DRAFT EPA/600/P-00/001Cb
DO NOT CITE OR QUOTE December 2003
MAS Review Draft
www.epa.gov/ncea
Exposure and Human Health Reassessment
of 2,3,7,8-Tetrachlorodibenzo-p-Dioxin (TCDD)
and Related Compounds
Part I: Estimating Exposure to Dioxin-Like Compounds
Volume 1: Sources of Dioxin-Like Compounds in the United States
Exposure Assessment and Risk Characterization Group
National Center for Environmental Assessment - Washington Office
Office of Research and Development
U.S. Environmental Protection Agency
Washington, DC
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DISCLAIMER
This document is a draft. It has not been formally released by the U.S. Environmental
Protection Agency and should not at this stage be construed to represent Agency policy.
Mention of trade names or commercial products does not constitute endorsement or
recommendation for use.
DRAFT-DO NOT QUOTE OR CITE l-i December 2003
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TABLE OF CONTENTS
1. BACKGROUND AND SUMMARY 1-1
1.1. BACKGROUND 1-1
1.2. DEFINITION OF DIOXIN-LIKE COMPOUNDS 1-3
1.3. TOXIC EQUIVALENCY FACTORS 1-5
1.4. OVERVIEW OF SOURCES AND EMISSIONS INVENTORY
METHODOLOGY 1-10
1.4.1. Overview and Organization of Source Analysis 1-10
1.4.2 Quantitative Inventory of Sources 1-12
1.5. GENERAL FINDINGS OF THE EMISSIONS INVENTORY 1-17
1.6. GENERAL SOURCE OBSERVATIONS 1-20
1.7. CONGENER PROFILES OF CDD/CDF SOURCES 1-25
2. MECHANISMS OF FORMATION OF DIOXIN-LIKE COMPOUNDS DURING
COMBUSTION OF ORGANIC MATERIALS 2-1
2.1. MECHANISM 1: CDD/CDF CONTAMINATION IN FUEL AS A SOURCE
OF COMBUSTION STACK EMISSIONS 2-3
2.2. MECHANISM 2: FORMATION OF CDD/CDFs FROM PRECURSOR
COMPOUNDS 2-5
2.3. MECHANISM 3: THE DE NOVO SYNTHESIS OF CDDS/CDFS
DURING COMBUSTION OF ORGANIC MATERIALS 2-12
2.4. THE ROLE OF CHLORINE IN THE FORMATION OF CDDS AND
CDFS IN COMBUSTION SYSTEMS 2-20
2.4.1. Review of Laboratory-Scale Studies 2-20
2.4.2. Review of Full Scale Combustion Systems 2-24
2.5. POTENTIAL PREVENTION OF CDD/CDF FORMATION IN COMBUSTION
SYSTEMS 2-27
2.6. THEORY ON THE EMISSION OF POLYCHLORINATED BIPHENYLS . . . 2-28
2.7. SUMMARY AND CONCLUSIONS 2-30
2.7.1. Mechanisms of Formation of Dioxin-Like Compounds 2-30
2.7.2. Role of Chlorine 2-31
2.7.3. General Conclusion 2-33
3. COMBUSTION SOURCES OF CDD/CDF: WASTE INCINERATION 3-1
3.1. MUNICIPAL SOLID WASTE INCINERATION 3-2
3.1.1. Description of Municipal Solid Waste Incineration
Technologies 3-2
3.1.2. Characterization of MSWI Facilities in Reference Years 1995
and 1987 3-7
3.1.3. Estimation of CDD/CDF Emissions from MSWIs 3-8
3.1.4. Summary of CDD/CDF (TEQ) Emissions from MSWIs for
1995 and 1987 3-10
3.1.5 Congener Profiles of MSWI Facilities 3-11
3.1.6 Estimated CDD/CDFs in MSWI Ash 3-11
3.1.7 Recent EPA Regulatory Activities 3-14
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TABLE OF CONTENTS (continued)
3.2. HAZARDOUS WASTE INCINERATION 3-15
3.2.1. Furnace Designs for Hazardous Waste Incinerators 3-16
3.2.2. APCDs for Hazardous Waste Incinerators 3-18
3.2.3. Estimation of CDD/CDF Emission Factors for Hazardous
Waste Incinerators 3-20
3.2.4. Emission Estimates for Hazardous Waste Incinerators 3-22
3.2.5. Recent EPA Regulatory Activities 3-23
3.2.6. Industrial Boilers and Furnaces Burning Hazardous Waste . . 3-24
3.2.7. Solid Waste from Hazardous Waste Combustion 3-25
3.3. MEDICAL WASTE INCINERATION 3-25
3.3.1. Design Types of MWIs Operating in the United States .... 3-26
3.3.2. Characterization of MWIs for Reference Years 1995 and
1987 3-27
3.3.3. Estimation of CDD/CDF Emissions from MWIs 3-29
3.3.4. EPA/OAQPS Approach for Estimating CDD/CDF Emissions
from MWIs 3-30
3.3.4.1. EPA/OAQPS Approach for Estimating Activity
Level 3-30
3.3.4.2. EPA/OAQPS Approach for Estimating CDD/CDF
Emission Factors 3-31
3.3.4.3. EPA/OAQPS Approach for Estimating Nationwide
CDD/CDF TEQ Air Emissions 3-32
3.3.5. AHA Approach for Estimating CDD/CDF Emissions from
MWIs 3-33
3.3.6. EPA/ORD Approach for Estimating CDD/CDF Emissions from
MWIs 3-34
3.3.6.1. EPA/ORD Approach for Classifying MWIs and
Estimating Activity Levels 3-34
3.3.6.2. EPA/ORD Approach for Estimating CDD/CDF
Emission Factors 3-36
3.3.7. Summary of CDD/CDF Emissions from MWIs 3-37
3.3.8. Recent EPA Regulatory Activities 3-40
3.4. CREMATORIA 3-40
3.5. SEWAGE SLUDGE INCINERATION 3-42
3.5.1. Emission Estimates from Sewage Sludge Incinerators 3-43
3.5.2. Solid Waste from Sewage Sludge Incinerators 3-45
3.6. TIRE COMBUSTION 3-45
3.7. COMBUSTION OF WASTEWATER SLUDGE AT BLEACHED CHEMICAL
PULP MILLS 3-46
3.8. BIOGAS COMBUSTION 3-47
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TABLE OF CONTENTS (continued)
4. COMBUSTION SOURCES OF CDD/CDF: POWER/ENERGY GENERATION 4-1
4.1. MOTOR VEHICLE FUEL COMBUSTION 4-1
4.1.1. Tailpipe Emission Studies 4-2
4.1.2. Tunnel Emission Studies 4-6
4.1.3. National Emission Estimates 4-9
4.2. WOOD COMBUSTION 4-16
4.2.1. Residential Wood Combustion 4-17
4.2.2. Industrial Wood Combustion 4-20
4.2.3. Solid Waste from Wood Combustion 4-25
4.3. OIL COMBUSTION 4-30
4.3.1. Residential/Commercial Oil Combustion 4-31
4.3.2. Utility Sector and Industrial Oil Combustion 4-31
4.4. COAL COMBUSTION 4-33
4.4.1. Utilities and Industrial Boilers 4-34
4.4.2. Residential/Commercial Coal Combustion 4-36
4.4.3. Solid Wastes from Coal Combustion 4-38
5. COMBUSTION SOURCES OF CDD/CDF: OTHER HIGH TEMPERATURE
SOURCES 5-1
5.1. CEMENT KILNS AND LIGHTWEIGHT AGGREGATE KILNS 5-1
5.1.1. Process Description of Portland Cement Kilns 5-1
5.1.2. Cement Kilns That Burn Hazardous Waste 5-3
5.1.3. Air Pollution Control Devices Used on Cement Kilns 5-4
5.1.4. CDD/CDF Emission Factors for Cement Kilns 5-5
5.1.5. National Estimates of CDD/CDF Emissions from Cement
Kilns 5-8
5.1.6. Recent EPA Regulatory Activities 5-10
5.1.7. Solid Waste from Cement Manufacturing 5-10
5.2. ASPHALT MIXING PLANTS 5-12
5.3. PETROLEUM REFINING CATALYST REGENERATION 5-14
5.4. CIGARETTE SMOKING 5-19
5.5. PYROLYSIS OF BROMINATED FLAME RETARDANTS 5-22
5.6. CARBON REACTIVATION FURNACES 5-23
5.7. KRAFT BLACK LIQUOR RECOVERY BOILERS 5-26
5.8. OTHER IDENTIFIED SOURCES 5-28
6. COMBUSTION SOURCES OF CDD/CDF: MINIMALLY CONTROLLED AND
UNCONTROLLED COMBUSTION SOURCES 6-1
6.1. COMBUSTION OF LANDFILL GAS 6-1
6.2. ACCIDENTAL FIRES 6-2
6.2.1. Soot and Ash Studies 6-3
6.2.2. Fume and Smoke Studies 6-5
6.2.3. Data Evaluation 6-6
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TABLE OF CONTENTS (continued)
6.3. LANDFILL FIRES 6-9
6.4. FOREST AND BRUSH FIRES 6-11
6.5 BACKYARD BARREL BURNING 6-15
6.5.1. Emission Estimates from Backyard Barrel Burning 6-16
6.5.2. Barrel Burning Ash Composition 6-18
6.6. UNCONTROLLED COMBUSTION OF POLYCHLORINATED BIPHENYLS
(PCBs) 6-18
6.7. VOLCANOES 6-19
7. METAL SMELTING AND REFINING SOURCES OF CDD/CDF 7-1
7.1. PRIMARY NONFERROUS METAL SMELTING/REFINING 7-1
7.1.1. Primary Copper Smelting and Refining 7-1
7.1.2. Primary Magnesium Smelting and Refining 7-2
7.1.3. Primary Nickel Smelting and Refining 7-4
7.1.4. Primary Aluminum Smelting and Refining 7-5
7.1.5. Primary Titanium Smelting and Refining 7-6
7.2. SECONDARY NONFERROUS METAL SMELTING 7-6
7.2.1. Secondary Aluminum Smelters 7-7
7.2.2. Secondary Copper Smelters 7-10
7.2.3. Secondary Lead Smelters 7-16
7.3. PRIMARY FERROUS METAL SMELTING/REFINING 7-19
7.3.1. Sinter Production 7-19
7.3.2. Coke Production 7-22
7.4 SECONDARY FERROUS METAL SMELTING/REFINING 7-22
7.5. FERROUS FOUNDRIES 7-24
7.6. SCRAP ELECTRIC WIRE RECOVERY 7-26
7.7. DRUM AND BARREL RECLAMATION FURNACES 7-28
7.8. SOLID WASTE FROM PRIMARY/SECONDARY IRON/STEEL
MILLS/FOUNDRIES 7-29
8. CHEMICAL MANUFACTURING AND PROCESSING SOURCES 8-1
8.1. BLEACHED CHEMICAL WOOD PULP AND PAPER MILLS 8-1
8.2. MANUFACTURE OF CHLORINE, CHLORINE DERIVATIVES, AND METAL
CHLORIDES 8-5
8.2.1. Manufacture of Chlorine 8-5
8.2.2. Manufacture of Chlorine Derivatives and Metal Chlorides . . . 8-6
8.3. MANUFACTURE OF HALOGENATED ORGANIC CHEMICALS 8-6
8.3.1. Chlorophenols 8-7
8.3.2. Chlorobenzenes 8-10
8.3.3. Chlorobiphenyls 8-12
8.3.4. Polyvinyl Chloride 8-16
8.3.5. Other Aliphatic Chlorine Compounds 8-24
8.3.6. Dyes, Pigments, and Printing Inks 8-25
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TABLE OF CONTENTS (continued)
8.3.7. TSCA Dioxin/Furan Test Rule 8-26
8.3.8. Halogenated Pesticides and FIFRA Pesticides Data Call-In . . 8-28
8.4. OTHER CHEMICAL MANUFACTURING AND PROCESSING SOURCES . 8-38
8.4.1. Municipal Wastewater Treatment Plants 8-38
8.4.2. Drinking Water Treatment Plants 8-43
8.4.3. Soaps and Detergents 8-44
8.4.4. Textile Manufacturing and Dry Cleaning 8-46
9. BIOLOGICAL SOURCES OF CDD/CDF 9-1
9.1. BIOTRANSFORMATION OF CHLOROPHENOLS 9-1
9.2. BIOTRANSFORMATION OF HIGHER CDD/CDFS 9-4
10. PHOTOCHEMICAL SOURCES OF CDD/CDF 10-1
10.1. PHOTOTRANSFORMATION OF CHLOROPHENOLS 10-1
10.2. PHOTOLYSIS OF HIGHER CDD/CDFS 10-3
10.2.1 Photolysis in Water 10-3
10.2.2 Photolysis on Soil 10-4
10.2.3 Photolysis on Vegetation 10-6
10.2.4 Photolysis in Air 10-6
11. SOURCES OF DIOXIN-LIKE PCBs 11-1
11.1. GENERAL FINDINGS OF THE EMISSIONS INVENTORY 11-1
1 1.2 RELEASES OF COMMERCIAL PCBs 11-2
11.2.1. Approved PCB Disposal/Destruction Methods 11-6
11.2.2. Accidental Releases of In-Service PCBs 11-9
11.2.3. Municipal Wastewater Treatment 11-12
11.3. CHEMICAL MANUFACTURING AND PROCESSING SOURCES 11-13
11.4. COMBUSTION SOURCES 11-14
11.4.1 Municipal Solid Waste Incineration 11-14
11.4.2. Industrial Wood Combustion 11-15
11.4.3. Medical Waste Incineration 11-16
11.4.4. Tire Combustion 11-16
11.4.5. Cigarette Smoking 11-17
11.4.6. Sewage Sludge Incineration 11-18
11.4.7. Backyard Barrel Burning 11-18
11.4.8. Petroleum Refining Catalyst Regeneration 11-20
1 1.5. NATURAL SOURCES 1 1-21
11.5.1. Biotransformation of Other PCBs 11-21
11.5.2. Photochemical Transformation of Other PCBs 11-24
11.6. PAST USE OF COMMERCIAL PCBS 11-26
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TABLE OF CONTENTS (continued)
1 2. RESERVOIR SOURCES OF CDD/CDF AND DIOXIN-LIKE PCBs 12-1
12.1. POTENTIAL RESERVOIRS 12-2
12.2. CHARACTERIZATION OF RESERVOIR SOURCES 12-3
12.2.1. Soil 12-3
12.2.2. Water 12-13
12.2.3. Sediment 12-16
12.2.4. Biota 12-19
12.3. SUMMARY AND CONCLUSIONS 12-23
12.3.1. Reservoir Sources 12-23
12.3.2. Implications for Human Exposure 12-25
13. BALL CLAY 13-1
13.1 INTRODUCTION 13-1
13.2 CHARACTERISTICS OF MISSISSIPPI EMBAYMENT BALL CLAYS .... 13-1
13.3 LEVELS OF DIOXIN-LIKE COMPOUNDS IN BALL CLAY 13-2
13.4 EVIDENCE FOR BALL CLAY AS A NATURAL SOURCE 13-3
13.5 ENVIRONMENTAL RELEASES OF DIOXIN-LIKE COMPOUNDS FROM
THE MINING AND PROCESSING OF BALL CLAY 13-5
REFERENCES R-1
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LIST OF TABLES
Table 1-1.
Table 1-2.
Table 1-3.
Table 1-4.
Table 1-5.
Table 1-6.
Table 1-7.
Table 1-8.
Table 1-9.
Table 1-10.
Table 1-11.
Table 1-12.
Table 2-1.
Table 2-2.
Table 2-3.
Table 3-1.
Table 3-2.
Table 3-3.
Table 3-4a.
Table 3-4b.
Table 3-5a.
Table 3-5b.
Table 3-6.
Table 3-7.
Table 3-8.
The TEF Scheme for I-TEQDF
The TEF Scheme for Dioxin-Like PCBs, as Determined by the World
Health Organization in 1994
from
The TEF Scheme for TEQDFP-WH098
Nomenclature for Dioxin-Like Compounds
List of Known and Suspected CDD/CDF Sources
Confidence Rating Scheme for U.S. Emission Estimates . .
Inventory of Environmental Releases (grams/year) of I-TEQDF
Known Sources in the United States for 1995 and 1987
Inventory of Environmental Releases (grams/year) of TEQDF-WH098
from Known Sources in the United States for 1995 and 1987
I-TEQDF Emission Factors Used to Develop National Emission
Inventory Estimates of Releases to Air
TEQDF-WH098 Emission Factors Used to Develop National Emission
Inventory Estimates of Releases to Air
Identification of Products Containing CDD/CDF in 1995 and 1987 . . .
Identification of Products Containing CDD/CDF in 1995 and 1987 . . .
Concentration of CDD/CDFs on Municipal Incinerator Fly Ash at
Varying Temperatures
CDD/CDFs Formed from the Thermolytic Reaction of 690 mg Benzene
+ FeCI3Silica Complex
De Novo Formation of CDDs/CDFs after Heating Mg-AI Silicate, 4%
Charcoal, 7% Cl, 1 % CuCI2.2H20 at 300°C
Inventory of MSWIs in 1995 by Technology, APCD, and Annual
Activity Level
Inventory of MSWIs in 1987 by Technology, APCD, and Annual
Activity Level
CDD/CDF TEQ Emission Factors (ng TEQ per kg waste) for Municipal
Solid Waste Incineration
Annual I-TEQDF Emissions (g/yr) from MSWIs Operating in 1995
Annual TEQDF-WH098 Emissions (g/yr) from MSWIs Operating in 1995
Annual I-TEQDF Emissions to the Air From MSWIs Operating in 1987 . .
Annual TEQDF-WH098 Emissions to the Air From MSWIs Operating in
1987
Fly Ash from a Municipal Incinerator
Comparison of the Amount of TEQs Generated Annually in MSWI Ash
CDD/CDF Emission Factors for Hazardous Waste Incinerators and
Boilers
1-28
1-29
1-30
1-31
1-32
1-35
1-36
1-38
1-40
1-41
1-42
1-42
2-34
2-35
2-36
3-49
3-51
3-53
3-55
3-56
3-57
3-58
3-59
3-60
3-61
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December 2003
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LIST OF TABLES (continued)
Table 3-9. Summary of Annual Operating Hours for Each MWI Type 3-62
Table 3-10. OAQPS Approach: PM Emission Limits for MWIs and Corresponding
Residence Times in the Secondary (2°) Combustion Chamber 3-63
Table 3-11. OAQPS Approach: Estimated Nationwide I-TEQDF Emissions (g/yr) for
1995 3-64
Table 3-12. AHA Approach: I-TEQDF Emission Factors Calculated for Air Pollution
Control 3-65
Table 3-13. AHA Assumptions of the Percent Distribution of Air Pollution Control on
MWIs Based on PM Emission Limits 3-66
Table 3-14. AHA Approach: Estimated Annual Nationwide I-TEQDF Emissions .... 3-67
Table 3-15. Comparison Between Predicted Residence Times and Residence
Times Confirmed by State Agencies in EPA/ORD Telephone Survey . . 3-68
Table 3-16. EPA/ORD Approach: TEQ Emissions from Medical Waste Incineration
for Reference Year 1995 3-69
Table 3-17. Summary of Annual TEQ Emissions from Medical Waste Incineration
(MWI) for Reference Year 1987 3-70
Table 3-18. Comparison of Basic Assumptions Used in the EPA/ORD, the
EPA/OAQPS, and the AHA Approaches to Estimating Nationwide
CDD/CDF TEQ Emissions from MWIs in 1995 3-71
Table 3-19. CDD/CDF Air Emission Factors for a Crematorium 3-72
Table 3-20. CDD/CDF Emission Factors for Sewage Sludge Incinerators 3-73
Table 3-21. CDD/CDF Air Emission Factors for Tire Combustion 3-74
Table 3-22. CDD/CDF Emission Factors for Combustion of Bleached-Kraft Mill
Sludge in Wood Residue Boilers 3-75
Table 4-1. Descriptions and Results of Vehicle Emission Testing Studies for CDDs
and CDFs 4-40
Table 4-2. Diesel-Fueled Automobile CDD/CDF Congener Emission Factors 4-41
Table 4-3. Diesel-Fueled Truck CDD/CDF Congener Emission Factors 4-42
Table 4-4. Leaded Gasoline-Fueled Automobile CDD/CDF Congener Emission
Factors 4-43
Table 4-5. Unleaded Gasoline-Fueled (Without Catalytic Converters) Automobile
CDD/CDF Congener Emission Factors 4-44
Table 4-6. Unleaded Gasoline-Fueled (With Catalytic Converters) Automobile
CDD/CDF Congener Emission Factors 4-45
Table 4-7. European Tunnel Study Test Results 4-46
Table 4-8. Baltimore Harbor Tunnel Study: Estimated Emission Factors for
Heavy-Duty (HD) Diesel Vehicles 4-47
Table 4-9. CDD/CDF Emission Factors for Industrial Wood Combustors 4-48
Table 4-10. CDD/CDF Concentrations in Residential Chimney Soot from Wood
Stoves and Fireplaces 4-49
Table 4-11. CDD/CDF Concentrations in Residential Bottom Ash from Wood Stoves
and a Fireplace 4-50
Table 4-12. CDD/CDF Concentrations in Chimney Soot (Bavaria, Germany) 4-51
Table 4-13. Fly Ash from Wood Working Industry 4-52
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December 2003
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LIST OF TABLES (continued)
Table 4-14. Electrostatic Precipitator Waste Ash from Wood-Fired Industrial
Boiler 4-53
Table 4-15. Estimated CDD/CDF Emission Factors for Oil-Fired Residential
Furnaces 4-54
Table 4-16. CDD/CDF Emission Factors for Oil-Fired Utility/Industrial Boilers 4-55
Table 4-17. CDD/CDF Concentrations in Stack Emissions from U.S. Coal-Fired
Power Plants 4-56
Table 4-18. Characteristics of U.S. Coal-Fired Power Plants Tested by DOE 4-57
Table 4-19. CDD/CDF Emission Factors for Coal-Fired Utility/Industrial Power
Plants 4-58
Table 4-20. CDD/CDF Emission Factors from Residential Coal Combustors 4-59
Table 4-21. Coal-Fired Utility Solid Wastes 4-60
Table 5-1. CDD/CDF Emission Factors for Cement Kilns 5-30
Table 5-2. CDD/CDF Emission Factors for Petroleum Catalytic Reforming Units . . 5-31
Table 5-3. CDD Concentrations in Japanese Cigarettes, Smoke, and Ash 5-32
Table 5-4. CDD/CDF Emissions in Cigarette Smoke 5-33
Table 5-5. CDD/CDF Concentrations in Cigarette Tobacco 5-34
Table 5-6. CDD/CDF Emission Factors for Black Liquor Recovery Boilers 5-35
Table 5-7. Concentrations of CDD/CDF in Candle Materials and Emissions 5-36
Table 5-8. CDD/CDF Concentrations in Ash Samples from Cement Kiln Electric
Static Precipitator and LWA Kiln Fabric Filter 5-37
Table 6-1. CDD/CDF Emission Factors for a Landfill Flare 6-21
Table 6-2. CDD/CDF Air Emission Factors from Barrel Burning of Household
Waste 6-22
Table 6-3. PCDD/PCDF Analysis for Composite Ash Samples from Barrel Burning . 6-23
Table 6-4. PCB Analysis for Composite Ash Samples from Barrel Burning 6-24
Table 6-5. CDD/CDF in Dust Fall and Ashes from Volcanoes 6-25
Table 7-1. CDD/CDF Emission Factors for Secondary Aluminum Smelters 7-31
Table 7-2. CDD/CDF Emission Factors for Secondary Copper Smelter 7-32
Table 7-3. CDD/CDF Emission Factors for Secondary Lead Smelters 7-33
Table 7-4. CDD/CDF Emission Factors for Sinter Plants 7-34
Table 7-5. Operating Parameters for U.S. Iron Ore Sinter Plants 7-35
Table 7-6. CDD/CDF Emission Factors for a Ferrous Foundry 7-36
Table 7-7. CDD/CDF Emission Factors for a Scrap Wire Incinerator 7-37
Table 7-8. Geometric Mean CDD/CDF Concentrations in Fly Ash and Ash/Soil
at Metal Recovery Sites 7-38
Table 7-9. CDD/CDF Emission Factors for a Drum and Barrel Reclamation Furnace 7-39
Table 8-1. CDD/CDF Concentrations in Pulp and Paper Mill Bleached Pulp,
Wastewater Sludge, and Effluent (circa 1988) 8-49
Table 8-2. CDD/CDF Concentrations in Pulp and Paper Mill Bleached Pulp,
Wastewater Sludge, and Effluent (circa 1996) 8-50
Table 8-3. Summary of Bleached Chemical Pulp and Paper Mill Discharges of
2,3,7,8-TCDD and 2,3,7,8-TCDF 8-51
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LIST OF TABLES (continued)
Table 8-4. CDD/CDF Concentrations in Graphite Electrode Sludge from Chlorine
Production 8-52
Table 8-5. CDD/CDF Concentrations in Metal Chlorides 8-53
Table 8-6. CDD/CDF Concentrations in Mono- through Tetra-Chlorophenols .... 8-54
Table 8-7. CDD/CDF Concentrations in Historical and Current Technical
Pentachlorophenol Products 8-55
Table 8-8. Historical CDD/CDF Concentrations in Pentachlorophenol-Na 8-57
Table 8-9. Summary of Specific Dioxin-Containing Wastes That Must Comply
with Land Disposal Retrictions 8-58
Table 8-10. CDD/CDF Concentrations in Chlorobenzenes 8-60
Table 8-11. Concentrations of CDD/CDF Congener Groups in Unused Commercial
PCB Mixtures 8-61
Table 8-12. 2,3,7,8-Substituted Congener Concentrations in Unused PCB
Mixtures 8-62
Table 8-1 3. Reported CDD/CDF Concentrations in Wastes from PVC Manufacture . 8-63
Table 8-14. CDD/CDF Measurements in Treated Wastewater and Wastewater Solids
from U.S. EDC/VCM/PVC Manufacturers 8-64
Table 8-15. CDD/CDF Measurements in Products from U.S. EDC/VCM/PVC
Manufacturers 8-65
Table 8-16. CDD/CDF Concentrations in Dioxazine Dyes and Pigments (Canada) . . 8-66
Table 8-17. CDD/CDF Concentrations in Printing Inks (Germany) 8-67
Table 8-18. Chemicals Requiring TSCA Section 4 Testing under the Dioxin/Furan
Rule 8-68
Table 8-19. Congeners and Limits of Quantitation (LOQ) for Which Quantitation is
Required under the Dioxin/Furan Test Rule and Pesticide Data Call-In . 8-69
Table 8-20. Precursor Chemicals Subject to Reporting Requirements under TSCA
Section 8(a) 8-70
Table 8-21. Results of Analytical Testing for Dioxins and Furans in the Chemicals
Tested to Date under Section 4 of the Dioxin/Furan Test Rule 8-71
Table 8-22. CDDs and CDFs in Chloranil and Carbazole Violet Samples Analyzed
Pursuant to the EPA Dioxin/Furan Test Rule 8-72
Table 8-23. Status of First Pesticide Data Call-in: Pesticides Suspected of Having
the Potential to Become Contaminated with Dioxins if Synthesized
under Conditions Favoring Dioxin Formation 8-73
Table 8-24. Status of Second Pesticide Data Call-in: Pesticides Suspected of
Being Contaminated with Dioxins 8-77
Table 8-25. Summary of Results for CDDs and CDFs in Technical 2,4-D and 2,4-D
Ester Herbicides 8-80
Table 8-26. Summary of Analytical Data Submitted to EPA in Response to Pesticide
Data Call-Ins 8-81
Table 8-27. CDD/CDF Concentrations in Samples of 2,4-D and Pesticide
Formulations Containing 2,4-D 8-82
Table 8-28. Mean CDD/CDF Measurements in Effluents from Nine U.S. POTWs . . . 8-83
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December 2003
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LIST OF TABLES (continued)
Table 8-29. CDD/CDF Concentrations Measured in EPA's National Sewage Sludge
Survey 8-84
Table 8-30. CDD/CDF Concentrations Measured in 99 Sludges Collected from U.S.
POTWs During 1 994 8-85
Table 8-31. Quantity of Sewage Sludge Disposed of Annually by Primary, Secondary,
or Advanced Treatment POTWs and Potential Dioxin TEQ Releases . . . 8-86
Table 8-32. CDD/CDF Concentrations in Swedish Liquid Soap, Tall Oil, and Tall
Resin 8-87
Table 11-1. List of Known and Suspected Source Categories for Dioxin-like PCBs 11-28
Table 11-2. Quantitative Inventory of Dioxin-Like PCB TEQp-WH098 Releases in the
United States 11 -29
Table 11-3. Weight Percent Concentrations of Dioxin-like PCBs in Aroclors,
Clophens, and Kanechlors 11-31
Table 11-4. Disposal Requirements for PCBs and PCB Items 11-33
Table 11-5. Off-site Transfers of PCBs Reported in TRI (1 988-1 996) 11 -34
Table 11 -6. Releases of PCBs Reported in TRI (1 988-1 996) 11 -35
Table 11-7. Aroclor Concentrations Measured in EPA's National Sewage Sludge
Survey 11-36
Table 11-8. Dioxin-Like PCB Concentrations Measured in Sludges Collected from
74 U.S. POTWs During 1 994 11 -37
Table 11-9. Dioxin-Like PCB Concentrations in Sludges Collected from a U.S. POTW
During 1999 11 -38
Table 11-10. Quantity of Sewage Sludge Disposed of Annually by Primary,
Secondary, or Advanced Treatment POTWs and Potential Dioxin-Like
PCB TEQ Releases 11-39
Table 11-11. PCB Congener Group Emission Factors for Industrial Wood
Combustors 11 -40
Table 11-12. PCB Congener Group Emission Factors for Medical Waste Incinerators
(MWIs) 11-41
Table 11-13. PCB Congener Group Emission Factors for a Tire Combustor 11 -42
Table 11-14. Dioxin-Like PCB Concentrations in Cigarette Tobacco 11-43
Table 11-15. Dioxin-Like PCB Concentrations in Stack Gas Collected from a
U.S. Sewage Sludge Incinerator 11-44
Table 11-16. Dioxin-Like PCB Emission Factors from Backyard Barrel Burning .... 11-45
Table 11-17. PCB Congener Group Emission Factors for a Petroleum Catalytic
Reforming Unit 11-46
Table 11-18. Estimated Tropospheric Half-Lives of Dioxin-Like PCBs with Respect
to Gas-Phase Reaction with the OH Radical 11-47
Table 11-19. Estimated PCB Loads in the Global Environment as of 1985 11-48
Table 11 -20. Domestic Sales of Aroclors (1 957-1 974) 11 -49
Table 11-21. Estimated U.S. Usage of PCBs by Use Category (1 930-1 975) 11 -50
Table 11-22. Estimated Direct Releases of Aroclors to the U.S. Environment
(1930-1974) 11-51
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LIST OF TABLES (continued)
Table 11-23. Estimated Releases of Dioxin-Like PCB TEQs to the U.S. Environment
During 1930-1977 11-52
Table 12-1. Historical Production, Sales, and Usage Quantities for 2,4-D 12-27
Table 12-2. Historical Production, Sales, and Usage Quantities 2,4,5-T 12-29
Table 12-3. CDD/CDF Concentrations in Recent Sample of 2,4,5-T 12-31
Table 12-4. PCB 138 Fluxes Predicted by Harner et al. (1995) 12-32
Table 12-5. Summary of Flux Calculations for Total PCBs in Green Bay, 1989 . . . 12-33
Table 12-6. Comparison of Estimated PCB Concentrations with Observed Values . 12-34
Table 13-1. Concentrations of CDDs Determined in Eight (8) Ball Clay Samples in
the U.S 13-7
Table 13-2. Comparison of the Mean CDD/CDF Congener Group Distribution in
Ball Clay to the Mean Congener Group Distributions in Urban and
Rural Soils in North America 13-8
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LIST OF FIGURES
Figure 1-1. Chemical Structure of 2,3,7,8-TCDD and Related Compounds 1-43
Figure 1-2. Estimated CDD/CDF I-TEQ Emissions to Air from Combustion Sources
in the United States (Reference Time Period: 1995) 1-44
Figure 1-3 Estimated CDD/CDF I-TEQ Emissions to Air from Combustion Sources
in the United States (Reference Time Period: 1987) 1-45
Figure 1-4. Comparison of Estimates of Annual I-TEQ Emissions to Air (grams
l-TEQ/year) for Reference Years 1987 and 1995 1-46
Figure 1-5. Estimated CDD/CDF WHO-TEQ Emissions to Air from Combustion
Sources in the United States (Reference Time Period: 1995) 1-47
Figure 1-6. Estimated CDD/CDF WHO-TEQ Emissions to Air From Combustion
Sources in the United States (Reference Time Period: 1987) 1-48
Figure 1-7. Comparison of Estimates of Annual WHO-TEQ Emissions to Air (grams
WHO-TEQ/year) for Reference Years 1987 and 1995 1-49
Figure 1-8. The Congener Profiles (as fractional distributions to total CDD/CDF)
of Anthropogenic Sources of Chlorinated Dibenzo-p-Dioxins and
Chlorinated Dibenzofurans in the United States 1-50
Figure 2-1. The de novo Synthesis of CDD/CDFs from Heating Carbon Particulate
at 300°C at Varying Retention Times 2-37
Figure 2-2. Temperature Dependence on CDD/CDF Formation 2-38
Figure 2-3. The Association Between Vapor Phase CI2 and the Formation of
CDDs/CDFs 2-39
Figure 3-1. Typical Mass Burn Waterwall Municipal Solid Waste Incinerator 3-76
Figure 3-2. Typical Mass Burn Rotary Kiln Combustor 3-77
Figure 3-3. Typical Modular Excess-Air Combustor 3-78
Figure 3-4. Typical Modular Starved-Air Combustor with Transfer Rams 3-79
Figure 3-5. Typical Dedicated RDF-Fired Spreader Stoker Boiler 3-80
Figure 3-6. Fluidized-Bed RDF Incinerator 3-81
Figure 3-7. MSWI Design Classes for 1987 3-82
Figure 3-8. MSWI Design Classes for 1995 3-83
Figure 3-9. Congener and Congener Group Profiles for Air Emissions from a
Mass-Burn Waterwall MSWI, Equipped with a Dry Scrubber and
Fabric Filter 3-84
Figure 3-10. Congener Profile for Air Emissions from Hazardous Waste
Incinerators 3-85
Figure 3-11. Congener and Congener Group Profiles for Air Emissions from Boilers
and Industrial Furnaces Burning Hazardous Waste 3-86
Figure 3-12. Congener and Congener Group Profiles for Air Emissions from Medical
Waste Incinerators without APCD 3-87
Figure 3-13. Congener and Congener Group Profiles for Air Emissions from
Medical Waste Incinerators Equipped with a Wet Scrubber, Baghouse,
and Fabric Filter 3-88
Figure 3-14. Congener and Congener Group Profiles for Air Emissions from a
Crematorium 3-89
Figure 3-15. Congener and Congener Group Profiles for Air Emissions from Sewage
Sludge Incinerators 3-90
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December 2003
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LIST OF FIGURES (continued)
Figure 3-16. Congener and Congener Group Profiles for Air Emissions from a Tire
Combustor 3-91
Figure 4-1. Congener and Congener Group Profiles for Air Emissions from
Diesel-fueled Vehicles 4-61
Figure 4-2. Congener and Congener Group Profiles for Air Emissions from Leaded
Gas-fueled Vehicles 4-62
Figure 4-3. Congener and Congener Group Profiles for Air Emissions from Unleaded
Gas-fueled Vehicles 4-63
Figure 4-4. Tunnel Air Concentrations 4-64
Figure 4-5a. Congener and Congener Group Profiles for Air Emissions from Industrial
Wood Combustors 4-65
Figure 4-5b Congener and Congener Group Profiles for Air Emissions from Bleached
Kraft Mill Bark Combustors 4-66
Figure 4-6. Congener Group Profile for Air Emissions from Residential Oil-fueled
Furnaces 4-67
Figure 4-7. Congener and Congener Group Profiles for Air Emissions from
Industrial Oil-fueled Boilers 4-68
Figure 4-8. Congener and Congener Group Profiles for Air Emissions from
Industrial/Utility Coal-fueled Combustors 4-69
Figure 4-9. Congener Group Profile for Air Emissions from Residential
Coal-fueled Combustors 4-70
Figure 5-1. Congener Profile for Air Emissions from Cement Kilns Burning Hazardous
Waste 5-38
Figure 5-2. Congener and Congener Group Profiles for Air Emissions from Cement
Kilns Not Burning Hazardous Waste 5-39
Figure 5-3. Congener and Congener Group Profiles for Air Emissions from
Petroleum Catalytic Reforming Units 5-40
Figure 5-4. CDD Profiles for Japanese Cigarettes, Smoke, and Ash 5-41
Figure 5-5. Congener Group Profiles for Mainstream and Sidestream Cigarette
Smoke 5-42
Figure 5-6. Congener Group Profiles for Cigarette Tobacco from Various Countries 5-43
Figure 5-7. Congener and Congener Group Profiles for Air Emissions from Kraft
Black Liquor Recovery Boilers 5-44
Figure 6-1. Congener Profile for Landfill Flare Air Emissions 6-26
Figure 7-1. Congener and Congener Group Profiles for Air Emissions from
Secondary Aluminum Smelters 7-40
Figure 7-2a. Congener Group Profile for Air Emissions from a Secondary Copper
Smelter 7-41
Figure 7-2b. Congener and Congener Group Profiles for a Closed Secondary Copper
Smelter 7-42
Figure 7-3. Congener and Congener Group Profiles for Air Emissions from Secondary
Lead Smelters 7-43
Figure 7-4. Congener Profiles for Air Emissions from U.S. Iron Ore Sinter Plants . . 7-44
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LIST OF FIGURES (continued)
Figure 7-5. Congener Group Profile for Air Emissions from a Scrap Wire
Incinerator 7-45
Figure 7-6. Congener Group Profile for Air Emissions from a Drum Incinerator .... 7-46
Figure 8-1. 104 Mill Study Full Congener Analysis Results for Pulp 8-88
Figure 8-2. 104 Mill Study Full Congener Analysis Results for Sludge 8-89
Figure 8-3. 104 Mill Study Full Congener Analysis Results for Effluent 8-90
Figure 8-4. Congener and Congener Group Profiles for Technical PCP 8-91
Figure 8-5. Congener Profile for 2,4-D (salts and esters) 8-92
Figure 8-6. Congener Profiles for Sewage Sludge 8-93
Figure 12-1. Fluxes Among Reservoirs 12-35
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LIST OF ACRONYMS
AHA
AISI
AICI3
AMSA
ARC
APCD
atm
ATSDR
BAAQMD
BCF
BDDs
BDFs
BHF
Btu
CAA
CaCI2
CARB
CBI
°C
CD
CDDs
CDFs
CEQ
CERCLA
C-ESP
CETRED
CFR
CSF
CKD
cr
CI2
cm
CO
C02
CRWQCB
CR
CuCI
CuCI2
D
D
DBF
DCBz
DCI
American Hospital Association
American Iron and Steel Institute
Aluminum Chloride
Association of Metropolitan Sewerage Agencies
Air Pollution Control
Air Pollution Control Device
Atmosphere
Agency for Toxic Substances and Disease Registry
Bay Area Air Quality Management District
Bioconcentration Factor
Polybrominated Dibenzo-p-dioxins or PBDDS
Polybrominated Dibenzofurans or PBDFs
Bag House Filter
British Thermal Unit
Clean Air Act
Calcium Chloride
California Air Resources Board
Confidential Business Information
Degree Celsius
Compact Disk
Polychlorinated Dibenzo-p-dioxins or PCDDs
Polychlorinated Dibenzofurans or PCDFs
Council on Environmental Quality
Comprehensive Environmental Response, Compensation and Liability Act (also
know as Superfund)
Cold-side Electrostatic Precipitator
Combustion Emissions Technical Resource Document
Code of Federal Regulations
Confidential Statement of Formula
Cement Kiln Dust
Chloride
Dichloride
Centimeters
Carbon Monoxide
Carbon Dioxide
California Regional Water Quality Control Board
Confidence Rating
Copper (Cupric) Chloride
Copper (Cupric) Dichloride
Symbol for Congener Class: Dibenzo-p-dioxin
Symbol for di (i.e., Two Halogen Substitution)
Dibenzofuran
Dichlorobenzene
Data Call-In
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LIST OF ACRONYMS (continued)
DCP
DHHS
DL
DOC
DOE
dP
dscm
DSI
EAF
EDC
EEI
EF
e.g.
EGB
EIA
EPA
EPRI
ESP
F
FF
°F
FB-RDF
FCEM
FeCI2
FeCI3
FGD
FIFRA
ft
ft3
g
GAC
GC-ECD
GC-MS
gpig
gpg
gr/dscf
HAPs
HCI
HCBz
HD
HDD
HDF
H-ESP
Hp
Dichlorophenols
U.S. Department of Health and Human Services
Detection Limit
U.S. Department of Commerce
U.S. Department of Energy
Physical Diameter
Dry Standard Cubic Meter
Dry Sorbent Injection
Electric Arc Furnaces
Ethylene Dichloride
Edison Electric Institute
Emission Factor
For example
Electro Granular Bed
Energy Information Administration
U.S. Environmental Protection Agency
Electric Power Research Institute
Electrostatic Precipitator
Symbol for Congener Class: Dibenzofuran
Fabric Filter
Degree Fahrenheit
Fluidized Bed Refuse-Derived Fuel
Field Chemical Emissions Measurement
Ferric (Iron) Dichloride
Ferric (Iron) Trichloride
Flue Gas Desulfurization
Federal Insecticide, Fungicide, and Rodenticide Act
Feet
Cubic Feet
Gram
Granular Activated Carbon
Gas Chromatography/Electron Capture Detector
Gas Chromatography/Mass Spectrometry
Grams Per Leaded Gallon
Gram Per Gallon
Grains Per Dry Standard Cubic Foot
Hazardous Air Pollutants
Hydrogen Chloride
Hexachlorobenzene
Heavy Duty
Polyhalogenated Dibenzo-p-Dioxins
Pollyhalogenated Dibenzofurans
Hot-side Electrostatic Precipitator
Symbol for Hepta (i.e., Seven Halogen Substitution)
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LIST OF ACRONYMS (continued)
hr Hour
HWI Hazardous Waste Incineration
Hx Symbol for Hexa (i.e., Six Halogen Substitution)
HxCB Hexachlorobiphenyl
i.e. That Is
ITC U.S. International Trade Commission
IUPAC International Union of Pure and Applied Chemistry
J Joules
KCI Potassium Chloride
kg Kilogram
km Kilometer
kW Kilowatt
L Liter
Ib Pound
III Liquid Injection Incinerator
LOQ Limits of Quantitation
M Symbol for Mono (i.e., One Halogen Substitution)
MACT Maximum Achievable Control Technologies
MB Mass Burn
MB-RC Mass Burn Rotary Kiln
MB-REF Mass Burn Refractory-Walled
MB-WW Mass Burn Waterwall
MCBz Monochlorobenzene
MCP Monochlorophenol
MgCI2 Magnesium Chloride
MgO Magnesium Oxide
fjg Microgram
mg Milligram
mm Millimeter
mol Mole (Unit of Substance)
MOD/EA Modular Excess Air
MOD/SA Modular Starved Air
MSW Municipal Solid Waste
MSWI Municipal Solid Waste Incinerator
MWI Medical Waste Incineration
m2 Square Meter
m3 Cubic Meter
Na Sodium
NA Not Applicable
NaCI Sodium Chloride
NaOCI Soldium Hypochlorite
NATO North Atlantic Treaty Organization
ng Nanogram
NCASI National Council of the Paper Industry for Air and Stream Improvement
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LIST OF ACRONYMS (continued)
ND Not Detected
NCEA National Center for Environmental Assessment
NEG Expected To Be Negligible or Non-existent
NESHAP National Emission Standards for Hazardous Air Pollutants
NiCI2 Nickel Chloride
NiO Nickel Oxide
Nm3 Standard Cubic Meter
NMOC Nonmethane Organic Compounds
NR Not Reported
NYSDEC New York State Department of Environmental Conservation
NYSDOH New York State Department of Health
0 Symbol for Octa (i.e., Eight Halogen Substitution)
OAQPS Office of Air Quality Planning and Standards
02 Molecular Oxygen
OH Hydroxide ion
QMS Office of Mobile Sources
OPP Office of Pesticide Programs
OPPT Office of Pollution Prevention and Toxics
ORD Office of Research and Development
OSW Office of Solid Waste
Pa Pascals (i.e., Unit of Pressure)
PAH Polycyclic Aromatic Hydrocarbons
Pb Lead
PBDDs Polybrominated Dibenzo-p-dioxins or BDDs
PBDFs Polybrominated Dibenzofurans or BDFs
PBS Packed Bed Scrubber
PCA Portland Cement Association
PCBs Polychlorinated Biphenyls
PCDDs Polychlorinated Dibenzo-p-dioxins or CDDs
PCDFs Polychlorinated Dibenzofurans or CDFs
PCP Pentachlorophenol
PCP-Na Pentachlorophenate
PCT Polychlorinated Terphenyl
Pe Symbol for Penta (i.e., Five Halogen Substitution)
PeCB Pentachlorobiphenyl
PeCBz Pentachlorobenzene
pg Picogram
PL Public Law
PM Particulate Matter
POM Polycyclic Organic Matter
POTW Publicly Owned Treatment Works
ppb Parts Per Billion
ppm Parts Per Million
ppmv Parts Per Million (Volume Basis)
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LIST OF ACRONYMS (continued)
ppq
ppt
ppt/v
PUF
PVC
QA/QC
QWASI
RCRA
RDF
RT
SAB
sec
SIC
SiCI4
SIPs
SNUR
S02
sq
T
TCBz
TCDD
TCDF
TCLP
TeCB
TeCP
TEF
TEQ
TEQ/yr
TiCI4
Tr
TrCBs
TrCP
TRI
TSCA
2,4-D
2,4-DB
2,4-DCP
2,4-DP
2,4,5-T
2378
fJQ
UK
UNC
USDA
Parts Per Quadrillion
Parts Per Trillion
Parts Per Trillion (Volume Basis)
Polyurethane Foam Plug
Polyvinyl Chloride
Quality Assurance/Quality Control
Quantitative Water Sediment Interaction
Resource Conservation and Recovery Act
Refuse-Derived Fuel
Residence Time
Science Advisory Board
Second
Standard Industrial Code
Silium Tetrachloride
State Implementation Plans
Significant New Use Rule
Sulfur Dioxide
Square
Symbol for Tetra (i.e., Four Halogen Substitution)
Trichlorobenzene
2,3,7,8-tetrachlorobidenzo-p-dioxin
2,3,.7,8-tetrachlorobidenzofuran
Toxicity Characteristic Leachate Procedure
Tetrachlorobiphenyls
Tetrachlorophenols
Toxicity Equivalency Factor
Toxicity Equivalents
Toxicity Equivalents Per Year
Titanium Tetrachloride
Symbol for Tri (i.e., Three Halogen Substitution)
Trichlorobiphenyls
Trichlorophenols
Toxics Release Inventory
Toxics Substances Control Act
2,4-Dichlorophenoxyacetic Acid
4-(2,4-Dichlorophenoxy) Butyric Acid
2,4-Dichlorophenol
2-(2,4-Dichlorophenoxy) Propionic Acid
2,4,5-Trichlorophenoxy (Phenoxy Herbicides)
Halogen Substitutions in the 2,3,7,8 Positions
Microgram
United Kingdom
Uncontrolled
U.S. Department of Agriculture
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LIST OF ACRONYMS (continued)
USITC U.S. International Trade Commissiion
USWAG Utility Solid Wastes Activity Group
UV Ultraviolet
VCM Vinyl Chloride Monomer
v/v Volume per Volume
WHO World Health Organization
wk"1 Per Week
WS Wet Scrubber
yr Year
SYMBOLS:
@ At
/ Per
fj Micro
% Percent
-< Less than
>- Greater than
< Less than or equal to
> Greater than or equal to
Difference
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1.0. BACKGROUND AND SUMMARY
1.1. BACKGROUND
This reassessment is comprised of three reports:
Part 1. Estimating Exposure to Dioxin-Like Compounds (U.S. EPA, 2000b) (which
expanded upon a 1988 draft exposure report titled, Estimating Exposure to 2,3,7,8-
TCDD [U.S. EPA, 1988d]);
Part 2. Health Assessment of 2,3, 7,8-Tetrachlorodibenzo-p-Dioxin (TCDD) and
Related Compounds (U.S. EPA, 1994a; U.S. EPA, 2000c); and
Part 3. Dioxin: Integrated Summary and Risk Characterization for 2,3,7,8-
Tetrachlorodibenzo-p-Dioxin (TCDD) and Related Compounds (U.S. EPA, 2000d).
Throughout the remainder of this document, these three parts as a whole will be
abbreviated as the Reassessment Documents, and the individual parts will be referred to as
the Exposure Reassessment Document, the Health Reassessment Document, and the Risk
Characterization. The Exposure Reassessment Document has expanded to three volumes,
as discussed below. Volumes 1 and 2 of the Exposure Reassessment Document are
summarized in Section 4 of the Risk Characterization.
The process for developing the Reassessment Documents has been open and
participatory. Each of the documents has been developed in collaboration with scientists
from inside and outside the Federal Government. Each document has undergone extensive
internal and external review, including review by EPA's Science Advisory Board (SAB). In
September 1994, drafts of each document were made available for public review and
comment. This included a 150-day comment period and 11 public meetings around the
country to receive oral and written comments. These comments, along with those of the
SAB (U.S. EPA, 1995a), have been considered in the drafting of this final document. The
Dose-Response Chapter of the Health Document underwent peer review in 1997 (U.S.
EPA, 1997a); an earlier version of the Integrated Summary and Risk Characterization
underwent development and review in 1997 and 1998, and comments have been
incorporated. In 1998, EPA released a workshop review version of the sources inventory
(U.S. EPA, 1998a), one of the three volumes of the Exposure Reassessment Document. In
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addition, as requested by the SAB, a chapter on Toxic Equivalency has been developed and
underwent external peer review in parallel with the Integrated Summary and Risk
Characterization in July 2000. The November 2000, review by the SAB of the Dose-
Response Chapter, the Toxic Equivalency Chapter and the Integrated Summary and Risk
Characterization was the final step in this open and participatory process of reassessment.
The full set of background documents and the integrative summary and risk
characterization replace the previous dioxin assessments as the scientific basis for EPA
decision-making.
The final Exposure Reassessment Document reflects changes made as a result of
both review comments and analyses of a variety of other types of information that has
come available. These include relevant information obtained from published peer-reviewed
literature, EPA program offices, and other Federal agencies. This version of the Exposure
Reassessment Document is current in this regard through 2000.
The purpose of the Exposure Reassessment Document is threefold: 1) to inventory
the known sources of release of dioxins into the environment, 2) to develop an
understanding of dioxins in the environment, including fate and transport properties,
environmental and exposure media concentrations, background as well as elevated
exposures, and temporal trends in exposure, and 3) provide site-specific procedures for
evaluating the incremental exposures due to specific sources of dioxin-like compounds.
Following this structure, the Exposure Reassessment Document is presented in three
volumes:
Volume 1 - Sources of Dioxin-Like Compounds in the United States
This volume presents a comprehensive review of known sources of environmental
releases of dioxin-like compounds in the United States. It includes an inventory of
known source activity in terms of estimates of annual releases of dioxin-like
compounds into the U.S. environment (i.e., air, water and land). This inventory is
specific for two reference years, 1987 and 1995. From these data, it is possible to
compare and contrast releases of dioxin-like compounds among the sources and
between the reference years.
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Volume 2 - Properties, Environmental Levels, and Background Exposures
This volume presents and evaluates information on the physical-chemical properties,
environmental fate, environmental and exposure media levels, background and
elevated human exposures, and temporal trends of dioxin-like compounds in the
U.S. environment during the 20th century.
Volume 3 - Site-Specific Assessment Procedures
This volume presents procedures for evaluating the incremental impact from sources
of dioxin release into the environment. The sources covered include contaminated
soils, stack emissions, and point discharges into surface water. This volume
includes sections on: exposure parameters and exposure scenario development;
stack emissions and atmospheric transport modeling; aquatic and terrestrial fate,
and food chain modeling; demonstration of methodologies; and uncertainty
evaluations including exercises on sensitivity analysis and model validation, review
of Monte Carlo assessments conducted for dioxin-like compounds, and other
discussions.
The primary technical resource supporting the development of the inventory of
sources of dioxin-like compounds discussed in Volume I (above) is the Database of Sources
of Environmental Releases of Dioxin-Like Compounds in the United States (EPA/600/C-
01/01 2. March, 2001). This database includes congener-specific CDD and CDF emissions
data extracted from original engineering test reports. It has been published independently
from the Reassessment and is available on Compact Disk-Read only Memory (CD-ROM),
without cost, from EPA's National Service Center for Environmental Publications (NSCEP)
in Cincinnati, Ohio (telephone: 1-800-490-9198, or 513-489-8190; fax: 513-489-8695).
Summary files from the database will be available for downloading from the Web page of
the National Center for Environmental Assessment, www.epa.gov/ncea/dioxin.htm.
Instructions on how to order and obtain the CD-ROM will also be available on the Web
page.
1.2. DEFINITION OF DIOXIN-LIKE COMPOUNDS
This assessment addresses specific compounds in the following chemical classes:
polychlorinated dibenzo-p-dioxins (PCDDs or CDDs), polychlorinated dibenzofurans (PCDFs
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or CDFs), polybrominated dibenzo-p-dioxins (PBDDs or BDDs), polybrominated
dibenzofurans (PBDFs or BDFs), and polychlorinated biphenyls (PCBs), and describes this
subset of chemicals as "dioxin-like." Dioxin-like refers to the fact that these compounds
have similar chemical structure, similar physical-chemical properties, and invoke a common
battery of toxic responses. Because of their hydrophobic nature and resistance towards
metabolism, these chemicals persist and bioaccumulate in fatty tissues of animals and
humans. The CDDs include 75 individual compounds; CDFs include 135 different
compounds. These individual compounds are referred to technically as congeners.
Likewise, the BDDs include 75 different congeners and the BDFs include an additional 135
congeners. Only 7 of the 75 congeners of CDDs, or of BDDs, are thought to have dioxin-
like toxicity; these are ones with chlorine/bromine substitutions in, at a minimum, the 2, 3,
7, and 8 positions. Only 10 of the 135 possible congeners of CDFs or of BDFs are thought
to have dioxin-like toxicity; these also are ones with substitutions in the 2, 3, 7, and 8
positions. This suggests that 17 individual CDDs/CDFs, and an additional 17 BDDs/BDFs,
exhibit dioxin-like toxicity. The database on many of the brominated compounds regarding
dioxin-like activity has been less extensively evaluated, and these compounds have not
been explicitly considered in this assessment.
There are 209 PCB congeners. Only 13 of the 209 congeners are thought to have
dioxin-like toxicity; these are PCBs with 4 or more lateral chlorines with 1 or no
substitution in the ortho position. These compounds are sometimes referred to as
coplanar, meaning that they can assume a flat configuration with rings in the same plane.
Similarly configured polybrominated biphenyls (PBBs) are likely to have similar properties.
However, the database on these compounds with regard to dioxin-like activity has been
less extensively evaluated, and these compounds have not been explicitly considered in
this assessment. Mixed chlorinated and brominated congeners of dioxins, furans, and
biphenyls also exist, increasing the number of compounds potentially considered dioxin-like
within the definitions of this assessment. The physical/chemical properties of each
congener vary according to the degree and position of chlorine and/or bromine substitution.
Very little is known about occurrence and toxicity of the mixed (chlorinated and
brominated) dioxin, furan, and biphenyl congeners. Again, these compounds have not
been explicitly considered in this assessment. Generally speaking, this assessment focuses
on the 17 CDDs/CDFs and a few of the coplanar PCBs that are frequently encountered in
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source characterization or environmental samples. While recognizing that other "dioxin-
like" compounds exist in the chemical classes discussed above (e.g., brominated or
chlorinated/brominated congeners) or in other chemical classes (e.g., halogenated
naphthalenes or benzenes, azo- or azoxybenzenes), the evaluation of less than two dozen
chlorinated congeners is generally considered sufficient to characterize environmental
"dioxin."
The chlorinated dibenzodioxins and dibenzofurans are tricyclic aromatic compounds
with similar physical and chemical properties. Certain of the PCBs (the so-called coplanar
or mono-ortho coplanar congeners) are also structurally and conformationally similar. The
most widely studied of this general class of compounds is 2,3,7,8-tetrachlorodibenzo-p-
dioxin (TCDD). This compound, often called simply "dioxin," represents the reference
compound for this class of compounds. The structure of TCDD and several related
compounds is shown in Figure 1-1. Although sometimes confusing, the term "dioxin" is
often also used to refer to the complex mixtures of TCDD and related compounds emitted
from sources, or found in the environment or in biological samples. It can also be used to
refer to the total TCDD "equivalents" found in a sample. This concept of toxic equivalency
is discussed below.
1.3. TOXIC EQUIVALENCY FACTORS
CDDs, CDFs, and PCBs are commonly found as complex mixtures when detected in
environmental media and biological tissues, or when measured as environmental releases
from specific sources. Humans are likely to be exposed to variable distributions of CDDs,
CDFs, and dioxin-like PCB congeners that vary by source and pathway of exposures. This
complicates the human health risk assessment that may be associated with exposures to
variable mixtures of dioxin-like compounds. In order to address this problem, the concept
of toxic equivalency has been considered and discussed by the scientific community, and
TEFs have been developed and introduced to facilitate risk assessment of exposure to
these chemical mixtures.
On the most basic level, TEFs compare the potential toxicity of each dioxin-like
compound comprising the mixture to the well-studied and understood toxicity of TCDD,
the most toxic member of the group. The background and historical perspective regarding
this procedure is described in detail in Part II, Chapter 9, Section 9.1, 9.2, and in Agency
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documents (U.S. EPA, 1987e; 1989a,b; 19911). This procedure involves assigning
individual TEFs to the 2,3,7,8-substituted CDD/CDF congeners and "dioxin-like" PCBs. To
accomplish this, scientists have reviewed the toxicological databases along with
considerations of chemical structure, persistence, and resistance to metabolism, and have
agreed to ascribe specific, "order of magnitude" TEFs for each dioxin-like congener relative
to TCDD, which is assigned a TEF of 1.0. The other congeners have TEF values ranging
from 1.0 to 0.00001. Thus, these TEFs are the result of scientific judgment of a panel of
experts using all of the available data and are selected to account for uncertainties in the
available data and to avoid underestimating risk. In this sense, they can be described as
"public health conservative" values. To apply this TEF concept, the TEF of each congener
present in a mixture is multiplied by the respective mass concentration and the products
are summed to represent the 2,3,7,8-TCDD Toxic Equivalence (TEQ) of the mixture, as
determined by Equation (1-1):
TEQ = £i-n(Congenerj x TEF^+ (Congener: x TEF:)+ (Congenern x TEFn j (1-1)
The TEF values for PCDDs and PCDFs were originally adopted by international convention
(U.S. EPA, 1989a). Subsequent to the development of the first international TEFs for
CDD/CDFs, these values were further reviewed and/or revised and TEFs were also
developed for PCBs (Ahlborg et al., 1994; van den Berg et al., 1998). A problem arises in
that past and present quantitative exposure and risk assessments may not have clearly
identified which of three TEF schemes was used to estimate the TEQ. This reassessment
introduces a new uniform TEQ nomenclature that clearly distinguishes between the
different TEF schemes and identifies the congener groups included in specific TEQ
calculations. The nomenclature uses the following abbreviations to designate which TEF
scheme was used in the TEQ calculation:
1. I-TEQ refers to the International TEF scheme adopted by EPA in 1989 (U.S. EPA,
1989a). See Table 1-1.
2. TEQ-WH094 refers to the 1 994 WHO extension of the I-TEF scheme to include 1 3
dioxin-like PCBs (Ahlborg et al., 1994). See Table 1-2.
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3. TEQ-WH098 refers to the 1998 WHO update to the previously established TEFs for
dioxins, furans, and dioxin-like PCBs (van den Berg et al., 1998). See Table 1-3.
The nomenclature also uses subscripts to indicate which family of compounds is
included in any specific TEQ calculation. Under this convention, the subscript D is used to
designate dioxins, the subscript F to designate furans and the subscript P to designate
PCBs. As an example, "TEQDF-WH098" would be used to describe a mixture for which only
dioxin and furan congeners were determined and where the TEQ was calculated using the
WH098 scheme. If PCBs had also been determined, the nomenclature would be "TEQDFP-
WH098." Note that the designations TEQDF-WH094 and I-TEQDF are interchangeable, as the
TEFs for dioxins and furans are the same in each scheme. Note also that in this document,
I-TEQ sometimes appears without the D and F subscripts. This indicates that the TEQ
calculation includes both dioxins and furans.
This reassessment recommends that the WH098 TEF scheme be used to assign
toxic equivalency to complex environmental mixtures for assessment and regulatory
purposes. Sections in the Health Reassessment Document, and summarized in the Risk
Characterization, describe the mode(s) of action by which dioxin-like chemicals mediate
biochemical and toxicological actions. These data provide the scientific basis for the
TEF/TEQ methodology. In its 20-year history, the approach has evolved, and decision
criteria supporting the scientific judgment and expert opinion used in assigning TEFs has
become more transparent. Numerous states, countries, and several international
organizations have evaluated and adopted this approach to evaluating complex mixtures of
dioxin and related compounds. It has become the accepted methodology, although the
need for research to explore alternative approaches is widely endorsed. Clearly, basing risk
on TCDD alone or assuming all chemicals are equally potent to TCDD is inappropriate on
the basis of available data. Although uncertainties in the use of the TEF methodology have
been identified (which are described in detail in the Health Reassessment Document,
Chapter 9, Section 9.5), one must examine the use of this method in the broader context
of the need to evaluate the potential public health impact of complex mixtures of
persistent, bioaccumulative chemicals. It can be generally concluded that the use of TEF
methodology for evaluating complex mixtures of dioxin-like compounds decreases the
overall uncertainties in the risk assessment process as compared to alternative approaches.
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Use of the latest consensus values for TEFs assures that the most recent scientific
information informs this "useful, interim approach" (U.S. EPA, 1989a; Kutz et al., 1990) to
dealing with complex environmental mixtures of dioxin-like compounds. As stated by the
U.S. EPA Science Advisory Board (U.S. EPA, 1995a), "The use of the TEFs as a basis for
developing an overall index of public health risk is clearly justifiable, but its practical
application depends on the reliability of the TEFs and the availability of representative and
reliable exposure data." EPA will continue to work with the international scientific
community to update these TEF values to assure that the most up-to-date and reliable data
are used in their derivation and to evaluate their use on a periodic basis.
A chemical is assigned a TEF value based on all the available data comparing the
chemical to either TCDD or PCB 126. In addition, there are weighting criteria that place
more emphasis on chronic and subchronic studies examining toxic endpoints (van den Berg
et al., 1998). There is a broad range in the quantity and quality of the data available for
individual congeners. For example, the TEF for PCB 1 26 is based on over 60 in vivo
endpoints examining responses as diverse as enzyme induction, developmental toxicity,
immunotoxicity, hepatic toxicity, alterations in hormones and tumor promotion, while the
TEF for 3,4,4',5-tetrachlorobiphenyl (PCB 81) is based on in vitro CYP1A induction and
QSAR calculations. Fortunately, PCB 81 does not significantly contribute to human TEQ
exposures. There are 5 congeners that contribute approximately 80% of the total TEQ in
humans: 2,3,7,8-TCDD, 1,2,3,7,8-PCDD, 1,2,3,6,7,8-HxCDD, 2,3,4,7,8-PCDF, and PCB
1 26 (See Part I, Volume 2 and Section 4.4.3 of this document). With the exception of
1,2,3,6,7,8-HxCDD, the TEFs for these chemicals are based on a number of different
endpoints from multiple studies performed in different laboratories. The TEF for
1,2,3,6,7,8-HxCDD is based on a two-year bioassay in which rats were exposed to a
mixture of 1,2,3,6,7,8-HxCDD and 1,2,3,7,8,9-HxCDD. The TEFs for 2,3,4,7,8-PCDF and
PCB 1 26 are similar to the mean REP value for all in vivo endpoints and are similar to their
REPs for tumor promotion. The TEF for 12378-PCDD is based largely on its REP for tumor
promotion in rats. From these data, it is clear that the chemicals that contribute
approximately 80% to the total human TEQ are well studied and the assigned TEFs provide
reasonable estimates of the relative potency of these chemicals. In contrast, while there
are some chemicals in the TEF methodology which have minimal data sets to reliably
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assess their relative potency, these chemicals do not contribute substantially to the human
blood TEQ.
The ability of the TEF methodology to predict the biological effects of mixtures
containing dioxin-like chemicals has been evaluated in a number of experimental systems.
These studies generally demonstrate that the assumption of additivity provides a
reasonable estimate of the dioxin-like potential of a mixture (described in the Health
Reassessment Document, Chapter 9, Section 9.4). In addition, there are examples of non-
additive interactions between dioxins and non-dioxins. Both greater than additive and less
than additive interactions have been observed in these studies. In general the non-additive
interactions between the dioxins and non-dioxins have been observed at doses that are
considerably higher than present background human exposures.
There are a number of natural chemicals that bind and activate the aryl hydrocarbon
receptor (AhR) and induce some dioxin-like effects. It has been proposed by some
scientists that these chemicals contribute significantly to the total TEQ exposures and that
these exposures far out weigh those from PCDDs, PCDFs and PCBs (Safe, 1995). While
this hypothesis is intriguing, there are several limitations to these analyses. The in vivo
data on the natural aromatic hydrocarbon receptor (AhR) ligands is limited to enzyme
induction and a single developmental study. Few, if any, toxicology studies demonstrating
clear dioxin-like toxicities have been published. The natural AhR ligands are rapidly
metabolized and result in both transient tissue concentrations and transient effects. The
natural ligands also have significant biological effects that are independent of the AhR and
it is not clear as to the role of the AhR in the biological effects of these chemicals. Clearly
this issue requires further research in order to better understand the relative potential
health effect of dioxin and related chemicals as compared to natural AhR ligands.
One of the limitations of the use of the TEF methodology in risk assessment of
complex environmental mixtures is that the risk from non-dioxin-like chemicals is not
evaluated in concert with that of dioxin-like chemicals. Another limition of the TEF
methodology is their application to non-biological samples. The fate and distribution of
PCDDs, PCDFs and PCBs are not necessarily related to their TEF. Thus, the use of the TEF
for non-biological media must be done cautiously. Future approaches to the assessment of
environmental mixtures should focus on the development of methods that will allow risks
to be predicted when multiple mechanisms are present from a variety of contaminants.
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1.4. OVERVIEW OF SOURCES AND EMISSIONS INVENTORY METHODOLOGY
In the United States, the major identified sources of environmental release have
been grouped into five broad categories for the purposes of this report:
Combustion Sources: CDD/CDFs are formed in most combustion systems. These
can include waste incineration (such as municipal solid waste, sewage sludge,
medical waste, and hazardous wastes), burning of various fuels (such as coal,
wood, and petroleum products), other high temperature sources (such as cement
kilns), and poorly or uncontrolled combustion sources (such as forest fires, building
fires, and open burning of wastes).
Metals Smelting, Refining and Processing Sources: CDD/CDFs can be formed
during various types of primary and secondary metals operations including iron ore
sintering, steel production, and scrap metal recovery.
Chemical Manufacturing: CDD/CDFs can be formed as by-products from the
manufacture of chlorine bleached wood pulp, chlorinated phenols (e.g.,
pentachlorophenol - PCP), PCBs, phenoxy herbicides (e.g., 2,4,5-T), and chlorinated
aliphatic compounds (e.g., ethylene dichloride).
Biological and Photochemical Processes: Recent studies suggest that CDD/CDFs
can be formed under certain environmental conditions (e.g., composting) from the
action of microorganisms on chlorinated phenolic compounds. Similarly, CDD/CDFs
have been reported to be formed during photolysis of highly chlorinated phenols.
Reservoir Sources: Reservoirs are materials or places that contain previously
formed CDD/CDFs or dioxin-like PCBs and have the potential for redistribution and
circulation of these compounds into the environment. Potential reservoirs include
soils, sediments, biota, water and some anthropogenic materials. Reservoirs
become sources when they have releases to the circulating environment.
1.4.1. Overview and Organization of Source Analysis
Only sources judged to have a reasonable likelihood for releases to the "circulating
environment" were addressed in ths document. Examples of the circulating environment
system boundary are as follows:
CDD/CDFs and dioxin-like PCBs in air emissions and wastewater discharges were
included; whereas, CDD/CDFs and dioxin-like PCBs in intermediate products or
internal wastestreams were excluded. For example, the CDD/CDFs in a
wastestream going to an on-site incinerator would not be addressed in this
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document, but any CDD/CDFs in the stack emissions from the incinerator would be
included.
• CDD/CDFs and dioxin-like PCBs in wastestreams applied to land in the form of "land
farming" are included; whereas, those disposed in permitted landfills were excluded.
Properly designed and operated landfills are considered to achieve long-term
isolation from the circulating environment. Land farming, however, involves the
application of wastes directly to land, clearly allowing for releases to the circulating
environment.
The sources addressed in this document (as defined above) can be divided into two
subclasses: 1) contemporary formation sources (sources which have essentially
simultaneous formation and release) and 2) reservoir sources (materials or places that
contain previously formed CDD/CDFs or dioxin-like PCBs that are re-released to
environment). The contemporary formation sources are discussed in Chapters 2 through
11 and the reservoir sources are discussed in Chapter 12. The presence of CDD/CDFs in
ball clay is discussed in Chapter 13. Table 1-5 provides a comprehensive list of all known
or suspected sources of CDDs/CDFs in the United States. The checkmarks indicate how
each source was classified in terms of the following six categories:
• Contemporary formation sources with reasonably well quantified releases (referred
to in this document as the Quantitative Inventory of Sources). These sources are
listed in Table 1-5 and release estimates are shown in Tables 1-7 and 1-8.
• Contemporary formation sources with preliminary release estimates. These sources
are listed in Table 1-5 and release estimates are shown in Tables 1-7 and 1-8.
• Contemporary formation sources without quantified release estimates. These
sources are listed in Table 1-5.
• Reservoir sources with reasonably well quantified releases. These sources are listed
in Table 1-5.
• Reservoir sources with preliminary release estimates. These sources are listed in
Table 1-5 and release estimates are shown in Tables 1-7 and 1-8.
• Reservoir sources without quantified releases. These sources are listed in Table 1-5.
This document includes discussions on products which contain dioxin-like
compounds. Some of these, such as 2,4-D, are considered to be sources since they are
clearly used in ways that result in environmental releases. These products have been
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classified into one of the above six groups. Other products containing dioxin-like
compounds, such as vinyl chloride products, do not appear to have environmental releases
and are not considered sources. For all CDD/CDF containing products, this document
summarizes the available information about the contamination levels and, where possible,
makes estimates of the total amount of CDD/CDF produced annually in these products.
Estimates of the CDD/CDF TEQ amounts in products are summarized in Tables 1-11 and 1-
12.
Throughout this document, environmental release estimates are presented in terms
of TEQs. This is done for convenience in presenting summary information and to facilitate
comparisons across sources. For purposes of environmental fate modeling, however, it is
important to use the individual CDD/CDF and PCB congener values, rather than TEQs. This
is because the physical/chemical properties of individual CDD/CDF congeners vary and,
consequently, the congeners will behave differently in the environment. For example, the
relative mix of congeners released from a stack cannot be assumed to remain constant
during transport through the atmosphere and deposition to various media. The full
congener-specific release rates for most sources are given in an electronic database which
is available as a companion to this document (Database of Sources of Environmental
Releases of Dioxin-Like Compounds in the United States, EPA/600/R-01/01 2). In Part I-
Volume 3, site-specific procedures are provided for estimating the impact of emissions on
local populations and emphasizes that congener-specific emission values should be used in
modeling environmental fate. Finally, it is important to understand that this series of
documents does not use source release estimates to generate background population
intake/risk estimates (rather these estimates are derived in Volume 2 primarily from food
levels and consumption rates).
1.4.2 Quantitative Inventory of Sources
EPA's Science Advisory Board (SAB) reviewed an earlier draft of the national dioxin
source emissions inventory and commented that the effort was comprehensive and
inclusive of most known sources (U.S. EPA, 1995f). However, the SAB emphasized that
source emissions are time-dependant, and recommended that emissions be associated with
a specific time reference. In consideration of this recommendation, EPA developed in this
report emission estimates for two reference years: 1987 and 1995.
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EPA selected 1987 primarily because, prior to this time, little empirical data existed
for making source specific emission estimates. The first study providing the type of data
needed for a national inventory was EPA's National Dioxin Study (U.S. EPA, 1987a). The
year 1987 also corresponds roughly with the time when significant advances occurred in
emissions measurement techniques and in the development of high resolution mass
spectrometry and gas chromatography necessary for analytical laboratories to achieve low
level detection of CDD and CDF congeners in environmental samples. Soon after this time,
a number of facilities began upgrades specifically intended to reduce CDD/CDF emissions.
Consequently, 1987 is also the latest time representative of the emissions occurring before
widespread installation of dioxin-specific emission controls.
EPA selected 1995 as the latest time period that could practically be addressed
consistent with the time table for producing the rest of the document. The data collected
in the companion document to this document on CDD/CDF and dioxin-like PCB levels in
environmental media and food were used to characterize conditions in the mid-1990s. So
the emissions data and media/food data in these two volumes are presented on a roughly
consistent basis. Since 1995, EPA has promulgated regulations limiting CDD/CDF
emissions for a number of the source categories that contribute to the inventory including
municipal waste combustors, medical waste incinerators, hazardous waste incinerators,
cement kilns burning hazardous waste, and pulp and paper facilities using chlorine
bleached processes. Consequently, the estimate of releases in the1995 inventory should
not be assumed to accurately represent post-1995 releases. EPA intends to periodically
revise this inventory.
A key element of the inventory is the method of extrapolation from tested facilities
to national estimates of environmental releases. Because not every U.S. facility in each of
the source categories have been tested for CDD/CDF emissions and releases, an
extrapolation procedure was developed to estimate national emissions for most source
categories. Many of the national emission estimates were, therefore, developed using a
"top down" approach. The first step in this approach is to derive from the available
emission monitoring data an emission factor (or series of emission factors) deemed to be
representative of the source category (or segments of a source category that differ in
configuration, fuel type, air pollution control equipment, etc.). The emission factor relates
mass of CDD/CDFs or dioxin-like PCBs released into the environment per some measure of
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activity (e.g., kilograms of material processed per year, vehicle miles traveled per year,
etc.). The emission factor is then multiplied by a national value for the activity level basis
of the emission factor (e.g., total kilogram [kg] of material processed in the United States
annually).
With the exception of certain releases from the bleached chemical wood pulp/paper
industry, no source category has estimates developed from a true "bottom up" approach
(i.e., estimates developed using site-specific emissions and activity data for all individual
sources in a category and then summed to obtain a national total). Existing facility-specific
emissions testing and activity level data for some source categories (e.g., municipal solid
waste incinerators) supported a semi- "bottom up" approach. In this approach, facility-
specific annual emissions were calculated for those facilities with adequate data. For the
untested facilities in the class, a subcategory (or class) emission factor was developed by
averaging the emission factors for the tested facilities in the class. This average emission
factor was then multiplied by the measure of activity for the non-tested facilities in the
class. Emissions were summed for the tested facilities and non-tested facilities. In
summary, this procedure can be represented by the following equations:
total ~ E tested,! E untested,!
Etotal = E^tesfed,/ + E("I * Attested
Where: Etota, = annual emissions from all facilities (g TEQ/yr)
Etested j = annual emissions from all tested facilities in class I (g TEQ/yr)
Euntested,i = annual emissions from all untested facilities class I (g TEQ/yr)
Efj = mean emission factor for tested facilities in class I (g TEQ/kg)
Aj = activity measure for untested facilities class I (kg/yr)
Some source categories are made up of facilities that vary widely in terms of design
and operating conditions. For these sources, as explained above, an attempt was made to
create subcategories that grouped facilities with common features and then to develop
separate emission factors for each subcategory. Implicit in this procedure is the
assumption that facilities with similar design and operating conditions should have similar
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CDD/CDF release potential. For most source categories, however, the specific combination
of features that contributes most to CDD/CDF or dioxin-like PCB release is not well
understood. Therefore, how to best subcategorize a source category was often
problematic. For each subcategorized source category in this report, a discussion is
presented about the variability in design and operating conditions, what is known about
how these features contribute to CDD/CDF or dioxin-like PCB release, and the rationale for
subcategorizing the category.
As discussed above, each source emission calculation required estimates of an
"emission factor" and the "activity level." For each emission source, the quantity and
quality of the available information for both terms vary considerably. Consequently, it is
important that emission estimates be accompanied by some indicator of the uncertainties
associated with their development. For this reason, a qualitative confidence rating scheme
was developed as an integral part of the emission estimate in consideration of the
following factors:
• Emission Factor - The uncertainty in the emission factor estimate depends primarily
on how well the tested facilities represent the untested facilities. In general,
confidence in the emission factor increases with increases in the number of tested
facilities relative to the total number of facilities. Variability in terms of physical
design and operating conditions within a class or subclass must also be considered.
The more variability among facilities, the less confidence that a test of any single
facility is representative of that class or subclass. The quality of the supporting
documentation also affects uncertainty. Whenever possible, original engineering
test reports were used. Peer reviewed reports from the open literature were also
used for developing some emission factors. In some cases, however, draft reports
that had undergone more limited review were used. In a few cases, unpublished
references were used (such as personal communication with experts) and are clearly
noted in the text.
• Activity Level - The uncertainty in the activity level estimate was judged primarily
on the basis of the extent of the underlying data. Estimates derived from
comprehensive surveys (including most facilities in a source category) were
assigned high confidence. As the number of facilities in the survey relative to the
total decreased, confidence also decreased. The quality of the supporting
documentation also affects uncertainty. Peer reviewed reports from the open
literature (including government and trade association survey data) were considered
most reliable. In some cases, however, draft reports that had undergone more
limited review were used. In a few cases, unpublished references were used (such
as personal communication with experts) and are clearly noted in the text.
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The confidence rating scheme, presented in Table 1-6, presents the qualitative
criteria used to assign a high, medium, or low confidence rating to the emission factor and
activity level terms for those source categories for which emission estimates can be
reliably quantified. The overall "confidence rating" assigned to an emission estimate was
determined by the confidence ratings assigned to the corresponding "activity level" term
and "emission factor" term. If the lowest rating assigned to either the activity level or
emission factor terms is "high," then the category rating assigned to the emission estimate
is high (also referred to as "A"). If the lowest rating assigned to either the activity level or
emission factor terms is "medium," then the category rating assigned to the emission
estimate is medium (also referred to as "B"). If the lowest rating assigned to either the
activity level or emission factor terms is "low," then the category rating assigned to the
emission estimate is low (also referred to as "C"). It is emphasized that this confidence
rating scheme should be interpreted as subjective judgements of the relative uncertainty
among sources, not statistical measures.
For many source categories, either emission factor information or activity level
information were inadequate to support development of reliable quantitative release
estimates for one or more media. For some of these source categories, sufficient
information was available to make preliminary estimates of emissions of CDD/CDFs or
dioxin-like PCBs; however, the confidence in the activity level estimates or emission factor
estimates was so low that the estimates cannot be included in the sum of quantified
emissions from sources with confidence ratings of A, B and C. These preliminary
estimates were given an overall confidence class rating of D (see Tables 1-7 and 1-8). As
preliminary estimates of source magnitude, they can be used, however, to help prioritize
future research and data collection. The actual magnitude of emissions from these sources
could be significantly lower or higher than these preliminary estimates. Although EPA has
chosen not to include them in the more thoroughly characterized emissions of the national
inventory, some of these poorly characterized sources have the potential of being major
contributors of releases to the environment. As the uncertainty around these sources is
reduced, they will be included in future inventory calculations. For other sources, some
information exists which suggests that they may release dioxin-like compounds; however,
the available data were judged to be insufficient for developing any quantitative emission
estimate. These source categories were assigned a confidence category rating of "E" and
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also were not included in the national inventory (See listings in Table 1-5 under the "Not
Quantifiable" column).
The emission factors developed for the emissions inventory are intended to be used
for estimating the total emissions for a source category rather than for individual facilities.
EPA has made uncertainty determinations for each of these emission factors based, in part,
on the assumption that by applying them to a group of facilities, the potential for
overestimating or underestimating individual facilities will to some extent be self
compensating. This means that in using these emission factors one can place significantly
greater confidence in an emission estimate for a class than can be placed on an emission
estimate for any individual facility. Given the limited amount of data available for deriving
emission factors, and the limitations of our understanding about facility-specific conditions
that determine formation and control of dioxin-like compounds, the current state of
knowledge cannot support the development of emission factors that can be used to
accurately estimate emissions on an individual facility-specific basis.
1.5. GENERAL FINDINGS OF THE EMISSIONS INVENTORY
Nationwide emission estimates of I-TEQDF and TEQDF-WH098 for the United States
inventory are presented in Tables 1-7 and 1-8, respectively, for those source categories for
which emission estimates can be reliably quantified. Nationwide emission estimates for
dioxin-like PCBs are presented in Chapter 11. Figures 1-2 and 1-3 are charts that visually
display the range of I-TEQDF emission estimates to air that are reported in Table 1-7 with
confidence ratings of A, B, or C. Figure 1-4 compares the I-TEQDF emission estimates to
air for the two reference years (i.e., 1987 and 1995). Figures 1-5 and 1-6 are charts that
visually display the range of TEQDF-WH098 emission estimates to air that are reported in
Table 1-8 with confidence ratings of A, B, or C. Figure 1-7 compares the TEQDF-WH098
emission estimates to air for the two reference years.
Table 1-9 lists the I-TEQDF emission factors used to derive the emission estimates
presented in Table 1-7 with confidence ratings of A, B, or C. Table 1-10 lists the TEQDF-
WH098 emission factors used to derive the emission estimates presented in Table 1-8 with
confidence ratings of A, B, or C. The emission factors used to calculate these emission
estimates were derived by setting "not detected" (ND) values in test reports as zeros.
Because detection limits were not always reported in test reports, it was not possible to
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consistently develop emission factors on any other basis (e.g., values set at one-half the
detection limit) for all source categories. When detection limits were reported for all test
reports for a given source category, emission factors were calculated and are presented in
this report for both ND equals zero and ND equals one-half the detection limit.
Tables 1-7 and 1-8 also present preliminary indications of the potential magnitude
of I-TEQDF and TEQDF-WH098 emissions, respectively, from category D sources in reference
year 1995. These estimates are based on very limited data whose representativeness is
unknown. The estimates were developed primarily as a tool to direct future investigations
and studies.
EPA's best estimates of releases of CDD/CDFs to air, water, and land from
reasonably quantifiable sources were approximately 3,000 gram (g) I-TEQDF (3,300 g
TEQDF-WHO98) in 1995 and 12,800-g I-TEQDF (14,000 g TEQDF-WHO98J in 1987.
The environmental releases of CDD/CDFs in the United States occur from a wide
variety of sources, but are dominated by releases to the air from combustion sources. The
current (i.e., 1995) inventory indicates that quantifiable emissions from combustion
sources are more than an order of magnitude greater than quantifiable emissions from all
other categories combined. Approximately 71 % of all quantifiable environmental releases
were dominated by air emissions from just three source categories in 1995: municipal
waste incinerators (representing 38% of total environmental releases); backyard burning of
refuse in barrels (representing 19% of total releases); and medical waste incinerators
(representing 14% of total releases).
The decrease in estimated emissions of CDD/CDFs between 1987 and 1995
(approximately 77 percent) was due primarily to reductions in air emissions from municipal
and medical waste incinerators, and further reductions are anticipated. For both
categories, these emission reductions have occurred from a combination of improved
combustion and emission controls and from the closing of a number of facilities. EPA's
regulatory programs estimate that full compliance with recently promulgated regulations
should result in further reductions in emissions from the 1995 levels (i.e., a reduction of
more than 1,800 grams I-TEQDF by the year 2005). Specifically, the Office of Air and
Radiation estimates that full compliance with Maximum Achievable Control Technology
(MACT) standards under the Clean Air Act (CAA) will result in annual emissions of 1 2 g I-
TEQDF from municipal solid waste incinerators and 6 g I-TEQDF from medical waste
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incinerators by the year 2005. The Office of Solid Waste anticipates that full compliance
with regulations promulgated under the combined authorities of the CAA and the Resource
Conservation and Recovery Act (RCRA) will result in annual emissions from hazardous
waste incinerators and cement kilns burning hazardous waste of about 4 and 8 g I-TEQDF,
respectively, by 2002. The Office of Water estimates that full compliance with effluent
guidelines promulgated under the Clean Water Act for the pulp and paper industry will
result in annual releases to water of 5 g I-TEQDF. However, no Federal regulations are in
place or currently under development for limiting CDD/CDF emissions from backyard
burning of refuse in barrels. A number of states have general restrictions on the practice
of backyard trash burning.
Insufficient data are available to comprehensively estimate point source releases of
dioxin-like compounds to water. Sound estimates of releases to water are only available
for chlorine bleached pulp and paper mills (356 g I-TEQDF or TEQDF-WH098 for 1987 and 28
g I-TEQDF or TEQDF-WH098 for 1995) and manufacture of ethylene dichloride (EDQ/vinyl
chloride monomer (VCM) «1 g I-TEQDF or TEQDF-WH098 in 1995). Other releases to
water bodies that cannot be quantified on the basis of existing data include effluents from
POTWs and most industrial/commercial sources.
Based on the available information, the quantitative inventory of sources includes
only a limited set of activities that result in direct environmental releases to land. The only
releases to land quantified in the national inventory are land application of sewage sludge
or commercial sludge products (106.5 g I-TEQDF or 79 g TEQDF-WH098 in 1995), land
application of pulp and paper mill wastewater sludges (2.0 g I-TEQDF or TEQDF-WH098 in
1995), use of 2,4-D pesticides (18.4 g I-TEQDF or 28.9 g TEQDF-WH098), and
manufacturing wastes from EDC/VCM « 1 g I-TEQDF or TEQDF-WH098). Not included in
the Inventory's definition of an environmental release is the disposal of sludges and ashes
into approved and regulated landfills.
Significant amounts of dioxin-like compounds produced annually are not considered
environmental releases and, therefore, are not included in the national inventory. Examples
include dioxin-like compounds generated internal to a process, but destroyed before
release, waste streams which are disposed of in approved landfills and are therefore
outside the definition of annual environmental releases, and products which contain dioxin-
like compounds but for which environmental releases, if any, cannot be estimated.
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The procedures and results of the U.S. inventory may have underestimated releases
from contemporary sources. A number of investigators have suggested that national
inventories may underestimate emissions because of the possibility of unknown sources.
This claim has been supported with mass balance analyses suggesting that deposition
exceeds emissions (Rappe et al., 1991; Harrad et al. 1992b; Bruzy and Hites, 1995). The
uncertainty, however, in both the emissions and deposition estimates for the United States
prevents the use of this approach for reliably evaluating the issue (U.S. EPA, 1995a). As
explained below, this document has instead evaluated this issue by making preliminary
estimates of poorly characterized sources and listing other sources that have been reported
to emit dioxin-like compounds but cannot be characterized on even a preliminary basis.
A number of sources were not included in the inventory even though limited
evidence exists indicating that these sources can emit CDD/CDFs. These sources
include various components of the metals industries such as electric arc furnaces
and foundries and uncontrolled or minimally controlled combustion practices (e.g.,
backyard trash burning and accidental fires at landfills). Tables 1-11 and 1-12
present preliminary estimates of U.S. national emissions using the emission factors
reported in these other studies as though they were representative of emission
factors for U.S. facilities.
The possibility remains that truly unknown sources exist. Many of the sources that
are well accepted today were only discovered in the past 10 years. For example,
CDD/CDFs were found unexpectedly in the wastewater effluent from bleached pulp
and paper mills in the mid 1980s. Ore sintering is now listed as one of the leading
sources of CDD/CDF emissions in Germany, but was not recognized as a source
until the early 1990s.
1.6. GENERAL SOURCE OBSERVATIONS
For any given time period, releases from both contemporary formation sources and
reservoir sources determine the overall amount of the dioxin-like compounds that are being
released to the open and circulating environment. Because existing information is
incomplete with regard to quantifying contributions from contemporary and reservoir
sources, it is not currently possible to estimate total magnitude of release for dioxin-like
compounds into the U.S. environment from all sources. For example, in terms of 1995
releases from reasonably quantifiable sources, this document estimates releases of 3,000 g
T-TEQDF (3,300 g TEQDF-WH098) for contemporary formation sources and 2,900 g I-TEQDF
or TEQDF-WH098 for reservoir sources. In addition, there remains a number of
DRAFT-DO NOT QUOTE OR CITE 1 -20 December 2003
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unquantifiable and poorly quantified sources. No quantitative release estimates can be
made for agricultural burning or for most CDD/CDF reservoirs or for any dioxin-like PCB
reservoirs. The preliminary estimate of 1995 poorly characterized contemporary formation
sources is 1,500 g I-TEQDF or TEQDF-WH098.
The contribution of dioxin-like compounds to waterways from nonpoint source
reservoirs is likely to be greater than the contributions from point sources. Current data
are only sufficient to support preliminary estimates of nonpoint source contributions of
dioxin-like compounds to water (i.e., urban storm water runoff and rural soil erosion).
These estimates suggest that, on a nationwide basis, total nonpoint releases are
significantly larger than point source releases.
Current releases of CDD/CDFs to the U.S. environment result principally from
anthropogenic activities. This finding applies to both sources of newly formed dioxin-like
compounds and reservoir sources. Four lines of evidence support this finding:
• As discussed in Volume 2, the companion document to this report, studies of
sediment corings in lakes in the United States show a consistent pattern of change
in CDD/CDF concentration in the sediments over time. The time period when
increases are observed in CDD/CDF levels in sediments coincides with the time
period when general industrial activity began increasing rapidly. CDD/CDF
concentrations in sediments began to increase around the 1930s, and continued to
increase until the 1960s and 1970s. Decreases appear to have occurred only
during the most recent time periods (i.e., 1970s and 1980s). These trend
observations are consistent among the dated sediment cores collected from over 20
freshwater and marine water bodies in various locations throughout the United
States and Europe. Levels of CDD/CDF in sediments from these lakes are
considered to be a reasonable indicator of the rate of environmental deposition. The
period of increase generally matches the time when a variety of industrial activities
began rising and the period of decline appears to correspond with growth in
pollution abatement. Some of these pollution abatement actions are likely to have
resulted in decreased CDD/CDF emissions (i.e., elimination of much open burning of
solid waste, installation of particulate controls on combustors, phase out of leaded
gasoline, and bans or restrictions on PCBs, 2,4,5-T, and PCP).
• In at least one case, soil erosion to surface waters, reservoir sources are thought to
be a significant contributor to the environment. However, the principal source of
CDD/CDFs in surface soils is air deposition. As discussed in the first bullet, it
appears that CDD/CDFs associated with air deposition are primarily of
anthropogenic origin.
• No large natural sources of CDD/CDF have been identified. EPA's current estimate
of emissions from all sources of CDD/CDFs suggests that forest fires are a minor
source of emissions compared to anthropogenic combustion activity. Recently
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CDD/CDFs have been discovered in ball clay deposits in western Mississippi,
Kentucky, and Tennessee. Although the origin of the CDD/CDFs in these clays may
be natural, it has not been confirmed.
As discussed in Volume 2, the companion document to this report, CDD/CDF levels
in human tissues from the general population in industrialized countries are higher
than levels observed in less-industrialized countries. Human populations in Europe
and North America have significantly higher mean tissue levels (e.g., blood, adipose
tissues, and breast milk) than human populations in developing countries of Asia.
Although chlorine is an essential component for the formation of CDD/CDFs in
combustion systems, the empirical evidence indicates that for commercial scale
incinerators, chlorine levels in feed are not the dominant controlling factor for rates of
CDD/CDF stack emissions. Important factors which can affect the rate of CDD/CDF
formation include the overall combustion efficiency, post-combustion flue gas temperatures
and residence times, and the availability of surface catalytic sites to support CDD/CDF
synthesis. Data from bench, pilot and commercial scale combustors indicate that
CDD/CDF formation can occur by a number of mechanisms. Some of these data, primarily
from laboratory and pilot scale combustors, have shown direct correlation between chlorine
content in fuels and rates of CDD/CDF formation. Other data, primarily from commercial
scale combustors, show little relation between availability of chlorine in feeds and rates of
CDD/CDF formation. These studies are summarized below:
Evidence from laboratory studies - A number of laboratory studies indicate that
changes in the chlorine content of feed materials may result in changes in the
amount of CDD/CDFs formed in the post-combustion region of a bench scale
combustion system (Kanters and Louw, 1994; Kanters et al., 1996; De Fre and
Rymen 1989; Wagner and Green, 1993).
Evidence from pilot-scale studies - Recent evidence from a pilot-scale combustion
study suggests that the amount of CDD/CDFs formed is not strongly correlated with
chlorine content of the feed material when the feed material contains less than one
percent chlorine; when chlorine in the feed is above one percent, the chlorine feed
content appears to be directly proportional to the amount of CDD/CDFs formed
(Wikstrom et al., 1996). Other pilot-scale studies indicate a strong relationship
between the amount of HCI formed (from organically-bound chlorine in feeds) and
the amount of CDD/CDFs formed (Bruce et al., 1991; Wagner and Green, 1993).
Wagner and Green (1993) concluded that a decrease in the levels of organically-
bound chlorine in the feed leads to a decrease in chlorinated organic emissions.
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Evidence from studies of full-scale systems - Combustors having poor combustion
characteristics and hot-sided particulate control devices show a positive correlation
between chlorine in feeds/fuels and CDD/CDF stack emissions (Thomas and Spiro
1995; U.S. EPA, 1987a). Combustors with high combustion efficiency, cool-sided
particulate control devices, and advanced dioxin-specific air pollution control
systems, however, do not show a strong correlation between chlorine amounts in
feeds/fuels and the amount of CDDs/CDFs emitted from the stack (Rigo et al.,
1995). This conclusion has been questioned in a paper by Costner (1998) who
claims that many of the facilities assessed by Rigo et al. (1996) show a positive
(though small) correlation between chlorine in feed and CDD/CDF emissions.
Conversely, Costner (1998) also found that about half the facilities showed a weak
inverse relationship. The American Society of Mechanical Engineers (ASME) has
concluded that, "Whatever effect chlorine has on PCDD/CDF emissions in
commercial scale systems is masked by the effect of ARCS (air pollution control
systems), temperature, ash chemistry, combustion conditions, measurement
imprecision, and localized flow stratification (ASME, 1995)."
The conclusion that chlorine in feed is not a strong determinant of CDD/CDF
emissions applies to the overall population of commercial scale Combustors. For any
individual commercial scale combustor, circumstances may exist in which changes in
chlorine content of feed could affect CDD/CDF emissions. For uncontrolled combustion,
such as open burning of household waste, the chlorine content of the waste may play a
more significant role in rates of CDD/CDF formation and release than is observed at
commercial scale combustors.
Dioxins are present in some ball clays, but insufficient data are available to estimate
whether environmental releases occur during the mining and use. Recent studies in the
U.S. and Europe have measured dioxins (principally CDDs) in some ball clays and other
related clays. As discussed in Chapter 13, it is likely that the CDDs present in ball clay are
of a natural origin. Ball clay is principally used in the manufacture of ceramics which
involves firing the clay in high temperature kilns. This activity may cause some portion of
the CDDs contained in the clay to be released into the air, but emission tests have not yet
been conducted which would allow characterizing these releases.
Data are available to estimate the amounts of CDD/CDFs contained in only a limited
number of commercial products. No systematic survey has been conducted to determine
levels of dioxin-like compounds in commercial products. The available data does, however,
allow estimates to be made of the amounts of dioxin-like compounds in bleached pulp (40
g I-TEQDF or TEQDF-WH098 in 1995), POTW sludge used in fertilizers (3.5 g I-TEQDF or 2.6 g
DRAFT-DO NOT QUOTE OR CITE 1 -23 December 2003
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TEQDF-WH098 in 1995), pentachlorophenol-treated wood (8,400 g I-TEQDF or 4,800 g
TEQDF-WH098 in 1995), dioxazine dyes and pigments «1 g I-TEQDF or TEQDF-WH098 in
1995) and 2,4-D (18.4 g I-TEQDF or 28.9 g TEQDF-WH098 in 1995).
No significant release of newly formed dioxin-like PCBs is occurring in the United
States. Unlike CDD/CDFs, PCBs were intentionally manufactured in the United States in
large quantities from 1929 until production was banned in 1977. Although it has been
demonstrated that small quantities of dioxin-like PCBs can be produced during waste
combustion, no strong evidence exists that the dioxin-like PCBs make a significant
contribution to TEQ releases during combustion. The occurrences of dioxin-like PCBs in
the U.S. environment most likely reflect past releases associated with PCB production, use,
and disposal. Further support for this finding is based on observations of reductions since
the 1980s in PCB concentrations in Great Lakes sediment and other areas.
It is unlikely that the emission rates of CDD/CDFs from known sources correlate
proportionally with general population exposures. Although the Inventory shows the
relative contribution of various sources to total emissions, it cannot be assumed that these
sources make the same relative contributions to human exposure. It is quite possible that
the major sources affecting CDD/CDF concentrations in food (see discussion in Section 2.6
of Volume 2) may not be those sources that represent the largest fractions of current total
emissions in the United States. The geographic locations of sources relative to the areas
from which much of the beef, pork, milk, and fish are produced are important to consider.
That is, many of the agricultural areas that produce dietary animal fats are not located near
or directly down wind of the major sources of dioxin-like compounds.
The contribution of reservoir sources to human exposure may be significant.
Several factors support this finding:
• Because the magnitude of releases from current sources of newly formed PCBs are
most likely negligible, human exposure to the dioxin-like PCBs is thought to be
derived almost completely from reservoir sources. Key pathways involve releases
from both soils and sediments to both aquatic and terrestrial food chains. As
discussed in Volume 2, one third of general population TEQDFP exposure is due to
PCBs. Thus, at least one third of the overall risk from dioxin-like compounds comes
from reservoir sources.
• CDD/CDF releases from soil via soil erosion and runoff to waterways may be
significant. These releases appear to be greater than releases to water from the
primary sources included in the inventory. CDD/CDFs in waterways can
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bioaccumulate in fish leading to human exposure via consumption of fish. As
discussed in Volume 2, fish consumption makes up about one third of the total
general population CDD/CDF TEQ exposure. This suggests that a significant portion
of the CDD/CDF TEQ exposure could be due to releases from the soil reservoir. It is
not known, however, how much of the soil erosion and runoff represents recently
deposited CDD/CDFs from primary sources or longer term accumulation. Much of
the eroded soil comes from tilled agricultural lands which would include a mix of
CDD/CDFs from various deposition times. The age of CDD/CDFs in urban runoff is
less clear.
Potentially, soil reservoirs could have vapor and particulate releases which deposit
on plants and enter the terrestrial food chain. The magnitude of this contribution,
however, is unknown. EPA plans future studies in agricultural areas which will
compare modeled air concentrations from primary sources to measured levels as a
way to get further insight to this issue.
1.7. CONGENER PROFILES OF CDD/CDF SOURCES
This section summarizes congener profiles of known sources of dioxin-like
compounds in the United States (Cleverly et. al, 1998). Congener profiles are the
fractional distribution of CDD/CDF congeners in an environmental release, in an
environmental sample, or in a biological sample. Under some circumstances, these
congener profiles may assist researchers in: (1) identification of specific combustion source
contributions to near field air measurements of CDD/CDFs; (2) comparing sources in terms
of discerning differences in the types and amplitude of CDD/CDF congeners emitted; and
(3) providing insights on formation of CDDs and CDFs in various sources and chemicals.
There are numerous procedures one could elect to use to derive a congener profile, and
there is no single agreed-upon convention (Cleverly et al., 1998; Lorber et al., 1996;
Hagenmaier et al., 1994). In this report, congener profiles were developed primarily by
calculating the ratio of specific 2,3,7,8-substituted CDDs and CDFs in the emission or
product to the total (CI4 - CI8) CDDs/CDFs. With respect to combustion sources, the
profiles were derived by: (a) dividing the congener-specific emission factors by the total
(CI4 - CI8) CDD/CDF emission factor for each tested facility; and (b) then averaging the
congener profiles developed for all tested facilities within the combustor type. For
chemical processes and commercial chemicals, CDD/CDF profiles were typically generated
by dividing average congener concentrations (ppt) in the chemical by the total CDD/CDF
present. Profiles for selected source categories are presented in Figure 1-8.
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On the basis of inspection and comparisons of the average CDD/CDF congener
profiles across combustion and non-combustion sources, the following observations are
made (Cleverly et al., 1998): (These generalizations are derived from this data set, and
their application beyond these data is uncertain).
i. It appears that combustion sources emit all 2,3,7,8-substituted CDDs and CDFs,
although in varying percentages of total CDD/CDF.
ii. In combustion source emissions, 2,3,7,8-TCDD is usually 0.1 to 1.0 percent of total
CDD/CDF. The exception to this are stack emissions from industrial oil-fired boilers
where the available, but limited data, indicate that 2,3,7,8-TCDD constitutes an
average of 7 percent of total CDD/CDF emissions.
iii. It cannot be concluded that OCDD is the dominant congener for all combustion
generated emissions of CDD/CDFs. OCDD dominates total emissions from: mass
burn municipal solid waste incinerators (MSWI) that have dry scrubbers and fabric
filters (DS/FF) for dioxin controls; industrial oil-fired boilers; industrial wood-fired
boilers; unleaded gasoline combustion; diesel fuel combustion in trucks; and sewage
sludge incinerators. The dominant congeners for other combustion sources are:
1,2,3,4,6,7,8-HpCDF in emissions from mass burn MSWIs equipped with hot-sided
electrostatic precipitators (ESPs); OCDF in emissions from medical waste
incineration; 1,2,3,4,6,7,8-HpCDF in hazardous waste incinerators; 2,3,4,7,8-
PeCDF in cement kilns burning hazardous waste; 2,3,7,8-TCDF in cement kilns not
burning hazardous waste; OCDF in industrial/utility coal-fired boilers; 1,2,3,4,6,7,8-
HpCDF in secondary aluminum smelters; and 2,3,7,8-TCDF in secondary lead
smelters.
iv. The 1,2,3,4,6,7,8-HpCDF appears to be the dominant congener in the following
sources: secondary aluminum smelters; MSWIs equipped with hot-sided ESPs;
hazardous waste incinerators; and 2,4-D salts and esters.
v. Evidence for a shift in the congener patterns potentially caused by the application of
different air pollution control systems within a combustion source-type can be seen
in the case of mass burn MSWIs. For mass burn MSWIs equipped with hot-sided
ESPs, the most prevalent CDD/CDF congeners are: 1,2,3,4,6,7,8-HpCDF; OCDD;
1,2,3,4,6,7,8-HpCDD/1,2,3,4,7,8-HxCDF; 2,3,4,6,7,8-HxCDF/OCDF; 1,2,3,6,7,8-
HxCDF. The most prevalent congeners emitted from MSWIs equipped with DS/FF
are: OCDD; 1,2,3,4,6,7,8-HpCDD; 1,2,3,4,6,7,8-HpCDF; OCDF; 2,3,7,8-
TCDF/1,2,3,4,7,8-HxCDD; 2,3,4,6,7,8-HxCDF.
vi. There is evidence of marked differences in the distribution of CDD/CDF congeners
between cement kilns burning and not burning hazardous waste. When not burning
hazardous waste as supplemental fuel, the dominant congeners appear to be
2,3,7,8-TCDF; OCDD; 1,2,3,4,6,7,8-HpCDD, and OCDF. When burning hazardous
waste, the dominant congeners are: 2,3,7,8-PeCDF; 2,3,7,8-TCDF; 1,2,3,4,7,8-
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HxCDF; and 1,2,3,4,6,7,8-HpCDD. When burning hazardous waste, OCDD and
OCDF are minor constituents of stack emissions.
vii. The congener profile of 2,4-D salts and esters seems to mimic a combustion source
profile in the number of congeners represented, and in the minimal amount of
2,3,7,8-TCDD relative to all 2,3,7,8-substituted congeners. A major difference is
the prevalence of 1,2,3,7,8-PeCDD in 2,4-D (i.e., 14 percent), which is not seen in
any other combustion or non-combustion sources presented here.
viii. There are similarities in the congener profiles of pentachlorophenol (PCP), diesel
truck emissions, unleaded gasoline vehicle emissions, and industrial wood
combustors. In these sources, OCDD dominates total emissions, but the relative
ratio of 1,2,3,4,6,7,8-HpCDD to OCDD is also quite similar.
ix. The congener profiles for diesel truck exhaust and air measurements from a tunnel
study of diesel traffic are quite similar.
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Table 1-1. The TEF Scheme for I-TEQ
DF
Dioxin (D) Congener
2,3,7,8-TCDD
1,2,3,7,8-PeCDD
1,2,3,4,7,8-HxCDD
1,2,3,6,7,8-HxCDD
1,2,3,7,8,9-HxCDD
1,2,3,4,6,7,8-
HpCDD
OCDD
TEF
1.0
0.5
0.1
0.1
0.1
0.01
0.001
Furan (F) Congener
2,3,7,8-TCDF
1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
1,2,3,4,7,8-HxCDF
1,2,3,6,7,8-HxCDF
1,2,3,7,8,9-HxCDF
2,3,4,6, 7,8-HxCDF
1,2,3,4,6,7,8-HpCDF
1,2,3,4,7,8,9-HpCDF
OCDF
TEF
0.1
0.05
0.5
0.1
0.1
0.1
0.1
0.01
0.01
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December 2003
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Table 1-2. The TEF Scheme for Dioxin-Like PCBs, as Determined
by the World Health Organization in 1994
Chemical Structure
3,3',4,4'-TeCB
2,3,3',4,4'-PeCB
2,3,4,4', 5-PeCB
2,3',4,4',5-PeCB
2',3,4,4',5-PeCB
3,3',4,4',5-PeCB
2,3,3',4,4',5-HxCB
2,3,3',4,4',5'-HxCB
2,3',4,4',5,5'-HxCB
3,3',4,4',5,5'-HxCB
2,2',3,3',4,4',5-HpCB
2,2',3,4,4',5,5'-HpCB
2,3,3',4,4',5,5'-HpCB
IUPAC Number
PCB-77
PCB-105
PCB-114
PCB-118
PCB-123
PCB-126
PCB-156
PCB-157
PCB-167
PCB-169
PCB-170
PCB-180
PCB-189
TEF
0.0005
0.0001
0.0005
0.0001
0.0001
0.1
0.0005
0.0005
0.00001
0.01
0.0001
0.00001
0.0001
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December 2003
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Table 1-3. The TEF Scheme for TEQDFP-WH098
Dioxin Congeners
2,3,7,8-TCDD
1,2,3,7,8-PeCDD
1,2,3,4,7,8-HxCDD
1, 2,3,6, 7,8-HxCDD
1,2,3,7,8,9-HxCDD
1,2,3,4,6,7,8-HpCDD
OCDD
TEF
1.0
1.0
0.1
0.1
0.1
0.01
0.0001
Furan Congeners
2,3,7,8-TCDF
1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
1,2,3,4,7,8-HxCDF
1,2,3,6,7,8-HxCDF
1,2,3,7,8,9-HxCDF
2,3,4,6, 7,8-HxCDF
1, 2,3,4,6, 7,8-HpCDF
1,2,3,4,7,8,9-HpCDF
OCDF
TEF
0.1
0.05
0.5
0.1
0.1
0.1
0.1
0.01
0.01
0.0001
Chemical Structure
3,3',4,4'-TeCB
3,4,4', 5-TCB
2,3,3',4,4'-PeCB
2,3,4,4', 5-PeCB
2,3',4,4',5-PeCB
2',3,4,4',5-PeCB
3,3',4,4',5-PeCB
2,3,3',4,4',5-HxCB
2,3,3',4,4',5'-HxCB
2,3',4,4',5,5'-HxCB
3,3',4,4',5,5'-HxCB
2,3,3',4,4',5,5'-HpCB
IUPAC Number
PCB-77
PCB-81
PCB-105
PCB-114
PCB-118
PCB-123
PCB-126
PCB-156
PCB-157
PCB-167
PCB-169
PCB-189
TEF
0.0001
0.0001
0.0001
0.0005
0.0001
0.0001
0.1
0.0005
0.0005
0.00001
0.01
0.0001
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1-30
December 2003
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Table 1-4. Nomenclature for Dioxin-Like Compounds
Term/Symbol
Congener
Congener
Group
Isomer
Specific
Isomer
D
F
M
D
Tr
T
Pe
Hx
Hp
0
CDD
CDF
PCB
2378
Definition
Any one particular member of the same chemical family (e.g., there are 75 congeners of
chlorinated dibenzo-p-dioxins).
Group of structurally related chemicals that have the same degree of chlorination (e.g.,
there are eight congener groups of CDDs, monochlorinated through octochlorinated).
Substances that belong to the same congener group (e.g., 22 isomers constitute the
congener group of TCDDs).
Denoted by unique chemical notation (e.g., 2,4,8,9-tetrachlorodibenzofuran is referred to
as 2,4,8,9-TCDF).
Symbol for congener class: dibenzo-p-dioxin
Symbol for congener class: dibenzofuran
Symbol for mono (i.e., one halogen substitution)
Symbol for di (i.e., two halogen substitution)
Symbol for tri (i.e., three halogen substitution)
Symbol for tetra (i.e., four halogen substitution)
Symbol for penta (i.e., five halogen substitution)
Symbol for hexa (i.e., six halogen substitution)
Symbol for hepta (i.e., seven halogen substitution)
Symbol for octa (i.e., eight halogen substitution)
Chlorinated dibenzo-p-dioxins, halogens substituted in any position
Chlorinated dibenzofurans, halogens substituted in any position
Polychlorinated biphenyls
Halogen substitutions in the 2,3,7,8 positions
Source: Adapted from U.S. EPA (1989)
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1-31
December 2003
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Table 1-5. List of Known and Suspected CDD/CDF Sources
Emission Source Category
I. COMBUSTION SOURCES
Waste Incineration
Municipal waste incineration
Hazardous waste incineration
Boilers/industrial furnaces
Medical waste/pathological incineration
Crematoria
Sewage sludge incineration
Tire combustion
Pulp and paper mill sludge incinerators
BioGas combustion
Power/Energy Generation
Vehicle fuel combustion
- leaded"
- unleaded
- diesel
Wood combustion - residential
- industrial
Coal combustion - residential
- industrial/utility
Oil combustion - residential
- industrial/utility
Other High Temperature Sources
Cement kilns (haz waste burning)
Cement kilns (non haz waste burning)
Asphalt mixing plants
Petro. refining catalyst regeneration
Cigarette combustion
Carbon reactivation furnaces
Kraft recovery boilers
Contemporary Formation
Sources
Quantifiable
/
/
/
/
/
/
/
/
/
/
/
/
/
/
/
/
/
/
/
/
/
Preliminary
Estimate
/
/
/
/
Not
Quantifiable
Reservoir
Sources
Quantifiable
Preliminary
Estimate
Not
Quantifiable
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December 2003
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Table 1-5. List of Known and Suspected CDD/CDF Sources (continued)
Emission Source Category
Manufacture of ball clay products
Contemporary Formation
Sources
Quantifiable
Preliminary
Estimate
Not
Quantifiable
/
Reservoir
Sources
Quantifiable
Preliminary
Estimate
Not
Quantifiable
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December 2003
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Table 1-5. List of Known and Suspected CDD/CDF Sources (continued)
Emission Source Category
Minimally Controlled or Uncontrolled Combustion
Combustion of landfill gas in flares
Landfill fires
Accidental fires (structural)
Accidental fires (vehicles)
Forest, brush, and straw fires
Backyard barrel burning
Uncontrolled combustion of PCBs
II. METAL SMELTING/REFINING
Ferrous metal smelting/refining
- Sintering plants
- Coke production
- Electric arc furnaces
- Ferrous foundries
Nonferrous metal smelting/refining
- Primary aluminum
- Primary copper
- Primary magnesium
- Primary nickel
- Secondary aluminum
- Secondary copper
- Secondary lead
Scrap electric wire recovery
Drum and barrel reclamation
III. CHEMICAL MANUFACTURING
(Releases to the Environment)
Bleached chemical wood pulp and paper mills
Mono- to tetrachlorophenols
Pentachlorophenol
Chlorobenzenes
Contemporary Formation
Sources
Quantifiable
/
/
/
/
/
/
/
/
/
Preliminary
Estimate
/
/
/
/
/
/
/
/
/
Not
Quantifiable
/
/
/
/
/
/
Reservoir
Sources
Quantifiable
Preliminary
Estimate
Not
Quantifiable
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December 2003
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Table 1-5. List of Known and Suspected CDD/CDF Sources (continued)
Emission Source Category
Chlorobiphenyls (leaks/spills)
Ethylene dichloride/vinyl chloride
Dioxazine dyes and pigments
2,4-Dichlorophenoxy acetic acid
Municipal wastewater treatment
Tall oil-based liquid soaps
IV. BIOLOGICAL AND PHOTOCHEMICAL
PROCESSES
V. RESERVOIR SOURCES
Natural
- Land
-Air
- Water
- Sediments
Anthropogenic Structures
- PCP Treated Wood
Contemporary Formation
Sources
Quantifiable
/
Preliminary
Estimate
/
Not
Quantifiable
/
/
/
/
/
Reservoir
Sources
Quantifiable
Preliminary
Estimate
/
Not
Quantifiable
/
/
/
/
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December 2003
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Table 1-6. Confidence Rating Scheme for U.S. Emission Estimates
Confidence Rating
Activity Level Estimate
Emission Factor Estimate
Categories/Media for Which Releases Can Be Reasonably Quantified
High
Medium
Low
Derived from comprehensive survey
Based on estimates of average plant activity
level and number of plants or limited survey
Based on data judged possibly
nonrepresentative
Derived from comprehensive survey
Derived from testing at a limited but reasonable
number of facilities believed to be representative
of source category
Derived from testing at only a few, possibly
nonrepresentative facilities or from similar
source categories
Categories/Media for Which Releases Cannot Be Reasonably Quantified
Preliminary
Estimate
Not Quantified
Based on extremely limited data, judged to be
clearly nonrepresentative
No data available
Based on extremely limited data, judged to be
clearly nonrepresentative
1) Argument based on theory but no data, or
2) Data available indicating formation, but not
in a form that allows developing an
emission factor
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December 2003
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Table 1-7. Inventory of Environmental Releases (grams/year) of I-TEQDF
From Known Sources in the United States for 1995 and 1987
Emission Source Category
Confidence Rating8
Reference Year1995
A
B
C
D
Confidence Rating8
Reference Year 1987
A
B
C
Releases (g TEQ/yr) to Air
WASTE INCINERATION
Municipal waste incineration
Hazardous waste incineration
Boilers/industrial furnaces
Medical waste/pathological incineration
Crematoria
Sewage sludge incineration
Tire combustion
Pulp and paper mill sludge incinerators6
Biogas Combustion
POWER/ENERGY GENERATION
Vehicle fuel combustion - leaded1"
- unleaded
- diesel
Wood combustion - residential
- industrial
Coal combustion - utility boilers
Coal Combustion - residential
Coal Combustion - commercial/Industrial
Oil combustion - industrial/utility
Oil combustion - residential
OTHER HIGH TEMPERATURE SOURCES
Cement kilns (hazardous waste burning)
Lightweight aggregate kilns burning hazardous
waste
Cement kilns (non hazardous waste burning)
Asphalt mixing plants
Petroleum Refining Catalyst Regeneration
Cigarette combustion
Carbon reactivation furnaces
Kraft recovery boilers
MINIMALLY CONTROLLED OR UNCONTROLLED
COMBUSTION"
Backyard barrel burning*
Combustion of Landfill Gas
Landfill fires
Accidental Fires (Structural)
Accidental Fires (Vehicles)
Forest and Brush Fires
METALLURGICAL PROCESSES
Ferrous metal smelting/refining
- Sintering plants
- Coke production
- Electric arc furnaces
- Foundries
1,100
5.7
14.7
26.2
60.9
2.3
25.1
0.38
461
9.1
0.11
1.7
5.6
33.5
62.8
9.3
145.3
3.3
16.6
2.11
0.8
0.08
595
>1
30.0
40.0
6.0
7
7.0
1,000
>20
30.0
200
7.0
40.0
20.0
7,915
5.0
6.0
25.1
51.4
2.0
0.77
2,440
5.5
0.11
31.9
3.3
26.3
89.6
15.5
109.6
2.4
12.7
2.14
1.0
0.06
573
29.3
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December 2003
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Table 1-7. Inventory of Environmental Releases (grams/year) of I-TEQDF
From Known Sources in the United States for 1995 and 1987 (continued)
Emission Source Category
Nonferrous metal smelting/refining
- Primary copper
- Secondary aluminum
- Secondary copper
- Secondary lead
- Primary Magnesium
Drum and barrel reclamation
CHEMICAL MANUFAC. /PROCESSING SOURCES
Ethylene dichloride/vinyl chloride
TOTAL RELEASES TO AIRC
Confidence Rating8
Reference Year1995
A
B
<0.5
1.63
11.2
C
27.4
266
0.08
D
15.0
2,888
Confidence Rating8
Reference Year 1987
A
B
<0.5
1.22
C
15.3
966
0.08
12,331
Releases (g TEQ/yr) to Water
CHEMICAL MANUF. /PROCESSING SOURCES
Bleached chemical wood pulp and paper mills
POTW (municipal) wastewater
Ethylene dichloride/vinyl chloride
RESERVOIR SOURCES
Urban runoff to surface water
Rural soil erosion to surface water
TOTAL RELEASES TO WATER0
28.0
0.43
10
190
2,700
28.43
356
356
Releases (g TEQ/yr) to Land
CHEMICAL MANUF. /PROCESSING SOURCES
Bleached chemical wood pulp and paper mill
sludge
Ethlyene dichloride/vinyl chloride
Municipal wastewater treatment sludge
Commercially marketed sewage sludge
2,4-Dichlorophenoxy acetic acid
TOTAL RELEASES TO LAND0
OVERALL RELEASES (g/yr)TO THE OPEN and
CIRCULATING ENVIRONMENT
2.0
103
3.5
18.4
0.73
127.6
3,044
(SUM OF COLUMNS A, B, C )
14.1
103
3.5
21.3
141.8
12,829 (SUM OF
COLUMNS A, B, C )
V The most reliable estimates of environmental releases are those sources within Categories A, B and C (see footnote 'a'
for definitions).
a A = Characterization of the Source Category judged to be Adequate for Quantitative Estimation with High Confidence
in the Emission Factor and High Confidence in Activity Level.
B = Characterization of the Source Category judged to be Adequate for Quantitative Estimation with Medium
Confidence in the Emission Factor and at least Medium Confidence in Activity Level.
C = Characterization of the Source Category judged to be Adequate for Quantitative Estimation with Low Confidence
in either the Emission Factor and/or the Activity Level.
D = Preliminary Indication of the Potential Magnitude of I-TEQDF Emissions from "Unqualified" (i.e., Category D)
Sources in Reference Year 1995. Based on extremely limited data, judged to be clearly nonrepresentative.
b Leaded fuel production and the manufacture of motor vehicle engines requiring leaded fuel for highway use have been
prohibited in the United States. (See Section 4.1 for details.)
0 TOTAL reflects only the total of the estimates made in this report.
d This refers to conventional pollutant control, not dioxin emissions control. Very few of the sources listed in this inventory
control specifically for CDD/CDF emissions.
e Included within estimate for Wood Combustion - industrial.
f This term refers to the burning of residential waste in barrels.
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December 2003
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Table 1-8. Inventory of Environmental Releases (grams/year) of TEQDF-WH098
From Known Sources in the United States for 1995 and 1987
Emission Source Category
Confidence Rating8 II Confidence Rating8
Reference Yearl 995 || Reference Year 1 987
A
B
C
D || A
B
C
Releases (g TEQ/yr) to Air
WASTE INCINERATION
Municipal waste incineration
Hazardous waste incineration
Boilers/industrial furnaces
Medical waste/pathological incineration
Crematoria
Sewage sludge incineration
Tire combustion
Pulp and paper mill sludge incinerators'
Biogas Combustion
POWER/ENERGY GENERATION
Vehicle fuel combustion - leaded1"
- unleaded
- diesel
Wood combustion - residential
- industrial
Coal combustion - utility boilers
Coal Combustion - residential
Coal Combustion - commercial/Industrial
Oil combustion - industrial/utility
Oil combustion - residential
OTHER HIGH TEMPERATURE SOURCES
Cement kilns (hazardous waste burning)
Lightweight aggregate kilns burning hazardous
waste
Cement kilns (non hazardous waste burning)
Asphalt mixing plants
Petroleum Refining Catalyst Regeneration
Cigarette combustion
Carbon reactivation furnaces
Kraft recovery boilers
MINIMALLY CONTROLLED OR UNCONTROLLED
COMBUSTION"
Backyard barrel burning9
Combustion of Landfill Gas
Landfill fires
Accidental Fires (Structural)
Accidental Fires (Vehicles)
Forest and Brush Fires
METALLURGICAL PROCESSES
Ferrous metal smelting/refining
- Sintering plants
- Coke production
- Electric arc furnaces
- Foundries
1,250
5.8
14.8
27.6
60.1
2.3
28
0.39
488
9.1e
0.11
2.0
5.6
33.5
62.8s
10.7
156.1
3.3e
17.8
2.21
0.8
0.08s
628
< 1
30
40
6
7
7
1,000
> 20
30
200
7.0
40
20
8,877
5.0
6.1
26.4
50.8
2.0
0.78
2,590
5.5s
0.11
37.5
3.6
27.8
89.6s
17.8
117.8
2.4s
13.7
2.24
1.0
0.06s
604
32.7
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December 2003
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Table 1-8. Inventory of Environmental Releases (grams/year) of TEQDF-WH098
From Known Sources in the United States for 1995 and 1987 (continued)
Emission Source Category
Nonferrous metal smelting/refining
- Primary copper
- Secondary aluminum
- Secondary copper
- Secondary lead
- Primary Magnesium
Drum and barrel reclamation
CHEMICAL MANUFAC. /PROCESSING SOURCES
Ethylene dichloride/vinyl chloride
TOTAL RELEASES TO AIRC
Confidence Rating8
Reference Year1995
A
B
<0.5e
1.72
11. T
C
29.1
271
0.08
D
15.0
3,125
Confidence Rating8
Reference Year 1987
A
B
<0.5e
1.29
C
16.3
983
0.08
13,515
Releases (g TEQ/yr) to Water
CHEMICAL MANUF. /PROCESSING SOURCES
Bleached chemical wood pulp and paper mills
POTW (municipal) wastewater
Ethylene dichloride/vinyl chloride
RESERVOIR SOURCES
Urban runoff to surface water
Rural soil erosion to surface water
TOTAL RELEASES TO WATER0
19.5
0.43s
10
190
2,700
19.93
356
356
Releases (g TEQ/yr) to Land
CHEMICAL MANUF. /PROCESSING SOURCES
Bleached chemical wood pulp and paper mill
sludge
Ethlyene dichloride/vinyl chloride
Municipal wastewater treatment sludge
Commercially marketed sewage sludge
2,4-Dichlorophenoxy acetic acid
TOTAL RELEASES TO LAND0
OVERALL RELEASES (g/yr)TO THE OPEN and
CIRCULATING ENVIRONMENT
1.4
76.6
2.6
28.9
0.73s
110.23
3,255
(SUM OF COLUMNS A, B, C )
14.1
76.6
2.6
33.4
126.7
13,998 (SUM OF
COLUMNS A, B, C )
V The most reliable estimates of environmental releases are those sources within Categories A, B and C (see footnote 'a'
for definitions).
a A = Characterization of the Source Category judged to be Adequate for Quantitative Estimation with High Confidence in
the Emission Factor and High Confidence in Activity Level.
B = Characterization of the Source Category judged to be Adequate for Quantitative Estimation with Medium
Confidence in the Emission Factor and at least Medium Confidence in Activity Level.
C = Characterization of the Source Category judged to be Adequate for Quantitative Estimation with Low Confidence in
either the Emission Factor and/or the Activity Level.
D = Preliminary Indication of the Potential Magnitude of I-TEQDF Emissions from "Unqualified" (i.e., Category D)
Sources in Reference Year 1 995. Based on extremely limited data, judged to be clearly nonrepresentative.
b Leaded fuel production and the manufacture of motor vehicle engines requiring leaded fuel for highway use have been
prohibited in the United States. (See Section 4.1 for details.)
0 TOTAL reflects only the total of the estimates made in this report.
d This refers to conventional pollutant control, not dioxin emissions control. Very few of the sources listed in this inventory
control specifically for CDD/CDF emissions.
e Congener-specific emissions data were not available; the I-TEQDF emission estimate was used as a surrogate for the TEQDF-
WH098 emission estimate.
f Included within estimate for Wood Combustion - industrial.
9 This term refers to the burning of residential waste in barrels.
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1-40
December 2003
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Table 1-9. I-TEQDF Emission Factors Used to Develop National
Emission Inventory Estimates of Releases to Air
Emission Source
Waste Incineration
Municipal waste incineration
Hazardous waste incineration
Boilers/industrial furnaces
Medical waste/pathological incineration
Crematoria
Sewage sludge incineration
Tire combustion
Pulp and paper mill sludge incinerators
Power/Energy Generation
Vehicle fuel combustion - leaded1"
- unleaded
- diesel
Wood combustion - residential
- industrial
Coal combustion - utility
Oil combustion - industrial/utility
Other High Temperature Sources
Cement kilns burning hazardous waste
Cement kilns not burning hazardous waste
Petroleum refining catalyst regeneration
Cigarette combustion
Carbon reactivation furnaces
Kraft recovery boilers
Minimally Controlled or Uncontrolled Combustion
Backyard barrel burning
Metallurgical Processes
Ferrous metal smelting/refining
- Sintering plants
Nonferrous metal smelting/refining
- Primary copper
- Secondary aluminum smelting
- Secondary copper smelting
- Secondary lead smelters
Drum and barrel reclamation
Chemical Manuf ./Processing Sources
Ethylene dichloride/vinyl chloride
I-TEQDF Emission Factor
1995
38. T
3.83
0.64
598a
17
6.94
0.282
b
45
1.5
172
2
0.56 to 13.2°
0.079
0.20
1.04 to 28.58"
0.27
1.52
0.00043 to
0.0029
1.2
0.029
72.8
0.55 to 4.14
<0.31
21.1
d
0.05 to 8.31
16.5
0.95a
1987
573a
3.83
0.64
1,706a
17
6.94
0.282
b
45
1.5
172
2
0.56 to 13.2°
0.079
0.20
1.04 to 28.58"
0.27
1.52
0.00043 to
0.0029
1.2
0.029
72.8
0.55 to 4.14
<0.31
21.1
d
0.05 to 8.31
16.5
Emission Factor Units
ng TEQ/kg waste combusted
ng TEQ/kg waste combusted
ng TEQ/kg waste combusted
ng TEQ/kg waste combusted
fjg TEQ/body
ng TEQ/kg dry sludge
combusted
ng TEQ/kg tires combusted
pg TEQ/km driven
pg TEQ/km driven
pg TEQ/km driven
ng TEQ/kg wood combusted
ng TEQ/kg wood combusted
ng TEQ/kg coal combusted
ng TEQ/L oil combusted
ng TEQ/kg clinker produced
ng TEQ/kg clinker produced
ng TEQ/barrel reformer feed
ng TEQ/cigarette
ng TEQ/kg of reactivated carbon
ng TEQ/kg solids combusted
ng TEQ/kg waste combusted
ng TEQ/kg sinter
ng TEQ/kg copper produced
ng TEQ/kg scrap feed
ng TEQ/kg scrap consumed
ng TEQ/kg lead produced
ng TEQ/drum
nq TEQ/kq EDC produced
a Different emission factors were derived for various subcategories within this industry; the value listed is a weighted
average.
b Included within total for Wood Combustion - Industrial.
c Emission factor of 0.56 ng l-TEQDF/kg used for non-salt-laden wood; emission factor of 13.2 ng l-TEQDF/kg used for salt-
laden wood.
d Facility-specific emission factors were used ranging from 3.6 to 16,600 ng l-TEQDF/kg scrap consumed.
e Emission factor of 1.04 ng l-TEQDF/kg used for kilns with APCD inlet temperatures less than 450°F; emission factor of
28.58 ng l-TEQDF/kg used for kilns with APCD inlet temperatures greater than 450°F.
TEQ = Toxic equivalency factor.
ng = nanogram.
kg = kilogram.
pg = picogram.
DRAFT-DO NOT QUOTE OR CITE
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December 2003
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Table 1-10. TEQDF-WH098 Emission Factors Used to Develop National
Emission Inventory Estimates of Releases to Air
Emission Source
Waste Incineration
Municipal waste incineration
Hazardous waste incineration
Boilers/industrial furnaces
Medical waste/pathological incineration
Crematoria
Sewage sludge incineration
Tire combustion
Pulp and paper mill sludge incinerators
Power/Energy Generation
Vehicle fuel combustion - leadedb
- unleaded
- diesel
Wood combustion - residential
- industrial
Coal combustion - utility
Oil combustion - industrial/utility
Other High Temperature Sources
Cement kilns burning hazardous waste
Cement kilns not burning hazardous waste
Petroleum refining catalyst regeneration
Cigarette combustion
Carbon reactivation furnaces
Kraft recovery boilers
Minimally Controlled or Uncontrolled Combustion
Backyard barrel burning
Metallurgical Processes
Ferrous metal smelting/refining
- Sintering plants
Nonferrous metal smelting/refining
- Primary copper
- Secondary aluminum smelting
- Secondary copper smelting
- Secondary lead smelters
Drum and barrel reclamation
Chemical Manuf ./Processing Sources
Ethylene dichloride/vinyl chloride
TEQDF-WHO98 Emission Factor
1995
43.4s
3.88
0.65
633a
17f
7.04
0.281
b
53
1.6
182
2f
0.60 to 13.2
0.078
0.23
1.11 to 30.70"
0.29
1.59
0.00044 to
0.0030
1.2f
0.028
76. 8f
0.62 to 4.61
<0.31f
22.4
d
0.05 to 8.81
17.5
0.95a-f
1987
644a
3.88
0.65
1,811a
17f
7.04
0.281
b
53
1.6
182
2f
0.60 to 13.2
0.078
0.23
1.11 to 30.70"
0.29
1.59
0.00044 to
0.0030
1.2f
0.028
76. 8f
0.62 to 4.61
<0.31f
22.4
d
0.05 to 8.81
17.5
Emission Factor Units
ng TEQ/kg waste combusted
ng TEQ/kg waste combusted
ng TEQ/kg waste combusted
ng TEQ/kg waste combusted
fjg TEQ/body
ng TEQ/kg dry sludge
combusted
ng TEQ/kg tires combusted
pg TEQ/km driven
pg TEQ/km driven
pg TEQ/km driven
ng TEQ/kg wood combusted
ng TEQ/kg wood combusted
ng TEQ/kg coal combusted
ng TEQ/L oil combusted
ng TEQ/kg clinker produced
ng TEQ/kg clinker produced
ng TEQ/barrel reformer feed
ng TEQ/cigarette
ng TEQ/kg of reactivated carbon
ng TEQ/kg solids combusted
ng TEQ/kg waste combusted
ng TEQ/kg sinter
ng TEQ/kg copper produced
ng TEQ/kg scrap feed
ng TEQ/kg scrap consumed
ng TEQ/kg lead produced
ng TEQ/drum
nq TEQ/kq EDC produced
a Different emission factors were derived for various subcategories within this industry; the value listed is a weighted
average.
b Included within total for Wood Combustion - Industrial.
c Emission factor of 0.60 ng TEQDF-WH098/kg used for non-salt-laden wood; emission factor of 13.2 ng l-TEQDF/kg used for
salt-laden wood.
d Facility-specific emission factors were used ranging from 3.6 to 16,900 ng TEQDF-WH098/kg scrap consumed.
e Emission factor of 1.1 1 ng TEQDF-WH098/kg used for kilns with APCD inlet temperatures less than 450°F; emission factor
of 30.70 ng TEQDF-WH098/kg used for kilns with APCD inlet temperatures greater than 450°F.
f Congener-specific data were not available; the I-TEQDF emission factor was used as a surrogate for the TEQDF-WH098
emission factor.
TEQ = Toxic equivalency factor.
ng = nanogram.
kg = kilogram.
pg = picogram.
DRAFT-DO NOT QUOTE OR CITE
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December 2003
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Table 1-11. Identification of Products Containing CDD/CDF in 1995 and 1987
(g l-TEQDF/yr)
Product
Bleached chemical wood pulp
Ethylene dichloride/vinyl chloride
Dioxazine dyes and pigments
Pentachlorophenol
Total Amounts in Products
1995
40
0.02
0.36
8,400
8,440
1987
505
NA
64.0
36,000
36,569
NA = information not available
Table 1-12. Identification of Products Containing CDD/CDF in 1995 and 1987
(gTEQDF-WH098/yr)
Product
Bleached chemical wood pulp
Ethylene dichloride/vinyl chloride
Dioxazine dyes and pigments
Pentachlorophenol
Total Amounts in Products
1995
40
0.02
0.36
4,800
4,840
1987
505
NA
64.0
20,000
20,569
NA = information not available
DRAFT-DO NOT QUOTE OR CITE
1-43
December 2003
-------
Cl
2,3,7,8-Tetrachlorodibenzo-p-dioxin
ci
1,2,3,7,8-Pentachlorodibenzo-p-dioxin
ci
CI'\CI
3,3',4,4',5,5'-Hexachlorobiphenyl
ci
2,3,7,8-Tetrachlorodibenzofuran
ci
2,3,4,7,8-Pentachlorodibenzofuran
ci
3,3',4,4',5-Pentachlorobiphenyl
Figure 1-1. Chemical Structure of 2,3,7,8-TCDD and Related Compounds
DRAFT-DO NOT QUOTE OR CITE
1-44
December 2003
-------
Emission Source
(tested/total units)
Municipal Solid Waste Incineration (39/13O>
Backyard Barrel Burning (NA
Medical Waste Incineration (2O/2.4OO)
Secondary Copper Smelting (2/3)
Cement K||ns Burning Haz Waste (1O/34)
Residential Wood Burning (7/25.OOD.OOO)
Utility / Industrial Coal Combustion (11/7)
On-Road Diesel Fuel Combustion (NA)
Secondary Aluminum Smelting (6776)
Industrial Wood Burning (9
Iron Ore Sinter Plants (2/1
Cement K||ns Not Burning Haz Waste (15/178)
Sewage Sludge Incineration (13/257)
Manufacture of EDC/VC (?/?)
Utility / Industrial O|| Combustion (=>2/?)
Crematoria (16/1.555)
Hazardous Waste Incineration (1 7/16
On-Road Unleaded Gas Fuel Combustion (7/7)
Onload Leaded Gas Fuel Combustion (7/7)
Best Estimate of
I-TEQ Emission Factor
(ng/kg or ng/L)
Total Annual 'Activity"
(thousand metric tons/yr or
million L/yr)
Annual I-TEQ Emission
(g l-TEQ/yr)
)
I
I)
'>
*)
dj
tt
B)
O
J>
I)
'>
*
!}
r)
i
i
i
i
i
i
i
i
i
i
i
i
i
i
i
— i — i — i — i — i i i i
The figures include sources with annual I-TEQ emission estimates greater than 5 g l-TEQ/yr in
one or both of Reference Year 1995 and Reference Year 1987. Derivations of emission factors
and annual "activity" estimates (e.g., kg of waste Incinerated) are presented in the following
chapters of this report. The difference in bar shading indicates the degree of confidence in
the estimate. The set of numbers following the source categories indicates the number of
facilities/sites for which emission test data are available versus the number of facilities/sites
in the category. A question mark (?) Indicates that the precise number of facilities/sites could
not be estimated.
O O
O O
O O
T- O
Legend
Low Confidence
Medium Confidence
High Confidence
Figure 1-2. Estimated CDD/CDF I-TEQ Emissions to Air from Combustion Sources in the United States
(Reference Time Period: 1995)
DRAFT-DO NOT QUOTE OR CITE
1-45
December 2003
-------
Emission Source
(tested/total units)
Best Estimate of
I-TEQ Emission Factor
(ng/kg or ng/L)
Municipal Solid Waste Incineration (19/113)
Backyard Barrel Burning (NA>
Medical Waste Incineration (8/5,OOO)
Secondary Copper Smelting (2/4)
Cement K||ns Burning Haz Waste (1O/?>
Residential Wood Burning (7/25.OOO.OOO)
Utility / Industrial Coal Combustion (11/7)
On-Road Diesel Fuel Combustion (NA)
Secondary Aluminum Smelting (6/67)
Industrial Wood Burning (9/7)
Iron Ore Sinter Plants (2/7)
Cement K||ns Not Burning Haz Waste (15/?)
Sewage Sludge Incineration (13/199)
Manufacture of EDC/VC (?/7)
Utility / Industrial OH Combustion (>2/7)
Crematoria (16/7)
Hazardous Waste Incineration (17/171-227)
On-Road Unleaded Gas Fuel Combustion (7/7)
On-Road Leaded Gas Fuel Combustion (7/7)
Total Annual "Activity"
(thousand metric tons/yr or
million L/yr)
Annual I-TEQ Emission
(g l-TEQ/yr)
=
g i
o o
°. d
o
O T- T-
o o o o
T- O O O
T- O O
T- O
The figures include sources with annual I-TEQ emission estimates greater than 5 g l-TEQ/yr in
one or both of Reference Year 1995 and Reference Year 1987. Derivations of emission factors
and annual "activity" estimates (e.g., kg of waste incinerated) are presented in the following
chapters of this report. The difference in bar shading indicates the degree of confidence in the
estimate. The set of numbers following the source categories indicates the number of
facilities/sites for which emission test data are available versus the number of facilities/sites in
the category. A question mark (?) indicates that the precise number of facilities/sites could not
be estimated.
Legend
Low Confidence
Medium Confidence
High Confidence
Figure 1-3. Estimated CDD/CDF I-TEQ Emissions to Air from Combustion Sources in the United States
(Reference Time Period: 1987)
DRAFT-DO NOT QUOTE OR CITE
1-46
December 2003
-------
Municipal Solid Waste Incineration
Backyard Barrel Burning
Medical Waste Incineration
Secondary Copper Smelting
Cement Kilns Burning Haz Waste
Residential Wood Burning
Utility/Industrial Coal Combustion
On-Road Diesel Fuel Combustion
Secondary Aluminum Smelting
Industrial Wood Burning
Iran Ore Sinter Plants
Cement Kilns Not Burning Haz Waste
Sewage Sludge Incineration
Manufacture of EDC/VC
Utility/Industrial Oil Combustion
Crematoria
Hazardous Waste Incineration
On-Road Unleaded Gat Fuel Combustion
On-Road Leaded Gas Fuel Combustion
10
100
1000
10000
1995 U1987
Figure 1-4. Comparison of Estimates of Annual I-TEQ Emissions to Air (grams l-TEQ/year) for Reference Years 1987 and 1995
DRAFT-DO NOT QUOTE OR CITE
1-47
December 2003
-------
Emission Source
(tested/total units)
Best Estimate of WHO-
TEQ Emission Factor
(ng/kg or ng/L)
Municipal Solid Waste Incineration (39/13O>
Backyard Barrel Burning (NA)
Medical Waste Incineration (2O/2,4OO)
Secondary Copper Smelting (2/3)
Cement K||ns Burning Haz Waste (1O/34)
Residential Wood Burning (7/23,000,000)
Utility / Industrial Coal Combustion (11/?)
On-Road Diesel Fuel Conn bust Ion (NA)
Secondary Aluminum Smelting (6/76)
Industrial Wood Burning (9/7)
Iron Ore Sinter Plants (2/1 1}
Cement K||ns Not Burning Haz Waste (15/178)
Sewage Sludge Incineration (13/257)
Manufacture of EDC/VC (?/?>
Utility / Industrial O|| Combustion (>2/?)
Crematoria (16/1.555)
Hazardous Waste Incineration (17/162)
On-Road Unleaded Gas Fuel Combustion (7/7)
Onload Leaded Gas Fuel Combustion (7/7)
Total Annual •Activity"
(thousand metric tons/yr or
million L/yr)
Annual WHO-TEQ Emission
(g WHO-TEQ/yr)
o o o o
o o o „•
o o o o r- i-
" 2 8 § d
*- o
o o o
T- O O
i- O
The figures include sources with annual I-TEQ emission estimates greater than 5 g l-TEQ/yr in
one or both of Reference Year 1995 and Reference Year 1987. Derivations of emission factors
and annual "activity" estimates (e.g., kg of waste Incinerated) are presented in the following
chapters of this report. The difference in bar shading indicates the degree of confidence in
the estimate. The set of numbers following the source categories indicates the number of
facilities/sites for which emission test data are available versus the number of facilities/sites
in the category. A question mark (?) Indicates that the precise number of facilities/sites could
not be estimated.
o o
o o
T- O
Legend
Low Confidence
Medium Confidence
High Confidence
Figure 1-5. Estimated CDD/CDF WHO-TEQ Emissions to Air from Combustion Sources in the United States
(Reference Time Period: 1995)
DRAFT-DO NOT QUOTE OR CITE
1-48
December 2003
-------
Emission Source
(tested/total units)
Best Estimate of WHO-
TEQ Emission Factor
(ng/kg or ng/L)
Municipal Solid Waste Incineration (15/113)
Backyard Barrel Burning (NA)
Medical Waste Incineration (8/5,OOO)
Secondary Copper Smelting (2/4)
Cement K||ns Burning Haz Waste (1O/?>
Residential Wood Burning (7/25.OOO,OOO)
Utility / Industrial Coal Combustion (11/7)
On-Road Diesel Fuel Combustion (NA)
Secondary Aluminum Smelting (3/67)
Industrial Wood Burning (9/?)
Iron Ore Sinter Plants (2/7)
Cement K||ns Not Burning Haz Waste (15/7)
Sewage Sludge Incineration (13/199)
Manufacture of EDC/VC (?/?)
Utility / Industrial OH Combustion (>2/7>
Crematoria (16/7)
Hazardous Waste Incineration (17/171-227)
On-Road Unleaded Gas Fuel Combustion (7/7)
On-Road Leaded Gas Fuel Combustion (7/7)
Total Annual "Activity"
(thousand metric tons/yr or
million L/yr)
Annual WHO-TEQ Emission
(g WHO-TEQ/yr)
=
° § § §
T- O O
i- O
T- T- O
The figures include sources with annual I-TEQ emission estimates greater than 5 g l-TEQ/yr in
one or both of Reference Year 1995 and Reference Year 1987. Derivations of emission factors
and annual "activity" estimates (e.g., kg of waste incinerated) are presented in the following
chapters of this report. The difference in bar shading indicates the degree of confidence in the
estimate. The set of numbers following the source categories indicates the number of
facilities/sites for which emission test data are available versus the number of facilities/sites in
the category. A question mark (?) indicates that the precise number of facilities/sites could not
be estimated.
*
4
4
4
<
4
4
4
4
<
4
4
4
T- O O O C
i- O O C
T- 0 C
Legend
Low Confidence
Medium Confidence
High Confidence
Figure 1-6. Estimated CDD/CDF WHO-TEQ Emissions to Air From Combustion Sources in the United States
(Reference Time Period: 1987)
DRAFT-DO NOT QUOTE OR CITE
1-49
December 2003
-------
Municipal Solid Warts Incineration
Backyard Barrel Burning
Medical Waste Incineration
Secondary Copper Smelting
Cement Kilne Burning Haz Waste
Residential Wood Burning
Utility/Industrial Coal Combustion
On-Road Diesel Fuel Combustion
Secondary Aluminum Smelting
Industrial Wood Burning
Iron Ore Sinter Plants
Cement Kilns Not Burning Haz Waste
Sewage Sludge Incineration
Manufacture of EDC/VC
Utility/Industrial Oil Combustion
Crematoria
Hazardous Waste Incineration
On-Road Unleaded Gas Fuel Combustion
On-Road Leaded Gas Fuel Combustion
10
100
1000
10000
1995 U1987
Figure 1-7. Comparison of Estimates of Annual WHO-TEQ Emissions to Air (grams WHO-TEQ/year)
for Reference Years 1987 and 1995
DRAFT-DO NOT QUOTE OR CITE
1-50
December 2003
-------
MSWI (mass bum-water wall w/hot-sida ESP)
0 0.02 0.04 0.06
2,3,7,6-TCDD
1,2,3,7,8fBCDD
1,2,3.4.7,8JtoCDD
1,2,3.8.7.8^x000
1,2,3,7,B,S-HxCDD
1.2.3.4.B.7,BHpCDD
1,2,3.4.6,7,8.9-OCDD
2,3,7.8-TCDF
1,2,3,7,&feCDF
2,3,4,7,8-PeCDF
1,2,3,4.7,B-HXCDF
1.2,3,6,7,9-HxCDF
1.2.3.7.8.9-HOXIF
2.3,4,6,7,8-HxCDF
1,2,3,4,6,7,WpCDF
1,2.3,4,7,8,94%>CDF
1,2,3,4,6,7,8,B4CDF
MSWI (mass bum-water wall w/ds &ff)
0 0.02 0.04 0.06
1,2,3,7,8-PeCDD
1.2,3,4.7.a-HxCDD
1,2,3,6.7,B-HxCDD
1.2,3,7,8.9-HxCDD
1,2,3,4,B,7,8-HpCDD
1,2.3.4.e,7,a.9-OCDD
2.3.7.B-TCDF
1,2.3,7,&faCDF
1,2.3.7,e,9-HxCDF
2,3.4,6J,S-HxCDF
1,2.3.4.7,8,tWpCDF
1.2,3,4,6.7.8,9-OCDF
2,3,7,B-TCDD
1A3.7.UWDO
1,2,3,4,7,6-HxCOD
1,2.3,6,7.6-HxCDD
1,2,3,4,6,7,8-HpCDD
1,2,3,4,6,7,8,^)000
2.3,7,8-TCDF
1,2,3,7,BJ>eCDF
2,3,4,7,8-PeCDF
1.2,3.4,7.8^x00=
1,2,3.B,7,8-HxCDF
1,2,3.4,6.7,8HpCDF
1,2.3,4,7,8,WpCDF
1.2,3,4,6,7,8,9-OCDF
Hazardous Waste Incinerators
0.05
Medical Waste Incinerators (Wo APCD)
2.3.7.B-TCDF
1,2.3,7,8-PeCDF
1,2.3,4.6.7.8,a-OCDF
Figure 1-8. The Congener Profiles (as fractional distributions to total CDD/CDF) of Anthropogenic
Sources of Chlorinated Dibenzo-p-Dioxins and Chlorinated Dibenzofurans in the United States
DRAFT-DO NOT QUOTE OR CITE
1-51
December 2003
-------
Cement Kilns Not Burning Haz Waste
0 0.01 0.02
2.3,7,8-TCDD
1,2.3,7,8-PaCDD
1,2,3,4,7,8^x000
1,S,3.7,8,WxCDD
1.2.3,4.6.7,8#pCDD
1,2,3,4,6,7,8,9-OCDD
2,3,7,8-TCDF
1,2,3,7,W9CDF
2.3,4,7.8-PeCDF
1.2,3,4.7,8HxCDF
1,2,3,6,7,8-HxCDF
1.2,3,7.8.9-HxCDF
2.3,4,e.7,9-HxCDF
1,2,3.4,6.7.8UpCDF
1,2,3,4,7,8,eHpCDF
1,2.3,4,6,7,8,9-OCDF
Cement Kilns Burning Haz Waste
0 0.005 0.01
2,3,7,8-TCDD
1,2.3,7,BfBCDD
1.2,3,4J,8-HxCDD
1,2,3,8,7,WxCDD
1,2,3,7,8,9-HxCDD
1,2.3.4,6.7.*HpCDD
1,2,3,4.6,7,B.B-OCDD
2,3.7,8-TtaJF
1.2.3.7.&feCDF
2.3,4,7.»PeCDF
J,44*7,WfeCDF
1.2.3,6,7.8-HxCDF
1,2.3,7,8.WxCDF
S,3,4,6,7,B-HxCDF
1.2,3,4.6,7,8-HpCDF
1,2,3.4,7,8,WpCDF
1,2,3,4,6,7,8,9-OCDF
Industrial Oil-Fired Boilers
0 0.05 0.1 0.15 0.2 0.25
1,2,3,7,8-PBCDD
1,2,3,4,7,8^x000
1,2,3,B,7,8-HxCDD
1,2,3,7,8,9-HxCDD
1,2,3.4,6,7,8-HflCDD
2,3,7,8-TCDF
2,3.4.7.8-PvCDF
1,2,3,8,7,B-HxCDF
2.3,4.6.7.B#xeDF
1.2,3,4,e.7,9-HpCDF
1,2.3,4,7.8.9-HpCDF
1,S,3,4,6,7,8,»-OCDF
I
I
I
^^
I
I
I
I
|
I
^^^
Industrial/Utility Coal Combustors
2,3,7,8-TCDD
1,2,3,7,B-PeCDD
1.2,3.4J,a-HxCDO
1,2,3,6,7,8-HxCDD
1.2,3,4.8,7,8,9-0000
2,3,7,8-7ta3F
1,2,3,7,8-PBCDF
2.3,4,7.8-PeCDF
1.2.3,4.7.8-HxCDF
1.2,3,B,7.B-HxCDF
1,2,3,7,8,9-HxCDF
2,3.4,6.7.8-HxCDF
1,2,3,4,7,8,B-HpCDF
1,2,3,4,e,7,8,*OCDF
Figure 1-8. The Congener Profiles (as fractional distributions to total CDD/CDF) of Anthropogenic
Sources of Chlorinated Dibenzo-p-Dioxins and Chlorinated Dibenzofurans in the United States
(continued)
DRAFT-DO NOT QUOTE OR CITE
1-52
December 2003
-------
Industrial Wood Combustors
0 0.05 0.1 0.15
2,3,7,8-TCDD
1,2,3,7,8fBCDD
1,2,3,4,7,B-HxCDD
1,2,3,6,7,8-HxCDD
1,2,3,7,8,6-HxCDD
1.2.3.4,6.7.8#pCDD
1,2,3,4,e,7,8,9-OCDD
2,3,7,8-TCDF
2.3.4.7,8fBCDF
1,2,3,4,7,8HxCDF
1,2,3,B,7,a-HxCDF
1, 2,3,7 A^HxCDF
2,3.4,6,7,8HxCDF
1
|
^M
^^^H
^B
^^H
^^^^^
B
^H
1,2,3,4.8,7,8-HpCDF ^^^^^^^^^^^^^^^^|
1,2,3,4.7,B,WpCDF •
1,2.3.4,6.7A9-OCDF
^^
Unleaded Gasoline Vehicles (w/ cat. com.)
2,3,7, B-TCDD
1,2,3,7,BfaCDD
1,2,3,4,7,8-HxCDD
1,2,3,8,7,8-HxCDD
1,2,3,7,3,9-HxCDD
1,2,3,4.B,7,WpCDD
1,2,3,4,6, 7,8,3-OCDD
2,3,7,8-TCDF
1.2,3.7.8faCDF
2,3,4,7.8-PeCDF
ttW,«*CDF
1,2,3,6,7,B-HxCDF
1.2,3,7.8.9-HxCDF
2,3,4,0.7,B-HxCDF
1.2.3.4,B.7.8MpCDF
1,2,3,4,7,8,9-HpCDF
0.05 0.1 0.15 0.2 0.25 0.3 0.3
1
I
I
I
I
^^^
™
•
1
1
1
^^^^
^^^^1
^^^*
5
D/ese/ Trucks (tailpipe emissions)
0 0.05 0.1 0.15 0.2 0.25 0.3
2,3,7,8-TCDD
1,2,3,e,7,a-HxCDD
1,2,3,4,6,7,8-HpCDD
1,2,3^,6,7, B.B-OCDD
2,3,7,8-TCDF
1,2,3,7,8-PeCDF
1,2,3,4,7,8-HxCDF
1,2,3,B,7,8-HaS3F
1,2.3,7.8.9-HxCDF
2,3,4,B,7,8-HxCDF
1.2,3,4.6,7,WpCBF
1,2,3,4,7,B,»+tpCDF
1.2.3.4.B,7.8.»OCDF
I
^^
|
1
1
|
|
1
^^^^|
|
^^^^™
Diesel Trucks (Baltimore Tunnel)
0 0.1 0.2 0.3 0.4 0.5
2,3,7,8-TCDD
1,2,3,7,8-PgCDD
1,2,3,4,7,8-HxCDD
1.2,3,7,8.9-HxCDD
1,2,3,4,6,7,8-HpCDD
2,3,7,8-TCDF
2,3,4,7,&foCDF
1,2,3.4,7,8MxCDF
I,2,3,7,B,WftCDF
2,3,4,8,7,8-HxCDF
1.2.3,4.6.7.WpCDF
1,2,3,4.7,B,fWpCDF
1,2,3.4,e,7,e.8-OCDF
\
\
1
^^*
1
1
1
1
I
1
•
•
Figure 1-8. The Congener Profiles (as fractional distributions to total CDD/CDF) of Anthropogenic
Sources of Chlorinated Dibenzo-p-Dioxins and Chlorinated Dibenzofurans in the United States
(continued)
DRAFT-DO NOT QUOTE OR CITE
1-53
December 2003
-------
Secondary Aluminum Smelters
1,2,3,4,7,8-HxCDD
1,2,3,6,7,8-HrCDD
1,2,3.7.8,tWxCDD
1,2,3.4.6,7,8.9-OCDD
2,3,7,8-TCDF
1.2.3,7.8-PeCDF
2,3,4,7.8-PeCDF
1,2,3,4.7,B-HXCDF
1,2,3,6,7,WxCDF
1,2,3,7,8,9-HxCDF
2.3.4,e.7.B-HxCDF
1,2.3,4,e,7,B-HpCDF
1,2.3.4,7,8.9-HpCDF
1,2,3,4,6,7,8,9-OCDF
2,3.7,6-TCDD
1,2,3,7,B-PeCDD
1,2,3,4.7,8-HxCDD
1.2,3,S,7.e-HxCDD
1,2,3,7,8,9^x0X1
1,2,3,4,B.7,B-HpCDD
1.2,3,4,S.7,a,9-OCDD
2,3,7,8-TCDF
1,2,3,7,8-PeCDF
2,3,4,7,8-PaCDF
1.2,3,4.7.8-HxCDF
1.2,3,B,7.8-HxCDF
1,2,3,7,8,9-HxCDF
2.3,4,6.7.8-HxCDF
1,2,3.4.6,7.8-HpCDF
1,2,3,4,7,8,9-HpCDF
1.2,3,4,6.7,3,9-OCDF
Secondary Lead Smelters
0.005 0.01 0.015
Sewage Sludge Incinerators
0 0.05 0.1 0.15 0.2 0.25 0.3
2,3,7,8-TCDD
1.2.3.4.7.WXCDD
1,2,3,6,7,B-HxCDO
1.2,3.7.8,9-HxCDO
1,2,3,4,B,7,B-HpCDD
2.3.7.8-TCDF
1,2,3,7,8-PBCDF
2.3,4,7.8-PeCDF
1,2.3,6,7.8-HxCDF
1.2.3.7.B.9-HXCDF
2,3,4,6,7,8-HxCDF
1,2,3,4,e,7,B4ipCDF
1.2,3,4.7,3,0-HpCOF
1,S,3.4,B,7.8,9-OCDF
I
I
I
^*
^^^^^f
1
•
1
1
I
•
•
Paper Pulp
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8
1.2.3,7,a-PBCDD
1.2,3,4.7.8-HxCDD
1.2.3,6,7.8-HxCDD
1,2,3,7,8,9-HxCDD
1,2,3.4,6,7,8-HpCDD
1.2,3,4.6.7,3,9-OCDD
I
2.3.7.B-TCDF ^^^^^^^|
1,2,3,7,a-PoCDF
2,3,4,7,8-PeCDF
1,2,3,4,7,8HxCDF
1,2.3.6.7,8#xCDF
1,2.3.7,B,9-HxeDF
2,3,4.B,7,B-HxCDF
1.2.3.4.e.7.WpCDF
1.2,3.4.7.B,SJ1pCDF
1,2,3,4,6,7,8,9-OCDF
Figure 1-8. The Congener Profiles (as fractional distributions to total CDD/CDF) of Anthropogenic
Sources of Chlorinated Dibenzo-p-Dioxins and Chlorinated Dibenzofurans in the United States
(continued)
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December 2003
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Technical Pentachlorophenol (PCP)
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8
S.3.7.8-TCOD
1,2,3.7,8-PeCDD
1,2,3,7,8,9-HxCDD
1,2,3,4,6, 7, WfeCDD
1,2,3.4.6,7,8.9-QCDD
£3,7,B-TCDF
1,2,3,7,8-PeCDF
2.3.4,7,8-PBCDF
1,2,3,4,7,WxCDF
2.3,4,6,7.8-HxCDF
1.2.3.4.7.8.WpCDF
1,2,3,4,6,7,8,9-OCDF
2,4-D Salts and Esters
2.3,7,B-TCDD
1.2.3,7,a-PBCDD
1.2,3,4.7.8-HxCDD
1,2,3,7,8,9-HxCDD
1,2.3,4,6,7.8-HpCDD
1.2,3,4.6.7,3,9-OCDD
2,3,4.7.B-PeCDF
1,2,3,4,7,WxCDF
1,Z,3,7,B,9-H*CDF
2,3,4.B,7,B-HxCDF
1.2.3.4.e.7.WpCDF
1.2,3.4.7.B,WpCDF
1,2,3,4,6,7,8,8-OCDF
Figure 1-8. The Congener Profiles (as fractional distributions to total CDD/CDF) of Anthropogenic
Sources of Chlorinated Dibenzo-p-Dioxins and Chlorinated Dibenzofurans in the United States
(continued)
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2.0. MECHANISMS OF FORMATION OF DIOXIN-LIKE COMPOUNDS DURING
COMBUSTION OF ORGANIC MATERIALS
More than a decade of combustion research has contributed to a general
understanding of the central molecular mechanisms that form CDDs and CDFs emitted
from combustion sources. Current understanding of the conditions necessary to form
CDDs and CDFs were primarily derived from studying full-scale municipal solid waste
incinerators (MSWIs), augmented with observations involving the experimental combustion
of synthetic fuels and feeds within the laboratory. However, the formation mechanisms
elucidated from these studies are generally relevant to most combustion systems in which
organic material is burned with chlorine. Intensive studies have examined MSWIs from the
perspective of identifying the specific formation mechanism(s) that occur within the
system. This knowledge may lead to methods that prevent the formation of CDDs and
CDFs and their release into the environment. Although much has been learned from such
studies, how to completely prevent CDDs/CDFs from forming during the combustion of
certain organic materials in the presence of a source of chlorine and oxygen is still
unknown. The wide variability of organic materials incinerated and thermally processed by
a wide range of combustion technologies that have variable temperatures, residence
times, and oxygen requirements adds to this complex problem. However, central chemical
events that participate in forming CDDs and CDFs can be identified by evaluating emission
test results from MSWIs in combination with laboratory experiments.
CDD/CDF emissions from combustion sources can potentially be explained by three
principal mechanisms, which should not be regarded as being mutually exclusive. The
first is that CDDs and CDFs are present as contaminants in the combusted organic
material, and pass through the furnace and are emitted unaltered. This mechanism is
discussed in Section 2.1. The second is that CDD/CDFs ultimately form from the thermal
breakdown and molecular rearrangement of precursor ring compounds, which are defined
as chlorinated aromatic hydrocarbons with a structural resemblance to the CDD and CDF
molecules. Ringed precursors emanated from the combustion zone are a result of the
incomplete oxidation of the constituents of the feed (i.e., products of incomplete
combustion). The precursor mechanism is discussed in Section 2.2. The third
mechanism, similar to the second and described in Section 2.3, is that CDD/CDFs are
synthesized de novo. De novo synthesis describes a pathway of forming CDD/CDFs from
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heterogeneous reactions on fly ash involving carbon, oxygen, hydrogen, chorine, and a
transition metal catalyst. With these reactions, intermediate compounds having an
aromatic ring structure are formed. Studies in this area suggest that aliphatic compounds,
which arise as products of incomplete combustion, may play a critical role in initially
forming simple ring molecules, which later evolve into complex aromatic precursors.
CDD/CDFs are then formed from the intermediate compounds. In both mechanisms (2)
and (3), formation occurs outside the furnace, in the so-called post-combustion zone.
Particulate bound carbon is suggested as the primary reagent in the de novo syntheses
pathway.
Section 2.4 gives an overview of studies that investigate the role that chlorine
plays in forming CDDs and CDFs. Although chlorine is an essential component for the
formation of CDD/CDFs in combustion systems, the empirical evidence indicates that for
commercial scale incinerators, chlorine levels in feed are not the dominant controlling
factor for rates of CDD/CDF stack emissions. There are complexities related to the
combustion process itself, and types of air pollution control equipment that tend to mask
any direct association. Therefore, the chlorine content of fuel and feeds to a combustion
source is not a good indicator of levels of CDDs and CDFs emitted from the stack of the
same source.
Section 2.5 discusses the generation and formation of coplanar PCBs. The
presence of coplanar PCBs in stack emissions to combustors is an area in need of further
research. Evidence to date suggests that PCB emissions are mostly attributed to PCB
contamination in waste feeds, and that emissions are related to mechanism (1). However,
newly published research has also indicated that it is possible to form PCBs in much the
same way as described in mechanisms (2) and (3) identified in the formation of
CDD/CDFs within the post-combustion zone.
Section 2.7 provides a closing summary of the three principal formation
mechanisms and the role of chlorine. From the discussion in this chapter, it should be
evident that no clear distinction exists between the precursor and de novo synthesis
mechanisms for forming CDDs and CDFs. Both formation pathways depend on the
evolution of precursors within combustion gases, the interaction of reactive fly ashes, a
generally oxidative environment, the presence of a transition metal catalyst, the presence
of gaseous chlorine, and a favorable range of temperature. Temperature of the
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combustion gases (i.e., flue gases) is perhaps the single most important factor in forming
dioxin-like compounds. Temperatures between 200° and 450° Celsius (C) are most
conducive to forming CDD/CDFs, with maximum formation occurring at around 350°C. If
temperature falls outside this range in temperature, the amount of CDD/CDFs formed is
minimized.
2.1. MECHANISM 1: CDD/CDF CONTAMINATION IN FUEL AS A SOURCE OF
COMBUSTION STACK EMISSIONS
The first mechanism involved in the stack emission of CDDs and CDFs is the
incomplete destruction of CDD/CDF contaminants present in the fuel or feeds delivered to
the combustion chamber. Not all of these molecules are destroyed by the combustion
system, thus allowing trace amounts to be emitted from the stack. Most work in this area
has involved the study of municipal solid waste incineration (MSWI), where CDDs and
CDFs were analytically measured in the raw refuse fed into the incinerator.
As discussed in Volume 2 to this report, CDD/CDFs are ubiquitous in the
environment (air, water, soil) and in foods and paper. Therefore, CDD/CDFs are clearly
present in municipal waste. Tosine et al. (1983) first reported detecting trace amounts of
HpCDD and OCDD in the MSW fed into an MSWI in Canada. HpCDD ranged in
concentration from 100 ppt to 1 ppb, and OCDD ranged from 400 to 600 ppt. Wilken et
al. (1992) separated the various solid waste fractions of MSW collected from
municipalities in Germany and analyzed them for the presence of CDD/CDFs and other
organochlorine compounds. Total CDD/CDFs were detected in all MSW fractions in the
following range of concentrations: paper and cardboard = 3.1 to 45.5 ppb; plastics,
wood, leather, and textiles combined = 9.5 to 109.2 ppb; vegetable matter = 0.9 to
16.9 ppb; and "fine debris" (defined as particles < 8 mm) = 0.8 to 83.8 ppb. Ozvacic
(1985) measured CDD/CDFs in the raw MSW fed into two MSWIs operating in Canada. In
one MSWI, CDDs were detected in the refuse; concentration ranged from 10 to 30 ppb,
but no CDFs were detected (detection limit: 1 pg/g). In the MSW fed to the second
MSWI, CDDs were detected in a range of 75 to 439 ppb, and CDFs were detected only in
one of three samples at a total concentration of 11 ppb. EPA has reported detecting
CDD/CDFs in refuse derived fuel (RDF) burned in a large urban MSWI (Federal Register,
1991 a). CDDs were detected in 13 MSW samples taken prior to incineration at
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concentrations ranging from 1 to 1 3 ppb; CDFs ranged from not detected to 0.6 ppb. In
these samples, OCDD predominated; the lower chlorinated congeners were not detected.
Clement et al. (1988) performed a mass balance involving an input versus output of
CDD/CDFs at two operational MSWIs in Canada. The mass balance showed that the
mass of CDDs and CDFs emitted at the stack point was much greater than the mass of
CDD/CDFs in the raw MSW fed into the incinerator, and that the profiles of the
distributions of CDD/CDF congeners were strikingly different. Primarily, higher chlorinated
congeners were detected as contaminants in the waste; whereas, the total array of tetra -
octa CDD/CDFs could be detected in the stack gases.
CDDs/CDFs present in the waste feeds may account for some fraction of the
CDD/CDFs released from the stack. However, mass balance studies have clearly shown
that more CDD/CDF can be detected downstream of the furnace than what is detected in
the feed, indicating that CDD/CDFs are being synthesized after the feed has been
combusted (Commoner et al., 1984, 1985, 1987; Clement et al., 1988; Hay et al., 1986;
Environment Canada, 1985). Moreover, it is expected that the conditions of thermal
stress imposed by high temperatures reached in typical combustion would destroy and
reduce the CDDs and CDFs present as contaminants in the waste feed to levels that are
0.0001 to 10 percent of the initial concentration, depending on the performance of the
combustion source and the level of combustion efficiency. Stehl et al. (1973)
demonstrated that the moderate temperature of 800°C enhances the decomposition of
CDDs at a rate of about 99.95 percent, but that lower temperatures result in a higher
survival rate. Theoretical modeling has shown that unimolecular destruction of
CDDs/CDFs at 99.99 percent can occur at the following temperatures and retention times
within the combustion zone: 977°C with a retention time of 1 second; 1,000°C at a
retention time of 1/2 second; 1,227°C at a retention time of 4 milliseconds; and 1,727°C
at a retention time of 5 microseconds (Schaub and Tsang, 1983). Thus, CDDs and CDFs
would have to be in parts per million concentration in the feed to the combustor to be
found in the part per billion or part per trillion level in the stack gas emission (Shaub and
Tsang, 1983). However, it cannot be ruled out is that CDDs/CDFs in the waste or fuel
may contribute (up to some percentage) to the overall concentration leaving the stack.
This leaves the only other possible explanation for CDD/CDF emissions from high
temperature combustion of organic material, formation outside and downstream of the
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furnace. These studies point to formation mechanisms other than simple pass through of
non-combusted feed contamination. These formation mechanisms are discussed and
reviewed in the sections which follow.
2.2. MECHANISM 2: FORMATION OF CDD/CDFs FROM PRECURSOR COMPOUNDS
The second mechanism involves the formation of CDDs and CDFs from aromatic
precursor compounds in the presence of a chlorine donor. This mechanism has been
elucidated from laboratory experiments involving the combustion of known precursors in
quartz ampules under starved-air conditions, and in experiments that investigate the role
of combustion fly ash in promoting the formation of CDD/CDFs from precursor
compounds. The general reaction in this formation pathway is an interaction between an
aromatic precursor compound and chlorine promoted by a transition metal catalyst on a
reactive fly ash surface (Dickson and Karasek, 1987; Liberti and Brocco, 1982). Examples
of well studied precursor compounds include chorobenzenes, chlorophenols, phenol, and
benzene (Esposito et al., 1980). Examples of diverse chlorine donor compounds are
polyvinyl chloride (PVC), and gaseous hydrogen chloride (HCI). CDD and CDF formation
results from heterogeneous gas-phase reactions involving chlorinated precursor
compounds and a source of chlorine. Chlorophenol and chlorobenzene compounds are
measured in flue gases from MSWIs (Dickson and Karasek, 1987). Precursors are carried
from the furnace to the flue duct as products of incomplete combustion. These
compounds can adsorb on the surface of combustion fly ash, or entrain in the gas phase
within the flue gases. In the post-combustion region outside the furnace, heterogeneous
reactions ensue to form CDD/CDFs. Laboratory experiments involving the controlled
combustion of precursor compounds have caused the breakdown of the precursor reagent
and the subsequent appearance of CDD/CDFs as products of the reaction. For example,
Jansson et al. (1977) produced CDDs through the pyrolysis of wood chips treated with
tri-, tetra-, and pentachlorophenol in a bench-scale furnace operated at 500-600°C. Stehl
and Lamparski (1977) combusted grass and paper treated with the herbicide 2,4,5-
trichlorophenoxyacetic acid (2,4,5-T) in a bench-scale furnace at 600-800°C and
generated ppmv levels of TCDD. Ahling and Lindskog (1982) reported CDD formation
during the combustion of tri- and tetrachlorophenol formulations at temperatures of 500-
600°C. Decreases in oxygen during combustion generally increased the CDD yield.
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Ahling and Lindskog (1982) noted that adding copper salts to the tetrachlorophenol
formulation significantly enhanced the yield of CDDs. This may have been an early
indication of copper's role in catalyzing the condensation of chlorophenol to dioxin.
Combustion of pentachlorophenol (PCP) resulted in low yields of CDDs. However, when
PCP was burned with an insufficient supply of oxygen, investigators noted the formation
of tetra- through octa-chlorinated congeners. Buser (1979) generated CDD/CDFs on the
order of 0.001-0.08 percent (by weight) by heating tri-, tetra-, and pentachlorobenzenes
at 620°C in quartz ampules in the presence of oxygen. It was noted that chlorophenols
formed as combustion byproducts; Buser (1979) speculated that these were acting as
reaction intermediates in the formation of CDD/CDFs.
Temperature of the combustion gases is, perhaps, the most dominant factor in the
formation of CDDs and CDFs from aromatic precursor compounds (Fangmark et al., 1994;
Vogg et al., 1987, 1992; Oberg et al.,1989; Weber and Hagenmaier, 1999). Vogg et al.
(1987) found that formation probably occurs outside and downstream from the
combustion zone of a furnace to a combustion source in regions where the temperature of
the combustion offgases has cooled within a range of 200° to 450°C.
After carefully removing organic contaminants from MSWI fly ash, Vogg et al.
(1987) added known concentrations of isotopically labeled CDD/CDFs to the matrix. The
MSWI fly ash was then heated for 2 hours in a laboratory furnace at varying temperatures.
The treated fly ash was exposed to increasing temperatures in 50°C increments in a
temperature range of 150° to 500°C. Table 2-1 summarizes these data. Because the
relative concentration of CDD/CDFs increased while exposed to varying temperature, it
was concluded that the temperature of the combustion gas is crucial to promoting the
formation of CDD/CDFs on the surface of fly ash. Within a temperature range of 200° to
450°C, the concentration of CDD/CDFs increases to some maxima; outside this range, the
concentration diminishes.
The region of cooler gas temperature is often referred to as the "post-combustion
zone." The heat loss may be inherent to the conduction and transfer through the
combustion gas metal ducting system, or related to adsorbing/exchanging heat to water in
boiler tubes. This region extends from near the exit of the furnace to the point of release
of the combustion gases at stack tip.
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Fangmark et al. (1994) found that CDD/CDFs exhibit a similar dependence on
temperature and residence times between 260° and 430°C, with maximum formation
occurring around 340°C. Using a pilot-scale combustor, Behrooz and Altwicker (1996)
found the formation of CDD/CDFs from the precursor 1,2-dichlorobenzene rapidly occurred
within the post-combustion region in a temperature range of 390° to 400°C, with
residence times of only 4-5 seconds. On the other hand, CDD/CDF formation from 1,2-
dichlorophenol seemed to require higher temperatures; still outside the furnace, but likely
in the exit to the furnace where gas temperatures are >400°C.
Oberg et al. (1989) investigated the role that temperature plays in the formation
kinetics using a full-scale hazardous waste incinerator operating in Sweden. Oberg et al.
(1989) observed that maximum CDD/CDF formation transpired in the boiler used to
extract heat for co-generation of energy. In this investigation, significant increases in total
concentration of I-TEQDF occurred between temperatures of 280° to 400°C, and
concentrations declined at temperatures above 400°C. Weber and Hagenmaier (1999)
showed that in gas phase reactions chlorophenols react in the presence of oxygen at
above 340°C to form CDDs and CDFs. Phenoxyradicals were formed which, in turn,
caused the formation of CDDs. Polychlorinated dihydroxybiphenyls were identified as
reaction intermediates in the gas phase dimerization of chlorophenols, and these
intermediates could form PCDFs.
Other conditions postulated to regulate the synthesis of CDDs and CDFs from the
aromatic precursor compound are adsorption and interaction with the reactive surface of
combustion generated fly ash (particulate matter) entrained in the combustion plasma, and
the presence of a transition metal catalyst (Vogg et al., 1987; Bruce et al., 1991; Cleverly
et al., 1991; Gullet et al., 1990a; Commoner et al., 1987; Dickson and Karasek, 1987;
Dickson et al., 1992). The molecular precursor leaves the gas-phase and condenses to
the fly ash particle. This places greater emphasis on heterogeneous surface reactions and
less emphasis on homogeneous gas-phase reactions. This condition was first postulated
by Shaub and Tsang (1983) using thermal-kinetic models based on heats of formation,
adsorption, and desorption. Shaub and Tsang (1983) modeled CDD production from
chlorophenols and concluded that gas-phase formation within an incineration system is
likely to be of low probability and importance given the short (i.e., seconds) residence time
of the combustion gases. Konduri and Altwicker (1994) proposed that rate limiting
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factors were the nature and the concentrations of the precursors, the reactivity and
availability of the fly ash surface, and the residence time in the post-combustion zone.
Dickson and Karasek (1987) investigated fly ash reactivity with 13C6-chlorophenol
compounds. Several fly ashes from a variety of combustion fuels were heated at 300°C
in quartz tubes under conditions known to catalyze the conversion of chlorophenols to
CDD/CDFs (i.e., MSWI, and copper smelter fly ashes). The MSW ashes included a sample
from a poorly-operated mass burn refractory incinerator and a sample from a well-operated
fluidized bed combustor. The MSWI fly ashes proved to be the most active catalytic
medium, despite similarities with respect to specific surface area and average pore
diameters. The ash from the refractory MSWI generated about seven times more mass of
dioxin-like compounds than the fluidized-bed MSW incinerator. In the MSW ashes, all
CDD/CDF congener groups were formed from labeled chlorophenols; however, only trace
amounts of heptachloro- and octachlorodioxin were formed with the copper
smelter/refiner. X-ray photoelectron spectroscopy revealed the presence of chlorine
adsorbed to the surface of the MSWI fly ashes, but an absence of chlorine sorbed to the
copper smelter fly ash.
CDD congener groups were postulated to form from the labeled pentachlorophenol
precursors by: (1) first forming octachlorodioxin by the condensation of two
pentachlorophenol molecules, and (2) forming other lower chlorinated dioxins through
dechlorination of the more highly chlorinated isomers. These steps seemed to proceed by
an increased reactivity of the chemisorbed precursor molecule caused by the removal of
one or more hydrogen or chlorine atoms along the ring structure (Dickson and Karasek,
1987), an observation consistent with the kinetic model of Shaub and Tsang (1983). In
related experiments, Dickson and Karasek (1987) more specifically reported on forming
CDD/CDFs from condensation reactions of chlorophenols on the surface of MSWI fly ash
heated in a bench-scale furnace. Their experiment was designed to mimic conditions of
MSW incineration, to identify the step-wise chemical reactions involved in converting a
precursor compound into dioxin, and to determine if MSWI fly ash could promote these
reactions. MSWI fly ash was obtained from facilities in Canada and Japan. The MSWI fly
ash was rinsed with solvent to remove any organic constituents prior to initiating the
experiment. Twenty grams of fly ash were introduced into a bench-scale oven (consisting
of a simple flow-tube combustion apparatus) and heated at 340°C overnight to desorb
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any remaining organic compounds from the matrix. 13C12 -labeled pentachlorophenol (PCP)
and two trichlorophenol isotopes (13C12- 2,3,5-Trichlorophenol and 3,4,5-Trichlorophenol)
were added to the surface of the clean fly ash matrix and placed into the oven for 1 hour
at 300°C. Pure inert nitrogen gas (flow rate of 10 mL/min) was passed through the flow
tube to maintain constant temperatures. Tetra- through octa- CDDs were formed from the
labeled pentachlorophenol experiment; over 100/vg/g of total CDDs were produced. The
congener pattern was similar to that found in MSWI emissions. The 2,3,5-Trichlorophenol
experiment primarily produced HxCDDs and very small amounts of tetra- and octa-CDD.
The 3,4,5-Trichlorophenol experiment mainly produced OCDD and 1,2,3,4,6,7,8-HpCDD.
Dickson and Karasek (1987) proposed that CDDs on the fly ash surface may result from
chlorophenol undergoing molecular rearrangement or isomerization as a result of
dechlorination, dehydrogenation, and trans-chlorination before condensation occurs.
These reactions were proposed as controlling the types and amounts of CDDs that are
ultimately formed. Born et al. (1993) conducted experiments on the oxidation of
chlorophenols with fly ash in a quartz tube reactor heated to about 300°C. The MSWI fly
ash mediated the oxidation of chlorophenols to produce carbon dioxide and carbon
monoxide as major products, and polychlorinated benzenes, monobenzofurans, and
nonhalogenated dibenzo-p-dioxins as trace species. Formation of these trace aromatic
species occurred after residence times of only 7 - 8 seconds which was consistent with
the later experimental result of Behrooz and Altwicker (1995) which showed the potential
for rapid formation from a precursor. Milligan and Altwicker (1996) fitted experimental
flow-tube reactor data to classical catalytic reaction models to empirically explain the
interaction of 2,3,4,6-tetrachlorophenol (as a model precursor) with reactive MSWI fly ash
during MSW incineration. The precursor was found to be highly adsorptive on fly ash,
with a first-order dependence on gas-phase precursor concentration to CDD formation.
Milligan and Altwicker (1996) concluded that chlorophenol's dependence on gas-phase
concentration to form CDD on fly ash reflects the highly heterogeneous nature of the fly
ash surface. Moreover the estimated 6 x 1018 adsorption sites per gram of fly ash
suggest the presence of highly energetic sites, which may be important in the surface-
catalyzed reactions forming CDDs. An interesting observation of Milligan and Altwicker
(1996) was that precursor molecules appeared to compete with oxygen molecules for the
reactive sites; therefore, chlorophenols are expected to adsorb less readily to the fly ash
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surface in the presence of oxygen. Experimental evidence suggests that condensation to
CDD of chlorophenol compounds via isomerization and the Smiles rearrangement on
reactive MSWI fly ash surfaces is a proven pathway for forming dioxins from a precursor
compound (Addink and Olie, 1995). However, no detailed mechanisms have been
presented for CDD/CDF formation from other precursors, such as chlorobenzenes under
conditions simulating incineration.
A condition to the synthesis of CDD/CDFs from aromatic precursor compounds is
that the presence of a transition metal catalyst promotes the chemical reaction on the
surface of fly ash. Copper chloride is a strong catalyst for promoting surface reactions on
particulate matter to convert aromatic precursor compounds to chlorinated dioxins and
dibenzofurans (Vogg et al., 1987). Copper chloride promotes ring condensation reactions
(e.g., chorophenols) on fly ash to form CDD/CDFs (Addink and Olie, 1995) via the Ullman
reaction (Born et al., 1993). In the Ullman reaction, copper catalyzes the formation of
diphenyl ethers by the reaction of halogenated benzenes with alkali metal phenolates (Born
et al., 1993), with copper participating in a nucleophilic aromatic substitution reaction.
Thus, Born et al. (1993) proposes a similar mechanism in catalyzing the formation of
dioxin-like compounds. Using the Ullman reaction as a model, Born et al. (1993) proposed
that the copper-catalyzed condensation of two ortho-substituted chlorophenol molecules
form chlorine-free dibenzo-p-dioxins. Vogg et al. (1987) proposed an oxidation reaction
pathway, giving rise to the formation of CDDs and CDFs in the post-furnace regions of the
incinerator in the following order: (1) hydrogen chloride gas (HCI) is thermolytically
derived as a product of the combustion of heterogeneous fuels containing abundant
chlorinated organic chemicals and chlorides; (2) oxidation of HCI, with copper chloride
(CuCI2) as a catalyst, yields free gaseous chlorine via the Deacon reaction; (3) phenolic
compounds (present from combustion of lignin in the waste or other sources) entrained in
the combustion plasma are substituted on the ring structure by contact with the free
chlorine; and (4) the chlorinated precursor to dioxin (e.g., chlorophenol) is further oxidized
(with copper chloride as a catalyst) to yield CDDs and CDFs and chlorine.
Gullett et al. (1990a; 1990b; 1991 a; 1991b; 1992) studied the formation
mechanisms through extensive combustion research at EPA, and verified the observations
of Vogg et al. (1987). It was proven that CDDs and CDFs could be ultimately produced
from low temperature reactions (i.e., 350°C) between CI2 and a phenolic precursor,
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combining to form a chlorinated precursor, followed by oxidation of the chlorinated
precursors (catalyzed by a copper catalyst such as copper chloride) as in examples (1) and
(2), below.
(1) The initial step in forming dioxin is the formation of chlorine from HCI in the
presence of oxygen (the Deacon process), as follows (Vogg et al., 1987; Bruce et al.,
1991):
A
2HCI + 1/2 02 > H20 + CI2
(2) Phenolic compounds adsorbed on the fly ash surface are chlorinated to form
the dioxin precursor, and the dioxin is formed as a product from the breakdown and
molecular rearrangement of the precursor. The reaction is promoted by copper chloride
acting as a catalyst (Vogg et al., 1987; Dickson and Karasek, 1987; Gullett et al., 1992):
(a) phenol + CI2 > chlorophenol (dioxin precursor)
CuCI2
(b) 2-chlorophenol + 1/2 02 > dioxin + CI2
On the other hand, Eklund et al. (1986) observed the high temperature formation of a
large variety of chlorinated toxic compounds, including CDDs and CDFs, from precursors
during a simple experiment in which phenol was oxidized with HCI at 550°C. One
milligram of phenol was placed in a quartz tube reactor with an aqueous solution (10/vL) of
HCI and heated at 550°C for 5 minutes. Trichlorobenzene, dichlorophenol,
dichlorobenzofuran, tetrachlorobenzene, trichlorophenol, and tetrachlorophenol were
identified as major products formed. Monochlorobenzene, chlorophenol, dichlorobenzene,
tetrachloropropene, pentachloropropene, trichlorobenzofuran, tetrachlorodibenzofuran,
trichlorodibenzodioxin, tetrachlorodibenzodioxin, hexachlorodibenzodioxin,
hexachlorodibenzofuran, pentachlorobenzene, pentachlorobiphenyl, and
pentachlorodihydroxycylohexane were seen as minor products. Trace species formed
included: monochlorodibenzofuran, pentachlorodibenzofuran, pentachlorodibenzodioxin,
octachlorodibenzofuran, and octachlorodibenzodioxin. Eklund et al. (1986) hypothesized
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that chlorinated organic compounds can be produced from phenols, acids, and any
chlorine source in the hot post-combustion region (e.g., just exit to the furnace). The
reaction was seen as very sensitive to HCI concentration. At HCI < 10"3 moles, no
chlorinated compounds could be detected. Nestrick et al. (1987) reported that the
thermolytic reaction between benzene (an unsubstituted precursor) and iron (III) chloride
on a silicate surface yielded CDD/CDFs at temperatures >150°C. The experimental
protocol introduced 100 - 700 mg of native and 13C6-benzene into a macro-reactor
system, consisting of a benzene volatilization chamber connected to a glass tube furnace.
The investigators noted the relevance of this experiment to generalizations about
combustion processes because benzene is the usual combustion byproduct of organic
fuels. Inert nitrogen gas carried the benzene vapor to the furnace area. The exit from the
glass tubing to the furnace was plugged with glass wool, and silica gel was introduced
from the entrance end to give a bed depth of 7 cm to which the FeCI3 was added to form
a FeCI3/silica reagent. The thermolytic reaction took place in a temperature ranging from
150-400°C, at a residence time of 20 minutes. Although di- through octa-CDD/CDF were
formed by this reaction at all the temperatures studied, the percent yields were extremely
small. Table 2-2 summarizes these data.
2.3. MECHANISM 3: THE DE NOVO SYNTHESIS OF CDDS/CDFS DURING
COMBUSTION OF ORGANIC MATERIALS
The third and last mechanism, de novo synthesis, promotes CDD/CDF formation in
combustion processes from the oxidation of carbon particulate catalyzed by a transition
metal in the presence of chlorine. As in mechanism 2, synthesis is believed to occur in
regions outside of the furnace zone of the combustion process, where the combustion
gases have cooled to a range of temperatures considered favorable to formation
chemistry. A key component to de novo synthesis is the production of intermediate
compounds (either halogenated or nonhalogenated) that are precursors to CDD/CDF
formation. Research in this area has produced CDD/CDFs directly by heating
carbonaceous fly ash in the presence of a transition metal catalyst, without the apparent
generation of reactive intermediates. Thus, the specific steps involved in the de novo
process have not been fully and succinctly delineated. However, laboratory
experimentation has proven that MSWI fly ash, itself, is a reactive substrate, and the
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matrix can actually catalyze the de novo formation chemistry. Typically, fly ash is
composed of an alumina-silicate construct, with 5-10 percent concentrations of silicon,
chlorine (as inorganic chlorides), sulfur, and potassium (NATO, 1988). Twenty percent of
the weight of fly ash particles are carbon, and the particles have specific surface areas in
the range of 2-4 m2(NATO, 1988). The de novo synthesis essentially is the oxidative
breakdown of macromolecular carbon structures, and CDD/CDFs are formed partially from
the aromatic carbon-oxygen functional groups embedded in the carbon skeleton (Huang et
al., 1999). The distinguishing feature of the de novo synthesis over the precursor
synthesis is the oxidation of carbon in particulate at the start of the process to yield
precursor compounds. In mechanism 2, the precursor compound is the starting molecule
to the condensation reactions forming CDD/CDFs (Dickson et al., 1992). By this
distinction, however, one could argue that mechanism 3 is really an augmentation to
mechanism 2, because the production of CDD/CDFs may still require the formation of a
CDD/CDF precursor as an intermediate species. Nevertheless, a distinction is presented
here to describe additional pathways suggested for the thermal formation of these
compounds.
To delineate the de novo synthesis of CDD/CDFs, Stieglitz et al. (1989a) conducted
experiments that involved heating particulate carbon containing adsorbed mixtures of Mg-
Al silicate in the presence of copper chloride (as a catalyst to the reaction). The authors
described heating mixtures of Mg-AI silicate with activated charcoal (4 percent by weight),
chloride as potassium chloride (7 percent by weight), and 1 percent copper chloride
(CuCI2) (in water) in a quartz flow tube reactor at 300°C. The retention time was varied
at 15 minutes, 30 minutes, and 1, 2, and 4 hours to obtain differences in the amounts of
CDD/CDFs that could be formed. The results are summarized in Table 2-3. In addition to
the CDD/CDFs formed as primary products of the de novo synthesis, the investigators
observed precursors formed at the varying retention times during the experiment. In
particular, similar yields of tri- though hexa-chlorobenzenes, tri- through hepta-
chloronaphthalenes, and tetra- through hepta-chlorobiphenyls were quantified; this was
seen as highly suggestive of the role these compounds may play as intermediates in the
continued formation of CDD/CDFs. Stieglitz et al. (1989a) made the following
observations:
• The de novo synthesis of CDD/CDFs via the oxidation of carbonaceous particulate
matter occurred at a temperature of 300°C. Additionally, the experiment yielded
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ppb to ppm concentrations of chlorinated benzenes, chlorinated biphenyls, and
chlorinated naphthalenes through a similar mechanism. When potassium bromide
was substituted for potassium chloride as a source of halogen for the organic
compounds in the reaction, polybrominated dibenzo-p-dioxins and dibenzofurans
formed as reaction products.
• The transition metal compound copper chloride catalyzed the de novo synthesis of
CDD/CDFs on the surface of particulate carbon in the presence of oxygen, yielding
carbon dioxide and chlorinated/brominated aromatic compounds.
• Particulate carbon, which is characteristic of combustion processes, may act as the
source for the direct formation of CDD/CDFs, as well as other chlorinated organics.
More recently, Stieglitz et al. (1991) investigated the role that particulate carbon
plays in the de novo formation of CDD/CDFs from fly ash containing appreciable quantities
of organic chlorine. Stieglitz et al. (1991) found that the fly ash contained 900/vg/g of
bound organic chlorine. Only 1 percent of the organic chlorine was extractable. Heating
the fly ash at 300-400°C for several hours caused the carbon to oxidize, leading to a
reduction in the total organic chlorine in the matrix and a corresponding increase in the
total extractable organic chlorine (TOX) (e.g., 5 percent extractable TOX at 300°C and
25-30 percent extractable total organic chlorine at 400°C). From this, Stieglitz et al.
(1991) concluded that the oxidation and degradation of carbon in the fly ash are the
source for the formation of CDD/CDFs; therefore, they are essential in the de novo
synthesis of these compounds.
Addink et al. (1991) conducted a series of experiments to observe the de novo
synthesis of CDD/CDFs in a carbon-fly ash system. In this experiment, 4 grams of
carbon-free MSWI fly ash were combined with 0.1 gram of activated carbon and placed
into a glass tube between two glass wool plugs. The glass tube was then placed into a
furnace at a specific temperature, ranging from 200 to 400°C. This was repeated for a
series of retention times and temperatures. The investigators observed that CDD/CDF
formation was optimized at 300°C and at the furnace retention times of 4-6 hours.
Figure 2-1 displays the relationship between retention time, temperature, and CDD/CDF
production from the heating of carbon particulate. Addink et al. (1991) also investigated
the relationship between furnace temperature and CDD/CDF production from the heating
of carbonaceous fly ash. Figure 2-2 displays this relationship. In general, the
concentration began to increase at 250°C and crested at 350°C, with a sharp decrease in
concentration above 350°C. The authors also noted a relationship between temperature
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and the CDD/CDF congener profile; at 300°C to 350°C, the lower chlorinated tetra- and
penta-CDD/CDF congeners increased in concentration, while hexa-, hepta-, and octa-
CDD/CDF congeners either remained the same or decreased in concentration. The
congener profile of the original MSWI fly ash (not subject to de novo experimentation) was
investigated with respect to changes caused by either temperature or residence time in the
furnace. No significant changes occurred, leading the authors to propose an interesting
hypothesis for further testing: after formation of CDD/CDFs occurs on the surface of fly
ash, the congener profile remains fixed and insensitive to changes in temperature or
residence time, indicating some form of equilibrium is reached in the formation kinetics.
Gullett et al. (1994) used a pilot-scale combustor to study the effect of varying the
combustion gas composition, temperature, residence time, quench rate, and sorbent
(Ca[OH]2) injection on CDD/CDF formation. The fly ash loading was simulated by injecting
on fly ash collected from a full-scale MSWI. Sampling and analysis indicated CDD/CDF
formed on the injected fly ash at levels representative of those observed at full-scale
MSWIs. A statistical analysis of the results showed that, although the effect of
combustor operating parameters of CDD/CDF formation is interactive and very
complicated, substantial reduction in CDD/CDF formation can be realized with high
temperature sorbent injection to reduce HCI or CI2 concentrations, control of excess air
(also affects ratio of CDDs to CDFs formed), and increased quench rate.
Several steps may be involved in the copper-catalyzed formation of CDDs and
CDFs, with residual carbon on fly ash at 300°C (Addink and Olie, 1995). Copper initially
reacts with chlorine to form CuCI2, and then the ligand transfers the halide to a carbon
atom of an organic macromolecule. The chlorinated macromolecular structure oxidizes
into small compounds. Milligan and Altwicker (1995) found that increases in the carbon
gasification rate caused increases in the amounts of CDDs and CDFs formed, and gave
further evidence linking the oxidation of carbon to the formation of CDD/CDFs. Neither
the gas-phase C02 nor CO (products of carbon oxidation) act as precursors to
chlorobenzenes or CDD/CDF from reactions with carbon particulate (Milligan and
Altwicker, 1995). Activated carbon, with a high surface area and excellent adsorptive
characteristics, also has the highest gasification rate of all residual carbon (Addink and
Olie, 1995). Experimental evidence suggest that the conditions for the de novo synthesis
of CDDs and CDFs from carbon are: (a) the carbon consists of imperfect and degenerated
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layers of graphite; (b) oxygen must be present; (c) chlorine must be present; (d) the
reactions are catalyzed by copper chloride or some other transition metal; and (e)
temperatures in the range of 200°C to 350°C (Huang and Buekens, 1995). The oxidation
of carbon in fly ash is apparently inhibited at temperatures below 200°C, thus indicating
the lower temperature limit for the thermal inertization of de novo synthesis (Lasagni et
al., 2000). Lasagni et al. (2000) determined that at a temperature of 250° C, the primary
product of the gasification of carbon in fly ash is C02 , but in a temperature range of 250-
325°C, organic compounds are formed as products of the oxidation of the carbon.
Addink and Olie (1995) raised the possibility that the molecular backbone of CDDs and
CDFs may be present in carbon. If this is the case, the generation of dioxins and furans
from the oxidation of carbon would not require the formation of intermediate aromatic ring
structures. More work is needed to identify these possibilities.
The de novo synthesis of CDD/CDFs also involves the possibility that aromatic
precursors could be formed within the post-combustion zone as in the following manner:
(1) fuel molecules are broken into smaller molecular species (e.g., C1f C2 molecules) during
primary combustion; and (2) these simple molecules recombine in the post-combustion
zone to form larger molecular aromatic species (i.e., chlorobenzenes and chlorophenols)
(Altwicker et al., 1993). Thus, small molecular products that evolve in the hot-zone of
the furnace as a consequence of the incomplete fuel or feed material combustion may be
important foundation molecules to the subsequent formation of precursor compounds in
the cooler, post-combustion region. Eklund et al. (1988) reported formation of a wide
range of chlorinated organic compounds, including CDDs, CDFs, and PCBs, from the
oxidation of methane with HCI at temperatures of 400° to 950°C in a quartz flow tube
reactor. No active catalysts nor reactive fly ashes were added to the combustion system.
From these experimental results, Eklund et al. (1988) hypothesized that chlorocarbons,
including CDDs and CDFs, are formed at high temperatures via a series of reversible
reactions starting with chloromethyl radicals. The chloromethyl radicals can be formed
from the reaction of methyl radicals and hydrogen chloride in a sooting flame. Methane is
chlorinated by HCI in the presence of oxygen at high temperatures, forming chlorinated
methanes, which react with methyl radicals at higher temperature (e.g., 800°C) to form
aromatic compounds. In an oxidative atmosphere, chlorinated phenols are formed, but
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alkanes and alkenes are the primary products. The chlorinated phenols then act as
precursors for the subsequent formation of CDD/CDFs.
Aliphatic compounds are common products of incomplete combustion, and may be
critical to the formation of simple ring structures in the post-combustion zone (Weber et
al., 1999; Sidhu, 1999; Froese and Hutzinger, 1996a; Froese and Hutzinger, 1996b;
Jarmohamed and Mulder, 1994). The aromatic precursor compounds may be formed in a
potentially rich reaction environment of aliphatic compounds, reactive fly ash particles,
HCI, and oxygen. Sidhu (1999) noted that combustion of acetylene on carbon (a common
combustion effluent) in the presence of gaseous HCI and copper chloride (as a catalyst) at
300°C formed intermediate precursors, and subsequently, CDDs and CDFs. Propene
oxidized at 350° to 550°C in contact with reactive MSWI fly ash in a flow tube reactor
forms a wide range of chlorinated aromatic compounds, when the resulting combustion
gases are mixed with hydrogen chloride gas (Jarmohamed and Mulder, 1994). Although
the conversion was low (i.e., 1-3 percent), the oxidation of propene on fly ash in the
presence of HCI can yield chlorinated benzenes and monobenzofurans. Incorporating an
oxygen atom into the monobenzofuran structure leads then to the formation of
monodibenzofuran. The HCI contributes chlorine to the aromatic ring through the Deacon
reaction, and cyclization on the fly ash surface can yield cyclohexadienyl-substituted
benzenes, which, in turn, can be further oxidized into CDFs (Jarmohamed and Mulder,
1994). Froese and Hutzinger (1996a) investigated the heterogeneous combustion
reactions of the nonchlorinated C2 aliphatics. Acetylene, as a model aliphatic compound,
was allowed to react with pre-cleaned MSWI fly ash in a tube flow reactor at ca. 600°C.
Metal oxides (e.g., Si02, Fe203, and CuO) were added separately as catalysts, instead of
the metal chlorides used in other precursor experiments. The reactants were put into
contact with HCI vapor, introduced at a constant flow rate. The acetylene flow was set
at 1.1 mL/min and constantly fell to near 0.9 mL/min over 30 minutes. Regulated air flow
maintained homeostatic oxidation conditions. Chorobenzenes and chlorophenols were
formed, with isomer patterns generally resembling isomer patterns of chlorobenzene and
chlorophenol emissions from MSWIs. CuO was seen as catalyzing condensation and
chlorination reactions under heterogeneous conditions to form the chlorinated CDD/CDF
precursor compounds. Additional more volatile compounds formed were short-chain
aliphatic products, such as chloromethane, dichloromethane, and chloro-and
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dichloroacetylene. Chlorobenzene congeners were not the major products formed;
perchlorinated aliphatic compounds dominated as gas-phase reaction products. Froese
and Hutzinger (1997) noted that perchlorinated aliphatic compounds (e.g.,
hexachloropropene, hexachloro-1,3-butadiene, and hexachlorocyclopentadiene) are
important intermediates in aromatic ring formation; they concluded that the catalytic
reaction of C2 aliphatic compounds at 600°C dramatically contributes to formation of
chlorinated and nonchlorinated aromatic compounds during combustion. Thus, aliphatic
compounds can form CDD/CDF precursor compounds. Variable temperature effects were
observed in the formation of CDD/CDF in the same reactions. Maximal OCDD formation
occurred at 400°C, and the tetra-hepta homologue groups were maximally formed at
600°C. For CDFs, production of higher chlorinated homologues occurred at 400°C, and
500°C favored the formation of tetrachlorodibenzofurans. Froese and Hutzinger (1996a)
noted a 100-fold increase in TCDF formation at 500°C, when compared to formation at
400°C. An explanation for this is that the higher temperature of 500°C maximized the
formation CDD/CDF precursor (chlorophenol) from the aliphatic starting compound;
whereas, at the lower temperature of 300°C, practically no ring structures were observed.
Froese and Hutzinger (1996b) have produced polychlorinated benzene and phenol
compounds from the high temperature (i.e., 300° to 600°C) heterogeneous combustion
reactions of ethylene and ethane over fly ash in the presence of HCI, oxygen, and a metal
catalyst in a combustion flow tube. No chlorobenzene congener precursors were formed
from ethylene and ethane at 300°C; however, the formation rate increased with
temperature, until a maximum production was achieved at 600°C. No definitive
temperature dependence was observed for the formation of chlorophenols from the
aliphatic starting compounds. However, at 500°C, 2,4,6-trichlorophenol dominated the
reaction products; at 300°C, pentachlorophenol was initially produced. Froese and
Hutzinger (1996b) also investigated the effects of elemental catalysts on potentiating the
heterogeneous combustion reactions by measuring the amount of chlorobenzene and
chlorophenol product formed from the reactions of ethylene/HCI over each catalyst at
600°C. The reaction with Si02 did not have a catalytic effect. AI203 catalytic action
showed high intensity for the dichlorobenzene isomers, and decreasing intensity for the
higher chlorinated isomers. Comparison of the amount of dichlorobenzene product formed
indicated that an equal quantity was produced with either AI203 or fly ash; however, AI203
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formed four to five times more product than the CuO catalyst. For tri- to hexa-
chlorobenzene congeners, MSWI fly ash reactions produced 5 to 10 times more product
that the metal catalysts. However, the presence of the CuO catalyst in these reactions
produced a chlorobenzene congener pattern comparable to the fly ash reactions. With
regard to chlorophenol production, AI203 also produced a unique dichlorophenol pattern,
suggesting that AI203 has a unique catalytic effect in the high-temperature reactions of C2
aliphatic compounds. Reactions over CuO produced additional products, including
chlorinated methyl compounds, chlorinated C2 aliphatics, and perchlorinated C3 - C5 alkyl
compounds. Froese and Hutzinger (1996b) noted that these perchlorinated alkyl groups,
formed by reacting ethylene and ethane over fly ash in the presence of the CuO catalyst,
are key intermediate compounds to the formation of first aromatic rings in typical
combustion systems. This emphasizes the importance of copper's catalytic effects in a
combustion fly ash system. AI203 catalyzed reactions produced nonchlorinated
naphthalene and akyalbiphenyl compounds. Furthermore, the organic chlorine in aliphatic
compounds may also act as a direct source of chlorine for the formation of CDDs, CDFs in
a carbon fly ash system (Weber et al., 1999).
In an earlier experiment using a similar flow-tube apparatus, Froese and Hutzinger
(1994) formed chlorinated benzenes and phenols in fly ash catalyzed reactions with
trichloroethylene at temperatures of 400° to 500°C. In this case, metal oxides (CuO,
Fe03, AI203) were used as catalysts, but no HCI was added for oxychlorination of product
compounds. Under combustion conditions, a temperature dependent formation of
chlorinated aromatics occurred from the trichloroethylene starting compound. Reaction
with fly ash at 600°C formed hexachlorobenzene in concentrations that were about
1,000 times greater than at 400° and 500°C, with similar results for chlorophenols.
Froese and Hutzinger (1994) hypothesized that key aromatic precursors for CDD/CDFs are
formed in the higher temperature region of a post-combustion zone (ca. 600°C), which
are then carried to the cooler post-combustion region (ca. 300°C), where the precursors
form CDDs and CDFs.
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2.4. THE ROLE OF CHLORINE IN THE FORMATION OF CDDS AND CDFS IN
COMBUSTION SYSTEMS
The formation of CDDs and CDFs in the post-combustion region of combustion
systems via either the precursor or de novo synthesis pathways requires the availability of
gaseous chlorine (Luijk et al., 1994; Addink et al., 1995). Chlorine concentration in this
region is related somehow to the chlorine content of combustion fuels and feed materials
in incineration/combustion systems, because there can be no other source. The central
question of the role of chlorine in forming CDDs and CDFs is whether or not their exists a
positive and direct correlation between the amount of chlorine in feeds and the amount of
CDDs and CDFs formed and emitted from the stack. If a direct relationship appears, then
reductions in the chlorine content of fuels/feeds prior to combustion should result in a
corresponding reduction in the concentrations of CDDs and CDFs formed after
combustion. If the oxychlorination reactions require a number of steps, then the
relationship between chlorine in uncombusted fuels and CDD/CDFs formed after
combustion may not be linear, although still dependent in some nonlinear association. The
central question can best be addressed by examining both formation mechanisms revealed
in laboratory scale combustion experiments and correlations between Cl inputs with
CDD/CDF outputs in commercial scale combustors.
2.4.1. Review of Laboratory-Scale Studies
A wide body of experimental evidence has elucidated the direct and indirect
associations between chlorine in feeds and fuels and the potential formation of CDDs and
CDFs during combustion. The de novo synthesis of CDDs and CDFs requires two basic
reactions: the transfer of chlorine to residual carbon particulate with subsequent formation
of carbon-chlorine bonds, and the oxidation of this macromolecular complex to yield
carbon dioxide and volatile and semivolatile organic compounds as side products (Weber
et al., 1999). Transition metal compounds, such as copper chloride, catalyze these
reactions. Hydrogen chloride gas is the major direct source of chlorine available for
participating in the formation of CDD/CDFs, which is initially formed as a combustion by-
product from the inorganic and organic chlorine contained in the fuel (Vogg et al., 1987;
Bruce et al., 1991; Gullet et al., 1990; Commoner et al., 1987; Addink et al., 1995; Luijk
et al., 1994; Dickson et al., 1992; Wagner and Green, 1993; Halonen et al., 1994; Rigo
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et al., 1995; Rigo, 1998; Altwicker et al.,1993). MSW contains approximately 0.45-0.90
percent (by weight) chlorine (Domalski et al., 1986). If left uncontrolled, MSW
incinerators are a major stationary combustion source of HCI air emissions, which average
between 400 to 600 ppm in the combustion gas (U.S. EPA, 1987a). In the presence of
oxygen, HCI may oxidize to yield free chlorine gas by the Deacon process, and the free
chlorine directly chlorinates a CDD/CDF precursor along the aromatic ring structure.
Further oxidation of the chlorinated precursor in the presence of a transition metal catalyst
(of which copper chloride was found to be the most active) yields CDDs and CDFs
(Altwicker et al.,1993). Increasing the yield of chlorine in vapor phase from HCI oxidation
generally increases the rate of CDD/CDF formation. Formation kinetics are most favored
at temperatures between 200°C to 450°C. Chlorine production can be reduced either by
limiting initial HCI concentration or by shortening the residence time in the Deacon process
temperature (Bruce et al., 1991; Gullett et al., 1990b; Commoner et al., 1987). Bruce et
al. (1991) observed a general increase in CDD and CDF formation, with increases in the
vapor phase concentration of chlorine. Figure 2-3 shows the apparent dependence of the
extent of formation of CDDs and CDFs upon chlorine concentration in the vapor phase.
Bruce et al. (1991) verified a dependence on the concentration and availability of gaseous
chlorine in the formation of CDD/CDFs in the post-combustion zone. This is in agreement
with a simple experiment of Eklund et al. (1986) in which unsubstituted phenol was mixed
with HCI at 550°C in a quartz tube reactor and formed a wide range of toxic chlorinated
hydrocarbons, including CDDs and CDFs as reaction products. Eklund et al. (1988) also
found a dependence of the amounts of chlorinated phenol product formed from the
nonchlorinated starting material with the increased amount of HCI introduced into the
reaction. Under the conditions of this experiment, no chlorinated compounds were formed
at an HCI concentration of less than 10"3 moles, and maximum chlorophenol concentration
occurred at ca. 10 + 8 M. Born et al. (1993) also observed that increasing levels of HCI
give rise to increasing rates of oxychlorination of precursors, with increasing chances for
the post-combustion formation of CDDs and CDFs. However, recently Addink et al.
(1995) observed that an HCI atmosphere and/or chlorine produced approximately equal
quantities of CDD/CDFs during the de novo synthesis from oxidation of particulate carbon.
These experimental results suggest that chlorine production via the Deacon process
reaction in the de novo synthesis may not be the only chlorination pathway, and may
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indicate that the HCI molecule can be a direct chlorinating agent. In addition, some
chlorine is expected to be formed from the oxidation of metal chlorides (e.g., CuCI2), but
CI2 formation from the Deacon process is greater because of the continuous supply of HCI
delivered from the combustion chamber (Bruce et al., 1991). In this case, a first order
dependence of HCI to CI2is observed.
Wagner and Green (1993) investigated the correlation of chlorine content in feed to
stack emissions of chlorinated organic compounds in a pilot-scale incinerator, using HCI
flue gas measurements as a surrogate for fuel-bound organic chlorine. In addition to MSW
as a fuel, variable amounts of PVC resin were added during 6 of 18 stack test runs. The
resulting data were regressed to determine the coefficient of correlation between HCI
measurements and total chlorobenzene compound emission measurements. In nearly all
of the different regression analyses performed, the relationship between HCI emission and
emissions of chlorinated organic compounds was positive and well-defined. In addition,
Wagner and Green (1993) found a direct dependence of HCI emission levels to the level of
PVC in the waste, with generally increasing amounts of HCI formed as increasing amounts
of PVC were added. From these experiments, Wagner and Green (1993) concluded that
decreases in the levels of organically bound chlorine in the input to an incinerator led to
decreases in chlorinated organic compound stack emissions. Kanters and Louw (1994)
investigated a possible relationship of chlorine content in waste feed to chlorophenol
emissions in a bench-scale thermal reactor. MSWI, with a higher content of chlorine,
caused a higher emission of chlorophenols via the de novo synthesis pathway. Kanters
and Louw (1994) lowered the chlorine content of the prototype MSWI by replacing Cl-
containing fractions with cellulose. They observed appreciable decreases in the amounts
of chlorophenol formed from combustion. Kanters and Louw (1994) concluded that
reductions in the chlorine content of waste feeds or elimination of PVC prior to MSWI
combustion should result in a corresponding reduction in chlorophenol and CDD/CDF
emissions.
In a similar experiment, Wikstrom et al. (1996) investigated the influence of
chlorine in feed materials to the formation of CDDs, CDFs, and benzenes in a laboratory-
scale fluidized bed reactor. An artificial fuel (composed of 34 percent paper, 30 percent
wheat flour, 14 percent saw dust, 7 percent polyethylene, and 2 percent metals) to which
varying amounts of organic chlorine (PVC) and inorganic chlorine (CaCI2. 6H20) were
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added, was combusted. Seven fuels were studied, and the chlorine content was varied
from 0.12 to 2 percent. Flue gases were sampled for CDDs, CDFs, and chlorobenzenes.
All combustion was performed with a high degree of combustion efficiency (e.g., 99.999
percent) to avoid forming polyvinylidene chloride and naphthalenes as products of
incomplete combustion of pure PVC. With the combustion conditions held constant, only
the chlorine content of the fuel was varied. From these experiments, about 1,000-fold
higher concentrations of PCB isomers were produced, as compared to CDD/CDF
(expressed as concentration of I-TEQDF). Moreover, a correlation was found between I-
TEQDF and PCB levels in the flue gases and the chlorine content of the fuel. A 5-fold
increase in both I-TEQDF and PCB concentration was observed in the flue gases from
combustion of fuels containing 0.5 and 1.7 percent total chlorine. Moreover, no
differences were observed in the amount of chlorinated product produced or whether the
source of chlorine in the fuel was organic or inorganic. No correlation was observed
between total CDD/CDF and PCB formation and total chlorine in the feed when chlorine
levels in feed were 0.5 percent or lower. Highest amounts of CDD/CDFs and PCBs were
formed from the fuel having the highest total chlorine content (1.7 percent). Under the
conditions of this experiment, Wikstrom et al. (1996) observed that a chlorine fuel
content of 1.0 percent was a threshold for forming excess CDDs, CDFs, and PCBs during
combustion. The authors noted that Swedish MSW contains about 0.7 percent chlorine,
of which approximately 40 percent are organic chlorine. They concluded that Swedish
MSW is below the observed threshold value of 1.0 percent chlorine associated with a
general increase in CDD, CDF, and PCB formation in the post-combustion region.
Wikstrom et al. (1996) stated that their study does not support the thesis that elimination
of only PVC from the waste prior to combustion will cause a significant reduction of
CDD/CDF emissions if the combustion process is well controlled (high combustion
efficiency).
A primary byproduct of combusting PVC is the generation of HCI. Paciorek et al.
(1974) thermally degraded pure PVC resin at 400°C and produced 550 mg/g HCI vapor as
a primary thermolysis product, which was observed as being 94 percent of the theoretical
amount based on the percent weight chlorine on the molecule. Ahling et al. (1978)
concluded that HCI can act as a chlorine donor to ultimately yield chlorinated aromatic
hydrocarbons from the thermolytic degradation of pure PVC, and that these yields are a
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function of transit time, percent oxygen, and temperature. They observed data from 11
separate experiments, conducted with a range of temperatures from 570 to 1,130°C.
These data indicated that significant quantities of various isomers of dichloro-, trichloro-,
tetrachloro-, and hexachlorobenzenes could be produced. Choudhry and Hutzinger (1983)
proposed that the radical species Cl- and H- generated in the incineration process may
attack the chlorinated benzenes thus formed, and abstract hydrogen atoms to produce
ortho-chlorine substituted chlorophenol radicals. These intermediate radical species then
react with molecular oxygen to yield ortho-substituted chlorophenols. As a final step, the
ortho-substituted chlorophenols act as ideal precursors to yield CDD/CDFs with heat and
oxygen. The chlorine in aliphatic compounds has been observed as both yielding high
amounts of HCL during combustion, and also acting as a direct chlorine source for the de
novo synthesis of CDDs/CDFs (Weber et al., 1999).
Recently Addink and Altwicker (1999) have reported on the role of the inorganic
chloride ion in the formation of CDD/CDFs using the labeled compound, Na37CI. The
inorganic chloride ion forms carbon-chlorine bonds on soot particles during combustion.
The chlorine in the soot can both be directly inserted into a CDD/CDF molecule during
formation, or can exchange with the chloride ions in the transitional metal catalyst which
promotes CDD/CDF formation. Thus, the inorganic chlorine ion participates as a chlorine
donor to CDD/CDF formation.
De Fre and Rymen (1989) reported on forming CDDs and CDFs from hydrocarbon
combustion in a domestic gas/oil heating burner in the presence of 15 and 300 ppm
concentrations of HCI. Over 100 chlorinated organic compounds were detected in the flue
gases whenever HCI was injected into the system. De Fre and Rymen (1989) observed
formation of CDDs and CDFs in all experiments where HCI was injected in a hydrocarbon
flame. In this case, CDFs were always more abundant that CDDs. De Fre and Rymen
(1989) concluded that the relationship between the HCI concentration and the emitted
concentration of CDD/CDF under fixed combustion conditions appeared to be exponential
for a wide range in temperature (e.g., 240° to 900°C).
2.4.2. Review of Full Scale Combustion Systems
The review of experimental data clearly indicates an association between chlorine
content of feed/fuels and the potential synthesis of CDDs and CDFs. Paradoxically, the
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review of full-scale operating incineration processes does not yield such unequivocal
results indicating that complex kinetic events make strong associations difficult in full-
scale systems. The following is a review of studies of the association between chlorine in
feeds and stack releases of CDD/CDFs in full-scale incineration systems. In the stack
testing of a variety of industrial stationary combustion sources during the National Dioxin
Study in 1987, EPA made a series of qualitative observations on the relationship between
total chlorine present in the fuel/waste and the magnitude of emissions of CDDs and CDFs
from the stack of the tested facilities (U.S. EPA, 1987a). In general, combustion units
with the highest CDD emission concentrations had greater quantities of chlorine in the
fuel; conversely, sites with the lowest CDD emission concentrations contained only trace
quantities of chlorine in the feed. The typical chlorine content of various combustion fuels
was reported by Lustenhouwer et al. (1980) as: coal: 1,300/vg/g; MSW: 2,500/vg/g;
leaded gasoline: 300-1,600//g/g; and unleaded gasoline: 1-6//g/g.
Thomas and Spiro (1995) also analyzed the relationship of CDD/CDF emissions
from combustion to the chlorine content of feed materials. Thomas and Spiro (1996)
plotted average CDD/CDF emission factors for a variety of combustion processes (black
liquor boilers, unleaded gasoline combustion, leaded gasoline combustion, wire
incineration, cigarette combustion, sewage sludge incineration, MSWI, PCP-treated wood
combustion, hazardous waste incineration, and hospital waste incineration) against the
average chlorine concentration of the combusted material. The plot showed that average
CDD/CDF emissions of combustion source categories tend to increase with the average
chlorine content of the combusted fuel. The analysis clearly indicated that combustion
sources with relatively high combustion efficiency and adequate air pollution controls
tended to have two order of magnitude lower emissions than poorly operated sources.
This suggested a strong dependence on chlorine concentration in fuels and the magnitude
of CDD/CDF emissions in the more poorly controlled combustion sources. The slope of
the log-log plot was between 1 and 2, indicating that the relationship of chlorine content
to CDD/CDF emissions was more than proportional.
Recently Costner (1998) reported finding a positive correlation between chlorine
content of feed material and CDD/CDF emissions at a full-scale hazardous waste
incinerator. Costner concluded that emissions at this facility were dependent on chlorine
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input at a chlorine concentration as low as 0.031 percent, and that there was no evidence
of a threshold in the relationship between chlorine in feed and CDD/CDF emissions.
Rigo et al. (1995) summarized the results of a study commissioned by the
American Society of Mechanical Engineers (ASME, 1995). The study was a statistical
evaluation of the relationship of HCI concentration in flue gases to various combustion
systems (i.e., MSWI, hospital waste incineration, hazardous waste incineration, biomass
combustors, laboratory and bench-scale combustors) to the stack emission of total CDDs
and CDFs. HCI was a surrogate for total chlorine feed content in this study. The data
analysis was sufficient for 92 facilities in the data base that showed both HCI and
CDD/CDF emissions. From the 92 facilities, 72 did not show statistically significant
relationship between chlorine input and CDD/CDF output in emissions streams; 2 facilities
showed increasing CDD/CDF concentrations with increasing chlorine; and 8 facilities
showed decreasing CDD/CDF concentrations with increasing chlorine. AMSE (1995)
concluded that:
"The failure to find simultaneous increases in most cases and finding inverse
relationships in a few indicates that any effect chlorine has on PCDD/F
emissions is smaller than the variability of other causative factors. Whatever
effect chlorine has on PCDD/F emissions in commercial scale systems is
masked by the effect of ARCS (air pollution control systems) temperature,
ash chemistry, combustion conditions, measurement imprecision, and
localized flow stratification."
Liberson and Belanger (1995) reported the results of an analysis of the formation
and emission of CDDs and CDFs as a function of total chlorine in combustion feed
materials at a rotary kiln hazardous waste incinerator (HWI). The data were generated
from multiple test series conducted over a 13-month period at the HWI, while operating a
carbon injection system specifically designed to control and reduce CDD and CDF stack
emissions. The chlorine feed rates ranged from 0 to 3,300 pounds per hour, while the
CDD/CDF emission rates ranged between 0.7 and 39 ng/DSCM. The authors noted that
multiple series of CDD/CDF control systems were employed on this HWI (e.g., a high
temperature secondary combustion chamber, a spray dryer-evaporative quench that
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further cools the combustion gases, activated carbon injection to adsorb semi-volatile
organics, and a cool-side electrostatic precipitator followed by an acid gas scrubber to
collect HCI and CI2). From the analyses of data, Liberson and Belanger (1995) concluded
no correlation exists between CDD/CDF emissions and chlorine feed in a modern MWI,
using carbon injection for CDD/CDF control.
2.5. POTENTIAL PREVENTION OF CDD/CDF FORMATION IN COMBUSTION SYSTEMS
Given what is currently understood about oxychlorination reactions in the
synthesis of CDDs and CDFs, researchers have identified certain interventions that could
be taken to reduce or impede formation in combustion systems. Recently, Haghunathan
and Gullett (1996) demonstrated in a pilot-scale incinerator that sulfur compounds can
combine with the metal catalyst necessary to stimulate the Deacon reaction of HCI and 02
to yield CI2, thereby, neutralizing the catalyzing agent and reducing the formation of CDDs
and CDFs. The Deacon reaction, which forms free chlorine in the combustion plasma, is
seen as only occurring in the presence of a catalyst. Thus, the S02 molecule (formed
when sulfur in the fuel combines with oxygen) can either inhibit the catalytic activity of
the fly ash by combining with a metal-based Deacon catalyst in the fly ash, or by
depleting the CI2 formed. Haghunathan and Gullett (1996) observed that the principal
action of sulfur for inhibiting the formation of CDDs and CDFs in combustion systems is
through S02 depletion of CI2, as follows:
CI2 + S02 + H20 « 2HCI + S02
The relevance of this finding is that the co-combustion of municipal solid waste with coal
(that contains sulfur) should lead to dramatic reductions in the amount of CDDs and CDFs
formed and emitted, and may explain why, in the United States, coal combustion at power
plants results in over a magnitude lower CDD/CDF emission rate than MSWIs.
Naikwadi and Karasek (1989) investigated the addition of calcium oxide (CaO) and
triethylamine (TEA) to the flue gases of a combustion system as an inhibitor of the
catalytic activity of fly ash. They placed 500//g C-13-labeled pentachlorophenol (a dioxin
precursor) in a combustion flow tube and allowed it to react with organic-extracted MSWI
fly ash at 300°C under an air stream. Under these condition, CDD/CDFs were formed at
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concentrations ranging from 1,660 ng to 2,200 ng per 100/vg 13C-PCP. The experimental
method was then modified by mixing reactive MSWI fly ash with either CaO or TEA. The
results showed that the amount of CDD/CDF formed could be reduced by an order of
magnitude from the reaction of PCP with fly ash and the addition of TEA as an inhibitor.
When CaO was mixed with fly ash, the amount of CDD/CDFs formed decreased by over
20 times.
2.6. THEORY ON THE EMISSION OF POLYCHLORINATED BIPHENYLS
The air emission of PCBs from MSW incineration is less well studied. Probably the
formation mechanisms that apply to CDDs/CDFs would also apply to PCBs. Mechanism 1
(pass through) is implicit in the TSCA rule which requires 99.9999 percent destruction in
hazardous waste incinerators. When this occurs, 0.0001 percent of the initial amount of
PCBs fed into the hazardous waste incinerator may be emitted out the stack. This may
indicate that some small fraction of the PCBs present in the fuel fed into an incineration
process may result in PCB emissions from the stack of the process.
PCBs have been measured as contaminants in the raw refuse prior to incineration in
an MSWI (Choudhry and Hutzinger, 1983; Federal Register, 1991 a). Using this
information, it is possible to test Theory mechanism 1 involved in CDD/CDF emissions:
that the PCB contamination present in the fuel is responsible for emissions from the stack.
The mass balance of total PCB, beginning with measurement in the raw refuse and ending
with measurement at the stack to an RDF MSW incinerator (Federal Register, 1991 a), can
be used to calculate the destruction rated efficiency (ORE) of incineration of the PCB
contaminated MSW. Using results from test number 11 at the RDF facility (Federal
Register, 1991 a), a computation of ORE can be made with the following equation
(Brunner, 1984):
W. - Wn
ORE = —! 2 x 100%
W,
Where:
W, = mass rate of contaminant fed into the incinerator system
W0 = mass rate of contaminant exiting the incinerator system
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In test 11, 811 nanograms of total PCBs/gram of refuse (ng/g) were measured in
the MSW fed into the incineration system, and 9.52 ng/g of total PCB were measured at
the inlet to the pollution control device (i.e., outside the furnace region, but preceding
emission control). From these measurements, a ORE of 98.8 percent can be calculated.
Therefore, it appears that PCB contamination in the raw MSW, which was fed into this
particular incinerator, may have accounted for the PCB emissions from the stack of the
MSW incinerator.
PCBs can be thermolytically converted into CDFs (Choudhry and Hutzinger, 1983;
U.S. EPA, 1984). This process occurs at temperatures somewhat lower than typically
measured inside the firebox of an MSWI. Laboratory experiments conducted by EPA (U.S.
EPA, 1984) indicate that the optimum conditions for CDF formation from PCBs are near a
temperature of 675°C in the presence of 8 percent oxygen and a residence time of 0.8
seconds. This resulted in a 3 to 4 percent efficiency of conversion of PCBs into CDFs.
Because 1 to 2 percent of the PCBs present in the raw refuse may survive the thermal
stress imposed in the combustion zone to the incinerator (Federal Register, 1991 a), then it
is reasonable to presume that PCBs in the MSW may contribute to the total mass of CDF
emissions released from the stack of the incinerator.
Although it appears that contamination of waste feeds with PCBs may be an
important factor to detecting PCBs in stack emissions from combustion processes, recent
research has indicated the possibility that these compounds may also be formed in the
post-combustion zone either from de novo synthesis or from precursor compounds.
Zheng et al. (1999) observed the formation of PCBs in the post-combustion zone from the
pyrolysis of chlorobenzenes using a laboratory scale furnace. Zheng and coworkers
(1999) observed that PCBs were optimally formed from lower chlorinated chlorobenzenes
(i.e., 1,3-dichlorobenzene) catalyzed by copper chloride. In this experiment, maximum PCB
production occurred at a temperature of 350°C. Wikstrom et al. (1998) reported
secondary formation of PCB in the post-combustion zone similar to the de novo synthesis
of CDDs and CDFs, albeit, PCBs were formed in only small amounts relative to CDD/CDFs.
Fangmark and coworkers (1994) have postulated that formation of PCBs, CDDs, and
CDFs in the post-combustion zone may occur either from a common precursor, or by side
reactions affected in a similar way by temperature and residence time. On the other hand,
Blumenstock et al. (1998) produced results in a pilot-scale furnace that were inconsistent
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with the de novo formation of CDDs and CDFs in the post-combustion zone (i.e., PCBs
seemed to be optimally formed at high temperatures in oxygen deficient atmospheres).
Shin and Chang (1999) have noted a positive correlation between PCB concentrations on
MSW incineration fly ash and fly ash concentrations of CDDs and CDFs, suggesting that
high PCBs levels in fly ash may be a contributory cause to the post-combustion formation
of CDDs and CDFs (i.e., PCBs are precursors to CDD/CDFs). Nito et al. (1997) noted the
formation of CDFs and CDDs from the pyrolysis of PCBs in a fluidized bed system
indicating that PCBs in feeds may account for CDFs formed in municipal solid waste
incineration. More combustion related research needs to be conducted to firmly establish
whether or not PCB contamination in feeds or post-combustion formation (or both) may
explain the presence of PCBs in combustion flue gases.
2.7. SUMMARY AND CONCLUSIONS
2.7.1. Mechanisms of Formation of Dioxin-Like Compounds
There are three primary mechanisms for CDD/CDF emissions from combustion
sources:
Mechanism 1: This refers to CDD/CDFs contained in the feed which pass through
the combustor intact and are subsequently released to the environment. For most
systems, this is not thought to be a major contributor to CDD/CDF emissions for two
reasons. First, for commercial systems with good combustion controls, the temperatures
and residence times should result in the destruction of most CDD/CDFs in the feed.
Second, mass balance studies of a number of combustion systems show that more
CDD/CDFs can be detected downstream of the furnace than in the feed. Consequently
synthesis appears to be a more important mechanism than pass through.
Mechanism 2: This is the formation of CDD/CDFs from the thermal breakdown and
molecular rearrangement of aromatic precursors either originating in the feed or forming as
a product of incomplete combustion. Actual synthesis of CDD/CDF occurs in the post-
combustor environment. The CDD/CDFs form when the precursors sorb onto binding sites
on the surface of fly ash particles. This reaction has been observed to be catalyzed by the
presence of a transition metal sorbed to the particulate. The most potent catalyst is
copper chloride. Heat in a range of 200 to 450°C has been identified as a necessary
condition for these reactions to occur, with either lower or higher temperatures inhibiting
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the process. Since these reactions involve heterogenous chemistry, the rate of emissions
is less dependent on reactant concentration than conditions that promote formation such
as temperature, retention time and availability of catalytic surfaces. For this mechanism
to be significant, two broad conditions are needed.
Mechanism 3: This is the formation of CDD/CDFs in the post-combustion
environment in the absence of aromatic precursors. This de novo synthesis involves the
oxidative breakdown of macromolecular carbon structures (e.g., graphite) leading to the
formation of aromatic CDD/CDF precursors. These precursors then undergo the
transformations associated with mechanism 2 to form CDD/CDFs. As with mechanism 2,
since this mechanism involves heterogenous chemistry, the rate of emissions is dominated
by the same physical conditions as discussed in mechanism 2. Mechanisms 2 and 3 can
occur simultaneously, share a number of common reaction pathways, occur in the same
physical environment and are controlled by many of the same physical conditions. In well
designed and operated combustion systems, the precursor species needed for mechanism
2 are not in abundant supply; consequently de novo synthesis can become the dominant
pathway for formation. In systems with incomplete combustion, it is difficult to sort out
the relative contribution of these two mechanisms to total emissions. Both mechanisms,
however, can be curtailed if steps are taken to minimize the physical conditions needed to
support formation (i.e., time, temperature and reactive surface).
The combustion formation chemistry of PCBs is less well-studied than for
CDD/CDFs, but it is reasonable to assume that these same three mechanisms would
apply. For waste incineration, PCBs can exist in significantly higher concentrations in the
feed than CDD/CDFs. Consequently, mechanism 1 may play a more prominent role in PCB
emissions from some forms of waste combustion.
2.7.2. Role of Chlorine
From the various analyses on the role and relationship of chlorine in feeds to
CDD/CDF formation and emissions, the following observations and conclusions are made:
1. Although chlorine is an essential component for the formation of CDD/CDFs in
combustion systems, the empirical evidence indicates that, for commercial scale
incinerators, chlorine levels in feed are not the dominant controlling factor for rates of
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CDD/CDF stack emissions. Important factors which can affect the rate of CDD/CDF
formation include the overall combustion efficiency, post-combustion flue gas
temperatures and residence times, and the availability of surface catalytic sites to support
CDD/CDF synthesis. Data from bench, pilot and commercial scale combustors indicate
that CDD/CDF formation can occur by a number of mechanisms. Some of these data,
primarily from laboratory and pilot scale combustors, have shown direct correlation
between chlorine content in fuels and rates of CDD/CDF formation. Other data, primarily
from commercial scale combustors, show little relation with availability of chlorine and
rates of CDD/CDF formation. The conclusion that chlorine in feed is not a strong
determinant of CDD/CDF emissions applies to the overall population of commercial scale
combustors. For any individual commercial scale combustor, circumstances may exist in
which changes in chlorine content of feed could affect CDD/CDF emissions. For
uncontrolled combustion, such as open burning of household waste, chlorine content of
wastes may play a more significant role in affecting levels of CDD/CDF emissions than
observed in commercial scale combustors.
2. Both organic and inorganic chlorine in combustion fuels yield HCI in the post-
combustion region. HCI vapor is the dominant source of chlorine leading to the formation
of CDD/CDFs. The reaction proceeds via the oxidation of HCI in the presence of an
inorganic chloride catalyst (the Deacon reaction). Although the preponderance of
scientific evidence suggest that this is a dominant pathway for producing chlorinated
compounds in emissions, it is still unclear if HCI can also directly chlorinate aromatics, or
must first be oxidized to yield free chlorine.
3. Laboratory scale experiments have examined correlations between chlorine content of
feeds with total CDD/CDF formation. These experiments have suggested that for feeds
containing less than 1 % Cl, the rate of CDD/CDF formation is independent of Cl. For
feeds with Cl content greater than 1 %, a positive correlation is seen. Although this
relationship is observed at the laboratory scale, it has been shown not to apply to
commercial scale combustors (see 1 above). It has not been determined, however, if
these relationships are relevant to other types of combustion such as backyard barrel
burning, landfill fires and agricultural burning.
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2.7.3. General Conclusion
The trace chemistry of combustion appears to involve a wide variety of formation
pathways indicating that the chemistry of CDD/CDF formation is more complicated than
the relatively simple constructs described in this review. Despite this complexity, the
current weight of evidence would suggest that the role of chlorine and the formation
mechanisms outlined above will account for most of the CDD/CDF emissions associated
with combustion.
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Table 2-1. Concentration of CDD/CDFs on Municipal Incinerator Fly
Ash at Varying Temperatures
Congener
CDD
Tetra
Penta
Hexa
Hepta
Octa
CDF
Tetra
Penta
Hexa
Hepta
Octa
CDD/CDF Concentration on Fly Ash (ng/g)
Temperature (°C)
200°
15
40
65
100
90
122
129
61
48
12
250°
26
110
217
208
147
560
367
236
195
74
300°
188
517
1029
1103
483
1379
1256
944
689
171
350°
220
590
550
430
200
1185
1010
680
428
72
400°
50
135
110
60
15
530
687
260
112
12
Source: Adapted from Vogg et al. (1987).
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Table 2-2. CDD/CDFs Formed from the Thermolytic Reaction of
690 mg Benzene + FeCI3Silica Complex
Congener
DiCDD
TriCDD
TCDD
PeCDD
HxCDD
HpCDD
OCDD
Total CDDs
DiCDF
TriCDF
TCDF
PeCDF
HxCDF
HpCDF
OCDF
Total CDFs
Mass Produced
(ng)
4.9
54
130
220
170
98
20
696.9
990
7,800
12,000
20,000
33,000
40,000
74,000
187,790
Number of Moles
Produced
0.019
0.019
0.400
0.620
0.440
0.230
0.040
1.940
4.200
29.00
39.00
59.00
88.00
98.00
167
484.2
Percent Yield3
4.3 E-7
4.3 E-6
9.0 E-6
1.4 E-5
9.9 E-6
5.2 E-6
9.0 E-7
4.4 E-5
9.5 E-5
6.6 E-4
8.8 E-4
1.3 E-3
2.0 E-3
1.1 E-3
3.8 E-3
1.1 E-2
a Percent yield = (number of moles of CDD or CDF/moles benzene) x 100.
Source: Nestrick et al. (1 987)
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Table 2-3. De Novo Formation of CDDs/CDFs after Heating Mg-AI
Silicate, 4% Charcoal, 7% Cl, 1 % CuCI2.2H20 at 300°C
Congener
TCDD
PeCDD
HxCDD
HpCDD
OCDD
Total CDD
TCDF
PeCDF
HxCDF
HpCDF
OCDF
Total CDF
Concentrations of CDD/CDF (ng/g)
Reaction Time (hrs)
0.25
2
110
730
1700
800
3342
240
1360
2500
3000
1260
8360
0.5
4
120
780
1840
1000
3744
280
1670
3350
3600
1450
10350
1
14
250
1600
3500
2000
7364
670
3720
6240
5500
1840
17970
2
30
490
2200
4100
2250
9070
1170
5550
8900
6700
1840
24160
4
100
820
3800
6300
6000
17020
1960
8300
14000
9800
4330
38390
Source: Stieglitz et al. (1 989a).
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CDDs/CDFs Formed at 300
1000
e
*
h
m
m
E
o
e
800-
600-
400-
200
Total CDDs
Total COFs
Retention Tims-hours
8
Olit (1891)
Figure 2-1. The de novo Synthesis of CDD/CDFs from Heating Carbon Particulate
at 300°C at Varying Retention Times
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on CDD/CDF
-------
Figure 2-3. The Association Between Vapor Phase CI2 and
the Formation of CDDs/CDFs
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3. COMBUSTION SOURCES OF CDD/CDF: WASTE INCINERATION
Incineration is the destruction of solid, liquid, or gaseous wastes through the
application of heat within a controlled combustion system. The purposes of incineration
are to reduce the volume of waste that needs land disposal and to reduce the toxicity of
the waste, making it more sterile. In keeping with this definition, incinerator systems can
be classified by the types of wastes incinerated: municipal solid waste incineration;
medical and pathological waste incineration; hazardous waste incineration; sewage sludge
incineration; tire incineration; and biogas flaring. Each of these types of incinerators are
discussed in this chapter. The purposes of this chapter are to: characterize and describe
waste incineration technologies in the United States and to derive estimates of annual
releases of CDDs and CDFs into the atmosphere from these facilities for reference years
1987 and 1995.
Combustion research has developed three theories on the mechanisms involved in
the emission of CDDs and CDFs from combustion systems: (1) CDD/CDFs can be
introduced into the combustor with the feed and pass through the system unchanged,
(2) CDD/CDFs can be formed during combustion, or (3) CDD/CDFs can be formed via
chemical reactions in the post-combustion portion of the system. The total CDD/CDF
emissions are likely to be the net result of all three mechanisms; however, their relative
importance is often uncertain. To the extent practical with the available data, the
combustors in each source category were divided into classes judged to have similar
emission factors. This classification effort attempted to reflect the emission mechanisms
described above. The emission mechanisms suggest that the aspects of combustor
design and operation that could affect CDD/CDF emissions are furnace design,
composition of the waste feed, temperature in the post-combustion zone of the system,
and type of air pollution control device (APCD) used to remove contaminants from the flue
gases. Therefore, incineration systems that are similar in terms of these factors should
have similar CDD/CDF emissions. Accordingly, this chapter proposes classification
schemes that divide combustors into a variety of design classes based on these factors.
Design class, as used here, refers to the combination of furnace type and accompanying
APCD.
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3.1. MUNICIPAL SOLID WASTE INCINERATION
As discussed previously, CDD/CDF emission theory suggests that CDD/CDF
emissions can be related to several factors, including furnace design, composition of the
waste feed, temperature in the post-combustion zone of the system, and type of APCD
used to remove contaminants from the flue gases. Accordingly, this chapter proposes a
classification scheme that divides municipal solid waste incinerators (MSWIs) into a variety
of design classes based on those factors. Some APCDs are operated at different
temperatures; therefore, operating temperature is used to define some design classes.
Because the theory also suggests that feed can influence CDD/CDF emissions, the
proposed furnace classification system distinguishes refused-derived fuel from normal
municipal solid waste (MSW). This section begins with a description of the MSWI
technology and then proposes the design classification scheme. Using this scheme, the
MSWI industry is characterized for the reference years 1987 and 1995. Finally, the
procedures for estimating emissions are explained, and results summarized.
3.1.1. Description of Municipal Solid Waste Incineration Technologies
For purposes of this report, MSWI furnace types are divided into three major
categories: mass burn, modular, and refuse-derived fuel. Each of these furnace types is
described below, followed with a description of the APCDs used with these systems.
Furnace Types
Mass Burn: Historically, this furnace type derived its name because it burned MSW
as received (i.e., no preprocessing of the waste was conducted other than removal of
items too large to go through the feed system). Today, a number of other furnace types
also burn unprocessed waste (as described below). Mass burn furnaces are distinguished
from these others because they burn the waste in a single stationary chamber. In a
typical mass burn facility, MSW is placed on a grate that moves through the combustor.
The 1995 inventory indicates that the combustion capacity of facilities ranges from 90 to
2,700 metric tons of MSW per day. Three subcategories of mass burn (MB) technologies
are described below:
• Mass burn refractory-walled (MB-REF) systems represent an older class of MSWIs
(generally built in the late 1970s to early 1980s) that were designed only to reduce
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the volume of waste in need of disposal by 70 to 90 percent. These facilities
usually lacked boilers to recover the combustion heat for energy purposes. In the
MB-REF design, the MSW is delivered to the combustion chamber by a traveling
grate and/or a ram feeding system. Combustion air in excess of stoichiometric
amounts (i.e., more oxygen is supplied than needed for complete combustion) is
supplied both below and above the grate.
Mass burn waterwall (MB-WW) facilities represent enhanced combustion efficiency,
as compared with MB-REF incinerators. Although it achieves similar volume
reductions, the MB-WW incinerator design provides a more efficient delivery of
combustion air, resulting in sustained higher temperatures. Figure 3-1 is a
schematic of a typical MB-WW MSWI. The term 'waterwall ' refers to a series of
steel tubes, running vertically along the walls of the furnace. The tubes contain
water, which when heated by combustion, transfer energy from the heat of
combustion to the water. The water reaches boiling temperature, and steam is
produced. The steam is then used to drive an electrical turbine generator or for
other industrial needs. This transfer of energy is termed 'energy recovery.'
Mass burn rotary kiln combustors (MB-RC) use a water-cooled rotary combustor,
which consists of a rotating combustion barrel configuration mounted at a 15-20°
angle of decline. The refuse is charged at the top of the rotating kiln by a hydraulic
ram (Donnelly, 1992). Preheated combustion air is delivered to the kiln through
various portals. The slow rotation of the kiln (i.e., 10 to 20 rotations/hour) causes
the MSW to tumble, thereby exposing more surface area for complete burnout of
the MSW. These systems are also equipped with boilers for energy recovery.
Figure 3-2 is a schematic of a typical MB-RC MSWI.
Modular Incinerator: This is the second general type of MSWI furnace used in the
United States. As with the mass burn type, modular incinerators burn waste without
preprocessing. Modular MSWIs consist of two vertically mounted combustion chambers
(i.e., a primary and secondary chamber). In the 1995 inventory, modular combustors'
combustion capacity ranged from 4 to 270 metric tons/day. The two major types of
modular systems, "excess air" and "starved air," are described below.
• The modular excess-air system consists of a primary and secondary combustion
chamber, both of which operate with air levels in excess of stoichiometric
requirements (i.e., 100 to 250 percent excess air). Figure 3-3 illustrates a typical
modular excess air MSWI.
• Starved (or controlled) air is another type of modular system in which air is supplied
to the primary chamber at sub-stoichiometric levels. The products of incomplete
combustion entrain in the combustion gases that are formed in the primary
combustion chamber, then pass into a secondary combustion chamber. Excess air
is added to the secondary chamber, and combustion is completed by elevated
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temperatures sustained with auxiliary fuel (usually natural gas). The high and
uniform temperature of the secondary chamber, combined with the turbulent
mixing of the combustion gases, results in low-levels of particulate matter and
organic contaminants being formed and emitted. Therefore, many existing modular
units lack post-combustion air pollution control devices. Figure 3-4 is a schematic
view of a modular starved air MSWI.
Refuse-Derived Fuel (RDF): The third major type of MSWI furnace technology is
designed to combust refuse-derived fuel (RDF). RDF is a general term that describes MSW
from which relatively noncombustible items are removed, thereby enhancing the
combustibility of the MSW. RDF is commonly prepared by shredding, sorting, and
separating out metals to create a dense MSW fuel in a pelletized form having a uniform
size. Three types of RDF systems are described below.
The dedicated RDF system burns RDF exclusively. Figure 3-5 shows a typical
dedicated RDF using a spreader-stoker boiler. Pelletized RDF is fed into the
combustor through a feed chute, using air-swept distributors; this allows a portion
of the feed to burn in suspension and the remainder to burn out after falling on a
horizontal traveling grate. The traveling grate moves from the rear to the front of
the furnace, and distributor settings are adjusted so that most of the waste lands
on the rear two-thirds of the grate. This allows more time to complete combustion
on the grate. Underfire and overfire air are introduced to enhance combustion, and
these incinerators typically operate at 80 to 100 percent excess air. Waterwall
tubes, a superheater, and an economizer are used to recover heat for production of
steam and/or electricity. The 1995 inventory indicates that dedicated RDF facilities
range in total combustion capacity from 227 to 2,720 metric tons/day.
Cofired RDFs burn both RDF and normal MSW.
The fluidized-bed RDF (FB-RDF) burns the waste in a turbulent and semi-suspended
bed of sand. The MSW may be fed into the incinerator either as unprocessed
waste or as a form of RDF. The RDF may be injected into or above the bed
through ports in the combustor wall. The sand bed is suspended during
combustion by introducing underfire air at a high velocity, hence the term
"fluidized." Overfire air at 100 percent stoichiometric requirements is injected
above the sand suspension. Waste-fired FB-RDFs typically operate at 30 to
100 percent excess air levels and at bed temperatures around 815°C (1,500°F).
A typical FB-RDF is presented as Figure 3-6. Technology has two basic design
concepts: (1) a bubbling-bed incineration unit and (2) a circulating-bed incineration
unit. The 1995 inventory indicates that fluidized-bed MSWIs have capacities
ranging from 184 to 920 metric tons/day. These systems are usually equipped
with boilers to produce steam.
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Air Pollution Control Devices (APCDs)
MSWIs are commonly equipped with one or more post-combustion APCDs to
remove various pollutants prior to release from the stack (e.g., particulate matter, heavy
metals, acid gases, and/or organic contaminants) (U.S. EPA, 1992d). These APCDs
include:
Electrostatic precipitator (ESP),
Fabric filter (FF),
Dry scrubber (DS),
Dry sorbent injection (DSI), and
Wet scrubber (WS)
Electrostatic Precipitator: The ESP is generally used to collect and control
particulate matter that evolves during MSW combustion, by introducing a strong electrical
field in the flue gas stream; this, in turn, charges the particles entrained in the combustion
gases (Donnelly, 1992). Large collection plates receive an opposite charge to attract and
collect the particles. CDD/CDF formation can occur within the ESP at temperatures in the
range of 1 50 to about 350°C. As temperatures at the inlet to the ESP increase from
150 to 300°C, CDD/CDF concentrations have been observed to increase by approximately
a factor of two for each 30°C increase in temperature (U.S. EPA, 1994f). As temperature
increases beyond 300°C, formation rates decline. Although ESPs in this temperature
range efficiently remove most particulates and the associated CDD/CDFs, the formation
that occurs can result in a net increase in CDD/CDF emissions. This temperature related
formation of CDD/CDF within the ESP can be applied to distinguish hot-side ESPs from
cold-side ESPs. For purposes of this report, ESPs are classified as follows:
A cold-side ESP operates at or below 230°C.
• A hot-side ESP operates at an inlet temperature greater than 230°C.
Fabric Filters (FF): FFs are also particulate matter control devices, which remove
dioxins associated with particles and any vapors that adsorb to the particles. Six- to 8-
inch diameter bags, made from woven fiberglass material, are usually arranged in series.
An induction fan forces the combustion gases through the tightly woven fabric. The
porosity of the fabric allows the bags to act as filter media and retain a broad range of
particles sizes (i.e., down to less than 1 micrometer in diameter). The FF is sensitive to
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acid gas; therefore, it is usually operated in combination with spray dryer adsorption of
acid gases.
Dry Scrubbers (PS): DSs, also called spray dryer adsorption, involve both the
removal of acid gas and particulate matter from the post-combustion gases. By
themselves, these units probably have little effect on dioxin emissions. In a typical DS
system, hot combustion gases enter a scrubber reactor vessel. An atomized hydrated lime
slurry (water plus lime) is injected into the reactor at a controlled velocity (Donnelly,
1992). The hydrated lime slurry rapidly mixes with the combustion gases within the
reactor. The water in the hydrated lime slurry quickly evaporates, and the heat of
evaporation causes the combustion gas temperature to rapidly decrease. The neutralizing
capacity of hydrated lime reduces the combustion gas content of acid gas constituents
(e.g., hydrogen chloride gas, and sulfur dioxide gas) by greater than 70 percent. A dry
product, consisting of particulate matter and hydrated lime, settles to the bottom of the
reactor vessel. DS technology is used in combination with ESPs. The DS reduces ESP
inlet temperatures to make a cold-side ESP. DS/FFs have achieved greater than 95
percent reduction and control of CDD/CDFs in MSWI emissions (U.S. EPA, 1992d).
Dry Sorbent Injection (DSD: DSI is used to reduce acid gas emissions. By
themselves, these units probably have little effect on dioxin emissions. DSI involves the
injection of dry hydrated lime or soda ash either directly into the combustion chamber or
into the flue duct of the hot post-combustion gases. In either case, the reagent reacts
with and neutralizes the acid gas constituents (Donnelly, 1992).
Wet Scrubber (WS): WS devices are designed for acid gas removal, and are more
common to MSWIs in Europe than in the United States. They should help reduce
emissions of dioxin in both vapor and particle forms. WS devices consist of two-stage
scrubbers. The first stage removes hydrogen chloride (HCI), and the second stage
removes sulfur dioxide (S02) (Donnelly, 1992). Water is used to remove the HCI, and
caustic or hydrated lime is added to remove S02 from the combustion gases.
In addition to the APCDs described above, some less common types are also used
in some MSWIs. An example is the Electro Granular Bed (EGB), which consists of a
packed bed of activated carbon. An electric field is passed through the packed bed;
particles entrained in the flue gases are given a negative charge, and the packed bed is
given a positive charge. EGB systems function much like an ESP. Particulate matter is
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collected within the bed; therefore, they will remove dioxins associated with collected
particles and any vapors that adsorb to the particles. Only one facility in the United States
currently employs the EGB system, a fluidized bed-RDF MSWI.
Classification Scheme
Based on the array of MSWI technologies described above, a classification system
for deriving CDD/CDF emission estimates was developed. As discussed earlier, it is
assumed that facilities with common design and operating characteristics have a similar
potential for CDD/CDF emissions. The MSWIs operating in 1987 and 1995 were divided
according to the eight furnace types and seven APCDs described above. This resulted in
17 design classes in 1987 and 40 design classes in 1995. Because fewer types of
APCDs were used in 1987 than in 1995, fewer design classes are needed for estimating
emissions. This taxonomy is summarized in Figures 3-7 and 3-8.
3.1.2. Characterization of MSWI Facilities in Reference Years 1995 and 1987
Table 3-1 lists by design/APCD type, the number of facilities and activity level (kg
MSW incinerated per year) for MSWIs in the reference year 1995. A similar inventory is
provided for reference year 1987 in Table 3-2. This information was derived from four
reports: U.S. EPA (1987b), Systems Applications International (1995), Taylor and Zannes
(1996), and Solid Waste Technologies (1994). In general, these studies collected the
information via telephone interviews with the plant operators.
Using Tables 3-1 and 3-2, a number of comparisons can be made between the two
reference years:
The number of facilities stayed about the same (113 in 1987 and 130 in 1995),
but the amount of MSW incinerated more than doubled (13.8 billion kg in 1987
and 28.8 billion kg in 1995).
The dominant furnace technology shifted from modular in 1987 (57 units and 1.4
billion kg) to mass burn waterwall facilities in 1995 (57 units and 17 billion kg).
The dominant APCD technology shifted from hot-sided ESPs in 1987 (54 units and
11 billion kg) to fabric filters in 1995 (55 units and 16 billion kg).
The use of hot-sided ESPs dropped from 54 facilities in 1987 (11 billion kg) to 16
facilities in 1995 (2.2 billion kg).
The number of uncontrolled facilities dropped from 38 in 1987 (0.6-billion kg) to
10 facilities in 1995 (0.2 billion kg).
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3.1.3. Estimation of CDD/CDF Emissions from MSWIs
Compared to other CDD/CDF source categories, MSWIs have been more
extensively evaluated for CDD/CDF emissions. Within the context of this report, adequate
emission testing for CDD/CDFs were available for 11 of the 113 facilities in the 1987
inventory and 27 of the 130 facilities in the 1995 inventory. Nationwide CDD/CDF air
emissions from MSWIs were estimated using a three-step process as described below.
Step 1. Estimation of emissions from all stack tested facilities. The EPA stack testing
method (EPA Method 23) produces a measurement of CDD/CDF in units of mass
concentration of CDD/CDF (i.e., nanograms per dry standard cubic meter of combustion
gas [ng/dscm]) at standard temperature and pressure (20°C and 1 atmosphere), and
adjusted to a measurement of 7 percent oxygen in the flue gas (U.S. EPA, 1995b). This
concentration is assumed to represent conditions at the point of release from the stack
into the air. Equation 3-1 below was used to derive annual emission estimates for each
tested facility:
c _ CxVxCFxH
TEQ - 7T^ ; (Eqn. 3-1)
109 ng/g
Where:
ETEQ = Annual TEQ emission (g/yr)
C = Combustion flue gas TEQ concentration (ng/dscm) (20°C, 1 atm;
adjusted to 7% 02)
V = Volumetric flow rate of combustion flue gas (dscm/hour) (20°C, 1
atm; adjusted to 7% 02)
CF = Capacity factor, fraction of time that the MSWI operates (i.e., 0.85)
H = Total hours in a year (8,760 hr/yr)
After calculating annual emissions for each tested facility, the emissions were summed
across all tested facilities for each reference year. [Note: Many of the emission tests do
not correspond exactly to these 2 years. In these cases, the equipment conditions
present at the time of the test were compared to those during the reference year to
determine their applicability.]
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Step 2. Estimation of emissions from all non-tested facilities. This step involves
multiplying the emission factor and annual activity level for each MSWI design class and
then summing across classes. The activity levels for reference years 1995 and 1987 are
summarized in Tables 3-1 and 3-2, respectively. The emission factors were derived by
averaging the emission factors across each tested facility in a design class. The emission
factor for each facility was calculated using the following equation:
C x Fv
EFmswi = j (Eqn. 3-2)
w
Where:
EFmswi = Emission factor, average ng TEQ per kg of waste burned
C = TEQ or CDD/CDF concentration in flue gases (ng TEQ/dscm) (20°C,
1 atm; adjusted to 7% 02)
Fv = Volumetric flue gas flow rate (dscm/hr) (20°C, 1 atm; adjusted to
7% 02)
lw = Average waste incineration rate (kg/hr)
Example: A mass burn waterwall MSWI equipped with cold-sided ESP.
Given:
C = 10 ng TEQ/dscm (20°C, 1 atm; adjusted to 7% 02)
Fv = 40,000 dscm/hr (20°C, 1 atm; adjusted to 7% 02)
lw = 10,000 kg MSW/hr
FF = 10 "9 40,000 dscm hr
tl~MBWW ~ ~- X : X
dscm hr 10,000 kg
40 ng TEQ
kg MSW burned
EPA was not able to obtain engineering test reports of CDD/CDF emissions for a
number of design classes. In these cases, the above procedure could not be used to
derive emission factors. Instead, the emission factors of the tested design class that was
judged most similar in terms of dioxin control was assumed to apply to the untested class.
The following logic was used to make this decision:
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1. The tested APCDs for the furnace type of the untested class were reviewed to see
if any operated at a similar temperature.
2. If any operated at similar temperatures, the one with most similar technology was
assumed to apply.
3. If none operated at a similar temperature, then the most similar furnace type with
same control device was assumed to apply.
Table 3-3 lists all design categories with no tested facilities and shows the class with
tested facilities that was judged most similar.
It should be understood that the emission factors for each design class are the
same for both reference years. This is because the emission factor is determined only by
the design and operating conditions and is independent of the year of the test.
Step 3. Sum emissions from tested and untested facilities. This step simply involves
summing emissions from all tested and untested facilities. This process is shown in
Tables 3-4 and 3-5 for the reference years 1995 and 1987, respectively. The tables are
organized by design class and show separately the emission estimates for the tested and
untested facilities. The calculation of emissions from untested facilities is broken out to
show the activity level and emission factor for each design class.
3.1.4. Summary of CDD/CDF (TEQ) Emissions from MSWIs for 1995 and 1987
The activity level estimates (i.e., the amount of MSW that is annually combusted
by the various MSWI technologies) are given a high confidence rating for both 1987 (i.e.,
13.8 billion kg of waste) and 1995 (i.e., 28.8 billion kg of waste). For both years,
comprehensive surveys of activity levels were conducted by independent sources on
virtually all facilities (U.S. EPA, 1987b; Systems Application International, 1995; Taylor
and Zannes, 1996; Solid Waste Technologies, 1994).
The emission factor estimates are given a medium confidence rating for both 1987
and 1995. A moderate fraction of the facilities were tested in both years: 11 of 11 3
facilities in 1987 (10 percent), and 27 of 130 facilities (21 percent) in 1995. Moreover,
the tested facilities represent 21 and 27 percent of the total activity level of operating
MSWIs in 1987 and 1995, respectively. These tests represent most of the design
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categories identified in this report. The emission factors were developed from emission
tests that followed standard EPA protocols, used strict QA/QC procedures, and were well
documented in engineering reports. Because all tests were conducted under normal
operating conditions, some uncertainty exists about the magnitude of emissions that may
occur during other conditions (i.e., upset conditions, start-up and shut-down).
These confidence ratings produce an overall medium confidence rating in the
annual emission estimates of 7,915 g I-TEQDF (8,877 g TEQDF-WH098) in 1987 and 1,100
g I-TEQDF (1,250 g TEQDF-WH098) in 1995.
3.1.5 Congener Profiles of MSWI Facilities
The air emissions from MSWIs contain a mixture of CDD and CDF congeners.
These mixtures can be translated into what are termed 'congener profiles,' which
represent the distribution of total CDDs and CDFs present in the mixture. A congener
profile may serve as a signature of the types of CDDs and CDFs associated with particular
MSWI technology and APCD. Figure 3-9 is a congener profile of a mass-burn waterwall
MSWI equipped with a dry scrubber and fabric filter (i.e., the most common type of MSWI
and APCD design in use today). This congener profile indicates that OCDD dominates
CDD/CDF emissions and that every toxic CDD/CDF congener is detected in the emissions.
3.1.6 Estimated CDD/CDFs in MSWI Ash
Ash from MSWIs is required to be disposed of in permitted landfills from which
releases to the general environment are controlled. For background purposes, however,
some information is presented below about the quantities of CDD/CDFs in ash from
MSWIs.
An estimated 7 million metric tons of total ash (bottom ash plus fly ash) were
generated by MSWIs in 1992 (telephone conversation between J. Loundsberry, U.S. EPA
Office of Solid Waste, and L. Brown, Versar, Inc., on February 24, 1993). U.S. EPA
(1991b) indicated that 2 to 5 million metric tons of total ash were produced annually in
the late 1980s from MSWIs, with fly ash comprising 5 to 1 5 percent of the total. U.S.
EPA (1990c) reported the results of analyses of MSWI ash samples for CDDs and CDFs.
Ashes from five state-of-the-art facilities located in different regions of the United States
were analyzed for all 2,3,7,8-substituted CDDs and CDFs. The TEQ levels in the ash (fly
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ash mixed with bottom ash) ranged from 106 to 466 ng l-TEQDF/kg, with a mean value of
258 ng l-TEQDF/kg. CDD/CDF levels in fly ash are generally much higher than in bottom
ash. For example, Fiedler and Hutzinger (1992) reported levels of 13,000 ng l-TEQDF/kg in
fly ash.
In another study (Washington, 1998), CDD/CDF congener data were reported for
ash and other solid residuals from three municipal incinerators (Fort Lewis, Bellingham
[municipal plus medical wastes], and Spokane). The data were compiled and evaluated to
determine a total I-TEQ concentration and loading. Non-detect values were included as
either zero, 1/2 DL, or at the DL. The results were as follows, assuming that non-detect
values were at zero concentration:
Location
Ft. Lewis
Bellingham
Spokane
Type of Residual
Bottom Ash
Fly Ash
Mixed Ash (avg. of 3
tests)
Mixed Ash
Fly Ash
Bottom Ash
T-TEQ (yt/g/kg)
0.00
4.98
0.038
0.163
0.51
0.0001
I-TEQ (mg/day)
0.00
0.76
1.14
38.0
24.3
0.02
In Shane (1990), five municipal incinerator ashes were analyzed for a number of
constituents including CDDs (not CDFs) and PCBs. For dioxins, three of the incinerators
were at non-detectable levels (detection limit of 1 /vg/kg). The other two incinerators had
detectable levels of five CDD congener groups. (No analyses were reported for individual
congeners.) The average for those two units were:
TCDD
PeCDDs
HxCDDs
HpCDDs
OCDDs
26 //g/kg
59 //g/kg
53 //g/kg
25 //g/kg
1 2 //g/kg
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These levels were much higher that those reported in U.S. EPA (1990c).
For PCBs, the five sets of ashes were analyzed for 10 congener groups. All groups
were detected for one of the incinerators. However, the other four incinerators contained
little or no octa-, nona-, or deca- congeners. The average PCB concentration (all congener
groups) for the five incinerators was 216//g/kg, with a range of 99-322//g/kg.
No generation rates of the ashes were given in Shane (1990). Therefore, the
measured concentrations cannot be readily converted to quantities of CDDs or PCBs. The
ashes from each of the five incinerators were disposed of in multiple fashions. For two of
the incinerators, the ash was sent to metal recovery and also landfilled. For a third, the
fly ash was sold. For a fourth, the ashes were only landfilled. For the fifth, the ashes
were used in road building and also landfilled. For those incinerators with more than one
ash disposition, no breakdown was given of how much went to each location. There
were 15 other incinerators discussed in Shane (1990). Thirteen of them disposed of their
ash exclusively in landfills, and the other two partially disposed of their ash in landfills.
Table 7 of Clement (1988) presented 13 data sets for CDD/CDF congener groups
for a municipal incinerator ash. The average data for each congener group and the ranges
of each group are given in Table 3-6. No data were presented in Clement (1988) for
individual congeners, nor were there data for ash quantities.
In Table 3-3 of U.S. EPA (1987a), there were data stating that ashes from three
incinerators (one in North America, one in Europe, and one in Japan) had mean CDD
concentrations of 363, 588, and 2.6//g/kg, respectively. The ranges of those data were
from <0.5 to 3.537/vg/kg. Similarly, for CDFs, the respective mean concentrations for
the first two incinerators were 923 and 288/vg/kg. The third incinerator was not
reported. The CDF range for the two incinerator ashes was <0.5 to 1,770 /vg/kg. No
data were given for individual congeners, nor were there any data for quantities of the
ashes.
In Table 1 of Lahl (1991), data were presented for the concentrations of total
CDDs and for total CDFs for the ash from an electrostatic precipitator from a municipal
incinerator. Data were reported for summer sampling and for winter sampling. The total
CDDs in the summer were 140.46/vg/kg, and for the winter were 86.00/vg/kg. The total
CDFs in the summer were 54.97 /vg/kg, and for the winter were 73.85 /vg/kg. No data
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were given for individual congeners, nor was there information about the quantity of
precipitator ash generated. It was assumed that the data were not for TEQs.
In Table 3-11 of U.S. EPA (1987a), a wire reclamation incinerator was reported to
have 0.41 /vg/kg of CDDs and 11.6 /vg/kg of CDFs in fly ash from its "stack" emissions.
For the same incinerator, the "furnace" ash concentrations were reported as 0.58 //g/kg
CDDs and 0.73/vg/kg CDFs. Again, no data were given for individual congeners, nor
were there any data for quantities of the ashes.
Data from the aforementioned sources have been compiled in Table 3-7 for
comparison purposes. Annual TEQ amounts were estimated by multiplying the mean TEQ
total ash concentration by the estimated amount of MSWI ash generated annually
(approximately 7 million metric tons in 1995 and 5 million metric tons in 1987). Where
possible, ash quantities were broken down into fly ash and/or bottom ash categories. Fly
ash is assumed to be 10% of the total ash and bottom ash is assumed to be 90% of the
total ash.
Each of the five facilities sampled in U.S. EPA (1990c) had companion ash disposal
facilities equipped with leachate collection systems or some means of collecting leachate
samples. Leachate samples were collected and analyzed for each of these systems.
Detectable levels were only found in the leachate at one facility (3 ng I-TEQDF/L); the only
detectable congeners were HpCDDs, OCDD, and HpCDFs.
3.1.7 Recent EPA Regulatory Activities
On December 19, 1995, EPA promulgated CDD/CDF emission standards for all
existing and new MSWI units at facilities with aggregate combustion capacities greater
than 35 metric tons per day (Federal Register, 1995e). In response to a court remand, the
regulations were subsequently amended to remove small MWC units (i.e., units with
capacities ranging from 35 to 225 kkg/day) (Federal Register, 1997c). The specific
emission standards (expressed as ng/dscm of total CDD/CDF - based on standard dry gas
corrected to 7 percent oxygen) are a function of the size, APCD configuration, and age of
the facility as listed below:
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1995 Emission standard
(ng total CDD/CDF/dscm) Facility age, size, and APCD
60 • Existing; >225 metric tons/day; ESP-
based APCD
30 • Existing; >225 metric tons/day; non-
ESP-based APCD
13 • New; > 225 metric tons/day
EPA reproposed emission standards for small MWCs (defined as units with
capacities of between 32 and 224 kkg/day) on August 30, 1999 (Federal Register,
1999c). The proposed emission standard is 125 ng total CDD/CDF per dscm at 7 percent
oxygen.
States have up to 3 years from promulgation of the Federal standards to submit
revised State Implementation Plans to EPA for approval. Once approved, States have the
primary responsibility to implement the new standards. EPA's Office of Air Quality
Planning and Standards (OAQPS) estimates that the full compliance by all MSWIs with the
1995 standards and 1999 proposed standards will result in an annual emission of about
12 g l-TEQDF/yr (U.S. EPA, 2000a).
3.2. HAZARDOUS WASTE INCINERATION
Hazardous waste incineration (HWI) is the controlled pyrolysis and/or oxidation of
potentially dangerous liquid, gaseous and solid waste. HWI is one technology used to
manage hazardous waste under the Resource Conservation and Recovery Act (RCRA) and
Comprehensive Environmental Response, Compensation, and Liability Act (CERCLA)
(Superfund) programs. As described below, hazardous wastes are burned in a variety of
situations and are covered in a number of different sections in this report.
• Much of the hazardous waste is burned in facilities dedicated to burning hazardous
waste. Most of these dedicated facilities are located on-site at chemical
manufacturing facilities and only burn waste associated with their on-site industrial
operations. Hazardous waste is also burned at dedicated facilities located off-site
from manufacturing facilities and accept waste from multiple sources. These fixed
location facilities dedicated to burning hazardous waste at both on- and off-site
locations are addressed in Sections 3.2.1 to 3.2.4.
• Hazardous waste is also burned in industrial boilers and furnaces that are permitted
to burn the waste as supplemental fuel. These facilities have significantly different
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furnace designs and operations than dedicated HWIs; therefore, they are discussed
in Section 3.2.5.
A number of cement kilns and lightweight aggregate kilns are also permitted to
burn hazardous waste as auxiliary fuel; these are discussed separately in Section
5.1.
Mobile HWIs are typically used for site cleanup at Superfund sites and operate for a
limited duration at any given location. These units are mobile in the sense that
they can be transported from one location to another. Due to the transitory nature
of these facilities, they are not included in this inventory at this time.
The following subsections review the types of HWI technologies commonly in use
in the United States, and present the derivation of emissions estimates of CDD/CDFs from
all facilities operating in 1995 and 1987.
3.2.1. Furnace Designs for Hazardous Waste Incinerators
The four principal furnace designs employed for the combustion of hazardous
waste in the United States are: rotary kiln, liquid injection, fixed hearth, and fluidized-bed
incinerators (Dempsey and Oppelt, 1993). The majority of commercial operations are of
the rotary kiln incinerator type. On-site (noncommercial) HWI technologies are an equal
mix of rotary kiln and liquid injection facilities, with a few additional fixed hearths and
fluidized bed operations (U.S. EPA, 1996h). Each of these HWI technologies is discussed
below:
Rotary Kiln HWI: Rotary kiln incinerators consist of a rotary kiln, coupled with a
high temperature afterburner. Because these are excess air units designed to combust
hazardous waste in any physical form (i.e., liquid, semi-solid, or solid), rotary kilns are the
most common type of hazardous waste incinerator used by commercial off-site operators.
The rotary kiln is a horizontal cylinder lined with refractory material. Rotation of the
cylinder on a slight slope provides for gravitational transport of the hazardous waste
through the kiln (Buonicore, 1992a). The tumbling action of the rotating kiln causes
mixing and exposure of the waste to the heat of combustion, thereby enhancing burnout.
Solid and semi-solid wastes are loaded into the top of the kiln by an auger or rotating
screw. Fluid and pumpable sludges and wastes are typically introduced into the kiln
through a water-cooled tube. Liquid hazardous waste is fed directly into the kiln through
a burner nozzle. Auxiliary fuel (natural gas or oil) is burned in the kiln chamber at start-up
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to reach elevated temperatures. The typical heating value of hazardous waste (i.e., 8,000
Btu/kg) is sufficient to sustain combustion without auxiliary fuel (U.S. EPA, 1996h). The
combustion gases emanating from the kiln are passed through a high temperature
afterburner chamber to more completely destroy organic pollutants entrained in the flue
gases. Rotary kilns can be designed to operate at temperatures as high as 2,580°C, but
more commonly operate at about 1,100°C.
Liquid Injection HWI: Liquid injection incinerators (Ills) are designed to burn liquid
hazardous waste. These wastes must be sufficiently fluid to pass through an atomizer for
injection as droplets into the combustion chamber. The Ills consist of a refractory-lined
steel cylinder mounted either in a horizontal or vertical alignment. The combustion
chamber is equipped with one or more waste burners. Because of the rather large surface
area of the atomized droplets of liquid hazardous waste, the droplets quickly vaporize.
The moisture evaporates, leaving a highly combustible mix of waste fumes and
combustion air (U.S. EPA, 1996h). Secondary air is added to the combustion chamber to
complete the oxidation of the fume/air mixture.
Fixed Hearth HWI: Fixed hearths are starved air or pyrolytic incinerators. Waste is
ram-fed into the primary chamber and incinerated below stoichiometric requirements (i.e.,
at about 50 to 80 percent of stoichiometric air requirements). The resulting smoke and
pyrolytic combustion products are then passed though a secondary combustion chamber
where relatively high temperatures are maintained by the combustion of auxiliary fuel.
Oxygen is introduced into the secondary chamber to promote complete thermal oxidation
of the organic molecules entrained in the gases.
Fluidized-bed HWI: The fourth hazardous waste incineration technology is the
fluidized-bed incinerator, which is similar in design to that used in municipal solid waste
incineration. (See Section 3.1.) In this configuration, a layer of sand is placed on the
bottom of the combustion chamber. The bed is preheated by underfire auxiliary fuel at
startup. During combustion of auxiliary fuel at start-up, the hot gases are channeled
through the sand at relatively high velocity, and the turbulent mixing of combustion gases
and combustion air causes the sand to become suspended (Buonicore, 1992a). This takes
on the appearance of a fluid medium, hence the incinerator is termed a 'fluidized bed'
combustor The incinerator is operated below the melting point temperature of the bed
material. Typical temperatures of the fluid medium are within the range of 650 to 940°C.
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A constraint on the types of waste burned is that the solid waste particles must be
capable of being suspended within the furnace. When the liquid or solid waste is
combusted in the fluid medium, the exothermic reaction causes heat to be released into
the upper portion of the combustion chamber. The upper portion is typically much larger
in volume than the lower portion, and temperatures can reach 1,000°C (Buonicore,
1992a). This high temperature is sufficient to combust volatilized pollutants emanating
from the combustion bed.
3.2.2. APCDs for Hazardous Waste Incinerators
Most HWIs use APCDs to remove undesirable components from the flue gases that
evolved during the combustion of the hazardous waste. These unwanted pollutants
include suspended ash particles (particulate matter or PM), acid gases, metal, and organic
pollutants. The APCD controls or collects these pollutants and reduces their discharge
from the incinerator stack to the atmosphere. Levels and kinds of these combustion
byproducts are highly site-specific, depending on factors such as waste composition and
incinerator system design and operating parameters (e.g., temperature and exhaust gas
velocity). The APCD is typically comprised of a series of different devices that work
together to clean the exhaust combustion flue gas. Unit operations usually include
exhaust gas cooling, followed by particulate matter and acid gas control.
Exhaust gas cooling may be achieved using a waste heat boiler or heat exchanger,
mixing with cool ambient air, or injection of a water spray into the exhaust gas. A variety
of different types of APCDs are employed for the removal of particulate matter and acid
gases. Such devices include: wet scrubbers (such as venturi, packed bed, and ionizing
systems), electrostatic precipitators, and fabric filters (sometimes used in combination
with dry acid gas scrubbing). In general, the control systems can be grouped into the
following three categories: wet, dry, and hybrid wet/dry systems. The controls for acid
gases (either dry or wet systems) cause temperatures to be reduced preceding the control
device. This impedes the extent of formation of CDDs and CDFs in the post-combustion
area of the typical HWI. It is not unusual for stack concentrations of CDD/CDFs at a
particular HWI to be in the range of 1 to 100 ng CDD/CDF/dscm (Helble, 1993), which is
low in comparison to other waste incineration systems. The range of total CDD/CDF flue
gas concentrations measured in the stack emissions of HWIs during trial burns across the
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class of HWI facilities, however, has spanned four orders of magnitude (ranging from 0.1
to 1,600 ng/dscm) (Helble, 1993). The APCD systems are described below:
• Wet Systems: A wet scrubber is used for both particulate and acid gas control.
Typically, a venturi scrubber and packed-bed scrubber are used in a back-to-back
arrangement. Ionizing wet scrubbers, wet electrostatic precipitators, and
innovative venturi-type scrubbers may be used for more efficient particulate
control. Wet scrubbers generate a wet effluent liquid wastestream (scrubber
blowdown), are relatively inefficient at fine particulate control compared to dry
control techniques, and have equipment corrosion concerns. However, wet
scrubbers do provide efficient control of acid gases and have lower operating
temperatures (compared with dry systems), which may help control the emissions
of volatile metals and organic pollutants.
• Dry Systems: In dry systems, a fabric filter or electrostatic precipitator (ESP) is
used for particulate control. A fabric filter or ESP is frequently used in
combination with dry scrubbing for acid gas control. Dry scrubbing systems, in
comparison with wet scrubbing systems, are inefficient in controlling acid gases.
• Hybrid Systems: In hybrid systems, a dry technique (ESP or fabric filter) is used for
particulate control, followed by a wet technique (wet scrubber) for acid gas
control. Hybrid systems have the advantages of both wet and dry systems (lower
operating temperature for capture of volatile metals, efficient collection of fine
particulate, efficient capture of acid gases), while avoiding many of the individual
disadvantages. In some hybrid systems, known as "zero discharge systems," the
wet scrubber liquid is used in the dry scrubbing operation, thus minimizing the
amount of liquid byproduct waste.
• Uncontrolled HWIs: Facilities that do not use any air pollution control devices fall
under a separate and unique category. These are primarily liquid waste injection
facilities, which burn low ash and chlorine content wastes; therefore, they are low
emitters of PM and acid gases.
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3.2.3. Estimation of CDD/CDF Emission Factors for Hazardous Waste Incinerators
For purposes of estimating emission factors, this document considers subdividing
the combustors in each source category into design classes judged to have similar
potential for CDD/CDF emissions. As explained below, it was decided not to subdivide
dedicated HWIs.
Combustion research has identified three mechanisms involved in the emission of
CDD/CDFs from combustion systems: (1) CDD/CDFs can be introduced into the
combustor with the feed and pass through the system not completely burned/destroyed;
(2) CDD/CDFs can be formed by chemical reactions inside the combustion chamber; and
(3) CDD/CDFs can be formed by chemical reactions outside the combustion chamber. The
total CDD/CDF emissions are likely to be the net result of all three mechanisms; however,
the relative importance of the mechanisms can vary among source categories. In the case
of HWIs, the third mechanism (i.e., post-combustion formation) is likely to dominate,
because HWIs are typically operated at high temperatures and long residence times, and
most have sophsiticated real-time monitoring and controls to manage the combustion
process. Therefore, any CDD/CDFs present in the feed or formed during combustion are
likely to be destroyed before exiting the combustion chamber. Consequently, for purposes
of generating emission factors, it was decided not to subdivide this class on the basis of
furnace type.
Emissions resulting from the post-combustion formation in HWIs can be minimized
through a variety of technologies:
• Rapid Flue Gas Quenching: The use of wet and dry scrubbing devices to remove
acid gases usually results in the rapid reduction of flue gas temperatures at the inlet
to the PM APCD. If temperature is reduced below 200°C, the low-temperature
catalytic formation of CDD/CDFs is substantially retarded.
• Use of Particulate Matter (PM) Air Pollution Control Devices: PM control devices
can effectively capture condensed and adsorbed CDD/CDFs that are associated
with the entrained particulate matter (in particular, that which is adsorbed on
unburned carbon containing particulates).
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• Use of Activated Carbon: Activated carbon injection is used at some HWIs to
collect (sorb) CDD/CDFs from the flue gas. This may be achieved using carbon
beds or by injecting carbon and collecting it in a downstream PM APCD.
All of these approaches appear very effective in controlling dioxin emissions at
dedicated HWIs, and insufficient emissions data are available to generalize about any
minor differences. Consequently, for purposes of generating emission factors, it was
decided not to subdivide this class on the basis of APCD type.
EPA compiled a data base summarizing the results of stack testing for CDDs and
CDFs at 17 HWIs (U.S. EPA, 1996c). Most facilities were tested between 1993 and
1996. For purposes of this report, CDD/CDF emission factors were estimated based on
the results of the emission tests contained in this data base. The breakdown of furnace
types of tested HWI facilities is as follows: 10 rotary kiln incinerators, 4 liquid injection
incinerators, 1 fluidized-bed incinerator, and 2 fixed-bed.
As stated earlier, EPA/ORD decided not to subclassify the dedicated HWI designs
for purposes of deriving an emission factor (EF). Instead, the emission factor was derived
as an average across all 1 7 tested facilties. First, an average emission factor was
calculated for each of 17 HWIs with Equation 3-3.
C x F
EFhwi = j (Eqn. 3-3)
w
Where:
EFhwi = Emission factor (average ng TEQ per kg of waste burned)
C = TEQ or CDD/CDF concentration in flue gases (ng TEQ/dscm) (20°C,
1 atm; adjusted to 7% 02)
Fv = Volumetric flue gas flow rate (dscm/hr) (20°C, 1 atm; adjusted to
7% 02)
lw = Average waste incineration rate (kg/hr)
After developing average emission factors for each HWI, the overall average congener-
specific emission factor was derived for all 17 tested HWIs using Equation 3-4.
EF = l* EF+ EF+ ........ + EF > / N
avgHwin=1_17 = wi,* Hwi2+ Hwi3+ ........ + Hwi17 (Eqn. 3-4)
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Where:
EFHWi = Average emission factor for the 17 tested HWIs, (ng/kg)
N = Number of tested facilities (i.e., 17)
Table 3-8 presents the average emission factors developed for specific congeners, total
CDDs/CDFs, and TEQs for the tested HWIs. The average congener emission profile for
the 17 tested HWIs are presented in Figure 3-10. The average I-TEQDF and TEQDF-WH098
emission factors for the 17 tested HWIs are 3.83 ng l-TEQDF/kg of waste feed and 3.88
ng TEQDF-WH098/kg of waste feed (assuming not detected values are zero). The available
data did not support development of different emission factors for the two reference
years, 1987 and 1995.
3.2.4. Emission Estimates for Hazardous Waste Incinerators
Although emissions data were available for 10 percent of the HWIs (i.e., 1 7 of 162
have been tested), the emission factor estimates are assigned a medium confidence rating
due to uncertainties resulting from:
• Variability of the waste feeds. The physical and chemical composition of the waste
can vary from facility to facility and even within a facility. Consequently, CDD/CDF
emissions measured for one feed may not be representative of other feeds.
• Trial bums. Much of the CDD/CDF emissions data were collected during trial
burns, which are required as part of the RCRA permitting process and are used to
establish Destruction Rated Efficiency (ORE) of principal hazardous organic
constituents in the waste. During trial burns, a prototype waste is burned, which
is intended to maximize the difficulty in achieving good combustion. For example,
chlorine, metals, and organics may be added to the waste. The HWI may also be
operated outside normal operating conditions. The temperature of both the furnace
and the APCD may vary by a wide margin (high and low temperatures), and the
waste feed system may be increased to maximum design load. Accordingly, it is
uncertain how representative the CDD/CDF emissions measured during the trial
burn will be of emissions during normal operating conditions.
Dempsey and Oppelt (1993) estimated that up to 1.3 million metric tons of
hazardous waste were combusted in HWIs during 1987. EPA estimated that 1.5 million
metric tons of hazardous waste were combusted each year in the early 1990s in HWIs
(Federal Register, 1996b). This activity level estimate for 1995 is assigned a high
confidence rating, because it is based on a review by EPA of the various studies and
surveys conducted in the 1990s to assess the quantity and types of hazardous wastes
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being managed by various treatment, storage, and disposal facilities. A confidence rating
of medium is assigned to the activity level estimate for 1987.
The annual TEQ emissions for the reference years 1995 and 1987 were estimated
using Equation 3-5.
EHWI = EFHWI x AHWI (Eqn. 3-5)
Where:
EHWI = Annual emissions from all HWIs, tested and non-tested (g TEQ/yr)
EFHW, = Mean emission factor for HWIs (ng TEQ/kg of waste burned)
AHWI = Annual activity level of all operating HWIs (million metric tons/yr)
Applying the average TEQ emission factors for dedicated HWIs (3.83 ng l-TEQDF/kg
and 3.88 ng TEQDF-WH098/kg waste) to these production estimates yields estimated
emissions of 5.7 g I-TEQDF (or 5.8 g TEQDF-WH098) in 1995 and 5.0 g TEQ (I-TEQDF or
TEQDF-WH098) in 1987 for HWIs. The medium confidence rating assigned to the emission
factor, combined with the medium confidence rating for the 1995 activity level and
medium confidence rating for the 1987 activity level, yields an overall medium confidence
rating for both years.
3.2.5. Recent EPA Regulatory Activities
EPA/OSW has also developed estimates of the CDD/CDF emissions from HWIs as
part of the development of the Hazardous Waste Combustors Rule (Federal Register,
1999b). Like ORD, OSW also decided not to subdivide the HWIs on the basis of design.
Instead of an emission factor approach, OSW used an imputation method to estimate
emissions at untested facilities. This procedure involved randomly selecting measured
CDD/CDF flue gas concentrations (ng/dscm) from the pool of tested HWI facilities and
assigning them to the untested facilities. With this procedure, all non-tested HWIs have an
equal chance of being assigned any flue gas concentration from the pool of measured
values. The flue gas concentrations were combined with flue gas flow rates for each
facility to estimate the emission rate. Using this approach, EPA/OSW estimated that I-
TEQDF emissions in 1997 were 24.8 grams and that the emissions would be reduced to
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3.5 g after full implementation of the rule. A key difference in these approaches is that
ORD uses waste feed rate directly in the calculation of emissions and the OSW approach
is independent of waste feed rate. Both procedures are reasonable ways to deal with the
broad range of uncertainties and both yield similar emission estimates. ORD has not
identified any inherent advantage of one approach over the other and elected to use the
emission factor approach primarily because it is consistent with the methods used in this
document to characterize CDD/CDF emissions from all other source categories.
3.2.6. Industrial Boilers and Furnaces Burning Hazardous Waste
In 1991, EPA established rules that allow the combustion of some liquid hazardous
waste in industrial boilers and furnaces (Federal Register, 1991c). These facilities typically
burn oil or coal for the primary purpose of generating electricity. Liquid hazardous waste
can only be burned as supplemental (auxiliary) fuel, and usage is limited by the rule to no
more than 5 percent of the primary fuels. These facilities typically use an atomizer to
inject the waste as droplets into the combustion chamber and are equipped with
particulate and acid gas emission controls. In general, they are sophisticated, well
controlled facilities, which achieve good combustion.
The national data base contains congener-specific emission concentrations for two
tested boilers burning liquid hazardous waste as supplemental fuel. The average congener
and congener group emission profiles for the industrial boiler data set are presented in
Figure 3-11. The average congener and TEQ emission factors are presented in Table 3-8.
The limited set of emissions data prevented subdividing this class for the purpose of
deriving an emission factor. The equation used to derive the emission factor is the same
as Equation 3-4 above. The average TEQ emission factor for the two industrial boilers is
0.64 ng l-TEQDF/kg of waste feed (or 0.65 ng TEQDF-WH098/kg of waste feed). These
emission factors are assigned a low confidence rating, because they reflect testing at only
2 of the 136 hazardous waste boilers/furnaces.
Dempsey and Oppelt (1993) estimated that approximately 1.2 billion kg of
hazardous waste were combusted in industrial boilers/furnaces in 1987. EPA estimates
that each year in the early 1990s approximately 0.6 billion kg of hazardous waste were
combusted in industrial boilers/furnaces (Federal Register, 1996b). It is possible that
cement kilns and lightweight aggregate kilns burning hazardous waste were included in
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this estimate by Dempsey and Oppelt for 1987; the estimate for 1995 does not appear to
include these hazardous waste burning kilns. This activity level estimate for 1995 is
assigned a medium confidence rating, because it was based on a review by EPA of the
various studies and surveys conducted in the 1990s to assess the quantity and types of
hazardous wastes being managed by various treatment, storage, and disposal facilities. A
confidence rating of low is assigned to the estimated activity level for 1987. The 1987
estimate was largely based on a review of State permits (Dempsey and Oppelt,1993).
Equation 3-5, used to calculate annual TEQ emissions for dedicated HWIs, was also
used to calculate annual TEQ emissions for industrial boilers/furnaces. Multiplying the
average TEQ emission factors by the total estimated kg of liquid hazardous waste burned
in 1995 and 1987 yields annual emissions in g-TEQ/yr. From this procedure, the
emissions from all industrial boilers/furnaces burning hazardous waste as supplemental
fuel are estimated as 0.38 g I-TEQDF (or 0.39 g TEQDF-WH098) in 1995 and 0.77 g I-TEQDF
(or 0.78 g TEQDF-WH098) in 1987. Because of the low confidence rating for the emission
factor, the overall confidence rating is low for both the 1987 and 1995 emission
estimates.
3.2.7. Solid Waste from Hazardous Waste Combustion
U.S. EPA (1987a) contains limited data on ash generated from hazardous waste
incineration. Table 3-8 of U.S. EPA (1 987a) indicates that 538 //g/kg and 2,853 //g/kg
were the mean concentrations of CDDs and CDFs, respectively, from a hazardous waste
incinerator with an afterburner. Specific data for congeners and for ash quantities were
not given.
3.3. MEDICAL WASTE INCINERATION
Medical waste incineration (MWI) is the controlled burning of solid wastes
generated primarily by hospitals, veterinary, and medical research facilities. The U.S. EPA
defines medical waste as any solid waste generated in the treatment, diagnosis, or
immunization of humans or animals, or research pertaining thereto, or in the production or
testing of biologicals (Federal Register, 1997b). The primary purposes of MWI are to
reduce the volume and mass of waste in need of land disposal, and to sterilize the
infectious materials. The following subsections review the basic types of MWI designs
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used to incinerate medical waste, review the distribution of APCDs used on MWIs,
summarize the derivation of dioxin TEQ emission factors for MWIs, and summarize the
national dioxin TEQ emission estimates for reference years 1995 and 1987.
3.3.1. Design Types of MWIs Operating in the United States
For purposes of this document, EPA has classified MWIs into three broad
technology categories: modular furnaces using controlled-air, modular furnaces using
excess-air, and rotary kilns. Of the MWIs in use today, the vast majority are believed to
be modular furnaces using controlled-air. EPA has estimated that 97 percent are modular
furnaces using controlled-air, 2 percent are modular furnaces using excess air, and 1
percent are rotary kiln combustors (U.S. EPA, 1997b).
Modular Furnaces Using Controlled-air: Modular furnaces have two separate
combustion chambers mounted in series (one on top of the other). The lower chamber is
where the primary combustion of the medical waste occurs. Medical waste is ram-fed
into the primary chamber, and underfire air is delivered beneath the incinerator hearth to
sustain good burning of the waste. The primary combustion chamber is operated at below
stoichiometric levels, hence the terms "controlled" or "starved-air." With sub-
stoichiometric conditions, combustion occurs at relatively low temperatures (i.e., 760 to
985°C). Under the conditions of low oxygen and low temperatures, partial pyrolysis of
the waste occurs, and volatile compounds are released. The combustion gases pass into
a second chamber. Auxiliary fuel (such as natural gas) is burned to sustain elevated
temperatures (i.e., 985 to 1,095°C) in this secondary chamber. The net effect of
exposing the combustion gases to an elevated temperature is more complete destruction
the organic contaminants entrained in the combustion gases emanating from the primary
combustion chamber. Combustion air at 100 to 300 percent in excess of stoichiometric
requirement is usually added to the secondary chamber. Gases exiting the secondary
chamber are directed to an incinerator stack (U.S. EPA, 1997b; U.S. EPA, 1991d;
Buonicore, 1992b). Figure 3-12 displays a schematic of a typical modular furnace using
controlled-air. Because of it's low cost and good combustion performance, this design has
been the most popular choice for MWIs and has accounted for more than 95 percent of
systems installed over the past two decades (U.S. EPA, 1990d; U.S. EPA, 1991d;
Buonicore, 1992b).
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Modular Furnaces Using Excess-air: These systems use the same modular furnace
configuration as described above for the controlled air systems. The difference is that the
primary combustion chamber is operated at air levels of 100 percent to 300 percent in
excess of stoichiometric requirements. Hence the name "excess-air." A secondary
chamber is located on top of the primary unit. Auxiliary fuel is added to sustain high
temperatures in an excess-air environment. Excess-air MWIs are typically smaller in
capacity than controlled-air units and are usually batch-fed operations. This means that
the medical waste is ram-fed into the unit and allowed to burn completely before another
batch of medical waste is added to the primary combustion chamber.
Rotary Kiln MWI: This technology is similar in terms of design and operational
features to the rotary kiln technology employed in both municipal and hazardous waste
incineration. (See description in Section 3.1.) Because of their relatively high capital and
operating costs, few rotary kiln incinerators are in operation for medical waste treatment
(U.S. EPA, 1990d; U.S. EPA, 1991d; Buonicore, 1992b).
MWIs can be operated in three modes: batch, intermittent, and continuous. Batch
incinerators burn a single load of waste, typically only once per day. Waste is loaded, and
ashes are removed manually. Intermittent incinerators, loaded continuously and frequently
with small waste batches, operate less than 24 hours per day, usually on a shift-type
basis. Either manual or automated charging systems can be used, but the incinerator
must be shut down for ash removal. Continuous incinerators are operated 24 hours per
day and use automatic charging systems to charge waste into the unit in small, frequent
batches. All continuous incinerators operate using a mechanism to automatically remove
the ash from the incinerator (U.S. EPA, 1990d; U.S. EPA, 1991d).
3.3.2. Characterization of MWIs for Reference Years 1995 and 1987
MWI remains a poorly characterized industry in the United States in terms of
knowing the exact number of facilities operational over time, the types of APCDs installed
on these units, and the aggregate volume and weight of medical waste that is combusted
in any given year (U.S. EPA, 1997b). The primary reason for this is that permits were not
generally required for the control of pollutant stack emissions from MWIs until the early
1990s when State regulatory agencies began setting limits on emissions of particulate
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matter and other contaminants (Federal Register, 1997b). Prior to that timeframe, only
opacity was controlled.
The information available to characterize MWIs comes from national telephone
surveys, stack emission permits, and data gathered by EPA during public hearings (Federal
Register, 1997b). This information suggests the following:
• The number of MWIs in operation was approximately 5,000 in 1987 (U.S. EPA,
1987d) and 2,375 in 1995 (Federal Register, 1997b).
• The amount of medical waste combusted annually in the United States was
approximately 1.43 billion kg in 1987 (U.S. EPA, 1987d) and 0.77 billion kg in
1995 (Federal Register, 1997b).
These estimates indicate that, between 1987 and 1995, the total number of operating
MWIs and the total amount of waste combusted decreased by more than 50 percent.
Certain activities caused this to occur, including more stringent air pollution control
requirements by State regulatory agencies and the development of less expensive medical
waste treatment technologies, such as autoclaving (Federal Register, 1997b). Because
many MWIs have small waste charging capacity (i.e., about 50 metric tons per day), the
installation of even elementary APCDs proved not to be cost effective. Thus, a large
number of facilities elected to close rather than retrofit.
The actual controls used on MWIs on a facility-by-facility basis in 1987 are
unknown, and EPA generally assumes that MWIs were mostly uncontrolled (U.S.
EPA,1987d). However, the modular design does cause some destruction of organic
pollutants within the secondary combustion chamber. Residence time within the
secondary chamber is key to inducing the thermal destruction of the organic compounds.
Residence time is the time that the organic compounds entrained within the flue gases are
exposed to elevated temperatures in the secondary chamber. EPA has demonstrated with
full-scale MWIs that increasing residence time from 1/4 second to 2 seconds in the
secondary chamber can reduce organic pollutant emissions, including CDD/CDFs, by up to
90 percent (Federal Register, 1997b). In this regard, residence time can be viewed as a
method of air pollution control.
EPA estimates that about two-thirds of medical waste burned in MWIs in 1995
went to facilities equipped with some method of air pollution control (Federal Register,
1997b). The types of APCDs installed and the methods used on MWIs include: dry
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sorbent injection, fabric filters, electrostatic precipitators (ESPs), wet scrubbers, and fabric
filters combined with packed-bed scrubbers (composed of granular activated carbon).
Some organic constituents in the flue gases can be adsorbed by the packed bed. Within
the uncontrolled class of MWIs, about 1 2 percent of the waste were combusted in
facilities with design capacities of <200 Ibs/hr, with the majority of waste burned
facilities >200 Ibs/hr. The estimated breakdown of controlled facilities is: 70 percent of
the aggregate activity level are associated with facilities equipped with either wet
scrubbers, fabric filters, or ESPs; 29.9 percent are associated with facilities utilizing dry
sorbent injection, combined with fabric filters, and less than 1 percent is associated with
facilities having the fabric filter/packed-bed APCD (AHA, 1995; Federal Register, 1997b).
3.3.3. Estimation of CDD/CDF Emissions from MWIs
Emission tests reported for 24 MWIs (i.e., about 1 percent of existing facilities)
were collected for use in this report. Consequently, most facilities have unmeasured
emission levels of dioxin-like compounds. Because so few have been evaluated, the
estimation of annual air emissions of CDD/CDFs from MWIs is quite dependent on
extrapolations, engineering judgement, and the use of assumptions. In addition, the
information about the activity levels of these facilities is also quite limited. With these
data limitations, two approaches have been used in the past to estimate CDD/CDF
emissions from MWIs, and a third is proposed here. These three approaches are as
follows:
1. EPA/OAQPS Approach: EPA's Office of Air Quality Planning and
Standards (OAQPS) used this approach in support of the
promulgation of final air emission standards for
hospital/medical/infectious waste incinerators (Federal Register,
1997b).
2. AHA Approach: The American Hospital Association proposed an approach
in its comments on drafts of this document and on the proposed MWI
emissions regulations (AHA, 1995).
3. EPA/ORD Approach: In preparation of this document, EPA's Office of
Research and Development (ORD) has developed a third approach.
Given the limitations with existing information, both the EPA/OAQPS and AHA approaches
are reasonable methods for calculating annual releases of CDD/CDFs from MWIs. Both
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methods relied heavily on a series of assumptions to account for missing information. In
developing a third approach, EPA/ORD built upon the other two approaches by utilizing the
most logical features of each. Because of the uncertainties with existing data, it is
currently not known which approach gives the most accurate estimate of CDD/CDF air
emissions from all MWIs, nationwide. The three approaches yield different air emission
estimates, but the estimates all agree within a factor of four. As discussed below, the
EPA/ORD approach used the strengths of the other two approaches, and represents some
improvement in estimating CDD/CDF emissions.
3.3.4. EPA/OAQPS Approach for Estimating CDD/CDF Emissions from MWIs
On September 15, 1997, EPA promulgated final standards of performance for new
and existing MWIs under the Clean Air Act Amendments (Federal Register, 1997b).
CDD/CDF stack emission limits for existing MWIs were established as follows: 1 25
ng/dscm of total CDD/CDF (at 7 percent 02, 1 atm), equivalent to 2.3 ng/dscm TEQ. In
order to evaluate emissions reductions that will be achieved by the standard, OAQPS
estimated, as a baseline for comparison, nationwide annual CDD/CDF emissions from all
MWIs operating in 1995.
3.3.4.1. EPA/OAQPS Approach for Estimating Activity Level
As a starting point for deriving the national estimates, OAQPS constructed an
inventory of the numbers and types of MWIs believed to be operating in 1995. The
inventory was based on an inventory of 2,233 MWIs prepared by the American Hospital
Association (AHA, 1995), supplemented with additional information compiled by EPA.
This created a listing of 2,375 MWIs in the United States. Next a series of assumptions
were used to derive activity level estimates, as follows:
1. The analysis divided MWIs into three design types based on the mode of
daily operation: batch, intermittent, and continuous. This was done using
the information from the inventory on design-rated annual incineration
capacity of each facility. The smaller capacity units were assumed to be
batch operations, and the others were classified as either intermittent or
continuous, assuming a ratio of three to one.
2. The activity level of each facility was estimated by multiplying the design-
rated annual incineration capacity of the MWI (kg/hr) by the hours of
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operation (hr/yr). The annual hours of operation were determined by
assuming a capacity factor (defined as the fraction of time that a unit
operates over the year) for each design type of MWI (Randall, 1995). Table
3-9 is a summary of the OAQPS estimated annual operating hours per MWI
design type.
3.3.4.2. EPA/OAQPS Approach for Estimating CDD/CDF Emission Factors
Based on information obtained from AHA and State regulatory agencies, one-third
of the population of MWIs operating in 1995 was etimated to have had no APCDs (i.e.,
were uncontrolled), and two-thirds had some type of APCD. CDD/CDF TEQ emission
factors were then developed for uncontrolled and controlled MWIs. The procedure was as
follows:
Estimating TEQ Emission Factors for Uncontrolled Facilities: The uncontrolled
category of facilities was subdivided by residence time of the secondary combustion
chamber. Based on tests at three MWIs, OAQPS concluded that stack emissions of
CDD/CDFs from uncontrolled facilities were dependent on the residence time (i.e., the
duration of time the compounds are exposed to elevated temperatures within the
secondary combustion chamber) (Strong, 1996). The tests demonstrated that when the
residence time in the secondary chamber was short (i.e., < 1 sec), the stack emissions of
CDDC/CDFs would increase; conversely, the longer the residence time (i.e., >1 sec), the
CDD/CDF emissions decrease. The emissions testing at these MWIs provided the basis for
the derivation of I-TEQDF emission factors for residence times of 1/4-sec, 1-sec and 2-sec.
Table 3-10 is a summary of the emission factors developed for each MWI type as a
function of residence time.
The OAQPS inventory of MWIs in 1995 did not provide residence times for each
facility. OAQPS overcame this data gap by assuming that residence time in the secondary
combustion chamber approximately corresponds with the particulate matter (PM) stack
emission limits established in State air permits. This approach assumed that the more
stringent PM emission limits would require longer residence times in the secondary
chamber in order to further oxidize carbonaceous soot particles and reduce PM emissions.
Table 3-10 lists the assumed residence times in the secondary chamber corresponding to
various State PM emission limits. State Implementation Plans (SIPs) were reviewed to
determine the PM emission limits for incinerators, and from this review, both a residence
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time and an I-TEQDF emission factor were assigned to each uncontrolled MWI on the
inventory.
Estimating TEQ Emission Factors for Controlled MWIs: Two-thirds of the MWI
population were assumed to have some form of APCD. As previously discussed, APCDs
typically used by MWIs consist of one or more of the following: wet scrubber, dry
scrubber, and fabric filter combined with a packed bed. The EPA/OAQPS approach also
included the addition of activated carbon to the flue gases as a means of emissions
control (i.e., dry scrubbers combined with carbon injection). TEQ emission factors were
developed for these control systems based on incinerator emissions testing data gathered
in support of the regulations (U.S. EPA, 1997b). Because the inventory did not list the
APCDs for all MWIs, State requirements for PM control were used to make assumptions
about the type of APCD installed on each facility in the inventory. These assumptions are
summarized in Table 3-11.
3.3.4.3. EPA/OAQPS Approach for Estimating Nationwide CDD/CDF TEQ Air
Emissions
Annual TEQ emissions for each MWI facility were calculated as a function of the
design capacity of the incinerator, the annual waste charging hours, the capacity factor,
and the TEQ emission factor as shown in Equation 3-6.
Emwi = ( C x H x C, } x FTEQ (Eqn. 3-6)
Where:
Emwi = Annual MWI CDD/F TEQ stack emissions (g/yr)
C = MWI design capacity (kg/hr)
H = Annual medical waste charging hours (hr/yr)
G! = Capacity factor (unitless)
FTEQ = CDD/CDF TEQ emission factor (g TEQ/kg)
The annual TEQ air emission of all MWIs operating in 1995 is the sum of the annual
emissions of each individual MWI. The following equation is applied to estimate annual
TEQ emissions from all MWIs.
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Emwi(nationwide) = (Emmwi, + Emmwi2 + + + ErT1mwi2375 > (Eqn. 3-7)
Where:
Emwi(nationwide) = Nationwide MWI TEQ emissions (g/yr)
Table 3-11 is a summary of I-TEQDF emissions for 1995 estimated using the EPA/OAQPS
Approach.
3.3.5. AHA Approach for Estimating CDD/CDF Emissions from MWIs
In 1995, the American Hospital Association (AHA) submitted written comments to
EPA in response to EPA's request for public comment of the 1994 draft public release of
this document (AHA, 1995). As part of these comments, the AHA attached an analysis
of CDD/CDF emissions from MWIs prepared by Doucet (1995) for the AHA. Doucet
(1995) estimated the total number of MWIs operating in 1995, the distribution of APCDs,
CDD/CDF TEQ emission factors, and the nationwide TEQ emissions. The following is a
brief discussion of the AHA inventory and the Doucet (1995) analysis.
From a national telephone survey of member hospitals conducted between
September and November 1994, the AHA developed what is generally considered as the
first attempt to systematically inventory MWIs in the United States. Approximately 6
percent of the hospitals with MWIs were contacted (AHA,1997). The AHA survey
showed that, as of December 1994, 2,233 facilities were in operation. Doucet (1995)
subdivided the AHA MWI inventory into two uncontrolled categories based on combustor
design-rated capacity and two controlled categories based on APCD equipment. Doucet
(1995) then developed CDD/CDF emission factors for each MWI category. Test reports of
19 MWIs were collected and evaluated. Average CDD/CDF TEQ flue gas concentrations
(i.e., ng/dscm @7 percent 02) were derived by combining tests from several MWIs in each
capacity range category and APCD. The average TEQ flue gas concentrations were then
converted to average TEQ emission factors, which were in units of Ib TEQ/106 Ibs of
medical waste incinerated (equation for conversion not given). Table 3-12 lists the I-
TEQDF emission factors calculated by Doucet (1995) for each level of assumed APCDs on
MWIs.
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Similar to the EPA/OAQPS Approach (Section 3.3.4), the distribution of the APCD
categories was derived by assuming that State particulate emission (PM) limits would
indicate the APCD on any individual MWI (Doucet, 1995). Table 3-13 displays the AHA
assumptions of air pollution control (APC) utilized on MWIs based upon PM emission
limits.
With the activity levels, the percent distribution of levels of controls, and the
CDD/CDF TEQ emission factors having been calculated with existing data, the final step of
the AHA Approach was the estimation of annual I-TEQDF emissions (g/yr) from MWIs,
nationwide. Although no equation is given, it is presumed that the emissions were
estimated by multiplying the activity level for each MWI size and APCD category by the
associated I-TEQDF emission factor. The sum of these calculations for each designated
class yields the estimated annual I-TEQDF emissions for all MWIs, nationwide. Doucet
(1995) indicates that these computations are appropriate for I-TEQDF emissions in 1995.
Table 3-14 summarizes the nationwide annual I-TEQDF emissions from MWIs using the
AHA Approach.
3.3.6. EPA/ORD Approach for Estimating CDD/CDF Emissions from MWIs
Because of limitations in emissions data and on activity levels, the EPA/ORD
approach used many of the logical assumptions developed in the EPA/OAQPS and AHA
approaches. The discussion below describes the rationale for how these decisions were
made, and presents the resulting emission estimates.
3.3.6.1. EPA/ORD Approach for Classifying MWIs and Estimating Activity Levels
As with the EPA/OAQPS and AHA approaches, the EPA/ORD approach divided the
MWIs into controlled and uncontrolled classes. The decisions about further dividing these
two classes are described below:
Uncontrolled MWIs: For purposes of assigning CDD/CDF emission factors and
activity levels to the uncontrolled class of MWIs, the EPA/OAQPS approach divided this
class on the basis of residence time within the secondary combustion chamber. This
approach has theoretical appeal, because it is logical to expect more complete combustion
of CDD/CDFs with longer residence times at high temperatures. Unfortunately, the
residence times on a facility-by-facility basis are not known, making it difficult to assign
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emission factors and activity levels on this basis. As discussed earlier, the EPA/OAQPS
approach assumed that residence time would strongly correlate with State PM stack
emission requirements (i.e., the more stringent the PM requirements, the longer the
residence time required to meet the standard). This PM method for estimating residence
time resulted in the following distribution of residence times: 6 percent of the waste
incinerated at MWIs with 1/4-sec residence time; 26 percent of the waste incinerated at
MWIs with 1-sec residence time; and 68 percent of the waste incinerated at MWIs with 2-
sec residence time. Thus, about two-thirds of the activity level within the uncontrolled
class were assumed in the EPA/OAQPS approach to be associated with facilities with the
longest residence time and the lowest CDD/CDF emission factor.
The AHA approach subcategorized the uncontrolled class on the basis of design-
rated capacity. There is also theoretical support for this approach. Smaller capacity
operations (i.e., <200 Ib/hr) are likely to have higher emissions, because they are more
likely to be operating in a batch mode. The batch mode results in infrequent operation
with more start-up and shut-down cycles. Thus, the batch-operated MWI usually spends
more time outside of the ideal range of operating conditions. In support of this approach,
the AHA presented limited empirical evidence indicating that CDD/CDF emission factors
calculated from emission test reports for the low capacity units were about a factor of
two higher than the emission factors for the high capacity units (Doucet, 1995).
Thus, both the EPA/OAQPS and AHA approaches have a sound theoretical basis
but lack strong supporting data. In order to decide which of the two approaches to use,
ORD first tested the assumption that there is a strong relationship between State PM
requirements and residence time. ORD conducted a limited telephone survey of regulatory
agencies in four States where a large number of MWI facilities were in operation:
Michigan, Massachusetts, New Jersey, and Virginia (O'Rourke, 1996). The results of the
limited survey, summarized in Table 3-15, did not verify the existence of a strong
dependent relationship between PM emission limits and residence time in the secondary
chamber at MWIs.
Next, the available emission testing data for small and high capacity units (i.e., less
than and greater than 200 Ib/hr) were evaluated to determine if, as posited in the AHA
approach, smaller capacity units have greater emission factors than large capacity units.
This evaluation indicated a distinct difference in the emission factors between the two
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capacity categories, although the difference in the set of data evaluated was not as great
as the difference observed in the data set evaluated in the AHA approach. The EPA/ORD
approach, therefore, adopted the subcategorization scheme used in the AHA approach.
Controlled MWIs: Both the EPA/OAQPS approach and the AHA approach
subcategorized the controlled MWIs on the basis of APCD equipment. However, the two
approaches differed in the subcategories developed. The AHA approach divided the
controlled class into two groups: facilities equipped with wet scrubbers (alone, with an
ESP, or with a fabric filter), and facilities equipped with dry sorbent injector and a fabric
filter (Doucet, 1995). The EPA/OAQPS approach divided the controlled class into three
groups: facilities equipped with wet scrubbers, facilities equipped with dry scrubbers
(with or without carbon injection), and facilities equipped with fabric filters and packed
bed scrubbers. This third category is comprised of a few facilities primarily located in the
Northeast United States (O'Rourke, 1996). The EPA/ORD approach adopted the two
subcategories of the AHA approach and the third subcategory of the EPA/OAQPS
approach. For 1995, EPA/ORD used the activity levels for each facility as reported in the
EPA/OAQPS inventory; the activity levels were then summed across facilities for each
APCD subclass.
For 1987, the EPA/ORD approach assumed that every MWI was uncontrolled. An
EPA study of MWI incineration conducted at that time indicates that MWIs operating in
1987 did not need controls, because they were not subject to State or Federal limits on
either PM or organic pollutant emissions (U.S. EPA, 1987d). The activity level estimates
were derived from data presented in U.S. EPA (1987d). This approach resulted in the
following activity level assumptions for 1987: (a) 15 percent of the activity level (i.e.,
0.22 billion kg medical waste) were incinerated/yr by MWIs with capacities less than or
equal to 200 Ib/hr, and (b) 85 percent of the activity level (i.e., 1.21 billion kg/yr) were
incinerated by facilities with capacities greater than 200 Ib/hr.
3.3.6.2. EPA/ORD Approach for Estimating CDD/CDF Emission Factors
EPA/ORD collected the engineering reports of 24 tested MWIs. After reviewing
these test reports, 20 met the criteria for acceptability. (See Section 3.1.3 for further
details on the criteria.) In some cases, CDD/CDF congener-specific data were not
reported, or values were missing. In other cases, the protocols used in the laboratory
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analysis were not described; therefore, no determination of the adequacy of the laboratory
methods could be made.
The EPA stack testing method (EPA Method 23) produces a measurement of
CDD/CDFs in units of mass concentration (i.e., nanograms per dry standard cubic meter of
combustion gas (ng/dscm)) at standard temperature and pressure and one atmosphere and
adjusted to a measurement of 7 percent oxygen in the flue gas (U.S. EPA, 1995b). This
concentration is assumed to represent conditions at the point of release from the stack
into the air, and to be representative of routine emissions. The emission factors were
derived by averaging the emission factors across each tested facility in a design class. The
emission factor for each tested MWIs was calculated using the following equation:
C x Fv
EFmwi = (Eqn. 3-8)
w
Where:
EFmwi = Emission Factor per MWI (average ng TEQ per kg of medical
waste burned)
C = Average TEQ concentration in flue gases of tested MWIs (ng
TEQ/dscm) (20°C, 1 atm; adjusted to 7% 02)
Fv = Average volumetric flue gas flow rate (dscm/hr) (20°C, 1 atm;
adjusted to 7% 02)
lw = Average medical waste incineration rate of the tested MWI
(kg/hr)
The emission factor estimate for each design class and the number of stack tests used to
derive it are shown in Table 3-16. Figures 3-12 and 3-13 present congener and congener
group profiles for air emissions from MWIs lacking APCDs and for MWIs equipped with a
wet scrubber/baghouse/fabric filter APCD system, respectively.
3.3.7. Summary of CDD/CDF Emissions from MWIs
Because the stack emissions from so few facilities have been tested (i.e., 20 test
reports) relative to the number of facilities in this industry (i.e., 2,375 facilities in 1995
and 5,000 facilities in 1987) and because several tested facilities are no longer in
operation or have installed new APCD after testing, the EPA/ORD approach did not
calculate nationwide CDD/CDF emissions by calculating emissions from the tested
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facilities and adding those to calculated emissions for the non-tested facilities. Rather, the
EPA/ORD approach (as well as the EPA/OAQPS and AHA approaches) calculated
nationwide CDD/CDF emissions by multiplying the emission factor and activity level
developed for each design class and then summing the calculated emissions for all
classes. Tables 3-16 and 3-17 summarize the resulting national TEQ air emissions for the
reference years 1995 and 1987, respectively. Tables 3-16 and 3-17 also indicate the
activity level and the TEQ emission factor used in estimating annual TEQ emissions.
In estimating annual TEQ emissions in both reference years, a low confidence
rating was assigned to the estimate of the activity level. The primary reason for the low
confidence rating is that very limited information is available on a facility level basis for
characterizing MWIs in terms of the frequency and duration of operation, the actual waste
volume handled, and the level of pollution control. The 1987 inventory of facilities was
based on very limited information. Although the 1995 EPA/OAQPS inventory was more
comprehensive than the 1987 inventory, it was still based on a fairly limited survey of
operating facilities (i.e., approximately 6 percent).
The emission factor estimates were given a low confidence rating, because only
the reports of 20 tested MWI facilities could be used to derive emissions factors
representing the 2,375 facilities operating in 1995 (i.e., less than 1 percent of estimated
number of operating facilities). Even fewer tested facilities could be used to represent the
larger number of facilities operating in 1987 (i.e., 8 tested facilities were used to represent
5,000 facilities). The limited emission tests available do cover all design categories used
here to develop emission factors. However, because of the large number of facilities in
each of these classes, it is very uncertain whether the few tested facilities in each class
capture the true variability in emissions. As shown in Table 3-16, the TEQ emissions in
1995 are estimated to have been 461 g I-TEQDF or 488 g TEQDF-WH098. As shown in
Table 3-17, the TEQ emissions in 1987 are estimated to have been 2,440 g I-TEQDF or
2,590 g TEQDF-WH098.
As explained above, the EPA/ORD approach to estimating national CDD/CDF TEQ
emissions is a 'hybridization' of the EPA/OAQPS and AHA approaches. Table 3-18
compares the main features of each of the three approaches. The 1995 TEQ emissions
estimated here (461 g l-TEQDF/yr) are about 3.5 times higher than those of OAQPS and
AHA (141 and 138 g l-TEQDF/yr, respectively). Most of this difference is due to
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differences in the emission estimates for the uncontrolled facilities (ORD - 432 g I-
TEQDF/yr, OAQPS - 1 36 g l-TEQDF/yr, AHA - 1 20 g l-TEQDF/yr). An analysis of the
differences in how these groups estimated emissions from the uncontrolled facilities are
presented below:
• Differences between the EPA/ORD and AHA Approaches: The ORD approach
adopted the classification scheme of the AHA approach for the uncontrolled class
and assumed similar activity levels. Thus, the difference in emission estimates is
primarily due to differences in the emission factors used. Both groups use similar
emission factors for facilities with design capacities less than or equal to 200 Ibs/h,
but the emission factor for MWIs >200 Ibs/hr used in the EPA/ORD approach was
higher than that used in the AHA approach by a factor of three. This results from
the fact that the two approaches used different sets of emission tests to derive
their emission factors.
• Differences between the EPA/ORD and EPA/OAQPS Approaches: Because the two
approaches subcategorized the uncontrolled facilities into different classes, the
activity levels and emission factors cannot be directly compared. Considering the
class as a whole, however, both approaches used essentially identical activity
levels. The EPA/OAQPS approach assigned 68 percent of the total activity to the
class with the lowest emission factor (i.e., those with >2-sec residence time). The
emission factor for this class, 74 ng l-TEQDF/kg, is considerably lower than either
emission factor used in the EPA/ORD approach (1,680 and 1,860 ng l-TEQDF/kg).
Given the uncertain data base available for making these estimates, it is difficult to
know which of these three estimation approaches yields the most accurate annual TEQ
estimate. However, despite the differences in methodologies and assumptions used, the
three approaches yield annual TEQ estimates that are not fundamentally different; the
estimates differ from each other by a factor of four or less. Because the EPA/ORD
approach was the last of the three to be developed, it has the benefit of being able to
utilize the most logical and supportable features of the previously developed EPA/OAQPS
and AHA approaches.
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3.3.8. Recent EPA Regulatory Activities
Regardless of the approach taken to estimate what the CDD/CDF emissions from
2,375 MWIs were in 1995, the National Emission Standards promulgated by EPA in
September 1997 (Federal Register, 1997b) require substantial reductions of CDD/CDF air
emissions from MWIs. As a result of these standards, MWI emissions will be thoroughly
assessed for purposes of compliance with the CDD/CDF standard. Compliance testing will
allow the development of a more comprehensive emissions data base and more accurate
characterization of this industry. EPA projects that, following full compliance with these
standards, annual emissions will be 5 to 7g l-TEQDFyear.
3.4. CREMATORIA
Bremmer et al. (1994) measured CDD/CDF emissions at two crematoria in The
Netherlands. The first, a "cold" type furnace with direct uncooled emissions, was
calculated to yield 2.4/vg I-TEQDF per body. In the cold type furnaces, the coffin is placed
inside at a temperature of about 300°C. Using a burner, the temperature of the chamber
is increased to 800 to 900°C and kept at that temperature for 2 to 2.5 hours. The
second furnace, a "warm" type with cooling of flue gases to 220°C prior to discharge,
was calculated to yield 4.9 fjg I-TEQDF per body. In the warm type furnace, the coffin is
placed in a chamber preheated to 800°C or higher for 1.2 to 1.5 hours. The chamber
exhausts from both furnace types were incinerated in an after burner at a temperature of
about 850°C. The higher emission rate for the warm-type furnace was attributed by
Bremmer et al. (1994) to the formation of CDD/CDF during the intentional cooling of the
flue gases to 220°C.
Jager et al. (1992) (as reported in Bremmer et al., 1994) measured an emission
rate of 28 /vg I-TEQDF per body for a crematorium in Berlin, Germany. No operating
process information was provided by Bremmer et al. (1994) for the facility.
Mitchell and Loader (1993) reported even higher emission factors for two
crematoria in the United Kingdom. The first facility tested was manually-operated, had
primary and secondary combustion chambers preheated to 650°C, and had a residence
time of 1 second in the secondary combustion chambers. The second tested facility was
computer-controlled, had primary and secondary combustion chambers heated to 850°C,
and had a residence time of 2 seconds in the secondary combustion chamber. The
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measured stack gas concentrations of I-TEQDF ranged from 42.0 to 71.3 ng l-TEQDF/m3 (at
11 % 02) at the first facility and from 25.4 to 45.5 ng l-TEQDF/m3 (at 11 % 02) at the
second facility. Emission factors based on these test results and gas generation rates
reported by Bremmer et al. (1994) were calculated to range from 70 to 80/vg I-
TEQDF/body (HMIP, 1995).
Takeda et al. (1998) measured CDD/CDF emissions at 10 crematoria in Japan.
Although there are more than 1,600 crematoria in Japan, the 10 tested facilities handle
four percent of the cremations carried out in Japan annually. A wide range in emission
factors was observed. When not-detected values are treated as zero, the range was
0.042 to 62//g l-TEQDF/body (mean of 9.2//g l-TEQDF/body). When not-detected values
are treated as one-half the detection limit, the range was 0.45 to 63 /vg l-TEQDF/body
(mean of 11 //g l-TEQDF/body).
In the United States, CDD/CDF emissions have been measured at one crematorium
(CARB, 1 990c) classified as a warm type facility using the criteria of Bremmer et al.
(1994). The combusted material at this facility was comprised of the body, as well as 4
pounds of cardboard, up to 6 pounds of wood, and an unquantified amount of unspecified
plastic wrapping. The three emission tests conducted at this facility yielded an average
emission factor of 0.50//g l-TEQDF/body (or 0.54//g TEQDF-WH098/body). Table 3-19
presents the congener-specific emission factors for this facility. Figure 3-14 presents
CDD/CDF congener and congener group emission profiles based on these emission factors.
The emission factor measured at the one tested U.S. facility is at the lower end of
the range reported for 10 Japanese facilities by Takeda et al. (1998) and is also lower
than the results reported by Bremmer et al. (1994) for two Dutch facilities, by Jager et al.
(1992) for one German facility, and by Mitchell and Loader (1993) for two British
facilities. The average emission factor for these 16 tested facilities is 1 7 fjg l-TEQDF/body
(assuming not-detected values are zero). Because congener-specific results were not
provided in the non-U.S. reports, it was not possible to calculate the average emission
factor in units of TEQDF-WH098. This average emission factor is assigned a low
confidence rating because it is based primarily on tests conducted at non-U.S. facilities.
In 1995, there were 1,155 crematories reported in the United States. However,
there are no readily available data on the number of cold versus warm crematoria
furnaces. In 1995, 21.1 percent of the deceased bodies were cremated (i.e., 488,224
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cremations), and 1 5.2 percent of the deceased were cremated in 1 987 (i.e., 323,371
cremations). Cremations are projected to increase to 25 percent in the year 2000 and 37
percent in the year 2010. A high confidence rating is assigned to these activity level
estimates, because they are based on recent data provided by the Crematoria Association
of North America (Springer, 1997).
Combining this average emission rate of 1 7 fjg l-TEQDF/body with the number of
cremations in 1995 (488,224) yields an estimated annual release of 9.1 g I-TEQDF.
Combining the emission rate of 1 7 /vg l-TEQDF/body with the number of cremations in
1987 (323,371) yields an estimated release of 5.5 g.
3.5. SEWAGE SLUDGE INCINERATION
The three principal combustion technologies used to incinerate sewage sludge in
the United States are the multiple-hearth incinerator, fluidized-bed incinerator, and the
electric furnace (Brunner, 1992; U.S. EPA, 1995b). All of these technologies are "excess-
air" processes (i.e., they combust sewage sludge with oxygen in excess of theoretical
requirements). Approximately 80 percent of operating sludge incinerators are multiple-
hearth design. About 20 percent are fluidized-bed incinerators, and less than 1 percent
are electric incinerators. Other types of technologies not widely used in the United States
are single-hearth cyclones, rotary kilns, and high-pressure wet-air oxidation (U.S. EPA,
1997b; Maw, 1998).
Multiple-hearth Incinerator: This consists of refractory hearths arranged vertically
in series, one on top of the other. Dried sludge cake is fed to the top hearth of the
furnace. The sludge is mechanically moved from one hearth to another through the length
of the furnace. Moisture is evaporated from the sludge cake in the upper hearths of the
furnace. The center hearths are the burning zone, where gas temperatures reach 871 °C.
The bottom hearths are the burn-out zone, where the sludge solids become ash. A waste-
heat boiler is usually included in the burning zone, where steam is produced to provide
supplemental energy at the sewage treatment plant. Air pollution control measures
typically include a venturi scrubber, an impingement tray scrubber, or a combination of
both. Wet cyclones and dry cyclones are also used (U.S. EPA, 1995b).
Fluidized-bed Incinerator: This is a cylindrical refractory-lined shell with a steel
plate structure that supports a sand bed near the bottom of the furnace (Brunner, 1992).
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Air is introduced through openings in the bed plate supporting the sand. This causes the
sand bed to undulate in a turbulent air flow; hence, the sand appears to have a fluid
motion when observed through furnace portals. Sludge cake is added to the furnace at a
position just above this fluid motion of the sand bed. The fluid motion promotes mixing in
the combustion zone. Sludge ash exits the furnace with the combustion gases; therefore,
air pollution control systems typically consist of high-energy venturi scrubbers. Air
pollution control measures typically include a venturi scrubber or venturi/impingement tray
combinations (U.S. EPA, 1995b).
Electric Furnaces: Also called infrared furnaces, these consist of a long rectangular
refractory-lined chamber. A belt conveyer system moves the sludge cake through the
length of the furnace. To promote combustion of the sludge, supplemental heat is added
by electric infrared heating elements within the furnace that are located just above the
traveling belt. Electric power is required to initiate and sustain combustion. Emissions are
usually controlled with a venturi scrubber or some other wet scrubber (Brunner, 1992;
U.S. EPA, 1995b).
3.5.1. Emission Estimates from Sewage Sludge Incinerators
EPA measured CDD/CDF emissions at three multiple-hearth incinerators as part of
Tier 4 of the National Dioxin Survey (U.S. EPA, 1987a). During the pre-test surveys, two
of the facilities were judged to have "average" potential and one facility was judged to
have "high" potential for CDD/CDF emissions with respect to other sewage sludge
incinerators. The results of these tests include congener group concentrations in stack
gas, but lack measurement results for specific congeners other than 2,3,7,8-TCDD and
2,3,7,8-TCDF. EPA measured CDD/CDF emissions (including all 17 toxic congeners) at a
fluidized-bed incinerator and a multiple hearth incinerator in 1990 (U.S. EPA, 1990f). In
1995, the Association of Metropolitan Sewerage Agencies (AMSA) submitted to EPA the
results of stack tests conducted at an additional 13 sewage sludge incinerators (Green et
al., 1995). Two of these data sets were considered not useable by EPA, because either
detection limits or feed rates and stack flow rates were not provided. The average
congener and congener group emission factors are presented in Table 3-20 for the three
facilities from U.S. EPA (1987a) and the 11 AMSA facilities from Green et al. (1995). A
wide variability was observed in the emission factors for the tested facilities. The total
DRAFT-DO NOT QUOTE OR CITE 3-43 December 2003
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CDD/CDF emission factors for the three U.S. EPA (1987a) facilities ranged from 90 to
3,400 ng/kg. The total CDD/CDF emission factors for the two facilities reported in U.S.
EPA (1990f) were 79 to 846 ng/kg. For the 11 facilities reported in Green et al. (1995), a
similarly large variability in emission factors was observed. Figure 3-15 presents the
average congener and congener group profiles based on these data.
The average TEQ emission factor based on the data for the 11 AMSA facilities and
the two facilities reported in U.S. EPA (1990f) is 6.94 ng l-TEQDF/kg of dry sludge
combusted (or 7.04 ng TEQDF-WH098/kg of dry sludge), assuming nondetected values are
zero. Other countries have reported similar results. Bremmer et al. (1994) reported an
emission rate of 5 ng l-TEQDF/kg for a fluidized-bed sewage sludge incinerator, equipped
with a cyclone and wet scrubber, in The Netherlands. Cains and Dyke (1994) measured
CDD/CDF emissions at two sewage sludge incinerators in the United Kingdom. The
emission rate at an incinerator equipped with an electrostatic precipitator and wet
scrubber ranged from 2.75 ng l-TEQDF/kg to 28.0 ng l-TEQDF/kg. The emission rate
measured at a facility equipped with only an electrostatic precipitator was 43.0 ng I-
TEQDF/kg.
In 1988, approximately 199 sewage sludge incineration facilities combusted about
0.865 million metric tons of dry sewage sludge (Federal Register, 1993b). In 1995,
approximately 257 sewage sludge incinerators (some of which were backup or alternate
incinerators) combusted about 2.11 million dry metric tons of sewage sludge (Maw,
1998). Given these estimated amounts of sewage sludge incinerated/yr, the estimate of
TEQ emissions to air is 6.0 g I-TEQDF (or 6.1 g TEQDF-WH098) in 1987 and 14.6 g I-TEQDF
(or 14.8 g TEQDF-WH098) in 1995, using the average TEQ emission factor of 6.94 ng
l-TEQDF/kg (7.04 ng TEQDF-WH098/kg).
A medium confidence rating is assigned to the average TEQ emission factor
because it was derived from stack testing at 13 U.S. sewage sludge incinerators. The
1988 activity level estimate (used as a surrogate for the 1987 activity level) is assigned a
high confidence rating, because it is based on an extensive EPA survey to support
rulemaking activities. The 1995 activity level estimate is assigned a medium confidence
rating because assumptions concerning hours of operation, operating capacity, and design
capacity were made for numerous facilities.
DRAFT-DO NOT QUOTE OR CITE 3-44 December 2003
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3.5.2. Solid Waste from Sewage Sludge Incinerators
In Table 5-16 of U.S. EPA (1987a), data are presented indicating that 2,3,7,8-
TCDD was not detected in the bottom ash or scrubber water filtrate from three sewage
sludge incinerators. However, total CDDs for the three incinerators and the filtrate were:
non-detect, 20 ng/kg, 10 ng/kg, and 0.3 ng/kg, respectively. For total CDFs, the
respective values were: non-detect, 70 ngk/kg, 50 ng/kg, and 4.0 ng/kg. No data were
given for any congeners (other than 2,3,7,8-TCDD), nor were there any data on the
quantities of ash or filtrate.
3.6. TIRE COMBUSTION
Emissions of CDD/CDFs from the incineration of automobile tires were measured
from a dedicated tire incinerator tested by the California Air Resources Board (CARB,
1991 a). The facility consists of two excess air furnaces equipped with steam boilers to
recovery the energy from the heat of combustion. Discarded whole tires were fed to the
incineration units at rates ranging from 2,800 to 5,700 kg/hr during the 3 test days. The
facility was equipped with a dry acid gas scrubber and fabric filter for the control of
emissions prior to exiting the stack. Table 3-21 presents the congener-specific emission
factors for this facility. Figure 3-16 presents CDD/CDF congener and congener group
profiles based on these TEQ emission factors. From these data, the average emission
factor is estimated to be 0.282 ng l-TEQDF/kg of tires incinerated (or 0.281 ng TEQDF-
WH098) when all not detected values are treated as zero.
Cains and Dyke (1994) reported much higher emission rates for two tire
incinerators equipped only with simple grit arresters in the United Kingdom, 188 and 228
ng l-TEQDF/kg of combusted tire.
EPA estimated that approximately 0.50-million metric tons of tires were incinerated
in 1990 in the United States (U.S. EPA, 1992a). This activity level estimate is given a
medium confidence rating, because it is based on both published data and professional
judgement. The use of scrap tires as a fuel was reported to have increased significantly
during the late 1980s; however, no quantitative estimates were provided in U.S. EPA
(1992a) for this period. In 1990, 10.7 percent of the 242 million scrap tires generated
were burned for fuel. This percentage is expected to continue to increase (U.S. EPA,
1992a). Of the tires burned for energy recovery purposes, approximately 46 percent were
DRAFT-DO NOT QUOTE OR CITE 3-45 December 2003
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utilized by pulp and paper facilities, 23 percent were utilized by cement kilns, and 19
percent were utilized by one tire-to-energy facility (U.S. EPA, 1995c). Estimates of
CDD/CDF emissions from cement kilns (inclusive of emissions from combustion of tires)
are addressed in Section 5.1 of this report.
If it is assumed that 385 million kilograms of discarded tires were incinerated in the
United States in 1987 and 1995 by facilities other than cement kilns (i.e., 500 million kg
less approximately 115 million kg burned by cement kilns), then, using the TEQ emission
factor derived from stack data from the one tested facility, an average of 0.11 grams of
I-TEQDF (or 0.11 g TEQDF-WH098) per year are estimated to have been emitted to the air in
both of these reference years. It must be noted that this may be an underestimate of
emissions from this source category, because the one facility tested is a dedicated tire
combustion facility and is equipped with a dry scrubber combined with a fabric filter for air
pollution control. These devices are capable of greater than 95 percent reduction and
control of dioxin-like compounds prior to discharge from the stack. It is not known to
what extent other facilities combusting tires are similarly controlled. If such facilities are
not so equipped, then the emission of CDD/CDF TEQ could be much greater than the
estimates developed above. Therefore, the estimated emission factor for tire incineration
is given a low confidence rating.
3.7. COMBUSTION OF WASTEWATER SLUDGE AT BLEACHED CHEMICAL PULP MILLS
Approximately 20.5 percent of the wastewater sludges generated at bleached
chemical pulp mills are dewatered and burned in bark boilers at the pulp mills. These
sludges can contain CDD/CDFs and elevated levels of chloride. However, the level of heat
input from sludge in the mixed feed to bark boilers rarely exceeds 10 percent (NCASI,
1995).
NCASI (1995) provided congener-specific test results for four wood residue/sludge
boilers tested between 1987 to 1993. Sludge comprised 6 to 10 percent of the solids in
the feed. The average congener-specific emission factors derived from the stack test
results obtained from these facilities are presented in Table 3-22. The average TEQ
emission factors derived from the test results are 0.061 ng l-TEQDF/kg of feed (i.e., sludge
and wood residue) (or 0.062 ng I-TEQDF-WH098), assuming nondetected values are zero.
The range in facility-specific emission factors was wide (0.0004 to 0.118 ng l-TEQDF/kg
DRAFT-DO NOT QUOTE OR CITE 3-46 December 2003
-------
assuming nondetected values are zero). NCASI (1995) also presented stack emission test
results for five other bark boilers. These boilers combusted only bark during the tests
even though the boilers normally fire bark in combination with sludge and coal. These are
discussed in Section 4.2.2 for industrial facilities burning wood scrap/residues. The
average TEQ emission factor for these facilities was 0.4 ng l-TEQDF/kg of feed. The
emissions test data presented in NCASI (1995), and discussed above, indicate that the
CDD/CDF emission factors for bark/sludge combustors are similar to the emission factor
developed in Section 4.2.2 for industrial facilities burning only wood residues/scrap.
Based on the fact that wood residues comprise a far greater fraction of the feed to these
bark/sludge burners than does sludge, the national TEQ emission estimates derived in
Section 4.2.2 of this report for industrial wood burning facilities are assumed to include
emissions from these bark/sludge combustion units.
3.8. BIOGAS COMBUSTION
Using a specially developed sampling apparatus, Schreiner et al. (1992) measured
the CDD/CDF content of a flare combusting exhaust gases from an anaerobic sewage
sludge digester in Germany. The nozzle of the apparatus was moved through three
cross-sections of the flame and cooling zone. The CDD/CDF content at the bottom of the
flare was 1.4 pg l-TEQDF/Nm3, 3.3. pg l-TEQDF/Nm3 at the top of the flare, and 13.1 pg
l-TEQDF/Nm3 in the middle of the flare. Congener-specific results were not reported.
Using the theoretical ratio of flare gas volume to digester gas volume combusted, 78.6:1,
and the average CDD/CDF content of the three measurements, 5.9 pg l-TEQDF/Nm3, an
emission rate of 0.46 ng l-TEQDF/Nm3 of digester gas combusted is yielded.
During 1996, POTWs in the United States treated approximately 122 billion liters
of wastewater daily (U.S. EPA, 1997c). Although reliable data are not readily available on
the amount of sewage sludge generated by POTWs that is subjected to stabilization by
anaerobic digestion, a reasonable approximation is 25 percent of the total sludge
generated (i.e., the sludge generated from treatment of about 30 trillion liters per day of
wastewater). An estimated 196 kg of sludge solids are generated for every million liters
of wastewater subjected to primary and secondary treatment (Water Pollution Control
Federation, 1990). Thus, multiplying 30 billion liters per day (i.e., 25 percent of 122
DRAFT-DO NOT QUOTE OR CITE 3-47 December 2003
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billion liters) by 196 kg/million liters and 365 days/yr yields an annual estimate of 2 million
metric tons of sludge solids that may be anaerobically digested in POTWs annually.
The volume of sludge digester gas combusted in flares annually can be estimated
using operation parameters for a "typical" anaerobic digester system as described in Water
Pollution Control Federation (1990). Multiplying the annual amount of sludge solids of 2
million metric tons by the following parameters and appropriate conversion factors yields
an annual flared digester gas volume of 467-million Mm3:
• Fraction of total solids that are volatile solids = 75 percent;
• Reduction of volatile solids during digestion = 50 percent;
• Specific gas production = 0.94 m3/kg volatile solids reduced; and
• Fraction of produced gas that is flared = 66 percent.
Because there are no direct measurements of CDD/CDF emissions from U.S.
anerobic sludge digester flares and because of uncertainties about the activity level for
biogas combustion, no national emission estimate has been developed for inclusion in the
national inventory. However, a preliminary estimate of the potential annual TEQ emissions
from this source can be obtained by multiplying the emission factor of 0.46 ng I-
TEQDF/Nm3 of digester gas flared by the estimated volume of gas flared annually in the
United States, 467 million Mm3. This calculation yields an annual potential release of 0.22
grams. This estimate should be regarded as a preliminary indication of possible emissions
from this source category; further testing is needed to confirm the true magnitude of
these emissions.
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Table 3-1. Inventory of MSWIs in 1995 by Technology, APCD, and Annual Activity Level
MSWI
MB/RC
MB/REF
MB/WW
FB/RDF
RDF/Ded
MOD
-SA
MOD-EA
Total
Total
No.
Facilities
Activity
Level, kg/yr
No.
Facilities
Activity
Level, kg/yr
No.
Facilities
Activity
Level, kg/yr
No.
Facilities
Activity
Level, kg/yr
No.
Facilities
Activity
Level, kg/yr
No.
Facilities
Activity
Level, kg/yr
No.
Facilities
Activity
Level, kg/yr
No.
Facilities
Activity
Level, kg/yr
UNC
0
0
0
0
0
0
0
0
0
0
9
1.87E + 08
1
1.41E + 07
10
2.01E + 08
Hot
ESP
0
0
0
0
6
1.04E + 09
0
0
1
4.22E + 07
4
1.82E + 08
1
1.97E + 07
12
1.29E + 09
Cold ESP
2
2.00E + 08
1
1.69E + 08
8
2.81E + 09
0
0
4
1.81E + 09
4
1.25E + 08
3
8.28E + 07
22
5.19E + 09
DSI/H-ESP
0
0
0
0
1
4.22E + 08
0
0
1
2.00E + 08
0
0
1
1.41E + 07
3
6.37E+08
DS/FF
2
1.14E + 09
2
2.68E + 08
28
8.57E + 09
1
1.69E + 08
7
2.51E + 09
0
0
1
1.18E + 08
41
1.28E+10
DS/CI/FF
0
0
0
0
3
1.17E + 09
0
0
0
0
0
0
0
0
3
1.17E + 09
DS/FF/
C-ESP
0
0
0
0
0
0
0
0
1
5.63E + 08
0
0
0
0
1
5.63E + 08
WS/FF
0
0
0
0
0
0
0
0
0
0
1
2.82E + 07
0
0
1
2.82E + 07
WS
C-ESP
0
0
0
0
0
0
0
0
0
0
0
0
1
6.76E + 07
1
6.76E + 07
DS/C-ESP
0
0
1
4.22E + 08
8
2.31E + 09
0
0
4
1.75E + 09
0
0
0
0
13
4.49E + 09
DS/DSI/
C-ESP
0
0
0
0
0
0
0
0
0
0
1
7.60E + 07
0
0
1
7.60E + 07
DSI/
Cl/
H-ESP
0
0
0
0
1
2.75E + 08
0
0
0
0
0
0
0
0
1
2.75E + 08
DSI/
C-ESP
6
5.07E + 08
0
0
0
0
0
0
0
0
0
0
0
0
6
5.07E + 08
DSI/
FF
2
2.59E + 08
1
1.13E + 08
2
1.97E + 08
1
8.45E + 07
1
4.22E + 08
1
3.24E + 07
1
1.01E + 08
9
1.21E + 09
DSI/
EQB
0
0
0
0
0
0
1
1.13E + 08
0
0
0
0
0
0
1
1.13E + 08
WS
0
0
2
2.04E + 08
0
0
0
0
0
0
3
4.90E + 07
0
0
5
2.53E + 08
Total
12
2.10E + 09
7
1.18E + 09
57
1.68E+10
3
3.66E + 08
19
7.30E + 09
23
6.80E + 08
9
4.18E + 08
130
2.88E+10
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December 2003
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Table 3-1. Inventory of MSWIs in 1 995 by Technology, APCD, and Annual Activity Level (continued)
MB/RC = Mass Burn Rotary Kiln
MB/REF = Mass Burn Refractory Walled
MB/WW = Mass Burn Waterwalled
RDF/Ded = Dedicated Refuse-Derived Fuel
FB/RDF = Fluidized Bed Refuse-Derived Fuel
MOD/SA = Modular Starved Air
MOD/EA = Modular Excess Air
UNC = Uncontrolled
Hot ESP = Hot side Electrostatic Precipitator
Cold ESP = Cold side Electrostatic Precipitator
DS/FF = Dry Scrubber with Fabric Filter
FF = Fabric Filter
EGB = Electro Gravel Bed
WS = Wet Scrubber
DSI/FF = Dry Sorbent Injection with Fabric Filter
DS/CI/FF = Spray Dryer - Carbon Injection - Fabric Filter
DSI/EGB = Dry Sorbent Injection - Electro Gravel Bed
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CD
Table 3-2. Inventory of MSWIs in 1987 by Technology, APCD, and Annual Activity Level
MB/RC
MB/REF
MB/WW
RDF/Dedicated
RDF/cofired
MOD/SA
MOD/EA
MSWI Type
No. of
Facilities
Activity
Level, kg/yr
No. of
Facilities
Activity
Level, kg/yr
No. of
Facilities
Activity
Level, kg/yr
No. of
Facilities
Activity
Level, kg/yr
No. of
Facilities
Activity
Level, kg/yr
No. of
Facilities
Activity
Level, kg/yr
No. of
Facilities
Activity
Level, kg/yr
Total No. of
Facilities
Total Activity
Level, kg/yr
UNC
0
0
0
0
0
0
0
0
0
0
36
5.73E + 08
2
4.17E + 07
38
6.15E + 08
Hot ESP
3
3.94E + 08
12
2.00E + 09
19
5.20E + 09
7
3.01E + 09
3
2.53E + 08
2
1.17E + 08
0
0
54
1.12E + 10
DS/FF
0
0
1
1.41E + 07
1
1.55E + 08
0
0
0
0
0
0
0
0
2
2.96E + 08
FF
1
1.58E + 07
0
0
0
0
0
0
0
0
3
1.43E + 08
0
0
4
1.59E + 08
EGB
0
0
0
0
0
0
0
0
0
0
0
0
1
6.76E + 07
1
6.76E + 07
WS
0
0
7
9.01E + 08
0
0
2
3.38E + 08
0
0
4
5.30E + 07
1
1.27E + 08
14
1.42E + 09
Total
4
4.10E + 08
20
3.04E + 09
20
5.35E + 09
9
3.35E + 09
3
2.53E + 08
53
1.15E + 09
4
2.36E + 08
113
1.38E + 10
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CD
Table 3-2. Inventory of MSWIs in 1987 by Technology, APCD, and Annual Activity Level (continued)
o
o
-H
D
c;
O
O
53
MB/RC = Mass Burn Rotary Kiln
MB/REF = Mass Burn Refractory Walled
MB/WW = Mass Burn Waterwalled
RDF/Ded = Dedicated Refuse-Derived Fuel
RDF/cofired = RDF cofired with coal
MOD/SA = Modular Starved Air
MOD/EA = Modular Excess Air
UNC = Uncontrolled
Hot ESP = Hot side Electrostatic Precipitator
DS/FF = Dry Scrubber with Fabric Filter
FF = Fabric Filter
EGB = Electro Gravel Bed
WS = Wet Scrubber
kg/y = kilogram per year
CO
Ol
ro
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CD
O
CD
3
0"
CD
O
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CD
Table 3-3. CDD/CDF TEQ Emission Factors (ng TEQ per kg waste) for Municipal Solid Waste Incineration
Municipal Solid
Waste
Incinerator
Design
Mass Burn
Waterwall
Mass Burn
Refractory
Mass Burn
Rotary Kiln
RDF Dedicated
Air Pollution
Control
Device
C-ESP
DS/C-ESP
DS/CI/FF
DS/FF
DSI/CI/H-ESP
DSI/FF
DSI/H-ESP
H-ESP
C-ESP
DS/C-ESP
DS/FF
DSI/FF
H-ESP
WS
C-ESP
DS/FF
DSI/C-ESP
DSI/FF
FF
H-ESP
C-ESP
DS/C-ESP
DS/FF
DS/FF/C-ESP
DSI/FF
DSI/H-ESP
H-ESP
WS
Average
I-TEQDF
Emission
Factor
(ng/kg)
6.10
6.10
1.50
0.63
7.74
1.91
7.74
473
236
51.1
0.63
1.91
473
236
47.0
0.646
47.0
47.0
47.0
285
231
0.53
0.24
0.24
231
231
1,492
231
Average
TEQDF-WH098
Emission
Factor
(ng/kg)
6.54
6.54
1.61
0.72
8.22
2.07
8.22
535
254
53.2
0.72
2.07
535
254
93.1
0.68
93.1
93.1
93.1
316
253
0.56
0.26
0.26
253
253
1,679
253
Basis and Rationale
Based on MB-WW; DS/C-ESP, same furnace and most similar APCD temperature
Based on direct tests
Based on direct tests
Based on direct tests
Based on direct tests
Based on direct tests
Based on MB-WW; DSI/CI/H-ESP, same furnace and most similar APCD temperature
Based on direct tests
Based on direct tests
Based on direct tests
Based on MB-WW; DS/FF, most similar furnace and same APCD
Based on MB-WW; DSI/FF, most similar furnace and same APCD
Based on MB-WW; H-ESP, most similar furnace and same APCD
Based on MB-Ref;C-ESP, same furnace and similar APCD temperature
Based on MB-RK; DSI/FF, same furnace and similar emission control
Based on direct tests
Based on MB-RK; DSI/FF, same furnace and similar emission control
Based on direct tests
Based on MB-RK; DSI/FF, same furnace and similar emission control
Based on direct tests
Based on direct tests
Based on direct tests
Based on direct tests
Based on RDF-Ded; DS/FF, same furnace and similar APCD
Based on RDF-Ded; C-ESP, same furnace and similar emission control
Based on RDF-Ded; C-ESP, same furnace and similar emission control
Based on direct tests
Based on RDF-Ded; C-ESP, same furnace and similar APCD temperature
o
o
-H
D
c;
O
O
53
CO
Ol
GO
D
CD
O
CD
3
0"
CD
O
O
CO
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CD
Table 3-3. CDD/CDF TEQ Emission Factors (ng TEQ per kg waste) for Municipal Solid Waste Incineration (continued)
Municipal
Solid Waste
Incinerator
Design
Modular
Starved-air
Modular
Excess-air
Fluidized-
bed RDF
Air Pollution
Control
Device
C-ESP
DS/DSI/C-ESP
DSI/FF
FF
H-ESP
UNC
WS
WS/FF
C-ESP
DS/FF
DSI/FF
DSI/H-ESP
EGB
H-ESP
UNC
WS
WS/C-ESP
DS/FF
DSI/EGB
DSI/FF
Average 1-
TEQDF
Emission
Factor
(ng/kg)
^6.2
16.2
0.025
16.2
79.0
0.025
16.2
16.2
16.2
16.2
0.025
118
0.025
118
0.025
16.2
16.2
0.63
0.63
0.63
Average
TEQDF-WH098
Emission
Factor (ng/kg)
17.0
17.0
0.024
17
85.7
0.024
17.0
17.0
17.0
17.0
0.024
119
0.024
119
0.024
17.0
17.0
0.72
0.72
0.72
Basis and Rationale
Based on Mod-EA; C-ESP, similar furnace (modular design) and same APCD
Based on Mod-EA; C-ESP, similar furnace (modular design) and similar emission control
Based on direct tests
Based on Mod-EA; C-ESP, similar furnace (modular design) and similar emission control
Based on direct tests
Based on Mod-SA; DSI/FF, same furnace and most similar expected emissions
Based on Mod-EA; C-ESP, similar furnace (modular design) and similar APCD temperature
Based on Mod-EA; C-ESP, similar furnace (modular design) and similar APCD temperature
Based on direct tests
Based on Mod-EA; C-ESP, same furnace and similar temperature in APCD - may over-
estimate emissions
Based on Mod-SA; DSI/FF, similar (modular design) furnace and same APCD
Based on Mod-EA; H-ESP, same furnace and similar emissions
Based on Mod-SA; DSI/FF, same furnace and most similar expected emissions
Based on direct tests
Based on Mod-SA; DSI/FF, same furnace and most similar expected emissions
Based on Mod-EA; C-ESP, same furnace and similar APCD temperature
Based on Mod-EA; C-ESP, same furnace and similar APCD
Based on MB-WW; DS/FF, similar furnace and same APCD
Based on MB-WW; DS/FF, similar furnace - may under-estimate emissions
Based on MB-WW; DS/FF, similar furnace - may under-estimate emissions
o
o
-H
D
c;
O
O
53
CO
Ol
Key:
D
CD
O
CD
3
0"
CD
^
M
O
O
CO
ng/kg
DS/FF
DSI/FF =
DS/CI/FF
C-ESP
H-ESP
WS
UNC
= Nanograms TEQ per kilograms waste
= Dry scrubber combined with a fabric filter
Dry sorbent injection coupled with a fabric filter
= Dry scrubber coupled with carbon injection and a fabric filter
= Cold-sided electrostatic precipitator (temperature at control device is below < 230°C
= Hot-sided electrostatic precipitator (temperature at control device is above > 230°C
= Wet scrubber
= Uncontrolled (no APCD)
-------
Table 3-4a. Annual I-TEQDF Emissions (g/yr) from MSWIs Operating in 1995
Municipal Solid Waste
Incinerator Design
Mass Burn Waterwall
Mass Burn Refractory
Mass Burn Rotary Kiln
RDF Dedicated
Modular Starved-air
Modular Excess-air
Fluidized-bed RDF
Total
Air Pollution
Control
Device
C-ESP
DS/C-ESP
DS/CI/FF
DS/FF
DSI/CI/H-ESP
DSI/FF
DSI/H-ESP
H-ESP
Subtotal
C-ESP
DS/C-ESP
DS/FF
DSI/FF
WS
Subtotal
C-ESP
DS/FF
DSI/C-ESP
DSI/FF
Subtotal
C-ESP
DS/C-ESP
DS/FF
DSI/FF
DSI/H-ESP
H-ESP
DS/FF/C-ESP
Subtotal
C-ESP
DSI/FF
H-ESP
UNC
WS
WS/FF
DS/DSI/C-ESP
Subtotal
C-ESP
DS/FF
DSI/FF
DSI/H-ESP
H-ESP
UNC
WS/C-ESP
Subtotal
DS/FF
DSI/EGB
DSI/FF
Subtotal
I-TEQDF
Emissions
from Tested
Facilities
(g TEQ/yr)
0
2.09
0.635
2.01
2.12
0.279
0
163
170
39.8
21.6
0
0
0
61.4
0
0.245
0
5.29
5.54
32.5
0.321
0.0975
0
0
0
0
33
0
0.000801
8.01
0
0
0
0
8.01
0.0643
0
0
0
2.32
0
0
2.39
0
0
0
0
280
Average I-TEQDF
Emission Factor
(ng/kg)
6.10
6.10
1.50
0.63
7.74
473
0.63
1.91
236
47.0
0.646
47.0
47.0
231
0.53
0.24
231
231
1,492
0.24
16.2
79.0
0.025
16.2
16.2
16.2
16.2
16.2
0.025
118
0.025
16.2
0.63
0.63
0.63
Activity Level
Non-Tested
Facilities
(kg/yr)
2.81e + 09
1.88e + 09
7.44e + 08
5.98e + 09
0
0
4.22e + 08
1.79e + 08
0
0
2.68e + 08
1.13e + 08
2.04e + 08
2.00e + 08
7.57e + 08
5.07e + 08
1.46e + 08
1.67e + 09
1.14e + 09
1.58e + 09
4.22e + 08
2.00e + 08
4.22e + 07
5.63e + 08
1.25e + 08
0
8.03e + 07
1.87e + 08
4.90e + 07
2.82e + 07
7.60e + 07
6.25e + 07
1.18e + 08
1.01e + 08
1.41e + 07
0
1.41e + 07
6.76e + 07
1.69e + 08
1.13e + 08
8.45e + 07
I-TEQDF
Emissions
from Non-
Tested
Facilities
(g TEQ/yr)
17.1
11.4
1.12
3.77
0
0
3.27
84.5
121
0
0
0.168
0.216
48.1
48.5
9.4
0.489
23.8
6.85
40.6
385
0.603
0.379
97.6
46.2
63
0.135
593
2
0
6.34
0.00463
0.785
0.451
1.22
10.8
1
1.9
0.00251
1.66
0
0.000348
1.08
5.64
0.106
0.0709
0.0532
0.231
820
Total I-TEQDF
Emissions
from All
Facilities
(g TEQ/yr)
17.1
13.5
1.75
5.77
2.12
0.279
3.27
247
291
39.8
21.6
0.168
0.216
48.1
110
9.4
0.734
23.8
12.1
46.1
418
0.924
0.477
97.6
46.2
63
0.135
626
2
0.000801
14.4
0.00463
0.785
0.451
1.22
18.8
1.07
1.9
0.00251
1.66
2.32
0.000348
1.08
8.03
0.106
0.0709
0.0532
0.231
1,100
Key: DS/FF = Dry Scrubber combined with a Fabric Filter
DSI/FF = Dry Sorbent Injection coupled with a Fabric Filter
DS/CI/FF = Dry Scrubber -Carbon Injection-Fabric Filter
C-ESP = Cold-side Electrostatic Precipitator (Temperature at control device is below ^230°C)
H-ESP = Hot-side Electrostatic Precipitator (Temperature at control device is >230°C)
WS = Wet Scrubber
UNC = Uncontrolled (no APCD)
EGB = Electro Granular Activated Carbon Bed
ng/kg = nanogram per kilogram
kg/yr = kilograms per year
DRAFT-DO NOT QUOTE OR CITE
3-55
December 2003
-------
Table 3-4b. Annual TEQDF-WH098 Emissions (g/yr) from MSWIs Operating in 1995
Municipal Solid Waste
Incinerator Design
Mass Burn Waterwall
Mass Burn Refractory
Mass Burn Rotary Kiln
RDF Dedicated
Modular Starved-air
Modular Excess-air
Fluidized-bed RDF
Total
Air Pollution
Control
Device
C-ESP
DS/C-ESP
DS/CI/FF
DS/FF
DSI/CI/H-ESP
DSI/FF
DSI/H-ESP
H-ESP
Subtotal
C-ESP
DS/C-ESP
DS/FF
DSI/FF
WS
Subtotal
C-ESP
DS/FF
DSI/C-ESP
DSI/FF
Subtotal
C-ESP
DS/C-ESP
DS/FF
DSI/FF
DSI/H-ESP
H-ESP
DS/FF/C-ESP
Subtotal
C-ESP
DSI/FF
H-ESP
UNC
WS
WS/FF
DS/DSI/C-ESP
Subtotal
C-ESP
DS/FF
DSI/FF
DSI/H-ESP
H-ESP
UNC
WS/C-ESP
Subtotal
DS/FF
DSI/EGB
DSI/FF
Subtotal
TEQDF-WH098
Emissions
from Tested
Facilities
(g TEQ/yr)
0
2.24
0.68
2.10
2.26
0.30
0
183
191
43.0
22.5
0
0
0
65.4
0
0.265
0
10.5
10.8
35.6
0.34
0.10
0
0
0
0
36.1
0
0.0008
8.69
0
0
0
0
8.69
0.068
0
0
0
2.35
0
0
2.42
0
0
0
0
315
Average TEQDF-
WH098
Emission Factor
(ng/kg)
6.54
6.54
1.61
0.72
8.22
535
0.72
2.07
254
93.1
0.68
93.1
93.1
253
0.56
0.26
253
253
1,679
253
17.0
85.7
0.024
17.0
17.0
17.0
17.0
17.0
0.024
119
0.024
17.0
0.72
0.72
0.72
Activity Level
Non-Tested
Facilities
(kg/yr)
2.81e + 09
1.88e + 09
7.44e + 08
5.98e + 09
0
0
4.22e + 08
1.79e + 08
0
0
2.68e + 08
1.13e + 08
2.04e + 08
2.00e + 08
7.57e + 08
5.07e + 08
1.46e + 08
1.67e + 09
1.14e + 09
1.58e + 09
4.22e + 08
2.00e + 08
4.22e + 07
5.63e + 08
1.25e + 08
0
8.03e + 07
1.87e + 08
4.90e + 07
2.82e + 07
7.60e + 07
6.25e + 07
1.18e + 08
1.01e + 08
1.41e + 07
0
1.41e + 07
6.76e + 07
1.69e + 08
1.13e + 08
8.45e + 07
935
TEQDF-WH098
Emissions
from Non-
Tested
Facilities
(g TEQ/yr)
18.4
12.3
1.20
4.04
0
0
3.47
94.7
134
0
0
0.181
0.234
51.9
52.3
18.6
0.53
47.2
13.6
80.0
422
0.638
0.405
107
50.6
70.9
0.144
651
2.12
0
6.88
0.005
0.832
0.478
1.29
11.6
1.06
2.01
0.002
1.68
0
0.003
1.15
5.90
0.114
0.076
0.057
0.247
Total TEQDF-
WH098
Emissions
from All
Facilities
(g TEQ/yr)
18.4
14.54
1.88
6.14
2.26
0.30
3.47
278
325
43.0
22.5
0.181
0.234
51.9
117.8
18.6
0.80
47.2
24.1
90.8
458
0.98
0.50
107
50.6
70.9
0.144
687
2.12
0.0008
15.57
0.005
0.832
0.478
1.29
20.3
1.13
2.01
0.002
1.68
2.35
0.003
1.15
8.32
0.114
0.076
0.057
0.247
1,250
Key: DS/FF = Dry Scrubber combined with a Fabric Filter
DSI/FF = Dry Sorbent Injection coupled with a Fabric Filter
DS/CI/FF = Dry Scrubber -Carbon Injection-Fabric Filter
C-ESP = Cold-side Electrostatic Precipitator (Temperature at control device is below ^230°C)
H-ESP = Hot-side Electrostatic Precipitator (Temperature at control device is >230°C)
WS = Wet Scrubber
UNC = Uncontrolled (no APCD)
EGB = Electro Granular Activated Carbon Bed
ng/kg = nanogram per kilogram
kg/yr = kilograms per year
DRAFT-DO NOT QUOTE OR CITE
3-56
December 2003
-------
Table 3-5a. Annual I-TEQDF Emissions to the Air From MSWIs Operating in 1 987
Municipal Solid Waste
Incinerator Design
Mass Burn Waterwall
Mass Burn Refractory
Mass Burn Rotary Kiln
RDF Dedicated
RDF Cofired
Modular Starved-air
Modular Excess-air
Totals
Air
Pollution
Control
Device
DS/FF
H-ESP
Subtotal
DS/FF
H-ESP
WS
Subtotal
FF
H-ESP
Subtotal
H-ESP
WS
Subtotal
H-ESP
FF
H-ESP
UNC
WS
Subtotal
EGB
UNC
WS
Subtotal
I-TEQDF
Emissions
fromTested
Facilities
(g TEQ/yr)
0.0373
433
433
0
0
0
0
0
48.2
48.2
840
0
840
0
0
0.0643
0
0
0.0643
0
0
0
0
1 370
Average
I-TEQDF
Emission
Factor
(ng/kg)
473
0.63
473
236
47.0
285
1492
231
231
16.2
79.0
0.025
16.2
0.025
0.025
16.2
Activity Level
Non-Tested
Facilities
(kg/yr)
0
3.27e + 09
1.41e + 08
2.00e + 09
9.01e + 08
1.58e + 07
2.25e + 08
2.45e + 09
3.38e + 08
2.53e + 08
1.43e + 08
3.61e + 08
5.73e + 08
5.30e + 07
6.76e + 07
4.17e + 07
1.27e + 08
I-TEQDF
Emissions from
Non-Tested
Facilities
(g TEQ/yr)
0
1550
1550
0.0887
944
212
1,160
0.741
64.2
65
3660
78.1
3730
58.6
2.29
28.5
0.0142
0.848
31.6
0.0017
0.0010
2.03
2.03
6 590
Total I-TEQDF
Emissions from
All Facilities
(g TEQ/yr)
0.0373
1980
1980
0.0887
944
212
1,160
0.741
112
113
4500
78.1
4570
58.6
2.29
28.5
0.0142
0.848
31.7
0.0017
0.0010
2.03
2.03
7 915
Key: DS/FF = Dry Scrubber combined with a Fabric Filter
DSI/FF = Dry Sorbent Injection coupled with a Fabric Filter
DS/CI/FF = Dry Scrubber -Carbon Injection-Fabric Filter
C-ESP = Cold-side Electrostatic Precipitator (Temperature at control device is below <230°C)
H-ESP = Hot-side Electrostatic Precipitator (Temperature at control device is t230°C)
WS = Wet Scrubber
UNC = Uncontrolled (no APCD)
EGB = Electro Granular Activated Carbon Bed
ng/kg = nanogram per kilogram
kg/yr = kilograms per year
DRAFT-DO NOT QUOTE OR CITE
3-57
December 2003
-------
Table 3-5b. Annual TEQDF-WH098 Emissions to the Air From MSWIs Operating in 1987
Municipal Solid Waste
Incinerator Design
Mass Burn Waterwall
Mass Burn Refractory
Mass Burn Rotary Kiln
RDF Dedicated
RDF Cofired
Modular Starved-air
Modular Excess-air
Totals
Air
Pollution
Control
Device
DS/FF
H-ESP
Subtotal
DS/FF
H-ESP
WS
Subtotal
FF
H-ESP
Subtotal
H-ESP
WS
Subtotal
H-ESP
FF
H-ESP
UNC
WS
Subtotal
EGB
UNC
WS
Subtotal
TEQDF-
WH098
Emissions
fromTested
Facilities
(g TEQ/yr)
0.039
485
485
0
0
0
0
0
53.4
53.4
946
0
946
0
0
0.068
0
0
0.068
0
0
0
0
1 485
Average
TEQDF-WH098
Emission
Factor
(ng/kg)
-
535
0.72
535
254
93.1
316
1,679
253
253
17.0
85.7
0.024
17.0
0.024
0.024
17.0
Activity Level
Non-Tested
Facilities
(kg/yr)
0
3.27e + 09
1.41e + 08
2.00e + 09
9.01e + 08
1.58e + 07
2.25e + 08
2.45e + 09
3.38e + 08
2.53e + 08
1.43e + 08
3.61e + 08
5.73e + 08
5.30e + 07
6.76e + 07
4.17e + 07
1.27e + 08
TEQDF-WH098
Emissions from
Non-Tested
Facilities
(g TEQ/yr)
0
1,732
1,732
0.095
1,058
229
1,287
1.47
71.2
72.7
4,114
85.5
4,200
64.1
2.43
30.9
0.014
0.898
34.2
0.0016
0.0010
2.15
2.15
7 397
Total TEQDF-
WH098
Emissions from
All Facilities
(g TEQ/yr)
0.039
2,218
2,218
0.095
1,058
229
1,287
1.47
124.6
126.1
5,060
85.5
5,146
64.1
2.43
31.0
0.014
0.898
34.3
0.0016
0.0010
2.15
2.15
8 877
Key: DS/FF = Dry Scrubber combined with a Fabric Filter
DSI/FF = Dry Sorbent Injection coupled with a Fabric Filter
DS/CI/FF = Dry Scrubber -Carbon Injection-Fabric Filter
C-ESP = Cold-side Electrostatic Precipitator (Temperature at control device is below =<230°C)
H-ESP = Hot-side Electrostatic Precipitator (Temperature at control device is t230°C)
WS = Wet Scrubber
UNC = Uncontrolled (no APCD)
EGB = Electro Granular Activated Carbon Bed
ng/kg = nanogram per kilogram
kg/yr = kilograms per year
DRAFT-DO NOT QUOTE OR CITE
3-58
December 2003
-------
Table 3-6. Fly Ash from a Municipal Incinerator
(Concentrations in /vg/kg)
Congener Group
TCDD
PeCDD
HxCDD
HpCDD
OCDD
TOTAL CDDs
TCDF
PeCDF
HxCDF
HpCDF
OCDF
TOTAL CDFs
Average Concentration
3.7
6.4
9.1
2.3
1.5
23
12
17
14
2.9
1.2
47
Concentration Range
1.6 - 12
2.0 - 25
1.5-42
0.5 - 9.2
0.2 - 6.0
6.2 - 94
5.1 - 36
8.3 - 40
3.9 - 40
0.8 - 9.2
ND - 2.1
22 - 110
DRAFT-DO NOT QUOTE OR CITE
3-59
December 2003
-------
Table 3-7. Comparison of the Amount of TEQs Generated Annually in MSWI Ash
Data Source
USEPA, 1990c
Type of
Ash
Mixed
Mean Total
CDD/CDF
Concentration
(ng/kg)
12,383
Mean I-
TEQDF
(ng/kg)
258
Annual TEQ
Amount
1995 Value3
(g l-TEQDF/yr)
1,806
Annual TEQ
Amount
1987 Value3
(g l-TEQDF/yr)
1,290
Washington, 1998
Ft. Lewis
Bellingham
Spokane
Shane, 1990
Clement, 1988
Bottom
Fly
Mixed
Mixed
Fly
Bottom
Fly
Fly
0
71,280
1,884
1,414
10,320
100
175,000
70,000
0
4,980
38
163
510
0.1
-
-
0
3,486
266
1,141
357
1
-
-
0
2,490
190
815
255
0.05
-
-
USEPA, 1987a
North America
Europe
Japan
Wire Reclamation
Lahl. 1991
Fly
Fly
Fly
Fly
Bottom
Mixed
1,286,000
876,000
2,600
12,010
1,310
177.640
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
Indicates that values could not be calculated.
a. In calculating the Annual TEQ Amounts, fly ash and bottom ash were considered to
be 10% and 90% of the total ash, respectively.
DRAFT-DO NOT QUOTE OR CITE
3-60
December 2003
-------
Table 3-8. CDD/CDF Emission Factors for Hazardous Waste Incinerators and Boilers
Congener/Congener Group
2,3,7,8-TCDD
1, 2,3,7, 8-PeCDD
1,2, 3,4,7, 8-HxCDD
1,2,3,6,7,8-HxCDD
1,2,3,7,8,9-HxCDD
1,2,3,4,6,7,8-HpCDD
OCDD
2,3,7,8-TCDF
1, 2,3,7, 8-PeCDF
2,3,4,7, 8-PeCDF
1,2, 3,4,7, 8-HxCDF
1,2,3,6,7,8-HxCDF
1,2,3,7,8,9-HxCDF
2,3,4,6, 7, 8-HxCDF
1,2,3,4,6,7,8-HpCDF
1,2,3,4,7,8,9-HpCDF
OCDF
Total I-TEQDF
Total TEQDF-WH098
Total TCDD
Total PeCDD
Total HxCDD
Total HpCDD
Total OCDD
Total TCDF
Total PeCDF
Total HxCDF
Total HpCDF
Total OCDF
Total CDD/CDF
Incinerator Average
Mean emission factor
(17 facilities)
(ng/kg feed)
Nondetects
Set to 1/2
Det. Limit
0.44
0.18
0.22
0.32
0.49
1.77
4.13
2.96
2.36
2.56
9.71
3.95
0.31
2.70
16.87
1.74
13.79
4.22
4.29
NR
NR
NR
NR
4.13
NR
NR
NR
NR
13.78
153
Nondetects
Set to
Zero
0.14
0.14
0.18
0.28
0.48
1.74
3.74
2.69
2.33
2.51
9.71
3.95
0.29
2.70
16.68
1.71
13.46
3.83
3.88
NR
NR
NR
NR
3.74
NR
NR
NR
NR
13.46
153
Hot-Side ESP Boilers
Mean emission factor
(2 facilities)
(ng/kg feed)
Nondetects
Set to 1 12
Det. Limit
0.10
0.11
0.15
0.20
0.22
1.17
5.24
0.81
0.38
0.52
0.83
0.37
0.08
0.56
1.04
0.18
0.70
0.78
0.83
0.77
1.15
1.67
2.34
5.24
5.47
5.51
4.04
1.94
0.70
28.83
Nondetects
Set to
Zero
0.00
0.04
0.08
0.18
0.20
1.17
5.24
0.81
0.38
0.52
0.83
0.37
0.02
0.56
0.93
0.16
0.70
0.64
0.65
0.77
0.77
1.62
2.34
5.24
5.47
5.51
4.04
1.94
0.70
28.39
ng/kg = nanograms per kilogram
NR = not reported
Source: U.S. EPA (1996c).
DRAFT-DO NOT QUOTE OR CITE
3-61
December 2003
-------
Table 3-9. Summary of Annual Operating Hours for Each MWI Type
MWI Type
Continuous
commercial
Continuous
onsite
Intermittent
Batch
Capacity Ranges
(Ib/hr)
> 1,000
501 - 1,000
> 1,000
< 500
Case by case
Annual Charging
Hours
(hr/yr)
7,776
1,826
2,174
1,250
Case by case
Maximum Annual
Charging Hours
(hr/yr)
8,760
5,475
4,380
Capacity
Factor
0.89
0.33
0.40
0.29
Case by case
Ib/hr = pounds per hour
hr/yr = hours per year
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Table 3-10. OAQPS Approach: PM Emission Limits for MWIs and Corresponding
Residence Times in the Secondary (2°) Combustion Chamber
MWI Type
Intermittent and
Continuous
Batch
PM Emission Limit3
(gr/dscf)
>0.3
0.16 to < 0.30
0.10 to <0.16
>0.079
0.042 to <0.079
0.026 to <0.042
Residence Time
in 2° Chamber
(seconds)
0.25
1.0
2.0
0.25
1.0
2.0
I-TEQDF Emission
Factor
(kg l-TEQDF/kg waste)
3.96e-9
9.09e-10
7.44e-11
3.96e-9
9.09e-10
7.44e-11
gr/dscf = grains per dry standard cubic foot at standard temperature and pressure.
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Table 3-11. OAQPS Approach: Estimated Nationwide I-TEQDF Emissions (g/yr) for 1995
MWI
Type
Batch
Continuous
Continuous/
Intermittent
Intermittent
Subtotal:
Uncontrolled
Batch
Continuous
Continuous/
Intermittent
Intermittent
Subtotal: Controlled
w/Wet Scrubber
Continuous
Continuous/
Intermittent
Intermittent
Continuous
Continuous/
Intermittent
Subtotal: Controlled
w/Dry Scrubber
Intermittent
Total MWI
Residence
Time or
APCD
0.25 sec
1.00 sec
2. 00 sec
0.25 sec
1.00 sec
2. 00 sec
0.25 sec
1.00 sec
2.00 sec
0.25 sec
1.00 sec
2.00 sec
Wet Scrubber
Wet Scrubber
Wet Scrubber
Wet Scrubber
Dry Scrubber -
no carbon
Dry Scrubber -
no carbon
Dry Scrubber -
no carbon
Dry Scrubber -
with carbon
Dry Scrubber -
with carbon
Fabric Filter/
Packed Bed
CDD/CDF
Emission
Factor
(ng/kg)
193,997
44,500
3,650
193,997
44,500
3,650
193,997
44,500
3,650
193,997
44,500
3,650
426
426
426
426
365
365
365
70
70
33,400
I-TEQDF
Emission
Factor
(ng/kg)
3,960
909
74
3,960
909
74
3,960
909
74
3,960
909
74
10
10
10
10
7
7
7
2
2
681
Activity
Level
(kg/yr)
5.95e + 06
4.20e + 05
2.14e + 05
1.20e + 06
5.10e + 06
3.01e + 07
4.54e + 06
5.10e + 06
9.79e + 07
4.18e + 06
5.57e + 07
4.31e + 07
2.54e + 08
2.42e + 04
1.88e + 08
1.22e + 08
6.04e + 07
3.70e + 08
9.94e + 07
7.86e + 06
2.07e + 07
1.43e + 07
3.70e + 06
1.46e + 08
6.99e + 05
7.71e + 08
CDD/CDF
Emissions
(g/yr)
1.15e + 03
1.87e + 01
7.81e-01
2.33e + 02
2.27e + 02
1.10e + 02
8.81e + 02
2.27e + 02
3.57e + 02
8.11e + 02
2.48e + 03
1.57e + 02
6.66e + 03
1.03e-02
8.01e + 01
5.20e + 01
2.57e + 01
1.58e + 02
3.63e + 01
2.87e + 00
7.56e + 00
1.00e + 00
2.59e-01
4.80e + 01
2.34e + 01
6.88e + 03
I-TEQDF
Emissions
(g/yr)
2.36e + 01
3.82e-01
1.58e-02
4.75e + 00
4.64e + 00
2.23e + 00
1.80e + 01
4.64e + 00
7.24e + 00
1.66e + 01
5.06e + 01
3.19e + 00
1.36e + 02
2.42e-04
1.88e + 00
1.22e + 00
6.04e-01
3.70e + 00
6.96e-01
5.50e-02
1.45e-01
2.86e-02
7.40e-03
9.32e-01
4.76e-01
1.41e + 02
NA = Not applicable
ng/kg = nanograms per kilogram
kg/yr = kilograms per year
g/yr = grams per year
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Table 3-12. AHA Approach: I-TEQDF Emission Factors Calculated for Air Pollution Control
ARC Category
Uncontrolled
MWIs up to 200 Ib/hr
MWIs > 200 Ib/hr
Wet scrubber/BHF/ESPb
Dry sorbent injection/Fabric Filter
I-TEQDF Emission Factor
(lb/106 Ibs waste)
1.53e-03
5.51e-04
4.49e-05
6.95e-05
Number of MWI Test
Reports Used3
4
13
11
8
a The same MWI may have been used more than once in deriving emission factors.
b Wet scrubbers-bag house filters-electrostatic precipitators. Bag house is also called Fabric
Filter.
Source: Doucet (1995).
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Table 3-13. AHA Assumptions of the Percent Distribution of Air Pollution
Control on MWIs Based on PM Emission Limits
PM Emission Limits3
(gr/dscf)
> 0.10
0.08 to < 0.10
0.03 to < 0.08
< 0.03
Percent MWIs
Uncontrolled11
50%
25%
0%
0%
Percent MWIs with
Scrubbers/
BHFs/ESPsc
50%
75%
98%
30%
Percent MWIs
DI/FFd
0%
0%
2%
70%
Particulate matter (PM) emission limits at the stack, grains per dry standard cubic foot
(gr/dscf).
Uncontrolled means there is no air pollution control device installed on the MWI.
Scrubbers/BHFs/ESPs means wet scrubbers/bag house filters/electrostatic precipitators.
DI/FF means dry sorbent injection combined with fabric filters.
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Table 3-14. AHA Approach: Estimated Annual Nationwide I-TEQDF Emissions
APCDa
Uncontrolled
Subtotal:
Uncontrolled
WS/BHF/ESP
DI/FF
Subtotal: Controlled
Total
MWI Capacity"
(Ib/hr)
< 200
> 200
>200
>200
I-TEQDF Emission
Factor0
(g/kg waste)
1.54 e-06
5.51 e-07
4.49 e-08
6.95 e-08
MWI Activity
Leveld
(kg/yr)
2.28 e + 07
1.54 e + 08
1.77 e + 08
3.51 e + 08
2.60 e + 07
3.77 e + 08
5.54 e + 08
Annual I-TEQDF
Emissions
(g/yr)
3.51e + 01
8.48e + 01
1.20e + 02
1.58e + 01
1.81
1.76e + 01
1.38e + 02
a APCD = Air Pollution Control Device assumed by AHA. Uncontrolled means there is no air
pollution control device installed on the MWI. WS/BHF/ESP = Wet scrubber-bag house
filter-electrostatic precipitator. DI/FF = Dry sorbent injection-fabric filter.
b MWI capacity is the design capacity of the primary combustion chamber.
c I-TEQDF Emission Factor derived from tested facilities.
d Activity Level is the annual amount of medical waste incinerated by each APCD class.
Ib/hr = pounds per hour
g/kg = grams per kilogram
kg/yr = kilograms per year
g/yr = grams per year
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Table 3-15. Comparison Between Predicted Residence Times and Residence
Times Confirmed by State Agencies in EPA/ORD Telephone Survey
State
Michigan
Massachusetts
Virginia
New Jersey
Residence Time
Categories
1/4 second
1 .0 second
2.0 seconds
1/4 second
1 .0 second
2.0 seconds
1/4 second
1 .0 second
2.0 seconds
1/4 second
1 .0 second
2.0 seconds
Percentage of
Uncontrolled MWIs
Predicted by PM Method
2% (6/280 MWIs)
2% (5/280)
96% (269/280)
6% (6/94 MWIs)
0% (0/94)
94% (88/94)
1 1 % (6/56)
0 % (0/50)
89% (50/56)
0% (0/53 MWIs)
0% (0/53)
100% (53/53)
Percentage of Uncontrolled
MWIs Confirmed by State
Agency
96% (269/280 MWIs)
3% (9/280)
1% (1/280)
Unknown
Unknown
4% (2/50)
4.5% (1/22)
91% (20/22)
4.5% (1/22)
Unknown
Unknown
Unknown
Source: O'Rourke (1996).
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CD
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53
Table 3-16. EPA/ORD Approach: TEQ Emissions from Medical Waste Incineration for Reference Year 1995
MWI Class
(APCD )
Uncontrolled
Controlled
Total
MWI
Subclass
(Capacity or
APCD)
<200 Ib/hr
>200 Ib/hr
Wet
Scrubber/
Fabric Filter/
ESP
Dry Sorbent
Injection/
Fabric Filter
Fabric Filter/
Packed Bed
Scrubber
No. of
Tested
Facilities
3
5
9
2
1
Total
CDD/CDF
Emission
Factor
(ng/kg)
9.25e + 04
6.05e + 04
4.67e + 03
2.85e + 02
1.11e + 05
I-TEQDF
Emission
Factor
(ng/kg)
1.86e + 03
1.68e + 03
7.22e + 01
6.78
1.35e + 03
TEQDF-WH098
Emission
Factor
(ng/kg)
1.98e + 03
1.78e + 03
7.43e + 01
6.86
1.49e + 03
Activity
Level
(kg/yr)
3.06e + 07
2.23e + 08
3.71e + 08
1.46e + 08
6.99e + 05
7.71e + 08
Annual
CDD/CDF
Emissions
(g/yr)
2.83e + 03
1.35e + 04
1.73e + 03
4.16e + 01
7.76e + 01
1.82e + 04
Annual
I-TEQDF
Emissions
(g/yr)
5.71e + 01
3.75e + 02
2.68e + 01
9.90e-01
9.44e-01
4.61e + 02
Annual
TEQDF-WH098
Emissions
(g/yr)
6.06e + 01
3.98e + 02
2.76e + 01
1.00e + 00
1.04e + 00
4.88e + 02
CO
O5
CD
D
CD
O
CD
3
0"
CD
APCD = Air Pollution Control Devices
ng/kg = nanograms per kilogram
kg/yr = kilograms per year
g/yr = grams per year
Ib/hr = pounds per hour
O
O
CO
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53
Table 3-17. Summary of Annual TEQ Emissions from Medical Waste Incineration (MWI) for Reference Year 1987
MWI Class3
< 200 Ib/hr
> 200 Ib/hr
Total
No. of
Tested
Facilities
3
5
8
Total
CDD/CDF
Emission
Factorb
(g/kg)
9.25e-05
6.05e-05
I-TEQDF
Emission
Factor
(g/kg)
1 .86e-06
1.68e-06
TEQDF-
WH098
Emission
Factor
(g/kg)
1.98e-06
1.78e-06
Activity
Level
(kg/yr)
2.19e + 08
1.21e + 09
1.43e + 09
Annual
CDD/CDF
Emissions
(g/yr)
2.02e + 04
7.32e + 04
9.34e + 04
Annual
I-TEQDF
Emissions
(g/yr)
4.08e + 02
2.03e + 03
2.44e + 03
Annual
TEQDF-
WH098
Emissions
(g/yr)
4.34e+02
2.15e + 03
2.59e + 03
CO
•^J
o
D
CD
O
CD
3
0"
CD
a This uses the categorization scheme of the AHA Approach (Doucet, 1995).
kg/yr = kilograms per year
g/kg = grams per kilogram
g/yr = grams per year
Ib/hr = pounds per hour
O
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Table 3-18. Comparison of Basic Assumptions Used in the EPA/ORD, the EPA/OAQPS, and the
AHA Approaches to Estimating Nationwide CDD/CDF TEQ Emissions from MWIs in 1995
Assumptions
Reference Year
Number of MWIs
Estimated Activity
Level
Percent of Activity
Level at Uncontrolled
MWIs
Percent of Activity
Level at Controlled
MWIs
Subclassification of
Uncontrolled Class
Assumed Distribution
of Uncontrolled Class
APCDs Assumed for
Controlled Class
Assumed Distribution
of Controls
Emission Factor
Approach Used
No. of Tested MWIs
Used to Develop
Emission Factors
Uncontrolled I-TEQDF
Emission Factors
(ng/kg)
Controlled I-TEQDF
Emission Factors
(ng/kg)f
EPA/ORD Approach
1995
2,375
7.71 e + 08 kg/yr
33%
67%
Same as AHA
assumption
Same as AHA
assumption
WS/FF/ESP
DI/FF
FF/Packed Bed Scrub
Yes/ Analogous to
AHA method.
Yes
Uncontrolled: 8
Controlled: 1 1
1,865 = <200 Ib/hr
1,680 = >200 Ib/hr
WS/FF/ESP: 72.2
DSI/FF: 6.8
FF/PBS: 1,350
EPA/OAQPS Approach | AHA Approach
1995
2,375
7.71 e + 08 kg/yr
33%
67%
By residence times (RT) in
secondary chamber
By RT of 0.25, 1.0 and 2.0
sec by State PM emission
limits
WS
DS-no Carbon
DS-Carbon
FF/Packed Bed Scrub
Yes/ Analogous to AHA
method
Yes
Uncontrolled: 10
Controlled: 23
a/ 3,960 = 0.25 s RT
b/ 909 = 1 .0 s RT
c/ 74 =2. Os RT
WS: 10
DS no carbon: 7
DS with carbon: 2
FF/PBS: 681
1995
2,233
5.54 e + 08 kg/yr
32%
68%
By design capacity
By estimated annual
hrs of operation of
<200 Ib/hr and >200
Ib/hr design capacity
WS/FF/ESP
DI/FF
Yes/ Based on survey
and State PM emission
limits
Yes
Uncontrolled: 1 3
Controlled: 12
d/ 1,540 =<200 Ib/hr
el 551 = > 200 Ib/hr
WS/FF/ESP: 44.9
DSI/FF: 69.5
WS = Wet Scrubber; FF = Fabric Filter; ESP = Electrostatic Precipitator; DSI = Dry Sorbent Injection; DS =
Dry Scrubber; no carbon = without the addition of activated carbon; with carbon = with the addition of
activated carbon; PBS = Packed Bed Scrubber.
a 0.25 seconds (s) residence time (RT) in the secondary chamber.
b 1.0 seconds (s) residence time (RT) in the secondary chamber.
c 2.0 seconds (s) residence time (RT) in the secondary chamber.
d design capacities less than or equal to 200 Ibs/hr.
e design capacities greater than 200 Ibs/hr.
f emission factors as reported in Tables 3-9, 3-12, and 3-14.
Ib/hr = pounds per hour
kg/yr = kilograms per year
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Table 3-1 9. CDD/CDF Air Emission Factors for a Crematorium
Congener/Congener Group
2,3,7,8-TCDD
1, 2,3,7, 8-PeCDD
1,2, 3,4,7, 8-HxCDD
1,2,3,6,7,8-HxCDD
1,2,3,7,8,9-HxCDD
1,2,3,4,6,7,8-HpCDD
OCDD
2,3,7,8-TCDF
1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
1,2, 3,4,7, 8-HxCDF
1,2,3,6,7,8-HxCDF
1,2,3,7,8,9-HxCDF
2,3,4,6,7,8-HxCDF
1,2,3,4,6,7,8-HpCDF
1,2,3,4,7,8,9-HpCDF
OCDF
Total 2,3,7, 8-CDD
Total 2,3,7, 8-CDF
Total I-TEQDF
Total TEQDF-WH098
Total TCDD
Total PeCDD
Total HxCDD
Total HpCDD
Total OCDD
Total TCDF
Total PeCDF
Total HxCDF
Total HpCDF
Total OCDF
Total CDD/CDF
Mean Facility
Assuming
ND = zero
(ng/body)
28.9
89.6
108
157
197
1,484
2,331
206
108
339
374
338
657
135
1,689
104
624
4,396
4,574
501
543
554
860
2,224
3,180
2,331
4,335
2,563
4,306
2,030
624
23,007
Emission Factor
Assuming
ND = 1/2 det limit
(ng/body)
28.9
89.6
108
157
197
1,484
2,331
206
117
349
374
338
657
135
1,813
112
624
4,396
4,725
508
550
554
860
2,224
3,180
2,331
4,335
2,563
4,306
2,154
624
23,131
ng/body = nanograms per body
Source: CARB (1990c)
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Table 3-20. CDD/CDF Emission Factors for Sewage Sludge Incinerators
Congener
2,3,7, 8-TCDD
1, 2,3,7, 8-PeCDD
1,2, 3,4,7, 8-HxCDD
1,2,3,6,7,8-HxCDD
1,2,3,7,8,9-HxCDD
1,2,3,4,6,7,8-HpCDD
OCDD
2,3,7,8-TCDF
1, 2,3,7, 8-PeCDF
2,3,4,7,8-PeCDF
1,2, 3,4,7, 8-HxCDF
1,2,3,6,7,8-HxCDF
1,2,3,7,8,9-HxCDF
2,3,4,6,7,8-HxCDF
1,2,3,4,6,7,8-HpCDF
1,2,3,4,7,8,9-HpCDF
OCDF
Total TCDD
Total PeCDD
Total HxCDD
Total HpCDD
Total OCDD
Total TCDF
Total PeCDF
Total HxCDF
Total HpCDF
Total OCDF
Total I-TEQDF
Total TEQDF-WH098
Total CDD/CDF
U.S. EPA (1987a) - 3 facilities
Mean Emission Factor (ng/kg)
Nondetects
Set to
Zero
0.39
NR
NR
NR
NR
NR
46.2
179
NR
NR
NR
NR
NR
NR
NR
NR
109
37.6
2.66
16.6
53.9
46.2
528
253
75.4
144
109
NR
NR
1,266
Nondetects
Set to
1/2 Det. Limit
0.44
NR
NR
NR
NR
NR
46.2
179
NR
NR
NR
NR
NR
NR
NR
NR
109
37.7
2.81
16.9
54.0
46.2
528
253
75.9
144
109
NR
NR
1,268
Green et al. (1 995) - 1 1 facilities
Mean Emission Factor (ng/kg)
Nondetects
Set to
Zero
0.12
0.23
0.03
0.10
0.29
2.55
13.60
26.60
1.98
6.84
2.17
0.79
0.03
1.26
1.46
0.17
1.22
35.80
0.82
1.74
4.39
13.60
123.85
59.94
12.69
2.63
1.22
6.94
7.04
257
Nondetects
Set to
1/2 Det. Limit
0.23
0.32
0.11
0.16
0.36
2.70
14.00
26.63
2.08
6.89
2.24
0.83
0.08
1.46
1.64
0.27
1.62
37.81
1.63
2.25
5.03
14.00
124.10
60.16
13.50
3.12
1.62
7.19
7.33
263
ng/kg = nanograms per kilogram
NR = not reported
Sources: U.S. EPA (1 987a); Green et al. (1 995)
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December 2003
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Table 3-21. CDD/CDF Air Emission Factors for Tire Combustion
Congener/Congener Group
2,3,7,8-TCDD
1, 2,3,7, 8-PeCDD
1, 2,3,4,7, 8-HxCDD
1, 2,3,6,7, 8-HxCDD
1, 2,3,7, 8,9-HxCDD
1, 2,3,4,6,7, 8-HpCDD
OCDD
2,3,7, 8-TCDF
1, 2,3,7, 8-PeCDF
2,3,4,7, 8-PeCDF
1, 2,3,4,7, 8-HxCDF
1, 2,3,6,7, 8-HxCDF
1, 2,3,7, 8,9-HxCDF
2,3,4,6,7, 8-HxCDF
1, 2,3,4,6,7, 8-HpCDF
1,2, 3,4,7, 8,9-HpCDF
OCDF
Total 2,3,7, 8-CDD
Total 2,3,7, 8-CDF
Total I-TEQDF
Total TEQDF-WH098
Total TCDD
Total PeCDD
Total HxCDD
Total HpCDD
Total OCDD
Total TCDF
Total PeCDF
Total HxCDF
Total HpCDF
Total OCDF
Total CDD/CDF
Mean Facility Emission Factor
Assuming
ND = zero
(ng/kg)
0.149
0.006
0.018
0.055
0.036
0.379
4.156
0.319
0.114
0.086
0.103
0.059
0.036
0.100
0.000
0.027
0.756
4.799
1.600
0.282
0.281
0.153
0.032
0.391
0.695
4.156
1.204
0.737
0.710
0.119
0.802
8.999
Assuming
ND = 1/2 det limit
(ng/kg)
0.149
0.026
0.023
0.062
0.048
0.379
4.156
0.319
0.118
0.091
0.111
0.090
0.068
0.148
0.166
0.095
0.756
4.843
1.962
0.311
0.318
0.153
0.032
0.391
0.695
4.156
1.204
0.737
0.710
0.186
0.802
9.067
ng/kg = nanograms per kilogram
ND = not detected
Source: CARB (1991 a)
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December 2003
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Table 3-22. CDD/CDF Emission Factors for Combustion of Bleached-Kraft
Mill Sludge in Wood Residue Boilers
Congener
2,3,7,8-TCDD
1,2,3,7,8-PeCDD
1,2,3,4,7,8-HxCDD
1,2,3,6,7,8-HxCDD
1,2,3,7,8,9-HxCDD
1,2,3,4,6,7,8-HpCDD
OCDD
2,3,7,8-TCDF
1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
1,2, 3,4,7, 8-HxCDF
1,2, 3,6,7, 8-HxCDF
1,2,3,7,8,9-HxCDF
2, 3,4,6,7, 8-HxCDF
1,2, 3,4,6,7, 8-HpCDF
1,2, 3,4,7, 8,9-HpCDF
OCDF
Total TCDD
Total PeCDD
Total HxCDD
Total HpCDD
Total OCDD
Total TCDF
Total PeCDF
Total HxCDF
Total HpCDF
Total OCDF
Total I-TEQDF
Total TEQDF-WH098
Total CDD/CDF
Mean Emission Factors
(ng/kg feed)
Nondetects
Set to
Zero
0.005
0.005
0.012
0.050
0.035
0.301
1.189
0.104
0.022
0.019
0.069
0.043
0.036
0.004
0.274
0.081
0.187
0.101
0.030
0.599
0.956
1.189
0.560
0.469
0.748
1.102
0.187
0.061
0.062
5.941
Nondetects
Set to
1/2 Det. Limit
0.013
0.012
0.022
0.056
0.043
0.302
1.192
0.107
0.029
0.027
0.071
0.046
0.041
0.012
0.275
0.083
0.188
0.108
0.109
0.600
0.958
1.192
0.560
0.470
0.748
1.102
0.188
0.082
0.087
6.037
ng/kg = nanograms per kilogram
Source: NCASI (1995)
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Belt
Conveyor
Total
Ash
Discharge
Figure 3-1. Typical Mass Burn Waterwall Municipal Solid Waste Incinerator
Source: U.S. EPA (1997b)
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Figure 3-2. Typical Mass Burn Rotary Kiln Combustor
Source: U.S. EPA (1997b)
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Figure 3-3. Typical Modular Excess-Air Combustor
Source: U.S. EPA (1997b)
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ToStaDkor
Wstehtet Bailer
Secondary
PrirrBryAir
Secondary
Chanter
Secondary
CSsBuner
Mi
Cuench
Figure 3-4. Typical Modular Starved-Air Combustor with Transfer Rams
Source: U.S. EPA (1997b)
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Sipertieeter
Steck
Figure 3-5. Typical Dedicated RDF-Fired Spreader Stoker Boiler
Source: U.S. EPA (1997b)
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Exhaust and Ash
Thermocouple
Sludge
Inlet
Fluidizing
Air Inlet
Pressure Tap
Sight
Glass
Burner
Tuyeres
Fuel Gun
Pressure Tap
Startup
Preheat
Burner
for Hot
Windbox
Figure 3-6. Fluidized-Bed RDF Incinerator
Source: U.S. EPA (1997b)
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Muncipal Solid Waste
Incinerator Design
Classes for 1987
Key: DS/FF = Dry Scrubber combined with a Fabric Filter
H-ESP = Hot-side Electrostatic Precipitator (Temperature at control device is
>230°C)
WS = Wet Scrubber
UNC = Uncontrolled (no APCD)
EGB = Electro Granular Activated Carbon Bed
FF = Fabric Filter
Figure 3-7. MSWI Design Classes for 1987
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Muncipal Solid Waste
Incinerator Design
Classes for 1995
Waterwall
DS/FF
DSI/FF
DS/CI/FF
DS/C-ESP
DSI/H-ESP
DSI/CI/H-ESP
C-ESP
H-ESP
Refractory
Walled
-
WS
C-ESP
DS/FF
DSI/FF
Key: DS/FF = Dry Scrubber combined with a Fabric Filter
DSI/FF = Dry Sorbent Injection coupled with a Fabric Filter
DS/CI/FF = Dry Scrubber -Carbon Injection-Fabric Filter
C-ESP = Cold-side Electrostatic Precipitator (Temperature at control device is
below ^230°C)
H-ESP = Hot-side Electrostatic Precipitator (Temperature at control device is
>230°C)
WS = Wet Scrubber
UNC = Uncontrolled (no APCD)
EGB = Electro Granular Activated Carbon Bed
Figure 3-8. MSWI Design Classes for 1995
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Ratio (congener emission factor / total CDD/CDF emission factor)
0 O.O2 O.O4 O.O6 O.O8 O.I O.12
2,3,7,8-TCDD
1,2,3,7,8-PeCDD
1,2,3,4,7,8-HxCDD
1,2,3,6,7,8-HxCDD
1,2,3,7,8,9-HxCDD
1,2,3,4,6,7,8-HpCDD
1,2,3,4,6,7,8,9-OCDD
2,3,7,8-TCDF
1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
1,2,3,4,7,8-HxCDF
1,2,3,6,7,8-HxCDF
1,2,3,7,8,9-HxCDF
2,3,4,6,7,8-HxCDF
1,2,3,4,6,7,8-HpCDF
1,2,3,4,7,8,9-HpCDF
1,2,3,4,6,7,8,9-OCDF
Ratio (congener group emission factor / total CDD/CDF emission factor)
O.OS 0.1 0.15 0.2
0.2S
Figure 3-9. Congener and Congener Group Profiles for Air Emissions from a Mass-Burn
Waterwall MSWI, Equipped with a Dry Scrubber and Fabric Filter
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Ratio (congener emission factor / total CDD/CDF emission factor)
0 0.05 0.1 0.15
2,3,7,8-TCDD
1,2,3,7,8-PeCDD
1,2,3,4,7,8-HxCDD
1,2,3,6,7,8-HxCDD
1,2,3,7,8,9-HxCDD
1,2,3,4,6,7,8-HpCDD
1,2,3,4,6,7,8,9-OCDD
2,3,7,8-TCDF
1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
1,2,3,4,7,8-HxCDF
1,2,3,6,7,8-HxCDF
1,2,3,7,8,9-HxCDF
2,3,4,6,7,8-HxCDF
1,2,3,4,6,7,8-HpCDF
1,2,3,4,7,8,9-HpCDF
1,2,3,4,6,7,8,9-OCDF
Figure 3-10. Congener Profile for Air Emissions from Hazardous Waste Incinerators
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Ratio (congener emission factor / total CDD/CDF emission factor)
O O.O5 O.I O.15 O.2
2,3,7,8-TCDD
1,2,3,7,8-PeCDD
1,2,3,4,7,8-HxCDD
1,2,3,6,7,8-HxCDD
1,2,3,7,8,9-HxCDD
1,2,3,4,6,7,8-HpCDD
1,2,3,4,6,7,8,9-OCDD
2,3,7,8-TCDF
1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
1,2,3,4,7,8-HxCDF
1,2,3,6,7,8-HxCDF
1,2,3,7,8,9-HxCDF
2,3,4,6,7,8-HxCDF
1,2,3,4,6,7,8-HpCDF
1,2,3,4,7,8,9-HpCDF
1,2,3,4,6,7,8,9-OCDF
Ratio (congener group emission factor / total CDD/CDF emission factor)
0.05 0.1 0.15 0.2
0.25
Figure 3-11. Congener and Congener Group Profiles for Air Emissions from
Boilers and Industrial Furnaces Burning Hazardous Waste
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Ratio (mean congener emission factor / total CDD/CDF emission factor)
0 O.O5 O.I O.15
2,3,7,8-TCDD
1,2,3,7,8-PeCDD
1,2,3,4,7,8-HxCDD
1,2,3,6,7,8-HxCDD
1,2,3,7,8,9-HxCDD
1,2,3,4,6,7,8-HpCDD
1,2,3,4,6,7,8,9-OCDD
2,3,7,8-TCDF
1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
1,2,3,4,7,8-HxCDF
1,2,3,6,7,8-HxCDF
1,2,3,7,8,9-HxCDF
2,3,4,6,7,8-HxCDF
1,2,3,4,6,7,8-HpCDF
1,2,3,4,7,8,9-HpCDF
1,2,3,4,6,7,8,9-OCDF
Ratio (congener group emission factor / total CDD/CDF emission factor)
0.05 0.1 0.15 0.2
0.25
Nondetects set equal to zero.
Figure 3-12. Congener and Congener Group Profiles for Air Emissions
from Medical Waste Incinerators without APCD
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Ratio (congener emission factor / total CDD/CDF emission factor)
0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09
2,3,7,8-TCDD
1,2,3,7,8-PeCDD
1,2,3,4,7,8-HxCDD
1,2,3,6,7,8-HxCDD
1,2,3,7,8,9-HxCDD
1,2,3,4,6,7,8-HpCDD
1,2,3,4,6,7,8,9-OCDD
2,3,7,8-TCDF
1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
1,2,3,4,7,8-HxCDF
1,2,3,6,7,8-HxCDF
1,2,3,7,8,9-HxCDF
2,3,4,6,7,8-HxCDF
1,2,3,4,6,7,8-HpCDF
1,2,3,4,7,8,9-HpCDF
1,2,3,4,6,7,8,9-OCDF
Ratio (congener group emission factor / total CDD/CDF emission factor)
O.OS O.I O.1S
O.2
Nondetects set equal to zero.
Figure 3-13. Congener and Congener Group Profiles for Air Emissions from Medical
Waste Incinerators Equipped with a Wet Scrubber, Baghouse, and Fabric Filter
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Ratio (congener emission factor / total ODD/CDF emission factor)
O.O2 O.O4 O.O6 O.O8 O.I O.12
2,3,7,8-TCDD
1,2,3,7,8-PeCDD
1,2,3,4,7,8-HxCDD
1,2,3,6,7,8-HxODD
1,2,3,7,8,9-HxCDD
1,2,3,4,6,7,8-HpCDD
1,2,3,4,6,7,8,9-OCDD
2,3,7,8-TCDF
1,2,3,7,8-PeCDF
2,3,4,7,8-PeODF
1,2,3,4,7,8-HxCDF
1,2,3,6,7,8-HxCDF
1,2,3,7,8,9-HxCDF
2,3,4,6,7,8-HxCDF
1,2,3,4,6,7,8-HpCDF
1,2,3,4,7,8,9-HpCDF
1,2,3,4,6,7,8,9-OCDF
Ratio (congener group emission factor / total CDD/CDF emission factor)
0.05 0.1 0.15
0.2
Source: GARB (1990c); nondetects set equal to zero.
Figure 3-14. Congener and Congener Group Profiles for
Air Emissions from a Crematorium
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Ratio (congener emission factor / total CDD/CDF emission factor)
0.05 0.1 0.15 0.2 0.25 0.3
2,3,7,8-TCDD
1,2,3,7,8-PeCDD
1,2,3,4,7,8-HxCDD
1,2,3,6,7,8-HxCDD
1,2,3,7,8,9-HxCDD
1,2,3,4,6,7,8-HpCDD
1,2,3,4,6,7,8,9-OCDD
2,3,7,8-TCDF
1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
1,2,3,4,7,8-HxCDF
1,2,3,6,7,8-HxCDF
1,2,3,7,8,9-HxCDF
2,3,4,6,7,8-HxCDF
1,2,3,4,6,7,8-HpCDF
1,2,3,4,7,8,9-HpCDF
1,2,3,4,6,7,8,9-OCDF
Ratio (congener group emission factor / total CDD/CDF emission factor)
O.I O.2 O.3 O.4
O.5
entrations from Green et al. (1OSB); nondetectB eet equal to
Figure 3-15. Congener and Congener Group Profiles for
Air Emissions from Sewage Sludge Incinerators
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Ratio (congener emission factor / total CDD/CDF emission factor)
O.I O.2 O.3 O.4 O.S
2,3,7,8-TCDD
1,2,3,7,8-PeCDD
1,2,3,4,7,8-HxCDD
1,2,3,6,7,8-HxCDD
1,2,3,7,8,9-HxCDD
1,2,3,4,6,7,8-HpCDD
1,2,3,4,6,7,8,9-OCDD
2,3,7,8-TCDF
1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
1,2,3,4,7,8-HxCDF
1,2,3,6,7,8-HxCDF
1,2,3,7,8,9-HxCDF
2,3,4,6,7,8-HxCDF
1,2,3,4,6,7,8-HpCDF
1,2,3,4,7,8,9-HpCDF
1,2,3,4,6,7,8,9-OCDF
Ratio (congener group emission factor / total CDD/CDF emission factor)
O.I O.2 0.3 O.4
O.S
Source: CARB (1991a); nondetects set equal to zero.
Figure 3-16. Congener and Congener Group Profiles for
Air Emissions from a Tire Combustor
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4. COMBUSTION SOURCES OF CDD/CDF: POWER/ENERGY GENERATION
4.1. MOTOR VEHICLE FUEL COMBUSTION
Ballschmiter et al. (1986) reported detecting CDD/CDFs in used motor oil and thus
provided some of the first evidence that CDD/CDFs might be emitted by the combustion
processes in gasoline- and diesel-fueled engines. Incomplete combustion and the presence
of a chlorine source in the form of additives in the oil or the fuel (such as dichloroethane
or pentachlorophenate) were speculated to lead to the formation of CDDs and CDFs. The
congener patterns found in the used oil samples were characterized by Ballschmiter et al.
(1986) as similar to the patterns found in fly ash and stack emissions from municipal
waste incinerators.
Since 1986, several studies have been conducted to measure or estimate CDD/CDF
concentrations in emissions from vehicles. Although there is no standard approved
protocol for measuring CDD/CDFs in vehicle exhausts, researchers have developed and
implemented several measurement approaches for collecting and analyzing vehicle
exhausts. Other researchers have estimated vehicle exhaust emissions of CDD/CDFs
indirectly from studies of tunnel air. The results of these two types of studies are
summarized in chronological order in the following Section 4.1.1 and Section 4.1.2.
Estimates of national annual CDD/CDF TEQ emissions from on-road motor vehicles fueled
with leaded gasoline, unleaded gasoline, and diesel fuel are presented in Section 4.1.3
based on the results of these studies. It should be noted, however, that relatively few
tests on emissions from diesel and unleaded gasoline fueled vehicles are available
considering the variety and numbers of such vehicles currently in operation, and the range
of operational, technical, and environmental conditions in which they are operated. As a
result, the emission factors developed in this report for on-road motor vehicles are quite
uncertain.
National emission estimates have not been generated in this report for off-road
vehicles (i.e., construction and farm vehicles) or stationary sources using these fuel types
because of lack of emission factor data; activity level information, however, is presented
in this report for these potential source categories.
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4.1.1. Tailpipe Emission Studies
Marklund et al. (1987) provided the first direct evidence of the presence of CDDs
and CDFs in car emissions based on tailpipe measurements on Swedish cars.
Approximately 20 to 220 pg of I-TEQDF from tetra- and penta-CDD/CDFs were reported
per kilometer driven for four cars running on leaded gasoline. For this study, an unleaded
gasoline was used to which was added tetramethyl lead (0.15 g/L or 0.57 grams per
gallon) and dichloroethane (0.1 g/L as a scavenger). The fuel used may not have
accurately represented commercial fuels at that time, which typically contained a mixture
of chlorinated and brominated scavengers (Marklund et al., 1990). Also, the lead content
of the fuel used (0.15 g lead/L), although the normal lead content for Swedish fuels at the
time (Marklund et al., 1990), was higher than the lead content of leaded gasoline in the
United States during the late 1980s (lowered to 0.10 g lead/gallon or 0.026 g lead/L
effective January 1, 1986). Marklund et al. (1987) reported a striking similarity in the
TCDF and PeCDF congener profiles in the car exhausts and those found in emissions from
municipal waste incinerators. For two cars running on unleaded gasoline, CDD/CDF
emissions were below the detection limit, which corresponded to approximately 1 3 pg of
I-TEQDF per kilometer driven.
Table 4-1 presents a summary description of the results of the Marklund et al.
(1987) study and subsequent studies (presented in chronological order) discussed below.
Tables 4-2 and 4-3 present the results of tailpipe emission studies reported for diesel-
fueled cars and trucks, respectively. Table 4-4 presents the results of studies using
leaded gasoline-fueled cars, and Tables 4-5 and 4-6 present results of studies with cars
fueled by unleaded gasoline. Figures 4-1, 4-2, and 4-3 present congener and congener
group profiles for emissions from diesel-fueled vehicles, leaded gasoline-fueled vehicles,
and unleaded gasoline-fueled vehicles, respectively.
Virtually no testing of vehicle emissions in the United States for CDD/CDFs has
been reported. In 1987, the California Air Resources Board (CARB) produced a draft
report on the testing of the exhausts of four gasoline-powered cars and three diesel fuel-
powered vehicles (one truck, one bus, and one car) (CARB, 1987a). However, CARB
indicated to EPA that the draft report should not be cited or quoted to support general
conclusions about CDD/CDFs in motor vehicle exhausts because of the small sample size
of the study and because the use of low rather than high resolution mass spectrometry in
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the study resulted in high detection limits and inadequate selectivity in the presence of
interferences (Lew, 1993). CARB did state that the results of a single sample from the
heavy-duty diesel truck could be reported, because congeners from most of the
homologue groups were present in the sample at levels that could be detected by the
analytical method and there were no identified interferences in this sample. This test was
conducted under steady state conditions (50 km/hr) for 6 hours with an engine with a fuel
economy of 5.5 km/L. The TEQ emission factor of this one sample was equivalent to
7,290 pg I-TEQDF/L of fuel burned (7,190 pg TEQDF-WH098/L). Assuming a fuel economy
of 5.5 km/L yields an emission factor of 1,325 pg l-TEQDF/km (1,307 pg TEQDF-
WH098/km). Treating nondetected values as zeros yields TEQ emission factors of 3,720
pg I-TEQDF/L of fuel burned (or 676 pg l-TEQDF/km driven) and 3,280 pg TEQDF-WH098/L
(or 596 pg TEQDF-WH098/km driven) (Lew, 1996).
Haglund et al. (1988) sampled exhaust gases from three different vehicles (two
cars fueled with leaded and unleaded gasoline, respectively, and a heavy-duty diesel truck)
for the presence of brominated dibenzo-p-dioxins (BDD) and brominated dibenzofurans
(BDF). The authors concluded that the dibromoethane scavenger added to the tested
gasoline probably acted as a halogen source. TBDF emissions measured 23,000 pg/km in
the car with leaded gasoline and 240 pg/km in the car with unleaded gasoline. TBDD and
PeBDF emissions measured 3,200 and 980 pg/km, respectively, in the car with leaded
gasoline. All BDD/Fs were below detection limits in the diesel truck emissions.
Bingham et al. (1989) analyzed the exhausts of four cars using leaded gasoline
(0.45 g/L tetramethyllead, 0.22 g/L dichloroethane, and 0.2 g/L dibromoethane), and the
exhaust from one car using unleaded gasoline. Analytical results and detection limits were
reported for only five of the 1 7 toxic CDD/CDF congeners. TEQ emission rates for the
cars using leaded fuel, based on detected congeners only, ranged from 1 to 39 pg I-
TEQDF/km. CDD/CDFs were not detected in the exhaust from the vehicle using unleaded
fuel. The total I-TEQDF emission rate for this car using unleaded fuel, based on one-half
the detection limits for the five reported congeners, was 20 pg l-TEQDF/km.
Marklund et al. (1990) tested Swedish cars fueled with commercial fuels,
measuring CDD/CDF emissions before and/or after the muffler. Both new and old vehicles
were tested. Three cars were tested using unleaded gasoline, and two cars were tested
with leaded gasoline (0.15 g Pb/L and dichloroethane and dibromoethane scavengers).
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CDD/CDFs were not detected in the fuels at a detection limit of 2 pg I-TEQDF/L, but were
detected at a level of 1,200 pg I-TEQDF/L in the new semi-synthetic engine lube oil used in
the engines. The test driving cycle employed (i.e., 31.7 km/hr as a mean speed; 91.2
km/hr as a maximum speed; and 17.9 percent of time spent idling) yielded fuel economies
ranging from approximately 9 to 10 km/L or 22 to 24 miles/gallon in the various cars. The
reported ranges of emission factors were:
• Leaded gas/before muffler: 2.4 to 6.3 pg l-TEQDF/km (or 21 to 60 pg I-TEQDF/L of
fuel consumed);
• Leaded gas/in tailpipe: 1.1 to 2.6 pg l-TEQDF/km (or 10 to 23 pg I-TEQDF/L);
• Unleaded gas/catalyst-equipped/in tailpipe: 0.36 pg l-TEQDF/km (or 3.5 pg
I-TEQDF/L); and
• Unleaded gas/before muffler: 0.36 to 0.39 pg l-TEQDF/km (or 3.5 pg I-TEQDF/L).
The TEQ levels in exhaust gases from older cars using leaded gasoline were up to six
times greater when measured before the muffler than after the muffler. No muffler-related
difference in new cars running on leaded gasoline or in old or new cars running on
unleaded gasoline was observed.
Marklund et al. (1990) also analyzed the emissions from a heavy-duty diesel-fueled
truck for CDD/CDFs. None were detected; however, the authors pointed out that the test
fuel was a reference fuel and may not have been representative of commercial diesel fuel.
Also, due to analytical problems, a much higher detection limit (about 100 pg I-TEQDF/L)
was realized in this diesel fuel test than in the gasoline tests (5 pg I-TEQDF/L) conducted.
Further uncertainty was introduced because the diesel emission samples were only
collected prior to the muffler.
Hagenmaier et al. (1990) ran a set of tests using conditions comparable to the FTP-
73 test cycle on gasoline- and diesel-fueled engines for light duty vehicles in Germany.
The following average TEQ emission rates per liter of fuel consumed were reported:
• Leaded fuel: 1,080 pg I-TEQDF/L (1,287 pg TEQDF-WH098/L);
• Unleaded fuel (catalyst-equipped): 7.2 pg I-TEQDF/L (7.9 pg TEQDF-WH098/L);
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• Unleaded fuel (not catalyst-equipped): 50.9 pg I-TEQDF/L (60.2 pg TEQDF-
WH098/L); and
• Diesel fuel: 20.8 pg I-TEQDF/L (24.8 pg TEQDF-WH098/L).
The major findings of a German study of emissions of halogenated dibenzodioxins
and dibenzofurans from internal combustion engines running on commercial fuels were
published in 1991 (Schwind et al., 1991), and the full detailed report was published in
1992 (Hutzinger et al., 1992). The study was conducted by the Universities of Stuttgart,
Tubingen, and Bayreuth for the Federal Ministry for Research and Technology, the
Research Association for Internal Combustion Engines, and the German Association for
the Petroleum Industry and Coal Chemistry. Tests were conducted using engine test
benches and rolling test benches under representative operating conditions. Tests were
performed on leaded gasoline engines, unleaded gasoline engines, diesel car engines, and
diesel truck engines. The reported range of CDD/CDF emission rates across the test
conditions in units of pg TEQ per liter of fuel consumed are presented below. The results
from those tests conducted under normal operating conditions with commercial fuels and
for which congener-specific emission results were presented in Hutzinger et al. (1992) are
listed in Tables 4-2 through 4-6.
• Leaded fuel: 52 to 1,1 84 pg I-TEQDF/L (72 to 1,41 7 pg TEQDF-WH098/L);
• Unleaded fuel (not catalyst-equipped): 96 to 177 pg I-TEQDF/L (102 to 181 pg
TEQDF-WH098/L);
• Unleaded fuel (catalyst-equipped): 10 to 26 pg I-TEQDF/L (9.6 to 28.0 pg TEQDF-
WH098/L);
• Diesel fuel (cars): 10 to 1 30 pg I-TEQDF/L (12 to 140 pg TEQDF-WH098/L); and
• Diesel fuel (trucks): 70 to 81 pg I-TEQDF/L (79 to 82 pg TEQDF-WH098/L).
Although no specific details on the methodology used were provided, Hagenmaier
(1994) reported that analyses of emissions of a diesel-fueled bus run either on steady
state or on the "Berlin cycle" showed no CDD/CDF present at a detection limit of 1 pg/L
of fuel consumed for individual congeners.
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Gullett and Ryan (1997) recently reported the results of the first program to sample
diesel engine emissions for CDD/CDFs during actual highway and city driving. The
exhaust emissions from a 1991 Freightliner diesel tractor with a 10.3 L, 6-cylinder
Caterpillar engine, representative of the first generation of computerized fuel controlled
vehicles manufactured in the United States, were sampled during both highway and city
driving routes. The average emission factor for the three highway tests conducted (15.1
pg l-TEQDF/km; range 11.7-18.7 pg l-TEQDF/km; standard deviation of 3.5 pg l-TEQDF/km)
was a factor of three below the average of the two city driving tests (49.9 pg l-TEQDF/kg;
range 3.0-96.8 pg l-TEQDF/km). Detection limits were considered as zeros in the
calculation of these emission factors. The average of all five tests was 29.0 pg
l-TEQDF/km with a standard deviation of 38.3 pg l-TEQDF/km; this standard deviation
reflects the 30-fold variation in the two city driving route tests.
4.1.2. Tunnel Emission Studies
Several European studies and one recent U.S. study evaluated CDD/CDF emissions
from vehicles by measuring the presence of CDD/CDFs in tunnel air. This approach has
the advantage that it allows random sampling of large numbers of cars, including a range
of ages and maintenance levels. The disadvantage of this approach is that it relies on
indirect measurements (rather than tailpipe measurements), which may introduce unknown
uncertainties and make interpretation of the findings difficult. Concerns have been raised
that the tunnel monitors are detecting resuspended particulates that have accumulated
over time, leading to overestimates of emissions. Also, the driving patterns encountered
in these tunnel studies are more or less steady state driving conditions rather than the
transient driving cycle and cold engine starts that are typical of urban driving conditions
and that may affect emission levels. Each of these studies is summarized below in
chronological order.
Rappe et al. (1988) reported the CDD/CDF content of two air samples (60 m3 per
sample) collected from a tunnel in Hamburg, Germany, during January of 1986 to be 0.42
and 0.58 pg l-TEQDF/m3 (0.44 and 0.59 pg TEQDF-WH098/m3. Each sample was collected
over a period of about 60 hours. The tunnel handled 65,000 vehicles per day of which
17 percent were classified as "heavy traffic." The congener-specific results of the two
samples are presented in Table 4-7. Measurement of ambient air conducted in September
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of 1986 at a nearby highway in Hamburg was reported to contain CDD/CDF levels two to
six times lower than those measured in the tunnel.
Larssen et al. (1990) and Oehme et al. (1991) reported the results of a tunnel
study in Olso, Norway, performed during April/May of 1988. Oehme et al. (1991)
estimated total vehicle emissions by measuring CDD/CDF concentrations in tunnel inlet
and outlet air of both the uphill and downhill lanes. Emission rates for light-duty and
heavy-duty vehicle classes in the uphill and downhill lanes were estimated by counting the
number of light-duty vs. heavy-duty vehicles passing through the tunnel on workdays and
a weekend and assuming a linear relationship between the percentage of the light- or
heavy-duty traffic and the overall emission rate. Thus, the linear relationship for each
emission rate was based on only two points (i.e., the weekday and weekend
measurements). The emission rates, in units of Nordic TEQ, estimated in this study are:
• Light-duty vehicles using gasoline (approximately 70-75 percent using leaded
gas): uphill = 520 pg TEQ/km; downhill = 38 pg TEQ/km; mean = 280 pg
TEQ/km; and
• Heavy-duty diesel trucks: uphill = 9,500 pg TEQ/km; downhill = 720 pg
TEQ/km; mean = 5,100 pg TEQ/km.
The mean values are the averages of the emission rates corresponding to the two
operating modes: vehicles moving uphill on a 3.5 percent incline at an average speed of
37 miles per hour and vehicles moving downhill on a 3.5 percent decline at an average
speed of 42 miles per hour. Although Oehme et al. (1991) reported results in units of
Nordic TEQ, the results in I-TEQDF should be nearly identical (i.e., about 3 to 6 percent
higher), because the only difference between the two TEQ schemes is the toxic
equivalency factor assigned to 1,2,3,7,8-PeCDF (0.1 in Nordic and 0.05 in I-TEQDF), a
minor component of the toxic CDD/CDFs measured in the tunnel air. Table 4-7 presents
the congener-specific differences in concentrations between the tunnel inlet and outlet
concentrations.
Wevers et al. (1992) measured the CDD/CDF content of air samples taken during
the winter of 1991 inside a tunnel in Antwerp, Belgium. During the same period,
background concentrations were determined outside the tunnel. Two to four samples
were collected from each location with two devices: a standard high volume sampler with
a glass fiber filter and a modified two-phase high volume sampler equipped with a glass
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fiber filter and a polyurethane foam plug (PDF). The I-TEQDF concentration in the air
sampled with the filter/PDF device was 74 to 78 percent of the value obtained with the
high volume sampler. However, the results obtained from both sets of devices indicated
that the tunnel air had a CDD/CDF TEQ concentration about twice as high as the outside
air (filter and PUF: 80.3 fg l-TEQDF/m3 for tunnel air vs. 35 fg l-TEQDF/m3 for outside air;
filter only: 100 fg l-TEQDF/m3 for tunnel air vs. 58 fg l-TEQDF/m3 for outside air). The
authors presented the congener-specific results for only one tunnel air measurement; these
results are presented in Table 4-7.
During October/November 1995, Gertler et al. (1996, 1998) conducted a study at
the Fort McHenry Tunnel in Baltimore, Maryland, with the stated objective of measuring
CDD/CDF emission factors from in-use vehicles operating in the United States, with
particular emphasis on heavy-duty vehicles. The air volume entering and leaving the
tunnel bore that services most of the heavy-duty vehicles (i.e., approximately 25 percent
of the vehicles using the bore are heavy-duty) was measured, and the air was sampled for
CDD/CDFs during 7 sampling periods of 12-hour duration. Three of the samples were
collected during daytime (i.e., 6 am to 6 pm) and four samples were collected during the
night (i.e., 6 pm to 6 am). The air volume and concentration measurements were
combined with information on vehicle counts (obtained from videotapes) and tunnel length
to determine average emission factors. A total of 33,000 heavy-duty vehicles passed
through the tunnel during the seven sample runs. Heavy-duty vehicles accounted for 21.2
to 28.8 percent of all vehicles passing through the tunnel for the seven sample runs. The
emission factors calculated, assuming that all CDD/CDF emitted in the tunnel were from
heavy-duty vehicles, are presented in Table 4-8. The average I-TEQDF emission factor was
reported to be 172 pg l-TEQDF/km (182 pg TEQDF-WH098/km). The major uncertainties
identified by the authors in the study were tunnel air volume measurement, sampler flow
volume control, and analytical measurement of CDD/CDF.
EPA's Office of Mobile Sources (QMS) reviewed the Gertler et al. (1996) study
(Lorang, 1996) and found the study to be technologically well done, with no major
criticisms or comments on the test methodology or protocol. QMS found no reason to
doubt the validity of the emission factor determined by the study. QMS did note that the
particulate emission rate for heavy-duty vehicles measured in the study (0.32 g/mile) is
lower than the general particulate emission rate used by EPA (i.e., about 1 g/mile) and,
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thus, may underestimate CDD/CDF emissions under different driving conditions. QMS
cautioned that the reported emission factor should be regarded only as a conservative
estimate of the mean emission factor for the interstate trucking fleet under the driving
conditions of the tunnel (i.e., speeds on the order of 50 miles/hour with the entering
traffic slightly higher and the exiting traffic slightly lower.
Figure 4-4 graphically presents the results of the studies by Rappe et al. (1988),
Oehme et al. (1991), Wevers et al. (1992), and Gertler et al. (1996, 1998). The figure
compares the congener profiles (i.e., congener concentrations or emission factors
normalized to total concentration or emission factor of 2,3,7,8-substituted CDDs and
CDFs) reported in the four studies. The dominant congeners in the Rappe et al. (1988),
Wevers et al. (1992), and Gertler et al. (1996, 1998) studies are OCDD, 1,2,3,4,6,7,8-
HpCDD, OCDF, and 1,2,3,4,6,7,8-HpCDF. With the exception of OCDD, these congeners
are also major congeners reported by Oehme et al. (1991). The Oehme et al. (1991)
study also differs from the other two studies in that the total of 2,3,7,8-substituted CDFs
dominates total 2,3,7,8-substituted CDDs (by a factor of 2), whereas just the opposite is
observed in Rappe et al. (1988), Wevers et al. (1992), and Gertler et al. (1996, 1998).
4.1.3. National Emission Estimates
Estimates of national CDD/CDF TEQ emissions are presented in this section only for
on-road vehicles utilizing gasoline or diesel fuel. Because emission factors are lacking for
off-road uses (i.e., construction vehicles, farm vehicles, and stationary industrial
equipment), no emission estimates could be developed at this time; however, activity level
information for off-road uses is presented below.
Activity Information for On-Road Vehicles: The U.S. Federal Highway
Administration, as reported in U.S. Department of Commerce (DOC) (1997), reports that
1,586-billion total vehicle miles (2,552 billion km) were driven in the United States during
1994 by automobiles and motorcycles. Because 1994 is the last year for which data are
available, these data are used as a surrogate for 1995 activity levels. Trucks accounted
for 840-billion vehicle miles (1,351 billion km), and buses accounted for 6.4 billion vehicle
miles (10 billion km) (U.S. DOC, 1997). In 1992, diesel-fueled trucks accounted for 14.4
percent of total truck vehicle km driven; gasoline-fueled trucks accounted for the
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remaining 85.6 percent (U.S. DOC, 1995b). Applying this factor (i.e., 14.4 percent) to
the 1994 truck km estimate (i.e., 1,351 billion km) indicates that an estimated 195 billion
km were driven by diesel-fueled trucks in 1994. It is assumed that all other vehicle km
driven (3,718 billion km) (i.e., non-diesel trucks, all automobiles, all buses, and all
motorcycles) were those of gasoline-powered vehicles; although it is recognized that a
fraction of the buses and automobiles use diesel fuel, the exact numbers are not known.
It is further assumed that all of these km were driven by unleaded gasoline-powered
vehicles because in 1 992, only 1.4 percent of the gasoline supply were leaded fuel (EIA,
1993); usage should have further declined by 1995, because use of leaded fuel in motor
vehicles for highway use in the United States was prohibited as of December 31, 1995
(Federal Register, 1985a).
Similar information for 1987 is as follows. An estimated 3,092 billion km were
driven in the United States of which trucks accounted for 887 billion km (U.S. DOC,
1 995a). In 1 987, diesel-fueled trucks accounted for 1 7.2 percent of total truck km driven
(U.S. DOC, 1995b). Applying this factor (i.e., 17.2 percent) to the 1987 truck km
estimate (i.e., 887 billion km) indicates that an estimated 153 billion km were driven by
diesel-fueled trucks. It is assumed that all other vehicle km driven (2,939 billion km) were
those of gasoline-powered vehicles. Leaded gasoline accounted for 24.1 percent of the
gasoline supply in 1987 (EIA, 1993). Thus, it can be estimated that 708 billion km (i.e.,
24.1 percent of 2,939 billion km) were driven by leaded gasoline-fueled vehicles. The
remaining 2,231 billion km are estimated to have been driven by unleaded gasoline-fueled
vehicles. These mileage estimates are given a high confidence rating because they are
based on recent U.S. Bureau of the Census transportation studies.
Activity Information for Off-Road Uses: Although on-road vehicles are the largest
consumers of diesel fuel (accounting for about 50 percent of U.S. sales), other sectors of
the economy use significant volumes: farm use, railroad use, vessel bunkering, and other
off-highway uses. The following paragraphs define each of these uses and present
volumes of distillate fuel sales in each sector for reference years 1987 and 1995. For
these sectors, the majority of "distillate fuel" sales are diesel fuels; a small fraction are
fuel oils.
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Farm use includes sales for use in tractors, irrigation pumps and other agricultural
machinery, as well as that used for crops drying, smudge pot fuel and space heating of
buildings. Sales in 1987 and 1995 were 2,999 and 3,476 million gallons, respectively
(EIA, 1992; EIA, 1997a).
Railroad use includes sales to railroads, for any use, including diesel fuel for railroad
locomotive engines and fuel used for heating buildings operated by railroads. Sales in
1987 and 1995 were 2,850 and 3,429 million gallons, respectively (EIA, 1992; EIA,
1997a).
Vessel bunkering includes sales for the fueling of commercial or private boats, such
as pleasure craft, fishing boats, tug boats, and ocean-going vessels, including vessels
operated by oil companies. Excluded are volumes sold to the U.S. Armed Forces. Sales in
1987 and 1995 were 1,865 and 2,339 million gallons, respectively (EIA, 1992; EIA,
1997a).
Off-highway use includes sales for use in: (1) construction equipment including
earthmoving equipment, cranes, stationary generators, air compressors, etc.; and (2) sales
for non-construction other off-highway uses such as logging. Sales in 1987 and 1995
were 1,560 and 2,173 million gallons, respectively (EIA, 1992; EIA, 1997a).
Emission Estimates: Using the results of the studies discussed in Section 4.1.1,
separate annual national emission estimates are developed below for vehicles burning
leaded gasoline, unleaded gasoline, and diesel fuel. Estimates are provided for the years
1987 and 1995. The emission estimates for reference year 1995 are based on activity
data (i.e., kilometers driven) for calendar year 1994.
Leaded Gasoline: Literature indicates that CDD/CDF emissions do occur from
vehicles using leaded gasoline and that considerable variation occurs depending, at least in
part, on the types of scavengers used. Marklund et al. (1987) reported emissions ranging
from 20 to 220 pg l-TEQDF/km from four cars fueled with a reference unleaded fuel to
which lead (0.5 gplg) and a chlorinated scavenger were added. Marklund et al. (1990)
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reported much lower emissions in the exhaust of cars (1.1 to 6.3 pg l-TEQDF/km) using a
commercial leaded fuel (0.5 g/L) containing both dichloroethane and dibromoethane as
scavengers. Marklund et al. (1990) attributed the difference in the emission
measurements of the 1987 and 1990 studies to the different mix of scavengers used in
the two studies, which may have resulted in preferential formation of mixed chlorinated
and brominated dioxins and furans. Hagenmaier et al. (1990) reported TEQ emissions of
1,080 pg I-TEQDF/L of fuel (approximately 108 pg l-TEQDF/km or 129 pg TEQDF-WH098/km)
from a car fueled with a commercial leaded fuel (lead content not reported). Bingham et
al. (1989) reported emissions from four cars using gasoline with a lead content of 1.7 g/L
in New Zealand to range from 1 to 39 pg l-TEQDF/km. The German study reported by
Schwind et al. (1991) and Hutzinger et al. (1992) measured emissions of 52 to 1,184 pg
I-TEQDF/L (approximately 5.2 to 11 8 pg l-TEQDF/km or 7.2 to 142 pg TEQDF-WH098/km) for
cars under various simulated driving conditions. The tunnel study by Oehme et al. (1991)
estimated that emissions from cars running primarily on leaded gasoline (i.e., 70 to 75
percent of the cars) ranged from 38 to 520 pg Nordic TEQ/km.
As shown in Table 4-4, the average emission factor reported for the tailpipe
emission studies performed using commercial leaded fuel which reported analytical results
for all 17 toxic CDD/CDF congeners (i.e., Marklund et al., 1990; Hagenmaier et al., 1990;
and Schwind et al., 1991) is 450 pg I-TEQDF/L or 532 pg TEQDF-WH098/L. Assuming an
average fuel economy of 10 km/L, these emission factors are approximately 45 pg
l-TEQDF/km and 53 pg TEQDF-WH098/km. A low confidence rating is assigned to this
emission factor because it is based on European fuels and emission control technologies,
which may have differed from U.S. leaded-fuels and engine technology, and also because
the factor is based is based on tests with only nine cars.
Combining the average emission factor developed above (45 pg l-TEQDF/km or 53
pg TEQDF-WH098/km, assuming not detected values are zero) with the estimate for km
driven by leaded fuel-powered vehicles in 1987 (708 billion km) suggests that 31.9 g
I-TEQDF (or 37.5 pg TEQDF-WH098) were emitted from vehicles using leaded fuels in 1987.
Although there likely was minor use of unleaded fuel in 1995 in on-road vehicles, further
use of leaded fuel in motor vehicles for highway use in the United States was prohibited
as of December 31,1995 (Federal Register, 1 985a). In 1 992, the last year for which
data are available on consumption of leaded gasoline by on-road vehicles, only 1.4 percent
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of the gasoline supply was leaded gasoline (EIA, 1993). If it is conservatively assumed
that 1 percent of the total vehicle km driven in 1995 (i.e., 37.2 billion km of a total of
3,718 billion km) were driven by leaded fuel-powered vehicles, then combining the
emission factor of 45 pg l-TEQDF/km (or 53 pg TEQDF-WH098/km) with this activity level
estimate yields an annual emission of 1.7 g I-TEQDF (or 2.0 g TEQDF-WH098) in 1995.
Unleaded Gasoline: The literature documenting results of European studies
indicates that CDD/CDF emissions from vehicles burning unleaded fuels are less than the
emissions from vehicles burning leaded gas with chlorinated scavengers. It also appears,
based on the limited data available, that catalyst-equipped cars have lower emission
factors than noncatalyst-equipped cars. Marklund et al. (1987) did not detect CDD/CDF in
emissions from two catalyst-equipped cars running on unleaded gasoline at a detection
limit of 1 3 pg l-TEQDF/km. Marklund et al. (1990) reported emission factors of 0.36 and
0.39 pg l-TEQDF/km for two noncatalyst-equipped cars and an emission factor of 0.36 pg
l-TEQDF/km for one catalyst-equipped car. Hagenmaier et al. (1990) reported an emission
factor of 5.1 pg l-TEQDF/km for one noncatalyst-equipped car and 0.7 pg l-TEQDF/km for
one catalyst-equipped car. Schwind et al. (1991) and Hutzinger et al. (1992) reported
emission factors of 9.6 to 17.7 pg l-TEQDF/km for several noncatalyst-equipped cars
tested under various conditions; the reported emission factor range for catalyst-equipped
cars was 1.0 to 2.6 pg l-TEQDF/km.
All automobiles running on unleaded gasoline in the United States are equipped
with catalysts. As shown in Table 4-6, the average emission factor reported for the
tailpipe emission studies performed on catalyst-equipped cars (i.e., Hagenmaier et al.
1990; Schwind et al., 1991; and Hutzinger et al., 1992) is 14.9 pg I-TEQDF/L or 15.6 pg
TEQDF-WH098/L. Assuming an average fuel economy of 10 km/L yields emission factors
of 1.5 pg l-TEQDF/km and 1.6 pg TEQDF-WH098/km. A low confidence rating is assigned
to this emission factor because the European fuels and emission control technology used
may have differed from U.S. fuels and technology and also because the emission factor
range is based on tests with only three catalyst-equipped cars.
Combining the calculated mean emission factor of 1.5 pg l-TEQDF/km (or 1.6 pg
TEQDF-WH098/km) with the estimate derived above for vehicle km driven in 1995 by all
gasoline-powered vehicles (3,718 billion km) suggests that 5.6 g of I-TEQDF (or 5.9 g
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TEQDF-WH098) were emitted from vehicles using unleaded fuels in 1995. Applying the
same emission factors to the estimate derived above for vehicle km driven in 1987 by
unleaded gasoline-powered vehicles (2,231 billion km), suggests that 3.3 g of I-TEQDF (or
3.6 g TEQDF-WH098) may have been emitted in 1987.
Diesel Fuel: Few data are available upon which to base an evaluation of the extent
of CDD/CDF emissions resulting from diesel fuel combustion. The limited data available
address emissions only from on-road vehicles; no emissions data are available for off-road
diesel uses (i.e., construction vehicles, farm vehicles, and stationary equipment). Two
U.S. tailpipe studies are available: CARB (1987a) and Gullett and Ryan (1997). CARB
(1987a) reported a relatively high emission factor of 676 pg l-TEQDF/km (not detected
values assumed to be zero) for one tested heavy-duty truck with a fuel economy at 50
km/hr of 5.5 km/L. Gullett and Ryan (1997) reported a range of emission factors for one
diesel truck tested on six highway or city driving routes, 3.0 to 96.8 pg l-TEQDF/km (mean
of 29.0 pg l-TEQDF/km).
The results of several tailpipe studies conducted in Europe have also been
published. Marklund et al. (1990) reported no emissions at a detection limit of 100 pg I-
TEQDF/L (or 18 pg l-TEQDF/km assuming a fuel economy of 5.5 km/L) for one tested truck.
Schwind et al. (1991) and Hutzinger et al. (1992) reported emission factors of 32 to 81
pg I-TEQDF/L (or 6 to 1 5 pg l-TEQDF/km assuming a fuel economy of 5.5 km/L) for a truck
engine run under various simulated driving conditions. Hagenmaier (1994) reported no
emissions from a bus at a detection limit of 1 pg/L of fuel consumed for individual
congeners. For diesel-fueled cars, Hagenmaier et al. (1990) reported an emission factor of
24 pg I-TEQDF/L (or approximately 2.4 pg l-TEQDF/km) for one tested car. Schwind et al.
(1991) and Hutzinger et al. (1992) reported emission factors of 5 to 1 3 pg l-TEQDF/km for
a car engine run under various simulated driving conditions.
The tunnel study by Oehme et al. (1991) generated an estimated mean emission
factor of 5,100 pg TEQ/km and a range of 720 to 9,500 pg TEQ/km (in units of Nordic
TEQ) for diesel-fueled trucks. Insufficient information was provided in Oehme et al.
(1991) to enable an exact calculation of emission in units of I-TEQDF or TEQDF-WH098.
However, based on the information that was provided, the mean emission factor in units
of I-TEQ is approximately 5,250 to 5,400 pg l-TEQDF/km. These indirectly estimated
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emission factors are considerably larger than those reported from engine studies by
Marklund et al. (1990), Schwind et al. (1991), and Hutzinger et al. (1992); the CARB
(1987a) diesel truck emission factor falls at the low end of the range. Although aggregate
samples were collected in this study representing several thousand heavy duty diesel
vehicles, several characteristics of this study introduce considerable uncertainty with
regard to using the study's results as a basis for estimating emissions in the United
States. These factors include: (1) heavy-duty vehicles comprised only 3 to 1 9 percent of
total vehicle traffic in the tunnel; (2) the majority of the light-duty vehicles were fueled
with leaded gasoline the combustion of which, as noted above in Table 4-4, can release
considerable amounts of CDD/CDFs; and (3) technology differences likely existed between
the 1988 Norwegian and the 1987 and 1995 U.S. vehicle fleets.
The recent tunnel study conducted in Baltimore, Maryland, by Gertler et al. (1996,
1998) has the same disadvantages shared by all tunnel studies relative to tailpipe studies.
Specifically, tunnel studies rely on indirect measurements (rather than tailpipe
measurements), which may introduce unknown uncertainties, and the emission factors
calculated from these studies reflect driving conditions by the vehicle fleet using the
tunnel and not necessarily the overall vehicle fleet under other driving conditions.
However, the Gertler et al. (1996, 1998) study does have strengths lacking in the Oehme
et al. (1991) tunnel study. Also, the Gertler et al. (1996, 1998) study has benefits over
the two U.S. diesel truck tailpipe studies. These include (1) the study is a recent study
conducted in the United States and thus reflects current U.S. fuels and technology, (2)
virtually no vehicle using the tunnel used leaded gasoline, (3) the tunnel walls and streets
were cleaned 1 week prior to the start of sampling and, in addition, the study analyzed
road dust and determined that resuspended road dust contributed only about 4 percent of
the estimated emission factors, (4) heavy-duty vehicles comprised, on average, a relatively
large percentage (25.7 percent) of vehicles using the tunnel, and (5) a large number of
heavy-duty vehicles, approximately 33,000, passed through the tunnel during the
sampling period, which generates confidence that the emission factor is representative of
interstate trucks.
In consideration of the strengths and weaknesses of the available emission factor
data from the tailpipe and tunnel studies, the mean TEQ emission factor reported by
Gertler et al. (1996, 1998), 172 pg l-TEQDF/km (or 182 pg TEQDF-WH098/km), is assumed
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to represent the best current estimate of the average emission factor for on-road diesel-
fueled trucks. Because it may not be representative of emission rates for the entire fleet
of diesel-fueled trucks under the wide array of driving conditions encountered on the road,
this emission factor is assigned a low confidence rating.
Combining the calculated mean emission factors from Gertler et al. (1996, 1998)
with the above estimate for vehicle kms driven in 1995 in the United States by diesel-
fueled trucks (195 billion km) suggests that 33.5 g of I-TEQDF (or 35.5 g TEQDF-WH098)
were emitted from trucks using diesel fuel in 1995. Combining the same emission factors
to the estimate derived above for vehicle km driven in 1987 by diesel-fueled trucks (153
billion km) suggests that 26.3 g of I-TEQDF (or 27.8 g TEQDF-WH098) were emitted from
diesel-fueled trucks in 1987.
4.2. WOOD COMBUSTION
In 1995, wood fuel (including black liquor solids) provided about 2.6 percent (or
2,350-trillion Btu) of the total primary energy consumed in the United States (EIA,
1997b). During 1987, wood energy consumption is estimated to have been 2,437 trillion
Btu, or 3.2 percent of total primary energy consumed (EIA, 1997b). The industrial sector
is the largest consumer of wood fuel, accounting for almost 72 percent of total wood fuel
consumption in 1995 and 65 percent in 1987. The residential sector accounted for 25
percent of consumption in 1995 and 35 percent in 1987. The electric utility sector
accounted for less than 1 percent of total consumption in both years. There are no
accurate sources to provide reliable estimates of commercial wood energy use;
consumption is thought to be between 20 and 40 trillion Btu, or 2 to 4 percent of total
wood consumption (EIA, 1994, 1997b).
These energy consumption estimates, however, appear to include the energy value
of black liquor solids, which are combusted in recovery boilers by wood pulp mills. In
1987 and 1995, the energy value of combusted black liquor solids were 950 trillion Btu
and 1,078 trillion Btu, respectively (American Paper Institute, 1992; American Forest &
Paper Association, 1997). Subtracting these black liquor energy value estimates from the
national totals for wood fuel yields 1,487 trillion Btu in 1987 and 1,272 trillion Btu in
1995. Assuming that 1 kg of oven-dried wood (i.e., 2.15 kg of green wood) provides
approximately 19,000 Btu (EIA, 1994), then an estimated 66.9 million and 78.3 million
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metric tons of oven-dried wood equivalents were burned for energy purposes in 1995 and
1987, respectively. Of these totals, an estimated 31.4 million metric tons and 44.8
million metric tons were consumed by the residential sector in 1995 and 1987,
respectively. An estimated 35.5 million metric tons and 33.5 million metric tons were
consumed by the industrial sector in 1995 and 1987, respectively.
The following two subsections discuss the results of relevant emission studies for
the residential and industrial sectors, respectively, and present annual TEQ emission
estimates for the reference years 1987 and 1995.
4.2.1. Residential Wood Combustion
Four studies have provided direct measurement of CDD/CDFs in flue gas emissions
from wood stoves and/or fireplaces (Schatowitz et al., 1993; Vikelsoe et al., 1993;
Bremmer et al., 1994; Broker et al., 1992). The findings of each of these studies are
summarized in the following paragraphs.
Schatowitz et al. (1993) measured the CDD/CDF content of flue gas emissions
from several types of wood burners used in Switzerland: a household stove (6 kW),
automatic chip furnaces (110 to 1,800 kW), and a wood stick boiler (35 kW). The
emissions from combustion of a variety of wood fuels were measured (natural beech
wood, natural wood chips, uncoated chipboard chips, waste wood chips from building
demolition, and household paper and plastic waste). The results from the testing of the
household stove are most relevant for assessing releases from residential combustion.
The household stove was tested with the stove door both open and closed. The open
door stove can be assumed to be representative of fireplaces because both have an
uncontrolled draft. Although the congener/congener group analytical results were not
reported, the following emission factors (dry weight for wood; wet weight for household
waste) and emission rates (corrected to 13 volume% oxygen) for the household stoves
and furnaces were reported.
Stoves
• Open door burn of beech wood sticks: 0.77 ng l-TEQDF/kg (0.064 ng
l-TEQDF/Nm3);
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• Closed door burn of beech wood sticks: 1.25 ng l-TEQDF/kg (0.104 ng
l-TEQDF/Nm3); and
• Closed door burn of household waste: 3,230 ng l-TEQDF/kg (114.4 ng
l-TEQDF/Nm3).
Furnaces
• Natural wood chips: 0.79 to 2.57 ng l-TEQDF/kg
• Chipboard chips (uncoated): 0.29 to 0.91 ng l-TEQDF/kg
• Waste wood chips from building
demolition: 26.0 to 173.3 ng l-TEQDF/kg
Vikelsoe et al. (1993) studied emissions of CDD/CDF congener groups from
residential wood stoves in Denmark. The wood fuels used in the experiments were
seasoned birch, beech, and spruce, equilibrated to 18 percent absolute moisture. Four
different types of stoves (including one experimental stove) were evaluated under both
normal and optimal (i.e., well controlled with CO emission as low as possible) operating
conditions. Widely varying total CDD/CDF emissions were found for the 24 different
fuel/stove type/operating condition combinations. The emissions from spruce were about
twice as high as the emissions from birch and beech. Surprisingly, the optimal operating
condition led to significantly higher CDD/CDF emissions for two stove types, but not for
the other stoves. The predominant congener group for all experiments was TCDF. The
weighted average (considering wood and stove types) emission factor and flue gas
concentration for wood stoves were reported to be 1.9 ng Nordic-TEQ/kg and 0.18 ng
Nordic-TEQ/Nm3, respectively. Because Vickelsoe et al. (1993) did not measure congener
levels, the reported emission factor and emission rate were estimated by assuming the
same congener distribution in each congener group that had been found for municipal
waste incinerators.
Bremmer et al. (1994) reported results of testing performed with a cast-iron, wood
burning stove with a combustion chamber lined with fire refractory clay. Measurements
were conducted at three loads (maximum, average, and minimum) using clean wood as
fuel. The emission factors ranged from 1.0 to 3.3 ng l-TEQDF/kg (average of about 2.2 ng
l-TEQDF/kg). Bremmer et al. (1994) also reported results of testing conducted at a
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fireplace of a type that is common in The Netherlands. The measured emission factors
from burning of clean wood ranged from 13.0 to 28.5 ng l-TEQDF/kg (average of about 20
ng l-TEQDF/kg). Bremmer et al. (1994) noted that the measured emission factors for
fireplaces were considerably higher than those reported by others (see Broker et al., 1992,
below) and they, therefore, assigned "great uncertainty" to the emission factors.
Broker et al. (1992) reported results of a series of three tests with a wood stove
and a fireplace. The average of the minimum and maximum emission factors measured
for the woodstove tests ranged from 0.53 to 0.94 ng l-TEQDF/kg, respectively. The
geometric mean of these two average values is 0.71 ng l-TEQDF/kg. The average of the
minimum and maximum emission factors measured for the fireplace tests ranged from
0.20 to 1.06 ng l-TEQDF/kg, respectively. The geometric mean of these two average
values is 0.46 ng l-TEQDF/kg.
Based on the results reported by Schatowitz et al. (1993), Vikelsoe et al. (1993),
Bremmer et al. (1994), and Broker et al. (1992), 2 ng l-TEQDF/kg appear to be a
reasonable average emission factor for residential burning of clean wood in fireplaces and
stoves. Although the cited studies were conducted in Europe, residential wood burning
practices are probably sufficiently similar to apply to the United States. Nevertheless, a
low confidence rating was assigned to this estimate on the basis that it is derived from
only four direct measurement studies. With the exception of the Broker et al. (1992)
study, none of the cited studies presented results for the individual 2,3,7,8-substituted
congeners. The Broker et al. (1992) study reported congener-specific results for only one
of the test runs. Consequently, the data are not available from which to derive a
corresponding emission factor for TEQDF-WH098. For purposes of this inventory, an
emission factor of 2 ng TEQDF-WH098/kg is assumed.
Several studies have reported that combustion of non-clean wood in stoves and
fireplaces can result in significantly higher CDD/CDF emission factors. The results of
Schatowitz et al. (1993) for combustion of household waste in stoves and demolition
waste in wood furnaces are presented above. A few researchers (e.g., Vikelsoe et al,
1993) have reported high CDD/CDF emission rates when PCP-contaminated wood is
combusted in residential wood stoves and furnaces. The European Inventory (Quab and
Fermann, 1997) used the results of these studies to derive best estimates of CDD/CDF
emission factors for combustion of "slightly contaminated wood (excluding PCP)" and
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"PCP-contaminated wood" to be 50 and 500 ng l-TEQDF/kg, respectively. Although it is
likely that there is some residential combustion of these types of wood in the United
States, there are no corresponding activity level data upon which to base a national annual
estimate of emissions.
In 1987, 22.5 million households in the United States burned wood (EIA, 1991).
Of these households, wood was used in 1987 as the primary heating fuel in 5 million
households and as a secondary source for aesthetic purposes (i.e., fireplaces) in 17.4
million households (EIA, 1991; EIA, 1997b). Lower numbers were reported for 1995;
wood was reported to be used as the primary fuel in 3.53 million households (EIA,
1997b). More rural low-income households consume wood as a primary heating fuel than
do other sectors of the population. The majority of these households use wood-burning
stoves as the primary heating appliance. Although fireplaces are the most common type
of wood-burning equipment in the residential sector, only 7 percent of fireplace users
report use of fireplaces for heating an entire home (EIA, 1991; EIA, 1994).
Residential wood consumption in 1995 was 596 trillion Btu (31.4 million metric
tons), or 25 percent of total U.S. wood energy consumption (EIA, 1997b). In 1987,
residential wood consumption was 852 trillion Btu (44.8 million metric tons), or 35
percent of total U.S. consumption (EIA, 1997b). These production estimates are given
high confidence ratings because they are based on recent government survey data.
Combining the best estimate of the emission factor (2 ng l-TEQDF/kg wood) with
the mass of wood consumed by residences in the years 1995 and 1987 indicates that the
annual I-TEQDF air emissions from this source were approximately 62.8 grams in 1995 and
89.6 grams in 1987.
4.2.2. Industrial Wood Combustion
Emissions Data - Congener-specific measurements of CDD/CDFs in stack emissions
from industrial wood-burning furnaces were measured by the California Air Resources
Board at four facilities in 1988 (CARB, 1990b; CARB, 1990e; CARB, 1990f; CARB,
1990g). Measurements of CDD/CDF congener groups and 2,3,7,8-TCDD and 2,3,7,8-
TCDF were reported for one facility by EPA (U.S. EPA, 1987a). The National Council of
the Paper Industry for Air and Stream Improvement (NCASI) (1995) presented congener-
specific emission factors for five boilers tested during burns of bark/wood residue. The
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average congener emission factors derived from the four CARB and five NCASI studies are
presented in Table 4-9. Average congener and congener group profiles are presented in
Figure 4-5a for the four CARB studies and in Figure 4-5b for the five NCASI studies.
In CARB (1990b), CDD/CDFs were measured in the emissions from a quad-cell
wood-fired boiler used to generate electricity. The fuel consisted of coarse wood waste
and sawdust from nonindustrial logging operations. The exhaust gas passed through a
multicyclone before entering the stack. From this study, average emission factors for total
CDD/CDF and I-TEQDF are calculated to be 48.1 and 0.64 ng/kg of wood burned,
respectively.
In CARB (1990e), CDD/CDFs were measured in the emissions from two spreader
stoker wood-fired boilers operated in parallel by an electric utility for generating electricity.
The exhaust gas stream from each boiler is passed through a dedicated ESP after which
the gas streams are combined and emitted to the atmosphere through a common stack.
Stack tests were conducted both when the facility burned fuels allowed by existing
permits and when the facility burned a mixture of permitted fuel supplemented by urban
wood waste at a ratio of 70:30. From this study, average emission factors for total
CDD/CDF and I-TEQDF are calculated to be 29.2 and 0.82 ng/kg of wood burned,
respectively.
In CARB (1990f), CDD/CDFs were measured in the emissions from a twin fluidized
bed combustors designed to burn wood chips for the generation of electricity. The air
pollution control device (APCD) system consisted of ammonia injection for controlling
nitrogen oxides, and a multiclone and electrostatic precipitator for controlling particulate
matter. During testing, the facility burned wood wastes and agricultural wastes allowed
by existing permits. From this study, average emission factors for total CDD/CDF and I-
TEQDF are calculated to be 47.9 and 1.32 ng/kg of wood burned, respectively.
In CARB (1990g), CDD/CDFs were measured in the emissions from a quad-cell
wood-fired boiler. During testing, the fuel consisted of wood chips and bark. The flue
gases passed through a multicyclone and an ESP before entering the stack. From this
study, average emission factors for total CDD/CDF and I-TEQDF are calculated to be 27.4
and 0.50 ng/kg of wood burned, respectively.
NCASI (1995) presented stack emission test results for five boilers burning bark or
wood residues. One of these facilities, equipped with a multicyclone, normally burns bark
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in combination with sludge and coal. One other facility, equipped with an ESP, normally
fires pulverized coal. The other three facilities were spreader stokers equipped with
multicyclones or ESPs. Although stack gas flow rates were obtained during these tests,
accurate measurements of the amounts of bark/wood fired were not made and had to be
estimated by NCASI (1995) from steam production rates. The average TEQ emission
factor for these facilities was 0.40 ng l-TEQDF/kg of feed (or 0.46 ng TEQDF-WH098/kg).
The mean of the emission factors derived from the four CARB studies and five
NCASI studies, 0.56 ng l-TEQDF/kg wood (assuming nondetected values are zero) (or 0.60
ng TEQDF-WH098/kg wood), is used in this report as most representative of industrial wood
combustion. This emission factor was assigned a medium confidence rating.
It should be noted, however, that these mean emission factors may not be
appropriate emission factors to apply to the combustion of waste wood containing
elevated chlorine content. NCASI (1995) concluded that CDD/CDF emissions from
facilities burning salt-laden wood residue may be considerably higher than from those
burning salt-free wood. Similarly, Umweltbundesamt (1996) reported the results of stack
gas testing at approximately 30 facilities of varying design type as well as type of wood
fuel combusted and noted that elevated CDD/CDF emissions were observed when the
combustion conditions were poor, as evidenced by elevated carbon monoxide emissions,
and/or when the fuel contained elevated chlorine levels. Umweltbundesamt (1996)
attributed the correlation between elevated CDD/CDF emissions and elevated chlorine
content of the fuel to the fire retardant effects of chlorine, which may have inhibited
complete combustion. The chlorine content of untreated wood and bark were reported to
range from 0.001 to 0.01 percent by weight and 0.01 to 0.02 percent by weight,
respectively. Chipboard can contain up to 0.2 percent chlorine by weight because of
binding agents used to manufacture the chipboard. Preservative-treated wood and PVC-
coated wood were reported to contain chlorine contents as high as 1.2 and 0.3 percent
by weight, respectively.
The facility tested by EPA in 1987 was located at a lumber products plant that
manufactures overlay panels and other lumber wood products. Nearly all the wood fed to
the lumber plant had been stored in sea water adjacent to the facility and, therefore, had a
significant concentration of inorganic chloride. The wood-fired boiler tested was a three-
cell dutch oven equipped with a waste heat boiler. The feed wood was a mixture of bark,
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hogged wood, and green and dry planar shavings. The exhausted gases from the boiler
passed through a cyclone and fabric filter prior to discharge from the stack. From this
study, an average emission factor for total CDD/CDF of 1,020 ng/kg of wood burned
(range: 552 to 1,410 ng/kg) was reported for the three collected samples. An average
emission factor for I-TEQDF of 17.1 ng/kg of wood burned (range: 7.34 to 22.8 ng/kg)
was estimated by EPA using measured congener group concentrations and concentrations
of 2,3,7,8-TCDD and 2,3,7,8-TCDF. Similar emission factors were reported by Lutke et
al. (1998) for testing conducted during the 1990s at four Canadian coastal, salt-laden
wood power boilers: 1.4, 2.6, 17.4, and 27.6 ng l-TEQDF/kg wood combusted. The
overall average of the five tested Canadian and U.S. facilities is 13.2 ng l-TEQDF/kg of
wood combusted. The confidence rating assigned to this emission factor is low because
it is based on reporting of limited congener data at one U.S. facility and testing at four
non-U.S. sources and because the fraction of salt-laden wood combusted across facilities
is likely to be highly variable.
Activity Level Information - As discussed in Section 4.2, industrial wood
consumption in 1995 totaled 35.5 million metric tons. A similar amount, 33.5 million
metric tons, was burned for fuel in industrial furnaces in 1987. The majority of wood fuel
consumed in the industrial sector consists of wood waste (i.e., chips, bark, sawdust, and
hogged fuel). Consumption in the industrial sector is dominated by two industries: the
Paper and Allied Products industry and the Lumber and Wood Products industry (EIA,
1994). These activity level estimates are assigned a high confidence rating because they
are based on recent government survey data.
As noted above, the emission factor associated with combustion of salt-laden
wood appears to be greater than that associated with combustion of non-salt-laden wood.
However, activity level data on combustion of salt-laden wood are not normally collected.
Nonetheless, attempts have been made to estimate this activity level. NCASI combined
the results from a 1995 survey of combustion units in the pulp and paper industry with an
ad hoc telephone survey of mills in the Pacific Northwest (i.e., Oregon and Washington) to
produce a conservative (i.e., high end) estimate of the amount of salt-laden wood
combusted at U.S. pulp and paper mills in 1995: 254,000 metric tons (or 0.7 percent of
the estimated 35.5 million metric tons of industrial wood consumed that year). NCASI
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suspects that a similar fraction of industrial wood combustion in 1987 by pulp and paper
mills was salt-laden (Gillespie, 1998).
For purposes of the NCASI survey, salt-laden wood was defined as wood that had
been transported, stored, or otherwise exposed to saltwater prior to being processed as
fuel. None of the three responding mills in Oregon reported use of salt-laden wood. Eight
of the 13 responding mills in Washington reported some combustion of salt-laden wood.
The estimated percentage of salt-laden wood to total wood consumption in the
Washington mills was 1 7 percent.
As noted above, the majority of industrial wood combustion (i.e., 97 percent)
occurs in two industries: the Paper and Allied Products industry and the Lumber and
Wood Products industry. The relative amounts of wood combusted by each of these two
industries were the same in 1990 and 1992, the only years for which these statistics are
readily available (EIA, 1991, 1994). Therefore, it can be assumed that the percentage of
total wood combusted nationally by the Lumber and Wood Products industry that is salt-
laden is the same percentage as that reported by the Paper and Allied Products industry,
0.7 percent. Therefore, the total percentage of wood combusted by industry that is salt-
laden is 1.4 percent. On a mass basis, this equates to 0.5 million metric tons in 1 995 and
0.5 million metric tons in 1987. These activity level estimates are assigned a low
confidence rating.
Emission Estimates - Applying the average TEQ emission factor from the four CARB
and five NCASI studies (0.56 ng l-TEQDF/kg wood or 0.60 ng TEQDF-WH098/kg wood) to
the estimated quantities of non-salt-laden wood burned by industrial facilities in 1995 (35
million metric tons) and 1987 (33 million metric tons) yields estimated TEQ emissions to
air of 1 9.6 g I-TEQDF (or 21.0 g TEQDF-WH098) in 1 995 and 1 8.5 g I-TEQDF (or 1 9.8 g
TEQDF-WH098) in 1987.
Applying the average TEQ emission factor from the five studies on boilers
combusting salt-laden wood (13.2 ng l-TEQDF/kg wood) to the estimated quantities of salt-
laden wood burned by industrial facilities in 1995 (0.5 million metric tons) and 1987 (0.5
million metric tons) yields estimated TEQ emissions to air of 6.6 g TEQ in both 1995 and
1987.
Total emissions are estimated to have been 26.2 and 25.1 g I-TEQDF in 1995 and
1987, respectively. Total emissions of TEQDF-WH098 are estimated to have been 27.6
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and 26.4 g in 1995 and 1987, respectively. As noted above, these emissions are based
on tests conducted at nine facilities in two industries. These two industries account for
97 percent of total industrial wood fuel combustion. The remaining 3 percent of industrial
combustion and the combustion of wood by the commercial sector (for which no reliable
activity level estimates are available) may not be well represented by the emission factors
used above, particularly if poorly controlled combustors or treated wood (e.g., treated
with PCP or plastics) are combusted.
4.2.3. Solid Waste from Wood Combustion
The measurement of CDDs and CDFs in chimney soot and bottom ash from wood-
burning stoves and fireplaces has been reported by several researchers (Bumb et al.,
1980; Nestrick and Lamparski, 1982 and 1983; Clement et al., 1985b; Bacher et al.,
1992; Van Oostam and Ward, 1995; and Dumler-Gradl et al., 1995a).
Bumb et al. (1980) detected TCDDs (ND to 0.4//g/kg), HxCDDs (0.2 to 3//g/kg),
HpCDDs (0.7 to 1 6 //g/kg), and OCDD (0.9 to 25 //g/kg) in residues from the wall of a
home fireplace and from the firebrick of another home fireplace; for lack of a suitable
analytical method, analysis was not performed for PeCDDs. Neither of the fireplaces
sampled by Bumb et al. (1980) had burned preservative-treated wood.
Nestrick and Lamparski (1982, 1983) expanded the research of Bumb et al. (1980)
by conducting a survey of CDD concentrations in chimney soot from residential wood-
burning units in three different rural areas of the United States. Samples were collected
from the base of six chimneys in each of the three study areas. Samples were not
collected from units where any type of treated or manufactured wood had been burned.
For lack of a suitable analytical method, analysis was not performed for PeCDDs. The
results of this survey are summarized in Table 4-10. There was wide variation in the
results across soot samples with standard deviations for congeners and congener groups
often equal to or exceeding the mean value; however, CDDs in each congener group were
detected in the soot from almost all sampled units. Nestrick and Lamparski (1982, 1983)
concluded that the results do not appear to present any easily discernible patterns with
respect to geographic region, furnace operational parameters, or wood fuel type. Nestrick
and Lamparski (1982, 1983) attribute the wide variability observed to differences in
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design of the different units, which affected the sampling point and/or the conditions at
the sampling point, and/or possible contamination of the fuel wood.
Clement et al. (1985b) analyzed chimney soot and bottom ash from residential
woodstoves and fireplaces in Canada. The CDD/CDF congener concentrations are
presented in Table 4-10 (soot) and Table 4-11 (bottom ash). CDD/CDF congeners were
detected in all samples analyzed, although the relative amounts of the different congener
groups varied considerably and inconsistently within the type of wood burning unit and
between ash and soot samples from the same unit. Clement et al. (1985b) also presents
total CDD/CDF concentration data for bottom ashes from outside open-air burning of
wood. No analyses were reported for individual congeners. The results for the congener
groups are presented below. Clement et al. (1985b) did not present the quantities of
ashes produced by the outside open-air burning test, hence it is not possible to readily
determine the quantities of CDD/CDF disposed.
Congener group
TCDDs
PeCDDs
HxCDDs
HpCDDs
OCCDs
Concentration (/vg/kg)
0.8
4.2
7.2
11
10
Congener group
TCDFs
PeCDFs
HxCDFs
HpCDFs
OCDFs
Concentration (/vg/kg)
2.2
7.6
8.2
11
1.7
Bacher et al. (1992) characterized the full spectrum (i.e., mono- through octa-
substitution) of chlorinated and brominated dibenzo-p-dioxin and dibenzofuran congeners
in the soot from an old farmhouse in southern Germany. The chimney carried smoke from
an oven that had used untreated wood at the rate of about 5 m3 per year for more than
10 years. The sample was taken during the annual cleaning by a chimney sweep. The
only BDF detected was mono-BDF (230 ng/kg). No BDDs, BCDDs, or BCDFs were
detected at a detection limit of 20 ng/kg. The results for the tetra- through octa- CDDs
and CDFs are presented in Table 4-10. The results indicate that CDFs dominate the CDDs
in each congener group except octa. Also, the lower chlorinated congener groups
dominate the higher chlorinated congener groups for both the CDDs and CDFs. The TEQ
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December 2003
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content of the chimney soot was 720 ng l-TEQDF/kg (755 ng TEQDF-WH098/kg) of which
less than 30 percent was due to CDDs.
Van Oostdam and Ward (1995) analyzed soot from two wood stoves in British
Columbia, Canada. The average TEQ concentrations were 211 ng l-TEQDF/kg and 246 ng
TEQDF-WH098/kg. The congener-specific results are presented in Table 4-10. The soot
from a wood stove burning salt-laden wood in a coastal area was found to have an I-
TEQDF content of 7,706 ng l-TEQDF/kg or 20 to 90 times greater than the concentrations
found in the soot from the other two tested stoves.
Dumler-Gradl et al. (1995a) analyzed chimney soot samples collected by chimney
sweeps from 188 residences in Bavaria. The summary results of the survey, the largest
published survey of its kind to date, are presented in Table 4-12. As was observed by
Nestrick and Lamparski (1982, 1983) and Clement et al. (1985b), CDD/CDFs were
detected in all samples; however, there was wide variability in total TEQ concentrations
within and across unit type/fuel type combinations.
Washington (1998) reports CDD/CDF congener data for ash from hog fuel boilers at
three paper mills. The data are compiled and evaluated to determine a total I-TEQ
concentrations and loading. Non-detect values were included as either zero, 1/2 DL or at
the DL. The results are as follows, assuming that non-detect values are at zero
concentration:
Location
Daishowa America, Port Angeles
Ft. James
Rayonier
Type of Residual
Mixed Ash
Fly Ash
Filter Ash
Vacuum Filter & Grate
Filter Ash
Fly Ash
I-TEQDF (ng/kg)
0.31
35.4
12,640
1,150
2,299
225
I-TEQDF (mg/day)
0.012
0.544
68.9
6.27
12.5
1.23
Pohlandt (1994) presents CDD/CDF concentration data for various ashes
("bottom", "furnace", "boiler", "fly") from 12 wood burning boilers. The "fly ash"
samples from two wood working industry boilers appear to have the greatest
concentrations of CDD/CDF. Table 4-13 list the average congener concentration for those
two boilers.
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December 2003
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Three boiler bottom ash samples contain detectable amounts of only total
HpCDD/HpCDF and OCDD/OCDF. All the other boiler samples were from boilers that
burned copper/chrome/boron impregnated woods. These samples had total TEQs
(assumed to be l-TEQs) ranging from 0.07 - 89 ppt, the highest being the fly ash samples
(52 and 89). Pohlandt (1994) did not report the quantities produced by the boilers that
were tested, hence it is not possible to readily determine the quantities of CDD/CDF
disposed.
Carpenter (2001) reported the results of analyses of two ash samples from wood
burning facilities in New Hampshire. Both samples are from the burning of clean (i.e.,
untreated) wood chips, sawdust and bark. The first sample is a combination of fly ash
and bottom ash. The second sample is only fly ash, but it is a combination of fly ash from
two wood burning boilers. For the first sample, none of the 2,3,7,8-substituted
congeners were detected at detection limits that ranged from 0.98 ng/kg for 2,3,7,8-
TCDD and 2,3,7,8-TCDF to 9.80 ng/kg for OCDD and OCDF. (All other congeners had a
detection limit of 4.90 ng/kg.) For the second sample, except for two congeners, all
congeners were below detection limits (which ranged from 0.379 to 0.831 ng/kg). The
two congeners that exceeded detection limits were OCDD at 1.261 ng/kg, and
1,2,3,4,6,7,8-HpCDF at 1.022 ng/kg. For this sample, assuming that the non-detect
congeners are not present, I-TEQDF concentration is 0.011 ng/kg. The quantities of the
ash produced were not reported.
In a CARB report of emissions from a wood waste fired incinerator (CARB, 1990b)
data are given for CDDs and CDFs for four ash samples. The concentrations of 2,3,7,8-
substituted CDD/CDF congeners for each of those four tests were all below the method
detection limits (MDLs) except for OCDD, which was detected in three samples at
concentrations of 14,18, and 32 ng/kg, and 2,3,7,8-TCDF, which was detected in one
sample at a concentration of 2.2 ng/kg. The method detection limits for each CDD and
CDF congener ranged from 0.63 ppt (for 2,3,7,8-TCDD) to 9.5 ppt (for HpCDF
congeners). Total CDD and total CDF values are given for each of the four samples.
However, those values assume that non-detected congeners are at the MDL level.
Consequently, the total CDD and total CDF values are biased high. The average of the
four total CDD values is 28.8 ng/kg (with a range of 20.3 - 44.0). The average of the
four total CDF values is 21.9 ng/kg (with a range of 1 6.0 - 26.9).
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In another CARB report (CARB, 1990e), data are presented for CDDs and CDFs for
several samples of Electrostatic Precipitator (ESP) waste ash from a wood-fired boiler.
The report provides sample results for two weeks of sampling conducted at the facility.
During the first week, the boiler burned fuels that were allowed by the facility permit;
during the second week, the boiler burned a mixture containing 70 percent permitted fuel
and 30 percent urban wood wastes. For the six samples collected over the three days of
the first week, many of the concentrations of CDD/CDF congeners in the ESP ash were
below the detection limits. CARB reports the CDD concentrations in ESP waste ash
ranged from 24 to 264 ng/kg, and the CDF concentrations ranged from 1 2 to 151 ng/kg.
However, those values assume that non-detected congeners are present at the detection
level. One sample does not have any non-detect values for CDDs. The total CDD
concentration for this sample is 264 ng/kg, or about 8.3 ng/kg I-TEQDF and 11.4 ng/kg
WHO-TEQs. The I-TEQDF and TEQDF-WH098 CDF concentrations for this sample are both
less than 1.5 ng/kg. These values are less than 1 ng/kg for the other five samples. All of
the samples have some non-detects for the CDF analysis.
Six samples were also collected over three days during the second week of
sampling, when the 70/30 permitted/urban wood waste mix was burned. For the samples
from the second week, the CDD concentrations in ESP waste ash ranged from 1,365 to
3,1 90 ng/kg, and the CDF concentrations ranged from 2,866 to 11,282 ng/kg. CARB
(1990e) assumes that non-detected congeners are present at the detection level.
However, this is a reasonable estimate for this data set because there is only one non-
detect value. Table 4-14 presents the average congener concentrations for these
samples.
CARB (1990e) did not present quantities of ESP ashes produced by the boiler,
therefore, it is not possible to readily determine the quantities of CDD/CDF disposed.
Appendix II of Luthe (1998) reports TEQ concentrations (assumed to be I-TEQDF) in
ashes collected from air pollution control devices from "salt-laden" wood steam boilers.
The I-TEQDF content of ashes from three for primary multiclone hoppers varied
significantly, 0.0978, 0.1 86, and 9.375 //g/kg. For the secondary multiclone hoppers,
two samples of ash were taken. The secondary multiclone removes dust from the primary
multiclone emissions; and therefore, the ash is finer than primary dust). The I-TEQDF for
the ash were 1.073 and 20.879 /vg/kg. The I-TEQDF for two samples taken from the
DRAFT-DO NOT QUOTE OR CITE 4-29 December 2003
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electrostatic precipitator, which collects dust from the secondary multiclone emissions,
and is, therefore, finer than multiclone dust, are 3.926 and 8.044/vg/kg. No data are
given for individual congeners. In fact, because the reference discusses only "dioxins", it
is unclear whether the TEQ data are for CDDs, or CDDs plus CDFs. Quantities of
collected ash are not given.
Also for the burning of salt-laden wood in paper mill boilers, Table II of Luthe
(1996) presents data for the "TEQs [assumed to be l-TEQs] on particulates from
secondary collection device" for four different paper mills. Eight data points are given
(two for each mill), the average of which is 3.6 /vg/kg. The range of values is 1.3 to 8.0
/yg/kg. As with Luthe (1998), no data are given for individual congeners. It is also
unclear whether the TEQ data are for CDDs, or CDDs plus CDFs. Quantities of collected
ash are not given.
Table 5-16 of U.S.EPA (1987a) contains data indicating that the bottom ash from
wood combustion (it is not indicated whether the combustion source was a boiler) from
one source contained 140 ng/kg of 2,3,7,8-TCDD, 138,200 ng/kg of CDDs, and 7,400
ng/kg of CDFs. For a second wood combustion source, the ash contained no detectable
2,3,7,8-TCDD, but did contain about 125 ng/kg of CDDs and non detectable levels of
CDFs. The baghouse dust from the second source contained 100 ng/kg of 2,3,7,8-TCDD,
1,143,600 ng/kg of CDDs, and 315,600 ng/kg of CDFs. Specific data for congeners and
for ash/dust quantities were not given.
4.3. OIL COMBUSTION
Two major categories of fuel oil are burned by combustion sources: distillate oils
and residual oils. These oils are further distinguished by grade numbers, with Nos. 1 and
2 being distillate oils; Nos. 5 and 6 being residual oils; and No. 4 either distillate oil or a
mixture of distillate and residual oils. No. 6 fuel oil is sometimes referred to as Bunker C.
Distillate oils are more volatile and less viscous than residual oils. They have negligible
nitrogen and ash contents and usually contain less than 0.3 percent sulfur (by weight).
Distillate oils are used mainly in domestic and small commercial applications. Being more
viscous and less volatile than distillate oils, the heavier residual oils (Nos. 5 and 6) must
be heated for ease of handling and to facilitate proper atomization. Because residual oils
are produced from the residue remaining after the lighter fractions (gasoline, kerosene, and
DRAFT-DO NOT QUOTE OR CITE 4-30 December 2003
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distillate oils) are removed from the crude oil, they contain significant quantities of ash,
nitrogen, and sulfur. Residual oils are used mainly in utility, industrial, and large
commercial application (U.S. EPA, 1995b).
4.3.1. Residential/Commercial Oil Combustion
No testing of the CDD/CDF content of air emissions from residential/commercial oil-
fired combustion units in the United States could be located. However, U.S. EPA (1997b)
has estimated CDD/CDF congener group and I-TEQDF emission factors based on average
CDD/CDF concentrations reported for soot samples from 21 distillate fuel oil-fired furnaces
used for central heating in Canada, and a particulate emission factor for distillate fuel oil
combustors (300 mg/L of oil) obtained from AP-42 (U.S. EPA, 1995b). The I-TEQDF
emission factor estimate in U.S. EPA (1997b) was derived using the calculated emission
factors for 2,3,7,8-TCDD, 2,3,7,8-TCDF, and the 10 congener groups. These emission
factors are presented in Table 4-15, and the congener group profile is presented in Figure
4-6.
Because there are no direct measurements of CDD/CDF emissions in stack gases
from U.S. residential oil-fired combustors and because of uncertainties associated with
using chimney soot data to estimate stack emissions, no national emission estimates for
this category are proposed at this time. However, a preliminary estimate of potential
national TEQ emissions from this source category can be made using the emission factor
presented in Table 4-13 (150 pg I-TEQDF/L of oil combusted). Distillate fuel oil sales to the
residential/commercial sector totaled 39.7 billion liters in 1995 (EIA, 1997a). Application
of the emission factor of 150 pg I-TEQDF/L to this fuel oil sales estimate results in
estimated emissions of 6.0 g I-TEQDF in 1995. This estimate should be regarded as a
preliminary indication of possible emissions from this source category; further testing is
needed to confirm the true magnitude of the emissions.
4.3.2. Utility Sector and Industrial Oil Combustion
Preliminary CDD/CDF emission factors for oil-fired utility boilers developed from
boiler tests conducted over the past several years are reported in U.S. EPA (1997b). The
data are a composite of various furnace configurations and APCD systems. Table 4-16
lists the median emission factors presented in U.S. EPA (1997b). The congener and
DRAFT-DO NOT QUOTE OR CITE 4-31 December 2003
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congener group profiles based on these data are presented in Figure 4-7. The median I-
TEQDF emission factor was reported to be 314 pg/L of oil burned (or 366 pg TEQDF-
WH098/L).
In 1993, the Electric Power Research Institute (EPRI) sponsored a project to gather
information of consistent quality on power plant emissions. This project, the Field
Chemical Emissions Measurement (FCEM) project, included testing of two cold side ESP-
equipped oil-fired power plants for CDD/CDF emissions (EPRI, 1994). The averages of the
congener and congener group emission factors reported for these two facilities are
presented in Table 4-16. The average TEQ emission factors are 83.1 pg I-TEQDF/L and
93.6 pg TEQDF-WH098/L of oil burned (when nondetected values are treated as zero).
The TEQ emission factors reported in EPRI (1994) are a factor of three to four less
than the median TEQ emission factor reported in U.S. EPA (1997b). For purposes of this
assessment, emission factors of 200 pg I-TEQDF/L and 230 pg TEQDF-WH098/L (i.e., the
average of the EPA median and EPRI mean emission factors) are assumed to be current
best estimates of the average TEQ emission factors for utility/industrial oil burning. These
estimated emission factors are assigned a low confidence rating.
The emission factors derived above were based on combustion of virgin oil by
utility boilers. Significantly greater emission factors have been reported by Bremmer et al.
(1994) for combustion of used oil by smaller combustion units in The Netherlands. Flue
gases from a garage stove consisting of an atomizer fueled by spent lubricating oil from
diesel engines (35 mg ClYkg) were reported to contain 0.1 ng l-TEQDF/Nm3 (or 2,000 pg I-
TEQDF/kg of oil burned). The flue gases from a hot water boiler consisting of a rotary cup
burner fueled with the organic phase of rinse water from oil tanks (340 mg ClYkg)
contained 0.2 ng l-TEQDF/Nm3 (or 4,800 pg l-TEQDF/kg of oil burned). The flue gases from
a steam boiler consisting of a rotary cup burner fueled by processed spent oil (240 mg CI"
/kg) contained 0.3 ng l-TEQDF/Nm3 (or 6,000 pg l-TEQDF/kg of oil burned). The emission
factor for a ferry burning heavy fuel oil containing 11 ng/kg organic chlorine was 3,200 to
6,500 pg l-TEQDF/kg of oil burned. From these data, Bremmer et al. (1994) derived an
average emission factor for combustion of used oil of 4,000 pg l-TEQDF/kg of oil burned.
Bremmer et al. (1994) also reported measuring CDD/CDF emissions from a river barge and
a container ship fueled with gas oil (less than 2 ng/kg of organic chlorine). The exhaust
gases contained from 0.002 to 0.2 ng l-TEQDF/Nm3. From these data, Bremmer et al.
DRAFT-DO NOT QUOTE OR CITE 4-32 December 2003
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(1994) derived an average emission factor for inland oil-fueled vessels of 1,000 pg
l-TEQDF/kg oil burned. The applicability of these emission factors to used oil combustors
in the United States is uncertain. Therefore, estimates of potential emissions from used
oil combustion in the United States are not being developed at this time.
Residual fuel oil sales totaled 46.6 billion liters in 1995 and 77.3 billion liters in
1987 (EIA, 1992, 1997a). Vessel bunkering was the largest consumer (48 percent of
sales) followed by electric utilities and the industrial sector. A high confidence rating is
assigned to these production estimates. Application of the TEQ emission factor of 200 pg
I-TEQDF/L (230 pg TEQDF-WH098/L) to these residual fuel oil sales results in estimated TEQ
emissions of 9.3 g I-TEQDF (10.7 g TEQDF-WH098) in 1995 and 15.5 g I-TEQDF 1987 (17.8
g TEQDF-WH098) in 1987.
4.4. COAL COMBUSTION
During 1995, coal consumption accounted for approximately 22 percent of the
energy consumed from all sources in the United States (U.S. DOC, 1997). In 1995, 872
million metric tons of coal were consumed in the United States. Of this total, 88.4
percent (or 771 million metric tons) were consumed by electric utilities, 11.0 percent (or
96 million metric tons) were consumed by the industrial sector (including consumption of
30 million metric tons by coke plants), and 0.6 percent (or 5.3 million metric tons) were
consumed by residential and commercial sources (EIA, 1997b). Comparable figures for
1987 are: total consumption, 759 million metric tons; consumption by electric utilities,
651 million metric tons; consumption by coke plants, 33.5 million metric tons;
consumption by other industries, 68.2 million metric tons; and consumption by the
residential and commercial sectors, 6.3 million metric tons (EIA, 1995c). These
production estimates are assigned a high confidence rating because they are based on
detailed studies specific to the United States.
The following two subsections discuss the results of relevant emission studies for
the utility/industrial and residential sectors, respectively, and present annual TEQ emission
estimates for the reference years 1987 and 1995.
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4.4.1. Utilities and Industrial Boilers
Until fairly recently, few studies had been performed to measure CDD/CDF
concentrations in emissions from coal-fired plants, and several of these studies did not
have the congener specificity and/or detection limits necessary to fully characterize this
potential source (U.S. EPA, 1987a; NATO, 1988; Wienecke et al., 1992). The results of
more recent testing of coal-fired utility and industrial boilers in The Netherlands (Bremmer
et al., 1994), the United Kingdom (Cains and Dyke, 1994; CRE, 1994), Germany
(Umweltbundesamt, 1996), and the United States (Riggs et al., 1995; EPRI, 1994) have
achieved lower detection limits.
Bremmer et al. (1994) reported the results of emission measurements at two coal-
fired facilities in The Netherlands. The emission factor reported for a pulverized coal
electric power plant equipped with an ESP and a wet scrubber for sulfur removal was 0.35
ng l-TEQDF/kg of coal fired (or 0.02 ng l-TEQDF/Nm3 at 11 percent 02). The emission
factor reported for a grass drying chain grate stoker equipped with a cyclone APCD was
1.6 ng l-TEQDF/kg of coal fired (or 0.16 ng l-TEQDF/Nm3 at 11 percent 02).
Cains and Dyke (1994) reported an emission factor of 102 to 109 ng l-TEQDF/kg of
coal at a small-scale facility in the United Kingdom that was equipped with an APCD
consisting only of a grit arrester. CRE (1994) reported results of testing at 13
commercial/ industrial coal-fired boilers in the United Kingdom. The I-TEQDF emission
factors ranged from 0.04 to 4.8 ng l-TEQDF/kg coal combusted (mean value of 0.6 ng I-
TEQDF/kg). CRE (1994) also reported testing results for one coal-fired power plant, 0.06
ng l-TEQDF/kg coal combusted.
Umweltbundesamt (1996) reported that the I-TEQDF content of stack gases from
16 coal-burning facilities in Germany ranged from 0.0001 to 0.04 ng l-TEQDF/m3; the data
provided in that report did not enable emission factors to be calculated.
The U.S. Department of Energy sponsored a project in 1993 to assess emissions of
hazardous air pollutants at coal-fired power plants. As part of this project, CDD/CDF
stack emissions were measured at seven U.S. coal-fired power plants. The preliminary
results of this project (i.e., concentrations in stack emissions) were reported by Riggs et
al. (1995) and are summarized in Table 4-17. The levels reported for individual 2,3,7,8-
substituted congeners were typically very low (i.e., <0.033 ng/Nm3) or not detected. In
general, CDF levels were higher than CDD levels. OCDF and 2,3,7,8-TCDF were the most
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frequently detected congeners (i.e., at four of the seven plants). Table 4-18 presents
characteristics of the fuel used and APCD employed at each plant. Variation in emissions
between plants could not be attributed by Riggs et al. (1995) to any specific fuel or
operational characteristic.
During the early 1990s, EPRI also sponsored a project to gather information of
consistent quality on power plant emissions. This project, the Field Chemical Emissions
Measurement (FCEM) project, included testing of four cold-side ESP-equipped coal-fired
power plants for CDD/CDF emissions. Two plants burned bituminous coal and two burned
subbituminous coal. The final results of the DOE project discussed above (Riggs et al.,
1995) were integrated with the results of the EPRI testing and published in 1994 (EPRI,
1994). The average congener and congener group emission factors derived from this 11
facility data set, as reported in EPRI (1994), are presented in Table 4-19. Congener and
congener group profiles for the data set are presented in Figure 4-8. The average I-TEQDF
and TEQDF-WH098 emission factors, assuming nondetected values are zero, are 0.079 ng I-
TEQDF/kg of coal combusted and 0.078 ng TEQDF-WH098/kg. A medium confidence rating
is assigned to these emission factors derived from the DOE and EPRI studies because they
are based on recent testing at U.S. utilities.
Because the EPRI and DOE data only characterized emissions from units with cold-
side ESPs, there has been uncertainty regarding the applicability of the emission factors
derived from these data to units with hot-side ESPs. In July 1999, EPA conducted testing
of stack emissions at a coal-fired utility equipped with a hot-side ESP. The preliminary
results of this testing indicate that the TEQ emission factor for hot-sided ESPs is of the
same order of magnitude as the average TEQ emission factors derived above.
As stated above, consumption of coal by the U.S. utility sectors was 771 million
metric tons in 1995 and 651 million metric tons in 1987. Applying the TEQ emission
factors of 0.079 ng l-TEQDF/kg of coal combusted and 0.078 ng TEQDF-WH098/kg to these
production factors yields estimated annual emissions of 60.9 g I-TEQDF and 60.1 g TEQDF-
WH098 in 1995 and 51.4 g I-TEQDF and 50.8 TEQDF-WH098 in 1987 by the utility sector.
No testing of the CDD/CDF content of air emissions from commercial/industrial
coal-fired combustion units in the United States could be located. However, as noted
above, several studies have been performed in European countries (Bremmer et al., 1994;
CRE, 1994). It is uncertain whether the data collected in these European studies
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accurately represent U.S. sources, but the data suggest that emission factors for
commercial/industrial sources can be higher than those reported above for U.S. coal-fired
utilities. Therefore, no national emission estimate for this category is being derived at this
time. However, a preliminary estimate of potential national TEQ emissions from this
source category can be derived using the average emission factor reported in CRE (1994),
0.6 ng l-TEQDF/kg coal combusted. As noted above, 66 million metric tons of coal were
consumed by the industrial sector (excluding 30 million metric tons consumed by coke
plants). Applying the emission factor of CRE (1994) to this activity level estimate yields
an estimated national emission of 39.6 g I-TEQDF in 1995. This estimate should be
regarded as a preliminary indication of possible emissions from commercial/industrial coal-
fired boilers; further testing is needed to confirm the true magnitude of these emission.
4.4.2. Residential/Commercial Coal Combustion
Coal is usually combusted in underfeed or hand-stoked furnaces in the residential
sector. Other coal-fired heating units include hand-fed room heaters, metal stoves, and
metal and masonry fireplaces. Stoker-fed units are the most common design for warm-air
furnaces and for boilers used for steam or hot water production. Most coal combusted in
these units are either bituminous or anthracite. These units operate at relatively low
temperatures and do not efficiently combust the coal. Coal generally contains small
quantities of chlorine and CDD/CDF; therefore, the potential for CDD/CDF formation
exists. Typically, coal-fired residential furnaces are not equipped with particulate matter
or gaseous pollutant control devices that may limit emissions of any CDD/CDFs formed
(U.S. EPA, 1997b). No testing of the CDD/CDF content of air emissions from
residential/commercial coal-fired combustion units in the United States could be located.
However, several relevant studies have been performed in European countries.
Thub et al. (1995) measured flue gas concentrations of CDD/CDF from a household
heating system in Germany, fired either with salt lignite coal (i.e., total chlorine content of
2,000 ppm) or normal lignite coal (i.e., total chlorine content of 300 ppm). CDD/CDFs
were detected in the flue gases generated by combustion of both fuel types. (See
Table 4-20.) The congener profiles and patterns were similar for both fuel types, with
OCDD the dominant congener and TCDF the dominant congener group. However, the
emissions were higher for the "salt" coal (0.109 ng l-TEQDF/m3 or 2.74 ng l-TEQDF/kg of
DRAFT-DO NOT QUOTE OR CITE 4-36 December 2003
-------
coal) by a factor of eight than for the "normal" coal (0.015 ng l-TEQDF/m3 or 0.34 ng
l-TEQDF/kg of coal).
Eduljee and Dyke (1996) used the results of testing performed by the Coal
Research Establishment in the United Kingdom to estimate emission factors for residential
coal combustion units as follows:
• Anthracite coal: 2.1 ng l-TEQDF/kg of coal; and
• Bituminous coal: 5.7 to 9.3 ng l-TEQDF/kg of coal (midpoint of 7.5 ng
l-TEQDF/kg).
CDD/CDF emission factors for coal-fired residential furnaces were estimated in U.S.
EPA (1997b) based on average particulate CDD/CDF concentrations from chimney soot
samples collected from seven coal ovens, and particulate matter emission factors specific
to anthracite and bituminous coal combustion obtained from AP-42 (U.S. EPA, 1995b).
The I-TEQDF emission factors estimated in U.S. EPA (1997b) (i.e., 60.0 and 98.5 ng
l-TEQDF/kg of anthracite and bituminous coal, respectively) were derived using the
calculated emission factors for 2,3,7,8-TCDD 2,3,7,8-TCDF, and the 10 congener groups.
U.S. EPA (1997b) stated that the estimated factors should be considered to represent
maximum emission factors, because soot may not be representative of the particulate
matter actually emitted to the atmosphere. These emission factors are presented in Table
4-20, and congener group profiles are presented in Figure 4-9.
Although the congener group profiles of the Thub et al. (1995) measurements and
the U.S. EPA (1997b) estimates are similar, the I-TEQDF emission factors differ by factors
of 1 75 to 289 between the two studies. The emission factors used by Eduljee and Dyke
(1996) to estimate national annual emissions of I-TEQDF from residential coal combustion
in the United Kingdom fall in between those other two sets of estimates but are still about
one to two orders of magnitude greater than the estimated emission factor for industrial/
utility coal combustors. (See Section 4.4.1.)
Because there are no direct measurements of CDD/CDF emissions from U.S.
residential coal-fired combustors and because of uncertainties regarding the comparability
of U.S. and German and British coal combustion units, no national emission estimate for
this category is being derived at this time. However, a preliminary estimate of potential
DRAFT-DO NOT QUOTE OR CITE 4-37 December 2003
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national TEQ emissions from this source category can be derived using the emission
factors of Eduljee and Dyke (1996). As noted above, 5.3 million metric tons of coal were
consumed by the residential/commercial sector in 1995 (U.S. DOC, 1997). U.S. EPA
(1997b) reports that 72.5 percent of the coal consumed by the residential sector in 1990
were bituminous and 27.5 percent were anthracite. Assuming that these relative
proportions reflect the actual usage in 1995, then application of the emission factors from
Eduljee and Dyke (1996) (i.e., 2.1 ng l-TEQDF/kg of anthracite coal and 7.5 ng l-TEQDF/kg
of bituminous coal) to the consumption value of 5.3 million metric tons results in an
estimated I-TEQDF emission of 32.0 g TEQ in 1995. This estimate should be regarded as a
preliminary indication of possible emissions from this source category; further testing is
needed to confirm the true magnitude of these emissions.
4.4.3. Solid Wastes from Coal Combustion
A limited amount of CDD/CDF concentration data have been developed for utility
industry solid wastes (U.S.EPA, 1999b). These data are for utility industry solid wastes
that are comanaged (i.e., combinations of fly ash, bottom ash, boiler slag, and flue gas
desulfurization [FGD] wastes). Samples were taken from 11 disposal sites. A total of 15
samples were taken from the 11 sites. The average concentrations for each of the CDD
and CDF congeners are presented in the second column of Table 4-21. It should be noted
that most of the concentration values shown in Table 4-21 represent limits of detection.
Consequently, the values overestimate the actual concentration.
Section 3.3 of U.S.EPA (1999c) indicates that there were approximately 63 million
tons (assumed to be short tons, i.e., 2,000 pounds) of large-volume utility coal
combustion solid wastes produced in 1995. Of this amount, about 67 percent was
landfilled, and the balance was disposed of in surface impoundments. The concentration
data presented in Table 4-21 is only for the 53 million tons that were comanaged (or
about 84 percent of the total wastes). For purposes of this analysis it will be assumed
that the CDD/CDF concentrations in the comanaged wastes are the same as for the entire
waste quantity. Combining the concentration data with the 63 million tons of total waste
yields the total quantities of each congener disposed of in 1995. These data are
presented in the fourth column of Table 4-21. Section 4.4 of this document indicates that
total consumption of coal for electric utility boilers in 1987 was 98.4 percent of 1995
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consumption. Consequently, the quantities of CDD/CDF disposed of in 1987 is assumed
to be 98.4 percent of the 1995 values. These values are presented in column 3 of Table
4-21.
The 1995 congener quantities are converted into I-TEQDF and TEQDF-WH098 values
in columns 5 and 6 of Table 4-21 respectively. The values for 1987 are assumed to be
98.4 percent of the 1995 values based on the assumptions stated in the above paragraph.
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Table 4-1. Descriptions and Results of Vehicle Emission Testing Studies for CDDs and CDFs
Study
CARB (1987a); Lew (1996)
Marklund et al. (1987)
Bingham et al. (1989)
Marklund et al. (1990)
Hagenmaier et al. (1990)
Oehme et al. (1991) (tunnel
study)
Schwind et al. (1991)
Hutzinger et al. (1992)
Gertler et al. (1996, 1998)
(tunnel study)
Gullett and Ryan (1997)
Country
United States
Sweden
New Zealand
Sweden
Germany
Norway
Germany
United States
United States
Fuel Type
Diesel (truck)
Unleaded
Leaded
Unleaded
Leaded
Unleaded
Leaded
Unleaded
Leaded
Diesel (truck)
Unleaded
Unleaded
Leaded
Diesel (car)
Leaded
Unleaded
Unleaded
Diesel (car)
Diesel (truck)
Diesel (truck)
Diesel (truck)
Scavenger8
No
No
Yes
No
Yes
No
Yes
No
Yes
No
No
No
Yes
No
Yes
No
No
No
No
—
No
Catalyst
Equipped
NR
Yes
No
NR
NR
No
No
Yes
No
NR
No
Yes
No
NR
No
No
Yes
No
No
—
—
Number
of Test
Vehicles
1
2
4
1
4
2
2
1
2
1
1
1
1
1
(c)
1
1
1
1
1
(f)
1
TEQ
Emission Factor9
(pg/km driven)
676-1,325" [597-1,307]
not detected « 13)
approx. 20-220
not detected «20)
1-39
0.36-0.39
2.4-6.3
0.36
1.1-2.6"
not detected «18)b
5.1b [6.0]
0.7b [0.8]
108b [129]
2.1b [2.5]
520d
38d
avg = 280
9,500d
720d
avg = 5,100
5.2-118" [7.2-142]
9.6-17.7" [10.2-18.1]
1.0-2.6" [1.0-2.8]
1.0-13" [1.2-14]
13-15" [14-15]
mean = 172
mean - 29.0
Driving Cycle; Sampling Location
6-hr dynamometer test at 50 km/hr
A10 (2 cycles); muffler exhaust
A10 (2 cycles); muffler exhaust
A10 (3 or 4 cycles); muffler exhaust
A10 (3 or 4 cycles); muffler exhaust
FTP-73 test cycle; before muffler
FTP-73 test cycle; before muffler
FTP-73 test cycle; in tailpipe
FTP-73 test cycle; in tailpipe
U.S. Federal mode 13 cycle; before muffler
Comparable to FTP-73 test cycle; in tailpipe
Comparable to FTP-73 test cycle; in tailpipe
Comparable to FTP-73 test cycle; in tailpipe
Comparable to FTP-73 test cycle; in tailpipe
Cars moving uphill (3.5% incline) at 60 km/hr
Cars moving downhill (3.5% decline) at 70 km/hr
Car average
Trucks moving uphill (3.5% incline) at 60 km/hr
Trucks moving downhill (3.5% decline) at 70
km/hr
Truck average
Various test conditions (i.e., loads and speeds)
Various test conditions (i.e., loads and speeds)
Various test conditions (i.e., loads and speeds)
Various test conditions (i.e., loads and speeds)
Various test conditions (i.e., loads and speeds)
Mean of seven 1 2-hour samples
Mean of five sample routes
o
o
^1
D
C
O
§
-^
o
D
CD
O
CD
3
-
K>
O
O
CO
a Dichloroethane and dibromoethane, except for Marklund et al. (1 987), used as scavengers.
" Results reported were in units of pg TEQ/liter of fuel. For purposes of this table, the fuel economy factor used by Marklund et al. (1 990), 10 km/L or 24 miles/gal, was used to convert
the emission rates into units of pg TEQ/km driven for the cars. For the diesel-fueled truck, the fuel economy factor reported in CARB (1 987a) for a 1 984 heavy-duty diesel truck, 5.5
km/L (or 13.2 miles/gal), was used.
c Tests were conducted over portions of 4 days, with traffic rates of 8,000-14,000 vehicles/day. Heavy duty vehicles (defined as vehicles over 7 meters in length) ranged from 4-15%
of total.
d Emission factors are reported in units of pg Nordic TEQ/km driven; the values in units of l-TEQDF/km are expected to be about 3 to 6 percent higher.
e Table reflects the range of summary results reported in Marklund et al. (1 990); however, the congener-specific results for the single run reported indicate an emission rate of about
7.3 pg l-TEQDF/km.
f Tests were conducted over 5 days with heavy-duty vehicle rates of 1,800-8,700 vehicles per 1 2-hour sampling event. Heavy-duty vehicles accounted for 21-28 percent of all vehicles.
° Values listed are in units of I-TEQDF. Values in brackets are in units of TEQDF-WH098.
NR = Not Reported
-------
Table 4-2. Diesel-Fueled Automobile CDD/CDF Congener Emission Factors
Congener/Congener
Group
2,3,7, 8-TCDD
1,2,3,7,8-PeCDD
1,2,3,4,7,8-HxCDD
1,2,3,6,7,8-HxCDD
1, 2,3,7, 8,9-HxCDD
1,2,3,4,6,7,8-HpCDD
OCDD
2,3,7,8-TCDF
1,2,3,7,8-PeCDF
2,3,4,7, 8-PeCDF
1, 2,3,4,7, 8-HxCDF
1,2,3,6,7,8-HxCDF
1,2,3,7,8,9-HxCDF
2,3,4,6,7, 8-HxCDF
1, 2,3,4,6,7, 8-HpCDF
1,2,3,4,7,8,9-HpCDF
OCDF
Total 2,3,7, 8-CDD
Total 2,3,7, 8-CDF
Total I-TEQDF (ND = zero)
Total I-TEQDF (ND = 1/2 det limit)
Total TEQDF-WH098 (ND = zero)
Total TEQDF-WH09a (ND = 1/2 det limit)
Total TCDD
Total PeCDD
Total HxCDD
Total HpCDD
Total OCDD
Total TCDF
Total PeCDF
Total HxCDF
Total HpCDF
Total OCDF
Total CDD/CDF (ND = zero)
Total CDD/CDF (ND = 1/2 det limit)
Automobile Tailpipe Emission Study Results
63 km/hr
(Ref. A)
(pg/L)
7.9
9.0
ND (5.1)
ND (5.1)
ND (5.1)
44.1
440
20.5
ND (5.1)
7.1
6.5
6.7
ND (5.1)
ND (5.1)
40.7
8.5
94.4
501.0
184.4
20.8
22.2*
24.8
26.2
37.4
19.7
23.6
88.5
440.5
76.7
39.3
25.6
80.6
94.4
926.3
926.3
Idling
(test no. 25)
(Ref. B)
(pg/U
13.1
6.3
21.4
36
28
107
635
79
171
58.7
121
75
17.1
52
159
11.9
214
846.8
958.7
100.7
100.7
103.1
103.1
317
214
256
187
635
436
821
556
321
214
3,957
3,957
57 km/hr
(test no. 24)
(Ref. B)
(pg/U
2.4
4.1
1.0
1.4
2.0
22.9
525
18.1
1.8
3.4
4.1
3.0
0.8
ND (0.4)
18.9
7.1
101
558.8
158.2
10.4
10.4
11.9
1.9
31
22
20
77
525
58
36
26
72
101
968
968
57 km/hr
(full load)
(test no. 28)
(Ref. B)
(pg/U
22
23
7.8
21
10
166
560
236
111
85
68
55
4.7
31
214
7.8
305
809.8
1117.5
129.6
129.6
140.4
140.4
394
228
164
356
560
3093
1205
472
241
305
7,018
7,018
Mean Emission Factors
Assuming
ND = zero
(pg/L)
11.4
10.6
7.6
14.6
10.0
85.0
540
88.4
71.0
38.6
49.9
34.9
5.7
20.8
108.2
8.8
178.6
679.1
604.7
65.4
70.0
195
121
116
177
540
916
525
270
179
179
3,217
Assuming
ND = 1/2 det limit
(pg/U
11.4
10.6
8.2
15.2
10.6
85.0
540
88.4
71.6
38.6
49.9
34.9
6.3
21.4
108.2
8.8
178.6
681.0
606.7
65.7
70.4
195
121
116
177
540
916
525
270
179
179
3,217
o
o
^1
D
C
O
§
D
CD
O
CD
3
-
K>
O
O
CO
ND = Not detected; value in parentheses is the detection limit.
* = An I-TEQDF emission factor of 23.6 pg/L is reported in Ref. A; however, an I-TEQDF emission factor of 22.2 pg/L is calculated based on reported congener levels.
Ref. A: Hagenmaier et al. (1 990)
Ref. B: Schwind et al. (1 991); Hutzinger et al. (1 992)
-------
CD
o
o
-H
D
c;
O
O
53
Table 4-3. Diesel-Fueled Truck CDD/CDF Congener Emission Factors
Congener/Congener
Group
2,3,7,8-TCDD
1,2,3,7,8-PeCDD
1,2,3,4,7,8-HxCDD
1,2,3,6,7,8-HxCDD
1,2,3, 7,8, 9-HxCDD
1,2,3, 4,6,7, 8-HpCDD
OCDD
2,3,7,8-TCDF
1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
1,2,3,4,7,8-HxCDF
1,2,3,6,7,8-HxCDF
1,2,3, 7,8, 9-HxCDF
2,3,4,6,7,8-HxCDF
1,2,3, 4,6,7, 8-HpCDF
1,2,3,4,7,8,9-HpCDF
OCDF
Total 2,3,7,8-CDD
Total 2,3,7,8-CDF
Total I-TEQDF (ND = zero)
Total I-TEQDF (ND = % det limit)
Total TEQDF-WH098 (ND = zero)
Total TEQDF-WH098 (ND = % det limit)
Total TCDD
Total PeCDD
Total HxCDD
Total HpCDD
Total OCDD
Total TCDF
Total PeCDF
Total HxCDF
Total HpCDF
Total OCDF
Total CDD/CDF (ND = zero)
Total CDD/CDF (ND = % det limit)
Truck Tailpipe Study Results
50 km/hr
(test no. 40)
(Ref. A)
(pg/U
25
5
14.0
28
14
119
1,355
87
45
18
56
84
4.7
63
375
40
397
1,560
1,170
81
81
82
82
200
32
130
200
1355
763
230
524
509
397
4,340
4,340
90 km/hr
(full load)
(test no. 42)
(Ref. A)
(pg/U
16
18
5.7
6
6
74
353
53
34
51
29
31
5.1
23
71
5.4
104
478.7
406.5
70
70
79
79
208
117
67
155
353
694
736
268
76
104
2,778
2,778
50 km/hr
(Ref. B)
(pg/U
ND (560)
ND (1,340)
ND (2,160)
ND (1,770)
ND (2,640)
116,000
344,400
ND (605)
ND (4,750)
ND (5,190)
ND (8,210)
ND (6,480)
13,400
ND (7,780)
73,460
ND (11,700)
140,400
460,400
227,300
3,720
7,290
3,280
7,190
ND (3,760)
ND (3,020)
ND (45,300)
203,300
344,000
25,000
47,900
169,200
150,700
140,300
1,080,500
1,104,700
Mean Emission Factors
Assuming
ND = zero
(pg/U
13.7
7.7
6.6
11.3
6.7
38,731
115,369
46.7
26.3
23.0
28.3
38.3
4,469
28.7
24,636
15.1
46,981
154,146
76,292
1,290
1,150
136
49.7
65.7
67,892
115,252
8,831
16,294
56,670
50,414
46,932
362,538
Assuming
ND = 1/2 det lim
(pg/U
107
231
367
307
446
38,731
115,369
148
819
887
1,397
1,119
4,469
1,325
24,636
1,960
46,981
155,558
83,739
2,480
2,450
762
553
7,620
67,892
115,252
8,831
16,294
56,670
50,414
46,932
370,596
D
CD
O
CD
3
-
K>
O
O
CO
ND = Not detected; value in parentheses is the detection limit.
Ref. A: Schwind et al. (1991); Hutzinger et al. (1992)
Ref. B: Lew (1993, 1996)
-------
CD
Table 4-4. Leaded Gasoline-Fueled Automobile CDD/CDF Congener Emission Factors
Congener/Congener Group
2,3,7,8-TCDD
1,2,3,7,8-PeCDD
1,2,3,4,7,8-HxCDD
1,2,3,6,7,8-HxCDD
1,2,3, 7,8, 9-HxCDD
1,2,3, 4,6,7, 8-HpCDD
OCDD
2,3,7,8-TCDF
1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
1,2,3,4,7,8-HxCDF
1,2,3,6,7,8-HxCDF
1,2,3, 7,8, 9-HxCDF
2,3,4,6,7,8-HxCDF
1,2,3, 4,6,7, 8-HpCDF
1,2,3,4,7,8,9-HpCDF
OCDF
Total 2,3,7,8-CDD
Total 2,3,7,8-CDF
Total I-TEQDF ( ND = zero)
Total I-TEQDF ( ND = M> det limit)
Total TEQDF-WH098 (ND = zero)
Total TEQDF-WH098 (ND = M> det limit)
Total TCDD
Total PeCDD
Total HxCDD
Total HpCDD
Total OCDD
Total TCDF
Total PeCDF
Total HxCDF
Total HpCDF
Total OCDF
Total CDD/CDF (ND = zero)
Total CDD/CDF (ND = M> det limit)
Automotive Tailpipe Emission Study Results
FTP
cycle
(Ref. A)
(pg/U
ND
(14.4)
ND (36)
ND (54)
ND (54)
ND (54)
ND (54)
ND (90)
432
21.6
43.2
ND (54)
ND (54)
ND (54)
ND (54)
ND (54)
ND (54)
ND (90)
ND
496.8
65.9
102
65.9
111
5,220
ND (360)
ND (540)
ND (90)
ND (90)
15,300
2,430
ND (540)
ND (270)
ND (90)
22,950
23,940
63
km/hr
(Ref. B)
(pg/U
128
425
188
207
188
503
498
1,542
1,081
447
856
856
ND (76)
273
4,051
ND (76)
230
2,137
9,336
1,075
1,080
1,287
1,291
4,555
3,338
1,868
1,164
498
50,743
11,591
6,308
5,642
230
85,937
85,937
Idling
(test no.
12)
(Ref. C)
(pg/U
NR
43
17
32
NR
119
380
NR
49
26
33
22
NR
NR
170
NR
1115
> 591
> 1,415
> 52
> 52
> 72
> 72
517
658
354
194
380
2,167
452
192
170
1,115
6,199
6,199
Full load
(test no. 13)
(Ref. C)
(pg/U
60
106
15
35
NR
136
513
678
367
156
70
60
NR
25
NR
NR
NR
> 865
> 1,356
> 300
> 300
> 352
> 352
8,134
2,161
623
297
513
20,513
3,608
477
NR
NR
> 36,326
> 36,326
64 km/hr
(test no.
14)
(Ref. C)
(pg/U
141
468
206
228
206
554
549
1,697
1,190
492
942
942
NR
301
4,460
NR
253
2,352
> 10,277
> 1,184
> 1,184
> 1,417
> 1,417
5,012
3,675
2,056
1,281
549
55,857
12,757
6,947
6,210
253
94,597
94,597
Rated power
(test no. 15)
(Ref. C)
(pg/U
NR
40
16
30
NR
111
1166
78
45
24
31
20
NR
NR
158
NR
447
> 1,363
> 803
> 56
> 56
> 75
> 75
4,558
6,389
1,973
2,374
1,166
29,353
10,580
12,553
4,767
447
74,160
74,160
FTP cycle
(test no.
22)
(Ref. C)
(pg/U
5
73
41
62
35
518
1,581
214
218
225
381
375
85
1,033
2,301
109
1,128
2,315
6,069
419
419
454
454
921
359
996
988
1,581
4,290
3,165
3,132
2,920
1,128
19,480
19,480
Mean Emission Factors
Assuming
ND = zero
(pg/U
67
165
69
85
107
277
670
774
425
202
330
325
28
326
1857
36
529
1,440
4,832
> 450
> 532
4,131
2,369
1,124
900
670
25,460
6,369
4,230
3,285
529
> 49,066
Assuming
ND = 1/2
det limit
(pg/U
68
168
73
89
114
281
676
774
425
202
334
329
50
332
1861
58
536
1,469
4,900
> 456
> 539
4,131
2,394
1,163
906
676
25,460
6,369
4,268
3,307
536
> 49,212
o
o
-H
D
c;
O
O
53
-^
I
CO
D
CD
O
CD
3
-
K>
O
O
CO
NR = Not reported.
ND = Not detected; value in parentheses is the reported detection limit.
Ref. A: Marklund et al. (1 990); values in the table were calculated from the reported units of pg/km to pg/L using a fuel economy of 9 km/L for leaded gas as reported in Marklund et al. (1 990).
Ref. B: Hagenmaier et al. (1990)
-------
CD
Table 4-5. Unleaded Gasoline-Fueled (Without Catalytic Converters) Automobile CDD/CDF Congener Emission Factors
Congener/Congener Group
2,3,7,8-TCDD
1,2,3,7,8-PeCDD
1,2,3,4,7,8-HxCDD
1,2,3,6,7,8-HxCDD
1, 2,3,7,8, 9-HxCDD
1,2,3,4,6,7,8-HpCDD
OCDD
2,3,7,8-TCDF
1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
1,2,3,4,7,8-HxCDF
1,2,3,6,7,8-HxCDF
1,2,3, 7,8, 9-HxCDF
2,3,4,6,7,8-HxCDF
1,2,3, 4,6,7, 8-HpCDF
1,2,3,4,7,8,9-HpCDF
OCDF
Total 2,3,7,8-CDD
Total 2,3,7,8-CDF
Total I-TEQDF (ND = zero)
Total I-TEQDF (ND = M> det limit)
Total TEQDF-WH098 (ND = zero)
Total TEQDF-WH098 (ND = M> det limit)
Total TCDD
Total PeCDD
Total HxCDD
Total HpCDD
Total OCDD
Total TCDF
Total PeCDF
Total HxCDF
Total HpCDF
Total OCDF
Total CDD/CDF (ND = zero)
Total CDD/CDF (ND = M> det limit)
Automotive Tailp pe Emission Study Results
FTP cycle
(Ref. A)
(pg/U
ND (5)
ND (3)
ND (40)
ND (40)
ND (40)
ND (40)
ND (50)
64
ND (7)
ND (7)
ND (40)
ND (40)
ND (40)
ND (40)
ND (40)
ND (40)
ND (70)
ND
64
6.4
26.2
6.4
26.9
13
ND (3)
ND (40)
ND (10)
ND (5)
170
ND (7)
ND (40)
ND (20)
ND (7)
183
249
63 km/hr
(Ref. B)
(pg/U
2.6
19.1
16.6
17.1
17.6
40.4
176
44.0
44.5
20.7
41.9
21.2
37.8
54.3
27.9
16.6
119
289.4
427.9
50.9
50.9
60.2
60.2
435
481
305
93
176
569
931
378
476
119
3,963
3,963
FTP cycle
(test no. 21)
(Ref. C)
(pg/U
24
14
24
84
15
192
868
70
40
30
68
62
47
55
278
ND (1)
374
1,221
1,024
96.4
96.4
102
102
429
837
484
392
868
718
531
165
278
374
5,076
5,076
64 km/hr
(test no. 17)
(Ref. C)
(pg/U
44
31
26
28
29
66
280
71
72
34
68
34
61
88
45
27
194
504
694
122
122
138
138
706
784
496
147
280
923
1,513
615
773
194
6,431
6,431
64 km/hr
(test no. 20)
(Ref. C)
(pg/U
7
11
25
42
23
121
685
77
69
184
88
35
ND (1)
42
22
24
288
914
829
144
144
148
148
500
542
563
225
685
478
437
258
445
288
4,421
4,421
64 km/hr
(test no. 31/2)
(Ref. C)
(pg/U
8.9
14.1
16.3
60.1
17.1
197.8
2,634
295.2
161.8
135.2
129.1
113.2
36.9
82.1
418.0
54.5
991
2,948
2,417
177
177
181
181
304
170
114
301
2,634
6,379
1,969
1,226
1,088
991
15,176
15,176
Mean Emission Factors
Assuming
ND = zero
(pg/U
14.4
14.9
18.0
38.5
17.0
103
774
104
64.6
67.3
65.8
44.2
30.5
53.6
132
20.4
328
979
909
99.5
106
398
469
327
193
774
1540
897
440
510
328
5875
Assuming
ND = 1/2
det limit
(pg/U
14.8
15.1
21.3
41.9
20.3
106
778
104
65.1
67.9
69.2
47.6
33.9
56.9
135
23.8
334
998
936
103
109
398
469
330
194
774
1540
897
444
512
328
5886
o
o
-H
D
c;
O
O
53
D
CD
O
CD
3
-
K>
O
O
CO
ND = Not detected; value in parentheses is the reported detection limit.
Ref. A: Marklund et al. (1990); the pg/L values in the table were calculated from the reported units of pg/km assuming a fuel economy of 10 km/L for unleaded gas.
-------
CD
Table 4-6. Unleaded Gasoline-Fueled (With Catalytic Converters) Automobile CDD/CDF Congener Emission Factors
Congener/Congener Group
2,3,7,8-TCDD
1,2,3,7,8-PeCDD
1, 2,3,4,7, 8-HxCDD
1, 2,3,6,7, 8-HxCDD
1, 2,3,7, 8,9-HxCDD
1, 2,3,4,6,7, 8-HpCDD
OCDD
2,3,7,8-TCDF
1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
1, 2,3,4,7, 8-HxCDF
1, 2,3,6,7, 8-HxCDF
1, 2,3,7, 8,9-HxCDF
2, 3,4,6,7, 8-HxCDF
1, 2,3,4,6,7, 8-HpCDF
1, 2,3,4,7, 8,9-HpCDF
OCDF
Total 2,3,7,8-CDD
Total 2,3,7,8-CDF
Total I-TEQDF (ND = zero)
Total I-TEQDF (ND = 1/2 det limit)
Total TEQDF-WH098 (ND = zero)
Total TEQDF-WH098 (ND = 1/2 det limit)
Total TCDD
Total PeCDD
Total HxCDD
Total HpCDD
Total OCDD
Total TCDF
Total PeCDF
Total HxCDF
Total HpCDF
Total OCDF
Total CDD/CDF (ND = zero)
Total CDD/CDF (ND = 1/2 det limit)
Automotive Tailpipe Emission Study Test Results
63 km/hr
(Ref. A)
(pg/U
1.6
1.6
2.4
3.5
3.1
15.3
170
4.3
3.3
2.4
4.8
6.3
0.2
4.6
16.3
ND (0.2)
27.9
197.5
70.1
7.2
7.2
7.8
7.8
28.6
25.5
26.3
38.7
170
52.6
53.4
33.3
27.1
27.9
483.4
483.4
64 km/hr
(test no. 29!)
(Ref. B)
(pg/U
3.0
2.6
5.3
6.0
6.0
27.8
275
10.6
8.7
7.2
10.6
9.1
ND (3.8)
18.1
54.3
ND (3.8)
38
325.7
156.6
16.0
16.2
17.1
17.3
51
51
56
50
275
152
122
71
62
38
928
928
64 km/hr
(test no. 30/2)
(Ref. B)
(pg/U
ND (7.9)
ND (7.9)
ND (7.9)
6.4
ND (7.9)
78.1
427
12.7
5.1
6.2
4.5
3.9
2.1
8.2
154.2
7.9
106
511.5
310.8
10.1
16.8
9.6
18.3
13
ND (15)
36
163
427
79
29
60
174
106
1,095
1,087
64 km/hr
(test no. 18)
(Ref. B)
(pg/U
14
4
1
2
2
14
197
35
13
6
5
7
5
ND (1)
51
1
140
234
263
26.3
26.4
28.0
28.1
82
101
50
25
197
332
84
39
83
140
1,133
1,133
Mean Emission Factors
Assuming
ND = zero
(pg/L)
4.7
2.1
2.2
4.5
2.8
33.8
267
15.7
7.5
5.5
6.2
6.6
1.8
7.7
69.0
2.2
78.0
317
200
14.9
15.6
43.7
44.4
42.1
69.2
267.3
153.9
72.1
50.8
86.5
78.0
910
Assuming
ND = 1/2 det limit
(pg/U
5.6
3.0
3.2
4.5
3.8
33.8
267
15.7
7.5
5.5
6.2
6.6
2.3
7.9
69.0
2.7
78.0
321
201
16.6
17.9
43.7
46.3
42.1
69.2
267.3
153.9
72.1
50.8
86.5
78.0
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ND = Not detected; value in parentheses is the reported detection limit.
Ref. A: Hagenmaier et al. (1 990)
Ref. B: Schwind et al. (1 991); Hutzinger et al. (1 992)
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Table 4-7. European Tunnel Study Test Results
Congener/Congener Group
2,3,7,8-TCDD
1,2,3,7,8-PeCDD
1,2,3,4,7,8-HxCDD
1,2,3,6,7,8-HxCDD
1,2,3, 7,8, 9-HxCDD
1,2,3,4,6,7,8-HpCDD
OCDD
2,3,7,8-TCDF
1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
1,2,3,4,7,8-HxCDF
1,2,3,6,7,8-HxCDF
1,2,3, 7,8, 9-HxCDF
2,3,4,6,7,8-HxCDF
1,2,3,4,6,7,8-HpCDF
1, 2,3,4,7,8, 9-HpCDF
OCDF
Total 2,3,7,8-CDD
Total 2,3,7,8-CDF
Total I-TEQDF (ND = zero)
Total I-TEQDF (ND = 1/2 det limit)
Total TEQDF-WH098 (ND = zero)
Total TEQDF-WH098 (ND = 1/2 det limit)
Total TCDD
Total PeCDD
Total HxCDD
Total HpCDD
Total OCDD
Total TCDF
Total PeCDF
Total HxCDF
Total HpCDF
Total OCDF
Total CDD/CDF (ND = zero)
Total CDD/CDF (ND = 1 12 det limit)
Tunnel Air
Germany
(Ref. A)
(pg/m3)
ND (0.01)
0.31
0.37
1.19
0.44
1.9
6.3
0.17
0.40
0.19
0.26
0.16
ND (0.04)
0.12
1.2
ND (0.16)
ND (1.3)
10.51
2.50
0.58
0.59
0.73
0.74
0.23
2.5
7.8
3.4
6.3
3.5
3.6
2.0
1.9
ND (1.3)
31.2
31.9
Tunnel Air
Germany
(Ref. A)
(pg/m3)
0.06
0.28
ND (0.17)
0.66
ND (0.17)
2.0
6.4
0.72
0.36
NR
0.13
0.15
ND (0.05)
ND (0.05)
0.98
ND (0.17)
ND (1.0)
9.40
2.34
0.42
0.44
0.55
0.58
0.22
1.3
2.7
3.4
6.4
6.2
4.1
1.1
1.2
ND (1.0)
26.6
27.1
Tunnel Air
Belgium
(Ref. B)
(pg/m3)
0.002
0.025
0.025
0.042
0.030
0.468
2.190
0.013
0.143
0.039
0.073
0.093
0.143
0.004
0.499
0.074
0.250
2.782
1.330
0.096
0.096
0.106
0.106
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
Tunnel Air
Norway
(workdays)8
(Ref. C)
(pg/m3)
0.02
0.18
0.06
0.29
0.25
1.41
0.10
0.58
0.83
0.78
0.79
0.62
0.04
0.74
1.78
0.22
1.62
2.31
7.98
0.91
0.91
1.00
1.00
0.26
1.78
1.32
1.31
0.10
13.20
10.17
6.42
2.62
1.62
38.80
38.80
Tunnel Air
Norway
(weekend)8
(Ref. C)
(pg/m3)
0.02
0.04
0.03
0.03
0.06
0.16
0.50
0.07
0.75
0.58
0.34
0.31
0.03
0.13
0.93
0.14
2.54
0.84
5.82
0.48
0.48
0.49
0.49
0.16
0.41
0.12
0.23
0.50
1.70
7.91
2.08
1.41
2.54
17.06
17.06
ND = Not detected; value in parentheses is the detection limit.
Ref. A: Rappe et al. (1988)
Ref. B: Wevers et al. (1992)
Ref. C: Oehme et al. (1991)
a Listed values are the differences between the concentrations at the inlet and outlet of the northbound tunnel lanes.
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December 2003
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Table 4-8. Baltimore Harbor Tunnel Study: Estimated Emission Factors for Heavy-Duty (HD) Diesel Vehicles
Congener/Congener Group
2,3,7,8-TCDD
1,2,3,7,8-PeCDD
1,2,3,4,7,8-HxCDD
1,2,3,6,7,8-HxCDD
1, 2,3,7,8, 9-HxCDD
1,2,3,4,6,7,8-HpCDD
OCDD
2,3,7,8-TCDF
1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
1,2,3,4,7,8-HxCDF
1,2,3,6,7,8-HxCDF
1, 2,3,7,8, 9-HxCDF
2,3,4,6,7,8-HxCDF
1,2,3,4,6,7,8-HpCDF
1, 2,3,4,7,8, 9-HpCDF
OCDF
Total 2,3,7,8-CDD
Total 2,3,7,8-CDF
Total I-TEQDF
Total TEQDF-WH098
Total TCDD
Total PeCDD
Total HxCDD
Total HpCDD
Total OCDD
Total TCDF
Total PeCDF
Total HxCDF
Total HpCDF
Total OCDF
Total CDD/CDF
HD vehicles as % of total
vehicles
Run-Specific Emission Factors
Run No. 2
(pg/km)
24.5
40.2
18.2
37.5
53.6
0
0
0
0
24.5
15.4
0.3
27.7
15.2
12.6
0
0
174
95.7
73.8
93.8
245
110
677
0
0
0
124
136
0
0
1,291
21.2
Run No. 3
(pg/km)
61.6
20.6
25.2
28.2
56.5
401
3,361
94.3
48.9
75.7
139
75.1
14.8
82.5
280
58.5
239
3,954
1,108
175
182
0
21.9
0
802
3361
901
119
319
223
239
5,987
22.0
Run No. 5
(pg/km)
0.0
15.4
46.5
64.3
91.6
729
3,382
67.6
72.6
131
204
73.7
75.6
152
445
60.8
401
4,328
1,684
170
175
140
83.3
753
1,498
3,382
1,314
1,152
852
814
401
10,390
22.6
Run No. 6
(pg/km)
21.2
5.6
8.3
19.6
48.4
111
1,120
152.8
23.6
46.6
93.8
51.0
0
55.7
154
31.1
175
1,335
784
96
97
165
35.6
54.5
142
1,120
656
78.4
67.6
144
175
2,638
34.0
Run No. 8
(pg/km)
37.8
38.4
64.5
153
280
2,438
9,730
155.8
53.3
85.0
124
61.3
20.6
93.0
313
25.0
416
12,743
1,347
235
245
311
174
2,009
5,696
9,730
2,416
1,055
444
513
416
22,766
28.8
Run No. 9
(pg/km)
40.1
0.0
0.0
71.1
126
963
5,829
73.4
0.0
63.9
164
54.4
37.2
86.8
354
2.3
534
7,028
1,371
153
147
109
0.0
1,666
1,933
5,829
1,007
282
719
354
534
12,434
24.2
Run No. 10
(pg/km)
54.9
83.0
123
186
370
2,080
7,620
61.7
43.3
108
166
95.5
63.5
111
308
34.9
370
10,515
1,362
303
337
97.3
165
2,971
4,377
7,620
687
626
619
637
370
18,168
27.4
Mean
Emission
Factors
(pg/km)
34.3
29.0
40.8
80.0
147
960
4,435
86.5
34.5
76.4
129
58.8
34.2
85.2
267
30.4
305
5,725
1,107
172
182
152
84.2
1,162
2,064
4,435
997
491
451
384
305
10,525
25.7
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Source: Gertler et al. (1 996, 1 998)
Notes:
1) Listed values are based on the difference between the calculated chemical mass entering the tunnel and the mass exiting the tunnel.
2) All calculated negative emission factors were set equal to zero.
3) All CDD/CDF emissions were assumed to result from heavy-duty diesel fueled vehicles. The table presents in the last row the percent of total traffic that was heavy-duty vehicles.
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Table 4-9. CDD/CDF Emission Factors for Industrial Wood Combustors
Congener
2,3,7, 8-TCDD
1,2,3,7,8-PeCDD
1, 2,3,4,7, 8-HxCDD
1, 2,3,6,7, 8-HxCDD
1, 2,3,7, 8,9-HxCDD
1, 2,3,4,6,7, 8-HpCDD
OCDD
2,3,7, 8-TCDF
1,2,3,7,8-PeCDF
2,3,4,7, 8-PeCDF
1, 2,3,4,7, 8-HxCDF
1, 2,3,6,7, 8-HxCDF
1, 2,3,7, 8,9-HxCDF
2,3,4,6,7, 8-HxCDF
1, 2,3,4,6,7, 8-HpCDF
1, 2,3,4,7, 8,9-HpCDF
OCDF
Total TCDD
Total PeCDD
Total HxCDD
Total HpCDD
Total OCDD
Total TCDF
Total PeCDF
Total HxCDF
Total HpCDF
Total OCDF
Total I-TEQDF
Total TEQDF-WH098
Total CDD/CDF
Four Facilities Tested by CARB
Mean Emission Factors (ng/kg wood)
Nondetects Set
to Zero
0.007
0.044
0.042
0.086
0.079
0.902
6.026
0.673
0.790
0.741
0.761
0.941
0.343
0.450
2.508
0.260
1.587
0.151
1.039
1.748
2.936
6.026
4.275
9.750
7.428
3.747
1.588
0.82
0.84
38.69
Nondetects Set to
1/2 Det. Limit
0.016
0.054
0.055
0.096
0.132
0.905
6.026
0.673
0.790
0.741
0.768
0.941
0.350
0.491
2.749
0.344
1.590
0.154
1.039
1.748
2.936
6.026
4.275
9.750
7.428
3.988
1.590
0.85
0.87
38.93
Five Facilities Tested by NCASI
Mean Emission Factors
(ng/kg wood)
Nondetects
Set to Zero
0.066
0.110
0.179
0.191
0.522
0.635
1.317
0.707
0.145
0.159
0.108
0.071
0.064
0.015
0.072
0.017
0.049
1.628
1.958
1.792
1.120
1.317
4.532
1.548
0.536
0.111
0.049
0.40
0.46
14.593
Nondetects Set to
1/2 Det. Limit
0.068
0.112
0.183
0.193
0.524
0.637
1.317
0.719
0.149
0.164
0.111
0.073
0.067
0.017
0.074
0.020
0.060
1.629
1.980
1.796
1.132
1.317
4.552
1.549
0.543
0.116
0.060
0.41
0.46
14.674
Nine Facilities Tested by
CARB and NCASI
Mean Emission Factors (ng/kg wood)
Nondetects Set
to Zero
0.040
0.079
0.115
0.138
0.321
0.745
3.329
0.684
0.406
0.389
0.375
0.418
0.178
0.192
1.062
0.113
0.674
0.969
1.521
1.663
1.821
3.329
4.353
4.930
3.316
1.580
0.674
0.56
0.60
24.155
Nondetects Set to
1/2 Det. Limit
0.046
0.084
0.123
0.143
0.342
0.748
0.329
0.690
0.409
0.392
0.379
0.419
0.183
0.209
1.155
0.152
0.681
0.970
1.533
1.665
1.823
0.329
4.364
4.930
3.320
1.674
0.681
0.58
0.62
24.294
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Sources: CARB (1990b); CARB (1990e); CARB (1990f); CARB (1990g); NCASI (1995)
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Table 4-10. CDD/CDF Concentrations in Residential Chimney Soot from Wood Stoves and Fireplaces
Congener/Congener
Group
2,3,7,8-TCDD
1,2,3,7,8-PeCDD
1,2,3,4,7,8-HxCDD
1,2,3,6,7,8-HxCDD
1,2,3, 7,8, 9-HxCDD
1,2,3,4,6,7,8-HpCDD
OCDD
2,3,7,8-TCDF
1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
1,2,3,4,7,8-HxCDF
1,2,3,6,7,8-HxCDF
1,2,3, 7,8, 9-HxCDF
2,3,4,6,7,8-HxCDF
1,2,3,4,6,7,8-HpCDF
1, 2,3,4,7,8, 9-HpCDF
OCDF
Total 2,3,7,8-CDD
Total 2,3,7,8-CDF
Total l-TEQDf
Total TEQDF-WH098
Total TCDD
Total PeCDD
Total HxCDD
Total HpCDD
Total OCDD
Total TCDF
Total PeCDF
Total HxCDF
Total HpCDF
Total OCDF
Total CDD/CDF
U.S. East
Region
(Ref. A)
(ng/kg)
66
NR
250*
250*
208
1,143
2,033
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
3,450
NR
>125
>123
1,987
NR
2,183
2,104
2,033
NR
NR
NR
NR
NR
8,307
U.S. West
Region
(Ref. A)
(ng/kg)
13.3
NR
522*
522*
282
1,653
2,227
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
4,175
NR
>112
>110
269
NR
4,273
3,243
2,227
NR
NR
NR
NR
NR
10,012
U.S. Central
Region
(Ref. A)
(ng/kg)
66
NR
1,831*
1,831*
1,450
6,160
13,761
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
21,437
NR
>479
>467
1,511
NR
14,243
12,603
13,761
NR
NR
NR
NR
NR
42,118
German
Farmhouse
(Ref. B)
(ng/kg)
150
70
35
60
30
90
90
930
560
590
330
400
70
200
490
40
70
525
3,680
720
755
3,900
880
600
200
90
13,400
6,100
3,200
720
70
29,160
Canadian
Wood Stove
(Ref. C)
(ng/kg)
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
ND (10)
ND (10)
ND (50)
100
200
ND (10)
ND (10)
ND (50)
ND (50)
ND (50)
300
Canadian
Fireplace
(Ref. C)
(ng/kg)
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
ND (10)
500
1,700
500
400
300
1,400
1,700
400
100
7,000
Canadian
Wood Stove
(Ref. D)
(ng/kg)
ND (12)
70
ND (10)
625
281
948
530
235
58
68
51
57
8
24
97
20
41
2,454
659
211
246
11
608
3,450
1,550
530
1,010
948
482
154
41
8,783
NR = Not reported.
* = Analytical method could not distinguish between congeners; listed value is the sum of both congeners.
Ref. A: Nestrick and Lamparski (1982, 1983); mean values listed - six samples collected in each Region.
Ref. B: Bacher et al. (1992)
Ref. C: Clement et al. (1985b)
Ref. D: Van Oostdam and Ward (1995); mean of two samples - nondetected values assumed to be zero.
DRAFT-DO NOT QUOTE OR CITE
4-49
December 2003
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Table 4-11. CDD/CDF Concentrations in Residential Bottom Ash from Wood Stoves and a Fireplace
Congener/Congener Group
2,3,7,8-TCDD
1,2,3,7,8-PeCDD
1,2, 3,4,7, 8-HxCDD
1,2,3,6,7,8-HxCDD
1,2,3,7,8,9-HxCDD
1,2,3,4,6,7,8-HpCDD
OCDD
2,3,7,8-TCDF
1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
1, 2,3,4,7, 8-HxCDF
1, 2,3,6,7, 8-HxCDF
1, 2,3,7, 8,9-HxCDF
2,3,4,6,7, 8-HxCDF
1,2,3,4,6,7,8-HpCDF
1,2,3,4,7,8,9-HpCDF
OCDF
Total 2,3,7,8-CDD
Total 2,3,7,8-CDF
Total TEQ
Total TCDD
Total PeCDD
Total HxCDD
Total HpCDD
Total OCDD
Total TCDF
Total PeCDF
Total HxCDF
Total HpCDF
Total OCDF
Total CDD/CDF
Canadian
Wood Stove
Ash
(ng/kg)
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
ND (10)
ND (10)
ND (50)
300
2,600
9,100
2,200
1,000
700
ND (50)
15,900
Canadian
Wood Stove
Ash
(ng/kg)
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
100
3,000
10,000
1,200
900
400
4,600
9,300
1,000
100
30,600
Canadian
Wood Stove
Ash
(ng/kg)
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
100
200
700
500
100
100
200
500
300
ND (50)
2,700
Canadian
Fireplace
Ash
(ng/kg)
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
ND (10)
ND (10)
300
2,000
3,100
ND (10)
ND (10)
100
400
100
6,000
NR = Not reported.
Source: Clement et al. (1985b)
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December 2003
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Table 4-1 2. CDD/CDF Concentrations in Chimney Soot (Bavaria, Germany)
Unit Type
Oven
Tiled Stove
Heating System
Oven
Tiled Stove
Oven
Fuel Type
Wood
Wood
Wood
Wood/coal
Wood/coal
Wood, wood/coal,
waste
Number of
Samples
33
39
9
27
5
5
CDD/CDF Concentrations in Soot (ng l-TEQDF/kg)
Minimum
10.4
4.0
16.9
77.3
53.1
116.3
Mean
2,015
3,453
1,438
2,772
549
6,587
Maximum
15,849
42,048
20,450
10,065
4,911
10,652
Source: Dumler-Gradl et al. (1995a).
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December 2003
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Table 4-13 Fly Ash from Wood Working Industry
(Concentrations in ng/kg)
Congener
2,3,7,8-TCDD
Total TCDD
1,2,3,7,8-PeCDD
Total PeCDD
1,2, 3,4,7, 8-HxCDD
1,2,3,6,7,8-HxCDD
1,2,3,7,8,9-HxCDD
Total HxCDD
1,2,3,4,6,7,8-HpCDD
Total HpCDD
Total OCCD
TOTAL TCDD TEQs
2,3,7,8-TCDF
Total TCDF
1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
Total PeCDF
1,2, 3,4,7, 8-HxCDF
1,2,3,7,8,9-HxCDF
1,2,3,6,7,8-HxCDF
Total HxCDF
1,2,3,4,6,7,8-HpCDF
1,2,3,4,7,8,9-HpCDF
Total HpCDF
Total OCDF
TOTAL CDF TEQs
Concentration
<15
1,730
100
1,250
130
150
140
750
280
470
300
130
1,300
100
120
790
40
40
<10
150
320
<10
570
60
I-TEQDF
<15
-
50
-
13
15
14
-
3
-
0.3
95-110
13
-
5
60
-
4
4
<1
-
3
<0.1
-
0.06
89-90
TEQDF-WH098
<15
-
100
-
13
15
14
-
3
-
0.03
145-160
13
-
5
60
-
4
4
<1
-
3
<0.1
-
0.006
89-90
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December 2003
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Table 4-14. Electrostatic Precipitator Waste Ash from Wood-Fired Industrial Boiler
2,3,7,8-TCDD
Total TCDD
1,2,3,7,8-PeCDD
Total PeCDD
1,2,3,4,7,8-HxCDD
1,2, 3,6,7, 8-HxCDD
1,2,3,7,8,9-HxCDD
Total HxCDD
1,2,3,4,6,7,8-HpCDD
Total HpCDD
Total OCDD
Total CDD
2,3,7,8-TCDF
Total TCDF
1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
Total PeCDF
1,2, 3,4,7, 8-HxCDF
1,2,3,6,7,8-HxCDF
2,3,4,6,7,8-HxCDF
1,2,3,7,8,9-HxCDF
Total HxCDF
1,2,3,4,6,7,8-HpCDF
1,2,3,4,7,8,9-HpCDF
Total HpCDF
Total OCDF
Total CDF
Total Dioxins/Furans
Average3
Concentration
(ng/kg)
17.85
239.00
30.67
226.83
20.33
26.33
23.33
300.00
325.00
706.67
786.67
2,439.17
285.00
2,713.33
154.50
641.67
2,666.67
244.83
179.67
296.67
7.28
1,520.00
147.67
21.33
248.33
48.33
7,196.67
I-TEQDF
Concentration
(ng/kg)
17.85
-
15.33
-
2.03
2.63
2.33
-
3.25
-
0.79
44.22
28.50
-
7.73
320.83
-
24.48
17.97
29.67
0.73
-
1.48
0.21
-
0.05
431.64
475.86
TEQDF-WH098
Concentration
(ng/kg)
17.85
-
30.67
-
2.03
2.63
2.33
-
3.25
-
0.08
58.85
28.50
-
7.73
320.83
-
24.48
17.97
29.67
0.73
-
1.48
0.21
-
0.00
431.60
490.44
a Calculated from data in Table 30 of CARB 1990e).
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December 2003
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Table 4-15. Estimated CDD/CDF Emission Factors for Oil-Fired Residential Furnaces
Congener/Congener Group
2,3,7,8-TCDD
1, 2,3,7, 8-PeCDD
1,2, 3,4,7, 8-HxCDD
1,2,3,6,7,8-HxCDD
1,2,3,7,8,9-HxCDD
1,2,3,4,6,7,8-HpCDD
OCDD
2,3,7,8-TCDF
1, 2,3,7, 8-PeCDF
2,3,4,7, 8-PeCDF
1,2, 3,4,7, 8-HxCDF
1,2,3,6,7,8-HxCDF
1,2,3,7,8,9-HxCDF
2,3,4,6,7,8-HxCDF
1,2,3,4,6,7,8-HpCDF
1,2,3,4,7,8,9-HpCDF
OCDF
Total 2,3,7,8-CDD
Total 2,3,7,8-CDF
Total I-TEQDF
Total TCDD
Total PeCDD
Total HxCDD
Total HpCDD
Total OCDD
Total TCDF
Total PeCDF
Total HxCDF
Total HpCDF
Total OCDF
Total CDD/CDF
Mean Facility
Emission Factor
(pg/L oil)
56
NR
NR
NR
NR
NR
66
53
NR
NR
NR
NR
NR
NR
NR
NR
30
NR
NR
150
139
82
66
63
66
663
420
170
73
30
1,772
NR = Not reported.
Source: U.S. EPA (1997b)
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December 2003
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Table 4-16. CDD/CDF Emission Factors for Oil-Fired Utility/Industrial Boilers
Congener/Congener Group
2,3,7,8-TCDD
1,2,3,7,8-PeCDD
1,2, 3,4,7, 8-HxCDD
1,2,3,6,7,8-HxCDD
1,2,3,7,8,9-HxCDD
1,2,3,4,6,7,8-HpCDD
OCDD
2,3,7,8-TCDF
1, 2,3,7, 8-PeCDF
2,3,4,7,8-PeCDF
1,2, 3,4,7, 8-HxCDF
1,2,3,6,7,8-HxCDF
1,2,3,7,8,9-HxCDF
2,3,4,6,7,8-HxCDF
1,2,3,4,6,7,8-HpCDF
1,2,3,4,7,8,9-HpCDF
OCDF
Total 2,3,7, 8-CDD
Total 2,3,7, 8-CDF
Total I-TEQDF
Total TEQDF-WH098
Total TCDD
Total PeCDD
Total HxCDD
Total HpCDD
Total OCDD
Total TCDF
Total PeCDF
Total HxCDF
Total HpCDF
Total OCDF
Total CDD/CDF
U.S. EPA (1997b)
Median
Emission Factor
(pg/L oil)
117
104
215
97
149
359
413
83
77
86
109
68
104
86
169
179
179
1,453
1,141
314
366
102
104
145
359
413
90
131
172
27
179
1,722
EPRI (1994)
Mean Emission Factor
ND = zero
(pg/L oil)
0
24.7
63.3
65.8
79.7
477
2055
0
64.1
49.3
76.5
35.4
0
23.8
164
0
0
2,766
414
83.1
93.6
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
3,179
ND = 1/2 DL
(pg/L oil)
26.6
43.1
108
79.3
102
546
2141
35.7
73.9
59.6
94.9
45.2
37.7
42.2
218
137
139
3,047
883
147
167
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
3,931
Sources:
U.S. EPA (1997b) - number of facilities not reported.
EPRI (1 994) - based on two cold side ESP-equipped power plants.
Note: Assumes a density for residual fuel oil of 0.87 kg/L.
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December 2003
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CD
Table 4-17. CDD/CDF Concentrations in Stack Emissions from U.S. Coal-Fired Power Plants
Congener/Congener Group
2,3,7, 8-TCDD
1,2,3,7,8-PeCDD
1,2, 3,4,7, 8-HxCDD
1,2, 3,6,7, 8-HxCDD
1,2,3,7,8,9-HxCDD
1,2, 3,4,6,7, 8-HpCDD
OCDD
2,3,7, 8-TCDF
1, 2,3,7, 8-PeCDF
2,3,4,7,8-PeCDF
1, 2,3,4,7, 8-HxCDF
1, 2,3,6,7, 8-HxCDF
1, 2,3,7, 8,9-HxCDF
2, 3,4,6,7, 8-HxCDF
1, 2,3,4,6,7, 8-HpCDF
1, 2,3,4,7, 8,9-HpCDF
OCDF
Total 2,3,7,8-CDD
Total 2,3,7,8-CDF
Total TCDD
Total PeCDD
Total HxCDD
Total HpCDD
Total OCDD
Total TCDF
Total PeCDF
Total HxCDF
Total HpCDF
Total OCDF
Total CDD/CDF
Plant 1
(pg/Nm3)
ND (3.5)
ND (0.56)
ND (0.56)
ND (0.44)
ND (0.56)
ND (1.7)
ND (12)
ND (1.7)
ND (1.0)
2.4
3.3
1.1
ND (0.44)
ND (2.0)
2.0
ND (0.63)
5.6
0
14
1.8
ND (1.0)
1.3
3.4
ND (12)
ND (5.2)
5.4
7.6
4.3
5.6
29
Plant 2
(pg/Nm3)
ND (3.5)
ND (4.8)
ND (5.7)
5.0
4.9
29
32
8.1
ND (5.7)
ND (19)
16
ND (5.0)
11
ND (4.2)
29
ND (6.1)
33
71
97
12
4.4
18
45
32
29
33
39
34
33
279
Plant 3
(pg/Nm3)
1.0
ND (1.8)
ND (3.6)
ND (1.8)
ND (1.8)
ND (1.8)
ND (14)
7.8
7.2
6.6
8.4
2.9
ND (1.8)
3.0
6.0
ND (3.6)
2.4
1
44.3
12
6.0
2.7
ND (2.4)
ND (14)
78
61
29
9.0
2.4
200.1
Plant 4
(pg/Nm3)
ND (2.0)
ND (10)
ND (10)
ND (10)
ND (10)
ND (10)
ND (20)
ND (2.0)
ND (10)
ND (10)
ND (10)
ND (10)
ND (10)
ND (10)
ND (10)
ND (10)
ND (20)
0
0
NR
ND (10)
ND (10)
ND (10)
ND (20)
ND (2)
ND (10)
ND (10)
ND (10)
ND (20)
0
Plant 5
(pg/Nm3)
ND (3.3)
ND (4.7)
ND (15.4)
ND (9.9)
ND (12.1)
ND (26.4)
ND (131)
ND (3.3)
ND (3.2)
ND (3.2)
ND (16.4)
ND (5.8)
ND (8.8)
ND (16.4)
ND (23)
ND (15.4)
ND (131)
0
0
6.7
ND (4.7)
ND (26.3)
ND (26.4)
ND (131)
ND (3.3)
ND (6.6)
ND (16.4)
ND (29.5)
ND (131)
6.7
Plant 6
(pg/Nm3)
ND (2.6)
ND (3.2)
ND (2.7)
ND (4.2)
ND (4.3)
4.3
20
13
ND (5.7)
ND (4.8)
ND (5.1)
ND (4.0)
ND (6.9)
ND (2.5)
ND (30)
ND (5.0)
ND (19)
24.3
13
ND (2.6)
ND (3.2)
ND (4.0)
ND (14)
20
88
14
ND (5.0)
ND (20)
ND (19)
122
Plant 7
(pg/Nm3)
ND (1.7)
ND (1.8)
ND (2.0)
ND (1.4)
ND (1.2)
2.4
21.6
0.7
ND (1.1)
ND (1.4)
ND (1.8)
ND (1.3)
ND (1.5)
ND (2.0)
ND (2.2)
ND (2.1)
11.4
24
12.1
ND (55)
ND (32)
ND (24)
ND (8.1)
21.6
ND (37)
3.0
ND (27)
2.9
11.4
38.9
o
o
-H
D
c;
O
O
53
Oi
O)
D
CD
o
CD
3
-
K>
O
O
CO
ND = Not detected; value in parentheses is the detection limit.
NR = Not reported; suspected contamination problem.
Source: Riggs et al. (1995)
-------
Table 4-18. Characteristics of U.S. Coal-Fired Power Plants Tested by DOE
Plant
No.
1
2
3
4
5
6
7
Coal Type
Bituminous
Bituminous
Subbituminous
Subbituminous
Bituminous
Lignite
Bituminous
Coal
Chlorine
Content
(mg/kg)
800
1,400
300
390
1,400
400
1,000
Temperature (°C) at:
Pollution Control Device3
ESP
160
130
__
__
130
170
150
Bag
__
__
150
70
__
__
--
FGD
__
__
__
130
120
170
-
Stack
160
130
150
75
40
110
150
a ESP = Electrostatic precipitator, Bag = Baghouse, FGD = Flue gas desulfurization system.
Source: Riggs et al. (1995).
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December 2003
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Table 4-19. CDD/CDF Emission Factors for Coal-Fired Utility/Industrial Power Plants
Congener/Congener Group
2,3,7,8-TCDD
1, 2,3,7, 8-PeCDD
1, 2,3,4,7, 8-HxCDD
1, 2,3,6,7, 8-HxCDD
1, 2,3,7, 8,9-HxCDD
1, 2,3,4,6,7, 8-HpCDD
OCDD
2,3,7, 8-TCDF
1, 2,3,7, 8-PeCDF
2,3,4,7, 8-PeCDF
1, 2,3,4,7, 8-HxCDF
1, 2,3,6,7, 8-HxCDF
1, 2,3,7, 8,9-HxCDF
2,3,4,6,7, 8-HxCDF
1,2, 3,4,6,7, 8-HpCDF
1,2,3,4,7,8,9-HpCDF
OCDF
Total I-TEQDF
Total TEQDF-WH098
Total TCDD
Total PeCDD
Total HxCDD
Total HpCDD
Total OCDD
Total TCDF
Total PeCDF
Total HxCDF
Total HpCDF
Total OCDF
Total CDD/CDF
Mean Emission Factor
ND = zero
(ng/kg coal)
0.005
0
0
0.004
0.004
0.216
0.513
0.109
0.007
0.074
0.098
0.014
0.013
0.043
0.354
0.087
0.158
0.079
0.078
0.051
0.014
0.030
0.063
0.513
0.154
0.180
0.104
0.064
0.158
1.331
ND = 1/2 DL
(ng/kg coal)
0.018
0.016
0.034
0.028
0.035
0.241
0.644
0.117
0.021
0.084
0.120
0.030
0.038
0.060
0.385
0.112
0.281
0.124
0.131
0.052
0.015
0.030
0.074
0.644
0.158
0.180
0.104
0.064
0.281
1.602
Source: EPRI (1 994) - 11 facility data set.
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December 2003
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Table 4-20. CDD/CDF Emission Factors from Residential Coal Combustors
Congener/Congener Group
2,3,7,8-TCDD
1,2,3,7,8-PeCDD
1,2, 3,4,7, 8-HxCDD
1,2,3,6,7,8-HxCDD
1,2,3,7,8,9-HxCDD
1,2,3,4,6,7,8-HpCDD
OCDD
2,3,7,8-TCDF
1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
1,2, 3,4,7, 8-HxCDF
1,2,3,6,7,8-HxCDF
1,2,3,7,8,9-HxCDF
2,3,4,6,7,8-HxCDF
1,2,3,4,6,7,8-HpCDF
1,2,3,4,7,8,9-HpCDF
OCDF
Total 2,3,7,8-CDD*
Total 2,3,7, 8-CDF*
Total I-TEQDF*
Total TCDD
Total PeCDD
Total HxCDD
Total HpCDD
Total OCDD
Total TCDF
Total PeCDF
Total HxCDF
Total HpCDF
Total OCDF
Total CDD/CDF
"Salt" Lignite
Ref. A
(ng/kg coal)
0.58
0.73
0.63
0.60
0.40
3.24
16.19
2.49
2.24
2.09
0.38
1.86
0.07
1.01
2.59
0.25
0.63
22.37
13.60
2.74
14.23
14.15
11.14
7.06
16.19
80.34
29.21
12.72
3.87
0.63
189.5
"Normal" Lignite
Ref. A
(ng/kg coal)
0.06
0.08
0.06
0.09
0.06
0.59
2.42
0.50
0.43
0.31
0.13
0.36
0.02
0.12
0.95
0.06
0.30
3.38
3.20
0.34
9.00
2.22
1.81
0.82
2.42
20.33
8.98
3.78
1.27
0.30
50.9
Anthracite
Ref. B
(ng/kg coal)
1.60
NR
NR
NR
NR
NR
77
42.0
NR
NR
NR
NR
NR
NR
NR
NR
4.2
NR
NR
60.0
61.6
31
60
57
77
412
340
130
32
4.2
1,205
Bituminous
Ref. B
(ng/kg coal)
2.40
NR
NR
NR
NR
NR
120
63.0
NR
NR
NR
NR
NR
NR
NR
NR
6.3
NR
NR
98.5
92.4
46
90
86
120
613
550
190
47
6.3
1,841
NR = not reported.
* Values as reported in References A and B.
Sources: Ref A: Thub et al. (1995); listed results represent means of three flue gas samples.
Ref B: U.S. EPA (1997b); based on average particulate CDD/CDF concentrations from
chimney soot samples collected from seven coal ovens and particulate emission
factors for anthracite and bituminous coal combustion.
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December 2003
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Table 4-21. Coal-Fired Utility Solid Wastes
CDDs
2,3,7,8-TCDD (4)
,2,3,7,8-PeCDD (4)
,2,3,4,7,8-HxCDD (4)
,2,3,6,7,8-HxCDD (4)
,2,3,7,8,9-HxCDD (5)
,2, 3,4,6,7, 8-HpCDD (6)
OCDD (7)
CDFs
2,3,7,8-TCDF (8)
1,2,3,7,8-PeCDF (4)
2,3,4,7,8-PeCDF (4)
,2,3,4,7,8-HxCDF (5)
,2,3,6,7,8-HxCDF (4)
2, 3,4,6,7, 8-HxCDF (4)
,2,3,7,8,9-HxCDF (4)
,2, 3,4,6,7, 8-HpCDF (5)
,2,3,4,7,8,9-HpCDF (4)
OCDF (9)
Mean
Concentrationd)
(ng/kg)
0.17
0.25
0.35
0.28
0.30
0.59
10.54
0.19
0.17
0.17
0.25
0.18
0.28
0.24
0.29
0.35
0.59
Grams/Year
Disposed of
in Solid
Waste(3)
1987
10
14
20
16
17
33
593
11
10
10
14
10
16
14
16
20
33
Total Quantities:
Grams/Year
Disposed of
in Solid
Waste(2)
1995
10
14
20
16
17
34
603
11
10
10
14
10
16
14
17
20
34
l-TEQDF/Yr
(grams)
1995
9.72
7.15
2.00
1.60
1.72
0.34
0.60
1.09
0.49
4.86
1.43
1.03
1.60
1.37
0.17
0.20
0.03
35.41
TEQDF-
WH098/Yr
(grams)
1995
9.72
14.30
2.00
1.60
1.72
0.34
0.06
1.09
0.49
4.86
1.43
1.03
1.60
1.37
0.17
0.20
<0.01
41.98
(1) From Table 2-9, Data for Co-managed Wastes of U.S. EPA (1 999b).
(2) Based on EPRI estimate of 63 million tons/yr of large-volume utility coal combustion solid wastes.
From Section 3.3 of U.S. EPA (1999c). Assumes all waste characteristics are same as for Comanaged
Wastes.
(3) Assumes that solid waste quantity for 1987 is 98.4% of 1995, based on total utility coal use in those
years (see Section 4.4).
(4) All 1 7 analyses were non-detects.
(5) 1 6 of the 1 7 analyses were non-detects.
(6) 11 of the 1 7 analyses were non-detects.
(7) 5 of the 17 analyses were non-detects.
(8) 14 of the 1 7 analyses were non-detects.
(9) 1 5 of the 1 7 analyses were non-detects.
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Ratio (congener emission factor / total GDD/GDF emission factor)
O.OS O.I O.1S O.2 O.2S O.3 O.3S
2,3,7,B-TOr>r>
1,2,3,7,8-PeCDD
1,2,3,4, T.S-HxODD
1,2,3,6,7,8-HxCDD
1,2,3,7,8,9-HxCDD
1,2,3,4,6,7,8-HpCr>r>
1,2,3,4
7,8,9-OCDD
2,3,7,8-TODF
1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
1,2,3,4,7,8-HxCDF
1,2.3,6.7.8-HxCDF
1,2,3,7,8,9-HxCDF
2,3,4,6,7,e-Hje:Or>F
1,2,3,4,6,7,8-HpCDI
1,2,3,4,7,8,9-HpCDF 3
1,2,3,4,6,7,8,9-OCDF
Ratio (congener group emission factor / total CDD/CDF emission factor)
0.05 0.1 0.15 0.2 0.25 0.3
0.35
• Cars I I Trucks
Note: Based on profiles calculated from emission factors (ND =1/2 DL) from Tables 4-2 and 4-3.
Figure 4-1. Congener and Congener Group Profiles for Air Emissions from Diesel-fueled Vehicles
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Ratio (congener emission factor / total ODD/CDF emission factor)
0.01 0.02 0.03 0.04 0.05
a,3,7,8-TCDD
1 ,2,3,7,8-PeCDD
1 ,2,3,6,7,8-HxCDD
1 ,2,3,7,8,9-HxCDD
l,2,3,4,6,7,S-Hp>CDD
1,2,3,4,6,7,8,9-OCDD
2,3,7,8-TODF
1 ,2,3,7,8-PeODF
2,3,4,7,8-PeODF
1 ,2,3,4,7,8-HxCDF
1 ,2,3,6,7,8-HxCDF
1 ,2,3,7,8,9-HxCDF
2,3,4,6,7,8-HxCDF
1 ,2,3,4,6,7,8-HpCDF
1 ,2,3,4,7,8,9-HpCDF
1 ,2,3,4,6,7,8,9-OCDF
Ratio (congener o/roup emission factor / total OL>E>/OL>F emis
0.1 0.2 0.3 0.4
3n factor)
0.5
0.6
Note: Based on profiles calculatecl from emission factors (ND = 1/2 DIO from Table 4-4.
Figure 4-2. Congener and Congener Group Profiles for Air Emissions from Leaded Gas-fueled Vehicles
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Ratio (concfenez* emie
0.05 0.1
an factor / total CDD/CDF emission factor)
O.1S O.2 O.25 O.3 O.35
2,3,7,8-TCDD
1,2,3,7,8-PeCDD
1,2,3,4,7,8-HxCDD
1,2,3,6,7,8-HxCDD
1,2,3,7,8,9-HxCDD
1,2,3,4,6,7,8-HpCDD
1,2,3,4,6,7,8,9-OCDD
2,3,7,8-TCDF
1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
1,2,3,4,7,8-HxCDF
1,2,3,6,7,8-HxCDF
1,2,3,7,8,9-HxCDF
2,3,4,6,7,8-HxCDF
1,2,3,4,6,7,8-HpCDF
1,2,3,4,7,8,9-HpCDF
1,2,3,4,6,7,8,9-OCDF
Ratio (congener group emission factor / total CDD/CDF emission factor)
O O.O5 O.I O.15 O.2 O.25 O.3 O.35
OCDF
Note: Profiles are for catalytic converter equipped vehicles; based on data from Table 4-6.
Figure 4-3. Congener and Congener Group Profiles for Air Emissions from Unleaded Gas-fueled Vehicles
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g
I
o
CO
CO
•a
•+••
O
0.7
0.6
0.5
0.4
M 0.3
I 0-2
0.1
\ Norway (Row 1)
7 8 9 10 11 12 13
Congener Number
Germany (Row 2) ^1
14 15 16 17
I Germany (Row 3)
I Belgium (Row 4) I Baltimore, USA (Row 5)
Congener numbers refer to the congeners in order as listed in Table 4-7.
Figure 4-4. Tunnel Air Concentrations
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Ratio (congener emission factor / total CDD/CDF emission factor)
O.O5 O.I O.15
2,3,7,8-TCDD
1,2,3,7,8-PeCDD
1,2,3,4,7,8-HxCDD
1,2,3,6,7,8-HxCDD
1,2,3,7,8,9-HxCDD
1,2,3,4,6,7,8-HpCDD
1,2,3,4,6,7,8,9-OCDD
2,3,7,8-TCDF
1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
1,2,3,4,7,8-HxCDF
1,2,3,6,7,8-HxCDF
1,2,3,7,8,9-HxCDF
2,3,4,6,7,8-HxCDF
1,2,3,4,6,7,8-HpCDF
1,2,3,4,7,8,9-HpCDF
1,2,3,4,6,7,8,9-OCDF
Ratio (mean congener group emission factor / total CDD/CDF emission factor)
O.OB O.I O.1S O.2 O.2B O.3
Sources: CAKB (1890b; 1890e; 19901; 1890g); nondetects set equal to zero.
Figure 4-5a. Congener and Congener Group Profiles for Air Emissions
from Industrial Wood Combustors
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Ratio (congener emission factor / total CDD/CDF emission factor)
O 0.1 0.2 0.3 0.4
2,3,7,8-TCDD
1,2,3,7,8-PeCDD
1,2,3,4,7,8-HxCDD
1,2,3,6,7,8-HxCDD
1,2,3,7,8,9-HxCDD
1,2,3,4,6,7,8-HpCDD
1,2,3,4,6,7,8,9-OCDD
2,3,7,8-TCDF
1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
1,2,3,4,7,8-HxCDF
1,2,3,6,7,8-HxCDF
1,2,3,7,8,9-HxCDF
2,3,4,6,7,8-HxCDF
1,2,3,4,6,7,8-HpCDF
1,2,3,4,7,8,9-HpCDF
1,2,3,4,6,7,8,9-OCDF
Ratio (congener emission factor / total CDD/CDF emission factor)
O.I 0.2 O.3
O.4
e: NCASI (1995); nondetecta set equal to
Figure 4-5b Congener and Congener Group Profiles for Air Emissions
from Bleached Kraft Mill Bark Combustors
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Ratio (congener group emission factor / total CDD/CDF emission factor)
0.1 0.2 0.3
0.4
Source: U.S. EPA (1995c)
Figure 4-6. Congener Group Profile for Air Emissions from Residential Oil-fueled Furnaces
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Ratio Factor* (coxiorex
O O.OS
fir emission factor / total CDD/CDF emission factor)
0.1 0.1S 0.2 0.25
2 , 3 , 7 , 8-TCDE)
1 ,2,3,7,8-PeCDD
1 ,2,3,4,7,S-H-x:CE>D
1 ,2,3,e,7,8-H-x:CE)D
1 ,2,3,7,8,9-HxCDD
1,2,3,4,6,7,8-H-pCDD
1,2,3,4,6,T,8,9-OCE>E)
2,3,7,8-TCDF
1 ,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
1 ,2,3,4,7,8-H-KCDF
1 ,2,3,6,7,8-HxClDF
2,3,4,6,7,8-HxCDF
1 ,2,3,4,6,7,8-H-pCDF
1 ,2,3,4,7,8,S-H-pCE)F
1 ,2,3,4,6,-Z,8,9-OCE)F
Ratio (congener group emission factor / total CDD/CDF emission factor)
O.OS O.I O.15 O.2
O.25
Source: U.S. EPA (1995c; 1997h>)
Figure 4-7. Congener and Congener Group Profiles for Air Emissions from Industrial Oil-fueled Boilers
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Ratio (convener emission factor / total GDD/GDF emission factor)
O 0.01 0.02 0.03 0.04 O.OS O.O6 O.O7 O.O8 O.O9
2,3,7,8-TCDD
1,2,3,7,S-PeCDD
1,2,3,4,7,8-HxCDD
1,2,3,6,7,8-HxCDD
1,2,3,7,8,9-HxCDD
1,2,3,4,6,7,8-HpCDD
1,2,3,4,6,7,8,9-OCDD
2,3,7,8-TODF
1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
1,2,3,4,7,8-HxCDF
1,2,3,6,7,8-HxCDF
1,2,3,7,8,9-HxCDF
2,3,4,6,7,8-HxCDF
1,2,3,4,6,7,8-HpCDF
1,2,3,4,7,8,9-HpCDF
1,2,3,4,6,7,8,9-OCDF
Ratio (congener group emission factor / total CDD/CDF emission factor)
O.OS O.I O.15 O.2 0.25 O.3
O.35
ource: EPR1 (1984): nondetects set equal to zero.
Figure 4-8. Congener and Congener Group Profiles for Air Emissions
from Industrial/Utility Coal-fueled Combustors
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Ratio (congener group emission factor / total CDD/CDF emission factor)
0.1 0.2 0.3
I Anthracite (U.S. EPA, 1997b)
Bituminous (U.S. EPA, 1997b)
Figure 4-9. Congener Group Profile for Air Emissions from Residential Coal-fueled Combustors
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5. COMBUSTION SOURCES OF CDD/CDF: OTHER HIGH TEMPERATURE SOURCES
5.1. CEMENT KILNS AND LIGHTWEIGHT AGGREGATE KILNS
This section addresses CDD/CDF emissions from portland cement kilns. These
facilities use high temperatures to convert mineral feedstocks into portland cement and
other types of construction materials. For purposes of this analysis, cement kilns have
been subdivided into two categories, those that burn hazardous waste and those that do
not, and these two subcategories are further divided into kilns with inlet APCD
temperatures above and below 450°F. The following subsections describe cement kiln
technology, the derivation of TEQ emission factors for cement kilns that burn hazardous
waste as supplemental fuel and those that do not, and the derivation of annual TEQ air
emissions (g/yr) for 1995 and 1987.
Lightweight aggregate kilns that combust liquid hazardous wastes are not
addressed in detail in this report. Only 5 of the more than 36 lightweight aggregate kilns
in the United States combust hazardous waste. Those facilities are estimated to have
emitted 3.3 g I-TEQDF to air in 1990 (Federal Register, 1998b) and 2.4 g I-TEQDF in 1997
(Federal Register, 1999b). These estimates are used in this report as the estimates for
reference years 1987 and 1995, respectively. Regulations issued by EPA under the Clean
Air Act (CAA) and Resource Conservation and Recovery Act (RCRA) in 1999 (Federal
Register, 1999b) are expected to reduce those emissions to 0.4 g I-TEQDF within the next
3 to 4 years.
5.1.1. Process Description of Portland Cement Kilns
In the United States, the primary cement product is portland cement. Portland
cement is a fine, grayish powder consisting of a mixture of four basic materials: lime,
silica, alumina, and iron compounds. Cement production involves heating (pyroprocessing)
the raw materials to a very high temperature in a rotary (rotating) kiln to induce chemical
reactions that produce a fused material called clinker. The cement clinker is then ground
into a fine powder and mixed with gypsum to form the portland cement. The cement kiln
is a large, steel, rotating cylindrical furnace lined with refractory material. The kiln is
aligned on a slight angle, usually a slope of 3 to 6°. This allows the materials to pass
through the kiln by gravity. The upper end of the kiln, known as the "cold" end, is where
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the raw materials, or meal, are generally fed into the kiln. Midpoint injection is practiced
at some facilities. The lower end of the kiln is known as the hot end. The hot end is
where the combustion of primary fuels (usually coal and petroleum coke) transpires to
produce a high temperature. The cement kiln is operated in a counter-current
configuration, in which the hot combustion gases are convected up through the kiln while
the raw materials pass down toward the lower end. The kiln rotates about 50 to 70
revolutions per hour, and the rotation induces mixing and the forward progress of mixed
materials. As the meal moves through the cement kiln and is heated by the hot
combustion gases, water is vaporized and pyroprocessing of materials occurs.
When operating, the cement kiln can be viewed as consisting of three temperature
zones necessary to produce cement clinker. Zone 1 is at the upper end of the kiln where
the raw meal is added. Temperatures in this zone typically range from ambient up to
600°C. In this area of the kiln, moisture is evaporated from the raw meal. The second
thermal zone is known as the calcining zone. Calcining occurs when the hot combustion
gases from the combustion of primary fuels dissociates calcium carbonate from the
limestone to form calcium oxide. In this region of the kiln, temperatures range from
600°C to 900°C. Zone 3 is known as the burning or sintering zone. The burning zone,
the lowest region of the kiln, is the hottest. Here temperatures in excess of 1,500°C
induce the calcium oxide to react with silicates, iron, and aluminum in the raw materials to
form cement clinker. The formation of clinker actually occurs close to the combustion of
primary fuel. The chemical reactions that occur in Zone 3 are referred to as
pyroprocessing.
The cement clinker, which leaves the kiln at the hot end, is a gray, glass-hard
material consisting of dicalcium silicate, tricalcium silicate, calcium aluminate, and
tetracalcium aluminoferrite. At this point, the clinker is about 1,100°C. The hot clinker is
then dumped onto a moving grate, where it cools as it passes under a series of cool air
blowers. Once cooled to ambient temperature, the clinker is ground into a fine powder
and mixed with gypsum to produce the Portland cement product.
Cement kilns can be either wet or dry processes. In the wet process, the raw
materials are ground and mixed with water to form a slurry, which is fed into the kiln
through a pump. This is an older process. A greater amount of heat energy is needed in
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the wet process than in other types of kilns. These kilns consume about 5 to 7 trillion
BTUs per ton of clinker product to evaporate the additional water.
In the dry process, a preheater is used to dry the raw meal before it enters the kiln.
A typical preheater consists of a vertical tower containing a series of cyclone-type vessels.
Raw meal is added at the top of the tower, and hot exhaust gases from the kiln operation
preheat the meal, thus lowering the fuel consumption of the kiln. Dry kilns are now the
most popular cement kiln type. Portland cement clinker production in the United States is
estimated to have been 67.6 billion kg in 1995 and 52 billion kg in 1987 (U.S.
Department of Commerce, 1996).
5.1.2. Cement Kilns That Burn Hazardous Waste
The high temperatures achieved in cement kilns make the kilns an attractive
technology for combusting hazardous waste as supplemental fuel. Sustaining the
relatively high combustion temperatures (1,100°C to 1,500°C) that are needed to form
cement clinker requires the burning of a fuel with a high energy output. Therefore, coal or
petroleum coke is typically used as the primary fuel source. Because much of the cost of
operating the cement kiln at high temperatures is associated with the consumption of
fossil fuels, some cement kiln operators have elected to burn hazardous liquid and solid
waste as supplemental fuel. Currently about 75 percent of the primary fuel is coal.
Organic hazardous waste may have a similar energy output as coal (9,000 to 12,000
Btu/lb for coal). The strategy of combusting the waste as supplemental fuel is to offset
the amount of coal/coke that is purchased and burned by the kiln. The operator may
charge a disposal fee to the waste generator for the right to combust the hazardous waste
at the kiln, which also offsets the cost of kiln operation. Much of the high-energy and
ignitable wastes primarily comprise such diverse substances as waste oils, spent organic
solvents, sludges from the paint and coatings industry, waste paints and coatings from
auto and truck assembly plants, and sludges from the petroleum refining industry (Greer et
al., 1992).
The conditions inherent in the cement kiln mimic conditions of hazardous waste
incineration. For example, the gas residence time in the burning zone is typically three
seconds while at temperatures in excess of 1,500°C (Greer et al., 1992). The method of
introducing liquid and solid hazardous waste into the kiln is a key factor to the complete
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consumption of the waste during the combustion of the primary fuel. Liquid hazardous
waste is either injected separately or blended with the primary fuel (coal). Solid waste is
mixed and burned along with the primary fuel. Trial burns have consistently shown that
99.99 to 99.9999 percent destruction and removal efficiencies for the very stable organic
wastes can be achieved in cement kilns (Greer et al., 1992). However, although the
combustion of hazardous waste as supplemental or substitute fuel does have apparent
advantages, only 16 percent of the Portland cement kilns (34 of 212 kilns) in the United
States combusted hazardous waste in 1995 (Federal Register, 1996b). Other types of
supplemental fuel used by these facilities include automobile tires, used motor oil,
sawdust, and scrap wood chips.
5.1.3. Air Pollution Control Devices Used on Cement Kilns
The pyroprocessing of raw meal in a cement kiln produces fine particulates,
referred to as cement kiln dust. Cement kiln dust is collected and controlled with fabric
filters or electrostatic precipitators, or both. Acid gases such as S02 can be formed during
pyroprocessing of the sulfur-laden minerals, but the minerals have high alkalinity, which
neutralizes S02 gases. Most particulate matter (PM) control devices used at cement kilns
in 1995 and 1987 were considered to be hot-side control devices. A hot-sided control
device is one that operates at flue gas temperatures above 450°F (some EPA rules use
different definitions for hot-sided control devices for different industries).
Reducing the flue gas temperature in the PM control device is one factor shown to
have a significant impact on limiting dioxin formation and emissions at cement kilns (U.S.
EPA, 1997d). Recent emissions testing at a Portland cement kiln showed that CDD/CDFs
were almost entirely absent at the inlet to a hot-sided ESP, but CDDs and CDFs were
measured at the exit (U.S. EPA, 1997d), showing conclusively that dioxins were formed
within the hot-sided ESP. Reducing the flue gas temperature in the PM control device to
below 450°F has been shown to substantially limit CDD/CDF formation at cement kilns.
Lower temperatures are believed to prevent the post-combustion catalytic formation of
CDD/CDFs. Consequently a number of cement kilns have added flue gas quenching units
upstream of the APCD to reduce the inlet APCD temperature, thereby reducing CDD/CDF
stack concentrations. A quenching unit usually consists of a water spray system within
the flue duct. Thus, current CDD/CDF emissions from cement kilns are believed to be
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substantially lower than CDD/CDF emissions in 1987 and 1995; EPA/OAQPS estimated
emissions to be 13.1 g I-TEQDF in 1997 (Federal Register, 1999b).
5.1.4. CDD/CDF Emission Factors for Cement Kilns
For purposes of deriving emission factors, the general strategy used in this
document is to consider subdividing each source category on the basis of design and
operation. However, cement kilns are relatively uniform in terms of kiln design, raw feed
material, temperatures of operation, and APCDs. Therefore, no subdivisions were made
on these bases. An important potential difference among kilns, however, is whether or
not hazardous waste is burned as a supplementary fuel. The source emissions database
used in this report contains CDD/CDF emissions data for 16 cement kilns burning
hazardous waste and 15 cement kilns not burning hazardous waste as reported in U.S.
EPA (1996c). The majority of stack emissions data from cement kilns burning hazardous
waste were derived during trial burns and may overestimate the CDD/CDF emissions that
most kilns achieve during normal operations. Stack emissions data from kilns not burning
hazardous waste were derived from testing during normal operations. The average TEQ
emission factors are 20.91 ng l-TEQDF/kg clinker produced (22.48 ng TEQDF-WH098/kg
clinker) and 0.27 ng l-TEQDF/kg clinker produced (0.29 ng TEQDF-WH098/kg clinker) for
cement kilns burning and not burning hazardous waste, respectively. Accordingly, the
average emission factor for kilns burning hazardous waste is about 90 times greater than
that for kilns not burning hazardous waste. As discussed in Section 5.1.6 (Cement Kiln
Dust), a comparison of CDD/CDF concentrations in cement kiln dust samples from cement
kilns burning and not burning hazardous waste shows a similar relationship (i.e., the
cement kiln dust from kilns burning hazardous waste had about 100 times higher
CDD/CDF TEQ concentration than dust from kilns not burning hazardous waste).
Although the average emission factors for the two groups of kilns differ
substantially, the emission factors for individual kilns in the two groups overlap.
Therefore, other aspects of the design and operation of the kilns are likely to be affecting
CDD/CDF emissions, particularly the temperature of the APCD equipment as discussed in
Section 5.1.3.
Previous attempts to understand this issue through parametric testing of cement
kilns have yielded mixed results. EPA conducted a limited comparison of CDD/CDF TEQ
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stack gas concentrations (ng TEQ/dscm) between cement kilns burning hazardous wastes
and not burning hazardous wastes (U.S. EPA, 1997d). Those comparisons were made at
14 cement kilns. Operating conditions (e.g., APCD temperature), with the exception of
the fuel being burned, were the same or similar for each set of comparisons. Baseline
conditions used coal as the only primary fuel. The results of these comparisons showed
the following:
Seven kilns in which the baseline (i.e., no combustion of hazardous waste)
CDD/CDF TEQ stack gas concentrations were about the same as that for the
burning of hazardous wastes.
Two kilns in which the baseline CDD/CDF I-TEQDF stack gas concentrations
were about double that for the burning of hazardous wastes.
Five kilns in which the hazardous waste CDD/CDF I-TEQDF stack gas
concentrations were substantially greater (from 3 to 29 times greater) than
that for the baseline operating conditions.
Subsequently, EPA/ORD conducted analyses of the available emissions data to
evaluate, on a congener-specific basis, whether there were significant differences in
emission factors between (a) kilns burning hazardous waste and those not burning
hazardous waste; (b) kilns with APCD inlet temperatures greater than 450°F and those
with temperature less than 450°F; (c) hazardous waste burning and non-hazardous waste
burning facilities with APCD inlet temperatures greater than 450°F; (d) hazardous waste
burning and non-hazardous waste burning facilities with APCD inlet temperatures less
than 450°F; (e) hazardous waste burning facilities with APCD inlet temperatures less than
or greater than 450°F; and (f) non-hazardous waste burning facilities with APCD inlet
temperatures less than or greater than 450°F. The results of all analyses showed
significant differences in the sample mean values (p<0.05).
Currently no satisfactory explanation exists for the apparent differences in the
emission factors. Given the strong empirical evidence that real differences may exist,
EPA/ORD has decided to treat the kilns burning hazardous waste separately from those
not burning hazardous waste for the purposes of developing a CDD/CDF emissions
inventory, and to subdivide the hazardous waste burning category into subcategories by
APCD inlet temperature (i.e., less than 450°F or greater than 450°F). APCD inlet
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temperature data were available for 88 test runs at 14 cement kilns. The number of test
runs conducted at individual kilns ranged from 1 to 26. Each test run was treated as an
individual facility and each was classified according to APCD inlet temperature and
whether or not hazardous waste was burned. The emission factor (EF) for each cement
kiln test run was calculated using Equation 5-1.
EF
C x F
ck
(Eq. 5-1)
Where:
EF
ck
\r
Cement kiln emission factor (burning or not burning hazardous
waste), (ng TEQ/kg of clinker produced)
TEQ or CDD/CDF concentration in flue gases (ng TEQ/dscm)
(20°C, 1 atm; adjusted to 7% 02)
Volumetric flue gas flow rate (dscm/hr) (20°C, 1 atm;
adjusted to 7% 02)
Average cement kiln clinker production rate (kg/hr)
After developing the emission factor for each cement kiln test run, the overall average
congener-specific emission factor was derived for all test runs in each subcategory using
Equation 5-2 below.
EF
avgCK
EF
CK,
EFCK/
N
.+ EF
CKN
(Eq. 5-2)
Where:
EF
avgCK
Average emission factor of tested
cement kilns burning hazardous
waste as supplemental fuel and
with APCD inlet temperatures
either greater than or less than
450°F (ng TEQ/kg clinker)
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5-7
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N = Number of cement kiln test runs
The average emission factors representing these categories of cement kilns are
summarized in Table 5-1. Because the same test reports were used, the emission factors
are the same for both the 1995 and 1987 reference years. Average congener and
congener group profiles for cement kilns burning hazardous waste are presented in Figure
5-1 and for cement kilns not burning hazardous wastes in Figure 5-2.
5.1.5. National Estimates of CDD/CDF Emissions from Cement Kilns
Non-hazardous waste burning cement kilns produced 61.3 billion kg of cement
clinker in 1995 (Heath, 1995). Since a total of 67.6 billion kg of cement clinker were
produced in the United States in 1995 (U.S. DOC, 1996), it follows that cement kilns
burning hazardous waste produced 6.3 billion kg of clinker, or 9.3 percent of the clinker
produced. In 1987, approximately 52 billion kg of cement clinker were produced (U.S.
DOC, 1996). If it is assumed that 9.3 percent of this total clinker production was from
kilns burning hazardous waste, then about 4.8 billion kg of clinker were produced in
hazardous waste burning kilns in 1987. These activity level estimates are given a high
confidence rating for 1995 because they are based on recent survey data, but a medium
rating for 1987 because of uncertainty concerning the proportion produced by hazardous
waste burning kilns (U.S. EPA, 1996c).
The TEQ emission factors are given a low confidence rating for all subcategories.
The emission factor for non-hazardous waste burning kilns was given a low rating
because test data were available for only 15 of 178 facilities. The tested facilities may
not be representative of routine CDD/CDF emissions from all kilns not burning hazardous
waste. Although a higher percentage of the kilns burning hazardous waste (with reported
APCD temperature data) had been tested (10 out of 34; eight with APCD inlet
temperatures greater than 450°F and two with temperatures less than 450°F), greater
uncertainty exists about whether the emissions are representative of normal operations
because trial burn procedures were used. Accordingly, a low confidence rating was also
assigned to the estimated emissions factors for kilns burning hazardous waste.
National estimates of CDD/CDF air emissions (grams TEQ per year) from all
Portland cement kilns operating in 1995 and 1987 were made by multiplying the average
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TEQ emission factors by an estimate of the annual activity level (cement clinker produced)
for each of the three subcategories (hazardous waste burning kilns with APCD inlet
temperatures greater than 450°F, hazardous waste burning kilns with APCD inlet
temperatures less than 450°F, and kilns not burning hazardous waste).
Of the 10 hazardous waste burning kilns with APCD temperature data, 8 facilities
(80 percent) had APCD inlet temperatures greater than 450°F; 2 (20 percent) had APCD
inlet temperatures less than 450°F. If it is assumed that the percentages of hazardous
waste burning kilns less than and greater than 450°F represent the actual distribution of
activity level in the industry, then one can use these percentages, coupled with the TEQ
emission factors presented in Table 5-1 and the activity levels established at the beginning
of this section, to calculate the annual national TEQ emission estimates shown below.
Reference Year 1995
Category
HW >450°F
HW <450°F
NHW
TEQ Emission Factor
(ng/kg clinker)
I-TEQDF
28.58
1.04
0.27
TEQDF-WH098
30.70
1.11
0.29
TOTAL
Activity Level
(billion kg
clinker/yr)
5.04
1.26
61.3
67.6
Annual TEQ Emission
(g/yr)
I-TEQDF
144.0
1.3
16.6
162
TEQDF-WH098
154.7
1.4
17.8
174
Reference Year 1987
Category
HW >450°F
HW <450°F
NHW
TEQ Emission Factor
(ng/kg clinker)
I-TEQDF
28.58
1.04
0.27
TEQDF-WH098
30.70
1.11
0.29
TOTAL
Activity Level
(billion kg
clinker/yr)
3.8
1.0
47.2
52.0
Annual TEQ Emission
(g/yr)
I-TEQDF
108.6
1.0
12.7
122
TEQDF-WH098
116.7
1.1
13.7
132
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5.1.6. Recent EPA Regulatory Activities
In May 1999, EPA promulgated national emission standards under the authority of
the Clean Air Act for hazardous air pollutants (including CDD/CDFs) for new and existing
cement kilns not burning hazardous waste (Federal Register, 1999a). EPA/OAQPS
expects this rule to reduce emissions of I-TEQDF by existing and new facilities by 36
percent over the next 5 years (i.e., from an estimated 44 g I-TEQDF in 1997 to 29 g I-
TEQDF per year).
In July 1999, EPA promulgated national emission standards under the joint
authority of the Clean Air Act and the Resource Conservation and Recovery Act for
hazardous air pollutants (including CDD/CDFs) for hazardous waste combustion facilities
(including cement kilns burning hazardous waste). Within the next 3 to 4 years under the
final emissions limits, emissions of I-TEQDF by hazardous waste burning facilities are
projected by EPA/OAQPS to be reduced by 40 percent (i.e., from an estimated 13.1 g I-
TEQDF in 1997 to 7.7 g I-TEQDF per year) (Federal Register, 1999b).
5.1.7. Solid Waste from Cement Manufacturing
The solid residual generated during the manufacturing of cement is known as
Cement Kiln Dust (CKD). EPA characterized CKD in a Report to Congress (U.S. EPA,
1993g). The report was based in part on a 1991 survey of cement manufacturers
conducted by the Portland Cement Association (PCA). Survey responses were received
from 64 percent of the active cement kilns in the United States. On the basis of the
survey responses, EPA estimated that in 1990 the U.S. cement industry generated about
12.9 million metric tons of gross CKD and 4.6 million metric tons of "net CKD," of which
4.2 million metric tons were land disposed. The material collected by the APCD system is
called "gross CKD" (or "as generated" CKD). The gross CKD is either recycled back into
the kiln system or is removed from the system for disposal (i.e., "net CKD" or "as
managed" CKD) (U.S. EPA, 1993g).
In support of the Report to Congress, EPA also conducted sampling and analysis
during 1992 and 1993 of CKD and clinker. The purposes of the sampling and analysis
efforts were to: (1) characterize the CDD/CDF content of clinker and CKD; (2) determine
the relationship, if any, between the CDD/CDF content of CKD and the use of hazardous
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waste as fuel; and (3) determine the relationship, if any, between the CDD/CDF content of
CKD and the use of wet process versus dry process cement kilns (U.S. EPA, 1993g).
Clinker samples were collected from five kilns not burning hazardous waste and six
kilns burning hazardous waste (U.S. EPA, 1993g). CDD/CDFs were not detected in any
cement kiln clinker samples. Tetra- through octa-chlorinated CDDs and CDFs were
detected in the gross CKD samples obtained from 10 of the 11 kilns and in the net CKD
samples obtained from 8 of the 11 kilns. The CDD/CDF content of gross CKD ranged
from 0.008 to 247 ng l-TEQDF/kg and from 0.045 to 195 ng l-TEQDF/kg for net CKD.
Analyses for seven PCB congeners were also conducted, but no congeners were detected
in any clinker or CKD sample. The mean CDD/CDF concentrations in net CKD generated
by the kilns burning hazardous waste are higher (35 ng l-TEQDF/kg) than in net CKD
generated by the facilities not burning hazardous waste (3.0E-02 ng l-TEQDF/kg). These
calculations of mean values treated nondetected values ("nondetects") as zero. If the
nondetected values had been excluded from the calculation of the means, the mean value
for net CKD from kilns burning hazardous waste would increase by a factor of 1.2, and
the mean value for net CKD from kilns not burning hazardous waste would increase by a
factor of 1.7. One sampled kiln had a net CKD TEQ concentration more than two orders
of magnitude greater than the TEQ levels found in samples from any other kiln. If this kiln
was considered atypical of the industry (U.S. EPA, 1993g) and was not included in the
calculation, then the mean net CKD concentration for hazardous waste burning kilns
decreases to 2.9 ng l-TEQDF/kg.
In a recent report (Washington, 1998), CDD/CDF congener data for CKD from
Holnam, Inc., Seattle, Washington, were presented. The data were compiled and
evaluated to determine total I-TEQ concentrations and loadings. Non-detect values were
included as either zero, 1/2 DL, or at the DL. The results of three separate tests were as
follows, assuming that non-detect values were at zero concentration:
Date Location I-TEQ (ng/kg) I-TEQ (mg/day)
5/15/96 not stated 0.038 0.0038
10/21/97 HLMN Bin 0.67 0.0674
10/21/97 HLMN Final 0.95 0.0948
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In a recently compiled database, EPA (1999d) provided data for ashes from an
electric static precipitator connected to a cement kiln and a fabric filter connected to a
light weight aggregate (LWA) kiln. The average congener concentrations for the ash
samples are listed in Table 5-8.
The average concentrations for the cement kiln were determined from two different
waste streams, each with five sample burns. The average concentrations for the LWA
kiln were determined using one waste stream with three sample burns.
All CKD is normally disposed of in engineered landfills and is consequently not
categorized as an environmental release as defined in this emission inventory. The
amount of CDD/CDF associated with these materials is calculated for informational
purposes. The estimate of land-disposed CKD from the 1991 PCA survey (4.2 million
metric tons per year; basis year is 1990) was divided among kilns that burn hazardous
waste (34 kilns) and those that do not (1 78 kilns) on the basis of the number of kilns in
each category. The average TEQ concentration in the net CKD from kilns burning
hazardous waste (including the high value discussed above) was 35 ng l-TEQDF/kg. For
kilns that do not burn hazardous waste, the average concentration in the net CKD was
3.0E-02 ng l-TEQDF/kg. Multiplying these average concentrations by the estimated annual
net CKD production yields 24 g l-TEQDF/yr for kilns burning hazardous waste and 0.1 g I-
TEQDF/yr for kilns not burning hazardous waste, a total of 24.1 g l-TEQDF/yr for all kilns in
1990.
EPA is currently developing cement kiln dust storage and disposal requirements
(Federal Register, 1999b).
5.2. ASPHALT MIXING PLANTS
Asphalt consists of an aggregate of gravel, sand, and filler mixed with liquid
asphalt cement or bitumen. Filler typically consists of limestone, mineral stone powder,
and sometimes ash from power plants and municipal waste combustors. The exact
composition of an asphalt formulation depends on how it will be used. The aggregate
typically constitutes over 92 percent by weight of the total asphalt mixture. The
components of the aggregate are dried, heated to a temperature ranging from 275 to
325°F, and then mixed and coated with the bitumen at an asphalt mixing installation.
"Old" asphalt (i.e., asphalt from dismantled bridges and roads) can be heated and
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disaggregated to its original components and reused in the manufacture of new asphalt
(U.S. EPA, 1996i).
No data are available on levels of CDD/CDF emissions, if any, from U.S. asphalt
mixing operations. However, limited data are available for facilities in The Netherlands and
Germany.
Bremmer et al. (1994) reported the CDD/CDF emissions factor for an asphalt
mixing plant in The Netherlands at 47 ng I-TEQDF per metric ton of produced asphalt. No
congener-specific emission factors were reported. The facility they tested heated old
asphalt to about 150°C in an individual recycling drum with flue gases that were mixed
with ambient air and heated to a temperature of 300-400°C. Parallel to this recycling
drum, the main drum dried and heated the aggregate (sand and gravel/granite chippings)
to a temperature of about 220°C. The flue gases leaving the recycling drum were led
along the main burner of the main drum for incineration. The old asphalt, the minerals
from the main drum, and new bitumen from a hot storage tank (about 180°C) were mixed
in a mixer to form new asphalt. Natural gas fueled the tested facility during the sample
collection period and used old asphalt as 46 percent of the feed. The facility's APCD
system consisted of cyclones and a fabric filter.
Umweltbundesamt (1996) reported lower emission factors for three tested facilities
in Germany that were also equipped with fabric filters. These three facilities were fueled
by oil or butane gas and used old asphalt at rates ranging from 30 to 60 percent of the
feed. The emission factors calculated from the stack gas concentrations, gas flow rates,
and hourly throughputs for these three facilities were 0.2, 3.5, and 3.8 ng l-TEQDF/metric
ton of asphalt produced.
Approximately 25 million metric tons of asphalt bitumen were produced in the
United States in 1992. An identical quantity was produced in 1990 (U.S. DOC, 1995a).
Bitumen constitutes approximately 5 percent by weight of finished paving asphalt
(Bremmer et al., 1994). Thus, an estimated 500 million metric tons of paving asphalt are
produced in the United States annually.
Because there are no direct measurements of CDD/CDF emissions from U.S.
asphalt plants and because of uncertainties regarding the comparability of U.S. and Dutch
asphalt plant technologies and feed materials, no national emission estimate for this
category is proposed at this time. However, a preliminary estimate of the potential
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magnitude of annual TEQ emissions for U.S. production of asphalt can be obtained by
averaging the emission factors for the four facilities reported by Bremmer et al. (1994) and
Umweltbundesamt (1996). Applying this average emission factor (i.e., 14 ng
l-TEQDF/metric ton of asphalt produced) to the activity level of 500 million metric tons of
paving asphalt produced annually yields an annual emission of 7 g l-TEQDF/yr. This
estimate should be regarded as a preliminary indication of possible emissions from this
source category; further testing is needed to confirm the true magnitude of these
emissions. Congener-specific results were not reported in either report. Therefore, TEQDF-
WH098 estimates could not be calculated.
5.3. PETROLEUM REFINING CATALYST REGENERATION
Regeneration of spent catalyst from the reforming process at petroleum refineries is
a potential source of CDDs and CDFs according to limited testing conducted in the United
States (Amendola and Barna, 1989; Kirby, 1994), Canada (Maniff and Lewis, 1988;
Thompson et al., 1990), and The Netherlands (Bremmer et al., 1994). This section
summarizes the catalyst regeneration process, relevant studies performed to date, and the
status of EPA regulatory investigations of this source.
Catalytic reforming is the process used to produce high-octane reformates from
lower octane reformates for blending of high-octane gasolines and aviation fuels. The
reforming process occurs at high temperature and pressure and requires the use of a
platinum or platinum/rhenium catalyst. During the reforming process, a complex mixture
of aromatic compounds, known as coke, is formed and deposited onto the catalyst. As
coke deposits onto the catalyst, its activity is decreased. The high cost of the catalyst
necessitates its regeneration. Catalyst regeneration is achieved by removing the coke
deposits via burning at temperatures of 750 to 850°F and then reactivating the catalyst at
elevated temperatures (850 to 1,000°F) using chlorine or chlorinated compounds (e.g.,
methylene chloride, 1,1,1-trichloroethane, and ethylene dichloride). Burning of the coke
produces flue gases that can contain CDDs and CDFs along with other combustion
products. Because flue gases, if not vented directly to the atmosphere, may be scrubbed
with caustic or water, internal effluents may become contaminated with CDD/CDFs (Kirby,
1994; SAIC, 1994).
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There are three basic catalyst regeneration processes used: semi-regenerative,
cyclic, and continuous. During the semi-regenerative process, the entire catalytic reformer
is taken off-line. In the cyclic process, one of two (or more) reforming reactors is taken
off-line for catalyst regeneration; the remaining reactor(s) remains on-line so that reforming
operations continue. In the continuous process, aged catalyst is continuously removed
from one or more on-line stacked or side-by-side reactors, regenerated in an external
regenerator, and then returned to the system; the reforming system, consequently, never
shuts down (SAIC, 1994).
In 1988, the Canadian Ministry of the Environment detected concentrations of
CDDs in an internal waste stream of spent caustic in a petroleum refinery that ranged from
1.8 to 22.2 //gL, and CDFs ranging from 4.4 to 27.6 //g/L (Maniff and Lewis, 1 988). The
highest concentration of 2,3,7,8-TCDD was 0.0054/vg/L. CDDs were also observed in
the refinery's biological sludge at a maximum concentration of 74.5/vg/kg, and CDFs were
observed at a maximum concentration of 125/vg/kg. The concentration of CDD/CDFs in
the final combined refinery plant effluent was below the detection limits.
Amendola and Barna (1989) reported detecting trace levels of hexa- to octa-CDDs
and CDFs in untreated wastewaters (up to 2.9 pg I-TEQDF/L) and wastewater sludges
(0.26 to 2.4 ng l-TEQDF/kg) at a refinery in Ohio. The levels of detected total CDD/CDFs
in the wastewater and sludge were much lower (<3 ng/L and < 1 /vg/kg, respectively)
than the levels reported by Maniff and Lewis (1988). No CDD/CDFs were detected in the
final treated effluent (i.e., less than 0.2 ng I-TEQDF/L). The data collected in the study
were acknowledged to be too limited to enable identifying the source(s) of the CDD/CDFs
within the refinery. Amendola and Barna (1989) also present in an appendix to their
report the results of analyses of wastewater from the catalyst regeneration processes at
two other U.S. refineries. In both cases, untreated wastewaters contained CDDs and
CDFs at levels ranging from high pg/L to low ng/L (results were reported for congener
group totals, not specific congeners). However, CDD/CDFs were not detected in the only
treated effluent sample collected at one refinery.
Thompson et al. (1990) reported total CDD and CDF concentrations of 8.9 ng/m3
and 210 ng/m3, respectively, in stack gas samples from a Canadian petroleum refinery's
reforming operation. They also observed CDDs and CDFs in the internal wash water from
a scrubber of a periodic/cyclic regenerator in the pg/L to ng/L range.
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Beard et al. (1993) conducted a series of benchtop experiments to investigate the
mechanism(s) of CDD/CDF formation in the catalytic reforming process. A possible
pathway for the formation of CDFs was found, but the results could not explain the
formation of CDDs. Analyses of the flue gas from burning coked catalysts revealed the
presence of unchlorinated dibenzofuran (DBF) in quantities up to 220//g/kg of catalyst.
Chlorination experiments indicated that DBF and possibly biphenyl and similar
hydrocarbons act as CDF precursors and can become chlorinated in the catalyst
regeneration process. Corrosion products on the steel piping of the process plant seem to
be the most likely chlorinating agent.
In May 1994, EPA's Office of Water conducted a sampling and analytical study of
catalyst regeneration wastewater for CDD/CDFs at three petroleum refining plants (Kirby,
1994). The study objectives were to determine the analytical method best suited for
determining CDD/CDFs in refinery wastewater and to screen and characterize wastewater
discharges from several types of reforming operations for CDD/CDFs. The report for this
study (Kirby, 1994) also presented results submitted voluntarily to EPA by two other
facilities. The sampled internal untreated wastewaters and spent caustics were found to
contain a wide range of CDD/CDF concentrations, 0.1 pg I-TEQDF/L to 57.2 ng I-TEQDF/L.
The study results also showed that 90 percent of the TEQ was contained in the
wastewater treatment sludges generated during the treatment of wastewater and caustic
from the regeneration process.
In 1995, EPA issued a notice of its proposed intent not to designate spent reformer
catalysts as a listed hazardous waste under RCRA (Federal Register, 1995b). The final
rule was issued in August 1998 (Federal Register, 1998a). The Agency's assessment of
current management practices associated with recycling of reforming catalyst found no
significant risks to human health or the environment. The Agency estimated that 94
percent of the approximately 3,600 metric tons of spent reformer catalyst sent off-site by
refineries are currently recycled for their precious-metal content. However, EPA made no
determination of the "listability" of spent caustic residuals formed during regeneration of
spent reforming catalyst. The Agency did identify as being possibly of concern potential
air releases from the combustion of the reforming catalyst prior to reclamation. The
Agency requested comments on (a) opportunities for removing dioxin prior to discharge of
scrubber water into the wastewater treatment system; (b) opportunities to segregate this
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wastestream; and (c) potential health risks associated with insertion of dioxin-
contaminated media back into the refinery process (such as the coker). In this proposed
rulemaking, EPA also noted the possibility of dioxin releases to air during regeneration
operations.
As part of its regulatory investigation under RCRA, EPA's OSW commissioned a
study to analyze and discuss existing data and information concerning CDD/CDF formation
in the treatment of catalytic reformer wastes. This report (SAIC, 1994) also identified
potential process modifications that may prevent the formation of CDD/CDFs. SAIC
(1994) concluded that, although the available data indicate that CDD/CDFs can be
generated during the catalyst regeneration process, the available data indicate that
CDD/CDF concentrations in treated wastewater and in solid waste are minimal. Releases
to air could result from vented flue gases at some facilities. In addition, the CDD/CDFs
formed could possibly be reintroduced into other refining operations (e.g., the coker) and
resulting products.
In 1998, emissions from the caustic scrubber used to treat gases from the external
regeneration unit of a refinery in California were tested (CARB, 1999). This facility uses a
continuous regeneration process. The reactor is not taken off-line during regeneration;
rather, small amounts of catalyst are continuously withdrawn from the reactor and are
regenerated. The emissions from the regeneration unit are neutralized by a caustic
scrubber before being vented to the atmosphere. The catalyst recirculation rate during the
three tests ranged from 733 to 1,000 Ibs/hr.
All 2,3,7,8-substituted CDD/CDFs were detected in each of the three samples
collected. The average emission factors in units of ng/barrel of reformer feed are
presented in Table 5-2. The congener profile is presented in Figure 5-3. The samples
showed a wide range in concentrations of the CDD/CDF congeners (up to fivefold
difference); however, the congener profile was consistent in all samples. The
concentrations of the individual furan congener groups were always higher than the
concentrations of the corresponding dioxin congener group. The average I-TEQDF emission
factor for these three tests is 3.04 ng TEQ/barrel and the average TEQDF-WH098 is 3.18
ng TEQ/barrel.
In 1991, stack testing was performed on the exhaust from one of three semi-
regenerative catalytic reforming units of a refinery in California (Radian, 1991b). A caustic
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solution is introduced to the exhaust to neutralize hydrochloric acid emissions from the
catalyst beds prior to release to the atmosphere. The tested unit was considered to be
representative of the other units. Each unit is periodically (approximately once per year)
taken off-line so the catalyst beds can be regenerated. The tested unit has a feed
capacity of 7,000 barrels per day. Approximately 59,500 pounds of catalyst were
regenerated during the tested regeneration cycle, which tested for 62 hours.
The average emission factors for this facility (in units of ng/barrel of reformer feed)
are presented in Table 5-2 and the congener profile is presented in Figure 5-3. The
majority of the 2,3,7,8-substituted CDD congeners were not detected during testing. In
contrast, the majority of the 2,3,7,8-substituted CDF congeners were detected. The
average I-TEQDF emission factor (assuming not detected values are zero) is 1.01E-03 ng
TEQ/barrel and the average TEQDF-WH098 emission factor is 1.04E-03 ng TEQ/barrel.
These values are three orders of magnitude less than the emission factor reported in CARB
(1999). The calculation of these emission factors involved several assumptions: the unit
is regenerated once per year; the unit operates at capacity (i.e., 7,000 barrels/day); and
the facility operates 362 days per year.
The average of the two facility emission factors, 1.52 ng l-TEQDF/barrel of reformer
feed (1.59 ng TEQDF-WH098/barrel), is assigned a low confidence rating. Only one
continuous and only one semi-regenerative unit in the United States have been tested.
Combined, these two facilities represent less than 1 percent of the catalytic reforming
capacity in U.S. petroleum refineries in 1987 (3.805 million barrels per day) and in 1995
(3.867 million barrels per day) (EIA, 1997c). The average emission factor developed
above assumes that emissions are proportional to reforming capacity; however, emission
factors may be more related to the amount of coke burned, the APCD equipment present,
or other process parameters.
The 1987 and 1995 national daily average catalytic reforming capacities in the
United States were 3.805 and 3.867 million barrels per day (EIA, 1997c). If it is
conservatively assumed that all units operated at full capacity in 1987 and 1995, then
applying the average emission factors of TEQ/barrel yields annual emissions of 2.11 g
I-TEQDF in 1987 (2.21 g TEQDF-WH098) and 2.14 g I-TEQDF in 1995 (2.24 g TEQDF-
WH098).
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5.4. CIGARETTE SMOKING
Bumb et al. (1980) were the first to report that cigarette smoking is a source of
CDD emissions. Subsequent studies by Muto and Takizawa (1989), Ball et al. (1990),
and Lofroth and Zebuhr (1992) also reported the presence of CDDs as well as CDFs in
cigarette smoke. A recent study by Matsueda et al. (1994) reported the CDD/CDF
content of the tobacco from 20 brands of cigarettes from seven countries. Although a
wide range in the concentrations of total CDD/CDFs and total TEQs were reported in these
studies, similar congener profiles and patterns were reported. The findings of each of
these studies are described in this section.
No studies published to date have demonstrated a mass balance, and it is not
known whether the CDD/CDFs measured in cigarette smoke are the result of formation
during tobacco combustion, volatilization of CDD/CDFs present in the unburned tobacco,
or a combination of these two source mechanisms. The combustion processes operating
during cigarette smoking are complex and could be used to justify both source
mechanisms. As reported by Guerin et al. (1992), during a puff, gas phase temperatures
reach 850°C at the core of the firecone, and solid phase temperatures reach 800°C at the
core and 900°C or greater at the char line. Thus, temperatures are sufficient to cause at
least some destruction of CDD/CDFs initially present in the tobacco. Both solid and gas
phase temperatures rapidly decline to 200 to 400°C within 2 mm of the char line.
Formation of CDD/CDFs has been reported in combustion studies with other media in this
temperature range of 200 to 900°C. However, it is known that a process likened by
Guerin et al. (1992) to steam distillation takes place in the region behind the char line
because of high, localized concentrations of water and temperatures of 200 to 400°C. At
least 1,200 tobacco constituents (e.g., nicotine, n-paraffin, some terpenes) are transferred
intact from the tobacco into the smoke stream by distillation in this region, and it is
plausible that CDD/CDFs present in the unburned tobacco would be subject to similar
distillation.
Bumb et al. (1980), using low-resolution mass spectrometry, analyzed the CDD
content of mainstream smoke from the burning of a U.S. brand of unfiltered cigarette. A
package of 20 cigarettes was combusted in each of two experiments. Approximately 20
to 30 puffs of 2 to 3 seconds duration were collected from each cigarette on a silica
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column. Hexa-, hepta-, and octa-CDD were detected at levels of 0.004-0.008, 0.009,
and 0.02-0.05 ng/g, respectively.
Muto and Takizawa (1989) employed a continuous smoking apparatus to measure
CDD congener concentrations in the mainstream smoke generated from the combustion of
one kind of filtered cigarette (brand not reported). The apparatus pulled air at a constant
continuous rate (rather than a pulsed rate) through a burning cigarette and collected the
smoke on a series of traps (glass fiber filter, polyurethane foam, and XAD-II resin). The
CDD content of the smoke, as well as the CDD content of the unburned cigarette and the
ash from the burned cigarettes, were also analyzed using low-resolution mass
spectrometry. The results are presented in Table 5-3, and the congener group profiles are
presented in Figure 5-4. Table 5-3 and Figure 5-4 present the mainstream smoke results
on a mass per cigarette basis to enable comparison with the results of other studies. The
major CDD congener group that was found was HpCDD, which accounted for 84 percent
of total CDDs found in the cigarette, 94 percent of total CDDs found in smoke, and 99
percent of total CDDs found in the ash. The 2,3,7,8-HpCDDs also accounted for the
majority of the measured TEQ in the cigarettes and smoke; however, none were measured
in the ash. Although no PeCDDs were detected in the cigarette, PeCDDs were detected at
low levels in the smoke, indicating probable formation during combustion. On the basis of
the similarities in the congener group profiles for the three media, Muto and Takizawa
(1989) concluded that most of the CDDs found in the cigarette smoke are the result of
volatilization of CDD/CDFs present in the unburned cigarette rather than formation during
combustion.
Ball et al. (1990) measured the CDD/CDF content of mainstream smoke for the 10
best-selling German cigarette brands. The international test approach (i.e., 1 puff/min;
puff flow rate of 35 ml_/2 sec) was employed with an apparatus that smoked 20
cigarettes at a time in three successive batches with a large collection device. The
average TEQ content (on both an I-TEQDF and TEQDF-WH098 basis) in mainstream smoke
for the 10 brands tested, normalized to a mass per cigarette basis, was 0.09 pg/cigarette
(i.e., 16.5 times less than the value reported by Muto and Takizawa (1989) for a
Japanese cigarette brand). However, the congener group profiles were similar to those
reported by Muto and Takizawa (1989) with HpCDD and OCDD the dominant congener
groups found.
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Lofroth and Zebuhr (1992) measured the CDD/CDF content of mainstream and
sidestream smoke from one common Swedish cigarette brand. The cigarette brand was
labeled as giving 17 mg carbon monoxide, 21 mg tar, and 1.6 mg nicotine. The
international test approach (i.e., 1 puff/min; puff flow rate of 35 ml_/2 sec) was used, and
the smoke was collected on glass fiber filters followed by two polyurethane plugs. The
analytical results for mainstream and sidestream smoke are presented in Table 5-4. The
TEQ content in mainstream smoke, normalized to a mass per cigarette basis, was 0.90 pg
l-TEQDF/cigarette or 0.96 pg TEQDF-WH098/cigarette (i.e., about 2 times less than the value
reported by Muto and Takizawa (1989) and 10 times greater than the average value
reported by Ball et al. 1990). As was reported by Muto and Takizawa (1989) and Ball et
al. (1990), the dominant congener groups were HpCDDs and OCDD; however, HpCDFs
were also relatively high compared to the other congener group totals. The sidestream
smoke contained 1.96 pg I-TEQDF per cigarette (2.08 pg TEQDF-WH098 per cigarette), or
twice that of mainstream smoke.
Using high-resolution mass spectrometry, Matsueda et al. (1994) analyzed the
CDD/CDF content of tobacco from 20 brands of commercially available cigarettes
collected in 1992 from Japan, the United States, Taiwan, China, the United Kingdom,
Germany, and Denmark. Table 5-5 presents the study results. The total CDD/CDF
content and total I-TEQDF content ranged from 109 to 1,136 pg/pack and from 1.4 to
12.6 pg/pack (1.9 to 14.0 pg/pack on a TEQDF-WH098 basis), respectively. The Chinese
cigarette brand contained significantly lower CDD/CDFs and TEQs than any other brand of
cigarette. Figure 5-6 depicts the congener group profiles for the average results for each
country. A high degree of similarity is shown in the CDF congener group profiles between
the tested cigarette brands. The Japanese and Taiwanese cigarettes show CDD congener
group profiles different from the other countries' cigarettes.
In 1995, approximately 487 billion cigarettes were consumed in the United States
and by U.S. overseas armed forces personnel. In 1987, approximately 575 billion
cigarettes were consumed. Per capita U.S. cigarette consumption, based on total U.S.
population aged 16 and over, declined to 2,415 in 1995; the record high was 4,345 in
1963 (The Tobacco Institute, 1995; USDA, 1997). These activity level estimates are
assigned a high confidence rating.
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The available emission factor data presented above provide the basis for two
methods of estimating the amount of TEQs that may have been released to the air in the
United States in 1995 and in 1987 from the combustion of cigarettes. The confidence
rating assigned to the emission factor is low because of the very limited amount of testing
performed to date. First, an annual emission estimate for 1995 of 0.21 g TEQ (on an I-
TEQDF or TEQDF-WH098 basis) is obtained if it is assumed that (a) the average TEQ content
of seven brands of U.S. cigarettes reported by Matsueda et al. (1994), 8.6 pg I-
TEQDF/pack or 8.8 pg TEQDF-WH098/pack, are representative of cigarettes smoked in the
United States; (b) CDD/CDFs are not formed, and the congener profile reported by
Matsueda et al. (1994) is not altered during combustion of cigarettes; and (c) all
CDD/CDFs contributing to the TEQ are released from the tobacco during smoking. The
second method of estimating is based on the assumption that the TEQ emission rates for
a common Swedish brand of cigarette reported by Lofroth and Zebuhr (1992) for
mainstream smoke (0.90 pg l-TEQDF/cigarette or 0.96 pg TEQDF-WH098/cigarette) and
sidestream smoke (1.96 pg l-TEQDF/cigarette or 2.08 pg TEQDF-WH098/cigarette) are
representative of the emission rates for U.S. cigarettes. This second method yields an
annual emission estimate of 1.41 g I-TEQDF or 1.48 g TEQDF-WH098. For 1987, the two
methods yield estimates of 0.25 g TEQ (I-TEQDF or TEQDF-WH098 basis) and 1.67 g I-
TEQDF(or 1.75gTEQDF-WH098).
For purposes of this report, the best estimates of annual emissions are assumed to
be the average of the annual emissions estimated by the two methods for 1995 and 1987
(0.8 g TEQ and 1.0 g TEQ), respectively (I-TEQDF or TEQDF-WH098 basis). Although these
emission quantities are relatively small when compared to the emission quantities
estimated for various industrial combustion source categories, the emissions are
significant because humans are directly exposed to cigarette smoke.
5.5. PYROLYSIS OF BROMINATED FLAME RETARDANTS
The pyrolysis and photolysis of brominated phenolic derivatives and polybrominated
biphenyl ethers used as flame retardants in plastics (especially those used in electronic
devices), textiles, and paints can generate considerable amounts of polybrominated
dibenzo-p-dioxins (BDDs) and dibenzofurans (BDFs) (Watanabe and Tatsukawa, 1987;
Thoma and Hutzinger, 1989; Luijk et al., 1992). Watanabe and Tatsukawa (1987)
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observed the formation of BDFs from the photolysis of decabromobiphenyl ether.
Approximately 20 percent of the decabromobiphenyl ether was converted to BDFs in
samples that were irradiated with ultraviolet light for 16 hours.
Thoma and Hutzinger (1989) observed the formation of BDFs during combustion
experiments with polybutylene-terephthalate polymers containing 9 to 11 percent
decabromodiphenyl ether. Maximum formation of BDFs occurred at 400 to 600°C, with a
BDF yield of 16 percent. Although Thoma and Hutzinger (1989) did not provide specific
quantitative results for similar experiments conducted with octabromodiphenyl ether and
1,2-bis(tri-bromophenoxy)ethane, they did report that BDDs and BDFs were formed.
Luijk et al. (1992) studied the formation of BDD/BDFs during the compounding and
extrusion of decabromodiphenyl ether into high-impact polystyrene polymer at 275°C.
HpBDF and OBDF were formed during repeated extrusion cycles, and the yield of BDFs
increased as a function of the number of extrusion cycles. HpBDF increased from 1.5 to
9 ppm (in the polymer matrix), and OBDF increased from 4.5 to 45 ppm after four
extrusion cycles.
Insufficient data are available at this time from which to derive annual BDD/BDF
emission estimates for this source.
5.6. CARBON REACTIVATION FURNACES
Granular activated carbon (GAC) is an adsorbent that is widely used to remove
organic pollutants from wastewater and to treat finished drinking water at water treatment
plants. Activated carbon is manufactured from the pyrolytic treatment of nut shells and
coal (Buonicore, 1992a). The properties of GAC make it ideal for adsorbing and
controlling vaporous organic and inorganic chemicals entrained in combustion plasmas, as
well as soluble organic contaminants in industrial effluents and drinking water. The high
ratio of surface area to particle weight (600 to 1,600 m2/g), combined with the extremely
small pore diameter of the particles (15-25 angstroms), increases the adsorption
characteristics (Buonicore, 1992a). GAC eventually becomes saturated, and the
adsorption properties significantly degrade. When saturation occurs, GAC usually must be
replaced and discarded, which significantly increases the costs of pollution control. The
introduction of carbon reactivation furnace technology in the mid-1980s created a method
involving the thermal treatment of used GAC to thermolytically desorb the synthetic
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compounds and restore the adsorption properties for reuse (Lykins et al., 1987). Large-
scale regeneration operations, such as those used in industrial water treatment operations,
typically use multiple-hearth furnaces. For smaller-scale operations, such as those used in
municipal water treatment operations, fluidized-bed and infrared furnaces are used.
Emissions are typically controlled by afterburners followed by water scrubbers (U.S. EPA,
1997b).
The used GAC can contain compounds that are precursors to the formation of
CDD/CDFs during the thermal treatment process. EPA measured precursor compounds in
spent GAC that was used as a feed material to a carbon reactivation furnace tested during
the National Dioxin Study (U.S. EPA, 1987a). The total chlorobenzene content of the
GAC ranged from 150 to 6,630 ppb. Trichlorobenzene was the most prevalent species
present, with smaller quantities of di- and tetra-chlorobenzenes detected. Total
halogenated organics were measured to be about 150 ppm.
EPA has stack-tested two GAC reactivation furnaces for the emission of dioxin
(U.S. EPA, 1987a; Lykins et al., 1987). One facility was an industrial carbon reactivation
plant, and the second facility was used to restore GAC at a municipal drinking water plant.
EPA (1997b) also reported results of other testing performed at a county water facility in
California during 1990.
The industrial carbon reactivation plant processed 36,000 kg/day of spent GAC
used in the treatment of industrial wastewater effluents. This facility was chosen for
testing because it was considered to be representative of other facilities in the source
category (U.S. EPA, 1987a). Spent carbon was reactivated in a multiple-hearth furnace,
cooled in a water quench, and shipped back to primary chemical manufacturing facilities
for reuse. The furnace was fired by natural gas and consisted of seven hearths arranged
vertically in series. The hearth temperatures ranged from 480 to 1,000°C. Air pollutant
emissions were controlled by an afterburner, a sodium spray cooler, and a fabric filter.
Temperatures in the afterburner were about 930°C. The estimated I-TEQDF emission
factor (treating not-detected values as zero) was 0.64 ng l-TEQDF/kg carbon processed
(0.76 ng TEQDF-WH098). The emission factor for total CDD/CDF was 58.6 ng/kg.
Because analyses were performed only for 2,3,7,8-TCDD, 2,3,7,8-TCDF, OCDD, and
OCDF and the congener groups, equivalent concentrations were assumed for all toxic and
nontoxic congeners in each of the penta-, hexa-, and hepta-congener groups.
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The second GAC reactivation facility tested by EPA consisted of a fluidized-bed
furnace located at a municipal drinking water treatment plant (Lykins et al., 1987). The
furnace was divided into three sections: a combustion chamber, a reactivation section,
and a dryer section. The combustion section was fired by natural gas and consisted of a
stoichiometrically balanced stream of fuel and oxygen. Combustion temperatures were
about 1,038°C. Gases from the reactivation and combustion section were directed
through an acid gas scrubber and high-temperature afterburner prior to discharge from a
stack. Although measurable concentrations of dioxin-like compounds were detected in the
stack emissions, measurements of the individual CDD/CDF congeners were not performed;
therefore, it was not possible to derive TEQ emission factors for this facility. With the
afterburner operating, no CDD congeners below HpCDD were detected in the stack
emissions. Concentrations of HpCDDs and OCDD ranged from 0.001 to 0.05 ppt/v and
0.006 to 0.28 ppt/v, respectively. All CDF congener groups were detected in the stack
emissions even with the afterburner operating. Total CDFs emitted from the stack
averaged 0.023 ppt/v.
From the results of a test of the reactivation unit at a county water facility in
California in 1990, EPA reported a TEQ emission factor of 1.73 ng l-TEQDF/kg of carbon
processed (U.S. EPA, 1997b). The emission factor for total CDD/CDF was reported to be
47 ng/kg (i.e., similar to the total CDD/CDF emission factor of 58.6 ng/kg at the industrial
GAC facility). Because congener-specific results were not reported, it was not possible to
calculate the TEQDF-WH098 emission factor. The report also did not provide the
configuration and type of furnace tested; however, it did state that the emissions from the
furnace were controlled by an afterburner and a scrubber.
The industrial GAC reaction furnace test data indicate that an average of 0.64 ng
l-TEQDF/kg of GAC may be released. The I-TEQDF emission rate for the reactivation unit at
the county water treatment facility was 1.73 ng l-TEQDF/kg carbon. Low confidence
ratings are given to these emission factors because only two GAC reactivation furnaces
were stack-tested and not all congeners were analyzed at the industrial GAC facility.
The mass of GAC that is reactivated annually in carbon reactivation furnaces is not
known. However, a rough estimate, to which a low confidence rating is assigned, is the
mass of virgin GAC shipped each year by GAC manufacturers. According to the
Department of Commerce (1990c), 48,000 metric tons of GAC were shipped in 1987.
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EPA (1995c; 1997b) reported that in 1990, water and wastewater treatment operations
consumed 65,000 metric tons of GAC. The 1990 activity level is used in this document
as a surrogate for the 1995 activity level.
Applying the average TEQ emission factor of 1.2 ng I-TEQDF (or TEQDF-WH098) per
kg of reactivated carbon for the two tested facilities to the estimates of potential GAC
reactivation volumes, yields annual release estimates of 0.06 g I-TEQDF (or TEQDF-WH098)
in 1987 and 0.08 g I-TEQDF (or TEQDF-WH098) in 1995 (assuming that the activity level for
1990 is representative of the 1995 activity level).
5.7. KRAFT BLACK LIQUOR RECOVERY BOILERS
Kraft black liquor recovery boilers are associated with the production of pulp in the
making of paper using the Kraft process. In this process, wood chips are cooked in large
vertical vessels called digesters at elevated temperatures and pressures in an aqueous
solution of sodium hydroxide and sodium sulfide. Wood is broken down into two phases:
a soluble phase containing primarily lignin, and an insoluble phase containing the pulp.
The spent liquor (called black liquor) from the digester contains sodium sulfate and sodium
sulfide, which the industry recovers for reuse in the Kraft process. In the recovery of
black liquor chemicals, weak black liquor is first concentrated in multiple-effect
evaporators to about 65 percent solids. The concentrated black liquor also contains 0.5
to 4 percent chlorides by weight, which are recovered through combustion. The
concentrated black liquor is sprayed into a Kraft black liquor recovery furnace equipped
with a heat recovery boiler. The bulk of the inorganic molten smelt that forms in the
bottom of the furnace contains sodium carbonate and sodium sulfide in a ratio of about
3:1. The combustion gas is usually passed through an electrostatic precipitator (ESP) that
collects particulate matter prior to being vented out the stack. The particulate matter can
be processed to further recover and recycle sodium sulfate (Someshwar and Pinkerton,
1992).
In 1987, EPA stack-tested three Kraft black liquor recovery boilers for the emission
of dioxin in conjunction with the National Dioxin Study (U.S. EPA, 1987a). The three
sites tested by EPA were judged to be typical of Kraft black liquor recovery boilers at that
time. During pretest surveys, two facilities were judged to have average potential and one
was judged to have high potential for CDD/CDF emissions based on the amount of
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chlorine found in the feed to these units. Dry-bottom ESPs controlled emissions from two
of the boilers; a wet-bottom ESP controlled emissions from the third. The results of these
tests include congener group concentrations but lack measurement results for specific
congeners other than 2,3,7,8-TCDD and 2,3,7,8-TCDF. NCASI (1995) provided
congener-specific emission test results for six additional boilers tested during 1990 to
1993. Three boilers were of the direct contact type, and three were noncontact type. All
were equipped with ESPs. The average congener and congener group emission factors
are presented in Table 5-6 for the three facilities from U.S. EPA (1987a) and the six
facilities from NCASI (1995). Figure 5-7 presents the average congener and congener
group profiles based on the test results presented in NCASI (1995).
The average TEQ emission factor based on the data for the six NCASI facilities
with complete congener data is 0.029 ng l-TEQDF/kg of black liquor solids, assuming
nondetected values are zero (0.028 ng TEQDF-WH098/kg), and 0.068 ng l-TEQDF/kg
assuming nondetected values are present at one-half the detection limit (0.078 ng
TEQDF-WH098/kg). The results for the three facilities reported in U.S. EPA (1987a) were
not used in the derivation of the TEQ emission factor because congener-specific
measurements for most 2,3,7,8-substituted congeners were not made in the study. A
medium confidence rating is assigned to those emission factors because the emission
factors were derived from the stack-testing of six Kraft black liquor recovery boilers that
were judged to be fairly representative of technologies used at Kraft pulp mills in the
United States. A 1995 survey of the industry indicated that 215 black liquor recovery
boilers were in operation at U.S. pulp and paper mills. All but one of these boilers used
ESPs for control of particulate emissions; the one unique facility used dual scrubbers. In
addition, ESPs were reported to have been the predominant means of particulate control at
recovery boilers for the past 20 years (Gillespie, 1998).
The amounts of black liquor solids burned in Kraft black liquor recovery boilers in
the United States during 1987 and 1995 were 69.8 million metric tons and 80.8 million
metric tons, respectively (American Paper Institute, 1992; American Forest & Paper
Association, 1997). These activity level estimates are assigned a high confidence rating
because they are based on recent industry survey data. Combining the emission factors
derived above with the activity level estimates of 69.8 and 80.8 million metric tons in
1987 and 1995, respectively, yields estimated annual emissions from this source of
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approximately 2.0 g I-TEQDF (2.0 g TEQDF-WH098) in 1987 and 2.3 g I-TEQDF (2.3 g TEQDF-
WH098) in 1995.
5.8. OTHER IDENTIFIED SOURCES
Several manufacturing processes are identified as potential sources of CDD/CDF
formation because the processes use chlorine-containing components or involve
application of high temperatures. However, no testing of emissions from these processes
has been performed in the United States, and only minimal emission rate information has
been reported for these processes in other countries.
Burning of Candles. Schwind et al. (1995) analyzed the wicks and waxes of
uncolored candles, as well as the fumes of burning candles, for CDD/CDF, total
chlorophenol, and total chlorobenzene content. The results presented in Table 5-7 show
that beeswax contained the highest levels of CDD/CDF and total chlorophenols. In
contrast, the concentration of total chlorobenzenes in stearin wax was higher by a factor
of 2 to 3 times than that in paraffin or beeswax. The concentrations of the three analyte
groups were significantly lower in the wicks than in the waxes. Emissions of CDD/CDF
from all three types of candles were very low during burning. In fact, comparison of the
emission factor to the original CDD/CDF concentration in the wax indicates a net
destruction of the CDD/CDF originally present in the wax.
Information is not readily available on the volume of candles consumed annually in
the United States. However, in 1992, the value of wholesale shipments of candles in the
United States was nearly $360 million (U.S. DOC, 1996). Assuming that the average
wholesale cost per kg of candle is $1, then the volume of candles shipped was 360
million kg. If it is further assumed that 75 percent of the candle volume is actually burned
and that the CDD/CDF emissions rate is 0.015 ng/kg, then a rough preliminary estimate of
the potential annual emission from combustion of candles is 4 mg l-TEQDF/yr.
Glass Manufacturing. Bremmer et al. (1994) and Douben et al. (1995) estimated
annual emissions of less than 1 g l-TEQDF/yr from glass manufacturing facilities in The
Netherlands and the United Kingdom, respectively. Glass is manufactured by heating a
mixture of sand and, depending on the type of glass, lime, sodium carbonate, dolomite,
clay, or feldspar to a temperature of 1,400 to 1,650°C. In addition, various coloring and
clarifying agents may be added. Chlorine enters the process as a contaminant (i.e., NaCI)
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in sodium carbonate (Bremmer et al. 1994). However, the emission factors used by
Bremmer et al. (1994) and Douben et al. (1995) were not reported. Umweltbundesamt
(1996) reported relatively low emission factors (approximately 0.002 and 0.007 ng
l-TEQDF/kg) for two glass manufacturing facilities in Germany.
Lime Kilns. Annual emissions from lime kilns in Belgium and the United Kingdom
have been reported by Wevers and De Fre (1995) and Douben et al. (1995), respectively.
However, the emission factors used to generate those estimates were not provided.
Umweltbundesamt (1996) reported low emissions (0.016 to 0.028 ng l-TEQDF/kg) during
tests at two lime kilns in Germany.
Ceramics and Rubber Manufacturers. Douben et al. (1995) estimated annual
emissions from ceramic manufacturers and rubber manufacturers in the United Kingdom.
Lexen et al. (1993) had previously detected high concentrations of CDD/CDF in emissions
from a ceramic manufacturer in Sweden, which occasionally glazed ceramics by
volatilization of sodium chloride in a coal-fired oven. Lexen et al. (1993) also detected
high pg/L levels of I-TEQDF in the scrubber water from the vulcanization process at a
Swedish rubber manufacturer.
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Table 5-1. CDD/CDF Emission Factors for Cement Kilns
Congener/Congener Group
2,3,7, 8-TCDD
1, 2,3,7, 8-PeCDD
1, 2,3,4,7, 8-HxCDD
1, 2,3,6,7, 8-HxCDD
1, 2,3,7, 8,9-HxCDD
1, 2,3,4,6,7, 8-HpCDD
OCDD
2,3,7, 8-TCDF
1, 2,3,7, 8-PeCDF
2,3,4,7, 8-PeCDF
1, 2,3,4,7, 8-HxCDF
1, 2,3,6,7, 8-HxCDF
1, 2,3,7, 8,9-HxCDF
2,3,4,6,7, 8-HxCDF
1, 2,3,4,6,7, 8-HpCDF
1, 2,3,4,7, 8,9-HpCDF
OCDF
Total I-TEQDF
Total TEQDF-WH098
Total TCDD
Total PeCDD
Total HxCDD
Total HpCDD
Total OCDD
Total TCDF
Total PeCDF
Total HxCDF
Total HpCDF
Total OCDF
Total CDD/CDF
Kilns Burning Hazardous Waste-
Mean Emission Factor
(ND values set equal to zero)
(ng/kg clinker produced)
APCD Inlet
Temperature
>450°F
3.38
4.28
4.85
6.93
9.55
27.05
18.61
36.26
13.36
23.48
22.24
8.46
0.96
13.33
7.73
2.16
2.51
28.58
30.70
406.76
608.65
845.99
192.99
18.61
295.72
127.99
50.75
8.36
2.51
2558.33
APCD Inlet
Temperature
<450°F
0.02
0.13
0.29
0.42
0.40
3.16
1.08
3.24
0.23
0.65
0.55
0.27
0.06
0.52
0.34
0.16
0.37
1.04
1.11
1.78
0.89
0.69
0.42
1.08
11.52
3.83
1.88
0.47
0.37
22.92
Kilns Not Burning Hazardous
Waste— Mean Emission Factor
(ND values set equal to zero)
(ng/kg clinker produced)
0.012
0.034
0.028
0.042
0.048
0.426
0.692
0.729
0.102
0.224
0.185
0.054
0.007
0.082
0.146
0.005
0.234
0.27
0.29
1.97
2.07
5.96
0.84
0.69
6.82
2.00
0.60
0.24
0.23
21.44
NR = Not reported.
Source: U.S. EPA (1996c)
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December 2003
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Table 5-2. CDD/CDF Emission Factors for Petroleum Catalytic Reforming Units
Congener/Congener
Group
2,3,7,8-TCDD
1,2,3,7,8-PeCDD
1,2, 3,4,7, 8-HxCDD
1,2,3,6,7,8-HxCDD
1,2,3,7,8,9-HxCDD
1,2,3,4,6,7,8-HpCDD
OCDD
2,3,7,8-TCDF
1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
1,2,3,4,7,8-HxCDF
1,2, 3,6,7, 8-HxCDF
1,2,3,7,8,9-HxCDF
2, 3,4,6,7, 8-HxCDF
1,2,3,4,6,7,8-HpCDF
1,2, 3,4,7, 8,9-HpCDF
OCDF
Total 2,3,7,8-CDD
Total 2,3,7,8-CDF
Total I-TEQDF
Total TEQDF-WH098
Total TCDD
Total PeCDD
Total HxCDD
Total HpCDD
Total OCDD
Total TCDF
Total PeCDF
Total HxCDF
Total HpCDF
Total OCDF
Total CDD/CDF
Semi-regenerative Unit
(nq/barrel)
Nondetects Set
to Zero
ND
5.69e-05
4.22e-05
ND
ND
7.02e-04
2.55e-03
2.32e-04
4.68e-04
1 .09e-03
1 .06e-03
1.07e-03
ND
1.24e-03
2.94e-03
8.32e-04
1.01e-03
3.35e-03
9.94e-03
1.016-03
1 .04e-03
ND
3.56e-04
1.28e-03
1.396-03
2.55e-03
2.706-03
5.12e-03
7.856-03
4.88e-03
1.016-03
2.71e-02
Nondetects Set to
1/2 Det. Limit
2.35e-05
9.58e-05
8.09e-05
5.52e-05
5.10e-05
7.02e-04
2.55e-03
2.32e-04
4.68e-04
1 .09e-03
1 .06e-03
1.07e-03
6.82e-05
1.24e-03
2.94e-03
8.32e-04
1.01e-03
3.56e-03
1 .OOe-02
1 .08e-03
1.12e-03
2.35e-05
3.56e-04
1.286-03
1.39e-03
2.55e-03
2.70e-03
5.12e-03
7.856-03
4.88e-03
1.016-03
2.72e-02
Continuous Regeneration Unit
(nq/barrel)
Nondetects Set
to Zero
1.61e-02
2.87e-01
3.47e-01
8.45e-01
5.56e-01
3.02e + 00
1.71e + 00
6.106-01
1.72e + 00
2.336 + 00
4.70e + 00
3.58e + 00
4.34e-01
3.106 + 00
1.59e + 01
1.456 + 00
3.75e + 00
6.77e + 00
3.76e + 01
3.04e + 00
3.18e + 00
6.84e + 00
5.61e + 00
8.186 + 00
6.58e + 00
1.71e + 00
4.68e + 01
3.30e + 01
2.96e + 01
2.11e + 01
3.75e + 00
1.63e + 02
Nondetects Set to
1/2 Det. Limit
1.61e-02
2.87e-01
3.47e-01
8.45e-01
5.56e-01
3.02e + 00
1.71e + 00
6.106-01
1.72e + 00
2.336 + 00
4.70e + 00
3.58e + 00
4.34e-01
3.106 + 00
1.59e + 01
1.456 + 00
3.75e + 00
6.77e + 00
3.76e + 01
3.04e + 00
3.18e + 00
6.84e + 00
5.61e + 00
8.18e + 00
6.58e + 00
1.71e + 00
4.686 + 01
3.30e + 01
2.966 + 01
2.11e + 01
3.756 + 00
1.63e + 02
ND = Not detected.
Note: 1 barrel assumed to be equivalent to 139 kg.
Sources: Radian (1991b) and CARB (1999)
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December 2003
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Table 5-3. CDD Concentrations in Japanese Cigarettes, Smoke, and Ash
Congener/Congener Group
2,3,7,8-TCDD
1, 2,3,7, 8-PeCDD
1, 2,3,4,7, 8-HxCDD
1,2,3,6,7,8-HxCDD
1,2,3,7,8,9-HxCDD
1,2,3,4,6,7,8-HpCDD
OCDD
2,3,7,8-TCDF
1, 2,3,7, 8-PeCDF
2,3,4,7, 8-PeCDF
1,2, 3,4,7, 8-HxCDF
1,2,3,6,7,8-HxCDF
1,2,3,7,8,9-HxCDF
2,3,4,6,7,8-HxCDF
1,2,3,4,6,7,8-HpCDF
1,2,3,4,7,8,9-HpCDF
OCDF
Total 2,3,7,8-CDD
Total 2,3,7, 8-CDF
Total I-TEQDF
Total TEQDF-WH098
Total TCDD
Total PeCDD
Total HxCDD
Total HpCDD
Total OCDD
Total TCDF
Total PeCDF
Total HxCDF
Total HpCDF
Total OCDF
Total CDD/CDF
Cigarette
(pg/g)
ND (0.5)
ND (0.5)
2.01a
a
a
1,343
257
_
—
—
—
—
—
—
_
—
1,602
--
13.9
13.7
44.9
ND (0.5)
13.41
1,629
257
—
—
—
—
—
1,944
Concentrations
Mainstream Smoke
(ng/m3)
ND (0.22)
0.43
2.15a
a
a
783
240
_
—
—
—
—
—
—
_
—
1,026
--
8.5
8.3
68.0
1.51
7.51
4,939
240
—
—
—
—
—
5,256
Ash
(pg/g)
ND (0.5)
ND (0.5)
0.56a
a
a
ND (0.5)
ND (0.5)
_
—
—
—
—
—
—
_
—
0.56
--
0.06
0.06
4.63
ND (0.5)
5.01
3,211
ND (0.5)
—
—
—
—
—
3,221
ND = Not detected (detection limit is in parentheses).
— = Not reported.
a Value reported only for total 2,3,7,8-substituted HxCDDs.
Source: Muto and Takizawa (1989)
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December 2003
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Table 5-4. CDD/CDF Emissions in Cigarette Smoke
Congener/Congener Group
2,3,7,8-TCDD
1,2,3,7,8-PeCDD
1,2,3,4,7,8-HxCDD
1,2,3,6,7,8-HxCDD
1,2,3,7,8,9-HxCDD
1,2,3,4,6,7,8-HpCDD
OCDD
2,3,7,8-TCDF
1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
1,2,3,4,7,8-HxCDF
1,2,3,6,7,8-HxCDF
1,2,3,7,8,9-HxCDF
2,3,4,6,7,8-HxCDF
1,2,3,4,6,7,8-HpCDF
1,2,3,4,7,8,9-HpCDF
OCDF
Total 2,3,7,8-CDD
Total 2,3,7,8-CDF
Total I-TEQDF
Total TEQDF-WH098
Total TCDD
Total PeCDD
Total HxCDD
Total HpCDD
Total OCDD
Total TCDF
Total PeCDF
Total HxCDF
Total HpCDF
Total OCDF
Total CDD/CDF
Concentrations — Normalized to a per Cigarette Basis (pg/cig)
Ref. A
(1 Japanese brand)
(mainstream smoke)
ND (0.04)
0.075
0.376
b
b
137
42
—
179
1.49
1.49
11.9
0.264
1.31
864
42
919
Ref. B
(Avg of 10 German brands)
(mainstream smoke)
ND (0.03)
ND (0.03)
0.06
0.05
0.04
1.3
3.4
0.19
0.13
0.04
ND (0.03)
0.03
0.03
0.05
0.16
0.03
0.11
4.85
0.77
0.09
0.09
0.51
0.14
0.53
2.9
3.4
1.41
0.83
0.35
0.27
0.11
10.5
Ref. C
(1 Swedish brand)
(mainstream smoke)
0.028
0.15
0.10
0.34
0.25
6.05
22.1
1.2°
0.34C
0.34
1.3C
0.48
0.14
0.21
10.0
2.6
3.2
29.0
19.8
0.90
0.96
0.61
1.07
2.52
12.3
22.1
4.5
3.23
5.30
19.8
3.2
74.5
Ref. C
(1 Swedish brand)
(sidestream smoke)
0.07
0.32
0.19
0.60
0.55
12.2
38.8
2.T
0.80C
0.60
3.8C
1.2
0.39
0.50
23.5
5.0
10.7
52.7
48.6
1.96
2.08
0.67
2.14
5.2
21.3
38.8
5.75
6.35
12.9
47.8
10.7
152
Ref. A: Muto and Takizawa (1989)
Ref. B: Ball et al. (1990)
Ref. C: Lofroth and Zebiihr (1992)
ND = Not detected (detection limit is in parentheses).
— = Not reported.
a Emissions calculated assuming 0.0035 m3 of smoke are inhaled per 20 cigarettes smoked (Muto and Takizawa, 1992).
b Ref. A reported a value only for total 2,3,7,8-HxCDDs (0.38 pg/cig).
c Concentrations listed include the contribution of a coeluting non-2,3,7,8-substituted congener.
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December 2003
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Table 5-5. CDD/CDF Concentrations in Cigarette Tobacco
Congener/Congener
Group
2,3,7,8-TCDD
1,2,3,7,8-PeCDD
1,2, 3,4,7, 8-HxCDD
1,2,3,6,7,8-HxCDD
1,2,3,7,8,9-HxCDD
1,2, 3,4,6,7, 8-HpCDD
OCDD
2,3,7,8-TCDF
1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
1,2, 3,4,7, 8-HxCDF
1,2,3,6,7,8-HxCDF
1,2,3,7,8,9-HxCDF
2, 3,4,6,7, 8-HxCDF
1,2, 3,4,6,7, 8-HpCDF
1, 2,3,4,7, 8,9-HpCDF
OCDF
Total 2,3,7,8-CDD
Total 2,3,7,8-CDF
Total I-TEQDF
Total TEQDF-WH09S
Total TCDD
Total PeCDD
Total HxCDD
Total HpCDD
Total OCDD
Total TCDF
Total PeCDF
Total HxCDF
Total HpCDF
Total OCDF
Total CDD/CDF
Concentrations in Brands from Various Countries (pg/pack)
U.S. Brands
(Avg of 7 brands)
1.2
1.6
6.9
a
a
52.7
589.3
18.2
8.7
b
8.1
c
c
c
17.6
d
24.6
652
77.2
8.6
8.8
47.1
27.6
40.6
108.7
589.3
183.8
57.7
29.1
27.3
24.6
1136
Japan
(Avg of 6 brands)
0.5
1.4
4.8
a
a
17.8
244.0
4.8
5.3
b
8.1
c
c
c
11.1
d
10.5
268.5
39.8
4.6
5.1
296.3
33.6
29.2
40.0
244.0
102.1
45.9
26.4
16.6
10.5
845
United Kingdom
(Avg of 3 brands)
1.7
3.1
6.1
a
a
23.9
189.5
15.6
21.2
b
17.0
c
c
c
13.6
d
8.3
224.3
75.7
12.6
14.0
85.1
62.9
49.2
47.7
189.5
348.9
134.5
51.3
19.0
8.3
996
Taiwan
(1 brand)
1.0
3.3
12.2
a
a
26.4
272.7
11.0
16.0
b
12.9
c
c
c
13.2
d
13.9
315.6
67
9.3
10.7
329
150.5
99.4
62.0
272.7
372.1
149.1
45.8
18.5
13.9
1513
China
(1 brand)
ND
1.1
1.1
a
a
2.2
28.2
1.2
1.5
b
2.2
c
c
c
1.5
d
0.5
32.6
6.9
1.4
1.9
9.7
5.2
5.4
3.8
28.2
35.4
11.2
7.8
1.7
0.5
109
Denmark
(1 brand)
0.5
0.8
6.2
a
a
53.3
354.3
2.2
4.3
b
4.3
c
c
c
7.0
d
10.5
415.1
28.3
3.8
3.9
17.0
9.8
26.7
93.1
354.3
97.8
35.5
18.1
11.1
10.5
674
Germany
(1 brand)
1.1
3.3
5.7
a
a
32.7
288.6
7.9
14.4
b
13.2
c
c
c
12.9
d
13.9
331.4
62.3
9.1
10.5
49.5
40.8
40.6
60.2
288.6
233.4
97.5
40.8
21.2
13.9
887
Source: Matsueda et al. (1994)
a Value reported only for total 2,3,7,8-substituted HxCDDs.
b Value reported only for total 2,3,7,8-substituted PeCDFs.
c Value reported only for total 2,3,7,8-substituted HxCDFs.
d Value reported only for total 2,3,7,8-substituted HpCDFs.
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5-34
December 2003
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Table 5-6. CDD/CDF Emission Factors for Black Liquor Recovery Boilers
Congener
2,3,7,8-TCDD
1, 2,3,7, 8-PeCDD
1,2, 3,4,7, 8-HxCDD
1,2,3,6,7,8-HxCDD
1,2,3,7,8,9-HxCDD
1,2,3,4,6,7,8-HpCDD
OCDD
2,3,7,8-TCDF
1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
1,2, 3,4,7, 8-HxCDF
1,2,3,6,7,8-HxCDF
1,2,3,7,8,9-HxCDF
2,3,4,6,7,8-HxCDF
1,2,3,4,6,7,8-HpCDF
1, 2,3,4,7, 8,9-HpCDF
OCDF
Total TCDD
Total PeCDD
Total HxCDD
Total HpCDD
Total OCDD
Total TCDF
Total PeCDF
Total HxCDF
Total HpCDF
Total OCDF
Total I-TEQDF
Total TEQDF-WH098
Total CDD/CDF
U.S. EPA (1987) - 3 Facilities
Mean Emission Factors
(ng/kg feed)
Nondetects
Set to
Zero
0
NR
NR
NR
NR
NR
4.24
0.04
NR
NR
NR
NR
NR
NR
NR
NR
0.35
0.21
0.27
0.80
2.05
4.24
0.95
0.64
1.16
1.05
0.35
0.10*
0.10*
11.71
Nondetects
Set to
1/2 Det. Limit
0.04
NR
NR
NR
NR
NR
4.24
0.06
NR
NR
NR
NR
NR
NR
NR
NR
0.35
0.36
0.35
1.02
2.05
4.24
1.00
0.77
1.20
1.05
0.35
0.15*
0.16*
12.17
NCASI (1995) - 6 Facilities
Mean Emission Factors
(ng/kg feed)
Nondetects
Set to
Zero
0
0
0.001
0.003
0.006
0.108
1.033
0.040
0.030
0.033
0.007
0.012
0.005
0.010
0.024
0
0.113
0.106
0.013
0.104
0.252
1.033
1.270
0.370
0.102
0.024
0.113
0.029
0.028
3.386
Nondetects
Set to
1/2 Det. Limit
0.016
0.016
0.018
0.015
0.019
0.135
1.054
0.049
0.036
0.037
0.022
0.021
0.016
0.021
0.035
0.014
0.130
0.123
0.059
0.122
0.279
1.054
1.275
0.376
0.109
0.038
0.130
0.065
0.072
3.566
NR = Not reported.
* Estimated based on the measured data for 2,3,7,8-TCDD, 2,3,7,8-TCDF, OCDD, and OCDF and congener
group emissions (i.e., for the penta-, hexa-, and hepta-CDD and CDFs, it was assumed that the measured
emission factor within a congener group was the sum of equal emission factors for all congeners in that group,
including non-2,3,7,8-substituted congeners).
Sources: U.S. EPA (1987a); NCASI (1995)
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December 2003
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Table 5-7. Concentrations of CDD/CDF in Candle Materials and Emissions
Wax
Material
Paraffin
Stearin
Beeswax
Paraffin
Stearin
Beeswax
Candle
Component
Wax
Wax
Wax
Wick
Wick
Wick
Concentration
CDD/CDF
(ng l-TEQDF/kg)
0.59
1.62
10.99
0.18
0.12
0.08
Total
Chlorophenols
(/yg/kg)
14.8
32.3
256
1.23
0.94
0.74
Total
Chlorobenzenes
(A/g/kg)
130
330
120
0.67
0.34
0.35
Emission Factor
CDD/CDF
(ng l-TEQDF/kg burnt wax)
0.015
0.027
0.004
_
—
-
Source: Schwind et al. (1995)
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5-36
December 2003
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Table 5-8. CDD/CDF Concentrations in Ash Samples from Cement Kiln Electric
Static Precipitator and LWA Kiln Fabric Filter
(concentrations in ng/kg)
Congener
2,3,7, 8-TCDD
Total TCDD
1, 2,3,7, 8-PeCDD
Total PeCDD
1, 2,3,4,7, 8-HxCDD
1, 2,3,6,7, 8-HxCDD
1, 2,3,7, 8,9-HxCDD
Total HxCDD
12,2,3,4,6,7,8-HpCDD
Total HpCDD
OCDD
Cement
Kiln
Avg. Cone.
0.429
36.1
0.886
54.9
1.03
2.36
2.47
173
17.7
55.2
21.0
LWA Kiln
Avg. Cone.
3.97
333
17.3
467
15.4
35.6
56.6
500
133
300
133
Total TCDD TEQs
2,3,7, 8-TCDF
Total TCDF
1, 2,3,7, 8-PeCDF
2,3,4,7, 8-PeCDF
Total PeCDF
1, 2,3,4,7, 8-HxCDF
1,2,3,6,7,8-HxCDF
1,2,3,7,8,9-HxCDF
2,3,4,6,7,8-HxCDF
Total HxCDF
1,2,3,4,6, 7, 8-HpCDF
1, 2,3,4,7, 8,9-HpCDF
Total HpCDF
OCDF
4.65
18.1
1.04
2.59
31.8
2.13
0.869
0.523
2.14
9.26
1.84
0.739
3.06
1.43
833
4,630
100
267
2,930
267
100
7.80
133
1,230
167
22.6
2,670
39.2
Total TCDF TEQs
Cement Kiln
I-TEQ
0.429
-
0.443
-
0.103
0.236
0.247
-
0.177
-
0.021
1.66
0.465
-
0.0518
1.30
-
0.213
0.0869
0.0523
0.214
0.0184
0.00739
-
0.00143
2.41
WHO-TEQ
0.429
-
0.886
-
0.103
0.236
0.247
-
0.177
-
0.0021
2.08
0.465
-
0.0518
1.30
-
0.213
0.869
0.0523
0.214
0.0184
0.00739
-
0.000143
2.40
LWA Kiln
I-TEQ
3.97
-
8.65
-
1.54
3.56
5.66
-
1.33
-
0.133
2.48
83.3
-
5.00
133
-
26.7
10.0
0.780
13.3
1.67
0.226
-
0.0392
274
WHO-TEQ
3.97
-
17.3
-
1.54
3.56
5.66
-
1.33
-
0.133
33.4
83.3
-
5.00
133
-
26.7
10.0
0.780
13.3
1.67
0.226
-
0.00392
274
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December 2003
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Ratio (congener emission factor / total CDD/CDF emission factor)
0* O!b05 0.01 0.015 0.02
2,3,7,8-TCDD
1,2,3,7,8-PeCDD
1,2,3,4,7,8-HxCDD
1,2,3,6,7,8-HxCDD
1,2,3,7,8,9-HxCDD
1,2,3,4,6,7,8-HpCDD
1,2,3,4,6,7,8,9-OCDD
2,3,7,8-TCDF
1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
1,2,3,4,7,8-HxCDF
1,2,3,6,7,8-HxCDF
1,2,3,7,8,9-HxCDF
2,3,4,6,7,8-HxCDF
1,2,3,4,6,7,8-HpCDF
1,2,3,4,7,8,9-HpCDF
1,2,3,4,6,7,8,9-OCDF
! Nondetects set eaual to zero.
Figure 5-1. Congener Profile for Air Emissions from Cement Kilns Burning Hazardous
Waste
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December 2003
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Ratio (congener group emission factor / total CDD/CDF emission factor)
0.05 0.1 0.15 0.2 0.25 0.3
0.35
Nondetects set equal to zero.
Ratio (congener emission factor / total CDD/CDF emission factor)
0.01 0.02 0.03
0.04
2,3,7,8-TCDD
1,2,3,7,8-PeCDD
1,2,3,4,7,8-HxCDD
1,2,3,6,7,8-HxCDD
1,2,3,7,8,9-HxCDD
1,2,3,4,6,7,8-HpCDD
1,2,3,4,6,7,8,9-OCDD
2,3,7,8-TCDF
1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
1,2,3,4,7,8-HxCDF
1,2,3,6,7,8-HxCDF
1,2,3,7,8,9-HxCDF
2,3,4,6,7,8-HxCDF
1,2,3,4,6,7,8-HpCDF
1,2,3,4,7,8,9-HpCDF
1,2,3,4,6,7,8,9-OCDF
Nondetects set equal to zero.
Figure 5-2. Congener and Congener Group Profiles for Air Emissions
from Cement Kilns Not Burning Hazardous Waste
DRAFT-DO NOT QUOTE OR CITE
5-39
December 2003
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Figure 5-3. Congener and Congener Group Profiles for Air Emissions
from Petroleum Catalytic Reforming Units
DRAFT-DO NOT QUOTE OR CITE 5-40 December 2003
-------
1.1
1.0
I 0.9
I
I 0.8
! 0.7
S0.6
8
§•0.5
&0.4
s
M
§0.3
U
0.2
0.1
0
TCDD PeCDD
I Cigarettes (row 1)
I Ash (row 3)
HxCDD HpCDD OCDD
I Mainstream Smoke (row 2)
Figure 5-4. CDD Profiles for Japanese Cigarettes, Smoke, and Ash
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December 2003
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i '
o
£ °'9
Q
O 0.8
Q
O 0.7
g °-6
o
o
O, 0.5
s
0> 0.4
H
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£
8 0.2
0.1
TCDD PeCDDHxCDDHpCDD OCDD TCDF PeCDF HxCDF HpCDF OCDF
I Japanese Mainstream (row 1) I German Mainstream (row 2)
I Swedish Mainstream (row 3) I Swedish Sidestream (row 4)
Figure 5-5. Congener Group Profiles for Mainstream and Sidestream Cigarette Smoke
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0.6
.0.5
u
I
CL.
§0.4
Q
Q
O
gO.3
o
o
I
0.2
0.1
/A
/A
/A
TCDD PeCDD HxCDD HpCDD OCDD TCDF PeCDF HxCDF HpCDF OCDF
I China (column 1) I I Denmark (column 2) I Japan (column 3)
Germany (column 4) I United Kingdom (column 5) I I United States (column 6)
Source: Matsueda et al. (1994)
Figure 5-6. Congener Group Profiles for Cigarette Tobacco from Various Countries
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Ratio (congener emission factor / total CDD/CDF emission factor)
0.05 0.1 0.1S 0.2 0.25 0.3
0.38
2,3,7,8-TCDD
1,2,3,7,8-PeCDD
1,2,3,4,7,8-HxCDD
1,2,3,6,7,8-HxCDD
1,2,3,7,8,9-HxCDD
1,2,3,4,6,7,8-HpCD:
1,2,3,4,6,7,8,9-OCD:
2,3,7,8-TCDF
1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
1,2,3,4,7,8-HxCDF
1,2,3,6,7,8-HxCDF
1,2,3,7,8,9-HxCDF
2,3,4,6,7,8-HxCDF
1,2,3,4,6,7,8-HpCD:
1,2,3,4,7,8,9-HpCD:
1,2,3,4,6,7,8,9-OCD:
Ratio (mean congener group emission factor / total CDD/CDF emission factor)
0.1 0.2 0.3 0.4 0.5
Source: NCASI (1995); nondetects set equal to zero.
Figure 5-7. Congener and Congener Group Profiles for Air Emissions from Kraft Black Liquor Recovery Boilers
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6. COMBUSTION SOURCES OF CDD/CDF: MINIMALLY CONTROLLED
AND UNCONTROLLED COMBUSTION SOURCES*
6.1. COMBUSTION OF LANDFILL GAS
The U.S. EPA promulgated emission standards and guidelines in 1996 to control
emissions of landfill gas from existing and future landfills under the Clean Air Act (Federal
Register, 1996a). Those regulations require the largest landfills in the United States
(approximately 312) (i.e., largest on the basis of design capacity) to periodically measure
and determine their annual emissions of landfill gas. Landfills that emit annually more than
50 metric tons of nonmethane organic compounds (NMOC) must collect landfill gas and
reduce its NMOC content by 98 percent weight through use of a control device. EPA
estimates that, when implemented, these controls will reduce NMOC annual emissions
from existing landfills by 77,600 metric tons. The cost analysis supporting this
rulemaking based control device costs on open flares, because flares are applicable to all
the regulated facilities. Assuming that this mass reduction is achieved by use of flares,
the corresponding volume of landfill gas that will be burned is approximately 14 billion
m3/yr. The calculation is based on an assumed default NMOC concentration in landfill gas
of 1,532 ppmv and a conversion factor of 3.545 mg/m3 of NMOC per 1 ppmv of NMOC
(Federal Register, 1993d). EPA estimated that more than 100 of the approximately 312
landfills had some form of collection or control system, or both, in place in 1991 (Federal
Register, 1 991 b). Thus, a rough approximation of the volume of landfill gas that is
currently combusted is 4.7 billion m3/yr (or 33 percent of the future expected 14 billion
m3/yr reduction). This estimate is similar to the 2.0 to 4.0 billion m3 of landfill gas that
were estimated in EIA (1994) as collected and consumed for energy recovery purposes in
1992. The Energy Information Administration (EIA, 1992) estimated that between 0.9
and 1.8 billion m3 of landfill gas were collected and burned in 1990 for energy recovery
purposes.
Although no data could be located on the levels of CDD/CDFs in untreated landfill
gas, several studies have reported detecting CDD/CDFs in the emissions resulting from the
This chapter discusses combustion sources of CDD/CDF that have some (in the case of combustion
of landfill gas) or no post-combustion pollution control equipment for conventional pollutant emissions.
Note that very few of the CDD/CDF sources listed in this report control specifically for CDD/CDF
emissions.
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combustion of landfill gas. Only one study of CDD/CDF emissions from a landfill flare has
been reported for a U.S. landfill (CARB, 1990d). The I-TEQDF and TEQDF-WH098 emission
factor calculated from the results of this study is approximately 2.4 ng TEQ/m3 of landfill
gas combusted. The congener-specific results of this study are presented in Table 6-1.
Figure 6-1 presents the CDD/CDF congener emission profile based on these emission
factors. Bremmer et al. (1994) reported a lower emission factor, 0.4 ng l-TEQDF/m3, from
the incineration of untreated landfill gas in a flare at a facility located in The Netherlands.
No congener-specific emission factors were provided in Bremmer et al. (1994). The
average TEQ emission factor for the CARB (1990d) and Bremmer et al. (1994) studies is
1.4 ng l-TEQDF/m3 of landfill gas combusted. Umweltbundesamt (1996) reported even
lower TEQ emission factors for landfill gas burned in engines or boiler mufflers rather than
in a flare. The reported results for 30 engines and mufflers tested in Germany ranged
from 0.001 to 0.28 ng l-TEQDF/m3 with most values below 0.1 ng l-TEQDF/m3. However,
Bremmer et al. (1994) also reported an emission factor of 0.5 ng l-TEQDF/m3 from a landfill
gas-fired engine in The Netherlands.
The limited emission factor data that are available were judged inadequate for
developing national emission estimates that could be included in the national inventory.
However, a preliminary estimate of the potential annual TEQ release from landfills can be
obtained using the estimated volume of combusted gas and the available emission factors.
Combining the estimate of current landfill gas volume that is combusted (4.7 billion m3/yr)
with the emission factor of 1.4 ng l-TEQDF/m3 of flare-combusted gas yields an annual
emission estimate of 6.6 g I-TEQDF. This estimate should be regarded as a preliminary
indication of possible emissions from this source; further testing is needed to confirm the
true magnitude of those emissions.
6.2. ACCIDENTAL FIRES
Accidental fires in buildings and vehicles are uncontrolled combustion processes
that, because of poor combustion conditions, typically result in relatively high emissions of
incomplete combustion products (Bremmer et al., 1994). The incomplete combustion
products can include CDDs and CDFs. Polyvinyl chloride (PVC) building materials and
furnishings, chloroparaffin-containing textiles and paints, and other chlorinated organic
compound-containing materials appear to be the primary sources of the chlorine (Retard,
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1993). Although the results of several studies demonstrate the presence of CDD/CDF
concentrations in soot deposits and residual ash from such fires, few direct measurements
of CDD/CDFs in the fumes or smoke of fires have been reported. The results of some of
those studies are described below, followed by an evaluation of the available data.
6.2.1. Soot and Ash Studies
Christmann et al. (1989b) analyzed the soot formed during combustion and
pyrolysis of pure PVC and PVC cable sheathings in simple laboratory experiments designed
to mimic the conditions of fires. For the combustion experiments, 2 grams of a PVC
sample were incinerated with a laboratory gas burner. The combustion products were
collected on the inner walls of a cooled gas funnel placed above the sample. For the
pyrolysis experiments, about 50 mg of the sample were placed in a quartz tube and
heated to about 950°C for 10 minutes in either an air atmosphere or a nitrogen
atmosphere. The combustion experiments yielded CDD/CDF concentrations in soot of
110//g l-TEQDF/kg for a low-molecular-weight PVC, 450//g l-TEQDF/kg for a high molecular
weight PVC, and 270 /vg l-TEQDF/kg for PVC cable. The pyrolysis experiments in the air
atmosphere yielded lower CDD/CDF concentrations in soot: 24.4/vg l-TEQDF/kg for a low-
molecular-weight PVC, 18.7/yg l-TEQDF/kg for a high-molecular-weight PVC, and up to 41
/yg l-TEQDF/kg for PVC cable. In general, CDFs were predominantly formed over CDDs.
The lower chlorinated CDF congeners were dominant in the combustion experiments;
however, the HpCDF and OCDF congeners were dominant in the pyrolysis experiments.
No CDD/CDFs were detected in pyrolysis experiments under a nitrogen atmosphere. Also,
no CDD/CDFs were detected when chlorine-free polyethylene samples were subjected to
the same combustion and pyrolysis conditions.
Deutsch and Goldfarb (1988) reported finding CDD/CDF concentrations ranging
from 0.04 to 6.6/vg/kg in soot samples collected after a 1986 fire in a State University of
New York lecture hall. The fire consumed or melted plastic furnishings, cleaning products
containing chlorine, wood, and paper.
Funcke et al. (1988; as reported in Bremmer et al., 1994, and Retard, 1993)
analyzed 200 ash and soot samples from sites of accidental fires in which PVC was
involved. CDD/CDFs were detected in more than 90 percent of the samples at
concentrations in the ng l-TEQDF/kg to /jg l-TEQDF/kg range. Fires involving the
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combustion of materials containing relatively large amounts of PVC and other chlorinated
organic substances resulted in the highest levels of CDD/CDFs, with CDD/CDF
concentrations ranging from 0.2 to 110 /vg l-TEQDF/kg of residue.
Thiesen et al. (1989) analyzed residues from surfaces of PVC-containing materials
that were partially burned during accidental fires at sites in Germany that manufactured or
stored plastics. CDD/CDF concentrations in residues were reported as 0.5/vg l-TEQDF/kg
for soft PVC, 4.6 fjg l-TEQDF/kg for PVC fibers, and 28.3 /JQ l-TEQDF/kg for a hard PVC.
The ratio of total CDFs to total CDDs in the three samples ranged from 4:1 to 7:1. The
dominant 2,3,7,8-substituted CDF and CDD congeners in all three samples were
1,2,3,4,6,7,8-HpCDF and 1,2,3,4,6,7,8-HpCDD.
Following an accidental fire at a Swedish carpet factory in 1987, 200 metric tons
of PVC and 500 metric tons of PVC-containing carpet burned. Marklund et al. (1989)
analyzed snow samples within 1,500 meters downwind from the fire site and found
CDD/CDF concentrations in the top 2 cm ranging from 0.32//g l-TEQDF/m2 at 10 meters of
the site to 0.01 /vg l-TEQDF/m2 at 1,500 meters downwind of the site. Because of an
atmospheric inversion and very light wind at the time of the fire, the smoke from the fire
remained close to the ground. The soot deposited onto the snow was thus assumed to be
representative of the soot generated and released from the fire. Wipe samples of soot
from interior posts of the plant (5 and 20 meters from the fire) contained EADON TEQ
concentrations of 0.18 and 0.05/yg/m2, respectively. On the basis of these deposition
measurements, Marklund et al. (1989) estimated the total CDD/CDF emission from the fire
to be less than 3 mg I-TEQDF.
Carroll (1996) estimated a soot-associated CDD/CDF emission factor (i.e., not
including volatile emissions) of 28 to 138 ng l-TEQDF/kg of PVC burned for the Swedish
carpet factory fire using the following assumptions: (1) the PVC carpet backing was one-
half the weight of the carpet, (2) the carpet backing contained 30 percent by weight PVC
resin, and (3) 20 to 100 percent of the PVC and PVC carpet backing present in the
warehouse actually burned. Carroll (1996) also estimated a similar soot-associated
emission factor (48 to 240 ng l-TEQDF/kg of PVC burned) for a fire at a plastics recycling
facility in Lengerich, Germany. Carroll (1996) used the results of wipe samples collected
at downwind distances of up to 6,300 meters from the fire to estimate the emission
factor.
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Fiedler et al. (1993) presented a case study of CDD/CDF contamination and
associated remedial actions taken at a kindergarten in Germany following a fire, that
destroyed parts of the roof, windows, and furnishings. Soot collected from the building
contained CDD/CDFs at a concentration of 45/vg l-TEQDF/kg (or 1 5/vg l-TEQDF/m2). Fiedler
et al. (1993) attributed the CDD/CDFs detected to the combustion of plastic and wooden
toys, floors, and furnishings; however, no information was provided on the quantities of
those materials.
Fiedler and Lindert (1998) presented results of soot sampling following a serious
fire at Dusseldorf Airport in Germany. Polystyrene sheets and PVC-coated cables were
involved in the fire, together with PCB-containing condensers (bulbs). Surface wipe
samples contained up to 0.33 //g l-TEQDF/m2. Concentrations in soot ranged from 7 to
1 30/yg l-TEQDF/kg. Concentrations of BDD/BDFs were detected in soot at concentrations
as high as 0.9 mg/kg soot.
Wichmann et al. (1993, 1995) measured the CDD/CDF content of ash and debris
and deposited surface residues that resulted from experimental test burns of two cars (a
1974 Ford Taurus and a 1988 Renault Espace), one subway car, and one railway coach in
a tunnel in Germany. On the basis of measurements obtained from sampled ash and
debris and from soot collectors placed at regular intervals up to 420 meters downwind of
the burn site, the total amounts of CDD/CDF in the ash/debris and tunnel surface residues
from each vehicle burn experiment were estimated as follows: 1974 model car —0.044 mg
I-TEQDF; 1988 model car —0.052 mg I-TEQDF; subway car —2.6 mg I-TEQDF; and railway
coach—10.3 mg I-TEQDF. For each vehicle burn experiment, the mass of TEQ in tunnel
surface residue exceeded the mass in ash and debris; 73 to 89 percent were accounted
for by the tunnel surface residues and 11 to 27 percent by ash and debris. The average
CDD/CDF content of the ash and debris from each experimental burn was as follows:
new car —0.14/yg l-TEQDF/kg; old car —0.30/vg l-TEQDF/kg; subway car —3.1 /vg I-
TEQDF/kg; and railway coach —5.1 fjg l-TEQDF/kg.
6.2.2. Fume and Smoke Studies
Merk et al. (1995) collected fume and smoke generated during the burning of 400
kg of wood and 40 kg of PVC in a building (4,500 m3 volume) over a 45-minute period.
The sampling device consisted of dual glass fiber filters to collect particles greater than
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0.5 fjm, followed by a polyurethane foam filter to collect vapor phase CDD/CDFs. The
particulate phase and gas phase showed the same congener pattern, decreasing
concentration with increasing degree of chlorination, thus indicating no preferential
sorption of higher chlorinated congeners to smoke particulates. However, the CDD/CDF
found in the gas phase (about 5 ng l-TEQDF/m3) accounted for more than 90 percent of the
detected CDD/CDFs. Merk et al. (1995) also reported that the soot deposited from this
fire onto a 1 m2 aluminum sheet resulted in surface contamination of 0.050 /jg l-TEQDF/m2.
Although it was stated in Merk et al. (1995) that the building was 'closed,' subsequent
communication with one of the coauthors (Schramm, 1998) clarified that a 'gas cleaning'
system was in operation during the testing. Because a ventilation system was in
operation during the testing, there was likely some loss of vapor phase CDD/CDFs from
the hall. Therefore, the deposits (from particulate deposition and vapor phase
condensation) on the test aluminum plate may not reflect total CDD/CDF formation during
the fire.
Dyke and Coleman (1995) reported a fourfold increase in CDD/CDF TEQ
concentrations in the ambient air during "bonfire" night in Oxford, England. Bonfire night
(November 5) is an annual event in England during which it is customary to set off
fireworks and have bonfires to commemorate a failed plot to overthrow the king in 1605.
Air concentrations before and after bonfire night ranged from 0.15 to 0.17 pg l-TEQDF/m3.
The air concentration during the bonfire night was 0.65 pg l-TEQDF/m3. The dominant
congeners in all samples were the hepta- and octa-CDDs. The study was not designed to
collect data that would enable calculation of an emission rate nor to differentiate the
relative importance of the various materials combusted. However, the results do indicate
that open burning of materials likely to be combusted in accidental fires (with the
exception of fireworks) results in the release of CDDs and CDFs.
6.2.3. Data Evaluation
Structural Fires — The limited data available on structural fires were judged
inadequate for developing national emission estimates that could be included in the
national inventory. This conclusion was also reached in national emission inventories
developed for The Netherlands (Bremmer et al., 1994) and the United Kingdom (UK
Department of the Environment, 1995). Most cited studies involved situations (i.e., field
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and laboratory) where relatively high loadings of PVC or plastics were combusted. The
effects of different mixes of combusted materials, oxygen supplies, building
configurations, durations of burn, and so forth, likely to be found in accidental fires cannot
be accounted for by the factors that can be derived from these studies. Also, most of the
studies addressed only soot or ash residues and did not address potential volatile
emissions of CDD/CDFs which, according to Merk et al. (1995), may represent 90 percent
of the CDD/CDFs generated during burning of PVC.
Two recent reports (Carroll, 1996; Thomas and Spiro, 1995) attempted to quantify
CDD/CDF emissions from U.S. structural fires, and Lorenz et al. (1996) estimated
emissions from structural fires in the Federal Republic of Germany. The estimates derived
in these three studies are presented below, following a brief summary of the number and
types of accidental fires reported annually in the United States.
In 1995, approximately 574,000 structural fires were reported in the United
States. Of these, 426,000 were reported for residential structures, including 320,000
fires in 1-2 family units, 94,000 fires in apartments, and 12,000 fires in other residential
settings. The remaining 148,000 structural fires were broken down as follows:
1 5,000 —public assembly; 9,000 —educational; 9,000 —institutional; 29,000 —stores and
offices; 29,000 —special structures; 39,000 —storage; and 1 8,000 —industry, utility, and
defense. The latter two categories may be underreported as some incidents were handled
by private fire brigades or fixed suppression systems, which do not report (U.S. DOC,
1997).
Carroll (1996) estimated the total CDD/CDF content of soot and ash generated
from the 358,000 fires reported in U.S. DOC (1995a) for 1993 in 1-2 family unit
residential structural fires. Carroll (1996) developed detailed estimates of the PVC content
of typical homes, including plumbing, wiring, siding and windows, wallpaper, blinds and
shades, and upholstery. Using statistical data on fire loss (i.e., dollar value) provided the
typical loss per recorded fire (9.5 percent of value) which Carroll assumed also
represented the typical percentage of PVC burned. Extrapolating to all 358,000 1-2
family unit fires yielded an annual mass of 2,470 metric tons of PVC burned. Carroll
(1996) then developed TEQ emission factors from the results of Thiesen et al. (1989) and
Marklund et al. (1989). The estimated CDD/CDF content ranged from 0.47 to 22.8 g I-
TEQDF with 0.07 to 8.6 g I-TEQDF in soot and 0.4 to 14.2 g I-TEQDF in ash. Carroll derived
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a soot emission factor (i.e., grams of soot produced per gram of PVC combusted) from his
assumptions regarding the surface area of the soot collection funnel used by Christmann
et al. (1989a) and the soot deposition rate on that funnel. Carroll then applied these I-
TEQDF emission factors to the estimated 2,470 metric tons of PVC burned annually in 1-2
family unit residential fires to obtain estimates of the annual mass of TEQ that would be
found in the soot and ash of residential fires (i.e., 0.48 to 22.8 g l-TEQDF/yr). The average
emission per fire is thus 1.3 to 64 //g I-TEQDF.
Thomas and Spiro (1995) estimated that 20 g of I-TEQDF may be released annually
to air from structural fires. This estimate assumed an emission factor of 4 ng l-TEQDF/kg
of material combusted (i.e., the emission rate for "poorly controlled" wood combustion),
an assumed material combustion factor of 6,800 kg/fire, and 688,000 structural fires per
year. The average emission per fire is thus 29 /jg I-TEQDF.
Lorenz et al. (1996) estimated annual generation of CDD/CDF TEQs in the Federal
Republic of Germany using data on the number of residential and industrial/commercial
structural fires coupled with data on CDD/CDF content in soot and ash residues remaining
after fires. The potential annual I-TEQDF generation was estimated to be 78 to 212 grams.
Although, as stated above, the available data were judged to be inadequate to
support development of an emission estimate for the national inventory, a preliminary
estimate of the potential magnitude of TEQ emissions can be obtained using the estimates
of Carroll (1996) and Thomas and Spiro (1995), that annual releases are about 20 g
I-TEQDF.
There is very low confidence in these estimated emissions because of the
numerous assumptions employed in their derivation. If the conclusion of Merk et al.
(1995) is assumed to be correct, that 90 percent of the CDD/CDFs formed in fires are in
the gaseous phase rather than particulate phase (i.e., greater than 0.5 /vm diameter), and
it is also assumed that the estimates of Carroll (1996) and of Thomas and Spiro (1995) do
not totally account for volatile emissions, then the total CDD/CDF emissions estimated by
Carroll (1996) and by Thomas and Spiro (1995) may be underestimates. Further testing is
needed to confirm the true magnitude of these releases.
Vehicle Fires—The limited data available on vehicle fires were judged inadequate for
developing national emission estimates that could be included in the national inventory.
However, a preliminary estimate of the range of potential CDD/CDF emissions that may
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result from vehicle fires can be calculated using the results reported by Wichmann et al.
(1993, 1995) for controlled vehicle fires in a tunnel (0.044 mg I-TEQDF for an old car to
2.6 mg I-TEQDF for a subway car). Although Wichmann et al. (1993; 1995) did not
measure volatile CDD/CDFs (which were reported by Merk et al. (1995) to account for the
majority of CDD/CDFs formed during a fire), the study was conducted in a tunnel, and it is
likely that a significant fraction of the volatile CDD/CDFs sorbed to tunnel and collector
surfaces and were thus measured as surface residues. In 1995, approximately 406,000
vehicle fires were reported in the United States (U.S. DOC, 1997). If it is assumed that
99 percent of those involved cars and trucks (i.e., the approximate percentage of all U.S.
motor vehicles that are in-service cars and trucks; U.S. DOC, 1995a), and that the
applicable emission rate is 0.044 mg I-TEQDF per incident, then the annual TEQ formation
is 17.7 g I-TEQDF. The emission factor of 2.6 mg l-TEQDF/fire is assumed to be applicable
to the remaining 1 percent of vehicle fires, thus yielding an emission of 10.6 g l-TEQDF/yr.
The total TEQ annual emission is roughly estimated to be 28.3 g l-TEQDF/yr. This estimate
should be regarded as a preliminary indication of possible emissions from this source
category; further testing is needed to confirm the true magnitude of these emissions.
6.3. LANDFILL FIRES
In the late 1980s, two serious fires occurred in landfills near Stockholm, Sweden.
The first involved a fire in a large pile of refuse-derived fuel. Using measurements of
chlorobenzenes in the air emissions, it was estimated that 50 to 100 kg of chlorobenzenes
were released. CDD/CDF emissions were estimated to be several tens of grams, on the
assumption that the ratio of CDD/CDFs to chlorobenzenes in landfill fire emissions is
similar to the ratio observed in stack gases of municipal waste incinerators. In connection
with the second fire, which occurred at a large conventional landfill, birch leaves were
collected from trees close to the fire and at distances up to 2 km downwind of the fire, as
well as from nearby areas not affected by smoke from the fire. The discharge of
CDD/CDF necessary to cause the CDD/CDF concentrations measured on the leaves was
estimated to be several tens of grams (Persson and Bergstrom, 1991).
In response to these incidents, Persson and Bergstrom (1991) measured CDD/CDF
emissions from experimental fires designed to simulate surface landfill fires and deep
landfill fires. The experiments used 9-month-old domestic waste. The tests showed no
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significant difference in CDD/CDF content of the fire gas produced by the simulated
surface and deep fires. The average CDD/CDF emission rate was reported to be 1 /jg
Nordic TEQ/kg of waste burned. Persson and Bergstrom (1991) and Bergstrom and
Bjorner (1992) estimated annual CDD/CDF Nordic TEQ emissions in Sweden from landfill
fires to be 35 grams. The estimate was based on the emission rate of 1 /vg Nordic
TEQ/kg waste burned, an assumed average density of landfill waste of 700 kg/m3, an
assumed waste burn of 150 m3 for each surface landfill fire (167 fires in Sweden per
year), and an assumed waste burn of 500 m3 for each deep landfill fire (50 fires in
Sweden per year). The estimates of waste burn mass for each type of fire were the
average values obtained from a survey of 62 surface fires and 25 deep fires. The
estimated number of fires per year was based on the results of a survey of all Swedish
municipalities for fires reported during the years 1988 and 1989. In 1991, Sweden had
an estimated 400 municipal landfills (Persson and Bergstrom, 1991).
Ruokojarvi et al. (1995) measured the ambient air concentrations of CDD/CDF in
the vicinity of real and experimental landfill fires in Finland. The most abundant toxic
congeners were the hepta- and octa-CDDs and the penta-, hepta-, and octa-CDFs. The
highest contributions to the measured TEQ were made by 1,2,3,7,8-PeCDD and
2,3,4,7,8-PeCDF. In Finland, annual CDD/CDF emissions from landfill fires are estimated
to be 50-70 g Nordic TEQ (Aittola, 1993, as reported by Ruokojarvi et al., 1995).
Although no U.S. monitoring studies are available, an emission factor similar to the
Swedish emission factor would be expected in the United States, because the contents of
the municipal waste are expected to be similar between the United States and Sweden.
However, because no data could be located on characterization of landfill fires in the
United States (i.e., number, type, mass of waste involved), the limited data available were
judged inadequate for developing national emission estimates that could be included in the
national inventory. However, a preliminary estimate of the potential magnitude of TEQ
emissions associated with landfill fires in the United States can be obtained by assuming a
direct correlation of emissions to population size for the United States and Sweden or by
assuming a direct correlation between emissions and the number of landfills in each
country. Both countries are Western, industrialized countries. Although the per capita
waste generation rate in the United States is nearly 1.5 times that of Sweden, the
composition of municipal waste and the fraction of municipal waste disposed of in landfills
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in the two countries are nearly identical (U.S. EPA, 1996b). The 1995 population of
Sweden is 8,822,000 (U.S. DOC, 1995a). Thus, the per capita landfill fire-associated
Nordic TEQ emission factor is 4.0/vg TEQ/person/year (i.e., 35 grams/8,822,000 people).
Because congener-specific results were not provided in Persson and Bergstrom (1991) and
Bergstrom and Bjorner (1992), it was not possible to derive emission factors in units of
I-TEQDF or TEQDF-WH098. Applying this factor to the U.S. population (263,814,000) (U.S.
DOC, 1995a) results in an estimated annual emission of 1,050 g of TEQ. This estimate
should be regarded as a preliminary indication of possible emissions from this source
category; further testing is needed to confirm the true magnitude of these emissions. An
annual emission of similar size is obtained if it is assumed that the ratio of annual TEQ
emissions to number of landfills in Sweden, 87.5 mg TEQ/landfill (i.e., 35 grams/400
landfills), is applicable to the United States, which has 3,558 landfills (U.S. EPA, 1996b).
The resulting annual emission estimate is 311 g TEQ/yr.
6.4. FOREST AND BRUSH FIRES
Because CDD/CDFs have been detected both in the soot from residential wood
burning (Bumb et al., 1980; Nestrick and Lamparski, 1982, 1983; Bacher et al., 1992),
and in the flue gases from residential wood burning (Schatowitz et al., 1993; Vickelsoe et
al., 1993), it is reasonable to presume that wood burned in forest and brush fires may
also be a source of CDD/CDFs (Section 4.2 contains details on these studies).
Only one study could be found that reported direct measurements of CDD/CDFs in
the emissions from forest fires. This study, by Tashiro et al. (1990), reported detection of
total CDD/CDFs in air at levels ranging from about 15 to 400 pg/m3. The samples were
collected from fixed collectors located 10 meters above the ground and from aircraft flying
through the smoke. Background samples collected before and after the tests indicated
negligible levels in the atmosphere. These results were presented in a preliminary report;
however, no firm conclusions were drawn about whether forest fires are a CDD/CDF
source. The final report on this study, Clement and Tashiro (1991), reported total
CDD/CDF levels in the smoke of about 20 pg/m3. The authors concluded that CDD/CDFs
are emitted during forest fires but recognized that some portion of these emissions could
represent resuspension from residues deposited on leaves rather than newly formed
CDD/CDFs.
DRAFT-DO NOT QUOTE OR CITE 6-1 1 December 2003
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Although not designed to directly assess whether CDD/CDFs are formed during
brush fires, Buckland et al. (1994) measured the CDD/CDF levels in soil samples from both
burnt and unburnt areas in national parks in New Zealand 6 weeks after large-scale brush
fires. Four surface soil cores (2 cm depth) were collected and composited from each of
three burnt and three unburnt areas. Survey results indicated that brush fires did not have
a major impact on the CDD/CDF levels in soil. The I-TEQDF contents in soil sample
composites of the three unburnt areas were 3.0 ng/kg, 8.7 ng/kg, and 10.0 ng/kg. The
I-TEQDF contents in the soil sample composites of three burnt areas were 2.2 ng/kg, 3.1
ng/kg, and 36.8 ng/kg. Total CDD/CDF contents ranged from 1,050 to 7,700 ng/kg in
the unburnt area soil samples and from 1,310 to 27,800 ng/kg in the burnt area soil
samples. OCDD accounted for 94 to 97 percent of the total CDD/CDF content in all
samples.
Similarly, a survey of controlled straw-field burning in the United Kingdom (Walsh
et al., 1994) indicated that the straw burning did not increase CDD/CDF burden in the soil;
however, a change in congener distribution was observed. Soils from three fields were
sampled immediately before and after burning, along with ash from the fire. The mean
I-TEQDF concentrations in the preburn soil, postburn soil, and ash were 1.79 ng/kg, 1.72
ng/kg, and 1.81 ng/kg, respectively. Concentrations of 2,3,7,8-TCDF were lower in the
postburn soils than in the preburn soils. Conversely, the concentrations of OCDD were
higher in the postburn soils indicating possible formation of OCDD during the combustion
process.
Van Oostdam and Ward (1995) reported finding no detectable levels of 2,3,7,8-
substituted CDD/CDFs in three soil samples and four ash samples following a forest fire in
British Columbia. The detection limits on a congener-specific basis (unweighted for TEQ)
ranged from 1 to 2 ng/kg. Nondetected values were also reported by Van Oostdam and
Ward (1995) for ashes at a slash and burn site; the soil contained about 0.05 ng
l-TEQDF/kg, whereas background soil contained about 0.02 ng l-TEQDF/kg.
The concentrations presented by Clement and Tashiro (1991) cannot accurately be
converted to an emission factor, because the corresponding rates of combustion gas
production and wood consumption are not known. As a result, three alternative
approaches were considered to develop an emission factor.
DRAFT-DO NOT QUOTE OR CITE 6-12 December 2003
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Soot-Based Approach —This approach assumes that the levels of CDD/CDFs in
chimney soot are representative of the CDD/CDFs in emissions. The CDD/CDF emission
factor is calculated as the product of the CDD/CDF concentration in soot and the total
particulate emission factor. This calculation involves first assuming that the CDD/CDF
levels measured in chimney soot (720 ng l-TEQDF/kg) by Bacher et al. (1992) are
representative of the CDD/CDF concentrations of particles emitted during forest fires.
Second, the total particulate generation factor must be estimated. Ward et al. (1976)
estimated the national average particulate emission factor for wildfires as 150 Ib/ton
biomass dry weight using primarily data for head fires. Ward et al. (1993) estimated the
national average particulate emission factor for prescribed burning as 50 Ib/ton biomass
dry weight. Combining the total particulate generation rates with the I-TEQDF level in soot
results in emission factor estimates of 54 ng of I-TEQDF and 1 8 ng of l-TEQDF/kg of
biomass burned in wildfires and prescribed burns, respectively. These estimated factors
are likely to be overestimates, because the levels of CDD/CDF measured in chimney soot
by Bacher et al. (1992) may represent the accumulation and enrichment of CDD/CDFs
measured in chimney soot over time, leading to much higher assumed levels than what is
actually on emitted particles.
Carbon Monoxide (CO) Approach —Carbon monoxide is a general indicator of the
efficiency of combustion, and the emission factors of many emission products can be
correlated to the CO emission factor. The Schatowitz et al. (1993) data for emissions
during natural wood burning in open stoves suggest an emission factor of 10/vg I-
TEQDF/kg of CO. Combining this factor with the CO emission factor during forest fires
(roughly 0.1 kg CO/kg of biomass, Ward et al., 1993) yields an emission factor of 1,000
ng l-TEQDF/kg biomass. This factor is higher than the soot-based factor discussed above,
which is itself considered to be an overestimate. In addition, although the formation
kinetics of CDD/CDF during combustion are not well understood, CDD/CDF emissions
have not been shown to correlate well with CO emissions from other combustion sources.
(See Chapter 2.)
Wood Stove Approach —This approach assumes that the emission factor for
residential wood burning (using natural wood and open door, i.e., uncontrolled draft)
DRAFT-DO NOT QUOTE OR CITE 6-13 December 2003
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applies to forest fires. As discussed in Section 4.2.1, this approach suggests an emission
factor of about 2 ng l-TEQDF/kg of wood burned. This value appears more reasonable than
the factors suggested by the soot and CO approaches because it is based on direct
measurement of CDD/CDFs from combustion of wood rather than indirect techniques.
However, forest fire conditions differ significantly from combustion conditions in wood
stoves. For example, forest fire combustion does not occur in an enclosed chamber, and
the biomass consumed in forest fires is usually green and includes underbrush, leaves, and
grass. Given these differences and the uncertainties about the formation kinetics of
CDD/CDF during combustion, it is difficult to determine whether CDD/CDF emissions
would be higher or lower from forest fires than from wood stoves. Thus, although an
emission factor of 2 ng l-TEQDF/kg appears to be the best estimate that can be made
currently, it must be considered highly uncertain.
The limited emission factor data available and the degree of confidence in the three
approaches evaluated to derive an emission factor were judged inadequate for developing
national emission estimates that could be included in the national inventory. However, a
preliminary estimate of the potential annual TEQ release associated with forest and brush
fires can be obtained using estimates of the biomass burned annually in wildfire and
prescribed burns and the emission factor used for wood stoves (2 ng l-TEQDF/kg of
biomass). According to the Council on Environmental Quality's 25th Annual Report (CEQ,
1997), 5 million acres of forest were lost to wildfires in 1987 and 7 million acres were
lost in 1995. Estimates of the acreage consumed annually during prescribed burns are not
readily available for the reference years 1995 and 1997. An estimated 5.1 million acres
of biomass were burned in 1989 during prescribed burns (Ward et al., 1993). Prescribed
burning, also known as managed or controlled burning, is used as a forest, range, and
wetland management tool conducted under prescribed weather and fuel conditions. This
value of 5.1 million acres is assumed to be an appropriate value to use for reference years
1987 and 1995.
Combining these acreage estimates with biomass consumption rates of 9.43 metric
tons/acre in areas consumed by wildfires (Ward et al., 1976) and 7.44 metric tons/acre in
areas consumed in prescribed burns (Ward et al., 1993) indicates that 47 million metric
tons of biomass were consumed by wildfires in 1987, 66 million metric tons were
DRAFT-DO NOT QUOTE OR CITE 6-14 December 2003
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consumed by wildfires in 1995, and 38 million metric tons were consumed by prescribed
burns in 1987 and 1995.
Combining the emission factor developed using the wood stove approach (2 ng
l-TEQDF/kg biomass) with the amount of biomass consumed annually in wildfires and
prescribed fires (total of 85 million metric tons in 1987 and 104 million metric tons in
1995) yields I-TEQDF annual emission estimates of 1 70 g in 1987 and 208 g in 1995.
These estimates should be regarded as preliminary indications of possible emissions from
this source; further testing is needed to confirm the true magnitude of emissions.
6.5 BACKYARD BARREL BURNING
In many rural areas of the United States, disposal of residential solid waste may
take place via open backyard burning in barrels or similar homemade devices. Although no
national statistics on the prevalence of this practice have been reported, the results of a
telephone survey conducted in the early 1990s of residents in five central Illinois counties
indicate that about 40 percent of the residents in a typical rural Illinois county burn
household waste. The survey also found that, on average, those households that burn
waste dispose of approximately 63 percent of their household waste by burning it in
barrels (Two Rivers Region Council of Public Officials and Patrick Engineering, 1994).
Similar results were recently obtained in a survey conducted by Zenith Research
Group, Inc. for the Western Lake Superior Sanitary District of Minnesota (Zenith, 2000).
This survey of 760 residents of selected portions of Northwest Wisconsin and Northeast
Minnesota addressed, in part, use of burn barrels or other devices to burn household
garbage or other materials. The survey found that 71 percent of the respondents indicated
their residence currently had garbage hauling service. Of those respondents lacking a
garbage hauling service, 92 percent said they currently used a nearby garbage disposal
site. However, among all respondents, 27.5 percent admitted they currently use a burn
barrel or other device to burn household garbage or other materials. Of these respondents
who admitted burning, 39 percent indicated they burn at least weekly and 30 percent
indicated they burned once or twice monthly.
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6.5.1. Emission Estimates from Backyard Barrel Burning
The low combustion temperatures and oxygen-starved conditions associated with
barrel burning may result in incomplete combustion and increased pollutant emissions. In
1997, EPA's Control Technology Center, in cooperation with the New York State
Departments of Health (NYSDOH) and Environmental Conservation (NYSDEC), conducted
an initial study to examine, characterize, and quantify emissions from the simulated open
burning of household waste materials in barrels (Lemieux, 1997). A representative waste
to be burned was prepared based on the typical percentages of various waste materials
disposed of by New York State residents (i.e., nonavid recyclers); hazardous wastes (i.e.,
chemicals, paints, oils, etc.) were not included in the test waste. A variety of
compounds, including CDD/CDFs, were measured in the emissions from two simulated
open burnings of this "baseline" waste.
Combustion studies were subsequently performed by EPA to provide additional
"baseline" waste tests and to provide an initial indication of the impact of limited variation
in waste composition and combustion conditions on CDD/CDF emissions from a simulated
domestic backyard barrel burn of 6.8 kg of unshredded household waste (Gullet et al.,
1999; 2000a; 2000b; Lemieux et al., 2000; Lemieux, 2000).
The results of seven "baseline" waste tests were reported in these EPA studies.
These tests exhibited variation in the emissions of CDD/CDFs with a 1-2 order of
magnitude spread between the lowest and highest values for individual congeners,
congener groups, total CDD/CDFs, and TEQ values. The average TEQ emission factor for
the seven wastes was 72.8 ng l-TEQDF/kg of waste burned (setting not detected values
equal to zero) and 73.7 ng l-TEQDF/kg (setting not detected values equal to one-half the
detection limit). The corresponding TEQDF-WH098 values were 76.8 and 77.7 ng TEQDF-
WHOgg/kg. Table 6-2 presents the average congener and congener group results for these
tests.
Variation from the baseline waste chlorine content (0.2 percent by weight PVC)
included testing at three different PVC levels (0, 1.0, and 7.5 percent by weight PVC).
The average emissions from the 0, 1.0, and 7.5 percent PVC were, respectively, 14, 201,
and 4,916 ng l-TEQDF/kg. Two tests using waste impregnated with inorganic chloride
(i.e., CaCI2) at a 7.5 percent by weight level (and no PVC) averaged 734 ng l-TEQDF/kg.
DRAFT-DO NOT QUOTE OR CITE 6-16 December 2003
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Qualitative comparisons suggest that the tests with higher Cl, via PVC or CaCI2, resulted
in substantial increase in TEQ emissions.
Other variations in baseline waste composition included conducting one test with
compressed waste, one test with a double load of waste, and one test in which some of
the waste paper was wetted to simulate high moisture burns. These tests resulted in a
higher mean TEQ emission factor (534 ng I-TEQDF) than that of the baseline runs.
Several waste combustion variables were studied such as average temperatures at
prescribed barrel heights, duration that temperatures were within the favorable
temperature range for CDD/CDF formation, and measurement of CO, C02, 02, PM, and
HCI. Statistical analyses of the results indicated that an interactive term, the product of
the CO emissions and the temperature in the uppermost portion of the barrel, and CO
emissions were the best predictors of TEQ variation. However, the wide variability in test
results (i.e., from less than 10 to more than 6,000 ng l-TEQDF/kg) also indicates that a
high degree of CDD/CDF emission variation can be expected due to factors, such as waste
orientation, that are not wholly related to waste composition or burning practice.
The limited emission factor and activity level data available for developing national
emission estimates that could be included in the national inventory were assigned low
confidence ratings. The number of households nationwide burning waste in barrels and the
total amount and variability of burned waste can only be roughly estimated, and the
representativeness of the trash and burning conditions used in the baseline experiments to
conditions nationwide is uncertain. Combining the emission factors of 72.8 ng l-TEQDF/kg
of waste burned and 76.8 ng TEQDF-WH098/kg with the following information and
assumptions allows estimates to be made of the potential magnitude of national CDD/CDF
TEQ emissions from backyard household trash burning.
Of the rural population in the United States, 40 percent are assumed to burn
their household waste in a barrel (Two Rivers Region Council of Public Officials
and Patrick Engineering, 1994).
On average, each U.S. citizen generates 3.72 pounds of solid waste (excluding
yard waste) per day (or 616 kg/person-yr) (U.S. EPA, 1996b).
On average, in households that dispose of household waste by burning,
approximately 63 percent of waste generated is burned (i.e., 63 percent of 616
kg/person-yr = 388 kg/person-year) (Two Rivers Region Council of Public
Officials and Patrick Engineering, 1994).
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In 1994 (used for 1995 reference year), 52.7 million people lived in
nonmetropolitan areas. In 1990 (used for 1987 reference year), 50.7 million
people lived in nonmetropolitan areas (U.S. DOC, 1997).
Annual nationwide TEQ emissions were calculated using Equation 6-1.
ETEQ = EFTEQx Px Fx W (Eqn. 6-1)
where:
ETEQ = Annual TEQDF emissions (g/yr)
EFTEQ = TEQDF emission factor (g TEQDF/kg of waste)
P = Nonmetropolitan population of U.S. in reference year
F = Fraction of nonmetropolitan population assumedto burn household
waste (0.4)
W = Mass of household waste burned per year on a per capita basis (388
kg/person-year)
Therefore, estimated nationwide emissions in 1995 and 1987 were 595 g I-TEQDF (628 g
TEQDF-WH098) and 573 g I-TEQDF (604 g TEQDF-WH098), respectively.
6.5.2. Barrel Burning Ash Composition
Ash samples were also collected from the open barrel burning (Lemieux, 1997) and
analyzed for PCDD/PCDFs and PCBs. Ash samples from the experiments were combined
resulting in two composite samples, one for "avid" recyclers and one for non-recyclers.
The results for PCBs depict only the data for specific PCB congeners. Remaining PCB data
reported in Lemieux (1997) could not be related to a particular congener. The results are
depicted in Tables 6-3 and 6-4.
6.6. UNCONTROLLED COMBUSTION OF POLYCHLORINATED BIPHENYLS (PCBs)
The accidental combustion of PCB-containing electrical equipment or intentional
combustion of PCBs in incinerators and boilers not approved for PCB burning (40 CFR
761) may produce CDDs and CDFs. At elevated temperatures, such as in transformer
fires, PCBs can undergo reactions to form CDF and other by-products. More than 30
accidental fires and explosions involving PCB transformers and capacitors in the United
States and Scandinavia, which involved the combustion of PCBs and the generation of
CDDs and CDFs, have been documented (Hutzinger and Fiedler, 1991b; O'Keefe and
Smith, 1989; Williams et al., 1985). For example, analyses of soot samples from a
DRAFT-DO NOT QUOTE OR CITE 6-18 December 2003
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Binghamton, New York, office building fire detected 20 /vg/g of total CDDs (0.6 to 2.8
//g/g of 2,3,7,8-TCDD) and 765 to 2,1 60 //g/g of total CDFs with 1 2 to 270 //g/g of
2,3,7,8-TCDF. At that site, the fire involved the combustion of a mixture containing PCBs
(65 percent) and chlorobenzene (35 percent). Laboratory analyses of soot samples from a
PCB transformer fire, which occurred in Reims, France, indicated total CDD and CDF levels
in the range of 4 to 58,000 ng/g and 45 to 81,000 ng/g, respectively.
Using a bench-scale thermal destruction system, Erickson et al. (1984) determined
the optimum conditions for CDF formation to be 675°C, an excess oxygen concentration
of 8 percent, and a residence time of 0.8 seconds or longer. Combusting mineral oil and
silicone oil containing 5, 50, and 500 ppm of Aroclor 1254 at these conditions for 0.8
seconds yielded PCB to CDF conversion efficiencies as high as 4 percent. Up to 3 percent
conversion efficiency was observed when an askarel (70 percent Aroclor 1 260) was
combusted under the same conditions.
The use of PCBs in new transformers in the United States is banned, and their use
in existing transformers and capacitors is being phased out under regulations promulgated
under the Toxic Substances Control Act (TSCA).
Because of the accidental nature of these incidents, the variation in duration and
intensity of elevated temperatures, the variation in CDD/CDF content of residues, and
uncertainty regarding the amount of PCBs still in service in electrical equipment, EPA
judged the available data inadequate for developing national emission estimates that could
be included in the national inventory. However, Thomas and Spiro (1995) conservatively
estimated that about 1 5 g of TEQ may be generated annually from fires in commercial and
residential buildings each year. This estimate is based on the following assumptions:
(1) the I-TEQDF emission rate is 20//g/kg of PCB burned; (2) 74,000 metric tons of PCB
are still in use in various electrical equipment; and (3) 1 percent of the in-use PCBs is
burned during the course of structural fires annually.
6.7. VOLCANOES
To date, no studies demonstrating formation of CDD/CDFs by volcanoes have been
published. Given the available information from the studies discussed below, volcanoes
do not appear to be sources of CDD/CDF release to the environment.
DRAFT-DO NOT QUOTE OR CITE 6-19 December 2003
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Gribble (1994) summarized some of the existing information on the formation of
chlorinated compounds by natural sources, including volcanoes. Gribble (1994) reported
that several studies had demonstrated the presence of chlorofluorocarbons and simple
halogenated aliphatic compounds (one and two carbon chain length) in volcanic gases. In
addition, several chlorinated monoaromatic compounds as well as three PeCB congeners
were reported as having been detected in the ash from the 1980 eruption of Mount St.
Helens. Gribble hypothesized that the formation of these PCB compounds was the result
of rapid, incomplete high-temperature combustion of chloride-containing plant material in
the eruption zone. However, he presented no information indicating formation of
CDD/CDFs by volcanoes.
Lamparski et al. (1990) analyzed groundfall ash samples collected at various
distances and locations from Mount St. Helens following the eruption in 1980. The
findings of this study indicate that volcanic particulate emissions were free of detectable
PCBs and nearly free of detectable CDDs (0.8 ng/kg HpCDD detected) upon exiting the
volcano and remained so throughout their period of deposition in the blast zone.
However, upon transport through the atmosphere, measurable and increasing levels of
CDDs and PCBs were detected in deposited ash as the ash passed from rural to urban
environments. The authors hypothesized that CDDs and PCBs in the atmosphere became
associated with the volcanic ash particulates through gas-phase sorption or particulate
agglomeration.
Takizawa et al. (1994) sampled the dust fall from the active volcano, Fugendake,
as well as the volcanic ash from the active volcano, Sakurajima, for CDD and CDF
congener group concentrations. The study was not designed to determine whether the
CDD/CDFs observed were formed by the volcanoes or were scavenged from the
atmosphere by the falling dust and ash. The dust fall was collected for 1-month periods
during July and October 1992; two samples of the volcanic ash were collected in 1992.
The results of the sample analyses for 2,3,7,8-substituted CDDs and CDFs, presented in
Table 6-5, show that no 2,3,7,8-substituted congeners with less than 7 chlorines were
detected; however, Takizawa et al. (1994) reported that non-2,3,7,8-substituted
congeners in the lower chlorinated congener groups were detected.
DRAFT-DO NOT QUOTE OR CITE 6-20 December 2003
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Table 6-1. CDD/CDF Emission Factors for a Landfill Flare
Congener/Congener Group
2,3,7,8-TCDD
1,2,3,7,8-PeCDD
1, 2,3,4, 7,8-HxCDD
1,2,3,6,7,8-HxCDD
1,2,3,7,8,9-HxCDD
1, 2,3,4,6, 7,8-HpCDD
OCDD
2,3,7,8-TCDF
1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
1, 2,3,4, 7,8-HxCDF
1,2,3,6,7,8-HxCDF
1,2,3,7,8,9-HxCDF
2,3,4,6, 7,8-HxCDF
1, 2,3,4,6, 7,8-HpCDF
1,2,3,4,7,8,9-HpCDF
OCDF
Total 2,3,7,8-CDD
Total 2,3,7,8-CDF
Total I-TEQDF
Total TEQDF-WH098
Total TCDD
Total PeCDD
Total HxCDD
Total HpCDD
Total OCDD
Total TCDF
Total PeCDF
Total HxCDF
Total HpCDF
Total OCDF
Total CDD/CDF
Mean Facility Emission Factor*
(ng/m3 gas combusted)
0.018
0.092
0.074
0.074
0.259
0.755
4.414
14.074
0.385
1.136
1.455
0.422
0.110
0.681
1.215
0.073
0.639
5.686
20.192
2.392
2.433
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
* Assumes heat content of 1.86E+07 J/m3 for landfill gas (Federal Register, 1996a).
NR = not reported.
Source: CARB (1990d)
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6-21
December 2003
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Table 6-2. CDD/CDF Air Emission Factors from Barrel Burning of Household Waste
Congener/Congener Group
2,3,7,8-TCDD
1,2,3,7,8-PeCDD
1,2,3,4,7,8-HxCDD
1,2,3,6,7,8-HxCDD
1,2,3,7,8,9-HxCDD
1,2,3,4,6,7,8-HpCDD
OCDD
2,3,7,8-TCDF
1,2,3,7,8-PeCDF
2,3,4,7, 8-PeCDF
1,2,3,4,7,8-HxCDF
1, 2,3,6, 7,8-HxCDF
2,3,4,6,7,8-HxCDF
1,2,3,7,8,9-HxCDF
1,2,3,4,6,7,8-HpCDF
1,2,3,4,7,8,9-HpCDF
OCDF
Total 2,3,7,8-CDD
Total 2,3,7,8-CDF
Total I-TEQDF
Total TEQDF-WH098
Total TCDD
Total PeCDD
Total HxCDD
Total HpCDD
Total OCDD
Total TCDF
Total PeCDF
Total HxCDF
Total HpCDF
Total OCDF
Total CDD/CDF
Average Air Emission Factors a
(ng/kg waste burned)
Nondetects
Set to 1 12
Det. Limit
3.4
8.2
6.6
9.9
19.1
39.8
49.7
45.6
37.2
65.2
113.8
38.5
61.9
3.0
128.6
14.6
37.5
136.6
545.8
73.7
77.7
413
281
221
105
43
1,880
1,021
492
169
32
4,657
Nondetects
Set to
Zero
2.7
8.1
6.4
9.7
19.0
39.8
49.7
45.6
37.2
65.2
113.8
38.5
61.9
2.5
124.4
15.0
36.4
135.4
540.4
72.8
76.8
413
281
221
105
43
1,880
1,021
492
169
30
4,656
a Listed values are the arithmetic averages of seven tests for the congeners and the
averages of five tests for the congener groups.
Source: Lemieux (2000); Gullett et al. (1999; 2000a; 2000b).
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6-22
December 2003
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Table 6-3. PCDD/PCDF Analysis for Composite Ash Samples from Barrel Burning
(ng/kg of ash)
Congener/Congener Group
2,3,7,8-TCDD
1,2,3,7,8-PeCDD
1,2, 3,4,7, 8-HxCDD
1,2,3,6,7,8-HxCDD
1,2,3,7,8,9-HxCDD
1,2,3,4,6,7,8-HpCDD
OCDD
2,3,7,8-TCDF
1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
1,2,3,4,7,8-HxCDF
1,2, 3,6,7, 8-HxCDF
2,3,4,6,7,8-HxCDF
1,2,3,7,8,9-HxCDF
1,2,3,4,6,7,8-HpCDF
1,2, 3,4,7, 8,9-HpCDF
OCDF
Total TCDD
Total PeCDD
Total HxCDD
Total HpCDD
Total OCDD
Total TCDF
Total PeCDF
Total HxCDF
Total HpCDF
Total OCDF
Total CDD
Total CDF
Total CDD/CDF
Average Concentration in
Composite Ash Sample
Avid
Recycler
31
230
270
420
300
4,000
9,600
830
1,000
2,500
2,300
2,100
2,900
810
12,000
1,400
8,200
2,500
4,100
5,600
7,600
9,600
25,000
21,000
19,000
17,000
8,200
14,851
34,040
48,891
Non-
Recycler
9
53
44
74
56
630
690
220
270
690
480
490
670
150
2,100
170
560
490
740
1,300
1,300
690
8,200
6,600
4,600
2,900
560
1,556
5,800
7,356
I-TEQDF
Avid
Recycler
31
115
27
42
30
40
9.6
83
50
1,250
230
210
290
81
120
14
8.2
-
-
_
-
Non-
Recycler
9
26.5
4.4
7.4
5.6
6.3
0.69
22
13.5
345
48
49
67
15
21
1.7
0.56
-
-
_
-
TEQDF-WH098
Avid
Recycler
31
230
27
42
30
40
0.96
83
50
1,250
230
210
290
81
120
14
0.82
-
-
_
-
Non-
Recycler
9
53
4.4
7.4
5.6
6.3
0.069
22
13.5
345
48
67
15
21
1.7
0.056
-
-
_
-
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Table 6-4. PCB Analysis for Composite Ash Samples from Barrel Burning
(ng/kg of ash)
Compound
2-Chlorobiphenyl
2,3'-Dichlorobiphenyl
2,2',6-Trichlorobiphenyl
2,2',5-Trichlorobiphenyl
2,3',5-Trichlorobiphenyl
2,3',4-Trichlorobiphenyl
2,4',5-Trichlorobiphenyl
2,4,4'-Trichlorobiphenyl
2,2',4,6'-Tetrachlorobiphenyl
2,2',3,6'-Tetrachlorobiphenyl
2,2',5,5'-Tetrachlorobiphenyl
2,2',3,5'-Tetrachlorobiphenyl
2,2',4,4',5-Pentachlorobiphenyl
2,2',3,3',5-Pentachlorobiphenyl
2,2',3,4,5,5'-Hexachlorobiphenyl
Avid Recycler
<2,500
3,700
<500
32,000
800
<500
1,500
<500
<500
5,300
3,100
2,600
3,400
400
1,200
Non-Recycler
4,900
4,700
5,600
6,300
800
700
900
500
1,500
1,300
1,800
1,200
1,300
<500
<500
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Table 6-5. CDD/CDF in Dust Fall and Ashes from Volcanoes
2,3,7,8-Substituted
Congener Group
TCDD
PeCDD
HxCDD
HpCDD
OCDD
TCDF
PeCDF
HxCDF
HpCDF
OCDF
Dust Fall (mg/km2/month)a
July 1992
<0.5
<0.5
<0.5
9.2
14
<0.5
<0.5
<0.5
1.9
4.2
Oct. 1992
<0.5
<0.5
<0.5
5.2
11
<0.5
<0.5
<0.5
2.8
1.8
Volcanic Ash (ng/kg)b
Ash No. 1
<0.1
<0.1
<0.1
2.5
1.7
<0.1
<0.1
<0.1
1.2
<0.5
Ash No. 2
<0.1
<0.1
<0.1
1.8
2.2
<0.1
<0.1
<0.1
1.2
<0.5
a Dust fall measured from the active volcano, Fugendake.
b Volcanic ash measured from active volcano, Sakurajima.
Source: Takizawa et al. (1 994).
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Ratio (congener emission factor / total 2378-CDD/CDF emission factor)
0.1 0.2 0.3 0.4 0.5
0.6
2,3,7,8-TCDD
1,2,3,7,8-PeCDD
1,2,3,4,7,8-HxCDD
1,2,3,6,7,8-HxCDD
1,2,3,7,8,9-HxCDD
1,2,3,4,6,7,8-HpCDD
1,2,3,4,6,7,8,9-OCDD
2,3,7,8-TCDF
1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
1,2,3,4,7,8-HxCDF
1,2,3,6,7,8-HxCDF
1,2,3,7,8,9-HxCDF
2,3,4,6,7,8-HxCDF
1,2,3,4,6,7,8-HpCDF
1,2,3,4,7,8,9-HpCDF
1,2,3,4,6,7,8,9-OCDF
Source: CARB (1990d).
Figure 6-1. Congener Profile for Landfill Flare Air Emissions
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7. METAL SMELTING AND REFINING SOURCES OF CDD/CDF
7.1. PRIMARY NONFERROUS METAL SMELTING/REFINING
Little information has been published on the potential for the formation and
environmental release of CDD/CDFs from primary nonferrous metal manufacturing
facilities. Norwegian investigators (Oehme et al., 1989) have reported the presence of
CDD/CDFs in the wastewater of a magnesium refining facility and in the receiving water
sediments downstream of a nickel refining facility in Norway. Insufficient information is
available from this study for evaluating CDD/CDF emissions, if any, from the
smelting/refining of magnesium and nickel in the United States. The potential for
formation and release of CDD/CDFs by primary copper smelters in the United States has
been reported by Environmental Risk Sciences (1995) to be negligible. Lexen et al. (1993)
reported finding few or no CDD/CDFs in solid wastes from a primary aluminum smelter.
Bramley (1998) indicated that the smelting/refining of titanium may be a source of
CDD/CDFs. The findings of these studies are discussed in the following subsections.
7.1.1. Primary Copper Smelting and Refining
Environmental Risk Sciences (1995) prepared an analysis for the National Mining
Association on the potential for CDD/CDF emissions from the primary copper smelting
industry. The analysis included reviewing the process chemistry and technology of
primary copper smelting, identifying operating conditions, and comparing process stream
compositions from seven of the eight U.S. primary copper smelters that are members of
the National Mining Association. The analysis also included stack testing for CDD/CDFs at
two facilities. The stack testing (Secor International Inc., 1995a and 1995b) involved the
principal off-gas streams for copper smelters: main stack, plant tail gas stack, and the
vent fume exhaust. The two facilities that were tested (Phelps Dodge Mining Co. in
Playas, New Mexico, and Cyprus Miami Mining Co. in Claypool, Arizona) were selected as
representative of the other facilities in the industry because of their similarity to the other
facilities in terms of process chemistry, process stream composition, and process stream
temperatures.
The results of the assessment of the process chemistry and technology and the
operating conditions and process stream compositions indicate that although there is some
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potential for CDD/CDF formation in this industry, several factors lessen the probability of
CDD/CDF formation. These factors include (a) most of the energy used to melt copper is
derived from oxidation of copper sulfide ore minerals (i.e., CuFeS2) rather than carbon
(i.e., fossil fuels); (b) low concentrations of organic carbon and chloride are present in raw
materials and reagents; (c) high concentrations of sulfur dioxide are present in process
gases (6 to 40 percent S02 by volume); (d) high temperatures are maintained in the
furnaces and converters (1,100 to 1,500°C); and (e) copper (II) chloride is apparently
absent in process emissions.
The results of this assessment were supported by the stack test data from the two
tested facilities. CDD/CDFs were not detected in the air emissions from either facility. In
1995, eight primary smelters were in operation in the United States, one of which closed
at the end of the year (Edelstein, 1995). Total refinery production was 1.60 million metric
tons in 1995, including 0.36 million metric tons from scrap material (Edelstein, 1995) and
1.13 million metric tons in 1987 (USGS, 1997c). Conservatively assuming that all
nondetected values were present at one-half the detection limits, Environmental Risk
Sciences (1995) calculated the annual TEQ emission to air to be less than 0.5 g I-TEQDF in
1995 for the seven facilities (out of a total of eight) belonging to the National Mining
Association. Assuming that feed and processing materials were similar in 1987, 1987
releases can be estimated at less than 0.5 g I-TEQDF as well. The activity level estimates
are assigned a high confidence rating and the emission factor estimate a medium rating.
7.1.2. Primary Magnesium Smelting and Refining
Oehme et al. (1989) reported that the production of magnesium can lead to the
formation of CDDs and CDFs. Oehme et al. (1989) estimated that 500 g of I-TEQDF were
released in wastewater to the environment and 6 g I-TEQDF were released to air annually
from a magnesium production facility studied in Norway; CDFs predominated with a CDF
to CDD concentration ratio of 10:1. At the time of sampling, the magnesium production
process involved formation of MgO (magnesium oxide) from calcinated dolomite followed
by a step in which MgCI2 was produced by heating MgO/coke pellets in a shaft furnace in
a pure chlorine atmosphere to about 700 to 800°C. The MgCI2 was then electrolyzed to
form metallic magnesium and CI2. The CI2 excess from the MgCI2 process and the CI2
formed during electrolysis were collected by water scrubbers and directly discharged to
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the environment. The discharged wastewater contained 200-500 ppm of suspended
particulate matter. All but trace quantities of the hexa- through octa- congeners were
associated with the particulates; up to 10 percent of the tetra- and penta- congeners were
present in the water phase.
A recent study by the firm operating the facility (Musdalslien et al., 1998) indicates
that installation of a water treatment system has reduced annual emissions to water to
less than 1 g Nordic TEQ and emissions to air have been reduced to less than 2 g Nordic
TEQ. This study also presented results demonstrating that the carbon reducing agent
used in the MgCI2 production step and the operating conditions of the shaft furnace
greatly affect the formation of CDD/CDFs. Gases from the furnace were measured nine
times over sampling periods of 6 to 8 hours. The calculated emission factor to air (i.e.,
before any APCD controls) ranged from 468 to 3,860 ng Nordic TEQ per kg of MgCI2
produced. The APCD controls consist of three water scrubbers, a wet ESP, and an
incinerator.
U.S. production of primary magnesium was 142,000 metric tons in 1995. This
production was about 98 percent of the rated capacity of the three U.S. magnesium
production facilities. The United States has been the world's largest producer of metallic
magnesium for the past five decades (Kramer, 1996). Similar to the Norwegian plant, an
electrolytic process (i.e., electrolysis of magnesium chloride) is used at the plants in Texas
(capacity of 65,000 kkg/yr) and Utah (capacity of 40,000 kkg/yr) to recover metallic
magnesium from MgCI2. However, these two facilities reportedly use seawater and lake
brines as the source of magnesium, and the procedures to obtain and purify MgCI2 do not
involve chlorinating furnaces and carbonized pellets (Lockwood et al., 1981). A thermic
process is used to recover magnesium from dolomite at the facility in Washington
(capacity of 40,000 kkg/yr) (Kramer, 1995). In thermic processes, magnesium oxide
(MgO), a component of calcinated dolomite, is reacted with a metal such as silicon
(usually alloyed with iron) to produce metallic magnesium.
Monitoring of wastewater discharges for CDD/CDF content from U.S. magnesium
production facilities has not been reported. Wastewater discharge of CDD/CDF reported
for the Norwegian facility, discussed in the previous paragraphs, are not adequate to
support development of wastewater emission factors for U.S. facilities because of
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possible differences in the processes used to manufacture MgCI2 and pollution control
equipment.
Monitoring of air emissions for CDD/CDF content has recently been reported for
one of the three U.S. primary magnesium production facilities, the Magnesium Corporation
of America facility near Rowley, Utah. The average emission rates (for three tests)
reported for the melt reactor stack and the cathode stack were 0.31 mg l-TEQDF/hr and
0.16 mg l-TEQDF/hr, respectively (Western Environmental Services and Testing, Inc.,
2000). The confidence in the degree to which the one tested facility represents the
emissions from the other two U.S. facilities is currently very low. Therefore, the
emissions data were judged inadequate for developing at this time national emission
estimates that could be included in the national inventory. However, a preliminary
estimate of potential TEQ annual emissions from U.S. primary magnesium production
facilities can be made by assuming that the average total emission factor for the Utah
facility (i.e., 0.47 mg l-TEQDF/hr ) measured in May of 2000 is representative of the other
two facilities on a magnesium production basis. Specifically, if it is assumed that this
facility operated for 24 hours per day and 365 days in 1995, then the annual release in
1995 would have been 4.1 g I-TEQDF. If it is further assumed that this facility operated at
98 percent of it's rated capacity of 40,000 kkg/yr, then the production-based emission
factor is 105 ng l-TEQDF/kg of magnesium produced. Applying this emission factor to 98
percent of the industry's production capacity in 1995 (i.e., 142,000 kkg) yields an annual
emission estimate of 14.6 g I-TEQDF in 1995. This estimate should be regarded as a
preliminary indication of possible emissions from this source category; further testing is
needed to confirm the magnitude of these emissions.
7.1.3. Primary Nickel Smelting and Refining
Oehme et al. (1989) reported that certain primary nickel refining processes
generate CDDs and CDFs, primarily CDFs. Although the current low-temperature process
used at the Norwegian facility is estimated to result in releases to water of only 1 g I-
TEQDF per year, a high temperature (i.e., 800°C) NiCI2 to NiO conversion process that had
been used for 17 years at the facility is believed to have resulted in significant releases in
earlier years based on the ppb levels of CDFs detected in aquatic sediments downstream
of the facility (Oehme et al., 1989).
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The only nickel mining and smelting complex in the United States is located in
Oregon. This facility restarted operations in April 1995 and produced 8,290 metric tons
of nickel that year. The facility had been on standby since August 1993 and had no
production in 1994. The smelter has a capacity of 16,000 metric tons per year (Kuck,
1995).
Monitoring of discharges for CDD/CDF content at this one U.S. facility has not
been reported. Emissions of CDD/CDF were reported for a Norwegian facility in the late
1980s, as discussed in Section 7.1.4. The emissions information contained in the
Norwegian study, Oehme et al. (1989), is not adequate to support development of
emission factors for the U.S. facility.
7.1.4. Primary Aluminum Smelting and Refining
No sampling of air emissions from this industry for the presence of CDD/CDFs has
been reported. Lexen et al. (1993) reported that samples of filter powder and sludge from
a lagoon at the only primary aluminum production plant in Sweden showed no or little
CDD/CDF. A brief summary of the processes involved in primary aluminum smelting is
presented in the following paragraphs.
Bauxite ore, a hydrated oxide of aluminum consisting of 30 to 56 percent alumina
(AI203), is refined into alumina by the Bayer Process and the alumina is then shipped to a
primary aluminum smelter for electrolytic reduction to aluminum. Electrolytic reduction of
alumina occurs in shallow rectangular cells, or "pots," which are steel shells lined with
carbon. Carbon electrodes (petroleum coke mixed with a pitch binder) extending into the
pot serve as the anodes, and the carbon lining serves as the cathode. Three types of pots
are used: prebaked anode cell, horizontal stud Soderberg anode cell, and vertical stud
Soderberg anode cell. Most of the aluminum produced in the United States is produced
using the prebaked cells. Molten cryolite (Na3AIF6) functions as both the electrolyte and
the solvent for the aluminum. Aluminum is deposited at the cathode as molten metal
(U.S. EPA, 1998a).
Prior to casting, the molten aluminum may be batch treated in reverberatory
furnaces (like those used in secondary aluminum smelting) to remove oxides, gaseous
impurities, and active metals such as sodium and magnesium. One process consists of
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adding a flux of chloride and fluoride salts and then bubbling chlorine gas through the
molten mixture (U.S. EPA, 1998a).
U.S. production of primary aluminum was 3.343 million metric tons in 1987 and
3.375 million metric tons in 1995. In 1995, 13 companies operated 22 primary aluminum
reduction plants (USGS, 1997d, 1997e).
7.1.5. Primary Titanium Smelting and Refining
No sampling of emissions or products from this industry for CDD/CDF content has
been reported. However, Bramley (1998) and the Peer Review Panel (Eastern Research
Group, 1998) suggested that carbochlorination processes used in this industry may be a
source of CDD/CDFs. A brief summary of the processes used in this industry is presented
in the following paragraphs.
Titanium oxide ores and concentrates are chlorinated in fluidized-bed reactors in the
presence of coke at 925 to 1,010°C to form titanium tetrachloride (TiCI4). The TiCI4 is
separated from other chlorides by double distillation. The TiCI4 is then either oxidized at
985°C to form pigment-grade titanium dioxide or is reduced using sodium or magnesium
to form titanium sponge (i.e., metallic titanium) (Knittel, 1983). Titanium ingot is
produced by melting titanium sponge or scrap or a combination of both using electron
beam, plasma, and vacuum arc methods. Scrap currently supplies about 50 percent of
ingot feedstock (Gambogi, 1996).
Titanium sponge is currently produced at two facilities in the United States, one in
Albany, Oregon, and the other in Henderson, Nevada. In 1995, the U.S. production
volume of titanium sponge was withheld to avoid disclosing company proprietary data;
domestic sponge capacity was 29,500 metric tons per year. In 1987, U.S. production of
titanium sponge was 17,849 metric tons. The majority of titanium dioxide (i.e., greater
than 90 percent) is produced using the process described above. Titanium dioxide is
produced at nine facilities in the United States. Production volumes in 1987 and 1995
were 821,000 and 1,180,000 metric tons, respectively (Gambogi, 1996; USGS, 1997f).
7.2. SECONDARY NONFERROUS METAL SMELTING
Secondary smelters primarily engage in the recovery of nonferrous metals and
alloys from new and used scrap and dross. The principal metals of this industry both in
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terms of volume and value of product shipments are aluminum, copper, lead, zinc, and
precious metals (U.S. DOC, 1990a). Scrap metal and metal wastes may contain organic
impurities such as plastics, paints, and solvents. Secondary smelting/refining processes
for some metals (e.g., aluminum, copper, and magnesium) use chemicals such as NaCI,
KCI, and other salts. The combustion of these impurities and chlorine salts in the
presence of various types of metal during reclamation processes can result in the
formation of CDDs and CDFs, as evidenced by the detection of CDDs and CDFs in the
stack emissions of secondary aluminum, copper, and lead smelters (Aittola et al., 1992;
U.S. EPA, 1987a, 1997b).
7.2.1. Secondary Aluminum Smelters
Secondary aluminum smelters reclaim aluminum from scrap. This recycling
involves two processes —precleaning and smelting. Both processes may produce
CDD/CDF emissions.
Precleaning processes involve sorting and cleaning scrap to prepare it for smelting.
Cleaning processes that may produce CDD/CDF emissions use heat to separate aluminum
from contaminates and other metals; these techniques are "roasting" and "sweating."
Roasting uses rotary dryers with a temperature high enough to vaporize organic
contaminants, but not high enough to melt aluminum. An example of roasting is the
delacquering and processing of used beverage cans. Sweating involves heating aluminum-
containing scrap metal to a temperature above the melting point of aluminum, but below
the melting temperature of other metals such as iron and brass. The melted aluminum
trickles down and accumulates in the bottom of the sweat furnace and is periodically
removed (U.S. EPA, 1997b).
After precleaning, the treated aluminum scrap is smelted and refined. This usually
takes place in a reverberatory furnace. Once smelted, flux is added to remove impurities.
The melt is "demagged" to reduce the magnesium content of the molten aluminum by the
addition of chlorine gas. The molten aluminum is then transferred to a holding furnace
and alloyed to final specifications (U.S. EPA, 1997b).
CDD/CDF emissions to air have been measured at six U.S. secondary aluminum
operations. Four facilities were tested in 1995 and two facilities were tested in 1992.
Three of the four 1995 tests were conducted by EPA in conjunction with The Aluminum
DRAFT-DO NOT QUOTE OR CITE 7-7 December 2003
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Association for the purpose of identifying emission rates from facilities with potentially
MACT-grade operations and APCD equipment; the other test performed in 1995 (U.S.
EPA, 1995h) was performed by EPA. Two facilities tested by the California Air Resources
Board in 1992 were reported in two confidential reports.
The first facility tested in 1995 was a top-charge melt furnace (Advanced
Technology Systems, Inc., 1995). During testing, the charge material to the furnace was
specially formulated to contain no oil, paint, coatings, rubber, or plastics (other than
incidental amounts). The CDD/CDF emissions from such a clean charge, 0.26 ng
l-TEQDF/kg charge material (0.27 ng TEQDF-WH098/kg), would be expected to represent the
low end of the normal industry range.
The second facility operated a sweat furnace to preclean the scrap and a
reverberatory furnace to smelt the precleaned aluminum (U.S. EPA, 1995h). Stack
emissions were controlled by an afterburner operated at 1,450° F. The TEQ emission
factor for this facility was 3.22 ng l-TEQDF/kg aluminum produced (3.37 ng TEQDF-
WH098/kg).
The third facility employed a crusher/roasting dryer as a precleaning step, followed
by a reverberatory furnace (Galson Corporation, 1995). The emissions from the two units
were vented separately. The exhaust from the crusher/dryer was treated with an
afterburner and a baghouse. The exhaust from the furnace passed through a baghouse
with lime injection. Both stack exhausts were tested and the combined TEQ emission
factor was 12.95 ng l-TEQDF/kg aluminum produced (13.55 ng TEQDF-WH098/kg).
Because the activity level of the facility at the time of sampling was treated as confidential
business information, the calculated emission factor was based on the reported typical
production rates of the two operations, 26,000 Ibs/hr for the crusher/dryer and 6,700
Ibs/hr for the furnace.
The fourth facility operated a scrap roasting dryer followed by a sidewell
reverberatory furnace (Envisage Environmental Inc., 1995). The emissions from the two
units were vented separately. Exhaust from the dryer passed through an afterburner and a
lime-coated baghouse. The exhaust from the furnace passed through a lime-coated
baghouse. Both stack exhausts were tested and the combined TEQ emission factor was
36.03 ng l-TEQDF/kg of charge material (37.94 ng TEQDF-WH098/kg). Problems with the
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scrap dryer were discovered after the testing was completed. Also, operating conditions
during testing were reported to represent more worst-case than typical operations.
Two facilities tested by CARB in 1992 and reported in two confidential reports
(CARB, 1992a, as reported in U.S. EPA, 1997b; CARB, 1992b, as reported in U.S. EPA,
1997b) were reported to have TEQ emission factors of 52.21 and 21.67 ng l-TEQDF/kg of
scrap aluminum consumed (55.68- and 23.44-ng TEQDF-WH098/kg). One facility was
equipped with a venturi scrubber; the other was assumed in U.S. EPA (1997b) to be
uncontrolled.
The congener and congener group emission factors derived from these stack tests
are presented in Table 7-1. The average congener and congener group profiles are
presented in Figure 7-1. The average of the TEQ emission factors measured at the six
tested facilities (including the facility at which a specially formatted clean charge was
used) is 21.1 ng l-TEQDF/kg of scrap feed (22.4 ng TEQDF-WH098/kg). [Note: Although
the emission factors at two of the facilities tested in 1995 are based on the output rather
than input rate, the two rates are assumed, for purposes of this report, to be roughly
equivalent.] Although the testing was recently conducted at U.S. facilities, a low
confidence rating is assigned to this average emission factor because it is based on the
results of testing at only six facilities, several of which may have more effective APCD
than the other facilities in the industry.
For comparison purposes, The European Commission uses 22 ng l-TEQDF/kg scrap
aluminum as the "typical" emission factor for the European Dioxin Inventory (Quab and
Fermann, 1997). Umweltbundesamt (1996) reported stack testing results for 25
aluminum smelters/foundries in Germany. Sufficient data were provided in
Umweltbundesamt (1996) to enable calculation of TEQ emission factors for 11 of the
tested facilities. The calculated emission factors ranged from 0.01 to 167 ng l-TEQDF/kg
of scrap feed. Three facilities had emission factors exceeding 100 ng l-TEQDF/kg, and two
facilities had emission factors less than 1 ng l-TEQDF/kg. The mean emission factor for the
11 facilities was 42 ng l-TEQDF/kg.
An approximate total of 727,000 metric tons of scrap aluminum were consumed
by 67 secondary aluminum smelters in 1987 (U.S. DOC, 1995c). In 1995, consumption
of scrap aluminum by the 76 facilities that compose the secondary aluminum smelting
industry had nearly doubled to 1.3 million metric tons (USGS, 1997a; The Aluminum
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Association, 1997). A high confidence rating is assigned to these production estimates,
because they are based on government survey data. Applying the I-TEQDF emission factor
of 21.1 ng TEQ/kg of scrap feed to these consumption values yields estimated annual
emissions of 15.3 g I-TEQDF in 1987 and 27.4 g I-TEQDF in 1995. Applying the TEQDF-
WH098 emission factor of 22.4 ng TEQ/kg to the consumption values yields estimated
annual emissions of 16.3 g TEQDF-WH098 in 1987 and 29.1 g TEQDF-WH098 in 1995.
It should be noted that a significant amount of scrap aluminum is also consumed
by other segments of the aluminum industry. However, this scrap is generally scrap from
metal manufacturing processes, including metal and alloy production (e.g., borings,
turnings, and dross), rather than old scrap that results from consumer products (e.g.,
cans, radiators, auto shredders). Integrated aluminum companies consumed 1.4 million
metric tons of scrap aluminum in 1995, and independent mill fabricators consumed 0.68
million metric tons (USGS, 1997a).
7.2.2. Secondary Copper Smelters
Secondary copper smelting is part of the scrap copper, brass, and bronze
reprocessing industry. Brass is an alloy of copper and zinc; bronze is an alloy of copper
and tin. Facilities in this industry fall into three general classifications: secondary
smelting, ingot making, and remelting. Similar process equipment may be used at all three
types of facilities, so the distinguishing features are not immediately apparent (U.S. EPA,
1994g).
The feature that distinguishes secondary smelters from ingot makers and remelters
is the extent to which pyrometallurgical purification is performed. A typical charge at a
secondary smelter may contain from 30 to 98 percent copper. The secondary smelter
upgrades the material by reducing the quantity of impurities and alloying materials,
thereby increasing the relative concentration of copper. This degree of purification and
separation of the alloying constituents does not occur at ingot makers and remelters.
Feed material to a secondary copper smelter is a mixture of copper-bearing scrap such as
tubing, valves, motors, windings, wire, radiators, turnings, mill scrap, printed circuit
boards, telephone switching gear, and ammunition casings. Nonscrap items like blast
furnace slags and drosses from ingot makers or remelters may represent a portion of the
charge. The secondary smelter operator uses a variety of processes to separate the
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alloying constituents. Some purify the scrap in the reductive atmosphere of a blast
furnace. The charge may be subsequently purified in the oxidizing atmosphere of a
converter. Other secondary smelters perform all purification by oxidation in top-blown
rotary converters or in reverberatory furnaces (U.S. EPA, 1994g).
The ingot makers blend and melt scrap copper, brass, and bronze of various
compositions to produce a specification brass or bronze ingot. When necessary, the ingot
makers add ingots of other metals (e.g., zinc or tin) to adjust the metallurgy of the final
product. The feed materials for ingot makers contain relatively high amounts of copper.
Examples of feed materials include copper tubing, valves, brass and bronze castings,
ammunition shell casings, and automobile radiators. "Fire-refined" anode copper or
cathode copper may also be charged. Items such as motors, telephone switchboard
scrap, circuit board scrap, and purchased slags are not used by ingot makers. The
reductive step (melting in a reducing atmosphere, as in a blast furnace) that some
secondary smelters employ is not used by ingot makers. Ingot makers do, however, use
some of the other types of furnaces used by secondary smelters, including direct-fired
converters, reverberatory furnaces, and electric induction furnaces (U.S. EPA, 1994g).
Remelting facilities do not conduct any substantial purification of the incoming
feeds. These facilities typically just melt the charge and cast or extrude a product. The
feeds to a remelter are generally alloy material of approximately the desired composition of
the product (U.S. EPA, 1994g).
Emissions Data
Stack emissions of CDD/CDFs from a secondary copper smelter were measured by
EPA during 1984-1985 as part of the National Dioxin Tier 4 Study (U.S. EPA, 1987a).
The facility chosen for testing was estimated to have high potential for CDD/CDF
emissions because of the abundance of chlorinated plastics in the feed. This facility
ceased operations in 1986. The tested facility was chosen for testing by EPA because
the process technology and air pollution control equipment in place were considered
typical for the source category. Copper and iron-bearing scrap were fed in batches to a
cupola blast furnace, which produced a mixture of slag and black copper. Four to 5 tons
of metal-bearing scrap were fed to the furnace per charge, with materials typically being
charged 10 to 12 times per hour. Coke fueled the furnace and represented approximately
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14 percent by weight of the total feed. During the stack tests, the feed consisted of
electronic telephone scrap and other plastic scrap, brass and copper shot, iron-bearing
copper scrap, precious metals, copper-bearing residues, refinery by-products, converter
furnace slag, anode furnace slag, and metallic floor-cleaning material. The telephone scrap
made up 22 percent by weight of the feed and was the only scrap component that
contained plastic materials. Oxygen-enriched combustion air for combustion of the coke
was blown through tuyeres (nozzles) at the bottom of the furnace. At the top of the blast
furnace were four natural gas-fired afterburners to aid in completing combustion of the
exhaust gases. Fabric filters controlled particulate emissions, and the flue gas then was
discharged into a common stack. The estimated emission factors derived for this site are
presented in Table 7-2. The emission factors are based on the total weight of scrap fed
to the furnace. The TEQ emission factor, based on the measured congener and congener
group emission factors, is 779 ng l-TEQDF/kg of scrap metal smelted (810 ng
TEQDFWH098/kg). Figure 7-2a presents the congener group profile based on these
emission factors.
In 1992, stack testing of the blast furnace emissions of a secondary smelter
located in Philadelphia, Pennsylvania (Franklin Smelting and Refining Co.), was conducted
by Applied Geotechnical & Environmental Services Corporation (AGES, 1992). Similar to
the facility tested by EPA in 1984-1985, this facility processed low-purity copper-bearing
scrap, telephone switch gear, and slags, as well as higher copper content materials (U.S.
EPA, 1994g). The facility used a blast (cupola-type) furnace coupled with a pair of rotary
converters to produce blister copper. The blast furnace used coke as both the fuel and
the agent to maintain a reducing atmosphere. The black copper/slag mixture from the
blast furnace was charged to the rotary converters for further refining with the aid of
oxygen, sand, and oak logs (AGES, 1992; U.S. EPA, 1994g). The APCD equipment
installed on the blast furnace included an afterburner, cooling tower, and baghouse.
During testing, the afterburner was reported to be operating erratically and was
particularly low during one of the two sampling episodes. Stack gas flow was also low
during both sampling episodes because one or more baghouse compartments were
inoperable (AGES, 1992). The estimated emission factors derived for this site from the
AGES results are presented in Table 7-2. The emission factors are based on the total
weight of scrap fed to the blast furnace. The TEQ emission factor was 16,618 ng I-
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TEQDF/kg of scrap (16,917 ng TEQDF-WH098/kg). Figure 7-2b presents the congener and
congener group profiles based on these emission factors.
In 1991, stack testing of the rotary furnace stack emissions of a secondary smelter
located in Alton, Illinois (Chemetco, Inc.) was conducted by Sverdrup Corp. (1991). The
Chemetco facility used four tap down rotary (i.e., oxidizing) furnaces. Furnace process
gas emissions were controlled by a primary quencher and a venturi scrubber. The feed
was relatively high-purity copper scrap containing minimal, if any, plastics. The same
manufacturing process and APCD equipment were in place in 1987 and 1995 (U.S. EPA,
1994g). Because this facility operated under oxidizing rather than reducing conditions and
processed relatively high-purity scrap, the potential for CDD/CDF formation and release
was expected to be dramatically different than that of the two tested facilities reported
above. The estimated emission factors derived for this site from the results of Sverdrup
Corp. (1991) are presented in Table 7-2. The emission factors are based on the total
weight of scrap feed to the furnace. The TEQ emission factor was 3.60 ng l-TEQDF/kg of
scrap (3.66 ng TEQDF-WH098/kg).
Only limited data on emissions from secondary copper smelters are reported in the
European Dioxin Inventory (LUA, 1997). I-TEQDF emission factors reported for German
shaft furnaces/converters and reverberatory furnaces range from 5.6 to 110 ng l-TEQDF/kg
and from 0.005 to 1.56 ng l-TEQDF/kg. Emission factors reported for two "smelter and
casting furnaces" in Sweden in which "relatively clean scrap is used as input" are 0.024
and 0.04 ng l-TEQDF/kg. A smelter in Austria is reported to have an I-TEQ emission factor
of 4 ng l-TEQDF/kg. The minimum, typical, and maximum default emission factors
selected in LUA (1997) are 5, 50, and 400 ng l-TEQDF/kg, respectively.
Activity Level Information
In 1987, four secondary copper smelters were in operation: Franklin Smelting and
Refining Co. (Philadelphia, PA), Chemetco (Alton, IL), Southwire Co. (Carrollton, GA), and
a facility located in Gaston, SC, that was owned by American Telephone and Telegraph
(AT&T) until 1990 when it was purchased by Southwire Co. In 1987, estimated smelter
capacities were 13,600 kkg for the Franklin Smelting and Refining Co. facility, 120,000
kkg for the Chemetco facility, 48,000 kkg for the Southwire Co. facility, and 85,000 kkg
for the AT&T facility (Edelstein, 1999). In 1995, only three of these four facilities were in
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operation. The Southwire facility in Gaston (previously owned by AT&T) was closed in
January 1995. The Franklin facility subsequently ceased operations in August 1997.
Estimated smelter capacities in 1995 were 16,000 kkg for the Franklin Smelting and
Refining Co. facility, 135,000 kkg for the Chemetco facility, and 92,000 kkg for the
Southwire Co. facility (Edelstein, 1999).
Emission Estimates
Although little research has been performed to define the CDD/CDF formation
mechanism(s) in secondary copper smelting operations, two general observations have
been made (Buekens et al., 1997). The presence of chlorinated plastics in copper scraps
used as feed to the smelters is believed to increase the CDD/CDF formation. Second, the
reducing or pyrolytic conditions in blast furnaces can lead to high CDD/CDF concentrations
in the furnace process gases. As noted in "Emission Data," above, two of the U.S.
facilities that have been tested (i.e., U.S. EPA, 1987a; AGES, 1992) each had the
following characteristics. Both processed low-purity scrap containing significant quantities
of plastics, and both facilities used blast furnaces. The APCD equipment at both facilities
consisted of an afterburner, cooling tower (Franklin facility only), and a baghouse (U.S.
EPA, 1994g). The other tested U.S. facility (i.e., Sverdrup, 1991) used oxidizing rather
than reducing conditions and processed relatively high purity scrap.
For purposes of this report, the TEQ emission factor measured at the Franklin
Smelting and Refining Co. facility in 1992 is considered to be representative of the TEQ
emission factor in 1987 and 1995. Combining this emission factor (16,618 ng l-TEQDF/kg
scrap feed, or 16,917 ng TEQDF-WH098/kg) with the estimated smelter capacities (data are
not available on the amount of scrap processed) for this facility in 1987 (13,600 kkg) and
1995 (16,000 kkg) yields I-TEQDF emission estimates of 226 g (230 g TEQDF-WH098) in
1987 and 266 g (271 g TEQDF-WH098) in 1995. This facility ceased operations in 1997.
Similarly, for purposes of this report, the TEQ emission factor for the Chemetco,
Inc., facility is considered to be representative of the TEQ emission factor in 1987 and
1995. Combining this emission factor (3.60 ng l-TEQDF/kg scrap feed or 3.66 ng TEQDF-
WH098/kg) with the estimated smelter capacities (data are not available on the amount of
scrap processed) for this facility in 1987 (120,000 kkg) and 1995 (135,000 kkg) yields I-
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TEQDF estimates of 0.43 g (0.44 g TEQDF-WH098) in 1987 and 0.49 g (0.49 g TEQDF-
WH098) in 1995.
The facility in Gaston, South Carolina, was in operation during 1987, but not in
1995. Prior to 1990, when this facility was owned by AT&T, the plant processed a great
deal of high-plastics-content scrap (such as whole telephones). This scrap was fed to a
pyrolysis unit prior to entering the blast furnace. In addition to a blast furnace, the facility
also had an oxidizing reverberatory furnace for processing higher purity scrap. The facility
had separate baghouses for the blast furnace, the converters, and the reverberatory
furnace (U.S. EPA, 1994g). Because this facility processed low-purity, high-plastics-
content scrap in 1987, and presumably processed much of this in the reducing
atmosphere of a pyrolysis unit and blast furnace, the average of the TEQ emission factors
for the Tier 4 (U.S. EPA, 1987a) and Franklin facilities (8,700 ng l-TEQDF/kg, or 8,860 ng
TEQDF-WH098/kg) was used to estimate potential emissions in 1987 of 740 g I-TEQDF or
753 g TEQDF-WH098 (assuming an activity level of 85,000 kkg). This activity level is the
estimated capacity of the facility; data are not available on the amount of scrap
processed.
The Southwire facility in Carrollton, Georgia, had both a blast furnace and a
reverberatory furnace. In 1992, approximately 50 percent of incoming scrap was
processed in each furnace (U.S. EPA, 1994g). Unlike the two tested facilities and the
Gaston facility, the Southwire facility stopped processing plastic-coated scrap in the
1970s. In addition, this facility had a more complex APCD system, which may have
reduced the formation and release of CDD/CDFs. The blast furnace process gases passed
through an afterburner (1,600°F), U-tube coolers, and an evaporative spray system before
entering the baghouse at a temperature of 225 to 375°F. For these reasons, EPA has
determined that the existing emissions data for secondary smelters cannot reliably be used
to generate a quantitative estimate of potential emissions during 1987 and 1995 for this
facility.
A high confidence rating is assigned to the production estimates, because they are
based on government survey data. A low confidence rating is assigned to the TEQ
emission estimates because they are based on limited measurements made at three
smelters, one of which was not in operation in 1987 or 1995.
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It should be noted that a significant amount of scrap copper is consumed by other
segments of the copper industry. In 1995, brass mills and wire-rod mills consumed
886,000 metric tons of copper-based scrap; foundries and miscellaneous manufacturers
consumed 71,500 metric tons (USGS, 1997a). As noted above, however, these facilities
generally do not conduct any significant purification of the scrap. Rather, the scrap
consumed is already of alloy quality and processes employed typically involve only
melting, casting and extruding. Thus, the potential for formation of CDD/CDFs is
expected to be much less than the potential during secondary smelting operations.
7.2.3. Secondary Lead Smelters
The secondary lead smelting industry produces elemental lead through the chemical
reduction of lead compounds in a high-temperature furnace (1,200 to 1,260° C).
Smelting is performed in reverberatory, blast, rotary, or electric furnaces. Blast and
reverberatory furnaces are the most common types of smelting furnaces used by the 23
facilities that make up the current secondary lead smelting industry in the United States.
Of the 45 furnaces at these 23 facilities, 15 are reverberatory furnaces, 24 are blast
furnaces, 5 are rotary furnaces, and 1 is an electric furnace. The one electric furnace and
11 of the 24 blast furnaces are co-located with reverberatory furnaces, and most share a
common exhaust and emissions control system (U.S. EPA, 1994h).
Furnace charge materials consist of lead-bearing raw materials, lead-bearing slag
and drosses, fluxing agents (blast and rotary furnaces only), and coke. Scrap motor
vehicle lead-acid batteries represent about 90 percent of the lead-bearing raw materials at
a typical lead smelter. Fluxing agents consist of iron, silica sand, and limestone or soda
ash. Coke is used as fuel in blast furnaces and as a reducing agent in reverberatory and
rotary furnaces. Organic emissions from co-located blast and reverberatory furnaces are
more similar to the emissions of a reverberatory furnace than the emissions of a blast
furnace (U.S. EPA, 1994h).
The total annual production capacity of the 23 companies that make up the U.S.
lead smelting industry is 1.36 million metric tons. Blast furnaces not co-located with
reverberatory furnaces account for 21 percent of capacity (or 0.28 million metric tons).
Reverberatory furnaces and blast and electric furnaces co-located with reverberatory
furnaces account for 74 percent of capacity, or 1.01 million metric tons. Rotary furnaces
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account for the remaining 5 percent of capacity, or 0.07 million metric tons (U.S. EPA,
1994h). Actual production volume statistics by furnace type are not available. However,
if it is assumed that the total actual production volume of the industry, 0.97 million metric
tons in 1995 (USGS, 1997a) and 0.72 million metric tons in 1987 (U.S. EPA, 1994h),
reflect the production capacity breakdown by furnace type, then the estimated actual
production volumes of blast furnaces (not co-located), reverberatory and co-located blast/
electric and reverberatory furnaces, and rotary furnaces were 0.20, 0.72, and 0.05million
metric tons, respectively, in 1995, and 0.15, 0.53, and 0.04 million metric tons,
respectively, in 1987. In 1987, the industry consisted of 24 facilities.
CDD/CDF emission factors can be estimated for lead smelters using the results of
emission tests recently performed by EPA at three smelters (a blast furnace, a co-located
blast/reverberatory furnace, and a rotary kiln furnace) (U.S. EPA, 1992e, 1995d, 1995e).
The air pollution control systems at the three tested facilities consisted of both baghouses
and scrubbers. Congener-specific measurements were made at both APCD exit points at
each facility. Table 7-3 presents the congener and congener group emission factors from
the baghouse and the scrubber for each site. Figure 7-3 presents the corresponding
profiles for the baghouse emissions from the tested blast furnace and reverberatory
furnace. Although all 23 smelters employ baghouses, only 9 employ scrubber technology.
Facilities that employ scrubbers account for 14 percent of the blast furnace (not co-
located) production capacity, 52 percent of the reverberatory and co-located furnace
production capacity, and 57 percent of the rotary furnace production capacity. TEQ
emission factors (ng TEQ/kg lead processed) from the reported data for each of the three
furnace configurations are presented below as a range reflecting the presence or absence
of a scrubber.
Emission factors when nondetected values are set equal to zero:
• Blast furnace:
- 0.63 to 8.31 ng l-TEQDF/kg lead produced
- 0.64 to 8.81 ng TEQDF-WH098/kg lead produced
• Reverberatory/co-located furnace:
- 0.05 to 0.41 ng l-TEQDF/kg lead produced
- 0.05 to 0.42 ng TEQDF-WH098/kg lead produced
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• Rotary furnace:
- 0.24 to 0.66 ng l-TEQDF/kg lead produced
- 0.24 to 0.66 ng TEQDF-WH098/kg lead produced
If it is assumed that these ranges of emission rates are representative of those at
nontested facilities with the same basic furnace configuration and presence or absence of
scrubbers, then combining these emission rate ranges with the estimated volume of
secondary lead production derived above and the percentage of each configuration type
that have scrubbers, yields the following estimated air emissions in units of grams TEQ
per year:
Estimated Annual TEQ Emissions (g TEQ)*
Configuration
Blast furnaces w/scrubbers
Blast furnaces w/o scrubbers
Reverberatory furnaces w/ scrubbers
Reverberatory furnaces w/o scrubbers
Rotary furnaces w/ scrubbers
Rotary furnaces w/o scrubbers
Ref.
I-TEQDF
0.018
1.429
0.019
0.142
0.019
0.005
1.632
Year 1995
TEQDF-WH098
0.018
1.515
0.019
0.145
0.019
0.005
1.721
Ref.
I-TEQDF
0.013
1.072
0.014
0.104
0.015
0.004
1.223
Year 1987
TEQDF-WH098
0.013
1.136
0.014
0.106
0.015
0.004
1.288
* Calculated using emission factors based on nondetected values set equal to zero.
A medium confidence rating is assigned to the emission factors derived above
because stack test data were available for 3 of the 23 smelters in the United States (of
which only 16 were in operation as of December 1993), and the stack test data used
represent the three major furnace configurations. The activity level estimate has been
assigned a medium confidence rating because, although it is based on a U.S. Department
of Commerce estimate of total U.S. production, no production data were available on a
furnace type or furnace configuration basis.
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7.3. PRIMARY FERROUS METAL SMELTING/REFINING
Iron is manufactured from its ores (i.e., magnetic pyrites, magnetite, hematite, and
carbonates of iron) in a blast furnace, and the iron obtained from this process is further
refined in steel plants to make steel. The primary production of iron and steel involves
two operations identified by European researchers as potential emission sources of
CDD/CDFs: iron ore sinter production and coke production. Each of these potential
sources is discussed in the following subsections.
7.3.1. Sinter Production
At some iron manufacturing facilities, iron ores and waste iron-bearing materials
undergo sintering to convert the materials to usable feed for the blast furnace. In the
sintering process, iron ore fines and waste materials are mixed with coke fines, and the
mixture is placed on a grate, which is then heated to a temperature of 1,000-1,400°C.
The heat generated during combustion sinters the small particles. Iron-bearing dusts and
slags from processes in the steel plant are the types of iron-bearing waste materials used
as a feed mix for the sinter plant (Knepper, 1 981; Capes, 1 983; U.S. EPA, 1 995b).
Several European investigators have reported that iron ore sinter plants are major
sources of airborne emissions CDD/CDFs (Rappe, 1992b; Lexen et al., 1993; Lahl, 1993,
1994). Lahl (1993, 1994) reports that the management practice of recycling dusts and
scraps from other processes in the steel plant into the sintering plant introduces traces of
chlorine and organic compounds that generate the CDD/CDFs found in these plants.
Organic compounds that are potential precursors to CDD/CDF formation come
primarily from the oil, which is found in mill scale, as well as some blast furnace sludges
that are used as part of the sinter feed mixture. Most U.S. plants limit the amount of oil
because it increases emissions of volatile organic compounds (VOCs) and may create a
fire hazard. In addition, plants with baghouses must limit the oil content because the oil
tends to blind the fabric filters. Typical oil contents of the feed at U.S. sinter plants range
from 0.1 to 0.75 percent (Calcagni et al., 1998).
Sinter plants in Sweden were reported to emit up to 3 ng l-TEQDF/Nm3 stack gas or
2 to 4 g l-TEQDF/yr per plant to the air (Rappe, 1992b; Lexen et al., 1993). Bremmer et
al. (1994) reported the results of stack testing at three iron ore sintering plants in The
Netherlands. One facility equipped with wet scrubbers had an emission factor of 1.8 ng
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l-TEQDF/dscm (at 11 percent 02). The other two facilities, both equipped with cyclones,
had emission factors of 6.3 and 9.6 ng l-TEQDF/dscm (at 7 percent 02). Lahl (1993,
1994) reports stack emissions for sintering plants in Germany (after passage through
mechanical filters and electrostatic precipitators) ranging from 3 to 10 ng l-TEQDF/Nm3. A
recent compilation of emission measurements by the German Federal Environmental
Agency indicates stack emission concentrations ranging from 1.2 to 60.6 ng l-TEQDF/m3
(at 7 percent 02); the majority of emissions in 1996 were around 3 ng l-TEQDF/m3
(Umweltbundesamt, 1996).
EPA conducted tests at two of the nine U.S. sinter plants operating in 1997 in
order to quantify emissions of CDD/CDFs (Calcagni et al., 1998). In choosing
representative plants for testing, EPA considered a variety of issues, including the types
and quantities of feed materials, types of emission controls, and the oil content of the
sinter feed. EPA decided to test a plant with a baghouse and a plant with a venturi (or
wet) scrubber. Baghouses and wet scrubbers are the principal air pollution control devices
employed to control emissions from the sinter plant windbox. Four plants used a
baghouse and five plants used a wet scrubber. The types of feed materials and oil
content at the two selected plants were determined to be representative of other plants in
the industry. Sampling was performed over 3 days (4 hours per day) at each plant.
The average CDD/CDF TEQ concentrations measured in the stack emissions were
0.19 ng l-TEQDF/Nm3 and 0.81 ng l-TEQDF/Nm3 for the wet scrubber and baghouse,
respectively. The corresponding TEQ emission factors are 0.55 ng l-TEQDF/kg sinter (0.62
ng TEQDF-WH098/kg) and 4.14 ng l-TEQDF/kg sinter (4.61 ng TEQDF-WH098/kg),
respectively, for wet scrubbers and baghouses. These emission factors are assigned a
high rating because they are based on recent EPA testing at two facilities considered by
EPA to be representative of both current and 1995 standard industry practices.
Congener-specific emission factors for these two facilities are presented in Table 7-4.
Figure 7-4 presents the congener profiles for these facilities. Although concentrations
were higher from the baghouse than from the scrubber, both concentrations are low
relative to what had been reported from testing at German, Dutch, and Swedish sinter
plants. These differences may be due to differences between the operation or APCD of
U.S. sinter plants and the tested European plants. Most of the U.S. integrated iron and
steel plants, including those with sinter plants, have eliminated the purchase and use of
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chlorinated organics in their facilities. Their rolling mill oils (lubricants and hydraulic fluids)
do not contain chlorinated compounds. In addition, routine analysis of waste materials
going to the sinter plant have not detected any chlorinated solvents. Finally, none of the
U.S. plants currently use an electrostatic precipitator to control emissions from the sinter
windbox (Calcagni et al., 1998).
In 1996 (data were not readily available for 1995), 11 sintering plants were
operating in the United States, with a total annual production capacity of about 17.6
million metric tons (Metal Producing, 1996). Over the past two decades, the size of this
industry has decreased dramatically. In 1982, 33 facilities operated with a combined total
capacity of 48.3 million metric tons (U.S. EPA, 1982b). The nine currently operating U.S.
sinter plants have a combined capacity of 15.6 million metric tons (Calcagni et al., 1998).
In 1987, sinter consumption by iron and steel plants was 14.5 million metric tons (AISI,
1990); in 1995, consumption was 12.4 million metric tons (Fenton, 1995), or
approximately 70 percent of production capacity, assuming that production capacity in
1995 was the same as in 1996. These activity level estimates are assigned a confidence
rating of medium.
As shown in Table 7-5, 59 percent of current (i.e., 1998) sinter production
capacity is at facilities with wet scrubbers and 41 percent is at facilities with baghouses.
If it is assumed that these same relative proportions of APCD to production capacity
existed in 1995, and it is assumed that actual production in 1995 was equal to sinter
consumption at iron and steel plants (i.e., 12.4 million metric tons), then estimated TEQ
emissions from wet scrubber-equipped facilities were 4.0 g I-TEQDF (4.5 g TEQDF-WH098)
and emissions from baghouse-equipped facilities were 21.0 g I-TEQDF (23.4 g TEQDF-
WH098), for a total of 25.1 g I-TEQDF (28.0 g TEQDF-WH098). These emission estimates
are assigned an overall medium confidence rating on the basis of the medium rating for
the activity level estimates.
If these same assumptions are applied to the 1987 sinter consumption rate of 14.5
million metric tons, then estimated TEQ emissions from wet scrubber-equipped facilities
were 4.7 g I-TEQDF (5.3 g TEQDF-WH098) and emissions from baghouse-equipped facilities
were 24.6 g I-TEQDF (27.4 g TEQDF-WH098), for a total of 29.3 g I-TEQDF (32.7 g TEQDF-
WH098). These emission estimates are less certain than the estimates for 1995 because
of uncertainties concerning actual APCD in place in 1987 and content of waste feed (i.e.,
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oil content and presence of chlorinated organics in the oil) at that time. Consequently, a
low confidence rating is assigned to the emission factor.
7.3.2. Coke Production
Coke is the principal fuel used in the manufacture of iron and steel. Coke is the
solid carbonaceous material produced by the destructive distillation of coal in high-
temperature ovens. No testing of CDD/CDF emissions from U.S. coke facilities has been
reported. However, at a facility in The Netherlands, Bremmer et al. (1994) measured a
CDD/CDF emission rate to air during the water quenching of hot coke of 0.23 ng I-
TEQDF/kg of coal consumed. Minimal CDD/CDF air emissions, 0.002 ng l-TEQDF/kg of
coal, were estimated by Bremmer et al. (1994) for flue gases generated during the
charging and emptying of the coke ovens.
In 1995, an estimated 30 million metric tons of coal were consumed by coke
plants in the United States (EIA, 1997b). No testing of CDD/CDF emissions from U.S.
coke plants has been reported upon which to base an estimate of national emissions. The
limited data available were thus judged inadequate for developing national emission
estimates that could be included in the national inventory. However, a preliminary
estimate of potential TEQ annual emissions from U.S. coke plants can be made by
combining the consumption value of 30 million metric tons and the emission factor
reported by Bremmer et al. (1994) for a Dutch coke plant (0.23 ng l-TEQDF/kg of coal
consumed). This calculation yields an annual emission of 6.9 g I-TEQDF in 1995. This
estimate should be regarded as a preliminary indication of possible emissions from this
source category; further testing is needed to confirm the true magnitude of these
emissions.
7.4 SECONDARY FERROUS METAL SMELTING/REFINING
Electric arc furnaces (EAFs) have been reported to be sources of CDD/CDF
emissions in Europe; no testing has been reported at U.S. facilities. EAFs are used to
produce carbon and steel alloys primarily from scrap material. The production of steel in
an EAF is a batch process, and the input material is typically 100 percent scrap. Scrap,
alloying agents, and fluxing materials are loaded into the cylindrical, refractory-lined EAF,
and then carbon electrodes are lowered into the EAF. The current of the opposite polarity
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electrodes generates heat between the electrodes and through the scrap. Processing time
of a batch ranges from about 1.5 to 5 hours to produce carbon steel and from 5 to 10
hours to produce alloy steel (U.S. EPA, 1995b).
The melting of scrap ferrous material contaminated with metalworking fluids and
plastics that contain chlorine provides the conditions conducive to formation of
CDD/CDFs. Tysklind et al. (1989) studied the formation and releases of CDD/CDFs at a
pilot 10 ton electric furnace in Sweden. Scrap ferrous metal feedstocks containing
varying amounts of chlorinated compounds (i.e., PVC plastics, cutting oils, or CaCI2) were
charged into the furnace under different operating conditions (i.e., continuous feed, batch
feed into the open furnace, or batch feed through the furnace lid). During continuous
charging operations, the highest emissions, 1.5 ng Nordic TEQ/dry Mm3 (i.e., after a
baghouse filter), were observed with a feedstock consisting of scrap metal with PVC
plastics (1.3 g of chlorine per kg of feedstock). This emission equates to 7.7 ng Nordic
TEQ/kg of feedstock. The highest emissions during batch charging also occurred when
the scrap metal with PVC plastic was combusted (0.3 ng Nordic TEQ/dry Nm3 or 1.7 ng
Nordic TEQ/kg of feedstock). Much lower emissions (0.1 ng Nordic TEQ/dry Nm3 or 0.6
ng Nordic TEQ/kg of feedstock) were observed when scrap metal with cutting oils that
contained chlorinated additives (0.4 g of chlorine per kg of feedstock) was melted.
Although these cutting oil-related emissions were not significantly different than the
emissions observed from the melting of no-chlorine scrap metal, relatively high levels of
CDD/CDF (i.e., 110-ng Nordic TEQ/dry Nm3) were detected in flue gases prior to the
baghouse. The congener profiles of raw flue gas samples (i.e., prior to APCD) showed
that CDFs, rather than CDDs, were predominant in all three feedstock types. The
congener profile from the test burn with PVC-containing feedstock showed a higher
chlorinated congener content than was observed with the other feedstocks.
Eduljee and Dyke (1996) used a range of 0.7 to 10 ng l-TEQDF/kg of scrap feed to
estimate national emissions for the United Kingdom. The range was assumed to be
representative of no-chlorine and high-chlorine operations. However, Eduljee and Dyke
(1996) provided little information on the supporting emission test studies (i.e., tested
facility operational materials, feed rates, congener-specific emission rates).
Umweltbundesamt (1996) reported stack testing results for a variety of EAFs in
Germany. Sufficient data were provided in Umweltbundesamt (1996) to enable
DRAFT-DO NOT QUOTE OR CITE 7-23 December 2003
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calculation of TEQ emission factors for six of the tested facilities. Two facilities had
emission factors exceeding 1 ng l-TEQDF/kg of scrap processed, and two facilities had
emission factors less than 0.1 ng l-TEQDF/kg of scrap. The mean emission factor was
1.15 ng l-TEQDF/kg of scrap. The TEQ concentrations in the stack gases at these facilities
(corrected to 7 percent 02) ranged from less than 0.1 to 1.3 ng l-TEQDF/m3.
In 1995, electric arc furnaces accounted for 40.4 percent of U.S. steel production
(or 38.4 of the total 95.2 million metric tons of raw steel produced) (Fenton, 1996). No
testing of CDD/CDF emissions from U.S. electric arc furnaces has been reported upon
which to base an estimate of national emissions, and the limited European data available
were thus judged inadequate for developing national emission estimates that could be
included in the national inventory. However, a preliminary estimate of potential TEQ
annual emissions from U.S. electric arc furnaces can be made by combining the
production estimate of 38.4 million metric tons and the average emission factor derived
from the data reported in Umweltbundesamt (1996) for six EAFs (i.e., 1.15 ng l-TEQDF/kg
scrap). This calculation yields an annual emission estimate of 44.3 g I-TEQDF in 1995.
This estimate should be regarded as a preliminary indication of possible emissions from
this source category; further testing is needed to confirm the true magnitude of these
emissions.
7.5. FERROUS FOUNDRIES
Ferrous foundries produce high-strength iron and steel castings used in industrial
machinery, pipes, and heavy transportation equipment. Iron and steel castings are solid
solutions of iron, carbon, and various alloying materials. Castings are produced by
injecting or pouring molten metal into cavities of a mold made of sand, metal, or ceramic
material. Metallic raw materials are pig iron, iron and steel scrap, foundry returns, and
metal turnings (U.S. EPA, 1995b, 1997b).
The melting process takes place primarily in cupola (or blast) furnaces and to a
lesser extent in electric arc furnaces. About 70 percent of all iron castings are produced
using cupolas, although steel foundries rely almost exclusively on EAFs or induction
furnaces for melting. The cupola is typically a vertical, cylindrical steel shell with either a
refractory-lined or water-cooled inner wall. Charges are loaded at the top of the unit; the
iron is melted as it flows down the cupola, and is removed at the bottom. (EAFs are
DRAFT-DO NOT QUOTE OR CITE 7-24 December 2003
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discussed in Section 7.4.3.) Electric induction furnaces are batch-type furnaces in which
the charge is melted by a fluctuating electromagnetic charge produced by electrical coils
surrounding the unit (U.S. EPA, 1995b, 1997b).
Iron and steel foundries, particularly those using EAFs, are highly dependent on iron
and steel scrap. Of the estimated 72 million metric tons of iron and steel scrap consumed
by the iron and steel industry in 1995, 25 percent (or 18 million metric tons) were used
by ferrous foundries. The other 75 percent were used by primary ferrous metal smelters
(principally those using EAFs) (USGS, 1997b). Thus, foundries face the same potential for
CDD/CDF emissions as EAFs because of their use of scrap that contains chlorinated
solvents, plastics, and cutting oils. (See Section 7.4.3.) The potential for formation and
release of CDD/CDFs during the casting process (i.e., pouring of molten metal into molds
and cores made of sand and various organic binders and polymers) is not known.
The results of emissions testing have been reported for only one U.S. ferrous
foundry (CARB, 1993a, as reported in U.S. EPA, 1997b). The tested facility consisted of
a batch-operated, coke-fired cupola furnace charged with pig iron, scrap iron, scrap steel,
coke, and limestone. Emission control devices operating during the testing were an oil-
fired afterburner and a baghouse. The congener and congener group emission factors
derived from the testing are presented in Table 7-6. The calculated TEQ emission factor
for this set of tests is 0.37 ng l-TEQDF/kg of metal charged to the furnace (0.42 ng TEQDF-
WH098).
Umweltbundesamt (1996) reported stack testing results for a variety of ferrous
foundries in Germany. Sufficient data were provided in Umweltbundesamt (1996) to
enable calculation of TEQ emission factors for eight of the tested facilities. Three facilities
had emission factors exceeding 1 ng l-TEQDF/kg of metal charge, and four facilities had
emission factors less than 0.1 ng l-TEQDF/kg of metal charge. The emission factors span
more than four orders of magnitude. The mean emission factor was 1.26 ng l-TEQDF/kg of
metal feed.
Because of the wide range of emissions for the tested German foundries reported in
Umweltbundesamt (1996), the confidence in the degree to which the one tested U.S.
facility represents the mean emission factor for the approximate 1,000 U.S. foundries is
considered very low. Therefore, the limited data available were judged inadequate for
developing national emission estimates that could be included in the national inventory.
DRAFT-DO NOT QUOTE OR CITE 7-25 December 2003
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However, a preliminary estimate of potential TEQ annual emissions from U.S. ferrous
foundries can be made by combining the mean emission factor derived from the data
reported in Umweltbundesamt (1996) for eight foundries (1.26 ng l-TEQDF/kg of metal
feed) with an activity level for U.S. foundries. In 1995, U.S. shipments from the
approximate 1,000 U.S. ferrous foundries were 13.9 million metric tons, of which about
90 percent were iron castings and 10 percent were steel castings (Fenton, 1996). This
calculation yields an annual emission estimate of 17.5 g I-TEQDF in 1995. This estimate
should be regarded as a preliminary indication of possible emissions from this source
category; further testing is needed to confirm the true magnitude of these emissions.
7.6. SCRAP ELECTRIC WIRE RECOVERY
The objective of wire recovery is to remove the insulating material and reclaim the
metal (e.g., copper, lead, silver, and gold) in the electric wire. The recovery facility then
sells the reclaimed metal to a secondary metal smelter. Wire insulation commonly
consists of a variety of plastics, asphalt-impregnated fabrics, or burlap. Chlorinated
organics are used to preserve the cable casing in ground cables. The combustion of
chlorinated organic compounds in the cable insulation, catalyzed by the presence of wire
metals such as copper and iron, can lead to the formation of CDDs and CDFs (Van Wijnen
et al., 1992).
Although in the past, scrap electric wire was commonly recovered using thermal
processing to burn off the insulating material, current recovery operations no longer
typically involve thermal treatment according to industry and trade association
representatives. Instead, scrap electric wire is mechanically chopped into fine particles.
The insulating material is then removed by air blowing and, followed by gravitational
settling of the heavier metal (telephone conversations between T. Leighton, Versar, Inc.,
R. Garino, Institute of Scrap Recycling Industries, March 2, 1993; and J. Sullivan, Triple F.
Dynamics, March 8, 1993).
EPA measured dioxin-like compounds emitted to the air from a scrap wire
reclamation incinerator during its 1986 National Dioxin Study of combustion sources
(U.S. EPA, 1987a). Testing determined that the facility was typical of this industrial
source category at that time. Insulated wire and other metal-bearing scrap material were
fed to the incinerator on a steel pallet. The incinerator operated in a batch mode, with the
DRAFT-DO NOT QUOTE OR CITE 7-26 December 2003
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combustion cycles for each batch of scrap feed lasting between 1 and 3 hours. Natural
gas was used to incinerate the material. Although most of the wire had a tar-based
insulation, PVC-coated wire was also fed to the incinerator. Temperatures during
combustion in the primary chamber furnace were about 570°C. The tested facility was
equipped with a high-temperature natural gas-fired afterburner (980 to 1,090°C).
Emission factors estimated for this facility are presented in Table 7-7. The estimated TEQ
emission factor (based only on 2,3,7,8-TCDD, 2,3,7,8-TCDF, OCDD, and OCDF) is 16.9
ng l-TEQDF/kg scrap feed (15.8 ng TEQDF-WH098). Figure 7-5 presents a congener group
profile based on these emission factors.
Bremmer et al. (1994) reported emission factors for three facilities in The
Netherlands, which have subsequently ceased operations. Emission rates at a facility
burning underground cables and cables containing PVC ranged from 3.7 ng l-TEQDF/kg to
14 ng l-TEQDF/kg. The emission rate at a second facility ranged from 21 ng l-TEQDF/kg of
scrap (when burning copper core coated with greasy paper) to 2,280 ng l-TEQDF/kg of
scrap (when burning lead cable). The third facility, which burned motors, was reported to
have an emission rate of 3,300 ng l-TEQDF/kg of scrap. On the basis of these
measurements, Bremmer et al. (1994) used emission rates of 40 ng l-TEQDF/kg of scrap
and 3,300 ng l-TEQDF/kg of scrap for estimating national emissions in The Netherlands for
facilities burning wires and cables and those burning motors.
Although limited emission testing has been conducted at one U.S. facility, the
activity level for this industry sector in reference years 1987 and 1995 is unknown;
therefore, an estimate of national emissions cannot be made. It is uncertain how many
facilities still combust scrap wire in the United States. Trade association and industry
representatives state that U.S. scrap wire recovery facilities now burn only minimal
quantities of scrap wire. However, a recent inventory of CDD/CDF sources in the San
Francisco Bay area noted that two facilities in the Bay area thermally treat electric motors
to recover electrical windings (BAAQMD, 1996).
In addition to releases from regulated recovery facilities, CDD/CDF releases from
small-scale burning of wire at unregulated facilities and open air sites have occurred;
however, the current magnitude of small-scale, unregulated burning of scrap wire in the
United States is not known. For example, Harnly et al. (1995) analyzed soil/ash mixtures
from three closed metal recovery facilities and from three closed sites of open burning for
DRAFT-DO NOT QUOTE OR CITE 7-27 December 2003
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copper recovery near a California desert town. The geometric mean of the total CDD/CDF
concentrations at the facility sites and the open burning sites was 86,000 and 48,500
ng/kg, respectively. The geometric mean TEQ concentrations were 2,900 and 1,300 ng
l-TEQDF/kg, respectively. A significantly higher geometric mean concentration (19,000 ng
l-TEQDF/kg) was found in fly ash located at two of the facility sites. The congener-specific
and congener group results from this study are presented in Table 7-8. The results show
that the four dominant congeners in the soil samples at both the facility and open burning
sites were OCDF, 1,2,3,4,6,7,8-HpCDF, 1,2,3,4,7,8-HxCDF, and 2,3,7,8-TCDF. A
slightly different profile was observed in the fly ash samples, with 1,2,3,7,8-PeCDF and
1,2,3,4,7,8,9-HpCDF replacing OCDD and 2,3,7,8-TCDF as dominant congeners.
Van Wijnen et al. (1992) reported similar results for soil samples collected from
unpermitted incineration sites of former scrap wire and cars in The Netherlands. Total
CDD/CDF concentrations in the soil ranged from 60 to 98,000 ng/kg, with 9 of the 15 soil
samples having levels above 1,000 ng/kg. Chen et al. (1986) reported finding high levels
of CDD/CDFs in residues from open air burning of wire in Taiwan, and Huang et al. (1992)
reported elevated levels in soil near wire scrap recovery operations in Japan. Bremmer et
al. (1994) estimated an emission rate to air of 500 ng l-TEQDF/kg of scrap for illegal,
unregulated burning of cables in The Netherlands.
7.7. DRUM AND BARREL RECLAMATION FURNACES
Hutzinger and Fiedler (1991b) reported detecting CDD/CDFs in stack gas emissions
from drum and barrel reclamation facilities at levels ranging from 5 to 27 ng/m3. EPA
measured dioxin-like compounds in the stack gas emissions of a drum and barrel
reclamation furnace as part of the National Dioxin Study (U.S. EPA, 1987a).
Drum and barrel reclamation furnaces operate a burning furnace to thermally clean
used 55-gallon steel drums of residues and coatings. The drums processed at these
facilities come from a variety of sources in the petroleum and chemical industries. The
thermally cleaned drums are then repaired, repainted, relined, and sold for reuse. The
drum-burning process subjects used drums to an elevated temperature in a tunnel furnace
for a sufficient time so that the paint, interior linings, and previous contents are burned or
disintegrated. The furnace is fired by auxiliary fuel. Used drums are loaded onto a
conveyor that moves at a fixed speed. As the drums pass through the preheat and
DRAFT-DO NOT QUOTE OR CITE 7-28 December 2003
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ignition zone of the furnace, additional contents of the drums drain into the furnace ash
trough. A drag conveyor moves these sludges and ashes to a collection pit. The drums
are air cooled as they exit the furnace. Exhaust gases from the burning furnace are
typically drawn through a breeching fan to a high-temperature afterburner.
The afterburner at the facility tested by EPA operated at an average of 827°C
during testing and achieved a 95 percent reduction in CDD/CDF emissions (U.S. EPA,
1987a). Emission factors estimated for this facility are presented in Table 7-9. On the
basis of the measured congener and congener group emissions, the average TEQ emission
factor is estimated to be 16.5 ng I-TEQDF per drum (17.5 ng TEQDF-WH098/drum). The
congener group profile is presented in Figure 7-6.
Approximately 2.8 to 6.4 million 55-gallon drums are incinerated annually in the
United States (telephone conversation between C. D'Ruiz, Versar, Inc., and P. Rankin,
Association of Container Reconditioners, December 21, 1992). This estimate is based on
the following assumptions: (1) 23 to 26 incinerators are currently in operation; (2) each
incinerator, on average, handles 500 to 1,000 drums per day; and (3) on average, each
incinerator operates 5 days per week, with 14 days downtime per year for maintenance
activities. The weight of 55-gallon drums varies considerably; however, on average, a
drum weighs 38 Ibs (or 17 kg); therefore, an estimated 48 to 109 million kg of drums are
incinerated annually. Assuming that 4.6 million drums are burned each year (i.e., the
midpoint of the range) and applying the emission factors developed above, the estimated
annual emission of TEQ is 0.08 g I-TEQDF (0.08 g TEQDF-WH098). No activity level data
are available that would enable annual emission estimates to be made specifically for
reference years 1987 and 1995.
A low confidence rating is assigned to the activity level estimate because it is
based on expert judgment rather than a published reference. A low confidence rating is
also assigned to the emission factor, because it was developed from stack tests
conducted at just one U.S. drum and barrel furnace and thus may not represent average
emissions from current operations in the United States.
7.8. SOLID WASTE FROM PRIMARY/SECONDARY IRON/STEEL MILLS/FOUNDRIES
Literature on the Identification of Relevant Industrial Sources of Dioxins and Furans
in Europe (Quab, 1997), Table 17, contains summary data on the typical annual quantities
DRAFT-DO NOT QUOTE OR CITE 7-29 December 2003
-------
and ranges of TEQ (Norwegian-TEQ [NTEQ] and I-TEQ) from various solid residuals from
the metallurgical industries in Europe. No support information accompanies the tabular
data. Specific congeners are not discussed. However, the summary data for annual TEQ
generation in grams are as follows:
Grey Iron Foundries: baghouse dust and scrubber sludge
Steel Mill Coke Oven Door Leakage Dust
Steel Mill Coke Oven Door Leakage Dust
Pig Iron Tapping Slag
Basic Oxygen Furnace Scrubber Sludge
Electric Furnace Baghouse Dust
Electric Furnace Slag or Baghouse Dust
0.817 NTEQ
0.31 NTEQ
0.04 I-TEQ
0.041 NTEQ
1.53 NTEQ (range of
0.30 - 7.81)
3.1 I-TEQ (range of
0.4 - 2.4)
19.2 NTEQ
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December 2003
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CD
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o
-H
D
c;
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O
53
Table 7-1. CDD/CDF Emission Factors for Secondary Aluminum Smelters
Congener/Congener
Group
2,3,7,8-TCDD
1,2,3,7,8-PeCDD
1,2,3,4,7,8-HxCDD
1,2,3,6,7,8-HxCDD
1, 2,3,7,8, 9-HxCDD
1,2,3,4,6,7,8-HpCDD
OCDD
2,3,7,8-TCDF
1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
1,2,3,4,7,8-HxCDF
1,2,3,6,7,8-HxCDF
1, 2,3,7,8, 9-HxCDF
2,3,4,6,7,8-HxCDF
1,2,3,4,6,7,8-HpCDF
1, 2,3,4,7,8, 9-HpCDF
OCDF
*Total I-TEQDF
*Total TEQDF-WH098
Total TCDD
Total PeCDD
Total HxCDD
Total HpCDD
Total OCDD
Total TCDF
Total PeCDF
Total HxCDF
Total HpCDF
Total OCDF
Total CDD/CDF
Mean Facility
Emission Factor
(ng/kg scrap feed)
(Ref. 1)
ND (0.01)
0.02
0.05
0.13
0.15
0.51
0.42
0.44
0.06
0.17
0.32
0.11
0.02
0.30
0.07
0.03
0.30
0.26
0.27
NR
NR
NR
NR
0.42
NR
NR
NR
NR
0.30
NR
Mean Facility
Emission Factor
(ng/kg scrap feed)
(Ref. 2)
0.13
0.39
0.24
0.86
1.26
7.67
14.97
0.74
1.51
2.44
2.44
2.69
1.02
3.82
11.39
5.50
30.40
3.22
3.37
3.30
4.91
11.45
14.71
14.97
29.67
28.73
32.23
39.44
30.40
209.81
Mean Facility
Emission Factor
(ng/kg scrap feed)
(Ref. 3)
0.51
1.19
1.35
1.52
2.51
2.60
1.01
14.20
10.47
11.06
21.84
7.10
0.47
7.09
14.61
1.21
3.15
12.95
13.55
46.03
28.07
35.51
6.01
1.01
161.80
222.75
115.32
39.94
3.15
659.60
Mean Facility
Emission Factor
(ng/kg scrap feed)
(Ref. 4)
2.17
3.84
2.88
5.39
7.22
18.01
NR
47.12
20.01
29.60
52.32
16.31
1.20
22.96
35.29
5.17
18.77
36.03
37.94
NR
NR
NR
NR
NR
NR
NR
NR
NR
18.77
NR
Mean Facility
Emission Factor
(ng/kg scrap feed)
(Ref. 5)
1.97
7.10
4.26
5.30
5.30
28.9
33.2
23.2
33.8
48.0
46.1
46.1
22.0
39.0
122
27.1
60.5
52.21
55.68
47.8
64.0
78.0
58.5
33.2
620
585
515
247
60.5
2,309
Mean Facility
Emission factor
(ng/kg scrap feed)
(Ref. 5)
0.845
3.64
2.82
4.12
2.02
19.3
24.3
4.84
1.18
23.3
17.6
16.9
1.35
16.0
42.6
6.20
29.5
21.67
23.44
0.845
3.64
8.95
19.3
24.3
4.84
35.1
52.0
48.8
29.5
227
CO
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CD
O
CD
3
-
K>
O
O
CO
* TEQ calculations assume not-detected values are zero.
NR = Not reported.
ND = Not detected (value in parenthesis is the emission at the detection limit).
Sources: Ref. 1: Advanced Technology Systems, Inc. (1995)
Ref. 2: U.S. EPA (1995h)
Ref. 3: Galson Corporation (1 995)
Ref. 4: Envisage Environmental, Inc. (1995)
Ref. 5: CARB (1 992a, 1 992b), as reported in U.S. EPA (1 997b)
-------
Table 7-2. CDD/CDF Emission Factors for Secondary Copper Smelters
Congener/Congener Group
2,3,7,8-TCDD
1,2,3,7,8-PeCDD
1,2, 3,4,7, 8-HxCDD
1,2,3,6,7,8-HxCDD
1,2,3,7,8,9-HxCDD
1,2,3,4,6,7,8-HpCDD
OCDD
2,3,7,8-TCDF
1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
1,2, 3,4,7, 8-HxCDF
1,2,3,6,7,8-HxCDF
1,2,3,7,8,9-HxCDF
2,3,4,6,7,8-HxCDF
1,2,3,4,6,7,8-HpCDF
1, 2,3,4,7, 8,9-HpCDF
OCDF
•Total I-TEQDF
•Total TEQDF-WH098
Total TCDD
Total PeCDD
Total HxCDD
Total HpCDD
Total OCDD
Total TCDF
Total PeCDF
Total HxCDF
Total HpCDF
Total OCDF
Total CDD/CDF
Mean EPA Tier 4
Emission Factora-b
(ng/kg scrap feed)
127
NR
NR
NR
NR
NR
1,350
2,720
NR
NR
NR
NR
NR
NR
NR
NR
2,520
779C
81 Oc
736
970
1,260
2,080
1,350
13,720
8,640
4,240
3,420
2,520
38,890
Franklin Smelting
Facility Mean
Emission Factord
(ng/kg scrap feed)
227
846
1,476
1,746
2,132
17,065
55,668
4,457
9,455
5,773
70,742
20,524
5,362
12,082
37,251
7,570
82,192
16,618
16,917
14,503
30,248
55,765
38,994
55,668
108,546
71,136
164,834
66,253
82,192
688,139
Chemetco Smelting
Facility Mean
Emission Factor6
(ng/kg scrap feed)
ND (0.05)
0.21
0.39
0.70
1.26
8.95
22.45
2.11
1.47
2.63
7.30
2.15
4.06
0.27
11.48
2.74
21.61
3.60
3.66
3.05
5.19
9.62
16.71
22.45
46.42
27.99
27.96
23.38
21.61
204.33
* TEQ calculations assume not-detected values are zero.
NR = Not reported.
ND = Not detected (value in parenthesis is the emission at the detection limit).
a No nondetected values were reported for 2,3,7,8-TCDD, 2,3,7,8-TCDF, or any congener group in the
three test runs.
b Source: U.S. EPA (1987a).
c Estimated using the measured data for 2,3,7,8-TCDD, 2,3,7,8-TCDF, OCDD, and OCDF and congener
group emissions (i.e., for the penta-, hexa-, and hepta-CDDs and CDFs, it was assumed that the
measured emission factor within a congener group was the sum of equal emission factors for all
congeners in that group, including non-2,3,7,8-substituted congeners).
d Source: AGES (1992).
e Source: Sverdrup Corp. (1991).
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December 2003
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Table 7-3. CDD/CDF Emission Factors for Secondary Lead Smelters
Congener/Congener Group
2,3,7,8-TCDD
1,2,3,7,8-PeCDD
1,2,3,4,7,8-HxCDD
1,2,3,6,7,8-HxCDD
1,2,3, 7,8, 9-HxCDD
1,2,3,4,6,7,8-HpCDD
OCDD
2,3,7,8-TCDF
1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
1,2,3,4,7,8-HxCDF
1,2,3,6,7,8-HxCDF
1,2,3, 7,8, 9-HxCDF
2,3,4,6,7,8-HxCDF
1,2,3,4,6,7,8-HpCDF
1, 2,3,4,7,8, 9-HpCDF
OCDF
Total 2,3,7,8-CDD
Total 2,3,7,8-CDF
Total I-TEQDF (nondetects = 0)
Total TEQDF-WH098 (nondetects = 0)
Total TCDD
Total PeCDD
Total HxCDD
Total HpCDD
Total OCDD
Total TCDF
Total PeCDF
Total HxCDF
Total HpCDF
Total OCDF
Total CDD/CDF (nondetects = 0)
Total CDD/CDF (nondetects = % DL)
Blast Furnace (Ref. A)
(ng/kg lead produced)
Before
Scrubber
2.11
0.99
0.43
0.99
1.55
2.06
1.40
8.73
3.88
6.65
5.83
1.67
0.11
2.06
2.34
0.63
1.39
9.52
33.28
8.31
8.81
74.33
39.29
20.05
4.20
1.39
145.71
69.59
19.73
4.74
1.39
380.43
380.44
After
Scrubber
0.25
0.03
0.00
0.03
0.03
0.08
0.39
0.93
0.43
0.36
0.37
0.11
0.00
0.11
0.19
0.06
0.18
0.82
2.74
0.63
0.64
7.39
1.73
0.81
9.72
0.18
17.34
3.45
1.02
0.11
0.18
41.92
42.27
Blast/reverb (Ref. B)
(ng/kg lead produced)
Before
Scrubber
0.00
0.00
0.00
0.00
0.00
0.10
0.57
1.46
0.24
0.31
0.63
0.19
0.00
0.15
0.48
0.00
0.29
0.68
3.75
0.41
0.42
0.97
0.15
0.14
0.09
0.57
8.21
3.07
1.14
0.72
0.29
15.36
15.36
After
Scrubber
0.00
0.00
0.00
0.00
0.00
0.06
0.55
0.49
0.02
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.61
0.51
0.05
0.05
1.58
0.16
0.02
0.09
0.55
4.71
0.36
0.19
0.01
0.00
7.66
7.74
Rotary kiln (Ref. C)
(ng/kg lead produced)
Before
Scrubber
0.10
0.01
0.00
0.00
0.00
0.00
0.24
0.40
0.14
0.14
0.11
0.02
0.04
0.00
0.03
0.00
0.00
0.35
0.88
0.24
0.24
3.40
0.29
0.10
0.01
0.24
10.82
1.69
0.15
0.05
0.00
16.76
16.80
After
Scrubber
0.24
0.00
0.00
0.00
0.00
0.22
2.41
1.20
0.40
0.46
0.27
0.10
0.13
0.00
0.13
0.00
0.00
2.87
2.68
0.66
0.66
7.90
0.27
0.23
0.29
2.41
28.57
5.04
0.73
0.14
0.00
45.57
45.62
Note: Except where noted, emission factors were calculated assuming nondetected values are zero.
Sources: Ref. A: U.S. EPA (1995e)
Ref. B: U.S. EPA (1992e)
Ref. C: U.S. EPA (1995d)
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Table 7-4. CDD/CDF Emission Factors for Sinter Plants
Congener/Congener Group
2,3,7,8-TCDD
1,2,3,7,8-PeCDD
1,2,3,4,7,8-HxCDD
1,2, 3,6,7, 8-HxCDD
1,2,3,7,8,9-HxCDD
1,2, 3,4,6,7, 8-HpCDD
OCDD
2,3,7,8-TCDF
1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
1,2, 3,4,7, 8-HxCDF
1,2,3,6,7,8-HxCDF
1, 2,3,7, 8,9-HxCDF
2,3,4,6,7,8-HxCDF
1,2, 3,4,6,7, 8-HpCDF
1,2,3,4,7,8,9-HpCDF
OCDF
Total 2,3,7,8-CDD
Total 2,3,7,8-CDF
Total I-TEQDF
Total TEQDF-WH098
Total TCDD
Total PeCDD
Total HxCDD
Total HpCDD
Total OCDD
Total TCDF
Total PeCDF
Total HxCDF
Total HpCDF
Total OCDF
Total CDD/CDF3
Wet Scrubber APCD
(ng/kg sinter)
ND = 0
0.049
0.138
0.030
0.612
0.288
0.696
0.496
0.602
0.343
0.349
0.421
0.164
0.011
0.142
0.247
0.036
0.103
2.309
2.418
0.55
0.62
NR
NR
NR
NR
0.496
NR
NR
NR
NR
0.103
4.73
ND = 1/2DL
0.049
0.138
0.030
0.612
0.288
0.696
0.496
0.602
0.343
0.349
0.421
0.164
0.014
0.142
0.247
0.036
0.103
2.309
2.421
0.55
0.62
NR
NR
NR
NR
0.496
NR
NR
NR
NR
0.103
4.73
Baghouse APCD
(ng/kg sinter)
ND = 0
0.406
0.937
0.135
1.469
0.609
0.698
0.695
10.232
3.518
3.228
1.382
0.495
0.029
0.285
0.316
0.000
0.050
4.949
19.535
4.14
4.61
NR
NR
NR
NR
0.695
NR
NR
NR
NR
0.050
24.48
ND = 1/2DL
0.406
0.937
0.135
1.469
0.609
0.698
0.695
10.232
3.518
3.228
1.382
0.495
0.057
0.285
0.316
0.115
0.192
4.949
19.820
4.14
4.61
NR
NR
NR
NR
0.695
NR
NR
NR
NR
0.192
24.77
a The listed values for total CDD/CDF include only the 17 toxic congeners.
Source: Calcagni et al. (1998)
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Table 7-5. Operating Parameters for U.S. Iron Ore Sinter Plants
Company
A.K. Steel Corp.
A.K. Steel Corp.*
Bethlehem Steel
Bethlehem Steel
Geneva Steel
Inland Steel
LTV Steel
U.S. Steel
Weirton Steel*
Wheeling-Pittsburgh Steel
WCI Steel
Location
Middleton, OH
Ashland, KY
Burns Harbor, IN
Sparrows Point, MD
Provo, UT
East Chicago, IN
East Chicago, IN
Gary, IN
Weirton, WV
East Steubenville, WV
Warren, OH
TOTALS
1998 Capacity
(1,000 kkg/yr)
907
816*
2,676
3,856
816
1,089
1,270
3,992
1,179*
519
477
17,597**
Current APCD
WS
NA
WS
WS
BH
BH
WS
BH
NA
WS
BH
NA = Not available.
WS = Wet scrubber.
BH = Baghouse.
* Not in operation during 1 998 (Calcagni et al., 1 998)
* * Total 1 998 capacity was 1 5,600 thousand metric tons (i.e., excluding the Ashland, KY, and Weirton, WV,
facilities).
Sources: Metal Producing (1991, 1996); Calcagni et al. (1998)
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Table 7-6. CDD/CDF Emission Factors for a Ferrous Foundry
Congener/Congener Group
2,3,7,8-TCDD
1,2,3,7,8-PeCDD
1,2, 3,4,7, 8-HxCDD
1,2,3,6,7,8-HxCDD
1,2,3,7,8,9-HxCDD
1,2,3,4,6,7,8-HpCDD
OCDD
2,3,7,8-TCDF
1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
1,2,3,4,7,8-HxCDF
1,2,3,6,7,8-HxCDF
1,2,3,7,8,9-HxCDF
2,3,4,6,7,8-HxCDF
1,2,3,4,6,7,8-HpCDF
1,2,3,4,7,8,9-HpCDF
OCDF
Total 2,3,7, 8-CDD
Total 2,3,7, 8-CDF
Total I-TEQDF (for reported congeners)
Total TEQDF-WH098
Total TCDD
Total PeCDD
Total HxCDD
Total HpCDD
Total OCDD
Total TCDF
Total PeCDF
Total HxCDF
Total HpCDF
Total OCDF
Total CDD/CDF (not including OCDD)
Mean Facility Emission Factor
(ng/kg scrap feed)
(CARB, 1993a)
0.033
0.086
NR
0.051
NR
0.093
NR
0.520
0.305
0.350
0.190
0.170
NR
0.101
0.193
NR
0.059
0.262
1.888
0.372
0.415
3.96
1.76
0.55
0.19
NR
25.8
850
1.74
0.24
0.06
884.3
NR = Not reported.
Source: CARB (1993a), as reported in U.S. EPA, 1 997b
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Table 7-7. CDD/CDF Emission Factors for a Scrap Wire Incinerator
Congener/Congener Group
2,3,7,8-TCDD
1,2,3,7,8-PeCDD
1,2, 3,4,7, 8-HxCDD
1,2, 3,6,7, 8-HxCDD
1,2,3,7,8,9-HxCDD
1,2,3,4,6,7,8-HpCDD
OCDD
2,3,7,8-TCDF
1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
1,2, 3,4,7, 8-HxCDF
1,2,3,6,7,8-HxCDF
1,2,3,7,8,9-HxCDF
2,3,4,6,7,8-HxCDF
1,2, 3,4,6,7, 8-HpCDF
1,2,3,4,7,8,9-HpCDF
OCDF
Total 2,3,7,8-CDD
Total 2,3,7,8-CDF
Total I-TEQDF
Total TEQDF-WH098
Total TCDD
Total PeCDD
Total HxCDD
Total HpCDD
Total OCDD
Total TCDF
Total PeCDF
Total HxCDF
Total HpCDF
Total OCDF
Total CDD/CDF
Mean Facility
Emission Factor a
(ng/kg scrap feed)
0.374
NR
NR
NR
NR
NR
1,000
2.67
NR
NR
NR
NR
NR
NR
NR
NR
807
NR
NR
16. 9b
15.8
4.42
13.7
71.1
347
1,000
107
97.4
203
623
807
3,273
NR = Not reported.
a No nondetected values were reported for 2,3,7,8-TCDD, 2,3,7,8-TCDF, or any congener group in
the three test runs.
b Estimated based on the measured data for 2,3,7,8-TCDD, 2,3,7,8-TCDF, OCDD, and OCDF and
congener group emissions (i.e., for the penta-, hexa-, and hepta-CDDs and CDFs, it was assumed
that the measured emission factor within a congener group was the sum of equal emission factors
for all congeners in that group, including non-2,3,7,8-substituted congeners).
Source: U.S. EPA (1987a)
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Table 7-8. Geometric Mean CDD/CDF Concentrations in Fly Ash and Ash/Soil at Metal Recovery Sites
Congener/Congener Group
2,3,7,8-TCDD
1,2,3,7,8-PeCDD
1,2,3,4,7,8-HxCDD
1,2,3,6,7,8-HxCDD
1,2,3, 7,8, 9-HxCDD
1,2,3,4,6,7,8-HpCDD
OCDD
2,3,7,8-TCDF
1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
1,2,3,4,7,8-HxCDF
1,2,3,6,7,8-HxCDF
1,2,3, 7,8, 9-HxCDF
2,3,4,6,7,8-HxCDF
1,2,3,4,6,7,8-HpCDF
1, 2,3,4,7,8, 9-HpCDF
OCDF
Total TCDD
Total PeCDD
Total HxCDD
Total HpCDD
Total OCDD
Total TCDF
Total PeCDF
Total HxCDF
Total HpCDF
Total OCDF
Total I-TEQDF
Total CDD/CDF
Metal Recovery Facilities
Fly ash (2 sites)
Geom.
mean
(//g/kg)
*
400
1,200
2,300
1,700
12,000
18,000
15,000
35,000
10,000
46,000
12,000
5,000
5,000
71,000
25,000
100,000
*
2,000
4,000
24,000
18,000
23,000
110,000
88,000
110,000
100,000
19,000
510,000
Relative
%
of Total
CDD/CDF
0.1%
0.2%
0.5%
0.3%
2.4%
3.5%
2.9%
6.9%
2.0%
9.0%
2.4%
1 .0%
1 .0%
13.9%
4.9%
19.6%
*
0.4%
0.8%
4.7%
3.5%
4.5%
21.6%
17.3%
21.6%
19.6%
Ash/Soil (3 sites)
Geom.
mean
(//g/kg)
*
0.24
0.25
0.49
1.3
2.6
7.2
6.4
2.9
1.4
5.9
1.8
0.92
1.6
12
3
14
*
1.4
2.7
4.1
7.2
14
12
12
17
14
2.9
85
Relative
%
of Total
CDD/CDF
0.3%
0.3%
0.6%
1.5%
3.1%
8.5%
7.5%
3.4%
1.6%
6.9%
2.1%
1.1%
1.9%
14.1%
3.5%
16.5%
*
1.6%
3.2%
4.8%
8.5%
16.5%
14.1%
14.1%
20.0%
16.5%
Open Burn Sites
Ash/Soil (3 sites)
Geom.
mean
(//g/kg)
*
0.24
0.13
0.33
0.39
1.2
3.4
1.7
0.58
0.66
2.7
0.76
0.66
0.49
4.3
0.71
6.6
*
2.8
0.98
2.0
3.4
5.6
7.0
7.6
7.4
6.6
1.3
48.5
Relative %
of Total
CDD/CDF
0.5%
0.3%
0.7%
0.8%
2.5%
7.0%
3.5%
1.2%
1 .4%
5.6%
1.6%
1.4%
1.0%
8.9%
1.5%
13.6%
*
5.8%
2.0%
4.1%
7.0%
11.5%
14.4%
15.7%
15.3%
13.6%
* Analytical method used had low sensitivity for TCDDs; results were not reported.
Source: Harnly et al. (1995)
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Table 7-9. CDD/CDF Emission Factors for a Drum and Barrel Reclamation Furnace
Congener/Congener Group
2,3,7,8-TCDD
1,2,3,7,8-PeCDD
1,2, 3,4,7, 8-HxCDD
1,2,3,6,7,8-HxCDD
1,2,3,7,8,9-HxCDD
1,2,3,4,6,7,8-HpCDD
OCDD
2,3,7,8-TCDF
1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
1,2, 3,4,7, 8-HxCDF
1,2,3,6,7,8-HxCDF
1,2,3,7,8,9-HxCDF
2,3,4,6,7,8-HxCDF
1,2, 3,4,6,7, 8-HpCDF
1,2,3,4,7,8,9-HpCDF
OCDF
Total 2,3,7,8-CDD
Total 2,3,7,8-CDF
Total I-TEQDF
Total TEQDF-WH098
Total TCDD
Total PeCDD
Total HxCDD
Total HpCDD
Total OCDD
Total TCDF
Total PeCDF
Total HxCDF
Total HpCDF
Total OCDF
Total CDD/CDF
Mean Facility
Emission Factor3
(ng/drum)
2.09
NR
NR
NR
NR
NR
37.5
36.5
NR
NR
NR
NR
NR
NR
NR
NR
22.4
NR
NR
16. 5b
17.5
50.29
29.2
32.2
53.4
37.5
623
253
122
82.2
22.4
1,303
NR = Not reported.
a No nondetected values were reported for 2,3,7,8-TCDD, 2,3,7,8-TCDF, or any congener group in
the three test runs.
b Estimated based on the measured data for 2,3,7,8-TCDD, 2,3,7,8-TCDF, OCDD, and OCDF and
congener group emissions (i.e., for the penta-, hexa-, and hepta-CDDs and CDFs, it was assumed
that the measured emission factor within a congener group was the sum of equal emission factors
for all congeners in that group, including non-2,3,7,8-substituted congeners).
Source: U.S. EPA (1987a)
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Ratio (congener emission factor / total CDD/CDF emission factor)
0 O.O1 O.O2 O.O3 O.O4 O.O5 O.O6 O.O7 O.O8
2,3,7,8-TCDD
1,2,3,7,8-PeCDD
1,2,3,4,7,8-HxCDD
1,2,3,6,7,8-HxCDD
1,2,3,7,8,9-HxCDD
1,2,3,4,6,7,8-HpCDD
1,2,3,4,6,7,8,9-OCDD
2,3,7,8-TCDF
1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
1,2,3,4,7,8-HxCDF
1,2,3,6,7,8-HxCDF
1,2,3,7,8,9-HxCDF
2,3,4,6,7,8-HxCDF
1,2,3,4,6,7,8-HpCDF
1,2,3,4,7,8,9-HpCDF
1,2,3,4,6,7,8,9-OCDF
Ratio (congener group emission factor / total CDD/CDF emission factor)
0.05 0.1 0.15 0.2
0.25
Sources: U.S. EPA (1995h); Galson Corporation (1995)
Figure 7-1. Congener and Congener Group Profiles for Air Emissions
from Secondary Aluminum Smelters
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0
Ratio (congener group emission factor / total CDD/CDF emission factor)
0.1 0.2 0.3
0.4
Source: U.S. EPA (1987c)
Figure 7-2a. Congener Group Profile for Air Emissions from a Secondary Copper Smelter
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Ratio (congener emission factor / total CDD/CDF emission factor)
0.02 0.04 0.06 0.08 0.1 0.12 0.14
2,3,7,8-TCDD
1,2,3,7,8-PeCDD
1,2,3,4,7,8-HxCDD
1,2,3,6,7,8-HxCDD
1,2,3,7,8,9-HxCDD
1,2,3,4,6,7,8-HpCDD
1,2,3,4,6,7,8,9-OCDD
2,3,7,8-TCDF
1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
1,2,3,4,7,8-HxCDF
1,2,3,6,7,8-HxCDF
1,2,3,7,8,9-HxCDF
2,3,4,6,7,8-HxCDF
1,2,3,4,6,7,8-HpCDF
1,2,3,4,7,8,9-HpCDF
1,2,3,4,6,7,8,9-OCDF
Ratio (congener group emission factor / total CDD/CDF emission factor)
0.05 0.1 0.15 0.2 0.25
0.3
Source: AGES (1992)
Figure 7-2b. Congener and Congener Group Profiles for a Closed Secondary Copper Smelter
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Ratio (congener emission factor / total CDD/CDF emission factor)
O O.O1 O.O2 O.O3 O.O4 O.O5 O.O6 O.O7 O.O8 O.O9 O.I
2,3,7,8-TODD
1,2,3,7,8-PeCDD
1,2,3,4,7,8-HxCDD
1,2,3,6,7,8-HxODD
1,2,3,7,8,9-HxCDD
1,2,3,4,6,7,8-HpCDD
1,2,3,4,6,7,8,9-OCDD
2,3,7,8-TCDF
1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
1,2,3,4,7,8-HxODF
1,2,3,6,7,8-HxCDF
1,2,3,7,8,9-HxCDF
2,3,4,6,7,8-HxCDF
1,2,3,4,6,7,8-HpCDF
1,2,3,4,7,8,9-HpCDF
1,2,3,4,6,7,8,9-OCDF
Ratio (congener group emission factor / total CDD/CDF emission factor)
0.1 0.2 0.3 0.4 0.5
0.6
I Blast furnace I I Reverb/co-located furnace
Source: U.S. EPA (1992e); U.S. EPA (1995d); U.S. EPA (1995e)
Note: Profiles are for emissions from baghouses; nondetected values set equal to zero.
Figure 7-3. Congener and Congener Group Profiles for Air Emissions from Secondary Lead Smelters
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Figure 7-4. Congener Profiles for Air Emissions from U.S. Iron Ore Sinter Plants
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Ratio (congener group emission factor / total CDD/CDF emission factor)
0.05 0.1 0.15 0.2 0.25 0.3 0.35
Source: U.S. EPA (1987a)
Figure 7-5. Congener Group Profile for Air Emissions from a Scrap Wire Incinerator
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Ratio (congener group emission factor / total CDD/CDF emission factor)
0.1 0.2 0.3 0.4 0.5
0.6
Source: U.S. EPA (1987a)
Figure 7-6. Congener Group Profile for Air Emissions from a Drum Incinerator
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8. CHEMICAL MANUFACTURING AND PROCESSING SOURCES
8.1. BLEACHED CHEMICAL WOOD PULP AND PAPER MILLS
In March 1988, EPA and the U.S. pulp and paper industry jointly released the
results from a screening study that provided the first comprehensive data on formation
and discharge of CDDs and CDFs from pulp and paper mills (U.S. EPA, 1988d). This early
screening study of five bleached kraft mills ("Five Mill Study") confirmed that the pulp
bleaching process was primarily responsible for the formation of the CDDs and CDFs. The
study results showed that 2,3,7,8-TCDD was present in seven of nine bleached pulps,
five of five wastewater treatment sludges, and three of five treated wastewater effluents.
The study results also indicated that 2,3,7,8-TCDD and 2,3,7,8-TCDF were the principal
CDDs and CDFs formed.
To provide EPA with more complete data on the release of these compounds by the
U.S. industry, EPA and the U.S. pulp and paper industry jointly conducted a survey during
1988 of 104 pulp and paper mills in the United States to measure levels of 2,3,7,8-TCDD
and 2,3,7,8-TCDF in effluent, sludge, and pulp. This study, commonly called the 104 Mill
Study, was managed by the National Council of the Paper Industry for Air and Stream
Improvement, Inc. (NCASI), with oversight by EPA, and included all U.S. mills where
chemically produced wood pulps were bleached with chlorine or chlorine derivatives. The
final study report was released in July 1990 (U.S. EPA, 1990a).
An initial phase of the 104 Mill Study involved the analysis of bleached pulp (10
samples), wastewater sludge (9 samples), and wastewater effluent (9 samples) from eight
kraft mills and one sulfite mill for all 2,3,7,8-substituted CDDs and CDFs. These analyses
were conducted to test the conclusion drawn in the Five Mill Study that 2,3,7,8-TCDD
and 2,3,7,8-TCDF were the principal CDDs and CDFs found in pulp, wastewater sludge,
and wastewater effluent on a toxic equivalents basis. Although at the time of this study
there were no reference analytical methods for many of the 2,3,7,8-substituted
CDDs/CDFs, the data obtained were considered valid by EPA for the purposes intended
because of the identification and quantification criteria used, duplicate sample results, and
limited matrix spike experiments. Table 8-1 presents a summary of the results obtained in
terms of the median concentrations and the range of concentrations observed for each
matrix (i.e., pulp, sludge, and effluent). Figures 8-1 through 8-3 present congener profiles
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for each matrix (normalized to total CDD/CDF and to total I-TEQDF) using the median
reported concentrations. After examination of the raw, mill-specific data, EPA (1990a)
concluded that the congener profiles were fairly consistent across matrices within mills
and that 2,3,7,8-TCDD and 2,3,7,8-TCDF account for the majority of TEQ in the samples.
Using the median concentrations and treating nondetected values as either zero or one-
half the detection limit, EPA concluded that 2,3,7,8-TCDF accounted for 95.8 to 99.0
percent of the total I-TEQDF in pulp (95.4 to 99.5 percent of TEQDF-WH098), 94.1 to 95.8
percent of the I-TEQDF in sludge (94.1 to 96.5 percent of TEQDF-WH098), and 81.1 to 91.7
of the I-TEQDF in effluent (81.7 to 96.4 percent of TEQDF-WH098).
NCASI reported on a similar full-congener analysis study for samples collected from
eight mills during the mid-1990s (Gillespie, 1997). The results of these analyses are
presented in Table 8-2. The frequencies of detection of 2,3,7,8-TCDD and 2,3,7,8-TCDF
were significantly lower than in the 1988 study. Therefore, deriving meaningful summary
statistics concerning the relative importance of 2,3,7,8-TCDD and 2,3,7,8-TCDF to the
total TEQ is difficult. With all nondetected values assumed to be zero, 2,3,7,8-TCDD and
2,3,7,8-TCDF account for 91 percent of the total effluent I-TEQDF (97 percent of TEQDF-
WH098), 46 percent of the total sludge I-TEQDF (53 percent of TEQDF-WH098), and 87
percent of the total pulp I-TEQDF (87 percent of TEQDF-WH098). Because of the high
frequency of nondetects, when all nondetected values are one-half the detection limits,
2,3,7,8-TCDD and 2,3,7,8-TCDF account for only 13 percent of the total effluent I-TEQDF,
13 percent of the total sludge I-TEQDF, and 28 percent of the total pulp I-TEQDF.
In 1992, the pulp and paper industry conducted its own NCASI-coordinated survey
of 2,3,7,8-TCDD and 2,3,7,8-TCDF emissions (NCASI, 1993). Ninety-four mills
participated in the NCASI study, and NCASI assumed that the remaining 10 (of 104)
operated at the same levels as measured in the 1988 104 Mill Study. All nondetected
values were counted as half the detection limit. If detection limits were not reported, they
were assumed to be 10 pg/L for effluent and 1 ng/kg ppt for sludge or bleached pulp.
The data used in the report were provided by individual pulp and paper companies that
had been requested by NCASI to generate the data using the same protocols used in the
104 Mill Study.
As part of its efforts to develop revised effluent guidelines and standards for the
pulp, paper, and paperboard industry, EPA in 1993 published the development document
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for the guidelines and standards being proposed for this industry (U.S. EPA, 1993d). The
development document presents estimates of the 2,3,7,8-TCDD and 2,3,7,8-TCDF annual
discharges in wastewater from the mills in this industry as of January 1, 1993. To
estimate these discharges, EPA used the most recent information about each mill from
four databases (104 Mill Study, EPA short-term monitoring studies at 13 mills, EPA long-
term monitoring studies at 8 mills, and industry self-monitoring data submitted to EPA).
The 104 Mill Study data were used for only those mills that did not report making any
process changes subsequent to the 104 Mill Study and did not submit any more recent
effluent monitoring data.
Gillespie (1994, 1995) reported the results of 1993 and 1994 updates,
respectively, to the 1992 NCASI survey. As in the 1992 survey, companies were
requested to follow the same protocols for generating data used in the 104 Mill Study.
Gillespie (1994, 1995) reported that less than 10 percent of mills had 2,3,7,8-TCDD and
2,3,7,8-TCDF concentrations in effluent above the nominal detection limits of 10 pg/L and
100 pg/L, respectively. EPA obtained similar results in its short- and long-term sampling
for 18 mills; 2,3,7,8-TCDD was detected at four mills, and 2,3,7,8-TCDF was detected at
nine mills (U.S. EPA, 1993d). Gillespie (1994) reported that wastewater sludges at most
mills (i.e., 90 percent) contained less than 31 ng/kg of 2,3,7,8-TCDD and less than 100
ng/kg of 2,3,7,8-TCDF. Gillespie (1995) also reported that 90 percent of the mills
reported 2,3,7,8-TCDD and 2,3,7,8-TCDF concentrations in sludge of less than 1 7 ng/kg
and 76 ng/kg, respectively, in 1994. U.S. EPA (1993d) reported similar results but found
detectable levels of 2,3,7,8-TCDD and 2,3,7,8-TCDF in sludges from 64 percent and 85
percent of the facilities sampled, respectively. Gillespie (1994) reported that nearly 90
percent of the bleached pulps contained less than 2 ng/kg of 2,3,7,8-TCDD and less than
160 ng/kg of 2,3,7,8-TCDF. Gillespie (1995) reported that 90 percent of the bleached
pulps contained 1.5 ng/ng or less of 2,3,7,8-TCDD and 5.9 ng/kg or less of 2,3,7,8-
TCDF. The final levels in white paper products would correspond to levels in bleached
pulp, so bleached paper products would also be expected to contain less than 2 ng/kg of
2,3,7,8-TCDD.
On April 15, 1998, EPA promulgated effluent limitations guidelines and standards
for certain segments of the pulp, paper and paperboard industry (Federal Register, 1998c).
The industry segments covered by this rulemaking (i.e., the bleached papergrade kraft and
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soda subcategory and the papergrade sulfite subcategory) are those segments responsible
for more than 90 percent of the bleached chemical pulp production in the United States.
For this rule, EPA updated the estimates of baseline loadings made in 1993 for the
proposed rule by using more recent data collected by EPA, NCASI (including the 1994
NCASI survey), and individual facilities (U.S. EPA, 1997f). These revised estimates are
presented in the last column in Table 8-3. EPA projects that, after full compliance with
these rules, annual TEQ discharges will be reduced to 5 grams in effluent and 7 grams in
sludge.
Estimates of National Emissions in 1987 and 1995 - The U.S. annual discharges of
2,3,7,8-TCDD and 2,3,7,8-TCDF are summarized in Table 8-3 for each of the six surveys
discussed above. The release estimates for 1995 from U.S. EPA (1997f) and for 1988
from U.S. EPA (1990a) are believed to best represent emissions in the reference years
1995 and 1987, respectively. During the period between EPA's 104 Mill Study and
issuance of the development document (U.S. EPA, 1993d), the U.S. pulp and paper
industry reduced releases of CDD/CDFs primarily by instituting numerous process changes
to reduce the formation of CDD/CDFs during the production of chemically bleached wood
pulp. Details on the process changes implemented are provided in U.S. EPA (1993d) and
Gillespie (1995). Much of the reduction between 1988 and 1995 can be attributed to
process changes for pollution prevention.
The confidence ratings for these release estimates were judged to be high because
direct measurements were made at virtually all facilities, indicating a high level of
confidence in both the production and emission factor estimates. The best estimates of
annual emissions in 1995 (i.e., the 1995 estimates presented in Table 8-3) are 28 g
TEQ/yr for effluent, 50 g TEQ/yr for sludge, and 40 g TEQ/yr for pulp (i.e., TEQs that will
enter the environment in the form of paper products). The best estimates of annual
emissions in 1987 (i.e., the 1988 estimates presented in Table 8-3) are 356 g TEQ/yr for
effluent, 343 g TEQ/yr for sludge, and 505 g TEQ/yr for pulp.
In 1990, the majority of the wastewater sludge generated by these facilities was
placed in landfills or in surface impoundments (75.5 percent), with the remainder
incinerated (20.5 percent), applied to land directly or as compost (4.1 percent), or
distributed as a commercial product (less than 1 percent) (U.S. EPA, 1993e). Data more
recent than 1995 or earlier than 1988 are not available on disposition of wastewater
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sludges. On the basis of these statistics, the best estimate of TEQ applied to land (i.e.,
not incinerated or landfilled) in 1995 is 2.0 g (i.e., 4.1 percent of 50 g). The estimate for
1987 is 14.1 g TEQ (i.e., 4.1 percent of 343 g).
8.2. MANUFACTURE OF CHLORINE, CHLORINE DERIVATIVES, AND METAL
CHLORIDES
No testing of CDD/CDF emissions to air, land, or water from U.S. manufacturers of
chlorine, chlorine derivatives, and metal chlorides has been reported on which to base
estimates of national emissions. Sampling of graphite electrode sludges from European
chlorine manufacturers indicates high levels of CDFs. Limited sampling of chlorine
derivatives and metal chlorides in Europe indicates low-level contamination in some
products.
8.2.1. Manufacture of Chlorine
Chlorine gas is produced by electrolysis of brine electrolytic cells. Until the late
1970s, the primary type of electrolytic process used in the chloralkali industry to produce
chlorine consisted of mercury cells containing graphite electrodes. As shown in Table 8-4,
high levels of CDFs have been found in several samples of graphite electrode sludge from
facilities in Europe. The CDFs predominate in these sludges, and the 2,3,7,8-substituted
congeners account for a large fraction of the respective congener totals (Rappe et al.,
1990b; Rappe et al., 1991; Rappe, 1993; Strandell et al., 1994). During the 1980s,
titanium metal anodes were developed to replace graphite electrodes (U.S. EPA, 1982a;
Curlin and Bommaraju, 1991). Currently, no U.S. facility is believed to use graphite
electrodes in the production of chlorine gas (telephone conversation between L. Phillips,
Versar, Inc., and T. Fielding, U.S. EPA, Office of Water, February 1993).
Although the origin of the CDFs in graphite electrode sludge is uncertain,
chlorination of the cyclic aromatic hydrocarbons (such as dibenzofuran) present in the coal
tar used as a binding agent in the graphite electrodes has been proposed as the primary
source (Strandell et al., 1994). For this reason, sludges produced using metal electrodes
were not expected to contain CDFs. However, results of an analysis of metal electrode
sludge from a facility in Sweden, analyzed as part of the Swedish Dioxin Survey, showed
the sludge contained high levels of CDFs (similar to those of the graphite sludge) and
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primarily nondetectable levels of CDDs (Strandell et al., 1994). The sludge showed the
same type of CDF congener pattern reported by Rappe et al. (1991) and Rappe (1993).
Strandell et al. (1994) suggested that chlorination of PAHs present in the rubber linings of
the electrolytic cell may have formed the CDFs found in the one sample analyzed.
Although EPA does not regulate CDD/CDFs specifically, it issued restrictions under
the Resource Conservation and Recovery Act (RCRA) on the land disposal of wastewater
and sludges generated by chlorine manufacturers that use the mercury cell process and
the diaphragm process (with graphite electrodes) (Waste Codes K071, K073, and K106)
(40 CFR 268).
8.2.2. Manufacture of Chlorine Derivatives and Metal Chlorides
The limited sampling of chlorine-derivative products indicates that these products
contain very low, if any, concentrations of CDD/CDFs. Rappe et al. (1990c) analyzed a
sample of chlorine bleach consisting of 4.4 percent sodium hypochlorite. Most of the
2,3,7,8-substituted CDD/CDF congeners were below the limits of detection (0.3 to 7 pg/L
for all congeners, except OCDD and OCDF, which were 12 and 20 pg/L, respectively). No
2,3,7,8-substituted CDDs were detected. Tetra-, penta-, and hexa-CDFs were detected
at levels of 13 pg/L or lower. The TEQ content of the sample was 4.9 pg I-TEQDF/L.
Hutzinger and Fiedler (1991 a) reported finding no CDD/CDFs at a detection limit of 4
/yg/kg in chlorine gas or in samples of 10 percent sodium hypochlorite, 13 percent sodium
hypochlorite, and 31-33 percent hydrochloric acid at a detection limit of 1 /vg/kg.
Hutzinger and Fiedler (1991 a) reported the results of analyses of samples of FeCI2,
AICI3, CuCI2, CuCI, SiCI4, and TiCI4 for their content of HpCDF, OCDF, HpCDD, and OCDD.
The sample of FeCI3 contained HpCDF and OCDF in the low /vg/kg range, but no HpCDD or
OCDD were detected at a detection limit of 0.02 //g/kg. One of the two samples of AICI3
analyzed also contained a low /vg/kg concentration of OCDF. The samples of CuCI2 and
CuCI contained concentrations of HpCDF, OCDF, and OCDD less that 1 //g/kg. The
results are presented in Table 8-5.
8.3. MANUFACTURE OF HALOGENATED ORGANIC CHEMICALS
Several chemical production processes generate CDDs and CDFs (Versar, 1985;
Hutzinger and Fiedler, 1991 a). CDDs and CDFs can be formed during the manufacture of
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chlorophenols, chlorobenzenes, and chlorobiphenyls (Versar, 1985; Ree et al., 1988).
Consequently, disposal of industrial wastes from manufacturing facilities producing these
compounds may result in the release of CDDs and CDFs to the environment. Also, the
products themselves may contain these compounds, and their use or consumption, may
result in additional releases to the environment. CDD and CDF congener distribution
patterns indicative of noncombustion sources have been observed in sediments in
southwest Germany and The Netherlands. According to Ree et al (1988), the congener
patterns found suggest that wastes from the production of chlorinated organic compounds
may be important historical sources of CDD and CDF contamination in these regions. The
production and use of many of the chlorophenols, chlorophenoxy herbicides, and PCB
products are now banned or strictly regulated in most countries. However, these products
may have been a source of the environmental contamination that occurred prior to the
1970s and may continue to be a source of environmental releases under certain limited
use and disposal conditions (Rappe, 1992a).
8.3.1. Chlorophenols
Chlorophenols have been widely used for a variety of pesticidal applications. The
higher chlorinated phenols (i.e., tetrachlorophenol and pentachlorophenol) and their sodium
salts have been used primarily for wood preservation. The lower chlorinated phenols have
been used primarily as chemical intermediates in the manufacture of other pesticides. For
example, 2,4-dichlorophenol is used to produce the herbicides 2,4-Dichlorophenoxyacetic
acid (2,4-D), 4-(2,4-Dichlorophenoxy)butanoic acid (2,4-DB), 2-(2,4-Dichlorophenoxy)-
propanoic acid (2,4-DP), Nitrophen, Genite, and Zytron, and 2,4,5-trichlorophenol was
used to produce hexachlorophene, 2,4,5-T, Silvex, Erbon, Ronnel, and Gardona (Oilman et
al., 1988; Hutzinger and Fiedler, 1991 a). [Note: Sections 8.3.7 and 8.3.8 contain
information on EPA actions to control CDD/CDF contamination of pesticides (including
pentachlorophenol and its salts) and to obtain additional data on CDD/CDF contamination
of pesticides.]
The two major commercial methods used to produce chlorophenols are (1)
electrophilic chlorination of molten phenol by chlorine gas in the presence of catalytic
amounts of a metal chloride and organic chlorination promoters and stabilizers, and (2)
alkaline hydrolysis of chlorobenzenes under heat and pressure using aqueous methanolic
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sodium hydroxide. Other manufacturing methods include conversion of diazonium salts of
various chlorinated anilines, and chlorination of phenolsulfonic acids and benzenesulfonic
acids, followed by the removal of the sulfonic acid group (Oilman et al., 1988; Hutzinger
and Fiedler, 1991 a).
Because of the manufacturing processes employed, commercial chlorophenol
products can contain appreciable amounts of impurities (Oilman et al., 1988). During the
direct chlorination of phenol, CDD/CDFs can form either by the condensation of tri-, tetra-,
and pentachlorophenols or by the condensation of chlorophenols with
hexachlorocyclohexadienone (which forms from excessive chlorination of phenol). During
alkaline hydrolysis of chlorobenzenes, CDD/CDFs can form through chlorophenate
condensation (Ree et al., 1988; Oilman et al., 1988; Hutzinger and Fiedler, 1991 a).
The limited information on CDD/CDF concentrations in chlorophenols published in
the 1970s and early 1980s was compiled by Versar (1985) and Hutzinger and Fiedler
(1991 a). The results of several major studies cited by these reviewers (Firestone et al.,
1972; Rappe et al., 1978a, 1978b) are presented in Table 8-6. Typically, CDD/CDFs were
not detected in monochlorophenols (MCP) and dichlorophenols (DCP) but were reported in
trichlorophenols (TrCP) and tetrachlorophenols (TeCP). More recent results of testing of
2,4-dichlorophenol (2,4-DCP), performed in response to the Toxic Substances Control Act
(TSCA) Dioxin/Furan Test Rule, showed no detectable concentrations of 2,3,7,8-
substituted tetra- through hepta-CDD/CDFs. Other than a study by Hagenmaier (1986)
that reported finding 2,3,7,8-TCDD at a concentration of 0.3//g/kg in a sample of
2,3,4,5-tetrachlorophenol, no more recent data on concentrations of CDDs and CDFs
could be found in the literature for the mono- through tetra-chlorophenols. Tables 8-7 and
8-8 present summaries of several studies that reported CDD/CDF concentrations in PCP
and in PCP-Na products, respectively. Many of these studies do not report congener-
specific concentrations, and many are based on products obtained from non-U.S. sources.
Regulatory Actions - Section 8.3.8 of this report describes regulatory actions taken
by EPA to control the manufacture and use of chlorophenol-based pesticides.
In the mid-1980s, EPA's Office of Solid Waste promulgated land disposal
restrictions on wastes under RCRA (i.e., wastewaters and nonwastewaters) resulting from
the manufacture of chlorophenols (40 CFR 268). Table 8-9 lists all wastes in which CDDs
and CDFs are specifically regulated as hazardous constituents by EPA, including
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chlorophenol wastes (waste codes F020 and F021). The regulations prohibit the land
disposal of these wastes until they are treated to a level below the routinely achievable
detection limits in the waste extract listed in Table 8-9 for each of the following congener
groups: TCDDs, PeCDDs, HxCDDs, TCDFs, PeCDFs, and HxCDFs. Wastes from PCP-
based wood-preserving operations (waste codes K001 and F032) are also regulated as
hazardous wastes under RCRA (40 CFR 261).
EPA's Office of Water promulgated effluent limitations for facilities that
manufacture chlorinated phenols and discharge treated wastewater (40 CFR 414.70).
These effluent limitations do not specifically regulate CDDs and CDFs. The effluent
limitations for the individually regulated chlorinated phenols are less than or equal to 39
/yg/L for facilities that use biological end-of-pipe treatment.
DCPs and TrCPs are subject to reporting under the Dioxin/Furan Test Rule, which is
discussed in Section 8.3.7 of this report. Since the effective date of that rule (i.e., June
5, 1987), only the 2,4-DCP isomer has been commercially produced (or imported) in the
United States, and as noted in Table 8-6, no CDD/CDFs were detected in the product.
Testing is required for the other DCPs and TrCPs, if manufacture or importation resumes.
Similarly, TeCPs were subject to reporting under the Dioxin/Furan Pesticide Data Call-In or
DCI (discussed in Section 8.3.8 of this report). Since issuance of the DCI, the registrants
of TeCP-containing pesticide products have elected to no longer support the registration of
their products in the United States.
In January 1987, EPA entered into a Settlement Agreement with pentachlorophenol
(PCP) manufacturers, which set limits on allowed uses of PCP and its salts and set
maximum allowable concentrations of 2,3,7,8-TCDD and HxCDDs, effective in February
1989. Section 8.3.8 discusses the 1987 PCP Settlement Agreement and estimates
current releases of CDD/CDFs associated with use of PCP in the United States. Section
12.3.1 provides an estimate of the amount of CDD/CDFs that may have entered the
environment or that are contained within treated wood products as a result of prior use of
PCP and PCP-Na.
Since the late 1980s, U.S. commercial production of chlorophenols has been
limited to 2,4-dichlorophenol (2,4-DCP) and PCP. As noted above, disposal of wastes
generated during the manufacture of chlorophenols is strictly regulated, and thus releases
to the environment are expected to be negligible. With regards to releases associated
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with the use of 2,4-DCP, no CDD/CDFs have been detected in 2,4-DCP. Releases
associated with the use of PCP are presented in Sections 8.3.8 and 12.3.1.
8.3.2. Chlorobenzenes
Chlorobenzenes have been produced in the United States since 1909. U.S.
production operations were developed primarily to provide chemical raw materials for the
production of phenol, aniline, and various pesticides based on the higher chlorinated
benzenes. Because of [incremental] changes in the processes used to manufacture phenol
and aniline and the phaseout of highly chlorinated pesticides such as DDT and
hexachlorobenzene, by 1988 U.S. production of Chlorobenzenes had decreased to 50
percent of the peak production level in 1969.
Chlorobenzenes can be produced via three methods: (1) electrophilic substitution of
benzene (in liquid or vapor phase) with chlorine gas in the presence of a metal salt
catalyst; (2) oxidative chlorination of benzene with HCI at 150-300°C in the presence of
a metal salt catalyst; and (3) dehydrohalogenation of hexachlorocyclohexane wastes at
200-240°C with a carbon catalyst to produce trichlorobenzene, which can be further
chlorinated to produce higher chlorinated benzenes (Ree et al., 1988; Hutzinger and
Fiedler, 1991 a; Bryant, 1993).
All Chlorobenzenes currently manufactured in the United States are produced using
the electrophilic substitution process using liquid-phase benzene (i.e., temperature is at or
below 80°C). Ferric chloride is the most common catalyst employed. Although this
method can be used to produce mono- through hexachlorobenzene, the extent of
chlorination is controlled to yield primarily MCBz and DCBz. The finished product is a
mixture of Chlorobenzenes, and refined products must be obtained by distillation and
crystallization (Bryant, 1993).
CDD/CDFs can be produced inadvertently during the manufacture of
Chlorobenzenes by nucleophilic substitution and pyrolysis mechanisms (Ree et al., 1988).
The criteria required for production of CDD/CDFs via nucleophilic substitution are (1)
oxygen as a nuclear substituent (i.e., presence of chlorophenols) and (2) production or
purification of the substance under alkaline conditions. Formation via pyrolysis requires
reaction temperatures above 150°C (Ree et al., 1988; Hutzinger and Fiedler, 1991 a). The
liquid-phase electrophilic substitution process currently used in the United States does not
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meet either of these criteria. Although Ree et al. (1988) and Hutzinger and Fiedler
(1991 a) state that the criteria for formation of CDD/CDFs via nucleophilic substitution may
be present in the catalyst neutralization and purification/distillation steps of the
manufacturing process, Opatick (1995) states that the chlorobenzene reaction product in
U.S. processes remains mildly acidic throughout these steps.
Table 8-10 summarizes the very limited published information on CDD/CDF
contamination of chlorobenzene products. The presence of CDD/CDFs has been reported
in TCBz, PeCBz, and HCBz. No CDD/CDFs have been reported in monochlorobenzene
(MCBz) and DCBz. Conflicting data exist concerning the presence of CDD/CDFs in TCBz.
One study (Villanueva et al., 1974) detected no CDD/CDFs in one sample of 1,2,4-TCBz
at a detection limit of 0.1 /vg/kg. Hutzinger and Fiedler (1991 a) reported unpublished
results of Dr. Hans Hagenmaier showing CDD/CDF congener group concentrations ranging
from 0.02 to 0.074/vg/kg in a sample of mixed TCBz. Because the TCBz examined by
Hagenmaier contained about 2 percent hexachlorocyclohexane, it is reasonable to assume
that the TCBz was produced by dehydrohalogenation of hexachlorocyclohexane (a
manufacturing process not currently used in the United States).
Regulatory Actions - EPA has determined, as part of the Federal Insecticide,
Fungicide, and Rodenticide Act (FIFRA) Pesticide Data Call-In (discussed in Section 8.3.8),
that the 1,4-DCBz manufacturing processes used in the United States are not likely to
form CDD/CDFs. MCBz, DCBz, and TCBz are listed as potential precursor chemicals under
the TSCA Dioxin/Furan Test Rule and are subject to reporting. (See Section 8.3.7.) In
addition, EPA issued a Significant New Use Rule (SNUR) under Section 5(a)(2) of TSCA on
December 1, 1993 (effective January 14, 1994) for PeCBz and 1,2,4,5-TeCBz (Federal
Register, 1993c). This rule requires persons to submit a notice to EPA at least 90 days
before manufacturing, importing, or processing either of these compounds in amounts of
10,000 pounds or greater per year per facility for any use. All registrations of pesticide
products containing HCBz were cancelled in the mid-1980s (Carpenter et al., 1986).
EPA's Office of Solid Waste promulgated land disposal restrictions on wastes (i.e.,
wastewaters and nonwastewaters) resulting from the manufacture of chlorobenzenes (40
CFR 268). Table 8-9 lists all solid wastes for which EPA specifically regulates CDDs and
CDFs as hazardous constituents, including chlorobenzene wastes. The regulations prohibit
the land disposal of these wastes until they are treated to a level below the routinely
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achievable detection limits in the waste extract listed in Table 8-9 for each of the
following congener groups: TCDDs, PeCDDs, HxCDDs, TCDFs, PeCDFs, and HxCDFs.
EPA's Office of Water promulgated effluent limitations for facilities that
manufacture chlorinated benzenes and discharge treated wastewater (40 CFR 414.70).
These effluent limitations do not specifically address CDDs and CDFs. The following
chlorinated benzenes are regulated: chlorobenzene; 1,2-dichlorobenzene; 1,3-
dichlorobenzene; 1,4-dichlorobenzene; 1,2,4-trichlorobenzene; and hexachlorobenzene.
The effluent limitations for the individual regulated chlorinated benzenes are less than or
equal to 77 /vg/L for facilities that use biological end-of-pipe treatment and are less than or
equal to 196//g/L for facilities that do not use biological end-of-pipe treatment.
Since at least 1993, U.S. commercial production of chlorobenzenes has been
limited to MCBz, 1,2-dichlorobenzene (1,2-DCBz), 1,4-dichlorobenzene (1,4-DCBz), and,
to a much lesser extent, 1,2,4-trichlorobenzene (1,2,4-TCBz). As noted above, CDD/CDF
formation is not expected under the normal operating conditions of the processes currently
used in the United States to produce these four chemicals. No tetra-, penta-, or
hexachlorinated benzenes are now intentionally produced or used in the United States
(Bryant, 1993). Thus, releases of CDD/CDFs from manufacture of chlorobenzenes in
1995 were estimated to be negligible. Because the information available on CDD/CDF
content of MCBz to PeCBz is very limited and is based primarily on unpublished European
data, and because information on the chlorobenzene manufacturing processes in place
during 1987 is not readily available, no emission estimates can be made for 1987.
8.3.3. Chlorobiphenyls
PCBs are manufactured by the direct batch chlorination of molten biphenyl in the
presence of a catalyst, followed by separation and purification of the desired chlorinated
biphenyl fractions. During the manufacture of PCBs, the inadvertent production of CDFs
also occurrs. This section addresses potential releases of CDD/CDFs associated with
leaks and spills of PCBs. CDFs have been shown to form when PCB-containing
transformers and capacitors undergo malfunctions or are subjected to fires that result in
accidental combustion of the dielectric fluid. This combustion source of PCB-associated
CDFs is discussed in Section 6.6. Section 11.2 addresses releases of dioxin-like PCBs.
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PCB production is believed to have occurred in 10 countries. The total amount of
PCBs produced worldwide since 1929 (i.e., the first year of known production) is
estimated to total 1.5 billion kg. Initially, PCBs were primarily used as dielectric fluids in
transformers. After World War II, PCBs found steadily increasing use as dielectric fluids in
capacitors, as heat-conducting fluids in heat exchangers, and as heat-resistant hydraulic
fluids in mining equipment and vacuum pumps. PCBs also were used in a variety of
"open" applications (i.e., uses from which PCBs cannot be re-collected) including in
plasticizers, carbonless copy paper, lubricants, inks, laminating agents, impregnating
agents, paints, adhesives, waxes, additives in cement and plaster, casting agents,
dedusting agents, sealing liquids, fire retardants, immersion oils, and pesticides (DeVoogt
and Brinkman, 1989).
PCBs were manufactured in the United States from 1929 until 1977. U.S.
production peaked in 1970, with a volume of 85 million pounds. Monsanto Corporation,
the major U.S. producer, voluntarily restricted the use of PCBs in 1971, and annual
production fell to 40 million pounds in 1974. Monsanto ceased PCB manufacture in mid-
1977 and shipped the last inventory in October 1977. Regulations issued by EPA
beginning in 1977, principally under TSCA (40 CFR 761), strictly limited the production,
import, use, and disposal of PCBs. (See Section 4.1 for details on TSCA regulations.)
The estimated cumulative production and consumption volumes of PCBs in the United
States from 1930 to 1975 were 1,400 million pounds produced; 3 million pounds
imported (primarily from Japan, Italy, and France), 1,253 million pounds sold in the United
States; and 150 million pounds exported (ATSDR, 1993; DeVoogt and Brinkman, 1989).
Monsanto Corporation marketed technical-grade mixtures of PCBs primarily under
the trade name Aroclor. The Aroclors are identified by a four-digit numbering code in
which the last two digits indicate the chlorine content by weight percent. The exception
to this coding scheme is Aroclor 1016, which contains only mono- through
hexachlorinated congeners with an average chlorine content of 41 percent. The following
list shows the percentages of total Aroclor production, by Aroclor mixture, during 1957 to
1977, as reported by Brown (1994).
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1957-1977
U.S. Production
Aroclor (%)
1221 0.96
1016 12.88
1232 0.24
1242 51.76
1248 6.76
1254 15.73
1260 10.61
1262 0.83
1268 0.33
The trade names of the major commercial technical-grade mixtures of PCBs
manufactured in other countries included Clophen (Germany), Fenclor and Apirolio (Italy),
Kanechlor (Japan), Phenoclor and Pyralene (France), Sovtel (USSR), Delor and Delorene
(Czechoslovakia), and Orophene (German Democratic Republic) (DeVoogt and Brinkman,
1989). Some of the mixtures marketed under these trade names were similar in terms of
chlorine content (by weight percent and average number of chlorines per molecule) to
various Aroclors, as shown below. Mixtures that are comparable in terms of chlorine
content were marketed under several trade names, as shown below.
Aroclor Clophen Pyralene Phenoclor Fenclor Kanechlor
1232 2000 200
1242 A-30 3000 DP-3 42 300
1248 A-40 DP-4 400
1254 A-50 DP-5 54 500
1260 A-60 DP-6 64 600
During the commercial production of PCBs, thermal oxidative cyclization under
alkaline conditions resulted in the inadvertent production of CDFs in most of the
commercial PCB mixtures (Brown et al., 1988; ATSDR, 1993). Bowes et al. (1975a) first
reported detection of CDFs in Aroclor products; samples of unused Aroclors manufactured
in 1969 and 1970 were found to have CDF (i.e., TCDF through HxCDF) concentrations
ranging from 0.8 to 2.0 mg/kg. Bowes et al. (1975b) employed congener-specific
analytical methodology and detected 2,3,7,8-TCDF and 2,3,4,7,8-PeCDF at
concentrations ranging from 0.11 to 0.33 mg/kg and 0.12 to 0.83 mg/kg, respectively, in
unused samples of Aroclor 1254 and Aroclor 1260. The presence of CDDs in commercial
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PCB mixtures, although at much lower concentrations than those of the CDFs, was
reported by Hagenmaier (1987) and Malisch (1994). Table 8-11 presents the CDF and
CDD congener group concentrations reported by Bowes et al. (1975a) and those reported
in subsequent years for unused PCBs by Erickson (1986), ATSDR (1993), Hagenmaier
(1987), and Malisch (1994).
Several researchers reported concentrations of specific CDD/CDF congeners in
commercial PCB mixtures (Bowes et al., 1975b; Brown et al., 1988; Hagenmaier, 1987;
Malisch, 1994). Only the Hagenmaier (1987) and Malisch (1994) studies, however,
reported the concentrations of all 2,3,7,8-substituted CDDs and CDFs. Table 8-12
presents the results of these four studies. It is evident from the table that major variations
are found in the levels of 2,3,7,8-TCDF and 2,3,4,7,8-PeCDF in the Clophen mixtures
reported by Hagenmaier (1987) and Malisch (1994) and the corresponding levels in the
Aroclor mixtures reported by Bowes et al. (1975b) and Brown et al. (1988).
Brown et al. (1988) compared the levels of 2,3,7,8-TCDF, 2,3,4,7,8-PeCDF, and
1,2,3,7,8,9-HxCDF in used samples (i.e., samples from previously used capacitors and
transformers) and unused samples of Aroclors 1016, 1242, 1254, and 1260. The
concentration ranges reported for the used and unused Aroclors were similar, leading
Brown et al. (1988) to conclude that CDFs are not formed during the normal use of PCBs
in electrical equipment.
Amounts of CDD/CDF TEQ that may have been released to the environment during
1987 and 1995 from spills and leaks of in-service PCBs cannot be accurately estimated
because reliable data regarding leaked and spilled PCBs are not available. However,
preliminary estimates can be made using the release data reported to EPA's Toxics
Release Inventory (TRI) by those manufacturing facilities required to submit annual reports
to TRI. Table 11-6 in Section 11 lists the amounts of PCBs reported to TRI to have been
released to the environment during 1988 through 1996. These TRI data include emissions
to the air, discharges to bodies of water, and releases to land. On the basis of these data,
annual emissions of PCBs to air during 1988 and 1996 could have been as high as 2.7 kg
and as low as 0 kg, respectively. If it is further assumed that the ratio of TEQ (I-TEQDF
and TEQDF-WH098) to total PCB in the air emissions was 0.17:1,000,000 (i.e., the average
of the TEQ contents for Clophen A-30 and Clophen A-50, or 1 70 //g/kg, as reported by
Hagenmaier (1987) and presented in Table 8-12), then annual emissions of I-TEQDF to air
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in 1988 and 1995 could have been 0.5 and 0 mg, respectively. Similar assumptions for
PCB releases to water of 4.5 kg in 1988 and 0 kg in 1995 yield estimated TEQ emissions
during 1988 and 1995 of 0.8 and 0 mg, respectively. For land releases of 341 kg in
1988 and 0 kg in 1995, estimated TEQ emissions during 1988 and 1995 are 58 and 0
mg, respectively. All of these estimated releases are considered to be negligible.
8.3.4. Polyvinyl Chloride
Polyvinyl chloride (PVC) resins are produced when free radical initiators are used to
induce the polymerization of vinyl chloride monomer (VCM). VCM is typically produced by
the thermal dehydrochlorination (commonly known as cracking) of ethylene dichloride
(EDC). One plant in the United States still uses the catalytic reaction of acetylene and HCI
to manufacture VCM directly. The cracking of EDC requires elevated pressure (20 to 30
atmospheres) and temperature (450 to 650°C) and yields VCM and HCI at about a 1:1
molar ratio. EDC is produced by two different methods: (1) direct chlorination of ethylene
with chlorine in the presence of a catalyst at a temperature of 50 to 60°C and pressure of
4 to 5 atmospheres; and (2) oxychlorination, which involves reaction of ethylene with HCI
and oxygen in the presence of a catalyst at temperatures generally less than 325°C. The
primary source of HCI for the oxychlorination process is the HCI produced from the
cracking of EDC to form VCM; all VCM plants, with the exception of the one facility noted
above, are integrated with EDC production facilities (The Vinyl Institute, 1998).
Although it has generally been recognized that CDD/CDFs are formed during the
manufacture of EDC/VCM/PVC, manufacturers and environmental public interest groups
have disagreed as to the quantity of CDD/CDFs that are formed and released to the
environment in wastes and possibly in PVC products. Although EPA regulates emissions
from EDC/VCM production facilities under the Clean Water Act (40 CFR 61), the Clean Air
Act (40 CFR 414), and RCRA (40 CFR 268 - Waste Codes F024, K019, and K020),
CDD/CDFs are not specifically regulated pollutants; as a consequence, monitoring data for
CDD/CDFs in emissions are generally lacking.
In 1993, Greenpeace International issued a report on CDD/CDF emissions
associated with the production of EDC/VCM (Greenpeace, 1993). Greenpeace estimated
that 5 to 10 g I-TEQDF are released to the environment (air, water, and ground combined)
annually for every 100,000 metric tons of VCM produced. This emission factor was
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based on data gathered by Greenpeace on four European plants. The Vinyl Institute
responded with a critique of the Greenpeace report (ChemRisk, 1993). Miller (1993)
summarized the differing views of the two parties. According to Miller (1993), European
PVC manufacturers claim the emission factor is 0.01 to 0.5 g I-TEQDF/100,000 metric
tons of VCM. Although Greenpeace (1993) and ChemRisk (1993) used basically the same
monitoring information to develop their emission factors, Greenpeace adjusted the
emission factor to account for unquantified fugitive emissions and waste products that
contain unspecified amounts of CDD/CDFs.
In 1995, Greenpeace issued another report reiterating the organization's concern
that the generation and emission of CDD/CDFs may be significant and urging that further
work be initiated to quantify and prevent emissions (Stringer et al., 1995). Stringer et al.
(1995) presented the results of analyses of three samples of chlorinated wastes obtained
from U.S. EDC/VCM manufacturing facilities. The three wastes were characterized
according to EPA hazardous waste classification numbers as follows: (1) an F024 waste
(i.e., waste from the production of short chain aliphatics by free radical catalyzed
processes), (2) a K019 waste (i.e., heavy ends from the distillation of ethylene from EDC
production), and a probable K020 waste (i.e., heavy ends from distillation of VC in VCM
manufacture). Table 8-13 presents the analytical results reported by Stringer et al.
(1995). This study acknowledged that because EDC/VCM production technologies and
waste treatment and disposal practices are very site-specific, the limited information
available on CDD/CDF generation and emissions made it difficult to quantify amounts of
CDD/CDFs generated and emitted.
In response to the lack of definitive studies and at the recommendation of EPA,
U.S. PVC manufacturers initiated an extensive monitoring program, the Dioxin
Characterization Program, to evaluate the extent of any CDD/CDF releases to air, water,
and land, as well as any product contamination. Manufacturers performed emission and
product testing at various facilities that were representative of various manufacturing and
process control technologies. The Vinyl Institute has completed studies of CDD/CDF
releases in wastewater, wastewater treatment plant solids, and stack gases, as well as
studies of CDD/CDF content of products (i.e., PVC resins and "sales" EDC). The following
subsections discuss the results for each of these media (The Vinyl Institute, 1998).
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The Vinyl Institute created an External Advisory Group to advise the institute on
the conduct of the Dioxin Characterization Program and to provide an independent review
of the Program results. In their final evaluation report, the Advisory Group judged the
industry's coverage, in terms of the number of facilities and waste streams sampled, to be
fairly comprehensive. The number of samples of PVC product, stack emissions,
wastewaters, and wastewater sludges obtained from the different types of manufacturing
facilities was deemed by the Advisory Group to provide a sufficient database to evaluate
industrywide annual releases. The Advisory Group concluded that the process established
by The Vinyl Institute to ensure that data collected as part of its Dioxin Characterization
Program are representative of normal process operations was a good one. After auditing
The Vinyl Institute's estimates of annual releases, the Advisory Group concluded that the
data were properly validated and the results were extrapolated to annual industrywide
release estimates in a creditable, scientific manner.
EPA has reviewed The Vinyl Institute (1998) study and concurs with the
conclusions of the External Advisory Group. EPA assigns a high confidence rating to the
activity level estimates and a medium confidence rating to the emission factor estimates
developed by The Vinyl Institute (1998).
Wastewater - The Vinyl Institute (1998) presented results for treated wastewater
samples collected during April and May of 1995 at six sites that manufactured only PVC,
at three sites that manufactured EDC and VCM, and at one site that manufactured EDC,
VCM, and PVC. In terms of production, the six PVC-only sites represent approximately 15
percent of the total estimated 1995 U.S. and Canadian PVC production. The three
EDC/VCM sites and the one EDC/VCM/PVC site together represent 27 percent of the total
estimated 1995 U.S. EDC production. Samples taken from PVC-only sites were taken
from sites that manufactured suspension PVC resin as well as sites that manufactured
dispersion PVC resin. Samples for the other four sites were taken from sites that used
direct and oxychlorination processes, fixed and fluidized beds, and low- and high-
temperature direct chlorination. The wastewater samples from one of the EDC/VCM sites,
one of the PVC-only sites, and the EDC/VCM/PVC site were taken from effluents derived
from process areas not limited to EDC/VCM, EDC/VCM/PVC, or PVC manufacturing.
The results of the sampling are presented in Table 8-14. The method detection
limits (MDLs) for all congeners except OCDD and OCDF in all samples were 10 pg/L or
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less. The MDLs for OCDD and OCDF were 50 pg/L or less. CDD/CDFs were detected in
two of the six samples from PVC-only sites (0.52 and 2.0 pg I-TEQDF/L, assuming ND =
0). The overall mean TEQ concentrations were 0.88 pg I-TEQDF/L (ND = 0) and 4.7 pg
I-TEQDF/L (ND = 1/2 MDL). CDD/CDFs were detected in all four of the samples from
EDC/VCM/PVC sites. The overall mean TEQ concentrations were 0.42 pg I-TEQDF/L (ND
= 0) and 4.4 pg I-TEQDF/L (ND = 1/2 MDL).
Based on these sample results, The Vinyl Institute developed I-TEQDF emission
factors for PVC-only and EDC/VCM/PVC manufacturing facilities. First, individual site
release rates were estimated using the treated wastewater effluent flow rate recorded by
the site during sampling and assuming that the site continuously releases CDD/CDFs at its
calculated total I-TEQDF, 24 hours per day, 360 days per year, at the recorded water
effluent rate. The total releases from each site-type category (i.e., PVC-only or
EDC/VCM/PVC facilities) were then estimated by averaging the individual release rates on
a per-1,000-metric-ton-of-PVC basis or per-1,000-metric-ton-of-EDC basis using the
estimated 1995 PVC and EDC production statistics for the sampled sites. These values
were then "scaled up" to estimate total U.S. releases in treated wastewater from the site-
type categories. It is not possible using the data presented in the Vinyl Institute (1998) to
calculate emission factors for TEQDF-WH098. However, because 1,2,3,7,8-PeCDD was
not detected in any wastewater sample, the TEQDF-WH098 emission factors would be
lower than the I-TEQDF emission factors.
The mean emission factors derived from the sample results for the PVC-only
facilities are 2.3//g I-TEQDF/1,000 metric tons of PVC (ND = 0) and 29 //g I-TEQDF/1,000
metric tons of PVC (ND = 1/2 MDL). The mean emission factors for the EDC/VCM/PVC
facilities are 2.9//g I-TEQDF/1,000 metric tons (ND = 0) and 1 5//g I-TEQDF/1,000 metric
tons of EDC (ND = 1/2 MDL).
The Vinyl Institute (1998) combined these emission factors with 1995 industry
production statistics (i.e., 5,21 2 thousand metric tons of PVC and 11,115 thousand
metric tons of EDC), to yield release estimates of 0.011 grams I-TEQDF (ND = 0) and 0.15
g I-TEQDF (ND = 1/2 DL) from PVC-only manufacturing sites and 0.032 g I-TEQDF (ND =
0) and 0.17 g I-TEQDF (ND = 1/2 DL) from EDC/VCM and EDC/VCM/PVC facilities for a
total I-TEQDF release in 1995 of 0.043 g (ND = 0) and 0.32 g (ND = 1/2 DL).
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Wastewater Treatment Plant Solids - The Vinyl Institute (1998) presented results
for 14 samples collected in 1996 from 9 EDC/VCM/PVC manufacturing sites. Samples
were collected from 4 of the 5 U.S. sites that manufactured EDC, VCM, and PVC; 3 of
the 7 U.S. sites that manufactured EDC and VCM, but not PVC; and 2 of the 21 sites that
manufacture PVC, but not EDC or VCM. On the basis of 1995 production data, the two
PVC-only sites manufactured approximately 4.7 percent of the total estimated U.S. and
Canadian PVC resin production. The sampled EDC/VCM and EDC/VCM/PVC sites
manufactured 56 percent of the total estimated 1995 U.S. EDC production. Samples
from the PVC-only sites were taken from sites that manufactured suspension PVC resin as
well as sites that manufactured dispersion PVC resin. Samples taken from the EDC/VCM
and EDC/VCM/PVC sites were taken from sites that used direct and oxychlorination
processes; fixed and fluidized EDC reactor beds; low- and high-temperature direct
chlorination; and air, oxygen, and mixed air/oxygen feeds.
Using the sample results and their determination that the results for facilities using
different EDC reactor bed technologies (i.e., fluidized bed vs. fixed bed) appear to differ
significantly, The Vinyl Institute developed annual I-TEQDF emission estimates for three
categories: PVC-only, EDC/VCM/PVC fixed bed, and EDC/VCM/PVC fluidized bed
facilities. Nine U.S. sites use fixed bed technology and six use fluidized bed technology.
Four of each type of facility were sampled by The Vinyl Institute. It is not possible using
the data presented in The Vinyl Institute (1998) to calculate emission factors for TEQDF-
WH098. Because 1,2,3,7,8-PeCDD was detected in only 3 of 10 samples, but OCDD and
OCDF were detected in all samples, it is likely that the TEQDF-WH098 emission factors
would not be significantly different from the I-TEQDF emission factors.
Results of the sampling are presented in Table 8-14. The MDLs for all congeners
were less than 150 ng/kg, and usually were less than 10 ng/kg. CDD/CDFs were
detected in all samples. The ranges of TEQ concentrations (dry weight basis) for the two
PVC-only facilities were 1.1 to 2.6 ng l-TEQDF/kg (ND = 0) and 2.8 to 4.4 ng l-TEQDF/kg
(ND = 1/2 MDL). On an emission factor basis, the ranges were 1.7 to 46/vg
I-TEQDF/1,000 metric tons of PVC produced (ND = 0) and 4.3 to 78 //g I-TEQDF/1,000
metric ton of PVC produced (ND = 1/2 DL). The range of TEQ concentrations for the
samples from the EDC/VCM or EDC/VCM/PVC sites were 88 to 6,850 ng l-TEQDF/kg (ND
= 0) and 93 to 6,850 ng l-TEQDF/kg (ND = 1/2 DL). On an emission factor basis, the
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ranges were 28 to 4,000 fjg I-TEQDF/1,000 metric tons of EDC (ND = 0) and 29 to 4,000
//g I-TEQDF/1,000 metric tons of EDC (ND = 1/2 DL).
The annual amounts of I-TEQDF generated in 1995 in each of the three facility
categories were estimated by The Vinyl Institute as follows. First, total annual
contributions at each sampled site were estimated by multiplying the I-TEQDF from the
sample by the annual production of wastewater solids at that site. These annual site
contributions of I-TEQDF were then summed for each of the three facility types and
multiplied by the ratio of each category's total annual production of PVC or EDC to the
sum of the annual production of the sampled sites in that category.
The Vinyl Institute (1998) combined these emission factors with 1995 industry
production statistics (i.e., 5,21 2 thousand metric tons of PVC and 11,115 thousand
metric tons of EDC) to yield estimated amounts of I-TEQDF in wastewater treatment plant
solids. For PVC-only facilities, estimated amounts are 0.069 g I-TEQDF per year (ND = 0)
and 0.1 2 g I-TEQDF per year (ND = 1 /2 DL), assuming an annual PVC production of 5,21 2
thousand metric tons. For EDC/VCM/PVC fixed bed facilities, the estimated amounts of
TEQ are 1.0 g I-TEQDF per year (ND = 0 or ND = 1/2 DL), assuming an EDC annual
production volume of 5,400 thousand metric tons. For EDC/VCM/PVC fluidized bed
facilities, the estimated amount of TEQ is 11 g I-TEQDF per year (ND = 0 or ND = 1/2
DL), assuming EDC annual production volume of 5,600 thousand metric tons. Thus, total
amounts of TEQ in wastewater treatment plant solids are estimated to have been 12.1 g I-
TEQDF in 1995 (ND = 0 or ND = 1/2 DL).
Based on The Vinyl Institute survey data, Institute member companies dispose of
wastewater solids by three methods: (1) RCRA hazardous waste landfilling (approximately
1 percent of industry total solids), (2) landfarming (approximately 6 percent), and (3)
"secure" on-site landfilling (93 percent of industry total solids). Solids disposed of by
methods 1 and 3 are assumed to be well controlled to prevent release into the general
environment, whereas solids disposed of by landfarming are not as well controlled and
could be released to the environment. Therefore, an estimated 0.73 g I-TEQDF (i.e., 6
percent of 12.1 g I-TEQDF) can be considered as potentially released to the environment.
Stack Gas Emissions - By grouping similarities of design and service, The Vinyl
Institute (1998) subcategorized thermal destruction units at EDC/VCM and/or PVC
manufacturing units into three categories: (a) type A—vent gas incinerators at PVC-only
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resin plants; (b) type B —vent gas thermal oxidizers at EDC/VCM plants; and (c) type
C —liquid only and liquid/vent gas thermal oxidizers at EDC/VCM plants. Using an
industrywide survey, The Vinyl Institute (1998) identified 22 type A units at 11 facilities,
23 type B units at 10 facilities, and 17 type C units at 10 facilities. The Vinyl Institute
gathered test data from 5 of the 22 type A units (from three facilities representing 7
percent of total U.S. and Canadian EDC/VCM/PVC production in 1995), 14 of the 23 type
B units (from 8 facilities), and 13 of the 17 type C units (from 7 facilities). The sampled
types B and C units represent 70 percent of total U.S. and Canadian EDC/VCM/PVC
production in 1 995.
Annual I-TEQDF emission estimates were generated by The Vinyl Institute by
combining estimated emissions from tested units (i.e., based on measured stack gas
results and plant-specific activity data) with an estimate of emissions from untested units.
The emissions from the untested units were estimated by multiplying the average emission
factor for the tested units in the category (i.e., called the "most likely" estimate by The
Vinyl Institute) or by multiplying the average emission factor of the three highest tested
units in each class (i.e., the "upper bound" estimate) by the activity level for the untested
units. It is not possible using the data presented in The Vinyl Institute (1998) to calculate
emission factors for TEQDF-WH098.
The Vinyl Institute (1998) estimates of "most likely" and "upper-bound" emissions
during 1995 for these three categories are as follows:
"Upper Bound"
"Most Likely" Emission Emission Estimate (g I-
Category Estimate (g l-TEQDF/yr) TEQDF/yr)
PVC-only incinerators
EDC/VCM liquid and liquid/vents
EDC/VCM vents for VCM only
0.0014
3.7
6.9
0.0019
7.2
21.6
The Vinyl Institute (1998) also estimated emissions that may result from
incineration of EDC/VCM/PVC wastes processed by off-site third-party processing. Using
the emission factors for liquid and liquid/vents developed in their study, The Vinyl Institute
estimated that potential emissions to air from this source category would be 0.65 g I-
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TEQDF/yr (most likely estimate) and 2.3 g l-TEQDF/yr (upper-bound estimate). Including
these third-party release estimates with those developed above yields a most likely
estimate of 11.2 g l-TEQDF/yr and an upper-bound estimate of 31 g l-TEQDF/yr.
Products - The Vinyl Institute (1998) presented results for 22 samples from 14 of
the 24 U.S. and Canadian facilities manufacturing suspension and mass PVC resins (i.e.,
13 pipe resins, 3 bottle resins, and 6 packaging resins). The results are summarized in
Table 8-15. The 14 sampled sites represent approximately 74 percent of estimated 1995
U.S. and Canadian suspension and mass PVC resin production. CDD/CDFs were detected
in only one sample (0.043 ng l-TEQDF/kg, assuming ND = 0). The overall mean TEQ
concentrations were 0.002 ng l-TEQDF/kg (ND= 0) and 0.7 ng l-TEQDF/kg (ND = 1/2
MDL). The MDLs were 2 ng/kg or less for all congeners in all samples except for OCDD
and OCDF, which had MDLs of 6 ng/kg or less.
The Vinyl Institute (1998) also presented results for six samples from four of the
seven U.S. facilities manufacturing dispersion PVC resins. CDD/CDFs were detected in
five of the samples. The results are summarized in Table 8-15. In terms of production,
the four sampled sites represent approximately 61 percent of estimated 1995 U.S.
dispersion PVC resin production. The results ranged from not detected to 0.008 ng I-
TEQDF/kg (overall mean = 0.001 ng l-TEQDF/kg assuming ND = 0, and 0.4 ng l-TEQDF/kg,
assuming ND = 1/2 MDL). The MDLs were 2 ng/kg or less for all congeners in all
samples except OCDD and OCDF, which had MDLs of 4 ng/kg or less.
The Vinyl Institute (1998) also presented results for 5 samples from 5 of the 15
U.S. facilities manufacturing EDC. The results are summarized in Table 8-15. In terms of
production, the five sampled sites represent approximately 71 percent of total U.S.
estimated 1995 "sales" EDC production. CDD/CDFs were detected in only one sample
(0.03 ng l-TEQDF/kg). The overall mean TEQ concentrations were 0.006 ng l-TEQDF/kg
(ND = 0) and 0.21 ng l-TEQDF/kg (ND = 1/2 MDL). The MDLs for all congeners were 1
ng/kg or less.
Using 1995 U.S. production data (i.e., 4.846 million metric tons of suspension and
mass PVC, 0.367 million metric tons of dispersion PVC resins, and 1.362 million metric
tons of "sales" EDC) and the average TEQ observed for the samples analyzed, The Vinyl
Institute estimated the total I-TEQDF contents of suspension/mass PVC resins, dispersion
PVC resins, and "sales" EDC in 1995 to be 0.01 g, 0.004 g, and 0.008 g, respectively
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(ND = 0) and 3.39 g, 0.15 g, and 0.29 g, respectively (ND = 1/2 MDL). Therefore, total
I-TEQDF present in PVC in 1995 was estimated at between 0.02 g (ND = 0) and 3.83 g
(ND = 1/2 MDL). It is not possible using the data presented in The Vinyl Institute (1998)
to calculate emission factors for TEQDF-WH098. However, because neither 1,2,3,7,8-
PeCDD nor OCDD were detected in any sample, the TEQDF-WH098 emission factors would
be very similar to the I-TEQDF emission factors.
8.3.5. Other Aliphatic Chlorine Compounds
Aliphatic chlorine compounds are used as monomers in the production of plastics,
as solvents and cleaning agents, and as precursors for chemical synthesis (Hutzinger and
Fiedler, 1991 a). These compounds are produced in large quantities. In 1992, 14.6
million metric tons of halogenated hydrocarbons were produced (U.S. International Trade
Commission, 1946-1994). The production of 1,2-dichloroethane and vinyl chloride
accounted for 82 percent of this total production. Highly chlorinated CDDs and CDFs
(i.e., hexa- to octachlorinated congeners) have been found in nanograde-quality samples of
1,2-dichloroethane (55 ng/kg of OCDF in one of five samples), tetrachloroethene (47
ng/kg of OCDD in one of four samples), epichlorohydrin (88 ng/kg of CDDs and 33 ng/kg
of CDFs in one of three samples), and hexachlorobutadiene (360 to 425 ng/kg of OCDF in
two samples) obtained in Germany from the company Promochem (Hutzinger and Fiedler,
1991 a; Heindl and Hutzinger, 1987). No CDD/CDFs were detected in two samples of allyl
chloride, three samples of 1,1,1-trichloroethane, and four samples of trichloroethylene
(detection limit ranged from 5 to 20 ng/kg) (Heindl and Hutzinger, 1987). Because no
more recent or additional data could be found in the literature to confirm these values for
products manufactured or used in the United States, no national estimates of CDD/CDF
emissions are made for the inventory.
EPA's Office of Water promulgated effluent limitations for facilities that
manufacture chlorinated aliphatic chlorine compounds and discharge treated wastewater
(40 CFR 414.70). These effluent limitations do not specifically address CDDs and CDFs.
The following chlorinated aliphatic compounds are regulated: 68/vg/L for 1,2-
dichloroethane and 22 /vg/L for tetrachloroethylene. Similarly, EPA's Office of Solid Waste
promulgated restrictions on land disposal of wastes generated during manufacture of
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many chlorinated aliphatics (40 CFR 268); however, these restrictions do not specifically
regulate CDD/CDFs.
8.3.6. Dyes, Pigments, and Printing Inks
Several researchers analyzed various dyes, pigments, and printing inks obtained in
Canada and Germany for the presence of CDDs and CDFs (Williams et al., 1992;
Hutzinger and Fiedler, 1991 a; Santl et al., 1994c). The following paragraphs discuss the
findings of those studies.
Dioxazine Dyes and Pigments - Williams et al. (1992) analyzed the CDD/CDF
content in dioxazine dyes and pigments available in Canada. As shown in Table 8-16,
OCDD and OCDF concentrations in the ng/kg range, and HpCDD, HxCDD, and PeCDD
concentrations in the/vg/kg range were found in Direct Blue 106 dye (3 samples), Direct
Blue 108 dye (1 sample), and Violet 23 pigments (6 samples) (Williams et al., 1992).
These dioxazine pigments are derived from chloranil, which has been found to contain
high levels of CDD/CDFs and has been suggested as the source of contamination among
these dyes (Christmann et al., 1989a; Williams et al., 1992; U.S. EPA, 1992b). In May
1990, EPA received test results showing that chloranil was heavily contaminated with
dioxins; levels as high as 3,065 /jg l-TEQDF/kg (2,903 /jg TEQDF-WH098/kg) were measured
in samples from four importers (mean value of 1,754/vg l-TEQDF/kg or 1,388 /jg TEQDF-
WH098/kg) (U.S. EPA, 1992b; Remmers et al., 1992). (See Section 8.3.7 for analytical
results.)
In the early 1990s, EPA learned that I-TEQDF levels in chloranil could be reduced by
more than two orders of magnitude (to less than 20 /vg/kg) through manufacturing
feedstock and process changes. EPA's Office of Pollution Prevention and Toxics (OPPT)
subsequently began efforts to complete an industrywide switch from the use of
contaminated chloranil to low-dioxin chloranil. Although chloranil is not manufactured in
the United States, significant quantities are imported. As of May 1992, EPA had
negotiated agreements with all chloranil importers and domestic dye/pigment
manufacturers known to EPA that use chloranil in their products to switch to low-dioxin
chloranil. In May 1993, when U.S. stocks of chloranil with high levels of CDD/CDFs had
been depleted, EPA proposed a significant new use rule (SNUR) under Section 5 of TSCA
that requires industry to notify EPA at least 90 days prior to the manufacture, import, or
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processing, for any use, of chloranil containing CDD/CDFs at a concentration greater than
20//g l-TEQDF/kg (Federal Register, 1993a; U.S. EPA, 1993c).
In 1983, approximately 36,500 kg of chloranil were imported (U.S. ITC, 1984).
The U.S. International Trade Commission (ITC) has not published quantitative import data
for chloranil since 1984. If it is assumed that this import volume reflects actual usage of
chloranil in the United States during 1987, and the CDD/CDF contamination level was
1,754 /yg l-TEQDF/kg (1,388/vg TEQDF-WH098/kg), then the maximum release into the
environment via processing wastes and finished products was 64.0 g I-TEQDF. If it is
assumed that the import volume in 1995 was also 36,500 kg, but that the imported
chloranil contained 10 /vg l-TEQDF/kg on average, then the total potential annual TEQ
release associated with chloranil in 1995 was 0.36 g I-TEQDF (50.6 g TEQDF-WH098).
Phthalocyanine Dyes and Printing Inks - Hutzinger and Fiedler (1991 a) found
CDD/CDFs (tetra-, penta-, and hexachlorinated congeners) in the//g/kg range in a sample
of a Ni-phthalocyanine dye. No CDD/CDFs were detected (detection limit of 0.1 to 0.5
//g/kg) in two samples of Cu-phthalocyanine dyes and in one Co-phthalocyanine dye
(Hutzinger and Fiedler, 1991 a).
Santl et al. (1994) reported the results of analyses of four printing inks obtained
from a supplier in Germany. Two of the inks are used for rotogravure printing, and two
are used for offset printing. The results of the analyses are presented in Table 8-17. The
I-TEQDF content of the inks ranged from 15.0 to 88.6 ng/kg (17.7 to 87.2 ng/kg on a
TEQDF-WH098 basis). Primarily non-2,3,7,8-substituted congeners were found. The
identities of the dyes and pigments in these inks were not reported.
8.3.7. TSCA Dioxin/Furan Test Rule
Citing evidence that halogenated dioxins and furans may be formed as by-products
during chemical manufacturing processes (Versar, 1985), EPA issued a rule under
Section 4 of TSCA that requires chemical manufacturers and importers to test for the
presence of chlorinated and brominated dioxins and furans in certain commercial organic
chemicals (Federal Register, 1987c). The rule listed 12 manufactured or imported
chemicals that required testing and 20 chemicals not currently manufactured or imported
that would require testing if manufacture or importation resumed. These chemicals are
listed in Table 8-18. The specific dioxin and furan congeners that require quantitation and
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the target limits of quantitation (LOQ) that are specified in the rule are listed in Table 8-19.
Under Section 8(a) of TSCA, the final rule also required that chemical manufacturers
submit data on manufacturing processes and reaction conditions for chemicals produced
using any of the 29 precursor chemicals listed in Table 8-20. The rule stated that
subsequent to this data-gathering effort, testing may be proposed for additional chemicals
if any of the manufacturing conditions used favored the production of dioxins and furans.
Sixteen sampling and analytical protocols and test data for 10 of the 12 chemicals
that required testing were submitted to EPA (Holderman and Cramer, 1995). Data from
15 submissions were accepted; one submission is under review. Manufacture or import of
two substances (tetrabromobisphenol-A-bis-2,3-dibromopropylether and
tetrabromobisphenol-A-diacrylate) have stopped since the test rule was promulgated.
[Note: All data and reports in the EPA TSCA docket are available for public review and
inspection at EPA Headquarters in Washington, DC.]
Table 8-21 presents the results of analytical testing for CDDs and CDFs for the
chemicals that have data available in the TSCA docket. Five of these 10 chemicals
contained CDD/CDFs. Positive results were obtained for 2,3,5,6-tetrachloro-2,5-
cyclohexadiene-1,4-dione (chloranil), pentabromodiphenyloxide, octabromodiphenyloxide,
decabromodiphenyloxide, and 1,2-Bis(tribromophenoxy)-ethane. Table 8-22 presents the
quantitative analytical results for the four submitted chloranil samples, as well as the
results of an EPA analysis of a sample of carbazole violet, which is manufactured from
chloranil.
Although testing conducted under this test rule for 2,4,6-tribromophenol indicated
no halogenated dioxins or furans above the LOQs, Thoma and Hutzinger (1989) reported
detecting BDDs and BDFs in a technical-grade sample of this substance. Total TBDD,
TBDF, and PeBDF were found at 84 |Jg/kg, 1 2 |Jg/kg, and 1 M9/kg, respectively. No hexa-
, hepta-, or octa-BDFs were detected. Thoma and Hutzinger (1989) also analyzed
analytical-grade samples of two other brominated flame retardants, pentabromophenol and
tetrabromophthalic anhydride; no BDDs or BDFs were detected (detection limits not
reported).
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8.3.8. Halogenated Pesticides and FIFRA Pesticides Data Call-In
In the late 1970s and early 1980s, attention began to focus on pesticides as
potential sources of CDDs and CDFs in the environment. Up to that time, CDD and CDF
levels were not regulated in end-use pesticide products. Certain pesticide active
ingredients, particularly chlorinated phenols and their derivatives, were known or
suspected, however, to be contaminated with CDDs and CDFs. During the 1980s and
1990s, EPA took several actions to investigate and control CDD/CDF contamination of
pesticides.
Actions to Regulate 2,4,5-T and Silvex: In 1983, EPA cancelled the sale of Silvex
and 2,4,5-T for all uses (Federal Register, 1983). Earlier, in 1979, EPA had ordered
emergency suspension of the forestry, rights-of-way, and pasture uses of 2,4,5-T.
Emergency suspensions of the forestry, rights-of-way, pasture, home and garden,
commercial/ornamental turf, and aquatic weed control/ditch bank uses of Silvex were also
ordered (Federal Register, 1979; Plimmer, 1980). The home and garden,
commercial/ornamental turf, and aquatic weed control/ditch bank uses of 2,4,5-T had
been suspended in 1970.
Actions to Regulate pentachlorophenol (PCP): In 1984, EPA issued a notice of
intent to cancel registrations of pesticide products containing PCP (including its salts) for
all wood preservative uses (Federal Register, 1984). This notice specified modifications to
the terms and conditions of product registrations that were required in order to avoid
cancellation of the products. In response to this notice, several trade associations and
registrants requested administrative hearings to challenge EPA's determinations. After
carefully considering the comments and alternatives suggested during the prehearing stage
of the administrative proceedings, EPA concluded that certain changes to the 1984 notice
were appropriate. These were finalized in 1986 (Federal Register, 1986) and included the
following: (i) all wood preservative uses of PCP and its salts were classified as "restricted
use" only by certified applicators; (ii) specific worker protection measures were required;
(iii) limits were placed on the HxCDD content of PCP; and (iv) label restrictions for home
and farm uses of PCP prohibiting its application indoors and to wood intended for interior
use (with a few exceptions), as well as prohibiting application of these products in a
manner that may result in direct exposure of domestic animals or livestock, or in the
contamination of food, feed, or drinking and irrigation water.
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EPA subsequently amended the wood preservative uses Notice to establish reliable
and enforceable methods for implementing certified limits for HxCDD and 2,3,7,8-TCDD in
registered wood-preservative pesticide products (Federal Register, 1987a). Levels of
2,3,7,8-TCDD were not allowed to exceed 1.0 ppb in any product, and after February 2,
1989, any manufacturing-use PCP released for shipment could not contain HxCDD levels
that exceeded an average of 2 ppm over a monthly release or a batch level of 4 ppm (a
gradually phased in requirement). On January 21, 1987, EPA prohibited the registration
of PCP and its salts for most nonwood uses (Federal Register, 1987b). EPA deferred
action on several uses (i.e., uses in pulp/paper mills, oil wells, and cooling towers) pending
receipt of additional exposure, use, and ecological effects data. On January 8, 1993, EPA
issued a press advisory stating that the EPA special review of these deferred nonwood
uses was being terminated, because all of these uses either had been voluntarily cancelled
by the registrants or had been cancelled by EPA for failure of the registrants to pay the
required annual maintenance fees (U.S. EPA, 1993f).
Pentachlorophenol (PCP) was one of the most widely used biocides in the United
States prior to the regulatory actions to cancel and restrict certain wood and nonwood
preservative uses of PCP. PCP was registered for use as a herbicide, defoliant, mossicide,
and as a mushroom house biocide. It also found use as a biocide in pulp-paper mills, oil
wells, and cooling towers. These latter three uses were terminated on or before 1993
(U.S. EPA, 1993f). However, the major use (greater than 80 percent of consumption) of
PCP was and continues to be wood preservation.
The production of PCP for wood preserving began on an experimental basis in the
1930s. In 1947, nearly 3,200 metric tons of PCP were reported to have been used in the
United States by the commercial wood preserving industry. Use in this industry steadily
increased through the mid-1970s (American Wood Preservers Institute, 1977). Although
domestic consumption volumes are not available for all years, based on historical
production/export data for PCP reported in Mannsville (1983), it is estimated that 90 to
95 percent of production volume have typically been consumed domestically rather than
exported. A reasonable estimate of average annual domestic PCP consumption during the
period 1970 to 1995 is about 400,000 metric tons. This estimate assumes an average
annual consumption rate of 20,000 metric tons/yr during the 1970s, 15,000 metric
tons/yr during the 1980s, and 10,000 metric tons/yr during the 1990s.
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Table 8-7 presents a compilation of published data on the CDD/CDF content of
technical grade PCP. The only samples that have been analyzed for all dioxin-like
CDD/CDFs were manufactured in the mid to late 1980s. Figure 8-4 presents these data in
graphical form. It is evident from the figures that the predominant congener groups are
OCDD, OCDF, HpCDF, and HpCDD, and the dominant 2,3,7,8-substituted congeners are
OCDD, 1,2,3,4,6,7,8-HpCDD, and OCDF. Waddell et al. (1995) tested analytical grade
PCP (from Aldrich Chemical Co.) for CDD/CDF content and found the same congener
profile; however, the CDD/CDF levels were three to four orders of magnitude lower. Table
8-8 presents a similar compilation of published data on the CDD/CDF content of PCP-Na.
The table shows the same patterns of dominant congeners and congener groups reported
for PCP.
Samples of technical PCP manufactured during the mid to late 1980s contained
about 3 mg l-TEQ/kg, (1.7 mg TEQDF-WH098/kg) based on the data presented in Table 8-
7. No published reports could be located that present the results of any congener-specific
analyses of PCP manufactured since the late 1980s. However, monthly measurements of
CDD/CDF congener group concentrations in technical PCP manufactured for use in the
United States have been reported to EPA from 1987 to the present (KMG-Bernuth, 1997;
Pentachlorophenol Task Force, 1997; U.S. EPA, 1999a). The average congener group
concentrations reported to EPA for the years 1988 (i.e., 1 year after EPA regulations were
imposed limiting HxCDD and 2,3,7,8-TCDD concentrations in PCP) to 1999 are presented
in Table 8-7. In general, the average congener group concentrations during the period
1988-1999 are lower by factors of 2 to 4 than observed in the mid to late 1980s full
congener analysis samples. If it is assumed that the toxic CDD/CDF congeners have also
been reduced by similar factors, then the TEQ content of PCP manufactured since 1988 is
about 1 mg l-TEQ/kg (0.6 mg TEQDF-WH098/kg).
An estimated 8,400 metric tons of PCP were used for wood preservation in the
United States in 1994 (American Wood Preservers Institute, 1995); for purposes of this
report, it is assumed that an identical amount was used in 1995. An estimated 12,000
metric tons were used in 1987 (WHO, 1991). Combining these activity level estimates
with the TEQ concentration estimates presented above indicates that 8,400 g I-TEQDF
(4,800 g TEQDF-WH098) and 36,000 g I-TEQDF (20,000 g TEQDF-WH098) were incorporated
into PCP-treated wood products in 1995 and 1987, respectively. This estimate for 1987
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is assigned a high confidence rating, indicating high confidence in both the activity level
and TEQ concentration in PCP estimates. The estimate for 1995 is assigned a medium
confidence estimate because of uncertainties about the actual TEQ content of PCP
manufactured in 1995.
Although the estimates of the mass of TEQ in treated wood are fairly certain, no
studies are available that provide measured CDD/CDF release rate data from which a
reliable estimate can be made of the amount of CDD/CDFs that have or will volatilize or
leach from treated wood. Several recent field studies, discussed in the following
paragraphs demonstrate that CDD/CDFs do apparently leach into soil from PCP-treated
wood, but the studies do not provide release rate data. No studies were located that
provide any measured CDD/CDF volatilization rates from PCP-treated wood. Although
CDD/CDFs have very low vapor pressures, they are not bound nor react with the wood in
any way that would preclude volatilization. Several studies, discussed below, have
attempted to estimate potential CDD/CDF volatilization releases using conservative
assumptions or modeling approaches, but these estimates span many orders of
magnitude.
Gurprasad et al. (1995) analyzed three PCP-treated utility poles and their
surrounding surface soils for penta- through octa-CDD content. All three poles showed
significant levels of HxCDD (0.29 to 0.47 mg/kg), HpCDD (4.69 to 6.63 mg/kg), and
OCDD (27.9 to 42.1 mg/kg), but no PeCDD. Surface soils collected 2 cm from the poles
also had detectable levels of HxCDD, HpCDD, and OCDD; however, no consistent pattern
was found between the CDD concentrations in the poles and in the adjacent soils. The
soil concentrations did, however, show the same relative congener group pattern observed
in the wood. CDD concentrations in soils obtained 20 cm from the poles were an order of
magnitude less than the soil concentrations measured at 2 cm. Soils 26 meters from the
poles showed nondetected values or values close to the detection limit of 0.01 to 0.02
mg/kg.
In a study of the leaching of PCP from 31 utility poles, the Electric Power Research
Institute (EPRI) (1995) found similar patterns of PCP distribution in soils surrounding poles
as those found by Gurprasad et al. (1995) for CDDs. PCP concentrations decreased by as
much as two orders of magnitude between 7.5 cm from the poles and 20 cm from the
poles, with an average decrease of slightly more than one order of magnitude over this
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distance. EPRI (1995) also found no obvious trend between PCP concentration in the
wood (eight poles analyzed) and the age of the poles (4 to 11 years) or the PCP
concentration in the surface soil. Based on their results and those of EPRI (1995),
Gurprasad et al. (1995) concluded that CDDs probably leach from PCP-treated utility poles
with the PCP/oil carrier and travel in the soil in a similar manner.
Wan (1995) and Wan and Van Oostdam (1995) measured CDD/CDF concentrations
in waters and sediments from ditches surrounding utility poles and railroad ties and
demonstrated that chlorophenol-treated wood could serve as a source of CDD/CDFs to the
aquatic environment. Ten samples were collected at each of six utility pole sites and five
railroad tie sites 1 to 2 days after major rainfall events and then were composited into one
sample per site prior to analyses. Total CDDs (mean value of 76.7 mg/kg) and total CDFs
(mean value of 18.7 mg/kg) detected in chlorophenol/creosote-treated utility poles were
about 6 to 8 times greater, respectively, than the CDD and CDF concentrations detected
in chlorophenol/creosote-treated railroad ties. Total CDDs found in water from railway
ditches without utility poles (i.e., only treated railroad ties were present) were
approximately 20 times higher than the background level found in farm ditch water. Total
CDDs in railway ditches with utility poles were 4,300 times higher than the background
levels. Water from railway ditches without utility poles contained total CDF levels 13
times higher than background, whereas water in ditches adjacent to poles were 8,500
times higher than background. Total CDDs in ditches adjacent to, and 4 m downstream
of, utility poles were about 5,900 and 2,200 times, respectively, higher than background;
total CDFs for the same sites were about 8,100 and 1,700 times, respectively, higher
than background. Total CDDs found in ditch sediments of railway and ditch sediments
adjacent to utility poles were about 5 and 700 times, respectively, higher than
background; while total CDFs were about 9 and 1,800 times, respectively, higher than
background. Both CDDs and CDFs were found in utility ditch sediments 4 m downstream
of treated power poles, but at levels of 200 and 400 times, respectively, lower than those
found adjacent to poles, indicating that they were transported from point sources of
contamination. The corresponding values for CDFs were 5,400 and 8,000 times,
respectively, higher in concentration.
Bremmer et al. (1994) estimated an annual release of 15 to 125 g of I-TEQDF from
PCP-treated wood in The Netherlands. The lower estimate was based on three basic
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assumptions: (1) the half-life of PCP in treated wood is 15 years (according to industry
sources); (2) the half-life of CDD/CDFs in treated wood is 10 times that of PCP (i.e., 150
years) because of the lower vapor pressures of CDD/CDFs relative to PCP; and (3) the
typical CDD/CDF concentration in PCP has been 3,000/vg/kg. The higher estimate was
based on an assumed half-life of PCP in wood of 1 5 years and the results of an indoor air
study by Papke et al. (1989) conducted at several kindergartens where PCP-treated wood
had been used. Although Papke et al. (1989) found no clear correlation between indoor
air concentrations of CDD/CDF and PCP across the range of CDD/CDF concentrations
observed in the 20-plus samples (2.6 to 427 pg CDD/CDF/m3), there did appear to be a
positive correlation at the sites with more elevated CDD/CDF concentrations. Bremmer et
al. (1994) reported that the average ratio of PCP to I-TEQ DF air concentrations at these
elevated sites was found to be 1:5x10"6 (or about the same ratio as the concentration of I-
TEQ DF in technical PCP). The results of the Papke et al. (1989) study imply that
CDD/CDFs may be released from PCP-treated wood at the same rate as PCP, rather than
at a rate 10 times slower.
Rappe (1995) used the emission factor approach developed by Bremmer et al.
(1994) and an assumed U.S. usage volume of PCP over the past 50 years (0.5 million
metric tons) to estimate that as much as 10.5 kg of I-TEQDF could volatilize from PCP-
treated wood in the United States annually. Eitzer and Hites (1987) derived a dramatically
different estimate of CDD/CDF volatilization from PCP-treated wood in the United States,
3 kg of total CDD/CDF per year (or 66 g of I-TEQDF per year assuming an I-TEQDF content
in PCP of 3 mg/kg). Eitzer and Hites (1987) based their estimate on: (1) an assumption
that 0.1 percent of the PCP produced annually enters the atmosphere, and (2) that the
CDD/CDF contaminants present in the PCP (assumed to be 130 mg/kg) are released to the
atmosphere at the same rate as the PCP (i.e., 0.1 percent). The basis for the first
assumption of Eitzer and Hites (1987) is not clear because U.S. EPA (1980), which was
cited as the source of the 0.1 percent emission factor, does not appear to address
volatilization of PCP from in-service treated wood. The report does, however, estimate
that most PCP in treated wood leaches relatively rapidly from the wood, presumably to
land, within a period of 12 years.
Eduljee and Dyke (1996) and Douben et al. (1995) estimate that 0.8 g of I-TEQDF is
released to the air annually from PCP-treated wood in the U.K. This estimate is based on
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the assumed emission of 0.1 percent of the CDD/CDF present in PCP-treated wood during
the first year of the service life of the wood that was assumed by Eitzer and Hites (1987).
No emission is assumed for subsequent years of use of the treated wood.
The California Air Resources Board (Chinkin et al., 1987) generated estimates of
CDD/CDF volatilization releases at wood treatment facilities from bundles of treated wood
that remain on-site for 1 month prior to shipment. An "adapted" version of a model
developed by McCord (1981) was used for estimating volatile releases from a constantly
filling lagoon. The model is primarily driven by chemical-specific vapor pressures and air
diffusivity coefficients. Chinkin et al. (1987) do not provide all model input parameter
values used to generate the emission estimates. However, running the model with typical
dimensions for treated poles yields an I-TEQDF emission rate on the order of 6E-12 g/yr-
pole, an extremely low number (i.e., 170 billion poles would together emit 1 g TEQ/yr).
Actions to Identify Other Pesticides Containing CDD/CDFs: In addition to
cancelling some pesticide registrations and establishing product standards, EPA's Office of
Pesticide Programs (OPP) issued two Data Call-Ins (DCIs) in 1987. Pesticide
manufacturers are required to register their products with EPA in order to market them
commercially in the United States. Through the registration process, mandated by FIFRA,
EPA can require that the manufacturer of each active ingredient generate a wide variety of
scientific data through several mechanisms. The most common process is the five-phase
reregistration process with which the manufacturers (i.e., registrants) of older pesticide
products must comply. In most registration activities, registrants must generate data
under a series of strict testing guidelines, 40 CFR 1 58-Pesticide Assessment Guidelines
(U.S.EPA, 1988b). EPA can also require additional data from registrants, when necessary,
through various mechanisms, including the DCI process.
The purpose of the first DCI, dated June and October 1987, "Data Call-In Notice
for Product Chemistry Relating to Potential Formation of Halogenated Dibenzo-p-dioxin or
Dibenzofuran Contaminants in Certain Active Ingredients," was to identify, using an
analysis of raw materials and process chemistry, those pesticides that may contain
halogenated dibenzo-p-dioxin and dibenzofuran contaminants. The 93 pesticides (76
pesticide active ingredients) to which the DCI applied, along with their corresponding
Shaughnessey and Chemical Abstract code numbers, are presented in Table 8-23. [Note:
The Shaughnessey code is an internal EPA tracking system-it is of interest because
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chemicals with similar code numbers are similar in chemical nature (e.g., salts, esters, and
acid forms of 2,4-D).] All registrants supporting registrations for these chemicals were
subject to the requirements of the DCI, unless their product qualified for a Generic Data
Exemption (i.e., a registrant exclusively used a FIFRA-registered pesticide product(s) as the
source(s) of the active ingredient(s) identified in Table 8-23 in formulating their
product(s)). Registrants whose products did not meet the Generic Data Exemption were
required to submit the types of data listed below to enable EPA to assess the potential for
formation of tetra- through hepta-halogenated dibenzo-p-dioxin or dibenzofuran
contaminants during manufacture. Registrants, however, had the option to voluntarily
cancel their product or "reformulate to remove an active ingredient," to avoid having to
comply with the DCI.
• Product Identity and Disclosure of Ingredients: EPA required submittal of a
Confidential Statement of Formula (CSF), based on the requirements
specified in 40 CFR 158.108 and 40 CFR 158.120 - Subdivision D: Product
Chemistry. Registrants who had previously submitted still-current CSFs
were not required to resubmit this information.
• Description of Beginning Materials and Manufacturing Process: Under the
requirements mandated by 40 CFR 158.120 - Subdivision D, EPA required
submittal of a manufacturing process description for each step of the
manufacturing process, including specification of the range of acceptable
conditions of temperature, pressure, or pH at each step.
• Discussion of the Formation of Impurities: Under the requirements mandated
by 40 CFR 158.120 - Subdivision D, EPA required submittal of a detailed
discussion and assessment of the possible formation of halogenated
dibenzo-p-dioxins and dibenzofurans.
The second DCI, dated June and October 1987, "Data Call-In for Analytical
Chemistry Data on Polyhalogenated Dibenzo-p-Dioxins/Dibenzofurans (HDDs and HDFs),"
was issued for 68 pesticides (16 pesticide active ingredients) suspected to be
contaminated by CDD/CDFs. (See Table 8-24.) All registrants supporting registrations for
these pesticides were subject to the requirements of this DCI, unless the product qualified
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for various exemptions or waivers. Pesticides covered by the second DCI were strongly
suspected by EPA to contain detectable levels of CDD/CDFs.
Under the second DCI, registrants whose products did not qualify for an exemption
or waiver were required to generate and submit the following types of data in addition to
the data requirements of the first DCI:
• Quantitative Method for Measuring CDDs or CDFs: Registrants were required
to develop an analytical method for measuring the HDD/HDF content of their
products. The DCI established a regimen for defining the precision of the
analytical method. Target limits of quantitation were established in the DCI
for specific CDD and CDF congeners. (See Table 8-25.)
• Certification of Limits of CDDs or CDFs: Registrants were required to submit
a "Certification of Limits" in accordance with 40 CFR 158.110 and 40 CFR
158.120 - Subdivision D. Analytical results were required that met the
guidelines described above.
Registrants could select one of two options to comply with the second DCI. The
first option was to submit relevant existing data, develop new data, or share the cost to
develop new data with other registrants. The second option was to alleviate the DCI
requirements through several exemption processes, including a Generic Data Exemption,
voluntary cancellation, reformulation to remove the active ingredient of concern, an
assertion that the data requirements do not apply, or the application or award of a low-
volume, minor-use waiver.
The data contained in CSFs, as well as any other data generated under 40 CFR
158.120 - Subdivision D, are typically considered Confidential Business Information (CBI)
under the guidelines prescribed in FIFRA, because they usually contain information
regarding proprietary manufacturing processes. In general, all analytical results submitted
to EPA in response to both DCIs are considered CBI and cannot be released by EPA into
the public domain. Summaries based on the trends identified in that data, as well as data
made public by EPA, are summarized below.
The two DCIs included 161 pesticides. Of these, 92 are no longer supported by
registrants. Following evaluation of the process chemistry submissions required under the
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DCIs, OPP determined that formation of CDD/CDFs was not likely during the manufacture
of 43 of the remaining 69 pesticides; thus, analysis of samples of these 43 pesticides was
not required by OPP. Evaluation of process chemistry data is ongoing at OPP for an
additional seven pesticides. Tables 8-23 and 8-24 indicate which pesticides are no longer
supported, those for which OPP determined that CDD/CDF formation is unlikely, and those
for which process chemistry data or analytical testing results are under review in OPP
(U.S. EPA, 1995f).
OPP required that analysis of production samples be performed on the remaining 19
pesticides. (See Table 8-26.) The status of the analytical data generation/evaluation to
date is summarized as follows: (1) no detection of CDD/CDFs above the LOQs in
registrant submissions for 13 active ingredients, (2) detection of CDD/CDFs above the
LOQs for 2,4-D acid (two submissions) and 2,4-D 2-ethyl hexyl acetate (one submission),
and (3) ongoing data generation or evaluation for four pesticides.
Table 8-25 presents a summary of results obtained by EPA for CDDs and CDFs in
eight technical 2,4-D herbicides; these data were extracted from program files in OPP.
Because some of these files contained CBI, the data in this table were reviewed by OPP
staff to ensure that no CBI was being disclosed (Funk, 1996). Figure 8-5 presents a
congener profile for 2,4-D based on the average congener concentrations reported in Table
8-25.
Schecter et al. (1997) reported the results of analyses of samples of 2,4-D
manufactured in Europe, Russia, and the United States. (See Table 8-27.) The total TEQ
concentrations measured in the European and Russian samples are similar to those
measured in the EPA DCI samples; however, the levels reported by Schecter et al. (1997)
for U.S. samples are significantly lower.
As discussed in Section 12.2.1, an estimated 26,300 metric tons of 2,4-D were
used in the United States in 1995, making it one of the top 10 pesticides in terms of
quantity used (U.S. EPA, 1997e). An estimated 30,400 metric tons were used during
1987 (U.S. EPA, 1988c). On the basis of the average CDD/CDF congener concentrations
in 2,4-D presented in Table 8-25 (not including OCDD and OCDF), the corresponding I-
TEQDF concentration is 0.70//g/kg (1.10//g TEQDF-WH098/kg). Combining this TEQ
concentration with the activity level estimates for 1995 and 1987 indicates that 18.4 g
I-TEQDF (28.9 g TEQDF-WH098) may have entered the environment in 1995 and 21.3 g I-
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TEQDF (33.4 g TEQDF-WH098) may have entered the environment in 1987. These release
estimates are assigned a high confidence rating, indicating high confidence in both the
production and emission factor estimates.
8.4. OTHER CHEMICAL MANUFACTURING AND PROCESSING SOURCES
8.4.1. Municipal Wastewater Treatment Plants
Sources - CDD/CDFs have been measured in nearly all sewage sludges tested,
although the concentrations and, to some extent, the congener profiles and patterns differ
widely. Potential sources of the CDD/CDFs include microbial formation (discussed in
Chapter 9), runoff to sewers from lands or urban surfaces contaminated by product uses
or deposition of previous emissions to air (discussed in Section 12.2.1), household
wastewater, industrial wastewater, chlorination operations within the wastewater
treatment facility, or a combination of all the above (Rappe, 1992a; Rappe et al., 1994;
Horstmann et al., 1992; Sewart et al., 1995; Cramer et al., 1995; Horstmann and
McLachlan, 1995).
The major source(s) for a given Publicly Owned Treatment Works (POTW) is likely
to be site-specific, particularly in industrialized areas. For example, Rieger and
Ballschmiter (1992) traced the origin of CDDs and CDFs found in municipal sewage sludge
in Dim, Germany, to metal manufacturing and urban sources. The characteristics of both
sources were similar and suggested generation via thermal processing. However, in a
series of recent studies, Horstmann et al. (1992, 1993a, 1993b) and Horstmann and
McLachlan (1994a, 1994b, 1995) demonstrated that wastewater generated by laundering
and bathing could be the major source at many, if not all, POTWs that serve primarily
residential populations. Although runoff from streets during precipitation events,
particularly from streets with high traffic density, was reported by these researchers as
contributing measurably, the total contribution of TEQ from household wastewater was
eight times greater than that from surface runoff at the study city.
Horstmann et al. (1992) provided initial evidence that household wastewater could
be a significant source. Horstmann et al. (1993a) measured CDD/CDF levels in the
effluent from four different loads of laundry from two different domestic washing
machines. The concentrations of total CDD/CDF in the four samples ranged from 3,900
to 7,100 pg/L and were very similar in congener profile, with OCDD being the dominant
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congener followed by the hepta- and hexa-CDDs. Because of the similar concentrations
and congener profiles found, Horstmann et al. (1993a) concluded that the presence of
CDD/CDF in washing machine wastewater is widespread. A simple mass balance
performed using the results showed that the CDD/CDFs found in the four washing
machine wastewater samples could account for 27 to 94 percent of the total CDD/CDF
measured in the sludge of the local wastewater treatment plant (Horstmann and
McLachlan, 1994a).
Horstmann et al. (1993a) also performed additional experiments that showed that
detergents, commonly used bleaching agents, and the washing cycle process itself were
not responsible for the observed CDD/CDFs. To determine if the textile fabric or fabric
finishing processes could account for the observed CDD/CDFs, Horstmann et al. (1993b),
Horstmann and McLachlan (1994a, 1994b), and Klasmeier and McLachlan (1995)
analyzed the CDD/CDF content of eight different raw (unfinished) cotton cloths containing
fiber from different countries and five different white synthetic materials (acetate, viscose,
bleached polyester, polyamide, and polyacrylic), as well as more than 100 new textile
finished products. Low concentrations were found in most products (i.e., less than 50
ng/kg of total CDD/CDF), but a small percentage contained high concentrations up to 290
//g/kg of total CDD/CDF. On the basis of the concentrations and patterns found, the
authors concluded that neither unfinished new fabrics nor common cotton finishing
processes can explain the CDD/CDF levels found in wastewater. Rather, the use of
CDD/CDF-containing textile dyes and pigments and the use in some developing countries
of pentachlorophenol to treat unfinished cotton appear to be the sources of the detected
CDDs/CDFs.
Horstmann and McLachlan (1994a, 1994b, 1995) reported the results of additional
experiments that demonstrated that the small percentage of clothing items with high
CDD/CDF levels could be responsible for the quantity of CDD/CDFs observed in household
wastewater and sewage sludge. They demonstrated that the CDD/CDFs can be gradually
removed from the fabric during washing, can be transferred to the skin, subsequently
transferred back to other textiles, and then washed out, or can be transferred to other
textiles during washing and then removed during subsequent washings.
Releases to Water - The presence of CDD/CDFs in sewage sludge suggests that
CDD/CDFs may also be present in the wastewater effluent discharges of POTWs;
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however, few studies reporting the results of effluent analyses for CDD/CDFs have been
published.
Rappe et al. (1989a) tested the effluent from two Swedish POTWs for all 2,3,7,8-
substituted CDD/CDF congeners. OCDD was detected in the effluents from both facilities
at concentrations ranging from 14 to 39 pg/L. Rappe et al. detected 1,2,3,4,6,7,8-
HpCDD and 1,2,3,4,6,7,8-HpCDF in the effluent of one facility at concentrations of 2.8
and 2.0 pg/L, respectively. No 2,3,7,8-substituted tetra-, penta-, and hexa-CDDs and
CDFs were detected (detection limits of 0.2 to 20 pg/L).
Ho and Clement (1990) reported the results of sampling during the late 1980s of
37 POTWs in Ontario, Canada, for each of the five CDD/CDF congener groups with four
to eight chlorines. The sampled facilities included 27 secondary treatment facilities, 7
primary treatment facilities, 1 tertiary plant, and 2 lagoons. The facilities accounted for
about 73 percent of the sewage discharged by POTWs in Ontario. No CDDs/CDFs were
detected (detection limit in low ng/L range) in the effluents from the lagoons and the
tertiary treatment facility. Only OCDD and TCDF were detected in the effluents from the
primary treatment facilities (two and one effluent samples, respectively). HpCDD, OCDD,
TCDF, and OCDF were detected in the effluents from the secondary treatment facilities
(detected in four or fewer samples at levels ranging from 0.1 to 11 ng/L).
Gobran et al. (1995) analyzed the raw sewage and final effluent of an Ontario,
Canada, wastewater treatment plant for CDD/CDF congeners over a 5-day period.
Although HpCDD, OCDD, HpCDF, and OCDF were detected in the raw sewage (12 to
2,300 pg/L), no CDD/CDFs were detected in the final effluent at congener-specific
detection limits ranging from 3 to 20 pg/L.
The California Regional Water Quality Control Board (CRWQCB, 1996) reported the
results of effluent testing at nine POTWs in the San Francisco area. A total of 30 samples
were collected during 1992-1995; 1 to 6 samples were analyzed for each POTW. Table
8-28 summarizes the sampling results. With the exception of OCDD, most 2,3,7,8-
substituted CDD/CDF congeners were seldom detected.
The CRWQCB (1996) data were collected to provide representative effluent
concentrations for the San Francisco area; these data cannot be considered to be
representative of CDD/CDF effluent concentrations at the 16,000-plus POTWs nationwide.
Therefore, the data can only be used to generate a preliminary estimate of the potential
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mass of CDD/CDF TEQ that may be released annually by U.S. POTWs. Approximately
122 billion liters of wastewater are treated daily by POTWs in the United States (U.S.
EPA, 1997c). Multiplying this value by 365 days/year and by the "overall mean" TEQ
concentrations listed in Table 8-28 (i.e., 0.29 pg I-TEQDF/L and 0.27 pg TEQDF-WH098/L)
yields annual TEQ release estimates of 13 g of I-TEQDF or 1 2 g of TEQDF-WH098.
Sewage Sludge Land Disposal - EPA conducted the National Sewage Sludge Survey
in 1988 and 1989 to obtain national data on sewage sludge quality and management. As
part of this survey, EPA analyzed sludges from 1 74 POTWs that employed at least
secondary wastewater treatment for more than 400 analytes, including CDD/CDFs.
Although sludges from only 16 percent of the POTWs had detectable levels of 2,3,7,8-
TCDD, all sludges had detectable levels of at least one CDD/CDF congener (U.S. EPA,
1996a). I-TEQDF concentrations as high as 1,820 ng/kg dry weight were measured. The
congener-specific results of the survey are presented in Table 8-29. If all nondetected
values found in the study are assumed to be zero, then the mean and median I-TEQDF
concentrations of the sludges from the 1 74 POTWs are 50 and 11.2 ng/kg (dry weight
basis), respectively. If the nondetected values are set equal to the detection limit, then
the mean and median I-TEQDF concentrations are 86 and 50.4 ng/kg, respectively (U.S.
EPA, 1996a; Rubin and White, 1992).
Green et al. (1995) and Cramer et al. (1995) reported the results of analyses of 99
samples of sewage sludge collected from wastewater treatment plants across the United
States during the summer of 1994. These data are summarized in Table 8-30. To
calculate average results in units of TEQ, Green et al. (1995) averaged results from all
samples collected from the same facility to ensure that results were not biased toward the
concentrations found at facilities from which more than one sample were collected. Also,
eight samples were excluded from the calculation of the overall TEQ averages because it
was unclear as to whether they were duplicate samples from other POTWs. POTW
average TEQ concentrations were calculated for 74 POTWs. If all nondetected values are
assumed to be zero, then the overall study mean and median I-TEQDF concentrations were
47.7 and 33.4 ng l-TEQDF/kg (dry weight basis), respectively (standard deviation of 44.7
ng l-TEQDF/kg). The corresponding mean and median TEQDF-WH098 concentrations were
36.3 and 25.5 ng TEQDF-WH098/kg, respectively (standard deviation of 38.6 ng TEQDF-
WH098/kg). The mean and median results reported by Green et al. (1995) and Cramer et
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al. (1995) are very similar in terms of total TEQ to those reported by EPA for samples
collected 5 years earlier (U.S. EPA, 1996a; Rubin and White, 1992). The predominant
congeners in both data sets are the octa- and hepta-CDDs and -CDFs. Although not
present at high concentrations, 2,3,7,8-TCDF was commonly detected.
The CDD/CDF concentrations and congener group patterns observed in these two
U.S. surveys are similar to the results reported for sewage sludges in several other
Western countries. Stuart et al. (1993) reported mean CDD/CDF concentrations of 23.3
ng l-TEQDF/kg (dry weight) for three sludges from rural areas, 42.3 ng l-TEQDF/kg for six
sludges from light industry/domestic areas, and 52.8 ng l-TEQDF/kg for six sludges from
industrial/domestic areas collected during 1991-1992 in England and Wales. Na'f et al.
(1990) reported concentrations ranging from 31 to 40 ng l-TEQDF/kg (dry weight) in
primary and digested sludges collected from the POTW in Stockholm, Sweden, during
1989. Gobran et al. (1995) reported an average concentration of 15.7 ng l-TEQDF/kg in
anaerobically digested sludges from an industrial/domestic POTW in Ontario, Canada. In
all three studies, the congener group concentrations increased with increasing degrees of
chlorination, with OCDD the dominant congener. Figure 8-6 presents congener profiles,
using the mean concentrations reported by Green et al. (1995).
Approximately 5.4 million dry metric tons of sewage sludge are estimated by EPA
to be generated annually in the United States according to the results of the 1988/1989
EPA National Sewage Sludge Survey (Federal Register, 1993b). Table 8-31 lists the
volume, by use and disposal practices, of sludge disposed of annually. More recent
comprehensive survey data are not available to characterize sludge generation and
disposal practices during 1995. For this reason, and because the mean I-TEQDF
concentration values reported in the 1988/1989 survey (U.S. EPA, 1996a) and the 1995
survey (Green et al., 1995; Cramer et al., 1995) were very similar, the estimated amounts
of TEQs that may have been present in sewage sludge and been released to the
environment in 1987 and 1995 were assumed to be the same. These values, presented
in Table 8-31, were estimated using the average (i.e., 49 ng l-TEQDF/kg) of the mean I-
TEQDF concentration values (nondetected values set at detection limits) reported by EPA
(1996a) (i.e., 50 ng l-TEQDF/kg) and by Green et al. (1995) and Cramer et al. (1995) (i.e.,
47.7 ng l-TEQDF/kg). Multiplying this mean total TEQ concentration by the sludge volumes
generated yields an annual potential total release of 204 g I-TEQDF or 151 g TEQDF-WH098
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for nonincinerated sludges. Of this 204 g I-TEQDF, 3.5 g enter commerce as a product for
distribution and marketing. The remainder is applied to land (103 g) or is landfilled (97 g).
In units of TEQDF-WH098, the comparable estimates are 2.6 g to commerce, 76.6 g applied
to land, and 71.7 g landfilled.
These release estimates are assigned a high confidence rating for both the
production and emission factor estimates. The high rating was based on the judgment
that the 174 facilities tested by EPA (U.S. EPA, 1996a) and the 74 facilities tested by
Green et al. (1995) and Cramer et al. (1995) were reasonably representative of the
variability in POTW technologies and sewage characteristics nationwide.
8.4.2. Drinking Water Treatment Plants
There is no strong evidence that chlorination of water for drinking purposes results
in the formation of CDD/CDFs. Few surveys of CDD/CDF content in finished drinking
water have been conducted. The few that have been published only rarely report the
presence of any CDD/CDF even at low pg/L detection limits, and in those cases, the
CDD/CDFs were also present in the untreated water.
Rappe et al. (1989b) reported the formation of CDFs (tetra- through octa-
chlorinated CDFs) when tap water and double-distilled water were chlorinated using
chlorine gas. The CDF levels found in the single samples of tap water and double-distilled
water were 35 and 7 pg I-TEQDF/L, respectively. No CDDs were detected at detection
limits ranging from 1 to 5 pg/L. However, the water samples were chlorinated at a
dosage rate of 300 mg of chlorine per liter of water, which is considerably higher (by a
factor of one to two orders of magnitude) than the range of dosage rates typically used to
disinfect drinking water. Rappe et al. (1989b) hypothesized that the CDFs or their
precursors are present in chlorine gas. Rappe et al. (1990a) analyzed a 1,500-liter sample
of drinking water from a municipal drinking water treatment plant in Sweden. Although
the untreated water was not analyzed, a sludge sample from the same facility was. The
large sample volume enabled detection limits on the order of 0.001 pg/L. The TEQ
content of the water and sludge was 0.0029 pg I-TEQDF/L and 1.4 ng/kg, respectively.
The congener patterns of the drinking water and sludge sample were very similar,
suggesting that the CDD/CDFs detected in the finished water were present in the
untreated water.
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8.4.3. Soaps and Detergents
As discussed in Section 8.4.1, CDD/CDFs were detected in nearly all sewage
sludges tested, whether obtained from industrialized areas or rural areas. Because of the
ubiquitous presence of CDD/CDFs in sewage sludge, several studies have been conducted
to determine the source(s). A logical category of products to test, because of their
widespread use are detergents, particularly those that contain or release chlorine during
use (i.e., hypochlorite-containing and dichloroisocyanuric acid-containing detergents). The
results of studies conducted to date, which are summarized below, indicate that
CDD/CDFs are not formed during use of chlorine-free detergents, chlorine-containing or
chlorine-releasing detergents, and chlorine bleach during household bleaching operations.
Sweden's Office of Nature Conservancy (1991) reported that the results of a
preliminary study conducted at one household indicated that CDD/CDFs may be formed
during use of dichloroisocyanurate-containing dishwasher detergents. A more extensive
main study was then conducted using standardized food, dishes, cutlery, etc., and
multiple runs. Testing of laundry washing and fabric bleaching, and actual testing of the
CDD/CDF content of detergents, was also performed. The study examined
(1) hypochlorite- and dichloroisocyanurate-containing dish-washing machine detergents;
(2) sodium hypochlorite-based bleach (4.4 percent NaOCI) in various combinations with
and without laundry detergent; and (3) sodium hypochlorite-based bleach, used at a high
enough concentration to effect bleaching of a pair of imported blue jeans. CDD/CDFs
were not detected in either the chlorine-free detergent or the detergent with hypochlorite;
0.6 pg TEQ/g was detected in the detergent containing dichloroisocyanurate. The results
of all dish and laundry washing machine tests showed very low levels of CDD/CDFs, often
nondetected values. There was no significant difference between the controls and test
samples. In fact, the control samples contained higher TEQ content than some of the
experimental samples. The drain water from the dish washing-machine tests contained
< 1.0 to <3.0 pg I-TEQDF/L (the water-only control sample contained <2.8 pg I-TEQDF/L).
The CDD/CDF content of the laundry drain water samples ranged from < 1.1 to <4.6 pg
I-TEQDF/L (the water-only control sample contained <4.4 pg I-TEQDF/L).
Thus, under the test conditions examined by Sweden's Office of Nature
Conservancy (1991), CDD/CDFs are not formed during dish washing and laundry washing
nor during bleaching with hypochlorite-containing bleach. No definitive reason could be
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found to explain the difference in results between the preliminary study and the main
study for dish washing with dichloroisocyanurate-containing detergents. The authors of
the study suggested that differences in the foods used and the prewashing procedures
employed in the two studies were the likely causes of the variation in the results.
Rappe et al. (1990c) analyzed a sample of a Swedish commercial soft soap, as well
as a sample of tall oil and a sample of tall resin, for CDD/CDF content. Tall oil and tall
resin, by-products of the pulping industry, are the starting materials for the production of
soft, liquid soap. Crude tall oil, collected after the Kraft pulping process, is distilled under
reduced pressure at temperatures of up to 280-290°C, yielding tall oil and tall resin. The
measured TEQ content of the liquid soap was 0.447 ng I-TEQDF/L (0.647 ng TEQDF-
WH098/L). PeCDDs were the dominant congener group followed by HpCDDs, HxCDDs,
PeCDFs, and OCDD with some tetra-CDFs and -CDDs also present. The TEQ content of
the tall oil (9.4 ng l-TEQDF/kg, or 12.0 ng TEQDF-WH098/kg) and tall resin (200 ng I-
TEFDF/kg, 196 ng TEQDF-WH098/kg) was significantly higher than the level found in the
liquid soap. The tall oil contained primarily tetra- and penta-CDDs and -CDFs, while the
tall resin contained primarily HpCDDs, HxCDDs, and OCDD. Rappe et al. (1990c)
compared the congener patterns of the three samples and noted that although the
absolute values for the tetra- and penta-CDDs and -CDFs differed between the tall oil, tall
resin and liquid soap samples, the same congeners were present in the samples. The
congener patterns for the more chlorinated congeners were very similar. Table 8-32
presents the results reported by Rappe et al. (1990c).
In 1987, 118 million liters of liquid household soaps were shipped in the United
States (U.S. DOC, 1990b); shipment quantity data are not available for liquid household
soap in the 1992 U.S. Economic Census (U.S. DOC, 1996). Because only one sample of
liquid soap has been analyzed for CDD/CDF content (Rappe et al., 1990c), only a very
preliminary estimate of the annual release of CDD/CDF TEQ from liquid soap can be made.
If it is assumed that an average 118 million liters of liquid soap contain 0.447 ng I-TEQDF/L
(0.647 ng TEQDF-WH098/L), then the resulting estimate is 0.05 g l-TEQDF/yr (0.08 g TEQDF-
WH098/yr).
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8.4.4. Textile Manufacturing and Dry Cleaning
As discussed in Section 8.4.1, CDD/CDFs have been detected in nearly all sewage
sludges tested, whether obtained from industrialized areas or rural areas. To determine if
the textile fabric or fabric finishing processes could account for the observed CDD/CDFs,
several studies were conducted in Germany. These studies, summarized in the following
paragraphs, indicate that some finished textile products do contain detectable levels of
CDD/CDFs and that these CDD/CDFs can be released from the textile during laundering or
dry cleaning; however, textile finishing processes are typically not sources of CDD/CDF
formation. Rather, the use of CDD/CDF-containing dyes and pigments and the use in
some countries of pentachlorophenol to treat unfinished cotton appear to be the sources
of the detected CDD/CDFs.
Horstmann et al. (1993b) analyzed the CDD/CDF content of eight different raw
(unfinished) cotton cloths containing fiber from different countries and five different white
synthetic materials (acetate, viscose, bleached polyester, polyamide, and polyacrylic). The
maximum concentrations found in the textile fabrics were 30 ng/kg in the cotton products
and 45 ng/kg in the synthetic materials. Also, a cotton finishing scheme was developed
that subjected one of the cotton materials to a series of 16 typical cotton finishing
processes; one sample was analyzed following each step. The fabric finishing processes
showing the greatest effect on CDD/CDF concentration were the application of an
indanthrene dye and the "wash and wear" finishing process, which together resulted in a
CDD/CDF concentration of about 100 ng/kg. On the basis of the concentrations found,
the authors concluded that neither unfinished new fabrics nor common cotton finishing
processes can explain the CDD/CDF levels found in laundry wastewater.
Fuchs et al. (1990) reported that dry-cleaning solvent redistillation residues that
were collected from 12 commercial and industrial dry-cleaning operations contained
considerable amounts of CDD/CDFs. The reported I-TEQDF content ranged from 131 to
2,834 ng/kg, with the dominant congeners always OCDD and the HpCDDs. Towara et al.
(1992) demonstrated that neither the use of chlorine-free solvents nor variation of the dry-
cleaning process parameters lowered the CDD/CDF content of the residues.
Umlauf et al. (1993) conducted a study to characterize the mass balance of
CDD/CDFs in the dry-cleaning process. The soiled clothes (containing 16 pg total
CDD/CDF per kg) accounted for 99.996 percent of the CDD/CDF input. Input of CDD/CDF
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from indoor air containing 0.194 pg/m3 accounted for the remainder (i.e., 0.004 percent).
The dry-cleaning process removed 82.435 percent of the CDD/CDF in the soiled clothing.
Most of the input CDD/CDF (82.264 percent) was found in the solvent distillation
residues. Air emissions (at 0.041 pg/m3) accounted for 0.0008 percent of the total input,
which was less than the input from indoor air. The fluff (at a concentration of 36 ng/kg)
accounted for 0.1697 percent, and water effluent (at a concentration of 0.07 pg/L)
accounted for 0.0000054 percent.
Horstmann and McLachlan (1994a, 1994b, 1995) analyzed 35 new textile samples
(primarily cotton products) obtained in Germany for CDD/CDFs. Low levels were found in
most cases (total CDD/CDF less than 50 ng/kg). The dominant congeners found were
OCDD and the HpCDDs. However, several colored T-shirts from a number of clothing
producers had extremely high levels, with concentrations up to 290,000 ng/kg. Because
the concentrations in identical T-shirts purchased at the same store varied by up to a
factor of 20, the authors concluded that the source of CDD/CDFs is not a textile finishing
process, because a process source would have resulted in a more consistent level of
contamination. Klasmeier and McLachlan (1995) subsequently analyzed 68 new textile
products obtained in Germany for OCDD and OCDF. Most samples had nondetectable
levels (42 samples <60 ng/kg). Only four samples had levels exceeding 500 ng/kg.
Horstmann and McLachlan (1994a, 1994b) reported finding two different congener
group patterns in the more contaminated of the 35 textile products. One pattern agreed
with the congener pattern for PCP reported by Hagenmaier and Brunner (1987), while the
other pattern was similar to that reported by Remmers et al. (1992) for chloranil-based
dyes. The authors hypothesize that the use of PCP to preserve cotton, particularly when
it is randomly strewn on bales of cotton as a preservative during sea transport, is the
likely source of the high levels occasionally observed. Although the use of PCP for
nonwood uses was prohibited in the United States in 1987 (see Section 8.3.8), PCP is still
used in developing countries, especially to preserve cotton during sea transport
(Horstmann and McLachlan, 1994a).
Horstmann and McLachlan (1994a, 1994b) conducted additional experiments that
demonstrated that the small percentage of clothing items with high CDD/CDF levels could
be responsible for the quantity of CDD/CDFs observed in household wastewater. They
demonstrated that the CDD/CDFs can be gradually removed from the fabric during
DRAFT-DO NOT QUOTE OR CITE 8-47 December 2003
-------
washing, can be transferred to the skin and subsequently transferred back to other textiles
and then washed out, or can be transferred to other textiles during washing and then
removed during subsequent washings.
DRAFT-DO NOT QUOTE OR CITE 8-48 December 2003
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Table 8-1. CDD/CDF Concentrations in Pu\p and Paper Mill Bleached Pu\p, Wastewater Sludge, and Effluent (circa 1988)
Congener/Congener Group
2,3,7,8-TCDD
1,2,3,7,8-PeCDD
1,2,3,4,7,8-HxCDD
1,2,3,6,7,8-HxCDD
1,2,3,7,8,9-HxCDD
1,2,3,4,6,7,8-HpCDD
OCDD
2,3,7,8-TCDF
1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
1,2,3,4,7,8-HxCDF
1,2,3,6,7,8-HxCDF
1,2,3,7,8,9-HxCDF
2,3,4,6,7,8-HxCDF
1,2,3,4,6,7,8-HpCDF
1,2,3,4,7,8,9-HpCDF
OCDF
Total 2,3,7,8-CDD°-b
Total 2,3,7,8-CDF'-b
Total I-TEQDF (ND = zero)b
Total I-TEQDF (ND= Y, DLf
Total TEQDF-WHO98 (ND = zero)b
Total TEQDF-WH098 (ND = Y, DL)b
Total CDD/CDFb
Bleached Pulp
Median
(ng/kg)
6.4
ND (0.3)
ND (0.4)
ND (0.5)
ND (0.5)
3.3
46
18
ND (0.7)
ND (0.2)
ND (0.3)
ND (0.3)
ND (0.3)
ND (0.3)
ND (0.6)
ND (0.6)
2.2
55.7
18
8.28
8.56
8.24
8.59
120
Range
(ng/kg)
0.4 to 124
ND (0.1) to 1.4
ND (0.2) to 0.4
ND (0.2) to 1.6
ND (0.2) to 0.5
2.3 to 8.4
28 to 81
1.4 to 716
ND (0.1) to 3.9
ND (0.1) to 4.7
ND (0.2) to ND (0.6)
ND (0.1) to ND (0.4)
ND (0.1) to ND (0.4)
ND (0.2) to ND (0.4)
ND (0.1) to 0.8
ND (0.1) to ND (2.1)
ND(2.8) to 4.3
No. of
Detects
(1 0 samples)
10
2
1
2
1
10
10
10
4
3
0
0
0
0
3
0
8
89.47
91.7
Wastewater Sludge
Median
(ng/kg)
63
ND (2.5)
ND (3.1)
ND (3.2)
ND (3.9)
37
698
233
6.2
4.7
ND (2.5)
ND (1.4)
ND (1.7)
ND (1.7)
6.6
ND (1.6)
22
798
272.5
90.12
91.72
1,695
Range
(ng/kg)
ND (6.3) to 180
ND (1.4) to 28
ND (1.5) to 40
ND (1.7) to 95
ND (1.7) to 80
1 8 to 490
263 to 1,780
13 to 1,150
ND (1.2) to 22
ND (0.9) to 38
ND (0.9) to 31
ND (0.9) to 33
ND (0.9) to ND (4.0)
ND (0.9) to 34
ND (3.6) to 70
ND (1.2) to 10
ND (54) to 168
No. of
Detects
(9 samples)
8
1
1
1
1
9
9
9
6
6
2
1
0
1
7
1
8
Wastewater Effluent
Median
(pg/D
42
ND (9.6)
ND (12)
ND (12)
ND (12)
170
3,000
120
ND (7.2)
ND (6.3)
ND (8.4)
ND (7.1)
ND (6.2)
ND (8.2)
ND (23)
ND (22)
190
3,212
310
58.89
66.57
56.02
66.09
4,013
Range
(pg/L)
ND (1 1) to 98
ND (2.8) to ND (25)
ND (6.6) to ND (12)
ND (6.6) to ND (24)
ND (6.6) to ND (23)
77 to 270
1,000 to 4,600
1 2 to 840
ND (2.2) to 36
ND (2.2) to 33
ND (4.8) to ND (15)
ND (4.8) to ND (15)
ND (2.5) to ND (15)
ND (4.8) to ND (15)
ND (13) to 44
ND (6.4) to ND (41)
ND (180) to 230
No. of
Detects
(9 samples)
8
0
0
0
0
9
9
9
2
2
0
0
0
0
3
0
8
o
o
^1
D
C
O
§
00
-^
CD
D
CD
O
CD
3
-
ND = Not detected; value in parentheses is the detection limit.
ng/kg = nanograms per kilogram
pg/L = picograms per liter
a Calculated assuming nondetected values are zero.
b Sum of median values.
Source: U.S. EPA (1990a).
K>
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o
o
^1
D
C
O
§
Table 8-2. CDD/CDP Concentrations in Pulp and Paper Mill Bleached Pulp, Wastewater Sludge, and Effluent (circa 1996)
Congener/Congener Group
2,3,7,8-TCDD
1,2,3,7,8-PeCDD
1,2,3,4,7,8-HxCDD
1,2,3,6,7,8-HxCDD
1,2,3,7,8,9-HxCDD
1,2,3,4,6,7,8-HpCDD
OCDD
2,3,7,8-TCDF
1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
1,2,3,4,7,8-HxCDF
1,2,3,6,7,8-HxCDF
1,2,3,7,8,9-HxCDF
2,3,4,6,7,8-HxCDF
1,2,3,4,6,7,8-HpCDF
1,2,3,4,7,8,9-HpCDF
OCDF
Total 2,3,7,8-CDD°
Total 2,3,7,8-CDF'
Total I-TEQDF (ND = zero)"
Total I-TEQDF (ND = 1/2 DL)'
Total TEQDF-WHO9B (ND = zero)"
Total TEQDF-WH098 (ND = Y, DL)°
Bleached Pulp
Mean
ND = 0
(ng/kg)
0.3
0
0
0
0
0
2.4
10.3
0
0.4
0
0
0
0
0
0
0
2.7
10.7
1.53
6.4
1.5
7.6
Median
(ng/kg)
ND(1)
ND(5)
ND(5)
ND(5)
ND(5)
ND(5)
ND(10)
ND(1)
ND(5)
ND(5)
ND(5)
ND(5)
ND(5)
ND(5)
ND(5)
ND(5)
ND(10)
Range
(ng/kg)
ND(1) to 5
ND(3) to ND(7)
ND(3) to ND(7)
ND(3) to ND(7)
ND(3) to ND(7)
ND(3) to ND(7)
ND(10) to 15
ND(1) to 170
ND(3) to ND(7)
ND(3) to 7
ND(3) to ND(7)
ND(3) to ND(7)
ND(3) to ND(7)
ND(3) to ND(7)
ND(3) to ND(7)
ND(3) to ND(7)
ND(6) to ND(14)
No. of
Detects/
No. of
Samples
1/18
0/18
0/18
0/18
0/18
0/18
3/16
7/18
0/18
1/18
0/18
0/18
0/18
0/18
0/18
0/18
0/18
Wastewater Sludge
Mean
ND = 0
(ng/kg)
0.8
0
0.5
2.3
1.6
41.4
445
6.2
0
0.5
0
0
0
0.5
1.2
0
0
492
8.4
3.0
12.9
2.6
15.2
Median
(ng/kg)
ND(1)
ND(5)
ND(5)
ND(5)
ND(5)
7
150
3
ND(5)
ND(5)
ND(5)
ND(5)
ND(5)
ND(5)
ND(5)
ND(5)
ND(10)
Range
(ng/kg)
ND(1) to 4
ND(4) to ND(52)
ND(4) to 7
ND(4) to 18
ND(4) to 14
ND(4) to 330
21 to 2,900
ND(1) to 31
ND(4) to ND(52)
ND(4) to 7
ND(4) to ND(52)
ND(4) to ND(52)
ND(4) to ND(52)
ND(4) to 6
ND(4) to 10
ND(4) to ND(52)
ND(9) to ND(100)
No. of
Detects/
No. of
Samples
4/12
0/12
1/13
2/13
2/13
9/13
10/10
9/12
0/13
1/13
0/13
0/13
0/13
1/13
2/13
0/13
0/13
Wastewater Effluent
Mean
ND = 0
(pg/L)
1.2
0
0
0
0
3.2
99.0
2.3
0
0
0
0
0
0
0
0
0
103
2.3
1.5
53.6
1.4
66.5
Median
(ng/kg)
ND(11)
ND(53)
ND(53)
ND(53)
ND(53)
ND(53)
ND(110)
ND(1 1)
ND(53)
ND(53)
ND(53)
ND(53)
ND(53)
ND(53)
ND(53)
ND(53)
ND(106)
Range
(pg/L)
ND(10) to 21
ND(50) to ND(55)
ND(50) to ND(55)
ND(50) to ND(55)
ND(50) to ND(55)
ND(50) to 58
ND(100) to 370
ND(10) to 23
ND(50) to ND(55)
ND(50) to ND(55)
ND(50) to ND(55)
ND(50) to ND(55)
ND(50) to ND(55)
ND(50) to ND(55)
ND(50) to ND(55)
ND(50) to ND(55)
ND(104) to ND(110)
No. of
Detects/
No. of
Samples
1/18
0/18
0/18
0/18
0/18
1/18
6/14
2/18
0/18
0/18
0/18
0/18
0/18
0/18
0/18
0/18
0/18
00
Ol
o
D
CD
O
CD
3
-
ND = Not detected; value in parentheses is the detection limit.
ng/kg = nanograms per kilogram
pg/L = picograms per liter
° Sum of mean values.
Source: Gillespie (1997).
K>
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Table 8-3. Summary of Bleached Chemical Pulp and Paper Mill Discharges
of 2,3,7,8-TCDD and 2,3,7,8-TCDF
Matrix
Effluent
Sludge"
Pulp
Congener
2,3,7,8-TCDD
2,3,7,8-TCDF
TEQ
2,3,7,8-TCDD
2,3,7,8-TCDF
TEQ
2,3,7,8-TCDD
2,3,7,8-TCDF
TEQ
EPA 1988
Discharge3
(g/year)
201
1,550
356
210
1,320
343
262
2,430
505
NCASI
1992
Dischargeb
(g/year)
22
99
32
33
118
45
24
124
36
EPA 1 993
Discharge0
(g/year)
71
341
105
NR
NR
177
NR
NR
149
NCASI
1993
Dischargeb
(g/year)
19
76
27
24
114
35
22
106
33
NCASI
1994
Dischargeb
(g/year)
14.6
49.0
19.5
18.9
95.2
28.4
16.2
78.8
24.1
EPA 1995
Discharge6
(g/year)
16
120
28
NR
NR
50
NR
NR
40
NR = Not reported.
g/year = grams per year
a The total discharge rate of congener or TEQ (based only on 2,3,7,8-TCDD and 2,3,7,8-TCDF concentration)
was summed across all 104 mills. 104-Mill Study (U.S. EPA, 1990a).
b The total discharge rate of congener or TEQ (based only on 2,3,7,8-TCDD and 2,3,7,8-TCDF concentration)
was summed across all 104 mills. The daily discharge rates reported in NCASI (1993), Gillespie (1994), and
Gillespie (1995) were multiplied by a factor of 350 days/yr to obtain estimates of annual discharge rates.
NCASI 1992 Survey (NCASI, 1993), 1993 Update (Gillespie, 1994), and 1994 Update (Gillespie, 1995).
c The discharges in effluent and sludge were estimated in U.S. EPA (1 993d; 1 997f) for January 1, 1 993. The
TEQ discharge in pulp was estimated by multiplying the 1 988 discharge estimate by the ratio of the 1 993 and
1988 effluent discharge estimates (i.e., the estimate of the reduction in 1988 discharges achieved by pollution
prevention measures taken by the industry between 1988 and 1993).
d Approximately 20.5 percent of the sludge generated in 1990 were incinerated. The remaining 79.5 percent
were predominantly landfilled (56.5 percent) or placed in surface impoundments (18.1 percent); 4.1 percent
were land-applied directly or as compost, and 0.3 percent were distributed/marketed (U.S. EPA, 1993e).
e The discharges in effluent and sludge were estimated in U.S. EPA (1 997f) for mid-1 995. The TEQ discharge in
pulp was estimated by multiplying the 1988 discharge estimate by the ratio of the 1995 and 1988 effluent
discharge estimates (i.e., the estimate of the reduction in 1988 discharges achieved by pollution prevention
measures taken by industry between 1988 and 1995).
DRAFT-DO NOT QUOTE OR CITE
8-51
December 2003
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Table 8-4. CDD/CDF Concentrations in Graphite Electrode Sludge
from Chlorine Production
Congener/Congener Group
2,3,7,8-TCDD
1,2,3,7,8-PeCDD
1,2, 3,4,7, 8-HxCDD
1,2,3,6,7,8-HxCDD
1,2,3,7,8,9-HxCDD
1,2,3,4,6,7,8-HpCDD
OCDD
2,3,7,8-TCDF
1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
1,2, 3,4,7, 8-HxCDF
1,2,3,6,7,8-HxCDF
1,2,3,7,8,9-HxCDF
2,3,4,6,7,8-HxCDF
1,2, 3,4,6,7, 8-HpCDF
1,2,3,4,7,8,9-HpCDF
OCDF
Total 2,3,7,8-CDD*
Total 2,3,7,8-CDF*
Total I-TEQDF*
Total TEQDF-WH098*
Total TCDD
Total PeCDD
Total HxCDD
Total HpCDD
Total OCDD
Total TCDF
Total PeCDF
Total HxCDF
Total HpCDF
Total OCDF
Total CDD/CDF*
Sludge 1
Ot/g/kg)
ND (0.006)
ND (0.007)
ND (0.018)
ND (0.012)
ND (0.016)
0.095
0.92
26
25
12
32
7
1.3
0.87
9.1
8.1
31
1.02
152.37
14.2
14.1
ND (0.006)
ND (0.070)
ND (0.046)
0.22
0.92
64
75
68
24
31
263.14
Sludge 2
(jt/g/kg)
ND (0.009)
ND (0.009)
ND (0.026)
ND (0.016)
ND (0.022)
0.21
2.0
56
55
25
71
16
2.8
1.9
19
19
76
2.21
341.7
30.5
30.4
ND (0.009)
ND (0.009)
ND (0.064)
0.48
2
150
240
140
53
76
661.48
Sludge 3
(jt/g/kg)
ND (0.009)
ND (0.009)
ND (0.029)
ND (0.019)
ND (0.025)
0.25
2.2
57
56
24
73
15
2.6
2.0
19
20
71
2.45
339.6
30.2
30.2
ND (0.009)
ND (0.009)
ND (0.074)
0.56
2.2
140
240
140
54
71
647.76
Sludge 4
Ot/g/kg)
ND
ND (0.033)
ND (0.49)
ND (0.053)
ND (1.2)
0.055
0.65
52
55
27
44
12
1.7
1.3
15
14
81
0.70
303.0
27.7
27.6
NR
NR
NR
NR
0.65
NR
NR
NR
NR
81
NR
ND = Not detected value in parentheses is the reported detection limit.
NR = Not reported.
yc/g/kg = micrograms per kilogram
* Calculated assuming not detected values were zero.
Sources: Rappe et al. (1991), Rappe (1993).
DRAFT-DO NOT QUOTE OR CITE
8-52
December 2003
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CD
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o
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c;
O
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53
Table 8-5. CDD/CDF Concentrations in Metal Chlorides
Congener Group
Total TCDD
Total PeCDD
Total HxCDD
Total HpCDD
Total OCDD
Total TCDF
Total PeCDF
Total HxCDF
Total HpCDF
Total OCDF
FeCI3
(jt/g/kg)
NR
NR
NR
ND
ND
NR
NR
NR
12
42
AlClg
Ot/g/kg)
NR
NR
NR
ND
ND
NR
NR
NR
ND
ND
AlClg
(yt/g/kg)
NR
NR
NR
ND
0.1
NR
NR
NR
ND
34
CuCI2
(jt/g/kg)
NR
NR
NR
0.03
O.Q
NR
NR
NR
0.1
0.5
CuCI
(jt/g/kg)
NR
NR
NR
ND
0.03
NR
NR
NR
0.08
0.2
TiCI4
(yt/g/kg)
NR
NR
NR
ND
ND
NR
NR
NR
ND
ND
SiCI4
(jt/g/kg)
NR
NR
NR
ND
ND
NR
NR
NR
ND
ND
00
Ol
GO
D
CD
O
CD
3
-
NR = Not reported.
ND = Not detected; detection limit of 0.02
yc/g/kg = micrograms per kilogram
Source: Hutzinger and Fiedler (1991 a).
K>
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CD
Table 8-6. CDD/CDF Concentrations in Mono- through Tetra-Chlorophenols
o
o
-H
D
c;
O
O
53
Congener/
Congener Group
Total TCDD
Total PeCDD
Total HxCDD
Total HpCDD
Total OCDD
Total TCDF
Total PeCDF
Total HxCDF
Total HpCDF
Total OCDF
Total CDD/CDF
2-CP
(Ref. A)
(mg/kg)
ND (0.02)
ND (0.02)
ND (0.02)
ND (0.02)
ND (0.02)
+
ND
ND
ND
ND
--
2,4-DCP
(Ref. A)
(mg/kg)
ND (0.02)
ND (0.02)
ND (0.02)
ND (0.02)
ND (0.02)
ND
ND
ND
ND
ND
--
2,6-DCP
(Ref. A)
(mg/kg)
ND (0.02)
ND (0.02)
ND (0.02)
ND (0.02)
ND (0.02)
ND
ND
ND
ND
ND
--
2,4,5-TrCP
(Na salt)
(Ref. A)
(mg/kg)
ND (0.02) - 14
ND (0.02)
ND (0.02)
ND (0.02)
ND (0.02)
ND
ND
ND
ND
ND
--
2,4,5-TrCP
(Ref. A)
(mg/kg)
ND (0.02) -
6.5
ND (0.02) -
1.5
ND (0.02)
ND (0.02)
ND (0.02)
ND
ND
ND
ND
ND
-
2,4,6-TrCP
(Ref. A)
(mg/kg)
ND (0.02) - 49
ND (0.02)
ND (0.02)
ND (0.02)
ND (0.02)
+
+
+
ND
ND
-
2,4,6-TrCP
(Na salt)
(Ref. B &
C)
(mg/kg)
<0.02
<0.03
<0.03
<0.1
<0.1
1.5
17.5
36
4.8
__
-
2,3,4,6-TeCP
(Ref. A)
(mg/kg)
ND (0.02)
ND (0.02)
ND (0.02) - 15
ND (0.02) - 5.1
ND (0.02) - 0.17
+
+
+
+
+
-
2,3,4,6-TeCP
(Na salt)
(Ref. B & C)
(mg/kg)
0.7
5.2
9.5
5.6
0.7
0.5
10
70
70
10
-
00
Ol
D
CD
O
CD
3
-
K>
O
O
CO
ND = Not detected; value in parentheses is the detection limit, if reported.
+ = Detected but not quantified.
= Not reported.
mg/kg = milligrams per kilogram
Ref. A: Firestone et al. (1972); because of poor recoveries, authors stated that actual CDD/CDF levels may be considerably higher than those reported.
Ref. B: Rappe et al. (1978a); common Scandinavian commercial chlorophenols.
Ref. C: Rappe et al. (1978b); common Scandinavian commercial chlorophenols.
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CD
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c;
O
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53
Table 8-7. CDD/CDF Concentrations in Historical and Current Technical Pentachlorophenol Products
Congener/
Congener Group
2,3,7,8-TCDD
1,2,3,7,8-PeCDD
1,2,3,4,7,8-HxCDD
1,2,3,6,7,8-HxCDD
1,2,3,7,8,9-HxCDD
1,2,3,4,6,7,8-HpCDD
OCDD
2,3,7,8-TCDF
1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
1,2,3,4,7,8-HxCDF
1,2,3,6,7,8-HxCDF
1,2,3,7,8,9-HxCDF
2,3,4,6,7,8-HxCDF
1,2,3,4,6,7,8-HpCDF
1,2,3,4,7,8,9-HpCDF
OCDF
Tola 2,3,7,8-CDD*
Tola 2,3,7,8-CDF*
Tola I-TEQDF*
Tola TEQDF-WHOBB*
Tola TCDD
Tola PeCDD
Tola HxCDD
Tola HpCDD
Tota OCDD
Tola TCDF
Tota PeCDF
Tota HxCDF
Tota HpCDF
Tota OCDF
Total CDD/CDF*
PCP
(Ref. B)
(1973)
Ojg/kg)
NDI20)
NDI30)
5,500
98,000
220,000
40
250
22,000
150,000
160,000
655,800
PCP
(Ref. C)
(1978)
Ojg/kg)
..
--
--
-
-
-
--
..
--
--
-
-
--
--
--
--
130,000
..
--
--
--
..
--
--
--
--
900
4,000
32,000
120,000
130,000
1,280,000
PCP
(Ref. A)
(1979)
Ojg/kg)
..
--
--
-
-
-
--
..
--
--
-
-
--
--
--
--
--
..
--
--
--
..
--
10,100
296,000
1,386,000
--
1,400
9,900
88,000
43,000
1,834,400
PCP
(Ref. D)
(1984)
Ojg/kg)
ND (10)
ND (10)
--
2,200
100
100,000
610,000
ND (10)
--
--
-
-
--
--
--
--
130,000
712,300
--
1,970
1,304
ND (10)
ND (10)
4,500
135,000
610,000
ND (10)
--
--
62,000
130,000
941,500
PCP
(Ref. I)
(1985)
Ojg/kg)
ND (0.05)
ND (1)
6
2,565
44
210,000
1,475,000
ND (0.5)
ND (1)
ND (1)
49
5
5
ND (1)
34,000
4,100
222,000
1,687,615
260,159
4,445
2,918
ND
ND
4,694
283,000
1,475,000
6
10
1,982
125,000
222,000
2,111,692
PCB
(Ref. I)
(1986)
Ojg/kg)
ND (0.05)
ND (1)
8
1,532
28
106,000
930,000
ND (0.5)
ND (1)
ND (1)
34
4
ND (1)
ND (1)
29,000
6,200
233,000
1,037,568
268,238
2,736
1,689
ND
ND
2,925
134,000
930,000
ND
3
1,407
146,000
233,000
1,447,335
PCP
(Ref. E)
(1987)
Ojg/kg)
ND (0.03)
1
ND (1)
831
28
78,000
733,000
ND (0.1)
0.5
1.5
125
ND (1)
32
ND (1)
11,280
637
118,000
811,860
130,076
1,853
1,088
1.9
6.5
1,700
154,000
733,000
0.8
141
4,300
74,000
118,000
1,085,000
PCP
(Ref. F)
(1987)
Ojg/kg)
ND (0.05)
2
ND (1)
1,480
53
99,900
790,000
ND (0.1)
0.2
0.9
163
ND (1)
146
ND (1)
19,940
980
137,000
891,435
158,230
2,321
1,488
0.4
15.2
3,300
198,000
790,000
0.4
343
13,900
127,000
137,000
1,270,000
PCP
(Ref. I)
(1985-88)
Ojg/kg)
ND (0.05)
ND (1)
8
600
13
89,000
2,723,000
ND (0.5)
ND (1)
ND (1)
67
2
ND (1)
ND (1)
22,000
3,400
237,000
2,812,621
262,469
4,173
1,509
ND
ND
912
117,000
2,723,000
ND
200
1,486
99,000
237,000
3,178,598
PCP
(Ref. Q)
(1991)
Ojg/kg)
ND
ND
1,100,000
ND
ND
ND
170,000
> 1,270
>127
ND (10)
ND (10)
8,900
130,000
1,100,000
ND (10)
ND (10)
14,000
36,000
170,000
1,459,000
PCP
(Ref. H)
(1988-99)
Ojg/kg)
..
-
-
-
-
-
-
..
-
-
-
-
-
-
-
-
-
..
-
-
-
ND (1)
ND (10)
1,440
55,560
-
ND (10)
ND (10)
3,070
36,530
-
-
PCP
(Ref. J)
(1988-99)
Ojg/kg)
ND (0.5)
-
-
-
-
-
-
..
-
-
-
-
-
-
-
-
-
..
-
-
-
ND
3
1,490
48,430
191,700
48
520
13,650
76,090
136,310
468,240
PCP
(Ref. K)
(unknown)
Ojg/kg)
ND (10)
ND (10)
ND (10)
860
20
36,400
296,810
ND (10)
ND (10)
ND (10)
200
ND (20)
ND (20)
ND (20)
2,000
140
19,940
334,090
22,280
810
525
..
-
-
-
-
-
-
-
-
-
-
00
Ol
Ol
D
CD
O
CD
3
-
ND = Not detected; value in parentheses is the detection limit.
= Not reported.
= micrograms per kilogram
Calculated assuming not detected values are zero.
K)
O
O
CO
-------
CD Table 8-7. CDD/CDF Concentrations in Historical and Current Technical Pentachlorophenol Products (continued)
ii
~n
^
! Sources:
CD
O
> Ref. A: U.S. Department of Health and Human Services (1989); composite of technical-grade materials produced in 1 979 by Monsanto Industrial Chemical Co. (St. Louis, MO),
Q Reichhold Chemicals, Inc. (White Plains, NY), and Vulcan Materials Co. (Birmingham, AL).
~H Ref. B: Buser and Bosshardt (1976); mean of 10 samples of "high" CDD/CDF content PCP received from Swiss commercial sources in 1973.
D Ref. C: Rappe et al. (1978b); sample of U.S. origin, "presumably prepared by alkaline hydrolysis of hexachlorobenzene."
S Ref. D: Cull et al. (1984); mean of four "recent" production batches from each of two manufacturers of technical PCP using three different analytical methods; ANOVA showed no
~H statistically significant difference in CDD/CDF concentrations between the eight samples (samples obtained in the United Kingdom).
Ref. E: Hagenmaier and Brunner (1987); sample of Witophen P (Dynamit Nobel - Lot no. 7777) (obtained in Germany).
^ Ref. F: Hagenmaier and Brunner (1987); sample of PCP produced by Rhone Poulenc (obtained in Germany).
pj Ref. G: Harrad et al. (1991); PCP-based herbicide formulation from NY State Dept. Environmental Conservation.
^j Ref. H: Pentachlorophenol Task Force (1 997); average of monthly batch samples for the period Jan. 1 987 to Aug. 1 996.
171 Ref. I: Pentachlorophenol Task Force (1997); samples of "penta" manufactured in 1985, 1986, and 1988.
Ref. J: KMG-Bermuth, Inc. (1 997); average of monthly batch samples for the period Feb. 1 987 to Dec. 1 996 (excluding the following months, for which data were not available: Feb.
1993, Jan. 1992, Dec. 1991, Sept. 1991, Dec. 1988, and Sept. 1988).
Ref. K: Schecter et al. (1997); sample found stored in a barn in Vermont.
00
D
CD
O
CD
3
-
K)
O
O
CO
-------
Table 8-8. Historical CDD/CDF Concentrations in Pentachlorophenol-Na
Congener/Congener Group
2,3,7,8-TCDD
1,2,3,7,8-PeCDD
1,2,3,4,7,8-HxCDD
1,2,3,6,7,8-HxCDD
1,2,3, 7,8, 9-HxCDD
1,2,3,4,6,7,8-HpCDD
OCDD
2,3,7,8-TCDF
1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
1,2,3,4,7,8-HxCDF
1,2,3,6,7,8-HxCDF
1,2,3, 7,8, 9-HxCDF
2,3,4,6,7,8-HxCDF
1,2,3,4,6,7,8-HpCDF
1, 2,3,4,7,8, 9-HpCDF
OCDF
Total 2,3,7,8-CDD*
Total 2,3,7,8-CDF*
Total I-TEQDF*
Total TEQDF-WH098*
Total TCDD
Total PeCDD
Total HxCDD
Total HpCDD
Total OCDD
Total TCDF
Total PeCDF
Total HxCDF
Total HpCDF
Total OCDF
Total CDD/CDF*
PCP-Na
(Ref. A)
(1969)
(//g/kg)
--
-
__
-
3,600
„„
-
-
-
__
—
--
__
-
„„
-
--
--
„„
-
17,000
9,600
3,600
-
-
-
--
--
--
PCP-Na
(Ref. B)
(1973)
(//g/kg)
„„
-
-
__
-
-
„„
-
-
-
__
—
-
__
-
„„
-
-
-
140
40
140
1,600
4,000
ND (20)
60
1,400
4,300
4,300
15,980
PCP-Na
(Ref. C)
(1973)
(//g/kg)
„„
-
-
__
-
-
„„
-
-
-
__
—
-
__
-
„„
-
-
-
50
ND (30)
3,400
38,000
110,000
ND (20)
40
1 1 ,000
47,000
26,500
235,990
PCP-Na
(Ref. D)
(1987)
(//g/kg)
0.23
18.2
28.3
2,034
282
9,100
41,600
1.8
8.2
6.6
48
69
ND (1)
87
699
675
37,200
53,063
38,795
452
406
27
213
3,900
18,500
41,600
82
137
3,000
13,200
37,200
117,859
PCP-Na
(Ref. E)
(1987)
(//g/kg)
0.51
3.2
13.3
53.0
19.0
3,800
32,400
0.79
1.9
1.1
4.6
1.3
1.3
4.6
197
36
4,250
35,289
4,499
79.5
58.5
52
31
230
5,800
32,400
12
27
90
860
4,250
43,752
PCP-Na
(Ref. F)
(1992)
(//g/kg)
0.076
18.7
96
4,410
328
175,400
879,000
ND (1.0)
ND (4.0)
ND (4.0)
27.6
21.9
9.8
103
9,650
2,080
114,600
1,059,253
126,492
3,374
2,566
3.6
142.7
9,694
260,200
879,000
10.1
88.4
9,082.3
75,930
114,600
1,348,751
PCP-Na
(Ref. G)
(1980s)
(//g/kg)
ND (1.4)
28.3
ND (6.1)
4,050
ND (1.4)
33,800
81,000
149
319
324
ND (2.8)
225
480
ND (385)
6,190
154
36,000
118,878
43,841
1,201
1,096
1.9
140
14,000
100,000
81,000
1200
6400
49,000
91,000
36,000
378,742
ND = Not detected; value in parentheses is the detection limit.
= Not reported.
//g/kg = micrograms per kilogram
* Calculated assuming not-detected values are zero.
Sources:
Ref. A: Firestone et al. (1972); mean of two samples of PCP-Na obtained in the United States between 1967 and 1969.
Ref. B: Buser and Bosshardt (1976); mean of five samples of "low" CDD/CDF content PCP-Na received from Swiss
commercial sources.
Ref. C: Buser and Bosshardt (1976); sample of "high" CDD/CDF content PCP-Na received from a Swiss commercial source.
Ref. D: Hagenmaier and Brunner (1987); sample of Dowicide-G purchased from Fluka; sample obtained in Germany.
Ref. E: Hagenmaier and Brunner (1987); sample of Preventol PN (Bayer AG); sample obtained in Germany.
Ref. F: Santl et al. (1994c); 1992 sample of PCP-Na from Prolabo, France.
Ref. G: Palmer et al. (1988); sample of a PCP-Na formulation collected from a closed sawmill in California in the late 1980s.
DRAFT-DO NOT QUOTE OR CITE
8-57
December 2003
-------
Table 8-9. Summary of Specific Dioxin-Containing Wastes That Must Comply with Land Disposal Retrictions
EPA Hazardous
Waste Number
F020
F021
F022
F023
F026
F027
Waste Description
Wastes (except wastewater and spent carbon from hydrogen chloride
purification) from the production or manufacturing use (as a reactant,
chemical intermediate, or component in a formulating process) of tri- or
tetrachlorophenol, or of intermediates used to produce their pesticide
derivatives. (This listing does not include wastes from the production of
hexachlorophene from highly purified 2,4,5-trichlorophenol.)
Wastes (except wastewater and spent carbon from hydrogen chloride
purification) from the production or manufacturing use (as a reactant,
chemical intermediate, or component in a formulating process) of
pentachlorophenol, or of intermediates used to produce its derivatives.
Wastes (except wastewater and spent carbon from hydrogen chloride
purification) from the manufacturing use (as a reactant, chemical
intermediate, or component in a formulating process) of tetra-, penta-, or
hexachlorobenzenes under alkaline conditions.
Wastes (except wastewater and spent carbon from hydrogen chloride
purification) from the production of materials on equipment previously
used for the production or manufacturing use (as a reactant, chemical
intermediate, or component in a formulating process) of tri- and
tetrachlorophenols. (This listing does not include wastes from equipment
used only for the production or use of hexachlorophene from highly
purified 2,4,5-trichlorophenol.)
Wastes (except wastewater and spent carbon from hydrogen chloride
purification) from the production of materials on equipment previously
used for the manufacturing use (as a reactant, chemical intermediate, or
component in a formulating process) of tetra-, penta-, or
hexachlorobenzene under alkaline conditions.
Discarded unused formulations containing tri-, tetra-, or pentachlorophenol
or discarded unused formulations containing compounds derived from
these chlorophenols. (This listing does not include formulations containing
hexachlorophene synthesized from prepurified 2,4,5-trichlorophenol as the
sole component.)
Land Disposal
Restriction
Effective Date
November 8, 1988
November 8, 1988
November 8, 1988
November 8, 1988
November 8, 1988
November 8, 1988
Regulated
Waste
Constituent
TCDDs
PeCDDs
HxCDDs
TCDFs
PeCDFs
HxCDFs
TCDDs
PeCDDs
HxCDDs
TCDFs
PeCDFs
HxCDFs
TCDDs
PeCDDs
HxCDDs
TCDFs
PeCDFs
HxCDFs
TCDDs
PeCDDs
HxCDDs
TCDFs
PeCDFs
HxCDFs
TCDDs
PeCDDs
HxCDDs
TCDFs
PeCDFs
HxCDFs
TCDDs
PeCDDs
HxCDDs
TCDFs
PeCDFs
HxCDFs
Treatment Standard8 (ppb)
Wastewaters
(ug/U
0.063
0.063
0.063
0.063
0.035
0.063
0.063
0.063
0.063
0.063
0.035
0.063
0.063
0.063
0.063
0.063
0.035
0.063
0.063
0.063
0.063
0.063
0.035
0.063
0.063
0.063
0.063
0.063
0.035
0.063
0.063
0.063
0.063
0.063
0.035
0.063
Nonwastewaters
(//g/kg)
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
-------
CD
Table 8-9. Summary of Specific Dioxin-Containing Wastes That Must Comply with Land Disposal Retrictions (continued)
o
o
-H
D
c;
O
O
53
EPA Hazardous
Waste Number
F028
F039
K043
K099
Waste Description
Residues resulting from the incineration or thermal treatment of soil
contaminated with EPA Hazardous Waste Nos. F020-F023, F026, and
F027
Leachate (liquids that have percolated through land disposed wastes)
resulting from the disposal of more than one restricted waste classified as
hazardous under subpart D of 40 CFR 268. (Leachate resulting from the
disposal of one or more of the following EPA Hazardous Wastes and no
other Hazardous Wastes retains its EPA Hazardous Waste Number(s):
F020, F021, F022, F026, F027, and/or F028.)
2,6-dichlorophenol waste from the production of 2,4-D.
Untreated wastewater from the production of 2,4-D.
Land Disposal
Restriction
Effective Date
November 8, 1988
August 8, 1990
(wastewater)
May 8, 1992
(non-wastewater)
June 8, 1989
August 8, 1988
Regulated
Waste
Constituent
TCDDs
PeCDDs
HxCDDs
TCDFs
PeCDFs
HxCDFs
TCDDs
PeCDDs
HxCDDs
TCDFs
PeCDFs
HxCDFs
TCDDs
PeCDDs
HxCDDs
TCDFs
PeCDFs
HxCDFs
TCDDs
PeCDDs
HxCDDs
TCDFs
PeCDFs
HxCDFs
Treatment Standard8 (ppb)
Wastewaters
(//g/L)
0.063
0.063
0.063
0.063
0.035
0.063
0.063
0.063
0.063
0.063
0.035
0.063
0.063
0.063
0.063
0.063
0.035
0.063
0.063
0.063
0.063
0.063
0.035
0.063
Nonwastewaters
(//g/kg)
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
00
Ol
CD
D
CD
O
CD
3
-
K>
O
O
CO
a Treatment standards (i.e., maximum allowable concentration in waste extract) are based on incineration to 99.9999 percent destruction and removal efficiency.
//g/L = micrograms per liter
//g/kg = micrograms per kilogram
Source: 40 CFR 268
-------
CD
o
o
-H
D
c;
O
O
53
Table 8-10. CDD/CDF Concentrations in Chlorobenzenes
Congener/
Congener Group
Total TCDD
Total PeCDD
Total HxCDD
Total HpCDD
Total OCDD
Total TCDF
Total PeCDF
Total HxCDF
Total HpCDF
Total OCDF
Total CDD/CDF
MCBz
(Ref. A)
(A/g/kg)
ND (0.02)
ND (0.02)
ND (0.02)
ND (0.02)
ND (0.02)
ND (0.02)
ND (0.02)
ND (0.02)
ND (0.02)
ND (0.02)
--
1,2-DCBz
(for synthesis)
(Ref. A)
(A/g/kg)
0.3
ND (0.02)
ND (0.02)
ND (0.02)
ND (0.02)
ND (0.02)
0.5
ND (0.02)
ND (0.02)
ND (0.02)
--
1,2,4-TrCBz
("pure")
(Ref. B)
(A/g/kg)
ND (0.1)
ND (0.1)
ND (0.1)
ND (0.1)
ND (0.1)
ND (0.1)
ND (0.1)
ND (0.1)
ND (0.1)
ND (0.1)
--
Mixed TrCBz
(47%)
(Ref. A)
(A/g/kg)
0.027
0.140
0.259
0.253
0.081
0.736
0.272
0.091
0.030
0.016
1.904
1,2,4,5-TeCBz
(99%)
(Ref. A)
(A/g/kg)
ND (0.02)
0.2
0.5
0.8
0.4
0.03
0.2
0.8
1.5
2.1
--
PeCBz
(98%)
(Ref. A)
(A/g/kg)
ND (0.02)
ND (0.02)
0.02
0.02
0.05
0.02
ND (0.02)
ND (0.02)
0.1
0.1
--
HCBz
(97%)
(Ref. A)
(A/g/kg)
ND (20)
ND (20)
ND (20)
470
6,700
ND (20)
ND (20)
ND (20)
455
2,830
--
HCBz
(Ref. B)
(A/g/kg)
__
--
--
--
50- 212,000
-
-
-
-
350- 58,300
-
00
O5
O
D
CD
O
CD
3
-
ND = Not detected; value in parentheses is the detection limit, if reported.
= Not reported.
A/g/kg = micrograms per kilogram
Ref. A: Hutzinger and Fiedler (1991 a); unpublished results of tests performed at the Univ. of Bayreuth, Germany, and by Dr. H. Hagenmaier.
Ref. B: Villanueva et al. (1974); range of three samples of commercially available HCBz.
K>
O
O
CO
-------
CD
Table 8-11. Concentrations of CDD/CDF Congener Groups in Unused Commercial PCB Mixtures
PCB Mixture
Aroclor 1016
Aroclor 1242
Aroclor 1242
Aroclor 1242
Clophen A-30
Clophen A-30
Aroclor 1248
Clophen A-40
Kanechlor 400
Aroclor 1254
Aroclor 1254
Aroclor 1254
Aroclor 1254
Clophen A-50
Aroclor 1260
Aroclor 1260
Aroclor 1260
Aroclor 1260
Clophen A-60
Clophen A-60
Clophen A-60
Phenoclor DP-6
Clophen T-64
Prodelec 3010
Year of
Manufacture
1972
__
-
-
-
-
1969
-
-
1969
1970
-
-
-
1969
-
-
-
-
-
-
—
-
CDF Congener Group Concentrations (mg/kg)
TCDF
ND
0.07
2.3
0.25
6.377
0.713
0.5
1.289
-
0.1
0.2
0.02
0.05
5.402
0.3
0.1
0.8
0.2
15.786
16.340
1.4
0.7
0.3
1.08
PeCDF
ND
0.03
2.2
0.7
2.402
0.137
1.2
0.771
-
0.2
0.4
0.2
0.1
2.154
1.0
0.4
0.9
0.3
11.655
21.164
5.0
10.0
1.73
0.35
HxCDF
ND
0.003
ND
0.81
0.805
0.005
0.3
0.144
-
1.4
0.9
0.6
0.02
2.214
1.10
0.5
0.5
0.3
4.456
7.630
2.2
2.9
2.45
0.07
HpCDF
—
__
-
-
0.108
0.001
__
0.020
-
__
-
-
-
0.479
1.35
-
-
-
1.517
2.522
-
-
0.82
-
OCDF
__
-
-
0.016
ND
__
0.011
-
__
-
-
-
0.069
-
-
-
0.639
1.024
-
-
—
-
Total CDF
ND
0.15
4.5
1.9
9.708
0.855
2.0
2.235
20.0
1.7
1.5
0.8
0.2
10.318
3.8
1.0
2.2
0.8
34.052
48.681
8.6
13.6
5.4
2.0
CDD Congener Group Concentrations (mg/kg)
TCDD
—
__
-
-
0.0007
ND
__
ND
-
__
-
-
-
ND
-
-
-
0.0004
ND
-
-
—
-
PeCDD
—
__
-
-
ND
ND
__
ND
-
__
-
-
-
ND
-
-
-
0.002
ND
-
-
—
-
HxCDD
—
__
-
-
0.001
ND
__
ND
-
__
-
-
-
ND
-
-
-
0.002
ND
-
-
—
-
HpCDD
—
__
-
-
0.006
0.005
__
0.012
-
__
-
-
-
0.011
-
-
-
0.003
0.014
-
-
—
-
OCDD
—
__
-
-
0.031
0.025
__
0.030
-
__
-
—
-
0.027
-
-
-
0.015
0.032
-
-
—
-
Total
CDD
—
__
-
-
0.039
0.030
__
0.042
-
__
-
—
-
0.038
-
-
-
0.022
0.046
-
-
—
-
Referenc
e
Number
A
B, C
B, C
B
E
D
B
D
B, C
A
A
B, C
B
D
B, C
A
B, C
A
E
D
A
A
B
B
o
o
-H
D
c;
O
O
53
00
D
CD
O
CD
3.
CD
K>
O
0
CO
ND
—
mg/kg
Ref. A:
Ref. B:
Ref. C:
= Not detected.
= Not reported.
= milligram per kilogram
Bowes et al. (1975a).
Erickson (1986).
ATSDR (1993).
-------
CD
o
o
-H
D
c;
O
O
53
Table 8-12. 2,3,7,8-Substituted Congener Concentrations in Unused PCB Mixtures
Congener
2,3,7,8-TCDD
1,2,3,7,8-PeCDD
1,2,3,4,7,8-HxCDD
1,2,3,6,7,8-HxCDD
1,2,3,7,8,9-HxCDD
1,2,3,4,6,7,8-HpCDD
OCDD
2,3,7,8-TCDF
1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
1,2,3,4,7,8-HxCDF
1,2,3,6,7,8-HxCDF
1,2,3,7,8,9-HxCDF
2,3,4,6,7,8-HxCDF
1,2,3,4,6,7,8-HpCDF
1,2,3,4,7,8,9-HpCDF
OCDF
Total TCDD
Total PeCDD
Total HxCDD
Total HpCDD
Total OCDD
Total TCDF
Total PeCDF
Total HxCDF
Total HpCDF
Total OCDF
Total CDD/CDF*
Total I-TEQDF*
Total TEQDF-WHO98*
Congener Concentrations in Clophens (A/g/kg)
A-30
(Ref. A)
ND
ND
ND
0.8
ND
5.6
31.1
1032.6
135.8
509.2
301.4
65.3
ND
50.6
43.7
22.5
15.7
0.7
ND
1.2
5.6
31.1
6376.6
2402.4
804.8
108.3
15.7
9746.4
407.2
407.2
A-30
(Ref. B)
ND
ND
ND
ND
ND
2.4
24.7
36.9
14.9
13.1
1.9
0.8
ND
0.1
0.6
ND
ND
ND
ND
ND
5.4
24.7
713
136.5
5.1
0.8
ND
885.5
1 1.3
11.3
A-40
(Ref. B)
ND
ND
ND
ND
ND
4.4
30.3
250.2
52.7
171.3
48.4
19.6
0.7
6.8
7.0
2.8
11.4
ND
ND
ND
11.6
30.3
1289.4
770.8
143.6
19.5
11.4
2276.6
121.0
121.0
A-50
(Ref. B)
ND
ND
ND
ND
ND
5.3
26.9
1005.7
155.2
407.5
647.5
227.5
8.3
62.5
205.5
72.2
69.2
ND
ND
ND
11.0
26.9
5402.3
2153.7
2213.8
478.8
69.2
10355.7
409.6
409.5
A-60
(Ref. A)
ND
0.1
0.2
ND
ND
2.5
14.9
2287.7
465.2
1921.9
1604.2
157.6
42.8
369.5
480.6
321.7
639.2
0.4
2.0
1.8
3.0
14.9
15785.7
1 1654.6
4455.8
1517.0
639.2
34074.4
1439.2
1439
A-60
(Ref. B)
ND
ND
ND
ND
ND
6.8
32.3
3077.2
1750.8
2917.0
2324.1
351.3
19.0
408.3
1126.1
304.0
1024.3
ND
ND
ND
13.5
32.3
16340
21 164
7630.2
2522.3
1024.3
48726.5
2179
2178
Congener Concentrations in Aroclors (A/g/kg)
1016
(Ref. C)
-
-
-
0.10
-
1.75
-
0.08
-
-
-
-
..
-
-
-
1242
(Ref. C)
-
-
-
40.1
-
40.8
-
0.26
-
-
-
-
..
-
-
-
1248
(Ref. D)
-
-
-
330
-
830
-
-
-
-
-
-
..
-
-
-
1254
(Ref. C)
-
-
-
28.0
-
1 10
-
28.8
-
-
-
-
..
-
-
-
1254
(Ref. C)
-
-
-
20.9
-
179
-
28.7
-
-
-
-
..
-
-
-
1254
(Ref. C)
-
-
-
55.8
-
105
-
19.4
-
-
-
-
..
-
-
-
1254
(Ref. D)
-
-
-
1 10
-
120
-
-
-
-
-
-
..
-
-
-
1260
(Ref. C)
-
-
-
63.5
-
135
-
5.1
-
-
-
-
..
-
-
1260
(Ref. C)
-
-
-
6.88
-
58.2
-
9.7
-
-
-
-
-
-
-
1260
(Ref. C)
-
-
-
29.0
-
1 12
-
10.7
-
-
-
-
-
-
-
00
O5
ro
D
CD
o
CD
3
-
K>
O
O
CO
ND = Not detected.
= Not reported.
/jg/kg = micrograms per kilogram.
* Calculated assuming not-detected values are zero.
Ref. A: Malisch (1994).
Ref. B: Hagenmaier (1 987).
Ref. C: Brown et al. (1988).
Ref. D: Bowes (1975b).
-------
Table 8-13. Reported CDD/CDF Concentrations in Wastes from PVC Manufacture
Congener/Congener Group
2,3,7,8-TCDD
1,2,3,7,8-PeCDD
1, 2,3,4,7, 8-HxCDD
1,2,3,6,7,8-HxCDD
1,2,3,7,8,9-HxCDD
1,2,3,4,6,7,8-HpCDD
OCDD
2,3,7,8-TCDF
1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
1, 2,3,4,7, 8-HxCDF
1,2,3,6,7,8-HxCDF
1,2,3,7,8,9-HxCDF
2,3,4,6,7,8-HxCDF
1,2,3,4,6,7,8-HpCDF
1, 2,3,4,7, 8,9-HpCDF
OCDF
Total 2,3,7,8-CDD
Total 2,3,7,8-CDF
Total I-TEQDF
Total TEQDF-WH098
Total TCDD
Total PeCDD
Total HxCDD
Total HpCDD
Total OCDD
Total TCDF
Total PeCDF
Total HxCDF
Total HpCDF
Total OCDF
Total CDD/CDF
F024 Waste
U/g/kg)
0.37
0.14
.30
0.14
0.11
4.20
15.00
0.91
9.5
1.6
110
24.0
9.5
3.1
250
51.0
390
20.3
849.6
20.0
19.7
3.1
3.6
1.3
5.0
15.0
15.0
65.0
300
450
390
1,248
K019 Waste
U/g/kg)
260
890
260
330
620
920
1,060
680
975
1,050
10,100
9,760
21,800
930
13,400
1,340
43,500
4,340
103,535
5,928
6,333
1,230
3,540
3,950
1,270
1,060
20,600
45,300
63,700
16,600
43,500
200,750
K020 Waste
U/g/kg)
0.06
0.05
0.08
0.06
0.07
0.89
3.00
0.44
1.80
0.58
11.0
2.4
1.3
0.89
38.0
6.0
650
4.21
712.4
3.2
2.6
1.9
1.7
NR
1.7
3.0
6.0
11.0
27.0
58.0
650
760.3
NR = Congener group concentration reported in source is not consistent with reported
congener concentrations.
yc/g/kg = micrograms per kilogram
Source: Stringer et al. (1995).
DRAFT-DO NOT QUOTE OR CITE
8-63
December 2003
-------
CD
Table 8-14. CDD/CDF Measurements in Treated Wastewater and Wastewater Solids from U.S. EDC/VCM/PVC Manufacturers
Congener and
Congener Groups
2,3,7,8-TCDD
1,2,3,7,8-PeCDD
1,2,3,4,7,8-HxCDD
1,2,3,6,7,8-HxCDD
1,2,3, 7,8, 9-HxCDD
1,2,3,4,6,7,8-HpCDD
OCDD
2,3,7,8-TCDF
1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
1,2,3,4,7,8-HxCDF
1,2,3,6,7,8-HxCDF
1,2,3, 7,8, 9-HxCDF
2,3,4,6,7,8-HxCDF
1,2,3,4,6,7,8-HpCDF
1, 2,3,4,7,8, 9-HpCDF
OCDF
Mean I-TEQDF (ND = zero)
Mean I-TEQDF (ND = 1/2 DL)
Total TCDD
Total PeCDD
Total HxCDD
Total HpCDD
Total OCDD
Total TCDF
Total PeCDF
Total HxCDF
Total HpCDF
Total OCDF
Treated Wastewater
PVC-only Facilities
No. Detects/
No. Samples
0/6
0/6
0/6
0/6
0/6
2/6
1/6
0/6
0/6
0/6
1/6
1/6
0/6
1/6
1/6
1/6
2/6
0/6
0/6
0/6
2/6
1/6
0/6
0/6
1/6
1/6
2/6
Concentration
Range3
(ng
Min.
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
0.42
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
/L)
Max.
ND
ND
ND
ND
ND
26
260
ND
ND
ND
5.8
3.8
ND
6.1
26
6.2
33
4.4
ND
ND
ND
48
260
ND
ND
30
49
33
Treated Wastewater
EDC/VCM/PVC Facilities
No. Detects/
No. Samples
0/4
0/4
0/4
0/4
0/4
1/4
1/4
0/4
0/4
0/4
0/4
0/4
0/4
1/4
3/4
2/4
4/4
0/4
0/4
0/4
1/4
1/4
0/4
0/4
1/4
3/4
4/4
Concentration
Range3
(ng
Min.
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
0.88
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
/L)
Max.
ND
ND
ND
ND
ND
14
130
ND
ND
ND
ND
ND
ND
6.5
78
20
900
4.7
ND
ND
ND
22
130
ND
ND
14
140
900
Wastewater Solids
EDC/VCM/PVC Facilities
No. Detects/
No. Samples
4/8
3/8
4/8
7/8
6/8
8/8
8/8
8/8
8/8
8/8
8/8
8/8
8/8
7/8
8/8
7/8
8/8
6/8
5/8
7/8
8/8
8/8
8/8
8/8
8/8
8/8
8/8
Concentration
Range"-0
(ng/kg)
Min.
ND
ND
ND
ND
ND
74
390
18
36
50
180
74
78
ND
570
ND
1,800
1,680
ND
ND
ND
74
390
210
380
750
880
1,800
Max.
109
320
455
520
645
3,230
9,700
460
1,500
1,750
7,550
3,650
2,800
425
20,600
12,000
4,200,000
1,680
730
1,630
3,915
5,300
9,700
9,800
18,000
31,000
39,400
4,200,000
Wastewater Solids
PVC-only Facilities
No. Detects/
No. Samples
1/2
0/2
1/2
1/2
1/2
2/2
2/2
0/2
0/2
0/2
1/2
1/2
1/2
0/2
1/2
1/2
2/2
1/2
1/2
1/2
2/2
2/2
1/2
1/2
2/2
2/2
2/2
Concentration
Range"-0
(ng/kg)
Min.
ND
ND
ND
ND
ND
28
200
ND
ND
ND
ND
ND
ND
ND
9.7
ND
39
1.90
ND
ND
ND
58
200
ND
ND
1.5
11
39
Max.
2.0
ND
3.2
2.3
2.4
35
640
ND
ND
ND
3.6
2.4
3.8
ND
12
2.0
43
3.6
6.3
3.3
14
64
640
4.8
4.0
11
18
43
o
o
-H
D
c;
O
O
53
00
D
CD
O
CD
3
-
K>
O
O
CO
ND = Not detected.
a Method detection limits (MDLs) for individual samples were less than 10 pg/L for all congeners and congener groups except OCDD and OCDF, which had MDLs less
than 50 pg/L.
b Dry weight basis.
c MDLs for all congeners were less than 150 ng/kg, and usually were less than 10 ng/kg.
-------
CD
Table 8-15. CDD/CDF Measurements in Products from U.S. EDC/VCM/PVC Manufacturers
o
o
-H
D
c;
O
O
53
Congener and
Congener Groups
2,3,7,8-TCDD
1,2,3,7,8-PeCDD
1,2, 3,4,7, 8-HxCDD
1,2, 3,6,7, 8-HxCDD
1,2,3,7,8,9-HxCDD
1,2, 3,4,6,7, 8-HpCDD
OCDD
2,3,7,8-TCDF
1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
1,2, 3,4,7, 8-HxCDF
1,2, 3,6,7, 8-HxCDF
1,2,3,7,8,9-HxCDF
2,3,4,6,7,8-HxCDF
1,2, 3,4,6,7, 8-HpCDF
1,2, 3,4,7, 8,9-HpCDF
OCDF
Mean I-TEQDF (ND = zero)
Mean I-TEQDF (ND = 1/2 DL)
Total TCDD
Total PeCDD
Total HxCDD
Total HpCDD
Total OCDD
Total TCDF
Total PeCDF
Total HxCDF
Total HpCDF
Total OCDF
Suspension and Mass PVC Resins
No. Detects/
No. Samples
0/22
0/22
0/22
0/22
0/22
1/22
0/22
0/22
0/22
0/22
0/22
0/22
0/22
1/22
0/22
0/22
0/22
0/22
0/22
0/22
1/22
0/22
0/22
0/22
1/22
0/22
0/22
Range of
Concentrations3
(ng/kg)
Min.
nd
nd
nd
nd
nd
nd
nd
nd
nd
nd
nd
nd
nd
nd
nd
nd
nd
0.002
nd
nd
nd
nd
nd
nd
nd
nd
nd
nd
Max.
nd
nd
nd
nd
nd
0.64
nd
nd
nd
nd
nd
nd
nd
0.37
nd
nd
nd
0.7
nd
nd
nd
0.64
nd
nd
nd
0.37
nd
nd
Dispersion PVC Resins
No. Detects/
No. Samples
0/6
0/6
0/6
0/6
0/6
1/6
0/6
0/6
0/6
0/6
0/6
0/6
0/6
0/6
0/6
0/6
2/6
1/6
1/6
5/6
1/6
0/6
0/6
1/6
0/6
0/6
2/6
Range of
Concentrations0
(ng/kg)
Min.
nd
nd
nd
nd
nd
nd
nd
nd
nd
nd
nd
nd
nd
nd
nd
nd
nd
0.001
nd
nd
nd
nd
nd
nd
nd
nd
nd
nd
Max.
nd
nd
nd
nd
nd
0.8
nd
nd
nd
nd
nd
nd
nd
nd
nd
nd
0.38
0.4
0.24
0.32
0.97
1.3
nd
nd
0.3
nd
nd
0.38
"Sales" Ethvlene Dichloride
(EDC)d
No. Detects/
No. Samples
0/5
0/5
0/5
0/5
0/5
0/5
0/5
0/5
0/5
0/5
0/5
0/5
0/5
0/5
1/5
1/5
1/5
0/5
0/5
0/5
0/5
0/5
0/5
0/5
0/5
1/5
1/5
Range of
Concentrations6
(ng/kg)
Min.
nd
nd
nd
nd
nd
nd
nd
nd
nd
nd
nd
nd
nd
nd
nd
nd
nd
0.0006
nd
nd
nd
nd
nd
nd
nd
nd
nd
nd
Max.
nd
nd
nd
nd
nd
nd
nd
nd
nd
nd
nd
nd
nd
nd
1.1
0.40
11
0.21
nd
nd
nd
nd
nd
nd
nd
nd
2.02
11
00
O5
Ol
D
CD
O
CD
3
-
K>
O
O
CO
nd = Not detected.
ng/kg = nanograms per kilogram
a Method detection limits (MDLs) for individual samples were less than 2 ng/kg for all congeners and congener groups except OCDD and OCDF, which had MDLs less than 6
ng/kg.
b Two of these 22 samples were duplicate samples from two sites. The results were averaged and treated as one sample for each site.
c MDLs for individual samples were less than 2 ng/kg for all congeners and congener groups except OCDD and OCDF, which had MDLs less than 4 ng/kg.
d "Sales" EDC is defined as EDC sold commercially for non-VCM uses or exported from the United States.
e MDLs were less than 1 ng/kg for all congeners in all samples.
Source: The Vinyl Institute (1998).
-------
CD
Table 8-1 6. CDD/CDF Concentrations in Dioxazine Dyes and Pigments (Canada)
Congener/Congener Group
2,3,7,8-TCDD
1,2,3,7,8-PeCDD
1,2,3,4,7,8-HxCDD
1,2,3,6,7,8-HxCDD
1, 2,3,7,8, 9-HxCDD
1,2,3,4,6,7,8-HpCDD
OCDD
2,3,7,8-TCDF
1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
1,2,3,4,7,8-HxCDF
1,2,3,6,7,8-HxCDF
1, 2,3,7,8, 9-HxCDF
2,3,4,6,7,8-HxCDF
1,2,3,4,6,7,8-HpCDF
1, 2,3,4,7,8, 9-HpCDF
OCDF
Total 2,3,7,8-CDD
Total 2,3,7,8-CDF
Total I-TEQDF **
Total TEQDF-WH098 **
Total TCDD
Total PeCDD
Total HxCDD
Total HpCDD
Total OCDD
Total TCDF
Total PeCDF
Total HxCDF
Total HpCDF
Total OCDF
Total CDD/CDF**
Blue 106
(/yg/kg)
ND (0.3)
ND (0.3)
-
ND (0.3)
ND (0.3)
31
41,953
ND (0.3)
ND (0.3)
ND (0.3)
12
*
-
ND (0.3)
50
12,463
41,984
12,525
56.4
7.45
ND (0.3)
ND (0.3)
ND (0.3)
34
41,953
ND (0.3)
ND (0.3)
12
71
12,463
54,533
ND (0.3)
ND (0.3)
-
ND (0.3)
ND (0.3)
6
28,523
ND (0.3)
ND (0.3)
ND (0.3)
2
*
-
ND (0.3)
10
1,447
28,529
1,459
30.3
3.4
ND (0.3)
ND (0.3)
ND (0.3)
8
28,523
0.3
ND (0.3)
2
32
1,447
30,012
ND (0.3)
ND (0.3)
-
ND (0.3)
ND (0.3)
9
18,066
ND (0.3)
ND (0.3)
ND (0.3)
2
*
-
ND (0.3)
14
1,006
18,075
1,022
19.5
2.3
ND (0.3)
ND (0.3)
ND (0.3)
12
18,066
ND (0.3)
ND (0.3)
2
26
1,006
19,112
Blue 108
(/yg/kg)
ND (0.3)
ND (0.3)
-
ND (0.3)
ND (0.3)
ND (0.3)
23
ND (0.3)
ND (0.3)
ND (0.3)
ND (0.3)
*
-
ND (0.3)
9
11
23
20
0.1
0.1
ND (0.3)
ND (0.3)
1
ND (0.3)
23
ND (0.3)
ND (0.3)
ND (0.3)
12
11
47
Violet 23
(/yg/kg)
ND (0.3)
ND (0.3)
-
ND (0.3)
ND (0.3)
9
7,180
ND (0.3)
0.5
ND (0.3)
76
*
-
ND (0.3)
13
941
7,189
1,031
16.0
8.7
ND (0.3)
ND (0.3)
21
30
7,180
ND (0.3)
0.5
76
26
941
8,275
ND (0.3)
ND (0.3)
-
ND (0.3)
ND (0.3)
1
806
ND (0.3)
ND (0.3)
ND (0.3)
4
*
-
ND (0.3)
10
125
807
139
1.4
0.6
ND (0.3)
ND (0.3)
2
5
806
ND (0.3)
ND (0.3)
5
14
125
957
ND (0.3)
ND (0.3)
-
ND (0.3)
ND (0.3)
16
11,022
ND (0.3)
ND (0.3)
ND (0.3)
39
*
-
ND (0.3)
11
3,749
11,038
3,799
18.9
5.6
ND (0.3)
ND (0.3)
7
36
11,022
ND (0.3)
ND (0.3)
39
29
3,749
14,882
ND (0.3)
ND (0.3)
-
ND (0.3)
ND (0.3)
10
7,929
ND (0.3)
ND (0.3)
ND (0.3)
31
*
-
ND (0.3)
4
1,556
7,939
1,591
12.7
4.2
ND (0.3)
ND (0.3)
ND (0.3)
11
7,929
ND (0.3)
ND (0.3)
31
13
1,556
9,540
ND (0.3)
ND (0.3)
-
ND (0.3)
ND (0.3)
2
1,627
ND (0.3)
ND (0.3)
ND (0.3)
9
*
-
ND (0.3)
1
147
1,629
157
2.7
1.1
ND (0.3)
ND (0.3)
ND (0.3)
2
1,627
0.4
ND (0.3)
9
2
147
1,787
ND (0.3)
ND (0.3)
-
ND (0.3)
ND (0.3)
4
1,420
ND (0.3)
ND (0.3)
ND (0.3)
7
*
-
ND (0.3)
12
425
1,424
444
2.7
1.0
ND (0.3)
ND (0.3)
1
6
1,420
ND (0.3)
ND (0.3)
7
21
425
1,880
o
o
-H
D
c;
O
O
53
00
D
CD
O
CD
3
-
K>
O
O
CO
ND = Not detected; value in parenthesis is the detection limit.
-- = Not reported.
//g/kg = micrograms per kilogram
* = Results listed for 1,2,3,4,7,8-HxCDF include concentrations for 1,2,3,6,7,8-HxCDF.
** = Calculations assume nondetected values are equal to zero.
-------
Table 8-17. CDD/CDF Concentrations in Printing Inks (Germany)
Congener/Congener Group
2,3,7,8-TCDD
1, 2,3,7, 8-PeCDD
1,2, 3,4,7, 8-HxCDD
1,2,3,6,7,8-HxCDD
1, 2,3,7, 8,9-HxCDD
1, 2,3,4,6,7, 8-HpCDD
OCDD
2,3,7, 8-TCDF
1, 2,3,7, 8-PeCDF
2,3,4,7, 8-PeCDF
1, 2,3,4,7, 8-HxCDF
1, 2,3,6,7, 8-HxCDF
1, 2,3,7, 8,9-HxCDF
2,3,4,6,7, 8-HxCDF
1, 2,3,4,6,7, 8-HpCDF
1, 2,3,4,7, 8,9-HpCDF
OCDF
Total 2,3,7,8-CDD
Total 2,3,7,8-CDF
Total I-TEQDF *
Total TEQDF-WH098
Total TCDD
Total PeCDD
Total HxCDD
Total HpCDD
Total OCDD
Total TCDF
Total PeCDF
Total HxCDF
Total HpCDF
Total OCDF
Total CDD/CDF
Rotogravure
(2-color)
(ng/kg)
ND (1)
8
19
325
155
2,770
5,810
2.5
ND (2)
ND (2)
4
ND (3)
ND (3)
ND (3)
40
ND (4)
129
9,087
175.5
88.6
87.2
4
58
2,679
5,630
5,810
5.5
13
29
64
129
14,422
Rotogravure
(4-color)
(ng/kg)
ND (1.5)
ND (4)
ND (5)
310
105
1,630
2,350
14
ND (4)
ND (4)
7
ND (5)
ND (5)
ND (5)
14
ND (7)
ND (10)
4,395
35
62.4
60.3
ND (2)
145
2,485
3,460
2,350
28
ND (4)
45
14
ND (10)
8,527
Offset
(4-color)
(ng/kg)
ND (2)
15
16
82
42
540
890
7
ND (4)
ND (4)
27
ND (5)
ND (5)
ND (5)
315
11
960
1,585
1320
35.4
41.2
77
35
660
1,100
890
90
340
95
566
960
4,813
Offset
(4-color)
(ng/kg)
ND (2)
6
11
21
14
240
230
7
ND (3)
ND (3)
35
ND (5)
ND (5)
ND (5)
42
ND (6)
165
522
249
15.0
17.7
38
25
246
445
230
35
110
94
63
165
1,451
ND = Not detected; value in parenthesis is the detection limit.
= Not reported.
ng/kg = nanograms per kilogram.
* Calculations assume not-detected values are zero.
Source: Santl et al. (1994c).
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Table 8-1 8. Chemicals Requiring TSCA Section 4 Testing under the Dioxin/Furan Rule
CAS No.
Currently Manufactured or Imported as of June 5, 1 987a
Chemical Name
79-94-7
118-75-2
118-79-6
120-83-2
1163-19-5
4162-45-2
21850-44-2
25327-89-3
32534-81-9
32536-52-0
37853-59-1
55205-38-4
Tetrabromobisphenol-A
2,3,5,6-Tetrachloro-2,5-cyclohexadiene-1,4-dione
2,4,6-Tribromophenol
2,4-Dichlorophenol
Decabromodiphenyloxide
Tetrabromobisphenol-A-bisethoxylate
Tetrabromobisphenol-A-bis-2,3-dibromopropylether'
Allyl ether of tetrabromobisphenol-A
Pentabromodiphenyloxide
Octabromodiphenyloxide
1,2-Bis(tribromophenoxy)-ethane
Tetrabromobisphenol-A-diacrylate a
CAS No.
Not Currently Manufactured or Imported as of June 5, 1987b
Chemical Name
79-95-8
87-10-5
87-65-0
95-77-2
95-95-4
99-28-5
120-36-5
320-72-9
488-47-1
576-24-9
583-78-8
608-71-9
615-58-7
933-75-5
1 940-42-7
2577-72-2
3772-94-9
37853-61-5
Tetrachlorobisphenol-A
3,4',5-Tribromosalicylanide
2,6-Dichlorophenol
3,4-Dichlorophenol
2,4,5-Trichlorophenol
2,6-Dibromo-4-nitrophenol
2[2,4-(Dichlorophenoxy)]-propanoic acid
3,5-Dichlorosalicyclic acid
Tetrabromocatechol
2,3-Dichlorophenol
2,5-Dichlorophenol
Pentabromophenol
2,4-Dibromophenol
2,3,6-Trichlorophenol
4-Bromo-2,5-dichlorophenol
3,5-Dibromosalicylanide
Pentachlorophenyl laurate
Bismethylether of tetrabromobisphenol-A
Alkylamine tetrachlorophenate
Tetrabromobisphenol-B
Tetrabromobisphenol-A-bis-2,3-dibromopropylether and tetrabromobisphenol-A-diacrylate are no longer
manufactured in or imported into the United States (Cash, 1993).
As of August 5, 1995, neither manufacture nor importation of any of these chemicals had resumed in the
United States (Holderman, 1995).
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Table 8-19. Congeners and Limits of Quantitation (LOQ) for Which
Quantitation is Required under the Dioxin/Furan
Test Rule and Pesticide Data Call-in
Chlorinated Dioxins
and Furans
2,3,7,8-TCDD
1,2,3,7,8-PeCDD
1,2,3,4,7,8-HxCDD
1,2,3,6,7,8-HxCDD
1,2,3,7,8,9-HxCDD
1,2,3,4,6,7,8-HpCDD
2,3,7,8-TCDF
1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
1, 2,3,4, 7,8-HxCDF
1,2,3,6,7,8-HxCDF
1,2,3,7,8,9-HxCDF
2,3,4,6, 7,8-HxCDF
1,2,3,4,6,7,8-HpCDF
1,2, 3,4,7,8, 9-HpCDF
Brominated Dioxins
and Furans
2,3,7,8-TBDD
1,2,3,7,8-PeBDD
1,2,3,4,7,8-HxBDD
1,2,3,6,7,8-HxBDD
1,2,3,7,8,9-HxBDD
1,2,3,4,6,7,8-HpBDD
2,3,7,8-TBDF
1,2,3,7,8-PeBDF
2,3,4,7,8-PeBDF
1,2,3,4,7,8-HxBDF
1,2,3,6,7,8-HxBDF
1,2,3,7,8,9-HxBDF
2,3,4,6, 7,8-HxBDF
1,2,3,4,6,7,8-HpBDF
1,2, 3,4,7,8, 9-HpBDF
LOQ
U/g/kg)
0.1
0.5
2.5
2.5
2.5
100
1
5
5
25
25
25
25
1,000
1,000
/yg/kg = microgram per kilogram
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December 2003
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Table 8-20. Precursor Chemicals Subject to Reporting Requirements
under TSCA Section 8(a)
CAS No.
Chemical Name
85-22-3
87-61-6
87-84-3
89-61-2
89-64-5
89-69-0
92-04-6
97-74-6
94-81-5
95-50-1
95-56-7
95-57-8
95-88-5
95-94-3
95-50-7
99-30-9
99-54-7
106-37-6
106-46-7
108-70-3
108-86-1
108-90-7
1 17-18-0
120-82-1
348-51-6
350-30-1
615-67-8
626-39-1
827-94-1
Pentabromoethy I benzene
1,2,3-Trichlorobenzene
1,2,3,4,5-Pentabromo-6-chlorocyclohexane
1,4-Dichloro-2-nitrobenzene
4-Chloro-2-nitrophenol
2,4,5-Trichloronitrobenzene
2-Chloro-4-phenylphenol
4-Chloro-o-toloxy acetic acid
4-(2-Methyl-4-chlorophenoxy) butyric acid
o-Dichlorobenzene
o-Bromophenol
o-Chlorophenol
4-Chlororesorcinol
1,2,4,5-Tetrachlorobenzene
5-Chloro-2,4-dimethoxy aniline
2,6-Dichloro-4-nitroaniline
1,2-Dichloro-4-nitrobenzene
Dibromobenzene
p-Dichlorobenzene
1,3,5-Trichlorobenzene
Bromobenzene
Chlorobenzene
1,2,4,5-Tetrachloro-3-nitrobenzene
1,2,4-Trichlorobenzene
o-Chlorofluorobenzene
3-Chloro-4-fluoronitro benzene
Chlorohydroquinone
1,3,5-Tribromobenzene
2,6-Dibromo-4-nitroaniline
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December 2003
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Table 8-21. Results of Analytical Testing for Dioxins and Furans in the Chemicals
Tested to Date under Section 4 of the Dioxin/Furan Test Rule
CAS
Number
79-94-7
118-75-2
118-79-6
120-83-2
1163-19-5
25327-89-3
32536-52-0
378-53-59-1
32534-81-9
4162-45-2
Chemical Name
Tetrabromobisphenol-A
2,3,5,6-Tetrachloro-2,5-
cyclohexadiene-
1,4-dione (chloranil)
2,4,6-Tribromophenol
2,4-Dichlorophenol
Decabromodiphenyl oxide
Allyl ether of
tetrabromobisphenol-A
Octabromodiphenyl oxide
1 ,2-Bis(tribromo-phenoxy)-
ethane
Pentabromodiphenyl oxide
Tetrabromobisphenol-A-
bisethoxylate
No. of
Chemical
Companies
That Submitted
Data
3
4
1
1
3
1
3
1
2
1
No. of
Positive
Studies
0
4
0
0
3
0
3
1
2
0
Congeners Detected
(detection range: //g/kg)
NDa
See Table 8-22
NDa
NDa
2,3,7,8-PeBDD (ND-0.1)
1 ,2,3,4,7,8/1 ,2,3,6,7,8-HxBDD (ND-0.5)
1,2,3,7,8,9-HxBDD (ND-0.76)
1,2,3,7,8-PeBDF (ND-0.7)
1 ,2,3,4,7,8/1 ,2,3,6,7,8-HxBDF (ND-0.8)
1,2,3,4,6,7,8-HpBDF (17-186)
NDa
2,3,7,8-TBDD (ND-0.71)
1, 2,3,7,8-PeBDD (ND-0.1)
2,3,7,8-TBDF (ND-12.6)
1,2,3,7,8-PeBDF (ND-6.3)
2,3,4,7,8-PeBDF (ND-83.1)
1,2,3,4,7,8/1,2,3,6,7,8-HxBDF (ND-67.8)
1,2,3,7,8,9-HxBDF (ND-56.0)
1,2,3,4,6,7,8-HpBDF (ND-330)
2,3,7,8-TBDF (ND-0.04)
1 ,2,3,4,7,8/1 ,2,3,6,7,8-HxBDF (ND-0.03)
1,2,3,4,6,7,8-HpBDF (ND-0.33)
1, 2,3,7,8-PeBDD (ND-5.9)
1 ,2,3,4,7,8/1 ,2,3,6,7,8-HxBDD (ND-6.8)
1 ,2,3,4,7,8/1 ,2,3,6,7,8-HxBDD (ND-6.8)
1,2,3,7,8,9-HxBDD (ND-0.02)
2,3,7,8-TBDF(ND-3.1)
1,2,3,7,8-PeBDF (0.7-10.2)
2,3,4,7,8-PeBDF (0.1-2.9)
1,2,3,4,7,8/1,2,3,6,7,8-HxBDF (15.6-61.2)
1,2,3,4,6,7,8-HpBDF (0.7-3.0)
NDa
//g/kg = micrograms per kilogram
a No 2,3,7,8-substituted dioxins and furans detected above the Test Rule target limits of quantitation (LOQ). (See Table 8-
18.)
Source: Holderman and Cramer (1995).
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December 2003
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Table 8-22. CDDs and CDFs in Chloranil and Carbazole Violet
Samples Analyzed Pursuant to the EPA Dioxin/Furan Test Rule
Congener
2,3,7,8-TCDD
1,2,3,7,8-PeCDD
1,2, 3,4,7, 8-HxCDD
1,2,3,6,7,8-HxCDD
1,2,3,7,8,9-HxCDD
1,2,3,4,6,7,8-HpCDD
OCDD
2,3,7,8-TCDF
1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
1,2, 3,4,7, 8-HxCDF
1,2,3,6,7,8-HxCDF
1,2,3,7,8,9-HxCDF
2,3,4,6,7,8-HxCDF
1,2,3,4,6,7,8-HpCDF
1,2,3,4,7,8,9-HpCDF
OCDF
TOTAL I-TEQDF*
TOTAL TEQDF-WH098*
Concentration (|Jg/kg) in Chloranil
Importer
1
nd (1)
nd (2)
nd (3)
nd (3)
nd (1)
110
240,000
nd (1)
nd (1)
nd (1)
35
nd (5)
6
nd (5)
33
nd (15)
18,000
263
31
Importer
2
nd (1)
nd (2)
nd (10)
75
48
8,200
180,000
nd (2)
nd (1)
nd (1)
nd (860)
nd (860)
nd (680)
nd (680)
240,000
nd (100)
200,000
2,874
2,532
Importer
3
nd (2)
nd (5)
nd (5)
nd (5)
nd (5)
390
760,000
nd (1)
nd (3)
nd (3)
nd (4)
nd (4)
nd (4)
nd (4)
36
nd (15)
50,000
814
85
Importer
4
nd (2)
nd (6)
nd (3)
6
9
2,300
71,000
nd (2)
nd (5)
nd (5)
5,600
nd (600)
nd (600)
nd (600)
230,000
nd (400)
110,000
3,065
2,903
Concentration
(|Jg/kg) in
Carbazole
Violet
nd (0.8)
nd (0.5)
nd (1.2)
nd (1.2)
nd (1.2)
28
1,600
nd (1.6)
nd (0.9)
nd (0.9)
nd (20)
nd (20)
nd (20)
nd (20)
15,000
nd (20)
59,000
211
156
nd = Not detected; minimum limit of detection shown in parentheses.
/yg/kg = micrograms per kilogram.
* Calculated assuming not-detected values are zero.
Source: Remmers et al. (1 992).
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CD
Table 8-23. Status of First Pesticide Data Call-in: Pesticides Suspected of Having the Potential to
Become Contaminated with Dioxins if Synthesized under Conditions Favoring Dioxin Formation
Shaughnessey
Code
000014
008706
009105
012001
012101
019201
019202
019401
025501
027401
028201
028601
029201
029601
029902
029906
030602
031301
031503
031516
031563
034502
035502
035505
Pesticide [Active Ingredient]
Dichlorodifluoromethane
0-(4-Bromo-2,5-dichlorophenyl) 0,0-dimethyl phosphorothioate
Di methyl amine 2,3,5-triiodobenzoate
Neburon
Crufomate
MCPB, 4-butyric acid [4-(2-Methyl-4-chlorophenoxy)butyric acid]
MCPB, Na salt [Sodium 4-(2-methyl-4-chlorophenoxy)butyrate]
4-Chlorophenoxyacetic acid
Chloroxuron
Dichlobenil
Propanil [3',4'-Dichloropropionanilide]
Dichlofenthion [0-(2,4-Dichlorophenyl) 0,0-diethyl phosphorothioate)]
DDT [Dichloro diphenyl trichloroethane]
Dichlone [2,3-dichloro-1 ,4-naphthoquinone]
Ammonium chloramben [3-amino-2,5-dichlorobenzoic acid]
Sodium chloramben [3-amino-2,5-dichlorobenzoic acid]
Sodium 2-(2,4-dichlorophenoxy)ethyl sulfate
DCNA [2,6-Dichloro-4-nitroaniline]
Potassium 2-(2-methyl-4-chlorophenoxy)propionate
MCCP, DBA Salt [Diethanolamine 2-(2-methyl-4-chlorophenoxy)propionate]
MCPP, IDE [Isooctyl 2-(2-methyl-4-chlorophenoxy)propionate]
Dicapthon [0-(2-chloro-4-nitrophenyl) 0,0-dimethyl phosphorothioate]
Monuron trichloroacetate [3-(4-chlorophenyl)-1 ,1-dimethylurea trichloroacetate]
Diuron [3-(3,4-dichlorophenyl)-1 ,1-dimethylurea]
CAS Number
75-71-8
2104-96-3
17601-49-9
555-37-3
299-86-5
94-81-5
6062-26-6
122-88-3
1982-47-4
1194-65-6
709-98-8
97-17-6
50-29-3
117-80-6
1076-46-6
1954-81-0
136-78-7
99-30-9
1929-86-8
1432-14-0
28473-03-2
2463-84-5
140-41-0
330-54-1
Support
Withdrawn
Yes
Yes
Yes
Yes
Yes
No
No
No
Yes
No
No
Yes
Yes
Yes
Yes
Yes
Yes
No
Yes
Yes
No
Yes
Yes
No
Testing
Required
—
—
-
--
—
Yes
No
Yes
—
Yes
No
—
-
—
-
—
-
Yes
-
—
No
—
—
No
-------
CD
Table 8-23. Status of First Pesticide Data Call-In: Pesticides Suspected of Having the Potential to
Become Contaminated with Dioxins if Synthesized under Conditions Favoring Dioxin Formation (continued)
Shaughnessey
Code
035506
035901
053501
055001
055005
055201
057501
058102
058301
058802
059401
061501
062201
062202
062203
062204
062206
062207
062208
062209
062210
062211
062212
062213
Pesticide [Active Ingredient]
Linuron [3-(3,4-dichlorophenyl)-1 -methoxy-1 -methylurea]
Metobromuron [3-(p-bromophenyl)-1 -methoxy-1 -methylurea]
Methyl parathion [0,0-Dimethyl 0-p-nitrophenyl phosphorothioate]
Dichlorophene [Sodium 2,2'-methylenebis(4-chlorophenate)]
Dichlorophene, sodium salt [Sodium 2,2'-methylenebis(4-chlorophenate)]
1 ,2,4,5-Tetrachloro-3-nitrobenzene
Ethyl parathion [0,0-diethyl 0-p-nitrophenyl phosphorothioate]
Carbophenothion [S-(((p-chlorophenyl)thio)methyl) 0,0-diethyl phosphorodithioate]
Ronnel [0,0-dimethyl 0-(2,4,5-trichlorophenyl) phosphorothioate]
Mitin FF [Sodium 5-chloro-2-(4-chloro-2-(3-(3,4-dichlorophenyl)ureido)phenoxy)
benzenesulfonate]
Orthodichlorobenzene
Paradichlorobenzene
Chlorophene [2-Benzyl-4-chlorophenol]
Potassium 2-benzyl-4-chlorophenate
Sodium 2-benzyl-4-chlorophenate
2-Chlorophenol
2-Chloro-4-phenylphenol
Potassium 2-chloro-4-phenylphenate
4-Chloro-2-phenylphenol
4-Chloro-2-phenylphenol, potassium salt
6-Chloro-2-phenylphenol
6-Chloro-2-phenylphenol, potassium salt
4-Chloro-2-phenylphenol, sodium salt
6-Chloro-2-phenylphenol, sodium salt
CAS Number
330-55-2
3060-89-7
298-00-0
97-23-4
10254-48-5
117-18-0
56-38-2
786-19-6
229-84-3
3567-25-7
95-50-1
106-46-7
120-32-1
35471-49-9
3184-65-4
95-57-8
92-04-6
18128-16-0
not available
53404-21-0
85-97-2
18128-17-1
10605-10-4
10605-11-5
Support
Withdrawn
No
Yes
No
Yes
Yes
Yes
No
Yes
Yes
No
Yes
No
No
No
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Testing
Required
No
—
No
—
—
—
No
—
—
No
--
No
No
In review
In review
--
--
-
—
—
--
—
—
-
-------
CD
Table 8-23. Status of First Pesticide Data Call-in: Pesticides Suspected of Having the Potential to
Become Contaminated with Dioxins if Synthesized under Conditions Favoring Dioxin Formation (continued)
Shaughnessey
Code
062214
062215
064202
064208
064209
064214
064218
067707
069105
069144
077401
077406
078780
079202
079301
080403
080811
081901
082602
084101
Pesticide [Active Ingredient]
4 and 6-Chloro-2-phenylphenol, diethanolamine salt
2-Chloro-4-phenylphenol, sodium salt
4-Chloro-2-cyclopentylphenol
Fentichlor [2,2'-Thiobis(4-chloro-6-methylphenol)]
Fentichlor [2,2'-Thiobis(4-chlorophenol)]
4-Chloro-2-cyclopentylphenol, potassium salt of
4-Chloro-2-cyclopentylphenol, sodium salt
Chlorophacinone
ADBAC [Alkyl* dimethyl benzyl ammonium chloride *(50% C14, 40% C12, 10% C16)]
ADBAC [Alkyl* dimethyl 3,4-dichlorobenzyl ammonium chloride *(61% C12, 23% C14,
11% C16, 5% C18)]
Niclosamide [2-Aminoethanol salt of 2',5-dichloro-4'-nitrosalicylanilide]
5-Chlorosalicylanilide
2-Methyl-4-isothiazolin-3-one
Tetradifon [4-chlorophenyl 2,4,5-trichlorophenyl sulfone]
Chloranil [tetrachloro-p-benzoquinone]
6-Chlorothymol
Anilazine [2,4-Dichloro-6-(o-chloroanilino)-s-triazine]
Chlorothalonil [tetrachloroisophthalonitrile]
Sodium 2,3,6-Trichlorophenylacetate
Chlorfenvinphos
CAS Number
53537-63-6
31366-97-9
13347-42-7
4418-66-0
97-24-5
35471-38-6
53404-20-9
3691-35-8
68424-85-1
not available
1420-04-8
4638-48-6
Not available
116-29-0
118-75-2
89-68-9
101-05-3
1897-45-6
2439-00-1
470-90-6
Support
Withdrawn
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
No
No
No
Yes
Yes
Yes
Yes
Yes
Yes
No
Yes
Yes
Testing
Required
__
__
__
__
__
__
_
No
No
No
No
__
__
__
__
Yes
__
-------
CD
Table 8-23. Status of First Pesticide Data Call-In: Pesticides Suspected of Having the Potential to
Become Contaminated with Dioxins if Synthesized under Conditions Favoring Dioxin Formation (continued)
Shaughnessey
Code
100601
101001
101101
104301
106001
108201
109001
109301
109302
109801
109901
110902
111401
111601
111901
112802
113201
119001
123901
125601
128838
206600
Pesticide [Active Ingredient]
Fenamiphos
p-Chlorophenyl diiodomethyl sulfone
Metribuzin
Bifenox [methyl 5-(2,4-dichlorophenoxy)-2-nitrobenzoate]
Methazole [2-(3,4-dichlorophenyl)-4-methyl-1 ,2,4-oxadiazolidine-3,5-dione]
Diflubenzuron [N-(((4-chlorophenyl)amino)carbonyl)-2,6-difluorobenzamide]
Oxadiazon [2-tert-butyl-4-(2,4-dichloro-5-isopropoxyphenyD- delta 2 -1,3,4-
oxadiazoline-5-one]
Fenvalerate
Fluvalinate [N-2-Chloro-4-trifluoromethyl)phenyl-DL-valine ( + -)-cyano(3-phenoxy-
phenyllmethyl ester]
Iprodione [3-(3,5-Dichlorophenyl)-N-(1 -methylethyl)-2,4-dioxo-1 -
imidazolidinecarboxamide (9CA)]
Triadimefon [1 -(4-Chlorophenoxy)-3,3-dimethyl-1 -(1 H-1 ,2,4-triazol-1 -yl)-2-butanone]
Diclofop - methyl [methyl 2-(4-(2,4-dichlorophenoxy)phenoxy)propanoate]
Profenofos [0-(4-Bromo-2-chlorophenyl)-0-ethyl S-propyl phosphorothioate]
Oxyfluorfen [2-chloro-1-(3-ethoxy-4-nitroph en oxy)-4-(trifluoromethyl) benzene]
Imazalil [1 -(2-(2,4-Dichlorophenyl)-2-(2-propenyloxy)ethyl)-1 H-imidazole]
Bromothalin [N-Methyl-2,4-dinitro-n-(2,4,6-tribromophenyl)-6-
(trifuloromethyl)benzenamine]
Vinclozolin [3-(3,5-Dichlorophenyl)-5-ethenyl-5-methyl-2,4-oxazolidinedione (9CA)]
Fenridazon [Potassium 1-(p-chlorophenyl)-1 ,4-dihydro-6-methyl-4-oxo- pyridazine-
3-carboxylate]
Tridiphane [2-(3,5-Dichlorophenyl)-2-(2,2,2-trichloroethyl) oxirane]
Paclobutrazol
Linalool
Fenarimol [a-(2-chlorophenyl)-a-(4-chlorophenyl)-5-pyrimidinemethanol]
CAS Number
Not available
20018-12-6
21087-64-9
42576-02-3
20354-26-1
35367-38-5
19666-30-9
51630-58-1
69409-94-5
36734-19-7
43121-43-3
51338-27-3
41198-08-7
42874-03-3
35554-44-0
63333-35-7
50471-44-8
83588-43-6
58138-08-2
76738-62-0
78-70-6
60168-88-9
Support
Withdrawn
No
Yes
No
Yes
Yes
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
Testing
Required
No
—
No
—
--
Yes
Yes
In review
No
No
No
Yes
In review
In review
No
No
No
In review
No
No
In review
No
-------
Table 8-24. Status of Second Pesticide Data Call-in: Pesticides Suspected of Being Contaminated with Dioxins
Shaughnessey
Code
029801
029802
029803
030001
030002
030003
030004
030005
030010
03001 1
030013
030014
030016
030017
030019
030020
030021
030023
030024
030025
030028
030029
030030
030033
030034
Pesticide [Active Ingredient]
Dicamba [3,6-dichloro-o-anisic acid]
Dicamba dimethylamine [3,6-dichloro-o-anisic acid]
Diethanolamine dicamba [3,6-dichloro-2-anisic acid]
2,4-Dichlorophenoxyacetic acid
Lithium 2,4-dichlorophenoxyacetate
Potassium 2,4-dichlorophenoxyacetate
Sodium 2,4-dichlorophenoxyacetate
Ammonium 2,4-dichlorophenoxyacetate
Alkanol* amine 2,4-dichlorophenoxyacetate *(salts of the ethanol and ispropanol series)
Alkyl* amine 2,4-dichlorophenoxyacetate *(100% C12)
Alkyl* amine 2,4-dichlorophenoxyacetate *(100% C14)
Alkyl* amine 2,4-dichlorophenoxyacetate *(as in fatty acids of tall oil)
Diethanolamine 2,4-dichlorophenoxyacetate
Diethylamine 2,4-dichlorophenoxyacetate
Dimethylamine 2,4-dichlorophenoxyacetate
N,N-Dimethyloleylamine 2,4-dichlorophenoxyacetate
Ethanolamine 2,4-dichlorophenoxyacetate
Heptylamine 2,4-dichlorophenoxyacetate
Isopropanolamine 2,4-dichlorophenoxyacetate
Isopropylamine 2,4-dichlorophenoxyacetate
Morpholine 2,4-dichlorophenoxyacetate
N-Oleyl-1 ,3-propylenediamine 2,4-dichlorophenoxyacetate
Octylamine 2,4-dichlorophenoxyacetate
Triethanolamine 2,4-dichlorophenoxyacetate
Triethylamine 2,4-dichlorophenoxyacetate
CAS Number
1918-00-9
2300-66-5
25059-78-3
94-75-7
3766-27-6
14214-89-2
2702-72-9
2307-55-3
Not available
2212-54-6
28685-18-9
Not available
5742-19-8
20940-37-8
2008-39-1
53535-36-7
3599-58-4
37102-63-9
6365-72-6
5742-17-6
6365-73-7
2212-59-1
2212-53-5
2569-01-9
2646-78-8
Support
Withdrawn
No
No
Yes
No
No
Yes
No
Yes
Yes
Yes
Yes
Yes
No
Yes
No
Yes
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
No
Testing
Required
Yes
Yes
—
Yes
No
__
No
__
—
-
—
No
No
—
-
-
No
-
-
—
No
K>
O
o
CO
-------
CD
Table 8-24. Status of Second Pesticide Data Call-in: Pesticides Suspected of Being Contaminated with Dioxins (continued)
Shaughnessey
Code
030035
030039
030052
030053
030055
030056
030062
030063
030064
030065
030066
030072
030801
030804
030819
030853
030856
030863
031401
031419
031453
031463
Pesticide [Active Ingredient]
Triisopropanolamine 2, 4-dichlorophenoxy acetate
N,N-Dimethyl oleyl-linoleyl amine 2,4-dichlorophenoxyacetate
Butoxyethoxypropyl 2,4-dichlorophenoxyacetate
Butoxy ethyl 2,4-dichlorophenoxyacetate
Butoxypropyl 2,4-dichlorophenoxyacetate
Butyl 2,4-dichlorophenoxyacetate
Isobutyl 2,4-dichlorophenoxyacetate
lsooctyl(2-ethylhexyl) 2,4-dichlorophenoxyacetate
lsooctyl(2-ethyl-4-methylpentyl) 2,4-dichlorophenoxyacetate
lsooctyl(2-octyl) 2,4-dichlorophenoxyacetate
Isopropyl 2,4-dichlorophenoxyacetate
Propylene glycol butyl ether 2,4-dichlorophenoxyacetate
4-( 2, 4-Dichlorophenoxy) butyric acid
Sodium 4-( 2, 4-dichlorophenoxy) buty rate
Dimethylamine 4-( 2, 4-dichlorophenoxy Ibutyrate
Butoxy ethanol 4-( 2, 4-dichlorophenoxy Ibutyrate
Butyl 4-( 2, 4-dichlorophenoxy Ibutyrate
Isooctyl 4-( 2, 4-dichlorophenoxy Ibutyrate
2-(2,4-Dichlorophenoxy)propionic acid
Dimethylamine 2-( 2, 4-dichlorophenoxy Ipropionate
Butoxy ethyl 2-( 2, 4-dichlorophenoxy) propionate
Isooctyl 2-( 2, 4-dichlorophenoxy Ipropionate
CAS Number
32341-80-3
55256-32-1
1928-57-0
1929-73-3
1928-45-6
94-80-4
1713-15-1
1928-43-4
25168-26-7
1917-97-1
94-11-1
1320-18-9
94-82-6
10433-59-7
2758-42-1
32357-46-3
6753-24-8
1320-15-6
120-36-5
53404-32-3
53404-31-2
28631-35-8
Support
Withdrawn
No
Yes
Yes
No
Yes
Yes
Yes
No
Yes
Yes
No
Yes
No
No
No
Yes
Yes
Yes
No
No
No
No
Testing
Required
No
__
__
No
__
__
Yes
__
No
Yes
No
No
__
Yes
No
No
No
-------
CD
Table 8-24. Status of Second Pesticide Data Call-In: Pesticides Suspected of Being Contaminated with Dioxins (continued)
Shaughnessey
Code
031519
035301
044901
044902
044904
054901
063004
063005
063006
063007
064203
064212
064219
064220
064501
078701
079401
082501
083701
104101
Pesticide [Active Ingredient]
MCPP, DMA [Dimethylamine 2-(2-methyl-4-chlorophenoxy)propionate]
Bromoxynil [3,5-dibromo-4-hydroxybenzonitrile]
Hexachlorophene [2,2'-Methylenebis(3,4,6-trichlorophenol)]
Hexachlorophene, Na salt [Monosodium 2,2'-methylenebis(3,4,6-trichlorophenate)]
Hexachlorophene, K salt [Potassium 2,2'-methylenebis(3,4,6-trichlorophenate)[
Irgasan [5-Chloro-2-( 2, 4-dichlorophenoxy) phenol]
Tetrachlorophenols
Tetrachlorophenols, sodium salt
Tetrachlorophenols, alkyl* amine salt*(as in fatty acids of coconut oil)
Tetrachlorophenols, potassium salt
Bithionolate sodium [Disodium 2,2'-thiobis(4,6-dichlorophenate)]
Phenachlor [2,4,6-Trichlorophenol]
Potassium 2,4,6-trichlorophenate
2,4,6-Trichlorophenol, sodium salt
Phenothiazine
Dacthal-DCPA [Dimethyl tetrachloroterephthalate]
Endosulfan [hexachlorohexahydromethano-2,4,3-benzodioxathiepin-3-oxide]
Silvex [2-(2,4,5-trichlorophenoxy)propionic acid]
Tetrachlorvinphos [2-Chloro-1-(2,4,5-trichlorophenyl)vinyl dimethyl phosphate]
Edolan [Sodium 1 ,4',5'-trichloro-2'-(2,4,5-trichlorophenoxy)
methanesulfonanilide]
CAS Number
32351-70-5
1689-84-5
70-30-4
5736-15-2
67923-62-0
3380-34-5
25167-83-3
25567-55-9
not available
53535-27-6
6385-58-6
88-06-2
2591-21-1
3784-03-0
92-84-2
1861-32-1
115-29-7
93-72-1
961-11-5
69462-14-2
Support
Withdrawn
No
No
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
No
Yes
No
Yes
Testing
Required
No
Yes
__
__
Yes
__
__
__
__
Yes
No
Yes
-
-------
Table 8-25. Summary of Results for CDDs and CDFs in
Technical 2,4-D and 2,4-D Ester Herbicides
Congener
2,3,7,8-TCDD
1, 2,3,7, 8-PeCDD
1,2, 3,4,7, 8-HxCDD
1,2,3,6,7,8-HxCDD
1,2,3,7,8,9-HxCDD
1,2,3,4,6,7,8-HpCDD
OCDD
2,3,7, 8-TCDF
1, 2,3,7, 8-PeCDF
2,3,4,7, 8-PeCDF
1,2, 3,4,7, 8-HxCDF
1, 2,3,6,7, 8-HxCDF
1, 2,3,7, 8,9-HxCDF
2,3,4,6,7, 8-HxCDF
1, 2,3,4,6,7, 8-HpCDF
1, 2,3,4,7, 8,9-HpCDF
OCDF
EPA LOQa
(/vg/kg)
0.1
0.5
2.5
2.5
2.5
100
__
1
5
5
25
25
25
25
1000
1000
__
Total
Number of
Technicals
8
8
8
8
8
8
__
8
8
7
8
8
8
8
8
8
Number of
Technicals
Greater Than
LOQ
2
3
0
0
0
0
__
0
0
0
0
0
0
0
0
0
__
Observed
Maximum
Concentration
(/vg/kg)
0.13
2.6
0.81
0.77
0.68
1.5
__
0.27
0.62
0.73
1.6
1.2
1.4
1.1
8.3
1.2
__
TOTAL0
I-TEQDF
TEQDF-WH098
Average
Concentration13
(/vg/kg)
0.06
0.78
0.31
0.39
0.24
0.21
__
0.07
0.38
0.07
0.36
0.11
0.16
0.14
2.17
0.18
__
5.60
0.70
1.10
a Limit of quantitation required by EPA in the Data Call-in.
b Average of the mean results for multiple analyses of four technical 2,4-D and/or 2,4-D ester products for
which detectable CDD/CDF congener concentrations less than the LOQs were quantified; not-detected
values were assumed to be zero.
c Total equals the sum of the individual congener averages.
/yg/kg = micrograms per kilogram
- = Analyses not performed.
Source: U.S. EPA Office of Pesticide Program file.
DRAFT-DO NOT QUOTE OR CITE
8-80
December 2003
-------
CD
Table Q-26. Summary of Analytical Data Submitted to EPA in Response to Pesticide Data Call-ins
Shaughnessey
Code
019201
019401
027401
029801
029802
030001
030063
030801
031301
031401
031501
035301
054901
078701
081901
083701
108201
109001
110902
Pesticide
Common Name
MCPB, 4-butyric acid
4-CPA
Dichlobenil
Dicamba
Dicamba, dimethylamine
2,4-D
2,4-D, 2EH
2,4-DB
DCNA
2,4-DP
Mecoprop (MCPP)
Bromoxynil
Irgasan
Dacthal (DCPA)
Chlorothalonil
Tetrachlorvinphos
Diflubenzuron
Oxadiazon
Dichlofop-methyl
Chemical Name
4-(2-methyl-4-chlorophenoxy)butyric acid
4-Chlorophenoxyacetic acid
2,6-Dichlorobenzonitrile
3,6-Dichloro-o-anisic acid
3,6-Dichloro-o-anisic acid, dimethylamine salt
2,4-Dichlorophenoxy acetic acid
lsooctyl(2-ethylhexyl)2,4-dichlorophenoxyacetate
4-(2,4-Dichlorophenoxy) butyric acid
2,6-Dichloro-4-nitroaniline
2-(2,4-Dichlorophenoxy)propionic acid
2-(2-methyl-4-chlorophenoxy)propionic acid
3,5-Dibromo-4-hydroxybenzonitrile
5-Chloro-2-(2,4-dichlorophenoxy) phenol
Dimethyl tetrachloroterephthalate
Tetrachloroisophthalonitrile
2-Chloro-1 -(2,4,5-trichlorophenyl)vinyl dimethyl phosphate
N-(((4-chlorophenyl)amino)carbonyl)-2,6-difluorobenzamide
2-Tert-butyl-4(2,4-dichloro-5-isopropoxyphenyl)-delta2-1 ,3,4-oxadiazoline-5-one
Methyl-2-(4-(2,4-dichlorophenoxy)phenoxy) propanoate
Number of
Positive
Submissions8 to
Date
0
0
0
0
0
2
1
0
Pending
0
0
0
0
Pending
Pending
0
0
Pending
0
o
o
-H
D
c;
O
O
53
00
do
D
CD
o
CD
3
-
K>
O
O
CO
a "Positive" is defined as the detection of any congener at a concentration equal to or exceeding the LOQs listed in Table 8-24.
Sources: U.S. EPA (1995a); personal communication with S. Funk (EPA/OPP/HED) on March 27, 1996.
-------
Table 8-27. CDD/CDF Concentrations in Samples of 2,4-D and Pesticide Formulations Containing 2,4-D
Congener/Congener Group
2,3,7,8-TCDD
1,2,3,7,8-PeCDD
1,2,3,4,7,8-HxCDD
1,2,3,6,7,8-HxCDD
1,2,3,7,8,9-HxCDD
1,2,3,4,6,7,8-HpCDD
OCDD
2,3,7,8-TCDF
1,2,3,7,8-/1,2,3,4,8-PeCDF
2,3,4,7,8-PeCDF
1,2,3, 4,7, 8-/1, 2,3, 4,7, 9-HxCD
F
1,2,3,6,7,8-HxCDF
1,2,3,7,8,9-HxCDF
2,3,4,6,7,8-HxCDF
1,2,3, 4,6,7, 8-HpCDF
1,2,3,4,7,8,9-HpCDF
OCDF
Total 2,3,7,8-CDD (ND = 0)
Total 2,3,7,8-CDF (ND = 0)
Total I-TEQDF (ND = 0)**
Total TEQDF-WH098 (ND =
0)**
Total TCDD
Total PeCDD
Total HxCDD
Total HpCDD
Total OCDD
Total TCDF
Total PeCDF
Total HxCDF
Total HpCDF
Total OCDF
Total CDD/CDF
Acbar Super
(Gaza City*)
(//g/kg)
ND (0.1)
0.1
ND (0.1)
ND (0.1)
ND (0.1)
0.1
0.1
0.3
ND (0.1)
ND (0.1)
ND (0.1)
ND (0.1)
ND (0.1)
ND (0.1)
0.1
ND (0.1)
0.2
0.3
0.6
0.082
0.134
—
-
-
--
—
-
-
-
--
--
--
Amco Super
(Gaza City*)
(//g/kg)
ND (0.1)
ND (0.1)
ND (0.1)
0.2
ND (0.1)
1.2
2.6
ND (0.1)
0.2
ND (0.1)
0.1
ND (0.1)
ND (0.1)
ND (0.1)
0.8
ND (0.1)
3.8
4
4.9
0.066
0.061
—
-
-
--
—
-
-
-
--
--
--
(Bethlehem)*
(//g/kg)
ND (0.1)
1.2
ND (0.1)
0.6
0.4
0.3
0.1
ND (0.1)
0.7
0.1
0.4
0.1
ND (0.1)
0.1
0.1
ND (0.1)
0.4
2.6
1.9
0.850
1.449
—
-
-
--
—
-
-
-
--
--
--
Chimprom
(Russia)
(//g/kg)
ND (0.02)
0.03
0.02
0.05
ND (0.02)
0.23
0.85
ND (0.1)
1.2
0.06
0.08
0.11
ND (0.02)
0.05
0.24
0.02
0.46
1.18
2.22
0.142
0.156
—
-
-
--
—
-
-
-
--
--
--
Dragon
Lawn Weed
Killer
(//g/kg)
ND (0.001)
0.0014
ND (0.001)
0.0024
0.0010
0.0017
0.0063
0.0036
0.0010
0.0011
0.0013
ND (0.001)
ND (0.001)
0.0011
0.0016
ND (0.001)
0.0039
0.0128
0.0136
0.0023
0.0030
—
-
-
--
—
-
-
-
--
--
--
KGRO
(U.S.)
(//g/kg)
-
-
-
-
-
--
--
-
-
-
__
-
-
-
-
-
--
0.0144
0.1628
0.0009
—
-
-
--
—
-
-
-
--
--
--
Pro Care
Premium
(U.S.)
(//g/kg)
-
-
-
-
-
--
--
-
-
-
__
-
-
-
-
--
--
0.0143
0.4253
0.0012
—
-
-
--
—
-
-
-
--
—
--
Ortho
Weed-B-Gone
(U.S.)
(//g/kg)
-
-
-
-
-
--
--
-
--
--
__
-
--
--
-
--
--
0.0091
0.1095
0.0014
-
-
-
--
—
-
-
-
--
—
--
Sigma Co.
(U.S.)
(//g/kg)
-
-
-
-
-
--
--
-
--
--
__
-
--
--
-
--
--
0.127
3.0507
0.0013
-
-
-
--
—
-
-
-
--
—
--
American
Brand
Chemical Co.
(U.S.)
(//g/kg)
-
-
-
-
-
--
--
-
--
--
__
-
--
--
-
--
--
0.0278
0.0822
0.0019
-
-
-
--
—
-
-
-
--
—
--
o
o
^1
D
C
O
§
00
do
ro
o
CD
O
CD
3
-
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-- = Not reported.
//g/kg = micrograms per kilogram
* 2,4-D manufactured in Europe and packaged in Palestine.
** Calculated assuming not-detected values are zero.
-------
Table 8-28. Mean CDD/CDF Measurements in Effluents from Nine U.S. POTWs
Congener/Congener
Group
2,3,7,8-TCDD
1,2,3,7,8-PeCDD
1,2,3,4,7,8-HxCDD
1,2,3,6,7,8-HxCDD
1,2,3, 7,8, 9-HxCDD
1,2,3,4,6,7,8-HpCDD
OCDD
2,3,7,8-TCDF
1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
1,2,3,4,7,8-HxCDF
1,2,3,6,7,8-HxCDF
1,2,3, 7,8, 9-HxCDF
2,3,4,6,7,8-HxCDF
1,2,3,4,6,7,8-HpCDF
1, 2,3,4,7,8, 9-HpCDF
OCDF
Total 2,3,7,8-CDD
Total 2,3,7,8-CDF
Total I-TEQDF
Total TEQDF-WH098
Total TCDD
Total PeCDD
Total HxCDD
Total HpCDD
Total OCDD
Total TCDF
Total PeCDF
Total HxCDF
Total HpCDF
Total OCDF
Total CDD/CDF
No. Detections/
No. Samples
0/30
0/30
0/30
0/30
0/30
3/30
13/30
1/27
1/30
1/30
1/30
1/30
1/30
1/30
2/30
0/30
1/30
4/27
0/27
1/30
3/30
13/30
2/30
1/30
1/30
2/30
1/30
Range of
Detection
Limits
(pg/U
0.31 - 8.8
0.45 - 15
0.43 - 9.8
0.81 - 10
0.42 - 9.7
0.75 - 18
6.2-57
0.74-4.4
0.64 - 9.4
0.61 - 14
0.25 -6.8
0.23 -6.8
0.57 - 10
0.25 - 7.9
0.36-6.9
0.19- 11
0.86 - 28
1.2 - 8.8
0.62 - 200
0.84- 11
0.75 - 18
6.2 - 57
0.39-6.8
0.64 - 25
0.93 - 17
0.36 - 19
0.86-28
Range of Detected
Concentrations
(POTW mean basis)
Minimum
Detected
Cone.
(pg/U
nd
nd
nd
nd
nd
nd
nd
nd
nd
nd
nd
nd
nd
nd
nd
nd
nd
nd
nd
nd
nd
nd
nd
nd
nd
nd
nd
nd
nd
nd
nd
nd
Maximum
Detected
Cone.
(pg/D
nd
nd
nd
nd
nd
5.0
99.75
1.3
2.0
2.8
2.4
1.5
2.0
nd
4.6
nd
3.2
99.75
16.6
2.42
2.33
9.7
nd
1.7
8.4
99.75
25.0
20.0
13.0
4.6
3.2
99.75
Overall Means*
Mean
Cone.
(ND = 0)
(pg/D
0.00
0.00
0.00
0.00
0.00
1.06
29.51
0.14
0.22
0.31
0.27
0.17
0.22
0.00
0.68
0.00
0.36
30.57
2.37
0.29
0.27
1.23
0.00
0.19
1.83
29.51
6.61
2.22
1.44
0.68
0.36
42.00
Mean
Cone.
(ND = 1/2DL)
(pg/U
0.98
1.32
1.38
1.42
1.31
3.61
37.95
0.98
1.58
1.68
1.22
0.97
1.72
0.93
1.83
1.18
3.40
47.98
15.49
3.66
4.28
2.61
6.27
1.93
4.77
37.95
7.70
4.72
3.43
2.41
3.40
71.96
nd = Not detected.
pg/L = picograms per liter.
* The "overall means" are the means of the individual POTW mean concentrations rather than the means of the
individual sample concentrations.
Source: California Regional Water Quality Control Board (1996).
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December 2003
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CD
Table 8-29. CDD/CDF Concentrations Measured in EPA's National Sewage Sludge Survey
Congener
2,3,7,8-TCDD
1,2,3,7,8-PeCDD
1, 2,3,4,7, 8-HxCDD
1, 2,3,6,7, 8-HxCDD
1, 2,3,7, 8,9-HxCDD
1, 2,3,4,6,7, 8-HpCDD
OCDD
2,3,7, 8-TCDF
1, 2,3,7, 8-PeCDF
2,3,4,7, 8-PeCDF
1, 2,3,4,7, 8-HxCDF
1, 2,3,6,7, 8-HxCDF
1, 2,3,7, 8,9-HxCDF
2,3,4,6,7, 8-HxCDF
1, 2,3,4,6,7, 8-HpCDF
1, 2,3,4,7, 8,9-HpCDF
OCDF
Total I-TEQDF
Total 2,3,7,8-CDD/CDF
Percent
Detected
16
18
25
49
39
98
100
65
22
26
43
35
16
27
71
26
80
Maximum
Concentration
Detected
(ng/kg)
116
736
737
737
737
52,500
905,000
337
736
736
1,500
737
1,260
737
7,100
842
69,500
1,820
NR
Median Concentration (ng/kg)
Nondetects
Set to
Det. Limit
6.86
9.84
22.5
27.3
28.0
335
3,320
17.0
9.60
10.4
28.0
18.0
18.0
18.0
57.0
23.0
110
50.4
NR
Nondetects
Set to
Zero
0
0
0
0
0
335
3,320
3.90
0
0
0
0
0
0
36.0
0
80.0
11.2
NR
Mean Concentration (ng/kg)
Nondetects
Set to
Det. Limit
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
86*
NR
Nondetects
Set to
Zero
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
50*
NR
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o
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D
c;
O
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53
00
do
D
CD
o
CD
3
-
NR = Not reported.
ng/kg = nanograms per kilogram.
* Values presented by Rubin and White (1 992) for 1 75 rather than 1 74 POTWs.
Source: U.S. EPA (1996a); for POTWs with multiple samples, the pollutant concentrations were averaged before the summary statistics presented in the table were
calculated. All concentrations are in units of ng/kg dry weight.
K>
O
O
CO
-------
CD
Table 8-30. CDD/CDF Concentrations Measured in 99 Sludges Collected from U.S. POTWs During 1994
Congener
2,3,7, 8-TCDD
1,2,3,7,8-PeCDD
1, 2,3,4,7, 8-HxCDD
1, 2,3,6,7, 8-HxCDD
1, 2,3,7, 8,9-HxCDD
1, 2,3,4,6,7, 8-HpCDD
OCDD
2,3,7,8-TCDF
1,2,3,7,8-PeCDF
2,3,4,7, 8-PeCDF
1, 2,3,4,7, 8-HxCDF
1, 2,3,6,7, 8-HxCDF
1, 2,3,7, 8,9-HxCDF
2,3,4,6,7,8-HxCDF
1, 2,3,4,6,7, 8-HpCDF
1, 2,3,4,7, 8,9-HpCDF
OCDF
Average I-TEQDF (facility basis)b
Total 2,3,7,8-CDD/CDF
Average TEQDF-WH098 (facility basis)b
Percent
Detected
40
23
34
87
64
98
99
76
21
42
48
17
4
35
64
31
93
Maximum
Concentration
Detected
(ng/kg)
12.3
37.5
45.6
130
88.8
5,380
65,500
156
60.3
155
170
200
115
356
1,460
213
11,200
246
73,520
Median Concentration (ng/kg)
Nondetects
Set to
Det. Limit
1.95
8.23
5.25
25.6
12.3
642
6,630
7.53
7.91
9.70
11.5
14.0
7.53
9.85
91.7
11.7
286
49.6
7,916
44.6
Nondetects
Set to
Zero
0
0
0
24.7
9.48
642
6,630
6.28
0
0
0
0
0
0
31.8
0
281
33.4
7,881
25.5
Mean Concentration (ng/kg)
Nondetects
Set to
Det. Limit3
2.72 (2.40)
10.9 (7.80)
11.1 (8.13)
33.8 (27.6)
20.2 (17.7)
981 (977)
11,890 (12,540)
12.8 (19.6)
10.7 (11.3)
15.7 (19.8)
20.4 (25.3)
30.4 (53.6)
11.1 (13.6)
21.8 (40.4)
223 (271)
27.1 (34.8)
786 (1,503)
64.5 (50.1)
14,110 (14,390)
57.2 (44.4)
Nondetects
Set to
Zero3
1.71 (2.86)
3.34 (7.43)
6.03 (10.2)
32.2 (28.8)
17.0 (19.8)
981 (977)
11,890 (12,540)
11.1 (20.2)
3.53 (9.36)
10.5 (21.6)
14.0 (25.9)
5.13 (21.9)
1.56 (11.7)
13.6 (41.0)
97.5 (207)
15.0 (33.4)
775 (1,506)
47.7 (44.7)
13,880 (14,200)
36.3 (38.6)
o
o
-H
D
c;
O
O
53
00
do
01
D
CD
o
CD
3
-
K>
O
O
CO
a Values in parentheses are standard deviations.
b For POTWs with multiple samples, the sample TEQ concentrations were averaged to POTW averages before calculation of the total TEQ mean and median values
presented in the table. A total of 74 POTW average concentrations were used in the calculations. In addition, the following sample ID numbers were not included
in the averaging because, according to Green et al. (1995), it was not possible to determine if they were duplicate or multiple samples from other POTWs: 87, 88,
89, 90, 91, 97, 98, and 106.
Source: Green et al. (1995); Cramer et al. (1995).
-------
Table 8-31. Quantity of Sewage Sludge Disposed of Annually by Primary, Secondary,
or Advanced Treatment POTWs and Potential Dioxin TEQ Releases
Use/Disposal Practice
Land Application
Distribution and Marketing
Surface Disposal Site/Other
Sewage Sludge Landfill
Co-disposal Landfills8
Sludge Incinerators and Co-incinerators'1
Ocean Disposal
TOTAL
Volume Disposed
(thousands of dry
metric tons/year)
1,714
71
396
157
1,819
865
(336)d
5,357
Percent of
Total
Volume
32.0"
1.3
7.4
2.9
33.9
16.1
(6.3)d
100.0
Potential Dioxin Release0
(g of TEQ/yr)
I-TEQDF
84.0
3.5
19.4
7.7
89.1
(f)
(0)d
204.0
TEQDF-WH098
62.2
2.6
14.4
5.7
66.0
(f)
(0)d
151.0
Landfills used for disposal of sewage sludge and solid waste residuals.
Co-incinerators treat sewage sludge in combination with other combustible waste materials.
Potential dioxin TEQ release for nonincinerated sludges was estimated by multiplying the sludge volume generated (i.e.,
column 2) by the average of the mean I-TEQDF concentrations in sludge reported by Rubin and White (1992) (i.e., 50 ng/kg
dry weight) and Green et al. (1 995) and Cramer et al. (1 995) (i.e., 47.7 ng/kg). The calculations of TEQDF-WH098 used
the mean concentration of 36.3 ng TEQDF-WH098/kg for the results reported by Green et al. (1995) and Cramer et al.
(1995).
The Ocean Dumping Ban Act of 1988 generally prohibited the dumping of sewage sludge into the ocean after December
31, 1 991. Ocean dumping of sewage sludge ended in June 1 992 (Federal Register, 1 993b). The current method of
disposal of the 336,000 metric tons of sewage sludge that were disposed of in the oceans in 1 988 has not been
determined.
Includes 21.9 percent applied to agricultural land, 2.8 percent applied as compost, 0.6 percent applied to forestry land,
3.1 percent applied to "public contact" land, 1.2 percent applied to reclamation sites, and 2.4 percent applied in undefined
settings.
See Section 3.6.5 for estimates of CDD/CDF releases to air from sewage sludge incinerators.
DRAFT-DO NOT QUOTE OR CITE
8-86
December 2003
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Table 8-32. CDD/CDF Concentrations in Swedish Liquid Soap, Tall Oil, and Tall Resin
Congener/Congener Group
2,3,7,8-TCDD
1,2,3,7,8-PeCDD
1,2,3,4,7,8-HxCDD
1,2,3,6,7,8-HxCDD
1,2,3, 7,8, 9-HxCDD
1,2,3,4,6,7,8-HpCDD
OCDD
2,3,7,8-TCDF
1,2,3,4,8-/1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
1,2,3,4,7,8/9-HxCDF
1,2,3,6,7,8-HxCDF
1,2,3, 7,8, 9-HxCDF
2,3,4,6,7,8-HxCDF
1,2,3,4,6,7,8-HpCDF
1, 2,3,4,7,8, 9-HpCDF
OCDF
Total 2,3,7,8-CDD*
Total 2,3,7,8-CDF*
Total I-TEQDF*
Total TEQDF-WH098*
Total TCDD
Total PeCDD
Total HxCDD
Total HpCDD
Total OCDD
Total TCDF
Total PeCDF
Total HxCDF
Total HpCDF
Total OCDF
Total CDD/CDF*
Liquid Soap
(ng/L)
ND (0.009)
0.400
ND (0.020)
0.320
0.180
1.900
1.000
0.620
0.290
0.200
0.013
ND (0.004)
ND (0.004)
ND (0.004)
ND (0.005)
ND (0.010)
NA
3.8
1.123
0.447
0.647
0.120
15.000
3.400
3.600
1.000
1.000
1.300
0.150
ND (0.010)
NA
25.57
Tall Oil
(ng/kg)
3.6
5.3
ND (2)
ND (2)
ND (2)
ND (1)
5.3
17
4.2
1.9
1.4
0.7
ND (0.7)
ND (0.5)
ND (0.8)
ND (2)
NA
14.2
25.2
9.4
12.0
31
380
3.3
ND (1)
5.3
26
41
4.9
ND (2)
NA
491.5
Tall Resin
(ng/kg)
ND (1)
3.1
ND (4)
810
500
5,900
6,000
ND (2)
ND (0.4)
ND (0.5)
24
ND (1)
ND (0.7)
10
9.0
NA
13213.1
43
200
196
ND (1)
25
6,800
11,000
6,000
ND (2)
ND (0.5)
56
19
NA
23,900
* Calculations assume not-detected values are zero.
ng/kg = nanograms per kilogram.
Ng/L = nanograms per liter.
ND = Not detected; value in parentheses is the detection limit.
NA = Not analyzed.
= Not reported.
Source: Rappe et al. (1990c).
DRAFT-DO NOT QUOTE OR CITE
8-87
December 2003
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Ratio (median congener cone. / total CDD/CDF cone.)
O.I O.2 O.3 0.4
O.S
2,3,7,8-TCDD
1,2,3,7,8-PeCDD
1,2,3,4,7,8-HxCDD
1,2,3,6,7,8-HxCDD
1,2,3,7,8,9-HxCDD
1,2,3,4,6,7,8-HpCDD
1,2,3,4,6,7,8,9-OCDD
2,3,7,8-TCDF
1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
1,2,3,4,7,8-HxCDF
1,2,3,6,7,8-HxCDF
1,2,3,7,8,9-HxCDF
2,3,4,6,7,8-HxCDF
1,2,3,4,6,7,8-HpCDF
1,2,3,4,7,8,9-HpCDF
1,2,3,4,6,7,8,9-OCDF
Source: Median concentrations from U.S. EPA (199Oa); nondetects set equal to 1/2 detection limit.
Ratio (median congener TEQ cone./total TEQ cone.)
O.I O.2 0.3 O.4 O.S O.6 O.7
O.S
2,3,7,8-TCDD
1,2,3,7,8-PeCDD
1,2,3,4,7,8-HxCDD
1,2,3,6,7,8-HxCDD
1,2,3,7,8,9-HxCDD
1,2,3,4,6,7,8-HpCDD
1,2,3,4,6,7,8,9-OCDD
2,3,7,8-TCDF
1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
1,2,3,4,7,8-HxCDF
1,2,3,6,7,8-HxCDF
1,2,3,7,8,9-HxCDF
2,3,4,6,7,8-HxCDF
1,2,3,4,6,7,8-HpCDF
1,2,3,4,7,8,9-HpCDF
1,2,3,4,6,7,8,9-OCDF
Source: Median concentrations from U.S. EPA (199Oa); nondetects set equal to 1/2 detection limit.
Figure 8-1. 104 Mill Study Full Congener Analysis Results for Pulp
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Ratio (median congener cone. / total CDD/CDF cone.)
0.1 0.2 0.3 0.4
0.5
2,3,7,8-TCDD
1,2,3,7,8-PeCDD
1,2,3,4,7,8-HxCDD
1,2,3,6,7,8-HxCDD
1,2,3,7,8,9-HxCDD
1,2,3,4,6,7,8-HpCDD
1,2,3,4,6,7,8,9-OCDD
2,3,7,8-TCDF
1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
1,2,3,4,7,8-HxCDF
1,2,3,6,7,8-HxCDF
1,2,3,7,8,9-HxCDF
2,3,4,6,7,8-HxCDF
1,2,3,4,6,7,8-HpCDF
1,2,3,4,7,8,9-HpCDF
1,2,3,4,6,7,8,9-OCDF
Source: Median c
B from U.S. EPA (199Oa): nondetects set equal to 1/2 detection limit.
Ratio (median congener TEQ conc./total TEQ cone.)
O.I O.2 O.3 O.4 O.S O.6 O.7
O.8
2,3,7,8-TCDD
1,2,3,7,8-PeCDD
1,2,3,4,7,8-HxCDD
1,2,3,6,7,8-HxCDD
1,2,3,7,8,9-HxCDD
1,2,3,4,6,7,8-HpCDD
1,2,3,4,6,7,8,9-OCDD
2,3,7,8-TCDF
1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
1,2,3,4,7,8-HxCDF
1,2,3,6,7,8-HxCDF
1,2,3,7,8,9-HxCDF
2,3,4,6,7,8-HxCDF
1,2,3,4,6,7,8-HpCDF
1,2,3,4,7,8,9-HpCDF
1,2,3,4,6,7,8,9-OCDF
Source: Median concentrations from U.S. EPA (199Oa); nondetects set equal to 1/2 detection limit.
Figure 8-2. 104 Mill Study Full Congener Analysis Results for Sludge
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Ratio (median concfe
0.1 0.2 0.3
• cone./total CDD/GDF cone.)
0.4 O.S 0.6 0.7
0.8
1,2,3,7-,8-PeCDD
,2,3,4,e,7,B,9-OCDD
2,3,7,8-TCDF
1 ,2,3,7,8-PeCDF
2,3,4,Y,B-PeCr>F
1 ,2,3,4,7,8-HxCDF
1 ,2,3,6,7,8-HxGDF
2,3,4,6,7,8-HxCDK
1 ,2,3,4,6,7,8-Hp>CDF
1 ,2,3,4,7,8,9-HpCDF
1 ,2,3,4,6,7,8,9-OCDF
t ac[Lia.l to 1/3 detection, limit.
Ratio (median congener TEQ conc./total TEQ cone.)
0.1 0.2 0.3 0.4 0.5 O.f
2,3,7,8-TCDD
1,2,3,7,8-PeCDD
1,2,3,4,7,8-HxCDD
1,2,3,6,7,8-HxCDD
1,2,3,7,8,9-HxCDD
1,2,3,4,6,7,8-HpCDD
1,2,3,4,6,7,8,9-OCDD
2,3,7,8-TCDF
1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
1,2,3,4,7,8-HxCDF
1,2,3,6,7,8-HxCDF
1,2,3,7,8,9-HxCDF
2,3,4,6,7,8-HxCDF
1,2,3,4,6,7,8-HpCDF
1,2,3,4,7,8,9-HpCDF
1,2,3,4,6,7,8,9-OCDF
Source: Median concentrations from U.S. EPA (1990a); nondetects set equal to 1/2 detection limit.
Figure 8-3. 104 Mill Study Full Congener Analysis Results for Effluent
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Ratio (congener concentration / total CDD/CDF concentration)
0.1 0.2 0.3 0.4 0.5 O.6
0.7
2,3,7,8-TCDD
1,2,3,7,8-PeCDD
1,2,3,4,7,8-HxCDD
1,2,3,6,7,8-HxCDD
1,2,3,7,8,9-HxCDD
1,2,3,4,6,7,8-HpCDD
1,2,3,4,6,7,8,9-OCDD
2,3,7,8-TCDF
1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
1,2,3,4,7,8-HxCDF
1,2,3,6,7,8-HxCDF
1,2,3,7,8,9-HxCDF
2,3,4,6,7,8-HxCDF
1,2,3,4,6,7,8-HpCDF
1,2,3,4,7,8,9-HpCDF
1,2,3,4,6,7,8,9-OCDF
Ratio (congener group concentration / total CDD/CDF concentration)
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7
TCDD
PeCDD
IxCDD
IpCDD
OCDD
TCDF
PeCDF
HxCDF
flpCDF
OCDF
Source: Based on data reported in Table 8-7; nondetects set equal to zero.
Figure 8-4. Congener and Congener Group Profiles for Technical PCP
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Ratio (mean congener cone. / mean total 2378-CDD/CDF cone.)
0.1 0.2 0.3 0.4 0.5
2,3,7,8-TCDD
1,2,3,7,8-PeCDD
1,2,3,4,7,8-HxCDD
1,2,3,6,7,8-HxCDD
1,2,3,7,8,9-HxCDD
1,2,3,4,6,7,8-HpCDD
1,2,3,4,6,7,8,9-OCDD
2,3,7,8-TCDF
1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
1,2,3,4,7,8-HxCDF
1,2,3,6,7,8-HxCDF
1,2,3,7,8,9-HxCDF
2,3,4,6,7,8-HxCDF
1,2,3,4,6,7,8-HpCDF
1,2,3,4,7,8,9-HpCDF
1,2,3,4,6,7,8,9-OCDF
Source: Based on mean concentrations reported in Table 8-24; nondetects set equal to zero.
Figure 8-5. Congener Profile for 2,4-D (salts and esters)
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Ratio (mean congener cone. / total 2378-CDD/CDF cone.)
O.I O.2 O.3 O.4 O.5 O.6 O.7 O.8
O.9
2,3,7,8-TCDD
1,2,3,7,8-PeCDD
1,2,3,4,7,8-HxCDD
1,2,3,6,7,8-HxCDD
1,2,3,7,8,9-HxGDD
1,2,3,4,6,7,8-HpCDD
1,2,3,4,6,7,8,9-OCDD
2,3,7,8-TCDF
1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
1,2,3,4,7,8-HxCDF
1,2,3,6,7,8-HxCDF
1,2,3,7,8,9-HxCDF
2,3,4,6,7,8-HxCDF
1,2,3,4,6,7,8-HpCDF
1,2,3,4,7,8,9-HpCDF
1,2,3,4,6,7,8,9-OCDF
L
Source: Green at al. (1995); nondetects set equal to zero.
2,3,7,8-TCDD
1,2,3,7,8-PeCDD
1,2,3,4,7,8-HxCDD
1,2,3,6,7,8-HxCDD
1,2,3,7,8,9-HxCDD
1,2,3,4,6,7,8-HpCDD
1,2,3,4,6,7,8,9-OCDD
2,3,7,8-TCDF
1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
1,2,3,4,7,8-HxCDF
1,2,3,6,7,8-HxCDF
1,2,3,7,8,9-HxCDF
2,3,4,6,7,8-HxCDF
1,2,3,4,6,7,8-HpCDF
1,2,3,4,7,8,9-HpCDF
1,2,3,4,6,7,8,9-OCDF
Ratio (mean congener cone. / total 2378-CDD/CDF cone.)
0.1 0.2 O.3 O.4 O.5 O.6 O.7 O.8
O.9
Source: Green et al. (1998); nondetects set equal to 1/2 detection limit.
Figure 8-6. Congener Profiles for Sewage Sludge
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9. BIOLOGICAL SOURCES OF CDD/CDF
Recent laboratory and field research studies demonstrate that biochemical
formation of CDD/CDFs from chlorophenol precursors is possible. In addition, under
certain conditions, some CDD/CDFs can be biodegraded to form less chlorinated (and
possibly more toxic) CDD/CDFs. Both of these mechanisms are discussed in this chapter.
However, the extent to which CDD/CDFs are formed by either mechanism in the
environment is not known at present.
The origin of CDD/CDFs that were recently discovered in ball clay deposits is not
yet determined, and natural occurrence is still considered a possibility. Chapter 13
discusses this topic in detail.
9.1. BIOTRANSFORMATION OF CHLOROPHENOLS
Biochemical formation of CDD/CDFs, particularly the higher chlorinated congeners,
from chlorophenol precursors is possible, as indicated by laboratory studies with solutions
of trichlorophenols and pentachlorophenol (PCP) in the presence of peroxidase enzymes
and hydrogen peroxide (Svenson et al., 1989; Oberg et al., 1990; Wagner et al., 1990;
Oberg and Rappe, 1992; Morimoto and Kenji, 1995) and with sewage sludge spiked with
PCP (Oberg et al., 1992). However, the extent to which CDD/CDFs are formed in the
environment via this mechanism cannot be estimated at this time.
In 1 991, Lahl et al. (1 991) reported finding CDD/CDFs in all 22 samples of various
types of composts analyzed. The hepta- and octa-substituted CDDs and CDFs were
typically the dominant congener groups found. The I-TEQDF content of the composts
ranged from 0.8 to 35.7 ng l-TEQDF/kg. The CDD/CDFs found in compost may be
primarily the result of atmospheric deposition onto plants that are subsequently
composted but may also be caused by uptake of CDD/CDFs from air by the active
compost (Krauss et al., 1994). Similarly, CDD/CDFs are frequently detected in sewage
sludges. The CDD/CDFs found in sewage sludge may be primarily from the sources
identified in Section 8.4.1.
Peroxidases are common enzymes in nature. For example, the initial degradation of
the lignin polymer by white- and brown-rot fungi is peroxidase catalyzed (Wagner et al.,
1990). The conversion efficiency of chlorinated phenols to CDD/CDFs that has been
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observed is low. In the solution studies, Oberg and Rappe (1992) reported a conversion
efficiency of PCP to OCDD of about 0.01 percent, Morimoto and Kenji (1995) reported a
conversion efficiency of PCP to OCDD of 0.8 percent, and Wagner et al. (1990) reported a
conversion efficiency of trichlorophenol to HpCDD of about 0.001 percent. Oberg et al.
(1990) reported a conversion efficiency of trichlorophenols to CDD/CDFs of about 0.001
percent. In their sewage sludge study, Oberg et al. (1992) reported a conversion
efficiency of PCP to total CDDs of 0.0002 to 0.0004 percent.
Several researchers recently conducted both laboratory and field studies in an
attempt to better understand the extent of and factors affecting the fate or formation of
CDD/CDFs in composts and sewage sludges. The findings of several of these studies are
discussed in the following paragraphs. The findings are not always consistent, the
congener profiles and patterns detected, and the extent, if any, of CDD/CDF "formation,"
may vary with the compost materials studied, differences in experimental or field
composting design, and duration of the studies.
Harrad et al. (1991) analyzed finished composts and active compost windrows
from a municipally operated yard-waste composting facility in Long Island, New York.
Concentrations measured in 12 finished composts ranged from 14 to 41 ng l-TEQDF/kg
(mean of 3 ng l-TEQDF/kg). The concentrations in the five active compost samples (1 to
30 days in age) ranged from 7.7 to 54 ng l-TEQDF/kg (mean of 21 ng l-TEQDF/kg). The
authors observed that CDD/CDF concentrations measured in two soil samples from the
immediate vicinity of the composting facility were significantly lower (1.0 and 1.3 ng I-
TEQDF/kg) than the levels found in the composts, suggesting that the source(s) of
CDD/CDFs in the composts was different than the source(s) affecting local soils. The
authors also noted a strong similarity between the congener profiles observed in the
composts and the congener profile of a PCP formulation (i.e., predominance of
1,2,4,6,8,9-HxCDF and 1,2,3,4,6,8,9-HpCDF in their respective congener groups), which
indicated to the authors that leaching of CDD/CDFs from PCP-treated wood in the
compost piles was the likely source of the observed CDD/CDFs. The levels of PCP in the
12 finished composts ranged from 7 to 1 90//g/kg (mean of 33//g/kg), and the PCP levels
in the active compost samples ranged from 1 7 to 210 /vg/kg (mean of 68 /vg/kg). The
PCP level in both soil samples was 1.5 /vg/kg.
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Goldfarb et al. (1992) and Malloy et al. (1993) reported the results of testing of
composts at three municipal yard-waste composting facilities (5 to 91 ng l-TEQDF/kg;
mean of 30 ng l-TEQDF/kg), two municipal solid waste composting facilities (19 to 96 ng
l-TEQDF/kg; mean of 48 ng l-TEQDF/kg), and one municipal facility composting solid waste
and dewatered sewage sludge (37 to 87 ng l-TEQDF/kg; mean of 56 ng l-TEQDF/kg). All
facilities were located in the United States. Two general trends were observed. First, an
increase in analyte levels was observed, with an increasing degree of chlorination for each
compound type (i.e., CDDs, CDFs, chlorophenols, and chlorobenzenes). Second, an
increase in concentration of each congener or homologue group, with a progression from
yard waste to solid waste to solid waste/sewage sludge composts, was observed. As
noted above, TEQ concentrations showed this same trend, which was primarily due to
increasing levels of 1,2,3,4,6,7,8-HpCDD and OCDD. The mean PCP concentrations in
the three compost types were 20 //g/kg (yard waste), 215 //g/kg (solid waste), and 615
/yg/kg (solid waste/sewage sludge). Comparison of congener profiles by the authors
indicated that the CDD/CDF residue in PCP-treated wood in the compost feedstock was a
major but not exclusive contributor of the observed CDD/CDFs. The authors postulated
that biological formation of HxCDDs, HpCDDs, and OCDD from chlorophenols (tri-, tetra-,
and penta-) in the compost could be responsible for the elevated levels of these congener
groups relative to their presence in PCP.
Oberg et al. (1993) measured the extent of CDD/CDF formation in three
conventional garden composts; two were spiked with PCP, and one was spiked with
hexachlorobenzene. The two PCP-spiked composts were monitored for periods of 55
days and 286 days, respectively. A significant increase in the concentrations of the
higher chlorinated congeners, particularly the HpCDDs, OCDD, and, to a lesser extent,
OCDF, were observed. Similar results were reported for the hexachlorobenzene-spiked
compost, which was monitored for a period of 49 days. Oberg et al. (1993) state that for
a "typical" composting event, a two- to threefold increase in TEQ content corresponds to
an elevation by 0.2 to 0.5 ng l-TEQDF/kg dry weight.
Weber (1995) subjected sewage sludges from two German communities to
anaerobic digestion in laboratory reactors for 60 days. The two sludges were spiked with
2,3,5-trichlorophenol (10 to 25 mg/kg), a mixture of 2,3,5-trichlorophenol and
dichlorophenols (2.5 to 25 mg/kg), or a mixture of di-, tri-, and tetrachlorobenzenes (4 to
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40 mg/kg). In nearly all of the digestion experiments, the addition of these precursors did
not lead to any significant changes in CDD/CDF concentrations. The initial CDD/CDF
concentrations in the two sludges were 9 and 20 ng l-TEQDF/kg. The only exceptions
were increased 2,3,7,8-TCDF concentrations in the mixed chlorophenol experiments and
decreased 2,3,7,8-TCDF concentrations in the mixed chlorobenzene experiments.
However, the same increases or decreases for this congener were also observed in the
controls (i.e., no precursors added).
Researchers at the U.S. Department of Agriculture (USDA) (Fries et al., 1997)
reported that dairy cows that were fed PCP-treated wood excreted OCDD in amounts
almost four times what they ingested. Feil and Tiernan (1997) reported that rats fed
technical PCP had liver concentrations of HxCDD, HpCDD, HpCDF, OCDD, and OCDF two
to three orders of magnitude higher than rats fed purified PCP. These results suggest the
in vivo formation of CDD/CDFs from pre-dioxins (i.e., chlorinated phenoxy phenols present
as contaminants in the PCP). A followup USDA study (Huwe et al., 1998) investigated
the metabolic conversion of a pre-dioxin (monochloro-2-phenoxyphenol) to OCDD in a
feeding study with rats. The results of the study demonstrate the formation of OCDD
from the pre-dioxin, although the conversion was estimated to be less than 2 percent.
Interestingly, the study noted that the presence of added OCDD in the feed material
increased the percentage of pre-dioxin conversion.
Wittsiepe et al. (1998) demonstrated that CDD/CDF can be formed through
reaction of chlorophenols with myeloperoxidase (a component of neutrophile granulocytes,
a subgroup of human leucocytes). The CDD/CDFs formed showed different homologue
patterns and formation rates depending upon the degree of chlorination of the
chlorophenol substrate. The formation rates ranged from 1 to 1 6 u mol of CDD/CDF per
mole of chlorophenol substrate.
9.2. BIOTRANSFORMATION OF HIGHER CDD/CDFS
Results of several recent studies examining the fate of a range of CDD/CDF
congeners in pure cultures, sediments, and sludges indicate that under certain conditions
some CDD/CDF congeners will undergo biodegradation to form less chlorinated (and
possibly more toxic) CDD/CDFs. However, the extent to which more toxic CDD/CDFs are
formed in the environment via this mechanism cannot be estimated at this time. The
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following paragraphs discuss studies that examined the products of biodegradation in
sediments, compost, and sewage sludge.
Several recent reports indicate that CDDs and CDFs may undergo microbial
dechlorination in anaerobic sediments. Adriaens and Grbic-Galic (1992; 1993) and
Adriaens et al. (1995) reported the results of a series of microcosm studies using Hudson
River sediment (contaminated with Aroclor 1242) and aquifer material (contaminated with
CDDs) from Pensacola, Florida. Both types of substrates were spiked with several CDDs
(1,2,3,4,6,7,8-HpCDD; 1,2,3,4,7,8-HxCDD; and 1,2,4,6,8,9-71,2,4,6,7,9-HxCDD) and
CDFs (1,2,3,4,6,7,8-HpCDF and 1,2,4,6,8-PeCDF) and monitored over a 16-month
period, at an incubation temperature of 30°C. The Hudson River sediment was spiked
with 144/yg/kg of each congener, and the Pensacola aquifer material was spiked with 63
/yg/kg of each congener.
All of the congeners, with the exception of 1,2,3,4,6,7,8-HpCDF, showed a slow
decrease in concentration over time, attributed to biologically mediated reductive
dechlorination, with net disappearance rates ranging from 0.0031 week"1 to 0.0175 week"
1 (i.e., half-lives of approximately 1 to 4 years). However, Adriaens et al. (1995) conclude
that the actual half-lives may be orders of magnitude higher. The experiment with
1,2,3,4,6,7,8-HpCDD yielded formation of two HxCDDs (1,2,3,4,7,8- and 1,2,3,6,7,8-).
Thus, removal of the peri-substituted (1,4,6,9) chlorines was favored, with enrichment of
2,3,7,8-substituted congeners. No lesser chlorinated congeners were identified from
incubation with the other tested congeners. 1,2,4,6,8-PeCDF was also examined in
dichlorophenol-enriched cultures. After 6 months incubation, several TCDFs were
identified, which also indicated that peri-dechlorination was the preferred route of
reduction.
Barkovskii and Adriaens (1995, 1996) reported that 2,3,7,8-TCDD (extracted from
Passaic River sediments) was susceptible to reductive dechlorination when incubated at
30°C under methanogenic conditions in a mixture of aliphatic and organic acids inoculated
with microorganisms obtained from Passaic River sediments. The initial concentration of
2,3,7,8-TCDD (20 ± 4//g/L) decreased by 30 percent to 14 ± 2//g/L over a period of 7
months with the consecutive appearance and disappearance of tri-, di-, and mono-CDDs.
Experiments were also conducted by spiking the sediment with HxCDDs, HpCDDs, and
OCDD. Up to 10 percent of the spiked OCDD was converted to hepta-, hexa-, penta-,
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tetra-, tri-, di-, and monochlorinated isomers, but the reaction stoichiometry was not
determined. Two distinct pathways of dechlorination were observed: the peri-
dechlorination pathway of 2,3,7,8-substituted hepta- to penta-CDDs, resulting in the
production of 2,3,7,8-TCDD, and the pe/7-lateral dechlorination pathway of non-2,3,7,8-
substituted congeners.
Several studies reported that CDD/CDFs can be formed during composting
operations through biological action on chlorophenols present in the compost feed
material. The results of studies that specify likely involvement of chlorophenols are
described in Section 9.1. Another possible formation mechanism was suggested by
Vikelsoe et al. (1994), who reported that higher chlorinated CDD/CDF congeners are
formed when humic acid is reacted with a peroxidase enzyme, hydrogen peroxide, and
sodium chloride. It is expected that some organic material in compost and sewage sludge
has a humic-like structure. Several additional studies are described below in which the
potential involvement of chlorophenols could not be assessed because chlorophenol
concentrations in the composts were not reported.
Schafer et al. (1993) monitored the seasonal changes in the CDD/CDF content, as
well as the extent of CDD/CDF formation, in the composts from a vegetable and garden
waste composting operation in Germany. Finished compost samples were collected and
analyzed every 2 months for 1 year. An annual cycle was observed in TEQ
concentrations, with peak concentrations in the summer (approximately 8.5 ng l-TEQDF/kg)
that were 2.5 times higher than the lowest concentrations observed in the winter
(approximately 3.5 ng l-TEQDF/kg). No seasonal source was apparent that could explain
the observed differences in seasonal levels. The CDD/CDF contents of the starting waste
materials for two compost cycles (March and September) were measured to monitor the
extent of CDD/CDF formation during composting. For the March cycle sample, most
2,3,7,8-substituted CDD/CDF congeners decreased in concentration during composting.
Four CDF congeners showed a slight increase in concentration (less than 10 percent). For
the September cycle sample, OCDD and HpCDD concentrations increased 300 percent
during composting. Less than 10 percent increases were observed for HxCDDs and
OCDF; all other 2,3,7,8-substituted CDD/CDF congeners showed decreases in
concentrations during composting.
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Krauss et al. (1994) measured the extent of CDD/CDF formation during the
composting of household waste using a laboratory compost reactor. After 11 weeks, the
TEQ content of the compost increased from 3.0 to 4.5 ng. The largest increases in mass
content were observed for HpCDD (primarily 1,2,3,4,6,7,8-HpCDD) and OCDD. TCDD,
PeCDD, and HxCDD showed no change in mass content. All CDF congener groups
showed decreases in mass content; however, the concentrations in both the starting and
finished compost were close to the analytical detection limits.
Oberg et al. (1994) reported the results of monitoring of two household waste
composts and two garden composts. For the two household waste composts, total
CDD/CDF content decreased in both composts over the 12-week test period. Total CDD
content and PCB content decreased, but total CDF content increased in contrast to the
findings of Krauss et al. (1994). However, a small increase in OCDD content in both
composts was observed. The two garden composts were monitored by Oberg et al.
(1994) for a 60-week period. Total CDD/CDF concentration increased, with the largest
increases observed for OCDD and HpCDDs. The lower chlorinated CDFs decreased in
concentration.
As a followup to a preliminary study (Hengstmann et al., 1990) that indicated
CDD/CDF concentrations may increase and congener profiles may change during anaerobic
digestion of sewage sludge, Weber et al. (1995) subjected sewage sludges from two
German communities to anaerobic digestion and aerobic digestion in laboratory reactors
for 60 days and 20 days, respectively. The initial average I-TEQDF concentrations in the
raw sludges were 20 and 200 ng l-TEQDF/kg. No significant increase or decrease in total
CDD/CDF content or congener group content was observed with either sludge. In
contrast, a significant decrease in CDD/CDF content was observed in the aerobic digestion
experiments on both sludges. The greatest percentage decreases in congener group
concentrations (i.e., greater than 40 percent) were observed for TCDF, PeCDF, HxCDF,
TCDD, and PeCDD in the sludge initially containing 20 ng l-TEQDF/kg and for TCDF, TCDD,
HpCDD, and OCDD in the initially high-content sludge. The greatest percentage decreases
in congener concentrations (i.e., greater than 40 percent) were observed for non-2,3,7,8-
substituted congeners.
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10. PHOTOCHEMICAL SOURCES OF CDD/CDF
10.1. PHOTOTRANSFORMATION OF CHLOROPHENOLS
Several researchers demonstrated that CDD/CDFs can be formed via photolysis of
pentachlorophenol (PCP) under laboratory conditions. These studies are described below.
However, the extent to which CDD/CDFs are formed in the environment via this
mechanism cannot be estimated at this time.
Lamparski et al. (1980) conducted laboratory studies to determine the effect of
simulated summer sunlight on the formation of OCDD, HpCDDs, and HxCDDs in wood
that was pressure treated in the laboratory with PCP. In the first set of experiments,
wood veneers (southern pine) treated with purified PCP or with Dowicide EC-7, using
methylene chloride as the PCP carrier, were exposed to light for 70 days. The PCP
concentration in the treated wood was 5 percent by weight, which approximates the
concentration in the outer layer of PCP-treated wood utility poles. Photolytic
condensation of PCP to form OCDD was observed, with the OCDD concentration
increasing by a maximum factor of 3,000 for the purified PCP and by a factor of 20 for
EC-7 at about day 20 before leveling off. HpCDD and HxCDD were also formed,
apparently by photolytic degradation of OCDD rather than by condensation of PCP and
tetrachlorophenols. The HxCDD concentration increased by a factor of 760 for the
purified PCP and by a factor of 50 for EC-7 over the 70-day exposure period. The
predominant HpCDD congener formed was 1,2,3,4,6,7,8-HpCDD as a result of an
apparent preferential loss of chlorine at the peri position (i.e., positions 1, 4, 6, and 9).
In a second set of experiments conducted by Lamparski et al. (1980), a
hydrocarbon oil (P-9 oil) was used as the carrier to treat the wood. The increases
observed in the OCDD, HpCDD, and HxCDD were reported to be much lower relative to
the increases observed in the first set of experiments, which used methylene chloride as
the carrier. Results were reported only for OCDD. The OCDD concentration increased by
a maximum factor of 1.5 for both EC-7 and technical PCP, and by a factor of 88 for
purified PCP. The authors concluded that the oil either reduced condensation of PCP to
OCDD or accelerated degradation to other species by providing a hydrocarbon trap for
free-radical species.
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Vollmuth et al. (1994) studied the effect of irradiating laboratory water and landfill
seepage water that contained PCP under conditions simulating those used to purify water
with ultraviolet (UV) radiation (i.e., 5-hour exposure to 254 nm radiation from low-
pressure mercury lamps). Before irradiation, the three solutions tested contained
approximately 1 mg/L of PCP or PCP-Na but the CDD/CDF content varied dramatically
(1.5, 2,066, and 2,071 pg I-TEQDF/L). Irradiation resulted in nearly total destruction of
PCP (greater than 99 percent loss) in all three experiments. An overall net increase in I-
TEQDF content was observed in the initially low I-TEQDF content water, but a net decrease
was observed for the two initially high I-TEQDF content waters.
• Irradiation of laboratory water containing purified PCP showed an increase in
I-TEQDF concentration from 1.5 pg/L to 214.5 pg/L. The increase in I-TEQDF
was due entirely to the formation of 1,2,3,4,6,7,8-HpCDD, OCDD, and
1,2,3,4,6,7,8-HpCDF. Formation of non-2,3,7,8-substituted HpCDDs and
HpCDFs was also observed. The ratios of the concentrations of these non-
2,3,7,8-congeners to the concentrations of the 2,3,7,8-congeners were 0.6
for HpCDDs and 5.0 for HpCDFs. The HpCDD and HpCDF congeners
formed indicated that the operative mechanism is photoinduced
dechlorination of OCDD at a peri position and dechlorination of OCDF at only
the 1- and 9-peri positions.
• Irradiation of water containing technical PCP-Na (Dowicide-G) resulted in a
net loss in I-TEQDF content, from 2,065.5 pg/L to 112.7 pg/L. The only
2,3,7,8-substituted congener showing an increased concentration was
1,2,3,6,7,8-HxCDD. The other congeners originally present in the technical
PCP-Na showed reductions of 80.6 to 100 percent.
• The I-TEQDF content of seepage water from a landfill (2,071 pg I-TEQDF/L)
was reduced by a factor of two to 1,088 pg I-TEQDF/L. However, several
2,3,7,8-substituted congeners did increase in concentration (1,2,3,6,7,8-
HxCDD, 1,2,3,4,6,7,8-HpCDD, 1,2,3,4,6,7,8-HpCDF, and OCDF).
Waddell et al. (1995) also studied the effect of irradiating distilled laboratory water
containing PCP under conditions simulating those used to purify water with UV radiation.
The results obtained were similar to those of Vollmuth et al. (1994). Analytical-grade PCP
at a concentration of 10 mg/L was exposed for 12 minutes to 200-300 nm radiation from
a medium-pressure mercury lamp. All CDD/CDF congener groups increased in
concentration over the 1 2-minute exposure period, with the greatest increases observed
for OCDD (75-fold increase) and HpCDDs (34-fold increase). The I-TEQDF content of the
solution increased from 4.2 pg I-TEQDF/L to 137 pg I-TEQDF/L over the 1 2-minute period.
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The dominant congeners formed, in terms of both concentration and contribution to I-
TEQDF, were 1,2,3,4,6,7,8-HpCDD, OCDD, and 1,2,3,7,8,9-HxCDD.
10.2. PHOTOLYSIS OF HIGHER CDD/CDFS
Photolysis appears to be one of the few environmentally significant degradation
mechanisms for CDD/CDFs in water, air, and soil. Although in most studies good mass
balances were not obtained and the photolytic pathways for CDD/CDFs were not fully
identified, a major photolysis pathway appears to be photodechlorination, resulting in
formation of lower chlorinated CDD/CDFs. A preferential loss of chlorines from the peri
positions (i.e., chlorines at the 1, 4, 6, and 9 positions) rather than from the lateral
positions (i.e., chlorines at the 2, 3, 7, and 8 positions) was reported for some congener
groups when irradiated as dry films, sorbed to soil, and as gas-phase CDD/CDFs
(Choudhry and Webster, 1989; Kieatiwong et al., 1990; Sivils et al., 1994, 1995;
Tysklind et al., 1992). Several researchers reported that carbon-oxygen cleavage and
other mechanisms may be similarly or more important pathways for CDD/CDFs containing
four or fewer chlorines.
Because of the difficulties inherent in controlling experimental variables for
nonvolatile and highly lipophilic compounds like CDD/CDFs, few photolysis studies have
been performed on natural waters, on soils, or particulates, and on atmospheric gases to
examine the rates and products of photolysis under environmentally relevant conditions.
Thus, it is not possible at this time to quantitatively estimate the mass of various
CDD/CDF congeners formed in the environment annually via photolytic mechanisms.
Sections 10.2.1-10.2.4 summarize the key findings of recent environmentally significant
studies for the water, soil, and air media.
10.2.1 Photolysis in Water
Numerous studies demonstrate that CDD/CDFs will undergo photodechlorination
following first order kinetics in organic solution, with preferential loss of chlorine from the
lateral positions. Photolysis is slow in pure water, but it increases dramatically when
solvents serving as hydrogen donors such as hexane, benzene, methanol, acetonitrile,
hexadecane, ethyl oleate, dioxane, and isooctane are present. However, only a few
studies have examined the photolysis of CDD/CDFs using natural waters and sunlight.
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Choudhry and Webster (1989) experimentally determined the sunlight photolysis
half-life of 1,3,6,8-TCDD in pond water to be 3.5 days (i.e., more than 10 times greater
than the half-life predicted by laboratory experiments using a water/acetonitrile solution).
The authors attributed this significant difference in photolysis rates to the light
screening/quenching effects of dissolved organic matter.
Friesen et al. (1990) examined the photolytic behavior of 1,2,3,4,7-PeCDD and
1,2,3,4,6,7,8-HpCDD in watenacetonitrile (2:3, v/v) and in pond water under sunlight at
50 degrees north latitude. The observed half-lives of these two compounds in the
watenacetonitrile solution were 12 and 37 days, respectively, but much shorter in pond
water, 0.94 and 2.5 days, respectively. Similarly, Friesen et al. (1993) studied the
photodegradation of 2,3,7,8-TCDF and 2,3,4,7,8-PeCDF by sunlight using
watenacetonitrile (2:3, v/v) and lake water. The observed half-lives of the 2,3,7,8-TCDF
and 2,3,4,7,8-PeCDF in the watenacetonitrile solution were 6.5 and 46 days,
respectively, and 1.2 and 0.19 days, respectively, in lake water. The significant
differences between the natural water and watenacetonitrile solution results were
attributed to indirect or sensitized photolysis due to the presence of naturally occurring
components in the lake and pond water.
Dung and O'Keefe (1992), in an investigation of aqueous photolysis of 2,3,7,8-
TCDF and 1,2,7,8-TCDF, reported findings similar to those of Friesen et al. (1993). The
photolysis rates of the two TCDF congeners observed in the river and lake water (half-
lives of about 4 to 6 hours) were double the rates observed in pure water (half-lives of
about 8 to 11 hours). Dung and O'Keefe (1992) attributed the difference in rates to the
presence of natural organics in the river and lake water that may be acting as sensitizers.
10.2.2 Photolysis on Soil
Photolysis of CDD/CDFs on soil has not been well characterized. According to the
data generated to date, however, photolysis is an operative degradation process only in
the near-surface soil where UV light penetrates (i.e., the top few millimeters or less of
soil), and dechlorination of peri-substituted chlorines appears to occur preferentially.
Miller et al. (1989) studied the CDD degradation products resulting from irradiation
of 13C-labeled OCDD on two soil types using sunlamps. Approximately 38 to 42 percent
of the OCDD was degraded by day 5 of the experiment; no significant further loss of
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OCDD was observed over the following 10 days. Although the authors determined that
photodechlorination was not the dominant photolysis pathway, it was observed in both
soils; approximately 10 to 30 percent of the lower chlorinated congeners were produced
from the immediate higher chlorinated congeners. The HpCDD and HxCDD congeners
observed as degradation products were present in proportions similar to the number of
congeners in each congener group. However, Miller et al. (1989) observed greater yields
of 2,3,7,8-TCDD and 1,2,3,7,8-PeCDD than would be expected on the basis of the
number of potential TCDD and PeCDD congeners. One-fifth to one-third of the total yield
of PeCDDs was 1,2,3,7,8-PeCDD, and one-half of the total yield of TCDDs was 2,3,7,8-
TCDD.
Kieatiwong et al. (1990) performed similar experiments to those of Miller et al.
(1989) using natural sunlight rather than sunlamps for irradiation of 13C-labeled OCDD on
soils. Photodechlorination was estimated to account for approximately 10 percent of the
loss of OCDD. One-third to one-half of the total yield of PeCDDs was 1,2,3,7,8-PeCDD,
and one-half of the total yield of TCDDs was 2,3,7,8-TCDD. The findings of Miller et al.
(1989) and Kieatiwong et al. (1990) indicate that the 2,3,7,8-substituted TCDD and
PeCDD congeners were either preferentially formed or were photochemically less reactive
than the other congeners that were formed.
Tysklind et al. (1992) studied the sunlight photolysis of OCDD on soil and reported
results similar to those of Miller et al. (1989) and Kieatiwong et al. (1990).
Photodechlorination was observed with production of HpCDDs, HxCDDs, PeCDDs, and
TCDDs over the 16-day irradiation period. Photodechlorination at the peri-substituted
positions was the preferred photodechlorination mechanism; the proportions of 2,3,7,8-
substituted congeners present in the soils after 16 days for each congener group were as
follows: HxCDD - 65 percent; PeCDD —40 percent; and TCDD —75 percent. Tysklind et
al. (1992) also studied the sunlight photolysis of OCDF on soil. Photodechlorination was
observed; however, unlike the case with OCDD, photodechlorination of the lateral-
substituted positions was found to be the dominant photodechlorination mechanism,
resulting in a relative decreasing proportion of 2,3,7,8-substituted congeners during the
irradiation period. 2,3,7,8-TCDF was not observed in any of the irradiated samples.
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10.2.3 Photolysis on Vegetation
Photolysis of CDD/CDFs sorbed on the surface of vegetation has not been well
characterized, and the findings to date are somewhat contradictory. McCrady and
Maggard (1993) reported that 2,3,7,8-TCDD sorbed on the surface of reed canary grass
(Phalaris arundinacea L.) undergoes photolytic degradation with a half-life of 44 hours in
natural sunlight. In contrast, Welsch-Pausch et al. (1995) found little difference in the
CDD/CDF congener patterns between grass (Lolium multiflorum) grown on an outdoor plot
and grass grown in a greenhouse (i.e., UV light transmission blocked). In an attempt to
clarify this contradiction, Welsch-Pausch and McLachlan (1995) studied the
photodegradation of CDD/CDFs on pasture grass (Arrhenatherion e/atioris) during two
growing cycles (summer and autumn) using two greenhouses. One greenhouse was
constructed of glass that blocks UV transmission, and the other was constructed of
plexiglass (4 mm) with a UV-light transmission of greater than 50 percent in the 280-
320 mm range. In both the summer and autumn exposure periods, the concentrations of
CDD/CDFs (on a congener group basis) were similar in the grass exposed to UV light and
the grass that was not exposed. Welsch-Pausch and McLachlan (1995) concluded that if
photodegradation was occurring, it was a relatively insignificant factor in the accumulation
of CDD/CDF in pasture grass.
10.2.4 Photolysis in Air
Photolysis of CDD/CDFs in the atmosphere has not been well characterized. On
the basis of data generated to date, however, photolysis appears to be a significant
mechanism for degradation (principally, dechlorination of the peri-substituted chlorines) of
those CDD/CDFs present in the atmosphere in the gas phase. For airborne CDD/CDFs
sorbed to particulates, photolysis appears to proceed very slowly, if at all. Because of the
low volatility of CDD/CDFs, few studies have been attempted to measure actual rates of
photodegradation of gas-phase CDD/CDF, and only recently have studies examined the
relative importance of photolysis to particulate-bound CDD/CDFs.
Sivils et al. (1994; 1995) studied the gas-phase photolysis of several CDDs (2,3,7-
TrCDD; 2,3,7,8-TCDD; 1,2,3,4-TCDD; 1,2,3,7,8-PeCDD, and 1,2,4,7,8-PeCDD) by
irradiating the effluent from a gas chromatograph with broadband radiation in the
UV/visible region for periods up to 20 minutes. The irradiated sample was then introduced
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into a second gas chromatograph to measure the extent of dechlorination. The results
showed that degradation followed first order kinetics and that an inverse relationship
exists between the degree of chlorination and the rate of disappearance. Although the
lack of photoproducts prevented an independent confirmation of the preferential loss
mechanism, the results indicated that laterally substituted congeners (i.e., chlorines at the
2, 3, 7, and 8 positions) degrade at a slower rate than the peri-substituted congeners (i.e.,
chlorines at the 1, 4, 6, and 9 positions). Although Sivils et al. (1 994) did not present the
rate constants, the degradation rate for 2,3,7,8-TCDD (30 percent loss in 20 minutes)
was reported to be slower than the rates for all other tested CDDs. Also, 1,2,4,7,8-
PeCDD (with two perichlorines) degraded significantly faster than 1,2,3,7,8-PeCDD (with
only one perichlorine).
Mill et al. (1987) studied the photolysis of 2,3,7,8-TCDD sorbed onto small
diameter fly ash particulates suspended in air. The results indicated that fly ash confers
photostability on 2,3,7,8-TCDD. Little (8 percent) to no loss was observed on the two fly
ash samples after 40 hours of illumination. Tysklind and Rappe (1991) and Koester and
Hites (1992) reported similar results of photolysis studies with fly ash. Tysklind and
Rappe (1991) subjected fly ash from two German incinerators to various simulated
environmental conditions. The fraction of photolytically degradable CDD/CDF after 288
hours of exposure was in the range of 20 to 40 percent of the extractable CDD/CDF.
However, a 10 to 20 percent reduction was also observed in the darkened control
samples. With the exception of HpCDD and HpCDF, the concentration of all other
congener groups either increased or stayed the same during the exposure period from hour
144 to hour 288. Koester and Hites (1992) studied the photodegradation of CDD/CDFs
naturally adsorbed to fly ash collected from five electrostatic precipitators. They observed
no significant degradation in 11 photodegradation experiments performed on the ash for
periods ranging from 2 to 6 days. Koester and Hites (1992) concluded that (1) the
absence of photodegradation is not due to the absence of a hydrogen-donor organic
substance; (2) other molecules on the ash, as determined by a photolysis experiment with
an ash extract, inhibit photodegradation, either by absorbing light and dissipating energy
or by quenching the excited states of the CDD/CDFs; and (3) the surface of the ash itself
may hinder photolysis by shielding the CDD/CDFs from light.
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11. SOURCES OF DIOXIN-LIKE PCBs
The purpose of this chapter is twofold: (1) to identify sources that release dioxin-
like polychlorinated biphenyls (PCB) congeners into the environment and (2) to derive
national estimates for releases from these sources in the United States. PCBs have been
found in all media and all parts of the world. PCBs were produced in relatively large
quantities for use in commercial products such as dielectrics, hydraulic fluids, plastics, and
paints. They are no longer commercially produced in the United States, but continue to be
released to the environment through the use and disposal of these products. PCBs may
also be inadvertently produced as by-products during the manufacture of certain organic
chemicals and also as products of the incomplete combustion of some waste materials.
11.1. GENERAL FINDINGS OF THE EMISSIONS INVENTORY
Table 11-1 provides a list of known or suspected dioxin-like PCB-emitting source
categories in the United States. The source categories included in this table represent a
compilation of source categories for which dioxin-like PCB congener, PCB Aroclor, or PCB
congener group emission measurements have been reported in government, industry, and
trade association reports; in conference proceedings and journal articles; and in comments
submitted to the Agency on previous versions of this document. The intent of Table 11-1
is to clearly present those source categories and media (i.e., air, water, land, and
products) for which available data are either adequate or inadequate for reliably
quantifying emissions of dioxin-like PCBs.
Nationwide emission estimates for the United States inventory are presented in
Table 11-2 (emissions to air, water, land, and product) for those source categories for
which emission estimates can be reliably quantified (i.e., the category has been assigned a
confidence rating of A, B, or C) (see Section 1.4.2 of this report for details on confidence
ratings). Table 11-2 also lists, in the far right column, preliminary estimates of the
potential magnitude of emissions from "unquantified" sources (i.e., sources assigned a
confidence rating of D) in reference year 1995. Because of large uncertainties for these
category D estimates, they are not included in the "quantitative inventory."
Releases of "old" dioxin-like PCBs (i.e., dioxin-like PCBs manufactured prior to the
ban) to the environment can occur from ongoing use and disposal practices. Prior to
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regulations enacted beginning in the late 1970s that limited the manufacture/use/disposal
of PCBs, significant quantities of PCBs were released to the environment in association
with (1) the manufacture of PCBs; (2) the manufacture of products containing PCBs; and
(3) the use and disposal of products containing PCBs, as well as materials that may have
been contaminated with trace levels of PCBs from prior PCB use or disposal. Following
the ban on PCB production, releases from these first two categories ceased to exist. The
third type of releases, those associated with product use and disposal, will continue in at
least four ways:
• Products containing greater than 2 pounds of PCBs (e.g., dielectric fluids in
transformers and large capacitors) are controlled by disposal regulations that have
minimized environmental releases.
• Disposal of products containing small quantities of PCBs (e.g., small capacitors,
fluorescent lighting fixtures) or trace quantities of PCBs (e.g., wastepapers) are
subject to disposal as municipal solid waste but may result in some release to the
general environment.
• Leaks and spills occur in still-in-service PCBs.
• PCBs are disposed of illegally.
No significant release of newly formed dioxin-like PCBs is occurring in the United
States. Unlike CDD/CDFs, PCBs were intentionally manufactured in the United States in
large quantities from 1929 until production was banned in 1977. Although it has been
demonstrated that small quantities of dioxin-like PCBs can be produced during waste
combustion, no strong evidence exists that the dioxin-like PCBs are produced in significant
quantities as byproducts during combustion or chemical processes. The widespread
occurrence of dioxin-like PCBs in the U.S. environment most likely reflects past releases
associated with PCB production, use, and disposal. Further support for this finding is
based on observations of reductions since the 1980s in PCB concentrations in Great Lakes
sediment and other areas.
11.2 RELEASES OF COMMERCIAL PCBs
PCBs were commercially manufactured by the direct batch chlorination of molten
biphenyl with anhydrous chlorine in the presence of a catalyst, followed by separation and
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purification of the desired chlorinated biphenyl fractions. The degree of chlorination was
controlled by the chlorine contact time in the reactor. Commercial PCBs production is
believed to have been confined to 10 countries. Total PCBs produced worldwide since
1 929 (i.e., the first year of known production) has been estimated to total 1.5-million
metric tons. Initially, PCBs were primarily used as dielectric fluids in transformers. After
World War II, PCBs found steadily increasing use as dielectric fluids in capacitors, as heat-
conducting fluids in heat exchangers, and as heat-resistant hydraulic fluids in mining
equipment and vacuum pumps. PCBs also were used in a variety of "open" applications
(i.e, uses from which PCBs cannot be recollected) including: plasticizers, carbonless copy
paper, lubricants, inks, laminating agents, impregnating agents, paints, adhesives, waxes,
additives in cement and plaster, casting agents, dedusting agents, sealing liquids, fire
retardants, immersion oils, and pesticides (DeVoogt and Brinkman, 1989).
PCBs were manufactured in the United States from 1929 until 1977. U.S.
production peaked in 1970 with a volume of 39,000 metric tons. In 1971, Monsanto
Corporation, the major U.S. producer, voluntarily restricted the sales of PCBs to all
applications with the exception of "closed electrical systems," and annual production fell
to 18,000 metric tons in 1974. Monsanto ceased PCB manufacture in mid-1977 and
shipped the last inventory in October 1977. Regulations issued by EPA beginning in
1977, principally under the Toxic Substances Control Act (TSCA) (40 CFR 761), have
strictly limited the production, import, use, and disposal of PCBs. The estimated
cumulative production and consumption volumes of PCBs in the United States from 1930
to 1975 were 635,000 metric tons produced, 1,400 metric tons imported (primarily from
Japan, Italy, and France), 568,000 metric tons sold in the United States; and 68,000
metric tons exported (Versar, 1976). The reliability of these values is +5 percent and -20
percent (Versar, 1976).
Monsanto Corporation marketed technical grade mixtures of PCBs primarily under
the trade name Aroclor. The Aroclors are identified by a four-digit numbering code in
which the last two digits indicate the chlorine content by weight percent. The exception
to this coding scheme is Aroclor 1016, which contains only mono- through
hexachlorinated congeners with an average chlorine content of 41 percent. From 1957
until 1972, Monsanto also manufactured several blends of PCBs and polychlorinated
terphenyls (PCTs) under the trade names Aroclor 2565 and Aroclor 4465; manufacture
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and sales volumes are not available for these blends. Listed below are the percentages of
total Aroclor production during the years 1957 to 1977 by Aroclor mixture as reported by
Brown (1994).
1957-1977
U.S. Production
Aroclor (%)
1016 12.88
1221 0.96
1232 0.24
1242 51.76
1248 6.76
1254 15.73
1260 10.61
1262 0.83
1268 0.33
The trade names of the major commercial PCB technical grade mixtures
manufactured in other countries included Clophen (Germany), Fenclor and Apirolio (Italy),
Kanechlor (Japan), Phenoclor and Pyralene (France), Sovtel (USSR), Delor and Delorene
(Czechoslovakia), and Orophene (German Democratic Republic) (DeVoogt and Brinkman,
1989). The mixtures marketed under these trade names had similar chlorine content (by
weight percent and average number of chlorines per molecule) to those of various
Aroclors. Listed below are comparable mixtures in terms of chlorine content marketed
under several trade names.
Aroclor Clophen Pyralene Phenoclor Fenclor Kanechlor
1232 2000 200
1242 A-30 3000 DP-3 42 300
1248 A-40 DP-4 400
1254 A-50 DP-5 54 500
1260 A-60 DP-6 64 600
Major advances in analytical separation and resolution techniques beginning in the
1970s enabled various researchers to identify and quantify PCB congeners present in
Aroclors, Clophens, and Kanechlors (Jensen et al., 1974; Albro and Parker, 1979; Huckins
et al., 1980; Albro et al., 1981; Duinker and Hillebrand, 1983; Kannan et al., 1987;
Tanabe et al., 1987; Duinker et al., 1988; Schulz et al., 1989; Himberg and Sippola,
1990; Larsen et al., 1992; deBoer et al., 1993; Schwartz et al., 1993; Frame et al.,
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1996a; Frame et al., 1996b; and Frame, 1997). Schulz et al. (1989) were the first to
identify and quantify all PCB congeners present in a series of Aroclors and Clophens.
Frame (1995) reported preliminary results of a nearly completed round robin study, one
goal of which was to determine the distribution of all PCB congeners above 0.05 weight
percent in various Aroclors (1221, 1016, 1242, 1260, and 1262) using 18 state-of-the-
art gas chromatography/mass spectrometry (GC-MS) or electron capture detector (GC-
ECD) systems.
Table 11-3 presents mean summary statistics on the concentrations of the dioxin-
like PCBs in each mixture group (i.e., Aroclor 1248, Clophen A-40, and Kanechlor 400 are
in one mixture group) reported by these researchers. Table 11-3 also presents calculation
of the corresponding mean TEQ concentration of each congener in each mixture group as
well as the total mean TEQ concentration in the mixture group. For each mixture group,
the congeners detected were generally similar. There was, however, wide variability in
the concentrations reported by some researchers for some congeners. Brown et al.
(1995) compiled similar statistics using a somewhat different set of studies and derived
significantly lower mean concentrations of some congeners in several Aroclors. Frame
(1995) and Larsen (1995) attribute such differences either to potential limitations in the
GC columns used by various researchers to separate similar eluting congeners or to actual
differences in the congener concentrations in the Aroclor, Clophen, and Kanechlor lots
analyzed by various research groups. In addition to the specific congener concentrations,
the congener distributions also vary among the different mixtures. Therefore, the
calculated TEQs also vary. The congener distributions for various lots of Aroclor 1254,
and the corresponding TEQs, are presented in another study by Frame (1999). In this
study, Frame (1999) reports that the relative TEQs for late production lots are much
higher than the earlier production lots; however, the late production lots are estimated to
account for only about one percent of the total production volume of Aroclor 1254.
Therefore, the data for the later production lots were not included in the average TEQ
calculation for Aroclor 1254 in Table 11-3. Because of the wide variability in the reported
results, the uncertainty associated with the mean concentrations reported in Table 11-3 is
very large.
In the environment, PCBs also occur as mixtures of congeners, but their
composition will differ from the commercial mixtures. This is because after release to the
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environment, the composition of PCB mixtures changes over time, through partitioning,
chemical transformation, and preferential bioaccumulation (U.S. EPA, 1996g). Dioxin-like
PCB congeners differ by up to one to two orders of magnitude in their water solubilities,
vapor pressures, Kow values, and Henry's Law constants. Thus, although all the dioxin-like
PCB congeners are poorly soluble in water and have very low vapor pressures, they will
volatilize and leach at different rates. Similarly, because the congeners differ somewhat in
their rates of biodegradation, bioaccumulation, and photodegradation, the congener
patterns found in environmental media and biota will vary from those found in commercial
mixtures.
Although environmental mixtures are often characterized in terms of Aroclors, this
characterization can be both imprecise and inappropriate. Qualitative and quantitative
errors can arise from judgements in comparing GC/MS peaks for a sample with the
characteristic peak patterns for different Aroclors, particularly for environmentally altered
patterns (U.S. EPA, 1996g). For the same reason, it can be both imprecise and
inappropriate to infer concentrations of dioxin-like PCB congeners in an environmental
sample based on characterization of the sample's Aroclor content and knowledge of the
dioxin-like congener content in the commercial Aroclor. Safe (1994) wrote, "Regulatory
agencies and environmental scientists have recognized that the composition of PCBs in
most enviromental extracts does not resemble the compositions of the commercial
product." Similarly, ATSDR (1993) stated, "It is important to recognize that the PCBs to
which people may be exposed are likely to be different from the original PCB source
because of changes in congener and impurity composition resulting from differential
partitioning and transformation in the environment and differential metabolism and
retention."
11.2.1. Approved PCB Disposal/Destruction Methods
In 1978, EPA began regulating the disposal of PCBs and PCB-contaminated waste
under the TSCA, PL 94-469. The disposal regulations, published in the Code of Federal
Regulations, 40 CFR, Part 761, state that the preferred disposal method is incineration at
1,200°C or higher. If the waste contains material that can not be destroyed by
incineration, EPA clearance must be obtained to dispose of the waste in a chemical waste
landfill, or in another approved manner.
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The PCB disposal regulations describe disposal of three distinct types of PCB
waste: PCBs, PCB articles (i.e., items containing PCBs), and PCB containers. Within
these categories of PCB waste, further distinctions are made based on the PCB
concentration in the waste. The acceptable disposal methods are based on the PCB
concentrations in the specific waste to be destroyed. The acceptable disposal methods
are: Annex I incinerators, high-efficiency boilers, Annex II chemical waste landfills, and
other approved methods. The following subsections and Table 11-4 provide brief
descriptions of these disposal methods. More complete descriptions of the specific
methodologies are provided in the Code of Federal Regulations, 40 CFR, Part 761.
Approved Incinerators/High Efficiency Boilers - PCB Annex I incinerators must meet
the specific technical standards and criteria listed in Annex I of EPA's PCB regulations.
The minimum operating requirements for disposal of liquid wastes are 2 seconds at
1,200°C (2,1 90°F) with 3 percent excess oxygen (measured in the stack gas), or 1.5
seconds at 1,600°C (2,910°F) and 2 percent excess oxygen (measured in the stack gas).
Monitoring requirements, approval conditions, and trial burn requirements are prescribed in
Annex I. Commercial or industrial incinerators intending to destroy liquid PCB wastes
must demonstrate compliance with the Annex I requirements through a comprehensive
trial burn program. Annex I incinerators operating at optimum performance level should
destroy 99.997 percent of liquid PCB waste with a resulting maximum emission factor of
0.03 grams per kilogram (g/kg).
Criteria for Annex I incinerators were established for the destruction of liquid PCB
wastes; however, these incinerators also may be used for disposal of nonliquid PCB items
(such as capacitors), provided that a destruction and removal efficiency of 99.9999
percent and a maximum emission factor of 0.001 g/kg are met.
High-efficiency boilers may be used to destroy PCBs and PCB-contaminated waste
with PCB concentrations not exceeding 500 ppm. Conventional industrial and utility
boilers may be designated as high-efficiency boilers, if they are operated under the
prescribed combustion conditions defined in the PCB disposal regulations. The PCB
regulations do not specify a minimum PCB destruction efficiency for high-efficiency
boilers; however, EPA-approved boilers operated according to the regulations have
reported destruction efficiencies in excess of 99.99 percent, with a corresponding
maximum emission factor of 0.1 g/kg (U.S. EPA, 1987c).
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Approved Chemical Waste Landfills - Approved chemical waste landfills can be
used for the disposal of some, but not all, PCB wastes. PCB-contaminated materials
acceptable for land disposal in an approved landfill include PCB mixtures (e.g., certain
PCB-contaminated soil/solid debris, PCB-contaminated dredged materials, and PCB-
contaminated municipal sewage sludge), PCB articles that cannot feasibly be incinerated
(e.g., drained and flushed transformers), and drained PCB containers. EPA must issue
written approval to landfill PCB articles other than transformers. PCB-contaminated
materials not acceptable for land disposal in an approved landfill include nonliquid PCB
mixtures in the form of contaminated soil, rags, or other solid debris, and sealed
capacitors. Typically, PCBs disposed in these landfills are placed in sealed containers,
thereby, minimizing any PCB emissions.
Other Approved Disposal Methods - Other thermal and nonthermal destruction
techniques may be approved by EPA Regional Administrators, if these processes can
effect destruction of PCBs equivalent to that of incinerators or boilers. Subsequent to
April 29, 1983, all other PCB disposal technologies (thermal and nonthermal) that are to
be used in more than one EPA Region must be approved by EPA Headquarters. Examples
of thermal technologies approved for commercial-scale use or for research and
development projects include a pyrolysis process to treat contaminated soils, a fluid wall
reactor, a cement kiln, a diesel engine, a steam-stripping operation, an aluminum melting
furnace, and a molten salt process. Examples of approved nonthermal processes include
chemical dechlorination processes, physical/chemical extraction techniques, and biological
reduction methods. The physical/chemical techniques extract the PCBs from transformers
or capacitors and concentrate them for disposal; they do not destroy the PCBs.
Emission Estimates - Table 11-5 lists the amounts of PCBs reported in EPA's
Toxics Release Inventory (TRI) as transferred off-site for treatment, energy recovery, or
disposal during the years 1988 through 1996. These quantities do not necessarily
represent entry of PCBs into the environment. If it is assumed that all transferred PCBs
are incinerated in high-efficiency boilers with a destruction and removal efficiency of
99.99 percent, then annual emissions of PCBs to air during 1988 and 1993 could have
been as high as 264 kg and 31 kg, respectively. Because no stack testing data are
available for dioxin-like PCBs, it is not possible to estimate what fraction of these potential
PCB releases would have been the dioxin-like congeners.
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11.2.2. Accidental Releases of In-Service PCBs
EPA banned PCB production and use in open systems in 1977. Subsequent to the
1977 ban, releases of commercially produced PCB to the environment (aside from minimal
releases occurring during approved disposal and/or destruction) have been limited to
accidental release of in-service PCBs (U.S. EPA, 1987c). Accidental releases are the result
of leaks or spills during failure/breakage of an existing piece of PCB-containing equipment,
or incomplete combustion occurring during accidental fires involving PCB-containing
equipment. These two types of accidental releases are discussed in this section.
Leaks and Spills - PCBs that remain in active service at this time are those
contained in "closed system" (i.e., those pieces of electrical equipment that completely
enclose the PCBs and do not provide direct atmospheric access of the PCBs during normal
use). This equipment includes PCB transformers, capacitors, voltage regulators, circuit
breakers, and reclosures. With the exception of PCB transformers and probably small PCB
capacitors, the majority of the PCB-containing electrical equipment in-service during 1981
was owned by the electrical utility industry. Approximately 70 percent of the estimated
140,000 PCB transformers in-service in 1981 were owned by nonutilities. No information
was available on the relative distribution of small PCB capacitors (Versar, 1988).
The number of each of these items owned by the utility industry, the quantity of
PCBs each contains, and an estimate of the annual quantity of PCBs leaked and/or spilled
were investigated by the Edison Electric Institute and the Utility Solid Wastes Activity
Group (EEI/USWAG) for EPA in 1981. The findings of this investigation were reported in
a proposed modification to the PCB regulations (Federal Register, 1982a). The findings
indicated that over 99 percent of the total quantity of PCBs contained in utility-owned
electrical equipment in 1981 (73,700 metric tons) were in 40,000 PCB transformers
(those containing > 500 ppm of PCBs) and large PCB capacitors (those containing > 3 Ib
of PCBs). An upper-bound estimate of the mass of PCBs that leached or spilled from this
equipment in 1981 was 177 metric tons. Approximately 95 percent of the estimated
releases were the result of leaks from large PCB capacitors (Federal Register, 1982a).
Leaks/spills typically occur in transformers when the gasket joining the top to the body
corrodes, tears, or physically fails. PCBs can then leak past this failed section and
potentially spill onto the surrounding ground. PCB capacitors typically fail by rupturing,
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exposing the contained PCBs to the environment. Failure is caused by environmental and
weathering effects (e.g., lightning) or material failures (e.g., metal fatigue).
As of mid-1988, the total population of in-service PCB transformers and large PCB
capacitors was estimated to have decreased from 140,000 to 110,000 and from 3.3
million to 1.9 million, respectively (Versar, 1988). PCB transformers have normal
operating lifetimes of 30 years and 40 years, respectively. The accelerated retirement rate
over this 7-year period was attributed to EPA's PCB Electrical Use Rule (Federal Register,
1982b), which required the removal of 950 food/feed industry transformers by 1985 and
removal of 1.1 million unrestricted-access large PCB capacitors by October 1988. In
addition, EPA's PCB Transformer Fires Rule (Federal Register, 1985b) required the removal
by 1990 of 7,600 480-volt network transformers. More recent inventories of PCB-
containing electrical equipment are not available. However, a recent Information
Collection Request submitted by EPA to the Office of Management and Budget for
information on uses, locations, and conditions of PCB electrical equipment estimated that
there may be 150,000 owners of PCB-containing transformers used in industry, utilities,
government buildings, and private buildings (Federal Register, 1997a). It is expected, and
is demonstrated by the reported PCB transfers in TRI (see Table 11-5), that many owners
of PCB electrical equipment have removed PCB-containing equipment to eliminate potential
liability.
The proportion of spilled PCB that enters the atmosphere, runs off to surface
water, or remains in or on the surface depends on a variety of factors including the
porosity of the surface onto which the PCBs are spilled (concrete, soil), the PCB isomers
that are spilled, ambient conditions (i.e., temperature, wind speed, precipitation), and the
cleanup schedule. The number and diversity of factors affecting PCB emissions from spills
and leaks make estimation of an emission factor difficult. A rough approximation of the
annual amount that may be released to the environment from spills and leaks can be made
using the release data reported by manufacturing facilities to EPA's TRI. Table 11-6 lists
the amounts of PCBs reported in TRI to be released to the environment during 1988
through 1996. These data include emissions to the air, discharges to bodies of water,
releases at the facility to land, as well as contained disposal into underground injection
wells.
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On the basis of TRI data, annual reported emissions of PCBs to air during 1988 and
1995 could have been as high as 2.7 kg and 0 kg, respectively. For purposes of deriving
a preliminary rough estimate of potential releases of dioxin-like PCBs, it can be assumed
that the ratio of TEQ to total PCB in the air emissions was 67:1-million (i.e., the average
of the estimated mean TEQ contents for Aroclors 1242 and 1254 presented in Table 11-
3). Based on this assumption, annual emissions of PCB TEQs in 1988 and 1995 could
have been 0.2 and 0 grams, respectively. Similar assumptions for releases to water listed
in Table 11-6 yield estimated TEQ emissions during 1988 and 1995 of 0.3 and 0 grams,
respectively. For land, estimated TEQ emissions during 1988 and 1995 could have been
23 and 0 grams, respectively.
Accidental Fires - The available information is not adequate to support an estimate
of potential annual releases of dioxin-like PCBs from accidental electrical equipment fires.
For fires involving PCB transformers or capacitors, the amount of PCBs released is
dependent upon the extensiveness of the fire and the speed at which it is extinguished. A
number of these fires are documented. A New York fire, involving 200 gallons of
transformer fluid containing some 65 percent by weight PCBs, resulted in a release of up
to 1,300 pounds of PCBs. A capacitor fire that burned uncontrolled for 2 hours in
Sweden resulted in the destruction of 12 large utility capacitors containing an estimated
25 pounds of PCBs each, for a total potential release of 300 pounds. However, data are
incomplete on the exact amount of PCBs released as a result of these two fires.
EPA has imposed reporting requirements to ensure that the National Response
Center is informed immediately of fires involving PCB transformers (40 CFR 761). The
recordkeeping requirements are used to document the use, location, and condition of PCB
equipment. Responses are mandatory, but may be claimed by the submitter to be
confidential information. The annual number of PCB transformer fires is estimated at
approximately 20 per year; the number of PCB capacitor fires is unknown (U.S. EPA,
1987c). As these PCB items reach the end of their useful lives and are retired, their
susceptibility to fires will be eliminated, and the overall number of PCB transformer and
capacitor fires will be reduced.
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11.2.3. Municipal Wastewater Treatment
EPA conducted the National Sewage Sludge Survey in 1988 and 1989 to obtain
national data on sewage sludge quality and management. As part of this survey, EPA
analyzed sludges from 175 publicly owned treatment works (POTWs) that employed at
least secondary wastewater treatment for more than 400 analytes including 7 of the
Aroclors. Sludges from 19 percent of the POTWs had detectable levels of at least one of
the following Aroclors: 1 248, 1 254, or 1 260; none of the other Aroclors were detected in
any sample (detection limit was typically about 200//g/kg dry weight) (U.S. EPA, 1996a).
Analyses were not performed for dioxin-like PCB congeners. The Aroclor-specific results
of the survey are presented in Table 11-7. Gutenmann et al. (1994) reported similar
results in a survey of sludges from 16 large U.S. cities for Aroclor 1260 content. At a
detection limit of 250-//g/kg (dry weight), Gutenmann et al. (1994) detected Aroclor 1260
at only one facility (4,600/vg/kg). These results indicate that PCBs are not likely to be
formed at POTWs, but rather are present because of disposal of PCB products or
recirculation of previously disposed PCB.
Although PCBs, measured as Aroclors, were not commonly detected in sewage
sludge at//g/kg levels by U.S. EPA (1996a) and Gutenmann et al. (1994), the presence of
dioxin-like PCB congeners at lower concentrations may be more common. Green et al.
(1995) and Cramer et al. (1995) reported the results of analyses of 99 samples of sewage
sludge for PCB congener numbers 77, 81, 126, and 169. The sludge samples were
collected from 74 wastewater treatment plants across the United States during the
summer of 1994. These data are summarized in Table 11-8. Results from all samples
collected from the same facility were averaged by Green et al. (1995) and Cramer et al.
(1995) to ensure that results were not biased towards the concentrations found at
facilities from which more than one sample were collected. If all nondetected values are
assumed to be zero, then the POTW mean TEQP-WH094 and TEQP-WH098 concentrations
were 25.1 and 24.2 ng TEQ/kg (dry weight basis), respectively. If the nondetected values
are set equal to the detection limits, then the POTW mean TEQP-WH094 and TEQp-WH098
concentrations were 25.2 and 24.3 ng TEQ/kg, respectively.
EPA recently analyzed samples of sewage sludge collected from a POTW in Ohio
for all of the TEQP-WH094 and TEQP-WH098 dioxin-like PCB congeners, with the exception
of PCB 81 (Battelle, 1999). The results of the analyses presented in the draft test report
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are listed in Table 11-9. The average TEQ content of the POTW sludge was 158 ng TEQP-
WH094/kg (141 ng TEQP-WH098/kg). Three PCB congeners, 77, 126, and 169, accounted
for more than 97 percent of the total TEQ in each sample.
Approximately 5.4 million dry metric tons of sewage sludge are estimated by EPA
to be generated annually in the United States based on the results of the 1988/1989 EPA
National Sewage Sludge Survey (Federal Register, 1993b). Table 11-10 lists the volume
of sludge disposed of annually by use and disposal practices. Table 11-10 also lists the
estimated amount of dioxin-like PCB TEQs that may be present in sewage sludge and
potentially be released to the environment. These values were estimated using the POTW
mean TEQP-WH098 concentration calculated from the results reported by Green et al.
(1995) and Cramer et al. (1995). Multiplying this TEQ concentration by the sludge
volumes generated yields an annual potential total release of 101 g TEQP-WH098 for
nonincinerated sludges. Of this 101 g TEQP-WH098, 1.7 grams enter commerce as a
product for distribution and marketing. The remainder is applied to land (51.1 grams) or is
landfilled (48.2 grams).
These release estimates are assigned a confidence rating of B indicating high
confidence in the production estimate and "medium" confidence in the emission factor
estimates. The medium rating was based on the judgment that, although the 74 facilities
tested by Green et al. (1995) and Cramer et al. (1995) may be reasonably representative
of the variability in POTW technologies and sewage characteristics nationwide, the sample
size was still relatively small, and not all dioxin-like PCB congeners were monitored.
11.3. CHEMICAL MANUFACTURING AND PROCESSING SOURCES
In the early 1980s, EPA investigated the extent of inadvertent generation of PCBs
during the manufacture of synthetic organic chemicals (Hammerstrom et al., 1985). For
example, phthalocyanine dyes and diarylide pigments were reported to contain PCBs in the
mg/kg range. EPA subsequently issued regulations under TSCA (40 CFR 761.3) that
banned the distribution in commerce of any products containing an annual average PCB
concentration of 25 mg/kg (50 mg/kg maximum concentration at any time). In addition,
EPA required manufacturers with processes inadvertently generating PCBs and importers
of products containing inadvertently generated PCBs to report to EPA any process or
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import for which the PCB concentration is greater than 2 mg/kg for any resolvable PCB
gas chromatographic peak.
11.4. COMBUSTION SOURCES
11.4.1 Municipal Solid Waste Incineration
Municipal solid waste incinerators have long been identified as potential PCB air
emission sources. Stack gas concentrations of PCBs for three incinerators were reported
in U.S. EPA (1 987c), and the average test results yields an emission factor of 1 8 /vg
PCBs/kg refuse. Stack gas emissions of PCBs from the three incinerators were quantified
without determining the incinerator's PCB destruction efficiency. The PCB content of
various consumer paper products was analyzed as part of the study. This study indicates
that paper products such as magazine covers and paper towels contained up to 139
micrograms of PCB per kilogram of paper (/vg/kg). These levels, which were reported in
1981, were attributed to the repeated recycle of waste paper containing PCBs. For
example, carbonless copy paper manufactured prior to 1971 contained PCB levels as high
as 7 percent. This copy paper then became a component of waste paper, which was
recycled. The PCBs inevitably were introduced into other paper products, resulting in
continued measurable levels in municipal refuse some 4 years after the PCB manufacturing
ban was imposed. Refuse-derived fuel (RDF) manufactured from these paper products had
PCB levels of 8,500 //g/kg, indicating that this fuel could be a source of atmospheric
PCBs. Therefore, it was assumed in U.S. EPA (1987c) that municipal refuse does contain
detectable levels of PCBs, and that some of these PCBs may enter the atmosphere when
the refuse is incinerated.
Shane et al. (1990) analyzed fly ashes from five municipal solid waste (MSW)
incinerators for PCB congener group content. Total PCB levels ranged from 99 to 322
//g/kg in these ashes with the tri-, tetra-, and penta-congener groups occurring in the
highest concentrations. Shane et al. (1990) also analyzed seven bottom ashes and eight
bottom ash/fly ash mixtures for total PCB measured as Aroclor 1254. The detection limit
for this Aroclor analysis was 5 /vg/kg. Aroclor 1 254 was detected in two of the seven
bottom ash samples (26 and 8/vg/kg) and in five of the eight fly ash/bottom ash mixtures
(range of 6 to 33 //g/kg).
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The development of more sensitive analytical methodologies has enabled
researchers in recent years to detect dioxin-like PCB congeners in the stack gases and fly
ash from full-scale and pilot-scale MSW incinerators (Sakai et al., 1993a; Sakai et al.,
1993b; Boers et al., 1993; Schoonenboom et al., 1993; Sakai et al., 1994). Similarly,
the advances in analytical techniques have enabled researchers to determine that dioxin-
like PCBs can be formed during the oxidative solid combustion phase of incineration
presumably due to dimerization of chlorobenzenes. Laboratory-scale studies have also
recently demonstrated that dioxin-like PCBs can be formed from heat treatment of fly ash
in air (Schoonenboom et al., 1993; Sakai et al., 1994). However, the available data are
not adequate to support development of a quantitative estimate of a dioxin-like PCB
emission factor for this source category.
11.4.2. Industrial Wood Combustion
Emissions of PCB congener groups, not individual congeners, were measured
during stack testing of two industrial wood burning facilities by the State of California Air
Resources Board (CARB, 1990e; 1990f). Table 11-11 presents the average of the
congener group (i.e., mono- through decachlorobiphenyl) emission factors for these two
facilities. No tetra- or more chlorinated congeners (i.e., the congener groups containing
the dioxin-like PCBs) were detected at either facility at detection limits corresponding to
emission factors in the low range of ng/kg of wood combusted.
In CARB (1990e), PCBs were measured in the emissions from two spreader stoker
wood-fired boilers operated in parallel by an electric utility for generating electricity. The
exhaust gas stream from each boiler is passed through a dedicated ESP after which the
gas streams are combined and emitted to the atmosphere through a common stack.
Stack tests were conducted both when the facility burned fuels allowed by existing
permits and when the facility burned a mixture of permitted fuel supplemented by urban
wood waste at a ratio of 70:30.
In CARB (1990f), PCBs were measured in the emissions from twin fluidized bed
combustors designed to burn wood chips to generate electricity. The APCD system
consisted of ammonia injection for controlling nitrogen oxides, and a multiclone and
electrostatic precipitator for controlling particulate matter. During testing, the facility
burned wood wastes and agricultural wastes allowed by existing permits.
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11.4.3. Medical Waste Incineration
As discussed in Section 3.3, EPA recently issued nationally applicable emission
standards and guidelines for medical waste incinerators (MWI) that address CDD/CDF
emissions. Although PCBs are not addressed in these regulations, the data base of stack
test results at MWIs compiled for this rulemaking does contain limited data on PCB
congener group emission factors. Data are available for two MWIs lacking add-on APCD
equipment and for two MWIs with add-on APCD equipment in place. The average
congener group emission factors derived from these test data are presented in Table 11-
1 2. Because data are available for only 4 of the estimated 2,400 facilities that make up
this industry and because these data do not provide congener-specific emission factors, no
national estimates of total PCB or dioxin-like PCB emissions are being made at this time.
11.4.4. Tire Combustion
Emissions of PCB congener groups, not individual congeners, were measured
during stack testing of a tire incinerator by the State of California Air Resources Board
(CARB, 1991 a). The facility consists of two excess air furnaces equipped with steam
boilers to recovery the energy from the heat of combustion. Discarded whole tires were
fed to the incineration units at rates ranging from 2,800 to 5,700 kg/hr during the 3
testing days. The furnaces are equipped to burn natural gas as auxiliary fuel. The steam
produced from the boilers drives electrical turbine generators that produce 14.4
megawatts of electricity. The facility is equipped with a dry acid gas scrubber and fabric
filter for the control of emissions prior to exiting the stack. Table 11-13 presents the
congener group (i.e., mono- through decachlorobiphenyl) emission factors for this facility.
The emission factor for the total of the tetra- through hepta-chlorinated congener groups
is about 1.2 //g/kg of tire processed.
EPA estimated that approximately 0.50 million metric tons of tires were incinerated
in 1990 in the United States (U.S. EPA, 1992a). This production estimate is given a
medium confidence rating, because it is based on both published data and professional
judgment. The use of scrap tires as a fuel increased significantly during the late 1980s;
however, no quantitative estimates were provided in U.S. EPA (1992a) for this period. In
1990, 10.7 percent of the 242 million scrap tires generated were burned for fuel. This
percentage is expected to continue to increase (U.S. EPA, 1992a). Of the tires burned for
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energy recovery purposes, pulp and paper facilities used approximately 46 percent;
cement kilns, 23 percent; and one tire-to-energy facility, 19 percent (U.S. EPA, 1997b).
If it is assumed that 500 million kg of discarded tires are incinerated annually in the
United States, then, using the sum of the average emission factors for the total tetra-
through heptachlorinated congener groups (1.2//g/kg tire processed) derived from stack
data from the one tested facility, yields a total emission of 610 g/yr. However, it is not
known what fraction of this emission is dioxin-like PCBs.
11.4.5. Cigarette Smoking
Using high-resolution mass spectrometry, Matsueda et al. (1994) analyzed tobacco
from 20 brands of commercially available cigarettes collected in 1992 from Japan, the
United States, Taiwan, China, the United Kingdom, Germany, and Denmark for the PCB
congeners 77, 1 26, and 1 69. Table 11-14 presents the results of the study.
However, no studies have been reported which examined the tobacco smoke for
the presence of these congeners. Thus, it is not known whether the PCBs present in the
tobacco are destroyed or volatilized during combustion, or whether PCBs are formed
during combustion. The combustion processes operating during cigarette smoking are
complex and could be used to support either of these potential mechanisms. As reported
by Guerin et al. (1992), during a puff, gas phase temperatures reach 850°C at the core of
the firecone, and solid phase temperatures reach 800°C at the core and 900°C or greater
at the char line. Thus, temperatures are sufficient to cause at least some destruction of
CDD/CDFs initially present in the tobacco. Both solid and gas phase temperatures rapidly
decline to 200 to 400°C within 2 mm of the char line. Formation of dioxin-like PCBs has
been reported in combustion studies with other media in this temperature range (Sakai et
al., 1994). However, it is known that a process likened by Guerin et al. (1992) to steam
distillation takes place in the region behind the char line because of high localized
concentrations of water and temperatures of 200 to 400°C. At least 1,200 tobacco
constituents (e.g., nicotine, n-paraffin, some terpenes) are transferred intact from the
tobacco into the smoke stream by distillation in this area, and it is plausible that PCBs
present in the unburned tobacco would be subject to similar distillation.
In 1995, approximately 487 billion cigarettes were consumed in the United States
and by U.S. Armed Forces personnel stationed overseas. Per capita U.S. cigarette
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consumption in 1995, based on total U.S. population aged 16 and over, declined to 2,415
from a record high of 4,345 in 1963. In 1987, approximately 575 billion cigarettes were
consumed domestically (The Tobacco Institute, 1995; USDA, 1997).
A preliminary rough estimate of potential emissions of dioxin-like PCBs can be
made using the following assumptions: (1) the average TEQP-WH098 content of seven
brands of U.S. cigarettes reported by Matsueda et al. (1994), 0.64 pg/pack (or 0.032
pg/cigarette) is representative of cigarettes smoked in the United States; (2) dioxin-like
PCBs are neither formed nor destroyed, and the congener profile reported by Matsueda et
al. (1994) is not altered during combustion of cigarettes; and (3) all dioxin-like PCBs
contributing to the TEQ are released from the tobacco during smoking. Based on these
assumptions, the calculated annual emissions would be 0.018 g TEQP-WH098 and 0.016 g
TEQP-WH098 for reference years 1987 and 1995, respectively.
11.4.6. Sewage Sludge Incineration
U.S. EPA (1996f) derived an emission factor of 5.4//g of total PCBs per kg of dry
sludge incinerated. This emission factor was based on measurements conducted at five
multiple hearth incinerators controlled with wet scrubbers. In 1992, approximately 199
sewage sludge incineration facilities combusted 0.865 million metric tons of dry sewage
sludge (Federal Register, 1993b). Given this mass of sewage sludge incinerated, the
estimated annual release of total PCBs to air annually is 4,670 g. However, it is not
known what fraction of this annual emission is dioxin-like PCBs.
EPA recently conducted stack testing at a sewage sludge incinerator in Ohio
(Battelle, 1999) for all of the TEQP-WH094 and TEQP-WH098 dioxin-like PCB congeners
with the exception of PCB 81. The results of the analyses (ng/dscm) presented in the
draft test report are listed in Table 11-15. The average TEQ content of the stack gas was
0.119 ng TEQP-WH094/dscm (0.106 ng TEQP-WH098/dscm). Three PCB congeners, 77,
126, and 169, accounted for more than 97 percent of the total TEQ in each sample.
11.4.7. Backyard Barrel Burning
In many rural areas of the United States, disposal of residential solid waste may
take place via open backyard burning in barrels or similar homemade devices. Although no
national statistics on the prevalence of this practice have been reported, the results of a
DRAFT-DO NOT QUOTE OR CITE 11-18 December 2003
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telephone survey conducted in the early 1990s of residents in five central Illinois counties
indicate that about 40 percent of the residents in a typical rural Illinois county burn
household waste. The survey also found that, on average, those households that burn
waste dispose of approximately 63 percent of their household waste through burning in
barrels (Two Rivers Region Council of Public Officials and Patrick Engineering, 1994).
The low combustion temperatures and oxygen-starved conditions associated with
this method may result in incomplete combustion and increased pollutant emissions
(Lemieux, 1997). EPA's Control Technology Center, in cooperation with the New York
State Departments of Health (NYSDOH) and Environmental Conservation (NYSDEC),
recently conducted a study to examine, characterize, and quantify emissions from the
simulated open burning of household waste materials in barrels (Lemieux, 1997). A
representative waste to be burned was prepared based on the typical percentages of
various waste materials disposed by New York State residents (i.e., nonavid recyclers);
hazardous wastes (i.e., chemicals, paints, oils, etc.) were not included in the test waste.
A variety of compounds, including dioxin-like PCBs, were measured in the emissions from
the simulated open burning. The measured TEQ emission factors for waste, which had
not been separated for recycling purposes, were 1.02E-2 fjg TEQp-WH094/kg of waste
burned and 5.26E-03//g TEQp-WH098/kg (see Table 11-16).
The limited emission factor and activity level data available were judged inadequate
for developing national emission estimates that could be included in the national inventory.
The number of households nationwide burning waste in barrels and the total amount and
variability of burned waste is unknown. The representativeness of the trash and burning
conditions used in the experiments to conditions nationwide are unknown. However,
combining the emission factor of 5.26E-03 /vg TEQp-WH098/kg of waste burned with the
following information/assumptions, allows a preliminary order of magnitude estimate to be
made of potential national dioxin-like PCB TEQ emissions from backyard household trash
burning.
Forty percent of the rural population in the United States are assumed to burn
their household waste in a barrel (Two Rivers Region Council of Public Officials
and Patrick Engineering, 1994).
On average, each U.S. citizen generates 3.72 pounds of solid waste (excluding
yard waste) per day (or 616 kg/person-year) (U.S. EPA, 1996b).
DRAFT-DO NOT QUOTE OR CITE 11-19 December 2003
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On average, for those individuals burning household waste, approximately 63
percent of waste generated are burned (i.e., 63 percent of 616 kg/person-year
= 388 kg/person-year) (Two Rivers Region Council of Public Officials and
Patrick Engineering, 1994).
In 1992, 51.8 million people lived in nonmetropolitan areas (U.S. DOC, 1997).
Emissions = (51.8 x 106 people)(40%)(388 kg/person-yr)(5.26E-03 |Jg TEQP-
WH098/kg)(10-6g/|Jg)
42.3 g TEQp-WH098/yr (82.1 g TEQP-WH094/yr)
11.4.8. Petroleum Refining Catalyst Regeneration
As discussed in Section 5.3, regeneration of spent catalyst used in catalytic
reforming to produce high-octane reformates is a potential source of CDD/CDF air
emissions. In 1998, emissions from the caustic scrubber used to treat gases from the
external catalyst regeneration unit of a refinery in California were tested for CDD/CDFs, as
well as PCB congener groups (CARB, 1999). This facility uses a continuous regeneration
process. The reactor is not taken off line during regeneration; rather, small amounts of
catalyst are continuously withdrawn from the reactor and are regenerated. The emissions
from the regeneration unit are neutralized by a caustic scrubber before being vented to the
atmosphere. The catalyst recirculation rate during the three tests ranged from 733 to
1,000 Ib/hr.
All PCB congener groups were detected in each of the three samples collected.
The average congener group emission factors in units of ng per barrel of reformer feed are
presented in Table 11-17. The total PCB emission factor was 118 ng/barrel. This
emission factor assumes that emissions are proportional to reforming capacity; emission
factors may be more related to the amount of coke burned, APCD equipment present,
and/or other process parameters.
Because emissions data are available for only one U.S. petroleum refinery (which
represents less than 1 percent of the catalytic reforming capacity at U.S. refineries) and
because these data do not provide congener-specific emission factors, no national
estimates of total PCB or dioxin-like PCB emissions are being made at this time.
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11.5. NATURAL SOURCES
11.5.1. Biotransformation of Other PCBs
Studies show that under anaerobic conditions, biologically mediated reductive
dechlorination to less chlorinated congeners, followed by slow anaerobic and/or aerobic
biodegradation, is a major pathway for destruction of PCBs in the environment. Research
reported to date and summarized below indicates that biodegradation should result in a
net decrease rather than a net increase in the environmental load of dioxin-like PCBs.
Laboratory studies (e.g., Bedard et al., 1986; Pardue et al., 1988; Larsson and
Lemkemeier, 1989; Mickey, 1995; and Schreiner et al., 1995) have revealed that more
than two dozen strains of aerobic bacteria and fungi, which are capable of degrading most
PCB congeners with five or fewer chlorines, are widely distributed in the environment.
Many of these organisms are of the genus Pseudomonas or the genus Alcaligenes. The
major metabolic pathway involves addition of 02 at the 2,3-position by a dioxygenase
enzyme with subsequent dehydrogenation to the catechol followed by ring cleavage.
Several bacterial strains have been shown to possess a dioxygenase enzyme that attacks
the 3,4-position.
However, only a few strains have demonstrated the ability to degrade hexa- and
more chlorinated PCBs. The rate of aerobic biodegradation decreases with increasing
chlorination. The half-lives for biodegradation of tetra-PCBs in fresh surface water and soil
are 7 to 60+ days and 1 2 to 30 days, respectively. For penta-PCBs and higher
chlorinated PCBs, the half-lives in fresh surface water and soil are likely to exceed 1 year.
PCBs with all or most chlorines on one ring and PCBs with fewer than two chlorines in the
ortho position tend to degrade more rapidly. For example, Gan and Berthouex (1994)
monitored over a 5-year period the disappearance of PCB congeners applied to soil with
sewage sludge. Three of the tetra- and pentachlorinated dioxin-like PCBs (IUPAC Nos. 77,
105, and 118) followed a first-order disappearance model with half-lives ranging from 43
to 69 months. A hexa-substituted congener (IUPAC No. 167) and a hepta-substituted
congener (IUPAC No. 180) showed no significant loss over the 5-year period.
Until recent years, little investigation focused on anaerobic microbial dechlorination
or degradation of PCBs even though most PCBs eventually accumulate in anaerobic
sediments (Abramowicz, 1990; Risatti, 1992). Environmental dechlorination of PCBs via
losses of meta and para chlorines has been reported in field studies for freshwater,
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estuarine, and marine anaerobic sediments including those from the Acushnet Estuary, the
Hudson River, the Sheboygan River, New Bedford Harbor, Escambia Bay, Waukegan
Harbor, the Housatonic River, and Woods Pond (Brown et al., 1987; Rhee et al., 1989;
Van Dort and Bedard, 1991; Abramowicz, 1990; Bedard et al., 1995; and Bedard and
May, 1996). The altered PCB congener distribution patterns found in these sediments
(i.e., different patterns with increasing depth or distance from known sources of PCBs)
have been interpreted as evidence that bacteria may dechlorinate PCBs in anaerobic
sediment.
Results of laboratory studies reported recently confirm anaerobic degradation of
PCBs. Chen et al. (1988) found that "PCB-degrading" bacteria from the Hudson River
could significantly degrade the mono-, di-, and tri-PCB components of a 20 ppm Aroclor
1221 solution within 105 days. These congener groups make up 95 percent of Aroclor
1 221. No degradation of higher chlorinated congeners (present at 30 ppb or less) was
observed, and a separate 40-day experiment with tetra-PCB also showed no degradation.
Rhee et al. (1989) reported degradation of mono- to penta-substituted PCBs in
contaminated Hudson River sediments held under anaerobic conditions in the laboratory
(N2 atmosphere) for 6 months at 25°C. Amendment of the test samples with biphenyl
resulted in greater loss of PCB. No significant decreases in the concentrations of the more
highly chlorinated (i.e., more than five chlorines) were observed. No evidence of
degradation was observed in samples incubated in C02/H2 atmospheres. Abramowicz
(1990) hypothesized that this result could be an indication that, in the absence of C02, a
selection is imposed favoring organisms capable of degrading PCBs to obtain C02 and/or
low molecular weight metabolites as electron receptors.
Risatti (1992) examined the degradation of PCBs at varying concentrations (10,000
ppm, 1,500 ppm, and 500 ppm) in the laboratory with "PCB-degrading" bacteria from
Waukegan Harbor. After 9 months of incubation at 22°C, the 500 ppm and 1,500 ppm
samples showed no change in PCB congener distributions or concentrations, thus
indicating a lack of degradation. Significant degradation was observed in the 10,000 ppm
sediment with at least 20 congeners ranging from TrCBs to PeCBs showing decreases.
Quensen et al. (1988) also demonstrated that microorganisms from PCB-
contaminated sediments (Hudson River) dechlorinated most tri- through hexa-PCBs in
Aroclor 1242 under anaerobic laboratory conditions. The Aroclor 1242 used to spike the
DRAFT-DO NOT QUOTE OR CITE 1 1 -22 December 2003
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sediment contained predominantly tri- and tetra-PCBs (85 mole percent). Three
concentrations of the Aroclor, corresponding to 14-, 140-, and 700-ppm on a sediment
dry-weight basis, were used. Dechlorination was most extensive at the 700-ppm test
concentration; 53 percent of the total chlorine were removed in 16 weeks, and the
proportion of TeCBs through HxCBs decreased from 42 to 4 percent. Much less
degradation was observed in the 140-ppm sediment, and no observable degradation was
found in the 14-ppm sediment. These results and those of Risatti (1992) suggest that the
organism(s) responsible for this dechlorination may require relatively high levels of PCB as
a terminal electron acceptor to maintain a growing population.
Quensen et al. (1990) reported that dechlorination of 500-ppm spike
concentrations of Aroclor 1242, 1248, 1254, and 1260 by microorganisms from PCB-
contaminated sediments in the Hudson River and Silver Lake occurred primarily at the
meta- and para- positions; ortho-substituted mono- and di-PCBs increased in
concentration. Significant decreases over the up to 50-week incubation period were
reported for the following dioxin-like PCBs: 1 56, 1 67, 1 70, 1 80 and 1 89. Of the four
dioxin-like TeCBs and PeCBs detected in the Aroclor spikes (i.e., IUPAC Nos. 77, 105,
114, and 118), all decreased significantly in concentration, with the possible exception of
PeCB 114 in the Aroclor 1260-spiked sediment.
Nies and Vogel (1990) reported similar results with Hudson River sediments
incubated anaerobically and enriched with acetone, methanol, or glucose. Approximately
300 ppm of Aroclor 1242 (31-mole percent TeCBs, 7-mole percent PeCBs, and 1-mole
percent HxCBs) were added to the sediments prior to incubation for 22 weeks under an N2
atmosphere. Significant dechlorination was observed, with dechlorination occurring
primarily at the meta- and para-positions on the more highly chlorinated congeners (i.e.,
TeCBs, PeCBs, and HxCBs), resulting in the accumulation of less-chlorinated, primarily
ortho-substituted mono- through tri-substituted congeners. No significant dechlorination
was observed in the control samples (i.e., samples containing no added organic chemical
substrate and samples that were autoclaved).
Bedard and May (1996) also reported similar findings in the sediments of Woods
Pond, believed contaminated with Aroclor 1260. Significant decreases in the sediment
concentrations of PCBs 118,1 56, 1 70, and 1 80 (relative to their concentrations in
DRAFT-DO NOT QUOTE OR CITE 1 1 -23 December 2003
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Aroclor 1260) were observed. No increases or decreases were reported for the other
dioxin-like PCBs.
Bedard et al. (1995) demonstrated that it is possible to stimulate substantial
microbial dechlorination of the highly chlorinated PCB mixture Aroclor 1 260 in situ with a
single addition of 2,6-dibromobiphenyl. Bedard et al. (1995) added 365 g of 2,6-
dibromobiphenyl to 6-foot-diameter submerged caissons containing 400-kg sediment (dry
weight) and monitored the change in PCB congener concentrations for a period of 1 year.
At the end of the observation period, the hexa- through monochlorinated PCBs decreased
74 percent in the top of the sediment and 69 percent in the bottom. The average number
of chlorines per molecule dropped 21 percent from 5.83 to 4.61, with the largest
reduction observed in meta-chlorines (54 percent reduction) followed by para-chlorines (6
percent). The dechlorination stimulated by 2,6-dibromobiphenyl selectively removed meta-
chlorines positioned next to other chlorines.
The findings of these latter studies are significant, because removal of meta- and
para-chlorines from the dioxin-like PCBs should reduce their toxicity and bioaccumulative
potential and also form less chlorinated congeners that are more amenable to aerobic
biodegradation.
Van Dort and Bedard (1991) reported the first experimental demonstration of
biologically mediated ortho-dechlorination of a PCB and stoichiometric conversion of that
PCB congener (2,3,5,6-TeCB) to less chlorinated forms. In that study, 2,3,5,6-TeCB was
incubated under anaerobic conditions with unacclimated methanogenic pond sediment for
37 weeks, with reported dechlorination to 2,5-DCB (21 percent); 2,6-DCB (63 percent);
and 2,3,6-TrCB (16 percent).
11.5.2. Photochemical Transformation of Other PCBs
Photolysis and photo-oxidation may be major pathways for destruction of PCBs in
the environment. Research reported to date and summarized below indicates that ortho-
substituted chlorines are more susceptible to photolysis than are meta- and para-
substituted congeners. Thus, photolytic formation of more toxic dioxin-like PCBs may
occur. Oxidation by hydroxyl radicals, however, apparently occurs preferentially at the
meta- and para-positions thus resulting in a net decrease rather than a net increase in the
environmental load of dioxin-like PCBs.
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Based on the data available in 1983, Leifer et al. (1983) concluded that all PCBs,
especially the more highly chlorinated congeners and those that contain two or more
chlorines in the ortho-position, photodechlorinate. In general, as the chlorine content
increases, the photolysis rate increases. More recently, Lepine et al. (1992) exposed
dilute solutions (4 ppm) of Aroclor 1254 in cyclohexane to sunlight for 55 days in
December and January. Congener-specific analysis indicated that the amounts of many
higher chlorinated congeners, particularly mono-ortho-substituted congeners decreased,
while those of some lower chlorinated congeners increased. The results for the dioxin-like
PCBs indicated a 43.5 percent decrease in the amount of PeCB 114; a 73.5 percent
decrease in the amount of HxCB 156; and a 24.4 percent decrease in the amount of HxCB
157. However, TeCB 77 and PeCB 126 (the most toxic of the dioxin-like PCB congeners),
which were not detected in unirradiated Aroclor 1254, represented 2.5 percent and 0.43
percent, respectively, of the irradiated mixture.
With regard to photo-oxidation, Atkinson (1987) and Leifer et al. (1983), using
assumed steady-state atmospheric OH concentrations and measured oxidation rate
constants for biphenyl and monochlorobiphenyl, estimated atmospheric decay rates and
half-lives for gaseous-phase PCBs. Atmospheric transformation was estimated to proceed
most rapidly for those PCB congeners containing either a small number of chlorines or
those containing all or most of the chlorines on one ring. Kwok et al. (1995) extended the
work of Atkinson (1987) by measuring the OH radical reaction rate constants for 2,2'-,
3,3'-, and 3,5-dichlorobiphenyl. These reaction rate constants, when taken together with
the Atkinson's measurements for biphenyl and monochlorobiphenyl and the estimation
method described in Atkinson (1991), were used to generate more reliable estimates of
the gas-phase OH radical reaction rate constants for the dioxin-like PCBs. The persistence
of the PCB congeners increases with increasing degree of chlorination. Table 11-18
presents these estimated rate constants and the corresponding tropospheric lifetimes and
half-lives.
Sedlak and Andren (1991) demonstrated in laboratory studies that OH radicals,
generated with Fenton's reagent, rapidly oxidized PCBs (i.e., 2-mono-PCB and the DiCBs
through PeCBs present in Aroclor 1242) in aqueous solutions. The results indicated that
the reaction occurs via addition of a hydroxyl group to one nonhalogenated site; reaction
rates are inversely related to the degree of chlorination of the biphenyl. The results also
DRAFT-DO NOT QUOTE OR CITE 1 1 -25 December 2003
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indicated that meta- and para-sites are more reactive than ortho-sites due to stearic
hindrance effects. Based upon their kinetic measurements and reported steady-state
aqueous system OH concentrations or estimates of OH radical production rates, Sedlak
and Andren (1991) estimated environmental half-lives for dissolved PCBs (mono-through
octa-PCB) in fresh surface water and in cloud water to be 4 to 11 days and 0.1 to 10
days, respectively.
11.6. PAST USE OF COMMERCIAL PCBS
An estimated 1.5 million metric tons of PCBs were produced worldwide (DeVoogt
and Brinkman, 1989). Slightly more than one-third of these PCBs (568,000 metric tons)
were used in the United States (Versar, 1976). Although the focus of this section is on
reservoir sources of PCBs within the United States, it is necessary to note that the use
and disposal of PCBs in many countries, coupled with the persistent nature of PCBs, have
resulted in their movement and presence throughout the global environment. The ultimate
sink of most PCBs released to the environment will be aquatic sediments. Currently,
however, large quantities of PCBs are estimated to be circulating between the air and
water environments or are present in landfills and dumps, some of which may offer the
potential for re-release of PCBs into the air. Tanabe (1988) presented a global mass
balance for PCBs that indicated that as of 1985, 20 percent of the total PCBs produced
were present in seawater, whereas only 11 percent were in sediments. (See Table 11-
19.) Nearly two-thirds of total global PCB production was estimated by Tanabe (1988) to
still be in use in electrical equipment or to be present in landfills and dumps.
As discussed in Section 11.2, an estimated 568,000 metric tons of PCBs were
sold in the United States during the period 1930-1975 (Versar, 1976). Table 11-20
presents annual estimates of domestic sales by year for each Aroclor during the period
1957-1974. Estimates of PCB usage in the United States by usage category during the
period 1930-1975 are presented in Table 11-21. Prior to voluntary restrictions by
Monsanto Corporation in 1972 on sales for uses other than "closed electrical systems,"
approximately 13 percent of the PCBs were used in "semi-closed applications," and 26
percent were used in "open-end applications." Most of this usage of PCBs for "semi-
closed" and "open-end" applications occurred between 1960 and 1972 (Versar, 1976).
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Table 11-22 presents estimates of the amounts of individual Aroclors that were
released to the environment (i.e., to water, air, or soil) during the period 1930-1974.
Because detailed usage data were not available for the period 1930-1957, Versar (1976)
assumed that the usage pattern for this period followed the average pattern for the period
1957-1959. The basic assumptions used by Versar (1976) in deriving these estimates
were that 5 percent of the PCBs used in "closed electrical systems" were released; 60
percent of the PCBs used in "semi-closed applications" were released; 25 percent of the
PCBs used for plasticizers were released; and 90 percent of PCBs used for miscellaneous
industrial uses had escaped. The reliability of these release estimates was assumed to be
±30 percent (Versar, 1976).
In addition to these estimates of direct releases to the environment, Versar (1976)
estimated that 132,000 metric tons of PCBs were landfilled. This total was comprised of
50,000 metric tons from capacitor and transformer production wastes, 36,000 metric
tons from disposal of obsolete electrical equipment, and 46,000 metric tons from disposal
of material from "open-end applications." An additional 14,000 metric tons of PCBs,
although still "in service" in various "semi-closed" and "open-end" applications in 1976
were estimated to ultimately be destined for disposal in landfills.
An estimated 3,702 kg of TEQP-WH098 were released directly to the U.S.
environment during the period 1930-1977 (See Table 11-23). These estimates are based
on the Aroclor release estimates presented in Table 11-22 and the mean TEQP-WH098
concentrations in Aroclors that were presented in Table 11-3.
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Table 11-1. List of Known and Suspected Source Categories for Dioxin-like PCBs
Emission Source Category
Releases of Commercial PCBs
Approved disposal
Accidental releases
Municipal Wastewater Treatment
Nonincinerated sludge
Chemical Manufacturing/Processing Sources
Dyes and pigments
Combustion Sources
Municipal waste incineration
Industrial wood combustion
Medical waste incineration
Tire combustion
Cigarette combustion
Sewage sludge incineration
Backyard barrel burning
Source Categories for Which Emissions Can Be Reliably Quantif ed
Air
1995
1987
Water
1995
NA
NA
1987
NA
NA
Land
1995
•
NA
1987
•
NA
Product
1995
NA
NA
•
NA
NA
NA
NA
NA
NA
NA
1987
NA
NA
•
NA
NA
NA
NA
NA
NA
NA
Source Categories for Which Emissions Cannot Be Reliably Quantified
Air
1995
•
•
•
•
•
•
•
•
•
•
•
1987
•
•
•
•
•
•
•
•
•
•
•
Water
1995
•
•
•
•
•
•
•
•
NA
•
NA
1987
•
•
•
•
•
•
•
•
NA
•
NA
Land
1995
•
•
•
•
•
•
•
NA
•
•
1987
•
•
•
•
•
•
•
NA
•
Product
1995
NA
NA
•
NA
NA
NA
NA
NA
NA
NA
1987
NA
NA
•
NA
NA
NA
NA
NA
NA
NA
NA = This source category is not expected to generate releases to this environmental medium.
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December 2003
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Table 11-2. Quantitative Inventory of Dioxin-Like PCB TEQ -WH098 Releases in the United States
Emission Source Category
Quantitative Inventory
Confidence Rating8
Reference Year1995
A
B
C
Quantitative Inventory
Confidence Rating8
Reference Year 1987
A
B
C
Preliminary
Estimate for
1995"
Releases (g TEQ0-WHOga/yr) to Air
Releases of Commercial PCBs
Approved disposal
Accidental releases
Municipal Sludge Disposal
Nonincinerated sludge
Chemical Manufacturing/Processing Sources
Dyes and pigments
Combustion Sources
Municipal waste incineration
Industrial wood combustion
Medical waste incineration
Tire combustion
Cigarettes
Sewage sludge incineration
Backyard barrel burning
Petroleum refining catalyst regeneration
Total Quantified Releases to Airc
0
0
0
0
0
0
0
0.016
42.3
42.3
Releases (g TEQ0-WHO9a/yr) to Water
Releases of Commercial PCBs
Approved disposal
Accidental releases
Municipal Sludge Disposal
Nonincinerated sludge
Chemical Manufacturing/Processing Sources
Dyes and pigments
Combustion Sources
Municipal waste incineration
Industrial wood combustion
Medical waste incineration
Tire combustion
Sewage sludge incineration
Total Quantified Releases to Water0
0
0
0
0
0
0
0
0
Releases (g TEQ0-WHOga/yr) to Land
Releases of Commercial PCBs
Approved disposal
Accidental releases
Municipal Sludge Disposal
Nonincinerated sludge
Chemical Manufacturing/Processing Sources
Dyes and pigments
51.1
51.1
0
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December 2003
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Table 11-2. Quantitative Inventorty of Dioxin-Like PCB TEQP-WH098 Released in the United States (continued)
Emission Source Category
Combustion Sources
Municipal waste incineration
Industrial wood combustion
Medical waste incineration
Tire combustion
Sewage sludge incineration
Backyard trash burning
Total Quantified Releases to Land0
Quantitative Inventory
Confidence Rat nga
Reference Year1995
A
0
Releases (g TEQ
Municipal Sludge Disposal
Nonincinerated sludge
Chemical Manufacturing/Processing Sources
Dyes and pigments
Total Quantified Releases to Products0
0
B
51.1
C
0
Quantitative Inventory
Confidence Rating8
Reference Year 1987
A
0
B
51.1
C
0
Preliminary
Estimate for
1995"
0
-WHO9l/yr) to Products
1.7
1.7
0
0
1.7
1.7
0
0
A =
Characterization of the Source Category judged to be Adequate for Quantitative Estimation with High Confidence in the
Emission Factor and High Confidence in Activity Level.
Characterization of the Source Category judged to be Adequate for Quantitative Esimation with Medium Confidence in the
Emission Factor and at least Medium Confidence in Activity Level.
Characterization of the Source Category judged to be Adequate for Quantitative Estimation with Low Confidence in either
the Emission Factor and/or the Activity Level.
These are preliminary indications of the potential magnitude of emissions from "unquantified" sources in Reference Year 1995.
These estimates were assigned a "confidence category" rating of D and are not included in the Inventory.
TOTAL reflects only the total of the estimates made in this report.
B =
C =
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11-30
December 2003
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Table 11-3. Weight Percent Concentrations of Dioxin-like PCBs in Aroclors, Clophens, and Kanechlors
Dioxin-Like PCB Congener
AROCLOR 1016
3,3',4,4'-TCB
3,4,4', 5-TCB
2,3,3',4,4'-PeCB
2,3,4,4', 5-PeCB
2,3',4,4',5-PeCB
2',3,4,4',5-PeCB
3,3',4,4',5-PeCB
2,3,3',4,4',5-HxCB
2,3,3',4,4',5'-HxCB
2,3',4,4',5,5'-HxCB
3,3',4,4',5,5'-HxCB
2,2',3,3',4,4',5-HpCB
2,2', 3,4,4', 5,5'-HpCB
2,3,3',4,4',5,5'-HpCB
AROCLOR 1221
3,3',4,4'-TCB
3, 4,4', 5-TCB
2,3,3',4,4'-PeCB
2,3,4,4',5-PeCB
2,3',4,4',5-PeCB
2',3,4,4',5-PeCB
3,3',4,4',5-PeCB
2,3,3',4,4',5-HxCB
2,3,3',4,4',5'-HxCB
2,3',4,4',5,5'-HxCB
3,3',4,4',5,5'-HxCB
2,2',3,3',4,4',5-HpCB
2,2',3,4,4',5,5'-HpCB
2,3,3',4,4',5,5'-HpCB
AROCLOR 1242, Clophen A-30,
and Kanechlor 300
3,3',4,4'-TCB
3, 4,4', 5-TCB
2,3,3',4,4'-PeCB
2,3,4,4',5-PeCB
2,3',4,4',5-PeCB
2',3,4,4',5-PeCB
3,3',4,4',5-PeCB
2,3,3',4,4',5-HxCB
2,3,3',4,4',5'-HxCB
2,3',4,4',5,5'-HxCB
3,3',4,4',5,5'-HxCB
2,2',3,3',4,4',5-HpCB
2,2',3,4,4',5,5'-HpCB
2,3,3',4,4',5,5'-HpCB
AROCLOR 1248, Clophen A-40,
and Kanechlor 400
3,3',4,4'-TCB
3, 4,4', 5-TCB
2,3,3',4,4'-PeCB
2,3,4,4',5-PeCB
2,3',4,4',5-PeCB
2',3,4,4',5-PeCB
3,3',4,4',5-PeCB
2,3,3',4,4',5-HxCB
2,3,3',4,4',5'-HxCB
2,3',4,4',5,5'-HxCB
3,3',4,4',5,5'-HxCB
2,2',3,3',4,4',5-HpCB
2,2', 3,4,4', 5,5'-HpCB
2,3,3',4,4',5,5'-HpCB
IUPAC
Number
77
81
105
1 14
1 18
123
126
156
157
167
169
170
180
189
77
81
105
1 14
118
123
126
156
157
167
169
170
180
189
77
81
105
1 14
118
123
126
156
157
167
169
170
180
189
77
81
105
1 14
118
123
126
156
157
167
169
170
180
189
Number of
Samples
Analyzed
5
3
4
4
4
4
4
4
4
4
5
4
4
4
4
4
4
4
4
4
4
4
4
4
4
3
3
4
15
7
11
8
9
9
14
9
8
8
14
6
5
7
13
6
9
7
8
7
1 1
8
7
7
12
5
4
6
Number of
Detections
0
0
1
0
1
0
0
0
0
0
0
0
0
0
4
1
3
0
4
0
0
0
0
0
0
0
0
0
15
6
1 1
5
9
7
8
8
2
2
2
2
2
0
13
4
8
6
8
7
6
8
3
3
3
4
4
1
TEQP-WH098
Mean Cone. Cone.
(ND = 0) (ND = 0)
(g/kg) (mg/kg)
0 0
0 0
0.0375 0.00375
0 0
0.0125 0.00125
0 0
0 0
0 0
0 0
0 0
0 0
0 0
0 0
0 0
Total TEQP-WHO98 = 0.005
Total TEQP-WHO94 = 0.005
1.075 0.1075
0.0875 0.00875
0.3875 0.03875
0 0
1.725 0.1725
0 0
0 0
0 0
0 0
0 0
0 0
0 0
0 0
0 0
Total TEQP-WHO98 = 0.328
Total TEQP-WHO94 = 0.749
3.30 0.33
1.09 0.11
4.02 0.40
1.13 0.57
8.04 0.80
1.12 0.11
0.049 4.94
0.39 0.20
0.021 0.011
0.021 0.00021
0.000013 0.00013
0.19 0
0.16 0
0 0
Total TEQP-WHO98 = 7.47
Total TEQP-WHO94 = 8.70
4.36 0.44
1.76 0.18
10.12 1.01
3.39 1.69
20.98 2.10
1.48 0.15
0.11 10.55
1.13 0.56
0.19 0.09
0.16 0.0016
0.01 0.1006
0.96 0
1.24 0
0.0018 0.0001833
Total TEQp-WHO98 = 16.87
Total TEQP-WHO94 = 18.55
TEQP-WH098
Mean Conc.a Conc.a
(ND = 1/2DL) (ND = 1/2DL)
(g/kg) (mg/kg)
0 0
0 0
0.109 0.011
0 0
0.091 0.009
0 0
0 0
0 0
0 0
0 0
0 0
0 0
0 0
0 0
Total TEQp-WHO98 = 0.0200
Total TEQP-WH094 = 0.0200
1.078 0.108
0.116 0.012
0.4 0.04
0 0
1.725 0.173
0 0
0 0
0 0
0 0
0 0
0 0
0 0
0 0
0 0
Total TEQP-WH098 = 0.333
Total TEQP-WHO94 = 0.752
3.301 0.33
1.089 0.109
4.024 0.402
1.201 0.601
8.044 0.804
1.157 0.116
0.094 9.404
0.424 0.212
0.096 0.048
0.096 0.001
0.048 0.476
0.244 0
0.218 0
0 0
Total TEQP-WH098 = 12.50
Total TEQP-WHO94 = 13.74
4.36 0.44
1.77 0.18
10.12 1.01
3.40 1.70
20.98 2.10
1.48 0.15
0.14 13.51
1.13 0.56
0.20 0.10
0.16 0.0016
0.041 0.41
0.97 0
1.24 0
0.06 0.006
Total TEQP-WH098 = 20.16
Total TEQP-WHO94 = 21.83
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11-31
December 2003
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Table 11-3. Weight Percent Concentrations of Dioxin-like PCBs in Aroclors, Clophens, and Kanechlors (continued)
Dioxin-Like PCB Congener
AROCLOR 1254, Clophen A-50,
and Kanechlor 500
3,3',4,4'-TCB
3,4,4',5-TCB
2,3,3',4,4'-PeCB
2,3,4,4',5-PeCB
2,3',4,4',5-PeCB
2',3,4,4',5-PeCB
3,3',4,4',5-PeCB
2,3,3',4,4',5-HxCB
2,3,3',4,4',5'-HxCB
2,3',4,4',5,5'-HxCB
3,3',4,4',5,5'-HxCB
2,2',3,3',4,4',5-HpCB
2,2', 3,4,4', 5,5'-HpCB
2,3,3',4,4',5,5'-HpCB
AROCLOR 1260, Clophen A-60,
and Kanechlor 600
3,3',4,4'-TCB
3,4,4',5-TCB
2,3,3',4,4'-PeCB
2,3,4,4',5-PeCB
2,3',4,4',5-PeCB
2',3,4,4',5-PeCB
3,3',4,4',5-PeCB
2,3,3',4,4',5-HxCB
2,3,3',4,4',5'-HxCB
2,3',4,4',5,5'-HxCB
3,3',4,4',5,5'-HxCB
2,2',3,3',4,4',5-HpCB
2,2', 3,4,4', 5,5'-HpCB
2,3,3',4,4',5,5'-HpCB
IUPAC
Number
77
81
105
1 14
118
123
126
156
157
167
169
170
180
189
77
81
105
1 14
118
123
126
156
157
167
169
170
180
189
Number of
Samples
Analyzed
15
6
12
9
11
8
14
10
9
10
14
8
7
7
15
6
11
9
11
8
14
1 1
8
10
14
8
7
8
Number of
Detections
12
1
1 1
6
1 1
8
12
10
8
9
6
8
7
2
6
1
10
4
10
1
7
1 1
8
9
5
8
7
8
TEQP-WH098
Mean Cone. Cone.
(ND = 0) (ND = 0)
(g/kg) (mg/kg)
0.80 0.0795
7.85 0.79
35.83 3.58
12.17 6.08
81.65 8.17
4.59 0.46
0.99 99.46
11.08 5.54
1.91 0.95
2.74 0.0274
0.08 0.80
5.06 0
5.79 0
0.045 0.0045429
Total TEQP-WH098 = 125.94
Total TEQP-WHO94 = 126.04
0.13 0.01256
0.08 0.0075
1.59 0.16
0.71 0.35
9.51 0.95
0.0005 0.00005
1.81 180.89
6.89 3.45
1.59 0.79
2.87 0.03
0.16 1.64
32.94 0
82.61 0
1.74 0.1739792
Total TEQP-WH098 = 188.45
Total TEQn-WHO94 = 192.62
TEQP-WH098
Mean Conc.a Conc.a
(ND = 1/2DL) (ND = 1/2DL)
(g/kg) (mg/kg)
0.83 0.08
7.86 0.79
35.83 3.58
12.23 6.11
81.65 8.17
4.59 0.46
1.02 101.70
11.08 5.54
1.93 0.97
2.74 0.03
0.12 1.23
5.06 0
5.79 0
0.13 0.013
Total TEQP-WH098 = 128.67
Total TEQP-WHO94 = 128.78
0.17 0.017
0.10 0.010
1.59 0.16
0.77 0.39
9.51 0.95
0.08 0.008
1.84 183.82
6.89 3.45
1.59 0.79
2.87 0.03
0.19 1.92
32.94 0
82.61 0
1.74 0.17
Total TEQP-WH098 = 191.71
Total TEQn-WHO94 = 195.89
a Calculated for a congener only when at least one sample contained detectable levels of that congener.
References:
Schulz et al. (1989)
Duinker and Hillebrand (1983)
deBoer et al. (1993)
Schwartz et al. (1993)
Larsen, et al. (1992)
Kannan et al. (1987)
Huckins et al. (1980)
Albro and Parker (1979)
Jensen et al. (1974)
Albro et al. (1981)
Duinker et al. (1988)
Tanabe et al. (1987)
Himberg and Sippola (1990)
Frame et al. (1996a)
Frame et al. (1996b)
Frame (1997)
g/kg = grams per kilogram.
mg/kg = milligrams per kilogram.
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11-32
December 2003
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Table 11-4. Disposal Requirements for PCBs and PCB Items
CD
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53
PCBs
PCB Articles
PCB Containers
Waste Characterization
Mineral oil dielectric fluids from PCB
transformers
Mineral oil dielectric fluids from
PCB-contaminated transformers
PCB liquid wastes other than mineral oil
dielectric fluid
Nonliquid PCB wastes (e.g.,
contaminated materials from spills)
Dredged materials and municipal sewage
treatment sludges containing PCBs
Transformers
PCB capacitors
PCB hydraulic machines
Other PCB articles
Those used to contain only PCBs at a
concentration < 500 ppm
Other PCB containers
Those analyzing > 500 ppm PCB
Those analyzing 50-500 ppm PCB
Those analyzing > 500 ppm PCB
Those analyzing 50-500 ppm PCB
PCB transformers
PCB contaminated transformers
Those containing > 1,000 ppm PCB
Those containing < 1,000 ppm PCB
Those containing PCB fluids
Those not containing PCB fluids
Disposal Requirements
Annex I incinerator8
Annex I incinerator
High efficiency boiler (40 CFR 761 .10(a)(2)(iii))
Other approved incinerator'
Annex II chemical waste landfill0
Annex I incinerator
Annex I incinerator
High efficiency boiler (40 CFR 761 .10(a)(2)(iii))
Other approved incinerator'
Annex II chemical waste landfill0
Annex I incinerator
Annex II chemical waste landfill
Annex I incinerator
Annex II chemical waste landfill
Other approved disposal method (40 CFR 761 .10(a)(5)(iii)
Annex I incinerator
Drained and rinsed transformers may be disposed of in Annex II
chemical waste landfill
Disposal of drained transformers is not regulated
Annex I incinerator
Drained and rinsed machines may be disposed of as municipal solid
waste or salvaged
Drained machines may be disposed of as municipal solid waste or
salvaged
Drained machines may be disposed of per Annex I or Annex II
Annex I incinerator or Annex II chemical waste landfill
As municipal solid waste provided any liquid PCBs are drained prior to
disposal
Annex I incinerator
Annex II, provided any liquid PCBs are drained prior to disposal
Decontaminate per Annex IV
GO
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Annex I incinerator defined in 40 CFR 761.40.
Requirements for other approved incinerators are defined in 40 CFR 761.10(e).
Annex II chemical waste landfills are described in 40 CFR 761.41. Annex II disposal is permitted if the PCB waste contains less than 500 ppm PCB and is not ignitable as per 40 CFR
Part 761.41(b)(8)(iii).
Disposal of containerized capacitors in Annex II landfills was permitted until March 1, 1981; thereafter, only Annex I incineration has been permitted.
ppm = parts per million
-------
Table 1 1-5. Off-site Transfers of PCBs Reported in TRI (1988-1996)
Year
1996
1995
1994
1993
1992
1991
1990
1989
1988
No. of TRI Forms Filed
NA
NA
NA
16
20
26
NA
NA
122
Reported Transfers (kg)
Transfers
to POTWs
0
0
0
120
0
0
0
0.5
1 13
Transfers for
Treatment/
Disposal
160,802
308,347
466,948
463,385
766,638
402,535
1,181,961
2,002,237
2,642,133
TOTAL
TRANSFERS
160,802
308,347
466,948
463,505
766,638
402,535
1,181,961
2,002,237
2,642,246
NA = Not available
kg = kilograms
POTWs = Publicly owned treatment works
Sources: U.S. EPA (1993h), U.S. EPA (1995g), U.S. EPA (1998b)
DRAFT-DO NOT QUOTE OR CITE
11-34
December 2003
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CD
Table 11 -6. Releases of PCBs Reported in TRI (1 988-1 996)
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Year
1996
1995
1994
1993
1992
1991
1990
1989
1988
No. of TRI
Forms Filed
NA
NA
NA
16
20
26
NA
NA
122
Reported Releases (kg)
Fugitive or
Nonpoint Air
Emissions
2.3
0
0
0
0
0
2.3
0
2.7
Stack or
Point Air
Emissions
114
0
0
0
0
0
0
0
0
Surface
Water
Discharges
0
0
0
0
0
0
0
120
4.5
Underground
Injection
0
0
0
0
0
0
0
0
0
On-Site
Releases
to Land
4,179
0
0
120
0.5
0
32,372
453
341
TOTAL
ON-SITE
RELEASES
4,295
0
0
120
0.5
0
32,374
573
348
GO
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Sources: U.S. EPA (1993h); U.S. EPA (1995g); U.S. EPA (1998b)
O
CD
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CD
3
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NA = Not available.
-------
Table 1 1-7. Aroclor Concentrations Measured in EPA's National Sewage Sludge Survey
Aroclor
1016
1221
1232
1242
1248
1254
1260
Any Aroclor (total)
Percent
Detected
0
0
0
0
9
8
10
19
Maximum
Concentration
(ng/kg)
..
..
..
5.20
9.35
4.01
14.7
Median Concentration (ng/kg)
Nondetects
Set to
Det. Limit
..
..
..
0.209
0.209
0.209
1.49
Nondetects
Set to
Zero
0
0
0
0
0
0
0
0
Source: U.S. EPA (1996a); for POTWs with multiple samples, the pollutant concentrations were averaged before the summary statistics
presented in the table were calculated. All concentrations are in units of nanograms per kilogram (ng/kg) dry weight.
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11-36
December 2003
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CD
Table 11-8. Dioxin-Like PCB Concentrations Measured in Sludges Collected from 74 U.S. POTWs During 1 994a
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Congener
3,3',4,4'-TCB
3,4,4', 5-TCB
2,3,3',4,4'-PeCB
2,3,4,4',5-PeCB
2,3',4,4',5-PeCB
2',3,4,4',5-PeCB
3,3',4,4',5-PeCB
2,3,3',4,4',5-HxCB
2,3,3',4,4',5'-HxCB
2,3',4,4',5,5'-HxCB
3,3',4,4',5,5'-HxCB
2,2',3,3',4,4',5-HpCB
2,2',3,4,4',5,5'-HpCB
2,3,3',4,4',5,5'-HpCB
Total TEQP-WH094
Total TEQn-WHOfl8
IUPAC
Number
77
81
105
114
118
123
126
156
157
167
169
170
180
189
Percent
Detected
100
86
99
22
Maximum
Concentration
(ng/kg)
22,900
1,250
3,020
1,470
Median Concentration (ng/kg)
Nondetects
Set to 1 12
Det. Limit
783
27.3
91.6
8.5
9.5
9.3
Nondetects
Set to
Zero
783
27.0
91.6
0
9.5
9.2
Mean Concentration (ng/kg)
Nondetects
Set to 1 12
Det. Limit
2,243
65.2
237
32.5
25.2
24.3
Nondetects
Set to
Zero
2,243
63.5
237
26.2
25.1
24.2
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ng/kg = nanograms per kilogram
a For POTWs with multiple samples, the sample concentrations were averaged by Cramer et al. (1994) to POTW averages before
calculation of the total TEQ mean and median values presented in the table. The TEQP-WH094 and TEQP-WH098 values were
calculated on a facility-level basis.
NOTE: Blank cells indicate that no measurements of these congeners were made.
Source: Green et al. (1995); Cramer et al. (1995)
-------
Table 11-9. Dioxin-Like PCB Concentrations in Sludges Collected from a U.S. POTW During 1999
Congener
3,3',4,4'-TCB
3,4,4', 5-TCB
2,3,3',4,4'-PeCB
2,3,4,4',5-PeCB
2,3',4,4',5-PeCB
2',3,4,4',5-PeCB
3,3',4,4',5-PeCB
2,3,3',4,4',5-HxCB
2,3,3',4,4',5'-HxCB
2,3',4,4',5,5'-HxCB
3,3',4,4',5,5'-HxCB
2,2',3,3',4,4',5-HpCB
2,2',3,4,4',5,5'-HpCB
2,3,3',4,4',5,5'-HpCB
Total TEQP-WH094
Percent due to PCBs 77, 81 , 1 26, and 1 69
Total TEQp-WH098
Percent due to PCBs 77, 81 , 1 26, and 1 69
IUPAC
Number
77
81
105
114
118
123
126
156
157
167
169
170
180
189
Run 1
(ng/kg, dry)
40,899
7,015
691
12,250
231
1,118
1,772
472
878
453
2,526
6,002
181
141
97.3%
124
97.2%
Run 2
(ng/kg, dry)
41,096
7,389
674
13,497
276
1,214
1,883
565
968
601
2,572
6,780
198
152
97.3%
135
97.3%
Run 3
(ng/kg, dry)
45,386
7,289
738
12,856
241
1,479
1,876
536
959
656
2,776
6,711
218
181
97.8%
163
97.8%
Average
Cone.
(ng/kg)
42,460
7,231
701
12,868
249
1,270
1,844
524
935
570
2,625
6,498
199
158
97.5%
141
97.4%
* For POTWs with multiple samples, the sample TEQ concentrations were averaged to POTW averages
before calculation of the TEQ mean and median values presented in the table.
NOTE: Blank cells indicate that no measurements of these congeners were made.
Source: Battelle (1999)
DRAFT-DO NOT QUOTE OR CITE
11-38
December 2003
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Table 11-10. Quantity of Sewage Sludge Disposed of Annually by
Primary, Secondary, or Advanced Treatment POTWs
and Potential Dioxin-Like PCB TEQ Releases
Use/Disposal Practice
Land Application
Distribution and Marketing
Surface Disposal Site/Other
Sewage Sludge Landfill
Co-Disposal Landfills3
Sludge Incinerators and Co-
Incinerators"
Ocean Disposal
TOTAL
Volume Disposed
(thousands of dry
metric tons/year)
1,714
71
396
157
1,819
865
(336)d
5,357
Percent of
Total
Volume
32. Oe
1.3
7.4
2.9
33.9
16.1
(6.3)d
100.0
Potential
TEQP-WH098
Release0
(g of TEQ/yr)
41.5
1.7
9.6
4.2
44.0
(f)
(0)d
101.0
Potential
TEQP-WH094
Release0
(g of TEQ/yr)
43.0
1.8
9.9
3.9
45.6
104.2
a Landfills used for disposal of sewage sludge and solid waste residuals.
b Co-incinerators treat sewage sludge in combination with other combustible waste materials.
0 Potential TEQ release for nonincinerated sludges was estimated by multiplying the sludge volume
generated (i.e., column 2) by the mean dioxin-like PCB TEQ concentration in 74 POTW sludges reported
by Green et al. (1995) and Cramer et al. (1995) (i.e., 24.2 ng TEQp-WH098/kg and 25.1 ng TEQP-
WH094/kg).
d The Ocean Dumping Ban Act of 1 988 generally prohibited the dumping of sewage sludge into the ocean
after December 31, 1 991. Ocean dumping of sewage sludge ended in June 1992 (Federal Register,
1 993b). The current method of disposal of the 336,000 metric tons of sewage sludge that were
disposed in the oceans in 1988 has not been determined.
e Includes 21.9 percent applied to agricultural land, 2.8 percent applied as compost, 0.6 percent applied to
forestry land, 3.1 percent applied to "public contact" land, 1.2 percent applied to reclamation sites, and
2.4 percent applied in undefined settings.
f See Section 11.4.6 for for a discussion of dioxin-like PCB releases to air from sewage sludge incinerators.
Sources: Federal Register (1990); Federal Register (1 993b); Green et al. (1 995); Cramer et al. (1 995).
DRAFT-DO NOT QUOTE OR CITE
11-39
December 2003
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Table 11-11. PCB Congener Group Emission Factors for Industrial Wood Combustors
Congener Group
Monochlorobiphenyls
Dichlorobiphenyls
Trichlorobiphenyls
Tetrachlorobiphenyls
Pentachlorobiphenyls
Hexachlorobiphenyls
Heptachlorobiphenyls
Octachlorobiphenyls
Nonachlorobiphenyls
Decachlorobiphenyls
Number
of
Sites
2
2
2
2
2
2
2
2
2
2
Number
of
Detections
1
1
1
0
0
0
0
0
0
0
Maximum
Concentration
Detected
(ng/kg wood)
32.1
23.0
19.7
--
Mean Concentration
(ng/kg)
Nondetects
Set to
Det. Limit
39.4
50.9
42.3
22.7
17.6
17.0
17.9
15.8
25.0
36.3
Nondetects
Set to
Zero
16.0
11.5
9.8
__
__
__
__
__
__
--
ng/kg = nanograms per kilogram.
Source: CARB (1990e, 1990f)
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December 2003
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Table 11-12. PCB Congener Group Emission Factors for Medical Waste Incinerators (MWIs)
Congener Group
Monochlorobiphenyls
Dichlorobiphenyls
Trichlorobiphenyls
Tetrachlorobiphenyls
Pentachlorobiphenyls
Hexachlorobiphenyls
Heptachlorobiphenyls
Octachlorobiphenyls
Nonachlorobiphenyls
Decachlorobiphenyls
Mean Emission Factor (ng/kg)
(2 MWIs without APCD)
Nondetects
Set to
Det. Limit
0.059
0.083
0.155
4.377
2.938
0.238
0.155
0.238
0.155
0.155
Nondetects
Set to
Zero
0.059
0.083
0.155
4.377
2.938
0.238
0.155
0.238
0.155
0.155
Mean Emission Factor (ng/kg)
(2 MWIs with APCD)
Nondetects
Set to
Det. Limit
0.311
0.340
0.348
1.171
17.096
1.286
0.902
0.205
__
0.117
Nondetects
Set to
Zero
0
0
0
0
9.996
1.078
0
0
__
0
APCD = Air Pollution Control Device
ng/kg = nanograms per kilogram.
-- = Not reported.
Source: See Section 3.3 for details on tested facilities.
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December 2003
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Table 11-13. PCB Congener Group Emission Factors for a Tire Combustor
Congener Group
Monochlorobiphenyls
Dichlorobiphenyls
Trichlorobiphenyls
Tetrachlorobiphenyls
Pentachlorobiphenyls
Hexachlorobiphenyls
Heptachlorobiphenyls
Octachlorobiphenyls
Nonachlorobiphenyls
Decachlorobiphenyls
Number
of
Samples
3
3
3
3
3
3
3
3
3
3
Number
of
Detections
0
1
1
0
2
1
1
0
0
0
Maximum
Emission Factor
(ng/kg)
__
34.8
29.5
__
2,724
106.5
298.6
--
Mean Emission Factor
(ng/kg)
Nondetects
Set to
Det. Limit
0.04
11.7
11.8
10.0
1,092
55.9
107.7
20.9
17.7
41.9
Nondetects
Set to
Zero
__
11.6
9.8
__
1,092
35.5
99.5
__
__
--
ng/kg = nanograms per kilogram.
Source: CARB (1991 a)
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December 2003
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CD
Table 11-14. Dioxin-Like PCB Concentrations in Cigarette Tobacco
Congener
3,3',4,4'-TCB
3,4,4', 5-TCB
2,3,3',4,4'-PeCB
2,3,4,4',5-PeCB
2,3',4,4',5-PeCB
2',3,4,4',5-PeCB
3,3',4,4',5-PeCB
2,3,3',4,4',5-HxCB
2,3,3',4,4',5'-HxCB
2,3',4,4',5,5'-HxCB
3,3',4,4',5,5'-HxCB
2,2',3,3',4,4',5-HpCB
2,2',3,4,4',5,5'-HpCB
2,3,3',4,4',5,5'-HpCB
Total TEQP-WH094
Total TEQD-WH098
IUPAC
Number
77
81
105
114
118
123
126
156
157
167
169
170
180
189
Concentrations in brands from various countries (pg/pack)
U.S. Brands
(Avg of 7
brands)
105.7
6.2
0.9
0.68
0.64
Japan
(Avg of 6
brands)
70.2
7.8
0.9
0.82
0.80
United Kingdom
(Avg of 3
brands)
53.0
6.1
0.9
0.64
0.62
Taiwan
(1 brand)
133.9
14.5
2.4
1.54
1.49
China
(1 brand)
12.6
2.4
0.4
0.25
0.24
Denmark
(1 brand)
21.7
2.2
0.5
0.24
0.23
Germany
(1 brand)
39.3
7.3
1.6
0.76
0.75
o
o
-H
D
c;
O
O
53
GO
O
CD
O
CD
3
-
K>
O
O
CO
Source: Matsueda et al. (1994)
NOTE: Blank cells indicate that no measurements of these congeners were made.
-------
Table 11-15. Dioxin-Like PCB Concentrations in Stack Gas Collected from a U.S. Sewage Sludge Incinerator
Congener
3,3',4,4'-TCB *
3,4,4', 5-TCB
2,3,3',4,4'-PeCB
2,3,4,4',5-PeCB
2,3',4,4',5-PeCB
2',3,4,4',5-PeCB
3,3',4,4',5-PeCB
2,3,3',4,4',5-HxCB
2,3,3',4,4',5'-HxCB
2,3',4,4',5,5'-HxCB
3,3',4,4',5,5'-HxCB
2,2',3,3',4,4',5-HpCB
2,2',3,4,4',5,5'-HpCB
2,3,3',4,4',5,5'-HpCB
Total TEQP-WH094
Percent due to PCBs 77, 126, and 169
Total TEQp-WH098
Percent due to PCBs 77, 126, and 169
IUPAC
Number
77
81
105
114
118
123
126
156
157
167
169
170
180
189
Run 1
(ng/dscm)
(@ 7% 02)
49.20
5.23
0.76
11.20
0.23
1.37
1.26
0.43
0.76
1.10
2.12
5.27
0.19
1.76E-01
98.2%
1.56E-01
98.1%
Run 2
(ng/dscm)
(@ 7% 02)
38.18
4.32
0.60
9.27
0.20
1.03
0.99
0.32
0.59
0.82
1.70
4.18
0.13
1.33E-01
98.1%
1.17E-01
98.0%
Run 3
(ng/dscm)
(@ 7% 02)
13.26
1.75
0.25
4.10
0.07
0.39
0.39
0.15
0.25
0.26
0.81
1.59
0.08
4.94E-02
97.8%
4.40E-02
97.7%
Average
Cone.
(ng/dscm)
(@ 7% 02)
33.54
3.77
0.54
8.19
0.17
0.93
0.88
0.30
0.54
0.73
1.54
3.68
0.13
1.19E-01
98.1%
1.06E-01
98.0%
* PCB-77 concentrations were greater than the highest point on the lab's PCB calibration curve.
NOTE: Blank cells indicate that no measurements of these congeners were made.
Source: Battelle (1999)
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December 2003
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Table 11-16. Dioxin-Like PCB Emission Factors from Backyard Barrel Burning
Congener
3,3',4,4'-TCB
3,4,4', 5-TCB
2,3,3',4,4'-PeCB
2,3,4,4',5-PeCB
2,3',4,4',5-PeCB
2',3,4,4',5-PeCB
3,3',4,4',5-PeCB
2,3,3',4,4',5-HxCB
2,3,3',4,4',5'-HxCB
2,3',4,4',5,5'-HxCB
3,3',4,4',5,5'-HxCB
2,2',3,3',4,4',5-HpCB
2,2',3,4,4',5,5'-HpCB
2,3,3',4,4',5,5'-HpCB
Total TEQP-WH094
Total TEQP-WH098
IUPAC
Number
77
81
105
114
118
123
126
156
157
167
169
170
180
189
Emission Factors (ug/kg)
Test 1
9.3
5.9
8.3
18.6
7.93E-03
4.21 E-03
Test 2
15.2
4.9
14.3
28.7
1.24E-02
6. 31 E-03
Average
12.3
5.4
11.3
23.7
1.02E-02
5.26E-03
Source: Lemieux (1997)
NOTE: Blank cells indicate that the congener was not detected in either of the two duplicate samples.
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December 2003
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Table 11-17. PCB Congener Group Emission Factors for a Petroleum Catalytic Reforming Unit
Congener Group
Monochlorobiphenyls
Dichlorobiphenyls
Trichlorobiphenyls
Tetrachlorobiphenyls
Pentachlorobiphenyls
Hexachlorobiphenyls
Heptachlorobiphenyls
Octachlorobiphenyls
Nonachlorobiphenyls
Decachlorobiphenyls
Total PCBs
Number
of
Samples
3
3
3
3
3
3
3
3
3
3
Number
of
Detections
3
3
3
3
3
3
3
3
3
3
Mean
Concentration
(ng/dscm)
(at 12% 02)
166
355
743
849
914
780
1,430
698
179
41.3
6,155
Mean
Emission
Rate
(Ib/hr)
5.51E-08
1.17E-07
2.45E-07
2.81E-07
3.02E-07
2.57E-07
4.73E-07
2.32E-07
5.99E-08
1.39E-08
2.04E-06
Mean
Emission
Factor
(lb/1000bbl)
7.11E-09
1.52E-08
3.17E-08
3.62E-08
3.88E-08
3.30E-08
6.01E-08
2.95E-08
7.59E-09
1.76E-09
2.61E-07
Mean
Emission
Factor
(ng/barrel)
3.23E + 00
6.89E + 00
1.44E + 01
1.64E + 01
1.76E + 01
1.50E + 01
2.73E + 01
1.34E + 01
3.44E + 00
7.98E-01
1.18E + 02
Source: CARB (1999)
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December 2003
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CD
o
o
-H
D
c;
O
O
53
Table 11-18. Estimated Tropospheric Half-Lives of Dioxin-Like PCBs with Respect to Gas-Phase Reaction with the OH Radical
Congener Group
TCB
PeCB
HxCB
HpCB
Dioxin-Like Congener
3,3',4,4'-TCB
3,4,4', 5-TCB
2,3,3',4,4'-PeCB
2,3,4,4',5-PeCB
2,3',4,4',5-PeCB
2',3,4,4',5-PeCB
3,3',4,4',5-PeCB
2,3,3',4,4',5-HxCB
2,3,3',4,4',5'-HxCB
2,3',4,4',5,5'-HxCB
3,3',4,4',5,5'-HxCB
2,2',3,3',4,4',5-HpCB
2,2',3,4,4',5,5'-HpCB
2,3,3',4,4',5,5'-HpCB
Estimated OH Reaction Rate
Constant (10"12 cm3/molecule-sec)
0.583
0.710
0.299
0.383
0.299
0.482
0.395
0.183
0.214
0.214
0.266
0.099
0.099
0.125
Estimated Tropospheric
Lifetime (days)3
20
17
40
31
40
25
30
65
56
56
45
121
121
95
Estimated Tropospheric
Half-Life (days)3
14
12
28
22
28
17
21
45
39
39
31
84
84
66
O
CD
O
CD
3
-
cm3 = cubic centimeters.
a Calculated using a 24-hour, seasonal, annual, and global tropospheric average OH radical concentration of 9.7 x 105 molecule/cm3 (Prinn et al., 1995).
Source: Atkinson (1 995) [Based on Atkinson (1 991) and Kwok et al. (1 995)].
K>
O
O
CO
-------
Table 11-19. Estimated PCB Loads in the Global Environment as of 1985
Environment
Terrestrial and Coastal
Air
River and Lake Water
Seawater
Soil
Sediment
Biota
Total (A)
Open Ocean
Air
Seawater
Sediment
Biota
Total (B)
Total Load in Environment (A + B)
Degraded and Incinerated
Land-stocked3
World Production
PCB Load
(metric tons)
500
3,500
2,400
2,400
130,000
4,300
143,000
790
230,000
110
270
231,000
374,000
43,000
783,000
1,200,000
Percentage
of PCB Load
0.13
0.94
0.64
0.64
35
1.1
39.00
0.21
61
0.03
0.07
61.00
100
Percentage of
World
Production
31
4
65
100
a Still in use in electrical equipment and other products, and deposited in landfills and
dumps.
Source: Tanabe (1988); note that a world production of 1.2-million metric tons is
assumed by Tanabe (1988). DeVoogt and Brinkman (1989) estimated
worldwide production to have been 1.5-million metric tons.
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December 2003
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Table 11-20. Domestic Sales of Aroclors (1 957-1 974)
Year
1957
1958
1959
1960
1961
1962
1963
1964
1965
1966
1967
1968
1969
1970
1971
1972
1973
1974
TOTAL
S
% of
Total
Estimated Domestic Sales
Aroclor
1016
(metric tons)
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1,512
9,481
10,673
9,959
31,625
8.8%
Aroclor
1221
(metric tons)
10
7
115
47
43
64
164
270
167
239
200
62
230
670
1,005
78
16
26
3,412
0.9%
Aroclor
1232
(metric tons)
89
51
109
70
109
102
6
6
3
7
11
41
124
118
78
0
0
0
924
0.3%
Aroclor
1242
(metric tons)
8,265
4,737
6,168
8,254
8,993
9,368
8,396
10,692
14,303
17,943
19,529
20,345
20,634
22,039
9,970
330
2,812
2,815
195,596
54.2%
Aroclor
1248
(metric tons)
807
1,161
1,535
1,282
1,825
1,571
2,274
2,376
2,524
2,275
2,134
2,220
2,563
1,847
97
366
0
0
26,856
7.4%
Aroclor
1254
(metric tons)
2,023
3,035
3,064
2,761
2,855
2,869
2,681
2,849
3,509
3,191
3,037
4,033
4,455
5,634
2,114
1,585
3,618
2,805
56,120
15.6%
Aroclor
1260
(metric tons)
3,441
2,713
3,002
3,325
2,966
2,991
3,459
3,871
2,645
2,665
2,911
2,382
2,013
2,218
782
138
0
0
41,525
11.5%
Aroclor
1262
(metric tons)
14
83
163
148
164
196
188
202
253
348
381
327
323
464
0
0
0
0
3,255
0.9%
Aroclor
1268
(metric tons)
0
33
46
86
72
95
129
86
89
129
130
127
136
150
0
0
0
0
1,307
0.4%
Total
PCB
Releases
(metric tons)
14,651
11,821
14,202
15,973
17,027
17,256
17,296
20,352
23,494
26,797
28,334
29,536
30,479
33,140
15,559
11,978
17,119
15,605
360,620
100.0%
K>
O
o
CO
Source: Versar (1976)
-------
Table 11-21. Estimated U.S. Usage of PCBs by Use Category (1930-1975)
Use Class
Closed
Electrical
Systems
Semi-Closed
Applications
Open-End
Applications
TOTAL
Use Category
Capacitors
Transformers
Heat transfer
fluids
Hydraulics and
lubricants
Plasticizer uses
Carbonless copy
paper
Misc. industrial
Petroleum
additives
Amount Used
(1,000 metric
tons)
286
152
9
36
52
20
12
1
568
Percent of
Total Usage
50.3
26.8
1.6
6.3
9.2
3.5
2.1
<1
100
Reliability of
Estimate
±20%
±20%
±10%
±10%
±15%
±5%
±15%
±50%
Source: Versar (1976)
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December 2003
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Table 11-22. Estimated Direct Releases of Aroclors to the U.S. Environment (1 930-1 974)a
Year
1930-56
1957
1958
1959
1960
1961
1962
1963
1964
1965
1966
1967
1968
1969
1970
1971
1972
1973
1974
TOTALS
% of
Total
Estimated Environmental Releases
Aroclor
1016
(metric tons)
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
76
474
534
498
1,582
2.1%
Aroclor
1242
(metric tons)
8,486
903
649
1,042
1,340
1,852
1,811
1,655
2,085
2,689
3,180
3,376
3,533
4,165
4,569
1,466
22
141
141
43,103
56.1%
Aroclor
1248
(metric tons)
2,447
319
483
724
556
792
659
935
980
1,025
876
814
853
993
697
51
0
0
0
13,205
17.2%
Aroclor
1254
(metric tons)
2,269
307
416
518
449
587
554
529
555
660
566
525
733
985
1,168
325
104
181
140
11,572
15.0%
Aroclor
1260
(metric tons)
1,614
423
355
507
540
611
571
682
755
497
472
504
433
452
474
121
9
0
0
9,019
11.7%
Total
PCB
Releases
(metric tons)
14,817
1,952
1,903
2,792
2,885
3,841
3,594
3,801
4,375
4,872
5,094
5,219
5,552
6,596
6,907
1,963
135
322
281
76,898
100.0%
a Does not include an additional 132,000 metric tons estimated to have been landfilled during this period.
Source: Versar (1976)
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December 2003
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Table 11-23. Estimated Releases of Dioxin-Like PCB TEQs
to the U.S. Environment During 1 930-1 977
Aroclor
Aroclor 1016
Aroclor 1221
Aroclor 1232
Aroclor 1242
Aroclor 1248
Aroclor 1254
Aroclor 1260
Aroclor 1262
Aroclor 1268
Percent of
U.S. Sales3
(1957-1974)
12.88%
0.96%
0.24%
51.76%
6.76%
15.73%
10.61%
0.83%
0.33%
Estimated
PCB Releases
(1 930-1 974)b
(metric tons)
1,582
__
43,103
13,205
11,572
9,019
Estimated
Mean TEQP-WH098
Concentration0
(mg/kg)
d
0.328
__
7.47
16.87
125.94
188.45
Estimated
Total TEQP-WH098
Released
(kilograms)
d
__
322
223
1,457
1,700
__
__
Total = 3,702
/yg/kg = micrograms per kilogram.
"--" indicates that release estimates were not been made because of relatively low usage amounts.
a Sales during the period 1 957-1 974 constitute 63% of all PCB sales during 1 930-1 977; sales data for
individual Aroclors are not available for years prior to 1 957. However, sales of Aroclors 1 221, 1 232,
1 262, and 1 268 were minor even prior to 1 957.
b From Table 11-22.
c From Table 11-3 (assumes not detected values are zero).
d Data are available for only a few samples of Aroclor 1016 where only 2 dioxin-like PCB congeners were
detected. The total TEQP-WH098 released is less than 0.01 kilograms.
Source: Versar (1976)
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December 2003
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12.0 RESERVOIR SOURCES OF CDD/CDF AND DIOXIIM-LIKE PCBs
National CDD/CDF source inventories have been conducted in several nations,
including the United Kingdom, The Netherlands, Germany, Austria, and Sweden to
characterize emissions from various source categories and estimate annual CDD/CDF
emissions to air (and sometimes other media). These inventories focused primarily on
emissions from primary sources (i.e., emissions from the site or process where the
CDD/CDF are formed). The authors of these inventories (Rappe, 1991; Harrad and Jones,
1992b; Bremmer et al., 1994; Thomas and Spiro, 1995 and 1996; Eduljee and Dyke,
1996; Jones and Alcock, 1996; Duarte-Davidson et al., 1997) indicated that the annual
estimates of releases to air provided in these inventories may be underestimates of actual
emissions for several reasons. First, from an empirical basis, estimates of the amounts of
CDD/CDFs deposited annually from the atmosphere were greater than the estimates of
annual CDD/CDF emissions to the atmosphere. Second, the investigators indicated that
because of limited emission test data the inventories may underestimate releases from
known sources or may not identify all primary sources. Third, the investigators
acknowledged the existence of potential reservoir (or secondary) sources but were not able
to reliably quantify emissions from these sources. Potential reservoir sources identified by
these investigators included volatilization of CDD/CDFs from PCP-treated wood,
volatilization from soil, and resuspension of soil particles. Relatively little research of either
a monitoring or theoretical nature has been performed to identify reservoir sources and to
quantify the magnitude of current or potential future releases from these sources.
This chapter presents background information on reservoir sources of
CDD/CDF/PCBs. Section 12.1 presents a working definition for reservoir sources. Section
12.2 describes major environmental reservoirs (i.e., soil, water, sediment, and biota) and
presents information on: (1) the potential magnitude (mass) of CDD/CDF/PCBs in each
reservoir; (2) the chemical/physical mechanisms responsible for releases of these
compounds; and (3) estimates of potential annual releases from each reservoir, if such
estimates are feasible, given the available state of knowledge. Section 12.3 presents a
summary of the information presented in this chapter.
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12.1. POTENTIAL RESERVOIRS
Chapters 2 through 11 have discussed both known and suspected sources of newly
formed dioxin-like compounds to the environment in the United States. Once released into
the open environment, CDDs, CDFs, PCBs partition to air, soils, water, sediments and
biota according to both the nature of the release, and the contaminant's chemical and
physical properties (see Volume 2, Chapter 2). The definitions adopted for this analysis of
reservoirs and reservoir sources are as follows:
Reservoirs are materials or places that contain previously formed CDD/CDFs
or dioxin-like PCBs and have the potential for redistribution and circulation of
these compounds into the environment. Potential reservoirs include soils,
sediments, biota, water and some anthropogenic materials. Reservoirs
become sources when they have releases of dioxin-like compounds to the
circulating environment over a defined time and space. Like other sources
they would not include purely intermediate products or materials properly
disposed in a secure landfill.
Reservoir sources are not included in the quantitative inventory of contemporary sources
because they do not involve original releases, but rather the recirculation of past releases.
They can, however, contribute to human exposure and, therefore, are important to
consider. Figure 12-1 presents a conceptual diagram of flux and exchange of dioxin-like
compounds to multiple environmental compartments including the principal environmental
reservoirs: soil, water, air, sediment, and biota. This dynamic system consists of fluxes in
and out of the atmosphere, as well as other exchanges between reservoirs and the
atmosphere. Movement between media can be induced by volatilization, wet and dry
atmospheric particle and vapor deposition, adsorption, erosion and runoff, resuspension of
soils into air, and resuspension of sediments into water. The rate of movement from one
environmental medium to another is termed 'flux,' and refers to the direction and
magnitude of flow and exchange over a reference time period and space.
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12.2. CHARACTERIZATION OF RESERVOIR SOURCES
This section is organized according to reservoir type (soil, water, sediment, and
biota) with each subsection providing information in three parts: (1) the potential
magnitude (mass) of dioxin-like compounds in the reservoir; (2) the chemical/physical
mechanisms responsible for releases of these compounds; and (3) estimates of potential
annual releases from the reservoir, if such estimates are feasible, given the available state
of knowledge. Although, anthropogenic structures are potential reservoir sources, they are
not discussed here because they were covered earlier in Chapter 8 (the most detailed
discussion is on PCP, Section 8.3.8.).
12.2.1. Soil
Potential Mass of Dioxin-Like Compounds Present
The companion document to this report, Volume 2 (Fate, Environmental Levels, and
Exposure), presents a compilation of CDD/CDF/PCB concentrations in surface soils
reported in various published studies. Based on this compilation of studies, the mean TEQ
values for background urban and rural soils are estimated to be 13.4 and 4.1 ng l-TEQDF/kg
of soil, respectively (or 11.9 and 3.6 ng TEQDF-WH098/kg, respectively). It should be noted
that no comprehensive survey of CDD/CDF concentrations in U.S. soils has yet been
performed. The surface area of the United States (excluding Alaska) is approximately 7.85
million square kilometers (sq km). Land use statistics for 1992 indicate that 0.37 million
sq km are classified as "developed" and can be considered to be principally urban areas.
The remaining 7.48 million sq km are considered rural. Of this rural area, 1.55 million sq
km are cropland (U.S. DOC, 1997). Further breakdown of the rural land area into pasture
land, range land, and forest land is available, but the available monitoring data are
considered to be inadequate for deriving background CDD/CDF concentrations for these
land use types.
In estimating burdens for the United Kingdom, Harrad and Jones (1992) and Duarte-
Davidson et al. (1997) assumed that the majority of CDD/CDFs in soil is present in the top
5 centimeters (except possibly in cropland, which may involve a deeper depth due to
plowing) and that the soil density is 1,000 kg/m3. Coupling these assumptions regarding
depth of contamination and average soil density with the rural and urban U.S. surface
areas and TEQ concentrations yields soil burden estimates of 1,530 kg I-TEQDF (1,350 kg
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TEQDF-WH098) in rural soils and 250 kg I-TEQDF (220 kg TEQDF-WH098) in urban soils in the
United States.
Higher concentrations of CDD/CDFs than those presented above for background
urban and rural soils may be present in soils underlain by municipal and industrial waste
and in soils at contaminated industrial sites. The lack of comprehensive data on CDD/CDF
concentrations in these soils, as well as the lack of data on the mass of these soils
nationwide, precludes estimating total national soil burdens for these soils at present.
Higher concentrations of CDD/CDFs may also be present in the soils of areas that have
been treated with pesticides contaminated with CDD/CDFs. For the same reasons
presented above for industrially contaminated soil, it is not possible to estimate current soil
burdens of CDD/CDF associated with past pesticide use. However, estimates can be made
of the total mass of CDD/CDF TEQs that have been applied to soil from past use of the
pesticides 2,4-D and 2,4,5-T.
2,4-Dichlorophenoxy acetic acid (2,4-D) and its salts and esters are widely used in
agricultural and nonagricultural settings in the United States as post-emergence herbicides
for control of broadleaf weeds and brush. In terms of volumes of pesticides used annually,
2,4-D ranks among the top 10 pesticides in the United States (U.S. EPA, 1994b, 1997e)
and has been in large-scale, large-volume commercial use for many years (U.S. EPA,
1975). Commercial production of 2,4-D in the United States started in 1944 (Esposito et
al., 1980). Table 12-1 presents a compilation of historical domestic production, sales, and
usage volumes for 2,4-D and its salts and esters.
As described in Section 8.3.8, CDD/CDFs were detected in several formulations of
2,4-D and its derivatives during analyses performed to comply with EPA's 1987 Data Call-
in (DCI) for CDD/CDFs. Although the analytical results of these tests indicated that
CDD/CDFs were seldom above the regulatory limits of quantification (LOQ) established by
EPA for the DCI, several registrants detected and quantified CDD/CDFs at lower LOQs.
The results of these tests are summarized in Table 8-25. The average TEQ in these tests
was 0.70 ^g l-TEQDF/kg (1.10 ^g TEQDF-WH098/kg). Schecter et al. (1997) recently
reported similar concentrations in 2,4-D samples manufactured in Europe and Russia; lower
levels were observed in U.S. products. The results of Schecter et al. (1997) are presented
in Table 8-27.
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If it is assumed that the EPA DCI results (i.e., average TEQ level of 0.70 /J.Q
l-TEQDF/kg or 1.10^g TEQDF-WH098/kg) are typical of CDD/CDF levels in 2,4-D pesticides
over the past 20 years and that the average annual use of these pesticides in the United
States has been approximately 25,000 metric tons, then the estimated CDD/CDF TEQ
released to the environment from 2,4-D use during the period 1975 to 1995 was 350 g
I-TEQDF (550 g TEQDF-WH098).
2,4,5-Trichlorophenoxy acetic acid (2,4,5-T) was used in the United States for a
variety of herbicidal uses until the late 1970s to early 1980s. The major use of 2,4,5-T
(about 41 percent of annual usage) was for control of woody and herbaceous weed pests
on rights-of-way. The other major herbicidal uses were forestry (28 percent of usage),
rangeland (20 percent of usage), and pasture (5 percent of usage). As discussed in
Section 3.4.2.8, uses of 2,4,5-T for home or recreation areas and for lakes, ponds, and
ditches were suspended by EPA in 1970. Rights-of-way, forestry, and pasture uses were
suspended by EPA in 1979, and all uses were canceled in 1983.
Table 12-2 presents a compilation of historical domestic production, sales, and
usage volumes for 2,4,5-T and its salts and esters. As shown in Table 12-2, production
and use of 2,4,5-T generally increased each year following its introduction in the 1940s
until the late 1960s. Production, sales, and usage information for the 1970s are generally
not available but are reported to have steadily declined during that period (Federal Register,
1979; Esposito et al., 1980).
Some information is available on the 2,3,7,8-TCDD content of 2,4,5-T, but little
information is available on the concentrations of the other 2,3,7,8-substituted CDD/CDFs
that may have been present.
Plimmer (1980) reported that 2,3,7,8-TCDD concentrations as high as 70,000
were detected in 2,4,5-T during the late 1950s.
• In a study of 42 samples of 2,4,5-T manufactured before 1970, Woolson et al.
(1972) found 500 to 10,000 ^g/kg of TCDDs in 7 samples, and another 13
samples contained 10,000 to 100,000 ,wg/kg of TCDDs. HxCDDs were found in
four samples at levels between 500 and 10,000 ^g/kg and in one sample at a
concentration exceeding 10,000 ^g/kg, but less than 100,000 ^g/kg. The
detection limit in the study was 500 ^g/kg.
• The average 2,3,7,8-TCDD concentration in 200 samples of Agent Orange, a
defoliant containing about a 50/50 mixture of the butyl esters of 2,4,5-T and
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2,4-D that was used by the U.S. Air Force in Vietnam, was 1,910
(Kearney et al., 1973). Of the 200 samples, 64 (or 32 percent) contained more
than 500 ^g/kg of 2,3,7,8-TCDD, with the highest concentration reported to be
47,000 ^g/kg.
Storherr et al. (1971) reported detecting 2,3,7,8-TCDD at concentrations
ranging from 100 to 55,000 ^g/kg in five samples of 2,4,5-T.
Kearney et al. (1973) reported that production samples of 2,4,5-T obtained from
the three principal 2,4,5-T manufacturers in 1971 contained 2,3,7,8-TCDD at
levels of < 100 ^g/kg, lOO^g/kg, and 2,300
• A 1975 survey of 10 lots of a commercial formulation containing 2,4,5-T
showed 2,3,7,8-TCDD concentrations ranging from 10 to 40 ^g/kg (Dow
Chemical Co., undated).
• Analyses by EPA of 16 technical grade 2,4,5-T samples from five different
manufacturers revealed 2,3,7,8-TCDD contents ranging from < 10 to 25
(Federal Register, 1979).
• Schecter et al. (1997) recently reported the analytical results of one sample of
2,4,5-T purchased from Sigma Chemical Co. (product number T-5785, lot
number 16H3625). The results, presented in Table 12-3, indicate a total
I-TEQDF concentration of 2.88 ^g/kg (3.26 ^g TEQDF-WH098/kg).
Because of the wide variability (i.e., three orders of magnitude) in the available
limited information on the 2,3,7,8-TCDD content of 2,4,5-T (particularly the 2,4,5-T used
in the 1950s) and incomplete information on domestic usage, it is difficult to reliably
estimate the amount of 2,3,7,8-TCDD that was released to the U.S. environment as a
result of 2,4,5-T use. A very uncertain estimate can be made using the following
assumptions: (1) the average annual consumptions during the 1950s, 1960s, and 1970s
were 2,000 metric tons/yr, 4,000 metric tons/yr, and 1,500 metric tons/yr, respectively;
and (2) the average 2,3,7,8-TCDD concentrations in 2,4,5-T used over these three
decades were 10,000 ,wg/kg in the 1950s, 4,000 ,wg/kg in the 1960s, and 100 ,wg/kg in
the 1970s. Based on these assumptions, the very uncertain estimate of 2,3,7,8-TCDD
input from 2,4,5-T use over the period 1950-1979 is 36,000 g.
Another source contributing to the soil reservoir is CDD/CDF in sewage sludge
applied to land (i.e., surface disposal or land farming) is estimated to have been 103 g
I-TEQDF in 1995 (75 g TEQDF-WH098) [Note: See Section 8.4.1 for details]. If this same
amount of TEQ had been applied each year during the period 1975 to 1995, the total
amount applied would have been 2,000 g I-TEQDF (1,500 g TEQDF-WH098).
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Mechanisms Responsible for Releases from Surface Soils
Atmospheric deposition is believed to be the current primary means of supply of
dioxin-like compounds to surface soil. CDD/CDFs/PCBs are highly lipid soluble, low
volatility compounds, and tend to partition to soil instead of into air or water. Once
present in or on soils, physical/chemical and biological mechanisms (i.e., photolysis and
biodegradation) can slowly alter the composition and amount of CDD/CDFs/PCBs present.
Studies indicate that the dioxin-like compounds (particularly the more highly chlorinated
CDD/CDFs) exhibit little downward mobility once deposited in or on soil (Puri et al. 1989;
Freeman and Schroy, 1985; Orazio et al., 1992; and Paustenbach et al., 1992). However,
remobilization of the compounds to the atmosphere is possible through volatilization and
resuspension of soil particles.
For example, Young (1983) conducted field studies on the persistence and
movement of 2,3,7,8-TCDD during 1973-1979 on a military test area that had been
aerially sprayed with 73,000 kg of 2,4,5-T during 1962-1970. TCDD levels of 10 to
1,500 ng/kg could be found in the top 1 5 cm of soil 14 years after the last application of
herbicide at the site. Although actual data were not available on the amount of 2,3,7,8-
TCDD originally applied as a contaminant of the 2,4,5-T, best estimates indicated that less
than 1 percent of the applied 2,3,7,8-TCDD remained in the soil after 14 years.
Photodegradation at the time of and immediately after aerial application was believed by
Young (1983) to be responsible for most of the disappearance. However, once
incorporated into the soil, the data indicated a half-life of 10 to 12 years (Young, 1983).
Similarly, Paustenbach et al. (1992), concluded that half-lives of 2,3,7,8-TCDD in soils at
the surface might be 9 to 15 years and half-lives below the surface could be 25 to 100
years.
Ayris and Harrad (1997) studied the mechanisms affecting volatilization fluxes of
several PCB congeners (PCB numbers 28, 52, 101, 138, and 180) from soil and found
positive correlations between flux and soil temperature, soil moisture content, and soil PCB
concentration. For PCBs, secondary releases from soils (primarily via volatilization) are
believed to currently exceed primary emissions in the United Kingdom (Harner et al., 1995;
Jones and Alcock, 1996). Lee et al. (1998) recently quantified PCBs in air samples taken
every 6 hours over a 7-day period in the summer at a rural site in England and found a
strong correlation between air temperature and PCB congener concentrations. The
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concentrations followed a clear diurnal cycle, thus providing some evidence that rapid,
temperature controlled soil to air exchange of PCBs influences air concentrations and
enables regional/global scale cycling of these compounds.
CDD/CDFs and PCBs sorbed to soil and urban dust particles can also be moved
from the terrestrial environment to the aquatic environment via stormwater runoff/erosion.
Results of recent research indicate that, for at least some waterbodies, erosion/stormwater
runoff is currently the dominant mechanism for CDD/CDF input. For example, Smith et al.
(1995) analyzed CDD/CDF concentrations in sediment cores, air, precipitation, soil, and
stormwater runoff in an effort to determine the contributing sources of these compounds
to the lower Hudson River. The mass balance estimates developed from these data for
1990-1993 are: stormwater runoff entering tributaries (76 percent of total CDD/CDF
input), anthropogenic wastes (19 percent), atmospheric deposition (4 percent), and
shoreline erosion (less than 1 percent). Smith et al. (1995) also projected the percent
contribution of these same sources for 1970 as: anthropogenic wastes (70 percent),
stormwater runoff into tributaries (15 percent), atmospheric deposition (15 percent), and
shoreline erosion (0.1 percent).
Lebeuf et al. (1996) analyzed sediment cores from different locations in the lower
St. Lawrence River Estuary and the Gulf of St. Lawrence. The congener group profiles
found in the samples indicate that the input of CDD/CDFs is primarily from the atmosphere.
Comparison of the CDD/CDF concentrations in sediments collected from areas where
sediment accumulation is due primarily to fluvial transport with sediments from areas
where sediment accumulation is due primarily to direct atmospheric deposition onto the
water indicates that the contribution of CDD/CDF from direct atmospheric deposition
represents less than 35 percent of the sediment burden. Thus, the primary source of
CDD/CDFs is emissions to the atmosphere upwind of the Estuary that are deposited within
the watershed and subsequently transported downstream by fluvial waters.
Paustenbach et al. (1996) and Mathur et al. (1997) reported that stormwater runoff
from 15 sites in the San Francisco area contained CDD/CDF TEQ at levels ranging from
0.01 to 65 pg I-TEQDF/L; most samples contained less than 15 pg I-TEQDF/L. The sites
differed widely in land use; the highest levels measured were obtained from an urban, but
nonindustrialized area. A distinct variability was noted in the results obtained at the same
sampling location during different rain events. The profiles of CDD/CDFs in the urban
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stormwater samples were similar particularly in samples collected at the onset of rain
events. Stowe (1996) reported similar findings from analyses of sediments from three
stormwater basins collecting runoff from a military base, city street, and parking lots.
Fisher et al. (1998) reported that urban runoff samples from eight sites (15
samples) in the Santa Monica Bay watershed contained CDD/CDF TEQ at levels ranging
from 0.7 to 53 pg I-TEQDF/L (all but one sample were in the range of 0.7 to 10 pg I-
TEQDF/L). The samples were collected in 1988/1989 from continuously flowing storm
drains during both dry and storm periods. The mean concentration measured during storm
events, 18 pg I-TEQDF/L, was higher than concentration observed during dry periods, 1 pg
I-TEQDF/L.
Estimated Annual Releases from Soil to Water
Nonpoint sources of CDD/CDFs to waterways include stormwater runoff from urban
areas and soil erosion in rural areas during storms. Approaches to estimate national
loadings to water for both of these sources are described below. The estimate derived
below for the potential annual national loading of CDD/CDFs in urban runoff to waterways
is uncertain, but suggests that the loading may be comparable to the contribution from
known industrial point sources (at least 20 g I-TEQDF in 1995). Similarly, the estimate
derived below for the potential annual national loading of CDD/CDFs in rural eroded soils to
waterways is uncertain, but has a stronger analytical base than the urban run off estimate.
This loading estimate, however, is significantly higher than the contribution from known
industrial point sources.
Urban Runoff - Few data on CDD/CDF concentrations in urban runoff have been
reported. The most recent and largest data sets were reported in studies conducted in the
San Francisco Bay and Santa Monica Bay regions (Mathur et al, 1997; Fisher et al., 1998).
These studies found a wide range of CDD/CDF levels in samples of stormwater runoff from
23 sites, varying from 0.01 to 83 pg I-TEQDF/L. The wide variability and limited geographic
coverage of these data preclude derivation of a national emission estimate at this time.
However, by making a number of assumptions, a preliminary estimate of the potential
CDD/CDF magnitude from this source can be made. In order to estimate the amount of
rainfall in urbanized areas of the conterminous United States, a Geographic Information
Systems (GIS) analysis was performed to: determine the total area of every U.S. Census
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urbanized area; to determine the 30-year annual average rainfall for each of those areas;
and to calculate the product of the total areas of urbanized areas with the annual average
rainfall (Lockeed Martin, 1998). This approach yields an estimate of 1.9 x 1014 L/year. If
it is assumed that urban runoff in the United States averages 1 pg I-TEQDF/L (or 1 pg
TEQDF-WH098/L) (i.e., approximately the midpoint of the range reported by Mathew et al.
(1997) and Fisher et al. (1998)), this source could contribute a total of 1 90 g I-TEQDF or
TEQDF-WH098 per year to U.S. waterways. No data were available to make similar
estimates for PCBs.
Rural Soil Erosion - The total annual sheet and rill erosion in the United States
during 1 992 has been estimated as 1.6 billion metric tons from rangeland and 1.1 billion
metric tons from cropland (USDA, 1995). The total amount of eroded soil entering
waterways is greater than this value, because this value does not include soil erosion from
construction areas, forests, and other non-crop and non-rangelands. The data summarized
in the companion document to this report (Volume 2) suggest that typical concentrations
of CDD/CDFs in soils in rural areas is about 4.1 ng l-TEQDF/kg (or 3.6 ng TEQDF-WH098/kg.
It is not known how well this estimate represents eroded soil from cropland and rangeland.
If these soils contain an average of 1 ng TEQ/kg (i.e., slightly lower than the background
value for all types of rural soil), they would contribute 2,700 g TEQ/yr (I-TEQDF and TEQDF-
WH098) to the Nation's waterways. As with urban runoff, no data were available to make
similar estimates for PCBs.
Estimated Annual Releases from Soil to Air
No quantitative estimates of the mass of dioxin-like compounds that may be
released to the atmosphere annually from U.S. soils have been published in the literature
and none are developed in this report. As noted above, the vapor flux of these compounds
from soil to air is dependent on the soil and air concentrations of dioxin-like compounds,
and the temperature, moisture content, and organic carbon content of the soil. Most of
these parameters are not adequately characterized for the United States as a whole to
enable a reliable estimate to be made at present. Particle flux is dependent on many
factors including wind speed, vegetative cover, activity level, particle size, soil
type/conditions, moisture content and particle density. Through use of models and various
assumptions, Kao and Venkataraman (1995) estimated the fraction of ambient air
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CDD/CDF concentrations in the upper Midwest United States that may be the result of
atmospheric reentrainment of soil particles. Similarly, through use of models and various
assumptions, Jones and Alcock (1996) and Harner et al. (1995) reached tentative
conclusions about the relative importance of volatilization of dioxin-like compounds from
soils in the United Kingdom.
Modeling re-entrainment of soil to the atmosphere was conducted by Kao and
Venkataraman (1995). The soil re-entrainment model incorporated information on particle
sizes, deposition velocities, and concentrations of CDD/CDFs in soils. Smaller particulates,
with median diameters ranging from about 0.01 i^m to 0.3 /u,m, are primarily formed from
combustion sources when hot vapors condense and through accumulation of secondary
reaction products on smaller nuclei. Particles at the upper end of this size range will
deposit to the ground in several days. Large or coarse particles, having median diameters
of about 8 i^m, are generated from wind-blown dust, sea spray, and mechanically-
generated particles. CDD/CDFs absorbed onto re-entrained soil would be included in this
larger particle size. These larger particles have a lifetime in the atmosphere from a few to
many hours (Kao and Venkataraman, 1995).
The fraction of ambient air concentration of CDD/CDF that results from soil
re-entrainment was established based on the contribution of crustal sources to the ambient
aerosol. Data on typical crustal soil concentrations in air (15 to 50 ^g/m3 for rural areas
and 5 to 25 ^g/m3 for urban areas) were combined with data on the average
concentrations of CDD/CDFs in soils (73 ng/kg for rural, 2,075 ng/kg for urban, and 8,314
ng/kg for industrial soils) published by Birmingham (1990) for Ontario, Canada, and several
Midwest States. This analysis estimated the concentrations of CDD/CDFs in the ambient
aerosol that originate from soils to be 1 x 10"3 to 4 x 10"3 pg/m3 in rural areas and 0.01 to
0.05 pg/m3 in urban areas. These particulate dioxin concentrations were compared to
average total particulate dioxin levels of 1.36 pg/m3 by Eitzer and Hites (1989) to arrive at
the conclusion that soil re-entrainment could only account for 1 to 4 percent of the
particulate dioxins in the atmosphere in urban areas and 0.1 to 0.3 percent for rural
regions (Kao and Venkataraman, 1995).
This information on the size distribution of ambient aerosols and relative CDD/CDF
concentrations in different particle size fractions was integrated with particle size-
deposition velocities to estimate the relative contribution to the total mass deposition flux
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for small and large particle sizes. Even though re-entrained soil may constitute only a
small fraction of the atmospheric levels of CDD/CDFs, the contribution of dioxins in
re-entrained surface soil to the total deposition flux could be significant, because coarse
particles dominate dry deposition. Soil re-entrainment could possibly account for as much
as 70 to 90 percent of the total dry deposition of CDD/CDFs in urban areas and 20 to 40
percent in rural regions (Kao and Venkataraman, 1995).
Two approaches were used by Jones and Alcock (1996) to assess the potential
significance of CDD/CDF volatilization from soils: (1) the fugacity quotient concept and (2)
a simple equilibrium partitioning model. The fugacity quotient model compares fugacities
of individual CDD/CDF compounds in different environmental media to determine the
tendency for these compounds to accumulate in particular environmental compartments
(McLachlan, 1996). Fugacities for individual compounds, by media, were estimated by
Jones and Alcock (1996) based on physical/chemical properties of the compounds, as well
as the concentrations in the media. In this instance, fugacity quotients were calculated for
air and soil by dividing each compound's fugacity for air by that of soil. Quotients near 1
indicate equilibrium conditions between media; values greater than 1 represent a tendency
for flux (volatilization) from soil to air, while values less than 1 indicate a net flux to the
soil from the air. The equilibrium partitioning model used by Jones and Alcock (1996)
predicts the maximum (possible 'worst case') flux of CDD/CDFs from soil to the
atmosphere. Air phase to soil partition coefficients were calculated using the ratios of soil
and air fugacity capacities. Equilibrium air concentrations were then calculated using
typical U.K. soil concentrations for both urban and rural settings.
From the fugacity quotient model, Jones and Alcock (1996) concluded that the
lower-chlorinated CDD/CDFs may be close to soil-air equilibrium in the United Kingdom
while for other congeners, soil is a sink rather than a source to the atmosphere. Jones and
Alcock (1996) reported that the equilibrium partitioning model predicted that 0.15 kg I-TEQ
volatilizes annually from soil in the United Kingdom. However, the authors discounted this
estimate and concluded that soil volatilization is unlikely to be a significant contributor to
emissions. The likelihood that these estimates were high was attributed to the fact that:
(1) assumptions were made that the concentrations of CDD/CDFs in air were zero and (2)
the model does not consider the resistance of CDD/CDFs to volatilize from soil.
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Harner et al. (1995) developed a model to predict the long-term fate of PCBs in
soils, with emphasis on soil to air exchanges. Using data on levels of PCBs in air, soil, and
vegetation in the U.K., Harner et al. (1995) developed a mass balance model to simulate
the fate of PCBs in U.K. soils from 1935 to 1994. Specifically, monitoring data and
physical/chemical property data were compiled to calculate fugacities for PCB congeners
28, 52, 138, and 153. The model was designed to provide an order-of-magnitude-level of
accuracy, due in part to the inherent variability in the input data. The mass balance
equations in the model included a bell-shaped function for rates of emissions of PCBs, with
the maximum emission rate occurring in 1967. From these emissions rates, fluxes
between air and soil over several decades were estimated. Table 12-4 summarizes the
calculated fluxes. During the 1960s and 1970s, levels of total PCBs in U.K. soils reached
average levels of approximately 300 ^g/kg as a result of atmospheric deposition. Because
of restrictions on PCB use during the last two decades, air concentrations have fallen, and
the primary source to the atmosphere is now believed to be volatilization from soils. The
mass balance model estimated a net flux of 700 kg/yr of total PCBs from soils to the
atmosphere in 1994. However, this estimate is presented with the caveat that the model
tends to underestimate the rate of reduction of PCB concentrations in recent years, which
could be attributed to other mechanisms such as biodegradation, photolysis, and other
degradation processes.
12.2.2. Water
Potential Mass of Dioxin-Like Compounds Present
The surface area of inland waters (including the Great Lakes) in the United States is
about 359,000 sq km (U.S. DOC, 1995a). Assuming that the mean depth of inland water
is 10 meters (Duarte-Davidson et al., 1997), the total inland water volume is approximately
3,600 billion m3. No compilation of CDD/CDF measurements in inland surface waters is
made for this report. However, if it is assumed that the "typical" value used by Duarte-
Davidson et al. (1997) for rivers in the United Kingdom, 38 pg l-TEQDF/m3, is
representative of U.S. waters, then the burden is calculated to be 137 g I-TEQDF.
Mechanisms Responsible for Supply to and Releases from Water
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As discussed previously in Section 12.2.1, dioxin-like compounds enter surface
water from atmospheric deposition, stormwater runoff erosion, and discharges of
anthropogenic wastes. Volatilization is the primary mechanism for release of dioxin-like
compounds from the water column to the atmosphere. Several studies have addressed the
water-air exchange of dioxin-like PCBs through volatilization in the Great Lakes (Achman et
al., 1993; Hornbuckle et al., 1993; Swackhamer and Armstrong, 1986; Baker and
Eisenreich, 1990). No similar body of literature has been developed to address
volatilization of CDD/CDFs from water.
Most studies that have addressed PCB water/air exchange have used the two-film
model developed by Whitman (1927) and made popular by Liss and Slater (1974). When
assessing gas exchange between air and water, the interface between the two phases can
be considered as a two-layer (film) system consisting of well-mixed gas and liquid films
adjacent to the interface; the rate of transfer is controlled by molecular diffusion through
the stagnant boundary layer (Achman et al., 1993). Liss and Slater (1974) applied the
model to assess the flux of various gases, specifically in the air-sea systems, and indicated
the possibility of its use at any air-water interface in the environment, if the necessary data
are available. Hornbuckle et al. (1993) concluded that the two-film model is the best
available tool for estimating regional and local flux of PCBs from natural waters. The
following paragraph, from Achman et al. (1993), succinctly summarizes the model.
The basic equation used to describe the rate of transfer across the interface is
F = K0|(CW-C*) (Eqn. 12-1)
where F is the flux (mol/m2-day), Cw (mol/m3) is the dissolved PCB concentration in the
bulk water, and C* (P/H, mol/m3) is the air concentration expressed as a water
concentration in equilibrium with the air. The variable P is the vapor-phase air
concentration measured (mol/m3) and converted to units of pressure using the ideal gas
law. H is Henry's Law constant (atm«m3/mol). The overall mass-transfer coefficient, Kd,
has units of velocity (m/day). The concentration gradient determines the direction of flux
and drives the mass transfer; whereas, K0, is a kinetic parameter that quantifies the rate of
transfer. The value of Kd is dependent on the physical and chemical properties of the
DRAFT-DO NOT QUOTE OR CITE 12-14 December 2003
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compound as well as environmental conditions. The reciprocal of K0, is the total resistance
to transfer expressed on a gas- (RT/Hka) and liquid- (1/kw) phase basis:
1/K0| = 1/kw + RT/Hka (Eqn. 12-2)
where kw is the water-side mass-transfer coefficient (m/day) and ka is the air-side mass-
transfer coefficient (m/day). H is Henry's Law constant, R is the universal gas constant
(8.2057 x 10"5 atm«m3/mol K), and T is the absolute temperature, K.
Achman et al. (1993) and Hornbuckle et al. (1993) calculated the volatilization rates
of PCBs from Green Bay on Lake Michigan, based on air and water samples simultaneously
collected over a 14-day period above and below the air-water interphase and analyzed for
85 PCB congeners. Air samples collected over nearby land were also analyzed for the 85
PCB congeners. The direction and magnitude of flux for each congener were then
calculated using Henry's Law and meteorological and hydrological parameters in the "two-
film" model. (See Eqn. 12-1.)
The net total PCB transfer rate (i.e., sum of all congener transfer rates) was found
to be from water to air (i.e., volatilization). However, during cool water temperature
periods (i.e., October), the direction of transfer reversed for many congeners. Calculated
transfer rates to air ranged from 1 5 to 300 ng/m2 per day at low wind speeds (1-3 m/sec)
to 50 to 1,300 ng/m2 per day at higher wind speeds (4-6 m/sec). On a congener basis,
the lower chlorinated congeners dominated total fluxes. The summary of flux calculations
is presented in Table 12-5. The most important factors influencing the magnitude of
volatilization were the water concentration of PCBs, wind speed, and water temperature.
In addition, Achman et al. (1993) and Hornbuckle et al. (1993) found that (1) atmospheric
PCB concentrations are higher over contaminated water than over nearby land, (2)
atmospheric PCBs over water tend to increase with increasing dissolved PCB
concentrations, and (3) the congener distribution in the atmosphere correlates linearly with
the congener distributions in the adjacent water.
Achman et al. (1993) also summarized the PCB volatilization rates reported by other
researchers (Baker and Eisenreich, 1990; Swackhamer and Armstrong, 1986; Strachan and
Eisenreich, 1988; and Swackhamer et al., 1988) for Great Lakes water bodies. The results
of these other studies, presented below, also show net flux of PCBs from water to air.
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Water Body
Lake Superior
Lake Michigan
Lake Superior
Siskiwit Lake
Lake Michigan
Total PCB
Volatilization Rate
(ng/m2-day)
141
240
63
23
15
Reference
Baker and Eisenreich (1990)
Strachan and Eisenreich (1988)
Strachan and Eisenreich (1988)
Swackhamer et al. (1988)
Swackhamer and Armstrong (1986)
12.2.3. Sediment
Potential Mass of Dioxin-Like Compounds Present
EPA conducted congener-specific measurements of CDD/CDFS in the sediments
from 11 U.S. lakes located in areas relatively unimpacted by nearby industrial activity. The
mean TEQ concentration in the uppermost sediment layers from these 11 lakes is 5.3 ng
l-TEQDF/kg (dry weight) (or 5.3 ng TEQDF-WH098/kg). For most of the lakes, the uppermost
layer represents about 10 years worth of sedimentation. CDD/CDF concentrations in lakes
impacted by industrial activity may have higher concentrations. For example, Duarte-
Davidson et al. (1997) report a TEQ concentration of 54 ng l-TEQDF/kg for urban sediments
in the United Kingdom.
The surface area of inland waters in the United States is approximately 359,000 sq
km (U.S. DOC, 1995a). In their calculations of sediment burdens in the United Kingdom,
Duarte-Davidson et al. (1997) assumed that (1) the sediment surface area equals the
water surface area, (2) the majority of CDD/CDF are located in the top 5 cm of sediment,
and (3) that sediment density is 0.13 g dry weight/cm3. Applying these assumptions to
the water surface area and background TEQ concentration for U.S. sediments yields a
burden of at least 120 kg I-TEQDF (or 120 kg TEQDF-WH098).
Mechanisms Responsible for Supply to and Releases from Sediment
Because sediment is closely connected to the water column above it, evaluating the
potential for sediment to act as a reservoir of dioxin-like compounds is complex and likely
to be more difficult than studying dioxin-like compounds in a single medium such as water
or soil. Volatilization and sedimentation are two mechanisms whereby persistent
chemicals such as CDD/CDF/PCBs are lost from water bodies/columns. Numerous authors
DRAFT-DO NOT QUOTE OR CITE 12-16 December 2003
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have noted that sediments are a likely sink for persistent hydrophobia organic compounds,
because these compounds are likely to be strongly bound to organic particles in the
sediment (Swackhamer and Armstrong, 1986; Muir et al., 1985; Ling et al., 1993).
For example, Muir et al. (1985) radiolabeled 2,3,7,8-TCDD and studied its
dissipation from sediments (collected from a farm pond and a lake) to the water column in
laboratory studies under static aerobic conditions at 10°C. After 675 days, more than 80
percent of the labeled TCDD were still present in the pond sediment, and 87 percent were
still present in the lake sediment. Aeration had little effect on the dissipation rates.
The concept of fugacity is a useful way to estimate the behavior of dioxin-like
compounds in sediments. Fugacity (tendency of a chemical to escape from a phase) is
expressed in units of pressure (pascals or Pa) and is the partial pressure exerted by the
chemical in each medium. Fugacity models estimate equilibrium concentrations in specific
media at given chemical concentrations in the environment. Clark et al. (1988) suggested
evaluating contaminant concentrations in multiple environmental media by comparing
fugacity of adjoining media (e.g., comparing sediment fugacity with water column fugacity
to determine the chemical's tendency to move from one to the other). The authors
evaluated fugacities of certain organochlorine compounds, including PCBs, in air, water,
sediment, fish, fish-eating birds, and their eggs. The authors presented PCBs fugacities
developed from data collected in a study of the Lake Ontario region. The fugacities of
PCBs in various media can be ranked as birds>fish> water> bottom sediment, indicating
that PCBs and other similar chemicals are likely to remain in bottom sediment and are less
likely to re-enter the water column (Clark et al., 1988).
Estimated Annual Releases from Sediment to Water
Ling et al. (1993) evaluated the fate of various chemicals, including PCBs, in
Hamilton Harbour, located in Ontario, Canada, using a modified version of the Quantitative
Water Air Sediment Interaction (QWASI) fugacity model. Among the processes evaluated
were diffusion between air and water, and sediment and water; sediment deposition,
resuspension, and burial; and sediment transformation. Three primary compartments were
studied: air, water, and bottom sediments. The sediment was treated as a simple,
well-mixed surface layer of active sediment and the buried sediment underneath.
Chemicals in the active sediment were assumed to be able to exchange with the overlying
DRAFT-DO NOT QUOTE OR CITE 12-17 December 2003
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water; chemicals in the buried sediment were assumed to be isolated from the sediment-
water exchange. Sediment is assumed to be homogenous instead of heterogenous. The
epi- and hypolimnetic compartments of the water column were defined based on a
thermocline, and the atmosphere was defined as a semi-infinite medium of constant,
defined composition.
Ling et al. (1993) estimated rates of PCB movement based on 1987 loadings using
two models: a model with and a model without a thermocline. The results for the water-
sediment transfer using the model with a thermocline were: — 32 kg/yr enter the
hypolimnion from the epilimnion; — 27 kg/yr enter the surface sediment from the
hypolimnion; and — 1 8 kg/yr (> 50%) go to burial. For sediment to water transfer, — 7
kg/yr and 12.5 kg/yr transfer to the hypolimnion and then to the epilimnion, respectively.
Similar numbers were found in the single water column model (i.e., model without a
thermocline). Both models predicted volatilization from the water to the atmosphere: 1.6
and 1.8 kg/yr for thermocline and the single water column models, respectively. However,
the actual contribution of PCBs from sediment to air was not determined. A comparison of
estimated concentrations with observed values are presented in Table 12-6. For PCBs, 68
percent were buried in the sediment, 20 percent exported to Lake Ontario, 5.4 percent
degraded in the water and sediment, and 6 percent volatilize. The authors note that these
percentages are uncertain. At the sediment water exchange, more than 90 percent of
each chemical are contained in the sediment because of particle deposition and the high
affinity of the chemical for sediment. There was no indication that contaminants buried in
the bottom sediments are transferred through diffusion mechanisms back to the surface
sediments. However, episodic release of these chemicals from surface sediments can
occur through mechanisms such as resuspension during flooding or lake inversions and
uptake/ingestion by benthic biota.
12.2.4. Biota
Potential Mass of Dioxin-Like Compounds Present
The mass of CDD/CDFs in biota in the United States was not estimated as part of
this reassessment. However, to place perspective on the potential magnitude of this
reservoir, 82 g I-TEQDF have been estimated to be present in biota in the United Kingdom
(50 g in humans and 32 g in vegetation), which is about three orders of magnitude less
DRAFT-DO NOT QUOTE OR CITE 12-18 December 2003
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than that estimated to be present in U.K. surface soils (Duarte-Davidson et al., 1997;
Eduljee and Dyke, 1996). Applying this ratio to the estimates of CDD/CDF TEQ soil burden
in the United States that were presented in Section 12.2.1 (i.e., 1,780 kg I-TEQDF) yields a
biota burden in the United States of about 2 kg I-TEQDF.
Mechanisms Responsible for Supply to and Releases from Biota
Apparently, very little of the dioxin-like compounds contained in contaminated soil,
unlike certain other compounds and heavy metals, is ultimately taken up by the vegetation
growing in the soil. For example, Kjeller et al. (1991) analyzed concentrations of
CDD/CDFs in archived soil and grass samples collected at an English experimental station
from mid-1 840s to the present and found that only 0.006 to 0.02 percent of the soil
burden of CDD/CDFs was taken up by the grass. In addition, scientists generally agree
that, once taken up by plant tissue, CDD/CDFs are not translocated to other parts of the
plant (e.g., fruits, shoots, etc.) (Bacci and Gaggi, 1985; Hulster and Marschner, 1993,
1994; Nakamura et al., 1994).
Researchers have found that the concentration of dioxin-like compounds in the plant
should reach equilibrium with the vapor phase concentrations of dioxin-like compounds in
the surrounding air (Bacci et al., 1990a, 1990b; Frank and Frank, 1989; Horstman and
McLachlan, 1992; McCrady and Maggard, 1993; McLachlan et al., 1995; Paterson et al.,
1991; Simonich and Hites, 1994; Tolls and McLachlan, 1994; Welsch-Pausch et al.,
1995). Horstman and McLachlan (1992) stated that the leaf-air transfer of volatile
compounds is a reversible process governed by concentration gradients. If CDD/CDF
concentrations are higher in the surrounding air than they are in the air spaces within plant
tissue, CDD/CDF should diffuse into the plant. Once equilibrium is reached and CDD/CDF
concentrations in the plant equal that of surrounding air, no more CDD/CDF should be
taken into the plant. When CDD/CDF concentrations in surrounding air begin to decrease,
CDD/CDFs should diffuse (probably at a slow rate) out of the plant tissue. Apparently,
CDD/CDFs are not bioconcentrated to a significant extent in the lipid portion of the leaf
cuticle (Gaggi et al., 1985). The CDD/CDFs present in the leaf tissue are predominantly
released from the plant through leaf fall onto soil. As a result, vegetation is not likely to be
a long-term reservoir of dioxin-like compounds.
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Research suggests that dioxin-like compounds within animal tissue, unlike in
vegetation, seldom, if ever, reach equilibrium with vapor phase concentrations in the
surrounding atmosphere (or water column concentrations in the case of aquatic life).
Rather, animals exposed to dioxin-like compounds are known to bioaccumulate these
compounds, primarily in body fat (U.S. EPA, 1993a; 1993J). Nonetheless, animals, unlike
plants, can metabolize certain chlorinated hydrocarbons after they enter the body (Carlberg
et al., 1983). Dioxin-like compounds can be released from an animal's body (at congener-
specific rates) through metabolic processes or through weight loss, breast-feeding, or
sweating. McLachlan (1996) reported the half-life for the clearance of 2,3,7,8-TCDD from
humans to be 7 years. As a result, animal life has a greater potential than does vegetation
for being a long-term reservoir source of CDD/CDFs. The majority of the dioxin-like
compounds released by animals in the form of waste materials will be released to water or
soil. Similarly, upon death, the dioxin-like compounds remaining in the body will be
deposited onto soil or aquatic sediments or will be ingested by other animals.
Approaches to Measure and Estimate Releases from Biota - Researchers have
investigated the uptake and release of CDD/CDFs by vegetation through measurement of
actual concentrations of CDD/CDFs during uptake and release by vegetation grown in
closed systems (greenhouses). Bacci et al. (1992) conducted uptake and release studies
of 1,2,3,4-TCDD by plant foliage in a closed system (specially constructed greenhouse).
Concentrations of TCDD vapor in the greenhouse air were maintained during the 370-hour
uptake phase at a mean concentration of 0.0062 ng/L (air concentration varied slightly
from 0.0050 to 0.0075 ng/L). To begin the release phase, the TCDD vapor source
(amended sand), as well as the greenhouse walls were removed, and release of CDD/CDFs
from the leaves was measured for 500 hours. Bacci et al. (1992) concluded that during
uptake, TCDD concentration in the leaves varied as a function of time and was dependent
on the concentration of vapor-phase TCDD in the surrounding air. These researchers
estimated the release of TCDD from the vegetation to be relatively slow with a half-life of
TCDD of 3,300 hours.
McCrady and Maggard (1993) conducted a mass balance study of uptake and
release of dioxin in grass foliage. The results indicated a half-life of dioxin in grass of 128
hours (McCrady and Maggard, 1993). These researchers also noted that photodegradation
DRAFT-DO NOT QUOTE OR CITE 12-20 December 2003
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of dioxins on the foliage appeared to be a significant removal mechanism in addition to
volatilization. They calculated the photodegradation half-life to be 44 hours (McCrady and
Maggard, 1993).
Interpretation of uptake and release data over variable exposure times and
contaminant concentrations has led to the development of models describing air-to-
vegetation equilibrium and kinetics controlling the behavior of dioxin in vegetation. Some
earlier fugacity modeling attempts described the leaf of a plant as behaving as a single
compartment. One-compartment models were described by Bacci et al. (1990a; 1990b),
Trapp et al. (1990), and Schramm et al. (1987) (as cited in Tolls and McLachlan, 1994).
Researchers presenting most of the recently developed models claim that the available data
better support the concept of a leaf behaving as two-compartments (Riederer, 1990;
Paterson et al., 1991; Horstman and McLachlan, 1992; McCrady and Maggard, 1993;
Tolls and McLachlan, 1994; McLachlan et al., 1995). Input parameters considered by
most models include critical chemical characteristics of the contaminant, characteristics of
the plant, exposure times, and contaminant concentrations measured within the plant.
Riederer (1990) suggested treating a leaf as multiple compartments, having
different accessibility to the atmosphere and different diffusion resistances. Input
parameters for the two-compartment model are octanol/water coefficients, cuticle/water
partition coefficients, aqueous solubility, and saturation vapor pressure of the chemical of
concern. Outputs of the model are prediction of equilibrium concentration in different leaf
tissues, estimates of air-to-vegetation bioconcentration equilibria, and identification of leaf
compartments in which compounds are likely to accumulate. Riederer (1990) also
presents an approach for using the model to semiquantitatively assess the potential for
revolatilization of dioxins from vegetation.
One advantage of the model presented by Riederer (1990) is that it considers
critical plant characteristics in the release of dioxins. A plant is an active organism,
responding to changes in its environment, and acting accordingly to ensure its survival.
Certain plant characteristics, such as the action of stomata (specialized cells usually on the
lower leaf surface that open and close to control passage of vapors into and out of the leaf
interior) and total leaf volume, are important factors that effect the release rates of vapor
phase contaminants from vegetation.
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Paterson et al. (1991) also presented a two-compartment model for release of
dioxin-like compounds from vegetation. This model describes a plant as being made up of
compartments in terms of their volume fractions of air, water, and nonpolar (lipid-soluble,
or octanol-equivalent) organic matter. Paterson et al. (1991) attempted to show that leaf-
air equilibrium and kinetics can be correlated with chemical properties of the contaminant
and properties of the leaf. Paterson et al. (1991) suggest that the clearance rate constant
(k2) can be correlated with the bioconcentration factor (BCF). This model does not
consider critical plant characteristics, such as action of the stomata, and for this reason
may be less reliable than models that do consider plant characteristics, such as the model
presented by Riederer (1990).
Horstman and McLachlan (1992) developed a fugacity model to describe release of
semivolatile organic compounds from the surface of a solid (spruce needles). Their
approach was slightly different in that their goal was instrument/method development, but
their data supported the behavior of a leaf as a two-compartment system.
McCrady and Maggard (1993) also collected data supporting the importance of
viewing a leaf as a two-compartment system. They used a two-compartment model
similar to the one described by Paterson et al. (1991), which also does not consider critical
plant characteristics, and may be less reliable than models that do (e.g., Reiderer, 1990).
Tolls and McLachlan (1994) exposed grass cultures for up to 240 hours to several
semivolatile organic compounds and then measured the release of contaminants from the
grass. They developed a two-compartment partitioning model based on the data they
collected. The model consisted of a small surface compartment (the leaf cuticle) and large
interior reservoir (air spaces within the leaf). Their model assumes the flux of a chemical is
the product of the fugacity difference (surface fugacity minus reservoir fugacity) and the
conductance between the leaf compartments.
In an attempt to validate this model, McLachlan et al. (1995) compared
concentrations of semivolatile organic compounds measured in grass grown under field
conditions with concentrations predicted by their previous laboratory work with a fugacity
meter. The concentrations measured in the grass cultures agreed with results predicted by
the mathematical model described by Tolls and McLachlan (1994).
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12.3. SUMMARY AND CONCLUSIONS
As depicted in Figure 12-1 a set of complex relationships exist among reservoirs
and between reservoirs and contemporary formation sources. The significance of
reservoirs for human exposure is more dependent on their ability to affect the
concentration of dioxin-like compounds in other media than on their size or net release
rate. This Section, first summarizes and draws conclusions from the limited information
available regarding the character and magnitude of reservoir sources. Second, it uses this
information to discuss the implications of reservoir sources to human exposure.
12.3.1. Reservoir Sources
Noted below are some summary statements about soil reservoir sources
• Soil is likely to be the reservoir source with the greatest potential for release
of CDD/CDFs to other environmental media, particularly to water. This is
due in part to its relatively large mass of stored CDD/CDF, but more
importantly, it is due to the existence of demonstrated transport mechanisms
for intermedia exchange, i.e. soil erosion to surface waters and particle
resuspension to air.
• The preliminary estimates of CDD/CDF runoff from urban areas to
waterways is comparable to known industrial point source releases and
runoff from agricultural areas to surface waters is over 100 times greater. It
is unclear how much of the soil erosion and runoff represents recently
deposited CDD/CDFs from primary sources or longer term accumulation.
Much of the eroded soil comes from tilled agricultural lands which would
include a mix of CDD/CDFs from various deposition times. The age of
CDD/CDFs in urban runoff is less clear.
• Based on the limited information currently available (i.e., primarily fugacity
modeling), volatilization of CDD/CDFs from soils is not believed to
significantly alter ambient air concentrations. However, volatilization of
PCBs from soil may be a significant process.
• Based on the limited information currently available, resuspension of soil may
account for a small fraction (~4 percent) of CDD/CDF concentrations in air.
This resuspended soil may, however, constitute a more significant portion of
dry deposition.
Noted below are some summary statements about water reservoir sources:
• It is unclear if volatilization of CDD/CDFs from water can significantly alter
air concentrations. For PCBs, however, the water-air exchange appears to
DRAFT-DO NOT QUOTE OR CITE 12-23 December 2003
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be significant and for some water bodies results in a net transfer from water
to air.
• Water is the major media contributing CDD/CDF/PCBs to sediment. Note
that most of the CDD/CDF in sediments originally came from soils. For
specific water bodies, however, the CDD/CDFs/PCBs in sediments may have
been dominated by local industrial discharges to water.
Noted below are some summary statements about sediment reservoir sources:
• It is important to distinguish between surface and deep sediments. Surface
sediments are commonly resuspended and introduced back into the water
and deep sediments generally do not interact with the water column.
Surface sediments can contribute significantly to the CDD/CDF/PCB
concentration in water; whereas deep sediments do not.
• There is little, if any, movement of dioxin-like compounds once they are
buried in the bottom sediments. Bottom sediments may be considered as
sinks.
Noted below are some summary statements about biota reservoir sources:
• The mass of CDD/CDF in vegetation at any given time is likely to be small
compared to the mass in soil. Vegetation does play an important role in
transferring CDD/CDF from the air to the soil via the decay of plant biomass.
Release by volatilization from vegetation has been studied and modeled using
the fugacity approach, and half-lives have been estimated. Based on these
results, volatilization is not believed to be a significant mechanism for release
of CDD/CDFs or PCBs except possibly during forest/brush fires.
The mass of CDD/CDF in animals at any given time is likely to be small
compared to the mass in soil. Similarly releases are small and occur primarily
by excretion and decomposition of dead biomass.
12.3.2. Implications for Human Exposure
Although, the ability to make quantitative estimates of releases from reservoir
sources is limited at present, it is reasonable to conclude that the contribution of reservoir
sources to human exposure may be significant. As explained in Volume 2, the diet
accounts for over 95% of human exposure. Although the size of the biota reservoir is
small compared to the soil and sediment reservoirs, it is clearly the key contributor to
human exposure. The potential contribution of the other reservoirs to human exposure is
discussed below.
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PCB Reservoir Releases: Since current sources of newly formed PCBs are most
likely negligible, human exposure to the dioxin-like PCBs is thought to be derived almost
completely from current releases of old PCBs stored in reservoir sources. Key pathways
involve releases from both soils and sediments to both aquatic and terrestrial food chains.
As discussed in Volume 2, one third of general population TEQDFP exposure is due to PCBs.
Thus, at least one third of the overall risk to the general population from dioxin-like
compounds comes from reservoir sources.
CDD/CDF Releases from Soil and Sediments to Water and Exposure via the Aquatic
Pathway: The earlier discussion has shown that soils can have significant inputs to
waterways via soil erosion and runoff. Similarly the sediment reservoir contributes
significantly to CDD/CDF concentration in water. These releases appear to be greater than
those from the primary sources included in the inventory. Dioxins in waterways
bioaccumulate in fish and fish consumption causes human exposure. Fish consumption
makes up about one third of the total general population CDD/CDF TEQ exposure. This
suggests that a significant portion of the CDD/CDF TEQ exposure could be due to releases
from the soil and sediment reservoir.
CDD/CDF Releases from Soil to Air and Exposure via the Terrestrial Pathway:
Potentially, soil reservoirs could have vapor and particulate releases which deposit on
plants and enter the terrestrial food chain. The magnitude of this contribution, however, is
unknown. EPA plans future studies in agricultural areas which will compare modeled air
concentrations from primary sources to measured levels as a way to get further insight to
this issue.
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Table 12-1. Historical Production, Sales, and Usage Quantities for 2,4-Da
Year
1994/95
1993
1992
1991
1990
1989
1988
1987
1986
1985
1984
1983
1982
1981
1980
1979
1978
1977
1976
1975
1974
1973
1972
1971
1970
1969
1968
1967
1966
1965
1964
2,4-D, acid
Production
Volume
(metric tons)
„„
„„
„„
„„
„„
„„
„„
5,859
6,164
5,763
„„
„„
„„
24,948b
19,766
21,354
35,953
34,990
30,927
28,721
24,364
Sales
Volume
(metric tons)
„„
„„
„„
„„
„„
„„
„„
„„
„„
„„
„„
„„
„„
3,275
3,137
6,187
„„
„„
„„
„„
„„
„„
„„
5,619
7,159
8,521
10,352
15,432
12,710
11,816
11,343
Domestic Usage/
Disappearance
(metric tons)
21,800-26,300i
16,800-20,400°
1 6,800-20,400°
18,1 00-29, 500d
18,1 00-29, 500d
18,100-29,500e
23,600-30,400f
23,600-30,4009
17,418b
21,772b
15,700b
28,985h
22,906h
19,958h
2,4-D, esters and salts
(as reported)
Production
Volume
(metric tons)
„„
„„
„„
„„
„„
„„
„„
8,618
„„
„„
7,702
8,762
8,987
11,313
11,874
8,958
12,552
10,913
16,134
6,558
13,400
10,192
„„
„„
25,854
42,690
37,988
32,895
28,740
24,660
Sales
Volume
(metric tons)
„„
„„
„„
„„
„„
„„
„„
12,150
0
0
8,234
8,400
8,002
11,147
13,453
9,256
10,196
7,813
13,414
5,991
13,698
10,899
18,654
19,920
20,891
30,164
29,300
25,075
21,454
18,263
Domestic Usage/
Disappearance
(metric tons)
DRAFT-DO NOT QUOTE OR CITE
12-26
December 2003
-------
Table 12-1. Historical Production, Sales, and Usage Quantities for 2,4-Da (Continued)
Year
1963
1962
1961
1960
1959
1958
1957
1956
1955
1954
1953
1952
1951
1950
1949
1948
1947
1946
1945
2,4-D, acid
Production
Volume
(metric tons)
21,007
19,503
19,682
16,413
13,282
14,036
15,536
13,079
15,656
„„
11,761
13,933
„„
6,421
6,852
9,929
2,553
2,479
416
Sales
Volume
(metric tons)
9,446
7,716
7,591
„„
7,240
6,234
6,871
6,465
5,924
4,838
„„
„„
4,301
2,991
4,152
2,320
2,330
286
Domestic Usage/
Disappearance
(metric tons)
15,059h
16,284h
14,107h
14,107h
15,468h
9,662h
2,4-D, esters and salts
(as reported)
Production
Volume
(metric tons)
20,178
16,831
16,683
15,436
12,438
11,295
12,392
9,635
13,390
10,268
10,733
11,358
„„
5,274
5,829
2,458
1,468
515
--
Sales
Volume
(metric tons)
16,333
13,075
12,533
13,661
7,070
5,649
7,125
7,294
8,121
6,886
8,855
9,637
„„
3,219
3,211
1,598
1,108
81
--
Domestic Usage/
Disappearance
(metric tons)
"--" = Not reported to avoid disclosure of proprietary data.
a All values from the U.S. International Trade Commission's (USITC) annual report series entitled Synthetic Organic
Chemicals - United States Production and Sales unless footnoted otherwise (USITC, 1946-1994).
b U.S. EPA (1975).
c U.S. EPA (1994b).
d U.S. EPA (1992f).
e U.S. EPA (1991h).
f U.S. EPA (1990e).
9 U.S. EPA (1988c).
h USDAI1970).
' U.S. EPA (1997e).
DRAFT-DO NOT QUOTE OR CITE
12-27
December 2003
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Table 12-2. Historical Production, Sales, and Usage Quantities 2,4,5-Ta
Year
1993
1992
1991
1990
1989
1988
1987
1986
1985
1984
1983
1982
1981
1980
1979
1978
1977
1976
1975
1974
1973
1972
1971
1970
1969
1968
1967
1966
1965
1964
1963
2,4,5-T, acid
Production
Volume
(metric tons)
„„
„„
„„
„„
„„
„„
„„
„„
„„
„„
„„
„„
„„
„„
3,200-4,100b
„„
„„
„„
„„
„„
„„
„„
„„
„„
2,268
7,951
6,601
7,026
5,262
5,186
4,123
Sales
Volume
(metric tons)
„„
„„
„„
„„
„„
„„
„„
„„
„„
„„
„„
„„
„„
„„
„„
„„
„„
„„
„„
„„
„„
„„
„„
„„
„„
1,329
757
2,312
„„
1,691
1,928
Domestic Usage/
Disappearance
(metric tons)
900f
3,200h
4,100b
3,200h
900C
694d
3,200h
~7,000e'9
~7,000e'g
7,756e
3,266e
4,037e
3,266e
2,4,5-T, esters and salts
(as reported)
Production
Volume
(metric tons)
„„
„„
„„
„„
„„
„„
„„
„„
„„
„„
„„
„„
„„
„„
„„
„„
„„
„„
„„
„„
„„
„„
„„
5,595
5,273
19,297
12,333
8,191
6,131
5,880
4,543
Sales
Volume
(metric tons)
„„
„„
„„
„„
„„
„„
„„
„„
„„
„„
„„
„„
„„
„„
„„
„„
„„
„„
„„
„„
„„
„„
1,675
3,272
2,576
15,021
11,657
4,553
5,977
3,128
2,585
Domestic Usage/
Disappearance
(metric tons)
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12-28
December 2003
-------
Table 12-2. Historical Production, Sales, and Usage Quantities 2,4,5-Ta (continued)
Year
1962
1961
1960
1959
1958
1957
1956
1955
1954
1953
1952
1951
1950
1949
1948
1947
1946
1945
2,4,5-T, acid
Production
Volume
(metric tons)
3,796
3,134
2,874
2,516
1,668
2,419
2,345
1,327
1,223
2,395
1,583
„„
852
„„
„„
„„
„„
--
Sales
Volume
(metric tons)
1,021
1,196
1,039
692
„„
816
662
639
„„
„„
„„
297
„„
„„
„„
„„
--
Domestic Usage/
Disappearance
(metric tons)
3,674e
2,449e
2,676e
2,495e
1,724e
1 ,300h
1,100h
2,4,5-T, esters and salts
(as reported)
Production
Volume
(metric tons)
4,765
3,536
3,594
3,644
2,372
3,098
3,196
1,720
1,761
2,443
1,423
„„
„„
„„
„„
„„
„„
--
Sales
Volume
(metric tons)
2,543
2,372
1,891
1,843
1,151
1,337
1,473
1,077
615
1,817
569
„„
„„
„„
„„
„„
„„
--
Domestic Usage/
Disappearance
(metric tons)
"--" = Not reported to avoid disclosure of proprietary data.
a All values from the U.S. International Trade Commission's annual report series entitled Synthetic Organic Chemicals
United States Production and Sales (U.S. ITC, 1946-1994) unless footnoted otherwise.
b Federal Register (1979).
c U.S. EPA (1977).
d USDA (1 971); reflects farm usage only.
e USDA (1970); values include military shipments abroad.
f Esposito et al. (1980).
9 Kearney et al. (1 973) reports slightly lower domestic consumption for the years 1 967 and 1 968 than for 1 966.
h Thomas and Spiro (1995).
DRAFT-DO NOT QUOTE OR CITE
12-29
December 2003
-------
Table 12-3. CDD/CDF Concentrations in Recent Sample of 2,4,5-T
Congener/Congener Group
2,3,7,8-TCDD
1,2,3,7,8-PeCDD
1,2, 3,4,7, 8-HxCDD
1,2,3,6,7,8-HxCDD
1,2,3,7,8,9-HxCDD
1,2, 3,4,6,7, 8-HpCDD
OCDD
2,3,7,8-TCDF
1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
1,2, 3,4,7, 8-HxCDF
1,2, 3,6,7, 8-HxCDF
1,2,3,7,8,9-HxCDF
2, 3,4,6,7, 8-HxCDF
1,2,3,4,6,7,8-HpCDF
1,2,3,4,7,8,9-HpCDF
OCDF
Total 2,3,7,8-CDD*
Total 2,3,7,8-CDF*
Total I-TEQDF *
Total TEQDF-WH098 *
Total TCDD
Total PeCDD
Total HxCDD
Total HpCDD
Total OCDD
Total TCDF
Total PeCDF
Total HxCDF
Total HpCDF
Total OCDF
Total CDD/CDF
2,4,5-T Sample
(jt/g/kg)
1.69
0.412
0.465
2.28
1.35
18.1
33.9
0.087
0.102
0.183
1.72
0.356
ND (0.012)
0.126
2.90
0.103
3.01
58.2
8.59
2.88
3.26
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
ND = Nondetected; value in parenthesis is the detection limit.
NR = Not reported.
* = Calculation assuming not detected values are zero.
yc/g/kg = micrograms per kilogram
Source: Schecter et al. (1 997)
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12-30
December 2003
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Table 12-4. PCB 138 Fluxes Predicted by Harner et al. (1995)
Year
1950
1965
1975
1980
1994
Concentration
in Air
(pg/m3)
48
280
__
49
6
Fugacity in Air
(Pascals x 10'9)
0.24
1.5
__
--
Fugacity in Soil
(Pascals x 10'9)
1.1
12
16
__
8.3
Concentration
in Soil
(ng/g)
__
__
--
Net Flux/Direction
air -> soil (444 kg/yr)
air - soil (1,000 kg/yr)
soil - air (820 kg/yr)
soil - air (700 kg/yr)
-- = Not reported.
pg/m3 = picograms per cubic meter
ng/g = nanograms per gram
kg/yr = kilograms per year
Source: Harner et al. (1 995).
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12-31
December 2003
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Table 12-5. Summary of Flux Calculations for Total PCBs in Green Bay, 1989
Date
6-4
6-5
6-6
6-7
6-10
6-11
7-28
7-29
7-30
7-31
8-1
10-21
10-22
10-23
Site
18
18
10
10
4
10
18
21
14
10
4
14
10
4
p|uxa,b,c
(ng/m2-day)
40
40
95
155
325
13
330
70
225
90
800
555
1,300
30
a Numbers indicate water to air transfer of total PCBs.
b Represents the sum of individual PCB congener fluxes.
c Described as "daily" fluxes because they correspond to air samples collected over 5-10
hours and water samples collected over ~ 1 hour.
mg/m2-day = nanograms per square meter per day
Source: Achman et al. (1993).
DRAFT-DO NOT QUOTE OR CITE
12-32
December 2003
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Table 12-6. Comparison of Estimated PCB Concentrations with Observed Values
Variable
Observed Concentration [C]
Sediment
Water
Estimated [C] from Model Without Thermocline
Sediment
Water
Amount in Sediment
Amount in Water
Total Mass
Estimated [C] from Model with Thermocline
Sediment
Hypolimnion
Epilimnion
Amount in Sediment
Amount in Hypo
Amount in EPI
Total Mass
Units
A/g/g
A/g/m3
A/g/g
A/g/m3
kg
kg
kg
A/g/g
A/g/m3
A/g/m3
kg
kg
kg
kg
PCBs
0.23-1.04
<20
0.518
8.33
74.9
2.33
77.2
0.527
8.48
7.93
76.3
1.28
1.02
78.6
A/g/g = micrograms per gram
A/g/m3 = micrograms per cubic meter
kg = kilograms
Source: Ling et al. (1993).
DRAFT-DO NOT QUOTE OR CITE 12-33 December 2003
-------
Soaras
Import
foaii
Fluxes Among Dioxin Reservoirs
Figure 12-1. Fluxes Among Reservoirs
DRAFT-DO NOT QUOTE OR CITE
12-34
December 2003
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13. BALL CLAY
13.1 INTRODUCTION
The purpose of this chapter is to evaluate the potential for environmental releases
of dioxin-like compounds during the mining of ball clay and its subsequent uses. The
presence of dioxin-like compounds in ball clay was discovered in 1996 as a result of an
investigation to determine the sources of relatively high levels of dioxin found in two
chicken fat samples during a national survey of poultry. The survey was conducted jointly
by the U.S. Department of Agriculture and U.S. Environmental Protection Agency to
assess the national prevalence and concentrations of CDDs, CDFs, and coplanar PCBs in
poultry (Ferrario et al., 1997). The results of the investigation indicated soybean meal
added to chicken feed was the source of dioxin contamination (Ferrario, Byrne and
Cleverly, 2000). Further investigation showed that the CDD contamination came from the
ball clay added to the soymeal as an anticaking agent. The ball clay was added at
approximately 0.3% to 0.5% of the soybean meal. Samples of raw ball clay were
subsequently taken at the mine of origin in Mississippi. Analysis of the ball clay obtained
from the active mine showed elevated levels of CDDs having a congener profile similar to
the CDD profiles found in the soymeal, chicken feed and immature chickens.
13.2 CHARACTERISTICS OF MISSISSIPPI EMBAYMENT BALL CLAYS
The ball clays from the mine discussed above are part of a larger ball clay resource
which spans portions of western Kentucky, Tennessee, and Mississippi. These clays were
deposited along the shores of the Mississippi Embayment during the early to middle
Eocene Epoch which occurred approximately 40-45 million years ago. The Mississippi
Embayment ball clays are secondary clays comprised mainly of poorly defined crystalline
kaolinite. Other minerals present include illite, smectite, and chlorite. Quartz sand is the
major nonclay mineral. These deposits of ball clay occur in lenses surrounded by layers of
sand, silt, and lignite. These clays can have a gray appearance caused by the presence of
finely divided carbonaceous particles. It is not uncommon to find black carbonized
imprints of fossil leaves and other plant debris in the clay (Patterson and Murray, 1984).
The plasticity of ball clay makes this an important natural resource for the ceramic
industry. The breakdown of the ceramic uses of ball clay include: 33% floor and wall tile;
DRAFT-DO NOT QUOTE OR CITE 13-1 December 2003
-------
24% sanitary ware; 11 % pottery; and 32% other industrial and commercial uses (Virta,
2000). A minor use of ball clay was as an anticaking agent in animal feeds, which has
subsequently been discontinued by the U.S. Food and Drug Administration (Headrick et
al., 1999). Total mining of ball clay in 1999 was 1.14 million metric tons (Virta, 2000).
13.3 LEVELS OF DIOXIN-LIKE COMPOUNDS IN BALL CLAY
The joint EPA/FDA and USDA investigation of ball clay as a source of dioxin
contamination in animal feeds resulted in sampling the clay at an operational mine in
western Mississippi. Eight samples of raw (unprocessed) ball clay were collected from an
open mining pit at a depth of about 10 to 15m. Samples were prepared and analyzed by
EPA using high resolution gas chromatography coupled with high resolution mass
spectrometry (HRGC/HRMS) using EPA Method 1613 (Ferrario et al., 2000). The
concentrations of the CDD/CDFs present in the raw ball clay samples from the one mine
are shown in Table 13-1. The Limits of Detection (LOD):l_imits of Quantification (LOQ) for
the CDD/CDFs in the clay samples were 0.5:1 pg/g (ppt) d.w for the tetras, 1.0:2.0 pg/g
for the pentas, hexas, and heptas, and 5.0/10.0 pg/g for the octas. The mean
concentrations of all of the CDDs exceeded 100 ppt (d.w.). OCDD is found at the highest
concentration in all of the samples followed by either 1,2,3,4,6,7,8-HpCDD or
1,2,3,7,8,9-HxCDD. The maximum OCDD concentration in the eight samples was
approximately 59,000 pg/g. The most toxic tetra- and penta-congeners were present at
unusually high concentrations in all of the samples with average concentrations of 711
pg/g and 508 pg/g for 2,3,7,8-TCDD and 1,2,3,7,8-PeCDD, respectively. Although the
ball clays show elevated levels of 2,3,7,8-substituted CDDs, they show very low levels of
2,3,7,8-substituted CDFs. In addition, there was a consistent ratio within the HxCDD
congener distribution across all samples (i.e., 1,2,3,7,8,9-HxCDD was present at higher
concentrations than the other 2,3,7,8-substituted HxCDD congeners). The average
percent distribution among the three individual 2,3,7,8-hexa congeners was 5:17:78.
This congener pattern was observed in all the raw ball clay samples analyzed. The mean
total TEQDF-WH098 for the raw ball clay was determined to be 1,513 pg/g, d.w. 2,3,7,8-
TCDD accounted for 47% of the TEQDF-WH098, followed by 1,2,3,7,8-PeCDD at 34%.
As expected, even though present at the highest concentration, OCDD contributed less
than 1 % percent of the total TEQDF-WH098 due to its relatively small WHO-TEF. In
DRAFT-DO NOT QUOTE OR CITE 13-2 December 2003
-------
comparison, the typical range of background TEQDF-WH098 concentrations in North
American urban and rural surface soils samples are 2 to 21 pg/g and 0.1 to 6 pg/g,
respectively (see Volume 3: Properties, Environmental Levels, and Background Exposures,
EPA/600/P-00/001Bc). In soil samples, all 2,3,7,8-CDD/CDF congeners are detected, and
2,3,7,8-TCDD represents less than one percent of total CDD/CDF present. The most
prevalent congeners in soils are OCDD followed by OCDF. Table 13-2 compares the mean
CDD/CDF congener group concentrations in ball clay to the mean congener group
concentrations in rural and urban background soils. This comparison indicates there are
no similarities in the congener group distributions between the ball clay and soils.
13.4 EVIDENCE FOR BALL CLAY AS A NATURAL SOURCE
Several lines of evidence suggest that dioxin-like compounds in ball clay are of
natural origin:
• The clay samples were obtained from undisturbed deposits. It is unknown how
human activity could have contaminated these deposits without disturbing them.
• EPA's Laboratory in Athens, Georgia, analyzed the Mississippi mine clays using a
broad screen for anthropogenic contaminants and no compounds were found
outside of the normal range. All known anthropogenic sources of dioxin have
associated with them a wide variety of other contaminants. The absence of
elevated levels of other compounds is strong evidence that the dioxins found in the
clay are not the result of waste disposal.
• The congener profiles of ball clay do not match known anthropogenic sources
(these profiles are presented in Chapters 2 - 8 of this Volume). Cleverly et al.
(1997) reported on the congener profiles that are typical of known anthropogenic
sources of dioxin-like compounds in the United States. These analyses were used
as a basis for comparison to the profile of the raw ball clay.
The congener pattern characteristic of waste combustion sources differs
significantly from the ball clay profile in several aspects. In combustion source
emissions, all 2,3,7,8-substituted CDD and CDF congeners are measured and
2,3,7,8-TCDD is usually 0.1 to 1.0 percent of total CDD/CDF mass emitted. In
ball clay, 2,3,7,8-TCDD is approximately 5% of total mass of dioxins present.
As with the ball clay, the most prevalent 2,3,7,8-CI substituted CDD congeners
in most incinerator emissions are OCDD and 1,2,3,4,6,7,8-HpCDD. However,
combustion emissions contain appreciable amounts of CDFs of which the
1,2,3,4,6,7,8-HpCDF, OCDF, 1,2,3,4,7,8-HxCDF, 2,3,7,8-TCDF and
2,3,4,6,7,8-HxCDF congeners dominate.
The combustion of wood generates a congener profile not unlike that of waste
incinerator, (i.e., the ratio of CDD/CDF <1), and all laterally substituted
DRAFT-DO NOT QUOTE OR CITE 13-3 December 2003
-------
congeners can be detected in emissions. The combustion of tree bark produces
a congener profile in which the CDD/CDF ratio is > 1, showing only minimal and
barely detectable levels of CDFs in the smoke, the exception being that 2,3,7,8-
TCDF is present at approximately 2% of total mass. The dominant congener in
tree bark combustion emissions is OCDD (>30% total CDD/CDF mass),
followed by 1,2,3,4,6,7,8-HpCDD and 1,2,3,7,8,9-HxCDD.
The congener profile of 2,4-D salts and esters seems to mimic a combustion
source profile in the number of congeners represented, and in the minimal
amount of 2,3,7,8-TCDD relative to all 2,3,7,8-CI substituted congeners.
Nevertheless, unlike the combustion source profile, the 1,2,3,7,8-PeCDD and
the 1,2,3,4,6,7,8-HpCDF constitute major fractions of total CDD/CDF
contamination present in 2,4-D.
The congener profile of technical grade pentachlorophenol (PCP) is clearly
dominated by OCDD and 1,2,3,4,6,7,8-HpCDD. However, only trace amounts
of 2,3,7,8-TCDD are detected in PCP, and 1,2,3,4,6,7,8-HpCDF and OCDF
constitute roughly 15% of typical formulations.
Metal smelting and refining processes, such as secondary aluminum, copper
and lead smelting, also have all the 2,3,7,8-CI substituted CDD/CDF congeners
in stack emissions. In secondary aluminum smelting, 2,3,7,8-TCDD is less than
0.1 % of total CDD/CDFs whereas PeCDF is nearly 25% of total emissions of
dioxin-like compounds, and the CDD/CDF ratio is < 1. Secondary copper
operations show a similar pattern of CDD/CDF emissions, but with five
compounds dominating emissions: 1,2,3,4,7,8-HxCDF, 1,2,3,6,7,8-HxCDF,
1,2,3,4,6,7,8-HpCDF, OCDF, OCDD, and 1,2,3,4,6,7,8-HpCDD. In iron ore
sintering, the dominant congener in emissions of 2,3,7,8-CI substituted
compounds is 2,3,7,8-TCDF.
• A number of studies have shown that natural processes can produce chlorinated
aromatic compounds including dioxin-like compounds. Gribble (1994) reviewed the
biological production of a wide variety of halogenated organic compounds in
nature. For example, the Mississippi salt march grass "needlerush" (Juncus
roemerianus) contains the aromatic compound 1,2,3,4-tetrachlorobenzene and the
blue-green alga, Anacystis marina naturally contains chlorophenol (Gribble, 1994).
The soil fungus, Penicillium sp., produces 2,4-dichlorophenol, and the common
grasshopper is known to secrete 2,5-dichlorophenol (Gribble, 1994). Urhahn and
Ballschmiter (1998) also provide a good review of the chemistry of the biosynthesis
of chlorinated organic compounds under natural conditions. It has been
hypothesized that CDDs, CDFs, and other chlorinated aromatic compounds can be
naturally formed from halogenated humic substances and halomethanes through
chloroperoxidase-mediated reactions in undisturbed peat bogs (Silk et al., 1997). A
similar chloroperoxidase-mediated biochemical formation of CDD/CDFs from
chlorophenols was achieved under laboratory conditions by Oberg and Rappe
(1992). It has been observed that chlorophenols can be biosynthesized (Gribble,
1994; Silk et al., 1997), and that chorophenols are readily adsorbed into peat-
bentonite mixtures (Virarghavan and Slough, 1999). Hoekstra et al. (1999) offered
DRAFT-DO NOT QUOTE OR CITE 13-4 December 2003
-------
the hypothesis that 2,3,7,8-TCDD, 1,2,3,7,8-PeCDD, and 1,2,3,7,8,9-HxCDD can
be naturally formed in soils of coniferous forests from chlorinated phenol. These
same congeners are also the predominant congeners in the ball clay from the
Mississippi Embayment. Although none of these natural processes can be directly
attributed to the presence of dioxin in ball clay, the existence of such mechanisms
lends plausibility to a hypothesis that they are of natural origin.
CDD/CDFs have been found in other clays quite distant from Mississippi
Embayment ball clay deposits. No evidence of anthropogenic sources have been
discovered in these areas either. Recently the presence of the CDDs have been
discovered in kaolinitic clay mined in Germany (Jobst and Aldag, 2000). Because
no anthropogenic source could be determined to explain the presence and levels of
CDDs in the ball clay, Jobst and Aldag (2000) speculated that the CDDs were the
result of an unknown geologic process. In addition, the German clay also has a
congener profile similar to that observed in the Mississippi ball clay with an
absence of CDFs at comparable concentrations and the predominance of the
1,2,3,7,8,9-HxCDD among the toxic hexa-CDDs. The similarity in the congener
profiles in ball clay mined in the United States and Germany suggests a common
origin to the CDDs present in these clays (Ferrario, Byrne, and Cleverly, 2000).
In summary, no anthropogenic sources have been identified that explain the levels
and profiles of CDD/CDFs present in the clay. On the other hand, no definitive scientific
evidence has been brought forward that identifies the principal chemical and physical
mechanism involved to cause the selective chemical synthesis of CDDs under the
conditions inherent to the formation of ball clays some 40 million years ago. In order to
further understand the origin of CDDs in these clays, EPA is currently planning systematic
evaluation of the distribution of CDDs in these clay deposits and the surrounding area.
13.5 ENVIRONMENTAL RELEASES OF DIOXIN-LIKE COMPOUNDS FROM THE MINING
AND PROCESSING OF BALL CLAY
In 1995, approximately 993 million kg ball clay was mined in the United States
(Virta, 2000). Multiplication of the mean TEQDF-WH098 concentration in mined ball clay
by the total amount of ball clay mined in 1995 gives an estimate of 1,502 grams TEQDF-
WH098 contained in all the ball clay mined in 1995. It is unknown if any of these CDDs
are released to the environment during the mining, initial refining and product handling. As
discussed above, most ball clay is used to produce ceramics through a process of high
temperature vitrification. The temperatures found in ceramic kilns are well above the
levels needed for both volatilization and destruction of CDDs. Even though these high
DRAFT-DO NOT QUOTE OR CITE 13-5 December 2003
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temperatures exist, it is unclear whether some release occurs and no stack measurements
have yet been made. Therefore, insufficient evidence is available to make even a
preliminary estimate of releases and this activity is classified as a category "E" source.
Table 13-1. Concentrations of CDDs Determined in Eight (8) Ball Clay Samples in the U.S.
Congener
2,3,7,8-TCDD
1,2,3,7,8-PeCDD
1,2, 3,4,7, 8-HxCDD
1,2, 3,6,7, 8-HxCDD
1,2,3,7,8,9-HxCDD
1,2, 3,4,6,7, 8-HpCDD
OCDD
Total TEQ
Concentrations (pg/g, dry weight)
Mean
711
508
131
456
2,093
2,383
20,640
Median
617
492
134
421
1,880
2,073
4,099
Minimum
253
254
62
254
1,252
1,493
8,076
Maximum
1,259
924
193
752
3,683
3,346
58,766
TEQDF-WH098
711
508
13
46
209
24
2
1,513
Source: Ferrario et al. (2000).
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Table 13-2. Comparison of the Mean CDD/CDF Congener Group Distribution
in Ball Clay to the Mean Congener Group Distributions
in Urban and Rural Soils in North America
Congener Group
TCDD
TCDF
PeCDD
PeCDF
HxCDD
HxCDF
HpCDD
HpCDF
OCDD
OCDF
Total CDD/CDF
Mean Concentration (pg/g, dry weight)
Raw Ball Clay
3,729
6
4,798
2
6,609
6
6,194
9
11,222
11
32,586
Urban Background Soil
36.1
23.5
18.1
40.8
31.7
23.5
194.4
46.4
2,596
40.2
3,067.1
Rural Background Soil
2.3
6.8
4.1
12.7
22.7
21.9
114.7
37.3
565.1
33.5
821.3
Sources: Adapted from U.S. EPA (2000a) and Ferrario, Byrne and Cleverly (2000).
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DRAFT December 2003
DO NOT CITE OR QUOTE NAS Review Draft
www.epa.gov/ncea/dioxin
Exposure and Human Health Reassessment
of 2,3,7,8-Tetrachlorodibenzo-p-Dioxin (TCDD)
and Related Compounds
Part I: Estimating Exposure to Dioxin-Like Compounds
Volume 2: Properties, Environmental Levels, and Background Exposures
Exposure Assessment and Risk Characterization Group
National Center for Environmental Assessment - Washington Office
Office of Research and Development
U.S. Environmental Protection Agency
Washington, DC
-------
DISCLAIMER
This document is a draft. It has not been formally released by the U.S.
Environmental Protection Agency and should not at this stage be construed to represent
Agency policy. Mention of trade names or commercial products does not constitute
endorsement or recommendation for use.
DRAFT-DO NOT QUOTE OR CITE 2-1 December 2003
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TABLE OF CONTENTS
1.0. BACKGROUND AND SUMMARY 1-1
1.1. BACKGROUND 1-1
1.2. DEFINITION OF DIOXIN-LIKE COMPOUNDS 1-3
1.3. TOXIC EQUIVALENCY FACTORS 1-5
1.4. CONTENTS OF THIS VOLUME 1-8
1.5. SUMMARY OF FINDINGS IN THIS VOLUME 1-11
1.5.1. Physical and Chemical Properties and Fate 1-11
1.5.2. Environmental Media and Food Concentrations 1-12
1.5.3. Background Exposures 1-14
1.5.4. Potentially Highly Exposed Populations or Developmental
Stages 1-17
1.5.5. Temporal Trends Information 1-21
REFERENCES FOR CHAPTER 1 1-23
2. PHYSICAL AND CHEMICAL PROPERTIES AND FATE 2-1
2.1. INTRODUCTION 2-1
2.2. GENERAL INFORMATION 2-2
2.3. PHYSICAL/CHEMICAL PROPERTY EVALUATION METHODOLOGY .... 2-4
2.4. PHYSICAL/CHEMICAL PROPERTIES - CHLORINATED COMPOUNDS ... 2-6
2.4.1. Water Solubility 2-6
2.4.2. Vapor Pressure 2-7
2.4.3. Henry's Law Constant 2-9
2.4.4. Octanol/Water Partition Coefficient 2-10
2.4.5. Organic Carbon Partition Coefficient 2-12
2.4.6. Photo Quantum Yields 2-13
2.5. PHYSICAL CHEMICAL PROPERTIES - BROMINATED COMPOUNDS .. 2-14
2.6. ENVIRONMENTAL FATE - CHLORINATED COMPOUNDS 2-14
2.6.1. Environmental Fate of CDDs and CDFs 2-15
2.6.1.1. Summary 2-15
2.6.1.2. Transport Mechanisms in Air 2-16
2.6.1.2.1. Vapor/Particle (V/P) Partitioning 2-18
2.6.1.2.2. Dry Deposition 2-20
2.6.1.2.3. Wet Deposition 2-24
2.6.1.2.4. Mechanisms for Entry of CDD/CDFs into
the Terrestrial Food Chain 2-25
2.6.1.3. Transport Mechanisms in Soil 2-33
2.6.1.4. Transport Mechanisms in Water 2-36
2.6.1.4.1. Sorption to Particulates and
Sedimentation 2-37
2.6.1.4.2. Bioaccumulation 2-38
2.6.1.4.3. Mechanisms for Entry of CDD/CDFs Into the
Aquatic Food Chain 2-41
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TABLE OF CONTENTS (continued)
2.6.1.5. Transformation Processes 2-44
2.6.1.5.1. Photolysis 2-44
2.6.1.5.2. Photooxidation 2-53
2.6.1.5.3. Hydrolysis 2-55
2.6.1.5.4. Biotransformation and Biodegradation. 2-55
2.6.2. Environmental Fate of Dioxin-Like PCBs 2-58
2.6.2.1. Summary 2-58
2.6.2.2. Transport Mechanisms 2-59
2.6.2.3. Transformation Processes 2-60
2.6.2.3.1. Photolysis 2-60
2.6.2.3.2. Oxidation 2-62
2.6.2.3.3. Hydrolysis 2-63
2.6.2.3.4. Biotransformation and Biodegradation . 2-63
2.7. ENVIRONMENTAL FATE - BROMINATED COMPOUNDS 2-67
2.7.1. Summary 2-67
2.7.2. Transport Mechanisms 2-68
2.7.3. Transformation Processes 2-68
2.7.3.1. Photolysis 2-68
2.7.3.2. Oxidation 2-72
2.7.3.3. Hydrolysis 2-73
2.7.3.4. Biotransformation and Biodegradation 2-73
REFERENCES FOR CHAPTER 2 2-74
3. LEVELS OF CDD, CDF, AND PCB CONGENERS IN ENVIRONMENTAL MEDIA
AND FOOD 3-1
3.1. INTRODUCTION 3-1
3.2. CONCENTRATIONS IN AIR 3-4
3.2.1. U.S. Data 3-5
3.2.2. European Data 3-14
3.2.3. Air Observations and Trends 3-18
3.2.4. Air CDD/CDF Profiles and Background TEQ Concentrations . . 3-18
3.3. CONCENTRATIONS IN SOIL 3-20
3.3.1. North American Data 3-20
3.3.2. European Data 3-28
3.3.3. Soil Observations and Trends 3-30
3.3.4. Soil CDD/CDF Profiles and Background TEQ Concentrations . 3-31
3.4. CONCENTRATIONS IN WATER 3-33
3.4.1. North American Data 3-33
3.4.2. European and Japanese Data 3-34
3.4.3. Water Observations and Trends 3-34
3.4.4. Water CDD/CDF Profiles and Background TEQ Concentrations 3-35
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TABLE OF CONTENTS (continued)
3.5. CONCENTRATIONS IN SEDIMENT 3-35
3.5.1. North American Data 3-36
3.5.2. European Data 3-42
3.5.3. Vietnamese and Japanese Data 3-45
3.5.4. Sediment Observations and Trends 3-45
3.5.5. Sediment CDD/CDF Profiles and Background TEQ
Concentrations 3-46
3.6. CONCENTRATIONS IN FISH AND SHELLFISH 3-46
3.6.1. North American Data 3-47
3.6.2. European Data 3-54
3.6.3. Fish Observations and Trends 3-58
3.6.4. Fish CDD/CDF Profiles and Background TEQ Concentrations . 3-58
3.7. CONCENTRATIONS IN FOOD PRODUCTS 3-61
3.7.1. Migration of CDD/CDF from Paper Packaging Into Food .... 3-62
3.7.2. North American Food 3-63
3.7.3. European Food 3-88
3.7.4. Eastern European and Asian Food 3-95
3.7.5. Effects of Cooking and Trimming, or Processing on Residue
Levels in Foods 3-96
3.7.6. Food Observations and Trends 3-102
3.7.7. Food CDD/CDF Congener Profiles and Background TEQ
Concentrations 3-103
3.8. SUMMARY OF CDD/CDF AND PCB LEVELS IN ENVIRONMENTAL
MEDIA AND FOOD 3-103
REFERENCES FOR CHAPTER 3 3-105
4. HUMAN EXPOSURES TO CDD, CDF, AND PCB CONGENERS 4-1
4.1. INTRODUCTION 4-1
4.2. LEVELS OF DIOXIN-LIKE COMPOUNDS IN HUMAN TISSUE 4-2
4.2.1. Adipose Tissue and Blood Studies from the 1980s and Early
1990s 4-2
4.2.2. Breast Milk Studies from the 1980s and Early 1990s 4-9
4.2.3. The Blood Studies of the CDC Collaboration (1995-1997) ... 4-17
4.2.4. Additional Recent Tissue Studies 4-23
4.2.5. Summary of Human Tissue Levels 4-24
4.2.6. Body Burden Profiles 4-26
4.3. INTAKE ESTIMATES BASED ON TISSUE LEVELS AND
PHARMACOKINETIC MODELING 4-27
4.3.1. Steady State Approach 4-27
4.3.2. Non-Steady State Approach 4-31
4.4. INTAKE ESTIMATES BASED ON EXPOSURE MODELING 4-32
4.4.1. Previous Assessments of Background Exposures 4-32
4.4.2. Updated Assessment of Background Exposures on the Basis of
Media Levels and Contact Rates 4-42
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TABLE OF CONTENTS (continued)
4.4.3. Assessment of Background Exposures Among Children .... 4-47
4.4.4. Variability in Intake Estimates 4-48
4.4.5. Comparison of Previous North American Studies to This
Study 4-52
4.4.6. Relative Contribution of Exposure Pathways to Total Intake . 4-53
4.4.7. Geographical Contributions to Dietary Exposure 4-54
4.4.8. Contribution of CDD/CDF Congeners to Background Dose and
Body Tissue Concentration 4-57
4.4.8.1. Background Dose 4-60
4.4.8.2. Background Tissue Concentrations 4-62
4.5. Comparison of Assessment Approaches and Best Estimates of Intake . 4-63
REFERENCES FOR CHAPTER 4 4-66
5.0. POTENTIALLY ELEVATED EXPOSURES 5-1
5.1. INTRODUCTION 5-1
5.2. NURSING INFANTS 5-1
5.2.1. The Impact of Breast Feeding on Infant Body Burden 5-2
5.2.2. Calculation of an Average Daily Dose from Breast-Feeding . . . 5-4
5.2.3. Modeling the Impact of Breast-Feeding on Infant Body Burden . 5-7
5.2.3.1. Description of the Model 5-8
5.2.3.2. Validation of the Model 5-11
5.2.3.3. Scenario Evaluation 5-14
5.2.3.4. Sensitivity Analysis 5-16
5.3. SPORT AND SUBSISTENCE FISHERS 5-19
5.4. LOCALIZED IMPACTS 5-25
5.5. CIGARETTE SMOKERS 5-41
REFERENCES FOR CHAPTER 5 5-43
6. TEMPORAL TRENDS 6-1
6.1. INTRODUCTION 6-1
6.2. SEDIMENT CORE STUDIES OF TEMPORAL TRENDS 6-1
6.3. TEMPORAL TRENDS IN SOIL, VEGETATION, AND AIR 6-7
6.4. TEMPORAL TRENDS IN WILDLIFE 6-9
6.5. TEMPORAL TRENDS IN FOOD PRODUCTS 6-10
6.6. TEMPORAL TRENDS IN HUMAN EXPOSURE 6-12
6.7. TEMPORAL TRENDS IN HUMAN BODY BURDENS OF DIOXIN-LIKE
COMPOUNDS IN THE UNITED STATES 6-13
6.8. ADDITIONAL EVIDENCE OF TEMPORAL TRENDS IN BODY
BURDENS 6-22
6.9. A MODELING EFFORT TO RECONSTRUCT PAST DOSES OF
2,3,7,8-TCDD 6-24
REFERENCES FOR CHAPTER 6 6-30
APPENDIX A - Environmental Chemistry
APPENDIX B - Environmental Concentrations
APPENDIX C - Bioavailability of Dioxin
DRAFT-DO NOT QUOTE OR CITE 2-v December 2003
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LIST OF TABLES
Table 1 -1. The TEF Scheme for I-TEQDF 1-28
Table 1-2. The TEF Scheme for dioxin-like coplanar PCBs, as determined by the
World Health Organization in 1994 1-29
Table 1-3. The TEF Scheme for TEQDFP-WH098 1-30
Table 1-4. Summary of North American CDD/CDF and PCB TEQ-WH098 Levels in
Environmental Media and Food 1-31
Table 1-5. Background Serum Levels in the United States 1995 - 1997 1-32
Table 1-6. Adult Contact Rates and Background Intakes of Dioxin-like Compounds 1-33
Table 1-7. Variability in Average Daily TEQ Intake as a Function of Age 1-34
Table 1-8. Summary of Findings with Regard to Trends in Dioxin Levels in the
Environment and in Humans 1-35
Table 2-1. Possible Number of Positional CDD (or BDD) and CDF (or BDF)
Congeners 2-94
Table 2-2. Ranking Scheme for P-Chem Property Evaluation 2-95
Table 2-3. Selected Physical-Chemical Property Values for the "Dioxin-Like" CDD,
CDF, and PCB Congeners 2-96
Table 2-4. Summary of Selected Deposition Measurements Reported in the
Literature 2-100
Table 2-5. Percentages of CDD/CDFs in Particulate Phase Measured in Air
Monitoring Studies 2-101
Table 2-6. Predicted Fractions of CDD/CDF Congeners in Particulate Phase at
20°C in Four Airsheds 2-102
Table 2-7. Factors Influencing the Dry Deposition Removal Rate in the
Atmosphere 2-103
Table 2-8. Rain Scavenging Ratios (W) and Percent Washout Due to Particulates
(%P) for CDDs and CDFs in Bloomington and Indianapolis Ambient
Air 2-104
Table 2-9. Log BCF Values for CDD/CDFs in Fish 2-105
Table 2-10. CDD/CDF BSAFs and BEFs for Lake Ontario Lake Trout 2-106
Table 2-11. Photolysis Rates of CDDs/CDFs in Water and WatenAcetonitrile
Mixtures 2-107
Table 2-12. Estimated Tropospheric Half-Lives of CDDs/CDFs with Respect to
Gas-Phase Reaction with the OH Radical 2-108
Table 2-13. BAFs, BCFs, and BSAFs for Dioxin-Like PCBs 2-109
Table 2-14. Estimated Tropospheric Half-Lives of Dioxin-Like PCBs with Respect
to Gas-Phase Reaction with the OH Radical 2-110
Table 3-1. Mean CDD/CDF Ambient Air Concentrations from Sites Located
Upwind and Downwind of an Industrial Site 3-124
Table 3-2. Congener-Specific, Homologue, Total, and TEQ Concentrations
for the Four Clusters of Air Samples (pg/m3) 3-125
Table 3-3. Background Air Concentrations of CDD/CDFs at Mohawk Mountain,
Connecticut 3-126
Table 3-4. Ambient Air Concentrations Near a Roadway in Phoenix, Arizona . . . 3-127
DRAFT-DO NOT QUOTE OR CITE
2-vi
December 2003
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LIST OF TABLES (continued)
Table 3-5. Average Dioxin/Furan/PCB Concentrations at Nine NDAMN Sites,
Collected for Six Sampling Moments (n = 53) 3-128
Table 3-6. Annual Mean PCB Concentrations in Ambient Air, Ontario, Canada
(pg/m3) 3-129
Table 3-7. Annual Average Dioxin-Like PCB Concentrations in Ambient Air in
Germany (pg/m3) 3-130
Table 3-8. Mean Background CDD/CDF Profiles for Air 3-131
Table 3-9. TEQDF-WH098 Concentrations of CDD/CDFs in Air in the United States
(pg/m3) 3-132
Table 3-9. TEQDF-WH098 Concentrations of CDD/CDFs in Air in the United States
(pg/m3) 3-133
Table 3-10. Mean PCDD and PCDF Concentrations in Canadian Soil from 1987
(ppt) 3-134
Table 3-11. Dioxin/Furan Levels in Four Background Soil Samples from Elk River,
Minnesota (ppt) 3-135
Table 3-12. Dioxin/Furan Levels in British Columbia Soils 3-136
Table 3-13. Number of Positive Soil Samples and CDD/CDF Concentrations in
Background, Urban, and Impacted Sites Near a Waste-to-Energy
Facility in Ohio 3-137
Table 3-14. Mean Background CDD/CDF Profiles for Soil 3-138
Table 3-15. TEQDF-WH098 Concentrations of CDD/CDFs in North American Soil
(ppt) 3-139
Table 3-16. CDD/CDF Levels in British Columbia Sediments 3-141
Table 3-17. TEQDF Concentrations (ppt) and Ratios of 2,3,7,8-Substituted
CDD/CDF Concentrations to Total CDD/CDF Concentrations for the
Most Recent Sediment Core Sampling Periods for 11 U.S. Lakes . . . 3-142
Table 3-18. CDD/CDF and PCB Concentrations and Flux for 11 U.S.
Lakes/Reservoirs 3-143
Table 3-1 9. Average Total Concentrations of CDD/CDFs for Sediments (pg/g) .. 3-144
Table 3-20. Mean Background Profiles for Sediment 3-145
Table 3-21. TEQDF-WH098 Concentrations of CDD/CDFs in North American
Sediment (ppt) 3-146
Table 3-22. Background Data for Fish from the National Bioaccumulation Study . 3-147
Table 3-23. Levels of CDD and CDF l-TEQDFs in Fish From the Southern Mississippi
Region 3-148
Table 3-24. Summary of CDD, CDF, and PCB Analyses in Farm Raised Catfish
from the Southeastern United States 3-149
Table 3-25. FDA Fish and Shellfish Data for 1995-1999 Combined 3-150
Table 3-26. Levels of PCBs in Fish Tissue, Bivalves, and Sediment at a Site Near
a Pulp and Paper Mill 3-151
Table 3-27. TEQDFP-WH098 Concentrations in Marine Fish 3-152
Table 3-28. Mean CDD/CDF Profiles for Fish 3-153
Table 3-29. Background CDD/CDF TEQs in Fish and Shellfish, Consumption
Rates, and Intakes 3-1 54
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LIST OF TABLES (continued)
Table 3-30. FDA Dairy Data for 1995-1999 Combined 3-156
Table 3-31. l-TEQDFs in Foods From Southern Mississippi 3-157
Table 3-32. Summary of Dioxin/Furan Food Data Collected in the California
State Air Resources Board Study 3-158
Table 3-33. Summary of U.S. Food Data from NCASI Study 3-160
Table 3-34. Summary of Schecter et al. (1993a) Data on U.S. Foods 3-161
Table 3-35. CDD/CDFs and PCBs in Foods from Five Regions of the United
States 3-162
Table 3-36. I-TEQDF Levels in Cow's Milk and Infant Formula from the United
States and Thailand 3-163
Table 3-37. Maximum CDD/CDF Levels in Foods Collected in Canada (pg/g
fresh weight) as Reported by Birmingham et al. (1989) 3-164
Table 3-38. Summary of TEQ Levels in Toronto (1992) and Montreal (1993) . . . 3-165
Table 3-39. Example of Method for Estimating Fat Content (percent) of Beef . . . 3-166
Table 3-40. Summary of Coplanar PCBs in a Statistical Sample of Beef Fat in the
United States 3-167
Table 3-41. Concentration Levels of CDD/CDF Congeners in Back Fat and Ratios
of Muscle Fat/Back Fat in Cattle 3-168
Table 3-42. TEQDFP-WH098 Summary of Nationally Extrapolated Pork Results on
a Lipid Basis Assuming Nondetects (ND) Equal 1/2 Detection Limit . . 3-169
Table 3-43. TEQDFP-WH098 Summary of Nationally Exptrapolated Results From
U.S. Poultry Fat on a Lipid Basis Assuming Nondetects (ND) Equal
1/2 Detection Limit (Results are in ppt, or pg/g; ND = 0 results are in
parenthesis) 3-170
Table 3-44. CDD/CDF Concentrations in Eggs and TEQDF-WH098s 3-171
Table 3-45. Weighted Milk Fat Percent 3-172
Table 3-46. Average Congener Concentrations of 8 Composite Milk Samples
(pg/g lipid; ND = 0 in parenthesis) 3-173
Table 3-47. Calculation of Fractional Fat Content of Dairy Products Category . . . 3-174
Table 3-48. CDD/CDF Levels in German Food 3-175
Table 3-49. CDD/CDF Background Levels in Some European, Canadian, and New
Zealand Food 3-176
Table 3-50. CDD/CDF Levels in German Food (1993-1996) 3-177
Table 3-51. I-TEQDF Levels in Dairy Products in France 3-178
Table 3-52. I-TEQDF Concentrations in Food from the Netherlands 3-179
Table 3-53. I-TEQDF Concentrations in Food from Spain 3-180
Table 3-54. I-TEQDF Concentrations in UK Foods 3-181
Table 3-55. I-TEQDF Concentrations in Bottled Cow's Milk from the United
Kingdom 3-182
Table 3-56. Concentrations and Concentration Ranges (pg/g fresh weight) of
Four Dioxin-Like PCBs in Foods from Finland 3-183
Table 3-57. l-TEQDFs (ppt wet weight) in Foods From the Former Soviet Union . . 3-184
Table 3-58. Percentage Reduction of Total 2,3,7,8-TCDD Residues in Restructured
Carp Fillets from Cooking 3-185
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LIST OF TABLES (continued)
Table 3-59. Effects of Cooking and Trimming on PCB Levels in Lake Ontario Fish 3-186
Table 3-60. Means and Standard Deviations of PCB Cooking Losses (%) of
Stewed and Pressure Cooked Chicken Pieces 3-187
Table 3-61. Weighted Mean CDD/CDF Profiles for Foods 3-188
Table 3-62. Summary of CDD/CDF Levels in U.S. Food (pg/g fresh weight) .... 3-189
Table 3-63. Summary of TEQP-WH098 Levels in North American Food (pg/g
fresh weight) 3-190
Table 3-64. Summary of North American CDD/CDF and PCB TEQ-WH098 Levels
in Environmental Media and Food (whole weight basis) 3-191
Table 3-65. CDD/CDF Congeners that Contribute the Highest Percentage of
TEQDF-WH098 to the Total TEQDF-WH098 for All Congeners
Combined 3-193
Table 4-1. NHATS Mean Adipose Tissue Data (ppt, lipid adjusted) 4-79
Table 4-2. Estimated Mean I-TEQDF Concentrations (ppt) in Adipose Tissue for
U.S. Subpopulations from the 1987 NHATS 4-80
Table 4-3. Human Adipose Tissue Data (ppt, lipid adjusted) 4-81
Table 4-4. Mean Levels in Human Serum (ppt, whole weight basis) 4-82
Table 4-5. Mean TEQ Levels in Pooled Serum Samples 4-83
Table 4-6. CDD/CDF Levels in Human Blood from Various Countries 4-84
Table 4-7. CDD/CDF Levels in Human Adipose Tissues from Various Countries . . 4-85
Table 4-8. Levels of CDDs and CDFs 2,3,7,8-Substituted Found in Spanish
Human Adipose Tissue on Fat Weight Basis in pg/g (ppt).
(17 samples) 4-86
Table 4-9. Concentration of CDDs, CDFs, and PCBs in Human Milk on a Fat
Basis (pg/g) 4-87
Table 4-10. CDD/CDF Concentrations and I-TEQDF Levels in Human Milk 4-88
Table 4-11. CDD/CDF and PCB TEQ Concentrations in Breastmilk from Various
Countries and Regions Based on 1992/93 Sampling 4-89
Table 4-12. Comparison of Results from the First and Second Round of WHO-
Coordinated Human Milk Study 4-90
Table 4-13. PCB Concentrations in Cow's Milk and Human Milk from The
Netherlands (ppt, lipid basis) 4-91
Table 4-14. l-TEQDFs in Mother's Milk and Blood, and Infant's Blood (ppt) 4-92
Table 4-15. Mean Concentrations of CDD/CDFs and Coplanar PCB Congeners
from the Times Beach Exposure Study 4-93
Table 4-16. Results of Blood Sampling for the Comparison Population at Vertac in
Jacksonville, AK 4-94
Table 4-17. Congener-specific Average Concentrations for 29 North Carolina
Adults 4-95
Table 4-18. Results of CDC Compilation of Blood Data from Six Study Sites .... 4-96
Table 4-19. CDD/CDF Levels in Human Tissues in North America (ppt TEQDF-WH098,
lipid basis) (late 1980s to early 1990s) 4-97
Table 4-20. CDD/CDF Levels in Human Tissues in Europe and Japan (ppt
TEQDF-WH098, lipid basis) (ate 1980s to early 1990s) 4-98
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LIST OF TABLES (continued)
Table 4-21. PCB Levels in Human Tissues in North America (ppt TEQP-WH098,
lipid basis) (Iate1980s to early 1990s) 4-100
Table 4-22. PCB Levels in Human Tissues in Europe (ppt TEQP-WH098, lipid basis,
using WHO TEFs) (late 1980s to early 1990s) 4-102
Table 4-23. Weighted Mean CDD/CDF Profiles for Human Tissues from Studies in
the 1980s and Early 1990s 4-103
Table 4-24. Estimated Dose Based on Congener-Specific Half-Lives and Adipose
Tissue TEQDF-WH098 Concentrations, and Pharmocokinetic Modeling 4-104
Table 4-25. Predicted Average Daily Intake of 2,3,7,8-TCDD by the General
Population of the United States 4-105
Table 4-26. Predicted Average Daily Intake of 2,3,7,8-TCDD from Foods by the
General Population of the United States 4-106
Table 4-27. Daily Exposure to 2,3,7,8-TCDD and I-TEQDF from Air, Soil, Food, and
Nonfood in The Netherlands 4-107
Table 4-28. Estimated Lifetime Average Daily Exposure of Canadians to Dioxin
I-TEQ 4-108
Table 4-29. Estimated Upper Bound Dietary Intakes of CDD/CDFs by the
Average UK Consumer in 1982 and 1992 4-109
Table 4-30. Estimated CDD/CDF Mean Background Exposures for Adults in the
United States 4-110
Table 4-31. Estimated Dioxin-Like PCB Mean Background Exposures for Adults in
the United States 4-111
Table 4-32. Comparison of Adult Contact Rates, TEQDF Concentrations, and
Background Exposure Estimates from the 1994 Draft and Current
Version of This Document 4-11 2
Table 4-33. Background Exposures via Consumption of German Food 4-113
Table 4-34. Comparison of Contact Rates and Background TEQDF-WH098 Exposures
for Three Age Groups of Children to Adults 4-114
Table 4-35. Comparison of Contact Rates and Background TEQP-WH098 Exposures
for Three Age Groups of Children to Adults 4-115
Table 4-36. Percentage TEQDFP-WH098 Contribution of Each Media to Total Dose
by Age Group 4-116
Table 4-37. Variability in Fat Intake from the Bogalusa Heart Study 4-117
Table 4-38. Fat Intake (g/day) Among the Adult U.S. Population, Based on Data
from the 1 987 NHIS 4-118
Table 4-39. Estimated CDD/CDF Upper Percentile Background Exposures for
Adults in the United States 4-119
Table 4-40. Estimated Dioxin-Like PCB Upper Percentile Background Exposures
for Adults in the United States 4-1 20
Table 4-41. Comparisons of Predicted Average Daily Intake of 2,3,7,8-TCDD and
Total TEQDFs 4-121
Table 4-42. Example of the Calculation of the Picograms of TEQDF-WH098
Contributed by Individual CDD/CDF Congeners for the Beef
Consumption Pathway 4-1 22
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LIST OF TABLES (continued)
Table 4-43. Average Concentrations (not on a TEQDF-WH098 basis) and the
Fraction of TEQDF-WH098 Contributed by Each CDD/CDF Congener
for the Various Food Groups 4-1 23
Table 4-44. The Average Concentrations (not on a TEQP-WH098 basis) and the
Fraction of TEQP-WH098 Contributed by Each Dioxin-Like PCB
Congener for the Various Food Groups 4-124
Table 4-45. TEQDF-WH098 Contribution of Each CDD/F Congener to the Daily Dose
for Each Group and Overall (pg/day) 4-125
Table 4-46. TEQP-WH098 Contribution of Each Coplanar PCB Congener to the Daily
Dose for Each Group and Overall (pg/day) 4-126
Table 4-47. Average CDD/CDF Concentrations in Human Tissue and Fractional
Contribution of CDD/CDF Congeners to Total TEQDF-WH098
Tissue, Based on CDC Blood Data 4-127
Table 4-48. Average Coplanar PCB Concentrations in Human Tissue and
Percentage Contribution of CDD/F Congeners to Total TEQP-WH098
Tissue, Based on CDC Blood Data 4-128
Table 5-1. Concentrations of CDDs, CDFs, and Dioxin-Like PCBs in Blood (lipid
based) of a Breast-Fed and a Formula-Fed Infant at the Age of 11
and 25 Months 5-51
Table 5-2. Concentrations of CDDs and CDFs in Adipose Tissue (lipid based) of
Stillborn, Formula-Fed, and Breast-Fed Infants 5-52
Table 5-3. Parameters Used for Modeling the Impact of Nursing on Body Burden
and Body Lipid Concentrations of TEQs from Infancy to Adulthood . . 5-53
Table 5-4. Model Validation Data and Results (all concentrations in ppt
TEQDF-WH098 lipid basis) 5-54
Table 5-5. Results of PK Modeling for Formula Feeding and 4 Breast Feeding
Scenarios 5-55
Table 5-6. Sensitivity Analysis Testing of PK Model for Breast-Milk Impacts .... 5-56
Table 5-7. Estimated CDD/CDF/PCB Exposures for Adult Subsistence Fishermen . 5-57
Table 5-8. Levels of Different PCB Congeners in Blood Samples from Three
Groups of Men with Different Fish Consumption Habits 5-58
Table 5-9. Mean TEQ Levels in Pooled Serum Samples 5-59
Table 5-10. Mean CDD/CDF Levels in Serum of Consumers of Great Lakes Sport
Fish (ppt, lipid adjusted) 5-60
Table 5-11. Mean PCB Levels in Serum of Consumers of Great Lakes Sport Fish
(ppt, lipid adjusted) 5-61
Table 5.12. Average PCB and CDD/F TEQ-WH098 Concentrations (all concentrations
in pg/g whole weight) 5-62
Table 5-13. Comparison Between Mean PCB Levels in Fish-eating Populations and
Controls 5-63
Table 6-1. Lipid Based Concentrations of CDD/CDFs and PCBs in Samples of
Pooled Retail Milk Purchased in the United Kingdom 6-38
Table 6-2. CDD/F and PCB TEQ Concentrations and Percent Differences from
Current TEQ Levels 6-39
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LIST OF TABLES (continued)
Table 6-3. Estimated Upper Bound Dietary Intakes of CDD/CDFs and PCBs
by the Average UK Consumer in 1982 and 1992 6-40
Table 6-4. Summary of Studies with Body Burden Data of Dioxins and Furans . . 6-41
Table 6-5. Average Congener Concentrations for Body Burden Studies of Dioxins
and Furans 6-42
Table 6-6. Comparison of the 15-44 Age Group Average Concentration of
Selected Congeners from NHATS FY82 and NHATS FY87 6-44
Table 6-7. Trends in Blood CDD/CDF Levels in a German Population, 1991-1996 6-45
Table 6-8. Comparison of Results from the First and Second Round of WHO-
Coordinated Human Milk Study 6-46
Table 6-9. Comparison of CDD/CDF Concentrations in Human Milk from Finland
in 1987 and 1992-1994 6-47
Table 6-10. Mean Human Lipid TCDD Concentrations Reported in Various U.S.
Studies 6-48
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LIST OF FIGURES
Figure 1-1. Chemical Structure of 2,3,7,8-TCDD and Related Compounds 1-36
Figure 1-2. Lipid (a) and Body Burden (b) Concentrations in a Hypothethical
Female Until Age 70 Under Four Nursing Scenarios:
Formula Only, and 6-week, 6-month, and 1 year Nursing 1-37
Figure 2-1. Pathways for Entry of Dioxin-like Compounds into the Terrestrial
and Aquatic Food Chains 2-111
Figure 2-2. Intermedia Movement of CDD/CDFs and PCBs Among Major
Environmental Media 2-11 2
Figure 3-1. CDD/CDF Profiles for Rural Background Air 3-194
Figure 3-2. CDD/CDF Profiles for Urban Background Air 3-195
Figure 3-3. CDD/CDF Profiles for Rural Soils 3-196
Figure 3-4. CDD/CDF Profiles for Urban Background Soil 3-197
Figure 3-5. CDD/CDF Profiles for Sediment 3-198
Figure 3-6. CDD/CDF Congener Profiles for Fish and Shellfish 3-199
Figure 3-7. CDD/CDF Congener Profile for Beef 3-200
Figure 3-8. CDD/CDF Congener Profile for Pork 3-201
Figure 3-9. CDD/CDF Congener Profiles for Poultry and Eggs 3-202
Figure 3-10. CDD/CDF Congener Profiles for Milk and Dairy Products 3-203
Figure 3-11. CDD/CDF and PCB Mean Background Environmental Levels in
TEQ-WH098 3-204
Figure 4-1. TEQ (I-TEQ for CDD/CDF + WH094 for a Subset of Four Dioxin-Like
PCBs) Lipid Concentrations for a Comparison Population and the
Population of Mossville, Louisiana, as a Function of Age 4-129
Figure 4-2. CDD/CDF Profiles for Adipose Tissue, Blood and Human Milk
Based on Literature Studies from the 1980s to the Early 1990s .... 4-130
Figure 4-3. Congener Profile for the CDC Blood Data Set (1995-1997) 4-131
Figure 4-4. Background TEQDF-WH098 Exposure for North America, by Pathway . 4-132
Figure 4-5. Percent Contribution of Various Media to TEQDF-WH098 Dose, By Age
Group 4-133
Figure 4-6. Contribution of Various Media to 2,3,7,8-TCDD Exposure in North
America 4-134
Figure 4-7. Comparison of North American and European Background CDD/CDF
TEQ Exposures 4-1 35
Figure 4-8. TEQDF-WH098 Derived from Pork Production Data 4-136
Figure 4-9. TEQDF-WH098 Derived from Dairy Products Production Data 4-137
Figure 4-10. Comparison of Food Contributions to TEQDF-WH098 Production Data
and Dose 4-138
Figure 4-11. Total TEQ Production in Five Food Categories, Categorized by
Quartile 4-139
Figure 4-12. Fractions of the Background TEQ Dose and TEQ Tissue Concentration
Contributed by Each CDD/CDF Congener 4-140
Figure 4-13. Fractions of the Background TEQ Dose and TEQ Tissue Concentration
Contributed by Each PCB Congener 4-141
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December 2003
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LIST OF FIGURES (continued)
Figure 5.1. Assumptions used to model the lifetime impacts of breast-feeding,
including the assumptions for body lipid fraction (A and B) and full
body weight (C and D) for the first 10 years of life (A and C) and for
over the entire lifetime (B and D) 5-64
Figure 5-2. Comparison of the selected half-life of TEQs in the body with two
options that were available in the literature for 2,3,7,8-TCDD 5-65
Figure 5-3. Demonstration of the model for evaluating impacts on lipid
concentrations (A) and body burdens (B) of infants resulting from
various nursing scenarios during a lifetime 5-66
Figure 5-4. Demonstration of the model for evaluating impacts on lipid
concentrations (A) and body burdens (B) of infants resulting from
various nursing scenarios during the first 10 years of life 5-67
Figure 5-5. Results of sensitivity analysis showing the difference when making
modeling assumptions that lead to a high impact to the infant (high
impact scenario) and to a low impact to the infant (low impact
scenario) as compared to the baseline scenario for a 6-month
breast-feeding scenario (6-month scenario) 5-68
Figure 6-1. CDD/CDF Levels in Sediment, Beaver Lake, Washington 6-49
Figure 6-2. I-TEQDF Concentrations of Historical Food Samples from the U.S.
(results calculated at ND = 1/2 LOD) 6-50
Figure 6-3. TEQP-WH094 Concentrations of Historical Food Samples from the U.S.
(results calculated at ND = 1/2 LOD) 6-50
Figure 6-4. Average Adult Population TEQDF -WH098 Concentrations as a Function
of Year (all results in ppt lipid) 6-51
Figure 6-5. Age Trend Relationships for Three Studies 6-52
Figure 6-6. Examples of Temporal Exposure Curves for 2,3,7,8-TCDD, e(t) in
Units of pg/kg-day 6-53
Figure 6-7. Predicted Mean TCDD Lipid Concentrations (pg/g) in Males by Birth
Year and Specimen Year Derived Using e(t) Curve Labeled A in
Figure 6-6 6-54
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1.0. BACKGROUND AND SUMMARY
1.1. BACKGROUND
This reassessment is comprised of three reports:
Part 1. Estimating Exposure to Dioxin-Like Compounds (U.S. EPA, 2000a) (which
expanded upon a 1988 draft exposure report titled, Estimating Exposure to
2,3,7,8-TCDD [U.S. EPA, 1988]);
Part 2. Health Assessment of 2,3,7,8-Tetrachlorodibenzo-p-Dioxin (TCDD) and
Related Compounds (U.S. EPA, 1994; U.S. EPA, 2000b); and
Part 3. Dioxin: Integrated Summary and Risk Characterization for 2,3, 7,8-
Tetrachlorodibenzo-p-Dioxin (TCDD) and Related Compounds (U.S. EPA, 2000c).
Throughout the remainder of this document, these three parts as a whole will be
abbreviated as the Reassessment Documents, and the individual parts will be referred to
as the Exposure Reassessment Document, the Health Reassessment Document, and the
Risk Characterization. The Exposure Reassessment Document has expanded to three
volumes, as discussed below. Volumes 1 and 2 of the Exposure Reassessment
Document are summarized in Section 4 of the Risk Characterization.
The process for developing the Reassessment Documents has been open and
participatory. Each of the documents has been developed in collaboration with scientists
from inside and outside the Federal Government. Each document has undergone
extensive internal and external review, including review by EPA's Science Advisory Board
(SAB). In September 1994, drafts of each document were made available for public
review and comment. This included a 150-day comment period and 11 public meetings
around the country to receive oral and written comments. These comments, along with
those of the SAB (U.S. EPA, 1995), have been considered in the drafting of this final
document. The Dose-Response Chapter of the Health Document underwent peer review
in 1997 (U.S. EPA, 1997); an earlier version of the Integrated Summary and Risk
Characterization underwent development and review in 1997 and 1998, and comments
have been incorporated. In 1998, EPA released a workshop review version of the sources
inventory (U.S. EPA, 1998), one of the three volumes of the Exposure Reassessment
Document. In addition, as requested by the SAB, a chapter on Toxic Equivalency has
been developed and underwent external peer review in parallel with the Integrated
Summary and Risk Characterization in July 2000. The November 2000, review by the
SAB of the Dose-Response Chapter, the Toxic Equivalency Chapter and the Integrated
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Summary and Risk Characterization was the final step in this open and participatory
process of reassessment. The full set of background documents and the integrative
summary and risk characterization replace the previous dioxin assessments as the
scientific basis for EPA decision-making.
The final Exposure Reassessment Document reflects changes made as a result of
both review comments and analyses of a variety of other types of information that has
come available. These include relevant information obtained from published peer-reviewed
literature, EPA program offices, and other Federal agencies. This version of the Exposure
Reassessment Document is current in this regard through 2000.
The purpose of the Exposure Reassessment Document is threefold: 1) to inventory
the known sources of release of dioxins into the environment, 2) to develop an
understanding of dioxins in the environment, including fate and transport properties,
environmental and exposure media concentrations, background as well as elevated
exposures, and temporal trends in exposure, and 3) provide site-specific procedures for
evaluating the incremental exposures due to specific sources of dioxin-like compounds.
Following this structure, the Exposure Reassessment Document is presented in three
volumes:
Volume 1 - Sources of Dioxin-Like Compounds in the United States
This volume presents a comprehensive review of known sources of environmental
releases of dioxin-like compounds in the United States. It includes an inventory of
known source activity in terms of estimates of annual releases of dioxin-like
compounds into the U.S. environment (i.e., air, water and land). This inventory is
specific for two reference years, 1987 and 1995. From these data, it is possible to
compare and contrast releases of dioxin-like compounds among the sources and
between the reference years.
Volume 2 - Properties, Environmental Levels, and Background Exposures
This volume presents and evaluates information on the physical-chemical
properties, environmental fate, environmental and exposure media levels,
background and elevated human exposures, and temporal trends of dioxin-like
compounds in the U.S. environment during the 20th century.
Volume 3 - Site-Specific Assessment Procedures
This volume presents procedures for evaluating the incremental impact from
sources of dioxin release into the environment. The sources covered include
contaminated soils, stack emissions, and point discharges into surface water. This
volume includes sections on: exposure parameters and exposure scenario
development; stack emissions and atmospheric transport modeling; aquatic and
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terrestrial fate, and food chain modeling; demonstration of methodologies; and
uncertainty evaluations including exercises on sensitivity analysis and model
validation, review of Monte Carlo assessments conducted for dioxin-like
compounds, and other discussions.
The primary technical resource supporting the development of the inventory of
sources of dioxin-like compounds discussed in Volume I (above) is the Database of
Sources of Environmental Releases of Dioxin-Like Compounds in the United States
(EPA/600/C-01/012. March 2001). This database includes congener-specific CDD and
CDF emissions data extracted from original engineering test reports. It has been published
independently from the Reassessment and is available on Compact Disk-Read only
Memory (CD-ROM), without cost, from EPA's National Service Center for Environmental
Publications (NSCEP) in Cincinnati, Ohio (telephone: 1-800-490-9198, or 513-489-8190;
fax: 513-489-8695). Summary files from the database will be available for downloading
from the Web page of the National Center for Environmental Assessment,
www.epa.gov/ncea/dioxin.htm. Instructions on how to order and obtain the CD-ROM will
also be available on the Web page.
1.2. DEFINITION OF DIOXIN-LIKE COMPOUNDS
This assessment addresses specific compounds in the following chemical classes:
polychlorinated dibenzo-p-dioxins (PCDDs or CDDs), polychlorinated dibenzofurans (PCDFs
or CDFs), polybrominated dibenzo-p-dioxins (PBDDs or BDDs), polybrominated
dibenzofurans (PBDFs or BDFs), and polychlorinated biphenyls (PCBs), and describes this
subset of chemicals as "dioxin-like." Dioxin-like refers to the fact that these compounds
have similar chemical structure, similar physical-chemical properties, and invoke a common
battery of toxic responses. Because of their hydrophobic nature and resistance towards
metabolism, these chemicals persist and bioaccumulate in fatty tissues of animals and
humans. The CDDs include 75 individual compounds; CDFs include 135 different
compounds. These individual compounds are referred to technically as congeners.
Likewise, the BDDs include 75 different congeners and the BDFs include an additional 135
congeners. Only 7 of the 75 congeners of CDDs, or of BDDs, are thought to have dioxin-
like toxicity; these are ones with chlorine/bromine substitutions in, at a minimum, the 2,
3, 7, and 8 positions. Only 10 of the 135 possible congeners of CDFs or of BDFs are
thought to have dioxin-like toxicity; these also are ones with substitutions in the 2, 3, 7,
and 8 positions. This suggests that 17 individual CDDs/CDFs, and an additional 17
BDDs/BDFs, exhibit dioxin-like toxicity. The database on many of the brominated
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compounds regarding dioxin-like activity has been less extensively evaluated, and these
compounds have not been explicitly considered in this assessment.
There are 209 PCB congeners. Only 13 of the 209 congeners are thought to have
dioxin-like toxicity; these are PCBs with 4 or more lateral chlorines with 1 or no
substitution in the ortho position. These compounds are sometimes referred to as
coplanar, meaning that they can assume a flat configuration with rings in the same plane.
Similarly configured polybrominated biphenyls (PBBs) are likely to have similar properties.
However, the database on these compounds with regard to dioxin-like activity has been
less extensively evaluated, and these compounds have not been explicitly considered in
this assessment. Mixed chlorinated and brominated congeners of dioxins, furans, and
biphenyls also exist, increasing the number of compounds potentially considered dioxin-
like within the definitions of this assessment. The physical/chemical properties of each
congener vary according to the degree and position of chlorine and/or bromine
substitution. Very little is known about occurrence and toxicity of the mixed (chlorinated
and brominated) dioxin, furan, and biphenyl congeners. Again, these compounds have not
been explicitly considered in this assessment. Generally speaking, this assessment
focuses on the 17 CDDs/CDFs and a few of the coplanar PCBs that are frequently
encountered in source characterization or environmental samples. While recognizing that
other "dioxin-like" compounds exist in the chemical classes discussed above (e.g.,
brominated or chlorinated/brominated congeners) or in other chemical classes (e.g.,
halogenated naphthalenes or benzenes, azo- or azoxybenzenes), the evaluation of less
than two dozen chlorinated congeners is generally considered sufficient to characterize
environmental "dioxin."
The chlorinated dibenzodioxins and dibenzofurans are tricyclic aromatic compounds
with similar physical and chemical properties. Certain of the PCBs (the so-called coplanar
or mono-ortho coplanar congeners) are also structurally and conformationally similar. The
most widely studied of this general class of compounds is 2,3,7,8-tetrachlorodibenzo-p-
dioxin (TCDD). This compound, often called simply "dioxin," represents the reference
compound for this class of compounds. The structure of TCDD and several related
compounds is shown in Figure 1-1. Although sometimes confusing, the term "dioxin" is
often also used to refer to the complex mixtures of TCDD and related compounds emitted
from sources, or found in the environment or in biological samples. It can also be used to
refer to the total TCDD "equivalents" found in a sample. This concept of toxic
equivalency is discussed below.
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1.3. TOXIC EQUIVALENCY FACTORS
CDDs, CDFs, and PCBs are commonly found as complex mixtures when detected in
environmental media and biological tissues, or when measured as environmental releases
from specific sources. Humans are likely to be exposed to variable distributions of CDDs,
CDFs, and dioxin-like PCB congeners that vary by source and pathway of exposures. This
complicates the human health risk assessment that may be associated with exposures to
variable mixtures of dioxin-like compounds. In order to address this problem, the concept
of toxic equivalency has been considered and discussed by the scientific community, and
TEFs have been developed and introduced to facilitate risk assessment of exposure to
these chemical mixtures.
On the most basic level, TEFs compare the potential toxicity of each dioxin-like
compound comprising the mixture to the well-studied and understood toxicity of TCDD,
the most toxic member of the group. The background and historical perspective regarding
this procedure is described in detail in Part II, Chapter 9, Section 9.1, 9.2, and in Agency
documents (U.S. EPA, 1987; 1989a,b; 1991). This procedure involves assigning
individual TEFs to the 2,3,7,8-substituted CDD/CDF congeners and "dioxin-like" PCBs. To
accomplish this, scientists have reviewed the toxicological databases along with
considerations of chemical structure, persistence, and resistance to metabolism, and have
agreed to ascribe specific, "order of magnitude" TEFs for each dioxin-like congener relative
to TCDD, which is assigned a TEF of 1.0. The other congeners have TEF values ranging
from 1.0 to 0.00001. Thus, these TEFs are the result of scientific judgment of a panel of
experts using all of the available data and are selected to account for uncertainties in the
available data and to avoid underestimating risk. In this sense, they can be described as
TEQ = j_n(Congenerj xTEFj) + (Congener: xTEF: j+ (Congenern xTEFn j (1-1)
"public health conservative" values. To apply this TEF concept, the TEF of each congener
present in a mixture is multiplied by the respective mass concentration and the products
are summed to represent the 2,3,7,8-TCDD Toxic Equivalence (TEQ) of the mixture, as
determined by Equation (1-1):
The TEF values for PCDDs and PCDFs were originally adopted by international convention
(U.S. EPA, 1989a). Subsequent to the development of the first international TEFs for
CDD/CDFs, these values were further reviewed and/or revised and TEFs were also
developed for PCBs (Ahlborg et al., 1994; van den Berg et al., 1998). A problem arises in
that past and present quantitative exposure and risk assessments may not have clearly
identified which of three TEF schemes was used to estimate the TEQ. This reassessment
introduces a new uniform TEQ nomenclature that clearly distinguishes between the
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different TEF schemes and identifies the congener groups included in specific TEQ
calculations. The nomenclature uses the following abbreviations to designate which TEF
scheme was used in the TEQ calculation:
1. I-TEQ refers to the International TEF scheme adopted by EPA in 1989 (U.S. EPA,
1989a). See Table 1-1.
2. TEQ-WH094 refers to the 1994 WHO extension of the I-TEF scheme to include 13
dioxin-like PCBs (Ahlborg et al., 1994). See Table 1-2.
3. TEQ-WH098 refers to the 1998 WHO update to the previously established TEFs for
dioxins, furans, and dioxin-like PCBs (van den Berg et al., 1998). See Table 1-3.
The nomenclature also uses subscripts to indicate which family of compounds is
included in any specific TEQ calculation. Under this convention, the subscript D is used to
designate dioxins, the subscript F to designate furans and the subscript P to designate
PCBs. As an example, "TEQDF-WH098" would be used to describe a mixture for which
only dioxin and furan congeners were determined and where the TEQ was calculated using
the WH098 scheme. If PCBs had also been determined, the nomenclature would be
"TEQDFP-WH098." Note that the designations TEQDF-WH094 and I-TEQDF are
interchangeable, as the TEFs for dioxins and furans are the same in each scheme. Note
also that in this document, I-TEQ sometimes appears without the D and F subscripts. This
indicates that the TEQ calculation includes both dioxins and furans.
This reassessment recommends that the WH098 TEF scheme be used to assign
toxic equivalency to complex environmental mixtures for assessment and regulatory
purposes. Sections in the Health Reassessment Document, and summarized in the Risk
Characterization, describe the mode(s) of action by which dioxin-like chemicals mediate
biochemical and toxicological actions. These data provide the scientific basis for the
TEF/TEQ methodology. In its 20-year history, the approach has evolved, and decision
criteria supporting the scientific judgment and expert opinion used in assigning TEFs has
become more transparent. Numerous states, countries, and several international
organizations have evaluated and adopted this approach to evaluating complex mixtures of
dioxin and related compounds. It has become the accepted methodology, although the
need for research to explore alternative approaches is widely endorsed. Clearly, basing
risk on TCDD alone or assuming all chemicals are equally potent to TCDD is inappropriate
on the basis of available data. Although uncertainties in the use of the TEF methodology
have been identified (which are described in detail in the Health Reassessment Document,
Chapter 9, Section 9.5), one must examine the use of this method in the broader context
of the need to evaluate the potential public health impact of complex mixtures of
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persistent, bioaccumulative chemicals. It can be generally concluded that the use of TEF
methodology for evaluating complex mixtures of dioxin-like compounds decreases the
overall uncertainties in the risk assessment process as compared to alternative
approaches. Use of the latest consensus values for TEFs assures that the most recent
scientific information informs this "useful, interim approach" (U.S. EPA, 1989a; Kutz et
al., 1990) to dealing with complex environmental mixtures of dioxin-like compounds. As
stated by the U.S. EPA Science Advisory Board (U.S. EPA, 1995), "The use of the TEFs
as a basis for developing an overall index of public health risk is clearly justifiable, but its
practical application depends on the reliability of the TEFs and the availability of
representative and reliable exposure data." EPA will continue to work with the
international scientific community to update these TEF values to assure that the most up-
to-date and reliable data are used in their derivation and to evaluate their use on a periodic
basis.
A chemical is assigned a TEF value based on all the available data comparing the
chemical to either TCDD or PCB 126. In addition, there are weighting criteria that place
more emphasis on chronic and subchronic studies examining toxic endpoints (van den
Berg et al., 1998). There is a broad range in the quantity and quality of the data available
for individual congeners. For example, the TEF for PCB 1 26 is based on over 60 in vivo
endpoints examining responses as diverse as enzyme induction, developmental toxicity,
immunotoxicity, hepatic toxicity, alterations in hormones and tumor promotion, while the
TEF for 3,4,4',5-tetrachlorobiphenyl (PCB 81) is based on in vitro CYP1A induction and
QSAR calculations. Fortunately, PCB 81 does not significantly contribute to human TEQ
exposures. There are 5 congeners that contribute approximately 80% of the total TEQ in
humans: 2,3,7,8-TCDD, 1,2,3,7,8-PCDD, 1,2,3,6,7,8-HxCDD, 2,3,4,7,8-PCDF, and PCB
126 (See Part I, Volume 2 and Section 4.4.3 of this document). With the exception of
1,2,3,6,7,8-HxCDD, the TEFs for these chemicals are based on a number of different
endpoints from multiple studies performed in different laboratories. The TEF for
1,2,3,6,7,8-HxCDD is based on a two-year bioassay in which rats were exposed to a
mixture of 1,2,3,6,7,8-HxCDD and 1,2,3,7,8,9-HxCDD. The TEFs for 2,3,4,7,8-PCDF and
PCB 1 26 are similar to the mean REP value for all in vivo endpoints and are similar to their
REPs for tumor promotion. The TEF for 1 2378-PCDD is based largely on its REP for tumor
promotion in rats. From these data, it is clear that the chemicals that contribute
approximately 80% to the total human TEQ are well studied and the assigned TEFs
provide reasonable estimates of the relative potency of these chemicals. In contrast,
while there are some chemicals in the TEF methodology which have minimal data sets to
reliably assess their relative potency, these chemicals do not contribute substantially to
the human blood TEQ.
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The ability of the TEF methodology to predict the biological effects of mixtures
containing dioxin-like chemicals has been evaluated in a number of experimental systems.
These studies generally demonstrate that the assumption of additivity provides a
reasonable estimate of the dioxin-like potential of a mixture (described in the Health
Reassessment Document, Chapter 9, Section 9.4). In addition, there are examples of non-
additive interactions between dioxins and non-dioxins. Both greater than additive and less
than additive interactions have been observed in these studies. In general the non-additive
interactions between the dioxins and non-dioxins have been observed at doses that are
considerably higher than present background human exposures.
There are a number of natural chemicals that bind and activate the AhR and induce
some dioxin-like effects. It has been proposed by some scientists that these chemicals
contribute significantly to the total TEQ exposures and that these exposures far out weigh
those from PCDDs, PCDFs and PCBs (Safe, 1995). While this hypothesis is intriguing,
there are several limitations to these analyses. The in vivo data on the natural aromatic
hydrocarbon receptor (AhR) ligands is limited to enzyme induction and a single
developmental study. Few, if any, toxicology studies demonstrating clear dioxin-like
toxicities have been published. The natural AhR ligands are rapidly metabolized and result
in both transient tissue concentrations and transient effects. The natural ligands also have
significant biological effects that are independent of the AhR and it is not clear as to the
role of the AhR in the biological effects of these chemicals. Clearly this issue requires
further research in order to better understand the relative potential health effect of dioxin
and related chemicals as compared to natural AhR ligands.
One of the limitations of the use of the TEF methodology in risk assessment of
complex environmental mixtures is that the risk from non-dioxin-like chemicals is not
evaluated in concert with that of dioxin-like chemicals. Another limition of the TEF
methodology is their application to non-biological samples. The fate and distribution of
PCDDs, PCDFs and PCBs are not necessarily related to their TEF. Thus, the use of the
TEF for non-biological media must be done cautiously. Future approaches to the
assessment of environmental mixtures should focus on the development of methods that
will allow risks to be predicted when multiple mechanisms are present from a variety of
contaminants.
1.4. CONTENTS OF THIS VOLUME
The purpose of this volume is to: (1) summarize information on the physical and
chemical properties of dioxin-like compounds; (2) provide an overview of the levels of
dioxin-like compounds found in environmental media and food; (3) estimate background
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exposures to dioxin-like compounds for the general population of the United States; (4)
provide information on the potential for elevated exposures among certain subpopulations
of the United States; and (5) summarize the evidence that suggests a downward trend in
dioxin-like concentrations in the environment, as well as trends in exposure. These topics
are organized in this volume as follows:
Chapter 2 - Physical and Chemical Properties and Fate
This chapter summarizes available information regarding the physical and chemical
properties and fate of the dioxin-like compounds. Physical/ chemical properties
addressed in Chapter 2 include melting point, water solubility, vapor pressure,
Henry's Law constant, octanol/water partition coefficient, organic carbon partition
coefficient, and photochemical quantum yield. Fate and transport processes
addressed include photolysis, oxidation, hydrolysis, biodegradation, volatilization,
and sorption. Biologically-mediated transport properties (i.e., bioconcentration,
plant uptake, etc.) are also addressed in this volume. (These properties are also
addressed in Volume 3: Site-Specific Assessment Procedures.) These data were
compiled from a review of the current scientific literature on dioxin-like compounds.
Chapter 3 - Levels of CDD, CDF, and PCB Congeners
This chapter provides an overview of the concentrations at which dioxin-like
compounds have been found in the environment and food based on data presented
in the recent published literature. Data are presented for air, soil, sediment, water,
and foods. For foods, the general focus is on foods with relatively high fat content
(i.e., beef, pork, poultry and eggs, milk and dairy products, fish, and vegetable fats)
because these items are most likely to contain dioxins and related compounds.
Data from Government-sponsored monitoring studies and studies reported in the
peer-reviewed literature are used in this chapter to estimate U.S. background
concentrations of dioxin-like compounds in the various environmental media and
foods. In order to represent current exposure concentrations, data used for the
calculation of background media levels are based on studies published in the late
1980s and 1990s, but primarily in the 1990s. The studies used for the estimation
of background concentrations were also chosen on the basis of credibility and
representativeness. CDD/CDF profiles for environmental media are also presented
in this chapter.
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Chapter 4 - Human Exposures to CDD/CDF, and PCB Congeners
This chapter assesses background exposures to the dioxin-like compounds among
the general population of the United States. Recent assessments of background
exposures cited in the scientific literature are summarized, and background
exposure estimates, based on the data presented in Chapter 3, are presented. Data
on the concentrations of dioxin-like compounds in human tissue (i.e., adipose
tissue, blood, and human breast milk) are also presented. Two methods are used
in to estimate background daily intake of dioxin-like compounds. One method
estimates background exposures based on pharmacokinetic modeling using the
human tissue data. The other derives background exposure estimates from dietary
intake and contact with other media containing dioxin-like compounds. The
primary focus of this chapter is background exposure among the general
population.
Chapter 5 - Potentially Elevated Populations
This chapter focuses on elevated exposures that may occur among the general
population from dietary habits such as breast feeding or high rates of fish
ingestion, increased environmental levels of dioxin-like compounds from localized
sources, or cigarette smoking. This chapter does not, however, address
occupational or accidental exposure. Epidemiological studies that have evaluated
whether elevated dioxin exposure has occurred to certain workers in the chemical
industry, members of the Air Force who worked with Agent Orange, and residents
of Seveso, Italy, who were exposed as a result of a pesticide plant explosion are
fully discussed in the Epidemiology Chapter of the Dioxin Health Reassessment
Document.
Chapter 6 - Temporal Trends
This chapter describes trends in the levels of dioxin-like compounds that have been
observed in various environmental media and foods, as well as evidence of
downward trends in exposure to dioxin-like compounds in humans. The downward
trend in human exposure is supported by a modeling exercise that reconstructs the
most likely past doses of dioxin-like compounds contributing to observed body
burdens. Reviews of several studies and the modeling exercise are followed by
several key observations with regard to temporal trends of dioxin-like compounds.
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1.5. SUMMARY OF FINDINGS IN THIS VOLUME
1.5.1. Physical and Chemical Properties and Fate
The physical/chemical properties of individual dioxin congeners vary and the various
congeners behave differently in the environment. For example, the relative mix of
congeners released from a stack cannot be assumed to remain constant during transport
through the atmosphere and deposition to various media. Therefore, for purposes of
environmental fate modeling, it is important to use the individual CDD/CDF and PCB
congeners values, rather than TEQs. Estimates of environmental releases are presented in
Volume 1. Full congener-specific release rates for most sources are provided in an
electronic database which is available as a companion to this document (Database of
Sources of Environmental Releases of Dioxin-Like Compounds in the United States
(EPA/600/C-01/01 2). In Volume 3, site-specific procedures are provided for estimating
the impact of emissions on local populations and this section emphasizes that congener
specific emission values should be used in modeling their environmental fate. The
following paragraphs provide a summary of the fate of dioxin-like compounds.
Dioxin-like compounds are widely distributed in the environment as a result of a
number of physical and biological processes. The dioxin-like compounds are essentially
insoluble in water, generally classified as semivolatile, and tend to bioaccumulate in
animals. Some evidence has shown that these compounds can degrade in the
environment, but in general they are considered very persistent and relatively immobile in
soils and sediments. These compounds are transported through the atmosphere as vapors
or attached to airborne particulates and can be deposited on soils, plants, or other
surfaces (by wet or dry deposition). The dioxin-like compounds enter water bodies
primarily via direct deposition from the atmosphere, or by surface runoff and erosion.
From soils, these compounds can reenter the atmosphere either as resuspended soil
particles or as vapors. In water, they can be resuspended into the water column from
sediments, volatilized out of the surface waters into the atmosphere or become buried in
deeper sediments. Immobile sediments appear to serve as permanent sinks for the dioxin-
like compounds. Though not always considered an environmental compartment, these
compounds are also found in anthropogenic materials (such as PCP) and have the potential
to be released from these materials into the broader environment.
Atmospheric transport and deposition of the dioxin-like compounds are a primary
means of dispersal of these compounds throughout the environment. The dioxin-like
compounds can be measured in wet and dry deposition in most locations including remote
areas. Numerous studies have shown that they are commonly found in soils throughout
the world. Industrialized countries tend to show similar elevated concentrations in soil,
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and detectable levels have been found in nonindustrialized countries. The only
satisfactory explanation available for this distribution is air transport and deposition.
Finally, by analogy these compounds would be expected to behave similarly to other
compounds with similar properties, and this mechanism of global distribution is becoming
widely accepted for a variety of persistent organic compounds.
The two primary pathways for the dioxin-like compounds to enter the ecological
food chains and human diet are air-to-plant-to-animal and water/sediment-to-fish.
Vegetation receives these compounds via atmospheric deposition in the vapor and particle
phases. The compounds are retained on plant surfaces and bioaccumulated in the fatty
tissues of animals that feed on these plants. Vapor phase transfers onto vegetation have
been experimentally shown to dominate the air-to-plant pathway for the dioxin-like
compounds, particularly for the lower chlorinated congeners. In the aquatic food chain,
dioxins enter water systems via direct discharge or deposition and runoff from
watersheds. Fish accumulate these compounds through their direct contact with water,
suspended particles, bottom sediments, and through their consumption of aquatic
organisms. Although these two pathways are thought to normally dominate contribution
to the commercial food supply, others can also be important. Elevated dioxin levels in
cattle resulting from animal contact with PCP-treated wood have been documented by the
U.S. Department of Agriculture. Animal feed contamination episodes have led to
elevations of dioxins in poultry in the United States, milk in Germany, and meat/dairy
products in Belgium.
1.5.2. Environmental Media and Food Concentrations
Background levels of dioxin-like compounds in various environmental media
including food are presented in Table 1-4 in terms of means, variability and sample sizes
used to support the background estimates.
Estimates for background levels of dioxin-like compounds in environmental media
are based on a variety of studies conducted at different locations in North America. Of
the studies available for this compilation, only those conducted in locations representing
"background" were selected. The amount and representativeness of the data vary, but in
general these data were derived from studies that were not designed to estimate national
background means. The environmental media concentrations were similar to studies in
Western Europe. These data are the best available for comparing with site-specific values.
Because of the limited number of locations examined, it is not known if these estimates
adequately capture the full national variability. As new data are collected, these ranges
are likely to be expanded and refined. The limited data on dioxin-like PCBs in
environmental media are summarized in this document (Chapter 3).
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Estimates for levels of dioxin-like compounds in food are based on data from a
variety of studies conducted in North America. Beef, pork, and poultry were derived from
statistically based national surveys. Milk estimates were derived from a survey of a
nationwide milk sampling network. Dairy estimates were derived from milk fat
concentrations, coupled with appropriate assumptions for the amount of milk fat in dairy
products. The background egg concentrations were based on an analysis of 1 5 egg
samples collected from retail stores in 8 states (CA, OH, GA, NY, PA, OR, MN, WS; 2
samples/state except one in OR), where each sample was a composite of 24 individual
eggs (i.e., 15 samples represented 360 eggs). The fish data, as discussed below, were
derived from multiple studies with samples collected both directly from water bodies and
from retail outlets. All fish concentrations were expressed on the basis of fresh weight in
edible tissue. As with other environmental media, food levels found in the United States
are similar to levels found in Europe.
The procedure to evaluate background fish exposures emphasizes the use of both
species-specific consumption rates and species-specific concentrations. EPA's National
Bioaccumulation Study (U.S. EPA, 1992) provides some species-specific information on
freshwater/estuarine fish caught in the wild at various locations in the United States.
Additional species-specific data on store bought fish are available from studies conducted
by the Food and Drug Administration during the mid to Iatter1990s (Jensen and Bolger,
2000; Jensen et al., 2000). An important aspect of the U.S. Food and Drug
Administration (FDA) studies is that they include data on store-bought catfish, tuna,
shellfish, and salmon which are some of the most highly consumed species. Accordingly,
the data used to characterize CDD/CDF fish levels are much improved over previous
estimates with over 300 individual samples and good representation of the most highly
consumed species. However, the levels of dioxins in fish remain more uncertain than the
other foods. The compilation of data from different studies still lacks the geographic
coverage and statistical power of the other food surveys. The EPA and FDA studies did
not address dioxin-like PCBs, rather these are based on a much smaller data set derived
from the open literature. Also, the estimates of dioxin intake resulting from fish
consumption do not include consumption of fish oils. Currently insufficient data are
available to support estimates of dioxin intake from direct fish oil consumption.
The general population dioxin intake calculations used in this document are a
function of both consumption rate and dioxin concentration in food. The concentration
data used in this document were measured in raw foods. Therefore, if cooking
significantly alters the dioxin concentration in consumed portions it must be accounted for
in estimating dioxin intake. This issue has been examined in a number of studies which
measured the effects of cooking on the levels of CDDs, CDFs and PCBs in foods (see
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Chapter 3, Section 3.7.5). These studies have a range of results depending on food type
and cooking method. Most of the cooking experiments suggested that cooking reduces
the total amount of dioxins in food but causes relatively little change in its concentration.
Although some cooking experiments have shown increases and others have shown
decreases in dioxin concentrations, the relative prevalence of these impacts have not been
established. Therefore given that most experiments show little change and that others
show change in both directions, the most reasonable assumption that can be made from
the existing data is that dioxin concentration in uncooked food is a reasonable surrogate
for dioxin concentration in cooked food.
Some evidence from Europe suggests that during the 1990s a decline has
occurred in concentrations of dioxins and furans in food products, particularly dairy
products (see Chapter 6, Section 6.5). For example, the United Kingdom's Ministry of
Agriculture, Fisheries, and Food (MAFF) collected milk samples in 1990 and again from
similar locations in 1 995. In 1 990, the I-TEQDF ranged from 1.1 to 3.3 ppt, while the
1 995 I-TEQDF ranged from 0.7 to 1.4. In Germany, a sampling of 1 20 dairy products in
1994 found I-TEQDF concentrations that were 25% lower than a similar sampling program
in 1990. Liem et al. (2000) reports on a European cooperative study coordinated by the
National Institute of Public Health and the Environment in the Netherlands, and the
Swedish National Food Administration. Ten countries supplied data on food
concentrations, food consumption patterns, and other data used to evaluate exposure to
dioxins in Europe. Some of the data suggested reductions in concentrations over time,
but the available information was insufficient to draw general conclusions. No systematic
study of temporal trends in dioxin levels in food has been conducted in the United States.
Although not statistically based, one U.S. study examined dioxin levels in 14 preserved
food samples from various decades in the twentieth century (Winters et al., 1998). It was
found that meat samples of the 1950s through the 1970s had concentrations that were
2-3 times higher for the CDD/CDF TEQs and about 10 times higher for the PCB TEQs, as
compared to current meat concentrations.
1.5.3. Background Exposures
The average CDD/CDF/PCB tissue level for the general adult U.S. population
appears to be declining, and the best estimate of current (late 1990s) levels is 25 ppt
(TEQDFP-WHO98f lipid basis).
The tissue samples collected in North America in the late 1980s and early 1990s
showed an average TEQDFP-WH098 level of about 55 pg/g lipid. This finding is supported
by a number of studies which measured dioxin levels in adipose, blood, and human milk,
all conducted in North America. The number of people in most of these studies, however,
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is relatively small and the participants were not statistically selected in ways that assure
their representativeness of the general U.S. adult population. One study, the 1987
National Human Adipose Tissue Survey (NHATS), involved over 800 individuals and
provided broad geographic coverage, but did not address coplanar PCBs. Similar tissue
levels of these compounds have been measured in Europe and Japan during similar time
periods.
Because dioxin levels in the environment have been declining since the 1970s (see
trends discussion in Chapter 6), it is reasonable to expect that levels in food, human
intake, and ultimately human tissue have also declined over this period. The changes in
tissue levels are likely to lag the decline seen in environmental levels, and the changes in
tissue levels cannot be assumed to occur proportionally with declines in environmental
levels. CDC (2000) summarized levels of CDDs, CDFs, and PCBs in human blood
collected during the time period 1995 to 1997. The individuals sampled were all U.S.
residents with no known exposures to dioxin other than normal background. The blood
was collected from 316 individuals in six different locations with an age range of 20 to 70
years. While the samples in this data set were not collected in a manner that can be
considered statistically representative of the national population and lack wide geographic
coverage, they are judged to provide a better indication of current tissue levels in the
United States than the earlier data. PCBs 105, 11 8, and 1 56 are missing from the blood
data for the comparison populations reported by CDC (2000). These congeners account
for 62% of the total PCB TEQ estimated in the early 1990s. Assuming that the missing
congeners from the CDC study data contribute the same proportion to the total PCB TEQ
as in earlier data, they would increase our estimate of current body burdens by another
3.3 pg TEQ/g lipid for a total PCB TEQ of 5.3 pg/g lipid and a total of 25.4 pg TEQDFP-
WH098 /g lipid. A summary of the CDC (2000) data is shown in Table 1-5.
This finding regarding a current tissue level of 25.4 pg/g lipid TEQDFP-WH098 is
further supported by the observation that this mean tissue level is consistent with our best
estimate of current adult intake, i.e., 66 pg WH098-TEQDFP/d. Using this intake in a one-
compartment, steady-state pharmacokinetic model yields a tissue level estimate of about
11.2 pg TEQ/g lipid (assumes TEQDFP has an effective half-life of 7.1 yr, 80% of ingested
dioxin is absorbed into the body, and lipid weight is 25% of the adult assumed body
weight of 70 kg, or 17.5 kg). Because intake rates appear to have declined in recent
years and steady-state is not likely to have been achieved, it is reasonable to observe
higher measured tissue levels, such as the 25.4 pg TEQ/g lipid that was observed, than
predicted by the model.
Characterizing national background levels of dioxins in tissues is uncertain because
the current data cannot be considered statistically representative of the general
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population. It is also complicated by the fact that tissue levels are a function of both age
and birth year. Because intake levels have varied over time, the accumulation of dioxins in
a person who turned 50 years old in 1990 is different than in a person who turned 50 in
2000. Future studies should help address these uncertainties. The National Health and
Nutrition Examination Survey (NHANES) began a new national survey in 1999 that will
measure blood levels of CDDs, CDFs, and PCBs 126, 77, 169, and 81 in about 1,700
people per year (see http:www.cdc.gov/nchs/nhanes.htm). The survey is conducted at
15 different locations per year and is designed to select individuals statistically
representative of the civilian U.S. population in terms of age, race, and ethnicity. These
new data should provide a much better basis for estimating national background tissue
levels and evaluating trends than the currently available data.
Intake Estimates
Adult daily intakes of CDD/CDFs and dioxin-like PCBs are estimated to average 43
and 23 pg TEQDFP-Wt-fO9g/day, respectively, for a total intake of 66 pg/day TEQDFP-WHO98.
Daily intake is estimated by combining exposure media concentrations (food, soil, air) with
contact rates (ingestion, inhalation). Table 1-6 summarizes the media concentrations,
contact rates and resulting intake estimates.
The intake estimate is supported by an extensive database on food consumption
rates and estimates of dioxin-like compounds in food (as discussed above).
Pharmacokinetic (PK) modeling provides further support for the intake estimates. Applying
a simple steady-state PK model to an adult average blood level of 25 ppt TEQDFP-WH098
(on a lipid basis) yields a daily intake of 146 pg TEQDFP-WH098/day (assumes TEQDFP has
an effective half-life of 7.1 yr, 80% of ingested dioxin is absorbed into the body, and lipid
weight is 25% of the adult assumed body weight of 70 kg, or 17.5 kg). This PK-modeled
CDD/CDF/PCB intake estimate is about 2.2 times higher than the direct intake estimate of
66 pg TEQDFP-WH098/day. This difference is to be expected with this application of a
simple steady-state PK model to current average adipose tissue concentrations. Current
adult tissue levels reflect intakes from past exposure levels that are thought to be higher
than current levels (see Chapter 6). Because the direction and magnitude of the difference
in intake estimates between the two approaches are understood, the PK-derived value is
judged supportive of the pathway-derived estimate. It should be recognized, however,
that the pathway-derived value will underestimate exposure if it has failed to capture all
significant exposure pathways.
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Variability in Intake Levels
CDD/CDF and dioxin-like PCB intakes for the general population may extend to
levels at least three times higher than the mean. Variability in general population exposure
is primarily the result of the differences in dietary choices that individuals make. These
are differences in both quantity and types of food consumed. An increased background
exposure can result from either a diet that favors consumption of foods high in dioxin
content or a diet that is disproportionately high in overall consumption of animal fats.
The best data available to determine the variability of total fat consumption comes
from several analyses of the Bogalusa Heart Study (Cresanta et al., 1988; Nicklas et al.,
1993; Nicklas et al., 1995; Nicklas et al., 1995; Frank et al., 1986). These data show
that the 95th percentile of total fat consumption is about twice the mean and the 99th
percentile is approximately three times the mean. For a diet which has a broad
distribution of animal fats (as does the typical U.S. diet), this same distribution can be
assumed for dioxin intake.
Although body burden data cannot be assumed to be perfectly representative of
current intakes (because they reflect past exposures as well as current ones), they also
provide some support for this finding. This is based on the observation that the 95th
percentile blood level in the CDC (2000) study was almost twice the mean level.
Intakes of CDD/CDFs and dioxin-like PCBs are over three times higher for a young
child as compared to that of an adult, on a body weight basis. This is based on
combining age-specific food consumption rate and average food concentrations, as was
done above for adult intake estimates (see Table 1-7).
Only four of the 17 toxic CDD/CDF congeners and one of the 11 toxic PCBs
account for most of the toxicity in human tissue concentrations: 2,3,7,8-TCDD,
1,2,3,7,8-PCDD, 1,2,3,6,7,8-HxCDD, 2,3,4,7,8-PCDF, and PCB 126. This finding is
derived directly from the data described earlier on human tissue levels and is supported by
intake estimations indicating that these congeners are also the primary contributors to
dietary dose. These five compounds make up about 80% of the total WH098-TEQ tissue
level.
1.5.4. Potentially Highly Exposed Populations or Developmental Stages
As discussed earlier, background exposures to dioxin-like compounds may extend
to levels at least three times higher than the mean. This upper range is assumed to result
from the normal variability of diet and human behaviors. Exposures from local elevated
sources or exposures resulting from unique diets would be in addition to this background
variability. Such elevated exposures may occur in small segments of the population such
as individuals living near discrete local sources. Nursing infants represent a special case:
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for a limited portion of their lives, these individuals may have elevated exposures on a
body weight basis when compared with non-nursing infants and adults.
CDD/CDF contamination incidents involving the commercial food supply have
occurred in the United States and other countries. For example, in the United States,
contaminated ball clay was used as an anti-caking agent in soybean meal and resulted in
elevated dioxin levels in some poultry and catfish. This incident, which occurred in 1998,
involved less than 5% of the national poultry production and has since been eliminated.
Elevated dioxin levels have also been observed in a few beef and dairy animals where the
contamination was associated with contact with pentachlorophenol-treated wood.
Evidence of this kind of elevated exposure was not detected in the national beef survey.
Consequently its occurrence is likely to be low, but it has not been determined. These
incidents may have led to small increases in dioxin exposure to the general population.
However, it is unlikely that such incidents have led to disproportionate exposures to
populations living near where these incidents have occurred, because in the United States,
meat and dairy products are highly distributed on a national scale. If contamination events
were to occur in foods that are predominantly distributed on a local or regional scale, then
such events could lead to highly exposed local populations.
Elevated exposures associated with the workplace or industrial accidents have also
been documented. U.S. workers in certain segments of the chemical industry had
elevated levels of TCDD exposure, with some tissue measurements in the thousands of
ppt TCDD. There is no clear evidence that elevated exposures are currently occurring
among United States workers. Documented examples of past exposures for other groups
include certain Air Force personnel exposed to Agent Orange during the Vietnam War and
people exposed as a result of industrial accidents in Europe and Asia.
Consumption of breast milk by nursing infants leads to higher levels of exposure
and higher body burdens of dioxins during early years of life as compared with non-
nursing infants. Three German studies have compared dioxin levels in infants who have
been breast-fed with those who have been formula-fed. All have shown elevations in the
concentrations of dioxins in infants being breast-fed. Collectively these studies included
99 infants and found that blood levels (in units of pg TEQDF-WH098/g lipid - i.e., dioxin-like
PCBs not included) in infants aged 4-12 months were generally more than 20 in nursing
infants and less than 5 in formula fed infants. The most comprehensive of these studies
was by Abraham et al. (2000) who reported on 80 breast-fed infants. In that study, the
median concentration was 25.3 pg/g TEQDF-WH098. Six of the nursing infants in the
Abraham et al. (2000) study had lipid levels greater than 50 pg/g TEQDF-WH098 and the
maximum was 107 pg/g TEQDF-WH098. Five of these six children were from a region
where mother's milk was found to be elevated due to regional contamination by a copper
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recycling plant. These data suggest that breast-fed infants could have body burdens more
than five times higher than formula-fed infants, depending on length of breast-feeding,
dioxin concentrations in mother's milk, and other factors.
U.S. dioxin intakes from nursing were calculated using time dependent values for
breast milk concentrations, consumption rates and body weights. These calculations
estimated an intake immediately after birth of 242 pg TEQDFP-WH098/kg/day. This dropped
to 22 pg TEQDFP-WH098/kg/day after 12 months of nursing. The average intake over one
year of nursing was calculated to be 92 pg TEQDFP-WH098/kg/day. The cumulative intake
for a one year nursing scenario represented about 12% of the total lifetime cumulative
intake (see Chapter 5, Section 5.2 for details on these calculations).
The CDC (1997) reported that in 1995, 55% of all babies experience some breast
feeding, with about half of those breast feeding beyond 5 months. The average duration
of breast feeding was 28.7 weeks. In a policy statement, the American Academy of
Pediatrics (1997) stated that exclusive breast feeding is ideal nutrition and sufficient to
support optimal growth and development for 6 months after birth. They recommended
that breast feeding continue for at least 1 2 months, and thereafter for as long as mutually
desired.
To better evaluate the impact of nursing on infants, changes in body burden were
calculated using a one-compartment, first-order pharmacokinetic model. Changes in TEQ
tissue concentration over time were modeled for a variety of nursing scenarios: formula
only, 6 weeks nursing, 6 months nursing, and one year. These scenarios reasonably
capture the range of current nursing practice. This modeling effort required using the
intake assumptions described earlier and a variety of additional assumptions including: the
fraction of the oral dose which is absorbed into the body, changes in body weight over
time, and changes in body fat fraction over time. Assumptions were also made about
changes in the biological half-life of dioxins as a function of body fat fraction. For the
infant, the half-life was less than one year, and during adulthood the half-life increased as
the fraction of body fat increased. The short half-life at birth was based on a study by
Kreuzer et al. (1997) and the longer half-life during the later years of life, when body fat
fraction increased, was based on a model presented in Michalek et al. (1996). The
complete set of input values are listed in Chapter 5, Section 5.2.
The modeling results in terms of changes in lipid concentrations and body burdens
as a function of age are shown in Figure 1-2. Some key observations include:
• For the 6 and 1 2 month nursing scenarios, lipid concentrations peaked at around 4
months at about 46 ppt TEQDFP-WH098. The formula-fed infants peaked at less
than 10 ppt after the first year.
DRAFT-DO NOT QUOTE OR CITE 1-19 December 2003
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• In all four scenarios, the lipid concentrations merged at about 10 years of age, at a
concentration of about 13 ppt TEQDFP-WH098. Lipid and body burdens declined
slightly from age 10 to about age 20, and then rose gradually through adulthood.
This rise was due to the increase in half-life with age. At age 70, the modeled lipid
and body burden concentrations were 13 ppt TEQDFP-WH098 lipid and 5 ppt TEQDFP-
WH098.whole body weight.
A sensitivity analysis was performed to test the assumptions about changes in
breast milk concentrations during lactation and changes in half-life over time. In this
analysis, breast milk concentrations were held steady at 25 pg TEQDFP-WH098/g lipid for a
6-month nursing scenario, and the half-life of dioxins in the body remained steady at 7.1
years from birth until 70 years of age. With these two changes, the maximum infant lipid
concentration increased from 46 to 70 pg TEQDFP-WH098/g lipid. The major impact of a
steady half-life assumption, instead of one which increased with increasing body lipid
fractions in the aging adult, was that the lipid concentrations stabilized at about 8 pg
TEQDFP-WH098/g lipid in the adult, instead of rising to 1 3 pg TEQDFP-WH098/g lipid at age
70.
The above analysis indicates that the average annual infant intake resulting from
one year of nursing, 92 pg TEQDFP-WH098/kg/day, significantly exceeds the currently
estimated adult intake of 1 pg TEQDFP-WH098/kg/day. The impact of nursing on infant
body burdens, however, is much less, i.e. infant body burdens will not exceed adult body
burdens by 92 times. Rather, the modeling suggests that peak infant body burdens are
only about 2 times current adult body burdens (46 vs 25 pg TEQDFP-WH098/g lipid). The
reduced body burden impacts in nursing infants (relative to the intake) is thought to be
due to the rapidly expanding infant body weight and lipid volume and the possibly faster
elimination rate in infants. Impacts to nursing infants should decline in the future if, as
discussed earlier, general population exposures decline.
Consumption of fish, meat, or dairy products containing elevated levels of dioxins
and dioxin-like PCBs can lead to elevated exposures in comparison with the general
population. Most people eat some fish from multiple sources, both fresh and salt water.
The estimated dioxin concentrations in these fish and the typical rates of consumption are
included in the mean background calculation of exposure. People who consume large
quantities of fish at estimated contamination levels may have elevated exposures. These
kinds of exposures are addressed within the estimates of variability of background and are
not considered to result in highly exposed populations. If individuals obtain their fish from
areas where the concentration of dioxin-like chemicals in the fish is elevated, they may
constitute a highly exposed subpopulation. Although this scenario seems reasonable, very
DRAFT-DO NOT QUOTE OR CITE 1-20 December 2003
-------
little supporting data could be found for such a highly exposed subpopulation in the United
States. One study measuring dioxin-like compounds in the blood of sport fishers in the
Great Lakes area showed elevations over mean background, but within the range of
normal variability. Another study measuring 90 PCB congeners (seven of which were
dioxin-like PCBs, although PCB 126 was not measured) in the blood of sport fishers
consuming high amounts of fish caught from Lake Michigan (>26 pounds of sport fish/yr)
did, however, show significant elevations of PCBs in their blood as compared to a control
population (individuals consuming < 6 pounds of sport fish/yr). The average total
concentration of PCBs in the blood of these sport fishers was over three times higher than
that of the control population. Similarly, elevated levels of coplanar PCBs have been
measured in the blood of fishers on the north shore of the Gulf of the St. Lawrence River
who consume large amounts of seafood. Elevated CDD/CDF levels in human blood have
been measured in Baltic fishermen. For further details on these studies see Chapter 5.
High exposures to dioxin-like compounds as a result of consuming meat and dairy
products would most likely occur in situations where individuals consume large quantities
of these foods and the level of these compounds is elevated. Most people eat meat and
dairy products from multiple sources and, even if large quantities are consumed, they are
not likely to have unusually high exposures. Individuals who raise their own livestock for
basic subsistence have the potential for higher exposures if local levels of dioxin-like
compounds are high. One study in the United States showed elevated levels in chicken
eggs near a contaminated soil site. European studies at several sites have shown elevated
CDD/CDF levels in milk and other animal products near combustion sources, and some of
these have also documented elevations in the levels of dioxin-like compounds in blood
from the families consuming their home products.
1.5.5. Temporal Trends Information
Some general observations can be made about changes in levels of dioxin-like
compounds in the environment over time. These are discussed below and summarized in
Table 1-8.
Concentrations of CDD/CDFs and PCBs in the U.S. environment were consistently
low prior to the 1930s. Then, concentrations rose steadily until about 1970. At that
time, the trend reversed and the concentrations began to decline. That trend has
continued to the present. The most compelling supportive evidence of this trend for the
CDD/Fs and PCBs comes from dated sediment core studies. Sediment concentrations in
these studies are generally assumed to be an indicator of the rate of atmospheric
deposition. CDD/F and PCB concentrations in sediments began to increase around the
1930s, and continued to increase until about 1970. Decreases began in 1970 and have
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continued to the time of the most recent sediment samples (about 1990). Sediment data
from 20 U.S. lakes and rivers from seven separate research efforts consistently support
this trend. Additionally, sediment studies in lakes located in several European countries
have shown similar trends.
It is reasonable to assume that sediment core trends should be driven by a similar
trend in emissions to the environment. The period of increase generally matches the time
when a variety of industrial activities began rising and the period of decline appears to
correspond with growth in pollution abatement. Many of these abatement efforts should
have resulted in decreases in dioxin emissions, i.e. elimination of most open burning,
particulate controls on combustors, phase out of leaded gas, and bans on PCBs, 2,4,5-T,
hexachlorophene, and restrictions on use of pentachlorophenol. Also, the national source
inventory of this assessment documented a significant decline in emissions from the late
1980s to the mid-1990s. Further evidence of a decline in CDD/F levels in recent years is
emerging from data, primarily from Europe, showing declines in foods and human tissues.
In addition to the congener specific PCB data discussed earlier, a wealth of data on
total PCBs and aroclor mixtures exist which also supports these trends. It is reasonable to
assume that the trends for dioxin-like PCBs are similar to those for PCBs as a class
because the predominant source of dioxin-like PCBs is the general production of PCBs in
aroclor mixtures. PCBs were intentionally manufactured in large quantities from 1929
until production was banned in 1977. U.S. production peaked in 1970, with a volume of
39,000 metric tons. Further support is derived from data showing declining levels of total
PCBs in Great Lakes sediments and biota during the 1970s and 1980s. These studies
indicate, however, that during the 1990s the decline is slowing and may be leveling off.
Past human exposures to dioxins were most likely higher than current estimates.
This is supported by a study which applied a non-steady state pharmacokinetic model to
data on background U.S. tissue levels of 2,3,7,8-TCDD from the 1970s and 80s. Various
possible intake histories (pg/kg-day over time) were tested to see which best-fit the data.
An assumption of a constant dose over time resulted in a poor fit to the data. The "best-
fit" (statistically derived) to the data was found when the dose, like the sediment core
trends, rose through the 60s into the 70s, and declined to low current levels. Some
additional support for this finding comes from a limited study of preserved meat samples
from several decades in the twentieth century. One sample, from before 1910, showed
very low concentrations of dioxins and coplanar PCBs. Thirteen other samples, from the
1940s until the early 1980s, consistently showed elevated levels of all dioxin-like
compounds as compared to food surveys conducted during the 1990s.
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compounds. Part 2: Health assessment for 2,3,7,8-tetrachlorodibenzo-p-dioxin
(TCDD) and related compounds. National Center for Environmental Assessment,
Office of Research and Development. EPA/600/P-00/001 Be.
U.S. Environmental Protection Agency (2000c). Exposure and human health
reassessment of 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) and related
compounds. Part 3: Dioxin: Integrated summary and risk characterization for
2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) and related compounds. Washington,
DC: National Center for Environmental Assessment, Office of Research and
Development. EPA/600/P-00/001 Bf.
Van den Berg, M, L. Birnbaum, A.T.C. Bosveld, B. Brunstrom, P. Cook, M. Feeley, J.P.
Giesy, A. Hanberg, R. Hasegawa, S.W. Kennedy, T. Kubiak, J.C. Larsen, F.X.R.
van Leeuwen, A.K.D. Liem, C. Nolt, R.E. Peterson, L. Poellinger, S. Safe, D.
Schrenk, D. Tillitt, M. Tysklind, M. Younes, F. Warn, T. Zacharewski (1998)
Toxic equivalency factors (TEFs) for PCBs, PCDDs, PCDFs for humans and wildlife.
Environmental Health Perspectives 106: 775-792.
Vartiainen, T.; Mannio, J.; Korhonen, M.; Kinnunen, K.; Strandman, T. (1995) Levels of
PCDDs and PCDFs in dated sediment lake sediments in subarctic Finland.
Organohalogen Compounds. 24:369-372.
Winters, D.L.; Anderson, S.; Lorber, M.; et al. (1998) Trends in dioxin and PCB
concentrations in meat samples from several decades of the 20th century.
Organohalogen Compounds, Volume 38:75-78.
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Table 1-1. The TEF Scheme for I-TEQ
DF
Dioxin (D) Congener
2,3,7,8-TCDD
1,2,3,7,8-PeCDD
1, 2,3,4, 7,8-HxCDD
1, 2,3,6, 7,8-HxCDD
1,2,3,7,8,9-HxCDD
1,2,3,4,6,7,8-HpCDD
OCDD
TEF
1.0
0.5
0.1
0.1
0.1
0.01
0.001
Furan (F) Congener
2,3,7,8-TCDF
1,2,3,7,8-PeCDF
2,3,4, 7,8-PeCDF
1, 2,3,4,7, 8-HxCDF
1,2,3,6,7,8-HxCDF
1,2,3,7,8,9-HxCDF
2,3,4,6, 7, 8-HxCDF
1, 2,3,4,6, 7,8-HpCDF
1,2,3,4,7,8,9-HpCDF
OCDF
TEF
0.1
0.05
0.5
0.1
0.1
0.1
0.1
0.01
0.01
0.001
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December 2003
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Table 1-2. The TEF Scheme for dioxin-like coplanar PCBs, as determined
by the World Health Organization in 1994
Chemical Structure
3,3',4,4'-TeCB
2,3,3',4,4'-PeCB
2,3,4,4',5-PeCB
2,3',4,4',5-PeCB
2',3,4,4',5-PeCB
3,3',4,4',5-PeCB
2,3,3',4,4',5-HxCB
2,3,3',4,4',5'-HxCB
2,3',4,4',5,5'-HxCB
3,3'AA',5,5'-UxCB
2,2',3,3',4,4',5-HpCB
2,2',3,4,4',5,51-HpCB
2,3,3',4,4',5,5'-HpCB
IUPAC Number
PCB-77
PCB-105
PCB-114
PCB-118
PCB-123
PCB-126
PCB-156
PCB-157
PCB-167
PCB-169
PCB-170
PCB-180
PCB-189
TEF
0.0005
0.0001
0.0005
0.0001
0.0001
0.1
0.0005
0.0005
0.00001
0.01
0.0001
0.00001
0.0001
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December 2003
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Table 1-3. The TEF Scheme for TEQnFP-WHO
'98
Dioxin Congeners
2,3,7,8-TCDD
1,2,3,7,8-PeCDD
1,2,3,4,7,8-HxCDD
1,2,3,6,7,8-HxCDD
1,2,3,7,8,9-HxCDD
1,2,3,4,6,7,8-HpCDD
OCDD
TEF
1.0
1.0
0.1
0.1
0.1
0.01
0.0001
Furan Congeners
2,3,7,8-TCDF
1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
1, 2,3,4, 7,8-HxCDF
1,2,3,6,7,8-HxCDF
1,2,3,7,8,9-HxCDF
2,3,4,6, 7,8-HxCDF
1, 2,3,4,6, 7,8-HpCDF
1,2,3,4,7,8,9-HpCDF
OCDF
TEF
0.1
0.05
0.5
0.1
0.1
0.1
0.1
0.01
0.01
0.0001
Chemical Structure
3,3',4,4'-TeCB
3,4,4', 5-TCB
2,3,3',4,4'-PeCB
2,3,4,4', 5-PeCB
2,3',4,4',5-PeCB
2',3,4,4',5-PeCB
3,3',4,4',5-PeCB
2,3,3',4,4',5-HxCB
2,3,3',4,4',5'-HxCB
2,3',4,4',5,5'-HxCB
3,3',4,4',5,5'-HxCB
2,3,3',4,4',5,5'-HpCB
IUPAC Number
PCB-77
PCB-81
PCB-105
PCB-114
PCB-118
PCB-123
PCB-126
PCB-156
PCB-157
PCB-167
PCB-169
PCB-189
TEF
0.0001
0.0001
0.0001
0.0005
0.0001
0.0001
0.1
0.0005
0.0005
0.00001
0.01
0.0001
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December 2003
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Table 1-4. Summary of North American CDD/CDF and PCB TEQ-WH098
Levels in Environmental Media and Food
(whole weight basis; concentrations provided in parenthesis
for food products are calculated at ND = 0).
Media
Urban Soil, ppt
Rural Soil, ppt
Sediment, ppt
Urban Air, pg/m3
Rural Air, pg/m3
Freshwater Fish and Shellfish,
ppt
Marine Fish and Shellfish, ppt
Water, ppq
Milk, ppt
(Note: each composite for
CDD/F/PCB comprised of 40 +
U.S. regional samples)
Dairy, ppte
Eggs, ppt
(Note: each composite for CDD/F
data comprised of 24 eggs)
Beef ppt
Pork, ppt
Poultry, ppt
Vegetable Fats, ppt
CDD/CDFsa
n = 270
9.3 ± 10. 2d
Range = 2-21
n = 354
2.7d
Range = 0.1-6
n = 11
5.3 ± 5.8
Range = < 1 - 20
n = 106
0.12 ± 0.094
Range = 0.03 - 0.2
n = 60
0.013
Range = 0.004 - 0.02
n = 289
1.0 (NAb)
n = 158
0.26 (NAb)
n = 236
0.00056 ± 0.00079 (NAb)
n = 8 composites
0.018 ± 0.0012 (0.017)
n = 8 composites
0.12 ± 0.22 (0.12)
n = 15 composites
0.081C (0.013)
n = 63
0.18 ± 0.11 (0.061)
Range = 0.1 1 - 0.95
n = 78
0.28 ± 0.28 (0.080)
Range = 0.15-1.8
n = 78
0.068 ± 0.070 (0.043)
Range = 0.03 - 0.43
n=30
0.056 ± 0.24d (NAb)
PCBsa
n = 99
2.3d
n=62
0.59d
n = 11
0.53 ± 0.69
0.0009
n=53
0.00071
n = 1 composite of 10 samples plus
6 composites
1.2° (NAb)
n = 1 composite of 13 samples plus
5 composites
0.25s (NAb)
Nab
n = 8 composites
0.0088 (0.0088)
n = 8 composites
0.058 (0.058)
n = 18 plus 6 composites
0.10° (NAb)
n = 63
0.084 (0.084)
n = 78
0.0093 (0.006)
n = 78
0.026 (0.026)
n = 5 composites
0.037C
Values are the arithmetic mean TEQs, in ppt, and standard deviations. Nondetects were set to
one-half the limit of detection, except for soil and CDD/CDFs in vegetable fats for which
nondetects were set to zero.
NA = not available; Congener-specific PCB data, and data to calculate TEQ concentrations at
ND = 0, are limited.
Standard deviations could not be calculated due to limitations associated with the data (i.e.,
composite analyses).
TEQ calculated by setting nondetects to zero.
Dairy concentration calculated from milk lipid concentrations and then assuming a fat fraction
for dairy.
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Table 1-5. Background Serum Levels in the United States 1995 - 1997
Median
Mean
95th Percentile
TEQDFP-WH098 (pg/g lipid)
18.7
22.1*
38.8
2,3,7,8-TCDD (pg/g lipid)
1.9
2.1
4.2
* After adjusting to account for missing PCBs, the mean is 25.4 pg/g lipid.
Source: CDC (2000).
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Table 1-6. Adult Contact Rates and Background Intakes of Dioxin-like Compounds
Exposure
Route
Soil ingestion
Soil dermal
Freshwater
fish and
shellfish
Marine fish
and shellfish
Inhalation
Milk
Dairy
Eggs
Beef
Pork
Poultry
Other Meats
Vegetable fat
Water
Contact
Rate
50 mg/d
12g/d
5.9 g/d
9.6 g/d
13.3
m3/d
175 g/d
55 g/d
0.24
g/kg-d
0.71
g/kg-d
0.22
g/kg-d
0.50
g/kg-d
0.35
g/kg-d
17 g/d
1 .4 L/d
Dioxins and Furans
Concentrati
on
TEQDF-
WH098
9.3 pg/g
9.3 pg/g
1.0 pg/g
0.26 pg/g
0.12 pg/m3
0.018 pg/g
0.12 pg/g
0.081 pg/g
0.18 pg/g
0.28 pg/g
0.068 pg/g
0.18 pg/g
0.056 pg/g
0.0005
P9/L
Total
Intake
(pg TEQDF-
WH098/kg-
d)
0.0066
0.0016
0.084
0.036
0.023
0.045
0.094
0.019
0.13
0.062
0.034
0.062
0.014
0.000011
0.61
(43 pg/d)
Dioxin-like PCBS
Concentrati
on
TEQp-
WH098
2.3
2.3
1.2 pg/g
0.25 pg/g
NA
0.0088
pg/g
0.058 pg/g
0.10 pg/g
0.084 pg/g
0.012 pg/g
0.026 pg/g
0.041 pg/g
0.037 pg/g
NA
Intake
(pg TEQp-
WH098/kg-
d)
0.0016
0.00034
0.1
0.045
NA
0.022
0.046
0.024
0.060
0.0026
0.013
0.014
0.0090
NA
0.33
(23 pg/d)
Total
intake
(pg
TEQDFP-
WHO98
/kg-d)
0.0082
0.0019
0.18
0.070
0.023
0.067
0.14
0.043
0.19
0.065
0.047
0.076
0.023
0.000011
0.94
(66 pg/d)
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December 2003
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Table 1-7. Variability in Average Daily TEQ Intake as a Function of Age
Age range
1-5 yr
6-11 yr
12-19 yr
Adult
Intake, mass basis
pg TEQDFP-WH098/d
50
54
61
66
Intake, body weight basis
pg TEQDFP-WH098/kg-d
3.3
1.9
1.1
0.94
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December 2003
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~n
-H
Table 1-8. Summary of Findings with Regard to Trends in Dioxin Levels in the Environment and in Humans
Finding
Support
Uncertainty
Concentrations of CDD/CDFs in the environment
were consistently low for centuries until the 1930s.
Then, concentrations rose steadily until about the
1960s, at which point concentrations began to drop.
Evidence suggests that the drop in concentrations is
continuing to the present.
Sediment core studies show a trend of rising concentrations in
1930s and 1940s through the 1960s and 1970s and a subsequent
decline to the present.
- 11 lakes/reservoirs in the U.S.: Cleverly et al., 1996; Versar, 1996
- Lake Huron: Czuczwa et al., 1985
- Green Lake, NY: Smith et al., 1992, 1993
- Hudson River: Smith et al., 1995
Lakes Superior, Michigan, and Ontario: Pearson et al., 1995
- Straight of Georgia, British Columbia: MacDonald et al., 1992
- A remote arctic lake: Tan et al., 1993; Vartiainen et al., 1995
Analogous trends in environmental loadings
- Rise of the manufacture and use of chlorinated phenolic intermediates
and products
- Banning of leaded gasoline, certain phenoxy herbicides, PCBs
Reductions in pulp and paper mill discharges
- EPA National Source Inventory showing 60% reduction in CDD/CDF
TEQ emissions between 1987 and 1995 (see Volume 1)
Limited trend for other environmental concentrations
- Rises and declines in historical food products in the U.S.: Winters et
al., 1998
- Rises and declines in herbage, soil, and air measured in archived
samples in UK: Kjeller et al., 1991, 1996; Harneretal., 1995
- Reductions in the past two decades in herring gull eggs in the Great
Lakes and the Gulf of St. Lawrence River (Hebert et al., 1994); pike in
Sweden (DeWit et al., 1994); pike in Finland (Korhonen et al., 1995);
air in Germany (Hiester et al., 1995); German dairy products and
human milk between 1990 and 1994 (Furst and Wilmers, 1995; 1997)
Suggestive evidence of declines in human body burdens in recent
decade
- National Human Adipose Tissue Survey: U.S. EPA, 1991
- Ministry of Agriculture, Fisheries, and Food's calculations of declines
in dose based on market basket surveys showing reductions in levels
in combination with reductions in consumption of key food items:
MAFF(1995)
The assumption of nondegradation of
CDD/CDFs in sediment cores.
Indirect measure of environmental levels.
Very few archived environmental
measurements to ascertain trends
beyond the past decade or so. More
recent data showing a decline in trends
is limited.
Long half-lives in humans impede
responses in body burdens to changes in
environmental levels.
Environmental levels of coplanar PCBs began
increasing in the 1920s, peaking in the 1970s, and
decreasing after that
PCB production data
Rise in the manufacture from the latter 1920s to the early 1970s;
complete ban in 1977
Nearly all trends data are specific to
PCBs or Aroclors, and not to coplanar
PCBs.
PCB declining environmental levels
- Evidence of declines in biota and sediment levels in the Great Lakes
-------
Cl
2,3,7,8-Tetrachlorodibenzo-p-dioxin
ci
1,2,3,7,8-Pentachlorodibenzo-p-dioxin
ci
CI'\CI
3,3',4,4',5,5'-Hexachlorobiphenyl
ci
2,3,7,8-Tetrachlorodibenzofuran
ci
2,3,4,7,8-Pentachlorodibenzofuran
ci
3,3',4,4',5-Pentachlorobiphenyl
Figure 1-1. Chemical Structure of 2,3,7,8-TCDD and Related Compounds
DRAFT-DO NOT QUOTE OR CITE
1-36
December 2003
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(a)
I
a.
50.0
40,0
e 30.0
(5
•B
20.0
10.0
10.0
20.0
• Formula
•1 Year Nursing
30.0 40.0
Age, years
— 8-Week Nursing
50.0
60.0
70.0
6-Month Nursing
(b)
12.0
10.0
o
0.0
0.0 10.0 20.0 30.0 40.0 50.0 BO.O 70.0
Age, years
- Formula 6-Week Nursing 6-Month Nursing
•1 Year Nursing
Figure 1-2. Lipid (a) and Body Burden (b) Concentrations in a Hypothethical
Female Until Age 70 Under Four Nursing Scenarios:
Formula Only, and 6-week, 6-month, and 1 year Nursing
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2. PHYSICAL AND CHEMICAL PROPERTIES AND FATE
2.1. INTRODUCTION
This chapter summarizes available information regarding the physical and chemical
properties and fate of the dioxin-like CDDs, CDFs, BDDs, BDFs, and PCBs. Physical/
chemical properties addressed in this chapter include melting point, water solubility, vapor
pressure, Henry's Law constant, octanol/water partition coefficient, organic carbon
partition coefficient, and photochemical quantum yield. Fate and transport processes
addressed include photolysis, oxidation, hydrolysis, biodegradation, volatilization, and
sorption. Biologically-mediated transport properties (i.e., bioconcentration, plant uptake,
etc.) are also addressed in this volume, but are also addressed in the companion volume to
this report, Volume 3: Site-Specific Assessment Procedures.
Knowledge of physical and chemical properties is essential to understanding and
modeling the environmental transport and transformation of organic compounds such as
the dioxin-like compounds. The properties most important for understanding the
environmental behavior of the dioxin and dioxin-like compounds appear to be water
solubility (WS), vapor pressure (VP), octanol/water partition coefficient (Kow), organic
carbon partition coefficient (Koc), and photochemical quantum yield. The ratio of VP to
WS (VP/WS) can be used to calculate the Henry's Law constant (Hc) for dilute solutions of
organic compounds when the VP and WS are measured at the same temperature and for
the same physical state. Henry's Law constant is an index of partitioning for a compound
between the atmospheric and the aqueous phase (Mackay et al., 1982).
To maximize and optimize the identification of information on the physical/chemical
properties of these compounds, a thorough search of the recent literature was conducted.
A computer literature search was conducted using the on-line Chemical Abstracts (CA)
data base maintained by the Scientific Technical Network (STN). Printed abstracts were
obtained and screened, and selected literature were retrieved and critically evaluated. The
most definitive value for each physical/ chemical property for each congener was selected.
The evaluation method used to select the most definitive physical/chemical property
values is detailed in Section 2.3. The property values obtained from the scientific
literature are summarized in Appendix A. Sections 2.4 and 2.5 present the property
values for the dioxin-like compounds that are considered to be the most definitive. These
DRAFT-DO NOT QUOTE OR CITE 2-1 December 2003
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values are utilized in the modeling equations in the companion volume to this report,
Volume 3 Site-Specific Assessment Procedures. Appendix A lists reported chemical
property values for the CDDs, CDFs, and dioxin-like PCBs. Where technically feasible and
appropriate, estimation procedures have been used to provide values where measured data
are not available. For those compounds for which data could not be found and estimates
are not appropriate, the field is left blank, and a congener group average is presented as
the property value for that congener group. The congener group average was calculated
by averaging the selected (i.e., most definitive) property values listed for the congeners in
that group.
The values suggested in this document as most definitive are, in the authors'
opinion, the best values derivable from current data. Because the document has
undergone extensive review inside the Agency, by scientific community outside the
Agency, and by the Science Advisory Board, the values can be interpreted as generally
representative of the Agency and scientific community. The authors recommend that
document users consider the values as defaults in the sense that users are encouraged to
accept them as a starting point but should feel free to modify them as new data become
available.
Brief summaries of the recent and relevant scientific literature on the environmental
fate of the polychlorinated and polybrominated dibenzodioxins, dibenzofurans, and
biphenyls are provided in Sections 2.6 and 2.7.
2.2. GENERAL INFORMATION
Polychlorinated dibenzo-p-dioxins (CDDs), polychlorinated dibenzofurans (CDFs),
and polychlorinated biphenyls (PCBs) are chemically classified as halogenated aromatic
hydrocarbons. CDDs and CDFs can be formed as unintentional by-products through a
variety of chemical reactions and combustion processes. Both compound classes have a
triple-ring structure that consists of two benzene rings connected by a third oxygenated
ring. For CDDs, the benzene rings are connected by a pair of oxygen atoms. CDFs are
connected via a single oxygen atom. (See structures below.) PCBs are a class of
compounds formed by the chlorination of a biphenyl molecule.
There are 75 possible different positional congeners of CDDs and 135 different
congeners of CDFs. Likewise, there are 75 possible different positional congeners of
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BDDs and 135 different congeners of BDFs. (See Table 2-1.) The basic structure and
numbering of each chemical class is shown below.
ci
PCDDs
PCDFs
X = 0 to 4, Y = 0 to 4, X + Y > 1
There are 209 possible PCB congeners. (See Table 2-1.) The physical/
chemical properties of each congener vary according to the degree and position of chlorine
substitution. The list of dioxin-like PCBs can be found in Table 1-2. The PCBs assume a
dioxin-like structure when the substituent chlorines occupy: (a) usually no more than one
of the ortho positions; (b) both para positions; and (c) at least two meta positions; and (d)
the structure is not hindered from assuming the preferred planar configuration. The basic
structure and numbering scheme for PCBs is shown below.
X = 1 to 5, Y = 1 to 5, X + Y > 1
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2.3. PHYSICAL/CHEMICAL PROPERTY EVALUATION METHODOLOGY
As discussed above, a thorough search of the recent published scientific literature
was conducted to maximize and optimize the identification of measured physical/chemical
properties. For the purpose of identifying the most definitive of two or more
physical/chemical property values reported in the literature for a given dioxin-like
compound, a ranking methodology was developed to evaluate the degree of confidence in
the reported values. A property value with a ranking of 1 is considered to have the
highest level of confidence; a property value with a ranking of 5 is considered to have the
lowest level of confidence. The ranking scheme assumes that measured values are more
definitive than estimated values. The ranking scheme is based on five ranking criteria or
factors. These factors are described below:
Factor 1: Confirmation. Value, measured or derived, confirmed by at least
one other laboratory, or different experimental technique. Confirmation was
assumed if the reported values were within 50 percent of the highest value
(within 5 percent for values reported in logarithmic units).
Factor 2: Measurement Technique. Direct measurement technique used.
No measurements reported less than 10 times the method detection limit.
Factor 3: GLP Followed. Good Laboratory Practice was followed in the
experimental work. This includes the use of traceable, pure standards;
sensitive, selective detection technique was employed; repeatability of
measurements demonstrated; all experimental details sufficiently
documented so others could reproduce experiments; and sources of
determinate error considered - error analysis conducted.
Factor 4: Derived Value. Value derived from other directly measured
physical/chemical properties by use of known physical/chemical relationships
developed for structurally similar chemicals (e.g., other dioxin, furan, and
PCB congeners, multiple-ring halogenated compounds). The input value
(i.e., the independent variable) used to derive the property value of interest
from the equation (i.e., the physical/chemical relationship) is a directly
measured value.
Factor 5: Estimated Value. Value estimated using a physical/chemical
relationship that was developed using estimated values or a combination of
DRAFT-DO NOT QUOTE OR CITE 2-4 December 2003
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estimated and measured values; this includes QSAR (Quantitative Structure
Activity Relationship) methods. Also includes values derived from other
directly measured physical/chemical properties by use of known
physical/chemical relationships developed, in large part, for structurally
dissimilar compounds.
Although this ranking scheme is subjective in nature, it is a reasonable method for
identifying the most definitive physical/chemical property value. The ranking scheme has
several advantages. First, it identifies where more work is needed to obtain a more
definitive p-chem property value. Second, it allows for later adjustments in these values
when more definitive studies are conducted. A low ranking for a study does not mean
that a particular reported value is incorrect - only that insufficient evidence exists to
determine its accuracy. The ranking scheme is as follows:
Rank 1: Confirmed Measured Values. The reported value has met Factors 1,
2, and 3. (See Table 2-2.) This value is considered definitive.
Rank 2: Unconfirmed Measured Values. The reported value has met Factors
2 and 3. The value is considered accurate; it could be definitive subject to
confirmation.
Rank 3: Confirmed Derived Values. The reported value has met Factors 1,
3, and 4. The value is considered to be a close approximation.
Rank 4: Unconfirmed Derived Value. The reported value has met Factors 3
and 4. The value is considered to be an approximation.
Rank 5: Estimated Value. The reported value has met Factor 5 only. The
value is considered to be an "order-of-magnitude" estimate.
If two or more values have the same ranking, then the value that has been selected
as most definitive by Mackay et al. (1992a, 1992b), by other EPA offices, other
government agencies, or scientific data bases (e.g., the Syracuse Research Corporation
(SRC) Chemfate Data Base) was deemed to be the most definitive value for this
document. If two or more values with the same ranking have not been peer reviewed as
above, typically the most current value was chosen as the most definitive value. This
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decision was made on the assumption that the most current value would have been
developed by the latest scientific method; however, an evaluation of the techniques used
to derive the values was also considered in choosing the more definitive value. The
ranking of the literature can be found in Table A-2 in Appendix A. Table 2-3 lists the
property values for the dioxin-like compounds that are considered to be most definitive.
2.4. PHYSICAL/CHEMICAL PROPERTIES - CHLORINATED COMPOUNDS
Limited research has been conducted to determine physical and chemical properties
of CDFs, CDDs, and the dioxin-like PCBs. The CDD/CDF congeners having 2,3,7,8-
chlorination have received the most research attention, with 2,3,7,8,-TCDD being the
most intensely studied compound. All 2,3,7,8-substituted CDD/CDF congeners are now
available commercially, but many of these congeners have not been prepared in pure form.
Another factor that is likely to have limited research on these compounds is the high
toxicity of these compounds, which necessitates extreme precautions to prevent potential
adverse effects.
2.4.1. Water Solubility
Although water solubility data are not directly used in the exposure scenario
equations in Volume 3, water solubility data can be used to estimate Henry's Law
constants (using the VP/WS ratio technique) that are used in the equations in Volume 2.
Very few measured water solubility values are available in the literature. Marple et al.
(1986a) reported the water solubility of 2,3,7,8-TCDD as 19.3 ± 3.7 parts per trillion
(nanograms per liter, ng/L) at 22°C. Marple et al. (1986a) used a procedure of
equilibrating thin films of resublimed 2,3,7,8-TCDD with a small volume of water followed
by gas chromatography (GC) analysis with 63Ni electron capture detection. Other water
solubility values for 2,3,7,8-TCDD have been reported in the literature and are summarized
in U.S. EPA (1990) and Mackay et al. (1992a). Values ranging from 7.9 ng/L to 483 ng/L
are reported in U.S. EPA (1990) and Mackay et al. (1992a) with 19.3 ng/L selected as the
recommended value. The value of 19.3 ng/L was confirmed by Marple et al. (1987) using
both radio-labeled and unlabeled 2,3,7,8-TCDD. Marple et al. (1987) reported values of
10.6 ng/L and 10.4 ng/L for the labeled and unlabeled compounds respectively. Because
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the value of 19.3 ng/L was confirmed by other techniques and was recommended by U.S.
EPA (1990), it was chosen as the most definitive value.
Friesen et al. (1985) and Shiu et al. (1988) used high-performance liquid
chromatography (HPLC) generator column techniques to measure the water solubilities of
a series of chlorinated dioxins including the following dioxin-like congeners: 1,2,3,4,7,8-
HxCDD; 1,2,3,4,6,7,8-HpCDD; and OCDD. Reported water solubilities ranged from 4.42
ng/L to 0.074 ng/L for the 1,2,3,4,7,8-HxCDD and OCDD congeners, respectively.
Friesen et al. (1990b) used a gas chromatography/mass spectrometry detection (GC/MSD)
generator column technique to measure the water solubilities of a series of 2,3,7,8-
substituted CDFs (2,3,7,8-TCDF; 2,3,4,7,8-PeCDF; 1,2,3,6,7,8- and 1,2,3,4,7,8-HxCDF;
and 1,2,3,4,6,7,8-HpCDF) and reported a decrease in water solubility with an increase in
the number of chlorine substituents. The reported water solubility values ranged from
41 9 ng/L for 2,3,7,8-TCDF to 1.35 ng/L for 1,2,3,4,6,7,8-HpCDF.
Few measured data have been reported for the dioxin-like PCB compounds. The
selected water solubility values for the dioxin-like PCB compounds are typically one to two
orders of magnitude greater than the similarly chlorinated CDD and CDF congeners. For
example, the selected value for 3,3',4,4'-TCB is 1 /vg/l whereas the selected water
solubility values averages for 2,3,7,8-TCDD and 2,3,7,8-TCDF are 0.019 and 0.42 JJQ/\,
respectively.
For those compounds without reported measured water solubility values,
estimations were calculated by the congener group-average method. For example, for the
tetra-chlorinated dioxins, values reported in the literature were averaged to yield an
estimated water solubility value for the tetra-chlorinated dioxin congener group. A similar
procedure was used to develop the average value for each of the other CDD and CDF
congener groups. The most definitive value for each isomer was used to derive the
congener group average.
2.4.2. Vapor Pressure
Vapor pressure data are not directly used in the exposure scenario equations in
Volume 3. However, vapor pressure data can be used to estimate Henry's Law constant
using the VP/WS ratio technique. Very few measured vapor pressure values are available
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in the literature for the CDDs and CDFs, but the majority of the measured vapor pressures
are for the 2,3,7,8-substituted congeners.
Mackay et al. (1992a) reviewed the published vapor pressure data for 2,3,7,8-
TCDD and selected a recommended value of 1.50 x 10"9 mm Hg at 25°C. This value had
been measured by Rordorf (1987, 1989) using a gas-flow method in a saturation oven.
SRC (1991) reported this same value by extrapolating the vapor pressures measured by
Schroy et al. (1985) at four higher temperatures, 30°, 55°, 62°, and 71 °C. Rordorf
(1987, 1989) also reported experimental vapor pressure values for OCDD (8.25 x 10"13mm
Hg) and OCDF (3.75 x 10"12 mm Hg). These values were chosen as the most definitive
because they were the most current directly measured values and also because they were
selected by Mackay et al. (1992a) as the most definitive values.
Eitzer and Hites (1988) reported experimental vapor pressure values for several
dioxin-like compounds utilizing GC capillary column retention time data. The values were
reported as subcooled liquids and then converted to solid-phase vapor pressures. The
solid-phase vapor pressures ranged from 2.16 x 10"12 mm Hg to 9.48 x 10"10 mm Hg for
the CDDs and from 1.07 x 10"10 mm Hg to 8.96 x 10"9 for the CDFs. Rordorf (1 987,
1989) used a vapor pressure correlation method to predict the vapor pressures of 15
CDDs and 55 CDFs (including most of the 2,3,7,8-substituted CDDs and CDFs) based on
the measured vapor pressures for 10 CDDs, 4 CDFs, and the deduced boiling point and
enthalpy data for the larger series of CDDs and CDFs. Measured boiling point and
enthalpy data are in good agreement with the deduced data used in the correlation
method. The values from Rordorf (1987, 1989) were considered the more definitive
based on the review of the data from these two studies by Mackay et al. (1992a).
The ranges of selected values reported within various congener groups are as
follows:
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Congener Group Vapor Pressure Range (mm Ha)
TCDD 1.5 x 10'9 to 4.80x10'8
PeCDD 4.4 x10'10 to 6.6 x10'10
HxCDD 3.6 x 10'11 to 5.1 x 10'11
HpCDD 5.6 x 10'12
OCDD 8.2 x 10'13
TCDF 1.5 x 10'8 to 4.0 x 10'8
PeCDF 1.5 x 10'9 to 4.3 x 10'9
HxCDF 1.80X10'10 to 5.70X10'10
HpCDF 3.53x10'" to 5.8x10'11
OCDF 3.75 x 10'12
The selected vapor pressure values reported for the dioxin-like PCBs are typically
one to two orders of magnitude greater than the similarly chlorinated CDD and CDF
congeners. (See Table 2-3.) The directly measured values of Murphy et al. (1987) and
the derived values of Foreman and Bidleman (1985) were considered the most definitive.
As with the CDDs and CDFs, the vapor pressures of the PCBs decrease with an increase
in the number of chlorine substituents. The highest selected value for the dioxin-like PCBs
is 1.09 x 10'6 mm Hg for 2,3,3',4,4'-PeCB, and the lowest value selected is 1.46 x 10'8
mm Hg for 2,3,3',4,4',5,5'-HpCB.
Estimated vapor pressure values for those CDDs and CDFs for which measured
values were not found in the literature were calculated by the congener group-average
method using the literature-reported values within a congener group. For example, the
literature values for the TCDDs were averaged to obtain an estimated vapor pressure
assumed to apply to the TCDD congeners that did not have literature values. A similar
procedure was used to develop a congener-average for each of the other congener groups.
The most definitive value for each isomer was used to derive the congener group average.
Compounds with vapor pressures in the ranges reported for these compounds are
considered to have very low vapor pressures.
2.4.3. Henry's Law Constant
Henry's Law constants are used in Volume 3 to estimate the volatilization of the
dioxin-like compounds from soil. They are also utilized in estimating the vapor-phase
bioconcentration factor from air to plant leaves. Directly measured Henry's Law constants
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have been reported for only three compounds. Measured values have been reported for
1,2,3,4-TCDD, 1.99 x 10'5 atm-m3/mol (Santl et al., 1994); 1,3,6,8-TCDD, 6.81 x 10'5
atm-m3/mol (Webster et al., 1985); and for 3,3',4,4'-PCB, 9.4 x 10"5 atm-m3/mol
(Dunnivant and Elzerman, 1988). These three values were considered the most definitive.
Other values reported in the literature for CDDs, CDFs, and PCBs were calculated by the
vapor pressure/water solubility (VP/WS) ratio technique or by structure-activity
relationship techniques. A derived VP/WS ratio value, Rank 4, was determined to be more
definitive than an estimated value, Rank 5.
Congener group-average Henry's Law constants were estimated for each congener
group based on the selected congener values within that group. The Henry's Law
constant values for the PCBs are similar to those for the CDDs and CDFs.
Lyman et al. (1982) offers guidelines, though not specific to these compounds, for
comparing the degree to which organic compounds volatilize from water. These
guidelines suggest that volatilization of polycyclic aromatic hydrocarbons and halogenated
aromatics (which includes all the dioxin-like compounds) from water represents a
significant transfer mechanism from the aqueous to the atmospheric phase.
2.4.4. Octanol/Water Partition Coefficient
The octanol/water partition coefficient (Kow) is used in several exposure estimation
procedures in Volume 3. The log Kow is used to estimate log Koc when measured data are
not available, and it is utilized in estimating the root concentration factor (RCF). RCF is
used to estimate the uptake of contaminants by plant roots. Log Kow is also used to
estimate the vapor-phase bioconcentration factor from air to plant leaves.
Marple et al. (1986b) reported the octanol/water partition coefficient of
2,3,7,8-TCDD as 4.24 (± 2.73) x 106 at 22 ± 1 °C, yielding a log Kow of 6.64 (Table
A-1). Two similar experimental techniques were used, but the more reliable method
involved equilibration of water-saturated octanol, containing the 2,3,7,8-TCDD, with
octanol-saturated water, over 6 to 31 days. U.S. EPA (1990) reported that the available
low Kow data ranged from 6.15 to approximately 8.5. The 6.64 value reported by Marple
et al. (1986b) was the value recommended in U.S. EPA (1990). More recently, Mackay et
al. (1992a) evaluated all published measured and estimated log Kow values for 2,3,7,8-
TCDD and recommended a value of 6.80.
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Burkhard and Kuehl (1986) used reverse-phase High Pressure Liquid
Chromatography (HPLC) and Liquid Chromatography/Mass Spectrometry (LCMS) detection
to determine octanol/water partition coefficients for 2,3,7,8-TCDD and a series of seven
other tetrachlorinated planar molecules, including three other TCDD isomers (1,2,3,4-
TCDD; 1,3,7,9-TCDD; 1,3,6,8-TCDD), 2,3,7,8-TCDF, and 3,3',4,4'-tetrachlorobiphenyl.
The log Kow values for the four TCDD isomers ranged from 7.02 to 7.20. The log Kow for
2,3,7,8-TCDF was 5.82, and the log Kow for 3,3',4,4'-TCB was 5.81.
Burkhard and Kuehl (1986) also re-evaluated data on 13 CDDs and CDFs previously
reported by Sarna et al. (1984) under similar experimental techniques. In the re-
evaluation, Burkhard and Kuehl (1986) used experimental rather than estimated log Kow
values in correlations with gas chromatographic retention times. This approach yielded log
octanol-water partition coefficients ranging from about 4.0 for the nonchlorinated parent
molecules to about 8.78 for the octa-chlorinated compounds, much lower than the values
originally reported by Sarna et al. (1984).
Sijm et al. (1989) used a slow stirring method to obtain log Kow values for 73 CDD
and CDF congeners; values ranged from 6.10 to 7.92. Mackay et al. (1992a) reviewed all
published measured and estimated log Kow values for eight tetra- through octa-substituted
CDDs and seven tetra- through octa-substituted CDFs; recommended values were selected
by Mackay et al. (1992a) for the eight CDDs and for five of the CDFs. The most
definitive values chosen were either those selected by Mackay et al. (1992a), a directly
measured value, or the most current derived value. Selected values reported for
congeners within the various congener groups ranged as follows:
Congener Group Log K.
w
TCDD 6.1 to 7.1
PeCDD 6.2 to 7.4
HxCDD 6.85 to 7.8
HpCDD 8.0
OCDD 8.2
TCDF 5.6 to 6.79
PeCDF 6.19 to 6.92
HxCDF 7.0
HpCDF 7.4
OCDF 8.0
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The selected log Kow values for the PCBs are similar to those reported for the CDDs
and CDFs. The values range from 6.5 (measured) for 3,3',4,4'-TeCB to 7.71 (literature-
estimate) for 2,3,3',4,4',5,51-HpCB. The log Kow values increase with an increase in the
number of chlorine substituents.
Partition coefficient values were calculated for those compounds for which no
measured data were reported in the literature by averaging the literature values within
congener groups, as were done for vapor pressure and water solubility. Partition
coefficients in the ranges of these reported values indicate that the substances tend to
adsorb strongly to organic components in the soil and may bioconcentrate in those
organisms exposed to the compounds.
2.4.5. Organic Carbon Partition Coefficient
The organic carbon partition coefficient (Koc) is used in several exposure estimations
in Volume 3. Koc is used in the estimation of the adsorption partition coefficient, which
describes the partitioning of contaminants between suspended sediment and the water
column. Koc is also used in estimating the concentration of contaminants in below ground
vegetables grown in contaminated soil.
Log Koc values for 2,3,7,8-TCDD have been measured in several studies. Lodge and
Cook (1989) used contaminated sediments from Lake Ontario and distilled water in glass
cylinders to measure the log Koc of 2,3,7,8-TCDD. Log Koc values ranged from 7.25 to
7.59. Jackson et al. (1986) used 10 contaminated soil samples in a batch extraction
procedure to measure log Koc. The average log Koc of the 10 soils was reported as 7.39.
Marple et al. (1987) used two uncontaminated soils spiked by two different methods with
2,3,7,8-TCDD to obtain the log Koc value. The soil was stirred with water in 2-liter flasks.
The log Koc values ranged from 5.96 to 6.54 for both soils, with an average value of 6.40
for the red clay soil and 6.02 for the alluvial soil.
Puri et al., (1989) studied log Koc of 2,3,7,8-TCDD with several other co-
contaminants such as crankcase oils and surfactants. An average log Koc value of 5.68
was reported for 2,3,7,8-TCDD in the presence of 0.01 percent surfactant. Walters and
Guiseppi-Elie (1988) used several soils and water/methanol mixtures in a batch shake
testing procedure to determine the log Koc of 2,3,7,8-TCDD. The study resulted in a log
Koc value of 6.6.
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Five studies for log Koc of 2,3,7,8-TCDD were ranked number 1. The studies by
Jackson et al. (1986) and Lodge and Cook (1989) had confirming values of 7.39 and
7.42, respectively. The studies by Walters and Guiseppi-Elie (1988), Walters et al.
(1989), and Marple et al. (1987) had confirming values of 6.6, 6.66, and 6.4,
respectively. The 6.6 value reported by Walters and Guiseppi-Elie (1988) was chosen by
Syracuse Research Corporation (SRC) in the CHEMFATE Database (SRC, 1991) as the
most definitive. This value was determined in a mixed solvent system, water and
methanol; therefore, it is not considered as appropriate as a pure water equilibration
system determined value. The values reported by Marple et al. (1987) and Walters et al.
(1989) were determined in uncontaminated soil and with pure water; therefore, these
values are selected as the most definitive for this document.
Definitive values were not selected for other congeners because of few measured
data points and, oftentimes, considerable differences in the reported values for those
congeners with reported values. The ranges of reported values are presented in Table A-1
in Appendix A.
2.4.6. Photo Quantum Yields
Photo quantum yields, good semi-quantitative measures of phototransformation
efficiency (Van et al., 1995), have been reported for several CDD/CDF congeners. No
values were located for the dioxin-like PCBs.
Congener
Photo Quantum Yield
(mole/einstein)
1,2,3,7-TCDD
1,3,6,8-TCDD
2,3,7,8-TCDD
2,3,7,8-TCDD
2,3,7,8-TCDD
1,2,3,4,7-PeCDD
1,2,3,4,7,8-HxCDD
1,2,3,4,7,8-HxCDD
1,2,3,4,6,7,8-HpCDD
OCDD
OCDD
1, 2,4,7, 8-PeCDF
1,2,3,4,7,8-HxCDF
5.42 x 10'4
2.17 x 10'3
2.2 x 10'3
3.3 x 10'2
1.62 x 10'2
9.78 x 10'5
1 . 1 0 x 1 0'4
1.58 x 10'3
1.53 x 10'5
2.26 x 10'5
1.25 x 10'3
1.29 x TO'2
6.96 x 10'4
Reference
(Choudhry and Webster, 1989)
(Choudhry and Webster, 1989)
(Dulin et al., 1986)
(Rapaport and Eisenreich, 1984)
(Yan et al., 1995)
(Choudhry and Webster, 1987)
(Choudhry and Webster, 1987)
(Yan et al., 1995)
(Choudhry and Webster, 1987)
(Choudhry and Webster, 1987)
(Yan et al., 1995)
(Choudhry et al., 1990)
(Choudhry et al., 1990)
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All quantum yields were measured in a water-acetonitrile solution at 313 nm, except those
reported by Rapaport and Eisenreich (1984) which were measured in the vapor phase and
those reported by Van et al. (1995) which were carried out in a butanol/decane mixture.
Van et al. (1995) also examined the effect of co-contaminants (pentachlorophenol,
naphthalene, phenanthrene, and anthracene) on the photoquantum yield of OCDD and
2,3,7,8-TCDD. The presence of the co-contaminants decreased the photoquantum yield
at a degree dependent upon both the concentration and extinction coefficient of the co-
contaminants.
Congener group averages were not calculated because photo quantum yields are
very sensitive to chlorine position and also to the physical medium (e.g., vapor or dilute
solution) and conditions (e.g., the solvent system) used in the experiments (Van et al,
1995).
2.5. PHYSICAL CHEMICAL PROPERTIES - BROMINATED COMPOUNDS
Information on the physical and chemical properties of the polybrominated dioxins
and furans is very limited and has not been compiled for this report.
2.6. ENVIRONMENTAL FATE - CHLORINATED COMPOUNDS
CDD/CDFs and dioxin-like PCBs have been found throughout the world in
practically all media including air, soil, water, sediment, and biota. The widespread
occurrence observed is not unexpected considering the numerous sources that have
emitted these compounds into the atmosphere and the overall resistance of these
chemicals to abiotic and biotic transformation. Consequently, CDD/CDFs and PCBs
emitted to the atmosphere can be transported long distances in the atmosphere before
they are deposited onto vegetation, soil, and water via dry and wet deposition.
As depicted in Figure 2-1, deposition onto vegetation and subsequent ingestion of
that plant material by animals is hypothesized to be the primary mechanism by which
CDD/CDFs enter the terrestrial/agricultural food chain. Deposition onto soil with
subsequent erosion and runoff into water bodies with subsequent bioaccumulation by
aquatic biota is believed to be the major pathway by which CDD/CDFs enter the aquatic
food chain in most freshwater bodies. These two pathways are also expected to be major
pathways for entry of dioxin-like PCBs into the terrestrial and aquatic food chains.
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However, because PCBs are more mobile in the environment (i.e., greater vapor pressures
and water solubilities) than CDD/CDFs, there will be greater inter-media transport of PCBs
(e.g., greater volatilization from soil and water to air). In addition, because of the previous
widespread use and disposal of PCBs, localized sources of contamination may dominate
aquatic food chain sources in more water bodies than is the case for CDD/CDFs.
The growing body of literature from laboratory, field, and monitoring studies
examining the environmental transport, transformation, and distribution of CDD/CDFs and
dioxin-like PCBs has increased the understanding of the fate of these environmentally
ubiquitous compounds. The purpose of this section is to summarize the key findings from
the growing body of literature dealing with the environmental fate of CDD/CDFs and PCBs.
Figure 2-2 presents a conceptual diagram of the intermedia movement of
CDD/CDFs and PCBs among the five major environmental media: air, soil, water,
sediment, and biota. As will be discussed in this section, the primary mechanism
currently believed to be responsible for the widespread occurrence of CDD/CDFs and PCBs
is long range atmospheric transport and deposition onto vegetation and soil.
2.6.1. Environmental Fate of CDDs and CDFs
2.6.1.1. Summary
Because of their high lipophilicity and low water solubility, CDD/CDFs are primarily
associated with particulate and organic matter in soil, sediment, and the water column.
Current understanding of CDD/CDF behavior on atmospheric particulate matter is that
there is a partitioning between the particles and the gas phase. The two key parameters
controlling the phase in which a particular congener is predominantly found are the
congener's vapor pressure and the atmospheric temperature. Congeners with higher
vapor pressures (i.e., the less chlorinated congeners) are found to a greater extent in the
gas phase. CDD/CDFs sorbed to soil exhibit little potential for significant leaching or
volatilization once sorbed to particulate matter.
The available evidence indicates that CDDs and CDFs, particularly the tetra- and
higher chlorinated congeners, are extremely stable compounds under most environmental
conditions. The only environmentally significant transformation processes for these
congeners are believed to be atmospheric photooxidation and photolysis of nonsorbed
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species in the gaseous phase or at the soil or water-air interface. Several studies have,
however, indicated that certain ligninolytic fungi can degrade these higher-chlorinated
congeners and that anaerobic degradation in sediment may occur at a slow rate. To a
large extent, these degradation processes involve dechlorination to less-chlorinated (and
possibly more toxic) congeners.
Burial in-place or erosion of soil to water bodies appears to be the predominant fate
of CDD/CDFs sorbed to soil. CDD/CDFs entering the water column primarily undergo
sedimentation and burial with some uptake by aquatic biota. The ultimate environmental
sink of CDD/CDFs is believed to be aquatic sediments. CDD/CDFs entering the
atmosphere are removed either by photodegradation or by dry or wet deposition.
Vapor-phase dry deposition of CDD/CDFs onto vegetation is hypothesized to be the
primary route of entry of CDD/CDFs into the terrestrial/agricultural food chain.
Atmospheric deposition of CDD/CDFs onto land followed by runoff/erosion to water
bodies is hypothesized to be the major route of entry of CDD/CDFs into the aquatic food
chain of most freshwater bodies.
2.6.1.2. Transport Mechanisms in Air
Once released into the atmosphere, CDDs and CDFs can become widely dispersed
throughout the environment by atmospheric transport and deposition. In an assessment
of the atmospheric transport and deposition of CDDs and CDFs for EPA, Hites and Harless
(1991) generated data and analyses that support the contention that background
environmental levels and congener profiles of CDDs and CDFs in soils and sediment (i.e.,
higher rather than lower chlorinated congener patterns predominate) can be attributed, in
large part, to the atmospheric transport and transformation of CDDs and CDFs released
from combustion sources. More recently, Tysklind et al. (1993) reported the results of
measurements of CDD/CDFs in the ambient air from a rural site in Sweden collected during
1989 and 1990. The highest concentrations of total CDD/CDFs were measured during
sampling events with air masses coming with westerly to southerly winds, thus indicating
long range transport. The congener profiles were found to vary depending on wind
trajectories implicating source influences from industrialized and urbanized areas of
Europe.
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Deposition is a broad term defining a number of atmospheric phenomena, including
the wet and dry deposition of CDD/CDF-contaminated airborne particulate matter onto
soils and vegetation, and the wet and dry deposition of vapor-phase CDD/CDFs onto soils
and vegetation. Mass-balance studies have been conducted in several countries in an
effort to establish the mechanisms associated with the generation, transport, and
environmental fate of CDD/CDFs. A wide range of estimated deposition rates have been
developed in these studies. The variation in estimates may be attributed, in part, to the
likely differences in precision and accuracy of the various sampling methods used, as well
as the inherent difficulty in comparing results from differing collection devices. Table 2-4
presents a summary of some of the deposition rates generated by investigators in
Sweden, the United Kingdom, Belgium, Germany and the United States. As noted in
Table 2-4, deposition fluxes appear to be greater in urban areas than in rural areas.
A variety of methods have been developed by researchers in several countries for
measuring dry and wet deposition of CDD/CDFs. However, because of the complexity of
deposition as it occurs in the natural environment, all of these methods, with the possible
exception of wet deposition monitoring techniques, have major drawbacks which limit
their utility for generating data that provide a reliable and accurate measure of deposition
as it naturally occurs. Methods have also been developed for measurement of total
CDD/CDF concentrations in air; modifications to these methods have been developed that
can enable consistent and reproducible measurements of particulate and vapor-phase
ambient air concentrations. Mathematical models have also been developed to estimate
dry and wet deposition rates. [Volume 3 provides a detailed discussion of these modeling
techniques]. Because the current state of knowledge concerning deposition
mechanisms/rates and methods to accurately measure deposition is not well-developed,
the most effective program that could be deployed today to better understand deposition
would consist of ambient air measurements coupled with specific deposition studies
designed to improve the accuracy of deposition modeling.
Because of the importance of atmospheric deposition as a pathway for
contamination of the terrestrial/agricultural food chain, the remainder of this section (i.e.,
Section 2.6.1.2) presents an overview discussion of three areas: (1) vapor/particle
partitioning; (2) dry deposition processes relevant for CDD/CDF; and (3) wet deposition
processes relevant for CDD/CDF.
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2.6.1.2.1. Vapor/Particle (V/P) Partitioning. The relative importance of the various
deposition processes (and associated phases) is related to congener-specific vapor/particle
partitioning. Hites and Harless (1991), Hippelein et al. (1996), and others have
demonstrated that partitioning of CDD/CDFs between the vapor and particle-associated
phases occurs in the atmosphere. The key parameters controlling the phase in which a
particular congener is found are the congener's vapor pressure, the atmospheric
temperature, and the particulate matter concentration in the atmosphere. Congeners with
higher vapor pressures (i.e., the less-chlorinated compounds) are found to a greater extent
in the vapor phase. For a given congener, the fraction in the vapor phase increases with
increasing ambient temperature and decreases with increasing particle concentration. A
portion of the particle-associated compound appears to be freely exchangeable between
the particulate and vapor phases. A second portion may be irreversibly sorbed or
occluded by the particles and not in equilibrium with the gas phase.
A comprehensive review of the published literature addressing vapor/particle (V/P)
partitioning of CDD/CDFs in stack gases and ambient air is provided in Volume 3. The
Volume 3 review includes an evaluation of the results of stack testing data, ambient air
sampling data, and theory rooted in basic physical chemistry that either imply, directly
deduce, or theoretically calculate V/P partitioning. Table 2-5 presents a summary of the
Volume 3 review of the ambient air monitoring studies. The most comprehensive set of
partitioning data were collected by Hippelein et al. (1996); data were collected
continuously over the course of 48 weeks at six urban sites on the outskirts of Augsburg,
Germany, during 1992 and 1993.
A theoretical approach developed by Bidleman (1988) for predicting V/P
partitioning of CDD/CDFs is described in detail in the Volume 3 review. Table 2-6
presents, for each of the 2,3,7,8-substituted CDD/CDFs, the percentage of mass
predicted in Volume 3 using this theoretical approach to be in the particle phase under
four airshed conditions: "clean continental," "average background," "background plus
local sources," and "urban." From the review in Volume 3, the following conclusions
were made:
• The stack test methods in use today to monitor and measure the concentration of
CDD/CDFs emitted to the air from combustion sources have given inconclusive and
contradictory V/P partitioning results and thus do not provide a credible basis, at
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present, for determining V/P partitioning at the point of release. There is no
consistent pattern to the interpretation of V/P based on where the CDD/CDF
segregate in the instrument (e.g., the glass fiber filter or the XAD resin). Factors
that may contribute to this inconsistent pattern are: the relatively long residence
time spent traversing the stack interior; the location of the probe to the instrument
in a relatively hostile environment of the hot combustion gas; the static
temperature of the particulate filter caused by heating the particulate filter housing;
and the fact that located between the particulate trap and the vapor trap is a
condensing section consisting of glass tubing surrounded by an ice bath.
• The use of a high-volume ambient air sampler consisting of a glass fiber particulate
filter (GFF) and polyurethane foam adsorbent trap (PDF) is a reliable method for the
collection and retention of CDD/CDFs in ambient air. Because the sampler is not
artificially heated or cooled, but is allowed to operate at ambient air temperatures,
the method can be used to imply the V/P partitioning of CDD/CDFs in ambient air.
This is accomplished by separately extracting and analyzing the GFF and PDF.
However, the method may only give an approximate indication of the V/P ratio
since mass transfer of CDD/CDF from the particulate matter on the GFF to the PDF
cannot be ruled out. For example, it is possible that a portion of the CDD/CDFs
that are sorbed to particulate matter captured by the filter may be volatilized and
carried with the air flow to the PDF sorbent trap (blow-off effect). If this were to
occur, the observed V/P ratio would be overestimated. Also, the GFF will collect
particles >0.1 microns in diameter and, therefore, it is possible that smaller
particles will pass through the GFF and be trapped in the PDF. If this does occur,
the observed V/P ratio will be overestimated.
There are currently no empirical data that demonstrate the magnitude of these
effects or that these effects actually occur. However, the potential impact of
particle breakthrough may be ascertainable, if it is assumed that the CDD/CDF
congener group pattern is the same on all particle size fractions. This assumption
is supported by the findings of Kaupp et al. (1994) who demonstrated that
CDD/CDF congener group profiles were nearly identical in four particle size ranges
(1.35 to 4.05 urn; 0.45 to 1.35 urn; 0.15 to 0.45 urn; and <0.15 urn) collected in
a rural area of Germany during the summer of 1992. Thus, if OCDD is, as is
typical, the dominant congener in the collected material on the GFF, then the mass
of any other CDD/CDF on the PDF that may be due to particle breakthrough can be
estimated by multiplying: (1) the ratio of that congener's mass on the GFF to the
mass of OCDD on the GFF by (2) the mass of OCDD in the PUF.
• Neither the currently available monitoring techniques nor the available models
necessarily give the "correct" V/P partitions. Until the state of knowledge of
CDD/CDF partitioning in air is improved through development of improved
monitoring devices and laboratory investigations of the kinetics and
thermodynamics of CDD/CDF sorption, the theoretical construct described in
Chapter 3 of Volume 2 is the recommended approach for estimating V/P
partitioning of CDD/CDFs at this time. Key advantages to the theoretical approach
are that it relies on current adsorption theory, considers the molecular weight and
the degree of halogenation of the congeners, uses the boiling points and vapor
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pressures of the congeners, and uses the availability of surface area for adsorption
of atmospheric particles that correspond to a variety of ambient air shed
classifications having variable particulate matter densities.
2.6.1.2.2. Dry Deposition. Dry deposition can involve two phases, dry particulate
deposition and vapor phase deposition, the relative importance of which for a given
congener is dependent primarily on the V/P partitioning. First, dioxin-like compounds
associated with particulate matter can deposit by gravitational settling or turbulent
diffusion. Secondly, dioxin-like compounds can be deposited by vapor-phase diffusion
into the soil, vegetation, and the surface layer of water bodies. The rate at which
atmospheric chemicals are deposited is termed the "deposition flux". The deposition flux
is derived as the product of the concentration of the chemical in the vapor phase or on/in
the particulate and the deposition velocity of the contaminated particles. The downward
motion represented by deposition velocity is controlled by the gravitational settling
velocity, atmospheric resistance, surface resistance and the atmospheric surface friction
layer. The factors that most influence deposition flux can be divided into two types: (1)
meteorological influences and (2) the properties of the chemical influencing it's V/P
partitioning. A detailed list of the many factors that can affect dry deposition is shown in
Table 2-7.
Dry Particulate Deposition. Dry particulate deposition is the best characterized of
the dry deposition processes. As noted in Table 2-5, the vast majority of the atmospheric
burden of hepta- and octa-chlorinated CDD/CDF (and, to a lesser extent, the burden of
hexa- and penta-chlorinated congeners) is associated with particulate matter. As such,
dry particulate deposition is a major mechanism for removal of these congeners from the
atmosphere. The major factors controlling the transfer of particulate from some height
above the surface through the surface layer down to the immediate vicinity of the receptor
surface are the forces of gravity and turbulent diffusion. As a general rule, very large
particles (i.e., greater than 20/vm) will be removed from the atmosphere fairly rapidly by
the force of gravity (Kaupp et al., 1994). Particles less than 20 //m will be removed at a
slower rate primarily by atmospheric turbulence and Brownian diffusion through the
laminar sub-layer which often has a thickness of 10"1 to 10"2cm. The deposition flux for
these smaller particles is influenced by many factors, including: the distribution of particles
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by diameter and density; the atmospheric turbulence; and the friction and morphology of
the impacted ground and vegetative surfaces.
Few studies have been published that have attempted to measure only dry
particulate deposition of CDD/CDFs. Koester and Hites (1992a) used inverted frisbees and
flat glass plates to collect dry particulate deposition. The collectors were coated with
mineral oil and then deployed uncovered (except during precipitation events) for exposure
periods of several weeks. The mineral oil is removed from the frisbees/plates after the
exposure period and analyzed for CDD/CDF content. The extent to which these devices
may also be acting as vapor phase collectors is not thought to be significant but has not
been tested. Similarly, the extent of photodegradation of collected CDD/CDFs is not
thought to be significant but has not been tested. Hall and Upton (1988) had previously
conducted wind tunnel studies of the particle collection efficiency of inverted frisbees and
had found an overall collection efficiency of approximately 50 percent with efficiencies
decreasing as wind speed increased and particle diameter decreased.
The deposition velocity of particulate matter containing CDD/CDFs onto various
surfaces has not been well characterized and is a major source of uncertainty in modeling
particulate deposition. Bidleman (1988) estimated that particulates with diameters
ranging from 0.08 to 2 jjm have deposition velocities that vary from 0.003 to 0.036
cm/sec. Coarser particulates (i.e., >2/ym) were estimated to have much higher
deposition velocities, 0.5 to 2.5 cm/sec (Bidleman, 1988). From the results of their study
with inverted frisbees and glass plates, Koester and Hites (1992a) calculated an average
deposition velocity for particulate-associated CDD/CDFs of 0.2 cm/sec; calculated
deposition velocities for the tetra- through octa-chlorinated congener groups ranged from
0.086 to 0.6 cm/sec. Trapp and Matthies (1995) estimated that the fine particulates (i.e.,
diameters of 0.1 to 1.0 /vm) that are responsible for the long range transport of
atmospheric particle bound pollutants have deposition velocities of about 0.01 cm/sec.
Dry Vapor-Phase Deposition. Although not as well characterized, several studies
have concluded that the transfer of all non-hepta- and non-octa-chlorinated dioxin-like
compounds to leafy vegetation is dominated by vapor phase deposition which involves the
movement of vapor-phase dioxin from ambient air into leafy vegetation (Bacci, et. al.,
1990; Gaggi and Bacci, 1985; McLachlan, et. al., 1995; Rippen and Wesp, 1993;
Simonich and Hites, 1995). Dry particulate and wet deposition are believed to be the
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dominant mechanisms by which vegetation and soil are exposed to hepta- and octa-
chlorinated congeners. Vapor phase deposition directly onto soil is not believed to be a
dominant process in most settings because soil is usually covered by vegetation or
detritus which are likely to serve as more important exchange sites.
Major factors governing vapor-phase deposition include the ambient air
concentration of CDD/CDFs, the exposed surface area of vegetation, the plant
morphology/canopy density, and the air side resistance (i.e., a function of air turbulence
which is dependent on wind speed and canopy structure). The latter two factors control,
to a large extent, the vapor phase deposition velocity. Bidleman (1988) reported that
vapor phase deposition velocities calculated from the results of field studies with Aroclors,
p,p'-DDT, and chlordane ranged from 0.01 to 1.0 cm/sec. The limited studies that have
modeled deposition of CDD/CDFs onto vegetation have employed deposition velocities of
0.5 cm/sec or higher. Trapp and Matthies (1995) used a default vapor phase deposition
velocity of 0.5 cm/sec for modeling vapor phase deposition of CDD onto meadow
vegetation. Smith et al. (1995) used a deposition velocity of 0.78 cm/sec for deposition
of 2,3,7,8-TCDD onto tall grass. A deposition velocity of 0.5 cm/sec (calculated from the
data of a ryegrass experiment) was used by McLachlan et al. (1995) to predict gaseous
uptake of semivolatile organic compounds (such as CDD/CDFs and PCBs) by grass.
There are two principal applications for vegetation monitoring of gas phase
deposition of CDD/CDFs: (1) monitoring short-term trends (i.e., week, month or season),
and (2) long-term temporal trends (i.e., over the course of a year). To date, three
methods have been employed for each of these two principal applications. Each method
has its own advantages and disadvantages and each measures different components of
total deposition.
Short-term monitoring methods include collection of pasture grass, grass from
grass cultures, and passive collectors (e.g., McLachlan et al., 1995; McLachlan, 1995).
These three types of methods differ in their ability to serve as measures of "true"
deposition onto natural vegetation. However, the methods can be appropriate monitoring
tools depending upon the objective(s) of the monitoring task at hand. Collection of
pasture grass is easy and inexpensive and represents "true" deposition onto native pasture
grass. However, it cannot be standardized and is a function of uncontrollable factors (i.e.,
weather, species present, and growth rate). Collection of grass from a grass culture can
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be semi-standardized but it may not accurately represent "true" deposition on native
vegetation. Grass cultures can be elaborate and relatively costly, and it may be difficult to
replicate rate of growth across sites and times. Passive collectors are easy to deploy,
inexpensive, and easily standardized. However, there are no accepted, standardized
passive methods available at present and the results obtained are not likely to be
representative of "true" deposition on native vegetation.
The long-term methods include collection of conifer needles, canopy fall, and leaf
fall. Collection of conifer needles is easy, inexpensive, and can integrate an entire year's
deposition. Although the use of pine needles as CDD/CDF passive biomonitors has been
extensively reported in Europe, few studies have been reported to date in the United
States (Safe et al., 1992; Fiedler et al., 1995). Disadvantages of this method include
potential loss of some CDD/CDFs because of wax erosion from needles over the year.
Also, the method may not be an appropriate technique for a multi-year monitoring program
because the canopy structure of a conifer forest will change over the course of many
years. That is, the aerodynamic properties of a conifer forest, and consequently the
extent and nature of deposition, will differ significantly over a 10 or 20 year period.
Collection of canopy fall using vessels to collect leaf fall, dry deposition, and wet
deposition integrates the total deposition under the canopy for the entire exposure period.
It is maintenance intensive and the canopy structure may change over the course of many
years. The method captures most gas phase deposition, but not direct gaseous deposition
to the forest floor; deposition onto the forest floor is likely to be much smaller than the
deposition to the canopy. Collection of leaf fall in deciduous is easy, inexpensive, and can
be easily standardized. In conifer forests, because only a small fraction of total annual
deposition is reflected in fallen needles, the collection vessels must catch the eroded
needle waxes as well as fallen needles. Leaf fall collection will also include measurement
of some wet and dry particulate deposition that is retained on the leaves (personal
communication with Dr. Michael McLachlan, University of Bayreuth, July 1996).
Investigations into the role of conifer forests in removing CDD/CDFs from the
atmosphere and the consequences for accumulation in soil have recently been reported
(Horstmann et al., 1995; Horstmann and McLachlan, 1996). These researchers measured
bulk CDD/CDF deposition in a nature spruce forest and in an adjacent clearing over a 1-
year period. Litter fall samples were also collected in the forest. The annual deposition
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flux of hepta- and octa-chlorinated CDD/CDFs was approximately equal in the clearing and
in the spruce forest. However, the deposition flux of the lower chlorinated congeners was
up to five times higher in the forest. During the warmest month of the year, the bulk
deposition flux of some congener groups in the forest was up to 16 times higher than in
the clearing. However, litter fall accounted for only 16 percent (OCDF) to 48 percent
(TCDD) of total deposition in the forest, and canopy throughfall of wet or dry particulate
deposition was demonstrated not to be responsible for the large forest deposition rates
observed. The researchers hypothesize that the high deposition rates are due to dry
gaseous deposition onto conifer needles followed by shedding or erosion of needle waxes,
which may be enhanced during hot weather.
2.6.1.2.3. Wet Deposition. In the case of wet deposition, dioxin-like compounds can
enter the soil and water and impact on the vegetation in one of two phases: either
dissolved in the precipitation or associated with particulate material scavenged by the
precipitation. Over the long term, wet deposition processes are believed to dominate dry
deposition in terms of total mass deposition of CDD/CDFs. Wet deposition is the primary
mechanism responsible for removal of small particulates from the atmosphere. For
removal of particulate-associated chemicals, wet deposition flux is the product of the
particulate scavenging ratio and the chemical concentration on/in various particulate size
fractions. The scavenging ratio is calculated as the product of the scavenging coefficient
and precipitation rate. The scavenging coefficient depends on the size distribution of the
particulates and the intensity and form of precipitation (i.e., liquid or frozen). Scavenging
coefficients have been developed for varying types and intensities of precipitation relative
to different particle diameters based on measurements of scavenging of aerosol particles
during precipitation events.
CDDs and CDFs are removed physically from the atmosphere by wet deposition
(i.e., scavenged by precipitation), particle dry deposition (i.e., gravitational settling of
particles), and gas-phase dry deposition (i.e., sorption of CDD/CDFs in the vapor phase
onto plant surfaces) (Marklund et al., 1990; Rippen and Wesp, 1993; Welsch-Pausch et
al., 1993). Precipitation can be very effective in removing CDDs and CDFs from the
atmosphere. Listed in Table 2-8 are the average precipitation scavenging ratios for
congener groups reported by Hites and Harless (1991) and Koester and Hites (1992a) for
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Bloomington, Indiana, and Indianapolis, Indiana, respectively. The scavenging ratio is the
ratio of the concentration of a chemical in precipitation (rain in these studies) to the
concentration in the atmosphere and is a measure of the effectiveness of rain in removing
the chemical. Also listed in Table 2-8 are the percentages of congener groups scavenged
as particles in rain rather than as dissolved solutes in rain. Total rain scavenging ratios
ranged from 10,000 to 150,000; hepta- and octa- CDDs (i.e., the congeners most
strongly associated with particulates) were scavenged most efficiently.
Wet deposition samplers have been used to collect both rainfall and snow samples.
Several types are available, including those which are equipped with photovoltaic cells or
moisture sensors, which selectively open and close the sampler in response to weather
changes. Precipitation samplers not equipped with moisture sensors are uncovered at all
times and thus are subject to bias (i.e., due to collection of dryfall particles as well as wet
particulate deposition). Wet deposition collectors have been designed to measure total
wet deposition (i.e., dissolved and particulate-associated deposition combined) and
dissolved deposition and particulate deposition separately.
The Bergerhoff method is a standard method specified in the German Clean Air Act
for monitoring CDD/CDF deposition. The method consists of deploying open-mouth jars
for a one month exposure period. Ten Bergerhoff jars have a combined sample collection
area of about 0.1 m2. The Bergerhoff method was designed to collect wet and dry
particulate deposition. The samplers have the potential for evaporative loss of CDD/CDFs
both in the collected wet and dry deposition. The addition of a water/solvent solution to
the jar is reported to minimize this potential problem. The samplers are not thought to be
collecting gas phase CDD/CDFs but no confirmatory testing has been reported.
2.6.1.2.4. Mechanisms for Entry of CDD/CDFs into the Terrestrial Food Chain
Air to Plant to Animal Hypothesis. Based on information currently available, the
primary mechanism by which CDD/CDFs appear to enter the terrestrial food chain is by
vapor phase atmospheric deposition (and to a lesser extent, dry particulate deposition)
onto plant surfaces which are subsequently ingested by animals (e.g., cattle). This
hypothesis was originally advanced by McLachlan and Hutzinger (1990). Deposits onto
the soil can enter the food chain via direct ingestion (e.g., soil ingestion by earthworms,
fur preening by burrowing animals, incidental ingestion by grazing animals, etc).
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CDD/CDFs in soil can also become available to plants and thus enter the food chain by
volatilization and vapor sorption or particle resuspension and adherence to plant surfaces.
Although CDD/CDFs in soil can adsorb directly to underground portions of plants, uptake
from soil via the roots into above ground portions of plants is thought to be insignificant
(McCrady et al., 1990).
Support for this air-to-food hypothesis is provided by Hites and Harless (1991) who
concluded that "background environmental levels of PCD/F are caused by PCD/F entering
the environment through the atmospheric pathway." Their conclusion was based on
demonstrations that the congener profiles in lake sediments could be linked to congener
profiles of combustion sources. Further argument supporting this hypothesis is offered
below:
• Numerous studies have shown that CDD/CDFs are emitted into the air from a
wide variety of sources and that CDD/CDFs can be commonly detected in air at
low concentrations. (See Chapter 3 and Volume 1.)
• Studies have shown that CDD/CDFs can be measured in wet and dry deposition
in most locations including remote areas (Koester and Hites, 1992a; Rappe,
1991).
• Numerous studies have shown that CDD/CDFs are commonly found in soils
throughout the world. (See Chapter 3.) Atmospheric transport and deposition
is the most plausible mechanism that could lead to this widespread distribution.
• Models of the air-to-plant-to-animal food chain have been constructed.
Exercises with these models show that measured deposition rates and air
concentrations can be used to predict food levels that are similar to levels
actually measured in food (Travis and Hattemer-Frey, 1991; also Volume 3).
• Alternative mechanisms of uptake into food appear less plausible:
Uptake in food crops and livestock from water is minimal due to the
hydrophobic nature of these compounds. Travis and Hattemer-Frey (1987,
1991) estimate water intake accounts for less than 0.01 percent of the total
daily intake of 2,3,7,8-TCDD in cattle. Experiments by McCrady et al.
(1990) show very little uptake in plants from aqueous solutions.
Relatively little impact on the general food supply is expected from soil
residues that originate from site-specific sources such as sewage sludge and
other waste disposal operations. Sewage sludge application onto
agricultural fields is not currently a widespread practice in the United States.
Waste disposal operations can be the dominant source of CDD/CDFs in soils
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at isolated locations such as Times Beach, but are not sufficiently
widespread to explain the ubiquitous nature of these compounds.
The release of CDD/CDFs to the environment from the use of pesticides
contaminated with CDD/CDFs is believed to have declined in recent years;
however, the past and current impact of pesticide use on CDD/CDF levels in
the food supply is uncertain. CDD/CDFs have been associated with certain
phenoxy herbicides most of which are no longer produced or have restricted
uses. EPA has issued data call-ins requiring certain pesticide manufacturers
to test their products for CDD/CDF content. The responses, so far, indicate
that current levels in these products are below or near the limit of
quantitation. (See Volume 1.)
Current CDD/CDF levels in food resulting from the use of bleached paper
products containing CDD/CDFs appears to be minimal. In the early 1980s,
testing showed that CDD/CDFs could migrate from paper containers into
food. Current CDD/CDF levels in paper products are now much lower than
in the early 1980s. Also, testing of products such as milk and beef prior to
packaging has shown detectable levels which cannot be attributed to the
packaging. (See Chapter 3.)
A related issue is whether the CDD/CDFs in food result more from current or past
emissions. Sediment core sampling indicates that CDD/CDF levels in the environment
began increasing around the turn of the century, but also that CDF levels have been
declining since about 1980 (Smith et al., 1992). Thus, CDD/CDFs have been
accumulating for many years and may have created reservoirs that continue to impact the
food chain. Researchers in several countries have attempted to compare known emissions
with deposition rates. All of these studies suggest that annual atmospheric depositions
exceed annual emissions by a factor of 2 to 10. One possible explanation for this
discrepancy between source emissions and deposition may be that volatilization or particle
resuspension from these reservoir sources followed by atmospheric scavenging is
responsible. These mass balance studies are highly uncertain, and it remains unknown
how much of the food chain impact is due to current vs past emissions.
Plant Accumulation Models. McLachlan (1995) presents a simple "scavenging"
approach for predicting grass concentrations from air concentrations. He suggests that
grass scavenges the equivalent of 9 m3 or air per gram of grass, and that corn scavenges
4.5 m3 of air per gram of corn. These scavenging ratios were empirically derived from a
set of monitoring data including air, pasture, and corn samples. CDD/CDF concentrations
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in grass can be estimated by multiplying the air concentration by this scavenging
coefficient of 9. McLachlan (1995) suggests that this simple scavenging coefficient
would work with all CDD/CDF congeners since the deposition velocities of vapor and
particle bound CDD/CDF appear to be similar.
EPA has developed an air-to-leaf transfer approach to estimate the concentration of
CDD/CDFs in vegetation resulting from dry and wet deposition. The EPA approach, fully
described in Volume 3, was developed from field test data. The approach is summarized
below followed by a brief description of an alternative approach (the deposition velocity
approach) recently applied to CDD/CDFs by Smith et al. (1995) and Trapp and Mattheis
(1995).
EPA Empirical Air-to-Leaf Approach. Two processes, air-borne vapor phase
absorption and air-borne particle deposition, are assumed to contribute to above ground
vegetation concentrations:
Cabv = Cvpa + Cppa (Eqn. 2-1)
where:
Cabv = concentration in above-ground vegetation, expressed on a dry weight
basis (pg/g)
Cvpa = contribution of concentration due to vapor-phase absorption or
airborne contaminants (pg/g)
Cppa = contribution of concentration due to wet plus dry deposition
of contaminated particulates onto plant matter (pg/g)
The algorithm estimating plant concentrations as a function of vapor-phase air
concentrations, Cvpa, is:
Bvpa Cva VGgg
'vpa ~ -. (Eqn. 2-2)
where:
Cvpa = contribution concentration due to vapor-phase absorption or airborne
contaminants (pg/g)
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Bvpa = mass-based air-to-leaf biotransfer factor, [(pg contaminant/g plant
dry)/(pg contaminant/g air)]
Cva = vapor-phase concentration of contaminant in air (pg/m3)
VGag = empirical correction factor which reduces vegetative concentrations
considering that Bvpa was developed for transfer of air-borne
contaminants into leaves rather than into bulky above ground
vegetation
da = density of air (1,190 g/m3)
The steady state solution for plant concentrations attributed to wet plus dry particle
deposition, Cppa, is:
CPpa = --^7 (Eqn. 2-3)
where:
Cppa = vegetative concentration due to settling of contaminated particulates
onto plant matter (pg/g)
Fp = unit contaminant wet plus dry deposition rate onto plant surfaces
(pg/m2-yr)
kv = first-order dissipation constant (1/yr)
Yj = dry matter yield of crop j (g/m2)
The non-steady state solution has an additional term in the numerator of Equation 2-3,
1-e("kwt|, where t is the time to harvest or removal by grazing. Given that the kv is relatively
large (i.e., a relatively short half-life on the order of weeks), and the growing period for
vegetation of concern can be weeks to months, this additional term reaches unity quickly,
so a steady state solution is justified. The total deposition rate onto plants, Fp , is given
as:
(Eqn. 2-4)
where:
Fp = unit contaminant wet plus dry deposition rate onto plant surfaces
(pg/m2-yr)
Cpa = air-borne particulate phase contaminant concentration (pg/m3)
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Vd = deposition velocity (m/yr)
Ij = fraction of particulates intercepted by crop j during deposition
RN = annual rainfall (m/yr)
Rw = fraction of particles retained on vegetation after rainfall
Wp = scavenging coefficient (unitless)
The major uncertainties of this modeling approach are:
• Vapor/particle partitioning: The V/P partitioning modeling approach (i.e.,
Junge-Pankow model) yields different partitioning ratios than suggested by the
results of some monitoring studies (i.e., monitoring suggests a greater
percentage in the vapor phase). The model could be inaccurate or the
monitoring data could be biased to vapor (i.e., blow-off potential).
• Vapor transfers to vegetation: The basis for the vapor-phase biotransfer
factors (Bvpa) is the research performed Welsch-Pausch et al. (1995). There is
some uncertainty that the Welsch-Pausch experiments may not be
representative of field situations (e.g., pots were raised off the ground, and the
grass was a dense monoculture) and that more typical field situations (e.g., at
ground level with varied vegetation of lesser density) may lead to lower vapor-
phase transfer factors. However, because the vapor phase transfer factor is
also a function of the length of exposure, grass that is several months old may
have higher levels than the grass used by Welsch-Pausch et al. (1995).
• Particle deposition: There is uncertainty surrounding the following factors: Rw
(fraction retained on vegetation from wet deposition), the weathering half-life
on vegetation, absorption of particulate-associated dioxin by vegetation, and
the particle deposition velocity.
Deposition Velocity Approach for Vapor Phase CDD/CDFs. This alternative
approach is based on a transfer velocity (or conductance) term, a plant dissipation term,
and a plant yield term as shown in Equation 2-5. The approach is exactly analogous to
the EPA approach for modeling particle phase deposition. This approach has been
described and parameterized for vapor phase 2,3,7,8-TCDD impacts to grassy plants in
two articles, Trapp and Matthies (1995) and Smith et al. (1995). The steady state
solution for the transfer velocity approach is given as:
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Cvpa
where:
Cvpa = plant concentration due to vapor-phase transfer (pg/g dry weight)
Fv = deposition of vapor-phase congener (pg/m2-day)
kv = first-order dissipation constant (day"1)
Yj = yield of crop j (g/m2)
Like the solution for particle phase deposition to plants, the non-steady state solution for
Equation (2-5) has an additional term in the numerator, 1-e("kvt|, where t is the time to
harvest or removal by grazing. Both authors who applied this approach assumed a half-
life for vapor phase dioxins on plants on the order of days to weeks. Because the "time to
harvest" for grasses is also on the order of days to weeks, the additional term reaches
unity quickly and again a steady state solution is justified.
The two articles evaluated do diverge at this point. The Trapp and Matthies (1995)
approach is actually a comprehensive approach involving particle phase impacts and soil to
plant impacts. Their analysis suggests vapor phase impacts to foliar vegetation dominate
for 2,3,7,8-TCDD. They also present their solution in a more generalized fashion by
having a volume term in the denominator of Equation 2-5 instead of a plant yield term; the
volume term is easily converted to a mass (or yield) term with a plant density factor.
Their solution for Fv is:
Fv = A 9 Cva 86400 (Eqn. 2-6)
where:
Fv = deposition of vapor-phase congener (pg/m2-day)
A = leaf area index (m2 leaf area/m2 ground area)
g = conductance (m/sec)
Cva = vapor phase air concentration (pg/m3)
86400 = conversion factor (sec to days)
Trapp and Matthies (1995) used a default value of 0.1 cm/sec for the conductance term,
g, when modeling the deposition of 2,3,7,8-TCDD onto meadow grass. The appropriate
value to use for conductance for a given chemical depends upon the plant species, the
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environmental conditions, and the lipophilicity of the vapor phase chemical. The possible
range reported by Trapp and Matthies (1995) is 0.01 to 0.5 cm/sec with the appropriate
value increasing as lipophilicity increases. Trapp and Matthies (1995) used a value of 5
for the leaf area index, A, in their TCDD modeling exercise. The product of the A term
and the g term (i.e., 0.5 cm/sec) is analogous to a deposition velocity.
Smith et al. (1995) estimate the Fv as a multiplication of the vapor phase air
concentration, Cva (pg/m3), the transfer velocity, Vt, (cm/sec), and the plant interception
fraction (unitless). They state that the transfer velocity is represented as the inverse of
the sum of the resistances to transfer to the plant surface as:
V* = = — fFnq. 2-7)
where:
Vt = transfer velocity (cm/sec)
Ra = atmospheric resistance (sec/cm) a function of vertical turbulent transport
Rb = surface boundary layer resistance (sec/cm) a function of molecular
diffusivity
Rc = plant canopy/leaf resistance (sec/cm) a function of vegetative density,
stomatal uptake, surface effects, and humidity
Smith et al. (1995) assumed the following reasonably conservative resistance values for
deposition of 2,3,7,8-TCDD onto a flat open area with tall grass under neutral atmospheric
stability conditions: Ra = 0.4 sec/cm; Rb = 0.38 sec/cm; and Rc = 0.5 sec/cm. The
resulting transfer velocity is 0.78 cm/sec.
The deposition velocity approach is not uniform among researchers, and has the
two key uncertain quantities: the velocity itself (termed Vt by Smith et al. (1995) and
estimated as gA by Trapp and Matthies (1995)) and the plant degradation term - assigned
values ranging by a factor of three by the two research efforts. The companion document
to this report (i.e., Volume 3) provides a detailed comparison of the empirical EPA model,
the Trapp and Matthies (1995) model, and the Smith et al. (1995) model.
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2.6.1.3. Transport Mechanisms in Soil
Upon deposition of CDD/CDFs onto soil or plant surfaces, there can be an initial
loss due to photodegradation and/or volatilization. The extent of initial loss due to
volatilization and/or photodegradation is difficult to predict and is controlled by climatic
factors, soil characteristics, and the concentration and physical form of the deposited
CDD/CDFs (i.e., particulate-bound, dissolved in solvent, etc.) (Freeman and Schroy, 1989;
Paustenbach et al., 1992; Nicholson et al., 1993). For example, observations from the
Seveso incident indicated that the levels of 2,3,7,8-TCDD aerially deposited on the soil
surface decreased substantially in the first six months (diDomenico et al., 1982) but that
rate of disappearance then slowed by over two orders of magnitude (diDomenico et al.,
1990). Nash and Beall (1980) reported that 12 percent of the 2,3,7,8-TCDD applied to
bluegrass turf as a component (7.5 ppm concentration) of an emulsifiable Silvex
concentrate volatilized over a period of 9 months. Schwarz and McLachlan (1993)
observed no significant changes in CDD/CDF concentrations in sewage sludge amended
soil that was exposed to natural sunlight for six weeks in the late summer/early fall in
Germany. Similarly, Cousins et al. (1996) detected no volatilization from sludge-amended
soils through which air was pumped for 30 days.
Although few studies have evaluated quantitatively the transport of soil-bound
CDD/CDFs, the very low water solubilities, high Kocs, and persistent nature of these
chemicals indicate that erosion of soil to water bodies may be the dominant surface
transport mechanism for CDD/CDFs sorbed to soil in settings where erosion is possible
(Paustenbach et al., 1992; Nicholson et al., 1993). Because of their very low water
solubilities and vapor pressures, CDD/CDFs below the soil surface (i.e., below the top few
millimeters) are strongly adsorbed and show little upward or downward vertical migration,
particularly in soils with a high organic carbon content (Yanders et al., 1989). Freeman et
al. (1987) found no statistically meaningful changes in the concentration profile of
1,2,7,8-TCDD in the top 1 cm of Time Beach Soil over a 1 6-month period, with the
exception of the top 3 mm of soil exposed to water and sunlight in which 50 percent
reduction in 2,3,7,8-TCDD concentration was observed. In addition, the more chlorinated
congeners do not show any significant degree of degradation below the soil surface.
Although for several years it was believed that near-surface (i.e., the top 1cm)
CDD/CDFs could volatilize slowly to the surface (Freeman and Schroy, 1985), recent
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research has indicated that CDD/CDFs, particularly the tetra and higher chlorinated
congeners, show little or no movement upward or downward in the subsurface unless
surfactants or a carrier such as waste oil or diesel fuel is present to act as a solvent
(Kapila et al., 1989; Puri et al., 1989; Puri et al., 1990; Yanders et al., 1989; Schramm et
al., 1995). For example, Palausky et al. (1986) injected 2,3,7,8-TCDD dissolved in
various organic solvents into soil columns to determine the extent of vapor phase
diffusion; little movement due to volatilization was observed unless the soil was incubated
at 40°C. However, laboratory studies have shown that 2,3,7,8-TCDD moves readily
through soil with waste oil components and that mobility can also be enhanced by the
presence of surfactants such as sodium lauryl sulfate (Yanders et al., 1989; Puri et al.,
1989; Schramm et al., 1995). Overcash et al. (1991) developed a model that considers
diffusive transport of 2,3,7,8-TCDD in solvents and takes into account the rate of
adsorption and desorption of 2,3,7,8-TCDD from the soil particles.
Paustenbach et al. (1992) reviewed many major published studies on dioxin
persistence in soil and concluded that 2,3,7,8-TCDD probably has a half-life of 25 to 100
years in subsurface soil and 9 to 1 5 years at the soil surface (i.e., the top 0.1 cm).
Several major studies reviewed by Paustenbach et al. (1992) and additional recent studies
are summarized below. Some of these recent studies have concluded that the binding of
dioxin-like compounds to soil approaches irreversibility over time due to the encapsulation
of the compounds in soil organic and mineral matter (Puri et al., 1989; Puri et al., 1992;
Adriaens and Grbic-Galic, 1992).
McLachlan et al. (1996) presented data on CDD/CDF persistence in a sludge-
amended soil sampled from a long-term field experiment started in 1968. Over 50 percent
of the CDD/CDFs present in the soil in 1972 were still present in 1990. The
concentrations of all congeners were observed to decrease gradually and in the same
manner over this time, indicating that either physical loss of material from the
experimental plot had occurred or all congeners had undergone a uniform reduction in
extractability over time. Half-lives for the disappearance of CDD/CDFs from the sludge-
amended soil after 1972 were on the order of 20 years. These half-lives were believed by
McLachlan et al. (1996) to principally reflect physical removal rather than degradation.
Young (1983) conducted field studies on the persistence and movement of
2,3,7,8-TCDD during 1973-1979 on a military test area that had been aerially sprayed
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with 73,000 kg of 2,4,5-T during the period 1962-1970. TCDD levels of 10 to 1,500
ng/kg could be found in the top 15 cm of soil 14 years after the last application of
herbicide at the site. Although actual data were not available on the amount of 2,3,7,8-
TCDD originally applied as a contaminant of the 2,4,5-T, best estimates indicated that less
than one percent of the applied 2,3,7,8-TCDD remained in the soil after 14 years.
Photodegradation at the time of and immediately after aerial application was believed by
Young (1983) to be responsible for most of the disappearance. However, once
incorporated into the soil, the data indicated a half-life of 10 to 12 years.
Orazio et al. (1992) studied the persistence of di- to octa-chlorinated CDDs and
CDFs in sandy loam soil held in laboratory columns under water-saturated soil conditions
for a period of 1 5 months. Measurable upward movement was reported only for the
dichlorofurans and dichlorodioxins. Downward movement was only noticeable for the
dichloro- and trichloro-congeners. The mobility of the CDDs and CDFs was not
significantly affected by co-contaminants (i.e., pentachlorophenol and creosote
components) present at concentrations as high as 6,000 mg/kg. As much as 35 percent
loss of the di- and trichloro-congeners due to degradation was observed; no significant
degradation of the tetra- through octa-chlorinated congeners was reported (Orazio et al.,
1992).
Hagenmaier et al. (1992) collected soil samples around two industrial plants in
Germany in 1981, 1987, and 1989 at the same site and from the same depth, using the
same sampling method. There was no indication (within the limits of analytical accuracy
(+/- 20 percent)) of appreciable loss of CDDs and CDFs by vertical migration,
volatilization, or degradation over the 8-year period. Also, there were no significant
changes in the congener distribution pattern (i.e., tetra- through octa-) over this time
period.
Yanders et al. (1989) reported that 12 years after oil containing 2,3,7,8-TCDD was
sprayed on unpaved roads at Times Beach, Missouri, no dioxin was discovered deeper
than 20 cm. However, these roads were paved about 1 year after the spraying episode,
thus preventing volatilization to the atmosphere. Yanders et al. (1989) excavated this soil
and placed the soil in bins located outdoors, subject to the natural conditions of sunlight
and precipitation. They reported no appreciable loss nor vertical movement of 2,3,7,8-
TCDD from the soil, even in the uppermost sections, during a 4-year study period. Puri et
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al. (1992) reported no migration or loss of 1,2,3,4-TCDD, 1,2,3,7,8-PeCDD, OCDD, and
OCDF from samples of this soil which were examined for 2 years in controlled laboratory
column experiments.
Hallett and Kornelson (1992) reported finding 2,3,7,8-TCDD at levels as high as 20
pg/g in the upper 2 inches of soil obtained from areas of cleared forest in New Brunswick,
Canada, where the pesticides 2,4-D and 2,4,5-T had been applied in one or more
applications 24 to 33 years earlier.
Pereira et al. (1985) reported contamination by CDDs of the sand and gravel
aquifer underlying unlined surface impoundments at a wood-treatment facility that had
utilized creosote and pentachlorophenol. CDDs migrated both vertically and horizontally in
the subsurface. Puri et al. (1992), using soil column experiments in the laboratory,
demonstrated that pentachlorophenol and naphthalene and methylnaphthalene
(components of creosote) readily transported CDD/CDFs through soil. Puri et al. (1989)
and Kapila et al. (1989) demonstrated that application of waste oil and anionic surfactant
solutions to field and laboratory columns of Times Beach soil can move 2,3,7,8-TCDD
through soil. Walters and Guiseppe-Elie (1988) showed that methanol/water solutions
(1g/L or higher) substantially increase the mobility of 2,3,7,8-TCDD in soils.
2.6.1.4. Transport Mechanisms in Water
The dominant transport mechanism for removal of CDD/CDFs from the water
column is believed to be sedimentation and, ultimately, burial in sediments. Sediment
resuspension and remobilization of CDD/CDFs will vary on a site-by-site basis depending
on the nature and extent of physical processes (e.g., winds/waves/currents) and biological
processes (disturbance by benthic organisms) (Fletcher and McKay, 1992).
Even though CDD/CDFs have very low vapor pressures, they can volatilize from
water. However, volatilization is not expected to be a significant loss mechanism for the
tetra- and higher chlorinated CDD/CDFs from the water column under most non-spill
scenarios. Podoll et al. (1986) calculated volatilization half-lives of 15 days and 32 days
for 2,3,7,8-TCDD in rivers and ponds/lakes, respectively. Broman et al. (1992) used
measured concentrations of CDD/CDFs in ambient air (gaseous phase) and in Baltic Sea
water (truly dissolved concentrations) to calculate the fugacity gradient over the air-water
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interface. The fugacity ratios obtained indicated a net transport from air to water (ratios
between 0.4 and 0.004).
Aquatic organisms can bioaccumulate significant levels of CDD/CDFs. Although
the mass of CDD/CDFs in the biota in a given water body will account for only a small
fraction of the total mass of CDD/CDFs in that water body (Mackay et al., 1992a), these
bioaccumulated CDD/CDFs have entered the aquatic food chain and can lead to potentially
significant human and wildlife exposures and cause sensitive fish species to be at
increased risk (U.S. EPA, 1993).
2.6.1.4.1. Sorption to Particulates and Sedimentation
Most CDD/CDFs entering the aquatic environment are associated with particulate
matter (i.e., dry and wet deposition of atmospheric particles, eroded soil/stormwater
runoff solids, and solids in municipal and industrial discharges) and are likely to remain
sorbed to the particulate matter once in the aquatic environment. Recent studies have
demonstrated that dissolved CDD/CDFs entering the aquatic environment will, like other
lipophilic, low water solubility organic compounds, partition to suspended solids or
dissolved organic matter such as humic substances.
Muir et al. (1992) and Servos et al. (1992) recently reported that 48 hours after
the addition of 2,3,7,8-TCDF, 1,3,6,8-TCDD, and OCDD in a sediment slurry to natural
lake water/sediment limnocorrals, between 70 and 90 percent had partitioned to
suspended particulates. The proportion freely dissolved in water ranged from <2 percent
for 2,3,7,8-TCDF and OCDD to 10 to 15 percent for 1,3,6,8-TCDD. The remainder was
associated with dissolved organic substances.
Broman et al. (1992) analyzed water collected from nine sampling points in the
Baltic Sea selected to be representative of background levels. The concentration of
particle-associated (>0.45mm) total CDD/CDFs varied between 0.170 and 0.390 pg/L
with an average concentration of 0.230 pg/L (or 66 percent of total CDD/CDFs). The
total CDD/CDF concentration of the "apparently" dissolved fraction varied between 0.036
and 0.260 pg/L with an average concentration of 0.120 pg/L (or 34 percent of the total).
Subsequent calculations estimated that, on average, only 0.070 pg/L of the "apparently"
dissolved CDD/CDFs were truly dissolved.
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Servos et al. (1992) reported that the 1,3,6,8-TCDD and OCDD added as a
sediment slurry to lake limnocorrals rapidly partitioned/settled to surficial sediments where
they persisted over the 2 years of the study. The half-lives of 1,3,6,8-TCDD and OCDD in
the water column were reported as 2.6 and 4.0 days, respectively. Based on sediment
trap and mixed surface layer studies of the Baltic Sea, Broman et al. (1992) report that the
mass of CDD/CDFs in the mixed surface layer at any moment represents about 1 percent
of the total flux of CDD/CDFs to the sediment annually; this implies little recirculation of
these compounds within the water column of the Baltic Sea. Broman et al. (1992) also
reported that the concentration of CDD/CDFs in settling solids (i.e., sediment trap
collected material) is approximately one order of magnitude greater than the concentration
in suspended particulates. They attributed this elevated concentration to the capacity of
settling solids to scavenge the dissolved fraction as the solids settle through the water
column. Similar findings have been reported elsewhere (e.g., Baker et al., 1991) for PCBs
and PAHs in the Great Lakes.
2.6.1.4.2. Bioaccumulation
Fish and invertebrates can strongly bioaccumulate 2,3,7,8-substituted CDD/CDFs,
although the benthic and pelagic pathways by which the accumulation occurs are not well
understood. Organisms have been shown to accumulate CDD/CDFs when exposed to
contaminated sediments and also to bioconcentrate CDD/CDFs dissolved in water.
However, because most CDD/CDFs in the water column and sediment are associated with
particulate matter and dissolved organic matter, the accumulation observed in the
environment may be primarily food chain-based starting with uptake by benthic organisms
(e.g., mussels, chironomids) directly from sediment pore waters and/or by ingestion or
filtering of contaminated particles. Those organisms consuming benthic organisms (e.g.,
crayfish, suckers) would then pass the contaminants up the food chain (Muir et al., 1992;
Fletcher and McKay, 1992; U.S. EPA, 1993).
A thorough review of the concepts and available information on the
bioaccumulation of 2,3,7,8-substituted CDD/CDFs is presented in U.S. EPA (1993) and
U.S. EPA (1995). A brief overview of the material presented in these reports is provided
in the remainder of this section.
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Bioconcentration. For aquatic organisms, bioconcentration refers to the net
accumulation (i.e., intake less elimination and metabolic transformation) of a chemical
resulting from direct uptake from the water by gill membranes or other external body
surfaces. A bioconcentration factor (BCF) is the ratio (in L/kg) of a chemical's
concentration in the tissue of an aquatic organism to its concentration in the ambient
water (U.S. EPA, 1993; 1995):
BCF = -i (Eqn. 2-8)
Uw
where:
Ca = concentration of the chemical in the aquatic biota
Cw = concentration of the chemical in the ambient water
BCFs are measured in laboratory experiments. To be of most use in predicting
bioaccumulation in natural settings, BCFs should be determined under steady-state
conditions (i.e., conditions under which the concentrations in the biota and other ambient
water are stable over a period of time). For highly hydrophobic chemicals like CDD/CDFs,
steady-state takes a long time to reach and often may not be reached in laboratory
experiments particularly with larger organisms which tend to have slower uptake rates and
longer half-lives for elimination. Also, for highly hydrophobic compounds, a significant
fraction of the chemical concentration in the water can be associated with suspended
particles and dissolved organic matter and be less available for uptake by the organism.
In general, BCFs for a given species (particularly BCFs calculated using dissolved
chemical concentrations rather than total water concentrations) are expected to be largely
independent of site water characteristics (U.S. EPA, 1993). For hydrophobic compounds,
the lipid component of an organism is believed to dominate partitioning of the chemical
between the organism and water. Therefore, it is often useful to express BCFs on the
basis of organism lipid content in order to reduce variability among whole weight BCFs
reported for species differing in lipid content.
The expected equilibrium values for steady-state BCFs (lipid content basis) for
2,3,7,8-substituted CDD/CDFs are the corresponding Kow values. However, because most
reported BCFs for CDD/CDFs are calculated on the basis of total water concentrations, the
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reported BCFs are significantly less than these values. U.S. EPA (1993) presents a
compilation of measured steady-state BCFs for 2,3,7,8-TCDD. The log BCFs vary by
more than an order of magnitude (4.91 to 6.63 on a lipid content basis; 3.97 to 5.20 on a
whole body basis). This variability is likely due to incomplete characterization of exposure
concentrations or experimental shortcomings, including partitioning onto organic matter in
test systems, oversaturation of the chemical in water, and time-varying concentrations in
static systems (U.S. EPA, 1993). Table 2-9 presents log BCF values reported by various
researchers for CDD/CDFs.
Bioaccumulation. For aquatic organisms, bioaccumulation refers to the net
accumulation of a chemical from exposure via food and sediments as well as water. A
bioaccumulation factor (BAF) is the ratio (in L/kg) of a chemical's concentration in the
tissue of an aquatic organism to its concentration in the ambient water, in situations
where both the organism and its food are exposed and the ratio does not change
substantially over time (U.S. EPA, 1993; 1995).
C
BAF = — (Eqn. 2-9)
Lw
where:
Ca = concentration of the chemical in the aquatic biota
Cw = concentration of the chemical in the ambient water
The difference between BAFs and BCFs is primarily in the routes of exposure
involved and the levels of accumulation attained because of these exposure routes. BCFs
are measured in laboratory experiments designed to measure the chemical uptake by the
organism only from water. BAFs are usually determined from measurements of chemical
concentration in water and organism tissue samples that are obtained in the field from
aquatic systems at presumed steady-state exposure conditions. Thus, BAFs include both
direct uptake from the water as well as uptake from intake of food and sediments. The
previous discussion under "Bioconcentration" concerning the form of the chemical in the
water (i.e., dissolved or total) and the value of lipid normalization also applies to BAFs.
Because reliable measurements of trace levels of CDD/CDFs in ambient water are
generally not available, it is not practical to develop measured BAFs for these compounds.
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However, detectable concentrations of CDD/CDFs are generally measurable in sediments.
The relationship between chemical concentrations in organisms and sediments is defined
as the biota-sediment accumulation factor (BSAF). BSAFs can be used to measure and
predict bioaccumulation directly from measured concentrations of chemicals in surface
sediments and biota. They may also be used to estimate BAFs. Because BSAFs are
based on field measurements and incorporate uptake from water and food, and the effects
of metabolism and growth, BAFs estimated from BSAFs will incorporate the net affect of
all these factors (U.S. EPA, 1993; 1995).
BSAFs are measured by relating lipid-normalized concentrations of a chemical in an
organism to the organic carbon-normalized concentration of the chemical in surface
sediment samples associated with the average exposure of the organism (U.S. EPA,
1995). The BSAF equation is:
C,
BSAF = -I (Eqn. 2-10)
Us
where:
C, = lipid-normalized concentration of the chemical in aquatic biota
Cs = organic carbon-normalized concentration of the chemical in surface sediment
The ratios of BSAFs of CDD/CDFs to a BSAF for 2,3,7,8-TCDD yield
bioaccumulation equivalency factors (BEFs) which can be used to estimate the combined
toxic potential of CDD/CDFs as a toxic equivalence concentration. Table 2-10 presents
BSAFs and BEFs derived for CDD/CDFs from Lake Ontario lake trout and sediment. A
compilation of additional BSAFs is presented in the companion document to this report
(i.e., Volume 3). Chapter 3 of this report summarizes concentrations of CDD/CDFs in
aquatic organisms that have been reported in the literature.
2.6.1.4.3. Mechanisms for Entry of CDD/CDFs Into the Aquatic Food Chain
Air to Land to Water to Animal Hypothesis. Based on information currently
available, the primary mechanism by which CDD/CDFs enter the aquatic food chain in
most freshwater bodies is by atmospheric deposition onto land followed by transport of
the deposited material in stormwater runoff/erosion into water bodies. Once in the water
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body, entry into the food chain starts with uptake by benthic organisms as described in
Section 2.6.1.4.2.
CDD/CDFs can also enter aquatic systems directly from industrial and POTW
effluent discharges, from deposition of CDD/CDFs in the atmosphere directly onto water
bodies (of importance for the Great Lakes), and in erosion/stormwater runoff from areas
where dioxin-containing material is present (e.g., a contaminated industrial or waste
disposal site). Thus, for any given water body, the dominant transport mechanism will
depend on site-specific conditions. For example, Pearson and Swackhammer (1997)
report that atmospheric deposition is the dominant source of CDD/CDFs to Lake Superior,
but not to Lake Michigan or Lake Ontario. However, for most freshwater bodies today,
erosion/ stormwater runoff is the probable dominant mechanism for CDD/CDF input and
the CDD/CDFs present in that runoff can be attributed to atmospheric deposition. Several
studies support this hypothesis.
For example, Smith et al. (1995) analyzed CDD/CDF concentrations in sediment
cores, air, precipitation, soil, and stormwater runoff in an effort to determine the
contributing sources of these compounds to the lower Hudson River. The mass balance
estimates developed from these data are, for the period 1990-1993: stormwater runoff
entering tributaries (76 percent of total CDD/CDF input); anthropogenic wastes (19
percent); atmospheric deposition (4 percent); and shoreline erosion (less than 1 percent).
Smith et al. (1995) also projected the percent contribution of these same sources for the
year 1970 to be: anthropogenic wastes (70 percent); stormwater runoff into tributaries
(15 percent); atmospheric deposition (15 percent); and shoreline erosion (0.1 percent).
Lebeuf et al. (1996) analyzed sediment cores from different locations in the lower
St. Lawrence River Estuary and the Gulf of St. Lawrence. The congener group profiles
found in the samples indicate that the input of CDD/CDFs is primarily from the
atmosphere. Comparison of the CDD/CDF concentrations in sediments collected from
areas where sediment accumulation is due primarily to fluvial transport with sediments
from areas where sediment accumulation is due primarily to direct atmospheric deposition
onto the water indicates that the contribution of CDD/CDF from direct atmospheric
deposition represents less than 35 percent of the sediment burden. Thus, the primary
source of CDD/CDFs was determined to be emissions to the atmosphere upwind of the
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Estuary that are deposited within the watershed and subsequently transported
downstream by fluvial waters.
Paustenbach et al. (1996) and Mathur et al. (1997) reported that stormwater
runoff from 1 5 sites in the San Francisco area contained CDD/CDF I-TEQDF at levels
ranging from 0.01 to 65 pg/L; most samples contained less than 15 pg/L. The sites
differed widely in land use; the highest levels measured were obtained from an urban, but
nonindustrialized area. A distinct variability was noted in the results obtained at the same
sampling location during different rain events. However, the profiles of CDD/CDFs in the
urban stormwater samples were similar at all sites, particularly in samples collected at the
onset of rain events. Stowe (1996) reported similar findings from analyses of sediments
from three stormwater basins collecting runoff from a military base, city street, and
parking lots.
Further argument supporting this hypothesis is offered below:
• Much less data have been published on CDD/CDF discharges in wastewater
than have been published on emissions to air. Nonetheless, the general
scientific consensus is that because of their extremely low water solubilities and
the restrictions on suspended particulate discharges in wastewater discharges,
CDD/CDF emissions via wastewater are negligible compared to atmospheric
emissions and land-disposed waste material. This is the conclusion drawn in
the U.S. Inventory (see Volume 1), the European Dioxin Inventory (Quab and
Fermann, 1997), and the recent United Nations compilation of national and
regional CDD/CDF emissions inventories (UNEP, 1999).
• Studies have measured CDD/CDFs in wet and dry deposition in most locations,
including remote areas (see Table 2-4).
• Studies have measured CDD/CDFs in soils in most locations, including remote
areas (see Chapter 3). Volume 1 of this Reassessment presents preliminary
rough estimates of the potential magnitude of soil erosion via rural runoff
sources to be 2,700 grams of I-TEQDF annually.
• The Paustenbach et al. (1996) and Mather et al. (1997) studies cited above
detected CDD/CDFs in all stormwater samples collected in the San Francisco
area. However, CDD/CDFs were only rarely detected in discharges collected
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from nine POTWs in the San Francisco area during the same approximate time
period (California Regional Water Quality Control Board, 1996).
• Fisher et al. (1998) reported that urban runoff samples from eight sites (15
samples) in the Santa Monica Bay watershed contained CDD/CDF I-TEQDF at
levels ranging from 0.7 to 53 pg/L (all but one sample were in the range of 0.7
to 10 pg/L). The samples were collected in 1988/1989 from continuously
flowing storm drains during both dry and storm periods. Concentrations
measured during storm events (mean = 18 pg/L) were higher than
concentrations observed during dry periods (mean = 1 pg/L).
2.6.1.5. Transformation Processes
2.6.1.5.1. Photolysis. Photolysis appears to be one of the most environmentally
significant degradation mechanisms for CDD/CDFs in water and soil, and possibly in the
atmosphere. CDD/CDFs absorb electromagnetic radiation at wavelengths greater than
290 nm (i.e., the lower bound of the Sun's radiation reaching the Earth's surface) and,
therefore, can be expected to be subject to photolysis by sunlight. Numerous studies
have demonstrated that CDD/CDFs undergo relatively rapid photolysis in a variety of
organic solvents and at much slower rates in water, typically following first order kinetics.
Because of the difficulties inherent in controlling experimental variables for nonvolatile and
highly lipophilic compounds like CDD/CDFs, few photolysis studies have been performed
with natural waters, with CDD/CDFs sorbed to soil or particulate matrices, and with gas
phase CDD/CDFs. The photochemistry of CDD/CDFs has been reviewed by Miller and
Zepp (1987), Choudry and Webster (1987), Atkinson (1991; 1996), and others.
The major products from photolysis are complex and, in most studies, good mass
balances have not been obtained. Although, the photolytic pathway(s) for CDD/CDFs has
not been fully identified, a major photolysis pathway appears to be photodechlorination
resulting in formation of lower chlorinated CDD/CDFs. Several researchers have reported
that carbon-oxygen cleavage and other mechanisms may be similarly or more important
pathways for CDD/CDFs containing four or fewer chlorines (Choudry and Webster, 1989;
Dulin et al., 1986; Miller et al., 1989; Kieatiwong et al., 1990). Studies performed to
date suggest that the photolytic degradation products from irradiation of CDD/CDFs in
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organic solvents may be significantly different than those observed when surface-sorbed
and gas-phase CDD/CDFs are irradiated. A preferential loss of chlorines from the lateral
positions (i.e., chlorines at the 2, 3, 7, and 8 positions) rather than from the peri positions
(i.e., chlorines at the 1, 4, 6, and 9 positions) has been reported for solution studies
(Crosby et al., 1973; Dobbs and Grant, 1979; Tysklind and Rappe, 1991); the opposite
trend is observed for some congener groups when irradiated as dry films, sorbed to soil,
and as gas-phase CDD/CDFs (Choudry and Webster, 1989; Kieatiwong et al., 1990; Sivils
et al., 1994; Sivils et al., 1995).
Most studies performed until recently have been conducted using artificial light,
pure congeners, and reaction media consisting of homogenous solutions in aqueous-
organic solvent mixtures, silica gel, or clean solid surfaces. Thus, although photolysis of
CDD/CDFs at environmentally significant rates has been observed in laboratory studies,
the results of these studies may not be representative of photolysis rates that occur under
actual environmental conditions. The following subsections summarize the key findings of
recent environmentally significant studies for the water, soil, and air media.
Photolysis in Solution. Numerous studies have demonstrated that CDD/CDFs will
undergo photodechlorination following first order kinetics in solution with preferential loss
of chlorine from the lateral positions. Photolysis is slow in pure water but increases
dramatically when solvents serving as hydrogen donors are present such as hexane,
benzene, methanol, acetonitrile, hexadecane, ethyl oleate, dioxane, and isooctane (Buser,
1976; Buser, 1988; Choudry and Webster, 1987; Choudry et al., 1990; Crosby et al.,
1971; Crosby, 1978; Crosby, 1981; Dobbs and Grant, 1979; Dougherty et al., 1991;
Dulin et al., 1986; Friesen et al., 1990a; Hutzinger, 1973; Koester and Hites, 1992b;
Koshioka et al., 1990; Wagenaar et al., 1995; and others).
The photolytic behavior of CDD/CDFs in organic solvents or in pure water,
however, is not expected to accurately reflect the photolytic behavior of these compounds
in natural waters. Natural waters have differing quantities and types of suspended
particulates and dissolved organic material that could either retard or enhance the
photolysis of CDD/CDFs. However, only a few studies have been performed that have
examined the photolysis of CDD/CDFs using natural waters and sunlight. Several other
published studies have used a mixture of water and acetonitrile to enhance the solubility
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of CDD/CDFs. The following paragraphs summarize the results of these studies. Table 2-
11 presents selected results from these studies.
Dulin et al. (1986) studied the photolysis of 2,3,7,8-TCDD in a watenacetonitrile
solution (1:1, v/v) under sunlight and artificial light (i.e., a mercury lamp). The quantum
yield for photodegradation of 2,3,7,8-TCDD in water was three times greater under
artificial light at 313 nm than under sunlight which suggests that the medium-pressure
mercury lamp used was a more efficient light source than the sun (Koester and Hites,
1992b). Podoll et al. (1986) used the Dulin et al. (1986) reaction rate data from the
artificial light experiment to calculate seasonal half-life values for 2,3,7,8-TCDD at 40
degrees north latitude in clear near-surface water. The calculated seasonal values for
half-lives ranged from 0.9 days in summer to 4.9 days in winter. Using the Dulin et al.
(1986) reaction rate data from the sunlight experiment yields calculated seasonal values
for half-lives ranging from 2.7 days in summer to 16 days in winter.
Choudry and Webster (1989) studied the photolytic behavior of a series of CDDs in
a watenacetonitrile solution (2:3, v/v) using a low-pressure mercury lamp. Choudry et al.
(1990) investigated the photolytic behavior of two CDFs (1,2,4,7,8-PeCDF and
1,2,3,4,7,8-HxCDF) using a similar experimental setup but with a 1:1 watenacetonitrile
solution. Assuming that the quantum yields observed in these studies are the same as
would be observed in natural waters, Choudry and Webster (1989) and Choudry et al.
(1990) estimated the mid-summer half-life values at 40 degrees north latitude in clear
near-surface water to be as follows: 1,2,3,7-TCDD (1.8 days); 1,3,6,8-TCDD (0.3 days);
1,2,3,4,7-PeCDD (15 days); 1,2,3,4,7,8-HxCDD (6.3 days); 1,2,3,4,6,7,8-HpCDD (47
days); OCDD (18 days); 1,2,4,7,8-PeCDF (0.2 hours); and 1,2,3,4,7,8-HxCDF (6 hours).
However, Choudry and Webster (1989) also experimentally determined the sunlight
photolysis half-life of 1,3,6,8-TCDD in pond water to be 3.5 days (i.e., more than 10
times greater than the half-life predicted by laboratory experiments). The authors
attributed this significant difference in photolysis rates to the light screening/quenching
effects of dissolved organic matter.
Friesen et al. (1990a) examined the photolytic behavior of 1,2,3,4,7-PeCDD and
1,2,3,4,6,7,8-HpCDD in watenacetonitrile (2:3, v/v) and in pond water under sunlight at
50 degrees north latitude. The observed half-lives of these two compounds in the
watenacetonitrile solution were 12 and 37 days, respectively, which are very similar to
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the results observed by Choudry and Webster (1989) for these two congeners using
artificial light. However, in contrast to the results observed by Choudry and Webster
(1989) for 1,3,6,8-TCDD, Friesen et al. (1990a) found that the half-lives of 1,2,3,4,7-
PeCDD and 1,2,3,4,6,7,8-HpCDD were much shorter in pond water (0.94 and 2.5 days,
respectively) than in the watenacetonitrile solution. Similarly, Friesen et al. (1993)
studied the photodegradation of 2,3,7,8-TCDF and 2,3,4,7,8-PeCDF by sunlight using
watenacetonitrile (2:3, v/v) and lake water. The observed half-lives of the 2,3,7,8-TCDF
and 2,3,4,7,8-PeCDF in the watenacetonitrile solution were 6.5 and 46 days,
respectively, and 1.2 and 0.19 days in lake water, respectively. Friesen et al. (1990a)
and Friesen et al (1993) attributed the significant differences between the natural water
and watenacetonitrile solution results to indirect or sensitized photolysis due to the
presence of naturally occurring components in the lake and pond water.
Dung and O'Keefe (1992), in their investigation of aqueous photolysis of 2,3,7,8-
TCDF and 1,2,7,8-TCDF, reported findings similar to those of Friesen et al. (1993). Dung
and O'Keefe prepared aqueous solutions by pumping water through "generator columns"
containing particles coated with a thin film of the respective TCDF congener. Solutions in
high purity HPLC water, distilled water, Hudson River water, and Saratoga Lake water
were exposed to sunlight during September at approximately 42.5 degrees north latitude.
The photolysis rates of the two TCDF congeners observed in the river and lake water
(half-lives of about 4 to 6 hours) were double the rates observed in pure water (half-lives
of about 8 to 11 hours). Dung and O'Keefe (1992) attribute the difference in rates to the
presence of natural organics in the river and lake water that may be acting as sensitizers.
Kim and O'Keefe (1998) confirmed the results observed in Dung and O'Keefe
(1992) by photolyzing 1,2,7,8-TCDD; 2,3,7,8-TCDF; OCDD; and OCDF in natural water
from seven different locations. The values reported in Table 2-11 are the average values
for the seven natural waters. The half-lives of TCDD and TCDF were at least two times
shorter in all the natural waters than in pure water. However, the natural water half-lives
of OCDD and OCDF showed greater variability with some half-lives greater than pure
water and some less than pure water. Because there was no apparent relationship
between the dissolved organic content of the water and the observed half-lives, it was
hypothesized that certain waters may contain organic molecules which either act as
photosensitizers or as photodesensitizers.
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Photolysis on Soil. Photolysis of CDD/CDFs on soil has been reported but the
factors affecting the rate and extent of photolysis have not been well characterized.
Based on the data generated to date, photolysis is an operative degradation process only
in the near-surface soil where UV light penetrates (i.e., the top few millimeters or less of
soil) and dechlorination of peri-substituted chlorines appears to occur preferentially. Even
within this near surface area, the rate of photolysis is substantially slower than the rate of
photolysis that would be observed in a solution of same depth presumably as a result of
the light-attenuating effects of soils. Below this near-surface level, photolysis is not a
significant process, and CDD/CDFs present in soil at any greater depth are likely to persist
(Miller et al., 1989; Puri et al., 1989; Yanders et al., 1989; Kieatiwong et al., 1990). The
substantial research performed on the environmental persistence of 2,3,7,8-TCDD in the
area around the ICMESA chemical plant in Seveso, Italy, demonstrates that photolysis in
soils is a near-surface process. The Seveso area was contaminated when a trichloro-
phenol reaction vessel overheated in 1976, blowing out the safety devices and spraying
2,3,7,8-TCDD-contaminated material into the environment. The levels of dioxin in the soil
decreased substantially during the first 6 months following the accident before reaching a
relatively steady state of 1/5 to 1/11 of the initial levels (DiDomenico et al., 1982).
Kieatiwong et al. (1990) investigated the photolysis of 2,3,7,8-TCDD added to two
agricultural soils (350 ug/kg) of approximately 0.3 mm depth and irradiated for 15 days
with a mercury lamp. A loss of approximately 15 percent of 2,3,7,8-TCDD was observed
on the soil of higher organic carbon and clay content. A loss of approximately 45 percent
of 2,3,7,8-TCDD was observed on the soil of lower organic carbon and clay content.
There was no significant loss on either soil after the first 5 days.
Miller et al. (1989) studied the CDD degradation products resulting from irradiation
of 13C-labeled OCDD on two soil types using sunlamps. Approximately 38 to 42 percent
of the OCDD were degraded by day five of the experiment; no significant further loss of
OCDD was observed over the following 10 days. Although determined not to be the
dominant photolysis pathway, photodechlorination was observed in both soils;
approximately 10 to 30 percent of the lower chlorinated congeners were produced from
the immediate higher chlorinated congeners. The HpCDD and HxCDD congeners observed
as degradation products were present in approximately similar proportions to the number
of congeners in each congener group. However, Miller et al. (1989) found that, unlike the
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results reported for photolysis of OCDD in solution by Choudry and Webster (1989) and
Dobbs and Grant (1979), 2,3,7,8-TCDD and 1,2,3,7,8-PeCDD were observed in greater
yields than would be expected on the basis of the number of potential TCDD and PeCDD
congeners. One-fifth to one-third of the total yield of PeCDDs was 1,2,3,7,8-PeCDD, and
one-half of the total yield of TCDDs was 2,3,7,8-TCDD.
Kieatiwong et al. (1990) performed similar experiments to those of Miller et al.
(1989) using natural sunlight rather than sunlamps for irradiation of 13C-labeled OCDD on
soils. Photodechlorination was estimated to account for approximately 10 percent of the
loss of OCDD. One-third to one-half of the total yield of PeCDDs was 1,2,3,7,8-PeCDD,
and one-half of the total yield of TCDDs was 2,3,7,8-TCDD. The findings of Miller et al.
(1989) and Kieatiwong et al. (1990) indicate that the 2,3,7,8-substituted TCDD and
PeCDD congeners were either preferentially formed or were photochemically less reactive
than the other congeners that were formed.
Tysklind et al. (1992) also studied the sunlight photolysis of OCDD on soil and
reported results in good agreement with those of Miller et al. (1989) and Kieatiwong et al.
(1990). Photodechlorination was observed with production of HpCDDs, HxCDDs,
PeCDDs, and TCDDs over the 16-day irradiation period. Photodechlorination at the peri-
substituted positions was the preferred Photodechlorination mechanism; the proportions of
2,3,7,8-substituted congeners present in the soils after 16 days for each congener group
were as follows: HxCDD - 65 percent; PeCDD - 40 percent; and TCDD - 75 percent.
The sunlight photolysis of OCDF on soil was also studied by Tysklind et al. (1992).
Photodechlorination was observed. However, unlike the case with OCDD,
Photodechlorination of the lateral-substituted positions was found to be the dominant
Photodechlorination mechanism resulting in a relative decreasing proportion of 2,3,7,8-
substituted congeners during the irradiation period. 2,3,7,8-TCDF was not observed in
any of the irradiated samples.
Schwarz and McLachlan (1993) studied the photolysis of CDD/CDFs in an
experiment designed to simulate the application of sewage sludge to an agricultural field.
No significant changes in CDD/CDF concentrations were observed during the 43-day
exposure period to late summer/early fall natural sunlight. Although the OCDD
concentration in the sludge had been increased by a factor of 1,000 through spiking, no
increase in HpCDD concentrations were observed. The absence of any changes indicates
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that neither photodegradation nor volatilization are important mechanisms in the fate of
CDD/CDF in sewage sludge following agricultural applications.
The addition of solvents to soil can increase the rate and extent of photolysis.
Botre et al. (1978) reported the destruction of 8 //g/ml_ of 2,3,7,8-TCDD in 0.02 M
hexadecylpyridinium chloride (an aqueous surfactant) applied to soil in 4 hours.
Kieatiwong et al. (1990) reported that addition of hexadecane to soil containing 2,3,7,8-
TCDD resulted both in an increased rate of photolysis and continued photochemical loss of
2,3,7,8-TCDD beyond the point at which soil containing no hexadecane showed
photochemical loss.
Although only minimally related to soil environmental conditions, Buser (1988)
studied the photolytic decomposition rates of 2,3,7,8-TCDF, 1,2,3,4-TCDF, and 1,2,7,8-
TCDF dried as thin films on quartz vials. When exposed to sunlight, the substances
slowly degraded with reported half-lives of 5 days, 4 days, and 1.5 days, respectively.
Similarly, Koester and Hites (1992b) studied the photodegradation of a series of tetra-
through octa-chlorinated CDDs and CDFs on silica gel. In general, the CDFs degraded
much more rapidly than the CDDs, and half-lives increased with increasing level of
chlorination (1,2,7,8-TCDF excluded). The half-lives for CDDs ranged from 3.7 days for
1,2,3,4-TCDD to 11.2 days for OCDD. The half-lives for CDFs ranged from 0.1 day for
1,2,3,8,9-PeCDF to 0.4 days for OCDF.
Photolysis on Vegetation. Photolysis of CDD/CDFs sorbed on the surface of
vegetation has not been well characterized and the findings to date are somewhat
contradictory. McCrady and Maggard (1993) reported that 2,3,7,8-TCDD sorbed on the
surface of reed canary grass (Phalaris arundinacea L.) undergoes photolytic degradation
with a half-life of 44 hours in natural sunlight. In contrast, Welsch-Pausch et al. (1995)
found little difference in the CDD/CDF congener patterns between grass (Lolium
multiflorum) grown on an outdoor plot and grass grown in a greenhouse (i.e., UV-light
transmission blocked). In an attempt to clarify this contradiction, Welsch-Pausch and
McLachlan (1995) studied the photodegradation of CDD/CDFs on pasture grass
(Arrhenatherion elatioris) during two growing cycles (summer and autumn) using two
greenhouses. One greenhouse was constructed of glass which blocks UV transmission
and the other was constructed of plexiglass (4 mm) with an UV-light transmission of
greater than 50 percent in the 280-320 mm range. In both the summer and autumn
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exposure periods, the concentrations of CDD/CDFs (on a congener group basis) were
similar in the grass exposed to UV-light and the grass that was not exposed. Welsch-
Pausch and McLachlan (1995) concluded that if photodegradation is occurring, it is a
relatively insignificant factor in the accumulation of CDD/CDF in pasture grass.
Photolysis in Air. Photolysis of CDD/CDFs in the atmosphere has not been well-
characterized. Based on the data generated to date, however, photolysis appears to be a
significant mechanism for degradation (i.e., principally dechlorination of the peri-
substituted chlorines) of those CDD/CDFs present in the atmosphere in the gas phase. For
airborne CDD/CDFs sorbed to particulates, photolysis appears to proceed very slowly and
may be influenced by the organic content of the particle. Because of the low volatility of
CDD/CDFs, few studies have been attempted to measure actual rates of photodegradation
of gaseous-phase CDD/CDF, and only recently have studies been undertaken to examine
the relative importance of photolysis for particulate-bound CDD/CDFs.
Gas-Phase Photolysis - Podoll et al. (1986) estimated the photolysis rate of
2,3,7,8-TCDD vapors in the atmosphere based on the quantum yield for photolysis in
hexane. The half-life in summer sunlight at 40 degrees north latitude was estimated to be
1 hour as an upper limit.
Mill et al. (1987) reported that photolysis of vapor phase 2,3,7,8-TCDD at 145°C
in a flow system using a pulsed 308 nm laser light gave a photolysis quantum yield
ranging from 0.013 to 0.03, equivalent to an atmospheric half-life of a few hours.
However, the true gas phase quantum yield at 25°C is uncertain, and, therefore, the
atmospheric lifetime in sunlight is uncertain.
Orth et al. (1989) conducted photolysis experiments with vapor-phase
2,3,7,8-TCDD under illumination with a Hg-Xe light source and filters to achieve radiation
in the UV region from 250 nm to 340 nm. The temperature in the photolysis cell was
approximately 150°C. Carrier gases included air and helium. No significant difference in
the rate constants was observed in helium and air, 5.4 x 10"3 sec"1 and 5.9 x 10"3 sec"1,
respectively, which correspond to half-lives of a few minutes. The average quantum yield
in air over the absorption band was found to be 0.033 + 0.046. As with the results of
Mill et al. (1987), the true gas phase quantum yield at 25°C is uncertain, and, therefore,
the atmospheric lifetime in sunlight is uncertain.
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Sivils et al. (1994; 1995) studied the gas phase photolysis of several CDDs (2,3,7-
TrCDD; 2,3,7,8-TCDD; 1,2,3,4-TCDD; 1,2,3,7,8-PeCDD, and 1,2,4,7,8-PeCDD) by
irradiating the effluent from a gas chromatograph with broadband radiation in the
UV/visible region for periods of time up to 20 minutes. The irradiated sample was then
introduced into a second gas chromatograph to measure the extent of dechlorination. The
results showed that degradation followed first order kinetics and that there was an inverse
relationship between the degree of chlorination and the rate of disappearance. Although
the lack of photoproducts prevented an independent confirmation of the preferential loss
mechanism, the results indicated that laterally-substituted congeners (i.e., chlorines at the
2, 3, 7, and 8 positions) degrade at a slower rate than the peri-substituted congeners (i.e.,
chlorines at the 1, 4, 6, and 9 positions). Although the rate constants were not presented
in Sivils et al. (1994), the degradation rate for 2,3,7,8-TCDD (30 percent loss in 20
minutes) was reported to be slower than the rates for all other tested CDDs. Also,
1,2,4,7,8-PeCDD (with 2 peri-chlorines) degraded significantly faster than 1,2,3,7,8-
PeCDD (with only 1 peri-chlorine).
Particulate Phase Photolysis - The photolysis of 2,3,7,8-TCDD sorbed onto small
diameter fly ash particulates suspended in air was measured by Mill et al. (1987). The
results indicated that fly ash appears to confer photostability on 2,3,7,8-TCDD. Little (8
percent) to no loss was observed on the two fly ash samples after 40 hours of
illumination.
Koester and Hites (1992b) studied the photodegradation of CDD/CDFs naturally
adsorbed to five fly ashes collected from electrostatic precipitators (one from a hospital
incinerator, two from municipal incinerators, and two from coal-fired power plants) using a
rotary reactor and a medium-pressure mercury lamp. Although they found that CDD/CDFs
underwent photolysis in solution and when spiked onto silica gel, no significant
degradation was observed in 11 photodegradation experiments performed on the ashes for
periods ranging from 2 to 6 days. Three additional experiments were performed to
determine what factors may have been inhibiting photolysis. From the results of these
additional experiments, Koester and Hites (1992b) concluded that: (1) the absence of
photodegradation was not due to the absence of a hydrogen-donor organic substance; (2)
other molecules or the ash, as determined by a photolysis experiment with an ash extract,
inhibit photodegradation either by absorbing light and dissipating energy or by quenching
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the excited states of the CDD/CDFs; and (3) the surface of the ash itself may hinder
photolysis by shielding the CDD/CDFs from light.
Pennise and Kamens (1996) injected the emissions from the combustion of wood
chips treated with PCP, plastic PVC pipe shavings, and solid 2,4,6-trichlorophenol into 25-
m3 outdoor Teflon film chambers. The behavior of the eight congener groups was
monitored for approximately one day. Experiments were performed during January, June,
and October of 1994 in North Carolina. Experiments with combustion temperatures
ranging from 350 to 380°C were categorized as low-temperature experiments, and those
ranging from 760 to 800°C were categorized as high temperature experiments. Little or
no reactivity was observed for CDD/CDFs sorbed to particles resulting from the high
temperature experiments. Greater photochemical reactivity was observed for CDD/CDFs
sorbed to particles resulting from the low combustion experiments, where photolysis
appeared to compete with a CDD/CDF production mechanism believed to be associated
with non-combusted PCP. Photolysis rates appeared to decrease with increasing levels of
chlorination. On low temperature combustion particles, estimated TCDD half-lives
(excluding the impact of the observed formation from PCP) increased from 0.4 hours
under summer conditions to 17 hours under winter conditions. On high-temperature
particles, estimated TCDD half-lives ranged from 6.8 to 62 hours. Estimated OCDD half-
lives ranged from 5 to 38 hours in low combustion temperature experiments to 36 to 257
hours in high temperature combustion experiments.
2.6.1.5.2. Photooxidation. Until recently, the reaction rates of hydroxyl (OH) radicals,
ozone (03), and nitrate (N03) radicals with CDDs and CDFs had not been measured
because, in large part, the low vapor pressures of these compounds make direct
measurements very difficult with currently available techniques. In the absence of
experimental data, Podoll et al. (1986) and Atkinson (1987) estimated the half-life of
2,3,7,8-TCDD vapor via OH oxidation in the atmosphere to be 8.3 days and 3 days,
respectively. In a subsequent study, Atkinson (1991) used published reaction rate data
for other organic compounds to estimate the OH radical reaction rate constants for
vapor-phase dibenzofuran and dibenzo-p-dioxin, and from these estimates, Atkinson
(1991) estimated the OH radical reaction rate constants for the CDDs and CDFs. Based
on these empirical estimates, Atkinson (1991) concluded that the OH radical reaction is
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likely to be the dominant gas-phase transformation process for vapor phase CDDs and
CDFs. The tropospheric lifetimes calculated by Atkinson (1991) from the rate constant
estimates increased with increasing levels of chlorination from 2.0 days for 2,3,7,8-TCDD
and 4.4 days for 2,3,7,8-TCDF to 9.6 days for OCDD and 39 days for OCDF.
Kwok et al. (1994) expanded the work of Atkinson (1991) by experimentally
determining the room temperature gas-phase reaction rate constants of dibenzofuran and
dibenzo-p-dioxin with hydroxyl radical to be 3.9E-12 cm3 molecule"1 s"1 and 1.48E-11 cm3
molecule"1 s"1, respectively. These measured rate constants for dibenzo-p-dioxin and
dibenzofuran are lower than those estimated by Atkinson (1991) by factors of 2.5 and 8.
Assuming a 12-hour average daytime OH radical concentration of 8 x 105 molecule/cm3,
Kwok et al. (1994) estimated the atmospheric lifetimes for gas phase reactions of
dibenzofuran and dibenzo-p-dioxin with OH radicals to be 3.7 days and 1.0 day,
respectively. Also, based on experimental data, Kwok et al. (1994) calculated lifetimes
for the gas phase reaction of dibenzofurans with N03 and 03 to be greater than 7 years
and greater than 205 days, respectively; the calculated lifetimes for the gas phase
reaction of dibenzo-p-dioxin with N03 and 03 were 4.9 days and greater than 330 days,
respectively. The latter results indicate that reaction with the OH radical is the dominant
photooxidation mechanism.
Kwok et al. (1995) extended the work of Kwok et al. (1994) by measuring the OH
radical reaction rate constants for 1-chlorodibenzo-p-dioxin, diphenyl ether, and 1,2-
dimethoxybenzene. These new reaction rate data when taken together with the
measurements of Kwok et al. (1994) and the estimation method described in Atkinson
(1991) were used to generate more reliable estimates of the reaction rate constants for
the 2,3,7,8-substituted CDDs and CDFs (Atkinson, 1996). Table 2-12 presents these
recalculated rate constants and tropospheric lifetimes and half-lives. As can be seen from
Table 2-12, the persistence of CDD/CDFs increases with increasing degree of chlorination.
Recently, Brubaker and Hites (1997) measured the OH radical reaction rate
constants for dibenzo-p-dioxin, dibenzofuran, 2,7-dichlorodibenzo-p-dioxin (2,7-D), 2,8-
dichlorodibenzofuran (2,8-F), and 1,2,3,4-dibenzo-p-dioxin (1,2,3,4-D) over temperatures
ranging from 72 to 159°C. From these results, OH reaction rate constants were
estimated for each compound at 25°C. When these estimated values were compared to
the previously measured rate constants reported by Kwok et al. (1994; 1995) and the
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values predicted by Atkinson (1995; 1996), Brubaker and Hites (1997) concluded that
Atkinson's structure activity method is reliable.
2.6.1.5.3. Hydrolysis. There is no available evidence indicating that hydrolysis would be
an operative environmental process for degradation of CDDs or CDFs. The attachment of
chlorines directly to the aromatic ring in CDDs and CDFs confers hydrolytic stability.
Specifically, SN1 and SN2 reactions do not take place readily at sp2 hybridized carbons
(Leifer et al., 1983; Miller and Zepp, 1987).
2.6.1.5.4. Biotransformation and Biodegradation. Most investigations examining the
biodegradability of CDDs and CDFs have, until recently, focused on the microbial
degradation of 2,3,7,8-TCDD. Arthur and Frea (1989) provided a comprehensive review
of studies conducted during the 1970s and 1980s and concluded that 2,3,7,8-TCDD is
recalcitrant to microbial degradation. Several major studies conducted during that time
period as well as more recent studies are discussed below.
Matsumura and Benezet (1973) tested approximately 100 strains of
micro-organisms that were shown previously to degrade persistent pesticides; only five
strains showed any ability to metabolize 2,3,7,8-TCDD, based on autoradiographs of
thin-layer chromatograms. Mutter and Philippi (1982) concluded that although it is
possible that the less chlorinated dioxins are more susceptible to biodegradation, microbial
action on 2,3,7,8-TCDD is very slow under optimum conditions; the long-term incubations
of radiolabeled 2,3,7,8-TCDD yielded no radioactivity in carbon dioxide traps after 1 year,
and analyses of the cultures showed that, at most, 1 to 2 percent of the initial 2,3,7,8-
TCDD were recovered as a potential metabolite (assumed to be a hydroxylated derivative
of 2,3,7,8-TCDD). Camoni et al. (1982) added organic compost to contaminated soil
from the Seveso area to enrich the soil and enhance the 2,3,7,8-TCDD biodegradation
rate; however, the soil amendment had no clear effect on degradation. Quensen and
Matsumura (1983) reported that low concentrations (5 ppb) of radiolabeled 2,3,7,8-TCDD
were metabolized by pure cultures of Nocardiopsis spp. and Bacillus megaterium that had
been isolated from farm soil. The extent of metabolism after 1-week incubation was
strongly dependent on the carrier solvent used to dissolve and introduce the 2,3,7,8-
TCDD to the culture medium. The solvent ethyl acetate gave the best results; 52 percent
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of 14C were recovered as 2,3,7,8-TCDD out of a total of 77 percent 14C recovered.
However, incubation of 2,3,7,8-TCDD in farm soil, garden soil, and forest soil resulted in
little, if any, metabolism of 2,3,7,8-TCDD.
Bumpus et al. (1985) tested the white rot fungus, Phanerochaete chrysosporium,
which secretes a unique H202-dependent extracellular lignin-degrading enzyme system
capable of generating carbon-centered free radicals (Tien and Kirk, 1983; Tien and Kirk,
1984). Lignin is resistant to attack by all microorganisms except some species of fungi
and a relatively small number of bacteria species. Radiolabeled 2,3,7,8-TCDD was
oxidized to labeled C02 by nitrogen-deficient, ligninolytic cultures of P. chrysosporium;
because the label was restricted to the ring, it was concluded that the strain was able to
degrade halogenated aromatic rings. In 10 ml cultures containing 1,250 pmol of
substrate, 27.9 pmol of 2,3,7,8-TCDD were converted to Iabeled-C02 during the 30-day
incubation period; thus, only about 2 percent of the starting material were converted.
Hofmann et al. (1992) demonstrated that the fungi, Fusarium redo/ens, could
degrade 3-chlorodibenzofuran and, to a lesser degree, mono- and di-CDDs. Hoffman et al.
(1992) also identified 14 other strains of fungi that demonstrated the capability to degrade
dibenzofuran (nonchlorinated). The strains are members of the following genera: Mucor,
Chaetomium, Phoma, Fusarium, Paecilomyces, Papulaspora, Inonotus, Lentinus,
Phanerochaete, Polyporus, Pycnoporus, Schizophyllum, and Trametes.
Takada et al. (1994; 1996) reported significant degradation of 2,3,7,8-substituted
CDDs and CDFs by low-nitrogen medium cultures of the white rot fungus, Phanerochaete
sordida YK-624 strain. Tetra- through octa- CDDs and CDFs were incubated for 14 days
in glucose-amended cultures at 30°C. For both CDDs and CDFs, the 1,2,3,6,7,8-
congeners showed the highest degradation values, 75 percent and 70 percent,
respectively. The lowest degradation values were for 2,3,7,8-TCDD (40 percent),
1,2,3,7,8-TCDF (45 percent), and 1,2,3,7,8-PeCDF. Similar results were obtained under
the same conditions for P. chrysoporium IFO 31249 strain.
Several recent reports indicate that CDDs and CDFs, like PCBs, may undergo
microbial dechlorination in anaerobic sediments. Adriaens and Grbic-Galic (1992; 1993)
and Adriaens et al. (1995) have reported the results of a series of microcosm studies
utilizing Hudson River sediment (contaminated with Aroclor 1242) and aquifer material
(contaminated with CDDs) from Pensacola, Florida. Both types of substrates were spiked
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with several CDDs (1,2,3,4,6,7,8-HpCDD; 1,2,3,4,7,8-HxCDD; and 1,2,4,6,8,9-
71,2,4,6,7,9-HxCDD) and CDFs (1,2,3,4,6,7,8-HpCDF and 1,2,4,6,8-PeCDF) and
monitored over a period of 16 months at an incubation temperature of 30°C. The Hudson
River sediment was spiked with 144/vg/kg of each congener and the Pensacola aquifer
material was spiked with 63/vg/kg of each congener. Recoveries of the CDD/CDF
congeners from the control samples decreased with increasing incubation time indicating
that these congeners are strongly sorbed to the substrates. For example, after 50 days of
incubation, the fraction of CDD/CDF that could be recovered by manual extraction had
already decreased to 20-40 percent.
All of the congeners, with the exception of HpCDF, showed a slow decrease in
concentration over time attributed to biologically mediated reductive dechlorination with
net disappearance rates ranging from 0.0031 wk"1 to 0.0175 wk"1 (i.e., half-lives of
approximately 1 to 4 years). However, Adriaens et al. (1995) conclude that the actual
half-lives may be orders of magnitude higher. If it is assumed that transformation/
degradation occurs only for CDD/CDF in the aqueous phase, then the CDD/CDF that sorb
to the sediments may never be biologically available because of the apparent very slow
rates of desorption. The experiment with 1,2,3,4,6,7,8-HpCDD yielded formation of two
HxCDD (1,2,3,4,7,8- and 1,2,3,6,7,8-). Thus, removal of the peri-substituted (1,4,6,9)
chlorines was favored with enrichment of 2,3,7,8-substituted congeners. No lesser
chlorinated congeners were identified from incubations with the other tested congeners.
1,2,4,6,8-PeCDF was also examined in dichlorophenol-enriched cultures. After 6 months
incubation, several TCDFs were identified which also indicated that peri-dechlorination
was the preferred route of reduction.
Barkovskii et al. (1994) expanded the testing of Adriaens and Grbic-Galic (1992;
1993) by spiking the sediments with higher doses to determine if faster rates could be
achieved. Passaic River sediments (contaminated with CDD/CDFs) were spiked with
4,500 /vg/kg of OCDD and incubated under anaerobic conditions for 6 months. Although
no significant degradation of OCDD was observed, significant reductions in the
concentrations of the hepta-, hexa-, penta-, and tetra-CDDs were observed.
Barkovskii and Adriaens (1995; 1996) reported that 2,3,7,8-TCDD (extracted from
Passaic River sediments) was susceptible to reductive dechlorination when incubated at
30°C under methanogenic conditions in a mixture of aliphatic and organic acids inoculated
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with microorganisms obtained from Passaic River sediments. The initial concentration of
2,3,7,8-TCDD (20 ± 4 fjg/L) decreased by 30 percent to 14 ± 2 fjg/L over a period of 7
months with the consecutive appearance and disappearance of tri-, di-, and mono-CDDs.
Experiments were also conducted by spiking the sediment with HxCDDs, HpCDDs, and
OCDD. Up to 10 percent of the spiked OCDD was converted to hepta-, hexa-, penta-,
tetra-, tri-, di-, and monochlorinated isomers, but the reaction stoichiometry was not
determined. Two distinct pathways of dechlorination were observed: the peri-
dechlorination pathway of 2,3,7,8-substituted hepta- to penta-CDDs, resulting in the
production of 2,3,7,8-TCDD, and the pe/7-lateral dechlorination pathway of non-2,3,7,8-
substituted congeners. Direct evidence of further lateral dechlorination of 2,3,7,8-TCDD
was obtained from the historically contaminated incubations. Pasteurized cells exhibited
no pe/7-dechlorination pathway, and triCDDs were the least-chlorinated congeners
produced in these treatments. These results demonstrate that: (I) both freshly spiked and
aged CDDs are available to microbial reductive dechlorination; (ii) the peri and triCDD
dechlorinations are attributed to activities of nonmethanogenic, non-spore-forming
microbial subpopulations; and (iii) the 2,3,7,8-residue patterns in historically contaminated
sediments are likely affected by microbial activity.
2.6.2. Environmental Fate of Dioxin-Like PCBs
2.6.2.1. Summary
Little specific information exists on the environmental transport and fate of the
dioxin-like PCBs. However, the available information on the physical/chemical properties
of dioxin-like PCBs coupled with the body of information available on the widespread
occurrence and persistence of PCBs in the environment indicates that these dioxin-like
PCBs are likely to be associated primarily with soils and sediments, and to be thermally
and chemically stable. Soil erosion and sediment transport in waterbodies and
volatilization from the surfaces of soils/water bodies with subsequent atmospheric
transport and deposition are believed to be the dominant current transport mechanisms
responsible for the widespread environmental occurrence of PCBs. Photodegradation and
biologically-mediated reductive dechlorination to less chlorinated congeners followed by
slow anaerobic and/or aerobic biodegradation in soils/sediments are believed to be the
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principal paths for destruction of PCBs. Of note, however, is that the available photolysis
studies to date indicate that the more toxic coplanar PCBs are more resistant to photolysis
and are formed as products during the photolysis of more chlorinated less toxic
congeners.
2.6.2.2. Transport Mechanisms
Based on their low vapor pressures, low water solubilities, and high Koc values,
dioxin-like PCBs are expected primarily to be associated with soils, sediments, and
particulates. PCBs move from land to aquatic environments primarily associated with
runoff sediments (Gan and Berthouex, 1994). However, due to the stability and
persistence of dioxin-like PCBs via other transformation and transport pathways,
volatilization is likely to be a significant transport mechanism from a global perspective. It
should be noted that although dioxin-like PCBs have low vapor pressures and water
solubilities, the Henry's Law constants for the similarly substituted CDDs and CDFs are
expected to be one to two orders of magnitude lower. Therefore, it can be expected that
volatilization of PCBs from soil and water into air is likely to be a more significant transport
mechanism for PCBs than for CDDs and CDFs. For example, Cousins et al. (1996)
recently demonstrated that PCBs with fewer than eight chlorines will volatilize but
2,3,7,8-substituted CDD/CDFs do not volatilize from sewage sludge amended soil. The
rate of volatilization will be affected by soil characteristics (e.g., temperature, moisture
content, and organic carbon content) and the concentration and physical form of the PCBs
present (Ayris and Harrad, 1997).
Recent studies have shown that PCBs are cycling between the atmosphere and the
terrestrial and aquatic environments. For example, Murray and Andren (1992) studied the
precipitation scavenging of PCBs in the Great Lakes region. They reported that
atmospheric PCBs are largely in the gas phase (typically >90 percent) rather than bound
to particulate material. The results of their study support the hypothesis that precipitation
provides episodic inputs of PCBs to the Great Lakes. However, several published studies
indicate that, on an annual basis (and particularly during the summer and autumn), the
Great Lakes are net sources of PCBs to the atmosphere through the process of
volatilization. Shallow, warm, and impacted aquatic systems such as Green Bay, Lake
Michigan and Chesapeake Bay exhibit the highest net volatilization fluxes due partially to
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intimate contact with contaminated sediments (Baker and Eisenreich, 1990; Eisenreich,
1997). Gregor et al. (1992) reported similar findings in a study of PCB deposition in the
Arctic during the winter of 1990/1991. The results of the study indicated that snowfall
and ice crystal deposition were scavenging the full range of PCB congeners from the
atmosphere. The daily deposition rate during winter was about 2 ng/m2/day. Also, a
comparison of the PCB content of snow/ice samples from collectors and end of season
samples from the snowpack suggested that volatilization of the PCBs, particularly of the
lower chlorinated congeners, was occurring.
Like CDD/CDFs, the dioxin-like PCBs have a high potential for bioaccumulation in
aquatic organisms thereby leading to elevated exposures to humans and wildlife from
ingestion of contaminated fish. Table 2-13 presents BAFs, BCFs, and BSAFs reported in
the literature for dioxin-like PCBs. A compilation of additional reported BSAFs is provided
in the companion document to this report (i.e., Volume 3). Chapter 3 of this report
summarizes concentrations of dioxin-like PCBs in fish that have been reported in the
literature.
2.6.2.3. Transformation Processes
2.6.2.3.1. Photolysis. Based on the data available in 1983, Leifer et al. (1983)
concluded that all PCBs, especially the more highly chlorinated congeners and those that
contain two or more chlorines in the ortho position, photodechlorinate. In general, as the
chlorine content increases, the photolysis rate increases. The products of photolysis are
predominantly lower chlorinated PCBs.
More recently, Lepine et al. (1992) exposed dilute solutions (4 ppm) of Aroclor
1 254 in cyclohexane to sunlight for 55 days in December and January. Isomer-specific
analysis indicated that the amounts of many higher chlorinated congeners decreased while
those of some lower chlorinated congeners increased. These results are consistent with
the studies reviewed in Leifer et al. (1983) that indicated photodegradation of PCBs
proceeds through successive dechlorination of the biphenyl molecule. The results for the
dioxin-like PCBs indicated a 43.5 percent decrease in the amount of 2,3,4,4',5-PeCB; a
73.5 percent decrease in the amount of 2,3,3',4,4',5-HxCB; and a 24.4 percent decrease
in the amount of 2,3,3',4,4',5'-HxCB. However, the more toxic 3,3',4,4'-TeCB (PCB77)
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and 3,3',4,4',5-PeCB (PCB126), which were not detected in unirradiated Aroclor 1254,
represented 2.5 percent and 0.43 percent, respectively, of the irradiated mixture. The
authors postulated that formation of these two congeners probably occurred, at least in
part, from dechlorination at the ortho position of their mono-ortho-substituted precursors,
considering the greater reactivity of PCB ortho chlorines toward photodechlorination.
Dechlorination was reported to proceed by the loss of chlorine in the order of
ortho para >meta.
Brown et al. (1995) examined the changes in congener levels that occurred after
exposing hexane solutions of various Aroclors and specific congeners in quartz tubes to
direct summer sunlight (43°N). The reported disappearance rates and estimated half-lives
for several of the dioxin-like PCBs are as follows:
Substitution Disappearance Half-life
PCB Congener Pattern Rate (day1) (days)
105
156
167
170
180
189
Barr et al. (1997) irradiated several PCB congeners in solution on silica gel for up to
30 minutes. The results indicated that dechlorination in the ortho position is favored but
also that steric congestion and structural symmetry are major factors in determining the
relative reactivity of chlorines in the meta and para positions. The following conclusions
were made:
• In all cases, the ring with the greatest degree of chlorination is the primary ring
where the dechlorination occurs.
• Ortho-chlorine substituents and para-chlorine substituents that have two
adjacent chlorines were preferentially lost in coplanar (non-ortho-substituted)
PCBs.
• Chlorine substituents having neighboring chlorines are replaced more easily than
isolated chlorines.
• Para-chlorine substituents were lost preferentially from coplanar hexa-, penta- or
tetrachlorobiphenyls, while meta-chlorine was lost preferentially from
trichlorobiphenyls.
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234-34
2345-34
245-345
2345-234
2345-245
2345-345
0.11
0.3
0.08
0.006
0.004
0.08
6.3
2.3
8.7
115
173
8.7
-------
• More symmetrical isomers tend to be formed more easily, and are more stable.
2.6.2.3.2. Oxidation. Reaction of PCBs with common environmental oxidants such as
hydroperoxy radicals (H02) and ozone (03) has not been reported and is probably not very
important because only very strong oxidant species can react with PCBs (Sedlak and
Andren, 1991). However, reaction of gas-phase PCBs in the atmosphere and dissolved
PCBs in certain surface waters with hydroxyl radicals (OH) (one of the strongest
environmental oxidants known) may be an important degradation mechanism.
Oxidation in Air. Atkinson (1987) and Leifer, et al. (1983), using assumed steady-
state atmospheric OH concentrations and measured oxidation rate constants for biphenyl
and monochlorobiphenyl, estimated atmospheric decay rates and half-lives for gaseous-
phase PCBs. Atmospheric transformation was estimated to proceed most rapidly for
those PCB congeners containing either a small number of chlorines or those containing all
or most of the chlorines on one ring. The predicted half-lives for the congener groups
containing the 13 dioxin-like PCBs were as follows:
Congener Group Half-Life in Air (days)
TeCBs 11 to 20
PeCBs 12 to 31
HxCBs 32 to 62
HpCBs 94 +
Kwok et al. (1995) extended the work of Atkinson (1987) by measuring the OH
radical reaction rate constants for 2,2'-, 3,3'-, and 3,5-dichlorobiphenyl. These reaction
rate constants when taken together with the measurements of Atkinson (1987) for
biphenyl and monochlorobiphenyl and the estimation method described in Atkinson (1991;
1995; 1996) have been used to generate more reliable estimates of the gas-phase OH
radical reaction rate constants for the dioxin-like PCBs. Table 2-14 presents these
estimated rate constants and the corresponding tropospheric lifetimes and half-lives. As
can be seen from Table 2-14, the persistence of the PCB congeners increases with
increasing degree of chlorination.
Oxidation in Water. Sedlak and Andren (1991) demonstrated in laboratory studies
that OH radicals, generated with Fenton's reagent, rapidly oxidized PCBs (i.e., 2-mono-
PCB and the DiCBs through PeCBs present in Aroclor 1242) in aqueous solutions. The
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results indicated that the reaction occurs via addition of a hydroxyl group to one
nonhalogenated site; reaction rates are inversely related to the degree of chlorination of
the biphenyl. The results also indicated that meta and para sites are more reactive than
ortho sites due to stearic hindrance effects. Based upon their kinetic measurements and
reported steady-state aqueous system OH concentrations or estimates of OH radical
production rates, Sedlak and Andren (1991) estimated environmental half-lives for
dissolved PCBs (mono-through octa-PCB) in several water systems as listed below.
Water System Half-Life in Water (days)
Fresh surface water 4 to 11
Marine surface water 1,000 to 10,000
Cloud water 0.1 to 10
Estimates for dissolved PCBs in marine surface water are in excess of 1,000 days due to
the very low concentration of OH radicals in these waters (10"18M or about two orders of
magnitude lower than in freshwater systems). The results of studies to date indicate that
OH oxidation of PCBs dissolved in cloud water may be an important, although not very
fast, degradation mechanism for PCBs from a global perspective.
2.6.2.5.3. Hydrolysis. PCBs are unlikely to be affected by hydrolysis under
environmental conditions because the attachment of chlorines directly to the aromatic ring
in PCBs confers hydrolytic stability. Specifically, SN1 and SN2 reactions do not take place
readily at sp2 hybridized carbons (U.S. EPA, 1988; Leifer et al., 1983).
2.6.2.3.4. Biotransformation and Biodegradation. Leifer et al. (1983), Brown and Wagner
(1990), and Abramowicz (1990) summarized the available information on the degradation
of PCBs by microorganisms. Laboratory studies (e.g., Bedard et al., 1986; Pardue et al.,
1988; Larsson and Lemkemeier, 1989; Hickey, 1995; Schreiner et al., 1995; and Fukuda
et al., 1997) have revealed that more than two dozen strains of aerobic bacteria and fungi
are widely distributed in the environment that are capable of degrading most PCB
congeners with five or fewer chlorines. Many of these organisms are members of the
genus Pseudomonas or the genus Alcaligenes. Only a few strains have been
demonstrated to have the ability to degrade higher chlorinated congeners. The major
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metabolic pathway involves addition of 02 at the 2,3-position by a dioxygenase enzyme
with subsequent dehydrogenation to the catechol followed by ring cleavage. Several
bacterial strains have been shown to possess a dioxygenase enzyme that attacks the 3,4-
position.
In general, the rate of aerobic biodegradation decreases with increasing
chlorination. Growth on biphenyl as the sole carbon source is required for optimal PCB
degradative activity. Degradation in soil systems where numerous carbon sources are
present is more than 50-fold slower compared to biphenyl assays. The half-lives for
biodegradation of tetra-PCBs in fresh surface water and soil are 7 to 60+ days and 12 to
30 days, respectively. For penta-PCBs and higher chlorinated PCBs, the half-lives in fresh
surface water and soil are likely to exceed 1 year. PCBs with all or most chlorines on one
ring and PCBs with fewer than two chlorines in the ortho position tend to degrade more
rapidly. For example, Gan and Berthouex (1994) monitored over a 5-year period the
disappearance of PCB congeners applied to soil with sewage sludge. Three of the tetra-
and penta-chlorinated dioxin-like PCBs (IUPAC Nos. 77, 105, and 118) followed a first-
order disappearance model with half-lives ranging from 43 to 69 months. A hexa-
substituted congener (IUPAC No. 167) and a hepta-substituted congener (IUPAC No. 180)
showed no significant loss over the 5-year period.
Until recent years, little investigation focused on anaerobic microbial dechlorination
or degradation of PCBs even though most PCBs eventually accumulate in anaerobic
sediments (Abramowicz, 1990; Risatti, 1992). Environmental dechlorination of PCBs via
losses of meta and para chlorines has been reported in field studies for freshwater,
estuarine, and marine anaerobic sediments including those from the Acushnet Estuary, the
Hudson River, the Sheboygan River, New Bedford Harbor, Escambia Bay, Waukegan
Harbor, and the Housatonic River (Brown et al., 1987; Rhee et al., 1989; Van Dort and
Bedard, 1991; Abramowicz, 1990; Bedard et al., 1995). The altered PCB congener
distribution patterns found in these sediments (i.e., different patterns with increasing
depth or distance from known sources of PCBs) have been interpreted as evidence that
bacteria may dechlorinate PCBs in anaerobic sediment.
Results of laboratory studies have been reported recently that confirm anaerobic
degradation of PCBs. Chen et al. (1988) found that "PCB-degrading" bacteria from the
Hudson River could significantly degrade the mono-, di-, and tri-PCB components of a 20
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ppm Aroclor 1221 solution within 105 days. These congeners make up 95 percent of
Aroclor 1221. No degradation of higher chlorinated congeners (present at 30 ppb or less)
was observed, and a separate 40-day experiment with tetra-PCB also showed no
degradation.
Rhee et al. (1989) reported degradation of mono- to penta-substituted PCBs in
contaminated Hudson River sediments held under anaerobic conditions in the laboratory
(N2 atmosphere) for 6 months at 25°C. Amendment of the test samples with biphenyl
resulted in greater loss of PCB. No significant decreases in the concentrations of the more
highly chlorinated (i.e., more than five chlorines) were observed. No evidence of
degradation was observed in samples incubated in C02/H2 atmospheres. Abramowicz
(1990) hypothesized that this result could be an indication that, in the absence of C02, a
selection is imposed favoring organisms capable of degrading PCBs to obtain C02 and/or
low molecular weight metabolites as electron receptors.
VanDort and Bedard (1991) reported the first experimental demonstration of
biologically-mediated ortho dechlorination of a PCB and stoichiometric conversion of that
PCB congener (2,3,5,6-TeCB) to less-chlorinated forms. In that study, 2,3,5,6-TeCB was
incubated under anaerobic conditions with unacclimated methanogenic pond sediment for
37 weeks with reported dechlorination to 2,5-DCB (21 %); 2,6-DCB (63%); and 2,3,6-
TrCB (16%).
Risatti (1992) examined the degradation of PCBs at varying concentrations (10,000
ppm, 1,500 ppm, and 500 ppm) in the laboratory with "PCB-degrading" bacteria from
Waukegan Harbor. After 9 months of incubation at 22°C, the 500 ppm and 1,500 ppm
samples showed no change in PCB congener distributions or concentrations, thus
indicating a lack of degradation. Significant degradation was observed in the 10,000 ppm
sediment with at least 20 congeners ranging from TrCBs to PeCBs showing decreases.
Quensen et al. (1988) also demonstrated that microorganisms from PCB-
contaminated sediments (Hudson River) dechlorinated most PCBs in Aroclor 1242 under
anaerobic laboratory conditions. Aroclor 1242 contains predominantly tri- and tetra-PCBs.
Three concentrations of the Aroclor corresponding to 14, 140, and 700 ppm on a
sediment dry-weight basis were used. Dechlorination was most extensive at the 700 ppm
test concentration; 53 percent of the total chlorine were removed in 16 weeks, and the
proportion of TeCBs through HxCBs decreased from 42 to 4 percent. Much less
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degradation was observed in the 140 ppm sediment, and no observable degradation was
found in the 14 ppm sediment. These results and those of Risatti (1992) suggest that the
organism(s) responsible for this dechlorination may require relatively high levels of PCB as
a terminal electron acceptor to maintain a growing population.
Quensen et al. (1990) reported that dechlorination of Aroclor 1242, 1254, and
1260 by microorganisms from PCB-contaminated sediments in the Hudson River and
Silver Lake occurred primarily at the meta and para positions; ortho-substituted mono- and
di-PCBs increased in concentration.
Nies and Vogel (1990) reported similar results with Hudson River sediments
incubated anaerobically with acetone, methanol, or glucose. Approximately 300 //g/g of
Aroclor 1 242 was added to the sediments to increase the concentrations of higher
chlorinated congeners in the sediments prior to incubation for 22 weeks under an N2
atmosphere. Significant dechlorination over time was observed with dechlorination
occurring primarily at the meta- and para-positions on the highly chlorinated congeners
resulting in the accumulation of less-chlorinated, primarily ortho-substituted congeners.
No significant dechlorination was observed in the control samples (i.e., samples containing
no added organic chemical substrate and samples which had been autoclaved).
Bedard et al. (1995) demonstrated that it is possible to stimulate substantial
microbial dechlorination of the highly chlorinated PCB mixture Aroclor 1 260 in situ with a
single addition of 2,6-dibromobiphenyl. Bedard et al. (1995) added 365 g of 2,6-
dibromobiphenyl to 6-foot diameter submerged caissons containing 400 kg sediment (dry
weight) and monitored the change in PCB congener concentrations for a period of 1 year.
At the end of the observation period, the hexa- through mono-chlorinated PCBs had
decreased by 74 percent in the top of the sediment and 69 percent in the bottom. The
average number of chlorines per molecule dropped by 21 percent from 5.83 to 4.61 with
the largest reduction observed in meta-chlorines (54 percent reduction) followed by para-
chlorines (6 percent). The dechlorination stimulated by 2,6-dibromobiphenyl selectively
removed meta-chlorines positioned next to other chlorines.
The findings of these latter studies are significant because removal of meta and
para chlorines from the dioxin-like PCBs should reduce their toxicity and bioaccumulative
potential and also form less chlorinated congeners that are more amenable to aerobic
biodegradation. In support of the findings of these studies, Mousa et al. (1997)
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demonstrated that the PCBs present in extracts from PCB-contaminated sediments (i.e.,
Aroclor 1242 and 1254) that had been incubated for nine months under anaerobic
conditions had either reduced biological activities or did not manifest any significant
change, depending upon the toxicological endpoint used.
2.7. ENVIRONMENTAL FATE - BROMINATED COMPOUNDS
2.7.1. Summary
Although there are few published studies documenting measured fate rate
constants, relatively few studies with measured physical/chemical property data, and few
relevant environmental monitoring studies, it is possible to estimate the environmental
transport and transformation processes for the brominated dioxin-like compounds using
the available published information and using structure activity and property estimation
methods. Mill (1989) performed such an assessment, and much of the limited information
published since 1989 supports the conclusions of Mill (1989).
Mill (1989) concluded that the estimated physical/chemical properties of these
compounds indicate they will behave in a similar fashion to their chlorinated analogues. In
general, these chemicals are expected to be stable under normal environmental
conditions, relatively immobile in the environment, and primarily associated with
particulate and organic materials. The only environmentally significant path for
destruction is photodegradation. If discharged to the atmosphere, any vapor-phase
compounds will probably be rapidly photolyzed. The higher brominated congeners, like
their chlorinated counterparts, may be present primarily in a particle-bound rather than
gaseous phase. If so, they likely will be more resistant to photolysis and become more
widely dispersed in the environment.
Upon deposition onto surfaces, there can be an initial loss due to photodegradation
and/or volatilization. Once sorbed onto soils or sediments, however, they are expected to
be strongly sorbed with erosion and aquatic transport of sediment the dominant physical
transport mechanism. If discharged to water, they are expected to preferentially sorb to
solids. Volatilization may also be a significant transport mechanism for nonsorbed
chemicals even though they have negligible estimate vapor pressures.
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2.7.2. Transport Mechanisms
Little information exists on the environmental transport of brominated dioxin-like
compounds. For example, Jacobs et al. (1978) reported that less than 0.2 percent of
2,2',4,4',5,5'-hexa-PBB (14//g PBB/g of soil) and 2,2',3,4,4',5,5'-hepta-PBB (7//g PBB/g
of soil) volatilized from soil incubated for 1 year at 28°C. However, the available
information on the physical/chemical properties of these compounds and their chlorinated
analogs coupled with the body of information available on the widespread occurrence and
persistence of the chlorinated analogs in the environment indicate that these compounds
are likely to be strongly sorbed by soils, sediments, and other particulate material, and to
be resistant to leaching and volatilization.
2.7.3. Transformation Processes
2.7.3.1. Photolysis. Photolysis appears to be a major potential pathway for loss of
brominated dioxin-like compounds in water, air, and soil. The available data indicate that
BDDs and BDFs undergo photolytic degradation more readily than their chlorinated
analogs. Also, BCDDs and BCDFs appear to undergo debromination more readily than
dechlorination. However, no photolysis studies have been published that used natural
waters as the reaction medium or that measured gas-phase photolysis rates. Most studies
have been conducted using reaction media consisting of homogenous solutions in organic
solvent mixtures or clean solid surfaces. Thus, although photolysis of brominated dioxin-
like compounds at environmentally significant rates has been observed in laboratory
studies, the results of these studies may not be representative of photolysis rates that
occur under actual environmental conditions. The following subsections summarize the
key findings of recent environmentally significant studies for the water, soil, and air media.
Photolysis in Organic Solvents. Buser (1988) studied the photolytic decomposition
rates of the following compounds in dilute isooctane solutions in quartz vials and as solid
phases on quartz surfaces under sunlight (47 degrees north latitude): 1,2,3,4-TBDD;
2,3,7,8-TBDD; 2,3,7,8-TBDF; and mono- and dibrominated 2,3,7,8-TCDD and 2,3,7,8-
TCDF. Estimated half-lives were very short, on the order of minutes for solution
photolysis. Solid-phase photolysis was significantly slower with half-lives in the range of
7 to 35 hours. The major photolytic pathway was reductive dehalogenation with the
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formation of lower halogenated or unsubstituted dibenzo-p-dioxins and dibenzofurans.
The bromo-chlorodibenzofurans degraded faster than either the brominated or chlorinated
congeners. The major pathway of photolysis was debromination to form a chlorinated
dibenzofuran.
Lenoir et al. (1991) studied the photolysis in hexane and methanol of a series of
mono-through octa-substituted BDD. Several BDFs (di-, tetra-, and hepta-BDF) were also
studied as were a series of CDDs for comparison purposes. The results reported by Lenoir
et al. (1991) were similar to those reported by Buser (1988) with half-lives on the order of
minutes. Bromines at the lateral positions (i.e., 2, 3, 7, and 8 positions) reacted faster
than bromines at the peri-positions (i.e., 1, 4, 6, and 9 positions). The bromine
compounds reacted nearly an order of magnitude faster than the chlorine analogs.
Photolysis in methanol was found to be nearly six times faster than in hexane.
Chatkittikunwong and Greaser (1994) studied the fate of a mixture of mono-
through penta-substituted BDDs and BCDDs dissolved in dodecane in borosilicate glass
vials exposed to sunlight through a laboratory window. The results indicated that for both
the BDDs and the BCDDs the major mechanism of degradation was consecutive
debromination from higher congeners to lower congeners. The half-lives calculated by
Chatkittikunwong and Greaser (1994) for various congener groups (listed below) are much
greater than those reported by Buser (1988). Chatkittikunwong and Greaser (1994)
attribute the difference to the fact that borosilicate glass is more effective than quartz at
absorbing those wavelengths most likely to cause degradation of these compounds.
Estimated
Congener Average Half-Life
Group in Dodecane (hrs)
PeBDD 150
TBDD 480
Br2CI2DD 580
650
480
Br2CI3DD 995
Br3CI2DD 300
520
520
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Watanabe et al. (1994) extracted BDFs and BCDFs from the soil at a metal
reclamation facility using hexane and exposed the hexane solution to natural sunlight. The
half-lives calculated by Watanabe et al. (1994) for various congener groups are listed
below. The half-lives decrease with increasing number of bromines.
Estimated
Congener Average Half-Life
Group in Hexane (min)
BnClsDF 43
Br2CI2DF 1 1
BrsC^DF 6.5
TBDF 4.0
BnCLTJF 18
Br2CI3DF 4.9
Br3CI2DF 5.4
Br^DF 3.1
PeBDF 2.7
B^C^DF 9.8
Br2CI4DF 5.7
BCIDF 12
Photolysis in Water. No published studies were located that measured the
photolysis rates of brominated dioxin-like compounds in water. Mill (1989) used the
results obtained by Buser (1988) together with assumptions to overcome the lack of
quantum yield data from Buser (1988) to estimate the photolysis half-lives of the three
brominated-only compounds tested by Buser (1988). Mill (1989) estimated the following
half-lives in water (top 1 meter) for clear-sky conditions in mid-summer at 40 degrees
north latitude:
Estimated Half-Life
Congener in Water (hrs)
1,2,3,4-TBDD 7
2,3,7,8-TBDD 2
2,3,7,8-TBDF 1.7
Photolysis in Soil. Chatkittikunwong and Greaser (1994) studied the fate of a
mixture of mono- through penta-substituted BDDs and BCDDs spiked onto soil (5 mm
depth) exposed to full sun outdoors for a 3-month period. The pattern of degradation was
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similar to that observed in solution (i.e., debromination of higher congeners with formation
of lower congeners) although the rate of degradation was much slower (by a factor of
about 4) than observed in solution. For example, the calculated average half-lives for
PeBDDs and TBDDs were 600 hours and 2,330 hours, respectively.
Photolysis in Air. No published studies were located that measured the photolysis
rates of brominated dioxin-like compounds in the gas phase in air. Mill (1989) used the
results obtained by Buser (1988) together with assumptions to overcome the lack of
quantum yield data from Buser (1988) to estimate the photolysis half-lives of the three
brominated-only compounds tested by Buser (1988). Mill (1989) estimated the following
gas-phase half-lives (first kilometer above surface) for clear-sky conditions in mid-summer
at 40 degrees north latitude:
Estimated Half-Life
Congener in Air (min)
1,2,3,4-TBDD <1
2,3,7,8-TBDD 0.3
2,3,7,8-TBDF 0.2
Lutes et al. (1992a, 1992b) studied the short-term photochemistry of tetra- and
penta-BDDs and BDFs sorbed onto airborne soot particles in 25 m3 outdoor Teflon film
chambers. The emissions from high temperature (640 to 760°C) controlled burning of
polyurethane foam containing polybrominated diphenyl ether flame retardants served as
the source of the particulate-bound BDDs and BDFs. Initial experiments demonstrated that
more than 95 percent of the BDDs/BDFs were associated with airborne particulate
material; less than 5 percent were in the vapor phase. Particulate phase concentrations of
tetra- and penta-CDD/CDFs were monitored for 3 to 6 hours after introduction of the
emissions from the foam burn to the chamber under winter and spring temperatures and
sunlight regimes in Pittsboro, North Carolina. No significant reduction in concentration
was observed. The authors concluded that if photolytic degradation was occurring, then
the half-lives are much greater than 3 to 6 hours.
Birla and Kamens (1994) expanded the research of Lutes et al. (1992a; 1992b) by
examining the effect of combustion temperature on the atmospheric stability of BDDs and
BDFs generated using the same polyurethane combustion apparatus. Both "high
temperature" (745 to 780°C) and "low temperature" (400 to 470°C) combustion
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temperatures were studied. The results obtained from the high temperature experiments
were similar to those obtained by Lutes et al. (1992a; 1992b) in that there was little
evidence of any decay in particulate-bound BDDs and BDFs. In the low temperature
experiments, production of TBDFs and PeBDFs and decay of TBDDs were observed. Birla
and Kamens (1994) attributed the increase in particulate-bound TBDF and PeBDF levels to
photolysis of unburned polybrominated diphenyl ether flame retardants. The decay of
TBDD was attributed to differences in physical and chemical properties of the particles
generated from the high and low temperature experiments.
Watanabe et al. (1994) collected air dust on glass filters for a period of 24 hours in
Osaka, Japan, and then exposed the glass filters to 24 hours of sunlight. More than 10
congener groups of BCDFs as well as TBDFs and PeBDFs were measured in the collected
dust prior to irradiation. Although there was a reduction in the concentration of every
congener group over the exposure period with the largest decrease observed for the lower
halogenated congeners, Watanabe et al. (1994) concluded that most of the decrease was
probably due to volatilization rather than photolysis.
2.7.3.2. Oxidation
No reaction rate data for OH radicals with gas-phase brominated dioxin-like
compounds could be located. The low vapor pressures of these compounds make direct
measurements very difficult with the current techniques. However, Mill (1989), using a
structure activity relationship developed by Atkinson (1987), has estimated the half-lives
of OH oxidation for the tetra- through octa- BDDs and BDFs. The estimated half-lives
listed below indicate that OH oxidation is probably too slow to compete with photolysis.
Number of BDD Half-Life BDF Half-Life
Bromines in Air (hrs) in Air (hrs)
4 50 420
5 50 430
6 100 960
7 200 1900
8 770 3800
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2.7.3.3. Hydrolysis
No evidence is available indicating that hydrolysis would be a significant
degradation process for these compounds.
2.7.3.4. B/otransformation and Biodegradation
Although no data are available concerning the biodegradability of the brominated
dioxin-like compounds, it is expected that these brominated compounds, especially the
more halogenated congeners, will be recalcitrant to biodegradation. The limited data
available on PBBs (discussed below) indicate recalcitrance.
Jacobs et al. (1976) examined the distribution and fate of PBBs in the environment
following the accidental contamination of livestock feed in Michigan in 1973 with the
brominated flame retardant, FireMaster BPG. FireMaster BPG (a.k.a., PBB) was found by
Jacobs et al. (1976) to be comprised of 2,2',4,4',5,5l-hexabromobiphenyl as the major
component, two isomers of pentabromobiphenyl, three additional isomers of
hexabromobiphenyl, and two isomers of heptabromobiphenyl. Jacobs et al. (1976)
reported that PBBs are extremely persistent based on the results of aerobic and anaerobic
soil incubation studies for 24 weeks with the flame retardant, PBB. Only one major PBB
component, a pentabromobiphenyl isomer, showed any significant disappearance;
however, Jacobs et al. (1976) were not certain whether the disappearance was due to
microbial degradation, to poor soil extraction efficiency, or to sorption onto glassware.
Jacobs et al. (1976) also detected components of PBB in soils from a field that had
received manure from a PBB-contaminated dairy herd 10 months earlier (quantitative
changes in PBB were not possible because no earlier soil samples had been obtained).
Additional soil studies by Jacobs et al. (1978) found no degradation of 2,2',4,4',5,5'-
hexa-PBB (14//g/25g soil) or 2,2',3,4,4',5,5'-hepta-PBB (7//g/25g soil) after incubation at
28°C for 1 year.
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DRAFT-DO NOT QUOTE OR CITE 2-93 December 2003
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Table 2-1. Possible Number of Positional CDD (or BDD) and CDF (or BDF) Congeners
Halogen
Substitution
Mono
Di
Tri
Tetra
Penta
Hexa
Hepta
Octa
Nona
Deca
Number of Congeners
CDDs (or BDDs)
2
10
14
22
14
10
2
1
0
0
CDFs (or BDFs)
4
16
28
38
28
16
4
1
0
0
PCBs
3
12
24
42
46
42
24
12
3
1
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Table 2-2. Ranking Scheme for P-Chem Property Evaluation
Ranking
1
2
3
4
5
Factors
1
/
X
/
X
X
2
/
/
X
X
X
3
/
/
/
/
X
4
X
X
/
/
X
5
X
X
X
X
/
Notes:
^ indicates all specifications of the Factor have been met.
x indicates the specifications of the Factor have not been met, or the Factor does not
apply.
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Table 2-3. Selected Physical-Chemical Property Values for the "Dioxin-Like" CDD, CDF, and PCB Congeners
Chemical
CAS No. (IUPAC No.)
Melting Point
Value
(°C)a
Ref.
Water Solubility
Value
(mg/l)a-°
Temp.
(°C)
Ref.
[R]b
Vapor Pressure
Value
(mm Hg)ac
Temp.
(°C)
Ref.
[R]b
Henry's Constant
Value
(atm-m3/mol)a
Ref.
[R]b
Log Kow
Value
Ref.
[R]b
Tetrachlorodibenzo-p-dioxins (MW = 321 .98)
2,3,7,8-TCDD
1746-01-6
Congener Group Average
305-306
9
1.93E-05
(3.3E-04)
25
25
4,53
[1]
20
(1.50E-09)
(1.4E-08)
25
25
9,53
[2]
20
(3.29E-05)
(1.7E-05)
53
[4]
20
6.80
(6.5)
53
[1]
20
Pentachlorodibenzo-p-dioxins (MW = 356.42)
1,2,3,7,8-PeCDD
40321-76-4
Congener Group Average
240-241
9
(1.18E-04)
20
20
(4.4E-10)
(5.6E-10)
25
25
9
[4]
20
(2.6E-06)
20
6.64
(6.6)
10
[2]
20
Hexachlorodibenzo-p-dioxins (MW = 390.87)
1,2,3,4,7,8-HxCDD
39227-28-6
1,2,3,6,7,8-HxCDD
57653-85-7
1,2,3, 7,8, 9-HxCDD
1 9408-74-3
Congener Group Average
273-275
285-286
243-244
9
9
9
4.42E-06
(4.4E-06)
25
25
6,53
[2]
20
(3.8E-11)
(3.6E-11)
(4.9E-11)
(4.4E-11)
25
25
25
25
53
[4]
9
[5]
9
[5]
20
(1.07E-05)
(1.1E-05)
53
[4]
20
7.80
(7.3)
53
[4]
20
Heptachlorodibenzo-p-dioxins (MW = 425.3 1 )
1,2,3,4,6,7,8-HpCDD
35822-46-9
Congener Group Average
264-265
9
2.40E-06
(2.4E-06)
20
20
6,53
[2]
20
(5.6E-12)
(3.2E-11)
25
25
9,53
[4]
20
(1.26E-05)
(1.26E-05)
53
[4]
20
8.00
(8.0)
53
[4]
20
Octachlorodibenzo-p-dioxins (MW = 460.76)
1, 2,3,4,6,7,8, 9-OCDD
3268-87-9
325-326
6
7.4E-08
25
5,53
[2]
(8.25E-13)
25
9,53
[2]
(6.75E-06)
5,53
[4]
8.20
5,53
[2]
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Table 2-3. P-Chem Properties for the Dioxin-Like Congeners (continued)
Chemical
CAS No. (IUPAC No.)
Melting Point
Value
(°C)a
Ref.
Water Solubility
Value
(mg/l)a-°
Temp.
(°C)
Ref.
[R]b
Vapor Pressure
Value
(mm Hg)a-°
Temp.
(°C)
Ref.
[R]b
Henry's Constant
Value
(atm-m3/mol)a
Ref.
[R]b
Log Kow
Value
Ref.
[R]b
Tetrachlorodibenzofurans (MW = 305. 98)
2,3,7,8-TCDF
51207-31-9
Congener Group Average
227-228
21
4.19E-04
(4.2E-04)
22.7
22.7
11
[2]
20
(1.5E-08)
(2.5E-08)
25
25
21,53
[4]
20
(1.44E-05)
(1.4E-05)
53
[4]
20
6.1
(6.2)
53
[2]
20
Pentachlorodibenzofurans (MW = 340.42)
1,2,3,7,8-PeCDF
57117-41-6
2,3,4,7,8-PeCDF
57117-31-4
Congener Group Average
225-227
196-
196.5
21
21
2.36E-04
(2.4E-04)
22.7
22.7
11
[2]
20
(1.7E-09)
(2.6E-09)
(2.7E-09)
25
25
25
21
[4]
21,53
[4]
20
(4.98E-06)
(5.0E-06)
53
[4]
20
6.79
6.5
(6.4)
10
[2]
53
[2]
20
Hexachlorodibenzof urans (MW = 374.87)
1,2,3,4,7,8-HxCDF
70648-26-9
1,2,3,6,7,8-HxCDF
57117-44-9
1, 2,3,7,8, 9-HxCDF
72918-21-9
2,3,4,6,7,8-HxCDF
60851-34-5
Congener Group Average
225.5-
226.5
232-234
246-249d
239-240
21
21
21
21
8.25E-06
1.77E-05
(1.3E-05)
22.7
22.7
22.7
11
[2]
11
[2]
20
(2.4E-10)
(2.2E-10)
(2.0E-10)
(2.8E-10)
25
25
25
25
21,53
[5]
21
[5]
21
[5]
20
(1.43E-05)
(7.31E-06)
(1.1E-05)
19
[5]
53
[5]
20
(7.0)
(7.0)
53
20
Heptachlorodibenzof urans (MW = 409.3 1 )
1,2,3,4,6,7,8-HpCDF
67562-39-4
236-237
21
1.35E-06
22.7
11
[2]
(3.5E-11)
25
21,53
[4]
(1.41E-05)
53
[4]
(7.4)
53
[2]
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December 2003
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Table 2-3. P-Chem Properties for the Dioxin-Like Congeners (continued)
Chemical
CAS No. (IUPAC No.)
1, 2,3,4,7,8, 9-HpCDF
55673-89-7
Congener Group Average
Melting Point
Value
(°C)a
221-223
Ref.
21
Water Solubility
Value
(mg/l)a-°
(1.4E-06)
Temp.
(°C)
22.7
Ref.
[R]b
20
Vapor Pressure
Value
(mm Hg)a-°
1.07E-10
(4.7E-11)
(4.7E-11)
Temp.
(°C)
25
25
Ref.
[R]b
21,53
[4]
20
Henry's Constant
Value
(atm-m3/mol)a
(1.4E-05)
Ref.
[R]b
20
Log Kow
Value
(7.4)
Ref.
[R]b
20
Octachlorodibenzofurans (MW = 444.76)
1, 2,3,4,6,7,8, 9-OCDF
39001-02-0
258-260
21
(1.16E-06)
25
11
[2]
3.75E-12
25
21
[2]
(1.88E-06)
19
[4]
8.0
53
[4]
Tetrachloro-PCB (MW = 291 .99)
3,3',4,4'-TCB
32598-13-3 (77)
3,4,4', 5-TCB
70362-60-4 (81)
180-181
160-163
58
58
1 .OE-03
2.92E-03
25
25
56
[2]
17
[5]
4.47E-07
(7.85E-07)
25
25
56
[2]
18
[4]
1.70E-05
1.28E-04
56
[2]
41
[4]
6.5
(6.36)
56
[2]
15
[5]
Pentachloro-PCB (MW = 326.44)
2,3,3',4,4'-PeCB
32598-14-4 (105)
2,3,4,4', 5-PeCB
74472-37-0 (114)
2,3',4,4',5-PeCB
31508-00-6 (118)
2',3,4,4',5-PeCB
65510-44-3(123)
3,3',4,4',5-PeCB
57465-28-8 (126)
116.5-
117.5
98-99
111-113
134-135
160-161
56
58
58
58
58
(1.90E-03)
(2.58E-03)
(1.59E-03)
(1.64E-03)
(1.03E-03)
25
20
20
25
25
35
[5]
41
[2]
41
[2]
17
[5]
17
[5]
(8.28E-07)
(4.18E-07)
(3.14E-07)
(8.78E-07)
(2.96E-07)
25
20
20
25
25
18
[4]
41
[2]
41
[2]
18
[4]
18
[4]
(9.93E-05)
6.90E-05
8.50E-05
1.74E-04
(5.40E-05)
35
[5]
41
[4]
41
[4]
35
[5]
35
[5]
(6.0)
(6.65)
7.12
(6.74)
(6.89)
56
[2]
15
[5]
31
[4]
15
[5]
15
[5]
Hexachloro-PCB (MW = 360.88)
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December 2003
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Table 2-3. P-Chem Properties for the Dioxin-Like Congeners (continued)
Chemical
CAS No. (IUPAC No.)
2,3,3',4,4',5-HxCB
38380-08-4 (156)
2,3,3',4,4',5'-HxCB
69782-90-7 (157)
2,3',4,4',5,5'-HxCB
52663-72-6 (167)
3,3',4,4',5,5'-HxCB
32774-16-6 (169)
Melting Point
Value
(°C)a
129.5-
131
161-162
125-127
208-210
Ref.
58
58
58
58
Water Solubility
Value
(mg/l)a-°
(4.10E-04)
(3.61E-04)
(3.61E-04)
(3.61E-05)
Temp.
(°C)
20
25
25
25
Ref.
[R]b
41
[2]
17
[5]
17
[5]
17
[5]
Vapor Pressure
Value
(mm Hg)ac
(1.47E-07)
(5.47E-08)
(1.95E-07)
(1.81E-07)
Temp.
(°C)
25
25
25
25
Ref.
[R]b
18
[2]
18
[4]
18
[4]
56
[5]
Henry's Constant
Value
(atm-m3/mol)a
8.70E-04
5.80E-04
(1.10E-04)
(6.52E-05)
Ref.
[R]b
43
[4]
43
[4]
35
[5]
35
[5]
Log Kow
Value
7.16
7.19
7.09
7.46
Ref.
[R]b
14
[3]
14
[3]
14
[3]
14
[3]
Heptachloro-PCB (MW = 395.33)
2,3,3',4,4',5,5'-HpCB
39635-31-9 (189)
2,2',3,3',4,4',5-HpCB
35065-30-6 (170)
2,2',3,4,4',5,5'-HpCB
35069-29-3 (180)
162-163
136.5-
138.5
112.5-
114
58
58
58
(6.26E-05)
(2.27E-04)
(4.40E-04)
25
20
20
17
[5]
41
[2]
41
[2]
(1.31E-08)
(6.46E-09)
(2.72E-08)
25
25
25
18
[4]
41
[2]
41
[2]
(6.65E-05)
1.50E-05
3.20E-05
35
[5]
41
[4]
41
[4]
(7.71)
(7.27)
(7.36)
15
[5]
15
[5]
15
[5]
Values in ( ) are either estimated or are calculated/extrapolated from experimental values.
Footnote References
a Values are presented as they appeared in the referenced article.
b [R] is the ranking of the value from the cited reference.
c For several PCS congeners, subcooled liquid values were converted to solid values using the melting points presented in this table and the conversion methodology presented in Eitzer and Hites (1988) and Mackayet al. (1992).
In (PJPJ = 6.79 (Tm-T)/T
where: Psc = subcooled value
Ps = solid value
Tm = melting point (°K)
T = ambient temperature (°K)
The melting point value for this congener obtained from Ref. 21; however, it was attributed through a probably typographical error to 1,2,3,6,8,9-HxCDF.
Marple et al. (1986a) 11. Friesen et al. (1990b) 21. Rordorf (1989)
Dunnivant and Elzerman (1988) 22. Dulin et al. (1986)
Risby et al. (1990) 23. Choudhry and Webster (1 987)
Hawker and Connell (1988) 25. Choudhry et al. (1 990)
9.
10
USEPA (1990)
Podoll et al. (1986)
Marple et al. (1986b)
Shiu et al. (1988)
Friesen et al. (1985)
Burkhard and Kuehl (1986)
Rordorf (1987)
Sijm et al. (1989)
13.
14.
15.
16.
17.
18.
19.
20.
43.
45.
50.
Sabljic and Gusten (1989)
Abramowitz and Yalkowsky (1990)
Foreman and Bidleman (1985)
Calculated by the VP/WS ratio technique
Average of all literature values (measured and calculated)
30. Orth et al. (1989)
31. Rapaport and Eisenreich (1984)
33. Eitzer and Hites (1988)
35. Dunnivant et al. (1992)
41. Murphy et al. (1987)
Murphy et al. (1983)
Webster et al. (1986)
Marple et al. (1987)
51. Santl et al. (1994)
52. Rordorf et al. (1990)
53. Mackay et al. (1992a)
54. Eitzer and Hites (1989)
55. Sacan and Inel (1995)
56. Mackay et al. (1992b)
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December 2003
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Table 2-3. P-Chem Properties for the Dioxin-Like Congeners (continued)
within a homologue group 42. EPRI(1990) 58. Bolgar et al. (1 995)
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Table 2-4. Summary of Selected Deposition Measurements Reported in the Literature
Author
Horstmann and McLachlan
Smith et al.
Wallenhorst et al.
DeFre et al.
Hiester et al.
Liebl et al.
Andersson et al.
Fernandez
Koester and Hites
Year3
1996
1995
1995
1994
1993
1993
1992
1992
1992
a
Sampling Method
Bergerhoff
Wet deposition;
Ambient air samples
Bergerhoff
Bergerhoff
Bergerhoff
Bergerhoff
Cotton cloth; snow
collector
Wet and dry frisbee
collector
Frisbees; flat glass
plates; wet-only
collector
Analytes
CDD/CDF
CDD/CDF
CDD/CDF
CDD/CDF
CDD/CDF/PCB
CDD/CDF
CDD/CDF
CDD/CDF
CDD/CDF
Sampling Locations
Germany
Rural
New York, USA
Germany
Urban
Rural
Flanders, Belgium
Background
< 1 km from MSWI
Urban
Germany
Urban
Rural
Germany
Urban
Rural/Industrial
Rural
Umea, Sweden
United Kingdom
Urban-Semiurban
Indiana, USA
Range of Results
0.2-2.3 ng I- TEQDF/m2-yr
Total CDD/CDF flux
wet: 94 ng/m2-yr
dry: 100 ng/m2-yr
total: 194 ng/m2-yr
1 1 ng l-TEQDF/m2-yr
2-3 ng l-TEQDF/m2-yr
0.7-5.1 ng l-TEQDF/m2-yr
39-374 ng l-TEQDF/m2-yr
13-77 ng l-TEQDF/m2-yr
3.6-30.3 ng l-TEQDF/m2-yr
4.4 ng l-TEQDF/m2-yr
7.6 ng l-TEQDF/m2-yr
1.5 ng l-TEQDF/m2-yr
1 . 1 ng l-TEQDF/m2-yr
1 ng l-TEQDF/m2-yr
13-17 ng l-TEQDF/m2-yr
Total CDD/CDF flux
wet: 210-220 ng/m2-yr
dry: 160-320 ng/m2-yr
total: 370-540 ng/m2-yr
a Year represents year of publication, not measurement.
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December 2003
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Table 2-5. Percentages of CDD/CDFs in Particulate Phase Measured in Air Monitoring Studies
Reference
A
B
B
B
C
C
D
D
E (urban)
E (rural)
F
G
Temp.
(°C)
20
3
16-20
>28
18
18
NR
NR
18
9.5
Percent of Total Congener Group Mass in Particulate Phase
TCDD
23
40
8
5
21
3
NR
NR
ND
ND
10
31
PeCDD
37
87
28
13
20
5
NR
NR
0
ND
28
59
HxCDD
66
100
45
45
24
12
92
100
65
100
45
82
HpCDD
87
100
88
60
70
64
100
100
82
100
77
>96
OCDD
96
100
100
100
85
90
78
100
100
100
93
>97
TCDF
14
100
ND
ND
23
7
14
5
20
ND
9
18
PeCDF
31
60
28
0
26
12
42
43
71
ND
22
55
HxCDF
64
88
30
38
29
15
73
100
100
ND
48
79
HpCDF
87
100
93
78
59
43
100
100
100
ND
77
>93
OCDF
91
98
100
98
94
91
100
NR
100
ND
89
>94
o
o
^1
D
C
O
§
ro
o
ro
D
CD
O
CD
3
0"
CD
O
O
CO
NR = Not reported.
ND = Not detected.
Source: Volume 3. References are as follows:
Reference A: Eitzer and Hites (1989)
Reference B: Hites and Harlessd 991)
Reference C: Harless and Lewis (1992)
Reference D: Hunt and Maisel (1992)
Reference E: Bobet et al. (1990)
Reference F: Welsch-Pausch et al. (1995) (data provided by authors); values presented for HpCDD, OCDD, HpCDF, and OCDF represent
lower limits.
Reference G: Hippelein et al. (1996); values represent annual means for six sites in the outskirts of Augsburg, Germany.
-------
Table 2-6. Predicted Fractions of CDD/CDF Congeners in Particulate
Phase at 20°C in Four Airsheds
Congener
2,3,7,8-TCDD
1,2,3,7,8-PeCDD
1,2,3,4,7,8-HxCDD
1,2, 3,6,7, 8-HxCDD
1,2,3,7,8,9-HxCDD
1,2, 3,4,6,7, 8-HpCDD
OCDD
2,3,7,8-TCDF
1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
1,2,3,4,7,8-HxCDF
1,2, 3,6,7, 8-HxCDF
1,2,3,7,8,9-HxCDF
2, 3,4,6,7, 8-HxCDF
1,2,3,4,6,7,8-HpCDF
1,2, 3,4,7, 8,9-HpCDF
OCDF
Fraction in Particulate Phase by Airshed Type
Clean
Continental
0.10
0.44
0.78
0.78
0.78
0.93
0.98
0.09
0.27
0.38
0.63
0.63
0.74
0.74
0.86
0.92
0.98
Average
Background
0.29
0.74
0.93
0.93
0.93
0.98
>0.99
0.27
0.57
0.69
0.86
0.86
0.91
0.91
0.96
0.98
>0.99
Background
Plus Local
Sources
0.49
0.87
0.97
0.97
0.97
0.99
>0.99
0.47
0.75
0.84
0.93
0.93
0.96
0.96
0.98
0.99
>0.99
Urban
0.75
0.95
0.99
0.99
0.99
>0.99
>0.99
0.73
0.91
0.94
0.98
0.98
0.99
0.99
0.99
>0.99
>0.99
Source: Chapter 3 of Volume 3.
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December 2003
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Table 2-7. Factors Influencing the Dry Deposition Removal
Rate in the Atmosphere
Micrometeorological
Variables
Aerodynamic roughness
Mass transfer of
Particles
Gases
Heat transfer
Momentum transfer
Atmospheric stability
Diffusion
Friction velocity
Inversion layer
Pollutant concentration
Relative humidity
Seasonal variation
Solar radiation
Surface heating
Temperature
Terrain effects
Turbulence
Wind velocity
Zero plane
displacement effect
Mass transfer of
Particles
Gases
Heat transfer
Momentum transfer
Characteristics of
Particles
Agglomeration
Diameter
Diffusion effects
Brownian
Eddy
Particle
Momentum
Heat
Electrostatic effects
Attraction
Repulsion
Gravity settling
Hygroscopicity
Impaction
Interception
Momentum
Physical properties
Resuspension
Solubility
Thermophoresis
Characteristics of Gases
Chemical Activity
Diffusion effects
Brownian
Eddy
Partial pressure in
equilibrium with
the surface
Solubility
Surface Variables
Accommodation:
Exudates
Trichomes
Pubescence
Wax
Biotic surface
Canopy growth
Dormant
Expanding
Senescent
Canopy structure
Areal density
Bark
Bole
Leaves
Porosity
Soils
Stem
Type
Electrostatic
properties
Water
Pollutant
penetration of
canopy
Source: Adapted from Sehmel (1980).
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December 2003
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Table 2-8. Rain Scavenging Ratios (W) and Percent Washout Due to Particulates (%P)
for CDDs and CDFs in Bloomington and Indianapolis Ambient Air
Congener Group
TCDD
PeCDD
HxCDD
HpCDD
OCDD
TCDF
PeCDF
HxCDF
HpCDF
OCDF
Total CDD/CDF
Bloominc
W
a
10,000
10,000
62,000
90,000
22,000
14,000
11,000
34,000
21,000
—
ton, IN
%P
a
50
88
93
80
21
54
77
88
52
68
Indianapolis, IN
W
a
30,000
26,000
91,000
150,000
33,000
18,000
15,000
32,000
41,000
—
%P
a
67
69
78
60
24
35
74
79
87
64
a Rarely detected; no calculations performed.
Sources: Hites and Harless (1991); Koester and Hites (1992a).
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December 2003
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Table 2-9. Log BCF Values for CDD/CDFs in Fish
Congener
2,3,7,8-TCDD
1,2,3,7,8-PeCDD
1,2, 3,4,7, 8-HxCDD
1,2,3,6,7,8-HxCDD
1,2,3,7,8,9-HxCDD
1,2,3,4,6,7,8-HpCDD
OCDD
2,3,7,8-TCDF
1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
1,2, 3,4,7, 8-HxCDF
1,2,3,6,7,8-HxCDF
1,2,3,7,8,9-HxCDF
2,3,4,6,7,8-HxCDF
1,2, 3,4,6,7, 8-HpCDF
1,2,3,4,7,8,9-HpCDF
OCDF
Measured Log BCFs
Various Species
(Reference A)
3.73-5.90
3.23-4.00
2.71-3.32
1.90-3.97
3.39-4.82
3.70
2.77
Measured Log BCFs
Guppy
(Reference B)
5.24
5.27
5.01
4.94
4.93
4.68
4.13
5.14
4.95
4.46
3.90
Calculated Log BCFs
Guppy
(Reference C)
5.48
5.34
5.07
5.08
5.18
4.79
4.39
4.93
4.84
4.79
4.57
4.58
4.71
4.59
4.26
4.32
3.88
Reference A: Mackay et al. (1992a); wet weight BCFs.
Reference B: Covers and Krop (1996); lipid-adjusted BCFs.
Reference C: Covers and Krop (1996); values calculated with the Solubility Parameters for Fate
Analysis model.
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December 2003
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Table 2-10. CDD/CDF BSAFs and BEFs for Lake Ontario Lake Trout
Congener
2,3,7,8-TCDD
1,2,3,7,8-PeCDD
1,2, 3,4,7, 8-HxCDD
1,2,3,6,7,8-HxCDD
1,2,3,7,8,9-HxCDD
1,2,3,4,6,7,8-HpCDD
OCDD
2,3,7,8-TCDF
1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
1,2, 3,4,7, 8-HxCDF
1,2,3,6,7,8-HxCDF
2, 3,4,6,7, 8-HxCDF
1,2,3,7,8,9-HxCDF
1,2, 3,4,6,7, 8-HpCDF
1,2,3,4,7,8,9-HpCDF
OCDF
Estimated Log Kowa
7.02
7.50
7.80
7.80
7.80
8.20
8.60
6.5b
7.0b
7.0b
7.5b
7.5b
7.5b
7.5b
8.0b
8.0b
8.80
BSAF
0.059
0.054
0.018
0.0073
0.0081
0.0031
0.00074
0.047
0.013
0.095
0.0045
0.011
0.040
0.037
0.00065
0.023
0.001
BEF
1.0
0.92
0.31
0.12
0.14
0.051
0.012
0.80
0.22
1.6
0.076
0.19
0.67
0.63
0.011
0.39
0.016
Source: U.S. EPA (1995).
a Burkhard and Kuehl (1986).
b Estimated based on degree of chlorination and Burkhard and Kuehl (1986).
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December 2003
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Table 2-11. Photolysis Rates of CDDs/CDFs in Water and WaterAcetonitrile Mixtures
o
o
^1
D
C
O
§
ro
o
oo
o
CD
O
CD
3
0"
CD
O
O
CO
CONGENER
LIGHT
SOURCE
REACTION MEDIUM
PHOTOLYSIS RATE
CONSTANT (1/day)
HALF-LIFE (days)
DURING SUMMER
REFERENCE
CDDs
1,2,7,8-70000
1,3,6,8-TCDD
2,3,7,8-TCDD
2,3,7,8-TCDD
1,2,3,4,7,8-HxCDD
1,2,3,4,6,7,8-HpCDD
OCDD
OCDD
sunlight
Hg lamp
sunlight
Hg lamp
Hg lamp
Hg lamp
Hg lamp
sunlight
water from 7 ponds/lakes
pond water
wateracetonitrile (1:1 v/v)
wateracetonitrile (1:1 v/v)
wateracetonitrile (2:3 v/v)
wateracetonitrile (2:3 v/v)
wateracetonitrile (2:3 v/v)
water from 7 ponds/lakes
4.06
0.198
0.255
0.78
0.111
0.0148
0.0397
1.04
0.17
3.5
2.7
0.9
6.3
47
18
0.67
Kim and O'Keefe (1998)
Choudry and Webster (1989)
Podoll et al. (1986)
Podoll et al. (1986)
Choudry and Webster (1989)
Choudry and Webster (1989)
Choudry and Webster (1989)
Kim and O'Keefe (1998)
CDFs
2,3,7,8-TCDF
1,2,7,8-TCDF
1,2,7,8-TCDF
1,2,7,8-TCDF
1,2,7,8-TCDF
1,2,7,8-TCDF
2,3,7,8-TCDF
2,3,7,8-TCDF
2,3,7,8-TCDF
2,3,7,8-TCDF
2,3,7,8-TCDF
2,3,7,8-TCDF
2,3,7,8-TCDF
2,3,4,7,8-PeCDF
2,3,4,7,8-PeCDF
OCDF
sunlight
sunlight
sunlight
sunlight
sunlight
Hg lamp
sunlight
sunlight
sunlight
sunlight
sunlight
sunlight
Hg lamp
sunlight
sunlight
sunlight
water from 7 ponds/lakes
HPLC water
distilled water
Saratoga Lake
Hudson River
HPLC water
wateracetonitrile (1:2.5 v/v)
lake water
distilled water
HPLC water
Saratoga Lake
Hudson River
HPLC water
wateracetonitrile (1:2.5 v/v)
lake water
water from 7 ponds/lakes
3.87
1.96
2.18
3.53
3.96
24.5
0.106
0.58
1.49
1.56
2.64
2.83
16.8
0.015
3.59
1.19
0.18
0.35
0.32
0.20
0.18
0.03
6.5
1.2
0.47
0.44
0.26
0.25
0.04
46.2
0.19
0.58
Kim and O'Keefe (1998)
Dung and O'Keefe (1992)
Dung and O'Keefe (1992)
Dung and O'Keefe (1992)
Dung and O'Keefe (1992)
Dung and O'Keefe (1992)
Friesen et al. (1993)
Friesen et al. (1993)
Dung and O'Keefe (1992)
Dung and O'Keefe (1992)
Dung and O'Keefe (1992)
Dung and O'Keefe (1992)
Dung and O'Keefe (1992)
Friesen et al. (1993)
Friesen et al. (1993)
Kim and O'Keefe (1998)
-------
Table 2-12. Estimated Tropospheric Half-Lives of CDDs/CDFs with Respect to Gas-Phase Reaction with the OH Radical
Congener
Group
TCDD
PeCDD
HxCDD
HpCDD
OCDD
TCDF
PeCDF
HxCDF
HpCDF
OCDF
2,3,7,8-Substituted
Congener
2,3,7,8-TCDD
1,2,3,7,8-PeCDD
1,2,3,4,7,8-HxCDD
1,2,3,6,7,8-HxCDD
1,2,3,7,8,9-HxCDD
1,2,3,4,6,7,8-HpCDD
OCDD
2,3,7,8-TCDF
1, 2,3,7, 8-PeCDF
2,3,4,7,8-PeCDF
1,2,3,4,7,8-HxCDF
1,2,3,6,7,8-HxCDF
1,2,3,7,8,9-HxCDF
2,3,4,6,7,8-HxCDF
1,2,3,4,6,7,8-HpCDF
1,2,3,4,7,8,9-HpCDF
OCDF
Estimated OH Reaction Rate
Constant
(cm3/molecule-sec)
7.08E-13
4.59E-13
1.97E-13
2.95E-13
2.95E-13
1.30E-13
5.09E-14
4.26E-13
2.65E-13
2.49E-13
1.06E-13
1.51E-13
1.62E-13
1.40E-13
6.04E-14
6.78E-14
2.58E-14
Estimated Tropospheric
Lifetime3-15
(days)
17
26
61
40
40
92
234
28
45
48
113
79
74
85
198
176
462
Estimated Tropospheric
Half-Life3-0
(days)
12
18
42
28
28
64
162
19
31
33
78
55
51
59
137
122
321
a Calculated using a 24-hour, seasonal, and global tropospheric average OH radical concentration of 9.7 X 105molecule/cm3 (Prinn et al., 1995).
b Lifetime = [(reaction rate constantHOH concentration)]"1.
c Half-life = 0.693/[(reaction rate constantHOH concentration)].
Source: Based on Atkinson (1996).
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December 2003
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Table 2-13. BAFs, BCFs, and BSAFs for Dioxin-Like PCBs
PCB Congener/
Congener
Group
77
81d
105
118
126
156
167
170e
180
189
TeCB
PeCB
HxCB
HpCB
Log BAFsa
Zooplankton
7.47
7.36
7.37
8.20
7.66
Sculpin
7.48
7.82
7.86
9.15
8.45
Alewive
7.79
7.72
7.71
8.84
8.15
Salmonids
7.96
8.13
8.15
9.20
8.58
Log BCFsb
Various Species
3.24-4.15
3.95-4.79
5.0-5.30
5.39
5.80
BSAFsc
Lake Trout
0.29
0.67
2.70-4.49
1.72-4.09
3.21
3.97
0.69
2.06-4.17
3.26-3.78
0.71
a U.S. EPA (1995); citing data from Oliver and Niimi (1988).
b Mackay et al. (1992b)
c U.S. EPA (1995).
d Includes congeners 81, 56, and 60.
e Includes congeners 170 and 190.
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December 2003
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CD
o
o
-H
D
c;
O
O
53
ro
Table 2-14. Estimated Tropospheric Half-Lives of Dioxin-Like PCBs with Respect to Gas-Phase Reaction with the OH Radical
Congener Group
JCB
PeCB
HxCB
HpCB
Dioxin-Like Congener
3,3',4,4'-TCB
3,4,4', 5-TCB
2,3,3',4,4'-PeCB
2,3,4,4',5-PeCB
2,3',4,4',5-PeCB
2',3,4,4',5-PeCB
3,3',4,4',5-PeCB
2,3,3',4,4',5-HxCB
2,3,3',4,4',5'-HxCB
2,3',4,4',5,5'-HxCB
3,3',4,4',5,5'-HxCB
2,2',3,3',4,4',5-HpCB
2,2',3,4,4',5,5'-HpCB
2,3,3',4,4',5,5'-HpCB
Estimated OH Reaction Rate
Constant (10"12 cm3/molecule-sec)
0.583
0.710
0.299
0.383
0.299
0.482
0.395
0.183
0.214
0.214
0.266
0.099
0.099
0.125
Estimated Tropospheric
Lifetime (days)3
20
17
40
31
40
25
30
65
56
56
45
121
121
95
Estimated Tropospheric
Half-Life (days)3
14
12
28
22
28
17
21
45
39
39
31
84
84
66
O
CD
O
CD
3
0"
CD
cm3 = cubic centimeters.
a Calculated using a 24-hour, seasonal, annual, and global tropospheric average OH radical concentration of 9.7 x 105 molecule/cm3 (Prinn et al., 1995).
Source: Atkinson (1 995) [Based on Atkinson (1 991) and Kwok et al. (1 995)].
O
O
CO
-------
FOOD
n men t
Industrial
Processes
Combustion
Runoff
Figure 2-1. Pathways for Entry of Dioxin-like Compounds into the Terrestrial and Aquatic Food Chains
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Deep Soil
Sediment
Fluxes Among Dioxin Reserveiis
Figure 2-2. Intermedia Movement of CDD/CDFs and PCBs Among Major Environmental Media
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3. LEVELS OF CDD, CDF, AND PCB CONGENERS
IN ENVIRONMENTAL MEDIA AND FOOD
3.1. INTRODUCTION
Polychlorinated dibenzo-p-dioxins (CDDs), polychlorinated dibenzofurans (CDFs),
and polychlorinated biphenyls (PCBs) have been found throughout the world in practically
all media including air, soil, water, sediment, fish and shellfish, and other food products
such as meat and dairy products. Also, not unexpectedly, considering the recalcitrant
nature of these compounds and their physical/chemical properties (i.e., low water
solubilities, low vapor pressures, and high Kows and Kocs), the highest levels of these
compounds are found in soils, sediments, and biota (parts-per-trillion (ppt) and higher);
very low levels are found in water (parts-per-quadrillion (ppq) and lower) and air (pg/m3).
The widespread occurrence observed is not unexpected considering the numerous sources
that emit these compounds into the atmosphere (See Volume 1), and the overall
resistance of these compounds to biotic and abiotic transformation. (See Chapter 2 of this
volume.) Part-per-trillion levels of CDDs/CDFs have been found in everyday materials that
are contaminated with dust-clothes dryer lint (2.4 to 6.0 ng l-TEQDF/kg); vacuum cleaner
dust (8.3 to 12 ng l-TEQDF/kg); room air filters (27 to 29 ng l-TEQDF/kg); and house
furnace filter dust (170 ng l-TEQDF/kg) (Berry et al., 1993). Although Berry et al. (1993)
only analyzed one or two samples of these materials, the findings suggest that these
compounds may be ubiquitous.
This chapter provides an overview of the concentrations at which dioxin-like
compounds have been found in the U.S. environment and food based on data presented in
the published literature. This literature summary is not all inclusive, but is meant to
present the reader with a general overview of values reported in the recent literature.
Only data from Government-sponsored monitoring studies and studies reported in the
peer-reviewed literature are discussed in this chapter. Data are presented as presented in
the original studies/reports. No attempt was made to verify or assess the adequacy of the
quality assurance/quality control (QA/QC) measures employed in these studies beyond
those described in the published reports. In order to represent current exposure
concentrations, data used for the calculation of background media levels were based on
studies published in the late 1980s and 1990s, but primarily in the 1990s. The studies
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used for the estimation of background concentrations were also chosen on the basis of
credibility and representativeness.
CDD/CDF profiles for environmental media are also presented in this chapter.
CDD/CDF homologue group and 2,3,7,8-substituted congener profiles were calculated for
each medium by dividing the mean concentration of individual homologue groups or
congeners by the mean total CDD/CDF concentrations for a group of studies or samples.
Total CDD/CDF concentration was calculated as the sum of homologue group
concentrations. In some cases, however, homologue group concentrations were not
available. When this occurred (i.e., for foods), total concentration was redefined as the
sum of the concentrations of the 2,3,7,8-substituted congeners rather than the sum of
the homologue group concentrations. The fractions of the total for each congener add up
to 1.00 in this case rather than some fraction of 1.00, as they would if the total
concentration were, more appropriately, the sum of the homologue group concentrations.
The text carefully identifies where this occurred. Nondetects were assumed to be zero in
the calculation of CDD/CDF profiles. This was done as a matter of consistency - some
studies used did not report on the detection limits for some congeners; some had high
detection limits such that an assumption of one-half the detection limit would have led to
unreasonably high contributions of some congeners to total CDD/CDFs. When available,
data on media levels in European countries and in other parts of the world are also
presented for comparison to U.S. values. These data are not intended to provide
estimates that are representative of CDD/CDF levels in all parts of Europe or the world,
but are used to depict similarities or differences between U.S. levels and those observed
by researchers in other parts of the world.
Media levels discussed in this chapter that represent background conditions in the
United States are used in Chapter 4 to estimate background exposures to dioxin-like
compounds. For the purposes of this document, background is defined as the level of
dioxin-like compounds in samples of environmental media originating from sites not known
to be impacted by point source releases. For soil and air, background concentrations of
CDD/CDFs were calculated for both rural and urban background locations. However,
urban background concentrations were used in calculating human exposures to CDD/CDFs
because a large percentage of the population resides in urban environments. Also, it
should be noted that background levels in environmental media are represented by mean
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concentrations of multiple background samples. Because mean values are used, it is likely
that some background sites may have concentrations that are less than the mean
concentration, while others may have higher levels. It should be noted that background
concentrations were calculated based on the best available, current (i.e., late 1980s in a
few cases, but primarily from the 1990s) studies. For most media, background
concentrations used to assess exposure in Chapter 4 were calculated by setting
nondetectable concentrations to one-half the detection limit. For some media (i.e., soil,
vegetable oil), however, nondetects were set to zero because detection limits were
unavailable for some studies or the detection limits were too high. These instances are
noted in the text. In general, mean background concentrations were calculated as the
average value over all sample locations rather than the average concentration over all
samples. For example, if the available background data for a particular medium (e.g., soil,
air, fish) were derived from multiple samples collected at multiple locations, the overall
mean background concentration was calculated by first averaging the data for each site
and then calculating the mean of the site averages. This method ensures that each site is
weighted equally; heavily sampled sites do not have any greater impact on the mean than
sites with fewer samples.
Studies used for development of background concentrations were also chosen to
be representative of nationwide exposures. In general, data were selected that represent
typical exposure conditions, so that these levels could be combined with typical
ingestion/contact values to estimate background exposures (see Chapter 4). However,
the data and strategy used for estimating background media levels varied somewhat,
depending on the media:
Air and Soil - Urban data were used to derive background estimates because
most people are exposed to these levels. No data were included that were
collected near known uncommon point sources (i.e., large incinerators,
cement kilns, smelters, etc.). Vehicles, fireplaces, home heating furnaces,
etc., are all recognized potential point sources, but are so ubiquitous as to
be considered a normal part of the urban background. Estimates are also
presented for rural areas, and these are more relevant for evaluating impacts
in rural areas. Also a degree of uncertainty is expected from the air and soil
concentration estimates due to a lack of geographic coverage and non-
uniform study design.
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• Water and Sediment - No data were included that were collected near
known uncommon point sources (pulp and paper mills, POTWs, etc.). No
distinction was made between urban and rural sites. For water, the data for
treated drinking water were very limited (i.e., based on octa-chlorinated
compounds only). Sediment data were collected from non-impacted lakes.
• Fish - No data were included that were collected near known uncommon
point sources (pulp and paper mills, POTWs, etc.). Background data for
freshwater and marine fish and shellfish were based on species-specific data
from various studies, including a national survey conducted by EPA, market
basket surveys conducted by FDA, and individual site-specific studies were
used.
• Food - Only samples from national EPA/USDA surveys and grocery stores
(e.g., eggs) were used. National EPA/USDA surveys used statistically-based
sampling methods to collect samples representative of the national food
supply. Grocery store samples represent the most common source of foods.
3.2. CONCENTRATIONS IN AIR
Tables B-1 through B-3 (Appendix B) contain summaries of data from studies of
ambient air measurements of CDDs, CDFs, and PCBs in the United States and Europe.
Environmental levels of PCBs in North American air are based on a single source of
information (Hoff et al., 1992). Relatively few studies have been conducted to measure
ambient air levels of CDDs/CDFs. This may be, in part, because of the low analytical
detection limits required to detect the expected low concentrations of specific CDD/CDF
congeners and the relatively large volumes of air (e.g., 350 to 450 cubic meters of
ambient air over a 24-hour period) required to obtain subparts-per-trillion levels of
analytical detection. These low detection limits in ambient air samples were not achieved
until the mid 1980s. The results of several of these more recent ambient air studies are
summarized in the following paragraphs. It should be noted, however, that these studies
lack geographic coverage and may not be representative of the nation as a whole.
Currently, EPA is establishing a network of stations equipped with high-volume air
samplers capable of detecting concentrations of dioxin and dioxin-like compounds as low
as 0.1 parts per trillion. The network, known as the National Dioxin Air Monitoring
Network (NDAMN), will provide information on background concentrations of dioxin-like
compounds, as well as data for use in tracking long-range transport of dioxin and
calibrating atmospheric models. The sampling sites included in the network were selected
with the intent of covering a wide geographic region, with special attention to rural,
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agricultural areas. Many of the sites are shared with the National Atmospheric Deposition
Program (NADP), which is a collaborative effort involving dozens of public and private
research and educational institutions. The NDAMN project calls for a total of 29 sites in
24 states. Currently, results are available for 9 monitoring locations. These data are
reported in this chapter. Additional data from the other sites will eventually be used to
update the background air concentration data presented in this chapter.
It should also be noted that this chapter focuses on the concentrations of
CDD/CDF/PCBs in outdoor air. Data on indoor air concentrations are extremely limited.
PCB data for one recent indoor air study in the United Kingdom are presented. No
background CDD/CDF data for indoor environments were available.
3.2.1. U.S. Data
An extensive ambient air monitoring study of CDD/CDFs was conducted as part of
a multiyear monitoring effort at eight sampling locations in the Southern California area by
the Research Division of the California Air Resources Board from December 1987 through
March 1989 (Hunt et al., 1990). The monitoring network "included a number of sites
situated in primarily residential areas (San Bernadino, El Toro, and Reseda), as well as
several sites in the vicinity of suspected sources of CDD/CDFs (Cal. Trans, Commerce,
North Long Beach, and West Long Beach) (Hunt et al., 1990)." The seven sites
mentioned above were classified as urban locations by the definitions used in this
document, while the eighth site was classified as an industrial site (i.e., Carson-onsite at
manufacturer of gas cooking equipment). Additionally, four of the eight sites were part of
the South Coast Air Quality Management District (SCAQMD) monitoring network. All
totaled, there were nine sample collection intervals throughout this study. "Typically, five
to seven stations were in contemporaneous operation during a particular session" (i.e.,
samples were not collected from each location at each interval). Total tetra- through octa-
chlorinated CDDs and CDFs were screened for in the study as well as various 2,3,7,8-
substituted CDD and CDF congeners. A total of 34 analyses were performed throughout
the study for all congeners except for OCDD and OCDF, respectively, for which only 31
analyses were performed. Samples were collected over a maximum of seven intervals at
each site throughout the study (i.e., Reseda and El Toro-six dates, duplicate samples on
one date), while a sample was collected from the Commerce site during only a single
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collection interval. Sample collection intervals generally averaged 24 hours (Hunt et al.,
1990).
Generally, higher substituted CDD and CDF congeners accounted for the majority
of positive samples containing quantifiable CDD/CDF residues in this study (i.e., total
HxCDD/HxCDF and above). In fact, over 90 percent of the samples collected contained
quantifiable levels of 1,2,3,4,6,7,8-HpCDD, total HpCDD, and OCDD. Additionally,
approximately 50 to 70 percent of the samples collected contained quantifiable levels of
total HxCDD; 2,3,7,8-TCDF; total TCDF; total PeCDF; total HxCDF; 1,2,3,4,6,7,8-HpCDF;
total HpCDF; and OCDF. For all other congeners, quantifiable residues were detected in
less than 25 percent of the samples collected. All CDD congener concentrations ranged
from nonquantifiable levels (low limit of 0.0026 pg/m3) to an upper limit of 18.0 pg/m3.
Additionally, CDF congener levels ranged from nonquantifiable levels (low limit of 0.0040
pg/m3) to an upper limit of 2.70 pg/m3.
According to Hunt et al. (1990), "The highest concentration of CDDs/CDFs
congener class sums (CI4-CI8) and 2,3,7,8-substituted species were noted during a period
predominated by off-shore air flows in December 1987, suggesting a regional air mass and
transport phenomena. Concentrations of the CDDs/CDFs were diminished markedly in
subsequent sessions where air flow patterns were primarily off-shore or of coastal origin."
Hunt et al. (1990) indicated that the "CDD/CDF congener profiles (CI4-CI8) and 2,3,7,8-
substituted isomeric patterns strongly suggest combustion source influences in the
majority" of the samples collected.
Smith et al. (1989) quantified CDD/CDFs in air samples collected from two
locations in Niagara Falls, New York, over a 6-month period in 1986/87. One site was
located upwind of a large industrial complex (i.e., background), and the other site was
located downwind of the complex (i.e., industrial). OCDD concentrations at the
downwind location were more variable, but consistently higher, than at the upwind
location. The maximum OCDD concentration observed at the downwind site was 8.8
pg/m3. Total l-TEQDFs for the two locations were estimated to be 0.038 pg/m3 (TEQDF-
WH098 = 0.041 pg/m3) (n = 3) for the upwind (i.e., background) site and 0.84 pg/m3
(TEQDF-WH098 = 0.92 pg/m3) (n = 3) for the downwind site, using one-half the detection
limit to represent nondetects (Table 3-1). In another study, Smith et al. (1990a) analyzed
ambient air samples from several other New York cities for CDD/CDFs. Samples were
DRAFT-DO NOT QUOTE OR CITE 3-6 December 2003
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collected in Albany (n = 3), Binghamton (n = 1), and Utica (n = 2). Total CDD/CDF
concentrations ranged from 3.02 pg/m3 to 13.1 pg/m3. None of the samples had
detectable levels of 2,3,7,8-TCDD, but 2,3,7,8-TCDF was detected at concentrations
ranging from 0.1 8 pg/m3 to 1.24 pg/m3.
Maisel and Hunt (1990) reported on ambient air concentrations of CDD/CDFs in Los
Angeles, California, during the winter of 1987. Concentrations were highest for OCDD,
and the estimated I-TEQDF concentration was 0.12 pg/m3 (TEQDF-WH098 = 0.13 pg/m3,
assuming that nondetects equal one-half the detection limits. Edgerton et al. (1989)
measured CDD/CDFs in air samples from Ohio during 1987 to evaluate the impact of
potential CDD/CDF sources on ambient levels in air. Samples were collected from various
locations, including those at an industrial site, near a municipal refuse-derived fuel power
plant, near a sewage sludge incinerator, downwind of a municipal incinerator, at a high
traffic density site, and at a rural background site in Waldo, Ohio. Total CDD/CDF
concentrations were found to be higher in samples collected near incinerators than at the
background site. None of the samples had detectable levels of 2,3,7,8-TCDD, and the
hepta- and octachlorinated CDD/CDFs were the most abundant. Using congener profiles
developed for several source categories, Edgerton et al. (1989) compared the CDD/CDF
patterns in ambient air from this study to the profiles for each source and found that the
profile for the background sample was "almost identical to the profile constructed for
municipal incinerators."
The Ohio Environmental Protection Agency, Division of Air Pollution Control,
conducted an ambient air monitoring study in 1994/1995 for CDD and CDF compounds in
the vicinity of the Columbus Waste to Energy (WTE) facility. The purpose of the study
was to evaluate the impact of the facility on air quality. The Columbus WTE was a major
source of dioxins to the Columbus environment. Based on a 1992 stack emission test,
the Ohio EPA estimated that annual emissions from the facility exceeded 900 g l-TEQDF/yr
(OEPA, 1994a). The sampling in 1994 occurred while the facility was operating. The
facility ceased operation in December 1994; therefore, the 1995 sampling did not include
impacts from the facility. A total of seven sites were sampled; six were located in the
urban area of Columbus, within 1-2 miles of the facility, and mostly in the historically
predominant downwind direction, and the seventh was located 28 miles away in the
upwind direction in a rural background setting. Five urban samples were taken in both
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March and April 1994 (one of the six samplers was not operating on each of these
sampling dates), and six urban samples were taken in June 1995, for a total of 16 urban
samples. One rural sample was taken on each sampling date for a total of three
background samples. All samples were collected over a 48-hour sampling period using
modified high-volume air samplers. Further details on these studies can be found in OEPA
(1994b, 1995).
Table 3-2 presents the mean concentrations of congeners and homologue groups
from four groups of air samples:
1. The "impacted air" samples include one sample taken in each of the March
and April 1994 sampling periods. Wind rose data in OEPA (1994b) show
that the samplers from which these samples came from were downwind of
the Columbus WTE during the 48-hour sample.
2. The 1994 urban samples include the eight other samples taken in 1994
while the Columbus WTE was still operating.
3. The 1995 urban samples include the six samples taken in the urban setting
once the incinerator was no longer operating.
4. The three rural samples include those taken at a site 28 miles away in the
historical upwind direction from the Columbus WTE.
OEPA (1994b) also notes that the highest air concentrations were found in the downwind
samples.
Generally, total CDD/CDF concentrations were higher for the urban sites than for
the background sites. Overall, the average total urban background air CDD/CDF
concentration was 3.5 pg/m3, and the I-TEQDF and TEQDF-WH098 concentrations were
0.050 pg/m3 and 0.055 pg/m3, respectively. These values were the average of the 1994
and 1995 urban samples. The rural background total CDD/CDF concentration was 2.2
pg/m3, and the I-TEQDF and TEQDF-WH098 concentrations were 0.022 pg/m3 and 0.024
pg/m3, respectively. The impact of the Columbus facility can be seen by examining the
impacted air samples. As noted above, one sample in each of the two 1994 sampling
dates was downwind of the Columbus facility. In fact, the air sampler was the same in
both cases and was located about 1.5 miles in the easterly direction. The air
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concentration in this sampler was the highest in both these sampling dates, equaling 9.2
pg/m3 total and 0.17 pg l-TEQDF/m3 in March and 19.0 pg/m3 total and 0.35 pg l-TEQDF/m3
in April. The average total CDD/CDF concentration for these impacted samples was 14
pg/m3, and the I-TEQDF and TEQDF-WH098 concentrations were 0.26 pg/m3 and 0.29
pg/m3, respectively. Further analysis of these Columbus air data can be found in Lorber et
al. (1998a).
In accordance to the Connecticut Ambient Air Quality standards, the State of
Connecticut's Department of Environmental Protection (CDEP) implemented a monitoring
program which measured CDD/CDFs in ambient air at six sites in Connecticut (CDEP,
1995). The air monitoring program was conducted from fall 1993 through summer 1994
in the vicinity of five Resource Recovery Facilities (RRFs), located in Bridgeport, Hartford
(mid-Connecticut), Bristol, Preston, and Wallingford, Connecticut, as well as one
background rural site located at Mohawk Mountain. The monitoring activity involved four
quarterly 1-month sampling periods (CDEP, 1995). Ambient concentrations measured for
the four quarterly monitoring sessions at the rural background site (Mohawk Mountain) are
presented in Table 3-3. Based on the CDD congeners, OCDD had the highest background
concentration (0.451, 0.196, 0.155, and 0.056 pg/m3) for all four sampling periods. The
total l-TEQDFs for these rural background samples were 0.015 pg/m3, 0.009 pg/m3, 0.006
pg/m3, and 0.005 pg/m3 for the November 1993, February 1994, May 1994, and August
1994 sampling periods, respectively. The average I-TEQDF for these sampling periods was
0.0087 pg/m3 (TEQDF-WH098 = 0.010). The average I-TEQDF and TEQDF-WH098 values for
the five urban locations, over the four sampling periods, were 0.026 pg/m3 and 0.029
pg/m3, respectively. Data for all samples collected during the monitoring program show
that the I-TEQDF concentrations were below the 1.0 annualized pg/m3 ambient standard
adopted by the State of Connecticut for CDD/CDFs (CDEP, 1995).
In an earlier study of ambient air monitoring in the vicinity of Wallingford,
Connecticut, CDEP (1988) reported on CDD/CDF levels in 28 ambient air samples
collected in 1988. The mean total I-TEQDF for these samples was 0.05 pg/m3, when
nondetects were set to one-half the detection limit. Hunt and Maisel (1990) conducted
pre-operational air monitoring in the vicinity of the site of a municipal solid waste
incinerator. I-TEQDF concentrations, averaged over seven sites and all seasons, were 0.1
pg/m3, when nondetects set to one-half the detection limit. Mean concentrations were
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highest for the higher-chlorinated dioxin homologue groups and lower furan homologue
groups.
In a long-term study of CDD/CDFs in the ambient air around Bloomington, Indiana,
methods were developed for measuring individual CDD/CDFs at concentrations as low as
0.001 pg/m3 (Eitzer and Hites, 1989). Total CDD/CDF concentrations were 0.480 pg/m3
and 1.360 pg/m3 for the vapor phase and the particle-bound phase, respectively. For
individual congeners, CDFs were found to decrease in concentration with increasing levels
of chlorination, and CDD concentrations were found to increase with increasing levels of
chlorination (Eitzer and Hites, 1989).
Fiedler et al. (1995a) conducted a sampling and monitoring program in rural
Mississippi using pine needles as indicators of the presence of CDDs and CDFs in the
atmosphere. Pine needles have been shown to be passive samplers for lipophilic
substances present in the air, because their outer waxy surface absorbs these atmospheric
pollutants. Pine needle samples were collected from eight locations in southern
Mississippi. CDD and CDF I-TEQDF concentrations ranged from 0.11 to 0.23 pg/kg dry
mass for 1994 shoots and 0.07 to 0.51 pg/kg dry mass for 1993 shoots. The authors
concluded that the data suggest that the atmospheric concentrations of CDDs and CDFs
in rural Mississippi are relatively low, but higher concentrations were observed at more
populated sites.
In a subsequent study, Fiedler et al. (1997a) analyzed CDD/CDF levels in ambient
air in a rural area in southern Mississippi using three sampling methodologies: high-volume
ambient air sampling to measure direct atmospheric levels of CDD/CDFs; Bergerhoff
samplers to collect atmospheric deposition samples; and pine needles as passive samplers.
The study was conducted from December 1995 to January 1996 and from June to July
1996 to assess the concentrations of CDD/CDFs during these time periods. The sampling
location had no known local sources of CDD/CDFs. In general, winter CDD/CDF
concentrations were higher than summer concentrations. CDD/CDF concentrations
measured using high-volume air samples averaged 1.126 pg/m3 in the winter and 0.36
pg/m3 in the summer (i.e., three times higher in winter than in summer). The mean I-
TEQDF concentrations were 0.0109 pg l-TEQDF/m3 in the winter and 0.0037 pg l-TEQDF/m3
in the summer, when using one half the limit of quantification for nonquantifiable
congeners. The results of the deposition study suggested that deposition is also higher in
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winter than in summer. The mean CDD/CDF deposition rate was 152 pg/m3-day in winter
and 108 pg/m3-day in summer, when nonquantifiable congeners were set to one-half the
limit of quantification. Normalized to pg l-TEQDF/m3-day, the deposition rate was 2.6 pg
l-TEQDF/m3-day in winter and 0.58 pg l-TEQDF/m3-day in summer. Analysis of the pine
needles showed that CDD/CDF concentrations increase with increasing exposure times.
Concentrations in pine needles ranged from 10 to 54 pg/g d.m. (0.16 to 0.79 pg l-TEQDF/g
d.m.), when nonquantifiable congeners were set to one-half the limit of quantification.
The authors concluded that the CDD/CDF concentrations observed in ambient air in this
study (0.0023 to 0.017 pg l-TEQDF/m3) were lower than similar remote locations in
Germany (0.015 to 0.020 pg l-TEQDF/m3). Deposition rates were also lower in southern
Mississippi (0.42 to 3.1 pg l-TEQDF/(m2-day)) than in rural areas of Germany (5 to 7 pg I-
TEQDF/(m2-day).
Hunt et al. (1997) conducted a study in Phoenix, Arizona, aimed at assessing the
influence of motor vehicle emissions on ambient air concentrations of CDD/CDFs. Four
sets of 24-hour integrated samples were collected between December 15 and 20, 1994.
The sampling site was located near a heavily traveled roadway in metropolitan Phoenix.
The month of December was chosen for sampling, because inversion conditions are
expected during the winter months. CDD/CDFs were detected in all four sample sets.
Average total I-TEQDF values varied from 0.092 pg/m3 (December 15) and 0.094 pg/m3
(December 19) to 0.37 pg/m3 (December 16) and a high of 0.45 pg/m3 (December 20).
Average congener-specific I-TEQDF (and TEQDF-WH098) values are shown in Table 3-4.
The average total I-TEQDF value of 0.25 pg/m3 and TEQDF-WH098 values of 0.27 pg/m3 are
higher than the TEQ data reported for other U.S. urban locations, such as Los Angeles and
Connecticut (Maisel and Hunt, 1990). The first 2 sampling days of this study
demonstrated congener class profiles typical of those reported in the literature for urban
U.S. settings, showing a predominance of CDDs over CDFs. Hunt et al. (1997) also noted
that the "predominance of 1,2,3,4,6,7,8-HpCDD as the most persistent 2,3,7,8-
substituted CDD congener is consistent with the observations of others in the open
literature, and is prevalent at sites known to be influenced by stationary or mobile
combustion source emissions." Data from the last 2 sampling days of this study produced
distinctly different congener profiles from the first 2 days. The last 2 sample dates'
results showed a predominance of CDFs over CDDs. On December 20, for example,
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1,2,3,4,6,7,8-HpCDF was present at a level of 2.16 pg/m3, while the typically most
predominant isomer 1,2,3,7,8-HpCDD had a value of 1.30 pg/m3. Hunt et al. (1 997)
stated that further study "beyond examination of CDDs/CDFs data alone is warranted to
provide a more conclusive source determination." Data from this study are not included in
the ambient background level determinations in this chapter due to recommendations of
the authors that "due to site-specific bias likely introduced by vehicular traffic at the
Indian School Road site, the ambient CDDs/CDFs measured should not be construed to be
representative of ambient CDDs/CDFs burdens in metropolitan Phoenix, as a whole."
Beginning in September 1996, a Canadian survey of CDD/CDFs in air was
conducted at locations across Canada (Belzer et al., 1998). Some of the samples were
collected near a coastal pulp mill operation at a location 1 kilometer southeast of a mill
area. Data analysis indicated that CDD/CDFs concentrations from the mill area ranged
from 0.006 to 0.067 pg l-TEQDF/m3. These values were similar to those observed in other
Canadian urban sites (0.01 to 0.08 pg l-TEQDF/m3), but were lower than those measured
near industrial point sources (0.01 - 0.4 pg l-TEQDF/m3). The total CDD and CDF congener
values for the mill area ranged from 0.17-3.94 pg/m3, and the values of total homologue
groups ranged from 0.77-5.53 pg/m3. The results of this study suggest that production
of CDD/CDFs from combustion sources is highly dependent on combustion material,
temperature and moisture values (Belzer at al., 1998).
In 1997, EPA established the National Dioxin Air Monitoring Network (NDAMN) to
determine the temporal and geographical variability of atmospheric CDDs, CDFs, and
dioxin-like PCBs at rural locations through out the United States. NDAMN consists of 29
sampling stations whose three primary purposes are: (1) to determine the atmospheric
levels and occurrences of dioxin-like compounds in rural and agricultural areas where
livestock, poultry, and animal feed crops are grown; (2) to provide measurements of
atmospheric levels of dioxin-like compounds in different geographic regions of the U.S.;
and (3) to provide information regarding the long-range transport of dioxin-like compounds
in air over the U.S. The first phase of NDAMN, which operated from June 1998 to June
1999, consisted of an array of 10 monitors at 9 sites spread out across the mid- to
eastern-U.S. in the States of Pennsylvania (2), North Carolina, Florida, Wisconsin, Illinois,
Iowa, Arkansas, Kansas, and Oklahoma. The sampling regime consisted of sampling 24
days (i.e., 6 days per week for 4 weeks), every other month, starting in the month of
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June. This produced six sampling moments over a period of 1 year, with four composite
samples (i.e., 4 weeks) per sampling moment. The analytes of interest in this monitoring
program are the 1 7 CDD/CDFs and the coplanar PCBs (77, 105, 118, 1 26, 1 56, 1 57, and
169). The interim results from the nine monitoring stations are shown in Table 3-5, and
are summarized as follows:
1. The overall annual average TEQDF-WH098 air concentration was 1 2 fg/m3.
2. All congeners were detected at a frequency >95%.
3. There was a 6-fold range in TEQDF-WH098 annual average air concentrations from
the lowest to the highest: 4.2 fg/m3 at Lake Scott, Kansas, to 25.4 fg/m3 at
Monmouth, Illinois.
4. The variability of TEQDF-WH098 over 6 monitoring moments at the 9 sites indicate
a significant increase in TEQDF-WH098 across all sites during the November/
December monitoring period. During this month, the TEQDF-WH098 increased by up
to 9-fold over any other moment during the year. The increase in TEQ was
characterized by a large increase in actual measured concentrations of 1,2,3,7,8-
PeCDD and 2,3,7,8-TCDD. This is consistent with the seasonal patterns reported
in previous studies.
5. The TEQP-WH098 was small compared to TEQDF-WH098 (range: 0.2 to 1.3 fg/m3;
mean: 0.7 fg/m3).
PCBs in ambient Canadian air were evaluated by Hoff et al. (1992). A total of 143
air samples from Egbert, Ontario, Canada, taken in 1988 and 1989 were analyzed for
various vapor-phase PCB congeners. The annual mean concentrations for these samples
are presented in Table 3-6. These means were calculated by assuming that nondetectable
concentrations were zero. Based on the mean concentrations for the limited set of toxic
PCB congeners in Hoff et al. (1992) (i.e., PCBs 105, 114, 118, 156, 170, 180, and 189),
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the total TEQP-WH094 concentration is estimated to be 0.00094 pg/m3 (TEQP-WH098 =
0.00088 pg/m3).
3.2.2. European Data
Clayton et al. (1993) conducted a study of CDDs and CDFs in the ambient air of
three major cities (London, Manchester, and Cardiff) and an industrial town (Stevenage) in
the United Kingdom. Annual median I-TEQDF concentrations of CDDs and CDFs ranged
from 0.04 to 0.10 pg/m3. Hepta- and octachlorinated dioxin congeners contributed the
most to the total concentration of 2,3,7,8-substituted CDD/CDFs, and a large number of
nondetect values were reported for the tetra-, penta-, and hexachlorinated dioxins.
Congeners that contributed most to the total I-TEQDF concentrations were 2,3,7,8-TCDF;
1,2,3,4,7,8-; 1,2,3,6,7,8-; and 2,3,4,6,7,8-HxCDF. The United Kingdom's Department
of the Environment, Transport and the Regions has posted air monitoring data from 1991
through 1993 on the Internet (Department of the Environment, Transport and the Regions,
1998). Sampling locations include the same UK cities monitored by Clayton et al. (1993),
as well as an urban site in Middlesbrough and a rural site at Hazelrigg. Mean CDD/CDF
I-TEQDF concentrations from the quarterly sampling periods ranged from 0.15 to 0.34
pg/m3 for Cardiff (8 quarters monitored), 0.10 to 0.34 pg/m3 for Stevenage (5 quarters),
0.01 2 to 0.33 pg/m3 for Middlesbrough (7 quarters), 0.044 to 1.4 pg/m3 for Manchester
(12 quarters), 0.016 to 0.28 pg/m3 for London (12 quarters), and 0.004 to 0.21 pg/m3 for
Hazelrigg (12 quarters), when nondetects were set at the detection limit. When
nondetects were set to zero, mean I-TEQDF concentrations ranged from 0.063 to 0.30
pg/m3 for Cardiff, 0.034 to 0.30 pg/m3 for Stevenage, <0.001 to 0.21 pg/m3 for
Middlesbrough, 0.036 to 0.69 pg/m3 for Manchester, 0.008 to 0.24 pg/m3 for London,
and 0.003 to 0.1 7 pg/m3 for Hazelrigg. The sum of the quarterly mean PCB
concentrations of PCBs 2, 52, 101, 118, 1 38, 1 53, and 1 80, for these same cities,
ranged from 164 to 985 pg/m3 for Cardiff (10 quarters monitored), 189 to 395 pg/m3 for
Stevenage (5 quarters), 78 to 359 pg/m3 for Middlesbrough (9 quarters), 181 to 704
pg/m3 for Manchester (14 quarters), 651 to 2,482 pg/m3 for London (14 quarters), and 77
to 198 pg/m3 for Hazelrigg (8 quarters). These CDD/CDF values are relatively consistent
with the concentrations in ambient German air observed by Liebl et al. (1993) and Konig
et al. (1993a). Liebl et al. (1993) analyzed ambient air samples collected from 10 sites in
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Hessen, Germany, from 1 990 through 1 992. Concentrations ranged from 0.04 to 0.1 5
pg l-TEQDF/m3. The higher concentrations were presumed to result from direct local
industrial sources. Konig et al. (1993a) collected air samples from six sites in Hessen,
Germany. CDD/CDF concentrations ranged from 0.048 pg l-TEQDF/m3 at a rural reference
site to 0.146 pg l-TEQDF/m3 at an industrial site. The results of the study also indicated
that concentrations of CDDs and CDFs are typically higher in the winter than in the
summer. Sugita et al. (1993) also observed higher concentrations of CDDs and CDFs in
winter than in summer in an ambient air study in urban Japan. The average concentration
of CDDs and CDFs was 0.788 pg l-TEQDF/m3 in the summer and 1.464 pg l-TEQDF/m3 in
winter.
Fiedler et al. (1997b) conducted a 3-year air monitoring study that examined the
CDD/CDF levels near two municipal solid waste incinerators (MSWI) located in Bavaria,
Germany (Augsburg and Burgkirchen). The authors observed that the I-TEQDF
concentrations at these locations were comparable and concentrations were consistently
higher during winter months that in summer months. For example, at Augsburg, the
lowest concentration (0.009 pg l-TEQDF/m3) was obtained during the summer of 1995,
and the highest concentration (0.206 pg l-TEQDF/m3) was observed in the winter of
1994/1995. Background concentrations for these time periods ranged from 0.0076 to
01.29 pg l-TEQDF/m3. For Burgkirchen, the lowest concentration (0.0044 pg l-TEQDF/m3)
was observed during the summer of 1994, and the highest concentration (0.078 pg I-
TEQDF/m3) was observed during the winter of 1995/1996.
In a Swedish study, air samples were collected from a city center, suburb, remote
countryside, and open coastal area (Broman et al., 1991). Analyses of the samples for
dioxins and furans indicated that the concentrations of these compounds decreased with
increasing distance from the city center. Total CDD/CDF concentrations were 1.40 pg/m3,
1.10 pg/m3, 0.40 pg/m3, and 0.22 pg/m3 for the city center, suburb, countryside, and
open coastal areas, respectively. Similar patterns of decreasing concentrations with
increasing distances from urban areas were also observed for individual CDD/CDF
congeners (Broman et al., 1991). In a study of ambient air concentrations of CDDs and
CDFs in Flanders, samples were collected and analyzed at rural, industrial, and urban sites
(Wevers et al., 1993). Average ambient air concentrations ranged from 0.0696 pg I-
TEQDF/m3 at a rural site to 0.254 pg l-TEQDF/m3 at a site believed to be influenced by a
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chemical industry and a highway. Naf et al. (1990) analyzed urban air samples from a site
near a wastewater treatment plant in Sweden. The samples were collected as part of a
study to estimate the flux of CDD/CDFs through the treatment plant. All 2,3,7,8-
substituted congeners (except 1,2,3,7,8,9-HxCDD and 1,2,3,4,7,8,9-HpCDF) were
detected. The mean I-TEQDF for urban air was estimated to be 0.02 pg/m3.
Hiester et al. (1995) observed a decrease of CDD/CDF concentrations in Germany's
ambient air over a 6-year period. Ambient air samples were collected over 12 sampling
intervals from 4 sites in the heavily industrialized Rhine-Ruhr region of Germany during
1987/88 and 1993/94. Total l-TEQDFs for these sites ranged from 0.13 pg/m3 to 0.33
pg/m3 during 1 987/88 and from 0.04 pg/m3 to 0.1 2 pg/m3 for the 1 993/94 time period.
Reductions in l-TEQDFs ranged from 46 to 69 percent at these sites over the 6-year period
(i.e, from 0.22 pg/m3 to 0.13 pg/m3 at Dortmund and from 0.13 pg/m3 to 0.04 pg/m3 at
Koln). These reductions were attributed to abatement actions taken since 1989 (Hiester
et al. 1995).
Between November 1992 and October 1993, the Austrian Federal Environmental
Agency monitored six air stations for ambient air concentrations of CDD/CDFs
(Umweltbundesamt, 1994; 1996). One hundred samples were taken from industrial and
population sites; three sites in Vienna, one in Steyregg, one in Linz, and one in Graz. The
arithmetic annual average value of ambient levels for all samples ranged from 0.03 to
0.12 pg l-TEQDF/Nm3. Average winter levels ranged from 0.05 to 0.24 pg l-TEQDF/Nm3;
while the summer levels ranged from 0.02 to 0.04 pg l-TEQDF/Nm3. Winter levels
observed in Graz were two times the winter concentrations found at the other sampling
sites. Levels were consistently highest (i.e., two to three times higher) for the measuring
period between February 1 and 4 at all the measuring sites. This time period coincided
with an extremely stable meteorological condition or inversion. All locations demonstrated
a decrease in the proportion of CDFs from the tetra- to octachlorinated congeners, while
the opposite was true for CDDs. However, there were differences in the congener profiles
for different locations. For example, the Graz location showed a higher proportion of octa-
and heptachlorinated dioxins, while tetrachlorofurans predominated at the hospital site in
Vienna and also at the Steyregg and Linz sites.
Samples of ambient air were collected from 1 5 locations throughout Slovakia,
including urban, industrial, agricultural and rural sites, between October 1996 and August
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1 997 (Stenhouse et al., 1 998). A total of 11 3 samples were analyzed for CDD/CDFs.
The average ambient I-TEQDF concentrations for these locations ranged from 0.05 pg/m3
to 0.13 pg/m3 at urban/industrial areas (with an average of 0.1 pg/m3), 0.07 pg/m3 for
agricultural areas and 0.04 pg/m3 for rural background. The values of any congeners
below the detection limit (0.01 pg/m3) were included in the I-TEQDF at the detection limit.
Higher ambient I-TEQDF values were observed in the winter than in the summer at the
places where the major source was combustion. Stenhouse et al. (1998) suggested that
seasonal variation would not be expected if industrial processes and traffic were
significant contributors.
PCBs have also been evaluated in European air samples (Halsall and Jones, 1993;
Konig et al., 1993b). Halsall and Jones (1993) monitored urban air at two sites in the
United Kingdom. The annual mean total PCB concentrations were 520 and 590 pg/m3.
PCBs existed in ambient air predominantly in the vapor phase. This study also indicated
that summer PCB concentrations were higher than winter concentrations. These
researchers attributed the differences in seasonal patterns to volatilization from soil during
summer months. Ambient air concentrations of PCBs in Hessen, Germany, ranged from
350 to 1630 pg/m3 during the period of 1990 to 1992 (Konig et al., 1993b). Urban areas
characterized by industry and/or heavy traffic had the highest PCB concentrations in
ambient air. Hiester et al. (1995) also evaluated total PCB concentrations in ambient air
samples from several sites in Germany during 1993/94. Table 3-7 presents the annual
average dioxin-like PCB concentrations in ambient air of several German cities. Annual
mean total PCB concentrations ranged from 1,000 pg/m3 to 2,000 pg/m3 in urban
locations and from 100 pg/m3 to 300 pg/m3 in rural locations.
Recently, Currado and Harrad (1997) measured indoor air concentrations of PCBs
from nine different indoor environments, including two laboratories, two offices, and five
residential homes in the United Kingdom. The results indicated that the total PCB levels
found in indoor air (1.4 to 19.1 ng/m3; mean 7.1 ng/m3) were between 2 and 19 times
higher than the levels in outdoor air (0.77 to 0.87 ng/m3; mean 0.82 ng/m3). It should be
noted that the study did not focus on dioxin-like PCBs; only concentrations of four dioxin-
like PCB congeners were reported in indoor and outdoor areas. Thus, TEQ concentrations
in indoor air were not calculated. Studies that examine background CDD/CDF levels in
indoor environments were not available.
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3.2.3. Air Observations and Trends
Some general observations for CDD/CDF levels in air are possible from the various
air studies discussed in this chapter:
• Concentrations in urban settings are higher than those in rural settings.
• Concentrations associated with source impacted areas are the highest.
• As the degree of chlorination increases, so does the congener concentration.
• Based on the limited ambient air measurements made in selected cities in
the United States and Europe, there appears to be good agreement with
respect to the magnitude of specific congeners of CDDs and CDFs in
urbanized areas in the United States and Europe.
• Many of the air measurements tend to be very close to the current analytical
detection limit. This increases the probability that congeners indicated as
not detected (ND) may actually be present.
3.2.4. Air CDD/CDF Profiles and Background TEQ Concentrations
CDD/CDF profiles were calculated for rural and urban air. Rural air profiles used
data from OEPA (1995), CDEP (1988), and Cleverly et al. (2000). Urban air profiles used
data from CDEP (1988, 1995), Smith et al. (1989, 1990a), Maisel and Hunt (1990), Hunt
et al. (1990), and OEPA (1995). CDD/CDF homologue group and 2,3,7,8-substituted
congener profiles for air are presented in Table 3-8 and Figures 3-1 and 3-2. The
CDD/CDF homologue profile was calculated by dividing individual homologue group
concentrations by the total CDD/CDF concentration. This profile indicates that OCDD is
the predominant homologue group in rural and urban background air followed by HpCDD.
TCDD accounts for the lowest percentage of total CDD/CDFs. Congener group profiles
were calculated as the ratio of individual 2,3,7,8-substituted congener concentrations to
total CDD/CDF concentration (i.e., the sum of homologue group concentrations). The
concentration of 2,3,7,8-substituted congeners accounts for 57 percent of the total
CDD/CDF concentration in urban background air. In rural background air, 2,3,7,8-
substituted CDD/CDFs account for 59 percent of the total CDD/CDF concentration. Of
the 2,3,7,8-substituted CDD/CDF congeners, OCDD accounts for the highest percentage
(i.e., 39 percent rural; 34 percent urban) of total CDD/CDFs, followed by the
1,2,3,4,6,7,8-HpCDD in rural and urban background air.
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Table 3-9 presents a summary of the TEQDF-WH098 concentrations of CDD/CDFs in
the United States. Assuming that nondetects are equal to one-half the detection limit, the
mean TEQDF-WH098 concentration was 0.013 pg/m3 for rural background sites (i.e., sites
in Connecticut and Ohio, and NDAMN sites in Pennsylvania (2), North Carolina, Florida,
Wisconsin, Illinois, Iowa, Arkansas, Kansas, and Oklahoma) (n = 60; CDEP, 1995 (n = 4);
OEPA, 1995 (n = 3); Cleverly et al., 2000 (n = 53», and 0.12 pg/m3 for urban background
sites (i.e., from 14 sites in Connecticut, California, Ohio, and New York) (n = 106; CDEP,
1988; CDEP, 1995; Hunt and Maisel, 1990; Maisel and Hunt, 1990; OEPA, 1995; Smith
et al., 1989; Smith et al., 1990a). These mean concentrations represent the average of
the mean concentrations for the various sites and not the mean of all individual samples.
(See weighted mean in Table 3-9.) The mean value is used to ensure that each site is
weighted equally (i.e., heavily sampled sites do not have any greater impact on the overall
mean than sites with fewer samples). Samples collected from urban locations not
expected to be impacted by industrial point sources were assumed to represent
"background" conditions for the majority of the U.S. population. The "typical" urban
background TEQDF-WH098 level was estimated to be 0.12 pg/m3 based on the mean of the
background samples collected in urban environments. (The I-TEQDF value for these sites is
0.1 1 pg/m3). This value was used in Chapter 4 to characterize background exposures.
The mean TEQP-WH098 for rural sites was estimated to be 0.00071 pg/m3 based on data
from Cleverly et al. (2000).
Based on the results of European studies, ambient air concentrations of CDDs and
CDFs appear to be similar to those found in the United States. Based on the midpoints of
the European studies for which I-TEQDF concentrations were reported (Clayton et al. 1993;
Liebl et al. 1993; Konig et al. 1993a; Wevers et al. 1993), the I-TEQDF air concentration
for Europe is 0.11 pg/m3. Data for these European studies are not included in Tables B-1
and B-2 of Appendix B because individual congener data were not reported.
It is interesting to compare these background air values with the CDD/CDF
concentrations in air measured by Lugar (1993) in and around McMurdo Station,
Antarctica, a logistics and staging facility with a population of about 1,100. Four
locations were sampled: a site upwind of the station, downwind of the station, in the
center of the station, and a remote unpopulated island 30 kilometers distant from the
station. CDD/CDFs were not detected in the samples from the upwind site (congener
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detection limits ranged from <0.01 to 0.03 pg/m3), and few CDD/CDF congeners were
detected at the remote island sites (congener detection limits ranged from 0.001 to 0.008
pg/m3). CDD/CDFs were detected only sporadically at the downwind site (some
congeners detected in three of five samples) and in all five samples collected from the
station center site (mean I-TEQDF concentration of 0.0153 pg/m3). Similar results were
obtained in a follow-up study during the austral summers 1992/93 and 1993/94. A total
of 28 air samples were collected from these four sites (Lugar et al., 1996). CDD/CDFs
were not detected at the upwind or remote island sites and trace levels of only a few
CDD/CDFs were found in the downwind site. The highest CDD/CDF concentrations were
observed at the downtown site, where CDD concentrations ranged from 0.12 to 1.80
pg/m3, and CDFs ranged from 0.02 to 2.77 pg/m3. I-TEQDF values for this central
McMurdo location were 0.074 pg/m3 for 1992/93 and 0.0015 pg/m3 for 1993/94. The
most frequently detected congeners were the octa- and hepta-chlorinated CDDs.
3.3. CONCENTRATIONS IN SOIL
Tables B-4 and B-5 (Appendix B) contain summaries of data from several of the
numerous studies in the published peer-reviewed literature regarding concentrations of
CDDs and CDFs in soil. Only limited data on dioxin-like PCB congener soil concentrations
were found in the literature (e.g., EPA Region 8, 2000); most of the PCB soil
concentration data found in the literature were reported as either total PCB concentrations
or concentrations of Aroclor PCB mixtures. Descriptions of several of the studies
summarized in Appendix B are presented below. It should be noted that, the review of
soil data presented here is not based on a comprehensive review of the published studies
on CDD/CDFs in soil. Instead, it is intended to provide a brief overview of soil levels of
CDD/CDFs from a sampling of representative studies. Because of the lack of geographic
coverage and non-uniform study design associated with the soil data presented in this
section, there is a degree of uncertainty associated with the estimates of background
concentrations of CDD/CDFs in soil.
3.3.1. North American Data
Soil sampled in 1987 from the vicinity of a sewage sludge incinerator was
compared with soil from rural and urban sites in Ontario, Canada, by Pearson et al.
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(1990). Soil in the vicinity of the incinerator showed a general increase in CDD
concentration with increasing degree of chlorination (Table 3-10). Of the CDFs, only
OCDF was detected (mean concentration 43 ppt). Rural woodlot soil samples contained
only OCDD (mean concentration of 30 ppt). Soil samples from undisturbed urban parkland
settings revealed only HpCDDs and OCDD, but all CDF congener groups (CI4 to CI8) were
present. Those samples showed an increase in concentration from the HpCDDs to OCDD
and PeCDFs to OCDF. TCDFs had the highest mean value (29 ppt) of all the CDF
congener groups. Resampling of one urban site in 1988, however, showed high variability
in the concentrations of CDDs and CDFs.
Reed et al. (1990) analyzed background soil samples from a semi-rural location in
Elk River, Minnesota, as part of a baseline assessment prior to the operation of a refuse-
derived fuel-powered electric generation station. Four soil samples (two from an untilled
site and two from a tilled site) were collected and analyzed for CDD/CDFs. Of the
CDD/CDF congeners, OCDD concentrations were the highest, ranging from 340 ppt to
3,300 ppt. OCDF concentrations ranged from nondetect to 270 ppt. The 2,3,7,8-tetra
and penta chlorinated congeners were not detected in any of the samples analyzed (Table
3-11).
Data were collected on CDD and CDF levels in soil samples from industrial, urban,
and rural sites in Ontario and some U.S. Midwestern States (Birmingham, 1990).
CDD/CDF levels in rural soils were primarily nondetect (ND), although the HpCDDs and
OCDD were found in a few samples. In urban soils, the tetra- through octa-homologue
groups were measured for both CDDs and CDFs. The HpCDDs and OCDD dominated and
were two orders of magnitude greater than in the rural soils. These soils also contained
measurable quantities of the TCDDs and PeCDDs. Industrial soils did not contain any
TCDDs or PeCDDs, but they contained the highest levels of the TCDFs, HpCDFs, and
OCDF. Total CDD/CDF concentrations averaged 73 ± 50 ppt in rural soils (n = 30),
2,075 ± 3,608 ppt in urban soils (n = 47), and 8,314 ± 9,955 ppt in industrial soils (n
= 20) when nondetects were assumed to be zero. l-TEQDFs were also calculated for
these three types of sites by Birmingham (1990) by assuming that the 2,3,7,8-substituted
CDD/CDF congeners represent specified proportions of the homologue group
concentrations and by applying l-TEFDFs. Birmingham (1990) estimated the l-TEQDFs to be
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0.4 ±0.6 ppt for rural soil, 11.3 ± 21.8 ppt for urban soils, and 40.8 ± 33.1 for
industrial soils.
In another study, soils from industrialized areas of a group of cities from
Midwestern and Mid-Atlantic States (Michigan, Illinois, Ohio, Tennessee, Pennsylvania,
New York, West Virginia, Virginia) were analyzed for levels of 2,3,7,8-TCDD (Nestrick et
al., 1986). Many of the samples were taken within 1 mile of major steel, automotive, or
chemical manufacturing facilities, or municipal solid waste incinerators. Concentrations of
2,3,7,8-TCDD measured in this study ranged from ND to 9.4 ppt.
Nine background soil samples were collected from the Yarmouth Pole Yard Site
located in Yarmouth, Maine (Tewhey Associates, 1997). One of these samples, collected
from soil near the base of a utility pole, yielded an I-TEQDF concentration of 57,000 pg/g.
The I-TEQDF concentrations for the other eight samples ranged from 0.73 pg/g to 5.9 pg/g
when nondetects were assumed to be zero, and 1.46 pg/g to 6.07 pg/g when nondetects
were assumed to be one-half the detection limit. These samples are from rural
background locations. The mean I-TEQDF for these eight samples was 3.58 pg/g (TEQDF-
WH098 was 2.89 pg/g) when nondetects were set to zero and 3.93 pg/g when
nondetects were set to one-half the detection limit. The sample collected near the utility
pole was not included in these mean TEQ values, because its results were not considered
to be representative of typical rural background concentrations.
In an effort to determine whether incineration of municipal waste influenced
CDD/CDF levels in the immediate area of waste incineration facilities, soil samples were
collected from cities with, and without operating incinerators throughout Connecticut.
Between the years of 1987 and 1990, 34 soil samples were collected from eight different
Connecticut cities where no municipal waste incinerators were operating (MRI, 1992).
These pre-operational samples were considered to be representative of rural background
concentrations. The total I-TEQDF reported for these samples was 6.07 pg/g, with
nondetects assumed to be one-half the detection limit. When the total TEQ was
recalculated in units of TEQDF-WH098, the total TEQ for these samples was 5.74 pg TEQDF-
WHOgg/g. The proportion of nondetects ranged from 3 to 11 percent of samples for each
analyte, with the exception of 2,3,7,8-TCDD and 1,2,3,7,8,9-HxCDF, which had 56 and
49 percent nondetects, respectively (MRI, 1992).
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The Ministry of Environment in British Columbia conducted a 2-year monitoring
study during 1990/91 and 1991/92 to evaluate the levels of CDD/CDF contamination in
various types of environmental media (BC Environment, 1995). Soil samples were
collected from sites close to a source (primary sites), in the receiving environment
adjacent to a suspected source (secondary sites), and in areas not expected to be
contaminated (background). Primary and secondary sources were identified as chemical
or combustion sources. Chemical sources included sites associated with chlorophenol,
herbicide, or PCB contamination; oil refineries; pulpmill landfills; or sewage facilities.
Combustion sources included biomedical, industrial, municipal, or sewage sludge
incineration; PCB or forest fires; pulp mill boilers; salt-laden wood burning, woodwaste
burners, or slash burning; and scrap iron yards or smelters. The highest mean
concentrations of 2,3,7,8-TCDD and 2,3,7,8-TCDF were observed in primary and
secondary soils associated with chemical sources (Table 3-12). For the 53 background
samples, 2,3,7,8-TCDD was not detected, and 2,3,7,8-TCDF concentrations ranged from
nondetected to 3.2 ppt. For the purposes of calculating I-TEQDF values for this study,
nondetects were set to zero. l-TEQDFs were highest among samples associated with
primary and secondary chemical sources (Table 3-12). The mean I-TEQDF for the
background soil samples was 5.0 ppt (BC Environment, 1995). When the mean TEQ was
recalculated in units of TEQDF-WH098, the total TEQ for these samples was 4.4 pg TEQDF-
WH098/g.
Grundy et al. (1995) and Bright et al. (1995) collected soil samples from remote
locations in the Canadian Arctic as part of an environmental assessment of abandoned
military installations in the Canadian North. Four soil samples from remote pristine areas
(i.e., at least 20 km away from any human activity) were analyzed for CDD/CDFs. The
total I-TEQ concentrations for these samples ranged from 0.2 to 0.9 ppt (Grundy et al.,
1995). Of the CDD/CDF homologue groups, OCDD and TCDF levels were the highest
among these remote soil samples, and the HxCDFs made up the smallest portion of the
total CDD/CDF concentrations (Bright et al., 1995).
EPA conducted a 2-year nationwide study to investigate the national extent of
2,3,7,8-TCDD contamination (U.S. EPA, 1987). Results of this large study were
summarized broadly in the primary reference (i.e., the number and types of samples per
site and range of detection). The method used to analyze samples for five of the seven
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study "tiers" had a detection limit in soil, sediment, and water of 1 part per billion (ppb).
[Each "tier" of sites is a grouping of sites with a common past or present use (e.g.,
industrialized, pristine, etc.)]. Only Tier 5 (sites where pesticides derived from 2,4,5-
trichlorophenol (TCP) had been or were being used for commercial purposes) and Tier 7
(ambient sampling for fish and soil) had detection limits of 1 ppt. Consequently, the data
from this study are not included in the Appendix B tables; however, some observations
from this study with regard to soil contamination are discussed below.
Soil concentrations found in most of the 100 Tier 1 and 2 sites (i.e., sites already
on or expected to be on the NPL list) were in the ppb range; although in a few sites where
concentrated 2,4,5-TCP production wastes were stored or disposed, concentrations were
as high as 2,000 parts per million (ppm). Off-site soil contamination of concern was
confirmed in 7 of the 100 Tier 1 and 2 sites, with soil concentrations in the ppb range.
Eleven of the 64 Tier 3 sites (facilities and associated disposal sites where 2,4,5-TCP and
its derivatives were formulated into pesticide products) were found to have soil
concentrations exceeding 1 ppb, and in 7 of 11 sites where contamination was found,
only one or two soil samples were above 1 ppb. Fifteen of the 26 Tier 5 sites (areas
where 2,4,5-TCP and pesticide derivatives had been or were being used) had
concentrations above 1 ppt, and one of those had a single detection of 6 ppb. Two-thirds
of all detections at the Tier 5 sites were below 5 ppt. Three of the 18 Tier 6 sites (organic
chemical and pesticide manufacturing facilities where improper quality control on
production processes could have resulted in 2,3,7,8-TCDD being introduced into the
wastestreams) had soil concentrations that exceeded the detection limit of 1 ppb,
although these levels were limited to one or two samples per site. Seventeen of the 221
urban soil sites and 1 of the 138 rural sites from Tier 7 (background sites not expected to
have contamination) had soil concentrations exceeding 1 ppt. The highest concentration
detected (11.2 ppt) was found in an urban sample. Results from Tier 7 are consistent
with the other studies discussed in this chapter regarding soil concentrations of 2,3,7,8-
TCDD in nonindustrial settings.
Rappe et al. (1995a) and Fiedler et al. (1995a) analyzed soil samples collected from
rural sites in southern Mississippi for CDDs and CDFs. Sites not directly impacted by
human activities such as heavy traffic or dust were selected. A total of 36 composite soil
samples from 8 Mississippi counties were analyzed. The I-TEQDF concentration of
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CDD/CDFs in soil ranged from 0.16 to 22.9 ppt dry mass (Fiedler et al., 1995a). The
mean I-TEQDF concentration was 3.1 ppt dry mass, and the median I-TEQDF concentration
was 0.8 ppt dry mass (Fiedler et al., 1995a). CDDs were found at higher concentrations
than CDFs, and OCDD was the most dominant congener.
Soil samples were collected from the National Institutes of Health (NIH) campus in
Bethesda, Maryland, during 1995 in an effort to determine the effect of 30 years of
pathological waste incineration on the campus and its surroundings (NIH, 1995). Thirty-
seven samples were collected from the soil at a depth of 6 inches. The total I-TEQDF for
these samples was 7.83 pg/g, when nondetects were assumed to be zero, and 8.49 pg/g,
when nondetects were assumed to be one-half the detection limit. OCDD, at a I-TEQDF
concentration of 6.29 pg/g, was the principal contributor to the total I-TEQDF for these
samples, regardless of whether nondetects were assumed to be zero or one-half the
detection limit. It should be noted that using the new TEFDF-WH098s, the TEQ for OCDD
would be 10 times lower (i.e., 0.63 pg/g). This reduction would also result in a significant
decrease in the total TEQ. The total TEQDF-WH098 would be 2.21 pg/g, when nondetects
were set to zero. Samples were also collected at depths of 1 2 and 24 inches for
comparison to levels found in the shallow (6-inch) samples. While CDD/CDF
concentrations found at the surface indicate deposition, strong correlation with I-TEQDF
concentrations at the deeper depths were observed. This seemed to indicate either long-
term presence of the source (i.e., greater than 40 years), or soil mixing that has occurred
either during or after deposition. An expert panel (comprised of toxicologists, chemists,
soil scientists, engineers, risk assessors, and public health professionals) concluded that
the levels of I-TEQDF in the samples are low and not significantly different from
background. Thus, these samples are assumed to be representative of urban background
concentrations. The spatial pattern of I-TEQDF concentrations showed no particular trends
that could be related to the incinerator. Other anthropogenic activities, such as vehicular
traffic, other medical waste incinerators not related to NIH, and fireplaces burning in the
vicinity, may have contributed to the deposition (NIH, 1995).
U.S. EPA (1996) collected soil samples in the vicinity of a municipal waste-to-
energy (WTE) facility in Columbus, Ohio, to determine whether surface soils around the
incinerator contained higher CDD/CDF levels than soils collected from background sites.
The facility is not currently in operation, but CDD/CDF residues may be present in the soil
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near the facility as a result of past emissions. Samples were collected from (1) on-site,
(2) urban background locations near the incinerator, and (3) areas remote from the facility
(i.e., rural background sites). The results of the analyses indicate that soil from the rural
background sites had the lowest I-TEQDF concentrations and on-site samples had the
highest I-TEQDF concentrations (Table 3-13). For rural background soil samples, total
l-TEQDFs ranged from 0.9 to 1.3 ppt (n = 3) with a mean of 1.1 ppt (TEQDF-WH098= 0.9
ppt), when nondetects were assumed to be zero, and 1.0 to 2.0 ppt with a mean of 1.4
ppt (TEQDF-WH098= 1.3 ppt), when nondetects were set to one-half the detection limit.
Total l-TEQDFs for urban background soils ranged from approximately 3 to 60 ppt (n = 1 8)
with a mean of 19 ppt (TEQDF-WH098= 21 ppt), when nondetects were set to either zero
or one-half the detection limit. For on-site samples, all 2,3,7,8-CDD/CDF congeners were
detected in all samples (n=4). Total I-TEQDF concentrations ranged from 50 to 760 ppt
with a mean of 356 ppt (TEQDF-WH098= 444 ppt). Additional detail and analyses of
these data are presented in Lorber et al. (1998a).
Brzuzy and Hites (1995) examined soil cores from four U.S. locations to evaluate
the accuracy of using measurements of CDD/CDF homologue groups in estimating the
atmospheric flux of these compounds into the environment. Soil cores were collected
from undisturbed areas near Shingleton, Grayling, and Verona, Michigan, and near
Mitchell, Indiana. CDD/CDF concentrations varied according to depth of the soil samples,
with deeper samples having lower CDD/CDF concentrations. Approximately 80 percent of
the CDD/CDF load were contained in the top 1 5 cm of the cores, and CDD/CDF
concentrations were close to the detection limit in samples collected at a depth of 20-25
cm. Based on the graphs presented in Brzuzy and Hites (1995), total CDD/CDF
concentrations in the uppermost 5 cm of the core ranged from approximately 60 pg/g to
200 pg/g for the three Michigan sites. CDD/CDFs in these soil cores were also found to
be highly correlated with the organic carbon content of the soil, indicating that organic
carbon is an important factor in the sorption of CDD/CDFs to soil (Brzuzy and Hites,
1995). Higher concentrations of CDD/CDFs were observed in two cores taken from the
Indiana site. Concentrations in the uppermost layer (i.e., 9 cm) of these cores ranged
from approximately 700 pg/g to nearly 10,000 pg/g. CDD/CDF concentrations in these
cores peaked at a depth of approximately 40 to 50 cm with concentrations ranging from
approximately 1,000 pg/g to over 20,000 pg/g. Brzuzy and Hites (1995) used the
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Michigan data to estimate soil-derived CDD/CDF flux rates ranging from 264 ng/m2/yr for
upper Michigan to 663 ng/m2/yr for lower Michigan. These soil-derived flux estimates
were compared to sediment-derived fluxes from previous studies to determine if soil
samples can also be used to accurately predict atmospheric flux. Good agreement for the
fluxes to these two media was observed. In addition, the CDD/CDF homologue profiles
for soil and sediment were similar.
Recently, Washington State Department of Ecology (Rogowski et al., 1999)
collected soil samples as part of a study of metals and dioxin-like compounds in
agricultural fertilizers and soil amendments. Soils were analyzed to evaluate whether
these compounds had accumulated as a result of fertilizer use and to assess typical
concentrations of dioxin-like compounds in Washington State soils. A total of 30 soil
samples were collected from urban (N = 14), rangeland (N=8), and forested (N = 8)
locations. Each sample was a composite of 10 sub-samples collected within a 1-acre
sampling unit. The sampling units were selected to represent typical or background
locations for each land use. Mean TEQDF-WH098 concentrations were higher in urban
locations (4.1 ppt) than in open rangeland/forest locations (1.8 ppt). During a later
sampling event (Rogowski and Yake, 1999), agricultural soils were collected to
characterize typical or background concentrations of dioxin-like concentrations in soil.
Fifty-four samples were collected. Each sample was a composite of 10 sub-samples
collected from each sampling location to a depth of 5 cm. The mean TEQDF-WH098
concentration for the agricultural samples was 0.12 ppt.
U.S. EPA Region 8 (2000a) is conducting a set of four related studies on dioxin-like
compounds in surficial soils along Denver, Colorado's, Front Range. One of these studies
(U.S. EPA Region 8, 2000b) evaluated regional background soil; other sampling efforts
include characterization of the Rocky Mountain arsenal using random samples at the site
or from historic use sites. A large number of reference soils were collected and analyzed
for CDD/CDFs and dioxin-like PCBs. These data will be used to assess whether the soil
concentrations observed in the Western Tier Parcel of the Rocky Mountain Arsenal, an
EPA National Priority List site, are higher than regional background levels. U.S. EPA
Region 8 (2000b) collected and analyzed 162 surface soil samples for investigation into
background concentrations of dioxin-like compounds at multiple locations within 1,000
square miles of Denver, Colorado's, front range. The multi-land use areas that were
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sampled were situated on public lands and were categorized as agricultural (n = 27),
commercial (n = 31), industrial (n = 29), open space (n = 36), and residential (i.e., within
200 feet of private land) (n = 39). The fine-soil fractions of samples obtained in the upper
two inches of the soil were analyzed for the 17 dioxins and furans and 12 PCBs (77, 81,
105, 114, 118, 1 23, 1 26, 1 56, 1 57, 1 67, 1 69, and 1 89). The mean TEQDFP-WH098
ranged from less than 1 ppt TEQ to approximately 100 ppt TEQ (with two outliers of 142
and 155 ppt removed; one from a residential site and one from a commercial site). The
mean TEQDFP-WH098 values were: 1.9 ppt for agricultural sites, 8.5 ppt for commercial
sites, 15.4 ppt for industrial sites, 2.8 ppt for open space, and 8.6 ppt for residential
locations, with a total mean of 7.5 ppt when non-detects were set to one-half the
detection limit (U.S. EPA Region 8, 2000b, with revised data provided by Gerry
Henningsen, Region 8 to Jim Buchert, Versar, Inc., March 2001). PCBs comprised
approximately 20 percent of the TEQDFP-WH098. The analytical values indicate that open
space and agricultural lands have the lowest TEQDFP-WH098 concentrations, while
industrial, commercial, and residential locations have slightly higher concentrations. It
should be noted that because sieved samples were analyzed, these results may be higher
than if bulk samples had been analyzed. Further testing is being conducted to identify if
the increased total organic carbon content of agricultural and open space soils have a
higher affinity for dioxin-like compounds than other soil types, thereby skewing the
analytical results to produce lower than actual values.
3.3.2. European Data
Soil samples from rural and semi-urban sites in England, Wales, and lowland
Scotland showed a general increase in concentration from the TCDDs to OCDD, whereas
CDF levels showed very little variation between the congener groups (Greaser et al.,
1989). Concentrations of 2,3,7,8-TCDD at those sites ranged from <0.5 to 2.1 ppt.
The median values for the TCDDs to OCDD were 6.0, 4.6, 31, 55, and 143 ppt,
respectively. The median values for the TCDFs to OCDF were 16, 17, 32, 1 5, and 1 5
ppt. Evaluation of soil data from urban sites in the same geographical area showed that
the mean levels for the CDD and CDF congeners were significantly greater (p<0.01) than
those for rural and semi-urban background soils (Greaser et al., 1990). Concentrations of
2,3,7,8-TCDD at the urban sites ranged from <0.5 to 4.2 ppt. The median values for the
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TCDDs to OCDD were 40, 63, 141, 256, and 469 ppt, respectively. The median values
for the TCDFs to OCDF were 140, 103, 103, 81, and 40 ppt. Significantly elevated
levels of the lower congeners, together with higher overall CDD/CDF concentrations, are
indicative that local sources and short-range transport mechanisms are major contributors
of CDDs and CDFs to urban soils. Cox and Greaser (1995) evaluated soils from urban and
rural locations in the United Kingdom before the introduction of Integrated Pollution
Control in 1 991. l-TEQDFs for 11 rural locations ranged from 0.78 ppt to 1 7.48 ppt, with
a mean of 5.1 7 ppt, and the l-TEQDFs for 5 urban samples ranged from 4.88 pt to 87.34
ppt with a mean of 28.37 ppt.
Analysis of four sites in Hamburg, Germany, contaminated by an organochlorine
pesticide manufacturing company showed patterns of CDD and CDF distribution similar to
the urban and industrial sites examined in England, Wales, and Scotland (Sievers and
Friesel, 1989). The study indicated that CDDs and CDFs showed a regular increase in
concentration with increasing degree of chlorination (although individual data points were
not presented). Maximum concentrations of 2,3,7,8-TCDD ranged from 900 ppt to
874,000 ppt. Very high concentrations of 2,3,7,8-TCDD at the sites were attributed to
an admixture of wastes from 2,4,5-T production.
A soil sampling survey in Salzburg, Austria, also showed that the concentrations of
CDD/CDFs were higher in urban and industrial sites than in rural sites (Boos et al., 1992).
The total CDD content of the soils ranged from 33.7 to 1236.7 ppt for urban sites, 92.2
to 455 ppt for industrial sites, and 7.1 to 183.6 ppt for rural sites. The total CDF content
of the soils ranged from 45.6 to 260.8 ppt for urban sites, 53.0 to 355.3 ppt for
industrial sites, and 12.0 to 77.7 ppt for rural sites. l-TEQDFs ranged from 0.1 ppt to 3.1
ppt for rural sites, 1.0 ppt to 8.3 ppt for urban sites, and 3.5 ppt to 11.5 ppt for industrial
sites, when nondetects were assumed to be zero. When nondetects were set to one-half
the limit of detection, l-TEQDFs ranged from 1.3 ppt to 3.8 ppt for rural sites, 2.0 ppt to
8.6 ppt for urban sites, and 4.1 ppt to 12.5 ppt for industrial sites.
Rappe and Kjeller (1987) presented data on CDD/CDFs in soil collected from rural
(n = 3) and industrial (n = 2) sites in various parts of Europe. Concentrations were higher
among industrial soils than rural soils for all of the CDD/CDF homologue groups, and the
hepta-chlorinated compounds made up the largest portion of the total CDD/CDF
concentrations in both rural and industrial samples. HpCDDs ranged from nondetected to
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17 ppt in rural samples and 370 to 1,600 ppt in industrial samples. HpCDFs ranged from
14 to 22 ppt in rural soils and 260 to 4,500 ppt in industrial soils.
Retard et al. (1994) measured CDD/CDFs in soil samples collected from forest,
grassland, and plowland sites in western Germany. The highest mean concentration of
CDD/CDFs were found in the subsoil and topsoil layers of deciduous (38.0 ng l-TEQDF/kg
dry matter; n = 9) and coniferous forests (36.9 ng l-TEQDF/kg dry matter; n = 11).
Grassland and plowland sites had mean concentrations of 2.3 ng l-TEQDF/kg dry matter (n
= 7) and 1.7 ng l-TEQDF/kg dry matter (n = 14), respectively.
Stenhouse and Badsha (1990) collected baseline data for soils around a site
proposed for a chemical waste incinerator in Great Britain. All of the 2,3,7,8-substituted
CDD/CDF congeners, except PeCDD, were detected in all samples. Concentrations were
highest for the octa-chlorinated CDD/CDFs. Background I-TEQDF concentrations ranged
from 3 to 20 ppt. The mean I-TEQDF concentration was 8 ppt (n = 1 2), with a standard
deviation of 4 ppt.
Buckland et al. (1998) evaluated soils collected in New Zealand. Dry weight
CDD/CDF concentrations ranged from 0.17 to 1.99 pg l-TEQDF/g for pristine soils, 0.17 to
0.90 pg l-TEQDF/g for agricultural soils, and 0.52 to 6.67 pg l-TEQDF/g for urban soils. The
PCB concentrations ranged from 0.067 to 2.3 pg TEQP/g (the TEFs used for PCBs were
not identified) for provincial centers and 0.087 to 1.33 pg TEQP/g for metropolitan
centers. The congeners below the detection limit were included in the total TEQ using
half their limits of detection.
Masahide et al. (1998a) examined soil samples collected at the depth of 0-10 cm
from various sites located in Poland between 1990 and 1994. The mean dry weight total
PCB concentration was 8.6 ng/g for agricultural and forest soils, 170 ng/g (n = 31) for
urban soils, and 900 ng/g for the soils sampled at the military area. Dry weight PCB
concentrations increased from 21 ng/g in Northern Poland to 48-380 ng/g in highly
populated and industrialized regions in Southern Poland.
3.3.3. Soil Observations and Trends
Some general observations for CDD and CDF levels in soils are possible from the
data presented in the various soil studies discussed above:
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• As the degree of chlorination increases, the concentrations increase.
Concentrations of the hepta- and octa-chlorinated congeners are generally
higher than the tetra-, penta-, and hexa-chlorinated congeners.
• Concentrations in settings identified as urban are higher than those in areas
identified as rural.
• Concentrations associated with industrial sites clearly are the highest, with
concentrations in the hundreds to thousands of parts per trillion.
3.3.4. Soil CDD/CDF Profiles and Background TEQ Concentrations
CDD/CDF homologue group profiles for soil were calculated as the mean
homologue group concentrations divided by the mean total CDD/CDF concentration.
Nondetects were assumed to be zero in these calculations. For rural background soil,
homologue group profiles were calculated based on data from Reed et al. (1990),
Birmingham (1990), Pearson et al. (1990), BC Environment (1995), U.S. EPA (1985,
1996), Tewhey Associates (1997), MRI (1992), Rogowski et al. (1999), and Rogowski
and Yake (1999). They indicate that of the homologue groups, the higher chlorinated
compounds dominate. OCDD and HpCDD account for the highest percentages of total
CDD/CDFs (Figure 3-3; Table 3-14). CDD/CDF homologue group profiles for urban
background samples, based on Birmingham (1990), Pearson et al. (1990), NIH (1995),
U.S. EPA (1996), and Rogowski et al. (1999), are similar (Figure 3-4; Table 3-14). The
sum of 2,3,7,8-substituted congener concentrations account for 83 percent of the total
CDD/CDF concentrations in rural background soil and 91 percent in urban background soil.
Profiles based on the ratio of 2,3,7,8-substituted congeners to total CDD/CDFs in rural
background soil indicate that OCDD and 1,2,3,4,6,7,8-HpCDD account for the highest
percentages of total CDD/CDFs, followed by OCDF and 1,2,3,4,6,7,8-HpCDF (Reed et al.,
1990; BC Environment, 1995; U.S. EPA, 1996; Tewhey Assoc., 1997; MRI, 1992;
Rogowski et al., 1999; and Rogowski and Yake, 1999) (Figure 3-3). For urban
background soils, profiles were similar to those observed in rural background soils, based
on data from NIH (1995), U.S. EPA (1996), and Rogowski et al. (1999) (Figure 3-4).
Based on several of the studies described in this chapter, mean TEQDF-WH098 levels
were calculated (Table 3-15), based on the available data, to represent "typical"
background conditions in the North America. The mean rural background TEQDF-WH098
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level was estimated to be 2.6 ppt, and the "typical" urban background TEQDF-WH098 level
was estimated to be 8.8 ppt, assuming that nondetects equal zero. The mean rural
background concentration is based on data from eleven studies in seven U.S. States (i.e.,
Ohio, Minnesota, Illinois, Maine, Connecticut, Colorado, and Washington) and two areas in
Canada (i.e., British Columbia and Ontario) (n = 319; BC Environment, 1995; Birmingham,
1990; MRI, 1992; Pearson et al., 1990; Reed et al., 1990; Tewhey Associates, 1997;
U.S. EPA, 1985; U.S. EPA, 1996; Rogowski et al., 1999; Rogowski and Yake, 1999;
U.S. EPA Region 8, 2000b). The mean urban background concentration is based on data
from five U.S. States (i.e., Michigan, Maryland, Ohio, Colorado, and Washington) and
Ontario, Canada (n = 305; Birmingham, 1990; Nestrick et al., 1986; NIH, 1995; Pearson
et al., 1990; U.S. EPA, 1985; U.S. EPA, 1996; Rogowski et al., 1999; U.S. EPA Region
8, 2000b). The TEQP-WH098 was 0.31 ppt (n = 27) for rural soil and 2.0 ppt (n = 1 34) for
urban soil, based on soil data from U.S. EPA Region 8 (2000b) which were collected in
and around Denver, Colorado. In calculating the mean concentrations, each site was
weighted equally (i.e., the means are based on the average of the mean concentrations
from each location). In contrast, the weighted means in Table 3-15 represent average
concentrations for rural and urban sites that have been weighted according to the number
of samples at each site; all soil samples are treated individually regardless of location.
Thus, more heavily sampled locations have a greater impact on the weighted mean than
those sampled less frequently. Therefore, means, not weighted means, were used to
depict typical rural and urban background concentrations in North America. It should be
noted, however, that the means and weighted means in Table 3-15 are quite similar.
Background TEQDF-WH098 levels for soils were estimated by setting nondetects to
zero instead of one-half the detection limit, because congener-specific detection limits
were not available for some of the early studies. If one-half the detection limits had been
used to represent nondetected congeners, the estimated background TEQDF-WH098 levels
may have been slightly higher. Based on the results of European studies, it appears that
background TEQDF-WH098 concentrations in European soil are similar to those of the
United States. The TEQDF-WH098 concentration in urban background soil is used in
Chapter 4 to calculate background exposures. Urban soils are used because the majority
of the population lives in urban areas. Thus, using these concentrations would represent
typical exposure levels.
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3.4. CONCENTRATIONS IN WATER
Tables B-6 and B-7 (Appendix B) contain summaries of data from the limited
number of published studies regarding concentrations of CDDs and CDFs in water. Data
on dioxin-like PCB congener concentrations in water were not found in the literature.
Several of the CDD/CDF studies are discussed below.
3.4.1. North American Data
A survey of 49 drinking water supplies in Ontario, including supplies in the vicinity
of chemical industries and pulp and paper mills, was initiated in 1983 (Jobb et al., 1990).
As of February 1989, 4,347 congener analyses had been performed on 399 raw and
treated water samples. OCDD was detected in 36 of 37 positive results and ranged from
9 to 175 ppq in raw samples (33 positive samples) and 1 9 to 46 ppq in treated samples
(4 positive samples). These low concentrations were found primarily in water obtained
downstream of industrialized areas in the St. Clair/Detroit River system. No samples
contained detectable levels of 2,3,7,8-TCDD. Because CDDs and CDFs are hydrophobic
compounds, and consequently, have a tendency to sorb onto particulate matter in water,
conventional water treatment processes are expected to be effective in removing the
contaminants along with the particulates. This is substantiated by the fact that 33 of the
37 positive results were raw water samples. Because of the relatively low levels of CDDs
detected in the samples, it is difficult to ascertain whether the CDDs were particulate-
associated or dissolved.
A survey of 20 community water systems throughout New York State was
conducted in 1986 (Meyer et al., 1989). The sampling sites were representative of the
major surface source waters in New York. They included sources receiving industrial
discharges or known to contain dioxin-contaminated fish, as well as waters in more
remote areas. TCDFs were detected in the finished water at the Lockport facility
(duplicate samples had concentrations of 2.1 and 2.6 ppq). Except for a trace of OCDF
detected at one location, no other CDDs/CDFs were detected in finished water at any of
the other 19 community water systems surveyed. Raw water sampled at the Lockport
facility contained concentrations of TCDDs (1.7 ppq), as well as TCDFs to OCDF (1 8, 27,
85, 210, and 230 ppq, respectively). As can be seen from the data, the CDF congener
group concentrations increased with increasing chlorine number.
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3.4.2. European and Japanese Data
CDDs in surface water samples collected from the Eman River in southern Sweden
generally increased in concentration from the TCDDs to OCDD; whereas, the CDF levels
showed very little variation between the congener groups (Rappe et al., 1989a). In
general, however, the levels of CDFs were higher than the levels of CDDs. Concentrations
of 2,3,7,8-TCDF were 0.022 ppq in Jarnsjon and 0.026 ppq in Fliseryd. The filtered
water, before chlorination and distribution as drinking water, had no detectable tetra-,
penta-, or hexa-chlorinated congeners of CDDs or CDFs, but the HpCDDs and OCDD were
detected at 120 and 170 ppq, respectively.
A survey was conducted at the Venice lagoon in the north of Italy by the Ministry
of Justice and the Ministry of Works to assess the CDD/CDFs contamination produced by
industrial and municipal waste water discharges (Ramacci et al., 1998). The results
showed that four out of seven waste water samples were characterized by a prevalence
of OCDF; two had an almost equal distribution between OCDD and OCDF; and only one
collected from a septic tank of the city of Venice presented a prevalence of OCDD.
In Japan, water samples were collected from a coastal area, river, and pond in
Matsuyama between October 1996 and September 1997 (Seike et al., 1998). The total
concentrations of CDD/CDFs ranged from 1 5 to 170 pg/L (n = 3) in coastal waster with an
average of 51 pg/L, from under the detection limit to 1500 pg/L (n = 22) in river water
with an average of 180 pg/L, and from 44 to 530 pg/L (n = 3) in pond water with an
average of 260 pg/L. CDD/CDFs in coastal water were relatively lower than from other
sources, and were thought to be diluted with seawater and/or deposited to the coastal
sediment.
Raw and treated tapwater samples from Japanese water supplies were analyzed by
Magara et al. (2000) for CDD/CDFs and PCBs 77, 81, 1 26, 1 69, 105, 114, 118, 1 23,
1 56, 1 57, 1 67, and 1 89. The average TEQDFP-WH098 was 0.148 pg/L (ppq) for raw
water and 0.019 pg/L (ppq) for treated water. CDFs accounted for about 60 percent of
the total TEQ in the treated samples.
3.4.3. Water Observations and Trends
Some general observations for CDD and CDF levels are possible from the limited
data available from the various water studies described above:
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• CDD/CDFs are seldom detected in drinking water at ppq levels or higher.
• Raw water samples generally have higher concentrations of CDD/CDFs than
finished water samples.
• The concentration of CDDs and CDFs in surface water generally increases
from the tetra-chlorinated to the octa-chlorinated congener groups.
3.4.4. Water CDD/CDF Profiles and Background TEQ Concentrations
CDD/CDF congener profiles could not be generated for water because of the lack of
congener-specific data for treated drinking water. For the background studies reviewed
here, only data for OCDD and OCDF were available.
Based on the above studies, a total of 236 samples from Ontario, Canada, and
Lockport, New York, were selected as representing background conditions in North
America. The "typical" TEQDF-WH098 level was computed as 0.00056 ppq, assuming that
nondetects equal half the detection limit (Jobb et al., 1990). This value is used in Chapter
4 to estimate background exposures to the U.S. population from drinking water
consumption. It should be noted, however, that OCDD and OCDF were the only
congeners for which background data were available. Of the 214 samples analyzed for
OCDD, only 4 were positive, and only 2 out of 22 samples analyzed for OCDF were
positive. No appropriate data on drinking water concentrations could be found for Europe,
and only one study was available for Japan.
3.5. CONCENTRATIONS IN SEDIMENT
Tables B-8 through B-10 (Appendix B) contain summaries of data from several of
the numerous studies in the published literature regarding concentrations of CDDs, CDFs,
and dioxin-like PCB congeners in sediment. Several of these studies are discussed in the
following paragraphs. It should be noted that the review of sediment data presented here
is not based on a comprehensive review of the published studies on CDD/CDFs in
sediment. Instead, it is intended to provide a brief overview of sediment levels of
CDD/CDFs from a sampling of representative studies. In addition, because the levels of
CDD/CDFs and PCBs in sediment layers may be indicative of the cumulative history of
contamination at a site (i.e., reservoir sources), the studies presented here represent only
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those that analyzed surficial (i.e., recently deposited) sediment samples or the uppermost
layers from sediment cores, and not deep sediment core samples.
3.5.1. North American Data
In sediment samples collected from estuaries adjacent to an industrial site in
Newark, New Jersey, where chlorinated phenols had been produced, the level of OCDD
was many times higher than that of 2,3,7,8-TCDD (Bopp et al., 1991). Studies
conducted by Wenning et al. (1992; 1993) in Newark Bay also indicated that OCDD was
found in higher concentrations than the lower-chlorinated congeners in this water body.
Based on 19 sediment samples, OCDD levels ranged from 310 ppt to 17,000 ppt, and
2,3,7,8-TCDD levels ranged from 2.8 pt to 480 ppt. Observed congener patterns for this
area were similar to those found in sediments from several other U.S. and European water
bodies. The authors suggest that these similarities are a result of similar municipal and
industrial sources in heavily industrialized and populated areas. Based on the results of
principal components analyses, the congener profiles for sediments from Newark Bay are
closely related to those of several different potential sources, including chemical
manufacturing processes and municipal activities. Hudson River sediment samples
contained primarily the higher chlorinated (CI6 to CI8) CDD and CDF congeners (Petty et
al., 1982). Concentrations of the HpCDD and OCDD homologues ranged from 5 to 1 5
ppb, and the OCDD homologue in most instances accounted for more than half of the
total CDD residue. Likewise, the HpCDFs and OCDF occurred at the highest levels (ca. 1
ppb).
Surface sediment samples were collected from several estuaries in the United
States (Norwood et al., 1989). Sampling sites included Black Rock Harbor in Bridgeport,
Connecticut, (an industrialized urban estuary); central Long Island Sound (a relatively clean
reference site); Narragansett Bay, Rhode Island, (where chemical industries may have
contributed to the input); New Bedford Harbor, Massachusetts, (a section of which is a
National Superfund Site because of PCB contamination); and Eagle Harbor, Washington,
(the site of a creosote wood treatment facility). Sediments in New Bedford Harbor were
reported to be more heavily contaminated with CDFs than sediments from the other sites.
In particular, HxCDF congeners were greater by a factor of 40 (although individual data
points were not presented) at one of the more contaminated New Bedford Harbor sites. In
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contrast, sediments from Eagle Harbor were practically devoid of CDFs and showed a
large increase in the HpCDD and OCDD congeners closer to the treatment facility.
Narragansett Bay and Black Rock Harbor were similar in both concentration and
distribution of CDDs and CDFs, and Black Rock Harbor contained slightly higher levels of
the tetra- to hepta-CDD and CDF congeners. Sediment from Long Island Sound was
cleaner and had a distribution of CDFs between that of Narragansett Bay and Black Rock
Harbor. Sediment with the least contamination was collected in New Bedford Harbor, up-
river from the PCB facilities; the highest OCDD concentration (1,400 ppt) was detected in
Eagle Harbor.
Sediment samples from Siskiwit Lake, on Isle Royale, Lake Superior, were
examined to evaluate the atmospheric input of CDDs and CDFs to the lake (Czuczwa et
al., 1984). The water level in Siskiwit Lake is 17 meters higher than that in Lake
Superior, and in addition, there are no anthropogenic inputs in the drainage basin of
Siskiwit Lake. Consequently, the atmosphere is the only source of anthropogenic
chemicals in that lake. OCDD was most predominant, and the HpCDD and HpCDF
congeners also were abundant. The study indicated a considerable decrease in
concentration of all CDDs and CDFs between 6 and 8 cm of the sediment core depth (i.e.,
sediment believed to have been deposited about 1940).
Surficial sediments collected from Jackfish Bay on the north shore of Lake Superior
contained moderate concentrations of the TCDF and OCDD congeners, with trace
concentrations of other congeners (Sherman et al., 1990). The concentration of OCDD
was similar to that found in the Siskiwit Lake sediment samples. The OCDF and OCDD
profile for a sediment core collected from Moberly Bay was similar to the surficial
sediment pattern. These congener groups predominated at all depths where detectable
concentrations occurred. In addition, low concentrations of the HpCDD and PeCDF and
HpCDF congeners were detected. The concentration profile of the HpCDF congener group
showed a relatively high value that dropped abruptly to nondetectable (<60 ppt) below a
depth of 10 cm. This abrupt change corresponded to a section date 1973 that reflects an
operational change at the pulp mill.
A survey of surficial harbor sediments collected near a wood preserving plant in
Thunder Bay, Ontario, Canada, on the north shore of Lake Superior, found the highest
concentrations of CDD/CDFs at stations closest to the plant dock, and lower
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concentrations at locations further from the source (McKee et al., 1990). No CDDs or
CDFs were detected below the surficial layer. TCDD and PeCDD congeners were below
analytical detection limits in all samples. However, the concentrations of the HxCDDs to
OCDD congeners increased with the degree of chlorination. The maximum concentrations
of the HxCDDs to OCDD ranged from 5,700 ppt for the HxCDDs to 980,000 ppt for
OCDD. As with the CDD distribution profile, the HxCDFs to OCDF increased with the
degree of chlorination.
Sediment levels of CDD/CDFs in British Columbia were found to be higher in
samples collected from sites in the receiving environment adjacent to a suspected source
(secondary sites) than in areas not expected to be contaminated (background) (Table
3-16) (BC Environment, 1995). These observations are based on a 2-year study
conducted during 1990/91 and 1991/92 by British Columbia's Ministry of Environment.
Secondary sites were identified as being associated with chemical or combustion sources.
Background samples did not contain detectable levels of 2,3,7,8-TCDD, and the mean
2,3,7,8-TCDF concentration was 1.4 ppt. For the purposes of calculating I-TEQDF values
for this study, nondetects were set to zero. Mean I-TEQDF levels were 32.5 ppt for
secondary sites (all sources) and 3.9 ppt for background sites (Table 3-16).
Bottom surficial sediments (0-3 cm) were collected from the sedimentation basins
of Lake Ontario to assess the levels of the various PCB congeners (Oliver and Niimi,
1988). Concentrations of 2,3',4,4',5-PeCB (PCB 118); 2,3,3',4,4'-PeCB (PCB 105); and
2,3,3',4,4',5-HxCB (PCB 156) in the sediment were 15, 10, and 2.1 ppb, respectively. A
baseline assessment of CDDs and CDFs was performed on the Elk River, a semi-rural area
located about 25 miles northwest of Minneapolis-St. Paul, Minnesota (Reed et al., 1990).
Sediment samples were collected from Lake Orono, a reservoir on the Elk River, and from
an abandoned gravel pit. Although none of the sediment samples contained 2,3,7,8-
TCDD, the gravel pit sediments contained measurable concentrations of TCDFs. Only one
Lake Orono sample contained measurable concentrations of 2,3,7,8-TCDF (0.31 ppt) and
total TCDF (0.54 ppt). Gravel pit samples also contained HpCDDs to OCDD and PeCDFs
to OCDF. Lake Orono samples contained HpCDDs, OCDD, and HpCDF congeners.
HpCDDs ranged from 7.3 ppt in the lake inlet to 110 ppt in the gravel pit and in the lake
near the dam. OCDD concentration ranged from 450 ppt in the gravel pit to 600 ppt in
the middle of Lake Orono. The sediment profiles reflected combustion source influences.
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The Sheboygan River, a Wisconsin tributary to Lake Michigan, is polluted with
PCBs from the mouth to about 14 miles upstream (Sonzogni et al., 1991). That portion of
the river is a Superfund site, as well as one of the Great Lakes "Areas of Concern."
Sediment cores were collected at Rochester Park, near the original source of the PCBs,
about 14 miles upstream from the mouth. The PCB congeners 2,3',4,4',5-PeCB (PCB
118); 2,3,3',4,4'-PeCB (PCB 105); and 3,3',4,4'-TCB (PCB 77) were detected in all
samples and ranged from about 5 to 1,500 ppb. Remaining dioxin-like PCB congeners
were detected less frequently and ranged from nondetectable to slightly over 100 ppb.
The PCB congener 2,3',4,4',5-PeCB (PCB 118) appears to be the most common dioxin-
like PCB in environmental samples and was found in the Sheboygan River sediments in the
highest weight percent. The eight toxic PCBs detected in this study were present in
relatively low concentrations compared to total PCBs or other more abundant congeners.
Sediments collected from Waukegan Harbor in Lake Michigan contained the dioxin-
like PCB congeners 3,3',4,4'-TCB (PCB 77) and 2,3,3',4,4'-PeCB (PCB 105) (Huckins et
al., 1988). The percentage of 3,3',4,4'-TCB (PCB 77) in the samples averaged 0.16
percent ± 0.15, with concentrations ranging from 13 to 27,500 ppb. The percentage of
2,3,3',4,4'-PeCB (PCB 105) averaged 0.66 percent ± 0.37, with concentrations ranging
from 102 to 131,000 ppb. In another Lake Michigan study, sediment samples collected
from Green Bay contained concentrations of all 11 dioxin-like PCB congeners (Smith et al.,
1990b). The dominant congeners were 2,3,4,4',5-PeCB (PCB 114) and 2,3,3',4,4'-PeCB
(PCB 105), with concentrations of 11 and 5.8 ppb, respectively.
Fiedler et al. (1995b) and Rappe et al. (1995b) analyzed sediments samples from a
river system in southern Mississippi to determine if the concentrations of CDDs and CDFs
in the sediments were influenced by a local pulp and paper mill. The pulp mill, which is
located adjacent to the Leaf River and currently uses chlorine dioxide in its bleaching
process (elemental chlorine was used from 1984 through 1989), was suspected of
contributing to CDD and CDF contamination in the region (Fitzpatrick, 1995). A total of
61 sediment samples were collected from sites located upstream and downstream from
the mill. Study results indicated that most CDD and CDF congeners were present in all
sediment samples collected, but the predominant congeners were the HpCDDs and OCDD
(Rappe et al., 1995b). Congener profiles were generally similar for samples collected from
both populated, potentially polluted areas and pristine areas. In addition, for the majority
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of sampling sites in the study, the ratio of the sum of CDDs to CDFs ranged from 43 to
1,200. Rappe et al. (1995b) stated that this observation "is notable because, with the
exception of sewage sludge, no environmentally significant source has been identified
with such a dominance of CDDs." The mean I-TEQDF concentration observed in this study
was 10.6 ng/kg dry weight, and the median I-TEQDF concentration was 9.90 ng/kg dry
weight (Fiedler et al., 1995b). Median values reported in this study were consistent with
those of an earlier study conducted by the Mississippi Department of Environmental
Quality in 1992 in the same river system.
Rappe et al. (1997a) analyzed sediment core samples from five lakes in southern
Mississippi. The sediment cores were collected from five man-made recreational lakes
with no known industrial point source of CDD/CDFs and low atmospheric deposition rates.
Cores were subdivided into sections to evaluate temporal trends in deposition of
CDD/CDFs. I-TEQDF values for the lake cores ranged from 0.38 to 9.52 ppt (dry weight).
2,3,7,8-TCDD was present at levels below the detection limit in all of the sediment core
samples. The higher chlorinated congeners (hexa to octa CDD/CDFs) predominated.
OCDD levels ranged from 150 ppt dry weight to 5,500 ppt dry weight, while total CDD
levels ranged from 176 to 7,577 ppt dry weight. CDF levels ranged from nondetectable
to 14.4 ppt dry weight. As observed in another analysis of sediment cores from the
region (Rappe et al., 1997b), the CDD to CDF ratios were very high, ranging from 79.1 to
9,920. The CDD and CDF congener patterns were similar to those observed by Rappe et
al. (1995b) in previous sediment studies in the region. No observable trend for levels of
CDDs, homologues, or l-TEQDFs correlating to the age of the strata could be identified.
Rappe et al. (1997b) also studied sediment samples from 15 manmade lakes in the
same region of southern Mississippi. Lakes were selected from pristine areas, areas not
known to be impacted by industrial point sources of CDDs and CDFs. As in previous
studies from this region (Rappe et al., 1995b and Fiedler et al., 1995b), HpCDDs and
OCDD were the predominant congeners in these deep lake and sedimentation area
samples. OCDD levels ranges from 1,400 to 43,000 ppt dry weight (median 7,700 ppt).
The concentration of HpCDDs ranged from 63 ppt to 2,000 ppt dry weight (median 430
ppt). 2,3,7,8-TCDD was detected in 20 of the 27 sediment samples at levels not
exceeding 1.0 ppt dry weight. CDDs dominated, and the ratio of CDDs to CDFs ranged
from 19 to 764 (79 percent had ratios >100). The I-TEQDF concentration in the sediment
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samples ranged from 2 ppt dry weight to 63.7 ppt dry weight. Rappe et al. (1997b)
postulated that the high levels of OCDD found in this region of the United States are due
to natural formation. The authors base this theory on the fact that the sediment samples
were collected in pristine areas with no known industrial sources of CDD/CDFs and low
atmospheric deposition rates. However, they have observed consistently higher than
expected OCDD levels and high CDD/CDF ratios in this region, which do not correspond
with other levels in the published literature.
In an effort to determine whether incineration of municipal waste influenced
CDD/CDF levels in the immediate area of waste incineration facilities, sediment samples
were collected from surface water near cities with, and without, operating incinerators
throughout Connecticut. A total of 344 sediment samples were collected between 1987
and 1990. The mean total CDD/CDF concentrations for pre-operational and operational
status were 3,590 pg/g and 4,523 pg/g, respectively, when nondetects were assumed to
be one-half the detection limit (MRI, 1992). Based on the concentration data reported in
MRI (1992), mean I-TEQDF values for pre-operational and operational sediments were 21
pg/g and 24 pg/g, respectively.
Sediment samples collected at the lowermost Tennessee River (Kentucky Dam
Tailwater) and Kentucky Lake at the depth of 0-5 cm had total PCB concentrations
ranging from the detection limit (1.0 ng/g dry weight) to 26.36 ng/g dry weight
(Loganathan et al., 1998). The total PCB concentrations sampled from the lowermost
Tennessee River were generally higher that those from Kentucky Lake. The PCB congeners
found in the sediment samples included PCB-8, 29, 50, 28, 52, 44, 101, 87, 1 54, 11 8,
153, 105, 138, 187, 128, 200, 180, and 170; the dominant congeners were PCB-44 and
PCB-101. Of these congeners, only PCBs 11 8 and 105 are considered to be dioxin-like.
Recently, EPA conducted a time-trend study of dioxin-like compounds in sediment
cores (Versar, 1996a; Cleverly et al., 1996). Cores from 11 lakes/reservoirs were
collected, sectioned and dated, and analyzed for CDD/CDFs and PCBs 77, 105, 118, 126,
1 56, 1 57, and 1 69. The lakes were located in various geographic locations throughout
the United States and were selected to represent background conditions (i.e., no known
CDD/CDF sources). Based on the most recently deposited sediments (i.e., the uppermost
core sections), total I-TEQDF concentrations ranged from 0.11 ppt to 15.6 ppt with a mean
of 5.3 (TEQDF-WH098 concentrations ranged from 0.12 ppt to 16.3 ppt, with a mean of
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5.3 ppt) when nondetects were set to one-half the detection limit, and from 0.11 ppt to
14.3 ppt with a mean of 4.6 ppt (TEQDF-WH098 concentrations ranged from 0.12 ppt to
1 5.6 ppt, with a mean of 4.7 ppt) when nondetects were set to zero (Table 3-1 7).
Chandler Lake, an Arctic lake located in North Slope, Alaska, had the lowest TEQDF
concentration, and Canandaigua Lake in New York and Santeetlah Reservoir in North
Carolina, both eastern lakes, had the highest TEQDFs. In general, the higher chlorinated
CDD/CDFs accounted for the largest proportion of total TEQ concentrations. PCB TEQP-
WH098 and TEQP-WH094 ranged from 0.07 ppt for Chandler Lake to 2.2 ppt for
Canandaigua Lake, with a mean TEQP-WH098 of 0.53 ppt when nondetects were set to
either zero or one-half the detection limit. Total CDD/CDF concentrations for these 11
lakes are presented in Table 3-18. Total CDD/CDFs ranged from 9.1 ppt for Chandler
Lake, Alaska, to 2,916 ppt for Santeetlah Reservoir, North Carolina, with a mean of 926
ppt. Total PCBs ranged from 34 ppt for Chandler Lake, Alaska, to 2,116 ppt for
Canandaigua Lake, new York, with a mean of 489 ppt. Table 3-18 also presents the
estimated annual flux of CDD/CDFs to these lakes. Flux was calculated by multiplying
sediment concentrations by lake-specific sedimentation rates (g/cm2-yr), and dividing by
lake-specific sediment focusing (redistribution) factors. CDD/CDF Flux ranged from 0.05
pg/cm2-yr for Chandler Lake to 190 pg/cm2-yr for Santeetlah Reservoir. PCB flux ranged
from 0.19 pg/cm2-yr for Chandler Lake to 103 pg/cm2-yr for Santeetlah Reservoir. These
data are considered to be the best available data for characterizing sediment CDD/CDF and
dioxin-like PCB background concentrations in the United States because they are
representative of sites not expected to be impacted from within several geographic
regions.
3.5.2. European Data
This section presents a brief overview of some data on European sediments. It is
not intended to be a comprehensive review of all available studies on European sediments.
No attempt was made to characterize background levels of CDD/CDFs for Europe because
of the variability and limited scope of this review.
Sediment samples from the vicinity of a magnesium production plant in Norway
were analyzed for CDDs and CDFs (Oehme et al., 1989). The concentration distribution
of CDD and CDF congeners was rather homogeneous, except for a slight decrease at a
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sampling station downstream of the plant and higher levels in deeper sediments (4-6 and
11-13 cm depth) at that site. TCDF congener profiles were the same as those for
magnesium production. In addition, the PeCDF congener profiles were very similar to
those found in the wastewater.
Trapped sediments from the archipelago of Stockholm, Sweden, displayed CDD
and CDF congener distribution patterns that were very similar to those exhibited in total
air and air particulates (Rappe and Kjeller, 1987). HpCDDs, OCDD, and HpCDF were the
dominant congener groups in the sediment.
Bottom surface sediment samples collected from the Baltic Sea showed interesting
CDD and CDF distribution patterns (Rappe et al., 1989b). Background samples, one
between the Swedish and Soviet coasts and the other between the Swedish and Finnish
coasts, contained similar levels and distribution profiles. The study indicated that the
pattern of the TCDF congeners at these sites was typical of the "incineration pattern"
(i.e., patterns resulting from MSW incineration, car exhausts, steel mills, etc.), which also
had been found in samples of air and air particulates. However, sediment samples
collected at a distance of 4 to 30 km from a pulp mill revealed a congener distribution
pattern typical of bleaching mills. TCDFs found in the sediment 4 km from the pulp mill
contained only two major congeners. The sediment collected 30 km from the mill
displayed the same pattern.
Evaluation of sediments in Hamburg Harbor in Germany revealed high
concentrations of the TCDDs through OCDD (mean concentrations of 564, 1112, 2744,
4040, and 7560 ppt, respectively) and the TCDFs through OCDF (mean concentrations of
526, 2980, 4106, 2358, and 2712 ppt) (Gotz and Schumacher, 1990). The average
concentration of 2,3,7,8-TCDD was 375.3 ppt. High concentrations of 2,3,7,8-TCDD,
especially in the Moorfleeter Canal and the Auserer Vering Canal, were attributed to
discharges from an organochlorine pesticide manufacturing plant. Patterns of 2,3,7,8-
TCDD and the other HpCDD congeners are characteristic of the patterns resulting from the
production of 2,4,5-T and 2,4,6-trichlorophenol. In addition, the pattern of the HpCDF
congeners can be linked to emissions from thermal processes employed by chemical
industries in the production of chlorinated organic chemicals. High concentrations of
hepta- and octa-CDDs/CDFs may also be the result of other industrial combustion
processes in the Hamburg area.
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The Venice lagoon in the north of Italy, covering a surface of approximately 500
km2 with a depth of <2 m, has a limited water exchange with its surrounding area. The
pollution loading sources includes the effluent of water streams and industrial and
municipal waste water discharges, agricultural runoff, and an intense traffic of
motorboats. Two sediment studies were conducted at this lagoon. Ramacci et al. (1998)
used the data collected by the Ministry of Justice and the Ministry of Works to assess the
CDD/CDFs contamination. The sediment samples were collected from various depths,
including surficial to 60 cm. The dry weight I-TEQDF concentrations for various depth were
16.0 pg/g for surficial sediment, 14.3 pg/g and 19.8 pg/g for sediment samples from a
depth of 0-20 cm, 6.2 pg/g and 0.5 pg/g for sediment samples from a depth of 20-40 cm,
and 6.0 pg/g for the sediment at the depth of 40-60 cm. Di Domenico et al. (1998)
collected the data from the top 10-30 cm thick sediment layer of the Venice lagoon
bottom in 1992 and 1995. For the area under industrial or prevailing industrial exposure,
the concentrations of total CDD/CDFs ranged from 840 pg/g to 29,000 pg/g; the I-TEQDF
concentrations ranged from 12 pg/g to 570 pg/g; and the total PCB concentrations ranged
from 53 ng/g to 720 ng/g. For the area exposed to municipal waste water discharges, the
concentrations of total CDD/CDFs ranged from 210 pg/g to 1,400 pg/g; the I-TEQDF
concentrations ranged from 4.8 pg/g to 23 pg/g; and the total PCB concentrations ranged
from 71 ng/g to 610 ng/g. The CDD/CDF congener profile for sediments from the Venice
lagoon appeared to be strongly influenced by the waste water discharges. A prevalence
of OCDD and OCDF over other congeners was observed (Ramacci et al., 1998).
Masahide et al. (1998b) examined sediment samples collected from a depth of 0-
10 cm in the Districts of Gdansk, Szczecin, and Katowice in Poland in 1993-1994. The
dry weight total PCB concentrations sampled in Northern Poland ranged from 1.7 ng/g to
630 ng/g with an average of 110 ng/g. The dry weight total PCB concentrations were
1.7-2.2 ng/g at the area without industrial activity, 78-99 ng/g at the places receiving
untreated municipal effluents, and 46-630 ng/g at the sites under the influence of human
activities. The dry weight PCB concentrations sampled in Southern Poland ranged from
46 ng/g to 1,300 ng/g with an average of 540 ng/g. This highest concentration was
sampled from a pond receiving effluent water from a coal mine in Katowice.
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3.5.3. Vietnamese and Japanese Data
Schecter et al. (1989a) collected sediment samples from three rivers in Vietnam
and analyzed them for CDD/CDF residues. Rivers included the Red River in the
nonindustrialized North, the Saigon River in the industrialized South, and the Dong Nai
River in an area sprayed with Agent Orange, a TCDD-contaminated herbicide. Results of
these analyses are presented in Table 3-19. The average total concentrations of
CDD/CDFs were 240 pg/g dry weight for the Red River, 6,800 pg/g dry weight for the
Saigon River, and 1,200 pg/g dry weight for the Dong Nai River. Schecter et al. (1989a)
suggested that the total CDD/CDF levels in these Vietnamese rivers is comparable to the
total CDD/CDF levels observed in the lake sediments in the United States and Europe
(Table 3-19).
In Japan, sediment samples were collected from a coastal area, river, and pond in
Matsuyama between April 1995 and December 1997 (Seike et al., 1998). The dry weight
total concentrations of CDD/CDFs ranged from 2.0 to 16 ng/g (n=3) in the coastal area
with an average of 8.1 ng/g, from 0.95 to 4.3 ng/g (n = 22) in river water with an average
of 2.0 ng/g, and from 0.77 to 3.1 ng/g (n = 3) in pond water with an average of 2.3 ng/g.
TCDD and OCDD were the main contributors to total CDD concentrations in all sediment
samples, while CDF homologue compositions varied with the samples. Sediment samples
were also collected in June-July 1993 at eight sites from upper, mid- and downstream of
the Tama River, Japan, which was polluted by industrial and domestic wastewater
(Onodera et al., 1998). Total CDD/CDF dry weight concentrations ranged from 27.0 to
231.6 pg/g with an average of 90.7 pg/g. The total I-TEQDF concentrations ranged from
0.05 to 2.8 pg/g with an average of 1.2 pg/g.
3.5.4. Sediment Observations and Trends
Some general observations for CDD and CDF levels in the United States are
possible from the data presented in the various sediment studies above:
• CDD and CDF congener distribution patterns in sediment generally follow
those exhibited by the contaminant source.
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• The concentration of CDD and CDF congeners in sediment generally
increases with the degree of chlorination, but decreases uniformly with
distance from the source.
3.5.5. Sediment CDD/CDF Profiles and Background TEQ Concentrations
CDD/CDF homologue group profiles for sediment were calculated as the mean
homologue group concentrations divided by the mean total CDD/CDF concentrations.
Congener profiles are the ratio of 2,3,7,8-substituted congeners to total CDD/CDFs in
sediment. These congener profiles for sediment are presented in Figure 3-5. They are
based on data from the recent EPA sediment core study, which evaluated sediment data
from 11 non-source-impacted sites throughout the United States (i.e., 1 Alaska site, 3
New York sites, 1 North Carolina site, 1 Georgia site, 3 Utah sites, and 2 Washington
sites) (Cleverly et al., 1996; Versar, 1996a). Only the uppermost sediment core samples
(i.e., the most recently dated samples) were used in this analysis. The congener that
accounts for the highest proportion of total CDD/CDFs is OCDD, with 1,2,3,4,6,7,8-
HpCDD and OCDF also accounting for significant portions of total CDD/CDFs (Table 3-20).
For the homologue group profile for sediment, OCDD and HpCDD account for the highest
proportion of total CDD/CDFs.
Based on data from the uppermost sediment samples from EPA's recent sediment
core study (Cleverly et al., 1996; Versar, 1996a) (n = 11), the mean background TEQDF-
WH098 level was 5.3 ppt, assuming that nondetects equal half the detection limit (Table
3-21). When nondetects were set to zero, the mean TEQ was 4.7 ppt. These data were
considered to be the most appropriate data set for characterizing background CDD/CDF
TEQ concentrations, because they are representative of sites not expected to be impacted
from various geographic locations in the United States. Thus, the "typical" background
concentration in sediment is assumed to be 5.3 ppt TEQDF-WH098.
3.6. CONCENTRATIONS IN FISH AND SHELLFISH
Tables B-11 through B-13 (Appendix B) contain summaries of data from the
numerous studies in the published literature regarding concentrations of CDDs, CDFs, and
dioxin-like PCB congeners in fish and shellfish. It should be noted that some studies
reported fish concentrations on a whole weight basis and others reported concentrations
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for fish fillets. In the appendix tables and in the data used for calculation of background
fish levels, whole weight concentrations were converted to fillet concentrations, assuming
that the fillet contained one-half the concentration of the whole fish (U.S. EPA, 1990;
Branson et al., 1985). This was necessary for estimating human exposures, because it is
assumed that fish fillets, and not whole fish, are ingested by humans. In the following
studies, summaries of CDD/CDF and PCB concentrations are presented as reported by the
authors.
3.6.1. North American Data
A large quantity of fish data were collected as part of EPA's National Study of
Chemical Residues of Fish (NSCRF), more commonly referred to as the National
Bioaccumulation Study, during the period of 1986 to 1989 (U.S. EPA, 1992). Based on
these data, several summaries were prepared and are presented here. Tables B-11 and B-
12 include the dioxin and furan data collected as part of the National Bioaccumulation
Study. Samples were collected from a wide variety of sites across the United States,
including 314 sites thought to be influenced by point or nonpoint sources and over 30
sites identified as relatively free of influence from point and nonpoint sources. This latter
group of sites can be characterized as background per the definition used in this
document. Background data are presented in Table 3-22. Using the maximum
concentration from each site, the mean I-TEQDF concentration was 0.59 ppt for the
background sites, when nondetects were set at zero, and 1.2 ppt when non-detects were
set to one-half the detection limit. For other sites, I-TEQDF concentrations ranged from 0.7
ppt (POTWs) to 33.9 ppt (Superfund sites). Table B-1 3 includes similar data for the
various PCB congener groups from 362 National Bioaccumulation Study sites. EPA
recalculated the background TEQDF-WH098 concentrations for the background sites using
the mean concentration from each site instead of the maximum value for each site.
Additional adjustments were included to account for the fact that some samples were
analyzed on a whole body basis while others were analyzed on a fillet basis. All
concentrations were expressed on a wet weight basis. The background TEQDF-WH098
concentrations were 0.29 ppt when non-detects were set to zero and 1.3 ppt when non-
detects were set to one-half the detection limit. Because the specific PCB congeners
could not be identified, it is not known what percentage of these concentrations represent
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the PCBs identified as dioxin-like. Of these sites, 20 were identified as background sites.
The total PCB (all 209 congeners) mean concentration for these background sites was
46,900 ppt. Because the dioxin-like PCBs consist of only 11 of the 209 possible PCB
congeners, it may be that they are a small percentage of the total; however, only
congener specific analysis can ultimately confirm this. As discussed at the end of this
section (Section 3.6.4), this study was selected as the best basis for estimating
background fish levels in the United States.
Fish muscle and hepatopancreas samples of striped bass, blue crabs, and lobsters
collected from Newark Bay and the New York Bight all contained high levels (up to 6,200
ppt) of 2,3,7,8-chlorine substituted tetra- and penta-CDDs and CDFs (Rappe et al., 1991).
Levels of 2,3,7,8-TCDD were higher than any other New Jersey samples, and the highest
sample in this study (found in crab hepatopancreas) may be the highest level of 2,3,7,8-
TCDD ever reported for aquatic animals. Crustaceans resembled one another in congener
pattern. Specifically, they all contained both a large number and large amounts of CDD
and CDF congeners in addition to the 2,3,7,8-chlorine substituted compounds. The
striped bass samples, on the other hand, contained primarily the 2,3,7,8-chlorine
substituted congeners.
Carp, catfish, striped bass, large mouth bass, and lake trout were collected from
sites in the Hudson River and the Great Lakes Basin that were contaminated with
industrial chemicals or contained known or suspected levels of PCBs (Gardner and White,
1990). The congener 2,3,7,8-TCDF was detected in 12 fish fillets at levels that ranged
from 3 to 93 ppt. A 2,3,7,8-chlorine substituted PeCDF was detected in 14 fish fillets at
levels ranging from 4 to 113 ppt. An interesting observation in this study was that 2,4,6-
chlorine substituted CDFs were detected in four fish samples, suggesting that those fish
may have been exposed to chlorinated phenols. The study indicated that the 2,4,6-
chlorine substituted CDFs occurred in the fish at levels similar to those of the 2,3,7,8-
chlorine substituted CDFs, but with less frequency.
Composited whole fish samples of lake trout or walleye collected from each of the
Great Lakes and Lake St. Clair were analyzed for CDDs and CDFs (De Vault et al., 1989).
CDF and CDD concentrations in lake trout were substantially different for each lake and
between sites in Lake Michigan, probably reflecting differences in types and amounts of
loadings to the lakes. In all of the sampling sites (except Lake Ontario), 2,3,7,8-TCDF
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was the dominant CDF congener in lake trout and ranged from 14.8 ppt in Lake Superior
to 42.3 ppt in the whole fish samples of Lake Michigan. In Lake Ontario, the dominant
congener in lake trout was a 2,3,7,8-chlorine substituted PeCDF. The distribution of CDF
congeners in the Lake Erie walleye was very similar to that of the lake trout from Lake
Superior. With regard to CDDs, the concentrations of 2,3,7,8-TCDD in the whole fish
samples ranged from 1 ppt in Lake Superior to 48.9 ppt in Lake Ontario. With the
exception of Lake Ontario, the dominant CDD congener was a 2,3,7,8-chlorine substituted
PeCDD. A 2,3,7,8-chlorine substituted HxCDD also contributed significantly to the total
CDD concentrations. As with CDFs, the distribution of CDD congeners in the Lake Erie
walleye was very similar to that of the lake trout from Lake Superior. Total l-TEQDFs for
these samples ranged from 5.3 ppt to 67.0 ppt on a whole weight basis when
nondetected congeners were set to one-half the detection limit.
In another study, CDDs and CDFs were measured in four species of salmonids (lake
trout, coho salmon, rainbow trout, and brown trout) collected from Lake Ontario (Niimi
and Oliver, 1989a). Levels of 2,3,7,8-TCDD in whole fish ranged from 6 to 20 ppt, and
the HxCDD congener group was most dominant in all fish. High levels of OCDD also were
detected in lake trout and coho salmon, but not in rainbow trout or brown trout. Although
total CDF levels were about 25 percent lower than the total CDD concentrations, the
levels of 2,3,7,8-TCDF (which was the dominant component of the TCDF congener group)
were the same range as 2,3,7,8-TCDD (6 to 20 ppt). I-TEQDF concentrations ranged from
7.3 ppt to 22.3 ppt on a whole weight basis, when nondetects were set to zero. The
study suggested that, although collection sites can influence chemical levels and congener
composition, comparisons of chemical levels and congener frequencies may not be
suitable because of differences resulting from localized factors. The study also indicated
that the importance of the various CDD and CDF congeners can differ with location (i.e.,
the same species of fish collected at different locations in a study area may reveal that the
most common congener is different at each site).
Travis and Hattemer-Frey (1991) evaluated data generated as part of the National
Dioxin Study regarding 2,3,7,8-TCDD concentrations in fish. Fish were collected from
304 urban sites in the vicinity of population centers or areas with known commercial
fishing activity, including sites from the Great Lakes Region. Data from that study
indicated that concentrations of 2,3,7,8-TCDD in whole fish from urban sites ranged from
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nondetectable to 85 ppt. In addition, only 29 percent of the fillets from urban sites had
detectable levels of 2,3,7,8-TCDD, with a geometric mean concentration of 0.3 ppt.
Whole fish samples from the Great Lakes Region had higher 2,3,7,8-TCDD levels than fish
from urban areas (e.g., 80 percent vs. 35 percent detectable levels). In the Great Lakes
Region, 2,3,7,8-TCDD concentrations in whole fish samples ranged from nondetectable to
24 ppt, with a geometric mean of 3.8 ppt. These levels were 10 times higher than the
concentrations in whole fish from urban areas. Likewise, the mean concentration of
2,3,7,8-TCDD in Great Lakes Region fish fillets (2.3 ppt) was about seven times higher
than the levels in the fillets from urban areas (0.3 ppt). As with the whole fish samples,
fish fillet samples from the Great Lakes Region had higher 2,3,7,8-TCDD levels than fillets
from background urban areas (e.g., 67 percent vs. 29 percent detectable levels).
Comparable levels of 2,3,7,8-TCDD were detected in whole bottom feeders and predators
from the Great Lakes Region.
In an effort to determine whether incineration of municipal waste influenced
CDD/CDF levels in the immediate area of waste incineration facilities, fish samples were
collected from surface water near cities with, and without, operating incinerators
throughout Connecticut. A total of 550 fish samples were collected between the years of
1987 and 1990. The total CDD/CDF concentrations for pre-operational and operational
status were 28.44 pg/g and 58.38 pg/g, respectively, when nondetects were assumed to
be one-half the detection limit (MRI, 1992).
Cooper et al. (1995) and Fiedler et al. (1997c) collected fish samples from grocery
stores and local fish markets in southern Mississippi. All samples had detectable
concentrations of CDD/CDFs. High I-TEQDF concentrations were observed in farm-raised
catfish nuggets (mean = 2.1 ppt/sample I-TEQDF) and in the parts of the Crustacea
containing the digestive gland (Cooper et al., 1995). The congener profile for the shellfish
samples was similar to that observed for sediments collected in the same area and,
reported sewage sludge patterns. For marine fish fillets (i.e., Spanish mackerel and
mullet), the mean I-TEQDF concentration was 0.27 ppt. The meat of marine shellfish (i.e.,
claw and body of blue crab and whole American oysters) had I-TEQDF concentrations
averaging 0.63 ppt, and freshwater shellfish (i.e., crawfish) had concentrations averaging
1.0 ppt. I-TEQDF concentrations in fish and shellfish are presented in Table 3-23.
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To further examine the high TEQDF concentrations of the farm-raised catfish from
southern Mississippi, Cooper et al. (1997) and Fiedler et al. (1998) performed a follow up
to their 1995 study examining catfish feed and pond sediment along with catfish samples
from the previously tested facility and other sites in the southeastern United States. The
follow-up study also tested for PCB levels. Samples included three catfish fillets and three
catfish nugget (i.e., small pieces of fillet) samples from the same store and distribution
supplier as sampled in the previous study (Cooper et al., 1995), one catfish fillet from an
Alabama supplier, three catfish fillets and one feed and pond sediment sample from a
different catfish farm in Mississippi, and two catfish and one catfish feed samples from a
site in Arkansas. A summary of the results is presented in Table 3-24. Three farm-raised
catfish fillet samples and three catfish nugget samples from Mississippi had lipid-based
2,3,7,8-TCDD levels ranging from 2.1 to 4.7 ppt and total lipid-based TEQDFP-WH094
ranging from 10.9 to 30.2 ppt. Catfish samples from Arkansas had lipid-based 2,3,7,8-
TCDD levels ranging from 27 to 32 ppt and total lipid-based TEQDFP-WH094 ranging from
41.9 to 44.9 ppt. Similar results were observed in the feed samples. The feed from the
Mississippi aquaculture facility, which supplied the food for the Mississippi catfish fillet
and nugget samples, contained 2.7 ppt lipid 2,3,7,8-TCDD and a total lipid-based TEQDFP-
WH094 concentration of 10.5 ppt, compared with feed levels from the Arkansas facility
that contained 44 ppt lipid 2,3,7,8-TCDD, and a total lipid-based TEQDFP-WH094
concentration of 61. CDD/CDF congener profiles are also consistent between the catfish
and the respective feed suppliers' products with the exception of OCDD and
1,2,3,4,6,7,8-HpCDD. Pond sediment congener profiles were not consistent with the
profiles exhibited in the catfish samples, demonstrating significantly lower levels of most
2,3,7,8-substituted CDDs, and higher levels of 2,3,7,8-substituted CDFs. PCB analysis of
these same catfish samples demonstrated that for all but one sample, the highest
concentrations of PCBs were observed for congeners 153 and 138, respectively. PCB
TEQP-WH094 in the catfish samples (both fillet and nuggets) ranged from 0.45 to 4.9 ppt
lipid, with the PCB fraction of the total TEQP-WH094 ranging from 4 to 1 6 percent. The
feed sample TEQP-WH094 level from the Mississippi site was 3.31 ppt lipid, while the level
from the Arkansas site was 0.19 ppt lipid, and the pond sediment level contained a TEQP-
WH094 level of 0.04 ppt lipid. Rappe et al. (1997c) continued this investigation by
evaluating one combined catfish feed sample from Arkansas and its eight ingredients (i.e.,
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soybean meal, meat and bone meal, wheat, corn, fish meal, cottonseed meal, and midds).
The soybean meal had the highest I-TEQDF concentration (i.e., 576 pg/g lipid). The
2,3,7,8-TCDD concentration in this ingredient was 370 pg/g lipid. The combined catfish
feed sample had a I-TEQDF concentration of 101 pg/g lipid and a 2,3,7,8-TCDD
concentration of 67 pg/g lipid. Rappe et al. (1997c) suggested the ball clay anticaking
agent in the soybean meal as the source of CDD/CDFs in this ingredient.
Schecter et al. (1997) analyzed samples of freshwater (n = 10) and marine fish
(n = 1 3) collected from grocery stores in five U.S. cities (Binghamton, New York; Chicago,
Illinois; Louisville, Kentucky; Atlanta, Georgia; and San Diego, California). Whole weight
mean I-TEQDF values were 0.69 ppt for composites of freshwater fish and 0.25 ppt for
composites of ocean fish, when nondetects were set to one-half the detection limit. Total
mean CDD/CDF concentrations were 16.2 ppt (whole weight) for freshwater fish and 3.4
ppt (whole weight) for ocean fish, when nondetects were set to one-half the detection
limit. Schecter et al. (2001) reported mean whole weight TEQDFP-WH098 concentrations
for the same fish samples as 1.7 ppt for freshwater fish and 0.39 ppt for ocean fish when
nodetects were set to one-half the detection limit, and 1.6 ppt for freshwater fish and
0.16 ppt for ocean fish when nondects were set to zero. Additional detail on the Schecter
et al. (1997) study are presented in the section on concentrations in food products
(Section 3.7). Schecter et al. (1993a) analyzed five fish collected from a supermarket and
found an average of 0.05 ppt of I-TEQDF.
FDA analyzed fish and shellfish samples collected in 1996 through 1999 as part of
a market basket survey. The samples were collected from grocery stores from locations
around the country and were analyzed for CDD/CDFs. The results of these analyses have
been reported by Jensen and Bolger (2000) and Jensen et al. (2000). The combined
results from these two publications are presented in Table 3-25. TEQDF-WH098s ranged
from 0.22 ppt for pollack to 2.0 ppt for catfish.
Samples from all trophic levels in the Lake Ontario ecosystem were analyzed for
PCB congeners (Oliver and Niimi, 1988). Analysis revealed that the PCB concentration
increased from water to lower organisms to small fish to salmonids, demonstrating the
classical biomagnification process. In addition, the chlorine content of the PCBs increased
at the higher trophic levels. PCBs with the highest chlorine content (57 percent) were
found in sculpin, small bottom-living fish that feed on benthic invertebrates. TrCBs and
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TCBs comprised a much higher percentage of the PCBs in the lower trophic levels than in
salmonids and small fish. The percentage of PeCBs and OCPB in all samples was fairly
uniform, but the HxCBs and HpCBs comprised a much larger fraction of the PCBs in the
small fish and salmonids than in the lower trophic levels.
A study regarding the distribution of PCBs in Lake Ontario salmonids (brown trout,
lake trout, rainbow trout, and coho salmon) showed that the PeCBs and HxCBs were
dominant in all species (Niimi and Oliver, 1989b). The 10 most common PCB congeners
represented about 52 percent of the total content and did not appear to be influenced by
species or total concentration. Homologues observed averaged approximately 56 percent
chlorine by weight in whole fish and muscle. The analysis of the chlorine content
suggested that the more persistent congeners tend to behave as a homogeneous mixture
instead of as individual congeners.
Petreas (1991) conducted a study to assess the influence of a bleached pulp and
paper mill and other industrial facilities on the PCB congener levels in aquatic species and
sediment in northern California. Petreas (1991) collected samples of local fish species,
bivalves (freshwater clams that had been transplanted 2 months earlier), and sediment
samples at sites upstream, downstream, and within the vicinity of a pulp and paper mill
plant. Whole body fish samples were composited, or analyzed individually, based on size.
These samples were analyzed for PCBs 77, 126, and 169. Results of an analysis of the
raw data from these samples are presented in Table 3-26. Levels of PCBs in fish tissue
ranged from 1.2 pg/g for PCB congener 169 to 1,095 pg/g for PCB congener 77. Results
of the fish tissue analysis according to sampling location indicated that "no special impact
could be attributed to the pulp mill discharge" (Petreas, 1991). Bivalve concentrations
ranged from 0.7 pg/g for PCB congener 169 to 102 pg/g for PCB congener 77.
Concentrations of PCB congeners 77 and 126 were at least an order of magnitude higher
in bivalves than in sediments from the same sampling location, indicating that these
congeners bioconcentrate in the aquatic bivalve species evaluated. Bivalve and sediment
impacts that could be attributed to facility discharges were not observed in this study.
Krahn et al. (1995) analyzed marine fish and invertebrates collected from several
coastal sites of the northeastern United States for dioxin-like PCBs. Samples of winter
flounder (muscle tissue), northern lobster (muscle tissue and hepatopancreas), and blue
mussel (whole bodies) were analyzed for dioxin-like PCBs. Total mean PCB concentration
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ranged from 4 ppt to 351 ppt in muscle tissue from flounder and lobster, and blue mussel
whole bodies. Total PCB concentrations in lobster hepatopancreas ranged from 764 to
32,800 ppt. Total mean TEQP-WH094 for these PCBs ranged from 0.1 ppt to 6.9 ppt for
muscle tissue samples of winter flounder, 0.1 ppt to 3.7 ppt for whole blue mussels, and
0.1 ppt to 5.4 ppt for lobster muscle. Hepatopancreas tissue from lobster showed
considerably higher TEQP-WH094 concentrations, ranging from 5.2 ppt to 1,820 ppt.
Mes and Weber (1989) analyzed a freshwater fish composite sample and a canned
fish composite sample for PCBs 77, 126, and 169. The number of samples included in
these composites was not reported. Respective wet weight concentrations of these three
PCBs were 36 ppt, 8 ppt, and < 1 ppt for freshwater fish, and 8 ppt, 3 ppt, and < 1 ppt
for canned fish samples. In a later study, Mes et al. (1991) evaluated numerous PCB
congeners including dioxin-like PCBs 105, 114, 118, 1 56, 1 57, and 1 89 in foods. A total
of five composite freshwater fish, marine fish, and shellfish samples, each composite
taken from major Canadian city, were analyzed for these PCBs. The number of samples
included in these composites was not reported. Total PCB congener residues for
freshwater fish, marine fish, and shellfish were 31.9 ppt, 4.6 ppt, and 0.9 ng/g,
respectively, on a wet weight basis, based only on congeners observed in three out of five
composites. Schecter et al. (1997) also analyzed freshwater and marine fish samples for
dioxin-like PCBs 77, 105, 114, 118, 1 26, 1 69, and 1 80. Concentrations of these
congeners ranged from not detected to 1,800 ppt on a whole weight basis in fresh fish,
and from 0.20 ppt to 320 ppt in ocean fish. The total whole weight TEQP-WH094 was
0.7 ppt for fresh fish and 0.2 for ocean fish, when nondetects were set to one-half the
detection limit (Schecter et al., 1997).
3.6.2. European Data
Evaluation of fish in the Baltic Sea (Gulf of Bothnia) and northern Atlantic Ocean in
the vicinity of Sweden revealed that concentrations of CDDs and CDFs in composited
whole fish herring samples from the Atlantic Ocean were lower than those in the Gulf of
Bothnia (Rappe et al., 1989b). Detectable levels of 2,3,7,8-TCDD in salmon muscle were
found in both wild homing (4.6 to 19 ppt) and hatchery-reared (0.2 to 0.3 ppt) varieties in
the Gulf of Bothnia. In addition, concentrations of the same representative congeners of
the CI5 to CI8 CDD and CDF congener groups found in herring were found in both varieties
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of salmon. Levels of those congeners in the muscle of wild salmon, however, were five
to ten times higher than the herring levels, while the levels in the hatched salmon
essentially were the same as in the herring samples. Perch collected at a distance of 1-6
km from a pulp mill in the southern part of the Gulf of Bothnia contained 2,3,7,8-TCDD
and 2,3,7,8-TCDF; the levels were higher in the samples collected closer to the pulp mill.
These two compounds have been identified in bleaching effluents from pulp mills, as well
as in bleached pulp. Arctic char collected from Lake Vattern, a popular fishing lake in
southern Sweden, contained levels of 2,3,7,8-TCDD (6.5 to 25 ppt whole weight),
2,3,7,8-TCDF (21 to 75 ppt whole weight), and representative congeners of the PeCDD
and PeCDF homologues. There was a good correlation between the weight of the fish
and the levels of CDDs and CDFs. The main general pollution sources of the long, deep,
narrow lake are two pulp mills.
Fish (cod, haddock, pole flounder, plaice, flounder, and eel), mussels, and edible
shrimp from a fjord area contaminated by wastewater from a magnesium factory in
Norway were analyzed for CDDs and CDFs (Oehme et al., 1989). Certain magnesium
production processes can result in the formation of substantial amounts of CDDs and
CDFs as byproducts. The congener pattern of tetra- and penta-chlorinated CDDs and
CDFs released in wastewater during the magnesium production process is very
characteristic and is dominated by congeners with chlorine in the positions 1,2,3,7 and/or
8. Fish and shellfish differ in their ability to bioconcentrate CDD and CDF congeners. For
example, fish generally only concentrate the most toxic 2,3,7,8-substituted congeners;
whereas, shellfish can usually concentrate most of the congeners. Nearly all congeners
were present in the shrimp and mussel samples. Although these organisms displayed the
very characteristic PeCDF congener pattern of the magnesium production process, some
deviations were found in the TCDF congener distribution within those species. For fish,
the concentrations of CDDs and CDFs are dependent on the exposure level, fat content,
living habit, and the species degree of movement. The highest CDD and CDF levels were
found in comparatively high fat-content bottom fish collected close to the source. Cod
and haddock (lower fat-content nonstationary fish) had much lower concentrations, even
in the vicinity of the magnesium production factory. An interesting note is that the main
stream of the fjord follows the west coast; subsequently, cod and eel samples collected
along the west coast of the fjord had considerably higher levels of CDDs and CDFs than
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those collected from the eastern fjord entrance. Similarly, the level of 2,3,7,8-TCDD in
mussels decreased by one order of magnitude from the vicinity of the magnesium
production factory to the outer region of the fjord system.
Brown trout, grayling, barbel, carp, and chub collected in the Neckar River in
southwest Germany contained much higher levels of 2,3,7,8-TCDF than in eels collected
from the same river and the Rhine River (Frommberger, 1991). In addition, eels from both
rivers showed very similar patterns for CDD and CDF congener distribution; whereas, the
patterns of CDD and CDF distribution generally showed some degree of difference among
the other fish collected from the Neckar River. Perch and bream collected from various
locations in the vicinity of Hamburg Harbor, however, showed similar patterns in the
distribution of the CI4 to CI8 CDD and CDF congener groups (Gotz and Schumacher,
1990). In general, the levels of CDFs were higher than the level of CDDs in these fish,
especially with regard to the TCDFs to HxCDFs. Pooled samples of eels, collected at six
different localities in The Netherlands, contained low levels of CDDs and CDFs, the major
congeners of which were 2,3,7,8-chlorine substituted (Van den Berg, 1987).
Concentrations of the various congeners identified in the eel samples ranged from 0.1 to
9.1 ppt. The sample with the highest concentration of 2,3,7,8-TCDD (9.1 ppt) was
collected from Broekervaart in a location that was not far from a chemical waste dump
that contained high concentrations of the same congener.
In 1992, Falandysx et al. (1997) examined congener-specific PCB data in fish from
the southern part of the Baltic Sea. Eight fish species were collected from the Gulf of
Gdansk near Gdynia. Total PCB levels were high in whole fish samples from this area,
ranging from 1.4-million ppt lipid to 11-million ppt lipid. Measurements were performed
for 94 PCB congeners. Predominant homologue groups were PeCBs and HxCBs, which
constituted 33 to 46 percent and 36 to 46 percent of the total PCB concentrations,
respectively. Of the five dioxin-like PCBs examined, levels were highest for PCB 118, and
ranged from 160,000 ppt lipid in whole cod samples to 2-million ppt lipid in whole eelpout
specimens. Also detected, but in lesser quantities, were TrCBs (0 to 0.9 percent), TCBs
(5.5 to 11 percent), HpCBs (7.4 to 13 percent), OCBs (0.23 to 0.53 percent),
nonachlorobiphenyls (0 to 0.5 percent), and aldo decachlorobiphenyl (<0.01 to 0.01
percent). A large degree of variability in total PCB levels between species of fish was
observed. Total PCB levels in the whole fish samples ranged from 1,400 ng/g lipid weight
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(47.6 ng/g wet weight) in cod to 11,000 ng/g lipid weight (48 ng/g wet weight) in
pikeperch; 11,000 ng/g lipid weight (332.2 ng/g wet weight) in eelpout. The authors
observed that with the relatively similar lipid content of the fish, which varies from 3.02
to 6.26 percent, the interspecies variability in the PCB congener pattern could possibly be
attributed to variabilities in "enzyme activity, feeding behavior and trophic niche"
(Falandysx et al., 1997).
Lulek et al. (1997) examined five random freshwater fish samples from five
different Swiss and French lakes and rivers for 13 dioxin-like PCBs. The sampling
locations included two from Lake Geneva, one each from the upper and lower Maggia
River in Switzerland, and one from the Saone River in France. The analyzed fish included
one each of three different species (i.e., bream, burbot, and arctic char) and two each of a
fourth species (i.e., trout). Total PCB levels in the whole fish samples ranged from
1,1 37,000 ppt lipid (65,980 ppt wet weight) in the trout from the upper Maggia River to
3,235,000 ppt lipid (237,460 ppt wet weight) in the trout from the bottom of the Maggia
River to 1,594,000 ppt lipid (254,430 wet weight) in the arctic char, to 2,1 79,000 ppt
lipid (101,970 wet weight) in the burbot of Lake Geneva to 10,590,000 ppt lipid
(402,420 ppt wet weight) in the bream of the Saone River. Congener profiles were
similar in all species. Total TEQP-WH094 concentrations in the whole fish ranged from 104
ppt lipid in the burbot to 523 ppt lipid in the bream (Lulek et al., 1997).
In 1995, the Ministry of Agriculture, Fisheries and Food (MAFF, 1998) sampled
trout farms throughout England and Wales. Forty samples, each consisting of several
fillets of muscle, were analyzed. When setting nondetects to the limit of detection, lipid-
based CDD/CDF concentrations ranged from 2.1 to 13 ppt I-TEQDF (mean 5.1). Wet
weight concentrations ranged from 0.06 to 0.67 ppg I-TEQDF (mean 0.24). Lipid based
PCB concentrations varied from 8.9 to 51 ppt TEQP-WH094 (mean 19.0), and wet weight
levels ranged from 0.22 to 2.4 ppt TEQP-WH094 (mean 0.87). Fat content in the samples
ranged from 1.8 to 8.6 percent.
Robinson et al. (2000) analyzed over 100 samples of uncooked marine fish and fish
fingers (i.e., fish coated with bread crumbs) for CDD/CDFs and numerous dioxin-like PCB
congeners (e.g., 77, 81, 126, 169, 105, 114, 118, 156, 157, 167, 180, 189). Some of
these fish were caught in United Kingdom waters; others were imported. The
concentration of these compounds varied with fish species, fat content (i.e., fat weight
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concentrations were higher in oily fish than in fish with lower fat contents), and sampling
month (i.e., lower fat weight concentrations were seen in samples collected during
February than in those collected in November and May). The lipid-weight TEQ-WH098
concentrations are shown in Table 3-27. The method used for treating non-detected
values in calculating TEQs was not reported. Jacobs et al. (2000) collected 10 samples
of farmed Atlantic Salmon from several sites in Scotland and a site in Norway. The
samples were analyzed for CDD/CDFs and seven PCB congeners (i.e., 77, 105, 11 8, 1 26,
156, 157, 169). TEQDF-WH098s ranged from 5 to 1 8 ppt, on a lipid basis, when non-
detects were set to either one-half the detection limit or zero. TEQP-WH098s ranged from
9 to 25 ppt on a lipid basis. Lipid contents ranged from 3 to 1 5 percent. In general, the
highest levels were observed in the oldest fish.
3.6.3. Fish Observations and Trends
Some general observations for CDD and CDF levels are possible from the data
presented in the various fish and shellfish studies above:
For fish, the concentrations of CDDs and CDFs are dependent on the
exposure level, fat content, living habits, and the degree of movement of
the species. Comparatively high fat-content bottom fish, collected close to
the contaminant source, generally have the highest CDD/CDF levels;
whereas, lower fat content, nonstationary fish have much lower
concentrations, even in the vicinity of the contaminant source.
The National Dioxin Study indicated that the levels of 2,3,7,8-TCDD in fish
from the Great Lakes Region were higher than those from urban areas.
Comparable levels were detected in whole bottom feeders and predators
from the Great Lakes Region.
With regard to PCBs, concentrations increase from water to lower organisms
to small fish to salmonids, and the chlorine content of the PCBs increase at
the higher trophic levels.
3.6.4. Fish CDD/CDF Profiles and Background TEQ Concentrations
Example CDD/CDF congener profiles for freshwater fish were generated using data
for 10 freshwater fish samples collected from five U.S. States (i.e., New York, Illinois,
Kentucky, California, and Georgia), as reported by Schecter et al. (1997). These profiles
do not include all of the data used to estimate the background TEQDF-WH098 concentration
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in freshwater fish because congener-specific data were not available for all of the fish data
used in calculating the background TEQ concentrations. The CDD/CDF profile for
freshwater fish, based on Schecter et al. (1997), was generated by setting nondetects to
zero and calculating the ratio of individual congener concentrations to total 2,3,7,8-
substituted CDD/CDFs. This was done for consistency with other food profiles presented
in this chapter. For marine fish, congener-specific data for mackerel from Mississippi, as
reported in Fiedler et al. (1997c), were used to provide an example profile. The example
profile for marine fish was based on one mackerel sample. An example shellfish profile
was developed using data from Fiedler et al. (1997c), and is based on 13 samples, all
presumed to be freshwater species (i.e., crab (n = 6), oyster (n = 3), crayfish (n=4).
Profiles were generated in the same manner as for freshwater fish. Profiles for freshwater
fish, marine fish, and shellfish are presented in Table 3-28 and Figure 3-6. In general,
CDDs account for a higher percentage of the total 2,3,7,8-substituted CDD/CDFs than
CDFs. OCDD is the dominant congener for all three fish groups with 1,2,3,4,6,7,8-
HpCDD accounting for the second highest percentage.
Although a comprehensive market basket survey representing the most commonly
eaten fish species by the general population would probably provide the best information
on background concentrations of CDD/CDFs in fish and corresponding background
exposures from fish ingestion, these data are not available from a single source. Thus,
the background CDD/CDF TEQ concentrations for freshwater and marine finfish and
shellfish were estimated based on data from a variety of studies. These studies included
EPA's National Bioaccumulation Study (U.S. EPA, 1992), Fiedler et al. (1997), Jensen and
Bolger (2000), and Jensen et al. (2000). These studies were selected because they are
based on sampling from grocery stores and/or are based on a National sampling strategy.
It should be noted, however, that although the National Bioaccumulation Study data are
based on a National sampling, they may be more representative of wild caught fish (i.e.,
recreational fishing) than fish obtained by the general population at grocery stores. For
example, a large percentage of the trout consumed by the general population is likely to
be farm-raised. However, because no data were available on farm-raised (or grocery
store) trout, the concentration of CDD/CDFs in wild caught trout were used in estimating
background fresh and estuarine finfish concentrations. For catfish, which is also primarily
farm-raised, grocery store data from FDA's market surveys were used. Concentrations for
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several other species (i.e., mullet and mackerel) were based entirely on data collected in
the Mississippi area; and may not be entirely representative of levels seen in other
locations. Finally, concentrations for some species were averaged over several data sets.
In most cases, similar concentrations for these species were observed in the various
studies. Species-specific mean concentrations represent the average of the mean
concentrations for the various sites where samples were collected, and not the mean of all
individual samples. The mean is used to ensure that each site is weighted equally (i.e.,
heavily sampled sites do not have any greater impact on the overall mean that sites with
fewer samples).
Average background concentrations were estimated for freshwater fin- and
shellfish, and marine fin- and shellfish by weighting the species-specific fish
concentrations according to their species-specific fish consumption rates for the U.S.
population (U.S. EPA, 2000). The consumption data are based on an analysis of the
USDA's 1994-96 Continuing Survey of Food Intake Among Individuals (CSFII). Weighting
was accomplished by multiplying the consumption rates in g/day by the TEQDF-WH098
concentrations in pg/g. The resulting TEQ intakes in pg/day were then summed by
category. Finally, the total TEQ intake (g/day) for the category was divided by the total
TEQ consumption rate (g/day) to estimate the weighted average background concentration
(pg/g) (Table 3-29).
For consumption categories for which no species-specific concentration data were
available, default values were selected to represent that fish category. For example, for
freshwater and estuarine finfish, the average TEQDF-WH098 concentration from U.S. EPA
(1992) was used in conjunction with the total consumption rate for those species with no
corresponding concentration data. For freshwater/estuarine shellfish, the default value
used for the "other" category represents the average concentration for the
freshwater/estuarine shellfish species for which concentration data were available.
Likewise for marine finfish and shellfish, the default concentration used is the average
concentration for species with specific concentration data. This adds a degree of
uncertainty to the estimates. Based on this analysis of the available species-specific fish
data that are most representative of consumption among the U.S. population, the average
background TEQDF-WH098 concentration in freshwater fish and shellfish was estimated to
be 1.0 ppt, assuming non-detects are equal to one-half the detection limit. The
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background value for marine fish and shellfish was estimated to be 0.26 ppt TEQDF-
WH098, when non-detects were set to one-half the detection limit.
The background TEQP-WH098 concentration in freshwater fish and shellfish was
estimated to be 1.2 ppt, when nondetects were set to one-half the detection limit, based
on data from Schecter et al. (1997), Mes and Weber (1989), and Mes et al. (1991).
TEQp-WH098 for marine fish and shellfish was estimated to be 0.25 ppt when nondetects
were set to one-half the detection limit, based on Mes and Weber (1989), Mes et al.
(1991), and supermarket data from Schecter et al. (1997), as presented in Section 3.7.2.
3.7. CONCENTRATIONS IN FOOD PRODUCTS
Dietary intake is generally recognized as the primary source of human exposure to
CDD/CDFs (Rappe, 1992). Several studies estimated that over 90 percent of the average
daily exposure to CDD/CDFs are derived from foods (Rappe, 1992; Henry et al., 1992;
Furst et al., 1991). CDD/CDFs in fatty foods such as dairy, fish, and meat products are
believed to be the major contributors to dietary exposures (Rappe, 1992; Henry et al.,
1992). Travis and Hattemer-Frey (1991), using a fugacity model, estimated that the food
chain, especially meat and dairy products, accounts for 99 percent of human exposure to
2,3,7,8-TCDD.
Analysis of trace levels of CDD and CDF congeners in food has been hindered in
the past by lack of sensitive analytical detection methods, extraction difficulties from the
high-lipid content food products in which these chemicals are most often found, and the
presence of other potentially interfering organochlorine compounds. However, as the
analytical difficulties associated with detecting CDD and CDF congeners at ppt levels or
lower (Firestone, 1991) were overcome, more food data began to be generated. In recent
years, EPA, in association with the U.S. Department of Agriculture (USDA), has conducted
several studies of dioxin-like compounds in foods. The results of these studies are
presented in the following sections.
Tables B-14 and B-15 (Appendix B) contain summaries of data from the recent
published literature regarding concentrations of CDDs and CDFs in food products. Most of
the selected studies investigated "background" levels of CDDs and CDFs rather than
studies targeted at areas of known contamination. Table B-16 contains a summary of
PCB congener concentrations in food products.
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Studies summarized in Tables B-14 and B-15 primarily examined CDD and CDF
levels in products of animal origin (i.e., fish, meat, eggs, and dairy products). Because of
their lipophilic nature, CDDs and CDFs are expected to accumulate in these food groups.
Data in the tables indicate that CDDs and CDFs are found at levels ranging from the
intermediate ppq up to the low ppt range. As expected, the highest levels reported are
those measured in foods with high animal fat content. The highest reported congener
concentrations are for the HpCDDs and OCDD. In general, for the less-chlorinated
congener groups (i.e., CI4 - CI6), the CDD and CDF levels are both low and of similar
magnitude. However, for the CI7 and CI8 congener groups, CDDs are higher than CDFs.
3.7.1. Migration of CDD/CDF from Paper Packaging Into Food
In the past, low levels of CDDs and CDFs have been detected in bleached paper.
(See discussion in Volume 1.) Because bleached paper is sometimes used for food
packaging, concern has been expressed that CDD/CDFs may migrate from the paper into
the food.
Using refined and highly sensitive analytical methods, LaFleur et al. (1990)
observed the migration of 2,3,7,8-TCDD, 2,3,7,8-TCDF, and 1,2,7,8-TCDF from bleached
paper milk cartons into whole milk. After 12 days of exposure, 6.7 percent of the
2,3,7,8-TCDD; 1 8 percent of the 2,3,7,8-TCDF; and 1 3 percent of the 1,2,7,8-TCDF in
the milk carton leached into the milk. The concentrations of the three congeners in milk
were 8.5, 110, and 49 pg/kg for 2,3,7,8-TCDD; 2,3,7,8-TCDF; and 1,2,7,8-TCDF,
respectively. [Note: These data are not reported in Appendix B; only data for raw milk
are reported.] LaFleur et al. (1990) also analyzed a single background milk sample for
2,3,7,8-TCDD and 2,3,7,8-TCDF. The sample contained 2,3,7,8-TCDD at a concentration
of 1.8 pg/kg and nondetectable concentrations of 2,3,7,8-TCDF.
Study results reported by LaFleur et al. (1990) were performed by the National
Council of the Paper Industry for Air and Stream Improvement (NCASI) at the request of
the U.S. Food and Drug Administration (FDA) as part of a cooperative Federal agency
effort to assess the risks posed by dioxin contamination of paper products (i.e., the
Federal Interagency Working Group on Dioxin-in-Paper). In addition to assessing the
migration of CDDs and CDFs from milk cartons, studies were also conducted to assess the
extent of CDD/CDF migration into food from coffee filters, cream cartons, orange juice
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cartons, paper cups for hot beverages, paper cups for soup, paper plates for hot foods,
dual ovenable trays, and microwave popcorn bags. Migration of CDD/CDFs from the
paper into food was observed in all studies. Results of these migration studies and an
assessment of the risks to the general population posed by migration from paper are
addressed in detail in U.S. EPA (1990). CDD/CDF levels currently found in food due to
any leaching of dioxin-like compounds from paperboard containers are expected to be
significantly lower than those reported in U.S. EPA (1990) because of process changes
implemented by the pulp and paper industry to reduce formation of CDDs and CDFs (59
FR 17384).
In 1990, EPA referred the issue of potential CDD/CDF contamination from bleached
food-contact paper products to FDA, because the risks were considered to be
unreasonable in accordance with Section 9(a) of TSCA (55 FR 53047). In a 1994 Federal
Register notice (59 FR 17384), FDA outlined various options being considered to address
this issue, including the voluntary industry program to reduce TCDD in food-contact
bleached paper products that had been in effect since 1990. As discussed in Volume 1,
the paper industry has made process changes that they expect have generally reduced
dioxin levels in bleached paper pulp to less than 2 ppt of I-TEQDF. Similar or lower levels
could be expected in final paper products. NCASI reports that essentially no detectable
migration of dioxin to milk occurs from cartons at these levels. According to an industry-
wide survey conducted in 1993 by the American Forest and Paper Association, the
voluntary specification for 2 ppt or less TCDD has been met by industry (59 FR 17384).
This standard was still being met in 1995 (personal communication between G. Schweer,
Versar, Inc. and E. Machuga, FDA, October 5, 1995).
3.7.2. North American Food
Until recently, data on measured levels of CDDs, CDFs, and dioxin-like compounds
in U.S. food products have generally come from studies of a specific food product(s) in a
specific location(s) rather than from large survey studies designed to allow estimation of
daily intake of the chemicals for a population. For example, CDD/CDFs have not been
routinely monitored for in the U.S. FDA Surveillance Monitoring Program for domestic and
imported foods (conversation between Dr. S. Page, FDA, and G. Huse, Versar, Inc.,
February 8, 1993) nor have they been routinely monitored for by the U.S. Department of
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Agriculture (USDA) in the National Meat and Poultry Residue Monitoring Program
(conversation between Dr. E. A. Brown, USDA-FSIS, and G. Schweer, Versar, Inc.,
February 8, 1993).
However, USDA has developed some site-specific, though dated (late 1970s), CDD
monitoring data, and recently, EPA and USDA conducted joint statistically-based national
studies to evaluate the amount of CDD/CDF residues in animal products. Earlier efforts
by USDA to examine CDD/CDFs in animal products were in response to a decline in
general health noted by inspectors in several cattle herds in Michigan. Wood products in
the local barns and other cattle holding premises, presumed to be treated with
pentachlorophenol (PCP), were suspected as the cause of this health decline (Buttrill et al.,
1978; Tiernan and Taylor, 1978). PCP was suspected to contain trace CDD and CDF
levels as manufacturing contaminants at that time. In response to this incident, USDA
performed two national investigations. The first study analyzed peritoneal adipose and
liver samples collected from beef cattle in 23 States (Tiernan and Taylor, 1978), while the
second study analyzed adipose tissue samples (body region not specified) collected from
dairy cattle in 30 States (Buttrill et al., 1 978)-neither study specified the cattle breeds for
any sample. HxCDD, HpCDD, and OCDD were screened for in the analyses of samples
from each study. In the beef cattle study (Tiernan and Taylor, 1978), 220 samples were
analyzed: 189 peritoneal adipose samples and 31 liver samples. No residues were
detected in any liver samples. A total of 19 (i.e., 10 percent) of the 189 adipose samples
were found to contain HxCDD, HpCDD, or OCDD at levels >0.10 ppb (assumed to be on
a whole weight basis), while 56 (i.e., 30 percent) contained levels <0.10 ppb (assumed
to be on a whole weight basis) that were detectable based on the signal-to-noise ratio of
the analytical instrumentation. OCDD accounted for the majority of the samples that
positively contained CDDs (i.e., 17 or 9.0 percent), while only 3 samples contained
HxCDD and 2 samples contained HpCDD residues. In the dairy cattle study, 358 adipose
samples were analyzed (Buttrill et al., 1978). Nine samples (i.e., 2.5 percent) positively
contained CDD levels >0.19 ppb or the "level of reliable measurement," while another 30
samples (i.e., 8.4 percent) contained identifiable CDD levels that were below the "level of
reliable measurement" (i.e., not positively identified due to low concentration levels). As
with the beef cattle study results, OCDD accounted for the majority (eight) of positive
samples. HpCDD was identified in only a single sample that also contained OCDD.
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HxCDD was also identified in only a single sample. Data from the USDA studies are not
useful for estimating CDD/CDF exposure for two reasons. First, the samples were
analyzed for only 3 of the 17 CDD/CDF congeners with dioxin-like toxicity, and these
were reported on a homolog basis rather than a congener-specific basis. Second, the limit
of detection was at or above 0.1 ppb or 100 ppt. Background levels for individual
congeners appear to be much less than 100 ppt. For example, the highest congener
levels in beef fat analyzed by Furst et al. (1990) were 5.4 ppt for OCDD.
FDA has also conducted some limited analyses for the higher-chlorinated dioxins in
market basket samples collected under FDA's Total Diet Program (Firestone et al., 1986).
Food samples found to contain PCP residues >0.05 M9/9 were analyzed for
1,2,3,4,6,7,8-HpCDD and OCDD. Also, selected samples of ground beef, chicken, pork,
and eggs from the market basket survey were analyzed for these dioxin congeners,
regardless of the results of PCP residue analysis. Between 1979 and 1984, 16 ground
beef samples, 18 pork samples, 16 chicken samples, and 17 eggs samples with no PCP
contamination were collected at various locations throughout the United States and
analyzed for 1,2,3,4,6,7,8-HpCDD and OCDD. No dioxin residues were detected in any of
the ground beef or egg samples. OCDD was observed at detectable concentrations in
only 2 of the 18 pork samples (27 ppt 53 ppt) and 2 of the 16 chicken samples (29 ppt,
76 ppt). One chicken sample with PCP residues >0.05 M9/9 had detectable residues of
both 1,2,3,4,6,7,8-HpCDD (28 ppt) and OCDD (252 ppt). Egg samples from Houston,
Texas, and Mesa, Arizona, with PCP residues >0.05//g/g, had detectable 1,2,3,4,6,7,8-
HpCDD levels ranging from 21 ppt to 588 ppt, and OCDD levels ranging 80 ppt to 1,610
ppt. These levels were attributed to local PCP contamination (Firestone et al., 1986).
Milk samples, contaminated with PCP at levels ranging from 0.01 |Jg/g to 0.05 |Jg/g PCP,
contained no detectable dioxins. It should be noted that these food residue data were not
used in this assessment of dioxin exposures in the United States, because the reported
limits of detection (10 to 40 ppt) for the FDA analyses were considerably higher than the
levels of dioxins observed in foods from more recent studies. Also, the study only
analyzed for residues of 2 of the 17 toxic CDD/CDF congeners. Finally, the study focused
on samples with PCP contamination and, therefore, was not generally representative of
background exposures.
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FDA conducted a market basket survey of dairy products and commercial fish and
shellfish in 1995/96 (Bolger and Jensen, 1998). Analysis of the foods for CDD/CDFs
demonstrated that, other than the catfish samples, few of the food products had
quantifiable levels of CDD/CDFs below 1 ppt. Samples containing detectable levels below
1 ppt yielded uncertain results, highly dependent on what value nondetects were set to
(i.e., zero, one-half the detection limit, or the detection limit). Consequently, the market
basket survey results were not used in calculation of background estimates of CDD/CDFs.
Catfish fillet samples did, however, show quantifiable results. Twelve of the 19 catfish
samples were suspected of being linked to the use of ball clay as a feed additive.
CDD/CDF levels in these fillets ranged from 1.20 to 5.66 ppt I-TEQDF (mean = 3.11),
when nondetects were set to one-half the detection limit and also when nondetects were
set to the limit of detection. The seven uncontaminated catfish samples had levels
ranging from 0.03 to 0.70 ppt I-TEQDF (mean = 0.29), when nondetects were set to one-
half the detection limit, and 0.05 to 0.71 ppt I-TEQDF (mean = 0.31), when nondetects
were set to the limit of detection.
Jensen and Bolger (2000) and Jensen et al. (2000) reported on additional FDA
market basket dairy samples collected during 1996 through 1999. These data are shown
in Table 3-30. FDA also analyzed 15 composite egg samples collected in 1997 from
California, Ohio, Georgia, New York, Pennsylvania, Oregon, Minnesota, and Wisconsin
(two composite samples were collected from each state except Oregion, which had only
one composite) (Hayward and Bolger, 2000). Each composite contained 24 eggs. The
TEQDF-WH098 for these samples was 0.07 pg/g whole weight when only the positive
samples were included in the TEQ calculation. When all sample results were included in
the analysis, the mean TEQDF-WH098 concentration was 0.013 when non-detects were set
to zero and 0.081 when non-detects were setto one-half the detection limit.
Cooper et al. (1995) collected 38 samples of various food items from grocery
stores and local fish markets in southern Mississippi during the spring of 1994. Food
items "were selected based on their suspected high levels of CDD/CDF to the dietary
intake." Thus, locally consumed dairy products, meat, egg, and seafood samples were
collected, but items such as vegetables, fruit, grain, and cereal products were not
sampled. All 38 samples collected had detectable levels of CDD/CDFs. I-TEQDF
concentrations for each sample are reported in Table 3-31. In general, the levels of
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CDD/CDFs in fish and shellfish were higher than the levels in meat and dairy products, and
farm-raised catfish had the highest I-TEQDF concentrations of all the food types analyzed.
I-TEQDF concentrations in meat and dairy products were slightly lower than those reported
in other U.S. and European studies (Cooper et al., 1995).
As an extension of previous studies of food samples collected in southern
Mississippi, Fiedler et al. (1997d) examined the CDD/CDF I-TEQDF concentrations of seven
restaurant-prepared food dishes. Samples included: a veal chop, chicken strips and fries,
blackened amberjack fish fillet, seafood soup, pasta with cheese and cream sauce, cheese
sticks, and cheese cake. I-TEQDF values of CDD/CDFs ranged from 0.0197 ppt to 0.173
ppt on a fresh weight basis and from 0.1 28 ppt to 1.67 ppt on a lipid basis. The veal
chop contained the highest I-TEQDF levels with 1.67 ppt I-TEQDF (lipid) and 0.173 ppt
(whole weight), and also had measurable quantities of all the analyzed CDD/CDF
congeners with the exception of 2,3,7,8-TCDD. 2,3,7,8-TCDD was not detected in
quantifiable amounts in any of the restaurant samples. The major fraction of the total I-
TEQDF came from PeCDDs and HxCDDs in most of the samples. The authors observed
similar congener patterns in the dairy-based dishes (cheese sticks, pasta with cream
sauce, and cheese cake) and in the veal chop sample. Specifically, the ratios between the
HxCDDs (1,2,3,4,7,8-, 1,2,3,6,7,8-, and 1,2,3,7,8,9-) were approximately 1:4:1, and the
concentration of 1,2,3,7,8-PeCDD and 2,3,4,7,8-PeCDF was approximately the same in
dairy-based and veal chop dishes. The authors speculated that a dairy diet for the source
cattle would possibly explain the similar pattern in the veal chop and dairy-based dishes.
I-TEQDF levels in the restaurant-prepared seafood dishes were lower than I-TEQDF levels of
store purchased seafood items and farm-raised catfish items from southern Mississippi
studied by Cooper et al. (1995). An I-TEQDF level of 0.5 ppt (lipid) was found by Fiedler
et al. (1997d) in restaurant-prepared fish fillet samples, while a mean I-TEQDF level of 20.5
ppt (lipid) was observed in commercially bought fresh farm-raised catfish nuggets by
Cooper et al. (1995) in the same area of southern Mississippi. The restaurant-prepared
seafood soup had an I-TEQDF of 0.646 ppt (lipid), while the I-TEQDF levels of store
purchased fresh crab and crawfish ranged from 5.23 to 40.1 ppt (lipid). Fiedler et al.
(1997d) calculated the contribution of the seven restaurant food items to the percentage
of daily intake. Based on an assumed average daily dietary CDD/CDF intake of 100 pg I-
TEQDF/person, the veal chop would contribute 46 percent of the daily intake, the chicken
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strips 6.7 percent, the fish fillet 7.7 percent, the seafood soup 18 percent, the pasta with
cream sauce 51 percent, the cheese sticks 13 percent, and the cheese cake 15 percent.
The California Air Resources Board (CARB) collected multiple samples of seven
types of foods from commercial food sources in two urban areas of California (Stanley and
Bauer, 1989). Foods were collected randomly, but an emphasis was placed on food
stuffs of California origin (Stanley and Bauer, 1989). The types of food stuffs included
saltwater fish, freshwater fish, beef, chicken, pork, milk, and eggs. A total of 210
samples were collected in Los Angeles (30 individual samples of each of the 7 types of
foods), and 140 samples were collected in San Francisco (20 individual samples of each
of the 7 types of foods). Food items were composited before chemical analysis to obtain
a sample that was representative of average levels of CDDs and CDFs in the food stuffs,
increase the probability of detection, and reduce the cost of chemical analysis. Samples
were composited separately for each type of food stuff within each geographical area.
Each composite sample contained 6 to 10 individual food samples, and 5 to 8 composite
samples were analyzed for each food type. CARB data are summarized in Table 3-32.
Beef (ground beef), pork (bacon), chicken, fish, and milk samples were analyzed on a lipid
weight basis; however, for the purposes of this report, they were subsequently converted
to a wet weight basis by multiplying the lipid weight concentration of CDD/CDFs by the
fraction of fat contained in the food product of interest. Assumed lipid contents of 19,
15, and 4 percent for beef, pork and chicken, and milk were used. When nondetects
were set to one-half the detection limit, the mean l-TEQDFs were 0.29 ppt, 0.24 ppt, 0.21
ppt, and 0.06 ppt for beef, pork, chicken, and milk, respectively. When nondetects were
set to zero, l-TEQDFs were 0.03, 0.05, 0.08, and 0.02, respectively. Egg samples were
analyzed for CDD/CDFs on a wet weight basis. l-TEQDFs for eggs were 0.14 ppt, when
nondetects were set to one-half the detection limit and 0.004 when nondetects were set
to zero.
The NCASI study (as described by LaFleur et al., 1990; and Henry et al., 1992)
collected random food samples directly from the shelves of grocery stores located in the
southern, midwestern, and northwestern regions of the United States. The samples were
analyzed for 2,3,7,8-TCDD and 2,3,7,8-TCDF. These data are summarized in Table 3-33.
Schecter et al. (1993a) conducted complete congener analyses of 18 food samples
collected from a supermarket in Binghamton, New York, in early 1990. Samples included
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five fish, three types of beef (ground beef, beef sirloin tip, and beef rib steak), one
chicken drumstick, one porkchop, one lamb, one ham, one bologna, one heavy cream, and
four types of cheese. The following ranges of I-TEQDF levels on a whole weight basis
were found: fish: 0.02 - 0.24 ppt; meat: 0.03 - 1.5 ppt; and dairy products: 0.04 -
0.7 ppt. These data are summarized in Table 3-34.
In a more recent study, Schecter et al. (1997) analyzed food samples collected
directly from supermarkets in five U.S. cities: Binghamton, New York; Chicago, Illinois;
Louisville, Kentucky; Atlanta, Georgia; and San Diego, California. The types of foods
collected included: beef, chicken, ocean fish, fresh fish, and pork. Samples of each food
type from the five geographic regions were pooled and analyzed for CDD/CDFs and
selected PCBs. Ranges of CDD/CDF I-TEQDF values were calculated for each food group
by assigning either zero or the detection limit to undetected congeners. The following
ranges of CDD/CDF I-TEQDF levels on a lipid weight basis were found: beef: 0.89 - 2.86
ppt; chicken: 0.10-5.17 ppt; ocean fish: 2.45 - 21.14 ppt; fresh fish: 12.51 - 16.07
ppt; and pork: 0.64 - 3.97 ppt. These data are summarized in Table 3-35. Using the
reported lipid content of these pooled samples to calculate the whole weight
concentrations, the whole weight equivalents for these ranges are estimated to be: beef:
0.12 - 0.38 ppt; chicken: 0.005 - 0.28 ppt; ocean fish: 0.035 - 0.30 ppt; fresh fish: 0.60
- 0.78 ppt; and pork: 0.06 -0.36 ppt. PCB concentrations are also presented in Table
3-35. Schecter et al. (2001) reported again on the data collected from supermarkets in
five U.S. cites. In this paper, mean TEQDEP-WH098 concentrations for the same foods as
described previously in Schecter et al. (1997) were presented. The whole weight mean
TEQDFP.WH098 concentrations were as follows when non-detects were set to one-half the
detection limit: beef: 0.40 ppt; chicken: 0.33 ppt; pork: 0.39 ppt; ocean fish: 0.39 ppt;
and fresh fish: 1.7 ppt. When non detects were set to zero, the mean whole weight
TEQDFP-WH098 concentrations were: beef: 0.16 ppt; chicken: 0.14 ppt; pork: 0.12 ppt;
ocean fish: 0.16 ppt; fresh fish: 1.6 ppt.
Schecter and Li (1997) also analyzed four kinds of U.S. fast foods (i.e., hamburger,
pizza, fried chicken, and ice cream) for CDD/CDFs and dioxin-like PCBs (105, 108, 156,
180). The I-TEQDF concentrations were similar for hamburger, pizza, and chicken, ranging
from approximately 0.01 ppt wet weight, when a value of zero was used for nondetected
congeners, to 0.3 ppt wet weight, when the detection limit was used for nondetected
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congeners to calculate the total I-TEQDF value. These values are similar to those observed
for other raw food groups and may indicate that cooking does not have a significant effect
on the levels of CDD/CDFs in foods. (Further discussion on the effects of cooking can be
found in Section 3.7.5.) The I-TEQDF concentrations for ice cream ranged from 0.03 to
0.49 ppt. Only PCB 180 was detected in hamburger at a concentration of 126 ppt. In
pizza, PCB 118 (189 ppt) and PCB 180 (152 ppt) were detected, and in fried chicken,
only PCB 118 (250 ppt) was detected. None of the PCB congeners evaluated were
quantifiable in ice cream.
Schecter et al. (1989b) compared the levels of CDD/CDFs in cow's milk and infant
formulas from Thailand and the United States. Samples of cow's milk and infant formulas
were obtained from grocery stores in the Binghamton, New York, area in 1987. Thai
formulas were purchased in Bangkok, Thailand, in 1986. In general, the I-TEQDF levels, on
a lipid basis, were lower in infant formula than in cow's milk (Table 3-36). In addition, the
formulas that were purchased in the United States had lower I-TEQDF levels than those
purchased in Thailand. On a sample weight basis, the I-TEQDF level for whole cow's milk
was 0.04 ppt (i.e., 1.2 lipid based ppt x 3.4 percent fat). This is similar to I-TEQDF levels
observed for cow's milk in other parts of the United States and in Europe. Using a Nordic
model for calculating TEQs, the data for cow's milk and infant formulas were compared to
the Nordic-TEQ levels found in human milk samples from various countries. Human milk
from industrialized areas (i.e., United States, Germany, and South Vietnam) had higher
CDD/CDF Nordic-TEQ levels than either cow's milk or soy-based infant formulas.
U.S. EPA (1991) collected milk samples from several sites in the vicinity of a
municipal waste incinerator in Rutland, Vermont, and two background samples from a
dairy farm 123 kilometers from the incinerator, where no obvious industrial sources of
CDD/CDF were present. All samples were taken from bulk storage tanks at the farms.
The report indicated that facility emissions could not be correlated with the levels of
CDD/CDF and other contaminants measured in various environmental media. For all milk
samples, the majority of the congeners were not detected. It was reported that only
OCDD was consistently detected at levels from 0.2 to 2.4 pg/g in the farms near the
facility. The levels in milk from the three farms near the facility ranged from about 0.2 to
0.4 pg of l-TEQDF/g whole milk, and the I-TEQDF for the background samples collected
from the distant farm was 0.12 pg/g. l-TEQDFs were calculated by U.S. EPA (1991) by
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setting the nondetects equal to the detection limit. The 0.12 ppt I-TEQDF background
value estimated by EPA is nearly 2 orders of magnitude higher than the I-TEQDF for milk
based on the NCASI data. (This is probably due largely to the incomplete congener
analysis conducted by LaFleur et al., 1990.) Examination of the raw data supporting this
study indicated that all of the CDD/CDF congeners in the background sample were
nondetectable. Consequently, if nondetects are set to zero, the total background I-TEQDF
for milk would be zero. If half the detection limits are used to calculate the total I-TEQDF
level, the estimated value is 0.07.
Birmingham et al. (1989) analyzed CDD/CDF residues in food collected in Ontario,
Canada. Most of the food was grown in Canada, although some was from the United
States. They reported analyzing 25 composite samples from 10 food groups. The precise
number of samples in each food group was not reported. No TCDD, PeCDD, HxCDD,
TCDF, or PeCDF were found at detection limits of 0.1 to 7 ppt. Low ppt levels of some
of the higher chlorinated CDD/CDFs were detected in some foods. I-TEQDF levels were
also estimated for the major food groups. However, as shown in Table 3-36, these data
were reported on a homolog basis. It is unclear what procedure was used to convert the
homolog data to I-TEQDF. The text implies that nondetects were treated as zero for
purposes of estimating I-TEQDF. In addition to the animal food data shown in Table 3-37,
measurements were also made in potatoes, apples, tomatoes, peaches, and wheat. Only
OCDD was detected at levels ranging from 0.6 to 8 pg/g fresh weight. The I-TEQDF totals
for vegetables were reported as 0.004 ppt for fruit, 0.002 ppt for vegetables, and 0.0007
ppt for wheat- based products. The procedure used to develop these I-TEQDF estimates
was not clear.
Canada has a food safety program that analyzes total diet samples (i.e.,
representative food samples of the general population) for chemical substances, The
analyses are run on commercially bought and prepared foods. As a part of that program,
Ryan et al. (1997) analyzed CDD/CDFs and non-ortho PCBs in 44 food composites from
Toronto and also in 44 composite samples from Montreal during the summers of 1992
and 1993, respectively. To optimize specific congener detection, samples of primarily
animal origin were analyzed. Food groups included beef (ground beef, beef steak, beef
roast), cured pork, organ meat, and poultry, dairy products of varying lipid content (whole
milk, 1 percent milk, cream, cheddar cheese, and butter), fish (fresh water and marine),
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and cooking fats and salad oils. Congener-specific data were not presented in this report.
I-TEQDF levels and TEQP-WH094 levels were calculated by setting nondetects to one-half
the detection limit. These data were reported on a whole weight basis. TEQ values for
each city are summarized in Table 3-38. The highest concentrations of total TEQDFP-
WH094 were found in butter (0.93 ppt in Toronto and 0.62 ppt in Montreal), fresh water
fish (0.62 ppt in Toronto and 0.48 ppt in Montreal), oils (0.44 ppt in Toronto and 0.31
ppt in Montreal), and in ground beef (0.39 ppt in Toronto and 0.37 ppt in Montreal).
TEQs for dairy products increased with increasing lipid content. Butter (lipid content
approximately 70 percent) had a total TEQDFP-WH094 of 0.93 ppt in Toronto and 0.62 in
Montreal, while 1 percent milk (lipid content approximately 0.48 percent) had a TEQDFP-
WH094 of 0.036 ppt in Toronto and 0.025 ppt in Montreal. Fresh water fish, with a total
TEQDFP-WH094 of 0.62 ppt (Toronto) and 0.48 ppt (Montreal), were found to have higher
total TEQDFP-WH094 levels than marine fish (0.28 ppt in Toronto, and 0.12 ppt in
Montreal). PCBs constituted 58 percent of the total TEQDFP-WH094 value in Toronto (0.36
ppt) and 67 percent in Montreal (0.32 ppt). In the meat food group, more of the total
TEQDFP-WH094 was attributable to the CDD/CDF portion than the non-ortho PCB portion.
The proportion of total TEQDFP-WH094 attributable to non-ortho PCBs was greater in the
dairy products and fish than in the meat samples.
Mes and Weber (1989) analyzed one composite sample each of beef, butter,
cheese, eggs, organ meats, and poultry for PCBs 77, 126, and 169. The number of
individual samples included in these composites was not reported. Whole weight
concentrations of total PCBs (i.e., the sum of PCB 77, 1 26, and 1 69) were as follows:
beef - 2 ppt, butter - 24 ppt, cheese - 11 ppt, eggs - 3 ppt, organ meats - 4 ppt, and
poultry - 2 ppt. In a later study, Mes et al. (1991) evaluated numerous PCB congeners
including dioxin-like PCBs 105, 114, 118, 1 56, 1 57, and 1 89 in foods. Five composite
samples of the following food types were collected: milk, various dairy products, various
cuts of beef and pork, lamb, organ meats, fish, eggs, luncheon meats, cooking fats, and
soup. The number of individual samples included in each of these composites was not
reported. Each composite sample was taken from one of five major Canadian cities.
Among these foods, total whole weight PCB concentrations were highest for freshwater
(31.9 ng/g), canned (9.9 ng/g), and marine (4.6 ng/g) fish, followed by butter (3.0 ng/g)
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and cheese (1.7 ng/g). Concentrations were lowest (i.e., <0.1 to 0.1 ng/g) in canned
meat soup, margarine, milk, yogurt, and lamb.
Recently, EPA has worked in cooperation with USDA to estimate the levels of
CDD/CDFs and dioxin-like PCBs in U.S. food products. Analyses have been conducted for
beef, pork, poultry, and milk products. A study of CDD/CDFs in vegetable oils has also
been conducted. Data from the completed EPA/USDA studies are used in this Chapter to
estimate background levels of CDD/CDFs and dioxin-like PCBs in foods. These studies
were designed to be representative of the animal products used in the United States, and
are believed to be suitable for calculating national averages.
Beef
EPA conducted a joint study with USDA to evaluate the amount of CDD/CDF
residues in beef animals from federally inspected establishments (Winters et al., 1996a).
Using a statistically-based sampling plan, 63 back fat samples were collected. Back fat
was selected for sampling because: (1) it was assumed to be representative of fat people
consume, because it is an extension of the fat reservoir, which, at another point, is the fat
that is on rib cuts; (2) it was obtainable with little disruption by the USDA Federal
inspectors who collected the samples; and (3) it has high fat content, which would
optimize the analytical capability of measuring dioxins in the matrix. The average fat
content of the samples was 80 percent. The sampling plan was designed to provide
samples representative of the slaughtering establishments, cattle class (i.e., bulls, steer,
heifers, beef cows, and dairy cows) and region of the United States in order to provide a
national estimate of CDD/CDFs in beef. Tissue samples were analyzed for the residues of
the 1 7 toxic CDD/CDF congeners and for percent lipid content. Limits of detection for the
study were 0.05 ppt for the tetra-CDD/CDFs, 0.5 ppt for the penta-, hexa-, and hepta-
CDD/CDFs, and 3.0 ppt for OCDD/CDF, on a whole weight basis. Because the samples
were 80 percent fat, these whole weight detection limits translate to average lipid-based
detection limits of 0.0625 ppt for the tetra-CDD/CDFs, 0.625 ppt for the penta-, hexa-,
and hepta-CDD/CDFs, and 3.75 ppt for OCDD/OCDF.
Based on the analytical results, the most frequently detected CDD/CDF congener
was 1,2,3,4,6,7,8-HpCDD. This congener was detected in over 70 percent of the
samples and most frequently had the highest concentration of all the CDD/CDF congeners.
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The second most frequently detected congener was 1,2,3,6,7,8-HxCDD, which was
detected in approximately 32 percent of the samples. The most toxic congener, 2,3,7,8-
TCDD, occurred in 11 of the 63 samples. Congeners not detected in any of the 63 beef
samples included 2,3,7,8-TCDF, 1,2,3,7,8-PeCDF, 1,2,3,7,8,9-HxCDF, 1,2,3,4,7,8,9,-
HpCDF, and OCDF. Of the 17 congeners, 1,2,3,4,6,7,8-HpCDD and OCDD had the
highest mean concentrations for the 63 beef samples. Overall, total CDD concentrations
were higher than CDF concentrations in 44 of the 45 samples that had detectable
CDD/CDF concentrations. Of the 63 samples, 18 (i.e., 28.6 percent) had no detectable
CDD/CDFs. When nondetects were set to zero, the mean total CDD concentration
accounted for approximately 88 percent of the mean total CDD/CDF concentration, while
total CDFs accounted for only 1 2 percent. When nondetects were set to one-half the
detection limit, the mean total CDD concentration accounted for 70 percent of total
CDD/CDF concentrations, and CDFs accounted for 30 percent. Based on the cattle
classes, both I-TEQDF and TEQDF-WH098 concentrations were highest in bulls and lowest in
dairy cows.
The mean lipid-based TEQDF-WH098 value for the 63 beef samples was estimated to
be 0.36 ppt (I-TEQDF = 0.35 ppt), when nondetects were set to zero, and 1.06 ppt (I-
TEQDF = 0.89 ppt), when nondetects were set to one-half the detection limit. These
mean values were calculated by statistically extrapolating the sample size for each cattle
class to the percentage of the U.S. food supply that they represent. CDDs made up
almost 76 percent of the total TEQDF-WH098, while CDFs accounted for only about 24
percent, when nondetects were set to zero. When nondetects were set to one-half the
detection limit, CDD concentrations accounted for 65 percent of the total TEQDF-WH098
and CDFs accounted for 35 percent of the TEQDF-WH098.
Assuming that the TEQDF-WH098 concentration in the lipid portion of the back fat
samples is equivalent to the TEQDF-WH098 concentration in the lipid portion of edible cuts
of beef, the lipid-based results from this study may be used to estimate the TEQDF-WH098
concentration of CDD/CDFs in beef that is consumed by the general population. For
example, if it is assumed that the average fat content of edible cuts of beef is 1 7 percent,
then the TEQDF-WH098 concentration in beef consumed by the general population is 0.18
ppt (i.e., 0.1 7 times 1.06 ppt), when nondetects are set to one-half the detection limit,
and 0.06 ppt (i.e., 0.17 times 0.36), when nondetects are set to zero.
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The percentage of fat in beef was estimated using food consumption data and fat
content data for various beef products provided by David Haytowitz, USDA, to Linda
Phillips, Versar, Inc., by personal communication, January 2001. USDA obtained food
consumption data from the 1994-96 Continuing Survey of Food Intake Among Individuals
(CSFII). The total quantity (in grams) of each food item eaten by the survey population in
one survey day was tabulated and weighted to represent the quantity eaten by the entire
U.S. population in one day. The fat content of each of these food items was also
reported. To estimate the weighted mean percent of fat in beef products that are typically
consumed by the U.S. population, the total amount of each beef item was first multiplied
by the fraction of fat reported for that item to calculate the amount of beef fat consumed
from each beef item. Next, the total amount of beef fat consumed (in grams) was
calculated by summing the beef fat intakes for the individual beef items. The total
amount of beef consumed was also estimated by summing the beef intake for the
individual beef items. Finally, the weighted fraction of beef was estimated by dividing the
total beef fat intake by the total beef intake. An abbreviated (i.e., the total number of
beef items included in the analysis was 146; only a few beef items were included in the
example to demonstrate the methodology) example of this calculation is provided in Table
3-39.
EPA, in cooperation with USDA, also recently analyzed beef samples for dioxin-like
PCBs (Winters et al., 1996b; Saunders, 1997). The same samples that were analyzed for
CDD/CDFs, as described above, were analyzed for PCBs 77, 118, 105, 126, 156, 157,
and 169. Results of these analyses are presented in Table 3-40. Dioxin-like PCB
congeners were found to be present in beef fat at an occurrence of greater than 85
percent; however, PCB 77 was found in only 19 percent of the samples. Using a
statistical extrapolation of the data to account for the percentage of the different cattle
classes in the U.S. food supply, the mean lipid-based total TEQP-WH098 concentration for
these dioxin-like PCBs was estimated to be 0.49 ppt (TEQP-WH098 is also 0.49 ppt), when
nondetects were set to either zero or the detection limit. PCB 126 contributed the most
to the total TEQP-WH098. Assuming that the average fat content of edible cuts of beef is
1 7 percent, the PCB concentration in beef consumed by the general population was
estimated to be 0.084 ppt TEQP-WH098 (i.e., 0.49 times 0.17).
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To ensure the relationship between lipid concentrations of dioxin-like compounds in
the back fat of cattle is comparable to the level in edible meat products, EPA and USDA
collaborated on an additional beef study examining the CDD/CDF/PCB concentrations in
various cattle fat reservoirs (Lorber et al., 1997a). Fat matrices under examination
included back fat (60 to 90 percent lipid), perirenal (i.e., kidney) fat (70 to 90 percent
lipid), muscle tissue (less than 5 percent lipid), and liver (less than 5 percent lipid). Three
data sets, cited in Lorber et al. (1997a), were analyzed in the study. The first data set
came from a 1995 study in which the 17 dioxin and furan congeners and 6 coplanar PCBs
were measured in 5 tissue samples (i.e., back fat, muscle, liver, serum) from animals at 3
research facilities around the United States (Feil et al., 1995). The five selected animals
came from research facilities at Pennsylvania State University (PSU), North Dakota State
University (NDSU), and Oregon State University (OSU). These animals were raised under
the same conditions as cattle raised in routine U.S. feedlot operations, and were
slaughtered after about 1-1/2 years. The second data set came from a 1996 dosing study
in which four animals were fed high amounts of several, but not all, of the dioxin and
furan congeners (Feil et al., 1996). Dosed animals experienced unanticipated exposure to
some higher chlorinated congeners that exceeded the dosing levels. The likely source of
this exposure comes from the PCP-treated wood in the feeding facilities. Feil et al. (1996)
reported the homologue group concentrations in back fat, perirenal fat, muscle tissue,
serum, and liver. Lorber et al. (1997a) analyzed the unpublished congener-specific data
from this study. The third data set came from a 1995 depletion study of CDD/CDFs in
five animals from a herd in Bolsover, Derbyshire, England (Startin et al., 1994). The
animals in this herd had very high CDD/CDF concentrations in milk, which was traced to
locally contaminated feed.
Results reported below are based solely on analyses of the five animals in the first
data set, because they were believed to be representative of a typical food source (i.e.,
raised under routine feedlot conditions). The animals in the other two studies were not
used in the analysis because they experienced high levels of exposure. Examination of
total and TEQDF-WH098 lipid-based concentrations in back fat compared with intramuscular
fat concentrations, demonstrated that sampling for CDD/CDFs in back fat can be assumed
to be representative of the levels found in edible fat. Assumptions used in this document
to estimate CDD/CDF and PCB concentrations in edible beef from back fat are, therefore,
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believed valid. This finding is based on examination of the ratios derived by dividing TEQ
and total CDD/CDF/PCB concentrations in back fat by the same levels in intramuscular fat
as shown in Table 3-41. A ratio of 1.0 indicates that muscle and back fat concentrations
are equal. The ratio of total CDD/CDF concentrations in intramuscular fat (ppt lipid) to the
same level in back fat (ppt lipid) mostly ranged from 0.5 to 1.5 for the individual CDD/CDF
congeners, and the TEQDF-WH098 ratios ranged from 0.6 to 1.7 (average of 0.9) (Lorber et
al., 1997a). PCB comparisons are less straightforward. PCBs 77, 118, and 106 contain
total and TEQP-WH098 concentrations up to 16 times higher in muscle than in back fat.
The ratios for PCBs 1 26, 1 56, 1 57, and 1 69, which ranged from 0.3 to 1.5, however,
indicate that back fat levels are comparable to edible fat concentrations.
Pork
In addition to a national survey of CDD/CDFs and dioxin-like PCB residues in beef
animals, EPA and USDA recently reported on the completion of a second survey of these
compounds in pork (Lorber et al., 1997b). Using a statistically-based sampling plan, 78
belly fat samples were collected from 46 slaughtering establishments. The same
justification for collection of back fat samples from beef animals applies to the collection
of belly fat samples from the pork animals. These samples averaged 60 percent lipid
(standard deviation of 12 percent), similar to the 80 percent lipid of the beef back fat
samples. Tissue samples were analyzed for the 17 toxic CDD/CDF congeners and 7
coplanar PCBs, including PCBs 77, 11 8, 105, 1 26, 1 56, 1 57, and 1 69. Procedures for
analysis were similar to the procedures for beef fat reported in Ferrario et al. (1996a) for
CDD/CDFs and for PCBs in Ferrario et al. (1996b). Limits of detection for CDD/CDFs for
the study were: 0.1 ppt for the tetras; 0.5 for the pentas, hexas, and hepta; and 1.0 ppt
for the octas. Detection limits for the PCBs were: 1.5 ppt for PCB 77, 50.0 ppt for PCB
118, 26.0 for PCB 126, 10.0 for PCB 156, 2.5 ppt for PCB 157, and 0.1 ppt for PCB
169. These were detection limits for the sample matrix, and because the pork samples
were about 60 percent lipid, the lipid-based detection limits can be estimated by dividing
these detection limits by 0.60.
The sampling plan was designed to be representative of the pork class as a whole,
and its three major subclasses: barrows/gilts, sows, and boars/stags. These classes are
referred to by their common names: market hogs, sows, and boars, respectively. One
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major difference in the pork survey design as compared to the beef survey design was
that the two minor classes of pork animal (i.e., the sows and boars) were oversampled in
relation to their prevalence in the national slaughter of pork animals as a whole. In the
beef survey, the number of animals sampled from each cattle class (which included bulls,
steers, heifers, beef cows, and dairy cows) were proportional to their prevalence in the
national slaughter, with one exception. The exception was the sampling of two bulls;
whereas, sampling in accordance to their prevalence in the class would have required only
one sample. Results of the beef survey showed that the concentrations of CDD/CDFs in
the bull were 3 to 10 times higher than the other four cattle classes (Winters et al.,
1996b). However, it was difficult to draw conclusions and determine the variability in this
class because of the small sample size. Based on this experience, an alternate strategy of
oversampling the minor swine classes was adopted. This oversampling optimized the
ability to distinguish concentration patterns among the three classes, and allowed for an
estimate of the variability of the slaughter class estimates. Nationally, market hogs
comprise about 95 percent of the total slaughter, with sows comprising about 4 percent
and boars 1 percent of the slaughter. In the final sample of 78 animals, 56 were market
hogs, 11 were sows, and 11 were boars. These classes represent 71.8 percent, 14.1
percent, 14.1 percent of the total sample size, respectively.
The most toxic congener, 2,3,7,8-TCDD, occurred in only 3 of the 78 samples.
Congeners not detected in any of the 78 pork samples included 1,2,3,7,8-PeCDF;
1,2,3,7,8,9-HxCDF; and 1,2,3,4,7,8,9-HpCDF. Overall, CDD concentrations were higher
than CDF concentrations. The four most frequently detected congeners were
1,2,3,4,6,7,8-HpCDD; OCDD; 1,2,3,4,6,7,8-HpCDF; and OCDF, all at a frequency of 50
to 60 percent detected. Results also indicated important differences among the swine
classes. The TEQDF-WH098 concentration in sows appeared higher than market hogs:
1.85 ppt (I-TEQDF = 1.72 ppt) for sows versus 1.44 ppt (I-TEQDF = 1.26 ppt) for market
hogs. This may be due to a longer life span for sows (i.e., >2 years) than for market
hogs (i.e., <1 yr). With a longer life, sows accumulate more dioxins and have greater
body burdens than market hogs, despite also having the dissipation route of milk
excretion. Perhaps the most striking result, however, was for the boar class. While a
very small class in terms of exposure (only 1 percent of the pork food supply), older boars
were significantly different from all other classes, while younger boars were similar to the
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other pork classes. The older boars' lipid concentrations of 6.32 ppt TEQDF-WH098 (I-
TEQDF = 6.48 ppt) for CDD/CDFs and 0.54 ppt TEQDF-WH098 (TEQP-WH094= 0.54 ppt)
for coplanar PCBs were about 5 and 10 times higher than the overall averages for
CDD/CDFs and coplanar PCBs, respectively. Like the sows, age is the principal factor that
likely explains the higher concentrations. The average age of slaughter for market hogs is
less than 1 year, while the old boars live longer than 2 years.
As shown in Table 3-42, the mean lipid-based TEQDF-WH098 value for the 78 pork
fat samples was 1.48 ppt (I-TEQDF = 1.30 ppt), when nondetects were set equal to one-
half the detection limit, and 0.42 ppt (I-TEQDF = 0.46 ppt), when nondetects were set to
zero (Lorber et al., 1997b). Assuming the TEQDF-WH098 concentration in the lipid portion
of belly fat samples is equivalent to the TEQDF-WH098 concentration in the lipid portion of
edible cuts of pork, the lipid-based results of this study can be used to estimate the
TEQDF-WH098 concentrations people are exposed to by eating pork. The average fat
content of edible cuts of pork is assumed to be 19 percent (estimated using food
consumption data and fat content data for various pork products provided by David
Haytowitz, USDA, to Linda Phillips, Versar, Inc., by personal communication, January
2001). Thus, the average TEQDF-WH098 content of pork consumed by the general
population would be 0.28 ppt (i.e., 1.48 ppt times 0.19), when nondetects are set to one-
half the detection limit, and 0.080, when nondetects are set to zero. For PCBs, the mean
lipid-based TEQP-WH098 was 0.06 ppt, when nondetects were set to one-half the
detection limit, and 0.04 ppt, when nondetects were set to zero (the TEQP-WH094 are the
same as the TEQP-WH098 for pork) (Table 3-36) (Lorber et al., 1997b). Assuming the
average fat content of edible pork cuts is 19 percent, the TEQP-WH098 concentration in
pork consumed by the general population is estimated to be 0.012 ppt (i.e., 0.06 ppt
times 0.19), when nondetects are set to one-half the detection limit, and 0.0074 ppt
(i.e., 0.04 ppt times 0.19), when nondetects are set to zero.
Poultry and Eggs
EPA and USDA jointly participated in a study of dioxin-like compounds in the U.S.
poultry supply (Ferrario et al., 1997). The study is a companion report to the cooperative
studies on beef and pork (Winters et al., 1996a; Winters et al., 1996b; and Lorber et al.,
1997b), and is the basis for the background TEQ concentrations for poultry. Using a
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statistically based sampling plan, 80 abdominal samples were collected from 70 U.S.
slaughtering establishments. Abdominal fat was selected for sampling because it has a
very high lipid content, thereby optimizing the analytic capability of detecting and
quantifying dioxins in the samples. The average fat content of the samples was 80
percent. The sampling plan was designed to be representative of the four poultry classes:
young chickens, light fowl, heavy fowl, and young turkeys. Nationally, young chickens
account for about 95 percent of total poultry slaughter. In the final sample of 80 animals,
41 (51 percent) were young chickens, 12 (15 percent) were light fowl, 12 (15 percent)
were heavy fowl, and 15 (19 percent) were young turkeys. Samples were analyzed for
percentage lipid, the same 17 toxic CDD/CDF congeners, and the same coplanar PCBs as
the beef and pork samples, as discussed previously. Procedures for analysis of CDD/CDFs
are described in Ferrario et al. (1996a) and for analysis of coplanar PCBs in Ferrario et al.
(1996b). The detection limits for the study were 0.05 ppt for the tetra-CDD/CDFs; 0.25
ppt for the penta-, hexa-, and hepta-CDD/CDFs; and 0.5 ppt for the OCDD/CDF, on a
whole weight basis. The detection limits for PCBs were: 0.80 ppt for PCB 77, 30.0 ppt
for PCB 118,10.0 ppt for PCB 105, 0.10 ppt for PCB 1 26, 4.0 ppt for PCB 1 56, 1.0 ppt
for PCB 157, and 0.08 ppt for PCB 169. These were detection limits for the sample
matrix, and because the poultry samples were about 80 percent lipid, the lipid-based
detection limits can be estimated by dividing these detection limits by 0.80.
Laboratory analyses of the samples revealed that two young chicken samples could
be classified as outliers by the Dixon and Grubs outlier tests. These two samples
demonstrated significantly higher concentration levels of all the dioxin congeners, but CDF
and PCB levels were comparable to the results from the other young chickens and other
poultry classes. The results are reported below and also used to calculate national
background levels; they do not include the data from these two young chicken samples.
The most toxic congener, 2,3,7,8-TCDD, occurred in 67 percent of the young
chickens (mean 0.16 ppt lipid-based), 25 percent of the light fowl (mean 0.05 ppt lipid-
based), 92 percent of the heavy fowl (mean 0.43 ppt lipid-based), and 73 percent of the
young turkeys (mean 0.24 ppt lipid-based). No samples had detectable concentrations of
1,2,3,7,8,9-HxCDF. Ten percent of the young chicken samples had detectable levels of
1,2,3,4,7,8,9-HpCDF; none was detected in the other poultry classes. Overall, CDD
concentrations were higher than CDF concentrations, and a larger percentage of the
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samples had detectable levels of CDDs than CDFs. The most frequently detected
congeners were 1,2,3,4,6,7,8-HpCDD; OCDD; and 2,3,7,8-TCDF, at a detection
frequency of 94, 73, and 84 percent, respectively.
The mean lipid-based TEQDF-WH098 value for the 78 poultry fat samples was 0.77
ppt (I-TEQDF = 0.65 ppt), when nondetects were set to one-half the detection limit, and
0.48 ppt (I-TEQDF = 0.42 ppt), when nondetects were set to zero. For PCBs, the mean
lipid-based TEQP-WH098 (and TEQP-WH094) value for the same samples was 0.29 ppt,
when nondetects were set to either one-half the detection limit or when nondetects were
set to zero. Table 3-43 presents a summary of the lipid-based TEQDF-WH098 results on
the basis of poultry class.
Assuming the TEQDFP-WH098 concentration in the lipid portion of the abdominal fat
samples is equivalent to the TEQDFP-WH098 concentration in the lipid portion of generally
consumed poultry, the lipid-based results from this study may be used to estimate the
TEQDFP-WH098 concentration in poultry consumed by the general population. For example,
if it is assumed that the average fat content of poultry is 9 percent, then the TEQDF-
WH098 concentration in poultry consumed by the general population would be 0.070 ppt
(i.e., 0.09 times 0.77 ppt), when nondetects are set to one-half the detection limit, and
0.043 ppt (i.e., 0.09 times 0.48 ppt), when nondetects are set to zero. The percentage
of fat in poultry was estimated using food consumption data and fat content data for
various food products provided by David Haytowitz, USDA, to Linda Phillips, Versar, Inc.,
by personal communication, January 2001). Using the same assumptions, the TEQP-
WH098 concentration in poultry consumed by the general population is estimated to be
0.026 ppt (i.e., 0.09 times 0.29 ppt), when nondetects are set to zero and to one-half the
detection limit.
Background TEQDF-WH098s for eggs are based on whole weight data for 15
composite egg samples, each containing 24 eggs that were collected in 1997 by FDA
from sites in California, Ohio, Georgia, New York, Pennsylvania, Oregon, Minnesota, and
Wisconsin as part of a market basket survey (Hayward and Bolger (2000). The estimated
total TEQDF-WH098 for these eggs was 0.081 ppg whole weight, using one-half the
detection limit and 0.013 ppt whole weight, when non-detects were set to zero (Table
3-44). Cooper et al. (1995) and Fiedler et al. (1997c) obtained similar results for three
egg samples that were analyzed for CDD/CDFs. The estimated total TEQDF-WH098 for
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these eggs was 0.032 ppt (I-TEQDF = 0.026 ppt), using one-half the detection limit for
nondetectable concentrations, and 0.023 ppt (I-TEQDF = 0.017 ppt), using zero to
represent nondetectable concentrations. Schecter et al. (1997) also analyzed eggs.
However, many of the congeners were not detected at higher detection limits than those
in the Cooper et al. (1995) and Fiedler et al. (1997c) studies. The whole weight I-TEQDF
concentration reported by Schecter et al. (1997) was 0.31 ppt, four to ten times higher
than that reported in the other studies. Thus, the Schecter et al. (1997) data were not
used in calculating the background estimate for CDD/CDFs. Background TEQP-WH098
concentrations were estimated to be 0.1 ppt in eggs (TEQP-WH098 was also 0.1 ppt),
based on U.S. data from Schecter et al. (1997) and Canadian data from Mes and Weber
(1989) and Mes et al. (1991). Schecter et al. (1997) analyzed egg samples (n = 18) for
PCBs 118, 126, and 169. Mes and Weber (1989) analyzed one composite egg sample for
PCBs 77, 126, and 169, and Mes et al. (1991) analyzed five composite egg samples for
PCBs 105, 114, 118, 1 56, and 1 57.
Milk and Milk Products
Background TEQDF-WH098 concentrations in milk were based on data from a recent
study conducted by EPA that utilized the Environmental Radiation Ambient Monitoring
System (ERAMS) for collecting milk samples (Lorber et al., 1998b). ERAMS has 51
sampling stations in 41 of the 50 U.S. States, and Panama and Puerto Rico. Milk samples
from these ERAMS stations collected during four time periods (i.e., April, July, and
October 1996, and January 1997) were composited into duplicate composites samples
(n = 8) and analyzed for the 2,3,7,8-CDD/CDF congeners and dioxin-like coplanar PCBs 77,
105, 118, 1 26, 1 56, 1 57, and 1 69 to generate national estimates of the CDD/CDF
content of milk. In addition, samples from individual ERAMS stations, collected over the
same time period, were analyzed to evaluate geographic or temporal variability in
CDD/CDF/PCB concentrations. Composite samples had a mean TEQDF-WH098 content of
0.98 ppt (I-TEQDF = 0.82 ppt), when nondetects were set to one-half the detection limit,
and 0.97 ppt (I-TEQDF = 0.81 ppt), when nondetects were set to zero. For PCBs, the
lipid-based TEQP-WH098 concentration was 0.49 ppt (TEQP-WH094 was also 0.49 ppt),
when nondetects were set to either one-half the detection limit or zero. These whole milk
samples had a mean lipid content of 3.19 percent. However, not all of the milk consumed
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is whole milk. Therefore, a weighted mean lipid content was calculated, based on the fat
content of whole milk, low fat milk (1.3 percent), and skim milk (0.7 percent), as reported
by U.S. EPA (1997), and the proportion of total milk intake accounted for by these milk
types (USDA, 1995). Based on adult milk intake rates, the weighted milk fat percentage
is 1.8 percent (Table 3-45). Using this weighted lipid content, the whole-weight TEQDF-
WH098 concentration in milk, as consumed, would be 0.018 ppt (i.e., 0.018 times 0.98
ppt) and the TEQP-WH098 concentration in milk, as consumed, would be 0.0088 ppt (i.e.,
0.018 times 0.49 ppt). Congener-specific data for the eight composite samples are
presented in Table 3-46. Little evidence of a temporal trend in TEQDF-WH098
concentrations was observed, based on the results of individual station samples. Results
did, however, suggest a geographic trend with CDD/CDF concentrations in milk being
highest in the southeastern United States and lowest in the southwestern United States.
The TEQDF-WH098 estimates for U.S. milk obtained by this study are consistent with the
levels observed in previous, more limited milk studies (Schecter et al., 1989b; U.S. EPA,
1991).
Additionally, some idea of the total TEQDF level in milk samples can be gained by
assuming that levels in beef fat are similar to levels in milk fat. This assumption implies
that the differences in feeding/raising practices of dairy cattle vs. beef cattle do not cause
substantial differences in CDD/CDF exposure. Beef contains approximately 20 percent
fat, and whole milk is about 4 percent fat. Thus, on a whole food basis, CDD/CDF levels
in beef should be about five times higher than in milk. Support for this concept can be
seen in the German data presented by Furst et al. (1990, 1991). These data show that
the I-TEQDF level is 1.35 ppt in milk fat and 1.08 ppt in beef fat. On this basis, the North
American data for beef (0.20 ppt of TEQDF-WH098) suggest that milk would be about 0.04
ppt of TEQDF-WH098. This value is consistent with the TEQDF-WH098 value obtained from
the recent EPA study that utilized the ERAMS for sampling U.S. milk.
Whole weight TEQDFP-WH098 concentrations for dairy products (other than milk)
were derived from the TEQDFP-WH098 concentrations in milk fat by assuming that the
concentration of CDD/CDFs is the same in fat of dairy products as in milk fat. Whole
weight TEQDFP-WH098 concentrations were calculated by multiplying the milk fat TEQDFP-
WH098 concentrations by the fractional fat content of dairy products. However, because
the dairy products category included a variety of food types (i.e., cheese, yogurt, milk-
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based desserts, etc.), it was first necessary to calculate a fractional fat content value that
is representative of the percentage of fat in the diet, on average, that originates from dairy
products other than milk. This composite fractional dairy fat value was based on dietary
intake data from USDA's 1989-1991 Continuing Survey of Food Intake Among Individuals
(CSFII) (USDA, 1995), and fat content data from USDA's Agricultural Handbook Number 8
(USDA, 1979-1984), as reported in EPA's Exposure Factors Handbook (U.S. EPA, 1997),
as shown in Table 3-47. The composite percent of fat in dairy products was estimated to
be approximately 1 2 percent (i.e., 11.84 percent). Thus, the whole weight TEQDF-WH098
concentration in dairy products was estimated to be 0.12 ppt (i.e., 0.98 ppt [milk fat
concentration] times 0.12). The whole weight TEQP-WH098 concentration was estimated
to be 0.058 ppt (i.e., 0.49 ppt [milk fat concentration] times 0.12).
These values are similar to I-TEQ concentration estimates for dairy products based
on data from Schecter et al. (1992a), Jensen and Bolger (2000), Cooper et al. (1995),
Fiedler et al. (1997c) and Schecter et al. (2001). Schecter et al. (1992a) reported on the
analysis of 2,3,7,8-substituted CDD/CDFs in U.S. dairy products. Cottage cheese, soft
cream cheese, and American cheese samples were selected randomly from New York
supermarkets and analyzed on a wet-weight basis. All dairy products sampled had at least
13 detectable congeners out of the 17 evaluated, and only one congener (1,2,3,7,8,9-
HxCDF) was not detectable in any of the five dairy products. Whole weight l-TEQDFs
ranged from 0.04 to 0.72 ppt, when nondetects were set to one-half the detection limit.
Assuming a fat content of 25 percent for these cheeses, the lipid weight I-TEQDF content
would be 0.16 to 2.9 ppt. This lipid-based concentration range brackets the mean milk
fat concentration observed by Lorber et al. (1998b). Jensen et al. (2000) reported whole
weight TEQDF-WH098 values ranging from 0.082 to 0.38 ppt in various dairy products
when non-detects were set to one-half the detection limit (Table 3-30). When non-
detects were set to zero, TEQDF-WH098 values ranged from 0.0069 to 0.22 ppt (Table
3-30). Cooper et al. (1995) and Fiedler et al. (1997c) also reported on CDD/CDFs in three
samples of Cheddar cheese and three samples of butter. Lipid-based I-TEQDF
concentrations ranged from 0.70 to 0.97 ppt in butter and 0.74 to 0.86 ppt in Cheddar
cheese. Schecter et al. (2001) reported whole weight mean TEQDFP-WHO 98
concentrations of 0.47 ppt in cheese, 0.16 ppt in milk, and 1.1 ppt in butter when non-
detects were set to one-half the detection limit and 0.022 ppt in milk, 0.17 ppt in cheese,
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and 0.82 ppt in butter when nondetects were set to zero. In view of the similarities
between the milk fat I-TEQDF concentrations observed by Lorber et al. (1998b) and the
lipid-based I-TEQDF concentrations observed in dairy products, the use of the national milk
fat data to estimate CDD/CDF concentrations in dairy products from various studies is a
reasonable approach for estimating background concentrations of dioxin-like compounds
for this food group.
Fruits and Vegetables
Data on CDDs and CDFs in U.S. fruit and vegetable products are extremely limited.
The Ministry of the Environment, Ontario, conducted a study of CDDs and CDFs in locally
produced and imported fruits and vegetables, some of which originated in the United
States (Ministry of the Environment, 1988; Birmingham et al., 1989). Samples of fresh
apples, peaches, potatoes, tomatoes, and wheat products were analyzed. In general, the
minimum detection limits for these analyses were less than 1 ppt. The report indicated
that "fruit and vegetable samples were substantially free of PCDD and PCDF residues,
especially the more toxic tetra, penta, and hexachlorinated forms" (Ministry of the
Environment, 1988). OCDD was the only congener detected in any of the samples. One
apple and one peach sample contained detectable OCDD concentrations (8 ppt and 0.6
ppt, respectively). Detectable OCDD concentrations were found at concentrations ranging
from 1 to 3 ppt in potatoes and 0.6 to 0.7 ppt in wheat samples. None of the tomato
samples contained detectable levels of any CDD or CDF congeners. Based on these
results, Birmingham et al. (1989) estimated the l-TEQDFs for fruits, vegetables, and wheat
products to be 0.004 ppt, 0.002 ppt, and 0.0007 ppt, respectively.
As discussed in Volume 3, dioxin contamination of fruits and vegetables is thought
to occur primarily via particle deposition or vapor adsorption onto outer layers with little
penetration to inner portions. Plant uptake from the soil via the roots is generally
considered negligible. However, the work of Hulster and Marschner (1993) indicates that
zucchini and pumpkins were exceptions. For these plant species, it appears that root
uptake occurs and leads to a uniform concentration within the fruit. The concentration of
CDDs and CDFs in zucchini squash grown on "uncontaminated" soil (0.4 ppt I-TEQDF soil
concentration) ranged from 0.5 to 0.7 ppt I-TEQDF dry weight. These reported values may
be converted to whole weight I-TEQDF concentrations by using an assumed moisture
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content of 93.7 percent (USDA, 1979-1984). The resulting range of whole weight
concentrations for zucchini is 0.03 to 0.04 ppt I-TEQDF. Muller et al. (1993) also
evaluated CDDs and CDFs in vegetables (carrots, lettuce, and peas) grown at both
contaminated plots and control plots. For the control plots, the highest levels of CDDs
and CDFs were observed in carrot peels: 0.55 ppt I-TEQDF dry weight, or 0.07 ppt I-TEQDF
whole weight, assuming a moisture content for carrots of 87.8 percent (USDA, 1979-
1984). Lower concentrations were observed in samples from the cortex of the carrots,
indicating that the "contamination source for the peel of carrots is the soil" (Muller et al.,
1993). Lettuce concentrations ranged from 0.1 to 0.4 ppt I-TEQDF dry weight. This is
equivalent to a whole weight concentration range of 0.005 to 0.018 ppt I-TEQDF,
assuming a moisture content of 95.4 percent for lettuce (USDA, 1979-1984).
Concentrations in peas from contaminated plots ranged from 0.04 to 0.12 ppt I-TEQDF dry
weight (0.004 to 0.013 ppt I-TEQDF whole weight, assuming a moisture content of 88.9
percent). Lower concentrations in peas (i.e., close to the detection limit; exact value not
given) were reported for control plots. Similar data for vegetables grown in the United
States were not available.
Recently, Tomoaki et al. (2000) reported on the levels of CDD/CDFs and coplanar
PCBs in leafy vegetables in Japan. Whole weight TEQDF-WH098 concentrations were
0.196 pg/g for spinach (n = 7) and 0.094 pg/g for komatsuna (n = 7). Washing followed
by boiling reduced the total concentrations to 21 percent, 31 to 38 percent, and 60 to 61
percent of the original concentrations for CDDs, CDFs, and PCBs, respectively. Non-
detects were set to zero in the calculations. Kim et al. (2000a) analyzed cabbages and
radishes purchased in Korean markets fro CDD/CDFs. TEQDF-WH098s were 0.082 ppt for
cabbage (n = 1 5) and 0.0013 ppt for radishes (n = 1 5) when non-detects were set to zero.
Kim et al. (2000b) also evaluated cabbages and radishes, and observed similar results.
TEQDF-WH098s were 0.042 ppt for cabbages and 0.007 ppt for radishes. Kim et al.
(2000b) also analyzed pooled fruit samples (i.e., apples, oranges, and tangerines) and
observed an average whole weight TEQDF-WH098 of 0.006 ppt. The TEQDF-WH098 was
0.006 ppt for potatoes and 0.012 ppt for rice.
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Vegetable Oil
High fat levels in vegetable oil suggest that it may be important to consider as a
source of human exposure. Vegetable oils can be made from a variety of plants, including
soybeans, corn, olives, peanuts, sunflower seeds, safflower seeds, linseed, and cotton
seed. Many of these items are protected from atmospheric deposition, which implies that
their CDD/CDF levels would be low. However, Theelen (1991) estimated that vegetable
oil could contribute about 10 percent of a person's total daily intake in The Netherlands
(14 of 120 pg l-TEQ/d). This estimate was based on the Furst et al. (1990) study that
found nondetects for most congeners, except some of the higher chlorinated congeners of
CDD and CDF (detection limit = 0.5 ppt). Half the detection limit was used for the
nondetects, and most of the congeners were not detected. Consequently, the actual
value could be much lower.
Recently, EPA conducted a study to evaluate the levels of CDD/CDFs in vegetable
fats and oils using an adaptation of EPA Method 8290 (Versar, 1996b; Schrock et al.,
1996). A total of 30 oil samples collected from various geographical regions of the United
States were analyzed for CDD/CDFs. Samples included soybean, corn, peanut, canola,
olive, safflower, and sunflower oils, in addition to margarine, solid shortening, and canola
oil spray. OCDD was the only analyte detected in all 30 oil samples above background
levels found in method blanks. Concentrations of OCDD detected in the oil samples
ranged from 3.6 to 33.1 pg/g compared to OCDD method blank levels of 2.8 to 4.4 pg/g.
When subtracting out the appropriate method blank concentrations of OCDD from the
vegetable oil samples, the range of concentrations was 0.2 to 30.3 ppt, with a mean of
5.6 ppt. Detection limits were generally near 1 pg/g for all analytes and ranged from 0.1
to >2 pg/g. None of the oil samples or blanks with detection limits ranging from 0.2 to
1.8 ppt showed 2,3,7,8-TCDD. Other than OCDD, all detections were at or near the
detection limits. Because the occurrences of the CDD/CDFs in vegetable oil were near
detection limits and there was only a small percentage of occurrences overall (not
including OCDD), an average TEQDF-WH098 concentration calculated for the case where
nondetects were set equal to 0 is evaluated as more meaningful than a TEQDF-WH098
concentration calculated for nondetects set equal to one-half detection limit. The mean
TEQDF-WH098 calculated at ND = 0 was 0.056 ppt, and this value was used in calculating
background exposures. By way of comparison, the mean TEQDF-WH098 calculated at ND
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= 1/2 detection limit was 1.5 ppt. The difference between the mean concentration
calculated both ways is much larger than the difference seen for other food products.
This suggests that the detection limits for the vegetable oil were too high to render a
calculation of a mean at one-half the detection limit meaningful; for this reason, the mean
calculated this way is not used in Chapter 4 for calculating background exposures.
TEQp-WH098 concentrations for PCBs were based on data from Mes et al. (1991).
A total of five composite samples of cooking fats and salad oils were analyzed for PCBs.
The total TEQP-WH098 concentration for these samples was 0.037 ppt, whole weight,
based on the geometric mean of positive samples.
3.7.3. European Food
One of the most extensive investigations reported to date that involve testing of a
variety of randomly selected food samples collected within the framework of official food
control have been performed in the Federal Republic of Germany (Beck et al. 1989; Furst
et al., 1990; Malisch, 1998). Detailed results of these studies are included in Appendix B.
Furst et al. (1990) analyzed 107 food samples collected in Germany. The results of this
study are presented in Table 3-48. All samples, except some of the milk, were randomly
collected during official food monitoring programs. The authors speculated that a source
may have been near the areas where the milk samples were collected, because they
appeared higher than other milk tested in Germany, which showed levels around 1 ppt
I-TEQDF. In a later report, Furst et al. (1991) reported that a much larger survey of dairies
in Germany had been completed. This survey analyzed 168 samples of milk and milk
products collected at dairies prior to bottling in 1990. They found an arithmetic mean of
1.35 pg of l-TEQDF/g of fat. l-TEQDFs in these studies were estimated by assuming that
nondetects equaled half the detection limits. Except for 1,2,3,7,8,9-HxCDF and
1,2,3,4,7,8,9-HpCDF, the 2,3,7,8-substituted congeners were detected at a frequency of
greater than 99 percent in these samples (Furst, 1995). In a more recent study, Furst and
Wilmers (1995) compared the levels of CDD/CDFs found in dairy products from 1990 to
the levels in 120 dairy samples collected in 1994. Over the 4-year period, mean I-TEQDF
concentration in milk fat decreased by almost 25 percent from 1.35 ppt to 1.02 ppt.
Similar reductions were noted in human milk fat (Furst and Wilmers, 1995). Furst et al.
(1991) also provided a summary of the results of several European studies. The data
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summaries relevant to background levels in meat and dairy products from Furst et al.
(1991) are presented in Table 3-49. Furst et al. (1991) report that information on CDD
and CDF levels in vegetables and fruits is scarce and that the available data indicate a
background of below 1 ppt.
Beck et al. (1989) analyzed 12 food samples collected randomly from food markets
in West Berlin, Germany. Chicken, eggs, butter, pork, ocean perch, cod, herring,
vegetable oil, cauliflower, lettuce, cherries, and apples were analyzed for CDD/CDFs.
CDD/CDFs were detected in samples of animal origin in the ppq to ppt range (fat weight
basis). No CDD/CDF congeners were detected at a detection limit of 0.01 ppt (whole
weight basis) in samples of plant origin. Mayer (1995) analyzed 27 bulk milk samples
(i.e., background) collected from large dairies in Bavaria, Germany, and 160 milk samples
from farms in the vicinity of suspected dioxin sources between 1989 and 1993.
Background I-TEQDF concentrations ranged from 0.69 ppt to 1.12 ppt, with a mean of 0.9
ppt on a lipid basis. Nondetected congeners were assumed to equal one-half the
detection limit. Few of the potentially impacted samples had I-TEQDF concentrations
exceeding 5 ppt. Malisch et al. (1994) analyzed one background egg sample and one egg
sample from a contaminated site in Germany. The mean I-TEQDF concentrations were 1.2
pg/g fat for the background site and 12.7 pg/g fat for the contaminated site. These
results are based on analyses using four different analytical methods, which showed
similar results.
Malisch (1998) followed up with a more recent study of intake of food in Germany.
In this study, CDD/CDF levels in food from the southwestern part of Germany were
measured between 1993 and 1996. Malisch (1998) analyzed 1,414 food samples for
CDD/CDF concentrations. The results indicated that the more recent I-TEQDF
concentrations are lower than those previously observed by Furst et al. (1991) and Furst
et al. (1990) (Table 4-50).
Schmid and Schlatter (1992) found low background levels of CDD/CDFs in milk
samples from Switzerland. A total of 28 cow's milk samples and 1 goat's milk sample
were collected during 1990 and 1991 from industrial dairies and from both rural alpine
sites and potentially impacted sites (i.e., highly industrialized areas, and areas with waste
incineration and metal recycling). Due to insufficient analytical sensitivity, 2,3,7,8-TCDD
was not detected; thus, an assumed concentration was used for 2,3,7,8-TCDD in
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calculating l-TEQDFs. The lowest I-TEQDF concentrations were observed in samples from
rural and alpine areas. The mean lipid-based I-TEQDF for these seven milk samples ranged
from 0.70 ppt to 3.28 ppt. The goat milk sample had a I-TEQDF concentration of 0.88
ppt. Based on pooled milk samples from nine industrial dairies in Switzerland, the average
lipid-based I-TEQDF was 1.31 ppt. This is approximately equivalent to a whole weight I-
TEQDF concentration of 0.05 ppt, assuming a lipid content of 4 percent for these samples.
No significant differences were observed between samples stored in cardboard containers
and those stored in glass bottles. Higher I-TEQDF concentrations were observed in
samples collected from potentially impacted sites (2.02 ppt to 4.85 ppt on a lipid basis).
In 1996, the French Ministry of Agriculture, Fisheries and Food Products (FMAFFP)
(1997) undertook a study to investigate "background" levels of CDD/CDFs in the French
Republic (FMAFFP, 1997; Defour et al., 1997). This followed a 1993/94 FMAFFP study
examining dioxin levels found in French milk samples collected from areas near known
polluting industries. In this 1996 study, 40 dairy products, including cheese, butter, milk-
based desserts, and cream, were sampled from 34 regions of France. Also, 12 cow's milk
samples were collected from different locations in two regions (i.e., Seine-Maritime and
Pas-de-Calais). These two regions had shown elevated levels over other regions of the
country in the previous study, which targeted areas near pollution sources. The seven
milk samples from the Seine-Maritime region had a mean lipid-based I-TEQDF of 1.77 ppt.
The five milk samples from the Pas-de-Calais region had a mean I-TEQDF of 2.13 ppt.
Each of these regions had one sample with a I-TEQDF greater than 3.0 ppt; whereas, the
remaining samples clustered together at levels around 1.5 ppt and 1.9 ppt, respectively.
The congener profiles of the milk samples showed a predominance of OCDD, and the
furans typically were higher than the dioxins. Table 3-51 presents the mean I-TEQDF levels
of the dairy products tested by the FMAFFP. The lipid-based I-TEQDF values of the eight
butter samples ranged from 0.51 ppt to 2.10 ppt (mean of 1.01 ppt). The lipid-based I-
TEQDF values of the 20 cheese samples ranged from 0.54 ppt to 1.44 ppt (mean of 1.11
ppt). The 12 fresh cream and milk-based desserts had lipid-based I-TEQDF values ranging
from 0.78 ppt to 3.15 ppt (mean of 1.34 ppt). The sample from the Nord region had an I-
TEQDF value of 3.15 ppt, which was significantly higher than the other cream or milk-
based dessert samples, possibly reflecting industrial sources in the region. The mean
value of the remaining 11 samples was 1.18 ppt I-TEQDF.
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Theelen et al. (1993) collected food products from various locations in The
Netherlands and analyzed them for 2,3,7,8-chlorine substituted dioxins, furans, and planar
PCBs. Meat samples were collected from slaughter houses throughout The Netherlands.
Fish, mixed meats, and cheeses were gathered at various grocery stores. Mixtures of
foods in these categories were prepared based on the proportion of the average annual
consumption rate that different food items in these categories represented. The food
industry provided purified oils and fats. Mixtures of these items were also prepared in
proportion to their annual use in The Netherlands. The concentrations of CDD/CDFs in
these food products are presented in Table 3-52.
Food samples were collected in 1996 from both local markets and supermarkets
from Catalonia, Spain (Domingo et al., 1999). A total of 35 food samples were collected
and analyzed for CDD/CDF concentrations. The food samples included various types of
beef, pork, chicken, lamb, fish, seafood, canned fish, milk and dairy products, vegetables,
cereals, fruits, fats and oils, and eggs. The lipid-based and wet-weight I-TEQDF
concentrations in these foods are presented in Table 4-53. As shown in Table 4-53, both
the lipid-weight and wet-weight I-TEQDF concentrations were highest in fish and seafood.
It is interesting to note that reported concentrations of CDD/CDFs in fruits, vegetables,
and cereals were similar to those observed for meat and dairy products. However, the
number of samples collected from each food group was not reported, and the method
used for treating non-detects in calculating total l-TEQDFs was not reported.
CDDs and CDFs have been studied in dairy products in Spain. Ramos et al. (1999)
analyzed butter samples for CDD/CDFs/PCBs and estimated TEQDFP-WH094s. Eight of the
best known brands of butter were purchased from Spanish supermarkets. A total of 21
samples were analyzed. The results of the study indicated that the I-TEQ averages were
0.41 ppt for CDDs, 0.70 ppt for CDFs, and the TEQP-WH094 average was 0.09 ppt for
PCBs. The most toxic CDD congener, 2,3,7,8-TCDD, was found at detectable levels in
15 of the 21 samples analyzed and the most toxic CDF congener, 2,3,7,8-TCDF, was
found in all samples. Ramos et al. (1999) did not report whether non-detects were set to
zero or one-half the detection limit in calculating TEQs.
In the early 1990s, the United Kingdom's Ministry of Agriculture, Fisheries, and
Food (MAFF) conducted a survey of CDD/CDFs in foods collected as part of their Total
Diet Study (MAFF, 1992). Food samples were collected in 1988 from two UK locations:
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Port Talbot and Stonehaven, selected to represent an urban/industrial site and a rural site,
respectively. Additional samples were collected in Norwich. Selected food items
included: meat and meat products, milk products, fish, fats and oils, eggs, and fruits and
vegetables. l-TEQDFs were calculated by setting nondetects to either zero or to the limit of
detection to provide a range of possible I-TEQDF values for each food item. Results of
these analyses are presented in Table 3-54 in terms of total l-TEQDFs. Higher I-TEQDF
concentrations were found in fatty food products, such as fish, meats, and fats and oils,
than in food items with lower fat contents, such as fruits and vegetables. For some food
items, higher I-TEQDF concentrations were observed at the urban/industrial area than at the
rural site. Milk samples were collected from farms in rural/remote areas that were not
expected to be impacted and from farms closer to urban/industrialized areas. In addition,
retail milk samples were collected in 1990 from several UK locations during both winter
and summer months. The results of these analyses are presented in Table 3-55. After
laboratory procedures were developed for the fractionation of ortho and nonortho
substituted chlorobiphenyls, Kroskos et al. (1996) analyzed frozen aliquots of the same
1990 milk samples for PCB congeners. The mean concentration for seven congeners
(PCBs 28, 52, 101, 118, 138, 153, and 180) was 0.26//g/kg whole milk or 6.7//g/kg on
a lipid basis assuming a typical fat content of 3.9 percent for cow's milk. The mean
TEQP-WH094 concentration of PCBs 77, 118, 1 26, 1 69, and 1 80 in the milk samples was
0.06 ppt whole milk. PCB 11 8 and 1 26 accounted for 98 percent of the total PCB TEQ
(Kroskos et al., 1996). Additional MAFF analyses of Total Diet Study samples examining
temporal trends in dietary intake are summarized in Chapter 6.
Foxall et al. (1995) analyzed samples of fruits and vegetables from urban and rural
areas in Wales and England for CDD/CDF and PCB residues. The study was initiated as a
result of concerns over elevated CDD/CDF and PCB concentrations in the air and soil in
the vicinity of a chemical waste incinerator. Samples were collected from gardens at five
sites within a 1.5-mile radius of the incinerator and from five similar sites in three rural
areas for comparison. The produce evaluated included apples, courgettes, lettuce, and
potatoes. Median I-TEQDF concentrations ranged from 0.3 ppt to 0.4 ppt for the four fruit
and vegetable products, when nondetects were set to the limit of detection. In addition,
no significant differences were observed between CDD/CDF and PCB concentrations in
produce taken from urban and rural sites. The authors noted that CDD/CDF
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concentrations in produce directly exposed to atmospheric deposition (i.e., apples and
lettuces) are not significantly different from root vegetables (Foxall et al., 1995).
The Ministry of Agriculture, Fisheries, and Food (MAFF) also analyzed commercially
available cow's milk for dioxins and PCBs in samples collected in 1995 from 12 locations
in England (MAFF, 1997a). The locations were chosen to be representative of the
different regions. Full fat milk purchased in glass bottles was tested. Lipid-based
CDD/CDF concentrations ranged from 0.67 to 1.4 ppt I-TEQDF. Lipid-based PCB levels
ranged from 0.75 to 2.3 ppt TEQP-WH094. Concentration levels of dioxins found in the
1995 samples were lower than those found in a comparable MAFF survey conducted on
milk collected during 1990 (MAFF, 1992). (See Section 6.5, Temporal Trends in Food
Products, for details.)
In a Lancaster University study, Stewart and Jones (1996) also examined PCBs in
cow's milk from rural and urban dairy farms in the northwest of England. Sites were
chosen to be representative of farms providing milk for human consumption. Stewart and
Jones (1996) sampled pooled milk taken from 10 herds between 1993 and 1994. The
sum of the lipid based levels of PCBs 77, 105, 118, 1 26, 1 56, 1 69, 1 70, and 1 80 ranged
from 1.2 to 2.1 ppt TEQP-WH094.
The Ministry (MAFF) has also studied dioxin levels in samples of cow's milk from
individual farms located around known emission sources in the United Kingdom annually
since 1993. Additionally, beginning in 1994, the samples were analyzed for PCBs.
Between 1993 and 1995, MAFF collected samples from 93 farms in the vicinity of 29
industrial sites (MAFF, 1997b). The concentration of dioxins in the cow's milk (for
sample years 1993-1995) ranged from 0.87 to 11 ppt TEQ milk fat. In all but two of the
samples, dioxin levels were within or below the normal range of 1.1 to 7.1 ppt I-TEQ milk
fat previously described for the United Kingdom (MAFF, 1992). Two samples with
slightly elevated results were obtained from farms in the vicinity of a municipal waste
incinerator, which closed in 1996 due to noncompliance of plant emission standards. PCB
levels (sample years 1 994-1 995) ranged from 1.1 to 9.3 ppt TEQP-WH094 lipid. In 1 996,
the Ministry sampled 26 farms in the vicinity of 7 industrial sites and found dioxin levels
ranging from 0.81 to 8.6 ppt TEQP-WH094 milk fat. PCB concentrations ranged from 1.2
to 8.0 ppt TEQP-WH094 milk fat (MAFF, 1997c).
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The Ministry (MAFF) also analyzed 40 samples of cow's milk obtained from 20
dairies and farms in Northern Ireland during 1993 and 1994 (MAFF, 1997d). Sites were
chosen to be representative of all the regions of Northern Ireland, and the samples were
collected in polypropylene containers. Lipid-based dioxin concentrations in the retail
samples from dairies ranged from 0.74 to 2.7 ppt I-TEQDF lipid (mean = 1.2 ppt I-TEQDF),
and the concentrations in individual farm samples ranged from 0.84 to 3.0 ppt I-TEQDF
lipid (mean = 1.2 ppt I-TEQDF). l-TEQDFs were calculated by setting nondetects to the
limit of detection.
Vartiainen and Hallikainen (1994) conducted a survey of CDD/CDFs in cow's milk
from Finland's largest dairies, eggs from major Finnish producers, and meat (pork and
bovine) from major Finnish slaughter houses. Twenty samples in each food category were
analyzed for CDD/CDFs. Low levels of CDD/CDFs were observed in cow's milk. Based on
Nordic TEFDFs (N-TEFDFs), the mean lipid-based Nordic TEQDF (N-TEQDF) concentrations
were 0.83 ppt for milk stored in glass bottles, 1.17 ppt for milk stored in paper milk
cartons, and <0.5 ppt for meats. Whole-weight N-TEQDF concentrations in eggs averaged
0.12 ppt. The method used for treating nondetects in calculating these mean N-TEQDFs
was not described in the paper. Also, use of N-TEQDFs instead of l-TEQDFs adds
uncertainty to the interpretation of these data.
Himberg (1993) analyzed Finnish food samples for dioxin-like PCBs 77, 105, 126,
and 169 to estimate the average daily intake of these PCBs. A total of 34 food samples
were collected from food stores in the city of Helsinki. Concentrations of these PCBs in
foods are presented in Table 3-56. Concentrations were higher in fish than meat by
approximately one order of magnitude. Van Rhijr et al. (1993) analyzed 39 cow's milk
samples from various agricultural, industrial, and impacted sites in The Netherlands for
dioxin-like PCBs 77, 126, and 169 and CDD/CDFs. Mean lipid-based l-TEQDFs ranged
from 0.8 to 1.8 for agricultural sites, 2.7 ppt for an industrial site, and 3.6 ppt to 7.7 ppt
for sites near municipal waste incinerators. Lipid-based average TEQP-WH094 ranged from
1.1 ppt to 1.9 ppt for agricultural sites, 2.1 ppt for the industrial site, and 1.8 pt to 4.5
ppt for sites near municipal waste incinerators.
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3.7.4. Eastern European and Asian Food
Schecter et al. (1990, 1992b) analyzed foods collected from sites within the
former Soviet Union between 1988 and 1990 for CDD/CDF residues. A total of 31
samples were collected from markets and restaurants in four cities (i.e., Moscow, Irkutsk,
Novosibirsk, and Baikalsk). Fish samples were collected from local rivers in these areas.
The study compared CDD/CDF levels from Moscow, which represented an area that had
been industrialized for a long period of time, and Siberian cities, where industrialization
had occurred more recently. Fathead minnow samples were collected from Lake Baikal in
Baikalsk, because it was believed that they may have been impacted by a nearby pulp and
papermill. Results of these analyses are presented in Table 3-57. l-TEQDFs ranged from
0.02 to 0.7 ppt wet weight for samples from Moscow, 0.04 to 0.8 ppt wet weight for
samples from Irkutsk, 0.005 to 0.8 ppt wet weight for samples from Novosibirsk, and 0.9
to 1.4 ppt wet weight for samples from Baikalsk. Schecter et al. (1993b) analyzed foods
from the same cities within the former Soviet Union for PCBs and organochlorine
pesticides. PCB 180 was the only PCB congener analyzed for which a TEFP-WH094 had
been developed. Foods analyzed included pork, poultry, fish, beef, lamb, and cheese.
PCB 180 in these samples ranged from less than 0.001 to 0.007 ppm on a lipid basis.
Amirova et al. (1997) reported on the results of a 1996 examination of I-TEQDF
levels in 17 foods purchased from food stores in Ufa, a town in the agricultural region of
Bashkortostan in Russia. Foods were selected that contained high fat contents, such as
dairy products, fish, beef, port, poultry, and vegetable oil (Amirova et al., 1997). The
highest I-TEQDF concentrations, on a lipid basis, were found in freshwater fish (9.2 ppt),
cream (5.45 ppt), and milk (3.32 ppt). Using data on the food consumption patterns of
both rural and urban regions of the Republic of Bashkortostan, the dietary intake of
l-TEQDFs for the urban population of Bashkortostan was calculated to be 2.31 pg/kg/day,
while the rural population was estimated to have a lower level of 1.15 pg/kg/day.
Olie et al. (1989) analyzed food and wildlife samples from North and South
Vietnam for CDD/CDF residues. Samples were collected between 1985 and 1987 from
markets, fishermen, and women in the fields. The collection protocol used non-random
sampling and did not provide a statistically representative sample of foods in these regions
of the country. However, based on the limited number of samples collected, the study
results suggest that food samples collected in the South contain higher levels of
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CDD/CDFs than samples collected in the North. The authors suggest that these
differences may be due, in part, to differences in the level of industrialization in these
regions (i.e., the South is more industrialized than the North), and the spraying of South
Vietnam with Agent Orange during the Vietnamese War (Olie et al., 1989).
Concentrations of CDD/CDF in North Vietnamese food and wildlife samples ranged from
0.26 ppt I-TEQDF wet weight (catfish) to 3.30 ppt I-TEQDF wet weight (chicken fat) and
3.51 ppt I-TEQDF wet weight (cow fat) (Olie et al., 1989). For these same food products
(i.e., catfish and chicken fat; cow fat was not analyzed), I-TEQDF concentrations in South
Vietnamese food samples were higher (i.e., 5.68 and 31.54 ppt I-TEQDF wet weight,
respectively) than those observed in samples collected from North Vietnam. The highest I-
TEQDF concentrations in South Vietnamese wildlife were observed in turtle ovaries (85.71
ppt I-TEQDF wet weight). Schecter et al. (1990) reported the levels of PCBs in samples of
pork and chicken collected in Vietnam. PCB 180 was the only PCB congener analyzed for
which a TEFP-WH094 had been developed. PCB 180 ranged from less than 2.0 to 2.0 ppb
on a lipid basis in pork and 3.0 to 4.0 ppb on a lipid basis in chicken.
3.7.5. Effects of Cooking and Trimming, or Processing on Residue Levels in Foods
Data on the effects of cooking on the levels of dioxin-like compounds in food
products are limited, and the available data on this subject are somewhat contradictory.
Cooking losses of dioxin-like compounds are reported in the literature in two ways. One
method calculates losses by comparing total residues in a sample before cooking to total
residues after cooking (i.e., by comparing total micrograms of dioxin-like compounds in
raw and cooked foods). The other method calculates losses on the basis of the sample
weight (i.e., by comparing the concentrations of residues in raw and cooked food).
Losses of total residues are often accompanied by similar losses of water and/or fats.
Thus, although total residues are reduced by cooking, concentrations based on the
uncooked and cooked sample weights show little difference. Because dietary doses of
dioxin-like compounds are calculated on the basis of dioxin residues in uncooked foods
and intake rates of as-eaten (i.e., cooked) foods, changes in the concentrations of dioxin-
like compounds from cooking would be relevant to these calculations. In contrast, losses
of total residues, although an interesting phenomena, would have less effect on the
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results of these estimates. This section summarizes some of the data on the effects of
cooking on dioxin-like residues in foods, as reported in the scientific literature.
Stachiw et al. (1988) evaluated changes in the residue levels of 2,3,7,8-TCDD in
fish from cooking. Restructured carp fillets (i.e., fabricated fish products that use
mechanically deboned fish) from Saginaw Bay, Michigan, measuring either 7.5-cm or 10-
cm diameters, with a uniform thickness of 1 cm, were roasted (covered or uncovered) or
charbroiled to internal end temperatures of 60°C, 70°C, or 80°C. Both spiked samples
(i.e., spiked to levels approximating 100 ppt) and control samples (i.e., unspiked,
containing levels ranging from 37 to 45 ppt) were tested for residues of 2,3,7,8-TCDD
before and after cooking. Samples were also tested for total cooking losses (i.e., losses
of fat and moisture). Cooking losses ranged from approximately 5 to 20 percent,
depending on the cooking method. Results of the TCDD residue analyses indicated that
both cooking methods resulted in significant reductions of total 2,3,7,8-TCDD residues
(Table 3-58). Reductions of total TCDD residues by cooking ranged from 34.2 percent to
67.5 percent for control samples and 44.2 percent to 70.6 percent for spiked samples
(Stachiw et al., 1988). These percentage reductions were not significantly different for
control vs. spiked samples. Thus, the concentration of TCDD in the raw samples did not
appear to have a significant impact on the percent reduction by cooking. However,
increasing the end point temperature and surface area of the sample resulted in
significantly increased losses. Also, TCDD losses were two to eight times greater than
total cooking losses (i.e., fat and moisture losses) (Stachiw et al., 1988). When TCDD
levels were evaluated on a ppt whole weight concentration basis, reductions ranged from
24 to 60 percent for control samples and 38 to 65 percent for spiked samples.
In a similar study, Zabik et al. (1979) compared the reductions in PCBs in lake trout
achieved by various cooking methods. Samples of fat trout from Lake Superior, Michigan,
were analyzed before and after broiling, roasting, or microwave cooking. Significant total
residue losses of xenobiotics were observed for all cooking methods (i.e., 26 to 53
percent for PCBs). However, when PCB losses were calculated on a wet weight
concentration (ppm) basis, losses ranged from 5 to 1 6 percent, depending on the cooking
method. On a fat weight concentration basis (ppm), PCB losses ranged from 1 6 to 40
percent, depending on the cooking method. In a later study, Zabik et al. (1982) found
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that cooking was not an effective means of reducing the total residues or concentrations
of xenobiotics in the edible tissue of carp from Saginaw Bay.
Poston et al. (1994) and Moya et al. (1997) found that total PCB levels in winter
flounder from New Bedford Bay, Massachusetts, were reduced by one cooking method,
but not others. Significant reductions in total PCB residues was observed in deep fried
fish, but not in fillets pan fried or broiled. Similar results were obtained on an individual
congener basis. Deep frying reduced total PCB levels 47 percent. However, deep-fried
fillets also showed a weight loss of approximately 40 percent. Pan fried fillets showed a
much lower percent reduction in total PCB residues. Water losses were also significantly
lower for these two cooking methods (i.e., 7 percent and 15 percent, respectively) (Moya
et al., 1997).
Smith et al. (1973) reported that the total residue levels of PCBs (Aroclor 1248 and
1254) in chinook and coho salmon steaks from Lake Michigan were reduced only slightly
by baking and poaching; however, Cichy et al. (1979) observed significant losses (38 to
43 percent) of PCBs in Michigan lake trout that were irradiated and broiled. Losses were
calculated by comparing the total micrograms of PCBs in raw and cooked fish. Pan-frying
of white croaker fillets from Santa Monica Bay and Orange County, California, resulted in
total PCB (Aroclor 1242 and 1254) losses of 65 percent and 28 percent, respectively
(Puffer and Gossett, 1983). Trotter et al. (1989) found that, on the basis of
concentration, PCB levels in cooked and uncooked bluefish were similar. However, when
the results of cooked samples were corrected for moisture losses and compared to raw
samples based on total residue levels, PCBs were found to be reduced by 27 percent.
Based on a study of Atlantic bluefish collected near Long Island, New York,
Armbruster et al. (1989) concluded that trimming resulted in the largest reductions in PCB
residues in fish. Armbruster et al. (1989) reported that trimming bluefish fillets resulted in
an average total PCB residue reduction of 59.4 percent and that baking, broiling, frying, or
poaching resulted in further losses averaging only 7.5 percent. The magnitude of
reduction observed for the various cooking methods (combined with trimming) did not
differ significantly. In addition, Armbruster et al. (1989) analyzed oil drippings released
during cooking, and found that the total PCB residues in the oil did not account for the
total losses of PCBs that occurred during cooking. Based on these results, Armbruster et
al. (1989) concluded that "PCB losses by vaporization during the various cooking
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procedures may have constituted the major portion of the mean total (7.5 percent) loss
from cooking." Skea et al. (1979) also reported that trimming resulted in significantly
greater reductions in PCB concentrations in fish than cooking. Skea et al. (1979)
evaluated the effects of trimming and various cooking methods on the residue levels of
PCB, Mirex, and DDT in brown trout and smallmouth bass collected from Lake Ontario.
PCB results from this study are summarized in Table 3-59 according to the percent
reduction in concentration (//g/g) and total reduction of PCB residues (i.e., calculated by
comparing the total micrograms of PCBs in raw and cooked fish). Trimming alone resulted
in total percent reductions of approximately 80 percent. Of the cooking methods, deep
fat frying resulted in the greatest additional reductions in PCB residues Skea et al. (1979).
Zabik (1974) found that cooked chicken pieces had significantly lower
concentrations of PCBs than raw pieces. Ten hens that had been fed PCB Aroclor 1 254
were slaughtered, split in half, and cut into several pieces (i.e., drumsticks, breasts, thigh,
etc.). Pieces obtained from one-half of the chicken were analyzed raw, and pieces from
the other half were analyzed after stewing or pressure cooking. The two cooking methods
resulted in similar losses of PCBs (Table 3-60). Cooking resulted in greater losses from
abdominal adipose tissue and thigh skin. These pieces also had the highest fat content.
Recovery of PCBs was calculated by comparing the levels of Aroclor in cooked chicken
and broth to the levels in raw chicken. Percentage recoveries ranged from 60 to 95
percent.
Schecter et al. (1996) studied the effects of cooking on CDD/CDF levels in
hamburger. Ground beef was purchased from a supermarket in Binghamton, New York,
and divided into eight samples. Four samples were analyzed for CDD/CDFs uncooked, and
the other four samples were broiled and then analyzed for CDD/CDFs. Cooking produced
a 42 to 49 percent decrease in l-TEQDFs per hamburger. However, this decrease was
identical to the decrease in weight due to cooking. Thus, reduction in CDD/CDFs in
hamburger are due to loss of fat and water during cooking. The l-TEQDFs calculated on a
whole weight concentration basis were 0.128 to 0.134 ppt for uncooked samples and
0.116 to 0.145 ppt for cooked samples. These results indicated little change in total
I-TEQDF concentrations from cooking, despite the significant losses observed on the basis
of total residue level.
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Schecter et al. (1999) recently extended their studies to examine the effect of
broiling on CDD, CDF, and co-planar PCB levels in hamburger, bacon, and catfish. The
samples were again purchased from a supermarket in Binghamton, New York, and each
food type was divided into nine samples. Five samples of each food type were broiled in
an electric oven (one of each cooked food type sample was consumed to guarantee
edibility), and the other four samples were analyzed uncooked. Reductions of
approximately 50 percent in the total CDD/CDF/PCB TEQ levels (using l-TEFs for
CDD/CDFs and TEFP-WH094 for PCBs) were observed in the broiled samples. However,
after adjusting for moisture losses due to broiling (i.e., looking at concentration on a
weight basis), the following TEQ concentrations of CDD/CDF/PCBs were observed:
(1) TEQ concentrations in the hamburger remained the same (about 0.155 ppt); (2) TEQ
concentrations in the bacon increased 84 percent (from 0.079 ppt uncooked to 0.145 ppt
cooked); and (3) TEQ concentrations in the catfish decreased by 34 percent (from 0.577
ppt uncooked to 0.378 ppt cooked).
Petroske et al. (1997) studied the effect of pan frying on CDD/CDF concentrations
in ground beef. Samples were collected from four control animals and four dosed (16
congeners) cattle in a dioxin/furan feeding experiment. Muscle tissue (rib eye) and back
fat from the cattle were blended into patties containing approximately 20 percent fat. The
patties were then cooked in a stainless steel frying pan to an internal temperature of
74°C. During the cooking process, the fats, juices, and volatiles that formed were
collected on an inverted funnel placed over the frying pan, draining back into the pan.
These volatiles were analyzed along with the cooked patties. Analysis of the samples
showed significant reductions in total congener residue levels (pg/patty) after pan frying
(assuming the fats and juices are not consumed). Decreases after cooking ranged from
approximately 21 to 50 percent for the control samples and approximately 31 to 50
percent for the dosed animals. The majority of reductions after pan frying, for both types
of sample, were in the 40 to 50 percent range, which is similar to the reductions observed
by Schecter et al. (1996; 1999) after broiling. The findings were not analyzed on a
concentration (i.e., ppt per unit weight) basis, however. Consistent with the findings of
Stachiw et al. (1988), the concentration of CDDs and CDFs in the raw samples did not
appear to have a significant impact on the percent reduction by pan frying. Taking into
consideration the CDD/CDFs found in the volatiles and juices released during the cooking
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process, between 6 and 16 percent of the dosed CDD/CDF congeners were unaccounted
for after cooking. Unaccounted losses were greater for the lower chlorinated dioxin
congeners than for the more highly chlorinated congeners, while the opposite was
observed with the furans. Petroske et al. (1997) hypothesized that "pan frying of ground
beef patties, and likely non-patty ground beef significantly reduces the quantity of dioxin
and furan congeners consumed if the fat and juices are discarded, while congeners
releases as volatiles may pose a secondary mode of human exposure."
As indicated previously, Tomoaki et al. (2000) found that washing followed by
boiling significantly reduced the concentrations of CDD/CDFs and PCBs on leafy
vegetables. Concentrations were reduced to approximately 20 percent, 30 to 40 percent,
and 60 percent of the original concentrations of CDDs, CDFs, and PCBs, respectively.
To evaluate the potential for contamination of foods with CDD/CDFs during the
curing process, Mayer (1998) examined CDD/CDFs levels in 41 smoked ham samples
produced in southern Germany, and compared the results to CDD/CDFs in 21 untreated
pork samples. Only the results from samples of the outer layer of the smoked ham, about
1 cm thickness, were included in the comparison because it was assumed that CDD/CDFs
derived from the curing smoke would be concentrated in this surface. I-TEQDF
concentrations ranged from 0.08 pg/g fat to 85 pg/g fat, with an average of 6.2 pg/g fat
in the smoked ham samples. In comparison, the I-TEQDF concentrations in the untreated
pork samples were between 0.09 pg/g fat and 1.2 pg/g fat, with an average of 0.31 pg/g
fat. The median I-TEQDF levels for these two groups were approximately the same; 0.33
pg/g fat for the outer parts of smoked ham samples and 0.31 pg/g fat for the untreated
pork samples. Most pork samples had lipid-based I-TEQDF concentrations below 0.5 pg/g.
A total of 61 percent of the smoked ham samples, compared to 90 percent of the
untreated pork samples, had I-TEQDF concentrations lower than 0.5 pg/g fat. Mayer
(1998) also presented I-TEQDF levels based on all edible parts of the smoked ham samples.
The results indicated that 30 samples had I-TEQDF concentrations lower than 0.04 pg/g; 8
samples showed levels between 0.06 and 0.35 pg/g; and 3 samples were highly
contaminated with l-TEQDFs ranging from 1.7 to 3.7 pg/g. CDD/CDF homologue group
profiles illustrated that smoked ham with low CDD/CDF contamination had a similar profile
to that of untreated pork. Smoked ham with elevated levels of CDD/CDFs had a similar
profile to that of highly contaminated ham, but a quite different profile to that of untreated
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pork. Mayer (1998) indicated that the curing process may be a source of CDD/CDFs in
smoked ham.
Results of the preceding studies suggest that processes such as smoke curing may
increase CDD/CDFs in foods. Cooking may significantly reduce total residues of dioxin-like
compounds in foods. However, the data reported on the basis of concentration are
somewhat contradictory. Schecter et al. (1996, 1999) observed that concentrations of
dioxin-like compounds in beef were not significantly affected by cooking, but the effects
of cooking were more significant in bacon and fish. Skea et al. (1979) also observed
significant concentration changes of PCBs in fish. In contrast, the Zabik et al. (1979,
1982) studies did not show significant losses of PCB concentrations in fish from cooking.
Therefore, based on the existing data, it is not possible to draw conclusions with regard to
reductions in food concentrations of dioxin-like compounds from cooking. As a result,
potential reductions in concentrations from cooking are not accounted for in Chapter 4 for
the purpose of estimating dietary intake of dioxin-like compounds.
3.7.6. Food Observations and Trends
Some general observations for CDD/CDF levels are possible from the data
presented in the various food product studies above:
• TEQ concentrations of CDD/CDFs and PCBs are similar in the studies from
the United States and Europe.
• CDD/CDF levels in the higher chlorinated congeners (i.e., HpCDDs and
OCDD) are present in higher concentrations than the lower chlorinated
congeners. In the higher chlorinated congeners, CDD levels are present in
greater concentrations than the CDFs.
• PeCDD is frequently the highest contributors to total TEQs in foods.
• Food products of animal origin (i.e., fish, meat, eggs, and dairy products),
which have a high fat content, have a higher concentration of CDD/CDFs
than those food products that have lower lipid contents.
• Generally, of all the food products, fish and shellfish contain the highest
levels of CDD/CDFs.
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3.7.7. Food CDD/CDF Congener Profiles and Background TEQ Concentrations
The 2,3,7,8-substituted congener profile for various foods are presented in Table
3-61 and Figures 3-7 through 3-10. These profiles are calculated as the ratio of individual
congener concentrations to the sum of concentrations for all of the 2,3,7,8-substituted
congeners and are based on data from the studies discussed previously and footnoted on
Table 3-53. Profiles for beef, milk, and dairy products are similar, with 1,2,3,4,6,7,8-
HpCDD and OCDD dominating the profiles in nearly equal proportions. In contrast, OCDD
is the single dominant congener in both chicken, eggs, and pork.
U.S. food data on CDD/CDFs are summarized in Table 3-62. Background TEQDF-
WH098 estimates are presented first assuming that nondetects equal half the detection
limits and second assuming that nondetects equal zero. Large national surveys conducted
by EPA/USDA (i.e., beef, pork, poultry, and milk) provide an adequate basis for estimating
the concentrations of dioxin-like compounds believed to be representative of background
levels in U.S. foods. For some food groups, however, the small sample size and high
number of nondetects provide an uncertain basis for estimating national background
levels. Overall, the general agreement between the national U.S. estimates and the food
level estimates for Canada and Europe provides some reassurance that the U.S. values are
reasonable. For the purposes of calculating background exposures to CDD/CDFs via
dietary intake, the upper-range background TEQDF-WH098 (i.e., those calculated using one-
half the detection limit for the nondetects) were used, except for vegetable oil. (See
Chapter 4.)
North American food data on PCBs are summarized in Table 3-63. These data are
used in Chapter 4 to estimate background dietary exposures to PCBs.
3.8. SUMMARY OF CDD/CDF AND PCB LEVELS IN ENVIRONMENTAL MEDIA AND
FOOD
This chapter summarizes data on CDD/CDF and PCB levels in environmental media
and food with emphasis on "background levels." Data representative of background
conditions in environmental media are considered to be those collected in rural, pristine,
and urban (soil and air only) areas not believed to be impacted by any local sources (e.g.,
incinerators).
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The mean background levels for the various environmental media and foods
presented in this chapter are summarized in Table 3-64. These total background level
TEQDFP-WH098 are used in Chapter 4 to estimate typical exposure levels in the United
States. Standard deviations of the total mean TEQDF-WH098 for each media were also
calculated to depict the "range" of probable CDD/CDF levels in various media. For media
for which complete congener-specific data for multiple samples from the same study (i.e.,
beef, pork, poultry, milk, dairy, and sediments) were available, means and standard
deviations were calculated by conventional methods. However, for media for which mean
total TEQDF-WH098 were calculated by summing congener-specific TEQDF-WH098 from
multiple studies (e.g., soil, air), the use of typical methods for calculating standard
deviations was not possible. Therefore, standard deviations were based on the standard
deviation of the congener that contributed most to the total TEQDF-WH098. The
percentage deviation from the mean for that congener was applied to the total mean
TEQDF-WH098 for all congeners combined. The congeners selected for use in the standard
deviation estimates are presented in Table 3-65. The data in this table indicate that the
pentachlorinated dioxins and furans were frequently the highest contributors to total
TEQDF-WH098 in foods and other environmental media in the United States. Standard
deviations for fish could not be calculated because of the weighing method used in
developing the average background concentration for fish. Media levels presented in
Table 3-61 are shown graphically in Figure 3-11. Table 3-61 illustrates that of all the food
products, levels (whole weight basis) of CDD/CDF/PCBs are highest in freshwater fish.
DRAFT-DO NOT QUOTE OR CITE 3-104 December 2003
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DRAFT-DO NOT QUOTE OR CITE 3-123 December 2003
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Table 3-1. Mean CDD/CDF Ambient Air Concentrations from Sites Located Upwind and Downwind of an Industrial Site
2,3,7,8-TCDD
1,2,3,7,8-PeCDD
1,2, 3,4,7, 8-HxCDD
1,2,3,6,7,8-HxCDD
1,2,3,7,8,9-HxCDD
1,2,3,4,6,7,8-HpCDD
OCDD
2,3,7,8-TCDF
1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
1,2, 3,4,7, 8-HxCDF
1,2,3,6,7,8-HxCDF
2,3,4,6,7,8-HxCDF
1,2,3,7,8,9-HxCDF
1,2,3,4,6,7,8-HpCDF
OCDF
TOTAL I-TEQDF
TOTAL TEQDF-WH098
Upwind (Background)
No. of Positive
Samples
0/3
0/3
0/3
1/3
1/3
3/3
15/16
0/3
0/3
0/3
1/3
1/3
1/3
0/3
1/3
12/16
--
--
Mean
Concentration
nd = 1/2 LODa
(pg/m3)
0.0070
0.0070
0.0070
0.015
0.015
0.41
1.1
0.090
0.0050
0.0070
0.023
0.010
0.018
0.0070
0.053
0.089
0.038
0.041
Mean
Concentration
nd = Ob
(pg/m3)
0
0
0
0.010
0.010
0.41
1.1
0.090
0
0
0
0.010
0.010
0
0.050
0.071
0.019
0.020
Downwind
No. of Positive
Samples
0/3
1/3
3/3
3/3
2/3
2/3
14/14
3/3
3/3
2/3
2/3
3/3
1/3
1/3
3/3
8/15
--
--
Mean
Concentration
nd = 1/2 LODa
(pg/m3)
0.0070
0.17
0.24
0.39
0.062
2.0
3.0
1.5
0.25
0.69
0.11
0.45
0.76
0.038
2.1
0.62
0.84
0.92
Mean
Concentration
nd = Ob
(pg/m3)
0
0.16
0.24
0.39
0.060
2.0
3.0
1.5
0.25
0.68
0.11
0.45
0.76
0.038
2.1
0.59
0.83
0.91
o
o
^1
D
C
O
§
GO
ro
O
CD
O
CD
3
cr
CD
—^
ro
o
o
GO
a Nondetects assumed to be one-half the detection limit in calculating the mean.
b Nondetects assumed to be zero in calculating the mean.
Source: Smith et al. (1989).
-------
Table 3-2. Congener-Specific, Homologue, Total, and TEQ Concentrations
for the Four Clusters of Air Samples (pg/m3)
Congener
2,3,7,8-TCDD
1,2,3,7,8-PCDD
1,2, 3,4,7, 8-HxCDD
1,2,3,6,7,8-HxCDD
1,2,3,7,8,9-HxCDD
1,2,3,4,6,7,8-HpCDD
OCDD
2,3,7,8-TCDF
1,2,3,7,8-PCDF
2,3,4,7, 8-PCDF
1, 2,3,4,7, 8-HxCDF
1, 2,3,6,7, 8-HxCDF
1, 2,3,7, 8,9-HxCDF
2,3,4,6,7, 8-HxCDF
1, 2,3,4,6,7, 8-HpCDF
1, 2,3,4,7, 8,9-HpCDF
OCDF
TCDD
PCDD
HxCDD
HpCDD
TCDF
PCDF
HxCDF
HpCDF
TOTAL
I-TEQDF
TEQDF-WH098
1 994 Impacted Air
(n = 2)
0.019
0.062
0.081
0.095
0.086
0.633
1.765
0.051
0.121
0.169
0.205
0.302
0.006
0.189
0.939
0.131
0.411
0.761
0.939
1.193
1.290
1.793
2.373
2.044
1.542
14.11
0.26
0.29
1994 Urban
(n = 8)
0.003
0.012
0.017
0.028
0.029
0.248
1.062
0.012
0.024
0.028
0.038
0.056
0.003
0.033
0.165
0.027
0.124
0.097
0.158
0.331
0.533
0.374
0.420
0.363
0.287
3.75
O.C
O.C
1995 Urban
(n = 6)
0.007
0.008
0.011
0.024
0.020
0.205
0.807
0.017
0.022
0.020
0.063
0.058
0.003
0.027
0.165
0.038
0.159
0.110
0.082
0.252
0.416
0.378
0.294
0.361
0.325
3.18
50
55
Rural
(n = 3)
0.003
0.005
0.008
0.009
0.013
0.227
0.904
0.003
0.007
0.010
0.014
0.016
0.003
0.009
0.061
0.014
0.067
0.015
0.027
0.188
0.494
0.083
0.122
0.134
0.144
2.18
0.022
0.024
Note: Non-detects assumed to be one-half the detection limit.
Source: OEPA (1995).
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December 2003
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Table 3-3. Background Air Concentrations of CDD/CDFs at Mohawk Mountain, Connecticut
Parameter
2,3,7,8-TCDD
Total TCDD
1,2,3,7,8-PeCDD
Total PeCDD
1,2,3, 4,7, 8-HxCDD
1,2,3,6,7,8-HxCDD
1,2,3,7,8,9-HxCDD
Total HxCDD
1,2,3,4,6,7,8-HpCDD
Total HpCDD
OCDD
2,3,7,8-TCDF
Total TCDF
1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
Total PeCDF
1,2,3,4,7,8-HxCDF
1,2,3,6,7,8-HxCDF
2,3,4,6,7,8-HxCDF
1,2,3,7,8,9-HxCDF
Total HxCDF
1,2,3,4,6,7,8-HpCDF
1,2,3,4,7,8,9-HpCDF
Total HpCDF
OCDF
Sample Period
Oct. 29-Nov. 29, 1993
(pg/m3)
0.001
0.032
0.004
0.043
0.007
0.010
0.011
0.122
0.159
0.317
0.451
0.004
0.134
0.004
0.006
0.078
0.008
0.007
0.009
0.004
0.078
0.035
0.006
0.070
0.028
Jan. 20-Feb. 18, 1994
(pg/m3)
(0.000)
0.017
0.002
0.027
0.004
0.005
0.005
0.072
0.065
0.133
0.196
0.003
0.090
0.003
0.005
0.057
0.008
0.006
0.008
0.003
0.060
0.028
0.005
0.049
0.027
Apr. 26-May 26, 1994
(pg/m3)
(0.001)
0.012
0.002
0.022
0.002
0.003
0.002
0.032
0.029
0.061
0.155
0.002
0.095
0.003
0.004
0.057
0.004
0.003
0.004
0.001
0.041
0.012
0.001
0.020
0.011
Jul. 26-Aug. 25, 1994
(pg/m3)
(0.000)
0.007
(0.001)
0.009
(0.001)
0.002
(0.001)
0.020
0.016
0.033
0.056
0.004
0.112
0.003
0.004
0.073
0.004
0.004
0.004
(0.001)
0.041
0.016
0.002
0.029
0.011
o
o
^1
D
CO
ro
CD
O
CD
3
cr
CD
—^
ro
o
o
GO
( ) = Parameter not detected at the indicated detection limit.
Source: CDEPI1995).
-------
Table 3-4. Ambient Air Concentrations Near a Roadway in Phoenix, Arizona
Parameter
2,3,7,8-TCDD
1,2,3,7,8-PeCDD
1, 2,3,4,7, 8-HxCDD
1,2,3,6,7,8-HxCDD
1,2,3,7,8,9-HxCDD
1, 2,3,4,6, 7,8-HpCDD
OCDD
2,3,7,8-TCDF
1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
1,2,3,4,7,8-HxCDF
1,2,3,6,7,8-HxCDF
2,3,4,6, 7,8-HxCDF
1,2,3,7,8,9-HxCDF
1,2,3,4,6,7,8-HpCDF
1,2,3,4,7,8,9-HpCDF
OCDF
TOTAL TEQ
Average I-TEQDF (pg/m3)
0.0077
0.0254
0.0096
0.0220
0.0182
0.0295
0.0096
0.0033
0.0026
0.0562
0.0147
0.0127
0.0215
0.0078
0.0076
0.0010
0.0003
0.2499
Average TEQDF-WH098
0.0077
0.0508
0.0096
0.0220
0.0182
0.0295
0.00096
0.0033
0.0026
0.0562
0.0147
0.0127
0.0215
0.0078
0.0076
0.0010
0.00003
0.2664
Source: Hunt et al. (1997).
DRAFT-DO NOT QUOTE OR CITE
3-127
December 2003
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Table 3-5. Average Dioxin/Furan/PCB Concentrations at Nine NDAMN Sites,
Collected for Six Sampling Moments (n = 53)
Parameter
Mean Concentration (pg/m3)
ND = zero
ND = 1/2 DL
Dioxins
2,3,7,8-TCDD
1,2,3,7,8-PeCDD
1,2,3,4,7,8-HxCDD
1,2,3,6,7,8-HxCDD
1,2,3,7,8,9-HxCDD
1,2,3,4,6,7,8-HpCDD
OCDD
0.00070
0.0040
0.0053
0.010
0.0096
0.13
0.45
0.00071
0.0041
0.0053
0.010
0.0096
0.14
0.45
Furans
2,3,7,8-TCDF
1,2,3,7,8-PeCDF
2,3,4,7, 8-PeCDF
1,2,3,4,7,8-HxCDF
1,2,3,6,7,8-HxCDF
2,3,4,6,7,8-HxCDF
1,2,3,7,8,9-HxCDF
1, 2,3,4,6, 7,8-HpCDF
1, 2,3,4,7, 8,9-HpCDF
OCDF
Total Concentration (pg/m3)
Total Concentration (pg TEQ/m3)
0.0017
0.0019
0.0032
0.0038
0.0035
0.0045
0.0014
0.020
0.0026
0.017
0.68
0.012
0.0017
0.0019
0.0032
0.0038
0.0035
0.0045
0.0014
0.020
0.0026
0.017
0.68
0.012
Congener Groups
Total TCDF
Total TCDD
Total PCDF
Total PCDD
Total HxCDF
Total HxCDD
Total HpCDF
Total HpCDD
Total CDD/CDF (pg/m3)
PCB 77
PCB 118
PCB 105
PCB 126
PCB 156
PCB 157
PCB 169
Total PCBs (pg/m3)
Total Concentration (pg TEQ/m3)
0.068
0.017
0.041
0.033
0.047
0.13
0.035
0.30
0.67
0.053
0.82
0.30
0.0055
0.049
0.011
0.00060
1.2
0.00071
0.068
0.017
0.041
0.033
0.047
0.13
0.035
0.30
0.67
0.053
0.82
0.30
0.0055
0.049
0.011
0.00062
1.2
0.00071
DRAFT-DO NOT QUOTE OR CITE
3-128
December 2003
-------
Table 3-6. Annual Mean PCB Concentrations in Ambient Air, Ontario, Canada (pg/m3
PCB Congener
105
114
118
156
170
180
189
TOTAL TEQDF-WH094
TOTAL TEQDF-WH098
No. of Positive Samples
63/143
79/143
122/142
13/143
53/143
111/143
3/143
--
-
Annual Mean Concentration
(pg/m3)
0.16
1.2
2.3
0.07
0.48
1.1
0.01
0.00094
0.00088
Source: Hoff et al. (1992).
DRAFT-DO NOT QUOTE OR CITE
3-129
December 2003
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Table 3-7. Annual Average Dioxin-Like PCB Concentrations in
Ambient Air in Germany (pg/m3)
PCB 77
PCB 126
PCB 169
TOTAL (77 + 126 + 169)
Koln
3.0
0.27
0.02
3.3
Duesburg3
4.5-4.8
0.50-0.62
0.05
5.2-5.4
Essen
2.6
0.24
0.06
2.9
Dortmund
4.5
0.48
0.07
5.1
Two sites were sampled at this location.
Source: Hiester et al. (1 995).
DRAFT-DO NOT QUOTE OR CITE
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December 2003
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Table 3-8. Mean Background CDD/CDF Profiles for Air
2,3,7,8-Substituted
CDD/CDFs
2,3,7,8-TCDD
1,2,3,7,8-PeCDD
1,2,3,4,7,8-HxCDD
1,2,3,6,7,8-HxCDD
1,2,3,7,8,9-HxCDD
1,2,3,4,6,7,8-HpCDD
OCDD
2,3,7,8-TCDF
1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
1,2,3,4,7,8-HxCDF
1,2,3,6,7,8-HxCDF
1,2,3,7,8,9-HxCDF
2,3,4,6,7,8-HxCDF
1,2,3,4,6,7,8-HpCDF
1,2,3,4,7,8,9-HpCDF
OCDF
TOTAL
Rural Background8
Concentration
(pg/m3)
0.00059
0.0038
0.0051
0.0092
0.0090
0.14
0.47
0.0017
0.0022
0.0038
0.0045
0.0041
0.0013
0.0048
0.024
0.0035
0.022
0.71
Fraction of
Total
CDD/CDFs
0.00050
0.0032
0.0043
0.0077
0.0075
0.11
0.39
0.0014
0.0019
0.0032
0.0037
0.0034
0.0011
0.0040
0.020
0.0029
0.018
0.59
Urban Background'
Concentration
(pg/m3)
0.00065
0.0039
0.011
0.025
0.028
0.56
1.9
0.22
0.057
0.021
0.044
0.067
0.00077
0.020
0.13
0.0070
0.092
3.1
Fraction of
Total
CDD/CDFs
0.00012
0.00071
0.0020
0.0046
0.0051
0.10
0.34
0.040
0.010
0.0039
0.0080
0.012
0.00014
0.0037
0.023
0.0013
0.017
0.57
CDD/CDF
Homologue
Group
TCDD
PeCDD
HxCDD
HpCDD
OCDD
TCDF
PeCDF
HxCDF
HpCDF
OCDF
TOTAL
Rural Background8
Concentratio
n
(pg/m3)
0.016
0.032
0.13
0.30
0.47
0.073
0.050
0.055
0.045
0.022
1.2
Fraction of
Total
CDD/CDFs
0.014
0.026
0.11
0.25
0.39
0.061
0.042
0.046
0.038
0.018
1.000
Urban Background0
Concentration
(pg/m3)
0.017
0.041
0.23
0.89
1.9
1.2
0.69
0.28
0.18
0.091
5.4
Fraction of
Total
CDD/CDFs
0.0030
0.0075
0.042
0.16
0.34
0.22
0.13
0.052
0.032
0.017
1.0
o
o
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C
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NOTE: Non-detects are assumed to be zero.
Based on data from OEPA (1995) CDEP (1988), and Cleverly et al. (2000).
b Based on data from CDEP (1 988, 1 995); Smith et al. (1 989); Maisel and Hunt (1 990); Hunt et al. (1 990); and OEPA (1 995).
Based on data from CDEP (1988, 1995); Smith et al. (1989, 1990a); Maisel and Hunt (1990); Hunt et al. (1990); and OEPA (1995).
See Table 3-8 for sampling locations and numbers of samples from these studies.
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Table 3-9. TEQDF-WHO98 Concentrations of CDD/CDFs in Air in the United States (pg/m3
(ND = 1/2 LOD)
Reference
2,3,7,8-
TCDD
1,2,3,7,8-
PeCDD
2,3,7,8-
HxCDDs
1,2,3,4,6,7,8-
HpCDD
OCDD
2,3,7,8-
TCDF
1,2,3,7,8-
PeCDF
2,3,4,7,8-
PeCDF
2,3,7,8-
HxDCFs
2,3,7,8-
HpCDFs
OCDF
Total
RURAL BACKGROUND
CDEP, 1995
Mohawk Ml, CT (n = 4)
Ohio EPA, 1995
Rural Ohio (n = 3)
Cleverly et al., 2000
Arkadelphia, AR (n = 6)
Cleverly et al., 2000
Bixby, OK (n = 5)
Cleverly et al., 2000
Clinton Crops, NC (n = 4)
Cleverly et al., 2000
Everglades, FL (n =4)
Cleverly et al., 2000
Lake Dubay, Wl (n = 6)
Cleverly et al., 2000
Lake Scott, KS (n = 6)
Cleverly et al., 2000
McNay, IA (n = 6)
Cleverly et al., 2000
Monmouth, IL (n = 4)
Cleverly et al., 2000
Penn Nursery, PA (n = 1 2)
MEAN
SD
WEIGHTED MEAN
0.00038
0.0029
0.00050
0.00074
0.00045
0.00030
0.00043
0.00033
0.0012
0.0012
0.0011
0.00087
0.00073
0.00084
0.0021
0.0052
0.0027
0.0047
0.0029
0.0013
0.0025
0.0015
0.0058
0.0088
0.0064
0.0040
0.0023
0.0042
0.0013
0.0031
0.0019
0.0026
0.0016
0.0012
0.0018
0.0010
0.00032
0.0065
0.0026
0.024
0.0015
0.0024
0.00067
0.0023
0.0010
0.0013
0.00072
0.00087
0.0010
0.00057
0.0017
0.0036
0.0013
0.0014
0.00085
0.0013
0.000021
0.000090
0.000046
0.000045
0.000024
0.000034
0.000033
0.000024
0.000061
0.000097
0.000043
0.000047
0.000025
0.000045
0.00033
0.00028
0.00012
0.00028
0.00029
0.000069
0.00016
0.000040
0.00012
0.00024
0.00023
0.00020
0.000094
0.00019
0.00016
0.00033
0.000050
0.00016
0.00014
0.000039
0.000096
0.000023
0.000086
0.00015
0.00012
0.00012
0.000079
0.00011
0.0024
0.0048
0.00082
0.0024
0.0025
0.00071
0.0018
0.00039
0.0015
0.0025
0.0018
0.0020
0.0011
0.0018
0.0019
0.0041
0.00075
0.0020
0.0021
0.00062
0.0014
0.00034
0.0012
0.0020
0.0015
0.0016
0.0010
0.0015
0.00026
0.00076
0.00013
0.00032
0.00032
0.00011
0.00022
0.000057
0.00026
0.00037
0.00027
0.00028
0.00018
0.00026
0.0000019
0.0000067
0.0000012
0.0000021
0.0000027
0.00000076
0.0000017
0.00000047
0.0000021
0.0000027
0.0000019
0.0000022
0.0000016
0.0000020
0.010
0.024
0.008
0.015
0.011
0.005
0.009
0.004
0.015
0.026
0.015
0.0129
-
0.0126
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Table 3-9. TEQDF-WHO98 Concentrations of CDD/CDFs in Air in the United States (pg/m3) (continued)
(ND = 1/2 LOD)
Reference
2,3,7,8-
TCDD
1,2,3,7,8-
PeCDD
2,3,7,8-
HxCDDs
1,2,3,4,6,7,8-
HpCDD
OCDD
2,3,7,8-
TCDF
1,2,3,7,
8-PeCDF
2,3,4,7,
8-PeCDF
2,3,7,8-
HxDCFs
2,3,7,8-
HpCDFs
OCDF
Total
URBAN BACKGROUND
CDEP, 1988
Wallingford, CT (n = 28)
CDEP, 1995
Connecticut (n = 20)
Hunt and Maisel, 1990
Bridgeport, CT (n = 7)
Hunt et al., 1990
W. Long Beach, CA
(n = 2)
Hunt et al., 1990
Reseda, CA (n = 7)
Hunt et al., 1990
San Bernadino, CA
(n = 5)
Hunt et al., 1990
El Toro, CA (n = 7)
Hunt et al., 1990
N. Long Beach, CA
(n = 6)
Maisel and Hunt, 1990
Los Angeles, CA (n = 1 )
Ohio EPA, 1995
Franklin Co., OH (n = 14)
Smith et al., 1989
Niagra Falls, NY (n = 3)
Smith et al., 1 990a
Albany, NY (n = 3)
Smith et al., 1 990a
Binghampton, NY (n = 1 )
Smith et al., 1 990a
Utica, NY (n = 2)
MEAN
SD
WEIGHTED MEAN
0.0019
0.00012
0.012
0.0075
0.0080
0.013
0.010
0.013
0.0048
0.0048
0.0070
0.048
0.030
0.058
0.016
0.017
0.008
0.0063
0.0063
0.024
0.032
0.034
0.12
0.018
0.017
0.020
0.010
0.0070
0.027
0.032
0.018
0.0086
0.0036
0.015
0.0046
0.026
0.015
0.0075
0.020
0.012
0.0066
0.0037
0.01 1
0.007
0.010
0.0029
0.0014
0.0048
0.0034
0.024
0.0059
0.0014
0.0079
0.0025
0.0023
0.0041
0.0055
0.0063
0.0045
0.00055
0.000044
0.00021
0.00029
0.00054
0.00031
0.0001 1
0.00014
0.00019
0.00010
0.0001 1
0.000057
0.00014
0.00012
0.00021
0.00016
0.00026
0.0072
0.00093
0.0078
0.025
0.0028
0.0038
0.0015
0.0018
0.0021
0.0014
0.0090
0.094
0.018
0.12
0.021
0.035
0.009
0.00039
0.00046
0.0016
0.00078
0.0016
0.020
0.0017
0.0019
0.0039
0.0012
0.00025
0.0031
0.0054
0.0019
0.010
0.0080
0.024
0.00275
0.015
0.019
0.018
0.020
0.034
0.012
0.0035
0.015
0.010
0.013
0.012
0.0065
0.024
0.012
0.026
0.028
0.016
0.023
0.048
0.014
0.0058
0.020
0.011
0.015
0.0029
0.00081
0.0025
0.00081
0.0024
0.0029
0.00098
0.0031
0.0010
0.0020
0.00053
0.0018
0.00094
0.0020
0.000022
0.0000055
0.000021
0.000037
0.000012
0.000016
0.0000076
0.000015
0.0000056
0.000014
0.0000089
0.000028
0.000015
0.000031
0.000017
0.0000093
0.000015
0.053
0.029
0.1 15
0.088
0.140
0.232
0.076
0.110
0.132
0.055
0.041
0.142
0.048
0.173
0.120
0.081
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Table 3-10. Mean PCDD and PCDF Concentrations in Canadian Soil from 1987 (ppt)a
Homologue Group
TCDDs
PeCDDs
HxCDDs
HpCDDs
OCDDs
Total CDDs
TCDFs
PeCDFs
HxCDFs
HpCDFs
OCDFs
Total CDFs
Soil Near Sludge
Incinerator
(n = 12)
69 (ND-430)
81 (ND-540)
9 (ND-70)
43 (ND-300)
570 (ND-1,500)
772 (ND-2,770)
ND
ND
ND
ND
43 (ND-230)
43 (ND-230)
Urban Background
(n = 11)
ND
ND
ND
31 (ND-140)
1,461 (ND-1 1,000)
1,492 (ND-1 1,140)
29 (ND-1 20)
1 (ND-10)
7 (ND-35)
9 (ND-60)
16 (ND-1 60)
65 (ND-262)
Rural Background
(n = 26)
ND
ND
ND
ND
30 (ND-100)
30 (ND-100)
ND
ND
ND
ND
ND
ND
a Data collected in 1987 in Ontario Canada; range presented in parentheses.
Source: Pearson et al. (1990).
DRAFT-DO NOT QUOTE OR CITE
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December 2003
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Table 3-11. Dioxin/Furan Levels in Four Background Soil Samples
from Elk River, Minnesota (ppt)a
Congener
2,3,7,8-TCDD
Total TCDD
1,2,3,7,8-PeCDD
Total PeCDD
1,2, 3,4,7, 8-HxCDD
1,2, 3,6,7, 8-HxCDD
1,2,3,7,8,9-HxCDD
Total HxCDD
1,2, 3,4,6,7, 8-HpCDD
Total HpCDD
OCDD
2,3,7,8-TCDF
Total TCDF
1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
Total PeCDF
1,2, 3,4,7, 8-HxCDF
1,2, 3,6,7, 8-HxCDF
2,3,4,6,7,8-HxCDF
1,2,3,7,8,9-HxCDF
Total HxCDF
1,2, 3,4,6,7, 8-HpCDF
1,2,3,4,7,8,9-HpCDF
Total HpCDF
OCDF
Tilled (n = 2)
ND
ND
ND
ND-38
ND
ND
ND-8.7
12-99
37-360
62-640
340-3300
ND
ND-1.2
ND
ND
ND-41
ND
ND
ND
ND
6.7-86
11-80
ND
30-260
ND-270
Untilled (n = 2)
ND
ND
ND
ND
ND
ND-14
ND-9.9
29-53
78-300
150-530
680-2300
ND
ND
ND
ND
18-45
ND
ND
ND-7.1
ND
20-150
26-72
ND
30-82
60-120
a ND = Nondetected. Detection limits vary from 0.75 ppt to 2.9 ppt on a congener-specific basis.
Source: Reed et al. (1990).
DRAFT-DO NOT QUOTE OR CITE
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December 2003
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Table 3-1 2. Dioxin/Furan Levels in British Columbia Soils
Sample Category3
Background Soil
• 2,3,7,8-TCDD
• 2,3,7,8-TCDF
Primary Soil (all sources)
• 2,3,7,8-TCDD
• 2,3,7,8-TCDF
Primary Soil (chemical sources)
• 2,3,7,8-TCDD
• 2,3,7,8-TCDF
Primary Soil (combustion sources)
• 2,3,7,8-TCDD
• 2,3,7,8-TCDF
Secondary Soil (all sources)
• 2,3,7,8-TCDD
• 2,3,7,8-TCDF
Secondary Soil (chemical sources)
• 2,3,7,8-TCDD
• 2,3,7,8-TCDF
Secondary Soil (combustion sources)
• 2,3,7,8-TCDD
• 2,3,7,8-TCDF
Dioxin and Furan
Concentrations (pg/g)b
Range
NDe
ND - 32.0
ND - 85.0
ND - 520.0
ND - 85.0
ND - 520.0
ND - 3.5
ND - 160.0
ND - 550.0
ND - 550.0
ND - 550.0
ND - 550.0
ND - 5.6
ND - 180.0
Meand
ND (53)
3.2 (53)
5.2 (31)
47.9 (31)
8.4 (18)
60.3 (18)
0.8 (13)
30.7 (13)
5.4 (137)
25.1 (137)
15.4 (47)
60.7 (47)
0.09 (90)
6.5 (90)
I-TEQDF (pg/g)b-c
Range
0.0 -57.0
0.0- 2580.0
0.0- 2580.0
0.0- 125.7
0.0- 18721.8
0.0- 18721.8
0.0-472.6
Meand
5.0 (53)f
252.3 (31)
418.5 (18)
22.3 (13)
241.7 (137)
668.6 (47)
18.7 (90)
a Background samples were believed to be indicative of ambient levels of dioxins and furans in the
environment. Primary samples were collected immediately at a potential source of contamination.
Secondary samples were collected from areas directly impacted by the primary source and could be used to
indicate movement of contaminants.
b Concentrations in picograms/gram (pg/g) dry weight.
c l-TEQDFs are the sum of seventeen 2,3,7,8-substituted dioxins and furans after the concentration of each
individual dioxin or furan is multiplied by its International Toxicity Equivalency Factor (I-TEFDF). For samples
with nondetected levels of a dioxin or furan, zero was used as the concentration for the I-TEQDF calculation.
d Numbers in parentheses indicate the number of samples (n) used to calculate mean.
e ND = Not Detected.
f When the total TEQ was recalculated using TEFDF-WH098s, the TEQDF-WH098 was 4.4 pg/g.
Source: BC Environment (1 995).
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December 2003
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Table 3-13 Number of Positive Soil Samples and CDD/CDF Concentrations in Background, Urban, and Impacted Sites Near a Waste-to-Energy Facility in Ohio
2,3,7,8-TCDD
1,2,3,7,8-PeCDD
1,2,3,4,7,8-HxCDD
1,2,3,6,7,8-HxCDD
1,2,3, 7,8, 9-HxCDD
1,2,3,4,6,7,8-HpCDD
OCDD
2,3,7,8-TCDF
1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
1,2,3,4,7,8-HxCDF
1,2,3,6,7,8-HxCDF
1,2,3, 7,8, 9-HxCDF
2,3,4,6,7,8-HxCDF
1,2,3,4,6,7,8-HpCDF
1, 2,3,4,7,8, 9-HpCDF
OCDF
Mean Total I-TEQDF, ppt
(nondetects = % LOD)
Mean Total I-TEQDF, ppt
(nondetects = 0)
Mean Total TEQDF-WH098,
ppt (nondetects = Vi LOD)
Mean Total TEQDF-WH098,
ppt (nondetects = 0)
No. of
Positive
Samples
2/3
0/3
1/3
3/3
3/3
3/3
3/3
0/3
0/3
1/3
1/3
3/3
0/3
3/3
3/3
1/3
3/3
--
--
--
--
Background
Mean Cone, (ppt)
(nondetects = M> LOD)
0.39
0.14
0.35
0.82
1.23
17.7
160.9
0.45
0.17
0.21
0.19
0.52
0.15
0.64
4.06
0.27
10.72
1.4
1.1
1.3
0.92
No. of
Positive
Samples
15/18
18/18
18/18
18/18
18/18
18/18
18/18
18/18
17/18
17/18
15/18
17/18
6/18
18/18
18/18
16/18
18/18
--
--
--
--
Urban
Mean Cone, (ppt)
(nondetects = M> LOD)
2.27
6.58
6.14
10.9
10.8
190.1
1560.2
4.12
5.50
7.56
8.06
8.12
0.51
6.99
41.7
3.82
44.3
19.2
19.2
21.0
21.0
No. of
Positive
Samples
3/3
3/3
3/3
3/3
3/3
3/3
3/3
3/3
3/3
3/3
3/3
3/3
3/3
3/3
3/3
3/3
3/3
--
--
--
--
Impacted
Mean Cone, (ppt)
(nondetects = M> LOD)
28.5
180.0
142.3
137.8
201.6
765.2
1495.4
85.9
139.6
199.9
196.8
209.1
11.6
156.7
641.0
57.9
184.5
356.0
356.0
444.5
444.5
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Table 3-14. Mean Background CDD/CDF Profiles for Soil
CDD/CDFs
2,3,7,8-TCDD
1,2,3,7,8-PeCDD
1,2,3,4,7,8-HxCDD
1,2,3,6,7,8-HxCDD
1,2,3,7,8,9-HxCDD
1,2,3,4,6,7,8-
HpCDD
OCDD
2,3,7,8-TCDF
1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
1,2,3,4,7,8-HxCDF
1,2,3,6,7,8-HxCDF
1,2,3, 7,8, 9-HxCDF
2,3,4,6,7,8-HxCDF
1,2,3,4,6,7,8-
HpCDF
1,2,3,4,7,8,9-
HpCDF
OCDF
TOTAL
Rural Background8
Concentration
(PPt)
0.017
0.28
0.53
3.7
2.4
72
630
1.3
0.34
0.52
1.0
0.66
0.40
0.77
18
0.59
40
770
Fraction of
Total
CDD/CDFs
0.00019
0.00030
0.00057
0.0039
0.0025
0.077
0.68
0.0014
0.00037
0.00056
0.0011
0.00071
0.00043
0.00083
0.019
0.00064
0.043
0.83
Urban Background'
Concentration
(PPt)
0.87
2.4
2.7
5.3
5.1
99
2,700
2.3
1.8
3.2
4.0
3.6
0.75
2.6
17
1.5
23
2,900
Fraction of
Total
CDD/CDFs
0.00027
0.00077
0.00086
0.0017
0.0016
0.031
0.86
0.00073
0.00058
0.0010
0.0013
0.0011
0.00024
0.00080
0.0053
0.00046
0.0073
CDD/CDF
Homologue
Groups
TCDD
PeCDD
HxCDD
HpCDD
OCDD
TCDF
PeCDF
HxxCDF
HpCDF
OCDF
0.91 || TOTAL
Rural Background0
Concentration
(PPt)
2.1
3.9
21
96
470
7.3
10
18
31
28
680
Fraction
of Total
CDD/CDFs
0.0031
0.0057
0.030
0.14
0.68
0.011
0.015
0.027
0.046
0.041
1.00
Urban Background11
Concentration
(PPt)
32
19
34
170
2,100
37
37
22
41
36
2,600
Fraction of
Total
CDD/CDFs
0.012
0.0074
0.013
0.068
0.83
0.014
0.014
0.0085
0.016
0.014
1.00
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Based on data from Reed et al. (1990), BC Environment (1995), U.S. EPA (1996), MRI (1992), Tewhey Associates (1997), Rogowski
et al. (1999), and Rogowski and Yake (1999)..
Based on data from U.S. EPA (1996) NIH (1995); and Rogowski et al (1999).
Based on data from Reed et al. (1 990), Birmingham (1 990), Pearson et al. (1 990), BC Environment (1 995), U.S. EPA (1 985), U.S. EPA
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Table 3-15. TEQDF-WH098 Concentrations of CDD/CDFs in North American Soil (ppt)
(nondetects = 0)
Reference
2,3,7,8-
TCDD
1,2,3,7,8-
PeCDD
2,3,7,8-
HxCDDs
1,2,3,4,6,7,8-
HpCDD
OCDD
2,3,7,8-
TCDF
1,2,3,7,8-
PeCDF
2,3,4,7,8-
PeCDF
2,3,7,8-
HxDCFs
2,3,7,8-
HpCDFs
OCDF
Total
RURAL BACKGROUND
BC Environment, 1995
British Columbia (n=53)
background
Birmingham, 1990
Ontario (n=30)
rural, background
MRI, 1992*
Connecticut (n=34)
background
Pearson etal., 1990
Ontario (n=43)
rural, background
Reed etal., 1990
Minnesota (n=4)
semi-rural, background
Rogowski and Yake, 1999
Washington (n=54)
agricultural
Rogowski etal., 1999
Washington (n=16)
rangeland and forest
Tewhey Associates, 1997
Maine (n=8)
background
U.S. EPA, 1985
Illinois (n=13)
residential, background
U.S. EPA, 1985
Minnesota (n=4)
natural, background
U.S. EPA, 1985
Ohio (n=22)
residential, background
U.S. EPA, 1985
Ohio (n=5)
residential, background
U.S. EPA, 1985
Minnesota (n=3)
natural, background
0
-
0.61
-
0
0
0.024
0.28
0.15
0
1.1
-
-
0.16
-
0.87
-
0
0
0.52
0.43
-
-
-
-
-
1.7
-
0.67
-
0.82
0.014
0.55
0.65
-
-
-
-
-
1.4
-
0.55
-
1.9
0.029
0.20
0.70
-
-
-
-
-
0.068
0.070
0.081
0.0038
0.17
0.0024
0.012
0.097
-
-
-
0.24
0.014
0.32
-
0.48
-
0
0.025
0.067
0.040
-
-
-
-
-
0.016
-
0.090
-
0
0.0011
0.0031
0.010
-
-
-
-
-
0.090
-
1.2
-
0
0.0078
0.27
0.25
-
-
-
-
-
0.22
-
1.0
-
0.18
0.023
0.14
0.28
-
-
-
-
-
0.44
-
0.18
-
0.47
0.011
0.02
0.15
-
-
-
-
-
0.0077
0
0.0026
0
0.011
0.00062
0.00056
0.0040
-
-
-
0
0
4.41
-
5.74
-
3.58
0.12
1.8
2.89
-
-
-
-
-
-------
Table 3-15. TEQDF-WH098 Concentrations of CDD/CDFs in North American Soil (ppt) (continued)
(nondetects = 0)
Reference
U.S. EPA, 1996
Ohio (n=3)
background
U.S. EPA, 2000b
Colorado (n=62)
agricultural, open space
MEAN
S.D.
WEIGHTED MEAN
2,3,7,8-
TCDD
0.30
0.027
0.23
0.33
0.19
1,2,3,7,8-
PeCDD
0
0.27
0.28
0.29
0.28
2,3,7,8-
HxCDDs
0.23
0.19
0.60
0.48
0.61
1,2,3,4,6,7,8-
HpCDD
0.18
0.25
0.66
0.64
0.55
OCDD
0.016
0.016
0.060
0.073
0.038
2,3,7,8-
TCDF
0
0
0.12
0.17
0.15
1,2,3,7,8-
PeCDF
0
0.0077
0.016
0.028
0.019
2,3,4,7,8-
PeCDF
0.028
0.25
0.26
0.36
0.29
2,3,7,8-
HxDCF
0.12
0.14
0.27
0.30
0.27
2,3,7,8-
HpCDFs
0.042
0.036
0.17
0.18
0.15
OCDF
0.0011
0.00087
0.0024
0.0034
0.0022
Total
0.92
1.2
2.7
2.6
URBAN BACKGROUND
Birmingham, 1990
Ontario (n=47)
urban, background
Nestrick et al., 1986
Michigan (n=20)
urban/Indus, bckgrd.
NIH, 1995
Maryland (n=37)
urban
Pearson etal., 1990
Ontario (n=29)
urban, background
Rogowski etal., 1999
Washington (n=14)
urban
U.S. EPA, 1985
Michigan (n=6)
public areas
U.S. EPA, 1996
Ohio(n=18)
urban
U.S. EPA, 2000b
Colorado (n=99)
commercial, industrial,
residential background
MEAN
S.D.
WEIGHTED MEAN
~
2.1
0.056
~
0.35
~
2.2
0.86
1.1
0.89
0.92
~
~
0.098
~
0.71
~
6.5
1.8
2.3
2.5
1.8
~
~
0.34
~
0.77
~
2.8
1.7
1.4
0.95
1.4
—
—
0.60
—
0.48
—
1.90
1.8
1.2
0.65
1.4
0.16
~
0.63
0.25
0.033
0.51
0.16
0.089
0.26
0.21
0.28
~
—
0.028
~
0.30
~
0.37
0
0.17
0.16
0.046
—
~
0.0070
—
0
—
0.27
0.043
0.079
0.11
0.056
—
~
0.14
—
0.93
—
3.8
1.1
1.5
1.4
1.1
~
~
0.27
~
0.44
~
2.6
0.84
1.0
0.90
0.83
—
—
0.039
—
0.061
—
0.45
0.36
0.23
0.18
0.27
0.0059
—
0.00067
0.0050
0.0019
0.032
0.0044
0.0073
0.0082
0.010
0.0062
~
—
2.21
~
4.1
~
21.0
8.6
9.3
__
8.0
ro
o
o
GO
Calculated using nondetects = 1/2 LOD. Proportion of nondetects ranged from 3 to 1 1 percent of samples per each analyte, with the exception of 2,3,7,8-TCDD and
1,2,3,7,8-HxCDF, which had 56 and 49 percent nondetects, respectively.
-------
Table 3-16. CDD/CDF Levels in British Columbia Sediments
Sample Category3
Background Sediment
• 2,3,7,8-TCDD
• 2,3,7,8-TCDF
Secondary Sediment (all sources)
• 2,3,7,8-TCDD
• 2,3,7,8-TCDF
Secondary Sediment (chemical sources)
• 2,3,7,8-TCDD
• 2,3,7,8-TCDF
Secondary Sediment (combustion
sources)
• 2,3,7,8-TCDD
• 2,3,7,8-TCDF
Dioxin and Furan
Concentrations (pg/g)b
Range
NDe
ND - 17.0
ND - 2.7
ND - 33.0
ND - 2.7
ND - 33.0
ND - 1.1
ND - 12.0
Meand
ND (12)
1.4 (12)
0.2 (21)
3.5 (21)
0.2 (14)
3.8 (14)
0.2 (7)
3.0 (7)
l-TEQDFs (pg/g)b'c
Range
0.0 - 24.4
0.0-
172.0
0.0 -
172.0
0.0- 63.6
Meand
3.9 (12)
32.5 (21)
42.1 (14)
13.2 (7)
a Background samples were believed to be indicative of ambient levels of dioxins and furans in the
environment. Secondary samples were collected from areas directly impacted by the primary
source, and could be used to indicate movement of contaminants.
b Concentrations in picograms/gram (pg/g) dry weight.
c l-TEQDFs are the sum of 17 2,3,7,8-substituted dioxins and furans after the concentration of each
individual dioxin or furan is multiplied by its International Toxicity Equivalency Factor (I-TEFDF).
For samples with nondetected levels of a dioxin or furan, zero was used as the concentration for
the I-TEQDF calculation.
d Numbers in parentheses indicate the number of samples (n) used to calculate mean.
e ND = Not Detected.
Source: BC Environment (1995).
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o
o
^1
D
C
O
§
Table 3-17. TEQDF Concentrations (ppl) and Ratios of 2,3,7,8-Substituted
CDD/CDF Concentrations to Total CDD/CDF Concentrations for the Most Recent
Sediment Core Sampling Periods for 11 U.S. Lakes
Lake
Chandler Lake, AK
Canandaigua Lake, NY
Skaneateles Lake, NY
Great Sacandaga Reservoir, NY
Santeetlah Reservoir, NC
Blue Ridge Reservoir, GA
Deer Creek Reservoir, UT
Echo Lake, UT
Panguitch Lake, UT
Ozette Lake, WA
Beaver Lake, WA
Mean
I-TEQDF (ppt)
n = 1/2 LOD
0.11
15.0
10.1
6A
15.6
5.6
1.2
0.82
0.91
1.2
0.98
5.3
nd = 0
0.11
14.3
9.1
4.9
13.1
4.8
1.0
0.68
0.76
1.1
0.86
4.6
TEQDF-WH098 (ppt)
n = 1/2 LOD
0.12
16.3
10.8
6.1
15.4
4.9
1.0
0.67
0.82
1.3
0.97
5.3
nd = 0
0.12
15.5
9.7
4.8
13.0
4.2
0.83
0.50
0.60
1.2
0.80
4.7
Ratio of 2,3,7,8-
CDD/CDFs to Total
CDD/CDFs
n = 1/2 LOD
0.47
0.66
0.73
0.75
0.70
0.75
0.86
0.91
0.74
0.56
0.60
0.70
nd = 0
0.47
0.63
0.71
0.49
0.64
0.71
0.85
0.90
0.73
0.56
0.59
0.66
Range of Dates
Represented by
Uppermost Core Section
1956-1993
1981-1991
1984-1991
1974-1983
1974-1983
1974-1983
1973-1982
1973-1982
1976-1985
1977-1985
1974-1985
-
CO
ro
O
CD
O
CD
3
cr
CD
—^
ro
o
o
GO
Source: Cleverly et al. (1 996); Versar (1 996a).
-------
Table 3-18. CDD/CDF and PCB Concentrations and Flux for 11 U.S. Lakes/Reservoirs
Chandler Lake, AK
Canandaigua Lake, NY
Skaneateles Lake, NY
Great Sacandaga Reservoir, NY
Santeetlah Reservoir, NC
Blue Ridge Reservoir, GA
Deer Creek Reservoir, UT
Echo Lake, UT
Panguitch Lake, UT
Ozette Lake, WA
Beaver Lake, WA
Mean
Total Concentration
(pg/g, dry weight)3
CDD/CDFs
9.1
1790.6
1338.4
1257.4
2916.2
1785.8
255.3
236.9
265.4
203.4
132.0
926.4
Dioxin-Like
PCBs
34.0
2115.5
974.6
865.7
522.0
218.0
303.6
125.9
76.0
103.0
39.0
488.8
Flux (pg/cm2-yr)a
CDD/CDFs
0.051
86.9
58.6
82.0
190.0
158.6
46.4
39.5
15.5
7.9
31.6
65.2
Dioxin-Like
PCBs
0.19
102.7
42.7
56.5
34.0
19.4
55.2
21.0
4.5
4.0
9.3
31.8
a Nondetects set to one-half the detection limit.
Source: Cleverly et al. (1 996); Versar (1996a).
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December 2003
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Table 3-19. Average Total Concentrations of CDD/CDFs for Sediments (pg/g)
Vietnamese River Sediments
Site
Saigon River
Dong Nai River
Red River
Concentration
6,800
1,200
240
Lake Sediments
Site
Lake Huron
Lake Michigan
Lake Erie
Lake Ontario
Siskiwit Lake
Lake Zurich
Lake Balderg
Lake Lugano
Concentration
1,240
1,600
2,150
11,000
730
1,500
1,500
2,000
Source: Schecter et al. (1989a).
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December 2003
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Table 3-20. Mean Background Profiles for Sediment3
2,3,7,8-Substituted
CDD/CDFs
2,3,7,8-TCDD
1,2,3,7,8-PeCDD
1,2,3,4,7,8-HxCDD
1,2, 3,6,7, 8-HxCDD
1,2,3,7,8,9-HxCDD
1,2,3,4,6,7,8-HpCDD
OCDD
2,3,7,8-TCDF
1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
1,2,3,4,7,8-HxCDF
1,2, 3,6,7, 8-HxCDF
1,2,3,7,8,9-HxCDF
2,3,4,6,7,8-HxCDF
12,3,4,6,7,8-HpCDF
1,2, 3,4,7, 8,9-HpCDF
OCDF
TOTAL
Concentration
(ppt)
0.26
0.95
1.8
4.7
4.1
100.6
400.5
1.6
0.91
1.5
1.9
0.003
0.02
1.7
0.01
2.1
83.0
605.4
Fraction of Total
CDD/CDFs
0.0003
0.0010
0.0020
0.0051
0.0044
0.1086
0.4323
0.0017
0.0010
0.0016
0.0020
0.0000
0.0000
0.0018
0.0000
0.0023
0.0896
0.6536
CDD/CDF Homologue
Groups
TCDD
PeCDD
HxCDD
HpCDD
OCDD
TCDF
PeCDF
HxCDF
HpCDF
OCDF
Concentration
(ppt)
6.5
9.1
43.3
199.1
400.5
25.7
17.9
38.0
82.8
103.6
926.40
Fraction of Total
CDD/CDFs
0.007
0.0100
0.0468
0.2149
0.4323
0.0278
0.0193
0.0411
0.0893
0.1118
1.0
o
o
^1
D
C
O
§
CO
CJ1
o
CD
O
CD
3
cr
CD
—^
ro
o
o
GO
Based on Cleverly et al. (1996); Versar (1996a).
-------
o
o
^1
D
C
O
§
Table 3-21. TEQDF-WH098 Concentrations of CDD/CDFs in North American Sediment (ppt)
(nondetects = % LOD)
Location
Chandler Lake, AK
Cs date = 1952
Canadaigua Lake, NY
Cs date =1981
Skaneateles Lake, NY
Cs date = 1984
Great Sacandaga Reservoir, NY
Cs date = 1974
Santeetlah Reservoir, NC
Cs date = 1974
Blue Ridge Reservoir, GA
Cs date = 1974
Deer Creek Reservoir, UT
Cs date = 1973
Echo Lake, UT
Cs = 1973
Panguitch Lake, UT
Cs = 1976
Ozette Lake, WA
Cs = 1977
Beaver Lake, WA
Cs = 1974
Mean
S.D.
2,3,7,8-
TCDD
0.016
0.782
0.687
0.098
0.390
0.279
0.106
0.042
0.025
0.246
0.299
0.270
0.250
1,2,3,7,8-
PeCDD
0.025
3.91
2.610
0.726
2.160
0.682
0.124
0.055
0.124
0.345
0.125
0.990
1.249
2,3,7,8-
HxCDDs
0.010
3.512
1.672
0.613
4.054
0.854
0.161
0.127
0.169
0.270
0.163
1.055
1.368
1,2,3,4,6,7,8-
HpCDD
0.002
2.720
1.320
0.487
4.310
1.470
0.173
0.143
0.195
0.143
0.104
1.006
1.318
OCDD
0.0003
0.069
0.072
0.054
0.099
0.087
0.018
0.010
0.016
0.009
0.006
0.040
0.035
2,3,7,8-
TCDF
0.018
0.844
0.404
0.382
0.210
0.090
0.073
0.012
0.027
0.066
0.021
0.195
0.246
1,2,3,7,8-
PeCDF
0.003
0.153
0.147
0.098
0.043
0.023
0.017
0.005
0.014
0.007
0.007
0.047
0.055
2,3,4,7,8-
PeCDF
0.022
2.635
2.385
1.610
0.685
0.327
0.182
0.100
0.137
0.1 1 1
0.163
0.760
0.932
2,3,7,8-
HxDCFs
0.021
1.274
1.189
0.983
1.861
0.517
0.154
0.138
0.090
0.100
0.094
0.584
0.607
2,3,7,8-
HpCDFs
0.002
0.369
0.262
0.989
1.554
0.596
0.037
0.029
0.025
0.015
0.008
0.353
0.485
OCDF
0.00002
0.011
0.005
0.023
0.039
0.025
0.001
0.010
0.0007
0.0003
0.0002
0.010
0.012
Total
0.012
16.3
10.8
6.1
15.4
4.9
1.0
0.67
0.82
1.3
0.99
5.31
5 83
CO
O
CD
O
CD
3
CT
CD
—^
ro
o
o
GO
Source: Cleverly et al. (1 996); Versar (1 996a).
-------
Table 3-22. Background Data for Fish from the National Bioaccumulation Study
Congener
2,3,7,8-TCDD
2,3,7,8-TCDF
1,2,3,7,8-PeCDD
1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
Total HxCDDs
Total HxCDFs
No. of
Background
Sites
34
34
33
34
34
30
29
Concentration
Range3 (pg/g)
0.06 - 2.26
0.10- 13.73
0.15 - 2.67
0.10- 1.90
0.10 - 1.39
ND - 3.57
ND - 2.59
Mean Conc.a
(pg/g)
0.56
1.61
0.77
0.43
0.50
0.39
0.22
Standard
Deviation3
(pg/g>
0.38
2.51
0.54
0.31
0.36
0.8
0.66
Median
Conc.a
(pg/g>
0.50
0.90
0.54
0.39
0.42
ND
ND
Source: U.S. EPA (1992).
a Concentrations are picograms per gram (pg/g) wet weight. The mean, median, and standard
deviation were calculated using one-half the detection limit for samples that were below the
detection limit. In cases where multiple samples were analyzed per site, the value used
represents the highest concentration.
Note: ND = nondetect.
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December 2003
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Table 3-23. Levels of CDD and CDF l-TEQDFs in Fish From the Southern Mississippi Region
Food Sample
Catfish (farm-raised) Nuggetsb
Mullet Fillet
Spanish Mackerel Fillet
American Oyster Meat
Blue Crab
Claw Meat
Body (soft-shell)
Crawfish
Tail Muscle
Head and Digestive Gland
Number of
Observations
3
2
1
3
3
3
2
2
I-TEQDF
pg/g sample3
1.19, 2.64, 2.57
0.089, 0.027
0.72
0.62, 0.53, 0.60
0.06, 0.10, 0.09
1.09, 1.14, 1.44
0.033, 0.087
2.34, 1.55
a One-half the detection limit was used in calculating the l-TEQDFs.
b Nuggets are small pieces of fillet.
Source: Cooper et al. (1 995).
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Table 3-24. Summary of CDD, CDF, and PCB Analyses in Farm Raised
Catfish from the Southeastern United States3
Sample Type
Catfish Nuggets from Mississippi0
Catfish Fillet from Mississippi0
Catfish Fillet from Alabama
Catfish Fillet from Mississippi
Agriculture Facility
Catfish Fillet from Arkansas
Agriculture Facility (8% fish meal)
Feed - Mississippi (4% fish meal)
Feed - Arkansas (8% fish meal)
Sediment from Mississippi
Agriculture Facility
Number of
Observations
3
3
1
3
2
1
1
1
I-TEQDF
(mean)
pg/g Npid
9.7
22.67
13
7.93
40
7.2
61
3.5
TEQp-WH098
(mean) pg/g
lipid b
1.43
2.66
0.92
0.86
3.42
3.31
0.19
0.04
TEQDFP-WH098
(mean) pg/g
lipid
11.13
25.33
13.92
8.79
43.42
10.51
61.19
3.54
a One-half the detection limit was used in calculating the TEQs.
b Includes PCB 28, 52, 77, 101, 105, 118, 126, 138, 153, 156, 169, and 180.
c Purchased from same store and distributed by same supplier as those collected and analyzed in
Cooper et al. (1995).
Source: Cooper et al. (1997) and Fiedler et al. (1998).
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December 2003
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Table 3-25. FDA Fish and Shellfish Data for 1995-1999 Combined
(TEQs Based on 17 2,3,7,8-substituted dioxin and furan congeners)
Salmon
Catfish (all)
Rockfish/
striped bass
Pollack
Tuna
Cod
Lobster
Crawfish
Crab
Blue crab
Shrimp
Scallops
Oyster
Year
1998
1999
1996
1999
1999
1998
1999
1998
1996
1996
1998
1999
1998
1999
1998
1999
1996
1996
1999
1996
N
20
19
19
11
16
10
9
10
16
18
8
8
10
10
10
10
18
19
11
15
Average
TEQ
ND = 0
0.54
0.39
2.1
1.8
1.1
1.2
0.04
0.00
0.00022
0.00045
0.13
0.02
0.05
0.05
0.26
0.20
0.34
0.0016
0.00
0.44
Average
TEQ
ND = DL/2
0.63
0.51
2.1
1.9
1.1
1.2
0.19
0.24
0.055
0.15
0.31
0.21
0.26
0.19
0.36
0.36
0.35
0.074
0.16
0.44
N Total
39
30
26
19
16
20
38
Weighted
Average
ND = DL/2
0.57
2.0
1.2
0.22
0.26
0.23
0.36
Sources: Jensen and Bolger (2000) and Jensen et al. (2000).
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December 2003
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Table 3-26. Levels of PCBs in Fish Tissue, Bivalves, and Sediment
at a Site Near a Pulp and Paper Mill
Congener
Upstream
Mill Vicinity
Downstream
Fish Tissue (Whole Body)
PCB77 range (pg/g, wet wt.)
mean (pg/g, wet wt.)
mean TEQ (pg/g, wet wt.)
PCB126 range (pg/g, wet wt.)
mean (pg/g, wet wt.)
mean TEQ (pg/g, wet wt.)
PCB169 range (pg/g, wet wt.)
mean (pg/g, wet wt.)
mean TEQ (pg/g, wet wt.)
Total TEQP-WH094
26.0-120.6
56.0
0.028
12.3-30.5
20.7
2.07
1.2-2.8
2.0
0.020
2.12
14.2-1095.3
555.7
0.277
17.7
17.7
1.77
1.4
1.4
0.014
2.06
30.2-80.6
51.3
0.026
13.8-17.4
15.3
1.53
1.6-2.0
1.7
0.017
1.57
Bivalves
PCB77 range (pg/g, wet wt.)
mean (pg/g, wet wt.)
mean TEQ (pg/g, wet wt.)
PCB126 range (pg/g, wet wt.)
mean (pg/g, wet wt.)
mean TEQ (pg/g, wet wt.)
PCB169 range (pg/g, wet wt.)
mean (pg/g, wet wt.)
mean TEQ (pg/g, wet wt.)
Total TEQP-WH094
101.7
101.7
0.05
19.4
19.4
1.94
-
1.99
-
11.5
11.5
1.15
0.7
0.7
0.007
1.16
-
-
3.3
3.3
0.033
0.03
Sediment
PCB77 range (pg/g, wet wt.)
mean (pg/g, wet wt.)
mean TEQ (pg/g, wet wt.)
PCB126 range (pg/g, wet wt.)
mean (pg/g, wet wt.)
mean TEQ (pg/g, wet wt.)
PCB169 range (pg/g, wet wt.)
mean (pg/g, wet wt.)
mean TEQ (pg/g, wet wt.)
Total TEQP-WH094
9.5
9.5
0.005
0.9-1.1
1.0
0.10
1.5
1.5
0.015
0.12
-
-
-
-
27.8
27.8
0.009
1.38
1.38
0.14
0.5
0.5
0.005
0.16
NOTE: Results of sample analyses that showed interference or had recoveries below 40 percent or
above 120 percent (acceptability range specified by this study was 40 to 120 percent recovery)
were not included in the data set used here. Half the detection (or quantification) limit was used for
samples below the detection (or quantification) limit. TEFP-WH094s used in calculating the TEQP-
WH094s.
Source: Derived from Petreas (1991).
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Table 3-27. TEQDFP-WH098 Concentrations in Marine Fish
(pg/g lipid)
Fish Type
n
CDD/CDFs
Mean
Range
PCBs
Mean
Range
UK Landed
Cod
Haddock
Plaice
Whiting
Herring
Mackarel
Salmon
Fish Fingers
17
16
10
14
10
13
11
12
9
6.9
25
8.3
24
3.8
6.5
0.7
2.1 - 24
1.1- 14
3.6 -43
2.0 - 20
13-38
1.0- 9.0
4.6-11
0.3 - 2.4
17
7.4
42
23
59
14
19
1.6
3.3 - 76
2.2 - 22
9.5 - 55
2.4 - 91
12-110
2.5 - 31
12-30
1.3 - 6.2
Imported
Cod
Haddock
Plaice
Salmon
Red fish
13
10
3
1
2
6.1
4.6
20
3.4
14
1.4-18
1.9 - 8.5
16 - 27
3.4
12, 16
9.7
5.4
33
12
43
2.0 - 32
1.9-12
21 - 57
12
42, 44
Source: Robinson et al. (2000).
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Table 3-28. Mean CDD/CDF Profiles for Fish
2,3,7,8-Substituted
CDD/CDFs
2,3,7,8-TCDD
1,2,3,7,8-PeCDD
1,2,3,4,7,8-HxCDD
1,2,3,6,7,8-HxCDD
1,2,3, 7,8, 9-HxCDD
1,2,3,4,6,7,8-HpCDD
OCDD
2,3,7,8-TCDF
1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
1,2,3,4,7,8-HxCDF
1,2,3,6,7,8-HxCDF
1,2,3, 7,8, 9-HxCDF
2,3,4,6,7,8-HxCDF
1,2,3,4,6,7,8-HpCDF
1, 2,3,4,7,8, 9-HpCDF
OCDF
TOTAL
Freshwater Fisha
Concentration
Whole Weight
(PPt)
0.15
0.25
0.15
0.26
0.20
1.1
5.9
0.70
0
0.37
0
0
0
0
0
0
0
9.1
Fraction of
Total
CDD/CDFs
0.016
0.028
0.016
0.028
0.022
0.12
0.65
0.077
0
0.040
0
0
0
0
0
0
0
1.0
Marine Fishb
Concentration
Whole Weight
(PPt)
0.18
0.45
0.23
1.2
0.34
1.4
2.0
0.29
0.092
0.13
0.066
0.055
0
0.032
0.055
0
0.047
6.6
Fraction of
Total
CDD/CDFs
0.028
0.068
0.036
0.19
0.052
0.21
0.30
0.044
0.014
0.020
0.010
0.0084
0
0.0048
0.0084
0
0.0072
1.0
Shellfish"
Concentration
Whole Weight
(PPt)
0.15
0.41
0.36
0.67
0.62
2.3
8.6
0.67
0.089
0.17
0.18
0.077
0.018
0.066
0.24
0.012
0.066
10.2
Fraction of
Total
CDD/CDFs
0.014
0.040
0.035
0.066
0.061
0.23
0.84
0.066
0.0087
0.017
0.017
0.0075
0.0018
0.0065
0.024
0.0011
0.0065
1.0
Based on data from Schecter et al. (1997).
Based on data from Fiedler et al. (1997c).
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Table 3-29. Background CDD/CDF TEQs in Fish and Shellfish, Consumption Rates, and Intakes (Ages 18+ years)
Fish Class
Estuarine Finfish
Freshwater Finfish
Total Freshwater/Est. Finfish
Freshwater/Estuarine Shellfish
Total Freshwater/Est. Shellfish
Unknown Freshwater/Est. Species
Total Fresh. /Est. Fish
Marine Finfish
Species
Flounder (e)(f)
Catfish-nonfarmed(h)
Trout-nonfarmed (e,h)
Rockfish/Striped Bass (d)
Salmon (d)
Mullet (a)
Other
Flatfish
Perch
Croaker
Herring
Anchovy
Smelts
Eel
Sturgeon
Total Other*
Catfish-farmed (b,d,h)
Trout-farmed (e,h)
Perch (e) (walleye)
Carp (e)
Pike (e) (pickerel)
Salmon (d)
Other
Whitefish
Cisco
Smelts, Rainbow
Sturgeon
Total Other*
Shrimp (b,c)
Crab (b,d)
Crab (a)
Crab Average (i)
Oyster (b,d)
Oyster (a)
Oyster Average (i)
Scallop (d)
Crawfish (a)
Crayfish (e)
Crayfish (d)
Crayfish (i)
Other
Clam
Snails
Total Other**
Fish***
Tuna (c)
Cod (c)
Salmon (d)
Pollack (d)
Mackerel (a)
Consumption
Rate
(g/day)
0.58
0
0
0.043
0.042
0.034
0.39
0.19
0.13
0.12
0.042
0.0074
0.0038
0.00017
0.88
0.90
0.41
0.17
0.14
0.035
0.00083
0.012
0.0012
0.00050
0.00017
0.014
3.3
2.0
0.30
0.15
0.0011
0.0090
0.014
0.0017
0.0157
2.5
0.14
5.9
3.1
1.4
1.3
0.25
0.11
N
3
0
0
26
39
2
0
0
0
0
0
0
0
0
0
30
6
3
4
3
39
0
0
0
0
0
116
19
38
6
33
15
3
18
11
4
1
20
25
0
0
0
106
0
222
16
18
39
19
1
CDD/CDF TEQ
Cone.
(Pg/g fresh wt.)
1.8
1.2
0.57
0.068
1.3
2.0
1.9
1.2
1.2
0.49
0.57
1.3
1.6
0.080
0.36
0.84
0.37
0.45
0.69
0.47
0.16
1.0
1.0
0.23
0.30
0.43
0.14
1.3
1.0***
0.060
0.15
0.57
0.22
0.95
CDD/CDF
TEQ Intake
(pg/day)
1.0
0.052
0.024
0.0023
1.1
1.8
0.78
0.20
0.17
0.017
0.00047
0.018
5.3
0.16
0.11
0.070
0.00018
0.0027
0.0068
0.35
0.18
5.8
0.19
0.21
0.74
0.055
0.10
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Fish Class
Marine Finfish (continued)
Total Marine Finfish
Marine Shellfish
Total Marine Shellfish
Unknown Marine Species
Total Marine Fish and Shellfish
TOTAL FISH
Species
Other
Porgy
Haddock
Whiting
Squid
Perch
Sardine
Sea Bass
Swordfish
Pompano
Octopus
Flatfish
Halibut
Snapper
Whitefish
Smelt
Shark
Roe
Total Other*****
Scallop (d)
Lobster (d)
Crab (d)
Other
Clams
Mussels
Conch
Snails
Total Other*****
Seafood (g)***
Fish***
Consumption
Rate
(g/day)
0.36
0.31
0.26
0.17
0.13
0.12
0.10
0.098
0.084
0.073
0.045
0.035
0.032
0.012
0.0066
0.0046
0.0011
1.8
8.0
0.19
0.19
0.16
0.70
0.070
0.0021
0.0017
0.77
1.3
0.080
0.220
9.6
15.5
N
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
93
11
16
38
0
0
0
0
0
65
0
0
158
292(i)
CDD/CDFTEQ
Cone.
(Pg/g fresh wt.)
0.39
0.25
0.16
0.26
0.36
0.26
0.26
0.39
0.39
0.26****
0.53
CDD/CDF
TEQ Intake
(pg/day)
0.72
2.0
0.030
0.049
0.058
0.20
0.34
0.031
0.09
2.5
8.3
(a) Fiedler et al., 1997
(b) Jensen and Bolger, 2000
(c) Jensen (2000); personal communication by facsimile
(d) Jensen et al., 2000, with additional clarification by personal communication with FDA
(e) U.S. EPA, 1992
(f) Classified as marine by U.S. EPA, 1992
(g) Assumed to be marine, based on recommendation by EPA, Office of Water
(h) Catfish and trout were assumed to be entirely farm-raised
(i) Calculated as the average over all locations assuming that Jensen et al. (2000) and Jensen and Bolger (2000) collected
samples at different locations and Fiedler et al. (1992) collected samples from one location.
(j) Total N does not equal the sum of Ns of total freshwater/estuarine fish and total marine fish because the same data are used
in both categories for salmon, scallop, and crab. Thus, the N for these data were not double counted.
* For freshwater/estuarine species for which species-specific concentration data were not available, the average value from
U.S. EPA, 1992 was used
** For freshwater/estuarine shellfish species for which species-specific data were not available, the average value from the
species with known concentrations was used.
*** For unclassified fish, 39% of the consumption was assumed to be freshwater/estuarine and 61% was assumed to be marine,
based on recommendations by EPA, Office of Water.
**** This concentration is a species-specific ingestion-weighted average value.
***** For marine species for which species-specific concentration data were not available, the average value for the available
species shown here was used.
NOTE: Data from U.S. EPA, 1992 for the following species were not used here, except in the average freshwater/estuarine fish
concentration, because corresponding consumption data were not available: freshwater bass, crappie, dolly varden, redhorse,
rockbass, sucker, and sunfish.
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Table 3-30. FDA Dairy Data for 1995-1999 Combined
(WH098 TEQs Based on 17 2,3,7,8-substituted dioxin and furan congeners)
Ice cream
Yogurt
Butter
Milk
Cream
American cheese
Various (romano, Mozz)
Swiss cheese
Cheddar cheese
Cottage cheese
Cream cheese
Mozzarella cheese
Eggs
Year
1996
1996
1996
1996
1998
1996
1998
1998
N
40
20
22
44
19
30
19
14
39
24
25
18
20
Average
TEQ (ppt)
ND = 0
0.058
0.0069
0.060
0.029
0.22
0.067
0.21
0.05
Average
TEQ (ppt)
ND = DL/2
0.10
0.082
0.31
0.061
0.27
0.10
0.38
0.17
N Total
169
Weighted
Average
ND = DL/2
0.13
Sources: Jensen and Bolger (2000) and Jensen et al. (2000).
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Table 3-31. l-TEQDFs in Foods From Southern Mississippi
Food Sample
Catfish (farm-raised) Nuggets
Mullet Fillet
Spanish Mackerel Fillet
American Oyster Meat
Blue Crab
Claw Meat
Body (soft-shell)
Crawfish
Tail Muscle
Head and Digestive Gland
Butter
Milk
Cheddar Cheese
Eggs
Ground Beef
Chicken
Chicken Liver
Sausage
Number of
Observations
3
2
1
3
3
3
2
2
3
3
3
3
3
3
3
3
I-TEQDF
pg/g sample3
1.19, 2.64, 2.57
0.089, 0.027
0.72
0.62, 0.53, 0.60
0.06, 0.10, 0.09
1.09, 1.14, 1.44
0.033, 0.087
2.34, 1.55
0.683, 0.770, 0.552
0.025, 0.026, 0.012
0.300, 0.247, 0.254
0.038, 0.020, 0.019
0.196, 0.254, 0.152
0.043, 0.085, 0.053
0.031, 0.064, 0.070
0.178, 0.221, 0.282
a One-half the detection limit was used in calculating the l-TEQDFs.
Source: Cooper et al. (1 995).
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Table 3-32. Summary of Dioxin/Furan Food Data Collected in the California State Air Resources Board Study
Congener
2,3,7, 8-TCDD
1,2,3,7,8-PeCDD
1 ,2,3,4,7,8/1 ,2,3,6,7,8-HxCDD
1,2,3,7,8,9-HxCDD
1,2,3,4,6,7,8-HpCDD
OCDD
2,3,7, 8-TCDF
1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
1,2,3,4,7,8-HxCDF
1,2,3,6,7,8-HxCDF
1,2,3,7,8,9-HxCDF
2,3,4,6,7,8-HxCDF
1,2,3,4,6,7,8-HpCDF
1,2,3,4,7,8,9-HpCDF
OCDF
Beef
Fraction of
composite
samples
that were
positive
0/8
0/8
3/8
0/8
7/8
7/8
3/8
0/8
0/8
0/8
0/8
0/8
0/8
4/8
0/8
0/8
Concentration
range of
positive
samples
(pg/g)a-b
-
-
0.72-3.96
_
3.53-8.95
7.75-11.90
0.63-1.56
-
-
-
-
-
-
0.48-1.15
_
-
Pork
Fraction of
composite
samples
that were
positive
0/8
0/8
2/8
0/8
8/8
8/8
0/8
0/8
0/8
0/8
0/8
0/8
0/8
7/8
0/8
5/8
Concentration
range of
positive
samples
(pg/g)a-b
-
-
2.83-3.50
_
3.04-45.50
13.70-254.0
-
-
-
-
-
-
-
1.57-10.60
_
1.24-9.36
Chicken
Fraction of
composite
samples
that were
positive
3/8
0/8
1/8
2/8
7/8
7/8
1/8
0/8
0/8
0/8
0/8
0/8
0/8
6/8
0/8
2/8
Concentration
range of
positive
samples
(pg/g)a-b
0.31-1.67
-
2.29
2.14-4.30
1.10-35.20
2.61-96.20
0.67
-
-
-
-
-
-
1.01-24.60
_
3.79-26.00
CO
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CD
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Table 3-32. Summary of Dioxin/Furan Data Collected in the California State Air Resources Board Study (continued)
Congener
2,3,7, 8-TCDD
1,2,3,7,8-PeCDD
1 ,2,3,4,7,8/1 , 2,3,6,7, 8-HxCDD
1, 2,3,7, 8,9-HxCDD
1, 2,3,4,6,7, 8-HpCDD
OCDD
2,3,7,8-TCDF
1,2,3,7,8-PeCDF
2,3,4,7, 8-PeCDF
1, 2,3,4,7, 8-HxCDF
1, 2,3,6,7, 8-HxCDF
1, 2,3,7, 8,9-HxCDF
2,3,4,6,7, 8-HxCDF
1, 2,3,4,6,7, 8-HpCDF
1, 2,3,4,7, 8,9-HpCDF
OCDF
E
Fraction of
composite
samples that
were positive
0/8
0/8
0/8
0/8
0/8
1/8
1/8
0/8
0/8
0/8
0/8
0/8
0/8
1/8
0/8
0/8
ggs
Concentration
range of positive
samples (pg/g)c
_
-
_
_
_
1.30
0.011
-
-
-
-
-
-
0.065
_
-
[
Fraction of
composite
samples that
were positive
1/8
0/8
1/8
0/8
7/8
6/8
8/8
0/8
0/8
0/8
0/8
0/8
0/8
1/8
0/8
0/8
\/lilk
Concentration
range of positive
samples (pg/g)a-b
1.46
-
0.59
_
2.08-4.25
2.23-6.12
1.30-6.11
-
-
-
-
-
-
0.70
_
-
(freshwate
Fraction of
composite
samples that
were positive
8/10
6/10
7/10
4/10
5/10
10/10
10/10
0/10
0/10
0/10
0/10
0/10
0/10
2/110
1/10
0/10
zish
r & saltwater)
Concentration
range of positive
samples (pg/g)a'b
0.73-9.78
1.67-23.6
1.19-84.1
3.91-38.9
3.15-201
4.37-1490
0.83-28.2
-
-
-
-
-
-
2.21-92.9
13.3
0
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a Concentration reported on a lipid weight basis.
b For some of the concentrations reported, the ratio of characteristic ions were outside the qualitative identification data quality objectives.
c Concentration reported on a whole weight basis.
Source: Stanley and Bauer (1989).
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Table 3-33. Summary of U.S. Food Data from NCAS\ Study
Food
Milk
Half & Half
Ground beef
Corned beef hash
Beef hot dogs
Ground pork
Chicken broth
Coffee
Orange juice
Number
of
Samples
1
2
3
14
3
3
3
2
3
2,3,7, 8-TCDD Level3
(Food basis, pg/kg)
1.8
7.2; 8.7
17; 18; 62
7.2-20
12; 15; 37
ND(5.8); ND(6.5); ND(6.5)
1.1; 1.3; 1.5
ND(0.2); 0.08
ND(0.3); ND(0.3); ND(0.4)
2,3, 7, 8-TCDD Level3
(Lipid basis, pg/kg)
48
55; 67
71; 141; 352
54-144
44; 56; 128
ND(18); ND(22); ND(27)
(lipid content unknown)
NR
NR
2,3,7,8-TCDF Level3
(Food basis, pg/kg)
ND
NR
ND(3.8); ND(4.8); 5.2
ND(5.9); ND(17); 4.7-12
ND(7.7); 11; 11
13; 13; 20
NR
NR
NR
2,3,7,8-TCDF Level3
(Lipid basis, pg/kg)
ND
NR
ND(16); ND(27); 41
ND(39); ND(120); 33-103
ND(28); 38; 41
45; 53; 62
NR
NR
NR
CO
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3
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CD
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NOTE: ND = Not detected; NR = Not reported
3 Values in parentheses are detection limits.
Sources: Henry et al. (1 992); LaFleur et al. (1 990).
-------
Table 3-34. Summary of Schecter et al. (1993a) Data on U.S. Foods
Haddock Fillet
Crunchy Haddock
Perch
Cod
Ground Beef
Beef Rib Sirloin Tip
Beef Rib Steak
Pork Chop
Cooked Ham
Lamb Sirloin
Lebanon Bologna
Chicken
Cottage Cheese
Blue Cheese
Cream Cheese
American Cheese
Heavy Cream
CDD/CDF
I-TEQDF (pg/g)a
Assuming
ND = 0.5 DL
0.03
0.13
0.24
0.023
1.5
0.04
0.65
0.26
0.029
0.4
0.12
0.03
0.04
0.73
0.38
0.31
0.35
CDD/CDF
I-TEQDF (pg/g)a
Assuming
ND = 0
0.02
0.13
0.24
0.012
1.5
0.04
0.65
0.26
0.024
0.4
0.11
0.03
0.04
0.70
0.38
0.31
0.33
Number of
Samples'5
2
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
ND = nondetect; DL = detection limit
a Concentrations reported on whole food, wet weight basis.
b Samples collected from a supermarket in New York.
Source: Schecter et al. (1 993a).
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Table 3-35. CDD/CDFs and PCBs in Foods from Five Regions of the United States
Congener
% Lipid
CDD/Fs (pa/a, lipid-based)
2,3,7,8-TCDF
2,3,7,8-TCDD
1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
1,2,3,7,8-PeCDD
1,2,3,4,7,8-HxCDF
1,2,3,6,7,8-HxCDF
2,3,4,6,7,8-HxCDF
1,2,3, 7,8, 9-HxCDF
1,2,3,4,7,8-HxCDD
1,2,3,6,7,8-HxCDD
1,2,3, 7,8, 9-HxCDD
1,2,3,4,6,7,8-HpCDF
1,2,3,4,7,8,9-HpCDF
1,2,3,4,6,7,8-HpCDD
1, 2,3,4,6,7,8, 9-OCDF
1, 2,3,4,6,7,8, 9-OCDD
l-TEQnCs (pg/g)
lipid-based min.
lipid-based max.
whole-weight min.
whole-weight max.
PCBs (ng/g, lipid-based)
# 118
# 114
# 153
# 105
# 138
# 128
# 156
# 180
Beef
N = 9
13.13
0.488
ND (.19)"
ND (.95)b
ND (.95)b
ND (.95)b
ND (1.22 EMPC)a
ND (2.15 EMPC)a
ND (.95)b
ND (.95)b
ND (1.39 EMPC)a
4.92
1.04
ND (10.8 EMPC)a
ND (.95)b
20.9
ND (3 EMPC)a
32.7
0.89
2.86
0.12
0.38
0.717
-
0.629
-
-
-
-
-
Chicken
N = 7
5.33
ND (1.88 EMPC)a
ND (.467)"
ND (2.34)b
2.6
ND (2.34)b
ND (2.34)b
ND (2.84 EMPC)a
ND (2.34)b
ND (2.91)b
ND (2.34)b
ND (2.34)b
ND (2.34)b
ND (5.49 EMPC)a
ND (2.34)b
8.09
ND (4.67)b
20.2
0.10
5.17
0.005
0.28
3.70
-
2.08
1.47
0.747
-
-
4.32
Ocean Fish
N = 13
1.43
ND (11.6 EMPC)a
2.3
ND (8.74)b
ND (8.74)b
ND (8.74)b
ND (10.8)b
ND (16.6 EMPC)a
ND (9.51)b
ND (14.5)b
ND (8.74)b
ND (8.74)b
ND (8.74)b
ND (48.7 EMPC)a
ND (8.74)b
11.7
ND (17.5)b
31.6
2.45
21.14
0.035
0.30
22.3
-
27.4
8.34
30.2
-
-
11.8
Fresh Fish
N = 10
4.83
14.4
3.09
ND (7.59 EMPC)a
7.56
5.2
ND (3.36 EMPC)a
ND (19.9 EMPC)a
ND (2.58)b
ND (2.58)b
3.01
5.31
4.11
ND (31 EMPC)a
ND (2.72)b
23.5
ND (5.17)b
122
12.51
16.07
0.60
0.78
36.3
-
39.3
12.4
37.4
5.59
7.27
12.6
Pork
N = 7
9.18
1.97
ND (.349 EMPC)a
ND (1.36)b
ND (1.36)b
ND (1.36)b
ND (1.47 EMPC)a
ND (6.46 EMPC)a
ND (1.36)b
ND (1.36)b
ND (1.36)b
1.81
ND (1.36)b
ND (20.2 EMPC)a
ND (1.36)b
17.1
ND (3.26 EMPC)a
87.1
0.64
3.97
0.06
0.36
-
-
0.785
-
1.07
-
-
-
Nondetected due to an interference. An estimated maximum possible concentration (EMPC) is given as the detection limit.
Nondetected, with the curve based detection limit in pg/g.
Calculated using a value of zero for nondetected congeners.
Calculated using the detection limit value for nondetected congeners.
Source: Schecter et al. (1997).
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Table 3-36. I-TEQDF Levels in Cow's Milk and Infant Formula
from the United States and Thailand
Product/Origin
Cow's Milk/NY
2% Cow's Milk/NY
Ultra Pasteurized
Heavy Cream/NY
Similac Formula/NY
Isomil Formula/NY
Prosoybee Formula/NY
Laclasoy UHT
Soy Milk/Thailand
Thai-Danish UHT/Thailand
Lipid-based I-TEQDF (ppt)
1.2
0.1
0.38
0.004
0.003
0.005a
0.23
0.32
Fat Content (%)
3.4
1.9
39.1
_b
3.2
2.5
2.9
3.5
a Contamination from cap liner.
b Not available.
Source: Schecter et al. (1 989b).
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Table 3-37. Maximum CDD/CDF Levels in Foods Collected in Canada
(pg/g fresh weight) as Reported by Birmingham et al. (1989)
TCDD
PeCDD
HxCDD
HpCDD
OCDD
TCDF
PeCDF
HxCDF
HpCDF
OCDF
I-TEQDF
Eggs
ND
ND
ND
ND
8a
ND
ND
5a
7a
12a
0.59
Beef
ND
ND
ND
ND
24a
ND
ND
ND
ND
ND
0.29
Milk
Products
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
0.11
Pork
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
0.03
Chicken
ND
ND
ND
15
210
ND
ND
ND
ND
ND
0.39
NR = Not Reported.
ND = Not Detected; detection limits ranged from 0.1 to 0.7 pg/g.
a Data for foods of U.S. origin collected in Canada.
Source: Birmingham et al. (1989).
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Table 3-38. Summary of TEQ Levels in Toronto (1992) and Montreal (1993)'
Food
Category
Meat
Dairy
Fish
Oils
Composite
Sample
Beef Ground
Beef Steak
Beef Roast
Pork Cured
Organ Meat
Poultry
Whole Milk
1 % Milk
Cream
Cheese Cheddar
Butter
Fresh Water
Marine
Cooking Fats
and Salad Oil
CDD/CDF
(ppt) I-TEQDF
Toronto
0.32
0.18
0.087
0.045
0.29
0.066
0.038
0.024
0.079
0.24
0.50
0.26
0.033
0.42
Montreal
0.32
0.17
0.14
0.044
0.37
0.043
0.031
0.021
0.076
0.20
0.33
0.16
0.013
0.28
Non-Ortho PCB
(ppt) TEQP-WH094
Toronto
0.067
0.017
0.014
0.007
0.034
0.010
0.033
0.012
0.066
.015
0.42
0.36
0.24
0.019
Montreal
0.045
0.016
0.018
0.004
0.052
0.019
0.096
0.004
0.062
0.16
0.29
0.32
0.105
0.029
Z CDD/CDF/PCB
TEQ (ppt)
Toronto
0.39
0.19
0.10
0.053
0.32
0.076
0.072
0.036
0.145
0.39
0.93
0.62
0.28
0.44
Montreal
0.37
0.18
0.16
0.049
0.42
0.062
0.041
0.025
0.138
0.36
0.62
0.48
0.12
0.31
* N = 44 composites for each city.
Source: Ryan et al. (1997).
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Table 3-39. Example of Method for Estimating Fat Content (percent) of Beef
Item
Number
1
2
3
4-146
SUM
Weighted
Average0
Beef Item Description
Beef, ground, regular, cooked,
broiled, medium
Beef, ground, lean, cooked,
broiled, medium
Beef, short loin, top serloin,
choice
-
-
Fat
Fraction
0.2069
0.1846
0.1097
-
-
Beef Intake3
(g)
5372916272
1804281336
791973251
17855973824
2.58E+10
Fat Intake from
Beefb
(g)
1111656377
333070335
86879466
2775999294
4.31E + 9
0.167
a Total beef intake for the U.S. population in a day based on survey data weighted to the U.S.
population.
b Fat intake from beef calculated as the beef intake multiplied by the fat fraction.
c Calculated as the sum of fat intake from beef (g) divided by the sum of beef intake (g).
Source: Data provided by David Haytowitz, USDA, by personal communication to Linda Phillips,
Versar, Inc., January 2001.
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o
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^1
D
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O
§
Table 3-40. Summary of Coplanar PCBs in a Statistical Sample of Beef Fat in the United States
Description
Number of Samples
Limits of Detection, ppt
Percent Positive
Mean, ppt
ND = 1/2 DL
ND = 0.00
Range, ppt
TEFP-WH094
TEQP-WH094 Concentration,
ppt (ND = 1/2 LOD)
TEFP-WH094
TEQP-WH094 Concentration,
ppt (ND = 1/2 LOD)
PCB 77
63
1.00
20
1.00
0.60
ND - 7.97
0.0005
5. Ox 10'4
0.0001
1 x 10'4
PCB 118
63
30.0
100
440.5
440.5
61 - 2295
0.0001
4.4x 10'2
0.0001
4.4x 10'2
PCB 105
63
14.0
88
91.5
90.6
ND - 438
0.0001
9.2 x 10'3
0.0001
9.2 x 10'3
PCB 126
63
0.3
100
4.0
4.0
0.74 - 23.2
0.1
4. Ox 10'1
0.1
4. Ox 10'1
PCB 156
63
4.0
100
58.7
58.7
4.87 - 426
0.0005
2.9 x 10'2
0.0005
2.9 x 10'2
PCB 157
63
1.0
99
13.4
13.4
ND - 91.7
0.0005
6.7 x 10'3
0.0005
6.7 x 10'3
PCB 169
63
0.2
94
0.69
0.69
ND - 2.4
0.01
6.9x 10'3
0.01
6.9 x 10'3
CO
O
CD
O
CD
3
CT
CD
—^
ro
o
o
GO
Source: Winters et al. (1 996b).
-------
Table 3-41. Concentration Levels of CDD/CDF Congeners in Back Fat and Ratios of Muscle Fat/Back Fat in Cattle
Congener
2,3,7,8-TCDD
1,2,3,7,8-PeCDD
1,2,3,4,7,8-HxCDD
1,2,3,6,7,8-HxCDD
1,2,3,7,8,9-HxCDD
1,2,3,4,6,7,8-
HpCDD
OCDD
2,3,7,8-TCDF
1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
1,2,3,4,7,8-HxCDD
1,2,3,6,7,8-HxCDF
1,2,3,7,8,9-HxCDF
2,3,4,6,7,8-HxCDF
1,2,3,4,6,7,8-HpCDF
1,2,3,4,7,8,9-HpCDF
OCDF
PCB77
PCB 118
PCB 105
PCB 126
PCB 156
PCB 157
PCB 169
I-TEQDF
TEQDF-WH098
TEQP-WH094 or
TEQp-WHOQR
Oregon State University
Back Fat
Concentration
(ppt lipid)
0.3
2.3
2.2
7.7
2.3
8.0
6.3
0
0
0.6
0.8
0.7
0
0.9
1.3
0
0
0.7
859
145
8.4
88.4
20.7
1.3
3.3
4.4
1.0
Ratio
Muscle/
Back Fat
1.2
0.5
0.6
0.8
0.7
0.8
1.7
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
12.6
1.7
2.8
0.9
0.9
1.0
1.2
0.60
0.58
1.0
North Dakota State
University
Back Fat
Concentration
(ppt lipid)
0.3
1.6
2.4
9.3
2.3
24.8
33.0
0
0
1.2
1.6
1.3
0
1.2
4.3
0
0
2.0
1087
237
11.0
105
26.3
4.7
3.8
4.6
1.3
Ratio
Muscle/
Back Fat
0.9
0.6
0.5
0.7
0.3
0.7
0.8
NA
NA
0.7
0.7
0.3
NA
0.2
0.4
NA
NA
2.8
1.2
1.2
1.3
1.0
1.0
1.1
0.64
0.64
1.2
Pennsylvania State
University
Back Fat
Concentration
(ppt lipid)
0.2
5.1
6.4
24.6
7.3
41.7
12.8
0
0
1.4
1.7
1.7
0
1.2
2.8
0
0
1.7
1332
233
8.8
102
23.1
1.6
8.3
10.8
1.1
Ratio
Muscle/
Back Fat
1.2
0.9
0.9
1.0
0.8
0.9
1.6
NA
NA
0.6
0.7
0.5
NA
0.3
0.6
NA
NA
7.3
1.8
2.8
1.1
1.5
1.4
1.1
0.86
0.86
1.3
Pennsylvania State
University
Back Fat
Concentration
(ppt lipid)
0.6
9.9
7.3
36.1
11.6
56.7
33.7
0
0
2.9
2.7
6.5
0
4.3
10.6
0
0.1
16.5
3551
612
27.8
390
83.4
4.5
14.6
19.5
3.5
Ratio
Muscle/
Back Fat
1.4
1.7
2.4
1.8
2.7
1.7
2.4
NA
NA
1.0
0.5
1.7
NA
1.3
3.4
NA
NA
16.7
2.9
5.7
0.8
1.5
1.4
0.7
1.7
1.7
1.1
Pennsylvania State
University
Back Fat
Concentration
(ppt lipid)
0.9
31.5
18.9
60.4
25.0
65.7
19.6
0
0
6.3
5.2
14.0
0
16.9
24.2
0.4
0.6
19.5
3649
486
18.1
281
69.7
2.7
34.8
50.5
2.4
Ratio
Muscle/
Back Fat
1.1
0.6
0.8
0.9
0.8
1.1
1.5
NA
NA
0.6
0.3
0.7
NA
0.4
1.0
NA
1.0
4.7
2.5
5.3
1.2
1.3
1.1
NA
0.71
0.69
1.5
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^1
D
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00
O
CD
O
CD
3
cr
CD
—^
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NA = Either (or both) intramuscular and back fat samples had nondetect concentration, such that a ratio could not be derived.
Source: Lorber et al. (1997a).
-------
Table 3-42. TEQDFP-WH098 Summary of Nationally Extrapolated Pork Results on a Lipid
Basis Assuming Nondetects (ND) Equal % Detection Limit
(results are in ppt, or pg/g; ND = 0 results are in parenthesis).
Description
Overall
Market Hogs
Sows
Boars
Dioxins and Furans
Mean
1.48
(0.46)
1.44
(0.42)
1.85
(0.94)
3.60
(3.03)
Standard
Deviation
1.47
(1.50)
1.17
(1.34)
1.46
(1.76)
6.31
(6.63)
Min/Max
0.76/22.47
(0/22.76)
0.78/9.30
(0/9.58)
0.80/5.63
(0/5.41)
0.76/22.47
(0/22.76)
Coplanar PCBs
Mean
0.06
(0.04)
0.06
(0.04)
0.06
(0.04)
0.27
(0.26)
Standard
Deviation
0.08
(0.09)
0.07
(0.08)
0.03
(0.04)
0.48
(0.48)
Min/Max
0.02/1.66
(0/1.66)
0.02/0.40
(0/0.11)
0.02/0.11
(0/0.11)
0.02/1.66
(0/1.66)
Source: Lorber et al. (1 997b).
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Table 3-43. TEQDFP-WH098 Summary of Nationally Exptrapolated Results
From U.S. Poultry Fat on a Lipid Basis Assuming
Nondetects (ND) Equal 1/2 Detection Limit
(Results are in ppt, or pg/g; ND = 0 results are in parenthesis)
Description
Overall
Young
Chickens
Light Fowl
Heavy Fowl
Young Turkeys
Dioxins and Furans
Mean
0.77
(0.48)
0.76
(0.47)
0.47
(0.16)
1.14
(0.90)
1.09
(0.88)
Max
4.72
4.08
0.92
2.60
4.72
Coplanar PCBs
Mean
0.29
(0.29)
0.28
(0.28)
0.27
(0.27)
0.34
(0.34)
0.65
(0.65)
Max
1.68
1.68
0.75
1.12
1.28
Source: Ferrario, et al. (1997).
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Table 3-44. CDD/CDF Concentrations in Eggs and TEQDF-WH098s
2,3,7,8-TCDD
1,2,3,7,8-PeCDD
1,2, 3,4,7, 8-HxCDD
1,2,3,6,7,8-HxCDD
1,2,3,7,8,9-HxCDD
1,2, 3,4,6,7, 8-HpCDD
OCDD
2,3,7,8-TCDF
1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
1,2, 3,4,7, 8-HxCDF
1,2,3,6,7,8-HxCDF
2,3,4,6,7,8-HxCDF
1,2,3,7,8,9-HxCDF
1,2, 3,4,6,7, 8-HpCDF
1,2, 3,4,7, 8,9-HpCDF
OCDF
TOTAL
Number of
Positive
Samples
1
0
0
0
0
13
1.4
2
3
3
1
1
NR
0
5
0
5
Mean Positive
Samples (ppt)
0.009
--
--
0.31
1.2
0.042
0.043
0.061
0.12
0.081
NA
--
0.11
0.12
Number of
Non-detect
Samples
14
15
15
15
15
2
1
13
12
12
14
14
NR
15
10
15
10
LOD (ppt)
0.02
0.04
0.08
0.09
0.14
0.2
0.04
0.05
0.046
0.095
0.094
NA
0.11
0.098
0.1
0.18
Mean Cone.
ND = 1/2
LOD (ppt)
0.0099
0.020
0.040
0.045
0.040
0.28
1.1
0.023
0.029
0.031
0.052
0.049
NA
0.055
0.069
0.050
0.10
Mean Cone.
ND=0
(PPt)
0.0006
0
0
0
0
0.27
1.1
0.0056
0.0086
0.01
0.008
0.0054
NA
0
0.037
0
0.04
TEQ
ND = 1/2
LOD (ppt)
0.0099
0.02
0.004
0.0045
0.004
0.0028
0.00011
0.0023
0.0014
0.015
0.0052
0.0049
NA
0.0055
0.00069
0.0005
0.00001
0.081
TEQ
ND = 0
(PPt)
0.0006
0
0
0
0
0.0027
0.0011
0.00056
0.00043
0.0061
0.0008
0.00054
0
0
0.00037
0
0.00004
0.013
Notes: ND = non-detect
NR = not reported
LOD = limit of detection
Source: Hayward and Bolger (2000).
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Table 3-45. Weighted Milk Fat Percent
Males >20 years
Females >20 years
Average > 20 years
Fraction of Total Milk Intake
Percent Fat
Weighted Percent Fat (fraction of total
milk intake * percent fat)
Whole Milk
Intake
(g/day)
66
46
56
0.326
3.19
1.04
Low Fat
Milk Intake
(g/day)
92
74
83
0.483
1.3
0.63
Skim Milk
Intake
(g/day)
32
34
33
0.192
0.7
0.13
Total Milk
Intake
(g/day)
190
154
172
1
-
1.80
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Table 3-46 Average Congener Concentrations of 8 Composite Milk Samples
(pg/g lipid; ND = 0 in parenthesis)
CDDs
2,3,7,8-TCDD
1,2,3,7,8-PCDD
1,2,3,4,7,8-HxCDD
1,2,3,6,7,8-HxCDD
1,2,3, 7,8, 9-HxCDD
1,2,3,4,6,7,8-
HpCDD
OCDD
Concentration
0.07 (0.07)
0.32(0.32)
0.39 (0.39)
1.87 (1.87)
0.55 (0.55)
5.03 (5.03)
4.89 (4.89)
CDFs
2,3,7,8-TCDF
1,2,3,7,8-PCDF
2,3,4,7,8-PCDF
1,2,3,4,7,8-HxCDF
1,2,3,6,7,8-HxCDF
1,2,3, 7,8, 9-HxCDF
2,3,4,6,7,8-HxCDF
1,2,3,4,6,7,8-HpCDF
1, 2,3,4,7,8, 9-HpCDF
OCDF
TEQDF-WH098
Concentration
0.08 (0.08)
0.05 (0)
0.28 (0.28)
0.39 (0.39)
0.25 (0.25)
0.05 (0)
0.28 (0.28)
0.83 (0.83)
0.05 (0)
0.05 (0)
0.98 (0.97)
PCBs
PCB77
PCB 118
PCB 105
PCB 126
PCB 156
PCB 157
PCB 169
TEQP-WH098
Concentration
10.6 (10.6)
685.3 (685.3)
170.3 (170.3)
3.6 (3.6)
60.1 (60.1)
13.8 (13.8)
0.5 (0.5)
0.49 (0.49)
Source: Lorber et al. (1998b).
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o
o
^1
D
C
O
§
Table 3-47 Calculation of Fractional Fat Content of Dairy Products Category
Milk or Dairy Product3
Whole milk
Lowfat milk
Skim milk
Yogurt
Cheese
Milk desserts and otherc
TOTAL
Mean Whole Weight
Dietary Dairy Intake
(g/day)b
56
83
33
4.5
14
39.5
230
Mean Fat
Content
(%)
3.16
1.33
0.17
1.47
25.3
8.25(d)
Total Dairy Fat
Intake (g/day)
1.77
1.10
0.06
0.07
3.54
3.26(d)
9.80
Mean Whole Weight
Dietary Intake of Dairy
Other Than Milk (g/day)
--
--
--
4.5
14
39.5
58
Percent Dietary
Intake of Dairy
Other Than Milk
(%)
--
--
--
7.76
24.14
68.10
100
Dairy
Product
Fat
(%)
--
--
--
0.11
6.11
5.62
11.84(e)
GO
-J
O
CD
O
CD
3
CT
CD
—^
ro
o
o
GO
a CSFII food categories taken from USDA (1 995).
b USDA (1995).
c Milk desserts = 21 g/day. Because total dairy = 230 g/day and total dairy minus milk desserts and other = 190.5; other is assumed to =
18.5 g/day (i.e., 230 minus 190.5 minus 21 = 18.5).
d No fat content data available for milk desserts and other dairy products. Total dietary fat intake for milk desserts and other calculated as the
total dietary fat intake (i.e., 9.8 g/day, based on adult total fat intake of 81.39 g/day [CDC, 1994; as cited in U.S. EPA, 1997]), times the
fraction of fat that comes from dairy products (i.e., 0.1 2, based on NLMB, 1 993; as cited in U.S. EPA, 1 997), minus the dairy fat intake of
the other milk and dairy foods [i.e., (81.39 g/day * 0.1 2) - (6.54 g/day) = 3.26 g/day]. The mean fat content of milk desserts and other was
calculated as the dairy fat intake (3.26 g fat/day) divided by the dietary intake (39.5 g/day).
e The overall fractional fat content of dairy products other than milk (11.84%) is calculated as the sum of the products of the fractions of dairy
intake other than milk times the fractional fat contents, on the various non-milk dairy products [i.e., (0.0776 * 0.0147) + (0.2414 * 0.253)
+ (0.6810 * 0.0825) = 0.1184].
-------
Table 3-48 CDD/CDF Levels in German Food
Cow's Milk
Cheese
Butter
Beef
Veal
Pork
Sheep
Chicken
Canned Meat
Lard
Fresh Water Fish
Salt Water Fish
Fish Oil
Cod Liver Oil
Salad Oil
Margarine
Infant Formula
Mean l-TEQDFa
(pg/g fat)
1.35
0.98
0.66
1.69
3.22
<0.4
1.23
1.41
1.29
0.47
13.25
16.82
2.64
13.31
<0.4
<0.4
0.5
Number of Samples
168
10
5
3
4
3
2
2
2
4
18
15
4
4
4
6
10
a I-TEQDF computed using one-half the detection limit for nondetects.
Sources: Milk data based on Furst et al. (1991); other data from Furst et al. (1990).
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Table 3-49. CDD/CDF Background Levels in Some European, Canadian,
and New Zealand Food
Country
Germany
United Kingdom
Netherlands
New Zealand
Germany
Canada
Food
Cow's milk
Cow's milk
Cow's milk
Cow's milk
Cow's milk
Pork
Beef
Veal
Sheep
Poultry
Canned Meat
Lard
Beef
Pork
Poultry
Source
Background contamination
Consumer's milk
Rural area
Background contamination
Background contamination
pg l-TEQDF/g
fat
1.0 - 2.8
0.8 - 2.6
1.3
0.7 - 2.5
0.18 - 0.22
0.5
3.5
7.4
2.0
2.3
1.7
0.8
2.9
0.2
2.6
Source: Furst et al. (1991).
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Table 3-50. CDD/CDF Levels in German Food (1993-1996)
Sample
Cow's Milk
Cheese
Butter
Beef
Veal
Lamb, Sheep, Mutton
Poultry
Eggs
Ham
Venison
Sausage
Kidney (sheep)
Horsemeat
Salt-water Fish
Trout
Rhine River Fish
Artificial Lake Fish
Vegetables growing underground (e.g.,
potatoes, carrots)
Vegetables growing on the ground (e.g.,
zucchini, Beetroot, kohlrabi, celery, and
onions)
Leak vegetables (e.g., lettuce, sugarloaf,
endive, savoy cabbage, leek, white cabbage)
Fruit growing above ground (e.g., apples and
tomatoes)
Mean I-TEQDF
(pg/g fat)
0.71
0.66
0.64
0.71
0.95
0.52
0.62
2.10
0.39
1.41
0.28
1.11
3.76
14.7
7.44
39
104.1
16.9
12.2
12.9
4.3
Number of Samples
538
99
222
14
11
13
19
218
8
6
1
1
1
42
61
19
14
13
18
22
4
Source: Malisch (1998).
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Table 3-51. I-TEQDF Levels in Dairy Products in France
Type of Sample
Cow's Milk
Cheese
Butter
Milk Dessert and Cream
Mean I-TEQDF
Total pg/g Fat
1.91
1.11
1.01
1.34
Number of Samples
12
20
8
12
Source: Defour et al. (1 997).
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Table 3-52. l-TEQnF Concentrations in Food from the Netherlands
Food Category
Beef
Cow's Liver
Pork
Pig's Liver
Poultry
Chicken's Liver
Mutton
Horse Meat
Game a
Butter
Cheese a
Nutsa
Cereals a
Eggs
Fatty Sea Fish a
Lean Fish a
Eel
Fresh Water Fish a
Mixed Meat Product a
Dairy Products
Soy Bean Oil
Rape-Seed Oil
Palm Oil
Sunflower Oil
Coconut Fat
Palm Fat
Fish Oil
Items with Vegetable Oil a
Items from Food Industry a
CDD/CDFs
(pg l-TEQDF/g fat)
1.75
5.7
0.43
15.3
1.65
3.25
1.85
13.85
16.8
1.8
1.4
0.2
0.34
2.0
6.65
48.65
28.0
2.4
0.67
1.58
0.025
0.006
0.030
0.006
0.024
0.010
2.2
0.02
0.41
a A proportional mixture of food items was analyzed.
Source: Theelen et al. (1993).
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Table 3-53. I-TEQDF Concentrations in Food from Spain
Food Category
Vegetables
Pulses
Cerales
Fruits
White Fish
Seafood
Tinned Fish
Blue Fish
Pork and Pork Products
Chicken and Chicken Products
Beef and Beef Products
Lamb
Eggs
Dairy Products
Whole Milk
Semi-Skimmed Milk
Oil
Margarine
CDD/CDFs
(pg l-TEQDF/g fat)
-
-
-
-
5.39
10.59
2.57
7.90
0.90
1.15
1.76
1.76
1.22
1.25
1.02
1.20
0.64
0.49
CDD/CDFs
(pg l-TEQDF/g wet weight)
0.14
0.19
0.25
0.09
0.27
0.42
0.24
0.76
0.11
0.11
0.13
0.13
0.12
0.04
0.18
0.06
-
-
Source: Domingo et al. (1999).
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Table 3-54. l-TEQnF Concentrations in UK Foods in 1988
Food Product
Fish3'"
Carcass Meat3
Offals (internal organs)3
Poultry3
Meat Products3
Milk Products3
Butterb
Cheddar Cheesec
Reduced Fat Cheesec
Fats and Oils3
Eggs3
Green Vegetables3
Other Vegetables3
Potatoes3
Fresh Fruit3
Mean I-TEQDF (ppt)
Norwich
nd = 0
0.69
—
—
1.2
0.16
0.09
—
-_
—
-_
—
--
nd = LOD
0.73
—
—
1.2
0.16
0.12
—
-_
—
-_
—
--
Port Talbot
nd = 0
0.63
1.1
0.20
0.37
0.20
0.08
-_
—
-_
0.84
0.22
0.01
0.05
0.04
<0.01
nd = LOD
0.63
1.1
0.22
0.37
0.20
0.33
-_
—
-_
0.88
0.22
0.02
0.12
0.04
0.04
Stonehaven
nd = 0
0.07
0.18
0.62
0.28
0.17
0.09
-_
—
-_
0.11
0.16
<0.01
0.06
0.02
0.04
nd = LOD
0.07
0.26
0.69
0.29
0.21
0.09
-_
—
-_
0.41
0.16
0.01
0.06
0.03
0.06
3 One sample analyzed from Port Talbot, and one sample analyzed from Stonehaven.
b Eight samples analyzed.
c Two samples analyzed.
Source: MAFF (1992).
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Table 3-55. I-TEQDF Concentrations in Bottled Cow's Milk
from the United Kingdom
Winter 1990 (n = 8)
Summer 1990 (n = 7)
Rural 1989 (n = 9)
Urban 1989 (n = 9)
Mean (ppt)
nd=0
0.08
0.06
0.04
0.19
Range (ppt)
nd=0
0.05-0.13
0.04-0.07
0.03-0.06
0.12-0.27
Mean (ppt)
nd = LOD
0.09
0.06
0.05
0.20
Range (ppt)
nd = LOD
0.05-0.13
0.05-0.07
0.04-0.06
0.12-0.27
Source: MAFF (1992).
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Table 3-56. Concentrations and Concentration Ranges (pg/g fresh weight)
of Four Dioxin-Like PCBs in Foods from Finland
Baltic Herring (n = 6)
Rainbow Trout (n = 4)
Other Fish (n = 4)
Beef (n = 6)
Pork (n = 3)
Poultry (n = 2)
Inner Organs (n = 5)
Eggs (n = 2)
Fish Liver Oil (n = 2)
Mean
Range
Mean
Range
Mean
Range
Mean
Range
Mean
Range
Mean
Mean
Range
Mean
Mean
PCB 77
97
33-136
100
8.0- 150
53
5.6 - 153
13
0.6-38
13
1.0-24
8.2
3.2
nd - 7.9
4.1
2700
PCB 105
1700
960 - 2700
1200
410- 2100
400
1 1 3 - 1 1 00
22
5.3-38
24
11-47
68
45
8.1 - 111
98
30000
PCB 126
17
7.4- 26
17
5.2-35
11
2.3 - 28
3.2
0.3-7.3
1.5
0.5 -3.7
1.2
2.6
0.4- 6.0
2.9
620
PCB 169
4.5
nd - 12
3.9
nd - 7.4
1.9
0.6 - 6.5
0.5
nd - 1.0
0.8
nd - 2.2
nd
0.3
nd -0.6
0.1
130
Note: nd = not detected.
Source: Himberg (1993).
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Table 3-57. l-TEQDFs (ppt wet weight) in Foods From the Former Soviet Union
Site
Moscow
Irkutsk
Novosibirsk
Baikalsk
Food Product
Lamb
Swiss Cheese
Sausage
Hot Sausage
Turkey Fat & Meat
Hamburger (cooked)
Beef
Ground Beef (cooked)
Fish (Motba)
Pork Fat
Pork Fat
Pork Meat
Lamb Fat
Soft Cheese
Duck Livers
Cow Cream
Uncooked Beef
Pork Fat
Cheese
Vanilla Ice Cream
Fish
Smoked Fish
Meatball
Chicken
Mintai Fish (cooked)
Cheese with Butter
Beef Fat
Fat Head Minnow
Pork
Butter
Lipid Content (%)
23.8
3.0
57.0
9.4
29.9
6.7
24.0
8.5
2.0
54.8
50.8
9.8
43.5
4.9
3.0
15.0
3.0
34.1
27.6
1.0
9.2
16.3
4.7
24.6
5.8
36.0
34.0
2.7
72.0
53.0
I-TEQDF Concentration
ppt wet weight 1
0.30
0.04
0.60
0.15
0.30
0.02
0.13
0.24
0.70
0.15
0.20
0.05
0.40
0.04
0.08
0.80
0.13
0.06
0.17
0.005
0.07
0.80
0.02
0.05
0.14
0.40
0.30
1.3
0.9
1.4
1 One-half the detection limit was used in calculating the l-TEQDFs.
Source: Schecter et al. (1 992b).
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Table 3-58. Percentage Reduction of Total 2,3,7,8-TCDD Residues
in Restructured Carp Fillets from Cooking
Cooking Method
Roasted covered
Roasted uncovered
Roasted uncovered
Charbroiled
Charbroiled
Fillet Diameter
7.5
7.5
10.0
7.5
10.0
End Temperature °C
60
70
80
60
70
80
80
60
80
80
TCDD Concentration Reduction
control3
41.4 ±4.0
50.5 ± 7.9
63.4 ± 4.3
34.2 ± 8.7
49.2 ± 6.3
56.6 ± 5.9
65.9 ± 2.6
55.3 ± 6.5
62.0 ± 4.3
67.5 ± 7.8
spikedb
44.2 ± 3.9
47.7 ± 2.1
55.0 ± 3.9
47.0 ± 4.4
51.3 ± 2.4
57.5 ± 2.0
59.2 ± 2.4
59.3 ± 5.2
63.6 ± 3.7
70.6 ±7.7
N = 4
Samples were spiked to approximately 100 ppt 2,3,7,8-TCDD.
Source: Stachiw et al. (1988).
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Table 3-59. Effects of Cooking and Trimming on PCB Levels in Lake Ontario Fish
Method
Trimming off fat and skin
Trimming off fat and skin
Deep frying trimmed fillets
Smoking untrimmed fillets
Baking untrimmed fillets
Broiling trimmed fillets
Species
Smallmouth Bass
Brown Trout
Smallmouth Bass
Brown Trout
Smallmouth Bass
Brown Trout
Reduction in
Concentration
(%)
64.3
43.2
45.9
12.0
0
0
Reduction in
Total Amount
(%)
80.0
77.8
74.0
26.7
16.4
0
Source: Skea et al. (1979).
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Table 3-60. Means and Standard Deviations of PCB Cooking Losses (%)
of Stewed and Pressure Cooked Chicken Pieces
Chicken
Piece
Breast
Drumstick
Thigh Meat
Thigh Skin
Abdominal Adipose Tissue
PCB Cooking Losses (%)
Stewed
31.73 ± 2.36
31.21 ± 2.38
36.82 ± 1.50
39.31 ± 8.18
85.94 ± 1.98
Pressure Cooked
30.24 ± 1.28
32.86 ± 1.14
38.10 ± 0.63
46.59 ± 4.69
88.88 ± 2.17
Source: Zabik (1974).
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Table 3-61. Weighted Mean CDD/CDF Profiles for Foods
2,3,7,8-Substituted
CDD/CDFs
2,3,7,8-TCDD
1,2,3,7,8-PeCDD
1,2,3,4,7,8-HxCDD
1,2,3,6,7,8-HxCDD
1,2,3,7,8,9-HxCDD
1, 2,3,4,6, 7,8-HpCDD
OCDD
2,3,7,8-TCDF
1, 2,3,7,8- PeCDF
2,3,4,7,8-PeCDF
1,2,3,4,7,8-HxCDF
1,2,3,6,7,8-HxCDF
1,2,3,7,8,9-HxCDF
2,3,4,6,7,8-HxCDF
1,2,3,4,6,7,8-HpCDF
1,2,3,4,7,8,9-HpCDF
OCDF
TOTAL
Beef-
Whole
Weight
Concentra-
tion (ppt)
0.0051
0.0068
0.031
0.21
0.044
0.75
0.55
0
0
0.010
0.046
0.020
0
0.017
0.13
0
0
1.8
Fraction of
Total 2,3,7,8-
Substituted
CDD/CDFs
0.0028
0.0037
0.017
0.11
0.024
0.41
0.31
0
0
0.0056
0.025
0.011
0
0.0094
0.070
0
0
1.0
Pork*
Whole
Weight
Concentra-
tion (ppt)
0.0019
0.0019
0.019
0.15
0.0076
1.9
10
0.00076
0
0.027
0.11
0.11
0
0.030
0.64
0.032
0.35
13
Fraction of
Total 2,3,7,8-
Substituted
CDD/CDFs
0.00014
0.00014
0.0014
0.011
0.00057
0.14
0.75
0.000057
0
0.0020
0.0086
0.0083
0
0.0023
0.048
0.0024
0.026
1.0
Poultry"
Whole
Weight
Concentra-
tion (ppt)
0.014
0.011
0.0043
0.031
0.025
0.13
0.45
0.026
0.0076
0.012
0.0089
0.0060
0
0.0069
0.017
0.0034
0.0059
0.76
Fraction of
Total 2,3,7,8-
Substituted
CDD/CDFs
0.018
0.014
0.0057
0.040
0.033
0.17
0.59
0.034
0.010
0.015
0.012
0.0079
0
0.0090
0.022
0.0045
0.0078
1.0
Eggsd
Whole
Weight
Concentra-
tion (ppt)
0.00060
0
0
0
0
0.27
1.1
0.0056
0.0086
0.012
0.0081
0.0054
0
0
0.037
0
0.04
1.51
Fraction of
Total 2,3,7,8-
Substituted
CDD/CDFs
0.00040
0
0
0
0
0.18
0.74
0.0037
0.0057
0.0081
0.0053
0.0036
0
0
0.024
0
0.027
1.0
Milk-
Whole
Weight
Concentra-
tion (ppt)
0.0013
0.0058
0.0070
0.034
0.010
0.091
0.088
0.0014
0
0.0050
0.0070
0.0045
0
0.0050
0.015
0
0
0.27
Fraction of
Total 2,3,7,8-
Substituted
CDD/CDFs
0.0046
0.021
0.026
0.12
0.036
0.33
0.32
0.0053
0
0.018
0.026
0.016
0
0.018
0.055
0
0
1.0
Dairyf
Whole
Weight
Concentra-
tion (ppt)
0.029
0.169
0.165
0.877
0.205
2.565
2.561
0.055
0.010
0.142
0.351
0.174
0
0.111
0.605
0.023
0.307
8.35
Fraction of
Total 2,3,7,8-
Substituted
CDD/CDFs
0.0035
0.0202
0.0198
0.1050
0.0246
0.3072
0.3067
0.0066
0.0012
0.0170
0.0421
0.0208
0
0.0133
0.0725
0.0028
0.0368
1.0
o
o
^1
D
C
O
§
CO
00
00
O NOTE:
CD
0
CD
3 '
D"
CD
— ^ e
to
0
o
GO
Non-detects are assumed to be zero.
Based on da
Based on da
Based on da
Based on da
Based on da
Based on da
a from Winters et al. (1996a).
a from Lorber et al. (1997b).
a from Ferrario et al. (1997).
a from Hayward and Bolger (2000)
a from Lorber et al. (1998b).
a from Lorber et al. (1998b).
-------
Table 3-62. Summary of CDD/CDF Levels in U.S. Food (pg/g fresh weight)
Beef
Pork
Poultry
Eggs
Dairy Products
Milk
Freshwater Fish
and Shellfish
Marine Fish and
Shellfish
Vegetable Fat
Mean TEQDF-
WH098
Assuming
ND = 0.5 DL
0.18
0.28
0.068
0.081
0.12
0.018
1.0b
0.26b
NAa
Mean TEQDF-
WH098
Assuming
ND = zero
0.061
0.080
0.043
0.013
0.12
0.017
c
c
0.056
Number of
Samples
63
78
78
15 composites
8 composites
8 composites
222
158
30
Reference
Winters et al. (1996a)
Lorber et al. (1997b)
Ferrario et al. (1997)
Hayward and Bolger (2000)
Based on data from Lorber
et al. (1998b)
Lorber et al. (1998b)
Fiedler et al. (1997);
Jensen and Bolger (2000);
Jensen et al.(2000);
U.S. EPA (1992)
Fiedler et al. (1997)
Jensen et al. (2000)
Versar (1996b)
a High detection limits led to a calculation of the mean with ND = 0.5 DL that was judged to be
misleading. See text for more detail.
b This concentration is a species-specific ingestion-weighted average value.
c Not calculated because of lack of congener-specific data for all species.
ND = Nondetect; DL = Detection Limit
NA = Not available
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Table 3-63. Summary of TEQP-WH098 Levels in North American Food
(pg/g fresh weight)
Beef
Pork
Poultry
Eggs
Dairy Products
Milk
Freshwater Fish
Marine Fish
Vegetable Fat
Mean TEQP-
WH098
Assuming
ND = 0.5DL
0.084
0.012
0.026
0.1
0.058
0.0088
1.2
0.25
0.037
Mean TEQP-
WH098
Assuming
ND = Zero
0.084
0.0074
0.026
NR
0.058
0.0088
NR
NR
NR
Number of
Samples
63
78
78
18
1 composite
5 composites
8 composites
8 composites
1 composite of
10 samples
1 composite
5 composites
1 composite of
13 samples
5 composites
5 composites
Reference
Winters et al. (1996b)
Lorber et al. (1997b)
Ferrario et al. (1997)
Schecter et al. (1997)
Mes and Weber (1989)
Meset al. (1991)
Based on data from
Lorber et al. (1998b)
Lorber et al. (1998b)
Schecter et al. (1997)
Mes and Weber (1989)
Meset al. (1991)
Schecter et al. (1997)
Meset al. (1991)
Meset al. (1991)
NOTE: Schecter et al. (1997) and Mes and Weber (1989) values based on one-half the limit of
detection for nondetects and Mes et al. (1991) data based on positive composite samples only. Five
additional composite samples labeled "shellfish" from Mes et al. (1991) were not used in estimating
background levels because information on the source of the shellfish (i.e., freshwater or marine) needed
to categorize the data were not available. It is therefore assumed that the species included in the
freshwater and marine categories are representative of both finfish and shellfish from those sources.
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Table 3-64. Summary of North American CDD/CDF and PCB TEQ-WH09S Levels in
Environmental Media and Food (whole weight basis)
Media
Urban Soil, ppt
Rural Soil, ppt
Sediment, ppt
Urban Air, pg/m3
Rural Air, pg/m3
Freshwater Fish and
Shellfish, ppt
Marine Fish and
Shellfish, ppt
Water, ppq
Milk, ppt
Dairy, ppt
Eggs, ppt
Beef ppt
CDD/CDFsa
n= 270
9.3 ± 10. 2b
Range = 2-21
n = 354
2.7b
Range = 0. 1 1 - 5.7
n = 11
5.3 ± 5.8b
Range = <1 - 20
n = 106
0.12 ± 0.094"
Range = 0.03 - 0.2
n = 60
0.013"
Range = 0.004 - 0.02
n = 222
1.0"
n = 158
0.26d
n = 236
0.00056 ± 0.00079
n = 8 composites
0.018s
n = 8 composites
0.1 2"
n = 1 5 composites
0.081s
n = 63
0.18 ± 0.11
Range = 0.1 1 - 0.95
References
Birmingham (1990), Nestrick et al.
(1986), NIH (1995), Pearson et al.
(1990), U.S. EPA (1985, 1996),
Rogowski et al. (1 999), U.S. EPA
Region 8 (2000b)
BC Environment (1995),
Birmingham (1990), MRI (1992),
Pearson et al. (1990), Reed et al.
(1 990), Tewhey Assoc (1 997),
U.S. EPA (1985, 1996), Rogowski
et al. (1999), Rogowski and Yake
(1999), U.S. EPA Region 8
(2000b)
Cleverly et al. (1996)
CDEP (1 988, 1 995), Hunt and
Maisel (1 990), Hunt et al. (1 990),
Maisel and Hunt (1990), OEPA
(1 995), Smith et al. (1 989, 1 990)
CDEP (1995), OEPA (1995)
Cleverly et al. (2000)
Fiedler et al. (1997), Jensen and
Bolger (2000), Jensen et al.
(2000), U.S. EPA (1992)
Fiedler et al. (1997a), Jensen et
al. (2000)
Jobb et al. (1 990), Meyer et al.
(1989)
Lorber et al. (1998b)
Based on data from Lorber et al.
(1998b)
Hayward and Bolger (2000)
Winters et al. (1996a)
PCBsa
n = 99
2.3
n = 62
0.59
n = 11
0.53 ± 0.69"
n = 53
0.0009'
n = 53
0.00071
n = 1 composite of 10
samples plus 6 composites
1.2d-s
n = 1 composite of 1 3
samples plus 5 composites
0.25d's
c
n = 8 composites
0.0088
n = 8 composites
0.058
n = 18 plus 6 composites
0. 1 Od's
n = 63
0.084
References
U.S. EPA Region 8
(2000b)
U.S. EPA Region 8
(2000b)
Cleverly et al. (1996)
Hoff et al. (1992)
Cleverly et al. (2000)
Schecter et al. (1997),
Mes and Weber (1989),
Mes et al. (1991)
Schecter et al. (1997),
Mes et al. (1991)
-
Lorber et al. (1998b)
Based on data from
Lorber et al. (1998b)
Schecter et al. (1997),
Mes and Weber (1989),
Mes et al. (1991)
Winters et al. (1996b)
Mean Total
CDD/CDF/PCBs
11.6
3.3
5.8
0.12
0.014
2.2
0.57
0.00056
0.027
0.18
0.13
0.26
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Table 3-64. Summary of North American CDD/CDF and PCB TEQ-WH09S Levels in
Environmental Media and Food (whole weight basis) (continued)
Media
Pork, ppt
Poultry, ppt
Vegetable Fats, ppt
CDD/CDFsa
n = 78
0.28 ± 0.28
Range = 0.15 - 1 .8
n = 78
0.068 ± 0.070
Range = 0.03 - 0.43
n = 30
0.056 ± 0.249
References
Lorber et al. (1997b)
Ferrario et al. (1997)
Versar (1996b)
PCBsa
n = 78
0.012
n = 78
0.026
n = 5 composites
0.037s
References
Lorber et al. (1997b)
Ferrario et al. (1997)
Mes et al. (1991)
Mean Total
CDD/CDF/PCBs
0.29
0.094
0.093
Values are the arithmetic mean TEQs, in ppt, and standard deviations. Nondetects were set to one-half the limit of detection, except for soil and CDD/CDFs in vegetable
fats for which nondetects were set to zero.
The values for environmental media are means of the data, but lack the spatial representativeness to be considered true national means.
Congener-specific PCB data are limited.
The values for fish lack the statistical significance to be considered true means; the values for the other food groups were derived from statistically-based surveys and can
be considered true national means. The CDD/CDF concentrations are species-specific ingestion weighted average values.
Standard deviations could not be calculated due to limitations associated with the data (i.e., composite analyses).
Based on data from Canadian air, as reported by Hoff et al. (1992). Not used in U.S. background exposure estimates in Chapter 4.
TEQ calculated from Versar (1 996b) by setting nondetects to zero.
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Table 3-65. CDD/CDF Congeners that Contribute the Highest Percentage of
TEQDF-WH098 to the Total TEQDF-WH098
for All Congeners Combined
Media
Urban Soil
Sediment3
Freshwater Fish and Shellfish
Marine Fish and Shellfish
Urban Air
Waterc
Milka
Dairy3
Eggsa
Beefa
Porka
Poultry3
Vegetable Fatsa'd
North America
1,2,3,7.8-PeCDD
1,2, 3,4,6,7, 8-HpCDD
__b
__b
1,2,3,7,8-PeCDD
OCDD
1,2,3,7,8-PeCDD
1,2,3,7,8-PeCDD
1,2,3,7,8-PeCDD
1,2,3,7,8-PeCDD
1,2,3,7,8-PeCDD
1,2,3,7,8-PeCDD
1,2,3,7,8-PeCDD
Percentage of Total
TEQDF-WH098
22.0
18.9
22.5
56.3
32.7
32.7
24.6
32.8
30.5
31.5
44.0
NOTE: Data were not available for all congeners in all media.
a Not used in calculation of the standard deviation because adequate congener-specific data were
available for all samples allowing for the calculation of means and standard deviations by traditional
methods.
b Not calculated due to lack of congener-specific data for all species.
c Data available for OCDD and OCDF only.
d Due to the large number of nondetects, zero was used to represent nondetects in determining the
congener that contributed the most to the total TEQDF-WH098.
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2,3,7,8-Substituted CDD/CDF Profile
2378TCDD
12378PeCDD
123478HxCDD
123678HxCDD
<» 123789HxCDD
c 1234678HpCDD
|> OCDD
<3 2378TCDF
t 12378PeCDF
Q 23478PeCDF
^ 123478HxCDF
K 123678HxCDF
"- 123789HxCDF
234678HxCDF
1234678HpCDF
1234789HpCDF
OCDF
Non-detects= zero
0.2
Ratio
0.4 0.6
0.8
Note: Based on data from OEPA (1995), CDEP (1998), and Cleverly et al. (2000).
CDD/CDF Homologue Group Profile
0.2
0.4
0.6
0.8
|Non-detects = zero j
Note: Based on data from OEPA (1995), CDEP (1998), and Cleverly et al. (2000).
Figure 3-1. CDD/CDF Profiles for Rural Background Air
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December 2003
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2,3,7,8-Substituted CDD/CDF Profile
0.2
Ratio
0.4 0.6
0.8
2378TCDD
12378PeCDD
123478HxCDD
-------
-------
-------
2,3,7,8-Sinstituted CDD/CDF Profile
0.2
Ratio
0.4 0.6
0.8
2378TCDD
2 12378PeCDD
c 123478HxCDD
D) 123678HxCDD
o 123789HxCDD
2 1234678HpCDD
g OCDD
Q 2378TCDF
-c, 12378PeCDF
a 23478PeCDF
.•3 123478HxCDF
^ 123678HxCDF
^ 123789HxCDF
oi 234678HxCDF
h~" 1234678HpCDF
« 1234789HpCDF
0X1 OCDF
|Non-detects = zero [
Note: Based on data from Cleverly et al. (1996) and Versar (1996a).
CDD/CDF Homologue Group Profile
Ratio
TCDD
PeCDD
•HxCDD
O HpCDD
0
g, OCDD
TCDF
PeCDF
HxCDF
O
HpCDF
OCDF
|Non-detects = zero [
0.2
0.4
0.6
0.8
Note: Based on data from Cleverly et al. (1 996) and Versar (1 996a).
Figure 3-5. CDD/CDF Profiles for Sediment
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December 2003
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CDD/CDF CongenerProfile -FreshwaterFish
I normalizedtotota!2,3,7,8 CDD/CDFs
Note: Based on data from Schecter et al. (1997).
CDD/CDF CongenerProfile -Marine Fish
normalized to tota!2,3,7,8 CDD/CDFs
0)
s,
3
|N.
1 ,2
1 ,2 ,3
1 ,2 ,3
1 ,2 ,3
1 ,2 ,3 ,4
1 ,2
2 ,3
1 ,2 ,3
1 ,2 ,3
1 ,2 ,3
2 ,3 ,4
1 ,2 ,3 ,4
1 ,2 ,3 ,4
2 ,
,3
,4
,6
,7
,6
2 ,
,3
,4
,4
,6
,7
,6
,6
,7
3 ,
,7
,7
,7
,8
,7
3 ,
,7
,7
,7
,7
,8
,7
,7
,8
7,8-
,8 -P
,8 -H
,8 -H
,9 -H
,8 -H
7 ,8 -
,8 -P
,8 -P
,8 -H
,8 -H
,9 -H
,8 -H
,8 -H
,9 -H
T
e
x
x
x
P
D
T
e
e
x
x
x
x
p
p
O
J
C
C
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
1
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
F
F
F
F
F
F
F
F
F
F
Note: Based on data from Fiedler et al. (1 997c).
CDD/CDF CongenerProfile -Shellfish
normalized to tota!2,3,7,8 CDD/CDFs
Oj
D)
3
|NO
1 ,
1 ,2
1 , 2
1 ,2
1 ,2,3,
1
2
1 ,2
1 , 2
1 ,2
2 ,3
1,2,3
1,2,3
n -d e te
2
3
3
3
4
2
3
3
3
3
4
4
4
'
2 ,
,3
,4
, 6
, 7
,6
2
, 3
,4
, 4
i 6
,7
,6
, 6
, 7
ts
3,7,
7 , 8
,7 ,8
,7,8
,8 ,9
7 , 8
3,7,
,7,8
,7 ,8
,7,8
,7,8
,8,9
,7,8
,7,8
,8 ,9
= ze
-
P
H
H
H
H
B-
P
P
-H
-H
-H
-H
H
H
ro
T
e
x
x
x
P
D
T
e
e
x
x
x
x
P
p
0
C
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
I
D D
D D
D D
D D
D D
D D
D D
D
D
D
D
D
D
D
D
D
D
Note: Based on data from Fiedler et. al. (1997c).
Figure 3-6. CDD/CDF Congener Profiles for Fish and Shellfish
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December 2003
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CDD/CDF Congener Profile
normalized to total 2,3,7,8 CDD/CDFs
Ratio
0.2
0.4
0.6
0.8
2,3,7,8-TCDD
1,2,3,7,8-PECDD
1,2,3,4,7,8-HXCDD
1,2,3,6,7,8-HXCDD
1,2,3,7,8,9-HXCDD
1,2,3,4,6,7,8-HPCDD
OCDD
2,3,7,8-TCDF
1,2,3,7,8-PECDF
2,3,4,7,8-PECDF
1,2,3,4,7,8-HXCDF
1,2,3,6,7,8-HXCDF
1,2,3,7,8,9-HXCDF
2,3,4,6,7,8-HXCDF
1,2,3,4,6,7,8-HPCDF
1,2,3,4,7,8,9-HPCDF
OCDF
Non-detects = zero
Note: Based on data from Winters et al. (1 996a).
Figure 3-7. CDD/CDF Congener Profile for Beef
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December 2003
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0 0.2
CDD/CDF Congener Profile
normalized to total 2,3,7,8 CDD/CDFs
Ratio
0.4 0.6
0.8
-------
2,3,7,8-TCDD
1,2,3,7,8-PCDD
1,2,3,4,7,8-HXCDD
1,2,3,6,7,8-HXCDD
1,2,3,7,8,9-HXCDD
1,2,3,4,6,7,8-HPCDD
OCDD
2,3,7,8-TCDF
1,2,3,7,8-PCDF
2,3,4,7,8-PCDF
1,2,3,4,7,8-HXCDF
1,2,3,6,7,8-HXCDF
1,2,3,7,8,9-HXCDF
2,3,4,6,7,8-HXCDF
1,2,3,4,6,7,8-HPCDF
1,2,3,4,7,8,9-HPCDF
OCDF
CDD/CDF Congener Profile - Poultry
normalized to total 2,3,7,8 CDD/CDFs
0.2
Ratio
0.4
0.6
Non-detects = zero|
Note: Based on Ferrario et al. (1997).
a
Q
O
2,3,7,8-TCDD
1,2,3,7,8-PeCDD
1,2,3,4,7,8-HxCDD
1,2,3,6,7,8-HxCDD
1,2,3,7,8,9-HxCDD
1,2,3,4,6,7,8-HpCDD
OCDD
2,3,7,8-TCDF
1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
1,2,3,4,7,8-HxCDF
1,2,3,6,7,8-HxCDF
1,2,3,7,8,9-HxCDF
2,3,4,6,7,8-HxCDF
1,2,3,4,6,7,8-HpCDF
1,2,3,4,7,8,9-HpCDF
OCDF
2,3,7,8-Substituted CDD/CDF Profile
0.2
Ratio
0.4 0.6
0.8
\
Note: Based on Fiedler et al. (1 997c).
Figure 3-9. CDD/CDF Congener Profiles for Poultry and Eggs
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December 2003
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CDD/CDF Congener Profile
normalized to total 2,3,7,8 CDD/CDFs
Ratio
0.2
0.4
0.6
0.8
2,3,7,8-TCDD
1,2,3,7,8-PeCDD
1,2,3,4,7,8-HxCDD
1,2,3,6,7,8-HxCDD
1,2,3,7,8,9-HxCDD
1,2,3,4,6,7,8-HpCDD
OCDD
2,3,7,8-TCDF
1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
1,2,3,4,7,8-HxCDF
1,2,3,6,7,8-HxCDF
1,2,3,7,8,9-HxCDF
2,3,4,6,7,8-HxCDF
1,2,3,4,6,7,8-HpCDF
1,2,3,4,7,8,9-HpCDF
OCDF
Non-detects = zero
Note: Based on data from Lorber et al. (1 998b).
Figure 3-10. CDD/CDF Congener Profiles for Milk and Dairy Products
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Urban Soil
Sediment
Rural Soil
Freshwater Fish & Shellfish
Marine Fish& Shellfish
Fork
Beef
Urban Air
Dairy
Poultry
Eggs
Vegetable Fats
Mlk
Rural Air
Water
CDD/CDF and PCB Mean Background Environmental Levels in "rEQ-vUHOcJ
53 ppt
0.25 p
0.0009 pg/m
-
0.0000001
• CmCDFs D FCBs
• 5-
iOE-07 ppt
1.00071 pg/m
0.0088 ppt
5
0.084 ppt
358 ppt
>pt
.068 ppt
0.1 ppt
0.081 ppt
7 ppt
156 ppt
m3
3t
3t
P*
9.3 ppt
ppt
0.000001 0.00001 0.0001 0.001 0.01 0.1 1 10 100 1000
I
Figure 3-11. CDD/CDF and PCB Mean Background Environmental Levels in TEQ-WH09
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4. HUMAN EXPOSURES TO CDD, CDF, AND PCB CONGENERS
4.1. INTRODUCTION
The purpose of this chapter is to assess background exposures to the dioxin-like
compounds. Recent assessments of background exposures cited in the scientific literature
are summarized, and background exposure estimates based on the data presented in this
report are presented. Two methods have been used in this chapter to estimate
background daily intake of dioxin-like compounds. One method estimates background
exposures based on pharmacokinetic modeling using body burden data. The other derives
background exposure estimates from dietary intake and contact with other media
containing dioxin-like compounds. These two approaches provide comparable estimates
of daily TEQ-WH098 intake of dioxin-like compounds.
The primary focus of this chapter is background exposure among the general
population. The general population consist of people who are exposed to background
levels of dioxin-like compounds in soil and air. Most of their exposure comes from the
commercial food supply and they do not have significant occupational exposure. People
outside the general population are those living in areas with elevated soil or air levels, or
whose dietary exposure is strongly influenced by food outside the commercial food supply
(i.e., nursing infants, sports or subsistence fishermen, etc.).
The term "background," as applied to exposure, can be used to represent different
concepts. Two common definitions are (1) the level of exposure that would occur in an
area without known point sources of the contaminant of concern or (2) the average level
of exposure occurring in an area whether sources are present or not. For the purposes of
this document, "background" is defined as suggested in the first definition above. To the
extent possible, background exposures estimated in this chapter are based on monitoring
data obtained from sites removed from known contaminant sources (i.e., food data
representative of the general food supply) and body burden data from nonoccupationally
exposed members of the general population. Most of the data are based on studies
published in the late 1980s and 1990s, but primarily the 1990s. These data are
considered to be the most useful for describing background exposure levels.
Chapter 5 also includes information on potentially elevated exposures. It describes
the potential for elevated exposures among subpopulations such as nursing infants, sport
DRAFT-DO NOT QUOTE OR CITE 4-1 December 2003
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and subsistence fishermen, cigarette smokers, and individuals living in areas that may be
affected by localized sources of dioxin-like compounds.
4.2. LEVELS OF DIOXIN-LIKE COMPOUNDS IN HUMAN TISSUE
4.2.1. Adipose Tissue and Blood Studies from the 1980s and Early 1990s
The most extensive U.S. study of CDD/CDF body burdens is the National Human
Adipose Tissue Survey (NHATS) (U.S. EPA, 1991 a). NHATS was designed to estimate
national population average levels of CDD/CDFs. The survey analyzed for CDD/CDFs in
48 human tissue samples that were composited from 865 samples. Each composite
contained an average of 18 specimens. These samples were collected during 1987 from
autopsied cadavers and surgical patients. The sample compositing prevents use of these
data to examine the distribution of CDD/CDF levels in tissue among individuals. Also, not
all 48 composites were used for all congeners in the statistical analysis of the data
because some components did not meet the data quality objectives of the study.
However, the study results allowed conclusions to be made in the following areas:
National Averages - The national population averages for all TEQ congeners
were estimated as listed in Table 4-1. Nondetects were treated as half the
detection limit for averaging purposes. As shown in this table, all congeners
except some CDFs, had a very low frequency of nondetects. Thus, the
overall TEQ estimate is not sensitive to how nondetects were treated in the
averaging.
Age Effects - Tissue concentrations of CDD/CDFs were found to increase
with age (Orban et al., 1994) (Table 4-2).
Geographic Effects - In general, the average CDD/CDF tissue concentrations
appeared fairly uniform geographically. Only one TEQ congener was found
to have a significant difference among geographic regions of the country.
This compound, 2,3,4,7,8-PeCDF, was found at the lowest level in the
West (4.49 pg/g) and the highest in the Northeast (13.7 pg/g).
Race Effects - No significant difference in CDD/CDF tissue concentrations
was found on the basis of race (Table 4-2).
Sex Effects - No significant difference in CDD/CDF tissue concentrations
was found between males and females (Table 4-2).
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• Temporal Trends - The 1987 survey showed decreases in tissue
concentrations relative to the 1982 survey for all congeners. However, it is
not known whether these declines were due to improvements in the
analytical methods or actual reductions in body burden levels. The percent
reductions among individual congeners varied from 9 percent to 96 percent.
Patterson et al. (1994) provided additional information on levels of dioxin-like
compounds in human tissue. Human adipose from 28 individuals was collected. The
individuals studied were ones who died suddenly in the Atlanta area during 1984 or 1986.
Their ages ranged from 1 9 to 78 years and averaged 49 years. 2,3,7,8-TCDD levels
varied with the upper end of the range equaling between three and four times the mean
concentration. The tissue data are summarized in Table 4-3. This table shows that the
mean PCB levels generally exceeded the mean 2,3,7,8-TCDD level and PCB-126 exceeded
the 2,3,7,8-TCDD level by over an order of magnitude. The mean TEQ levels for these
dioxin-like PCBs summed to about 14 ppt on a lipid basis (using either TEFP-WH094s or
TEFP-WH098s). A complete CDD/CDF congener analysis was conducted on tissues of four
of the individuals, resulting in an average of 26 ppt I-TEQDF (31 ppt TEQDF-WH098) on a
lipid basis. These tissue samples were also analyzed for PCBs 77, 126, and 169. The
lipid-based TEQP-WH094 levels for these dioxin-like PCBs summed to 5.4 ppt. Thus, PCBs
77, 126, and 169 contributed between 15 and 20 percent of the total CDD/CDF and PCB
TEQs. Patterson et al. (1994) also studied serum collected by the CDC blood bank in
Atlanta during 1982, 1988, and 1989. These samples were pooled from over 200
donors. The average levels for 2,3,7,8-TCDD and PCBs are summarized in Table 4-4 in
units of ppt on a whole weight basis. The serum data appear to indicate a decrease in
exposure to PCBs from 1982 to 1988/1989. The lipid-based TEQP-WH094 for the 1988
sample was 14 ppt based on PCBs 77, 1 26, 1 60, 105, 11 8, and 1 80. In general, the
Patterson et al. (1994) data suggest that the dioxin-like PCBs can contribute significantly
to body burdens of dioxin-like compounds. The data suggest that the dioxin-like PCBs can
increase the total background body burden to over 40 ppt of total TEQDFP-WH094. This
conclusion is uncertain because the people studied by Patterson et al. (1994) may not be
representative of the overall U.S. population.
Schecter et al. (1993) reported on the comparisons of congener-specific
measurements of CDDs, CDFs, and dioxin-like PCBs (77, 105, 118, 1 26, 1 56, 1 69, 1 70,
and 180) in whole blood samples of four individuals with known exposures to that of the
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general population. In this comparison, the analytical results of separate 450 ml blood
samples collected from 50 Michigan residents, and a pooled blood sample from 5 donors
at a blood bank in Missouri were used as the control group. Two of the exposed
individuals were pulp and paper plant workers with potential exposure to dioxins, and the
other two were Michigan residents who had elevated blood PCB levels from consuming
contaminated fish. It was found that the control group and the pulp and paper mill
workers who had no known exposures to PCBs had relatively high levels of coplanar,
mono-ortho, and di-ortho PCBs in their whole blood. On average, the Michigan and
Missouri control samples showed mean I-TEQDF concentrations of 27 ppt and 24 ppt
(TEQDF-WH098s were 31 ppt and 26 ppt), respectively. These same samples showed
TEQP-WH094 mean concentrations of 17 ppt for the Michigan controls, and 10 ppt for
Missouri controls.
Cole et al. (1995) reported on CDD/CDFs and PCBs in 132 serum samples (pooled
to 14) from Ontario Great Lakes anglers and control populations. Based on a preliminary
survey, anglers from the communities of Cornwall and Mississauga, Canada, were
categorized based on the numbers, species, and locations of fish caught and kept for
consumption, and on data reflecting the contaminant levels for the fish in these areas.
Individuals categorized as having the highest and lowest potential for having elevated
body burdens of CDD/CDFs and PCBs were selected for biological sampling. Individuals
who did not consume fish served as controls. Study participants were further categorized
by age (i.e., <38 years, 38-50 years, and >50 years). The results indicated that mean
CDD/CDF TEQ levels were similar for both eaters and noneaters of Great Lakes' fish in
these communities. l-TEQDFs ranged from 20.8 to 41.2 ppt for fish eaters and 24.7 to
36.8 ppt for noneaters. In general, mean l-TEQDFs increased with age (Table 4-5). PCBs
77, 126, and 169 were also evaluated in the serum samples collected from Cornwall
residents. TEQP-WH094s ranged from 2.6 to 17.3 ppt for fish eaters and noneaters
combined. Because no statistical differences were observed between fish eaters and
noneaters, the data from this study were assumed to represent background exposures and
were included in the background tissue level calculations in this chapter.
Schecter et al. (1989a) provided data on PCB levels in adipose samples from three
patients from North America with no known chemical exposure history. The mean TEQP-
WH094 level based on PCBs 118, 105, 1 56, and 1 80 was 1 2.2 ppt on a lipid basis (the
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TEQP-WH098, recalculated using TEFP-WH098s, was 11.5 ppt on a lipid basis). Williams
and LeBel (1991) reported on the mean residue levels of PCBs 126 and 169 in 62 adipose
tissue samples collected in Canada during 1984. The mean lipid-based TEQP for these
samples was estimated to be 28 ppt based on TEFP-WH094 or TEFP-WH098s for PCBs.
Kang et al. (1997) reported on the levels of PCBs 77, 126, and 169 in human
serum collected from white male paper mill workers (n = 46), as well as residents (n =
16) of a northeastern U.S. community. PCB 77 was not detected in any samples, but
PCBs 126 and 169 were detected in most samples. The mean lipid-based concentrations
of the two congeners (i.e., PCB 126 and 169) were 25 ppt and 31 ppt, respectively, for
paper mill workers, and 18 ppt and 27 ppt, respectively, for community residents. Using
TEFP-WH094s for these PCBs (PCB 1 26 - 0.1, PCB 1 69 - 0.01), the relative contribution of
these PCBs to the total CDD/CDF/PCB TEQ (using l-TEFDFs for CDD/CDFs) for all study
participants was approximately 10 percent. Kang et al. (1997) also observed that age,
body mass index, and consumption of locally caught fish were significant predictors of
coplanar PCB concentrations in human serum.
The levels of dioxin-like compounds found in human tissue/blood appear similar in
Europe and North America. Schecter (1991) compared levels of dioxin-like compounds
found in blood among people from U.S. (pooled samples from 100 subjects) and Germany
(85 subjects). Although mean levels of individual congeners differed by as much as a
factor of two between the two populations, the total I-TEQDF averaged 42 ppt in the
German subjects and 41 ppt in the pooled U.S. samples. Using TEFDF-WH098s, these
TEQDF-WH098 concentrations would be 49 ppt and 50 ppt, respectively. In later papers,
Schecter et al. (1992a; 1994a) reported human blood levels for the general population
from various countries. These data are presented in Table 4-6. Schecter (1991) reported
adipose tissue levels in various countries, as summarized in Table 4-7. The adipose tissue
data show more variation between countries, but also involved much fewer samples,
reducing confidence in the accuracy of the mean.
Gonzalez et al. (1993) reported that the levels and patterns of CDD/CDFs in the
adipose tissue obtained from the general population of Madrid, Spain, were similar to
those of other industrialized countries. A total of 17 adipose tissue samples were
collected from male and female patients ranging in age from 48 to 89 years. The lipid-
based mean I-TEQDF was 42 ppt (46 ppt using TEFDF-WH098s) and the mean level of
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2,3,7,8-TCDD was 3.28 ppt. CDDs were found to be higher than CDFs in these samples
with the higher-chlorinated CDDs accounting for the highest portion of the total
CDD/CDFs (Table 4-8). The mean lipid-based I-TEQDF concentration in the blood of 11
individuals from Madrid, Spain, was 15.7 ppt (Jimenez et al., 1995). The higher-
chlorinated CDDs (i.e., HpCDD and OCDD) were the dominant congeners observed in
these samples.
Schumacher et al. (1999a and 1999b) conducted two studies to analyze
background concentrations of CDD/CDFs in blood and adipose tissue from individuals from
Tarragona, Spain. In the first study (Schumacher et al., 1999a), blood plasma samples
were collected from 20 nonoccupationally exposed subjects living near an area where a
hazardous waste incinerator is being constructed. The reported mean blood lipid
CDD/CDF concentration was 27.0 ppt I-TEQDF with a range of 14.8 to 48.9 ppt. The
maximum TEQDF value observed in this study was approximately 1.7 times the mean.
CDD/CDF TEQs were higher in women (e.g., 27.7 ppt) than in men (e.g., 25.2 ppt). The
results, however, were not statistically significant. Schumacher et al. (1999b) conducted
a second study on adipose tissues of 15 autopsied subjects. The arithmetic mean I-TEQDF
was 30.98 ppt (range of 13.4 to 69.4 ppt). The maximum I-TEQDF value observed in this
study was approximately 2.2 times the mean. Unlike their previous study, l-TEQDFs were
statistically higher (p<00.1) in the fat of women (mean value: 45 ppt) than in men (mean
value: 24 ppt). Levels of CDD/CDFs were higher for those people that lived in
industrialized areas than the residents who lived in the city, but this difference was not
statistically significant.
Beck et al. (1994) reported on levels of CDD/CDFs in adipose tissue from 20 males
(mean age-50 years) from Germany. l-TEQDFs ranged from 18 ppt to 122 ppt with a mean
of 56 ppt (using TEFDF-WH098s, the mean TEQDF would be 65 ppt), on a fat weight basis.
The I-TEQDF maximum concentration in this study was approximately 2.4 times the mean.
Beck et al. (1994) also reported on CDD/CDF levels in various organs of the body. In
comparison to adipose tissue, the concentrations of CDD/CDFs in brain and placental
tissue were found to be low. Accumulation of CDD/CDFs was not found to occur in the
thymus, spleen, and liver, based on whole weight concentrations. Schecter et al. (1994a)
also reported on I-TEQDF levels in organs of two autopsy patients from New York. The
highest concentrations of CDD/CDFs were found in adipose tissue (28 ppt I-TEQDF),
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adrenal tissue (14 ppt I-TEQDF), and liver (12 ppt I-TEQDF), on a whole weight basis.
Lower concentrations were observed in spleen (4.6 ppt I-TEQDF), muscle (2.4 ppt I-TEQDF),
and kidney (0.8 ppt I-TEQDF). Schecter et al. (1994b) reported PCB levels for these two
autopsy patients. Total PCBs in adipose tissue were 280.7 ppb on a wet weight basis
and 344.2 ppb on a lipid weight basis.
Beck et al. (1994) also observed that CDD/CDF tissue levels were dependent on
the age of the individual. I-TEQDF concentrations in infants ranged from 2.1 pg/g to 22
pg/g on a lipid basis. 2,3,7,8-TCDD was found to increase at a rate of 0.12 pg/g fat per
year, and l-TEQs increased at a rate of 0.77 pg/g fat per year. Schecter et al. (1995a)
measured levels of CDD/CDFs in human fetal tissue (N = 10) at 8 to 14 weeks gestational
age and observed an average of 5 pg l-TEQDF/g on a lipid basis. Stillborn liver (N = 3)
concentrations averaged 10 pg l-TEQDF/g on a lipid basis. These levels are considerably
lower than those observed in adult tissues (Schecter et al., 1995a). Pa'pke et al. (1996)
also observed that I-TEQDF levels in human tissues were age dependent. Whole blood
samples collected in 1994 indicated that I-TEQDF concentrations increased with increasing
age. Similar age effects were noted for PCBs 77, 126, and 169 (Pa'pke et al., 1996).
Wuthe et al. (1995) studied body burdens of CDD/CDFs among children in
Germany. Three study groups were evaluated: blood from 11 nonexposed children, age 9
to 15 years; adipose and liver tissue from 20 stillborn or otherwise deceased infants, age
0 to 44 weeks, some of whom had been breast-fed; and pooled blood from 10-year-olds
from 3 different regions. The total I-TEQDF concentration for the first study group (i.e.,
blood from 11 children between the ages of 9 and 15 years) was 10.7 ppt. Based on the
other study groups, the authors made the following conclusions: (1) because CDD/CDFs
were found in stillborns, a diaplacental transfer of these compounds occurred; (2) breast
feeding has an impact on CDD/CDF concentrations (i.e., the mean I-TEQDF concentration
was 12.7 ppt for breast-fed infants and 3.6 ppt for formula-fed infants); and (3) body
burdens of CDD/CDFs are lower among children than adults.
Lanting et al. (1998) examined PCBs in adipose tissue, liver, and brain from nine
stillborns at varying gestational ages. Of the four PCB congeners examined, only PCB 118
was dioxin-like. The median levels reported for PCB 118 were 20 ppt for adipose tissue,
1 7 ppt for the liver, and 6 ppt for the brain. The results of the study indicated that there
was a significant relationship (correlation coefficient = 0.98; p <0.01) between adipose
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tissue concentrations and liver concentrations. Correlation between the levels of PCB
congeners in these tissues and gestational age of the infants were not significant;
correlation coefficients varied between 0.22 and 0.47.
Kruezer et al. (1997) reported CDD/CDF concentrations from lipids of adipose
tissue and livers from cadavers (3 stillborns and 17 infants aged 0.43 to 44 weeks old
who died from sudden infant death syndrome). I-TEQDF lipid-based concentrations were in
the range of 1.55 to 29.63 ppt for adipose tissue (n = 20) and 2.05 to 57.73 ppt (n = 19)
for liver. TCDD concentrations in lipids of breast-fed infants were higher compared to
nonbreast-fed infants.
Nagayama et al. (1995) studied the effect of birth order on the body burdens of
CDD/CDFs and PCBs among 50 healthy Japanese women. The concentrations of these
dioxin-like compounds in blood were found to be significantly higher among first-born
women than among other women. No relationship was found between the method by
which these women were fed (i.e., breast-fed, formula-fed, or mix between breast milk
and formula) and the blood concentrations of CDD/CDFs and PCBs.
Human breast tissue has also been analyzed for dioxin-like PCBs (Dahl et al., 1994;
Petreas et al., 1998). Dahl et al. (1994) examined breast tissue collected from 16 women
seeking hospital care for breast tumors in Sweden. PCB levels were observed to increase
with age. Based on PCBs 105, 114, 118, 1 56, 1 57, 1 70, 1 80, and 1 89, the mean total
TEQP-WH098 for these samples was 40 ppt. Petreas et al. (1998) studied human breast
adipose tissue collected from women undergoing breast surgery at Stanford University in
California to determine CDD/CDF and PCB levels. Of the 17 CDD/CDF congeners, only
OCDD, HpCDD, HxCDD, and PeCDF were observed to be above the limit of detection.
I-TEQDF lipid-based concentrations, using one-half LOD for non-detects, ranged from 6 ppt
to 78 ppt with a mean of 17.8 ppt (n = 62). Based on only the four detected congeners,
the I-TEQDF concentration ranged from 5 ppt to 42 ppt with a mean of 12.6 ppt (the
maximum I-TEQDF value is 3.3 times higher than the mean). Lipid-based PCB levels ranged
from 451 ppb to 3,830 ppb with a mean of 1,1 20 ppb, based on PCBs 1 53/1 32, 1 80,
74, 1 38, 1 82/1 87, 1 70, 1 96/203, 1 94, 1 99, 1 56, 1 1 8, 206, 1 83, 99/1 1 3, 1 77, 28,
105/127, 128/162, 157, and 101 (n = 61). The maximum concentration is 3.4 times the
mean. Lipid-based TEQP-WH094 levels for coplanar PCBs 77, 126, and 169 ranged from 7
ppt to 110 ppt with a mean of 38 ppt (the maximum TEQP-WH094 is 2.9 times higher than
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the mean). The most prevalent PCB congeners included PCBs 153/132, 180, 74, 138,
1 82/1 87, and 1 70, which, when summed, contributed over 50 percent of the total PCB
measure.
lida et al. (1999) analyzed blood samples from 50 young (i.e., approximately 20
years of age) Japanese women for dioxin-like compounds. The women were described as
"normal subjects" who had not yet had children, and the samples were collected in 1993
and 1994. The range if l-TEQDFs was 7.3 pt to 28.0 ppt with a mean of 16.4 ppt (the
maximum value is 1.7 times higher than the mean). The range of TEQP-WH094s (based on
PCBs 77, 1 26, and 1 69) was 1 ppt to 10 ppt with a mean of 4.9 ppt. The total TEQDFP-
WH094 was 21 ppt and the maximum value was 37 ppt. This maximum value is 1.8
times higher than the mean.
4.2.2. Breast Milk Studies from the 1980s and Early 1990s
Schecter et al. (1989b; 1992b) reported that in a study of 42 U.S. women, the
average I-TEQDF was 16 ppt (20 ppt of TEQDF-WH098) (3.3 ppt of 2,3,7,8-TCDD) in the
lipid portion of breast milk. Schecter et al. (1989b) also reported a total I-TEQDF of 27 ppt
(TEQDF-WH098 = 31 ppt) for human milk collected in Germany (n = 185). A much larger
study in Germany (n = 526) showed an average of 29 ppt of I-TEQDF (TEQDF-WH098= 34
ppt) in lipid portion of breast milk (Furst et al., 1994). Bates et al. (1994) analyzed breast
milk samples from 38 women in New Zealand and reported mean lipid-based l-TEQDFs of
16.5 ppt for urban women and 18.1 ppt for rural women (average I-TEQDF = 17.2 ppt;
average TEQDF-WH098= 21 ppt). The age of the mother was found to be positively
correlated with the concentration of CDD/CDFs in breast milk. Beck et al. (1994) reported
a mean I-TEQDF of 30 ppt (TEQDF-WH098= 35 ppt) in the milk fat based on 11 2 human
milk samples from Germany. The congeners that contributed the most to the total I-TEQDF
were 2,3,4,7,8-PeCDF (35 percent), total HxCDD (22 percent), and 1,2,3,7,8-PeCDD (21
percent). Beck et al. (1994) observed that CDD/CDF levels decreased with the number of
children and the duration of breast feeding, but increased with the age of the mother.
Beck et al. (1994) also compared the adipose tissue levels of breast-fed and bottle-fed
infants who had died of sudden infant death syndrome. The breast-fed infants had higher
tissue levels (5.4 to 22 pg/g fat; n = 4) than the bottle-fed infants (2.1 to 4.4 pg/g fat;
n = 2).
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Hirakawa et al. (1995) studied differences in CDD/CDF levels in human milk
collected from primipara and multipara Japanese women. Human milk samples were taken
from seven primiparas and eight multiparas between the ages of 22 and 40 years and
analyzed for CDD/CDFs and dioxin-like PCBs. Total lipid-based TEQ concentrations were
34.6 ppt for the primiparas and 30.7 for multiparas, using l-TEFDFs for CDD/CDFs and
TEFP-WH094 for PCBs. Significant differences were observed between the concentrations
of 2,3,7,8-TCDD; 1,2,3,7,8-PeCDD; 1,2,3,6,7,8-HxCDD; 1,2,3,7,8,9-HxCDD; 2,3,4,7,8-
PeCDF; and 1,2,3,6,7,8-HxCDF in primipara and multipara women. The concentrations of
these congeners varied by a factor ranging from 1.3 to 1.8 for the two study groups
(Table 4-9). The mean I-TEQDF plus three standard deviations indicates that the high-end
CDD/CDF concentration is approximately 2 times higher than the mean.
Van Cleuvenbergen et al. (1994) observed lipid-based I-TEQDF levels in human milk
ranging from 27 to 43 ppt with a mean of 34 ppt (TEQDF-WH098= 40 ppt), based on
samples from 9 women living in Belgium in 1992. The maximum I-TEQDF concentration
observed in this study was approximately 1.3 times higher than the mean. OCDD and
1,2,3,4,6,7,8-HpCDD accounted for the highest proportion of total CDD/CDFs, but
2,3,4,7,8-PeCDF accounted for the largest proportion of the total CDD/CDF I-TEQDF (i.e.,
approximately 45 percent (Table 4-10)). Similar I-TEQDF levels have been observed in
other countries. Schecter et al. (1989c) collected human milk samples from southern
Japan in 1986. The mean lipid-based total I-TEQDF for two composites, containing three
samples each, was 26 ppt. Based on data from Startin et al. (1989), the mean lipid-based
I-TEQDF for a pool of 80 human milk samples from the United Kingdom was 33 ppt (TEQDF-
WH098 was 39 ppt).
Pluim et al. (1994a) studied the influence of short-term dietary changes in fats and
carbohydrate intake on CDD/CDF concentrations in human milk. Two different diets were
administered to two groups of lactating women in The Netherlands. Sixteen women had a
low-fat/high-carbohydrate/low-dioxin diet, and 18 women had a high-fat/low-
carbohydrate/low-dioxin diet for 5 consecutive days. At the end of this dietary regimen,
milk samples were collected and analyzed for CDD/CDFs. No significant differences
between CDD/CDF levels were observed. The mean I-TEQDF values for mothers using the
low-fat/high-carbohydrate/low-dioxin diet were 30.2 ppt and 30.0 ppt before and after the
test period, and the mean I-TEQDF values for the mothers using the high-fat/low-
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carbohydrate/low-dioxin diet were 24.4 ppt and 24.0 ppt before and after the test period.
Pluim et al. (1994a) concluded that short-term dietary changes were not an effective
means of reducing dioxin concentrations in human milk. In another study, Pluim et al.
(1994b) measured the levels of CDD/CDFs in breastmilk as part of a study to evaluate
relationships between neonatal CDD/CDF exposure via breastmilk and potential
physiological effects. CDD/CDFs were measured in the breastmilk of 35 Dutch mothers
when their nursing infants were 11 weeks of age. The mean lipid-based I-TEQDF level in
these breastmilk samples was 28.1 ppt (TEQDF-WH098= 33.5 ppt).
In 1994 and 1996, Hooper et al. (1998) monitored levels of CDD/CDFs in breast
milk samples collected in Kazakstan, a country of the former Soviet Union. The mean
reported CDD/CDF levels ranged from 7.2 to 57 ppt I-TEQDF. The detection limit for the
sampling was 1 ppt, and only levels above the detection limit were reported.
Approximately 92 breast milk samples were collected in both of these years. The range
and mean values of individual and composite samples were similar by region and ethnicity.
In addition, this study found that CDD/CDF levels were significantly higher in breast milk
samples collected from rural sites (mean 46 ppt I-TEQDF, n = 23) than from a nonrural site
(mean 11 ppt I-TEQDF, n = 32). Hooper et al. (1 998) did not identify the reason for the
higher CDD/CDF concentrations in samples from rural women. Several postulations
include the high use of a pesticide (Hexachlorocyclohexane) in Kazakstan, the Kazakstan
diet may include more contaminated fish from the Ural River, and consumption of
cottonseed oil and kefir (a beverage of fermented cow's milk), which has been shown to
have high dioxin levels. Consumption of cottonseed oil and kefir is more common in the
rural areas than in urban areas.
Recently, Liem et al. (1996) reported on the results of the second round of a
human breast milk study conducted by the World Health Organization (WHO). Human
milk samples were collected from women in 19 countries during 1992/93 and analyzed for
CDDs, CDFs, and PCBs (i.e., non-ortho 77, 126, 169; mono-ortho 105, 118; markers 28,
52, 101, 138, 153, 180). The results were compared to the results of the first round of
sampling that occurred among 11 countries in 1987/88 to evaluate trends in exposure to
dioxin-like compounds. Based on the 1992/93 results of pooled human milk samples,
lipid-based I-TEQDF concentrations ranged from 3.8 pg/g for the Librazhd area of Albania to
27.1 pg/g for the Liege area of Belgium (Table 4-11). Overall, significantly lower l-TEQDFs
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and PCBs were observed in Albania, Hungary, and Pakistan (Table 4-11). The highest I-
TEQDF levels were observed in Belgium and The Netherlands (Table 4-11), and the highest
TEQP-WH094 levels were observed in Canada's Hudson Bay region and in regions of the
Czech and Slovak Republics. An analysis of individual samples from The Netherlands and
Denmark indicated a high level of variability among individuals (i.e., levels varied by a
factor of 3 to 5). Comparison of the 1992/93 data to the 1987/88 data indicated that the
levels of CDD/CDFs and marker PCBs in breast milk have declined in some countries with
concentrations decreasing up to 50 percent in some areas (Table 4-12). Liem et al.
(1996) estimated an overall annual decrease in CDD/CDFs of 7.2 percent over the 5-year
time period evaluated.
Vartiainen et al. (1997) reported CDD/CDF and PCB levels in the human milk of
167 women collected in 1987 from an urban area and a rural area in Finland. The average
CDD/CDF levels were significantly higher (p<0.001) in the urban area (26.3 pg l-TEQDF/g
fat; n = 47) than in the rural area (20.1 pg l-TEQDF/g fat; n = 37) for all primiparae
individuals. Similarly, the total PCB concentrations were higher (p<0.01) among urban
primiparae (496 ng/g fat; 36.8 pg TEQP-WH094/g; n-47) than among rural primiparae (396
ng/g fat/ 26.3 pg TEQP-WH094/g; n = 37). The CDD/CDF and PCB levels in the milk of
these women decreased with the increasing number of children breast-fed by them.
Vartiainen et al. (1997) estimated that a woman's third child would be exposed to about
70 percent of the CDD/CDF and PCB levels that her first-born child was exposed to, and
the eighth to tenth child would be exposed to only about 20 percent of the levels of the
first-born. In addition, Vartiainen et al. (1997) observed a possible correlation between
average I-TEQDF levels and total PCB concentrations (correlation coefficient (R) was 0.84
for the urban area and 0.71 for the rural area).
Kiviranta et al. (1999) coordinated a study from 1992-1994, which was designed
as a follow-up of the Vartianen et al. (1997) study, measuring CDD/CDF and PCB levels in
human milk in Finland. One round of 20 samples focused on urban areas (Helsinki,
Finland) and the second round of 64 samples focused on rural areas (Koupio, Finland, and
surroundings). Samples were divided into groups based on the number of children the
mother has nursed. The groups included women who have had 1, 2, 3, 4, 6, or 13
children. The average CDD/CDF levels reported were 13.6 pg l-TEQDF/g fat for rural areas
and 19.9 pg l-TEQDF/g fat for urban areas for all primiparae women. The average total
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PCB concentrations were 198 pg/g fat from rural areas, and 296 pg/g fat for urban areas.
The conclusions of the Kiviranta et al. (1999) study were identical to the Vartianen et al.
(1997) study. The differences between the breast milk l-TEQDFs and PCB concentrations
for rural and urban women remain and l-TEQDFs and PCB concentrations in breast milk also
decreased proportionally when women had two or more children. It was also evident that
there was a marked decrease in I-TEQDF and PCB levels when comparing to the values
reported in 1992-1994 to those in 1987.
Tuinstra et al. (1994) evaluated the CDD/CDF and dioxin-like PCB content of
human milk from The Netherlands. Samples were collected 10 and 42 days after delivery
from about 200 mothers. Based on these data, the mean total I-TEQDF was 31 ppt
(TEQDF-WH098 = 36 ppt) (Tuinstra et al., 1994), and the mean TEQP-WH094 for PCBs 77,
1 26, 1 69, 105, 118, 1 56, 1 70, and 1 80 was 36 ppt (TEQP-WH098 = 31 ppt) (Tuinstra et
al., 1994; Koopman-Esseboom et al., 1994).
Similar estimates of the dioxin-like PCB content of human milk have been obtained
for North America and Europe. Hong et al. (1992) analyzed human milk samples from
upstate New York for PCBs 77, 105, 114, 118, 1 23, 1 26, 1 56, 1 57, 1 67, 1 69, and 1 89.
PCB 118 accounted for the highest proportion of the total PCB concentration. The mean
lipid-based TEQP-WH094 and TEQP-WH098 for these samples was 13 ppt. The total TEQP-
WH094 for 96 pooled human milk samples from Canada was also 13 ppt (TEQP-WH098 =
10 ppt) (Dewailly et al., 1994). She et al. (1995) analyzed 12 human milk samples for
PCBs 77, 118, 105, 1 26, 1 56, 1 69, 1 70, and 1 80. The total TEQP-WH094 for these
samples was 16 ppt (TEQP-WH098= 14 ppt).
For European countries, the lipid-based TEQP-WH094 levels were 22 ppt (TEQP-
WH098= 18 ppt), based on 1990/91 data for PCBs 118, 156, 170, and 180 from 68
German women (Georgii et al., 1995) and 32 ppt (TEQP-WH098= 30 ppt), based on data
for PCBs 77, 1 26,1 69, 105, 118, 114, 1 56, 1 70, and 1 80 from 28 Norwegian mothers
(Johansen et al., 1994). Noren et al. (1990) and Noren and Lunden (1991) analyzed
human milk samples from Sweden in 1989 (n = 2) and in every 4 years between 1972 and
1988/89, respectively. Total TEQP-WH094s based on Noren et al. (1990) were 29 ppt
(TEQP-WH098 = 27 ppt) (PCBs 118, 105, 1 56, 1 80, 77, 1 26, and 1 69). Noren and
Lunden (1991) observed that the concentrations of PCBs in human milk declined between
1972 and 1984/85, but that the 1988/89 samples had similar concentrations as the
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1984/85 samples. Based on the 1988/89 sampling period, the total TEQP-WH094 was 19
ppt (TEQP-WH098= 18 ppt) based on PCBs 105, 156, 180, 77, 1216, and 169
(n=>100).
Van der Velde et al. (1994) compared the levels of PCBs 77, 126, and 169 in
cow's milk and human milk from The Netherlands. The concentrations of these
compounds were found to be higher in human milk than in cow's milk collected from a
background location (Table 4-13). Based on these data, the total TEQP-WH094 and TEQP-
WH098 for human milk was 9.4 ppt for these three dioxin-like PCBs.
Abraham et al. (1998) measured CDD/CDF and coplanar PCBs in blood of four
mothers before and after delivery and during lactation. Abraham et al. (1998) also
examined their breast milk and their infants blood for concentrations of CDD/CDF and
coplanar PCBs. CDD/CDF and coplanar PCBs were also quantified in the cord blood,
meconium, and transit stool. Table 4-14 presents a summary of the TEQDFs of mothers'
milk and blood, and infants' blood. For two of the mothers (mother 1 and mother 2), the
data were associated with their second delivery, and data were also available for their
first-born infants at the age of 11 to 1 2 months. Mother 3 was the only subject that did
not fully breastfeed her infant for at least 1 7 weeks. The results of this study suggest
that CDD/CDF and coplanar PCB TEQs in the blood of the second infants were only about
half as much as in the first born children (at the same age). This is likely a result of
reductions in CDD/CDF concentrations in breast milk as a result of previous lactation. In
addition, the infant that was not fully breast-fed had a lower I-TEQDF concentration in the
blood than the fully breast-fed infants. Lipid-based CDD/CDF concentrations in the
infants' tissues appeared to increase during the 11 months after birth, based on the
comparison of infants' blood CDD/CDF concentrations at 11 months and CDD/CDF
concentrations in cord blood concentrations.
Schecter et al. (1998) analyzed blood and milk from a mother that nursed twin
babies over a 38-month period. In this study, a woman gave birth to twins on December
15, 1992. Blood and milk samples were taken each month starting in February 1993 and
ending in September 1 995. Overall, CDD levels in milk decreased from 309 ppt to 1 73
ppt, CDF levels dropped from 21 ppt to 9 ppt, and total coplanar PCB levels decreased
from 151 to 21 ppt during that time period. Schecter et al. (1 998) estimated that the
mother reduced her dioxin body burden from 310 to 96 ng TEQDFP-WH098, or
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approximately 69 percent during that time period. Overall, the CDD/CDF/PCB
concentrations in the maternal whole blood dropped from 698 ppt to 262 ppt in lipids
during that time period. The twins' consumption of CDD/CDF and coplanar PCBs from
breast feeding was estimated to be approximately 1 1 5 ng TEQDFP-WH098 per twin.
The levels of dioxin-like compounds in human breast milk can be predicted on the
basis of the estimated dioxin intake by the mother. Such procedures have been developed
by Smith (1987) and Sullivan et al. (1991). The approach by Smith assumes that the
concentration in breast milk fat is the same as in maternal fat and can be calculated as:
m h f1
(Eqn.4-1)
where:
Cmiikfat = Concentration in maternal milk (pg/kg of milk fat);
m = Average maternal intake of dioxin (pg/kg of body weight/day);
h = Half-life of dioxin in adults (days);
fi = Proportion of ingested dioxin that is stored in fat; and
f2 = Proportion of mother's weight that is fat (kg maternal fat/kg total
body weight).
This steady-state model assumes that the contaminant levels in maternal fat remain
constant. Though not described here, Smith (1987) also presents more complex
approaches that account for changes in maternal fat levels during breast feeding. The
model developed by Sullivan et al. (1991) is a variation of the models proposed by
Smith (1987). The Sullivan model considers changes in maternal fat levels and predicts
chemical concentrations in milk fat as a function of time after breast feeding begins. The
model proposed by Smith assumes that infant fat concentration at birth is zero; whereas,
Sullivan assumes that the infant fat concentration at birth is equal to the mother's fat
concentration.
Flesch-Janys et al. (1996) estimated the half-life of 2,3,7,8-TCDD in humans to be
approximately 7 years. For the purpose of this preliminary analysis, it is assumed that a
7-year half-life applies to all of the dioxin-like compounds. Smith (1987) suggests values
of 0.9 for f! and 0.3 for f2. Using these assumptions and a background exposure level of
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1 to 3 pg of TEQDFP-WH098/kg-d (derived from diet analysis, see Section 4.4.2 and
previous assessments of background exposure), the concentration of dioxin-like
compounds in breast milk fat is predicted to be about 10 to 30 ppt of TEQ, which is
slightly lower than the measured values.
Uncertainty is introduced into this estimate by the assumption that the assumed
half-life rate and partitioning factors apply to all the dioxin related compounds. Although
these properties are likely to be similar among the various congeners, some variation is
expected. It is unknown whether the net effect of these uncertainties would lead to over
or under estimates of dose. However, the simple model appears to provide reasonable
predictions of background levels found in breast milk and was judged adequate for
purposes of a preliminary analysis. For detailed assessments, readers should consider
using the more complex models and developing chemical-specific property estimates.
Travis et al. (1988) presented an alternative approach to estimating breast milk
contaminant levels. They proposed a biotransfer approach:
Cm = Bm ' (Eqn. 4-2)
where:
Cm = Contaminant concentration in breast milk fat (mg/kg);
Bm = Biotransfer factor for breast milk fat (d/kg); and
I = Maternal intake of contaminant (mg/d).
Travis et al. (1988) also argued that the biotransfer factor is primarily a function of
the octanol-water partition coefficient (Kow ) and developed the following geometric mean
regression:
Bm = 6.2*10-4Kow (Eqn. 4-3)
This regression was derived from data on six lipophilic compounds (log Kow range: 5.16 to
6.5), but did not include any dioxins or furans. Assuming a log Kow of 6.6 for 2,3,7,8-
TCDD, a Bm of 3,700 d/kg is predicted. Combining this value with a maternal intake of 6
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pg/d, a breast milk concentration on a fat basis of 22 ppt is predicted. This prediction is
about 7 times higher than what has been measured for TCDD in breast milk in the United
States. Thus, this approach appears to overpredict TCDD levels while the approach
suggested by Smith (1987) appears to underpredict total TEQ levels.
4.2.3. The Blood Studies of the CDC Collaboration (1995-1997)
The Centers for Disease Control (CDC) has compiled data on blood concentrations
of dioxins, furans, and coplanar PCBs from individuals in the United States with no known
exposures to dioxins (CDC, 2000). These data come from site-specific studies (with
permission from principle investigators in those studies), and CDC has provided the
laboratory analyses of all the blood samples. All the samples were collected between
1995 and 1997. There are a total of 316 individuals included in their compilation from six
locations: 1) Manchester, Missouri (n = 61), 2) Times Beach, Missouri (n = 67), 3)
Jacksonville, Arkansas (n = 57), 4) Oregon (n = 9), 5) Wisconsin (n = 93), and 6) North
Carolina (n = 29). CDC is preparing manuscripts for peer literature publication of statistical
summaries and interpretations of this data. They have provided EPA with an overall
statistical summary of the congener-specific and overall TEQ results from this compilation
(Patterson, 2000), and those results will be described shortly. EPA judges these data to
be the best representation of current background concentrations of dioxin-like compounds
in the blood of US citizens, for these reasons: 1) all individuals were evaluated by the CDC
analysis group as appropriately representing US background conditions and EPA concurs
with this evaluation - that is, all individuals were judged to be exposed only through
background exposures, including inhalation of background ambient air (i.e., not impacted
by nearby high dioxin stack emitters), consumption of animal food products not known or
expected to be contaminated, no occupational exposures, and so on, 2) the blood was
analyzed using a consistent, high resolution, mass spectrometry state-of-the-art protocol
(Patterson and Turner, 1997) which included 4 dioxin-like coplanar PCBs, 3) the data
represent a wide range of adult ages, from 20 to over 70 years of age, and 4) the
sampling was of relatively recent origin - 1995 to 1997, more recent than other studies
reviewed in this chapter. Prior to describing this overall profile, information on four of the
six study sites have been made available to EPA, and these will be described first.
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With the assistance of the Agency for Toxic Substances and Disease Registry, the
Missouri Department of Health (MDOH, 1999) conducted an exposure study to evaluate
the potential impact of incinerating contaminated soil from Times Beach. Approximately
265,000 tons of soil and other materials containing 2,3,7,8-TCDD from 27 eastern
Missouri sites were burned at the Times Beach Superfund site during the period March 1 7,
1996 through June 20, 1997. MDOH (1999) undertook a study to evaluate the impact of
emissions from this incineration. Their approach was to take blood samples from a target
and a comparison population before, during, and after the incineration, and evaluate the
differences in blood levels of dioxin-like compounds between the populations and over
time. MDOH (1999) selected a target population based on air dispersion and deposition
modeling. This population resided within a 4-kilometer radius of the incinerator. A
comparison population from Manchester was located about 16 kilometers from the
incinerator. From a list of over 650 individuals from both populations, totals of 76 and 74
individuals were selected from the target and comparison groups, respectively, for blood
sampling. These selections considered demography, whether or not a woman was
pregnant or breast feeding (neither was selected), and other critical factors. Blood
samples were taken from all participants in September 1995, July 1996, and June 1997,
and questionnaires were administered each time. Mean concentrations of each of 1 5
dioxin and furan congeners, and 4 coplanar PCB congeners were determined assuming
non-detects were equal to one-half the detection limit. These detection limits, on a lipid
basis, were: 0.8 ppt for the tetra- and penta-CDD congeners and the tetra- through octa-
CDF congeners, 1.2 ppt for the hexa- through hepta-CDD congeners, 3.8 ppt for the
coplanar PCB congeners, and 15.4 ppt for OCDD. Concentrations for two hexa-CDD
congeners, 1,2,3,4,7,8-HxCDD and 1,2,3,6,7,8-HxCDD, and one hexa-CDF congener,
1,2,3,7,8,9-HxCDF, were not reported, and concentrations of one hepta-CDD congener
which is not assigned a TEF value, 1,2,3,4,6,7,9-HpCDD, was reported. The mean
concentrations for each congener for each testing period and study group, is shown in
Table 4-15. Further details on this study can be found in MDOH (1999).
The CDC compilation included only the data from 1997. For that year, 67 of the
76 individuals from Times Beach had available measurements for their compilation, and 61
of the 74 individuals from the comparison site, Manchester, had available measurements.
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MDOH (1999) concluded that there was no statistically significant differences
between the target and comparison groups for all the analytes measured except for PCB
126, which was slightly higher in the comparison group. MDOH (1999) concluded that
the values measured were some of the lowest values ever recorded on a human
population. As seen in Table 4-1 5, the TEQDFP-WH098 for the target group was 11.7 ppt
while for the comparison group it was 12.6 ppt (averaged over all sampling dates).
However, the actual TEQ concentrations would be higher than these since this study did
not report on measurements for the three congeners noted earlier. Other data suggest
that the hexa-CDD congeners not reported on in this study, mainly 1,2,3,6,7,8-HxCDD,
comprise in the range of one-fourth to one-third of the total body burden of TEQ. MDOH
(1999) also observed that there appeared to be a decrease in concentrations from pre- to
post-incineration for most analytes. Of all factors examined through questionnaires, only
two appeared to be important for dioxin body burdens: smoking and age. Combining both
populations, the average TEQ for participants living in homes with cigarette smokers as
12.8 ppt (I-TEQDF + TEQP-WH094), compared to 9.4 ppt (I-TEQDF + TEQP-WH094) in
homes that do not have smokers. No age-specific results were presented in MDOH
(1999), but a Pearson correlation of 0.525 for average TEQ concentration (statistical
significance <0.001, two-tailed) was found for age. The average age of participants in
both populations was about 43 years.
The Arkansas Department of Health (ADH) and the Agency for Toxic Substances
and Disease Registry (ATSDR) cooperated on the design and implementation of a study to
evaluate the exposure of individuals to dioxin-like compounds and other contaminants
manufactured and then disposed of through incineration at the Vertac/Hercules Superfund
Site (abbreviated the Vertac Site) in Jacksonville, Arkansas (ADH, 1995). The site had
been used from the 1950s to manufacture herbicides such as 2,4,-D, 2,4,5-T, and 2,4,5-
TP. It had changed hands several times until being abandoned by Vertac in 1987.
Incineration occurred between 1992 and 1994. One component of the study was to
sample and then analyze blood from three target groups of individuals: 1) residents living
near the Site for more than 15 years as of 1991 - 72 individuals recruited, 2) residents
living between 1 and 5 years as of 1991 - 36 recruited, and 3) residents living in a
comparison area - 72 recruited; 71 participated. The comparison area chosen was in
Mabelvale, Arkansas, a demographically similar community approximately 25 miles south
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of Jacksonville. Study participants ranged in age from 18 to 65 years old. The average
age of the comparison group at the first sampling in 1991 was 40 years. Blood samples
were taken in March, 1991, and participants also filled out an extensive questionnaire at
that time. Subsets of individuals from all three populations were sampled once again in
1994 and 1995 after the incineration had been completed.
The CDC compilation used only the data from 1995 in their compilation. This data
set included individuals who lived both in Jacksonville and in Mabelville - most of the
individuals followed into 1995 lived in Jacksonville. The number of individuals sampled in
1995 included in the CDC compilation is 57.
The 1991 and 1994 sampling were described in a draft report released by the
Arkansas Department of Health for public comment in 1995 (ADH, 1995). This report has
never been finalized. However, the blood data has been available and even used by one
researcher citing results from the Mabelville population sampled in 1991 as a comparison
group to his own study of dioxin-like compounds in the blood of a Great Lakes sport-
fishing population (Anderson et al., 1998). Individual results that are summarized here
have been provided to EPA via personal communication (Cranmer, 1996). The data
supplied for each dioxin-like congener was either: identified as a quantified concentration
(in serum, on a lipid basis), identified as "not detected" (ND), or identified as "not
reported"(NR). Detection limits were not specified. Therefore, for purposes of the
calculation of means, non-detects were assumed equal to zero. Measurements identified
as NR were not included in the calculation of means.
Table 4-16 summarizes the results from the comparison population only. This table
shows the results for the entire set of 71 individuals sampled in 1991. It also shows the
results for subsets of these individuals that were sampled in 1994 and 1995. For
comparison, the 1991 means for these same subsets are also provided. Unlike the target
population of the Times Beach study described earlier, there appeared to be measurable
impacts on the blood levels of dioxin-like compounds in the target populations at Vertac,
as evidenced by the 1991 sampling. However, these impacts have not been tied directly
to activities at Vertac. For example, in groups 1 (15 years residence near the site) and 2
(between 1 and 5 years residence), the mean lipid-based concentrations of 2,3,7,8-TCDD
were 8.5 and 4.2 ppt, while the mean for the background population was 2.5 ppt. The
high means for groups 1 and 2 were driven by a small number of very high concentrations
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(the three high concentrations from group 1 were 29.7, 84.9, and 94.8 ppt). However, if
these high values are excluded, the overall concentrations from these groups are still
higher than for the comparison group. The average TEQDFP-WH098 from the comparison
population in 1991 was 25.2 ppt. The select group of 18 individuals who were targeted
for resampling in 1994 were individuals whose lipid-based concentration of 2,3,7,8-TCDD
ranged from 2 to 5 ppt. Table 4-16 suggests that the average blood TEQDFP-WH098 level
for this group decreased between 1991 and 1994, from 26.8 to 22.6 ppt. However,
when evaluating the average CDD/CDF/PCB concentration of the 14 individuals resampled
in 1995 (a further subset of the 18 who provided samples in 1994), there appears to be
little evidence of a decline in TEQDFP-WH098. The TEQDFP-WH098 concentrations were 25.0
ppt in 1991 and 24.0 ppt in 1995 for this group. As with other studies, ADH (1995) also
reported on an important age effect - the levels of dioxins and furans increased with age.
Grassman et al. (1999) developed a method to evaluate inter-individual variation in
dioxin responsiveness among humans. Specifically, they developed a system that
measures dioxin-responsive biomarkers in peripheral blood lymphocytes challenged in vitro
with 10 nM TCDD during cell culture. Grassman et al. (1999) evaluated the capabilities of
this method by obtaining blood samples from 3 populations widely variable in the
magnitude and duration of their exposure to dioxin. One was a group of plant workers in
a German chemical manufacturing plant, one was comprised of men, women, and children
living in the vicinity of Seveso, Italy, during the accidental release of 2,3,7,8-TCDD in
1976, and the third was comprised of adult North Carolina volunteers, with no known
occupational or unusual exposures to dioxin. This third group is comprised of 29
individuals, with ages ranging from 21 to 52 years, mean of 34.5 years, and it is the
results from their analyses that are considered here as a U.S. background population.
Grassman et al. (1999) reported that their average lipid-based TEQDFP-WH094 was 14.2
ppt. Results of the study comparing the three study groups are reported in Grassman et
al. (1999).
The North Carolina participants were sampled in 1996. EPA was provided the
congener specific data for the 29 individuals of this study (Masten, 2000). Average
congener concentrations from this group are provided in Table 4-17. Interferences were
found in the analysis for 1,2,3,6,7,8-HxCDD, so this congener was not reported for any of
the individuals, and TEQs were calculated without this congener. Other body burden data
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suggests that this congener could comprise in the range of one-fourth to one-third of the
body burden of TEQDFP, so the overall TEQfor this population is underestimated. A small
number of additional measurements from other congeners were not reported, and these
were not considered in the generation of mean congener values. The mean values were
calculated by assuming that non-detects were equal to one-half the detection limit. With
this procedure, the lipid-based TEQDFP-WH098 was calculated to be 15.0 ppt. Assuming
that non-detects are equal to zero would not change these results by much; the lipid-
based TEQDFP-WH098 in this case was calculated as 13.0 ppt.
The CDC compilation includes these same data from the 29 North Carolina
individuals. The congener profile for the overall compilation done by CDC is shown in
Table 4-18. These averages were derived assuming non-detects were equal to 1/2 the
detection limit. These average congener concentrations were derived only using data from
the overall set where these congeners were reported. As noted in the above discussions,
there were some studies where congeners were not reported, such as 1,2,3,6,7,8-
HxCDD. Therefore, the number of observations that went into calculating overall averages
for each congener was less than or equal to the total number of individuals (n = 316) in
the study. These congener profiles were not used to generate TEQ concentrations for the
overall data base. Instead, Patterson (2000) supplied statistical results for the TEQDFP-
WH098 concentrations that were generated using substitution methods for each individual
included who had "not reported" (NR) for some of the congeners. Each time a congener
was NR in an individual's congener profile, the average concentration from other
individuals in the same study set was substituted for the individual who had the missing
data. When that congener was missing from an entire study set, then the average for that
congener from all other data sets where it was reported was substituted for all individuals
in the data set with the missing congener. With these substitution techniques, every
individual included in the overall data base had a complete set of congener results
including quantified concentrations, non-detects with known detection limits, and
substituted values. Then, each individual's TEQDFP-WH098 lipid-based concentration was
derived (assuming non-detects equal 1/2 detection limit), and from these TEQs, means and
percentiles were generated. By this discussion, it should be clear that one cannot derive
the TEQ concentrations in Table 4-18 from the congener profiles in Table 4-18, although
they will be close.
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As seen in Table 4-18, the average lipid-based TEQDFP-WH098 concentration was
22.1 ppt. It was found that the substituting ND = 1/2 LOD did not influence the TEQ
results. At ND = 0, the average TEQ concentration was only 1 ppt lower at 21.1 ppt
TEQDFP-WH098 However, this TEQDFP-WH098 concentration included only 4 of the 1 2
coplanar PCB congeners. The overall compilation of literature data on coplanar PCB
concentrations in human tissues, other than this CDC compilation, shown later in this
chapter in Table 4-21, includes data on 11 of the dioxin-like coplanar PCBs. That data
suggests a weighted mean TEQP-WH098 concentration in blood of 15.6 ppt TEQP-WH098,
of which these four congeners comprise 5.9 ppt. Therefore, the congeners missing from
the CDC data base account for 62% [ (15.6-5.9)715.6 * 100% ] of the total PCB TEQ
estimated in the early 1990's for blood. From the congener profile in Table 4-18, it is
calculated that the 4 PCB congeners add about 2.0 ppt TEQ to the overall mean
concentration of 22.1 ppt. Assuming that the missing congeners from the CDC study
data contribute the same proportion to the total PCB TEQ as in earlier data, they would
increase the estimate of current PCB blood concentrations by another 3.3 ppt TEQP-
WH098 lipid for a total PCB TEQ of 5.3 pg/g lipid and a total TEQDFP-WH098 of 25.4 ppt
lipid. This will be the TEQ lipid concentration assumed to represent current background
conditions in the United States.
4.2.4. Additional Recent Tissue Studies
Petreas, et al. (2000) reported on the analysis of breast adipose tissue samples for
the seventeen dioxin-like CDD/F congeners. Samples were taken in 1998 from women in
San Francisco area hospitals undergoing breast surgery for suspected breast cancer.
I-TEQDF concentrations were reported for 45 of these women who were found to be
cancer-free. The range of I-TEQDF concentrations found in this study population was 10 to
60 ppt lipid-basis, with a median concentration of 19 ppt. This was calculated assuming
non-detects were equal to 1/2 the detection limit. When assuming non-detects were equal
to zero, this dropped slightly to 16 ppt I-TEQDF. When recalculating TEQs using the
WH098 TEF scheme, Petreas et al. (2000) found the concentrations to increase by 2-3
ppt. These concentrations compare well to the mean concentration of WH098-TEQDF of
approximately 21.6 ppt lipid-basis found in the 316 samples of the CDC compilation
(Patterson, 2000) reported on earlier. These results were compared to a set of 17 adipose
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samples from other women patients undergoing surgeries for other reasons 10 years
earlier in 1988. From 17 samples, the range was similar at 1 3 to 63 I-TEQDF, but the
median was higher at 27.3 pg/g I-TEQDF lipid-basis. Other analyses by Petreas
demonstrate the apparent downward trend in body burdens in these adipose tissues.
4.2.5. Summary of Human Tissue Levels
Tables 4-19 and 4-20 present summaries of the TEQDF concentrations in human
tissues from North America, and Europe and Japan, respectively, as reported in the
literature. In general, these data represent studies conducted in the late 1980s and early
1990s. These data on human adipose tissue, blood, and breast milk indicate that mean
tissue concentrations of CDD/CDFs ranged from 20 to 50 ppt TEQDF-WH098 on a lipid
basis, with a midpoint of 35 ppt TEQDF-WH098 during that time period. The mean TEQDF-
WH098 from the U.S. studies was 32.7 ppt, and the mean from the European and
Japanese studies was 41.0 ppt. The assumption is made here that levels in all three
tissues are similar (on a lipid basis) and that levels in all of these tissues can be considered
representative of overall body burden. Van den Berg et al. (1994) reported that (on a lipid
basis) the serum-to-blood tissue ratio for 2,3,7,8-TCDD is approximately one and this ratio
increases with higher chlorinated CDD/CDFs. Van den Berg et al. (1994) also compared
lipid-based concentrations for all CDD/CDF congeners reported in human milk, blood, and
adipose, and concluded that the levels are strikingly similar across tissues.
It should be noted that all available human tissue studies have uncertainties that
prevented a precise, statistically-based estimate of the national mean. Except for NHATS,
the number of people in the available studies of CDD/CDFs in human tissues is relatively
small, and participants are not selected in a statistically based manner. Other biases may
have also been present in NHATS, as well as in other studies. Thus, it is uncertain how
representative these data were of the general population.
Tables 4-21 and 4-22 present summaries of PCB TEQ concentrations in human
tissues from North America and Europe, respectively, based on data from the 1980s and
early 1990s. The average tissue level of dioxin-like PCBs for the general U.S. population
was probably within the range of 10 to 30 ppt TEQP-WH098 on a lipid basis, with a
midpoint of about 20 ppt. The mean TEQP-WH098 from these U.S. studies was 16.7 ppt.
The mean from the European studies was 31.9 ppt. This indicates that on a TEQP-WH098,
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PCB levels were between one-half and two-thirds that of CDD/CDFs. Inclusion of dioxin-
like PCBs raised the estimate of U.S. human tissue levels to approximately 30 to 70 ppt
TEQDFP-WH098 (midpoint = 55 ppt) for the late 1980s and early 1990s.
As discussed above, the representativeness of these PCB studies for the general
population is unknown. The toxic equivalency factors for PCBs are not as well established
as the CDD/CDFs and increase uncertainty in these estimates. Uncertainty is also
increased by the high background levels of PCBs found in many laboratories, which can
create analytical difficulties. In addition, not all studies presented data for the same set of
PCB congeners. Therefore, studies were combined to calculate a total TEQP-WH098 based
on all PCB congeners for which TEFP-WH098s have been established. Total TEQP-WH098s
were calculated by summing weighted mean TEQP-WH098 concentrations (based on one or
more studies) for each toxic PCB congener.
The CDC data base includes 316 individuals from 6 sites in the time frame of
1995-1997. These data form the basis of the estimates of current background tissue
levels in the United States. The mean TEQ tissue level from the study data alone is 22.1
ppt TEQDFP-WH098. Because this concentration does not include important dioxin-like PCB
congeners, this average has been increased to 25.4 ppt TEQP-WH098 using information
from earlier studies of dioxin-like PCBs in blood. This concentration will be used to
represent current background conditions in the United States. This use includes an overall
conclusion for body burdens of dioxin-like compounds in this chapter, as well as an
assumption for mother's milk concentration in an evaluation of the impacts of nursing on
infants in Chapter 5.
It is important to note that the 95th percentile concentration from this study data
base is 38.8 ppt TEQP-WH098, which is nearly twice the mean of 22.1 ppt TEQP-WH098
from this study. Later in this chapter, variation in background dose is investigated using
data on dietary consumption of fats. Using statistical surveys on food consumption, it
was found that the 95th percentile of fat consumption was about twice the mean (and the
99th percentile is about 3 times the mean). Knowing that dioxins are transmitted primarily
through consumption of dietary fat, this result from the CDC blood compilation is
consistent with the dietary result; the 95th percentile consumption of dietary fat appears to
lead to the 95th percentile in body burden of dioxin-like compounds.
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A portion of the CDC blood data were plotted as a function of age. This plot is
shown in Figure 4-1. This figure was generated as part of a site-specific study conducted
by the Agency for Toxic Substances and Disease Registry at Mossville, Louisiana (ATSDR,
1999). The data shown in Figure 4-1 encompass the control population that was to be
compared against measurements in Mossville. This comparison population is a subset of
the full CDC (2000) population. Figure 4-1 shows that blood levels generally increase
with age, and also that the variability in blood levels increase with age. An age trend such
as this one has been observed in other studies, such as the NHATS tissue data described
earlier (U.S. EPA, 1991 a).
4.2.6. Body Burden Profiles
The profiles for CDD/CDF concentrations in human adipose tissue, blood, and
human milk are presented in Figure 4-2 and Table 4-23 based on the literature studies
from the 1980s and early 1990s. These profiles were generated by calculating the ratio
of the mean concentrations of the 2,3,7,8-substituted congeners to total concentration of
2,3,7,8-substituted CDD/CDFs when nondetects were set to one-half the detection limit.
In addition, it should be noted that some studies (i.e., adipose tissue - Schecter, 1991 and
U.S. EPA, 1991 a; blood - Schecter et al., 1994a and Cole et al., 1995) reported total
2,3,7,8-substituted HxCDD/F and HpCDD/F concentrations instead of reporting
concentrations for the individual HxCDD/F and HpCDD/F congeners. Thus, in order to
provide a complete profile based on all 17 of the 2,3,7,8-substituted congeners, the
concentrations of total HxCDDs, HxCDFs, HpCDDs, and HpCDFs from these studies were
apportioned among the individual HxCDD/F and HpCDD/F congeners based on the ratios of
individual congeners to total HxCDD/Fs and HpCDD/Fs reported in studies providing data
for the individual 2,3,7,8-substituted HxCDD/F and HpCDD/F congeners (i.e., adipose
tissue - Patterson et al., 1994; blood - Schecter et al., 1993). The profiles generated for
these three body tissues appear to be similar. In general, higher-chlorinated CDDs
dominate with OCDD accounting for over 65 percent of the total 2,3,7,8-substituted
CDD/CDFs. CDFs account for a relatively small portion of the total 2,3,7,8-substituted
CDD/CDFs.
The profile of 2,3,7,8-CDD/CDF congeners in human blood from the more recent
(i.e., 1995-1997) CDC blood data set was also generated. This profile is shown in Figure
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4-3 and is based on the data in Table 4-18. The profile is similar to that generated from
earlier human tissue data (Figure 4-2).
4.3. INTAKE ESTIMATES BASED ON TISSUE LEVELS AND PHARMACOKINETIC
MODELING
4.3.1. Steady State Approach
Examination of human tissue data provides a way to estimate exposures of humans
to CDD/CDFs. Average daily intake of CDD/CDFs may be estimated using human tissue
data and pharmocokinetic modeling as follows:
where:
D =
D
Ti/2
V
C
CF,
CF2
A
In2
L1/2
r
V * CF1 (C) CF2
/ (A)
Daily intake of CDD/CDF (pg/day);
Half-life of CDD/CDF (years);
Volume of body fat (kg);
Concentration of CDD/CDF in tissue (pg/g)
Conversion factor (1,000 g/kg);
Conversion factor (year/365 days); and
Fraction of dose that is absorbed.
(Eqn. 4-4)
The level of 2,3,7,8-TCDD found in human adipose tissue averages about 5.5 ppt
in the United States based on data from a variety of studies from the 1980s and mid
1990s, and 2.1 pt based on the CDC data set. These values may be used to estimate the
associated exposure levels using a simple pharmacokinetic model that back calculates the
dose needed to achieve the observed tissue levels under the assumption of steady-state
exposure/dose, as given above. (See Equation 4-4.) This model requires an estimate of
the fraction of the dose that is absorbed, the elimination rate constant, and body fat
volume.
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December 2003
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A complete summary of the literature on gastrointestinal, dermal, transpulmonary,
and parenteral absorption is provided in Part II - Health Assessment of 2,3,7,8-
Tetrachlorodibenzo-p-dioxin and Related Compounds, Chapter 1 - Disposition and
Pharmacokinetics. The summaries there pertaining to oral absorption justify the selection
of 0.8 as an absorption fraction for dioxin TEQs in simple exercises conducted in this
section on pharmacokinetic modeling. Most of the gastrointestinal absorption research
has been conducted on 2,3,7,8-TCDD and laboratory animals. Results suggest that
2,3,7,8-TCDD is absorbed at a rate greater than 50% in oil or in diet, with several studies
reporting average absorption at 70% or more: Rose, et al. (1976) found an average of
84% in rats where the vehicle was a mixture of acetone and corn oil; Piper, et al. (1973)
found an average of 70% on rats with the same vehicle; Diliberto et al. (1996) reported
88% in rats in a vehicle of vegetable oil, ethanol, and water; and Olson, et al (1980)
reported 70% in hamsters in a vehicle of olive oil. Similar and even higher absorption was
found for 2,3,7,8-TCDF, 1,2,3,7,8-PCDD, 2,3,4,7,8-PCDF, and 3,3',4,4'-TCB. Lower
absorption at 2 to 15% was found for OCDD (Birnbaum and Couture, 1988), but since
background TEQ doses are dominated by the lower chlorinated congeners, the low
absorption of OCDD may be less critical. In limited studies and evaluations of oral
absorption on humans, it is concluded that the more soluble congeners, such as 2,3,7,8-
TCDF are almost completely absorbed, whereas the extremely insoluble OCDD is poorly
absorbed. In one experiment, Poiger and Schlatter (1986) found that >87% of the oral
dose of TCDD in corn oil in a 42 year-old man was absorbed from the gastrointestinal
tract. Like some of the experiments on rats, the amount absorbed in some cases was
dose dependent, with lower absorptions at higher doses. Again, low absorption at high
doses is less critical for the current exercises, which focus on low background dose of
TEQs.
Flesch-Janys et al. (1996) estimated the half-life of 2,3,7,8-TCDD (and other
CDD/CDFs) based on blood levels of a group of occupationally exposed individuals. The
median half-life for 2,3,7,8-TCDD (n = 48) was estimated to be 7.2 years. Half-lives for
other CDD/CDF congeners ranged from 3.0 to 19.6 years. Van der Molen et al. (1998)
estimated the elimination rate constant of 2,3,7,8-TCDD using data on the TCDD blood
lipid levels of Vietnam veterans who had been involved in the spraying of Agent Orange.
The Van der Molen et al. (1998) model predicted half-lives ranging from 5.5 years in
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young adults to 11 years in elderly men. The model accounted for age-dependent body
composition, and age- and time-dependent background intake.
Ryan et al. (1997) reported the elimination rate constant of 2,3,7,8-TCDD by back
calculating from the levels in 1992 and 1996 blood samples collected from six of the
2,4,5-trichlorophenoxyacetic (2,4,5-T) workers in Russia. The elimination rate constants
of four of the six samples ranged from 6.9 to 17 years (6.9, 9.7, 9.7, and 17,
respectively), while those of two of the six samples were incalculable. Ryan et al. (1997)
stated that these four values were in the range reported by other investigators. However,
no supporting references were provided. Due to the large variability of values, the small
sample sizes (one single value for each sample), and a potential inconsistency in sample
analysis (samples were analyzed by two different laboratories at two different times),
there is uncertainty in these values. Therefore, these values require further consideration.
Based on available data, the elimination rate constant (i.e., half-life) for 2,3,7,8-
TCDD was assumed to be about 7.1 years, and the fat volume was assumed to be 17.5
kg (i.e., 70 kg body weight * 0.25 fat) which yielded a background TCDD dose of about
32 pg/day using the TCDD tissue estimate from the 1980s to mid 1990s (5.5 ppt), and
12 pg/day (0.18 pg/kg/day) using the TCDD tissue concentration from the CDC data set
(2.1 ppt). These estimates agree well with the background exposure estimates (to
2,3,7,8-TCDD only) of 35 pg/day by Travis and Hattemer-Frey (1991) and 25 pg/day by
Furst et al. (1991), but are somewhat higher than the current background exposure
estimate of 5.6 pg/day from this assessment (see Section 4.4.2), as derived using typical
media levels and contact rates. Using the current CDD/CDF TEQ body burden data
presented in Table 4-18 and the pharmacokinetic model presented in Equation 4-4, the
average daily intake of total CDD/CDFs is estimated to be 126 pg TEQDF-WH098/day. This
estimate assumes a half-life of TEQDF-WH098s in the body of 7.1 years, a fat volume of
1 7.5 kg, a concentration in the body fat of 21.6 ppt (i.e., the approximate mean TEQ
concentration for CDD/CDFs only, as calculated from the data in Table 4-18), and steady-
state conditions. This value is also three times higher than the current background
exposure estimate of 41 pg TEQDF-WH098/day from this assessment, as derived using
typical media levels and contact rates. If PCBs are included in this exercise (i.e., using the
current TEQDFP-WH098 background tissue concentration of 25.4 ppt) the estimated TEQDFP-
WH098 dose would be 146 pg/day. This estimate is approximately 2.2 times higher than
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the direct estimate from the dietary data of 65 pg TEQDFP-WH098/day. Because this model
was originally developed for use with 2,3,7,8-TCDD, the effect of using it to model
CDD/CDFs introduces uncertainty into these estimated values.
An important uncertainty in the modeling exercise described above was the
assumption that the half-life estimate for 2,3,7,8-TCDD (7.1 yr) would apply to TEQDF-
WH098s. Thus, the same pharmacokinetic model was applied to average human tissue
levels for each congener, using half-lives that are specific to each congener, and then
summing the estimated intakes for each congener. This approach yielded an estimated
intake of 87 pg TEQDF-WH098/day (Table 4-24). This value is approximately 2 times
higher that the current background estimate of 41 pg TEQDF-WH098/day.
Another, perhaps more important, uncertainty in using this approach to estimate
current dose is that the dose is assumed to be constant over time. If, in fact, the dose
which has resulted in current average body burden were constant over the past several
decades, than use of this steady-state PK model would provide quite reasonable estimates
of current dose. The only uncertainty in this case (beside the simplistic nature of it being
a one-compartment PK model) is the use of 7.1 years as the half-life (as described above).
If the dose regime instead was characterized by very low doses in the middle of the
twentieth century only to rise significantly in the latter part of the century, than this model
would, by definition, provide an underestimate of current dose. If, on the other hand,
doses were very much higher in the mid-portions of the twentieth century only to drop
towards the end of the century, than this steady state model would, by definition again,
provide an overestimate of the current dose. The steady-state model only provides an
average dose over time that could account for a given body burden - it obviously doesn't
address the possibility of changes in dose over time. As will be described in the next
section, there is a very large amount of evidence suggesting that doses were higher in the
mid-decades of the twentieth century, and may be significantly higher, as compared to the
latter decades. The tissue levels representing the "current average body burdens"
included a significant number of individuals living in this middle decades of the twentieth
century. This being the case, it is concluded that the steady state approach will
overestimate current dose.
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4.3.2. Non-Steady State Approach
Chapter 6 describes evidence supporting temporal trends in CDD/CDF/PCB
concentrations in environmental media, foods, and associated doses. It appears that the
levels of dioxin-like compounds have increased in the environment starting from the 1930s
through the 1960s, and loadings began to decline perhaps starting in the 1970s to the
present. Recent evidence collected on animal food products in the United States (Winters,
et al., 1998), combined with body burden data, provide evidence that human exposures to
dioxins may have followed the same trends. (See Chapter 6.)
Pinsky and Lorber (1998) used a non-steady state approach to reconstruct the
pattern of past exposure and estimate current exposure to 2,3,7,8-TCDD, using a simple
pharmacokinetic model that included a time-varying TCDD dose. A first order,
one-compartment PK model was used to compute an individual's body lipids TCDD
concentration through time. Key inputs for that model include: (1) a time-varying dose of
TCDD (expressed in units of pg/kg-day), (2) a fraction of dose absorbed into the body lipid
compartment (assumed to be constant), (3) the volume of the body lipid compartment
(assumed to be time varying), and (4) a rate of TCDD loss from the lipid compartment
(modeled as a function of the percent of body fat). In order to calculate the rate of TCDD
loss, a model of how body lipid volumes vary over time, in addition to a model of how
overall body weight varied over time, was required.
In this modeling exercise, all inputs were fixed, except the time-varying dose of
TCDD. Using Bayesian statistical approaches, the non-steady state dose was "calibrated"
to best-fit a set of data on TCDD concentration in body lipids from the 1970s to the
1990s. The results of this exercise indicated that the dose appears to have increased
from the 1940s through the 1960s, and began to drop through the 1970s, with a
baseline level being reached by the 1980s. The results suggest that TCDD exposures may
have been 20 times higher during the 1960s than the 1980s. Over a 10-year peak period
in the 1960s and early 1970s, daily exposures could have been as high as 1.5 to 2.0
pg/kg-day, possibly dropping as low as 0.10 pg/kg-day (7 pg/day) and less into the
1980s. This estimate of current dose of 7 pg 2,3,7,8-TCDD/day is quite similar to the
estimate of 6.1 pg 2,3,7,8-TCDD/day made using typical media levels and contact rates.
In another test, Pinsky and Lorber (1998) used the same modeling structure to test the
steady state assumption by forcing the dose to be constant over time. In that test, Pinsky
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and Lorber (1998) solved for a 'best-fit' dose of 0.35 pg/kg-day. This is higher than the
1980s calibrated 'current dose' of 0.10 pg/kg-day, derived by allowing the dose to vary
over time. As described in the previous section, if much higher doses of dioxin occurred
in the middle part of the twentieth century, than a steady state model will provide an
overestimate of current dose; in other words, this Pinsky and Lorber (1998) result is to be
expected. In addition, the steady-dose 'best-fit' solution provided a significantly poorer fit
to the data as compared to the non-steady dose solution, providing even more evidence
that doses have not been steady during the twentieth century. (See Chapter 6 for a
complete description of this modeling approach.)
4.4. INTAKE ESTIMATES BASED ON EXPOSURE MODELING
4.4.1. Previous Assessments of Background Exposures
Several researchers have published quantitative assessments of human exposures
to CDDs and CDFs. Some of the more recent assessments are discussed below (Travis
and Hattemer-Frey, 1991; Furst et al.,1990; Furst et al., 1991; Henry et al., 1992;
Theelen, 1991; Schuhmacher et al., 1997; Oilman and Newhook, 1991; Schrey et al.,
1995; MAFF, 1995; and Jacobs and Mobbs, 1997; Himberg, 1993; and Liem et al.
2000a, 2000b). It is generally concluded by these researchers that dietary intake is the
primary pathway of human exposure to CDDs and CDFs. Over 90 percent of human
exposure occur through the diet, with foods from animal origins being the predominant
sources.
Travis and Hattemer-Frey (1991) estimated that the average daily intake of
2,3,7,8-TCDD by the general population of the United States is 34.8 pg/day. Ingestion
exposures were estimated by multiplying the concentration of 2,3,7,8-TCDD in beef, milk,
produce, fish, eggs, and water (estimated using the Fugacity Food Chain model) times the
average U.S. adult consumption values for these products reported by Yang and Nelson
(1986). The calculations assume that 100 percent of the 2,3,7,8-TCDD ingested are
absorbed through the gut. Intake via inhalation was estimated by multiplying the
concentration in air times the amount of air inhaled per day (20 m3) assuming that 100
percent of inhaled 2,3,7,8-TCDD are absorbed through the lung. The results of their
assessment, summarized in Table 4-25, indicate that foods from animal origins comprise
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95 percent of the estimated total daily exposure. These foods include milk and dairy
products, beef, fish, and eggs. Exposure resulting from consumption of vegetables and
other produce was estimated to account for 3.4 percent of the total intake. Exposure
from ingestion of water, ingestion of soil, and inhalation of air together accounted for
about 1 percent of the total daily intake.
Furst et al. (1990) estimated human exposure to CDD/CDFs based on the analysis
of 107 food samples collected in the Federal Republic of Germany. The average daily I-
TEQDF intake was estimated to be 85 pg/person/day or 1.2 pg/kg body weight/day. Furst
et al. (1990) concluded that foods of animal origin contribute significantly to the human
body burden of CDD/CDFs. In a subsequent study, Furst et al. (1991) assessed human
exposure to CDDs and CDFs from foods using data from more than 300 randomly
selected food samples and food consumption data reflective of consumption habits of the
German population. These authors estimated that the German population's average daily
intake of CDDs and CDFs from food is 158 pg I-TEQDF per person of which 25 pg is
2,3,7,8-TCDD. Dairy products, meat and meat products (primarily beef), and fish and fish
products each contribute about 32 to 36 percent of the daily intake of I-TEQDF. Based on
the levels of CDD/CDFs observed in human samples, the average daily intake via food was
estimated to be in the range of 1 to 3 pg l-TEQDF/kg body weight.
Henry et al. (1992) of the U.S. Food and Drug Administration estimated the
average exposure to the U.S. population from 2,3,7,8-TCDD through the food supply
using the following assumptions: (1) all dairy products have background lipid 2,3,7,8-
TCDD levels equivalent to those found in milk and half-and-half, i.e., about 55 ppq (whole
dairy food levels were estimated using percent fat in each food); (2) levels averaging 35
ppq in beef tissue are present in all meat products; (3) ocean fish with tissue levels equal
to half of the detection limit (about 0.5 ppt) are the sole fish source in the diet; (4)
average food consumption figures (total-sample-basis) available from nationally
representative data bases were used for frequency of eating (Market Research Corporation
of America's (MRCA) Menu Census VI (1977-78)) and for serving sizes (U.S. Department
of Agriculture's 1977-78 National Food Consumption Survey). The concentration
assumptions used in the Henry et al. (1992) study were based on previously published
data. For example, most of the food data were based on La Fleur et al. (1990), and the
fish data were based on U.S. EPA (1992). These studies are described in Sections 3.7.2
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and 3.6.1, respectively. FDA's estimates of 2,3,7,8-TCDD intake were derived by
multiplying the food dioxin levels by the average amounts of food consumed per day. The
results of the FDA assessment, summarized in Table 4-26, indicate an average daily
exposure of 15.9 pg/day of 2,3,7,8-TCDD of which 4 percent are due to dairy and milk
products, 41 percent are due to meats, and 54 percent are due to ocean fish.
Theelen (1991), of The Netherlands National Institute of Public Health and
Environmental Protection, estimated the average daily intake of 2,3,7,8-TCDD and total
I-TEQDF by residents of The Netherlands for various possible routes of exposure. The
results, summarized in Table 4-27, indicate an average intake of 20 pg/day of 2,3,7,8-
TCDD and 115 pg/day of total I-TEQDF from food and 0.08 pg/day (2,3,7,8-TCDD) and
3.2 pg/day (I-TEQDF) from combined direct air and soil exposure. Milk and dairy products
make up about one-third of the total daily exposure. Animal fat in meat, poultry, and fish
(i.e., fish oil) also contribute about one-third. Fish consumption represents 18.5 percent
of total daily exposure. In a later study, Theelen et al. (1993) reported a median daily
intake for adults of 1 pg l-TEQDF/kg body weight, and a 95th percentile rate of 2 pg I-
TEQDF/kg body weight. These values were based on CDD/CDF residue levels in food
products and food consumption survey data.
Becher et al. (1998) estimated dietary intake of CDD/CDFs and dioxin-like PCBs in
the Norwegian population. Average food consumption data obtained from the 1992-1994
Norwegian consumer survey of 4,033 households was analyzed in conjunction with
measured CDD/CDF and dioxin-like PCB concentrations in basic foodstuffs to determine
dietary intake. Becher et al. (1998) investigated pooled samples from 20 to 25 seafood
samples and 10 to 15 samples of other foodstuffs. Average CDD/CDF dietary intake
ranged from 71 to 85 pg l-TEQDF/day and average PCB dietary intake ranged from 86 to
106 pg TEQP-WH094/day. Fish and fish products constituted the largest contribution to
the dietary intake of CDD/CDFs and PCBs. PCBs contributed more to the total dioxin
related toxicity (i.e.,TEQDFP-WH098) than CDD/CDFs in the following food groups: milk,
meat, eggs, and cod liver oil; while CDD/CDFs were the higher contributor in the fats food
group.
Buckland et al. (1998) estimated dietary intake of CDD/CDFs and PCBs in the
population of New Zealand. The estimate was based on 19 food group composites from
51 individual food samples purchased from retail outlets in four major cities and one
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provincial center. Estimated dietary intake was calculated based on two typical diets, an
average exposure diet of an adult male and a high-end exposure diet of an adolescent
male. Total dietary levels of all CDD/CDF congeners ranged from 14.5 to 30.6 pg I-
TEQDF/day (whole weight). Total dietary PCB levels ranged from 12.2 to 22.7 pg TEQP-
WH094 (whole weight). Total PCB concentration was based on levels of the following
PCBs: 28, 31, 52, 77, 101, 99, 123, 118, 114, 105, 126, 153, 138, 167, 156, 157,
169, 187, 183, 180, 170, 189, 202, 194, and 206. These calculations were made by
setting concentrations less than the LOD at one-half the LOD. Vegetable fats/oils, cereals,
cooked potatoes and hot chips, and processed meats constituted the largest contribution
to l-TEQDFs in the adolescent diet. Butter, processed meats, and milk constituted the
largest contribution of TEQP-WH094s to the adolescent diet. The authors noted that while
it is difficult to compare these total dietary TEQ results to countries with different dietary
patterns, the results appear to indicate that estimated dietary intake of CDD/CDFs and
PCBs is lower in New Zealand than in other countries that have conducted similar studies
(e.g., USA, UK, Spain, The Netherlands, Federal Republic of Germany, and Norway).
Schuhmacher et al. (1997) and Domingo et al. (1999) estimated dietary intake of
CDD/CDFs based on the analysis of 35 food samples from local supermarkets in Catalonia,
Spain. Most of the results are in agreement with the recent data reported elsewhere;
however, the levels in whole milk, vegetables, lentils and beans, and cereals are higher
than those reported in previous studies. The average intake per adult was estimated as
210 pg l-TEQDF/day. The contributions from vegetables and cereals were relatively high
(8.13 percent and 23.09 percent, respectively, of total intake) compared to previous
studies where the vegetable and cereal contributions are almost negligible. The high
contributions may be explained by high consumption of these foods in the Mediterranean
diet. Schuhmacher et al. (1997) stated that since the Mediterranean diet is typical
throughout most Spanish regions, the results reported could be a representative of the
dietary intake of CDD/CDFs in Spain.
Oilman and Newhook (1991), of the Canadian Department of National Health and
Welfare and the Ontario Ministry of the Environment, respectively, estimated an average
lifetime daily intake of 140 to 290 pg of I-TEQDF for the typical Canadian. Their results,
summarized in Table 4-28, indicate that between 94 and 96 percent of the estimated
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intake are from food sources. No breakdown of intake by food type was provided in the
report.
Schrey et al. (1995) estimated dietary intake of CDD/CDFs using the duplicate
method. A total of 14 food samples that were duplicates of the food eaten by seven
German men and seven German women (age 24-64 years) were collected and analyzed
for CDD/CDFs. The 3-day sampling period included both weekdays and weekends. All
samples contained detectable levels of 2,3,7,8-substituted CDD/CDFs, but OCDD had the
highest concentrations. Daily intake was estimated to range from 3.3 to 14 pg/day
(0.026 to 0.26 pg/kg-day) for 2,3,7,8-TCDD and 23 to 96 pg/day (0.1 8 to 1.7 pg/kg-day)
for l-TEQDFs. These values are slightly lower than those observed in earlier German
studies conducted by Beck et al. (1991), even though the dietary intake of fat was similar.
Recently, the United Kingdom's Ministry of Agriculture, Fisheries, and Food (MAFF,
1995) analyzed Total Diet Study samples collected during 1982 and 1992 for CDD/CDFs
to analyze trends in dioxin intake over recent years. Samples of 11 food groups collected
from 24 locations in the United Kingdom were analyzed. The average intake of dioxins
from each food group was calculated by multiplying the CDD/CDF residue concentration in
the food group by the average daily intake of the food based on data from the United
Kingdom's National Food Survey. Average daily intake of l-TEQDFs was estimated to be
240 pg/day in 1982 and 69 pg/day in 1992 (Table 4-29). These values represent upper
bound exposures because l-TEQDFs were calculated by setting nondetects to the limit of
detection. Based on these results, the authors concluded that the relative contributions of
the various food groups to total dioxin intake in the United Kingdom have changed over
the years. In the most recent study, the proportion of total exposure attributable to cereal
products increased, while exposures from fats, oils, and milk products decreased.
Jacobs and Mobbs (1997) conducted a reassessment of human dietary exposure to
CDD/CDFs in the UK. Based on the data of the UK Total Diet Survey (TDS) in 1992, the
levels of CDD/CDFs in 11 fat-containing food groups were recalculated. Instead of using
the food consumption data from the UK's National Food Survey as in the MAFF study,
Jacobs and Mobbs (1997) obtained individual dietary intake data from three other surveys
that included adults, children (aged 1.5 to 4.5 years), and infants (aged 6 to 1 2 months)
in the UK. Combining the dietary intake data with the data of CDD/CDF levels in foods,
Jacobs and Mobbs (1997) reported an adult daily dietary intake of CDD/CDF as 175.5 pg
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l-TEQDF/day (2.93 pg TEQ/kg/day), a value that is more than twice that estimated by
MAFF (1995). The levels for young children ranged from 54.19 pg l-TEQDF/kg/day at 6
months of age, to 0.25 pg TEQDF/kg/day at 4.5 years of age. It should be noted that for
infants (under 1 year of age), breast milk is the largest contributing source. Estimation of
the cumulative dietary I-TEQDF intake indicated that the levels peak sharply between age 0
and 1 year at about 80 pg l-TEQDF/kg/day, decrease until 10 years of age, and then rise to
about 15 pg TEQDF/kg/day at 22 years of age.
Dioxin-like PCBs can also contribute to TEQ exposures. Himberg (1993) evaluated
exposures to dioxin-like PCBs 77, 15, 126, and 169 in Finnish foods. Based on fish, beef,
pork, poultry, and inner organs, total TEQP-WH094 intake was estimated to be 11 8 pg/day,
calculated using TEQP-WH094 concentrations in foods and consumption data from
Finland's 1990 household survey. PCB congeners 105 and 126 contributed the most to
total TEQP-WH094 intake. Intake of PCBs in fish products accounted for the greatest
proportion (i.e., approximately 70 percent) of the total TEQP-WH094 intake from these
foods.
Currado and Harrad (1997) measured air concentrations of PCBs from 9 different
indoor environments, including two laboratories, two offices, and five residential houses in
the United Kingdom (UK). The results indicated that the total PCB levels found in indoor
air (1.4 to 19.1 ng/m3, mean = 7.1 ng/m3) were between 2 and 1 9 times higher than the
levels in outdoor air (0.77 to 0.87 ng/m3, mean = 0.82 ng/m3). Currado and Harrad
(1997) also calculated the daily human intake of PCBs via inhalation. The estimate ranged
from 36.9 to 176.5 ng/person/day (mean = 103.5 ng/person/day), and represented
between 10 and 33 percent of overall exposure to PCBs for a typical UK individual with a
340 ng/day dietary intake of PCBs (estimated by the UK Ministry for Agriculture, Fisheries
and Food (MAFF) in 1992). Currado and Harrad (1997) suggested that, compared to the
dietary intake of 340 ng/person/day of PCBs, inhalation of indoor air might be a significant
pathway for PCB exposure. It should be noted that the study did not focus on dioxin-like
PCBs; only concentrations of four dioxin-like PCB congeners were reported for indoor and
outdoor areas.
Liem et al. (2000a) reported on a European cooperative study coordinated by the
National Institute of Public Health and the Environment in the Netherlands, and the
Swedish National Food Administration. Ten countries, including Belgium, Denmark,
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Finland, France, Germany, Italy, The Netherlands, Norway, Sweden, and the United
Kingdom, delivered available data on the occurrence of PCDDs, PCDFs, and dioxin-like
PCBs in food products and human milk. When available, these countries also delivered
data on the consumption of these foods and other data on the dietary exposures of the
general populations of these countries. Consumption data were combined with
concentration data to arrive at exposure doses in pg TEQDFP-WH094. Concentrations and
doses expressed in terms of the more recent WHO 1998 TEF scheme are generally higher
than these earlier TEF schemes, by about 5-10%, in American food and environmental
media. Some countries also provided consumption data representative of the 95th, or
97.5, percentile of the population. These consumption data were combined with mean
concentration data from the countries to evaluate higher end exposures of the general
population. Liem et al. (2000) concluded that data were reasonably available for dioxins
and furans, but limited for the dioxin-like PCBs.
Based on the short summary of this effort in Liem et al. (2000a), it appears that
trends in European CDD/F food concentrations and exposures are consistent with those
from the United States, although dioxin-like PCB concentrations may be somewhat higher
in Europe. National average concentrations of CDD/Fs in eggs, fats and oils, meat
products, and milk products are generally less than 1 up to 2-3 pg/g fat, I-TEQDF basis.
Concentrations in fruits, vegetables and cereals were found to be generally close to the
limits of detection. Some of the data suggested reductions in concentrations over time,
but the available information was insufficient to draw general conclusions. Limited data
on dioxin-like PCBs suggest average TEQP-WH094 that are between 1 and 2 times higher
than I-TEQDF concentrations in all food products, more so in fish. PCB TEQ concentrations
in fish were between 0.25 and 10-20 pg/g fat WH094-TEQP. In contrast, in the United
States, TEQ concentrations of dioxin-like PCBs are roughly comparable, if not lower, than
TEQ concentrations of CDD/Fs in foods of terrestrial origin Some data on dioxin-like PCBs
in fish suggest higher concentrations than CDD/Fs, particularly from a recent study of
several fish species from the Great Lakes (Kolic et al., 2000a).
Given the limitations of the available data, Liem et al. (2000a) reported that for
eight countries and for the period after 1995, the average adult dietary intakes of CDD/Fs
ranged between 29 and 97 pg l-TEQ/day. This compares well to the estimate of 45 pg
TEQDF-WH098/day developed in this assessment. The upper percentiles estimates of
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dietary exposures, where 95 and 97.5% of consumption rates were combined with
average concentrations, was 2-3 times the mean intake. This analysis was only available
from data on consumption supplied by the Netherlands and the United Kingdom. This also
compares well to evaluations done in this assessment based on dietary fat (and total diet)
intake, which suggest that the TEQ intakes at the 95th (2 standard deviations above the
mean) and 99th (3 standard deviations above the mean) percentiles would be 2 and 3
times the mean intakes, respectively.
Their limited data on dioxin-like PCBs suggest perhaps more of an impact than
developed in this assessment. Their average daily intake estimates ranged between 48
and 110 pg TEQP-WH094, compared to the 25 pg TEQP-WH094/day calculated for the US
in this evaluation. For countries where data were available for both CDD/Fs and PCBs, the
dioxin-like PCBs contributed between a roughly equal amount (Finland, Netherlands,
Sweden, United Kingdom) to approximately 4 times (Norway) the TEQ contributions of the
CDD/Fs.
Other findings of interest include:
1) Based on concentrations in foods taken in the 1970s and 1980s, calculated doses
were much higher, ranging from 127 to 314 pg l-TEQ/day.
2) Similar to findings in this assessment, the highest dietary contributions were made
by milk and dairy products (between 16 and 39% of total TEQ intakes), meat and
meat products (6-32%), and fish and fish products (2-63%).
3) The intake to breast-fed children was estimated to be between 1 and 2 orders of
magnitude higher than adults, on a body weight basis. This is similar to the finding
in this assessment that the average infant dose of a year's worth of breast-feeding
would be about 77 pg TEQDFP-WH098/kg-day.
According to Liem et al. (2000b), "Countries that started to implement measures to
reduce dioxin emissions in the late 1980s, such as The Netherlands, United Kingdom, and
Germany, clearly show decreasing PCDD/PCDF and PCB levels in food and consequently a
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significantly lower dietary intake of these compounds by almost a factor of 2 with the
past 7 years".
As reported in Section 3.7.1, CDD/CDFs can migrate from bleached paper
packaging and paper food-contact articles to foods. Some investigators have included this
pathway in estimates of background exposure. U.S. EPA (1990) estimated that I-TEQDF
intake due to leaching from paper products into food from paper packaging was in the
range of 5.5 to 12.7 pg/d. Henry et al. (1992) estimated that daily intake of 2,3,7,8-
TCDD due to migration from paper to food could amount to 12 pg/d, almost as much as
the daily intake from unaffected food of 16 pg/d. (See Table 4-26.) As shown in Table 4-
27, Theelen (1991) estimated that out of a total of about 120 pg of l-TEQDF/d, 9 pg of I-
TEQDF/d could be due to migration from paper. These estimates are based on levels in
paper before recent changes in industry practices that are expected to substantially reduce
dioxin levels in paper. As discussed in Section 3.7.1, these reductions are expected to
have significantly lowered the CDD/CDF levels currently found in food due to any leaching
of dioxin-like compounds from paper.
Horstmann and McLachlan (1994) measured CDD/CDF levels in human skin using
an adhesive tape stripping method. Skin samples of the stratum corneum were collected
from the backs of eight volunteers of varying age and sex. Two additional layers of
increasing depth were collected from five people. All showed a decrease in CDD/CDF
levels with depth. The concentration in the first layer ranged from 1,000 to 7,800 pg/g
on a total CDD/CDF basis. The second layer was an average of 43 percent lower, and the
third layer was an average of 33 percent lower. OCDD was the dominant congener in all
three layers. Also, non-2,3,7,8 substituted congeners were identified, congeners which
are not normally present in human tissue. In addition, samples of the epidermis and
subcutis were analyzed. These analyses indicated that levels of the non-2,3,7,8
substituted congeners were much higher in the stratum corneum than in the epidermis,
and none were identified in the subcutis. The authors argue that because these congeners
could not be transported from inside the body to the stratum corneum, the CDD/CDF in
the stratum corneum must originate from external sources. Horstmann and McLachlan
(1994) hypothesized that textiles could be the source of skin contamination. Thirty-five
new textiles, primarily cotton products, were analyzed and found to have a total CDD/CDF
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level that was generally less than 50 ng/kg; however, several colored T-shirts had high
levels, with concentration up to 290,000 pg/g. The homolog patterns in the textiles were
similar to the patterns found in the skin. Experiments were then conducted measuring the
CDD/CDF levels in human skin before and after wearing T-shirts. Significant increases in
CDD/CDF levels in the skin occurred after wearing the highly contaminated shirts for 1-2
weeks, and significant decreases in CDD/CDF levels in the skin occurred after wearing the
uncontaminated shirts for 1-2 weeks. This work strongly suggests that dermal exposure
to textiles may be contributing to background exposures to CDD/CDFs. Horstmann and
McLachlan (1994) comment that although the levels of most CDD/CDF congeners in
humans can be explained on the basis of diet, the origins of OCDD in humans is less clear.
Because OCDD was found to be the dominant congener in textiles and skin, they
speculate that the human body burden of this congener may result from dermal
absorption. Horstman and McLachlan (1994) further discuss that human scale (stratum
corneum) contributes to house dust and could lead to exposure via inhalation.
Klasmeier et al. (1999) further studied the transfer of CDD/CDFs from textiles to
human skin. Spatial variability, variability among individuals, and the percent transfer from
different cotton textiles was examined. Spatial variability in transfer to the skin was
measured by placing 7 and 10 cm2 patches of contaminated and uncontaminated (for
background determination) textiles on the upper back of human volunteers for 8 hours.
The four samples collected from the outermost layers of the skin of the back of 1 2
volunteers contained similar concentrations of all detected congeners. The results
indicated that the skin surface properties determining the transfer of CDD/CDFs from
cotton textiles to the stratum corneum of the human back did not vary. An additional
volunteer wore a similarly contaminated cotton t-shirt for 72 hours. The mean percent
transfer for the 72 hour exposure was 1.6 to 2.5 times higher than for the 8-hour
exposure.
Matsueda et al. (1995) measured CDD/CDFs and PCBs in skin lipids from the faces
of eight Japanese men between the ages of 21 and 73 years. Skin lipids were collected
in the morning before washing the face, using facial wipes containing 70 percent alcohol.
The I-TEQDF concentrations in the samples ranged from 8.8 ppt to 22.3 ppt with a mean
of 15.3 ppt. I-TEQP-WH094 concentrations ranged from 7.3 ppt to 22.5 ppt. Matsueda et
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al. (1995) also collected serum samples from these same subjects. Blood l-TEQDFs ranged
from 13.5 ppt to 36.5 ppt, and TEQP-WH094 ranged from 7.1 ppt to 22.7 ppt.
4.4.2. Updated Assessment of Background Exposures on the Basis of Media Levels and
Contact Rates
Background exposures to CDD/CDFs and dioxin-like PCBs in North America were
estimated using: (1) the arithmetic mean TEQDFP-WH098 levels in environmental media and
food from Table 3-61; (2) the standard contact rates for ingestion of soil, water, and food,
and inhalation of ambient air; and (3) the appropriate unit conversion factors. The general
equation used to estimate background exposures is as follows:
i.i i mi mno Trmi j i Daily Contact Rate x Concentration x Unit Conversion Factors
Intake (pg WH098- TEQ/kfl- day) = —J- (Eqn. 4-5)
where:
Contact Rate = inhalation or ingestion rate (m3/day, mg/day, L/day, or g/day);
and
Concentration = residue level in media of concern (pg/m3, ppt, or ppq).
These background exposure estimates represent administered doses and not absorbed
doses.
The estimated exposures and assumptions made for adults concerning ingestion or
contact rates are presented in Table 4-30 for CDD/CDFs and Table 4-31 for PCBs.
Standard intake rates representative of the adult general population were used. The
background exposure estimates reported here do not account for individuals with higher
consumption rates of a specific food group (e.g., subsistence fishermen, cigarette
smokers, and individuals with exposures from localized impacts-these are discussed in
Chapter 5). The estimates are assumed to represent typical (i.e., "central tendency") U.S.
background exposures, and do not account for these types of variations in the population
as a result of differences in intake rates of the various food groups. Average contact rates
for ingestion of soil, water, beef, pork, poultry, other meats, and eggs, and inhalation
were derived from the revised Exposure Factors Handbook (U.S. EPA, 1997). The intake
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rate for other meats represents total meat intake minus the intake rates for beef, pork, and
poultry. Other meats could include lamb, game, etc. It should be noted that the
concentration of dioxin-like compounds in other meats was assumed to be similar to that
observed in beef, pork, and poultry, because data were not available for these other
meats. Thus, the TEQDFP-WH098 for other meats was estimated as the average of TEQDFP-
WH098 concentrations for beef, pork, and poultry. Mean fish ingestion rates were derived
from U.S. EPA (2000). Contact rates for milk, dairy, and vegetable fats were derived
from USDA (1995). The contact rate for dermal contact with soil was calculated as the
skin surface area that contacts the soil (cm2/day) x the soil adherence rate (mg/cm2) x the
dermal absorption fraction for CDD/CDFs (0.03) (U.S. EPA, 1999). The age-specific
surface areas and adherence factors were based on data and estimation methods
recommended in U.S. EPA (1997) and U.S. EPA (1999) for adult and child residents. The
soil ingestion rates used here are those recommended by U.S. EPA (1997). Soil ingestion
occurs commonly among children during activities such as mouthing of toys and other
objects, nonsanitary eating habits, and inadvertent hand-to-mouth transfers. In addition to
normal soil ingestion activities, some individuals exhibit behavior known as pica which
involves intentional soil ingestion. Soil ingestion rates associated with pica are probably
much higher. Some limited data suggest rates as high as 5 to 10 g/day for deliberate soil
ingestion rates for pica children. The current Exposure Factors Handbook (U.S. EPA,
1997) suggests a central tendency value for non-pica children of 100 mg/day. To a lesser
extent, soil ingestion also occurs among adults from activities such as hand-to-mouth
transfer when eating sandwiches or smoking, and other inadvertent ingestion of soil, such
as that in household dust. Data on soil ingestion are even more scarce for adults. Based
on limited data, a central tendency value of 50 mg/day is suggested by U.S. EPA (1997),
which is used here.
It should be noted that the contact rates used in this assessment for some food
products (e.g., meats) are lower than those used in an earlier 1994 draft (U.S. EPA, 1994)
of this document. The values in the earlier draft were based on the average of food
disappearance rates and intake rates. The intake rates were 1-day diary data derived from
the 1987/1988 USDA National Food Consumption Survey (MFCS) (USDA, 1995). This
type of survey is considered to be the best indicator of food consumption patterns, and
statistical designs used by USDA optimized the ability to correctly account for factors
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such as seasonality, geography, age of recipients, and other factors. The intake data
derived from the 1987/1988 USDA MFCS in the 1994 draft used assumptions to allocate
meat mixtures among the various meat groups. These assumptions were required
because the meat consumption rates available at the time did not account for meats
consumed as mixtures. These assumptions over-estimated intake for the various individual
meat groups because it was assumed that intake rates for the MFCS meat mixture
category included intake of foods made up of mixed meat items only. For example, it was
assumed that meat mixtures were made up of 40 percent beef, 17 percent pork, 32
percent poultry, and 11 percent fish. However, meat mixtures actually included food
items that had dietary components (i.e., grains, vegetables, etc.) other than meats.
Therefore, the individual meats accounted for a much smaller fraction of the mixtures than
assumed in 1994. "Disappearance rates" are derived as the total amount of food that
disappears (i.e., is used) from the U.S. commercial food supply divided by the number of
people in the U.S., corrected for removal of bone and fat, food that goes into pet foods,
and food that is imported (USDA, 1993). These rates are expected to overestimate
average daily intakes because they do not account for uneaten portions, spoilage, or
waste. In 1994, EPA used the USDA's report on Food Consumption, Prices, and
Expenditures between 1970 and 1992 (USDA, 1993) to derive disappearance rates for
each food type.
The intake data used in this current assessment are derived from a newer set of
USDA intake data. EPA recently conducted a statistical analysis of the USDA food data
from the 1989-1991 Continuing Survey of Food Intake among Individuals (CSFII) for
inclusion in the Exposure Factors Handbook (U.S. EPA, 1997). The USDA CSFII is a 3-
day survey that provides national data on the amount of food eaten by individuals over the
survey period. During 1989 through 1991, over 15,000 individuals participated in the
CSFII (USDA, 1995). Using a stratified sampling technique, individuals of all ages living in
selected households in the 48 coterminous states and Washington, D.C., were surveyed.
Individuals provided 3 consecutive days of data, including a personal interview on the first
day followed by 2-day dietary records. The survey uses a statistical sampling technique
designed to ensure that all seasons, geographic regions of the U.S., and demographic and
sociodemographic groups are represented (USDA, 1995). EPA's analysis of the CSFII data
tabulated intake rates for the major food groups, as well as individual food items. The
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analysis allocated intake of meat mixtures among the various meat groups and other
applicable food groups according to the percentages provided by USDA (1995), as
described in U.S. EPA (1997). For example, according to USDA (1995), meat mixtures
contained 20 percent beef, 2 percent pork, and 8 percent poultry. Intake of other food
groups (i.e., grains, vegetables, etc.) accounted for the balance of meat mixture intake.
These meat mixture fractions are considerably lower than those assumed for the 1994
draft.
As an example of the difference in the 1994 and the current food consumption
rates, the 1994 pork consumption rate, 47 g/day, was derived as the average of the
disappearance rate of 62 g/day and the intake rate of 32 g/day. (The intake rate of 32
g/day was estimated as the intake rate for pork of 14 g/day plus an assumed 17 percent
of meat mixtures.) The resulting value, 47 g/day, is higher than the intake rate used in
this current draft, 0.22 g/kg-day, or approximately 15 g/day assuming a 70 kg adult.
Other differences are also significant: 77 g/day beef (1994) versus 50 g/day (currently),
68 g/day poultry (1994) versus 35 g/day, and 67/251 dairy/milk g/day (1994) vs. 55/175
g/day (currently). Also, contact rates for some of the other media are lower (e.g., soil),
based on the revised Exposure Factors Handbook (U.S. EPA, 1997).
Another reason that current estimates of exposure are lower than in the 1994
document is that the estimated TEQDF-WH098 concentrations for several food items (i.e.,
beef, pork, poultry, milk, and dairy) are also lower in this assessment than in the earlier
(1994) draft. Estimates in the earlier draft were based on limited data sets for these
foods, whereas the current assessment uses data from the more recent statistically-based
national analyses of several food categories, as described in Chapter 3. Some of the older
studies had nondetectable congener concentrations and higher detection limits than the
newer studies, resulting in higher TEQ concentrations. For example, the beef
concentration assumed in the 1994 assessment was 0.48 pg l-TEQ/g whole weight basis,
while the current estimate is 0.18 pg TEQDF-WH098/g whole; poultry was 0.19 pg l-TEQ/g,
while here it is 0.068 pg TEQDF-WH098/g. Table 4-32 compares the contact rates, TEQ
concentrations, and background exposure estimates from the 1994 draft and this
assessment. It should be noted that the previous draft estimated l-TEQDFs, while TEQDF-
WHOqoS are used in the current assessment.
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Background exposure levels are also presented for Germany, based on data from
Furst et al. (1990; 1991). The current total background TEQDF-WH098 exposure shown in
Table 4-33 is approximately 43 pg/day for North America. Based on Furst et al. (1990;
1991), the estimated total CDD/CDF I-TEQ background exposure from food consumption
for Germany is 79 pg/day (Table 4-33). However, it should be noted that the estimated
background level for the United States and Germany are based on limited data, and
exposure to all food groups was not considered. Also, the addition of TEQs for multiple
pathways presumes that individuals are exposed by all pathways, and assumes that the
fraction absorbed into the body is the same for all ingestion and inhalation pathways (i.e.,
100 percent absorption in the gut and lungs is assumed). The dermal absorption pathway
assumes that 3 percent of the CDD/CDFs in soil that adheres to the skin surface is
dermally absorbed. The following sections present observations about CDD/CDF
exposures in North America, comparisons between exposure estimates from this and
previous studies, and comparisons between North American and European exposures to
CDD/CDFs.
Based on the data presented in this report, the adult general population total
background TEQDF-WH098 exposure for North America was estimated to be 0.61 pg/kg-
day (or 43 pg/day assuming a 70 kg adult), for all media combined. Exposure to 2,3,7,8-
TCDD accounts for approximately 13 percent (5.5 pg/day) of the total TEQ exposure.
Estimated exposures based on total TEQDF-WH098 from the various exposure pathways are
presented in Figure 4-4. The highest exposures were estimated to occur via ingestion of
CDD/CDFs in fish and shellfish (0.12 pg/kg-day) and beef (0.13 pg/kg-day), which
accounted for about 20 and 21 percent of the total TEQDF-WH098 exposure, respectively.
The ingestion of foods accounted for approximately 95 percent of the total TEQDF-WH098
exposure. Exposure to CDD/CDFs via ingestion of water appears to be very low.
Exposure via inhalation, soil ingestion, and dermal contact with soil are 0.023 pg/kg-day,
0.0063 pg/kg-day, and 0.0015 pg/kg-day, respectively. These exposures account for
approximately 5.0 percent of the total CDD/CDF TEQ exposure in North America.
Adult general population TEQP-WH098 exposure for North America was estimated
to be 0.33 pg/kg-day (or approximately 23 pg/day, assuming a 70 kg adult), for all foods
combined. This estimate is based on data on dioxin-like PCBs for food items and soil; PCB
congener data were not available for urban air or water. For CDD/CDFs, these
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environmental media accounted for about 3.8 percent of the overall TEQDF-WH098
exposure. Assuming that these media account for a similar percentage of dioxin-like PCB
exposure, total PCB exposure would be approximately 0.34 pg/kg-day (i.e., 0.33 pg/kg-
day x 1.038). Thus, TEQP-WH098 exposures from PCBs are approximately three quarters
the TEQDF-WH098 exposures from CDD/CDFs.
4.4.3. Assessment of Background Exposures Among Children
Exposures among other age groups of the U.S. population were also estimated
using the same media TEQ-WH098 concentrations that were used to estimate adult
exposures. However, age-specific contact rates and body weights were used. These
values were derived from data presented in U.S. EPA (1997) and USDA (1995).
Background exposures were estimated for three age groups (i.e., 1-5 years, 6-11 years,
and 12-19 years). Table 4-34 compares the contact rates and estimated CDD/CDF
exposures for these age groups to adult contact rates and exposures. Table 4-35 makes
similar comparisons for TEQP-WH098s. As shown in these tables, the dose per unit body
weight (pg/kg/day) decreases with increasing age, but the daily dose (pg/day) increases
with age. On a pg/kg-day basis, adult TEQDF-WH098 doses were 3.6 times lower than
those of 1 to 5 year old children and 2.1 times lower than those of 6 to 11 year old
children. Likewise, for PCBs, TEQP-WH098 adult doses were 3.3 times lower for 1 to 5
year old children and 1.8 times lower for 6 to 11 year olds. Table 4-36 presents the
percentage contribution of each environmental media and food group to total TEQ dose for
each age group. Figure 4-5 depicts these percentages for CDD/CDFs, grouped as
meat/fish/eggs, dairy, and other, for the four age groups.
Milk and dairy products accounted for approximately 56 percent of the total TEQDF-
WH098 and TEQP-WH098 exposures in 1 to 5 year old children, but only approximately 22
percent in adults. In contrast, meat and fish intake accounted for a much smaller portion
of total exposure in 1 to 5 year olds, and a higher portion in adults.
Patandin et al. (1999) observed similar results using data for adults and children in
The Netherlands. Data on CDD/CDF and PCB residues in foods were combined with food
consumption data for various age groups to model dietary intake of dioxin-like compounds
in the following age groups: 1 to 5 years, 6 to 10 years, 10 to 15 years, 1 6 to 20 years,
and 20 to 25 years. The doses, on a body weight basis, were higher than those
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estimated for the United Sates population, but the ratio of adult to child doses were
similar to those described above. For example, Patandin et al. (1999) estimated a daily
TEQDFP-WH094 dose of 6.5 pg/kg-day for male children, age 1 to 5 years; 3.9 pg/kg-day
for male children, age 6 to 10 years; and 2.4 pg/kg-day for adults, age 20 to 25 years.
The adult value is 2.7 and 1.6 times lower than the values for 1 to 5 year old males and 6
to 10 years old males, respectively. Patandin et al. (1999) also reported on the
contributions of various food group to total dietary intake of CDD/CDF/PCBs for various
age groups. The results are consistent with those described above for the U.S.
population.
4.4.4. Variability in Intake Estimates
The background adult daily intake values presented in Tables 4-30 and 4-31 are
representative of mean exposures among the adult general population because they are
based on mean TEQDF-WH098 and TEQP-WH098 concentrations and mean contact rates.
They do not account for individuals with higher contact rates for foods or environmental
media, or individuals who may be exposed to higher concentrations of dioxin-like
compounds such as those affected by localized contamination.
Exposures to dioxin-like compounds were estimated as the product of media
concentrations of CDD/CDF/PCBs times contact rates for these media with food ingestion
accounting for the vast majority of the dose. Assuming that, over the long-term, all
individuals in the general population are exposed to the mean TEQDFP-WH098 media
concentrations, variability among this population can be assessed by evaluating variations
in contact rates. The assumption that long-term media concentrations to which the
general population are exposed are represented by mean values is reasonable if temporarily
elevated concentrations are offset by lower concentrations during other time periods, and
if no regional trends are assumed (e.g., foods with varying CDD/CDF/PCB concentrations
are equally distributed in the market place). Also, because food intake accounts for such
a large percentage of the total dose, variations in long-term average food contact rates
(i.e., ingestion rates) are likely to have the greatest impact on long-term average dose.
Some sense of the variability in general population exposures to TEQDFP-WH098 can
be gained by evaluating either the variability in fat intake among the general population
(i.e., because fatty foods account for a high percentage of total exposure), or by
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evaluating the variability of specific dietary components (i.e., food groups of the total
diet). Published data on the variability in fat intake among the general population are
somewhat limited. However, Cresanta et al. (1988), Nicklas et al. (1993), and Frank et
al. (1986) analyzed dietary fat intake data as part of the Bogalusa heart study. The
Bogalusa study "is an epidemiologic investigation of cardiovascular risk-factor variables
and environmental determinants in a population that began 20 years ago" (Nicklas et al.,
1995). Among other things, the study collected fat intake data for children, adolescents,
and young adults. According to Nicklas (1995), "the diets of children in the Bogalusa
study are similar to those reported in national studies of children." Thus, these data are
useful in evaluating the variability in fat intake among the general population for the
purposes of evaluating variability in exposure for dioxin-like compounds among this group.
Based on data for 6 month old to 17 year old individuals during 1973 to 1982, maximum
total fat intakes are 2.5 to 5 times higher than mean fat intakes. Maximum animal fat
intakes for this group are 3 to 7.6 times mean animal fat intakes (Frank et al., 1986).
Based on the mean total fat intake plus three standard deviations for 10-year old children
during 1992 to 1994 and young adults (i.e., 1 9 to 28 years) during 1988 to 1990, upper-
range fat intake is between two to three times that of mean intake (Nicklas et al., 1993;
Nicklas et al., 1995). (Three standard deviations around the mean should represent
approximately 99 percent of the population.) These data are presented in Table 4-37.
Based on the assumption that variability in intake is the key contributing factor to
variability in exposure to dioxin-like compounds, and that the fat intake data from these
studies is representative of the general population of the United States, upper-range
exposures to dioxin-like compounds would be expected to be two to three times higher
than the mean background exposures estimated in this chapter.
Block (1992) and Norris (1997) estimated dietary fat intake among the adult
general population using data from National Health Interview Surveys (NHIS) conducted by
the National Center for Health Statistics (NCHS). Block (1992) used data for 20,143 men
and women, ages 18 to 80+ years, from the survey. The mean and standard deviation
fat intakes from this analysis are presented in Table 4-38. Assuming that the mean value
plus three standard deviations represents the upper end of the range of fat intake,
maximum fat intake is approximately two to three times higher than the mean. Norris
(1997) used data for 10,827 men and women from the 1992 NHIS. The mean fat intake
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was 64.4 g/day and the standard deviation was estimated to be 41.6 g/day. Using the
same assumption as stated above (mean plus three standard deviations), the upper end of
the range of fat intake wold be 189.2 g/day. This value is 2.9 times higher than the
mean. Thus, these data from a nationally representative sample of adults are consistent
with the data for children and young adults from the Bogulusa study. This variability is
also supported by the ranges of tissue CDD/CDF/PCB levels, as described in Section 4.2.
These data show that maximum tissue levels of dioxin-like compounds are typically two to
three times the mean values.
Another way to assess variability in CDD/CDF/PCB background doses among the
general U.S. population is to evaluate variability in total dietary intake and the contribution
of specific dietary components to total dietary intake. Recently, EPA conducted an
analysis of USDA's 1994-1996 CSFII data set to estimate total dietary intake as well as
the contribution of the major food groups (i.e., total dairy, total fish, total meats, total
fats, eggs, etc.) to the total diet. Intake data from this analysis were used in conjunction
with average CDD/CDF/PCB concentrations in foods to evaluate variability in background
dose of dioxin-like compounds.
The procedure used to evaluate variability in CDD/CDF/PCB doses from total dietary
intakes derived from the CSFII was developed as follows. First, estimates of "total dietary
intake" for individuals in the CSFII were determined as the sum of all food intakes reported
by the individuals included in the survey. For purposes of this exercise, specific food
items reported by each individual in the CSFII were grouped into classes, including total
dairy, total meats, total fish, total vegetables, total eggs, and total fats. Once these total
dietary intakes were compiled, CSFII survey adult individuals were ranked from lowest to
highest based on total dietary intake, and intake rates at specific percentiles, such as the
50th or 90th percentile were examined. From these percentiles, subsets were defined
including a "central" group of adults, which were those in the 45-55th percentile of total
intake, and an upper percentile group of adults, which were defined as those above the
90th percentile of total intake. For the purposes of evaluating variability in CDD/CDF/PCB
doses that extend above the average doses reported in this chapter, intake rates for the
upper percentile group of adults was of interest. To calculate upper percentile doses of
CDD/CDF/PCBs, point estimates of the intake rates for each of the major food groups
were calculated as the mean intake rate for the individuals within the upper percentile of
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total food intake (i.e., above the 90th percentile). As noted above, these intake rates
represented intakes for major food groups only (e.g., total meats) and not specific food
items (e.g., beef, pork, poultry). Therefore, to complete this exercise, it was necessary to
convert the intake rates of the major food groups to intake rates for the categories of
individual foods for which CDD/CDF/PCB concentration data were available. To do so, it
was assumed that the proportions of individual foods (e.g., beef, pork, poultry, and other
meats) making up a food group (e.g., total meats) were the same for the upper percentile
groups as for the average background individual assessed in Tables 4-30 and 4-31.
Finally, average concentrations of CDD/CDF/PCBs in the various individual food items (as
shown in Tables 4-30 and 4-31) were combined with the upper percentile intakes rates for
individual food items to arrive at the doses to an upper percentile adult. The results for
this exercise for the "upper percentile" intake rates are shown in Tables 4-39 and 4-40,
which also include the average non-food exposures associated with soil, water, and air.
As shown in Table 4-39 the estimated TEQDF-WH098 dose among adults in the "upper
percentile" of total food intake is 1.1 pg/kg/day or 77 pg day. This dose is 1.8 times
higher than the mean TEQDF-WH098 dose estimated in Table 4-30. The estimated TEQP-
WH098 dose for "upper percentile" adults is 0.65 pg/kg-day or 45 pg/day (Table 4-40).
This dose is 1.9 times higher the mean dose estimated in Table 4-31.
The variability in current dose of about 2 to 3 times above the mean is similar to
the range of tissue CDD/CDF/PCB levels, as described in Section 4.2. These data show
that maximum tissue levels of dioxin-like compounds are typically two to three times the
mean values. However, it was also discussed that important factors such as the age of
the individual and their past history of exposure also contributed to variability in tissue
levels, perhaps more so than their current dose. Therefore, this variability in tissue data,
while similar to the variability in intakes based on the dietary data discussed here, should
not be considered as important supportive evidence to a finding that elevated intakes of
dioxin-like compounds range up to 3 times higher than the average dose. Also of note is
that the 1994 Dioxin Reassessment documents developed an estimate of variability of
intake of between 3 and 7 times the mean intake rates. This variability estimate was
based on statistical extrapolations from a relatively small study measuring CDD/CDFs in
blood. The new variability estimates presented here are considered more strongly
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supported because they are based on larger studies, do not involve extrapolations, and
more directly reflect consumption.
4.4.5. Comparison of Previous North American Studies to This Study
Previous studies of CDD/CDF exposures in North America were presented in
Section 4.4.1 of this report. These studies reported CDD/CDF exposures based on the
most toxic congener, 2,3,7,8-TCDD, and not on the total TEQDF value for all congeners
combined. For the purposes of comparison, mean background levels of 2,3,7,8-TCDD in
North America from this assessment were used to calculate exposure via various
pathways. Background exposures were calculated using background environmental levels
of 2,3,7,8-TCDD, standard contact rates, and appropriate unit conversion factors, as
described previously. Total 2,3,7,8-TCDD exposure among adults for all pathways
combined was 5.5 pg/day for the current assessment compared to 15.9 and 34.8 pg/day
for the two previous studies of 2,3,7,8-TCDD exposure in North America (Henry et al.,
1992; and Travis and Hattemer-Frey, 1991). Figure 4-6 depicts the comparisons of the
percent contribution of various exposure pathways to total exposure to 2,3,7,8-TCDD for
the current assessment and for previous North American studies. Figure 4-6 indicates that
exposure via ingestion of meats accounted for a large portion of the exposure in all three
studies. However, fish accounted for a higher percentage, and dairy products accounted
for a lower percentage of the total 2,3,7,8-TCDD exposure in the Henry et al. (1992)
study and in the current assessment than in the Travis and Hattemer-Frey (1991) study.
These differences reflect differences in assumptions for food ingestion rates as well as in
TCDD levels. All three studies indicate that beef, dairy products, and fish comprise over
93 percent of the total exposure. Because of the data base weaknesses noted earlier, it is
not known if these differences can be considered significant.
European CDD/CDF exposure studies may also be compared to the exposures
estimated in U.S. reports and in the current assessment. Comparisons may be made
based on the 2,3,7,8-TCDD congener or on total TEQDF exposures (Table 4-41). Adult
general population exposures to 2,3,7,8-TCDD in North America range from 5.5 pg/day to
34.8 pg/day based on the current assessment and two other U.S. studies. These values
are comparable to the 2,3,7,8-TCDD exposures reported in Germany and The Netherlands
by Furst et al. (1991) and Theelen (1991). Furst et al. (1991) reported an estimated
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2,3,7,8-TCDD exposure of 25 pg/day based on ingestion of dairy products, meat, and
fish; Theelen (1991) reported an estimate of 20 pg/day based on dairy, meat, poultry, and
fish intake. Total TEQDF background exposure estimates for North America range from
approximately 43 pg TEQDF-WH098/day for the current assessment to 140 to 290 pg
l-TEQDF/day based on Oilman and Newhook's (1991) Canadian study. For Europe, total
I-TEQDF exposure estimates range from 79 pg/day based on Furst et al. (1990) to 158
pg/day based on Furst et al. (1991).
4.4.6. Relative Contribution of Exposure Pathways to Total Intake
Figure 4-7 depicts the contributions of various exposure pathways to total
background TEQ exposures for North America, Germany, the United Kingdom, and The
Netherlands based on data from the current assessment (Furst et al., 1990; MAFF, 1995;
and Theelen, 1991). For all three geographic regions, over 90 percent of the exposures
were attributed to ingestion of CDD/CDFs in foods. For the United States and Germany,
intake of meat, fish, and eggs account for over 60 percent of the daily exposure, while
milk and dairy consumption account for less than 30 percent, and soil ingestion,
inhalation, etc. account for less than 7 percent of the total exposure. For The Netherlands
and the United Kingdom, the meat/fish/eggs group accounts for somewhat less of the
total intake, while milk/dairy and the "other" category account for more of the exposure.
In particular, approximately 30 percent of the total exposure came from breads and cereals
in the United Kingdom. These food groups were not evaluated in the United States
estimates.
Based on the data presented in Figure 4-7, it is reasonable to expect that the
CDD/CDF body burden in vegetarians would be lower than the body burden in
nonvegetarians because vegetarians avoid the consumption of meat and fish and their
derivative products. Welge et al. (1993) tested this hypothesis by comparing the
CDD/CDF levels in the blood of 24 German vegetarians with the blood levels of 24
nonvegetarians, matched for age, sex, body weight, and height. With the exception of
two individuals, all vegetarians had practiced a diet without meat and fish for at least 3
years. The CDD/CDF levels in the vegetarian group ranged from 14.64 to 52.85 pg I-
TEQDF/g (lipid basis) with a mean of 32.60 pg l-TEQDF/g. In the nonvegetarian group, the
CDD/CDF levels ranged from 14.26 to 97.98 pg l-TEQDF/g (lipid basis) with a mean of
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34.32 pg l-TEQDF/g. There was no significant difference (a = 0.05) between the
vegetarian and nonvegetarian group in the mean levels of any of the 2,3,7,8-substituted
congeners, in the total CDD levels, in the total CDF levels, in the total CDD/CDF levels, or
in the total I-TEQDF levels (each on a lipid and on a whole weight basis). Welge et al.
(1993) suggested several reasons why no differences were found. First, all tested
vegetarians had at one time been nonvegetarians. The higher levels of exposure during
this nonvegetarian period coupled with the long biological half-life of CDD/CDFs may be
responsible for the apparent similarity in body burdens using blood as the measure of body
burden. Second, the vegetarians may have a higher level of consumption of dairy
products than the nonvegetarians and thus have a similar CDD/CDF exposure even
without consumption of fish and meat.
Schecter and Papke (1998) collected blood samples from two individuals (one male
and one female) who had been vegans for over 20 years and analyzed them for CDD/CDFs
and coplanar PCBs. These individuals were strict vegetarians, consuming no milk, cheese,
eggs, or other animal products. Total CDD/CDF and PCB concentrations, as well as I-
TEQDF and TEQP-WH094 concentrations among these vegans were compared to the levels
in two pooled samples from 100 men and 100 women from the general population. Total
concentrations of CDD/CDF/PCBs were 244 ppt and 330 ppt for male and female vegans,
respectively. These values were considerably lower than those observed in pooled
samples from the general population; 643 ppt and 906 ppt for male and female subjects,
respectively. Likewise, the TEQDFP-WH094 concentrations were lower among the vegans
(4.4 ppt and 8.7 ppt for males and females, respectively) than the general population
(24.2 ppt and 29.3 ppt for males and females, respectively). Both the total
concentrations and TEQ levels of CDD/CDFs and PCBs were higher in the samples
collected from those taken from males.
4.4.7. Geographical Contributions to Dietary Exposure
As indicated in the previous sections, dietary intake appears to be the primary
pathway of human exposure to dioxin-like compounds. Over 90 percent of the
background dose is obtained through the diet, with foods of animal origin being the
predominant sources. Aside from some episodes of localized contamination that may
result in elevated exposures among individuals who consume foods from contaminated
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areas (see Chapter 5), the general population of the United States is assumed to consume
foods, over the long-term, that contain average background concentrations of dioxin-like
compounds, resulting in background exposures that are similar across all regions of the
United States. Except for some of the more perishable foods (i.e., milk and eggs) most
foods are widely distributed in commerce. Thus, the general population of the United
States may consume foods from a wide variety of geographic locations. In addition, the
concentrations of foods grown in the various geographic regions may not vary widely.
The national studies of beef, pork, and poultry, conducted jointly by EPA and USDA
(Winters et al., 1996a; Winters et al., 1996b; Lorber et al. 1997; Ferrario et al., 1997),
indicated that there was little variation in the concentrations of dioxin-like concentrations,
based on geographic location. The milk study (Lorber et al., 1998) suggested the
possibility of a geographic trend, with CDD/CDF concentrations being somewhat higher in
the southeastern United States than in the southwestern United States.
Based on the distribution of foods in commerce, and the similarities of
concentrations in many foods, variations in dietary exposure on the basis of geography
would not be likely to be significant and the general population would be expected, over
the long term, to be exposed to similar concentrations of dioxin-like concentrations in
foods. However, the total amount of dioxin-like compounds entering the food supply
may vary geographically because of the predominance of certain types of food production
in certain regions of the country. For example, food such as pork is produced primarily in
the northern midwest and some areas on the southeastern part of the United States;
whereas poultry is produced primarily in the southeast.
The purpose of this section is to present the results of a study of the geographic
variability of dioxin production as indicated by variability in production of animal fats. EPA
conducted an analysis to determine the geographic origin (within the 48 contiguous United
States) of several food groups that are likely to contain dioxin-like compounds (e.g., meats
and dairy products). Cattle, chicken, and hog producer sales figures from the 1997
Census of Agriculture (USDA, 1997), enumerated by county, were converted to an
equivalent dioxin TEQ using data in Putnam and Allshouse (1999). The 1997 food
disappearance data for beef, pork, and chicken in this reference were used to convert the
USDA production data, expressed in units of individual animals sold, to grams of animal
fat entering the food chain. Food disappearance is the total supply at the start of the
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year, plus imports, minus exports and shipments to U.S. territories, minus stock at the
end of the year. It therefore includes all food eaten in the home, wasted by spoilage in
the home, lost in preparation, or left uneaten on the plate. The food disappearance data
were expressed as a boneless weight assuming a standard conversion factor for each
animal type (Putnam and Allshouse, 1999). This total weight was converted to dioxin
TEQs using CDD/CDF/PCB concentration values from the EPA meat/milk surveys and
WH098 TEFs. The total dioxin value was then divided by the total number of animals to
yield ng TEQ per animal. This value was multiplied by the county-level USDA data to yield
ng TEQ per year for every county.
Production figures for dairy products are not included in the Census of Agriculture,
but the number of dairy cows is provided for each county. State-level data on milk fat
production were apportioned among each state's counties on the basis of the number of
dairy cows in each county. This approach assumes that all milk cows in a given state are
equally productive. In a similar way, where only state-level egg production data are
available, county values were calculated by apportioning the state-level data among the
counties on the basis of the number of layers and pullets in each county. Examples of the
county-level production data are shown in Figures 4-8 and 4-9 for pork and dairy
products, respectively. Similar maps were produced for the other products (i.e., beef,
poultry, and eggs) and for the total TEQDF-WH098 over all five products. It should be
noted that the geographic variability in this analysis is based on variability in food
production only, and not in the concentration of dioxin-like compounds in the foods.
Thus, it does not indicate that the concentrations of dioxin-like compounds are higher in
some regions than in others. Instead it indicates that the production of dioxin-containing
foods is higher in some regions than in others. The relative contributions of the five food
products included in this study compare favorably with EPA's current estimates of total
TEQDF-WH098 dose based on 1989-91 CSFII food intake data (Figure 4-10).
This analysis may be useful, in conjunction with source analyses, in identifying
important food production areas where dioxin-like compounds are also being released. To
that end, major contributors to the total dioxin TEQ for the 48 contiguous states were
identified. The 3,048 counties in the database were sorted in descending order and
divided into four groups, with each group encompassing 25 percent of the 48-state total.
The resulting map (Figure 4-11) shows that the top 65 counties account for 25 percent of
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the total TEQ. The second, third, and fourth quartiles encompass 212, 498, and 2,303
counties, respectively. Assuming that the dominant pathway resulting in dioxin exposure
for domestic meat and dairy animals is air deposition onto feed crops, it necessarily
follows that the dioxin sources that dominate general population exposure have to be
those sources that dominate ambient air concentrations in the areas flagged by this
analysis. Future work is aimed at identifying these dioxin sources.
4.4.8. Contribution of CDD/CDF Congeners to Background Dose and Body Tissue
Concentration
The purpose of this section is to evaluate the contribution of individual congeners
to background dose and tissue concentrations. This section also evaluates whether the
congeners that are the primary contributors to dietary dose are consistent with those that
dominate the body burden. Section 4.4.2 derived a background dose of approximately 1
pg TEQDF-WH098/kg-day, which included doses of 0.61 pg TEQDF-WH098/kg-day for
CDD/Fs and 0.33 pg TEQDF-WH098/kg-day for coplanar PCBs. These doses were
calculated assuming average exposure media concentrations and contact rates for several
pathways. Food consumption made up most of this total dose, with the food
consumption pathways of beef, pork, chicken, fresh fish, marine fish, dairy, and milk
totaling 0.90 pg TEQDFP-WH098/kg-day. This exercise will focus on these pathways alone.
Section 4.2 examined body tissue concentrations of the dioxin-like congeners. The
average TEQDF-WH098 lipid concentration in blood was calculated at 21.6 pg TEQDF-
WH098/g. For dioxin-like PCBs, the average lipid concentration in blood was 2.0 TEQP-
WH098/g. These data were based on the CDC blood data, as described previously, and
represent recent body burdens. It should be noted, however, that PCB data were only
available for four congeners (i.e., PCBs 77, 81, 126, and 169). The exercise in this
section determines the percentage TEQDFP-WH098 contribution of each toxic CDD, CDF,
and dioxin-like PCB congener to the daily total background dose of TEQDFP-WH098 s. It
also determines the percentage TEQDFP-WH098 contribution of each toxic congener to the
body tissue TEQDFP-WH098 concentrations. The exercise concludes with a comparison of
the two sets of percentages.
The following general rules were applied in developing the information for this
exercise:
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1) The food surveys used to calculate average concentrations for background dose
calculation of TEQ-WHO98s were also used to calculate TEQ-WHO98 congener
profiles, when possible.
For all food groups except two, the same data used in calculating background
doses were used in this analysis. The total TEQDF-WH098 for these data are summarized
in Table 3-59 and the total TEQP-WH098 are summarized in Table 3-60. For freshwater
and marine fish, it was not possible to derive a CDD/CDF congener profile using the same
data as that used to calculate a background dose because the individual congener
concentrations were not provided in the core reference. Thus, data from Schecter et al.
(1995b) were used. These data represent a sampling of 10 freshwater fish from
supermarkets. For marine fish, data from Fiedler et al. (1997) were used.
2) Average concentration profiles for food were calculated assuming non-detects are
equal to one-half detection, which was the same procedure for calculating body
tissue concentration profiles.
This was the assumption used to calculate the background dose. However, it
should be noted that this could be problematic for some data, specifically when the
detection limits were high. The determination of the food concentration profiles in
Chapter 3 was accomplished assuming nondetects were equal to zero for this reason.
3) When more than one survey was used to determine the average representative
concentration profile in food or body tissue concentration, all samples were pooled
and assumed equally weighted for dioxins. However, for coplanar PCBs, the data
in the literature studies were developed by compositing methods that did not allow
for the calculation of weighted averages. Because of this, one concentration per
study was derived for each congener, and then the average concentration was
assumed to be the average over the number of studies.
Since many of the studies reporting CDD/CDF concentrations, particularly the food
studies, were grab sample studies, it seems most reasonable to simply treat all samples
equally. Also, mean food concentrations, for purposes of background dose derivation of
the CDD/CDFs, were calculated giving all samples equal weight. Therefore, the
determination of the representative profiles was made consistent with the dose
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calculation. The background dose calculation for the dioxin-like PCBs was done slightly
differently. In these cases, two of the principal studies, Mes and Weber (1989) and Mes
et al. (1991) composited several samples. In one study, nondetected congeners on
composite samples were set to one-half the detection limits for calculating mean congener
concentrations. However, in the other study, mean congener concentrations were based
on positive composites only. Thus, there was no simple method for calculating a
weighted mean for these studies.
4) For the dioxin-like PCBs, not all the studies evaluated the same coplanar congeners.
This occurred in both the food data and the tissue data. Therefore, this analysis is
incomplete with regard to estimating the full dose of dioxin-like PCBs as well as the
percentage of dose/body tissue TEQP-WH098 that can be attributed to each
congener. This appears to be an issue for two of the dioxin-like PCBs. However,
inclusion of the full information of these two congeners will unlikely change the
important qualitative finding in the dioxin-like PCB analysis - that PCB 126
dominates both tissue and body burden concentration.
There are 11 dioxin-like PCBs with some dioxin-like toxicity, based on the TEQP-
WH098 scheme (Younes, 1998). Using the TEFP-WH098 scheme, 13 PCB congeners were
considered to have dioxin-like toxicity. The CDC data set included data for only four of
the dioxin-like PCBs for human tissues (i.e., PCBs 77, 81, 126, and 169). There were no
reported concentrations in food for two of the congeners, PCBs 123 and 167. PCB 114
had some impact on total tissue concentrations and was included in some of the food
survey data. However, this congener was not included in the USDA/EPA national studies
on pork, beef, poultry, and milk. Other food studies also measured PCB 189, which was
not included in the USDA/EPA studies, but contributed an insignificant amount to coplanar
TEQp-WH098 concentration, so its exclusion in the USDA/EPA studies was not critical.
The net effect for exclusion of PCB 114 in these food groups is that the contribution of
PCB 126 to TEQp-WHOgg was overestimated while the contribution from PCB 114 was
underestimated. Likewise for human tissues, the contribution of PCB 126 to TEQP-WH098
is likely overestimated.
Further details on the procedures used in the forward dose calculations and the
body tissue concentrations are presented below.
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4.4.8.1. Background Dose
Approximately 90 percent of the background daily TEQDFP-WH098 dose is derived
from the following foods: freshwater fish, marine fish, milk, dairy, beef, pork, and
poultry. For CDDs/CDFs/PCBs, the total daily dose from these pathways is estimated to
be 58 pg TEQDFP-WH098/day. The remaining dose comes from: soil ingestion, marine
shellfish ingestion, inhalation, water ingestion, egg ingestion, and vegetable fat ingestion.
For ease of calculation, this exercise focuses on the higher contributing food groups rather
than on all routes of exposure. Further, when calculating the percentage of the total
TEQDFP-WH098 dose which can be attributed to each congener, it is assumed that the 58
pg TEQDFP-WH098/d represents 100 percent of the daily dose. For ease of understanding,
the CDD/CDF and PCBs are tabulated separately in the tables and figures. The TEQDF-
WH098 dose from CDDs/CDFs is 38 pg/day and the dose for coplanar PCBs is 20 pg TEQP-
WH098/day. The congener contributions from the dietary intake calculation is
characterized in terms of the percentage each congener contributes to the TEQDFP-WH098.
This will be compared to the congener contributions to body burdens, which are also
compiled on an individual percentage basis.
The procedure for doing the dietary intake calculations is described in the following
four steps:
1. Determine the representative congener concentrations in the food product. These
were determined as the average concentrations of the individual congeners from available
survey data, given the rules stated above. Based on the way in which the data were
reported in the literature, the basis for food concentrations was either on a lipid basis or
on a whole weight basis. Most of the CDD/CDF food data were reported on a lipid basis,
while most of the coplanar PCB data were reported in the literature on a whole food basis.
Although the basis for the food concentrations is important for calculating a dose because
the concentration data must be consistent with the intake data (i.e., if concentrations are
reported on a whole weight basis, whole weight intake rates must be used), it was not
important for calculating the fractional contribution of each congener to the total TEQ
since the same values would be calculated using either lipid-based or whole weight
concentrations. Therefore, lipid-based CDD/CDF concentrations were used for all foods,
and whole weight PCB concentrations were used for all foods.
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2. Determine the toxic equivalent concentrations in the food product. These were
easily determined as the product of the average congener concentration and the
appropriate TEF.
3. Determine the TEQ-WHOQg congener profiles as the fractional contribution of each
congener to total TEQ-WHOQP concentration. This was determined as the ratio of the
toxic equivalent concentration of each congener to the total TEQ-WH098
4. Determine the TEQ-WHOQg congener profile of the dietary dose by multiplying the
TEQ-WHOQg fractional contribution of each congener by food intake rate for that food
product. A multiplication of each food product's overall TEQ-WH098 concentration, in
pg/g, and the corresponding food consumption rate, in g/day, gives the pg TEQ-WH098
consumed per day by that food product. Further multiplication of this pg TEQ-WH098/day
and each congener's fractional contribution gives the pg TEQ-WH098/day contributed by
each congener. The representative food TEQ-WH098 concentrations described in Section
4.4.2 to determine background dose were expressed on a whole weight basis, to be
consistent with the consumption rates of the food products, which were also on a whole
weight basis. The whole concentration, in pg TEQDF-WH098/g for CDD/CDFs and TEQP-
WH098/g for coplanar PCBs for all food products were: beef - 0.18 pg/g CDD/CDFs and
0.084 pg/g PCBs; pork - 0.28 pg/g CDD/Fs and 0.012 pg/g PCBs; poultry - 0.068 pg/g
CDDs/CDFs and 0.026 pg/g PCBs; dairy - 0.12 pg/g CDD/CDFs and 0.058 pg/g PCBs;
milk - 0.018 pg/g CDDs/CDFs and 0.0088 pg/g PCBs, freshwater fish - 1.2 pg/g
CDD/CDFs and 1.2 pg/g PCBs; and marine fish - 0.36 pg/g CDD/Fs and 0.25 pg/g PCBs.
The consumption rates for this exercise were expressed in g/day, which were calculated
using the g/kg-day consumption rates given in Section 4.4.2 multiplied by a 70 kg adult:
beef - 49.7 g/day, pork - 15.4 g/day, poultry - 35 g/day, dairy - 55 g/day, milk - 175
g/day, freshwater fish - 5.9 g/day, and marine fish - 9.6 g/day.
The results of this four-step procedure are demonstrated in Table 4-42 for the beef
consumption pathway for CDDs/CDFs. Tables 4-43 and 4-44 show the average congener
concentrations of CDDs/CDFs and PCBs, respectively, derived for the food groups, the
total TEQ-WH098 concentration for each food group from this profile, and the TEQ-WH098
percentage contributions for each congener and food group. Tables 4-45 and 4-46 show
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the final results of this exercise for CDDs/CDFs and PCBs, respectively. Results suggest
that 72 percent of the total TEQDF-WH098 background dose of CDDs/CDFs comes from
four congeners: 1,2,3,7,8-PCDD (33 percent), 2,3,4,7,8-PCDF (17 percent), 2,3,7,8-
TCDD (10 percent), and 1,2,3,6,7,8-HxCDF (12 percent). PCB 126 comprises 61 percent
of the TEQp-WH098 dose of dioxin-like PCBs. When adding the doses of the CDDs/CDFs
to the coplanar PCBs, PCB 126 and 1,2,3,7,8-PCDD are the largest contributors at 21
percent, followed by the three CDD/CDF congeners at 2,3,4,7,8-PCDF (11 percent),
2,3,7,8-TCDD (7 percent), and 1,2,3,6,7,8-HxCDD (8 percent).
4.4.8.2. Background Tissue Concentrations
For the purposes of this exercise, the tissue concentrations from the CDC studies
reported in Table 4-18 were used. For coplanar PCBs, the main issue was that data for
only four PCB congeners were included (i.e., PCBs 77, 81, 126, and 169). As a result of
the exclusion of the other PCBs, their percent contribution to the TEQ tissue concentration
could not be calculated.
Once the concentrations were derived, the TEQ-WH098 contributions of individual
congeners to the total TEQ-WH098 were derived in a manner similar to the food results.
Tables 4-47 and 4-48 show the final results of this exercise for CDDs/CDFs and coplanar
PCBs, respectively, giving the derived actual congener concentrations, and the percentage
contribution to TEQ-WH098 for each congener. The studies used in this exercise are the
same as those used in Section 4.2.3 to estimate recent (i.e., 1990s) body burden levels.
Table 4-47 indicates that four congeners contribute 82 percent of CDD/CDF TEQDF-
WH098: 1,2,3,6,7,8-HxCDD (34 percent), 1,2,3,7,8-PCDD (24 percent), 2,3,4,7,8-PCDF
(14 percent), and 2,3,7,8-TCDD (10 percent). These are the same four congeners
contributing the most to background dose. From Table 4-48, it is seen that PCB 126
overwhelms all other congeners, and for all tissue types. PCB 1 26 comprises 90 percent
of the dose of dioxin-like PCBs. Figures 4-12 and 4-13 compare the fractional TEQ-
WH098 contributions of each congener to the total TEQ-WH098 background dose of
CDD/CDFs (Figure 4-12) and coplanar PCBs (Figure 4-13), to the TEQ-WH098
contributions of each congener to average body tissue TEQ-WH098 concentration of
CDD/CDFs (Figure 4-12) and coplanar PCBs (Figure 4-13). The match between the
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highest contributors is noteworthy from this figure, as is the lack of contribution from
other congeners. Some key observations that can be gleaned from this exercise include:
1) As noted, five congeners dominate the TEQ-WH098 body burden as well as the
TEQ dose. These are, 1,2,3,7,8-PCDD, 1,2,3,6,7,8-HxCDD, 2,3,4,7,8-PCDF, and
2,3,7,8-TCDD from the CDD/CDFs, and PCB 126 from the coplanar PCBs.
2) For the four dominant CDD/CDF congeners combined, the body burden had a
higher TEQ-WH098 contribution than the food: contributions from the four
congeners to body burden TEQ-WH098 equaled 82 percent while for food they
equaled 72 percent.
3) While 2,3,7,8-TCDD has been the focus of past exposure and health studies, it
would appear that the other CDD/CDF congeners found to be high contributors in
this exercise may also be important from an exposure and health standpoint.
4.5. Comparison of Assessment Approaches and Best Estimates of Intake
Two approaches were used in this chapter to estimate background exposures to
dioxin-like compounds among the general population of the Unites States. The first
approach used pharmacokinetic modeling to calculate a dose from tissue concentrations.
This was done using either a steady state or non-steady state approach. Using the steady
state approach, the TEQDF-WH098 dose was estimated to be 1 26 pg/day, when the half
life for TCDD (i.e., 7.1 years) was assumed to apply to the total TEQ, and 87 pg/day,
when congener specific half-lives were used. PCB doses could not be estimated in this
way because of the lack of congener-specific half-life information. The advantage of
modeling doses from tissue concentrations is that all pathways of exposure are accounted
for. However, because the half-lives of dioxin-like compounds in the body are relatively
long (i.e., 7.1 years for TCDD), modeled doses may reflect the cumulative effect of
previous doses and not current doses. This was demonstrated by a non-steady state
model used to reconstruct past doses of 2,3,7,8-TCDD. The results of the modeling
exercise indicated that current doses would be expected to be less than past doses.
Assuming that these results would apply to all dioxin-like congeners, and not just 2,3,7,8-
TCDD, the current total TEQDF-WH098 dose would be expected to be somewhat lower
than 88 pg/day, as estimated using the steady state approach.
The second approach used for estimating background doses to dioxin-like
compounds was to evaluate dioxin-like compounds in various dietary components (i.e.,
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meats, dairy products, fish, etc.) and environmental media (i.e., air, soil, water) to which
humans are exposed. By combining TEQDFP-WH098 concentrations in foods and these
media with the contact rates (i.e., ingestion, inhalation, dermal contact rates) for these
foods and media, CDD/CDF and PCB doses were calculated. Using this approach, the
daily TEQDF-WH098 dose was estimated to be 43 pg/day and the TEQP-WH098 was
estimated to be 23 pg/day. The advantage of using this approach is that, if current media
concentrations and intake estimates are used, the estimated doses should reflect current
exposures. In this analysis, the most recent data on the concentrations of dioxin-like
compounds in beef, pork, poultry, milk, and vegetable oil, collected by EPA, have been
used. Recent data from the published literature have also been used for freshwater and
marine fish and shellfish. Likewise, intake rates are based on EPA's recently published
Exposure Factors Handbook (U.S. EPA, 1997)which presented data from USDA's 1989-
1991 Continuing Survey of Food Intake Among Individuals (USDA, 1995) (a more recent
USDA data set has been released since the Exposure Factors Handbook was published,
but EPA has not yet completed its analysis of these data), and the most current data for
establishing contact rates for other media. It should be noted, however, that the dose
component approach may underestimate current doses if important pathways of exposure
are not accounted for in the component analysis. For example, in this assessment, fruits
and vegetables have not been considered as significant contributors to the overall dose.
Data for the concentrations of dioxin-like compounds in fruits and vegetables are limited,
but it expected that the concentrations would be lower in these foods than in fatty foods
such as meat, fish and dairy products. Thus, a fruit and vegetable component has not
been included in this analysis. If fruits and vegetables actually account for a more
significant portion of the exposure than expected, the dose estimated here may be lower
than that experienced by the general population of the United States. Other uncertainties
introduced by this approach include the use of soil ingestion rates that may or may not
account for all types of inadvertent soil ingestion (e.g., outdoor soil, household dust), the
lack of PCB residue data for soils and air, and non-representative sampling data for air.
For example, the adult soil ingestion rate cited in the Exposure Factors Handbook (U.S.
EPA, 1997) is based on a limited data set, but is used as a reasonable surrogate for all
forms of soil ingestion. The accuracy of this assumption is difficult to assess; however,
because soil ingestion accounts for a small percentage of the overall dose, this uncertainty
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is not expected to significantly affect one's confidence in the dose estimates. Likewise,
the lack of PCB soil and air data, and the non-representative nature of the CDD/CDF air
data would be expected to have little effect on the overall dose estimate, because these
pathways account for a small percentage of the overall dose.
Despite these uncertainties, the dose component approach is believed to provide
the best estimate of the mean current background dose to the general U.S. population.
Variability was evaluated using dietary fat data, high-end intake rates, and by evaluating
variability in body burden. In general, these data indicate that the high-end dose of dioxin-
like compounds is likely to be 2 to 3 times higher than the mean.
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Table 4-1. NHATS Mean Adipose Tissue Data (ppt, lipid adjusted)
Congener
2,3,7,8-TCDD
2,3,7,8-PeCDD
2,3,7,8-HxCDD
2,3,7,8-HpCDD
OCDD
2,3,7,8-TCDF
1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
2,3,7,8-HxCDF
2,3,7,8-HpCDF
OCDF
TOTAL
Congener
Concentration
(pg/g)
5.38
10.7
86.8
110
724
1.88
0.31
9.7
14.2
16
2.28
I-TEQDF
Concentration
(pg/g)
5.38
5.35
8.68
1.1
0.72
0.19
0.016
4.85
1.42
0.16
0.002
27.9
TEQDF-WH098
Concentration
(pg/g>
5.38
10.7
8.68
1.1
0.072
0.19
0.016
4.85
1.42
0.16
0.0002
32.6
Percent
Detected3
97
97
97
100
100
100
14
95
2 to 92
4 to 89
30
a Based on analysis of 48 samples composited from 865 samples
Source: U.S. EPA (1991 a).
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Table 4-2. Estimated Mean I-TEQDF Concentrations (ppt) in Adipose
Tissue for U.S. Subpopulations from the 1987 NHATS
Census Regions
Northeast
North Central
South
West
Age Groups
0-14 years
1 5-44 years
45 + years
Race
Caucasian
Non-Caucasian
Sex
Male
Female
Total Population
I-TEQDF Concentration
(ppt)
31.1
29.7
26.6
24.4
9.7
24.6
46.5
26.5
35.2
26.1
29.9
27.9
Percent of Population3
22
26
33
19
23
46
31
83
17
49
51
100
a Population percentage based on 1980 U.S. Census.
Source: Orban et al. (1994).
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Table 4-3. Human Adipose Tissue Data (ppt, lipid adjusted)
Chemical
2,3,7,8-TCDD
PCB 77
PCB 126
PCB 169
PCB 81
Range (ppt)
1.6 to 38
Nondetect to 27.9
14.6 to 371
29.5 to 174
1.5 to 21.3
Mean (ppt)
10.4
11.7
135
69
10.5
Source: Patterson et al. (1994).
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Table 4-4. Mean Levels in Human Serum (ppt, whole weight basis)
Chemical
2,3,7,8-TCDD
PCB 77
PCB 126
PCB 169
PCB 105
PCB 118
PCB 180
Total PCBs
1982
Not Measured
1.38
0.281
0.282
Not Measured
Not Measured
Not Measured
Not Measured
1988
0.159
0.481
0.183
0.151
33.2
366
466
3,100
1989
0.0165
0.251
0.135
0.192
Not Measured
Not Measured
Not Measured
Not Measured
Source: Patterson et al. (1994).
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Table 4-5. Mean TEQ Levels in Pooled Serum Samples
Cornwall
Sports Fishers
<38 years, lower
higher
38 years, lower
higher
> 50 years, higher
Nonfish Eaters
<38 years
38-50 years
>50 years
Mississauga
Sports Fishers
<38 years
38-50 years
>50 years
Nonfish Eaters
<38 years
38-50 years
>50 years
I-TEQDF
(ppt, lipid basis)
20.8
22.2
28.4
31.4
33.5
24.7
29.8
36.8
32.4
40.1
41.2
34.0
29.1
34.3
TEQP-WH094
(ppt, lipid basis)
-
3.6
3.1
9.5
17.3
2.6
6.8
9.7
-
-
—
-
-
-
Source: Adapted from Cole et al. (1995).
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Table 4-6. CDD/CDF Levels in Human Blood from Various Countries
Country
USA
Germany
S. Vietnam (Ho Chi Minn)
S. Vietnam (Dong Nai)
N. Vietnam (Hanoi)
Guam
Soviet Union (St. Petersburg)
Siberia (Baikalsk)
Japan
Mean Blood Level
(ppt I-TEQDF, lipid)
41 (50 ppt TEQDF-WH098)
42 (49 ppt TEQDF-WH098)
28
49
12
32
17
18
31 (35 ppt TEQDF-WH098)
Number of Samples
100
85
50
33
32
10
50
8
50-100
Source: Schecter et al. (1992a; 1994a).
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Table 4-7. CDD/CDF Levels in Human Adipose Tissues from Various Countries
Country
USA
Germany
China
Japan
Canada
S. Vietnam
N. Vietnam
Mean Tissue Level
(ppt I-TEQDF)
24 (27 ppt TEQDF-WH098)
69 (79 ppt TEQDF-WH098)
18
38 (43 ppt TEQDF-WH098)
36 (40 ppt TEQDF-WH098)
30
4
Number of Samples
15
4
7
6
46
41
26
Source: Schecter (1991]
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Table 4-8. Levels of CDDs and CDFs 2,3,7,8-Substituted Found
in Spanish Human Adipose Tissue on Fat
Weight Basis in pg/g (ppt). (17 samples)
Isomers
2,3,7,8-TCDD
2,3,7,8-TCDF
1,2,3,7,8-PCDF
2,3,4,7,8-PCDF
1,2,3,7,8-PCDD
1,2, 3,4,7, 8-HCDF
1,2, 3,6,7, 8-HCDF
2, 3,4,6,7, 8-HCDF
1,2,3,7,8,9-HCDF
1,2, 3,4,7, 8-HCDD
1,2, 3,6,7, 8-HCDD
1,2,3,7,8,9-HCDD
1,2, 3, 4,6, 7, 8-HCDF
1,2, 3,4,7, 8,9-HCDF
1,2, 3, 4,6, 7, 8-HCDD
OCDF
OCDD
CDDs
CDFs
CDDs + CDFs
No. of
Pos.
6
11
4
13
12
10
10
8
10
5
12
13
14
6
17
11
17
17
17
17
Range
(pg/g)
ND-13.86
ND-18.52
ND-25.87
ND-44.77
ND-22.57
ND-83.63
ND-68.10
ND-66.31
ND-76.40
ND-54.5
ND-152.4
ND-41.28
ND-102.2
ND-106.6
60.4-707.4
ND-293.7
91-2847.5
313.9-3457
23.9-649.7
963.7-3604.2
Mean
(pg/g>
3.28
3.98
2.01
25.14
10.74
18.77
14.92
10.87
20.63
6.52
65.64
19.9
23.63
9.40
187.4
72.30
1318.1
1608.3
203.4
1811.7
S.D.
(pg/g>
5.03
5.24
6.47
15.86
8.89
25.43
19.05
21.05
38.6
14.62
54.60
13.31
25.45
26.55
146.35
99.59
742.49
839.6
188.3
813.8
I-TEQDF (pg/g)
3.28
0.39
0.02
12.7
5.37
1.87
1.49
1.87
2.06
0.65
6.56
1.99
0.23
0.09
1.87
0.072
1.31
21.03
20.79
41.8 (TEQDF-WH098
= 46 pg/g)
ND = Not detected
Source: Gonzalez et al. (1 993).
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December 2003
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Table 4-9. Concentration of CDDs, CDFs, and PCBs in Human Milk on a Fat Basis (pg/g)
Congener
2,3,7,8-TCDD
1,2,3,7,8-PeCDD
1,2,3,4,7,8-HxCDD
1,2, 3,6,7, 8-HxCDD
1,2,3,7,8,9-HxCDD
1,2, 3,4,6,7, 8-HpCDD
OCDD
2,3,7,8-TCDF
1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
1,2,3,4,7,8-HxCDF
1,2, 3,6,7, 8-HxCDF
1,2,3,7,8,9-HxCDF
2, 3,4,6,7, 8-HxCDF
1,2,3,4,6,7,8-HpCDF
1,2, 3,4,7, 8,9-HpCDF
OCDF
3,3',4,4'-TeCB
3,3',4,4',5-PeCB
3,3',4,4',5,5'-HxCB
Total CDD
Total CDF
Total CDD/CDF
Total Dioxin-like PCB
I-TEQDF
Fat (%)
Age
Primipara (n = 7)
Mean
2.0
8.9
4.7
32.3
6.9
29.8
174.2
2.3
0.6
11.4
4.3
4.5
1.9
2.0
2.0
0.2
2.7
10.4
134.5
60.0
258.7
27.0
285.7
204.9
32.6
4.6
27.4
SD
0.4
1.7
4.3
8.1
2.7
15.4
137.0
0.8
0.5
1.3
0.5
0.5
0.8
1.3
0.7
0.2
1.3
6.4
70.7
33.4
144.7
4.2
145.8
94.3
9.6
1.8
3.8
Multipara (n = 8)
Mean
1.2
5.0
2.6
18.9
3.6
31.3
194.6
2.0
0.6
7.8
3.3
3.2
1.6
1.5
2.1
0.7
3.0
13.7
165.9
50.1
257.2
21.5
278.7
229.8
28.9
3.5
32.1
SD
0.3
2.6
1.1
6.4
1.2
15.6
75.5
0.5
0.5
3.0
1.2
1.3
0.4
0.7
0.5
0.9
1.3
7.3
87.4
21.1
78.9
5.7
83.5
105.9
8.9
0.9
4.2
Ratio
Pri/Multi
•I ~j * * *
1.8**
1.8
1.7**
1.9**
0.9
0.9
1.1
1.1
1.5*
1.3
1.4*
1.2
1.3
1.0
0.3
0.9
0.8
0.8
1.2
1.0
1.3*
1.0
0.9
1.1
1.3
0.9
*** p<0.01
** p<0.1
* p<0.5
Source: Hirakawa et al. (1995).
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December 2003
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Table 4-10. CDD/CDF Concentrations and I-TEQDF Levels in Human Milk
(ppt, lipid basis)
Congener
2,3,7,8-T4CDD
1,2,3,7,8-P5CDD
1,2, 3,4,7, 8-H6CDD
1,2,3,6,7,8-H6CDD
1,2,3,7,8,9-H6CDD
1,2,3,4,6,7,8-H7CDD
08CDD
2,3,7,8-T4CDF
1,2,3,7,8-P5CDF
2,3,4,7,8-P5CDF
1,2, 3,4,7, 8-H6CDF
1,2,3,6,7,8-H6CDF
1,2,3,7,8,9-H6CDF
2,3,4,6,7,8-H6CDF
1,2, 3,4,6,7, 8-H7CDF
1,2,3,4,7,8,9-H7CDF
08CDF
Total CDD/CDFs
Concentration
(pg/s
Mean
4.2
11.9
7.1
35.3
8.0
81.4
272
1.3
0.9
31.1
8.6
7.8
0.5
4.9
13.4
5.0
3.4
497
fat)
Range
2.9 - 5.1
8.4 - 16.6
5.0- 11.0
27.8 - 45.5
6.5 - 11.1
40.8 - 142
154 - 455
0.7 - 1.9
0.5 - 1.8
24.7 - 42.6
6.8 - 11.0
6.3 - 10.4
<0.1 - 1.0
2.0 - 7.0
5.4 -30.1
2.5 - 15.0
1.6 - 7.0
333 - 715
Toxicity Equivalents
(pg/g fat, as
Mean
4.21
5.94
0.71
3.53
0.80
0.81
0.27
0.13
0.045
15.56
0.86
0.77
0.05
0.49
0.13
0.05
0.0034
34.4 (TEQDF-WH098
= 40 ppt)
-TEQDF)
Range
2.92 - 5.06
4.18 - 8.30
0.50- 1.10
2.78 - 4.55
0.65 - 1.11
0.41 - 1.42
0.15 -0.46
0.07 - 0.19
0.02 -0.09
12.35 - 21.30
0.68 - 1.10
0.63 - 1.04
<0.01 -0.10
0.20 - 0.70
0.05 -0.30
0.03 - 0.15
0.00-0.01
27.3 - 43.2
Source: Van Cleuvenbergen et al. (1994).
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December 2003
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Table 4-1 1. CDD/CDF and PCB TEQ Concentrations in Breastmilk from Various
Countries and Regions Based on 1992/93 Sampling9
Country
Albania
Austria
Belgium
Canada
Croatia
Czech
Denmark
Finland
Germany
Hungary
Netherlands
Norway
Lithuania
Pakistan
Russia
Slovak
Spain
Ukraine
United
Kinadom
Area
Tirana
Librazhd
Vienna (urban)
Tulln (rural)
Brixlegg (industrial)
Brabant Wallou
Liege
Brussels
Maritimes 92
Quebec 92
Ontario 92
Prairies 92
British Columbia 92
All Provinces 92
Gaspe
Basse Cote-Nord
Ungave Bay
Hudson Bay
Kirk
Zagreb
Kladno
Uherske Hradiste
7 Different Cities
Helsinki
Kuopio
Berlin
Budapest
Scentes
Whole Country
Troms0 (coastal)
Hamar (rural)
Skien/Porsgrumm find)
Palanga (coastal)
Anykshchiai (rural)
Vilnius City (urban)
Lahore
Arkhankelsk
Karhopol
Michalovce
Nitra
Bizkaia
Gipuzkoa
Kiev nr. 1
Kiev nr.2
Birmingham
Glasaow
Indiv.
Samples
in Pool
10
10
13
21
13
8
20
6
20
20
20
20
20
100
12
4
4
5
10
13
1 1
1 1
48
10
24
10
20
10
17
10
10
10
12
12
12
14
1
1
10
10
19
10
5
5
20
23
Fat
(wt%)
5.84
4.72
4.10
3.80
3.40
3.79
2.98
2.81
2.76
3.06
3.09
3.20
2.97
2.96
3.52
3.63
3.31
3.26
3.80
3.26
5.41
4.92
3.61
4.14
4.49
5.00
4.97
4.97
2.73
2.56-2.70
2.51-2.76
2.75-3.00
4.00-4.83
3.56-4.10
2.69-2.87
4.31
5.17
3.64
4.77
3.61
3.75
3.86
3.40
3.76
3.09-3.10
3.40-3.45
CDD/CDF
(pg
l-TEQDF/g)
4.8
3.8
10.7
10.9
14.0
20.8
27.1
26.6
10.8-1 1.0
13.4-13.6
18.1-18.3
14.6-14.8
15.7-15.8
14.5-14.6
23.2-23.4
14.6-14.7
14.3-14.5
20.9-21.1
8.4
13.5
12.1
18.4
15.2
21.5
12.0
16.5-16.6
8.5-8.6
7.8
22.4-22.5
10.1
9.3
12.5-12.6
16.6
14.4
13.3
3.9
15.2
5.9
15.1-15.2
12.6
19.4
25.5
11.0
13.3
17.9
15.2
Non-Ortho
PCBs
(pg TEQp-
WH094/g)
1.3
1.0
8.3
9.4
15.1
3.8
1.7
4.0
2.9
5.1
5.8
2.3
2.5
3.8
9.5
19.6
9.8
13.3
3.8
5.2
2.5
4.1
2.3
1.9
1.0
9.0
0.8
0.9
8.8
16.1
7.4
6.7
12.8
12.9
1 1.6
1.9
2.9
2.0
6.4
3.6
6.7
3.8
9.3
6.0
2.5
2.6
Mono-Ortho
PCBs
(pg TEQp-
WH094/g)
1.1
0.7
3.4
3.0
3.8
3.6
3.1
3.9
1.2-1.4
1.7-1.9
1.8-2.0
0.9-1.1
1.0-1.2
1.5-1.7
3.2-3.4
5.7-6.0
4.3-4.6
8.0-8.3
2.2
2.7
3.5
5.7
2.2
2.7
1.4
2.7
0.8
0.5
2.5
3.4
3.0
2.9
7.6
7.8
8.9
0.4
5.7
2.9
7.0
2.5
3.9
4.4
5.6
5.6
1.8
1.3
£ [Marker PCBs]
(ng/g)
63
43-46
381
303
449
275-277
306-308
260-261
86-87
137-138
128-129
58-59
70-71
112-113
220-221
559-560
576
1361
218-219
219
532-533
1068
209-210
189
133-135
375
61-65
45-47
253-256
273
265-266
302
361
287
322
19-20
197
102
1015
489-490
461
452-453
264
191-192
129-131
131-133
° Results from the second round of WHO-coordinated exposure studies on levels of PCBs, PCDDs, and PCDFs (on fat basis) in human milk. In
calculating sums of the six marker PCBs and levels of PCDDs, PCDFs, non-ortho, and mono-ortho PCBs expressed in TEQ, both data are shown
when non-detect values are equal to zero and non-detect values are equal to the limit of detection. If no differences appeared, a single value is
presented.
l-TEFDFs used in calculating TEQDFs for CCD/CDFs; TEFP-WHO94s used in calculating TEQPs for PCBs.
Source: Liem et al. (1996).
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December 2003
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Table 4-12. Comparison of Results from the First and Second Round of
WHO-Coordinated Human Milk Study
Country
Austria
Belgium
Canada
Croatia
Denmark
Finland
Germany
Hungary
Netherlands
Norwayd
United
Kingdom
Area
Vienna (urban)
Tulln (rural)
Brabant Wallou
Liege
Brussels
All Provinces 1981
All Provinces 1982
Maritimes
Quebec
Ontario0
Prairies
British Columbia
Kirk
Zagreb
Several Regions/Cities
Helsinki
Kuopio
Berlin
North Rhine-Westphalia
Budapest
Scentes
Rural Area
Urban Area
All Regions
Troms0 (coastal)
Hamar (rural)
Skien/Porsgrumm find)
Birmingham
Glasgow
CDDs and CDFs (pg l-TEQDF/g)
1987/88b
17.1
18.6
33.7
40.2
38.8
15.6
18.1
17.6
19.4
23.0
12.0
11.8
17.8
18.0
15.5
32.0
31.6
9.1
1 1.3
37.4
39.6
34.2
18.9
15.0
19.4
37.0
29.1
n
54
51
19
34
76
31
23
14
41
42
38
31
40
79
100
50
13
13
10
1 1
10
10
1992/93
10.7
10.9
20.8
27.1
26.6
28.6
14.5
10.8
13.4
18.1
14.6
15.7
8.4
13.5
15.2
21.5
12.0
16.5
20.7°
8.5
7.8
22.4
10.1
9.3
12.5
17.9
15.2
n
13
21
8
20
6
200
100
20
20
20
20
20
10
13
48
10
24
10
20
10
17
10
10
10
20
23
V [Marker PCBs] (ng/g)
1987/88
558
609
500=
450=
830°
150
203
762
416
392
272
562°
507°
533°
n
12
21
14
41
10
38
31
143
10
10
96
10
10
8
1992/93
381
303
275
306
260
212
1 12
86
137
128
58
70
218
219
209
189
133
375
61
45
253
273 (536°)
265 (483°)
302 (468°)
129
131
n
13
21
8
20
6
200
100
20
20
20
20
20
10
13
48
10
24
10
20
10
17
10
10
10
20
23
NOTE: Results are expressed on a fat basis. £ (marker PCBs) and TEQs are calculated assuming non-detect values are equal to zero.
a Analyzed using packed column technique.
b Calculated using Nordic TEF-model.
c Ontario-1988 denotes proportional mean of two pooled samples analyzed in the first round.
d To compare results between first and second round, samples from 1 992/93 have been reanalyzed using (old) packed column technique (Becher
and Skare, personal communication).
e Dioxin levels in human milk samples from North Rhine-Westphalia collected in 1992 as reported by Furst19).
Source: Liem et al. (1996).
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December 2003
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Table 4-13. PCB Concentrations in Cow's Milk and Human Milk
from The Netherlands (ppt, lipid basis)
PCB 77
PCB 126
PCB 169
Cow's Milk
(background site)
3.5
14.4
2.8
Human Milk
13.7
88.1
55.2
Source: Van der Velde et al. (1 994).
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December 2003
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Table 4-14. l-TEQDFs in Mother's Milk and Blood, and Infant's Blood (ppt)
Time Period
Before 2nd
pregnancy
At or after
birth*
5 Months
after birth
1 1 Months
after birth
Samples Taken
Mother's blood
Milk
1st Infant's blood
Mother's blood
Milk
Placenta
Cord blood
Mother's blood
Milk
Mother's blood
Infant's blood
Mother/Child
Pair 1
12.3
16.3
29.2 (age 11
months
10.3
11.9
14.5
11.2
11.0
10.1
10.8 (2nd
infant)
Mother/Child
Pair 2
10.5
12.8
37.5 (age 12
months)
11.9
15.6
18.5
8.4
6.0
11.3
5.6
16.0 (2nd
infant)
Mother/Child
Pair 3
NA
13.4
11.8
9.7
4.1
No Data
11.5
4.2 (2nd
infant)
Mother/Child
Pair 4
NA
14.5
10.9
24.4
9.1
11.1
15.8
23.7 (2nd
infant)
NA - Not applicable
* Represents second birth for mothers 1 and 2, and first birth for mothers 3 and 4.
Source: Abraham et al. (1998).
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December 2003
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Table 4-15. Mean Concentrations of CDD/CDFs and Coplanar PCB Congeners
from the Times Beach Exposure Study
Target Population (n = 76)
Sep,
1995
July,
1996
June,
1997
Mean
n*
Comparison Population (n = 74)
Sep,
1995
July,
1996
June,
1997
Mean
n*
ODD Congeners
2,3,7,8-TCDD
1, 2,3,7, 8-PCDD
1, 2,3,7, 8,9-HxCDD
1, 2,3,4,6,7, 8-HpCDD
1,2,3,4,6, 7, 9-HpCDD
OCDD
TEQD-WH098
1.79
4.93
7.24
88.0
0.89
650.0
8.4
1.27
4.04
5.98
75.4
1.06
542.0
6.7
1.23
2.95
5.15
60.5
0.68
435.0
5.3
1.43
3.97
6.12
74.6
0.88
542.3
6.8
66
67
64
60
58
64
1.46
4.53
6.28
83.7
0.99
535.0
7.5
1.38
4.96
7.25
84.7
0.93
512.0
8.0
1.23
3.45
5.47
64.8
0.79
404.0
5.9
1.36
4.31
6.33
77.8
0.90
483.7
7.1
61
60
59
61
59
46
CDF Congeners
2,3,7, 8-TCDF
1, 2,3,7, 8-PCDF
2,3,4,7, 8-PCDF
1, 2,3,4,7, 8-HxCDF
1,2,3,6,7,8-HxCDF
2, 3,4,67, 8-HxCDF
1,2,3,4,6, 7, 8-HpCDF
1,2,3,4,7,8,9-HpCDF
OCDF
TEQF-WH098
0.48
0.42
5.73
7.36
6.40
0.46
14.4
0.40
1.13
4.5
0.56
0.46
5.00
6.40
5.07
0.45
12.1
0.47
1.08
3.9
0.53
0.46
4.12
5.03
4.01
0.47
9.0
0.42
0.56
3.2
0.52
0.45
4.95
6.26
5.16
0.46
11.83
0.43
0.92
3.9
62
66
61
64
65
63
63
65
53
0.56
0.48
5.43
6.18
5.19
0.77
11.5
0.45
1.22
4.1
0.54
0.49
5.82
7.24
5.86
0.80
11.7
0.42
1.08
4.5
0.45
0.46
4.52
4.91
4.03
0.63
8.30
0.40
1.46
3.4
0.52
0.48
5.26
6.11
5.03
0.73
10.5
0.42
1.25
4.0
51
60
59
59
58
55
59
58
48
Coplanar PCB Congeners
77
81
126
169
WH098 TEQp
2.43
1.97
9.97
16.4
1.2
1.90
1.92
8.80
14.4
1.0
2.52
1.91
8.15
10.8
0.9
2.28
1.93
8.97
13.9
1.0
63
65
66
63
1.90
2.13
12.8
16.2
1.4
2.47
2.10
14.2
16.4
1.6
2.22
2.07
12.3
13.1
1.4
2.20
2.10
13.1
15.2
1.5
59
54
59
59
* n = number of individuals with measurements of this congener for all three sampling dates.
Source: MDOH (1999).
DRAFT-DO NOT QUOTE OR CITE
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December 2003
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Table 4-16. Results of Blood Sampling for the Comparison Population
at Vertac in Jacksonville, AK
1991 Sampling
of 71
individuals
1991
1994 Resampling of 18
individuals
1991
1994
1995 Resampling of 14
individuals
1991
1995
ODD Congeners
2,3,7,8-TCDD
1,2,3,7,8-PCDD
1,2, 3,4,7, 8-HxCDD
1,2,3,6,7,8-HxCDD
1, 2,3,7, 8,9-HxCDD
1, 2,3,4,6,7, 8-HpCDD
OCDD
TEQD- WH098
2.5
6.1
7.7
70.8
8.6
124.1
970.8
18.6
3.0
6.6
7.9
70.4
8.9
115.0
944.7
19.6
2.7
5.7
12.4
56.0
7.2
77.2
608.7
16.8
3.1
5.9
7.4
66.4
9.8
102.9
690.6
18.4
3.3
5.9
NR
68.1
10.2
81.7
650.9
17.9
CDF Congeners
2,3,7,8-TCDF
1,2,3,7,8-PCDF
2,3,4,7,8-PCDF
1,2, 3,4,7, 8-HxCDF
1,2,3,6,7,8-HxCDF
1,2,3,7,8,9-HpCDF
2,3,4,6,7,8-HxCDF
1,2,3,4,6,7,8-HpCDF
1,2,3,4,7,8,9-HpCDF
OCDF
TEQF-WH098
2.0
0.1
5.4
8.1
5.0
0 (ND)
3.2
19.9
0.1
0.6
4.7
0.6
0.3
6.4
8.0
5.6
0 (ND)
4.0
18.0
0.3
0.8
5.2
0.2
0 (ND)
5.6
6.8
4.4
0 (ND)
2.6
13.5
0.2
0 (ND)
4.3
0.6
0.2
5.9
7.4
5.1
0 (ND)
4.0
18.9
0 (ND)
1.0
4.9
0.1
0 (ND)
5.6
6.6
4.9
0 (ND)
2.5
14.6
0 (ND)
0 (ND)
4.4
Coplanar PCB Congeners
77
81
126
169
WH098 TEQp
5.9
0 (ND)
17.2
16.3
1.9
3.1
0 (ND)
17.6
20.8
2.0
0 (ND)
0 (ND)
13.2
18.5
1.5
4.4
0 (ND)
15.4
18.2
1.7
NR
0.4
15.1
17.9
1.7
Source: ADH (1995) and Cranmer (1996).
DRAFT-DO NOT QUOTE OR CITE
4-94
December 2003
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Table 4-17. Congener-specific Average Concentrations for 29 North Carolina Adults
North Carolina Adults, n = 29, sampled in 1996
ODD Congeners
2,3,7,8-TCDD
1,2,3,7,8-PCDD
1,2, 3,4,7, 8-HxCDD
1,2,3,7,8,9-HxCDD
1,2, 3,4,6,7, 8-HpCDD
OCDD
TEQD-WH098
2.38
4.51
3.46
3.99
54.04
391.3
8.22
CDF Congeners
2,3,7,8-TCDF
1,2,3,7,8-PCDF
2,3,4,7,8-PCDF
1,2, 3,4,7, 8-HxCDF
1,2, 3,6,7, 8-HxCDF
2, 3,4,6,7, 8-HxCDF
1,2,3,7,8,9-HxCDF
1,2, 3,4,6,7, 8-HpCDF
1,2, 3,4,7, 8,9-HpCDF
OCDF
TEQF-WH098
1.01
1.16
6.26
5.44
4.67
1.66
1.37
11.77
1.32
2.80
4.74
Coplanar PCB Congeners
77*
81*
126*
169*
WH098 TEQp
51.00
4.11
17.95
14.95
2.00
* PCBs 77 and 81 were not detected in any sample, so the concentrations shown are the average of
1/2 detection limit for the 29 samples. PCBs 126 and 169 were detected in most of the samples, so the
average concentrations calculated at % detection limits reported above are very similar to average
concentrations calculated at ND = 0.
Source: Masten (2000).
DRAFT-DO NOT QUOTE OR CITE
4-95
December 2003
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Table 4-18. Results of CDC Compilation of Blood Data from Six Study Sites
(all results in pg/g lipid; n = 316)
Congener
Mean
75th Percentile
90th Percentile
95th Percentile
ODD Congeners
2,3,7,8-TCDD
1,2,3,7,8-PCDD
1,2, 3,4,7, 8-HxCDD
1,2, 3,6,7, 8-HxCDD
1,2,3,7,8,9-HxCDD
1,2, 3,4,6,7, 8-HpCDD
OCDD
2.1
5.2
6.2
73.1
7.1
79.2
664.0
2.7
6.5
7.8
87.6
8.8
94.9
793.6
3.5
7.8
10.9
116.9
10.7
131.3
1084.7
4.2
9.2
12.0
127.3
12.6
161.5
1394.0
CDF Congeners
2,3,7,8-TCDF
1,2,3,7,8-PCDF
2,3,4,7,8-PCDF
1,2, 3,4,7, 8-HxCDF
1,2, 3,6,7, 8-HxCDF
1,2,3,7,8,9-HxCDF
2, 3,4,6,7, 8-HxCDF
1,2, 3,4,6,7, 8-HpCDF
1,2, 3,4,7, 8,9-HpCDF
OCDF
0.7
0.8
6.2
6.5
5.3
0.7
2.2
13.2
1.3
2.1
0.9
1.0
7.5
7.8
6.2
0.8
2.6
15.4
1.5
2.6
1.2
1.4
10.2
10.5
8.4
1.2
3.3
21.2
2.1
3.3
1.5
1.7
12.2
12.2
9.8
1.4
4.0
25.8
2.6
4.0
Coplanar PCB Congeners
77
81
126
169
31.1
3.2
18.1
19.4
32.6
3.9
21.8
25.1
51.7
5.4
32.2
32.7
72.7
6.9
45.8
37.7
Toxic Equivalent Concentrations for the Entire Data Base *
TEQDFP-WH098
22.1
26.7
33.9
38.8
* This TEQ concentration was derived separately from the congener profile, and cannot be derived from the profile.
See text for more detail.
DRAFT-DO NOT QUOTE OR CITE
4-96
December 2003
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Table 4-1 9. CDD/CDF Levels in Human Tissues in North America (ppt TEQDF-WHO98, lipid basis) (late 1 980s to early 1 990s)
2,3,7,8-
TCDD
1,2,3,7,8-
PECDD
Total
HXCDD
1,2,3,4,6,7,8-
HPCDD
OCDD
2,3,7,8-
TCDF
1,2,3,7,8-
PECDF
2,3,4,7,8-
PECDF
Total
HXCDF
1,2,3,4,6,7,8-
HPCDF
OCDF
Total
TEQ*
ADIPOSE TISSUE
NHATS,
U.S. EPA, 1991 a
U.S. (n = 865; 48
composites)
Patterson et al., 1994
U.S. (n = 4)
Schecter, 1991
U.S. (n = 15)
Schecter, 1991
Canada (n = 46)
MEAN
SD
WEIGHTED MEAN
5.4
4.4
6.9
7.1
6.0
1.1
5.5
10.7
1 1.6
7.7
1 1
10.3
1.5
10.7
8.7
1 1.6
6.6
9.7
9.1
1.8
8.7
1.1
0.56
0.83
1.5
1.00
0.35
1.1
0.072
0.045
0.043
0.095
0.064
0.021
0.073
0.19
0.1 1
0.16
0.15
0.03
0.19
0.016
-
0.02
0.00
0.02
4.9
1.9
3.4
8.5
4.7
2.5
5.0
1.4
0.95
1.1
1.8
1.3
0.33
1.4
0.16
0.12
0.16
0.3
0.19
0.07
0.17
0.0002
0.00005
0.0001
0.0001
0.0002
32.6
31.3
26.8
40.0
32.7
32.8
BLOOD
Cole et al., 1995
Canada (n = 132; 14
composites)
Schecter et al., 1993
U.S. (n = 5;
composite)
Schecter et al., 1993
U.S. (n = 50)
Schecter et al., 1 994a
U.S. (n = 100)
MEAN
SD
WEIGHTED MEAN
4.4
3.4
3.8
5.2
4.20
0.68
4.5
9.9
7.0
9.2
21.0
11.8
5.4
13.6
8.5
8.1
9.1
1 1.2
9.3
1.2
9.6
1.1
1.6
1.2
1.9
1.4
0.32
1.4
0.053
0.12
0.08
0.12
0.093
0.028
0.081
0.18
0.3
0.2
0.31
0.27
0.04
0.28
0.1
0.1
0.14
0.08
0.04
0.11
8.3
3.5
4.4
6.5
5.7
1.9
6.9
3.0
2.1
2.3
3.3
2.7
0.48
3.0
0.12
0.5
0.23
0.36
0.26
0.06
0.28
0.001
0.001
0.0004
0.0008
0.0003
0.0006
35.8
26.4
30.9
50.0
35.8
39.8
HUMAN MILK
Schecter et al., 1 989b
U.S. (n = 42)
3.3
6.7
4.2
0.42
0.023
0.29
0.023
3.65
1
0.043
0.0004
19.7
ALL TISSUE TYPES
MEAN
SD
WEIGHTED MEAN
4.9
1.3
5.2
10.5
4.1
1 1.2
8.7
2.2
8.8
1.1
0.45
1.2
0.072
0.032
0.073
0.23
0.07
0.21
0.06
0.04
0.03
5.00
2.2
5.4
1.9
0.80
1.8
0.20
0.09
0.19
0.001
0.0004
0.0003
32.7
34.0
Sum of mean TEQDF-WHO9B concentrations for all congeners.
DRAFT-DO NOT QUOTE OR CITE
4-97
December 2003
-------
o
o
^1
D
C
O
§
Table 4-20. CDD/CDF Levels \n Human Tissues in Europe and Japan (ppt TEQDF-WH098, lipid basis) (ate 1980s to early 1990s)
2,3,7,8-
TCDD
1,2,3,7,8-
PeCDD
Total
HxCDD
1,2,3,4,6,7,8-
HpCDD
OCDD
2,3,7,8-
TCDF
1,2,3,7,8-
PeCDF
2,3,4,7,8-
PeCDF
Total
HxCDF
1,2,3,4,6,7,8-
HpCDF
OCDF
Total
TEQ*
ADIPOSE TISSUE
Becket al., 1994
Germany (n = 20)
Gonzalez et al., 1993
Spain (n = 1 7)
Schecter, 1991
Germany (n = 4)
Schecter, 1991
Japan (n = 6}
MEAN
SD
WEIGHTED MEAN
7.2
3.3
5.1
6.6
5.6
1.5
5.5
21.0
10.7
21.5
13.0
16.6
4.8
16.3
11.9
9.2
10.9
8.6
10.2
1.32
10.4
1.0
1.9
1.5
0.69
1.3
0.46
1.3
0.059
0.13
0.065
0.14
0.098
0.036
0.095
0.25
0.39
0.39
0.31
0.34
0.06
0.32
0.02
0.02
0.02
0.00
0.02
20.0
12.7
35.4
6.5
18.7
10.8
17.0
3.6
7.3
3.8
6.9
5.4
1.7
5.4
0.2
0.32
0.23
0.71
0.37
0.20
0.31
0.00004
0.0072
0.00042
0.003
0.003
0.003
65.2
45.9
78.9
43.4
58.4
56.6
BLOOD
Schecter et al., 1 992a
Germany (n = 102)
Schecter et al., 1 992a
Japan (n = 50-100)
MEAN
SD
WEIGHTED MEAN
3.6
3.2
3.4
0.20
3.4
13.8
1 1.7
12.8
1.1
12.9
7.6
6.1
6.8
0.78
7.0
0.92
0.59
0.76
0.17
0.78
0.061
0.14
0.10
0.040
0.095
0.23
0.51
0.37
0.14
0.35
0.1
0.038
0.07
0.03
0.07
18.5
10.3
14.4
4.10
15.0
3.5
2.5
3.0
0.49
3.1
0.25
0.13
0.19
0.06
0.20
0.00042
0.00031
0.0004
0.0001
0.0004
48.5
35.1
41.8
42.9
c
oo
O
CD
O
CD
3
cr
CD
—^
ro
o
o
GO
-------
Table 4-20. CDD/CDF Levels \n Human Tissues in Europe and Japan (ppt TEQDF = WH098, lipid basis) (late 1 980s to early 1 990s) (continued)
2,3,7,8-
TCDD
1,2,3,7,8-
PeCDD
Total
HxCDD
1,2,3,4,6,7,8-
HpCDD
OCDD
2,3,7,8-
TCDF
1,2,3,7,8-
PeCDF
2,3,4,7,8-
PeCDF
Total
HxCDF
1,2,3,4,6,7,8-
HpCDF
OCDF
Total
TEQ*
HUMAN MILK
Bates et al., 1994
New Zealand (n = 37)
Becket al., 1994
Germany (n = 1 1 2)
Furst et al., 1994
Germany (n = 526)
Pluimet al., 1 994b
The Netherlands (n = 35)
Schecter et al., 1 989b
Germany (n = 185)
Schecter et al. 1989c
Japan (n = 6}
Startin et al., 1989
United Kingdom (n = 80)
Tuinstra et al., 1994
The Netherlands (n = 200)
Van Cleuvenbergen et al.,
1994
Belgium (n = 9)
MEAN
SD
WEIGHTED MEAN
5.1
3.6
3.2
3.8
3
4.5
5.6
4.1
4.2
4.2
0.83
3.6
7.4
12.0
10.1
10.6
9.3
4.6
13.0
11.5
11.9
10.0
3.5
10.5
4.0
6.6
5.1
5.7
4.6
3.9
7.0
6.2
5.04
5.3
1.1
5.4
0.52
0.51
0.41
0.54
0.46
0.62
0.71
0.63
0.81
0.58
0.12
0.50
0.021
0.034
0.021
0.030
0.019
0.098
0.027
0.079
0.027
0.041
0.028
0.033
0.089
0.25
0.17
0.2
0.2
0.3
0.12
0.09
0.13
0.17
0.07
0.16
0.05
0.025
0.01
0.035
0.053
0.02
0.03
0.045
0.04
0.01
0.03
2.7
10
13.4
11.0
12
12.8
1 1
11.3
15.6
11.1
3.5
1 1.9
0.85
1.9
1.8
1.6
1.6
0.94
1.7
1.7
2.2
1.6
0.43
1.7
0.071
0.084
0.055
0.061
0.052
0.040
0.083
0.077
0.18
0.08
0.04
0.06
0.00016
0.00014
0.00013
0.00099
0.00069
0.00013
0.00034
0.0004
0.0003
0.0003
20.7
35.0
34.2
33.5
31.3
27.7
39.2
35.7
40.1
33.1
.
34.0
ALL T/SSUE TYPES
MEAN
SD
WEIGHTED MEAN
4.4
1.3
3.6
12.1
4.2
1 1.0
6.9
2.4
5.8
0.81
0.40
0.56
0.066
0.043
0.043
0.24
0.12
0.19
0.04
0.02
0.04
13.7
7.3
12.5
2.9
1.9
2.0
0.18
0.17
0.09
0.001
0.002
0.0004
41.0
.
35.8
o
o
^1
D
C
O
§
c
CD
o
CD
O
CD
3
cr
CD
—^
ro
o
o
GO
Sum of mean TEQ concentrations for all congeners.
-------
Table 4-21. PCB Levels \n Human Tissues in North America (ppt TEQP-WH098, lipid basis) (Iate1980s to early 1990s)
PCB Congeners
77
105
114
118
123
126
156
157
167
169
189
Total
TEQ*
ADIPOSE TISSUE
Mes and Weber, 1989
Canada (n = 1)
Patterson et al., 1994
U.S. (n = 28)
Schecter el al., 1989a
U.S. (n = 3)
Williams and LeBel, 1991
Canada (n = 62)
MEAN
SD
WEIGHTED MEAN
0.0003
0.0012
-
-
0.0008
0.0004
0.0012
-
-
6.0
-
6.00
0.00
6.00
-
-
-
_
_
_
-
-
1.5
1.50
0.00
1.50
-
-
-
-
_
_
_
2.0
13.5
26.7
14.1
10.1
22.4
-
-
4.0
4.0
0.00
4.0
-
-
-
_
_
_
-
-
-
-
_
_
_
0.0016
0.69
-
1.6
0.76
0.65
1.3
-
-
-
-
_
_
_
2.0
14.2
11.5
28.3
26.3
35.2
BLOOD
Cole et al., 1995
Canada (n = 7; pooled from 1 32)
Dewailly et al., 1994
Canada (n = 10-57)
Kang et al., 1997
U.S. (n = 14-16)
Patterson et al., 1994
U.S. (n = 2,3, pooled from 240)
Schecter et al., 1993
U.S. (n = 1 , pooled from 5)
Schecter et al., 1993
U.S. (n = 50)
MEAN
SD
WEIGHTED MEAN
0.013
-
-
0.010
0.003
0.008
0.009
0.004
0.011
-
-
-
0.72
0.32
0.69
0.58
0.18
0.71
-
-
-
-
-
-
_
_
-
-
2.5
(n = 51)
-
7.9
1.1
1.6
3.3
2.7
6.1
-
-
-
-
-
-
_
_
-
6.9
4.8
(n = 10)
1.8
(n = 14)
3.95
5.0
10.4
5.5
2.7
5.5
-
-
-
2.1
3.0
2.6
0.45
2.9
-
-
-
-
-
_
_
-
-
-
-
-
-
-
_
_
-
0.57
0.29
(n = 10)
0.27
(n = 16)
0.33
0.3
0.46
0.37
0.11
0.41
-
-
-
-
-
-
_
_
-
7.5
7.6
2.1
12.9
8.9
16.2
12.3
_
15.6
-------
o
o
^1
D
C
O
§
Table 4-21. PCB Levels \n Human Tissues in North America (ppt TEQP-WH098, lipid basis) (late 1 980s to early 1 990s) (continued)
PCB Congeners
77
105
114
118
123
126
156
157
167
169
189
Total
TEQ*
HUMAN MILK
Dewailly et al., 1994
Canada (n = 96; pooled to 1 6)
Hong et al., 1992
U.S. (n = 5)
Mes and Weber, 1989
Canada (n = "several" pooled samples)
She et al., 1995
U.S. (n = 12)
MEAN
SD
WEIGHTED MEAN
0.0008
0.034
0.0012
0.0007
0.0009
0.014
0.002
-
0.64
1.7
1.7
0.53
1.4
-
0.75
-
-
0.75
0.00
0.75
1.7
2.6
-
3.8
2.7
0.84
2.0
-
0.017
0.02
0.00
0.02
8.0
5.8
5.1
5.8
6.1
1.1
7.7
-
2.2
-
2.8
2.5
0.31
2.7
-
0.50
-
0.50
0.00
0.50
-
0.011
-
-
0.01
0.00
0.01
0.33
0.58
0.006
0.15
0.27
0.21
0.32
-
0.04
-
-
0.04
0.00
0.04
10.1
13.2
5.1
14.3
14.2
15.3
ALL TISSUE TYPES
MEAN
SD
WEIGHTED MEAN
0.007
0.010
0.009
1.7
2.0
0.79
0.75
0.00
0.75
2.9
2.1
5.1
0.02
0.00
0.02
7.7
6.3
8.2
2.8
0.68
2.9
0.50
0.0
0.50
0.01
0.00
0.01
0.43
0.39
0.52
0.04
0.00
0.04
16.7
18.8
o
CD
O
CD
3
cr
CD
—^
ro
o
o
GO
Sum of mean TEQ concentrations for all congeners.
-------
Table 4-22 . PCB Levels in Human Tissues in Europe (ppt TEQP-WH098, lipid basis, using WHO TEFs) (late 1980s to early 1990s)
PCB Congeners
77
105
114
118
123
126
156
157
167
169
189
Total
TEQ*
ADIPOSE TISSUE
Beck et al., 1989
Germany (n = 7)
0.006
-
-
-
-
-
-
-
-
-
-
0.006
HUMAN MILK
Beck et al., 1989
Germany (n = 10)
Georgii et al., 1995
Germany (n = 68)
Johansen et al., 1994
Norway (n = 28)
Noren et al., 1990
Sweden (n = 2)
Noren and Lunden, 1991
Sweden (n = 6,7; pooled from 120,140)
Koopman-Esseboom et al., 1994
The Netherlands (n = 1 95)
Van der Velde et al., 1994
The Netherlands (n = "several")
Dwarka et al., 1995
United Kingdom (n = 1 93)
Startin et al., 1989;
Duarte-Davidson et al., 1992
United Kingdom (n = 6; pooled from 57)
MEAN
SD
WEIGHTED MEAN
0.0022
-
0.046
0.0024
0.0027
0.002
0.0014
-
-
0.009
0.016
0.006
-
-
0.77
1.2
0.65
0.9
-
-
0.99
0.89
0.17
0.83
-
-
2.0
-
-
-
-
-
-
2.0
0.00
2.0
-
4.4
2.6
2.8
-
3.6
-
2.3
1.8
2.9
0.86
3.0
-
-
-
-
-
-
-
-
-
-
-
-
15.6
12.4
9.8
15.2
8.8
-
-
12.4
2.8
13.1
-
13.5
5.8
10.1
7.2
10.5
-
-
-
9.4
2.7
9.7
-
-
0.8
-
-
-
-
-
-
0.80
0.00
0.80
-
-
-
-
-
-
-
-
-
1.9
0.86
0.47
0.8
0.55
-
-
0.92
0.52
0.76
-
-
-
-
-
-
-
-
-
-
0.0022
17.9
29.6
27.4
18.1
31.0
9.4
2.3
2.8
29.3
-
30.2
BREAST TISSUE
Dahl et al., 1994
Sweden (n = 1 6)
1.1
1.5
5.5
-
17.0
2.4
-
0.38
27.8
ALL TISSUE TYPES
MEAN
SD
0.009
0.015
0.93
0.18
1.7
0.28
3.3
1.2
-
-
12.4
2.8
10.7
3.8
1.6
0.78
-
-
0.92
0.52
0.38
-
31.9
-------
Table 4-23. Weighted Mean CDD/CDF Profiles for Human Tissues from Studies in the 1980s and Early 1990s
2,3,7,8-Substituted
CDD/CDFs
2,3,7,8-TCDD
1,2,3,7,8-PeCDD
1,2,3,4,7,8-HxCDD
1,2,3,6,7,8-HxCDD
1,2,3,7,8,9-HxCDD
1,2,3,4,6,7,8-HpCDD
OCDD
2,3,7,8-TCDF
1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
1,2,3,4,7,8-HxCDF
1,2,3,6,7,8-HxCDF
1,2,3,7,8,9-HxCDF
2,3,4,6,7,8-HxCDF
1,2,3,4,6,7,8-HpCDF
1,2,3,4,7,8,9-HpCDF
OCDF
TOTAL
Adipose Tissue8
Concentration
(ppt, lipid)
5.49
10.7
3.82
70.6
12.7
111.4
725.6
1.89
0.32
12.1
5.89
9.24
__
—
21.6
__
1.97
993.2
Fraction of Total
2,3,7,8-substituted
CDD/CDFs
0.0055
0.0107
0.0038
0.0711
0.0128
0.1121
0.7306
0.0019
0.0003
0.0122
0.0059
0.0093
__
—
0.0218
—
0.0020
1.0
Blood"
Concentration
(ppt, lipid)
4.54
13.6
9.93
73.0
13.0
138.1
811.7
2.77
1.20
13.9
12.6
8.22
6.93
3.54
25.2
4.27
5.74
1,148.2
Fraction of Total
2,3,7,8-substituted
CDD/CDFs
0.0040
0.0119
0.0086
0.0636
0.0113
0.1202
0.7069
0.0024
0.0010
0.0121
0.0110
0.0072
0.0062
0.0031
0.0219
0.0037
0.0050
1.0
Human Milk0
Concentration
(ppt, lipid)
3.30
6.70
4.95
30.5
6.20
42.0
233.0
2.85
0.45
7.30
5.55
3.20
1.85
0.25
4.00
0.25
4.10
356.5
Fraction of Total
2,3,7,8-substituted
CDD/CDFs
0.0093
0.0188
0.0139
0.0856
0.0174
0.1178
0.6537
0.0080
0.0013
0.0205
0.0156
0.0090
0.0050
0.0007
0.0112
0.0007
0.0115
1.0
o
o
^1
D
C
O
§
O
GO
O
CD
O
CD
3
CT
CD
—^
ro
o
o
GO
Based on data from Patterson et al. (1994); Schecter (1991); and U.S. EPA (1991 a).
Based on data from Schecter et al. (1 993, 1 994a), and Cole et al. (1 995).
Based on data from Schecter et al. (1992b).
-------
Table 4-24. Estimated Dose Based on Congener-Specific Half-Lives and
Adipose Tissue TEQDF-WH098 Concentrations, and Pharmocokinetic Modeling
2,3,7,8-TCDD
1,2,3,7,8-PECDD
1,2, 3,4,7, 8-HXCDD
1,2,3,6,7,8-HXCDD
1,2,3,7,8,9-HXCDD
1,2,3,4,6,7,8-HPCDD
OCDD
2,3,7,8-TCDF
1,2,3,7,8-PECDF
2,3,4,7,8-PECDF
1,2, 3,4,7, 8-HXCDF
1,2,3,6,7,8-HXCDF
1,2,3,7,8,9-HXCDF
2, 3, 4,6, 7, 8-HXCDF
1,2, 3,4,6,7, 8-HPCF
1,2,3,4,7,8,9-HPCDF
OCDF
TOTAL TEQDF-WH098
1/2 Life d
7.2
15.7
8.4
13.1
4.9
3.7
6.7
7.2
15.7
19.6
6.2
6
6
5.8
3
3.2
6.7
Adipose Tissue Cone.
(ppt TEQDF-WH098)
2.1
5.2
0.62
7.3
0.71
0.79
0.066
0.07
0.04
3.1
0.65
0.53
0.070
0.22
0.13
0.013
0.00021
21.6
Dose e'f
(pg/day)
7.8
8.8
2.0
14.8
3.9
5.7
0.26
0.26
0.07
4.2
2.8
2.4
0.3
1.0
1.2
0.11
0.0008
87
Represents the mean half-life for all 2,3,7,8-substituted congeners in this class.
Half-life for this congener not available; half-life assumed to be the same as for the CDD with the
same chlorination pattern.
No half-life data available for this congener; assumed to be the same as for 1,2,3,6,7,8-HxCDF
Half-life data from Flesch-Janys et al. (1996).
Assumes a body fat volume of 17.5 kg.
Dose = [(ln2/T 0.5 yrs)*17.5 kg*Conc. (pg/g)*(1,000 g/kg)*(1 yr/365 days) / (0.8 absorption).
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December 2003
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Table 4-25. Predicted Average Daily Intake of 2,3,7,8-TCDD by the
General Population of the United States
Media
Inhalation
Water
Soil ingestion
Food
Produce
Milk and dairy products
Beef
Fish
Eggs
TOTAL
Predicted Media
Concentration3
0.02 (pg/m3)
0.003 (pg/L)
0.96 (ng/kg)
0.06 (ng/kg)
0.03 (ng/kg)
0.20 (ng/kg)
0.38 (ng/kg)
0.01 (ng/kg)
Media Intake
(person/day)
20 (m3)
1.33 Lb
20 mg
20 gb
266 gb
90 gb
18gb
25 gb
Daily Intake
of 2,3,7,8-
TCDD
(pg/day)
0.4
0.004
0.02
1.2
8.0
18.0
6.7
0.5
34.8
Percent of
Daily Intake
1.1
0.01
0.05
3.4
23.0
51.7
19.3
1.4
100
a Values predicted by the Fugacity Food Chain model.
b Inferred consumption rate calculated by dividing reported daily intake (column 4) by predicted
concentration (column 2).
Source: Travis and Hattemer-Frey (1991).
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December 2003
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Table 4-26. Predicted Average Daily Intake of 2,3,7,8-TCDD from
Foods by the General Population of the United States
Media
Milk
Cream
Sour cream
Cheese
Ice cream
Butter
Cottage cheese
Meats
Ocean fish
Coffee
Orange juice
TOTAL
2,3,7,8-TCDD
Concentration in
Food
(ng/kg)
0.0018
0.0072
0.010
0.016
0.0055
0.044
0.0021
0.035
0.500
0.0001
0.0002
Food Intake
(g/person/day)
108.9
2.0
0.7
19.4
7.5
2.6
5.5
187
17.2
363.6
33.5
Daily Intake of
2,3,7,8-TCDD
(pg/day)
0.20
0.01
0.01
0.31
0.04
0.11
0.01
6.55
8.6
0.04
0.01
15.9
Percent of Daily
Intake
1.2
<0.1
<0.1
1.9
0.3
0.7
<0.1
41.2
54.1
0.3
<0.1
100
Source: Henry et al. (1992).
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December 2003
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Table 4-27. Daily Exposure to 2,3,7,8-TCDD and I-TEQDF from Air, Soil, Food,
and Nonfood in The Netherlands
Media
Air inhaled
Air ingested
(particulates)
Soil dermal
Soil ingested
Uptake from air and soil
Leafy vegetables
Pork
Beef
Chicken and eggs
Milk
Cheese, butter
Sea fish
Freshwater fish
Fish oil
Vegetable oil
Intake from food
Intake from paper food
packaging
TOTAL INTAKE
Media Intake
(g/person/day)
20am3
b
150 mg
27 g
1 5 g fat
5 g fat
2.5 g fat
8 g fat
12.5 gfat
0.4 g fat
0.4 g fat
5.5 g
40 g
Daily Intake of
2,3,7,8-TCDD
(pg/day)
0.05
0.025
0.004
0.003
0.08
0.2-2
0.45
3
0.6
3.2
5
2
4
1.1
NDA
19.5-21.3
NDA
19.6-21.4
Daily Intake of I-TEQDF
(pg/day)
2
1
0.15
0.10
3.2
1.8-7
4.2
13
4.8
17
26
14
10
7.2
14
112-117
9.1
121-126
a Intake rate could not be determined from Theelen (1991).
b Assumes exposure of 2,000 cm2 of skin to 1 mg of soil/cm2. Soil concentrations assumed to be
7,000 mg l-TEQDF/kg and 175 mg of 2,3,7,8-TCDD/kg. Dermal absorption of 1 percent assumed.
NDA = No data available.
Source: Theelen (1991).
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December 2003
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Table 4-28. Estimated Lifetime Average Daily Exposure
of Canadians to Dioxin I-TEQDF
Media
Food
Air
Soil
Water
Consumer Products
Total Estimated
Lifetime Intake6
Daily Intake of Dioxin3 (I-TEQDF) (pg/day)
Adult Ab
132 - 282
3.5
1.75 - 1.90
<0.7 - 3.5
<0.7
140 - 290
Adult Bc
291 - 441
3.5
1.75 - 1.90
<0.7 - 3.5
<0.7
300 - 450
Adult Cd
132 - 282
12
1.75 - 1.90
<0.7 - 3.5
<0.7
150 - 300
These estimates represent the lifetime average daily intake calculated by dividing the total estimated
intakes for each life stage (i.e., adult, child, infant, neonate) by the 70-year exposure period. The
estimates in this table are based on the upper range of average national values and conservative
assumptions that overestimate rather than underestimate exposures. These estimates are only
approximations and not absolute values.
Adult a is an average 70-kg adult consuming average amounts of air (20 m3/day), water (1
liter/day), and soil (20 mg/day). Food intakes based on Nutrition Canada 1977 survey.
Adult B is similar to Adult a except that consumption of fish contaminated with CDDs and CDFs is
in excess of current Canadian guidelines.
Adult C is similar to Adult a except that he/she lives in close proximity to an incineration/combustion
source.
These estimates have been rounded off because of the uncertainty in the data.
Source: Oilman and Newhook (1 991).
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December 2003
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o
o
^1
D
C
O
§
Table 4-29. Estimated Upper Bound Dietary Intakes of CDD/CDFs by the Average UK Consumer in 1982 and 1992
Food Group
Bread
Other Cereal Products
Carcass Meat
Offals (internal organs)
Meat Products
Poultry
Fish
Oils and Fats
Eggs
Milk
Milk Products
TOTAL
1982
Consumption
(kg/person/day)
Mean
0.125
0.105
0.032
0.002
0.048
0.017
0.016
0.030
0.024
0.303
0.055
--
CDD/CDF Concentration
(ng l-TEQDF/kg fresh
weight)
Mean
0.02
0.13
0.49
1.57
0.32
0.50
0.41
1.26
0.92
0.16
1.20
--
CDD/CDF Intake
(pgi-
TEQDF/person/day)
Mean
3
14
16
3
15
8
7
38
22
48
66
240
1992
Consumption
(kg/person/day)
Mean
0.118
0.098
0.029
0.001
0.046
0.018
0.014
0.031
0.017
0.293
0.056
--
CDD/CDF Concentration
(ng l-TEQDF/kg fresh
weight)
Mean
0.03
0.17
0.13
0.59
0.08
0.13
0.21
0.20
0.17
0.06
0.16
--
CDD/CDF Intake
(pg l-TEQDF/person/day)
Mean
4
17
4
1
3
2
3
6
3
17
9
69
O
CD
O
CD
O
CD
3
CT
CD
—^
ro
o
o
GO
Note: Estimated total dietary intakes were calculated before rounding.
Source: MAFF (1995).
-------
Table 4-30. Estimated CDD/CDF Mean Background Exposures for Adults in the United States
Media
Soil ingestion
Soil dermal contact
Freshwater fish and shellfish ingestion
Marine fish and shellfish ingestion
Inhalation
Water ingestion
Milk ingestion
Dairy ingestion
Eggs ingestion
Beef ingestion
Pork ingestion
Poultry ingestion
Other meat ingestion
Vegetable fat ingestion
Cone. TEQDF-WH09Sa
9.3 ppt6
9.3 ppt
1.0 ppt1
0.26 ppt1
0.12 pg/m3
0.00056 ppq
0.018 ppt
0.12 ppt
0.081 ppt
0.18 ppt
0.28 ppt
0.068 ppt
0.18 ppta
0.056 ppt6
Contact Rateb
50 mg/day
1 2 mg/dayf
5.9 g/day
9.6 g/day
13.3 m3/day
1.4 L/day
1 75 g/day
55 g/day
0.24 g/kg/day
0.71 g/kg/day
0.22 g/kg/day
0.50 g/kg/day
0.35 g/kg/dayh
1 7 g/day
Total
Daily Intake0
(mg/kg-day)
6.6x TO'12
1.6x TO'12
8.4x TO'10
3.6x 10'"
2.3x 10'"
1.1 x TO'14
4.5 x 10'"
9.4x 10'"
1.9 x 10'"
1.3x TO'10
6.2 x 10'"
3.4x 10'"
6.2 x 10'"
1.4x 10'"
6.1 x TO'10
Daily Intake
(pg/kg-day)
6.6 x 1C'3
1.6x 1C'3
8.4 x 1C'2
3.6x 1C'2
2.3x 1C'2
1.1 x 1 O'5
4.5 x 10'2
9.4x 1C'2
1.9x 1C'2
1.3x 10'1
6.2 x 1C'2
3.4x 1C'2
6.2 x 1C'2
1.4x 10'2
6.1 x 10'1 d
% of Total
1.1
0.3
13.9
5.9
3.7
<0.01
7.4
15.5
3.2
21.0
10.1
5.6
10.1
2.2
100.0
Values from Table 3-64.
Values for adult soil ingestion, inhalation, water ingestion, and eggs, beef pork, and poultry ingestion from Exposure Factors Handbook (U.S. EPA, 1 997).
Contact rates for milk, dairy, and vegetable fats are based on data from USDA (1 995). Contact rates for fish from U.S. EPA (2000).
Daily intake (mg/kg-day) = [Contact rate (g/day; m3/day; L/day; mg/day) x Cone. TEQ x Unit Conversion (soil unit conversion = 10"12, all other media
unit conversion = 1 0"9)/Body Weight (kg)] or Contact rate (g/kg-day) x Cone. TEQ x Unit Conversion.
Approximately equivalent to 43 pg/day, assuming an adult body weight of 70 kg.
Calculated by setting nondetects to zero.
Calculated as the surface area of the body that contacts the soil (5,700 cm2/day) x the rate that soil adheres to the skin (0.07 mg/cm2) x the fraction of
CDD/CDFs absorbed through the skin (0.03); exposure factors based on recommendations in U.S. EPA (1 999) for an adult resident, which assumes that
the lower legs, forearms, hands, and head are exposed to the soil.
Estimated as the average of beef, pork, and poultry.
Calculated as the total meat intake rate minus the intake rates for beef, pork, and poultry (U.S. EPA, 1997).
DRAFT-DO NOT QUOTE OR CITE
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December 2003
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This concentration is a species-specific ingestion-weighted average value.
DRAFT-DO NOT QUOTE OR CITE 4-111 December 2003
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Table 4-31. Estimated Dioxin-Like PCB Mean Background Exposures for Adults in the United States
Media
Soil ingestion
Soil dermal contact
Freshwater fish and shellfish ingestion
Marine fish and shellfish ingestion
Inhalation
Water ingestion
Milk ingestion
Dairy ingestion
Eggs ingestion
Beef ingestion
Pork ingestion
Poultry ingestion
Other meat ingestion
Vegetable fat ingestion
Cone. WH098-TEQ3
2.3 ppt6
2.3 ppt
1.2 ppt
0.25 ppt
__
__
0.0088 ppt
0.058 ppt
0.10 ppt
0.084 ppt
0.012 ppt
0.026 ppt
0.0419
0.037 ppt
Contact Rateb
50 mg/day
1 2 mg/dayf
5.9 g/day
9.6 g/day
__
__
1 75 g/day
55 g/day
0.24 g/kg/day
0.71 g/kg/day
0.22 g/kg/day
0.50 g/kg/day
0.35 g/kg/dayh
1 7 g/day
Total
Daily Intake0
(mg/kg-day)
1.6x 10'12
3.9x ID'13
1.0x 10'10
3.4x 10'"
__
__
2.2x 10'"
4.6x 10'"
2.4x 10'"
6. Ox 10'"
2.6x 10'12
1.3x 10'"
1.4x 1Q-"
9. Ox 10'12
3.3x 10'10
Daily Intake
(pg/kg-day)
1.6x 10'3
3.9x 1C'4
1.0x 10'1
3.4x 10'2
__
__
2.2x 10'2
4.6 x 10'2
2.4x 10'2
6.0 x 10'2
2.6x 10'3
1.3x 10'2
1.4x 10'2
9. Ox 10'3
3.3x 10'1 d
% of Total
0.5
0.1
30.9
10.5
__
__
6.7
13.9
7.3
18.2
0.8
4.0
4.3
2.7
100.0
Values from Table 3-64.
Values for adult soil ingestion, eggs, beef pork, and poultry ingestion from Exposure Factors Handbook (U.S. EPA, 1 997). Contact rates for milk, dairy,
and vegetable fats are based on data from USDA (1995). Contact rates for fish from U.S. EPA (2000).
Daily intake (mg/kg-day) = [Contact rate (g/day; m3/day; L/day; mg/day) x Cone. TEQ x Unit Conversion (soil unit conversion = 10"12, all other media
unit conversion = 1 0"9)/Body Weight (kg)] or Contact rate (g/kg-day) x Cone. TEQ x Unit Conversion.
Approximately equivalent to 23 pg/day, assuming an adult body weight of 70 kg.
Calculated by setting nondetects to zero.
Calculated as the surface area of the body that contacts the soil (5,700 cm2/day) x the rate that soil adheres to the skin (0.07 mg/cm2) x the fraction of
CDD/CDFs absorbed through the skin (0.03); exposure factors based on recommendations in U.S. EPA (1 999) for an adult resident, which assumes that
the lower legs, forearms, hands, and head are exposed to the soil.
Estimated as the average of beef, pork, and poultry.
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December 2003
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Calculated as the total meat intake rate minus the intake rates for beef, pork, and poultry (U.S. EPA, 1997).
DRAFT-DO NOT QUOTE OR CITE 4-113 December 2003
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Table 4-32. Comparison of Adult Contact Rates, TEQDF Concentrations, and Background Exposure
Estimates from the 1994 Draft and Current Version of This Document
Media
Soil Ingestion
Soil Dermal Contact
Freshwater Fish and
Shellfish Ingestion
Marine Fish and Shellfish
Ingestion
Inhalation
Water Ingestion
Milk Ingestion
Dairy Ingestion
Eggs Ingestion
Beef Ingestion
Pork Ingestion
Poultry Ingestion
Other Meat Ingestion
Vegetable Ingestion
TOTAL
Previous
I-TEQDF
Concentration
8.0 ppta
--
1.2 ppt
--
0.095 pg/m3
0.0056 ppq
0.07 ppt
0.36 ppt
0.14 ppt
0.48 ppt
0.26 ppt
0.19 ppt
--
--
—
Current TEQDF-
WH098
Concentration
9.3 pptb
9.3 ppt
1 .0 pptc
0.26 pptc
0.12 pg/m3
0.00056 ppq
0.016 ppt
0.12 ppt
0.081 ppt
0.18 ppt
0.28 ppt
0.068 ppt
0.18 ppt
0.056 ppt
—
Previous
Contact Rate
100 mg/day
--
6.5 g/day
--
23 m3/day
1 .4 L/day
251 g/day
67 g/day
29 g/day
77 g/day
47 g/day
68 g/day
--
--
—
Current
Contact Rate
50 mg/day
12 mg/day
5.9 g/day
9.6 g/day
13.3 m3/day
1 .4 L/day
1 75 g/day
55 g/day
0.24 g/kg/day
0.71 g/kg/day
0.22 g/kg/day
0.50 g/kg/day
0.35 g/kg/day
1 7 g/day
—
Previous Daily
Intake Rate
(pg/kg-day)
1.1 x 1 0'2
--
1.1 x 10'1
--
3.1 x 10'2
1.1 x 1 0'4
2.5 x 10'1
3.4x 10'1
5.8x TO'2
5.3 x 10'1
1.7 x 10'1
1.8x 10'1
--
--
1.7 x 10°
(119 pg/day)
Current Daily
Intake Rate
(pg/kg-day)
6.6x 10'3
1.6x ID'3
8.4x 10'2
3.6x TO'2
2.3 x 10'2
1.1 x 10'5
4.5 x 10'2
9.4x TO'2
1.9 x 10'2
1.3x TO'1
6.2 x 10'2
3.4x TO'2
6.2 x 10'2
1.4x 10'2
6.1 x 10'1
(43 pg/day)
a Rural/pristine background sites
b Urban background sites
c This concentration is a species-specific ingestion-weighted average value.
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December 2003
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Table 4-33. Background Exposures via Consumption of German Food
Food
Cow's milk
Cheese
Butter
Beef
Veal
Pork
Chicken
Canned meat
Lard
Salad oil
Margarine
Fish and Fish Products
Freshwater fish
Saltwater fish
Fish oil
Cod liver oil
Total I-TEQDF
l-TEQDFa
concentration (fat
basis)
1.35
0.98
0.66
1.69
3.22
<0.4
1.41
1.29
0.47
<0.4
<0.4
13.25
16.82
2.64
13.31
Intake Rate"
(g fat/day)
6.0
5.2
12
10
0.1
14
1
2
1.5
5
14
1.8
TCDD -
Equivalent3
(pg/day)
8.1
5.1
7.9
16.9
0.3
5.6
1.4
2.6
0.7
1
2.8
27
79.4
a Milk data based on Fiirst et al. (1991); other data based on Fiirst et al. (1990).
b Based on data reported by Fiirst et al. (1990).
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December 2003
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Table 4-34. Comparison of Contact Rates and Background TEQDF-WH098 Exposures for Three Age Groups of Children to Adults
Media
Soil Ingestion
Soil Dermal Contact
Freshwater Fish
and Shellfish
Ingestion
Marine Fish and
Shellfish Ingestion
Inhalation
Water Ingestion
Milk Ingestion
Dairy Ingestion
Eggs Ingestion
Beef Ingestion
Pork Ingestion
Poultry Ingestion
Other Meats
Ingestion
Vegetable Fat
Ingestion
TOTAL
TEQDF-WH098
Concentrations
(whole weight)
9.3 ppt9
9.3 ppt9
1 .0 ppth
0.26 ppth
0.12 pg/m3
0.00056 ppq
0.018 ppt
0.12 ppt
0.081 ppt
0.18 ppt
0.28 ppt
0.068 ppt
0.18 ppt
0.056 ppt9
—
Age 1 -5 Years8
Contact Rate
1 00 mg/day
2.2 mg/daye
1.5 g/dayf
2.5 g/dayf
7.5 m3/day
0.69 L/day
348 g/day
1 03 g/day
0.75 g/kg/day
1 .4 g/kg/day
0.48 g/kg/day
1 .1 g/kg/day
1 .1 g/kg/day
4 g/day
—
Daily Intake
(pq/kq-day)
6.2 x 10'2
1.3 x 10'3
1.0x 10'1
4.3 x 10'2
6. Ox 10'2
2.6x 10'5
4.2 x 10'1
8.2 x 10'1
6.1 x 10'2
2.5 x 10'1
1.3 x 10'1
7.5 x 10'2
1.9 x 10'1
1.5 x 10'2
2.2 x 10°
(34 pq/day)
Age 6-1 1 Years"
Contact Rate
50 mg/day
3.2 mg/daye
1.9g/dayf
3.1 g/dayf
1 2 m3/day
0.79 L/day
357 g/day
88 g/day
0.41 g/kg/day
1 .1 g/kg/day
0.35 g/kg/day
0.87 g/kg/day
0.69 g/kg/day
9 g/day
—
Daily Intake
(pq/kq-day)
1.6x 10'2
9.8 x 10'4
6.3 x 10'2
2.7 x 10'2
4.8 x 10'2
1.5 x 10'5
2.1 x 10'1
3.5 x 10'1
3.3 x 10'2
2. Ox 10'1
9.8 x 10'2
5.9 x 10'2
1.2 x 10'1
1.7 x 10'2
1.3 x 10°
(37 pq/day)
Age 12-19 Years0
Contact Rate
50 mg/day
1 1 mg/daye
2.3 g/dayf
3.7 g/dayf
1 4 m3/day
0.97 L/day
308 g/day
77 g/day
0.24 g/kg/day
0.83 g/kg/day
0.27 g/kg/day
0.56 g/kg/day
0.42 g/kg/day
12 g/day
—
Daily Intake
(pq/kq-day)
8. Ox 10'3
1.8 x 10'3
4. Ox 10'2
1.7 x 10'2
2.9 x 10'2
9.4x 10'6
9.6x 10'2
1.6x 10'1
1.9 x 10'2
1.5 x 10'1
7.6x 10'2
3.8 x 10'2
7.4x 10'2
1.2 x 10'2
7.2 x 10'1
(42 pq/day)
Adult"
Contact Rate
50 mg/day
1 2 mg/daye
5.9 g/day
9.6 g/day
13.3 m3/day
1 .4 L/day
175 g/day
55 g/day
0.24 g/kg/day
0.67 g/kg/day
0.22 g/kg/day
0.49 g/kg/day
0.35 g/kg/day
1 7 g/day
—
Daily Intake
(pq/kq-day)
6.6x 10'3
1.6x 10'3
8.4x 10'2
3.6x 10'2
2.3 x 10'2
1 . 1 x 1 0'5
4.5 x 10'2
9.4x 10'2
1.9 x 10'3
1.3 x 10'1
6.2 x 10'2
3.4x 10'2
6.2 x 10'2
1.4x 10'2
6.1 x 10'1
(43 pq/day)
a 1 5 kg body weight assumed
b 30 kg body weight assumed
c 58 kg body weight assumed
d 70 kg body weight assumed
e Dermal contact rates based on the calculation: skin surface area contacting soil (cm2/day) x soil adherence rate (mg/cm2) x absorption fraction (0.03). Exposure factor values based
on recommended data and procedures in U.S. EPA (1 999) for adult and child residents. For all ages it was assumed that the head, hands, lower legs, and forearms were exposed
to soil. Adherence factors for ages 1 -5 years and 6-1 1 years were calculated using data for children playing in dry soil. For ages 1 2-1 9 years and adults, a gardening scenario was
assumed. Surface areas were assumed to be 2,400, 3,500, 5,300, and 5,700 cm2/day for ages 1-5 years, 6-1 1 years, 12-19 years, and adults, respectively. Adherence factors
for these age groups were estimated to be 0.03, 0.03, 0.07, and 0.07 mg/cm2, respectively.
f Fish intake rates for children based on data in Table 1 0-46 of EPA's Exposure Factors Handbook (U.S. EPA, 1 997). Total fish intake values apportioned among various fish categories
based on the proportions for adults.
g Calculated by setting nondetects to zero.
h This concentration is a species-specific ingestion-weighted average value.
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December 2003
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NOTE: Contact rates derived from U.S. EPA (1 997) except for milk, dairy, and vegetable fats which were derived from USDA (1 995). Dairy intake is assumed to be intake of total milk
and milk products minus fluid milk intake.
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Table 4-35. Comparison of Contact Rates and Background TEQP-WH098 Exposures for Three Age Groups of Children to Adults
Media
Soil Ingestion
Soil Dermal
Freshwater Fish
and Shellfish
Ingestion
Marine Fish and
Shellfish Ingestion
Inhalation
Water Ingestion
Milk Ingestion
Dairy Ingestion
Eggs Ingestion
Beef Ingestion
Pork Ingestion
Poultry Ingestion
Other Meats
Ingestion
Vegetable Fat
Ingestion
TOTAL
TEQP-WHO98
Concentrations
(whole weight)
2.3
2.3
1.2ppt
0.25 ppt
--
--
0.0088 ppt
0.058 ppt
0.10 ppt
0.084 ppt
0.012 ppt
0.026 ppt
0.041 ppt
0.037 ppt
--
Age 1 -5 Years8
Contact Rate
1 00 mg/day
2.2 mg/day
1.5 g/daye
2.5 g/daye
7.5 m3/day
0.7 L/day
348 g/day
1 03 g/day
0.75 g/kg/day
1 .4 g/kg/day
0.48 g/kg/day
1 .1 g/kg/day
1 .1 g/kg/day
4 g/day
--
Daily Intake
(pq/kq-day)
1.5 x 10'2
3.3 x 10'4
1.2 x 10'2
4.2 x 10'2
--
--
2. Ox 10'1
4. Ox 10'1
7.5 x 10'2
1.2 x 10'1
5.8 x 10'3
2.9 x 10'2
4.5 x 10'2
9.9 x 10'3
1.1 x 1 0°
(16 pg/day)
Age 6-1 1 Years"
Contact Rate
50 mg/day
3.2 mg/day
1.9g/daye
3.1 g/daye
1 1 m3/day
0.8 L/day
357 g/day
88 g/day
0.41 g/kg/day
1 .1 g/kg/day
0.35 g/kg/day
0.87 g/kg/day
0.69 g/kg/day
9 g/day
--
Daily Intake
(pq/kq-day)
3.8 x 10'3
2.4x 10'4
7.6x 10'2
2.6x 10'2
-
-
1 . 1 x 1 O'1
1.7 x 10'1
4.1 x 10'2
9.2 x 10'2
4.2 x 10'2
2.3 x 10'2
2.8 x 10'2
1 . 1 x 1 O'2
5.8 x 10'1
(17 pg/day)
Age 12-19 Years0
Contact Rate
50 mg/day
1 1 mg/day
2.3 g/daye
3.7 g/daye
1 4 m3/day
1 .0 L/day
308 g/day
77 g/day
0.24 g/kg/day
0.83 g/kg/day
0.27 g/kg/day
0.56 g/kg/day
0.42 g/kg/day
1 2 g/day
-
Daily Intake
(pq/kq-day)
2. Ox 10'3
4.4x 10'4
4.8 x 10'2
1.6x 10'2
-
-
4.7 x 10'2
7.7 x 10'2
2.4x 10'2
7. Ox 10'2
3.2 x 10'3
1.5 x 10'2
1.7 x 10'2
7.7 x 10'3
3.3 x 10'1
(19 pg/day)
Adult"
Contact Rate
50 mg/day
1 2 mg/day
5.9 g/day
9.6 g/day
13.3 m3/day
1 .4 L/day
1 75 g/day
55 g/day
0.24 g/kg/day
0.71 g/kg/day
0.22 g/kg/day
0.50 g/kg/day
0.35 g/kg/day
17 g/day
-
Daily Intake
(pq/kq-day)
1.6x 10'3
3.9 x 10'4
1.0x 10'1
3.4x 10'2
-
-
2.2 x 10'2
4.6x 10'2
2.4x 10'2
6. Ox 10'2
2.6x 10'3
1.3 x 10'2
1.4x 10'2
9. Ox 10'3
3.3 x 10'1
(23 pg/day)
a 1 5 kg body weight assumed
b 30 kg body weight assumed
c 58 kg body weight assumed
d 70 kg body weight assumed
e Fish intake rates for children based on data in Table 1 0-46 of EPA's Exposure Factors Handbook (U.S. EPA, 1 997). Total fish intake values apportioned among various fish categories
based on the proportions for adults.
NOTE: Contact rates derived from U.S. EPA (1 997) except for milk, dairy, and vegetable fats which were derived from USDA (1 995). Dairy intake is assumed to be intake of total milk
and milk products minus fluid milk intake.
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Table 4-36. Percentage TEQDFP-WH098 Contribution of Each Media to Total Dose by Age Group
o
o
^1
D
C
O
§
Media
Soil Ingestion
Soil Dermal Contact
Freshwater Fish
and Shellfish
Marine Fish and
Shellfish
Inhalation
Water
Milk
Dairy
Eggs
Beef
Pork
Poultry
Other Meat
Vegetable Fat
CDD/CDFs
1-5 Years
2.8
0.06
4.5
1.9
2.7
0.001
18.7
36.8
2.7
11.3
6.0
3.3
8.6
0.7
6-1 1 Years
1.2
0.08
5.1
2.2
3.8
0.001
17.2
28.2
2.7
15.9
7.9
4.7
9.7
1.3
12-19 Years
1.1
0.2
5.5
2.3
4.0
0.001
13.3
22.2
2.7
20.8
10.5
5.3
10.3
1.6
Adult
1.1
0.3
13.9
5.9
3.7
0.002
7.4
15.5
3.2
21.0
10.1
5.6
10.1
2.2
PCBs
1-5 Years
1.4
0.03
11.3
3.9
-
-
19.2
37.5
7.1
11.1
0.5
2.7
4.2
0.9
6-1 1 Years
0.7
0.04
13.1
4.5
-
-
18.1
29.3
7.1
15.9
0.7
3.9
4.8
1.9
12-19 Years
0.6
0.1
14.6
4.9
-
-
14.3
23.6
7.4
21.4
1.0
4.5
5.2
2.3
Adult
0.5
0.1
30.9
10.5
-
-
6.7
13.9
7.3
18.2
0.8
4.0
4.3
2.7
O
CD
O
CD
3
CT
CD
—^
ro
o
o
GO
-------
o
o
^1
D
C
O
§
Table 4-37. Variability in Fat Intake from the Bogalusa Heart Study
Age
(Years)
0.5
1
2
3
4
10
13
15
17
19-28
Total Fat Intake (g)
1973-1982 Data3
Mean
37.1
59.1
86.7
91.6
98.6
93.2
107.0
97.7
107.8
-
Maximum
107.6
152.7
236.4
232.5
584.6
529.5
282.2
251.3
327.4
-
Max/Mean
2.9
2.6
2.7
2.5
5.9
5.7
2.6
2.6
3.0
-
Animal Fat Intake (g)
1973-1982 Data3
Mean
18.4
36.5
49.5
50.1
50.8
54.1
56.2
53.8
64.4
-
Maximum
61.1
127.1
153.1
182.6
242.2
412.3
209.6
182.1
230.0
-
Max/Mean
3.3
3.5
3.1
3.6
4.8
7.6
3.7
3.4
3.6
-
Total Fat Intake (g)
1992-1994 Datab
Mean
-
-
-
-
-
84.6
-
-
-
-
Mean +
3SD
-
-
-
-
-
205.8
-
-
-
-
Mean +
3SD/Mean
-
-
-
-
-
2.4
-
-
-
-
Total Fat Intake (g)
1988-1991 Data0
Mean
-
-
-
-
-
-
-
-
-
98.5
Mean +
3SD
-
-
-
-
-
-
-
-
-
290.2
Mean +
3SD/Mean
-
-
-
-
-
-
-
-
-
2.9
ro
o
a Frank et al. (1986)
b Nicklas et al. (1993)
O c Nicklas et al. (1995)
o
to
O
o
GO
-------
o
o
^1
D
C
O
§
Table 4-38. Fat Intake (g/day) Among the Adult U.S. Population, Based on Data from the 1987 NHIS
Age (yrs)
18-34
35-49
50-64
65-79
80 +
Men
N
3,166
2,346
1,512
1,148
213
Mean
(g/day)
116.5
103.6
90.2
76.0
73.8
SD a
(g/day)
69.5
48.4
46.7
40.7
39.4
Mean +35D /
Mean
2.9
2.4
2.6
2.6
2.6
Women
N
4,296
2,923
2,092
1,926
521
Mean
(g/day)
67.6
65.4
57.8
50.7
50.5
SD a
(g/day)
32.8
43.3
27.4
26.3
18.3
Mean +35D /
Mean
2.5
3.0
2.4
2.6
2.1
ro
a Standard deviation calculated from standard error (SE) as follows: SD = SE x v n.
o
a>
3
cr
CD
—^
ro
o
o
GO
-------
Table 4-39. Estimated CDD/CDF Upper Percentile Background Exposures for Adults in the United States
Media
Soil ingestion
Soil dermal contact
Freshwater fish and shellfish
ingestion
Marine fish and shellfish ingestion
Inhalation
Water ingestion
Milk ingestion
Dairy ingestion
Eggs ingestion
Beef ingestion
Pork ingestion
Poultry ingestion
Other meats ingestion
Vegetable fat ingestion
Cone. TEQDF-WH09Sa
9.3 ppt6
9.3 ppt
1 .0 ppt9
0.26 ppt
0.12 pg/m3
0.00056 ppq9
0.018 ppt
0.12 ppt
0.081 ppt
0.18 ppt
0.28 ppt
0.068 ppt
0.18 ppt
0.056 ppt6
Contact Rateb
1 00 mg/day
51.3 mg/dayf
10.3 g/day
16.7 g/day
15.2 m3/day
2.0 L/day
421 g/day
1 32 g/day
0.39 g/kg/day
0.93 g/kg/day
0.30 g/kg/day
0.68 g/kg/day
0.48 mg/kg/day
28.8 g/day
Total
Daily Intake0
(mg/kg-day)
1.3x 10'"
6.8 x ID'12
1.5 x 10'10
6.2 x 10'"
2.6x 10'"
1.6x 10'14
1.9x 10'10
2.3x TO'10
3.2x 10'"
1.7x 10'10
8.4 x 10'"
4.6x 10'"
8.6 x 10'"
2.3 x 10'"
1.1 x 1 0'9
Daily Intake
(pg/kg-day)
1.3x 10'2
6.8 x 1C'3
1.5x 10'1
6.2 x 10'2
2.6x 10'2
1.6x 10'5
1.9x 10'1
2.3x 10'1
3.2x 10'2
1.7x 10'1
8.4 x 10'2
4.6 x 10'2
8.6 x 1C'2
2.3x 10'2
1.1 x 10+od
% of Total
1.2
0.6
13.3
5.6
2.4
<0.01
16.8
20.4
2.9
15.1
7.6
4.2
7.8
2.1
100.0
Values from Table 3-64.
Values for adult soil ingestion based on data in U.S. EPA (1 991 b). Inhalation rate based on data for males in Exposure Factors Handbook (U.S. EPA, 1 997).
Water ingestion rate based on high-end value in U.S. EPA (1997). Contact rates for fish, milk, dairy, eggs, meats, and vegetable fats are based on data
from an unpublished analysis of USDA's 1 994-1 996 CSFII data conducted by EPA.
Daily intake (mg/kg-day) = [Contact rate (g/day; m3/day; L/day; mg/day) x Cone. TEQ x Unit Conversion (soil unit conversion = 10~12, all other media
unit conversion = 1 0"9)/Body Weight (kg)] or Contact rate (g/kg-day) x Cone. TEQ x Unit Conversion.
Approximately equivalent to 77 pg/day, assuming an adult body weight of 70 kg.
Calculated by setting nondetects to zero.
Calculated as the surface area of the body that contacts the soil (5,700 cm2/day) x the rate that soil adheres to the skin (0.30 mg/cm2) x the fraction of
CDD/CDFs absorbed through the skin (0.03); exposure factors based on recommendations in U.S. EPA (1 999) for an adult resident, which assumes that
the lower legs, forearms, hands, and head are exposed to the soil.
DRAFT-DO NOT QUOTE OR CITE
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December 2003
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This concentration is a species-specific ingestion-weighted average value.
DRAFT-DO NOT QUOTE OR CITE 4-123 December 2003
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Table 4-40. Estimated Dioxin-Like PCB Upper Percentile Background Exposures for Adults in the United States
Media
Soil ingestion
Soil dermal contact
Freshwater fish and shellfish ingestion
Marine fish and shellfish ingestion
Inhalation
Water ingestion
Milk ingestion
Dairy ingestion
Eggs ingestion
Beef ingestion
Pork ingestion
Poultry ingestion
Other meats ingestion
Vegetable fat ingestion
Cone. WH098-TEQ3
2.3 ppte
2.3 ppt
1.2 ppt
0.25 ppt
--
--
0.0088 ppt
0.058 ppt
0.10 ppt
0.084 ppt
0.012 ppt
0.026 ppt
0.041 ppt
0.037 ppt
Contact Rate"
100 mg/day
51 .3 mg/day
10.3 g/day
16.7 g/day
--
--
421 g/day
1 32 g/day
0.39 g/kg/day
0.93 g/kg/day
0.30 g/kg/day
0.68 g/kg/day
0.48 mg/kg/day
28.8 g/day
Total
Daily Intake0
(mg/kg-day)
3.3x TO'12
1.7x TO'12
1.8x TO'10
6. Ox TO'11
--
--
9.6x TO'"
1.1 x TO'10
3.9 x TO'"
7.8x 10-"
3.6x TO'12
1.8 x 10'"
2. Ox TO'"
1.5 x TO'"
6.2 x ID'10
Daily Intake
(pg/kg-day)
3.3x ID'3
1.7x ID'3
1.8x 10'1
6. Ox ID'2
--
--
9.6x 10'2
1.1 x 10'1
3.9x 1C'2
7.8x ID'2
3.6x ID'3
1.8x ID'2
2. Ox 10'2
1.5x ID'2
6.2 x 10'1 d
% of Total
0.5
11.1
28.5
9.6
--
--
15.5
17.6
6.3
12.6
0.6
2.9
3.2
2.5
100.0
Values from Table 3-64.
Contact rates for fish, milk, dairy, eggs, meats, and vegetable fats are based on data from an unpublished analysis of USDA's 1994-1996
CSFII data conducted by EPA.
Daily intake (mg/kg-day) = [Contact rate (g/day; m3/day; L/day; mg/day) x Cone. TEQ x Unit Conversion (unit conversion = 10"9)/Body
Weight (kg)] or Contact rate (g/kg-day) x Cone. TEQ x Unit Conversion.
Approximately equivalent to 43 pg/day, assuming an adult body weight of 70 kg.
Calculated by setting nondetects to zero.
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December 2003
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Calculated as the surface area of the body that contacts the soil (5,700 cm2/day) x the rate that soil adheres to the skin (0.07 mg/cm2) x the fraction of
CDD/CDFs absorbed through the skin (0.03); exposure factors based on recommendations in U.S. EPA (1 999) for an adult resident, which assumes that
the lower legs, forearms, hands, and head are exposed to the soil.
DRAFT-DO NOT QUOTE OR CITE 4-125 December 2003
-------
Table 4-41. Comparisons of Predicted Average Daily Intake of
2,3,7,8-TCDD and Total TEQDFs
Location
United States3
United States'1
North America0
Canada01
Germany6
Germany'
Netherlands9
United Kingdom1"
United Kingdom'
Spain'
Daily Intake of
2,3,7,8-TCDD
(pg/day)
34.8
15.9
5.5
25.0
20.0
-
-
-
Daily Total TEQDF
intake
(pg/day)
-
—
43
140-290
85 (79)
158
121-126
69
175.5
210
Media
beef, milk, produce, fish,
eggs, water, inhalation
dairy, meat, fish
dairy, eggs, meat, poultry,
fish, inhalation, soil
ingestion, soil dermal contact
air, water, soil, food
dairy, meats, fish
dairy, meat, fish
dairy, meat, poultry, fish
meat, fish, dairy, poultry,
eggs, milk products, breads,
and cereals
meat, fish, dairy, poultry,
eggs, milk products, breads,
and cereals
vegetables, lentils and beans,
cereals, fruit, fish, meat,
eggs, dairy, milk, and oil
Travis and Hattemer-Frey (1991)
Henry et al. (1992)
Current Assessment; TEFDF-WH098s used
Oilman and Newhook (1991); l-TEFDFs used
Fiirst et al. (1990); value in parentheses is the corrected I-TEQDF value based on the milk data from
Fiirst et al. (1991); l-TEFDFs used
Fiirst et al. (1991); l-TEFDFs used
Theelen (1991); l-TEFDFs used
MAFF (1995); data from 1992; l-TEFDFs used
Jacobs and Mobbs (1997) l-TEFDFs used
Schuhmacher et al. (1997) and Domingo et al. (1999); l-TEFDFs used
DRAFT-DO NOT QUOTE OR CITE
4-126
December 2003
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Table 4-42. Example of the Calculation of the Picograms of TEQDF-WH098 Contributed by
Individual CDD/CDF Congeners for the Beef Consumption Pathway
Congener
2378-TCDD
12378-PCDD
123478-HxCDD
123678-HxCDD
123789-HxCDD
1234678-HpCDD
OCDD
2378-TCDF
12378-PCDF
23478-PCDF
123478-HxCDF
123678-HxCDF
234678-HxCDF
123789-HxCDF
1234678-HpCDF
1234789-HpCDF
OCDF
TOTAL
Average congener
concentration, pg/g
lipid
0.052
0.35
0.46
1.4
0.53
4.5
4.8
0.030
0.31
0.36
0.55
0.40
0.31
0.39
1.0
0.31
1.9
Average TEQDF-
WH098
concentration, pg/g
lipid
0.052
0.35
0.064
0.14
0.053
0.045
0.00050
0.0030
0.016
0.18
0.055
0.040
0.031
0.039
0.01
0.0031
0.00019
1.06
Fraction of TEQDF-
WH098 contributed
by each congener1
0.049
0.33
0.044
0.13
0.050
0.042
0.00045
0.0030
0.015
0.17
0.051
0.038
0.030
0.036
0.0093
0.0030
0.00018
1.00
TEQDF-WH098
contributions to the
diet by each
congener (pg/day)2
0.43
2.9
0.39
1.2
0.45
0.38
0.0040
0.026
0.13
1.5
0.46
0.33
0.26
0.33
0.084
0.026
0.0016
8.9
1 This is calculated as the picograms TEQDF-WH098 contributed by each congener divided by the total TEQDF-
WH098 concentration. For example, the 0.049 for 2,3,7,8-TCDD is calculated as 0.052/1.06.
2 Picograms contributed by each congener = (0.1 8 pg/g) (0.71 g/kg/day) (70kg) (TEQDF-WH098 fraction), where
0.1 8 pg/g is whole weight beef concentration as derived in Section 4.4.2, 0.71 g/kg/day is the consumption
rate, 70 kg is the average adult body weight, and the TEQDF-WH098 fraction is shown in the fourth column
above, just preceding this final column of results.
DRAFT-DO NOT QUOTE OR CITE
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December 2003
-------
Table 4-43. Average Concentrations (not on a TEQDF-WH098 basis) and the Fraction of TEQDF-WH098
Contributed by Each CDD/CDF Congener for the Various Food Groups
Congener
2378-TCDD
12378-PCDD
123478-HxCDD
123678-HxCDD
123789-HxCDD
1234678-HpCDD
OCDD
2378-TCDF
12378-PCDF
23478-PCDF
123478-HxCDF
123678-HxCDF
234678-HxCDF
123789-HxCDF
1234678-HpCDF
1234789-HpCDF
OCDF
TEQDF-WH098, pg/g
Beef
Cone
0.052
0.35
0.46
1.4
0.53
4.5
4.8
0.030
0.31
0.36
0.55
0.40
0.31
0.39
1.0
0.31
1.9
1.1
Frac
0.049
0.33
0.044
0.13
0.050
0.042
0.00045
0.0030
0.015
0.17
0.051
0.038
0.030
0.036
0.0094
0.0030
0.00018
Pork
Cone
0.10
0.45
0.52
1.1
0.47
10
53
0.090
0.45
0.56
0.98
0.58
0.57
0.45
3.6
0.57
2.3
1.5
Frac
0.068
0.31
0.035
0.075
0.032
0.069
0.0036
0.0061
0.015
0.19
0.066
0.039
0.039
0.031
0.024
0.0039
0.0016
Chicken
Cone
0.16
0.24
0.18
0.40
0.37
1.5
5.0
0.29
0.21
0.26
0.22
0.20
0.20
0.15
0.26
0.17
0.33
0.76
Frac
0.21
0.32
0.024
0.053
0.049
0.020
0.00066
0.038
0.014
0.17
0.029
0.026
0.026
0.020
0.0034
0.0022
0.000043
Other Meat
Cone
0.10
0.35
0.39
0.98
0.46
5.4
21
0.14
0.33
0.39
0.58
0.39
0.36
0.33
1.6
0.35
1.5
1.1
Frac
0.094
0.31
0.035
0.089
0.042
0.049
0.0019
0.012
0.015
0.18
0.053
0.036
0.033
0.030
0.015
0.0032
0.00014
Dairy
Cone
0.070
0.32
0.39
1.9
0.55
5.0
4.9
0.080
0.050
0.28
0.39
0.25
0.28
0.050
0.83
0.050
0.050
0.98
Frac
0.071
0.33
0.040
0.19
0.056
0.051
0.00050
0.0082
0.0025
0.14
0.040
0.025
0.029
0.0051
0.0085
0.00051
5.1E-6
Milk
Cone
0.070
0.32
0.39
1.9
0.55
5.0
4.9
0.080
0.050
0.28
0.39
0.25
0.28
0.050
0.83
0.050
0.050
0.98
Frac
0.071
0.33
0.040
0.19
0.056
0.051
0.00050
0.0082
0.0025
0.14
0.040
0.025
0.029
0.0051
0.0085
0.00051
5.1E-6
Fresh Fish
Cone
3.1
5.2
3.0
5.3
4.1
24
120
14
3.8
7.6
1.7
10
1.3
1.3
16
1.4
2.6
17
Frac
0.18
0.31
0.018
0.032
0.024
0.014
0.00071
0.083
0.011
0.23
0.010
0.060
0.0077
0.0077
0.0095
0.00083
0.000016
Marine Fish
Cone
7.1
17
9.0
47
13
52
76
11
3.5
5.1
2.5
2.1
1.2
0.21
2.1
0.22
1.8
36
Frac
0.20
0.47
0.025
0.13
0.036
0.014
0.00021
0.031
0.0049
0.071
0.0069
0.0058
0.0033
0.00058
0.00058
0.000061
0.0000050
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oo
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CD
O
CD
3
cr
CD
—^
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Note:
Cone = average concentration, pg/g lipid for all foods;
Frac = fractional contribution of each congener to TEQ concentration
-------
o
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^1
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§
Table 4-44. The Average Concentrations (not on a TEQP-WH098 basis) and the Fraction of TEQP-WH098 Contributed
by Each Dioxin-Like PCB Congener for the Various Food Groups
Congener
PCB 77
PCB 105
PCB 114
PCB 118
PCB 123
PCB 126
PCB 156
PCB 157
PCB 167
PCB 169
PCB 189
TEQDF-WH098, pg/g
Beef
Cone
0.17
16
--
76
--
0.69
10
2.3
--
0.12
--
0.084
Frac
0.00020
0.019
--
0.089
--
0.81
0.058
0.013
--
0.014
--
--
Pork
Cone
0.30
6.4
--
18
--
0.063
4.1
0.97
--
0.049
--
0.012
Frac
0.0025
0.054
--
0.15
--
0.53
0.17
0.041
--
0.042
--
--
Chicken
Cone
0.81
12
--
51
--
0.17
3.9
0.98
--
0.019
--
0.026
Frac
0.0031
0.046
--
0.20
--
0.65
0.075
0.019
--
0.0073
--
--
Other Meat
Cone
0.43
11
--
48
--
0.31
6.0
1.4
--
0.063
--
0.041
Frac
0.0010
0.027
--
0.12
--
0.75
0.073
0.017
--
0.015
--
--
Dairy
Cone
1.3
20
--
82
--
0.43
7.2
1.7
--
0.060
--
0.058
Frac
0.0022
0.035
--
0.14
--
0.74
0.061
0.014
--
0.010
--
--
Milk
Cone
0.19
3.1
--
12
--
0.065
1.1
0.25
--
0.0090
--
0.088
Frac
0.0022
0.035
--
0.14
--
0.74
0.061
0.14
--
0.010
--
--
Fresh Fish
Cone
25
350
170
1900
--
5.5
390
210
--
0.7
33
1.2
Frac
0.021
0.029
0.073
0.16
--
0.47
0.17
0.089
--
0.0060
0.0028
--
Marine Fish
Cone
6.2
160
74
330
--
0.83
83
68
--
0.2
8.0
0.25
Frac
0.0025
0.064
0.15
0.13
--
0.34
0.17
0.14
--
0.0081
0.0032
--
ro
CD
O
CD
O
CD
3
cr
CD
—^
ro
o
o
GO
Note:
Cone = average concentration, pg/g whole for all foods;
Frac = fraction contribution of each congener to TEQP-WH098 concentration;
blank spaces indicate that no information was available on the concentration
-------
Table 4-45. TEQDF-WH098 Contribution of Each CDD/F Congener to the Daily Dose for Each Group and Overall (pg/day)
o
o
^1
D
C
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§
GO
O
O
CD
O
CD
3
CT
CD
—^
ro
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GO
Congener
2378-TCDD
12378-PCDD
123478-HxCDD
123678-HxCDD
123789-HxCDD
1234678-HpCDD
OCDD
2378-TCDF
12378-PCDF
23478-PCDF
123478-HxCDF
123678-HxCDF
234678-HxCDF
123789-HxCDF
1234678-HpCDF
1234789-HpCDF
OCDF
TOTAL
Fraction
Beef
0.43
2.9
0.39
1.2
0.45
0.38
0.0040
0.026
0.13
1.5
0.46
0.34
0.26
0.33
0.084
0.025
0.0016
8.9
0.23
Pork
0.29
1.3
0.15
0.32
0.14
0.30
0.015
0.026
0.066
0.82
0.29
0.17
0.17
0.13
0.10
0.017
0.00067
4.3
0.11
Chicken
0.50
0.75
0.056
0.13
0.12
0.047
0.0016
0.091
0.033
0.41
0.069
0.063
0.063
0.047
0.0081
0.0053
0.00010
2.4
0.062
Other Meat
0.42
1.4
0.16
0.39
0.18
0.22
0.0084
0.055
0.065
0.79
0.23
0.16
0.14
0.13
0.064
0.014
0.00060
4.4
0.12
Dairy
0.47
2.2
0.26
1.3
0.37
0.34
0.0033
0.054
0.017
0.95
0.26
0.17
0.19
0.034
0.056
0.0034
0.000034
6.6
0.17
Milk
0.22
1.0
0.13
0.61
0.18
0.16
0.0016
0.026
0.0080
0.45
0.13
0.080
0.090
0.016
0.027
0.0016
0.000016
3.2
0.082
Fresh fish
1.1
1.8
0.11
0.19
0.14
0.084
0.0042
0.49
0.067
1.3
0.060
0.35
0.046
0.046
0.056
0.0049
0.000091
5.9
0.15
Ocean fish
0.49
1.2
0.062
0.33
0.090
0.036
0.00053
0.076
0.012
0.18
0.017
0.015
0.0083
0.015
0.015
0.00015
0.000012
2.5
0.065
TOTAL
4.0
12.6
1.3
4.4
1.7
1.6
0.039
0.85
0.40
6.4
1.5
1.3
0.97
0.73
0.40
0.073
0.0031
38
Fraction
0.10
0.33
0.034
0.12
0.044
0.041
0.0010
0.022
0.010
0.17
0.040
0.035
0.025
0.019
0.010
0.0019
0.000082
Note: The total background dose is estimated to be 43 pg/day. The pathways above add to 38 pg/day, or about 90 percent of total. All numbers above were rounded
and may not add up perfectly.
-------
o
o
^1
D
C
O
§
Table 4-4Q. TEQP-WH098 Contribution of Each Coplanar PCB Congener to the Daily Dose for Each Group and Overall
(pg/day)
Congener
PCB 77
PCB 105
PCB 114
PCB 118
PCB 123
PCB 126
PCB 156
PCB 157
PCB 167
PCB 169
PCB 189
TOTAL
Fraction
Beef
0.00083
0.078
--
0.37
--
3.4
0.24
0.056
--
0.059
--
4.2
0.20
Pork
0.00047
0.010
--
0.028
--
0.099
0.032
0.0076
--
0.0077
--
0.18
0.0090
Chicken
0.0028
0.042
--
0.18
--
0.59
0.068
0.017
--
0.0066
--
0.91
0.044
Other Meat
0.0010
0.028
--
0.12
--
0.75
0.073
0.017
--
0.015
--
1.0
0.049
Dairy
0.0070
0.11
--
0.45
--
2.4
0.20
0.046
--
0.033
--
3.2
0.16
Milk
0.0033
0.053
--
0.22
--
1.1
0.094
0.022
--
0.016
--
1.5
0.075
Fresh fish
0.015
0.21
0.51
1.1
--
3.3
1.2
0.63
--
0.042
0.020
7.1
0.35
Ocean fish
0.0060
0.15
0.36
0.32
--
0.80
0.40
0.33
--
0.019
0.0077
2.4
0.12
TOTAL
0.037
0.69
0.87
2.8
--
12
2.3
1.1
--
0.20
0.030
20
--
Fraction
0.0012
0.033
0.042
0.14
--
0.61
0.11
0.055
--
0.0097
0.0013
--
--
GO
O
CD
O
CD
3
CT
CD
—^
ro
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GO
Note: All numbers above were rounded and may not add up perfectly.
-------
Table 4-47. Average CDD/CDF Concentrations in Human Tissue and Fractional
Contribution of CDD/CDF Congeners to Total TEQDF-WH098
Tissue, Based on CDC Blood Data
Congener
2378-TCDD
12378-PCDD
123478-HxCDD
123678-HxCDD
123789-HxCDD
1234678-HpCDD
OCDD
2378-TCDF
12378-PCDF
23478-PCDF
123478-HxCDF
123678-HxCDF
234678-HxCDF
123789-HxCDF
1234678-HpCDF
1234789-HpCDF
OCDF
TEQDF-WH098
Average
cone
2.1
5.2
6.2
73
7.1
79
664
0.7
0.8
6.2
6.5
5.3
0.7
2.2
13.2
1.3
2.1
21.6
frac
0.097
0.24
0.029
0.34
0.034
0.037
0.0031
0.0033
0.0019
0.14
0.030
0.025
0.010
0.0032
0.0061
0.00060
9.7E-6
Note:
cone = Actual, not TEQDF-WH098, lipid-based concentration profile in pg/g.
frac = Fractional contribution to TEQDF-WH098 of each congener.
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December 2003
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Table 4-48. Average Coplanar PCB Concentrations in Human Tissue and Percentage
Contribution of CDD/F Congeners to Total TEQP-WH098
Tissue, Based on CDC Blood Data
Congener
PCB 77
PCB 81
PCB 105
PCB 114
PCB 118
PCB 123
PCB 126
PCB 156
PCB 157
PCB 167
PCB 169
PCB 189
TEQp- WH098
Average
cone
31
3.2
--
--
--
--
18
--
--
--
19
--
2.0
frac
0.0016
0.00016
--
--
--
--
0.90
--
--
--
0.095
--
--
Note:
cone = Lipid-based concentration profile in pg/g; and
frac = fractional contribution to TEQP-WH098 of each congener.
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December 2003
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.:••*•*.*• *
10
30
50
Age
70
90
Figure 4-1. TEQ (I-TEQ for CDD/CDF + WH094 for a Subset of Four Dioxin-Like PCBs)
Lipid Concentrations for a Comparison Population and the
Population of Mossville, Louisiana, as a Function of Age
Source: ATSDR, 1999b.
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December 2003
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CDD/CDF Body Burden Profile - Adipose Tissue |
2,3,7,8-TCDD
1 ,2,3,7,8-PECDD
1 ,2,3,4,7,8-HxCDD
1 ,2,3,6,7,8-HxCDD
1,2,3,7,8,9-HxCDD
1 ,2,3,4,6,7,8-HPCDD
OCDD
2,3,7,8-TCDF
1 ,2,3,7,8-PECDF
2,3,4,7,8-PECDF
1,2,3,4,7,8-HxCDF
1,2,3,6,7,8-HxCDF
2,3,4,6,7,8-HxCDF
1,2,3,7,8,9-HxCDF
1 ,2,3,4,6,7,8-HPCDF
1 ,2,3,4,7,8,9-HPCDF
OCDF
CDD/CDF Body Burden Profile - Blood I
CDD/CDF Body Burden Profile - Human Milk
2,3,7,8-TCDD
Figure 4-2. CDD/CDF Profiles for Adipose Tissue, Blood and Human Milk
Based on Literature Studies from the 1980s to the Early 1990s
DRAFT-DO NOT QUOTE OR CITE
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December 2003
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CDD/CDF Body Burden Profile - Blood
2,3,7,8-TCDD
1,2,3,7,8-PeCDD
1,2,3,4,7,8-HxCDD
1,2,3,6,7,8-HxCDD
1,2,3,7,8,9-HxCDD
1,2,3,4,6,7,8-HpCDD
OCDD
2,3,7,8-TCDF
1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
1,2,3,4,7,8-HxCDF
1,2,3,6,7,8-HxCDF
1,2,3,7,8,9-HxCDF
2,3,4,6,7,8-HxCDF
1,2,3,4,6,7,8-HpCDF
1,2,3,4,7,8,9-HpCDF
OCDF
Ratio
0.2
0.4
0.6
0.8
Figure 4-3. Congener Profile for the CDC Blood Data Set (1995-1997)
DRAFT-DO NOT QUOTE OR CITE
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Water Ingestion
Soil Dermal Contact
Soil Ingestion
Vegetable Fat Ingestion
Egg Ingestion
Inhalation
Chicken Ingestion
Milk Ingestion
Pork Ingestion
Other Meat
Dairy Ingestion
Fish and Shellfish Ingestion
Beef Ingestion
0.034
| 0.062
I 0.062
North American Daily CDD/CDF TEQ Intake (pg/kg-day)
Total Dose = 0.61 pg/kg-day (approx. 43 pg/day)
Figure 4-4. Background TEQDF-WH098 Exposure for North America, by Pathway
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Milk/Dairy
55%
Meat/Fish/
Eggs
38%
Age 1-5 years
2.2 pg/kg/day (34 pg/day)
Other
Milk/Dairy
45%
Meat/Fish/Eggs
48%
Age 6-11 years
1.3 pg/kg/day (37 pg/day)
Other
Milk/Dairy
23%
Meat/Fish/Eggs
70%
Adult
0.61 pg/kg/day (43 pg/day)
Other
Milk/Dairy
36%
iMeat/Fish/Eg
gs
57%
Age 12 - 19 years
0.72 pg/kg/day (42 pg/day)
Note: See text for a discussion of the media concentrations and contact rates used to assess dose among these populations.
Figure 4-5. Percent Contribution of Various Media to TEQDF-WH098 Dose, By Age Group
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Milk/Dairy
23%
34.8 pg/day
Source: Travis and Hattermer-Frey, 1991
Milk/Dairy
12%
5.5 pg/day
Source: This Assessment
Other
Milk/Dairy 1%
4%
Meats
41%
15.9 pg/day
Source: Henry et al., 1992
Note: Background exposures are the product of media-specific contact rates and residue concentrations. Reduction in the intake
of one food type may not result in CDD/CDF exposure if dietary intake of that food type is replaced by other high CDD/CDF
content foods.
Figure 4-6. Contribution of Various Media to 2,3,7,8-TCDD Exposure in North America
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Milk/Dairy
23%
Meat/Fish/Eggs
70%
North America (a)
43 pg TEQDF-WHOss/day
Other
18%
Milk/Dai
37%
Meat/Fish/Eggs
45%
The Netherlands (b)
117pgl-TEQDF/day
Other
39%
Meat/Fish/Eggs
23%
ilk/Dairy
38%
United Kingdom (d)
69 pg l-TEQDF/day
Other
Milk/Dairy
26%
Meat/Fish
69%
Germany (c)
79 pg l-TEQDF/day
Note:
(a) Current assessment. See Table 4-30. Other category includes inhalation (3.7%), soil ingestion (1.1%), soil dermal contact (0.3%),
vegetables oils (2.2%), and water (0.002%).
(b) Based on Theelen (1991). See Table 4-27. Other refers to inhalation (2.5%), soil ingestion (0.2%), leafy vegetables (3.4%), and vegetable
oil (11.9%).
(c) Based on Furst et al. (1990, 1991). See Table 4-33. Other category includes salad oil (1.3%), and margarine (3.5%).
(d) Based on MAFF (1995). See Table 4-29. Other refers to breads and cereals (30%), and oils and fats (9%).
Percentages rounded to nearest whole number.
Reduction in the intake of one food type may not result in a reduction in CDD/CDF exposure if dietary intake of that food type is replaced by
other high CDD/CDF content foods.
Figure 4-7. Comparison of North American and European Background CDD/CDF TEQ Exposures
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*8 *& W*'/
//, s; "A.A '/,
0-100
100-1000
1000-10000
10000-20000
20000-30000
30000-40000
40000-60000
60000-100000
^/X No Data
Figure 4-8. TEQDF-WH098 Derived from Pork Production Data
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TEQ
0-100
100-1000
1000-10000
10000 - 20000
20000 - 30000
30000 - 40000
40000 - 60000
60000 - 80000
No Data
Figure 4-9. TEQDF-WH098 Derived from Dairy Products Production Data
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Percent Contribution of Five Food Categories to CDD/CDF
TEQ Production
(1997 Production Figures)
Percent Contribution of Five Food Categories to Current TEQ
Dose
(Based on 1989-91 CSFII data)
Poultry
7%
Milk/Dairy
43%
Poultry
13%
Milk/Dairy
40%
Figure 4-10. Comparison of Food Contributions to TEQDF-WH098 Production Data and Dose
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1st quartile: 60 counties
2nd quartile: 205 counties
3rd quartile: 490 counties
4th quartile: 2323 counties
Figure 4-11. Total TEQ Production in Five Food Categories, Categorized by Quartile.
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Fraction of TEQ
CDD/CDF Congeners
C
2,3,7,8-TCDD
19070 Popnn
I ,£.,0,1 , O-r c \sLJLJ
1,2,3,4,7,8-HxCDD
12367 S-HxCDD
1,2,3,7,8,9-HxCDD
1,2,3,4,6,7,8-HpCDD
OCDD
2,3,7,8-TCDF
1,2,3,7,8-PeCDF
2347 8-PeCDF
1,2,3,4,7,8-HxCDF
1,2,3,6,7,8-HxCDF
2,3,4,6,7,8-HxCDF
1,2,3,7,8,9-HxCDF
1, 2,3,4,6, 7,8-HpCDF
1, 2,3,4, 7,8,9-HpCDF
OCDF
) 0.1 0.2 0.3 0.
I I I
^
a
P
i—
Dose
Tissue
Figure 4-12. Fractions of the Background TEQ Dose and TEQ Tissue Concentration
Contributed by Each CDD/CDF Congener
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0
0.2
Fraction of TEQ
0.4 0.6
0.8
I I I I
77 \-
81 U
105 ^
co 114 ^
^^ 1
g 118
&123
c
5H O /^
126
g 156
°- 157
167
169
189
^^^^
^^
i
^^^
^
—
1
i
Dose
Tissue
Figure 4-13. Fractions of the Background TEQ Dose and TEQ Tissue Concentration
Contributed by Each PCB Congener
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5.0. POTENTIALLY ELEVATED EXPOSURES
5.1. INTRODUCTION
Certain groups of people may have higher exposures to the dioxin-like compounds
than the general population. The following sections discuss higher exposures that may
result from dietary habits, localized impacts, and cigarette smoking. Other population
segments can be highly exposed due to occupational conditions or industrial accidents.
For example, several epidemiological studies have evaluated whether elevated dioxin
exposure has occurred to certain workers in the chemical industry, members of the Air
Force who worked with Agent Orange, and residents of Seveso, Italy, who were exposed
as a result of a pesticide plant explosion. These epidemiological studies are fully
discussed in the Epidemiology Chapter of the Dioxin Health Reassessment Document (U.S.
EPA, 1996) and should be consulted if further details are desired. This chapter, however,
does not address occupational or accidental exposure. Instead, it focuses on elevated
exposures among the general population from dietary habits such as breast feeding or high
rates of fish ingestion, localized sources, or cigarette smoking.
5.2. NURSING INFANTS
Nursing infants may be exposed to dioxin-like compounds via consumption of
breast milk. These compounds are deposited in the fatty tissues (i.e., adipose tissue,
blood lipids, and breast milk) of the mother and may be transferred to the infant during
nursing. Based on data from 1989, approximately 52 percent of U.S. mothers initiate
breastfeeding with their newborn infants, and 40 percent continue breastfeeding for 3
months or longer (MAS, 1991). At 5 to 6 months of age, only about 20 percent of infants
are breast-fed (MAS, 1991). This section will show how breast milk ingestion exposures,
which are higher during breast-feeding, on a body weight basis, than during any other
period in an individual's life, impact lifetime exposures and body burdens. First, data
showing the impact of breast milk ingestion to the infant body burden of CDD/CDF/PCBs
are reviewed. Then, an estimate of the dose (average daily dose, ADD, and lifetime
average daily dose, LADD) to the infant via breast milk is made. The section will close by
developing, testing, and applying a model on the impact of breast-feeding to body burdens
for growing infants.
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5.2.1. The Impact of Breast Feeding on Infant Body Burden
Abraham et al. (1994, 1995) studied CDD/CDF/PCB levels in the blood of a breast-
fed and a formula-fed infant at 11 and 25 months of age. Sampling of blood showed that
the body burden of dioxin-like compounds was more than an order of magnitude higher for
the breast-fed infant than the formula-fed infant during both time periods, with CDD/CDF
ranging from 34.7 (11 months) to 43.9 (25 months) ppt TEQDF-WH098 lipid-basis in the
breast-fed infant compared to 2.7 to 3.3 ppt TEQDF-WH098 for the formula-fed infant.
Dioxin-like PCB concentrations were similarly an order of magnitude different, with the
breast-fed infant having a concentration of 31.4 ppt TEQP-WH098, compared to 2.5 ppt
TEQp-WH098 for the formula-fed infant at 11 months (PCB 126 not measured at 25
months, so a comparison for that age is not informative). The full congener profiles for
these results are shown in Table 5-1. The increase in the lipid-based CDD/CDF TEQ
concentration in the blood of the breast-fed infant at 25 months was attributed to the
relative decrease in body fat mass during the period between sampling and slight increases
in body burden concentrations.
Abraham et al. (1994) also analyzed mother's milk at 1 month and mother's blood
along with the infants' blood at 11 months. They found mother's milk to contain 23.5
ppt TEQDF-WH098 at 1 month and mother's blood to contain 14.2 ppt TEQDF-WH098 at 10
months. If blood and milk concentrations in a mother during lactation are the same at any
given time, then these data suggest a reduction of about 40 percent in TEQ concentration
in the mother between the 1st and 10th month of lactation. The reduction in mother's milk
concentration of dioxins during nursing is discussed further in Section 5.2.3 below.
Kreuzer et al. (1997) developed and tested a toxicokinetic model of human lifetime
body burden of TCDD, starting with a model for breast-feeding. To support their model,
they presented adipose tissue and liver data on 3 stillborn and 17 infants who had died
from sudden infant death syndrome (SIDS). Nine of the 17 infants had spent some
portion of their lives breast-feeding, while the other 8 infants were formula-fed. Average
congener and TEQ concentrations for these three groups are shown in Table 5-2. The
highest TEQ concentrations were found in the infants who had some breast-feeding, with
adipose concentrations at 15.9 ppt TEQDF-WH098, as compared to formula-fed infants who
had concentrations at 4.3 ppt TEQDF-WH098. The breast-fed infants' concentrations
included four infants who were weaned several weeks prior to their death from SIDS.
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This may have generally led to reductions in their body burdens as their higher daily intake
from breast-feeding was reduced after weaning. The average TEQ concentration for the
five infants who died while still breast-feeding was 20.1 ppt TEQDF-WH098. The highest
concentration found was for the infant who was breast-fed the longest at 19 weeks, and
who died at that time; the TEQ concentration was 35 ppt TEQDF-WH098. Other breast-
fed infants, however, did not have as much impact - infants who died while breast-feeding
at 12 and 16 weeks had concentrations of 9 and 7.5 ppt TEQDF-WH098. While Kreuzer et
al. (1997) concluded that breast-fed infants had elevated concentrations compared to
formula-fed infants, they also observed that breast-fed infants had adipose TEQ
concentrations that were within the range or lower than the values published for adults.
Abraham et al. (2000) reported on a study in Germany in which the blood of 80
breast-fed infants between the ages of 4 and 11 months were analyzed for the 1 7
CDD/CDFs . Of these 80 infants, 27 were from a region where a copper recycling plant
led to elevations in the mother's milk. The I-TEQDF blood concentration in this group of 80
children at 11 months ranged between 2.0 and 107 pg/g lipid-basis, with a median of
25.3 ppt. Of these children, 6 had I-TEQDF concentrations greater that 50 ppt, and 5 of
these 6 were from the region impacted by the copper recycling plant. From a control
group of 21 children who had been formula-fed, individual dioxin measurements were
performed in 5 children. Concentrations were found to range narrowly from 1.9 to 3.2
ppt I-TEQDF lipid-basis at 11 months. With several measurements over time, Abraham et
al. (2000) found the blood concentrations of the breast-fed infants to increase over time.
Infant or time-specific concentration data were not presented in Abraham et al. (2000).
However, they did show the ratio between the concentration of specific compounds, as
well as the I-TEQDF concentrations, in the children's blood and the blood of the mothers.
When the ratio exceeded 1.0, this meant that the child's concentration was higher than
the mother's. Abraham et al. (2000) showed the I-TEQDF ratio to increase from 1.12 at
16-24 weeks (n = 11 meaning there were 11 paired measurements at that time) to 2.12
at 25-32 weeks (n = 29) to 3.20 at 33-40 weeks (n = 33) to 3.73 at 41-48 weeks
(n = 7). This was the most comprehensive data set found in the literature, although it was
not fully described in the Abraham et al. (2000) abstract.
Patandin et al. (1997) looked at the plasma levels of four polychlorinated biphenyls
(PCBs) in 173 Dutch children 3.5 years of age, 91 of which had been breast-fed and 82
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of which had been formula-fed. Children in the breast-fed group had significantly higher
median PCB levels in plasma (p<0.0001) than children in the formula fed group. The four
PCBs measured were 11 8, 1 38, 1 53, and 1 80. The median sums of these four PCBs in
the two groups of children were 0.75 |Jg/L in the breast-fed group versus 0.21 |Jg/L in the
formula fed group. By means of an extensive questionnaire on dietary history, combined
with data on the concentrations of dioxin and PCBs in foods provided by the Dutch
National Institute of Public Health and the Environment, Pantadin et al. (1997) were able
to determine that the TEQ intake via the diet was virtually indistinguishable in the two
groups. They found that PCB levels in the breast-fed children were significantly correlated
with the period of breast-feeding (r = 0.63), milk PCB levels (r = 0.39), and the total TEQ in
breast milk (r = 0.36). They concluded that the plasma PCB levels in Dutch children were
the result of exposure through breast milk and in utero exposure, and that the influence of
dietary intake of PCBs after weaning is small compared to the intake during breast-
feeding.
5.2.2. Calculation of an Average Daily Dose from Breast-Feeding
Using the estimated dioxin concentration in breast milk, the administered dose to
the infant can be estimated as follows:
Ann Cmilkfat * f3 * IRmilk * ED /|=
ADDinfant ~T (Eqn.
infant
where,
ADDinfant = Average daily dose to the infant (pg/kg-d);
Cmi|kfat = Concentration in milk fat (pg/g);
IRmiik = Ingestion rate of breast milk (kg/d);
ED = Exposure duration (yr);
BWinfant = Body weight of infant (kg);
AT = Averaging time (yr); and
f3 = Fraction of fat in breast milk.
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The administered dose can be converted to an absorbed dose by multiplying by the
fraction of ingested contaminant that is absorbed.
This approach assumes that all pertinent parameters, including the body weight of
the infant, the infant ingestion rate of breast milk, and perhaps most importantly, the
contaminant concentration in milk, represent the average over the breast feeding time
period.
Smith (1987) reported that a study in Britain found that the breast milk ingestion
rate for 7- to 8-month old infants ranged from 677 to 922 ml_/d and that a study in
Houston measured the mean production of lactating women to range from 723 to 751
g/d. Smith (1987) also reported that breast milk ingestion rates remain relatively constant
over an infant's life. For purposes of estimating the dose to breast feeding infants, a milk
ingestion rate of 800 ml_/d was assumed in the analysis presented in this section. Smith
(1987) also assumed that mother's milk has a 4 percent fat content, and that 80 percent
of the ingested contaminant are absorbed. The infant weight varies with time. For
example, a typical infant (average of male and female data) weighs about 3.3 kg at birth,
7.9 kg at 6 months, and 10.2 kg at 1.0 year (U.S. EPA, 1 997; Walker and Watkins,
1997).
The concentration of dioxin in the mother's milk is also expected to change, since
lactation provides a significant avenue of depuration. Lakind et al. (2000) cite several
references where measurements of breast milk concentrations of lipophilic compounds
(PCBs, DDE, DDT, CDD/CDFs) were shown to decline during the course of lactation. They
fit available data on 2,3,7,8-TCDD to a curve, and their resulting relationship showed an
86 percent loss over 6 months. This is comparable to a modeling effort by Kreuzer et al.
(1997), who modeled a 70 percent decline in TCDD concentrations after 6 months. Their
model was more mechanistic, and added the loss by breast milk to an overall female body
burden model which included inputs by food consumption and outputs by metabolic and
non-metabolic pathways. Patandin et al. (1999), in their modeling of dioxin exposures
from infancy to adulthood, cited data from Germany and England to conclude that breast
milk concentrations of TCDD decline by 20 percent every 3 months. The data described
in the previous section by Abraham et al. (1994) suggest a decline of 40 percent of TEQs
from 1 month to 10 months of lactation.
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For assignment of CmNkfat in Equation 5-1, therefore, one would have to assign a
concentration at birth and consider a decline in that concentration over time. For
purposes of this discussion, a concentration of 25 ppt TEQDFP-WH098 on a lipid basis is
assumed for mother's milk when lactation begins (which is the average tissue
concentration derived from recent studies of dioxins in blood in background settings of the
US, reviewed in Chapter 4). It is then assumed to linearly drop by 50 percent after 6
months, with an additional linear drop of 50 percent by the end of 12 months, for a total
decline of 75 percent from initial concentrations. These assignments in concentration
decline are in the middle of the range reported above. They translate to concentrations of
12.5 ppt TEQDFP-WH098 after 6 months and 6.3 ppt TEQDFP-WH098 after a year, given a
starting concentration of 25 ppt TEQDFP-WH098.
The proper way to derive an average dose to the child is to integrate Equation 5-1
over the time period of interest. At birth, for example, with a mother's milk concentration
of 25 ppt TEQDF-WH098, an infant body weight of 3.3 kg, an average ingestion rate of
800 g/d breast milk, the administered dose is predicted to be 242 pg TEQDF-WH098/kg
bw/day [(25 pg/g x 0.04 x 800 g/d) / (3.3 kg) = 242 pg/kg-d]. Table 5-3 shows infant
body weights, as well as doses of dioxin TEQDF-WH098 expressed in terms of pg/day for
each of the first 1 2 months of life. These body weight data are the averages for male and
female infants (U.S. EPA, 1997; Walker and Watkins, 1997), and along with other data,
were used in the pharmacokinetic exercise described in the next section. Calculating
monthly doses on the basis of body weight for each of the first twelve months of life and
then dividing by 12, results in an average dose to the infant of 87 pg TEQDF-WH098/kg
bw/day.
This value is much higher than the estimated average background TEQDFP-WH098
dose for adults of approximately 1 pg TEQDFP-WH098/kg-d. However, if a 70 year
averaging time is used for this one-year nursing scenario, then the LADD (Lifetime Average
Daily Dose, calculated as ADD * ED/LT where LT is lifetime typically assumed to be 70
years) is estimated to be 1.2 pg TEQDFP-WH098/kg-d ([87 pg/kg-d] * 1 yr/70 yr). This is
close to the adult background dose of 1.0 pg TEQDFP-WH098/kg-d. However, this can be
misleading because it ignores the difference in daily intake during potentially sensitive
stages in development. Also, it does not consider any exposures past the first year of life.
In order to calculate a true lifetime average daily dose, one needs to incorporate the
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changes in dose over various life stages. Using the estimates of dose derived in Chapter 4
for various ages in children: 1-5: 3.3 pg TEQDFP/kg-d, 6-11: 1.9 pg TEQDFP/kg-d, and 12-
19: 1.1 pg TEQDFP/kg-d, the following calculates the LADD for lifetime background
exposures considering one year of breast-feeding:
LADD = 87 -JV1- + 3.3 -to- + 1.9 -^ + 1.1 J52L + 1 .0 (Eqn. 5-2)
70yrs 70yrs 70yrs 70yrs 70yr
LADD = 2.45 — - ^ (Eqn. 5-3)
kg- day
On a mass basis, the cumulative dose to the infant after a year is about 238 ng TEQDFP-
WH098 (87 pg/kg-d x 7.5 kg x 365 d x ng/1 ,000 pg; 7.5 kg is an average annual weight
based on the average of 12 monthly body weights). Using the age-dependent doses
derived in Chapter 4 with assumed body weights, a dose from year 1 to year 70 in a 70-
year lifetime is estimated to be about 1,687 ng TEQDFP-WH098, so that a total lifetime
dose is 1,925 ng TEQDFP-WH098 (1,687 + 238). This suggests that about 12 percent of
lifetime dose (238/1925 * 100 percent) may occur as a result of breast feeding, if that
feeding occurred for one year.
This exercise describes accumulated dose over a lifetime, given a year of breast-
feeding. It was found that about 12 percent of lifetime dose came from breast-feeding. It
was also found that the LADD for this scenario, including average dioxin doses after the
first year until year 70, was 2.45 pg TEQDFP-WH098/kg-day. The issue of accumulative
dose is explored in more detail in the next section, where the quantity, "area under the
curve" is defined and used to assess overall exposure during a lifetime, or portions of a
lifetime, under different breast-feeding scenarios as well as a formula-only scenario where
the infant experiences only background exposures.
5.2.3. Modeling the Impact of Breast-Feeding on Infant Body Burden
The previous section described an approach to estimate doses received by an infant
due to breast-feeding, which included a starting concentration in mother's milk, a decline
of that concentration over time (and the resulting decline in dose delivered to the child),
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and the child's changing body weight. That information will be used in this section to
evaluate the impact of breast feeding on an infant's body burden of dioxins.
To better evaluate the impact of nursing on infants, a one-compartment non-steady
state pharmacokinetic model was used to evaluate dioxin tissue levels. As described
below, this model was validated using paired mother/child data on breast milk and infant
blood concentrations of TEQDF-WH098. Following this validation, several breast-feeding
scenarios were modeled and compared with a formula-feeding only scenario. Specifically,
changes in infant TEQDFP-WH098 tissue concentration over time were modeled for these
scenarios: formula only, 6 weeks nursing, 6 months nursing, 1 year nursing, and 2 years
nursing. The section closes with sensitivity analyses exercises which describe the model
response to changes in the key parameters describing the dose received by the infant via
breast milk and the rate of dissipation of dioxin TEQs in the infant.
5.2.3.1. Description of the Model
The pharmacokinetic model was based on the following differential equation
describing the mass balance of dioxin in lipids (Pinsky and Lorber, 1998):
da(t)/dt = f D(t) - k(t) a(t) Eqn. (5-4)
a(t)
c(t) = Tooovm Eqn- (5-5)
where:
a(t) = total mass of dioxins in lipid (pg) at time t;
c(t) = concentration of dioxins in lipid (pg/g) a time t;
D(t) = ingested dose of dioxins (pg/yr) at time t;
V(t) = lipid weight (kg) at time t;
k(t) = elimination rate constant (yrs"1) at time t;
t = time (yrs); and
f = fraction of ingested dose absorbed into lipid compartment (unitless).
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The lipid weight, V(t), is calculated as the product of the percent body lipid and the full
body weight of the infant, both of which are provided in Walker and Watkins (1997) for
infant boys and girls. This section demonstrates the approach using the average for infant
boys and girls. The dose regime for tested scenarios in this section, the body weight, lipid
fraction, and assumed half-lives of TEQDFP-WH098, are shown with other model parameters
in Table 5-3. The body lipid and body weight are also shown graphically in Figure 5-1, for
the 70 year life span and in more detail for the early years of life.
One key assumption of this simplistic framework is that dioxins are instantaneously
distributed to all body lipids. This is a common assumption for TCDD PK modeling in
humans, adopted by the multi-compartment model of van der Molen et al. (1996), and the
single-compartment models in Kreuzer et al. (1997), Campbell et al. (1996), and Lakind et
al. (2000). The model of Carrier et al. (1995a,b) alternately has a nonlinear response to
doses, with different partitioning to the liver and other body lipids as a function of body
concentration; when the overall body concentration is high, more of the dioxin dose is
partitioned to the liver, whereas at lower body concentrations, the partitioning to the liver
is lower.
The other key and important assumption for the model is that the TEQDFP-WH098
behaves as a single compound in humans, and can be described by a single dissipation
half-life. Ayotte et al. (1994) modeled TEQ body burdens from infancy to adulthood, but
it was unclear whether they modeled individual congeners or TEQs as one compound.
Campbell et al. (1996) used a framework similar to the one used here and modeled
individual congeners for an industrial exposure study.
The elimination rate constant, k(t), was developed in similar fashions by Pinsky and
Lorber (1998), Michalek et al. (1996), and Flesch-Janys et al. (1996), for 2,3,7,8-TCDD.
All three research groups derived a relationship in which the elimination rate constant was
a function of percent body fat. All three also curve-fit their empirical algorithms for k(t) on
data from adult individuals, whose percent body fat was about 25 percent. With body
fat percent increasing over time, particularly in older individuals, the elimination rate
constant decreased (equivalently, the half-life increased) significantly. Given a range of
body fat over time, from about a low of 1 5 percent to a high over 40 percent (for elderly
females), the relationship in Pinsky and Lorber (1998) results in a half-life of 2,3,7,8-
TCDD ranging from about 6 to over 20 years.
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None of these efforts, however, identified processes or factors critical for infants,
other than percent body fat. With a body fat of around 15 percent at birth, the half-life is
calculated to be about 6.4 years using the relationship in Pinsky and Lorber (1998).
Kreuzer et al. (1997), however, developed a procedure for modeling the elimination half-
lives for 2,3,7,8-TCDD in infants which considerd metabolic, tm (breakdown by enzymes),
and non-metabolic, tf (fecal elimination) processes. Kreuzer et al. (1997) combined these
two half-lives to solve for an overall half-life, t1/2. Other key parameters included total
body lipid mass and liver volumes, which change over time, and a reference half-life for an
adult. For their "reference adult" at age 40, they cited an overall half-life of 5 years,
based on information in Geyer et al. (1986). The Kreuzer et al. (1997) model showed a
rise in half-lives from a low of less than 0.5 years at birth to a high of 5 years at the total
body lipid mass of 20 kg. With their parameter assignments, perhaps most importantly
this assignment of a 5 year half-life for a reference adult, the half-life will not go far
beyond 5 years (as a function of body lipid mass, it would exceed 5 years when body lipid
mass exceeds 20 kg), which makes the model of Kreuzer et al. (1997) importantly
different than that of Pinsky and Lorber (1998), Michalek et al. (1996), and Flesch-Janys
et al. (1996), all of whom have half-lives varying from a value of 6 to over 20 years. In
short, the model of Kreuzer et al. (1997) has half-lives which mostly never exceed 5
years, while the other approaches have half-lives for TCDD which never go below 6 years.
As noted, the model of Kruezer et al. (1997) suggests relatively short half-lives for
infants. For infants, the overall half-life is driven by non-metabolic processes and the
resulting half-life for newborns is calculated to be about 0.4 years. It rises to about 2.0
years when the total body fat weight is about 5 kg, which occurs around ages 8-10 years
(25-35 kg overall body weight, about 20 percent body fat). Very clearly, this rapid a half-
life of dioxin intake will have an important impact on the accumulation of dioxin residues
during breast-feeding as compared to a model showing a 6 year or higher half-life. Lakind
et al. (2000) adopted the Kruezer et al. (1997) approach in their evaluation of the impacts
of breast-feeding on the 2,3,7,8-TCDD body burdens of infants.
For purposes of this assessment, it was assumed that the overall half-life for the
early years of life more closely follows the trend as derived in the modeling exercises by
Kruezer et al. (1997). For later years, it was felt that the empirical data upon which
Pinsky and Lorber (1998), Michalek et al. (1996), and Flesch-Janys et al. (1996) derived
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the half-life relationship for 2,3,7,8-TCDD is more valid. Therefore, a hybrid of these
assumptions, as shown in Figure 5-2, was adopted for this effort. The half-life at birth
starts at the low value of 0.4 yr and then slowly rises to the levels as modeled by Pinsky
and Lorber (1998) by about age 20. It is noted that had Kreuzer et al. (1997) established
reference half-lives for 40 year-olds more in the 6-20 year range, they would still have had
very low half-lives at birth, rising to these higher half-lives with age. The half-life
assumptions remain an obvious uncertainty for this type of modeling approach. Not only
is there a disparity in the literature with regard to this critical assumption, but the literature
is also only specific to 2,3,7,8-TCDD, not TEQDFP-WH098. The impact of this assumption
is examined later in the sensitivity analysis exercises.
The final assumption for the model is the initial lipid concentration in the infant. It
was assumed to be 10 ppt TEQDFP-WH098, which was reasonably similar to the 11.9 ppt
TEQDF-WH098 found in stillborn adipose tissue in Kreuzer et al. (1997). All other
assumptions and parameter assignments for this modeling exercise, including the half-life
change over time, are shown in Table 5-3, and Figures 5-1 and 5-2.
5.2.3.2. Validation of the Model
While demonstrating the impact of breast-feeding, the studies reviewed in Section
5.2.1. do not contain the type of information needed for model validation. What is
needed are breast milk concentrations that are taken at the same time infant body burden
measurements are taken. The breast milk concentrations are used to provide the
"independent" model driving term, the dose term, and the body burden measurements
provide the "dependent" model prediction, the infant body lipid concentration.
One study had a set of this kind of data. Abraham et al. (1998) studied
CDD/CDF/PCB levels in the blood of 6 breast-fed infants as well as the breast milk of the
mothers of these infants. A portion of this data set had been reported in their earlier
articles (Abraham et al., 1994; 1995). This analysis will focus on the TEQDF-WH098
concentrations reported in Abraham et al. (1998), since PCB concentrations were not
uniformly available for mother's milk and infant blood for all six mother/child pairs.
Two of the infants were the second children from mothers whose first child was
also tracked by Abraham and colleagues. It was interesting to note that, for these two
second children, both the mothers' milk and the infants' body burdens were significantly
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lower. Specifically, the comparison of first and second children, respectively, were: 34.7
ppt TEQDF-WH098 lipid compared to11.9 ppt TEQDF-WH098, and 44.2 ppt TEQDF-WH098
compared to 18.8 ppt TEQDF-WH098. The comparison of the mothers' milk from the first
to second children was similarly disparate: the first mother had concentrations ranging
from 14 to 24 ppt TEQDF-WH098 lipid for the first child, but 13 to 14 ppt TEQDF-WH098
lipid only for the second child. The other mother showed a range of 1 5 to 27 ppt TEQDF-
WH098 lipid for the first child, but only 1 3 to 18 ppt TEQDF-WH098 lipid for the second.
Apparently, breast-feeding of the first child resulted in a higher body burden for this infant
as compared to the second infant, and a lower body burden for the mother when the
second infant was breast-fed.
Table 5-4 shows the observed data that were available in Abraham et al. (1998) for
this model validation exercise. There were two concentrations measured in breast-milk for
each of 5 of 6 children. The one child whose mother had only one measurement was only
breast-fed for 7 weeks; all other children were breast-fed for periods ranging narrowly
from 26 to 32 weeks. Concentrations within the breast-feeding period were linearly
extrapolated from the two available data points. For example, for the first mother/child
pair listed in Table 5-4, mother's milk was analyzed during month 2 and month 11. TEQ
concentrations were 23.5 and 14.0 ppt TEQDF-WH098 lipid, respectively. These
concentrations were extrapolated backwards to give an estimated concentration at birth of
24.6 ppt TEQDF-WH098. Likewise, forwards extrapolation gave an estimated concentration
for month 3 of 22.4 TEQDF-WH098, assuming linear decline. The infant body burden was
ascertained by blood measurements at about 1 year of age for each child. The amount of
time of full breast-feeding was supplied by Abraham et al. (1998), and this is also listed in
Table 5-4.
Other assumptions for modeling the infant body burden were outlined above, and
these include the intake rate of mother's milk (IR = 800 ml/d), the fraction of fat in
mother's milk (0.04), the rate of absorption of dioxins (0.80), and the changing infant
body weight and lipid fraction (and hence lipid volume, V(t); Table 5-3). After weaning,
the dose to the infant was assumed to be 50 pg TEQDF-WH098/d. This is the dose
developed for the age range of 1-5 in Chapter 4. The dose by formula feeding or other
foods the infants may be consuming after weaning may be lower or higher, as little
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information is available on the dioxin content of baby formula or baby food. The assumed
initial body burden of the infant was 10 ppt TEQDF-WH098, as noted above.
The rate of dissipation of dioxin residues was identified as a principal uncertainty
for this model. Two lines of thought discussed above include the rapid dissipation (half-
life < 1 year) of TCDD residues in infants modeled in Kreuzer et al. (1997) and later
adopted by Lakind et al. (2000), and the much longer dissipation (half-life around 7 years)
of TCDD in adults described in Pinsky and Lorber (1998), Michalek et al. (1996), and
Flesch-Janys et al. (1996). The model validation exercise described in this section tested
the appropriateness of the lower infancy half-life approach adopted in this model, as
shown in Figure 5-2, against an assumption of a constant 7 year half-life for TEQDF-WH098
during the first year of life.
Table 5-4 shows the final results of this exercise. As seen, the model predictions
at the selected and more rapid dissipation rate were significantly nearer to observations as
compared to the predictions with the longer half-life. The average predicted concentration
for the rapid dissipation rate for the 6 infants was 26 ppt TEQDF-WH098 lipid, compared to
the average observed concentration of 23.5 ppt TEQDF-WH098 lipid. With a longer 7-year
half-life, the predicted concentrations were all higher, with an average of 39 ppt TEQDF-
WH098 lipid. The model also seemed very adequately responsive to lower or higher
infants' exposures. For the infant who was breast-fed for only 7 weeks with a low
concentration in the mother's milk, the blood concentrations measured 5.0 ppt TEQDF-
WH098 lipid at 13 months, compared to a predicted 10 ppt TEQDF-WH098 lipid (with the
rapid dissipation assumption). The infant exposed to the highest mother's milk
concentration had the highest body burden measurement at 44.2 ppt TEQDF-WH098 lipid
and also the highest predicted concentration at 36 ppt TEQDF-WH098 lipid.
In summary, it appeared that, even with the key uncertainties identified above,
including the use of a simple, one-compartment pharmacokinetic model and the modeling
of TEQDFP-WH098s as though they were a single compound, this approach appears to
predict infant TEQDFP-WH098 body burdens within the range observed, and is adequately
responsive to the different conditions of high and low exposure via breast-feeding.
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5.2.3.3. Scenario Evaluation
The scenarios evaluated include: formula only, 6 weeks of breast-feeding, 6
months of breast-feeding, 1 year of breast-feeding, and 2 years of breast-feeding. These
scenarios encompass current trends. In comprehensive documentation of statistics for
children born between 1990 and 1993, CDC (1997) reported that 55 percent of all babies
breastfed, with about half of those breastfeeding beyond 5 months. The average duration
of breastfeeding was 28.7 weeks. In a policy statement, the American Academy of
Pediatrics (1997) stated that exclusive breastfeeding is ideal nutrition and sufficient to
support optimal growth and development for 6 months after birth. They recommend that
breastfeeding continue for at least 1 2 months, and thereafter for as long as mutually
desired. Ryan (1997) documented a resurgence in breastfeeding between 1989 and
1995. In comprehensive surveys conducted in 1989 and 1995, he found a 14 percent
increase in the number of mothers who breastfed in the hospital, rising from 52 percent in
1989 to 59 percent in 1995. He also found a 19 percent increase in mothers who
continued to breastfeed at 6 months, rising from 18 percent in 1989 to 22 percent in
1995.
The specifics of these scenarios are:
Scenario #1: Formula Only: In this scenario, the dose to the infant was assumed to be
50 pg TEQDFP-WH098/d. This is the dose developed for the age range of 1-5, as described
in Chapter 4. The dose by formula feeding may be lower or higher, as little information is
available on the dioxin content of baby formula. TEQDFP-WH098 doses among individuals
from 6 to 11, 1 2 to 18, and greater than 1 8 years of age were 54, 65, and 66 pg TEQDFP-
WHOgg/d, respectively, as developed for these age ranges in Chapter 4.
Scenario #2: Six-Week Nursing: The dose to the infant was assumed to be a function of
the starting concentration of dioxins in the mother's milk, 25 pg TEQDFP-WH098/g lipid, and
other assumptions that have been described in this section: 800 g/day milk ingestion, 4
percent lipids in milk, resulting in an initial dose of 800 pg TEQDFP-WH098/d. This drops to
733 after 1 month, and then to 667 pg TEQDFP-WH098/d, when nursing stops. Doses
from then are as in Scenario #1.
Scenario #3: Six-Month Nursing: It was assumed that the dose drops linearly from 800
to an ending dose of 400 pg TEQDFP-WH098/day at month 6. From there, doses are as in
Scenario #1.
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Scenario #4: It was assumed that the doses drop linearly from 800 to 400 at 6 months
and then linearly again to 200 pg TEQDFP-WH098/day at the end of one year. From there,
doses again are as in Scenario #1.
Scenario #5: The 1 year dose of 200 pg TEQDFP-WH098/day assumes a mother's milk
concentration of 6.25 ppt TEQDFP-WH098 lipid, which represented a drop from an initial
concentration of 25 ppt TEQDFP-WH098 lipid. For purposes of this demonstration, it was
assumed that the mother's milk concentration stays at 6.25 ppt TEQDFP-WH098 lipid for
the second year of breast-feeding. From there, doses again are as in Scenario #1.
The results from this exercise are shown in Figures 5-3 and 5-4, which show the
lipid concentrations and the body burdens from birth up to 70 years of age (on Figure 5-
3), and then these two quantities for the narrower time frame of from birth to 10 years of
age (Figure 5-4). The body burden, defined as the whole body concentration, is simply
calculated as the lipid concentrations times the lipid fraction. Other results for these 5
scenarios are provided in Table 5-5, including the peak TEQDFP-WH098 concentrations in
the infant, the time when the peak occurred, the "area under the curve" (ADC),
corresponding to different times, and the ratio of that ADC for the breast-feeding
scenarios and the ADC for formula feeding only. The ADC is defined as:
AUC = I c(t) Eqn. (5-6)
where:
AUC = area under the curve, ppt TEQDFP-WH098-day
c(t) = lipid-based concentration in the infant each day, ppt TEQDFP-WH098
The AUC is a measure of accumulated exposure. For example, a year at a lipid-based
concentration of 10 ppt TEQDFP-WH098 would yield an AUC of 3,650 ppt TEQDFP-WH098-
day (10 ppt * 365 days). A lifetime at an average body lipid concentration of 10 ppt
would yield an AUC of 255,500 ppt TEQDFP-WH098-day (10 ppt * 70 yrs * 365 days/yr).
The AUC provides a parameter to compare accumulated exposure for different scenarios.
For that reason, the ratios of the AUCs of the various breast-feeding scenario and the
formula-only scenario are provided in Table 5-5. A ratio for a particular breast-feeding
scenario of 6, for example, would mean that the accumulated exposure for that scenario is
6 times that of the breast-feeding only scenario. This can easily be translated to a
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corresponding measure of percent above or below the baseline scenario of formula feeding
only. For example, if the ratio is 6, this is equivalent to saying that the accumulated
exposure for the breast-feeding scenario is 500 percent higher than the formula-only
scenario ((6-1)* 100 percent); if the ratio is 0.7, this is equivalent to saying that the
accumulated exposure for the breast-feeding scenario is 30 percent lower than the
formula-only scenario (-(0.7-1 )* 100 percent).
The lipid concentrations are predicted to rise to about 44 ppt TEQDFP-WH098 for the
6-month, 1-year, and 2-year scenarios. The time that these peaks occur is uniformly at 9
weeks. For the 6 week breast-feeding scenario, the peak is at 34 ppt and it occurs at the
6 week mark. Body burdens follow a similar trend, rising to about 9 ppt TEQDFP-WH098 for
both the 6-month, 1-year, and 2-year scenarios at 9 weeks. The body burdens decline for
these breast-feeding scenarios, but the decline is slower as the duration of time for breast-
feeding increases. For the 2-year scenario, the body lipid concentration stays near 40 ppt
past 2 years of age (Figure 5-4). The six-week scenario shows a rise in infant body burden
to above 30 ppt TEQDFP-WH098, but then shows a rapid decline, tracking the formula-only
scenario fairly well after about age 2. From Figure 5-3, it appears that all four scenarios
begin to merge at about age 10 years. The rise in concentrations seen in the later years in
Figure 5-3 is due to the rise in body fat percent and the subsequent rise in half-life as
predicted by elimination rate model of Pinsky and Lorber (1998).
The ADC results in Table 5-5 show how the accumulated exposure is higher for
each of the breast-feeding scenarios as compared to the formula-only scenarios. This
exceedance after one year is about a factor of 6 for breast-feeding for 6 months or more.
Even for the first 10 years of life, the accumulated exposure is about 2 times higher for
these breast-feeding scenarios as compared to formula feeding only. After a lifetime, the
ratios suggest that breast feeding results in a lifetime exposure only 1.03-1.18 times
higher than formula feeding, or expressed in terms of a percentage, from 3 to 1 8 percent
higher than formula feeding only.
5.2.3.4. Sensitivity A nalysis
A brief sensitivity analysis was conducted to study the impact of key parameters
and assumptions on this exercise. Sensitivity analysis exercises typically focus on the
parameters with these two characteristics: those that are the most uncertain and those
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that have an important impact on the results. It was ascertained that the parameters:
absorption, body weight, and lipid fraction, while important, are reasonably well known
and assigned appropriate mid-range values for this exercise. The initial concentration in
the infant at birth of 10 ppt TEQDFP-WH098 lipid is an unknown, but testing showed that
infant concentrations rapidly declined to about 5 ppt regardless of the initial concentration.
Therefore, this parameter does not appear to influence results like peak concentrations or
accumulated exposures. The two most important parameters, or groups of parameters,
were those used to determine dose to the infant - mother's milk concentration initially and
over time, and the dissipation rate of dioxin-like compounds in the infant. To test the
influence that these parameters have on results, six sensitivity analyses were devised.
These were all variations on a baseline scenario selected to be the 6-month breast-feeding
scenario. The six scenarios were:
Scenario #1: Use of the Pinsky and Lorber (1998) lipid-based function for dissipation rate
instead of the hybrid function used. The Pinsky and Lorber (1998) lipid-based function is
shown in Figure 5-2. This is expected to result in an increase in the predictions of infant
and childhood body impacts.
Scenario #2: Use of the Kreuzer et al. (1997) modeled dissipation rate throughout life
instead of the hybrid function used. The Kreuzer et al. (1997) function is shown in Figure
5-2. This is expected to result in a decrease in predictions of infant, childhood, and adult
impacts.
Scenario #3: Use of the assumption that mothers' milk concentrations do not decline
from 25 ppt TEQDFP-WH098 lipid during the 6 months of breast-feeding. This will
obviously result in an increase in the impact to the infant.
Scenario #4: Use of the assumption that mothers' milk concentrations begin at 15 ppt
TEQDFP-WH098 and decline to 7.5 ppt TEQDFP-WH098 after 6 months (instead of using
concentrations that begin at 25 ppt TEQDFP-WH098 and decline to 12.5 ppt TEQDFP-WH098
after 6 months). The assignment of an initial concentration of 25 ppt TEQDFP-WH098 is
based on the finding that this represents a reasonable average of adult body
concentrations of the sum of dioxin, furan, and dioxin-like PCB TEQs, described in Chapter
4. However, this average includes a wide age range of populations, including older
individuals. As will be discussed in the next chapter on trends, higher exposures in the
past have resulted in higher concentrations in older adults compared to today's younger
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adults. It may be more reasonable to assume a woman of child-bearing age today would
have concentrations closer to 15 ppt TEQDFP-WH098 rather than the full adult population
average of 25 ppt TEQDFP-WH098. Also, for children after the first-born, reduction in a
woman's body burden of dioxin-like compounds could occur from prior breast-feeding.
Scenario #5: For this scenario, both assumptions that would lead to a higher impact -
use of the Pinsky and Lorber (1998) lipid-based function for dissipation rate and the
assumption of mother's milk concentration not declining from 25 ppt TEQDFP-WH098 lipid -
were used.
Scenario #6: In contrast to Scenario #5, both assumptions that would lead to a lower
impact - the use of the Kreuzer et al. (1997) modeled dissipation rate and the assumption
of mothers' milk concentrations beginning at 15 ppt TEQDFP-WH098 and declining to 7.5
ppt TEQDFP-WH098 after 6 months - were used.
Results from this sensitivity analysis exercise are shown in Table 5-6 and Figure
5-5. It is seen that all results range from a reduction of 40 percent (ADC ratio of 0.6)
from baseline or an increase of 160 percent (ADC ratio of 2.6). The peak concentration
can rise as high as 54 ppt TEQDF-WH098, as seen in Table 5-6 for Scenario #5 or as low
as 28.5 ppt TEQDF-WH098 for Scenario #6. Perhaps the biggest impact is seen in
assuming the higher dissipation rate (lower half-life) for the childhood years. This is seen
in Scenarios #4 and #6, with the lowest peak concentration and the steepest reduction
from baseline: 30-40 percent. While the results displayed on Table 5-6 focus on the
difference between the baseline 6-month scenario and sensitivity analysis scenarios, also
of note is the difference between the two extremes as modeled by Scenarios #5 and #6.
The peak concentration predicted for Scenario #5 (infant impact maximized) is about twice
that of Scenario #6 (infant impact minimized): 54.0 versus 28.5 ppt TEQDFP-WH098. More
importantly, the accumulated exposure from Scenario #5 is significantly higher than from
Scenario #6. This can be seen In Figure 5-5, where the body concentrations are higher in
Scenario #5 than #6 throughout the modeled lifetime, particularly for the first 10 years of
life. After 10 years, the accumulated exposure, as measured by ADC, is about 4 times
higher for Scenario #5 as compared to #6 (this results from the 10-year ratios of 2.6 and
0.7 displayed on Table 5-6, divided by each other; 2.6/0.7 = 3.7).
In short, when using this modeling approach to evaluate exposure-related impacts
to infants from breast feeding, the assessor needs to be aware that assumptions relating
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to the dissipation of dioxin-like compounds from infants, and the dose they receive
through breast milk, are the two most important inputs. Data should be sought to further
validate the selected modeling procedure and parameters, if possible.
5.3. SPORT AND SUBSISTENCE FISHERS
The possibility of high exposure to dioxin as a result of fish consumption is most
likely to occur in situations where individuals consume a large quantity of fish from one
location where the dioxin level in the fish is elevated above background levels. Most
people eat fish from multiple sources, and even if large quantities are consumed, they are
not likely to have unusually high exposures. However, individuals who fish regularly for
purposes of basic subsistence are likely to obtain their fish from one source and have the
potential for elevated exposures. Such individuals may consume large quantities of fish.
U.S. EPA (1997) presents studies that indicate that Native American subsistence
fishermen consume 59 g fish/day (as a mean) and 170 g fish/day (as an upper estimate).
Wolfe and Walker (1987) found Native American subsistence fish ingestion rates up to
770 g/day in a study conducted in Alaska. Assuming that subsistence fishermen
consume 59 to 1 70 g of freshwater fish per day as their primary source of protein (i.e., no
meat or eggs are consumed) adult daily intake of CDD/CDFs/PCBs would be 2.2 to 5.7
pg/kg-day (Table 5-7). This estimate is based on the same CDD/CDF/PCB media
concentrations exposure assumptions, and exposure algorithms as those presented in
Chapter 4. The estimated values for subsistence fishermen are two to six times higher
than the adult general population mean daily intake from all food sources of 0.94 pg/kg-
day, as estimated in Chapter 4. It should be noted that fish ingestion rate data for
subsistence fishermen are limited. The ingestion rate values used here pertain only to
Native American subsistence populations (U.S. EPA, 1997), but are used to demonstrate
the potential for elevated exposures among groups of individuals whose diets are known
to consist of higher proportions of fish than the general population. Fish ingestion rates
for sports fishermen would generally be lower than for the Native American subsistence
population, but higher than for the general population.
Studies are underway to evaluate whether Native Americans living on the Columbia
River in Washington have high dioxin exposures as a result of fish consumption. These
Tribes consume large quantities of salmon from the river. As cited in U.S. EPA (1997), a
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study conducted by the Columbia River Intertribal Fish Commission (1994) suggested that
these individuals have an average fish consumption rate of 59 g/day and a 95th percentile
rate of 170 g/day. These data were used in the estimated dietary intake calculations for
subsistence fishermen, as shown above. Currently, studies are underway to measure
dioxin levels in fish from this region.
Svensson et al. (1991) found elevated blood levels of CDDs and CDFs in high fish
consumers living near the Baltic Sea in Sweden. Three groups were studied:
nonconsumers (n=9), moderate consumers (n = 9, 220 to 500 g/wk), and high consumers
(n = 11, 700 to 1,750 g/wk). The high consumer group was composed of fishermen or
workers in the fish industry who consumed primarily salmon (30 to 90 pg l-TEQDF/g) and
herring (8 to 18 pg l-TEQDF/g) from the Baltic Sea. The I-TEQDF blood level was found to
average about 60 pg l-TEQDF/g lipid among the high consumers and 20 pg l-TEQDF/g lipid
for the nonconsumers. This difference was particularly apparent for the PeCDFs.
Asplund et al. (1994) also found elevated plasma levels of dioxin-like PCBs in
Swedish fishermen who consumed large amounts of fish. A total of 37 individuals with
varying intake rates of fish from the Baltic Sea was studied. These individuals were
categorized as high-fish eaters, moderate fish-eaters, and nonfish-eaters. The estimated
weekly intake of fish correlated positively with plasma PCB levels among this group (Table
5-8).
Cole et al. (1995) reported on CDD/CDFs and PCBs in 132 serum samples (pooled
to 14) from Ontario Great Lakes anglers and control populations. Based on a preliminary
survey, anglers from the communities of Cornwall and Mississauga, Canada, were
categorized based on the numbers, species, and locations of fish caught and kept for
consumption, and on data reflecting the contaminant levels for the fish in these areas.
Individuals categorized as having the highest and lowest potential for having elevated
body burdens of CDD/CDFs and PCBs were selected for biological sampling. Individuals
who did not consume fish served as controls. Study participants were further categorized
by age (i.e., <38 years, 38-50 years, and >50 years). The results, however, indicated
that mean I-TEQDF levels were similar for both eaters and noneaters of Great Lakes' fish in
these communities. l-TEQDFs ranged from 20.8 to 41.2 ppt for fish eaters and 24.7 to
36.8 ppt for noneaters. In general, mean l-TEQDFs increased with age (Table 5-9). PCBs
77, 126, and 169 were also evaluated in the serum samples collected from Cornwall
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residents. Mean TEQP-WH098s ranged from 2.6 to 17.3 ppt for fish eaters and noneaters
combined. Again, significant differences between the two groups were not observed and
the serum CDD/CDF and PCB levels are within the range of values observed for the
general population, as presented in Chapter 4.
Health departments of five Great Lakes states: Wisconsin, Michigan, Ohio, Illinois,
and Indiana, formed a consortium to study blood levels of chemical residues in fish
consumers of three Great Lakes: Michigan, Huron, and Erie. Anderson et al. (1998)
reported on a feasibility study to determine which compounds might be found in very
frequent Great Lakes sport fish consumers. Anderson et al. (1998) selected 32 angling
enthusiasts who reported eating at least one sport fish meal per week from one of three
Great Lakes (i.e., 11 Lake Huron anglers, 11 Lake Erie anglers and 10 Lake Michigan
anglers). The analysis included examination of serum levels of 7 CDDs, 10 CDFs, 4
coplanar PCBs (i.e., 77, 81,126, and 169), and 32 other PCB congeners. One individual
was excluded from the data summary due to unusually high occupational/environmental
exposures. The blood CDD/CDF/PCB levels for these anglers were compared to
CDD/CDF/PCB blood levels for a comparison group (n = 70) from Jacksonville, Arkansas.
Data for this Arkansas population are discussed in Chapter 4. The comparison groups
represented the general population with no known exposure to the contaminants of
concern (Anderson et al., 1998). The mean CDD/CDF lipid adjusted serum concentrations
for both the sport fishing populations and the comparison group used by Anderson et al.
(1998) are shown in Table 5-10. The mean coplanar and other PCB lipid adjusted serum
concentrations are reported in Table 5-11. The average lipid-based I-TEQDFP concentration
for Great Lakes fish consumers was calculated at 56.8 ppt, with the breakdown as
follows: I-TEQD = 27.5 ppt; I-TEQF = 11.9 ppt, and TEQP-WH094 = 17.4 ppt. Anderson
et al (1998) suggested that these values were higher than the background population used
in the comparison.
The Anderson et al. (1998) study led to a larger study, in which the blood of 100
additional sport fishers were sampled, and a comparison population of 100 other
individuals were sampled. Falk et al. (1999) reported on the results of the blood sampling
from 96 (of the 100) additional sport fishers. Results for the 100 comparison population
were not provided. Falk et al. (1999) presented results in terms of I-TEQDF and TEQP -
WH094 (congener-specific data were not provided), and also examined relationships
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between the CDD/CDF/PCB measurements in blood and factors such as: age, gender,
which Great Lakes the fish came from, the type of sport fish consumed, and amount of
sport fish consumed, as reported by the participants. The median lipid-based TEQDFP-
WH094 from the 96 participants was 21.3 ppt, with the breakdown as follows: I-TEQD =
9.6 ppt; I-TEQF = 7.4 ppt, and TEQP-WH094 = 4.3 ppt. This finding of 21.3 ppt TEQDFP-
WH094 appears significantly lower than the original finding of 56.7 TEQDFP-WH098. One
reason for this is that the lower finding from the 96 participants was a median, while the
finding from the 31 individuals in the pilot study was a mean. It also appears that the
smaller population had a few individuals with very high levels of dioxins which resulted in
a higher mean concentration. Other differences that could be identified from Anderson et
al. (1998) and Falk et al. (1999) include: 1) the time of sampling of the two studies; 2)
the age of the participants; and 3) the sport fish consumption rates. Anderson et al.
(1998) reported that sampling of the initial 31 individuals in the pilot study occurred in
1993. Although Falk et al. (1999) did not identify the date at which the followup study
occurred, it appears likely to have been in 1995 or 1996. Although not expected to be a
large factor explaining the differences in the populations, it is possible that average body
burdens within the population decreased during this time period. The mean age of the 31
participants in the pilot study was 52 years (range 36 to 76), while the mean age in the
second population of 96 participants was 46 years (range 27-67). A clear age relationship
has been demonstrated in other studies, showing that older individuals have higher body
burdens of dioxins. The followup study population of 96 individuals clearly showed lower
consumption of Great Lakes fish as compared to the pilot population of 31 individuals, as
evidenced by questionnaire response data. The followup study population reported an
average of 52 fish meals consumed per year, while the pilot group reported an average of
77 fish meals per year. Likewise, consumption of Great Lakes fish was lower for the
followup group than the pilot group: 43 Great Lakes fish meals per year and 49 Great
Lakes fish meals per year, respectively. Also, the followup group reported a lower number
of years consuming Great Lakes sports fish (26 years) than the pilot group (33 years). In
summary, while the larger population of 96 sport fishers in the full survey appeared to
show a much lower body burden of dioxin-like compounds as compared to pilot population
of 31 sport fishers, the differences could be explained by factors of data description
(median vs. mean), year of sampling, age of participants, and exposure to dioxins in fish.
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Another observation from the Anderson et al. (1998) study was that Lake Erie
sport fish consumers had consistently lower CDD/CDF/PCB serum concentrations than
consumers of sport fish from Lakes Michigan and Huron. Serum levels observed for the
Lake Michigan and Lake Huron fish consumers were similar and higher than those
observed in consumers of Lake Erie sport fish. These interlake differences parallel the
pattern observed in previously reported EPA sport fish tissue monitoring data from the
respective lakes (Anderson et al., 1998) and indicate that serum concentrations may also
be affected by variations in fish concentrations among the lakes.
Kolic et al. (2000) reported on sampling of fish for dioxin-like CDD/F/PCBs
conducted by the Ontario Ministry of the Environment between 1996 and 1998 in the
Ontario Great Lakes region. Table 5-1 2 presents the data on this sampling effort as it was
reported in Kolic et al. (2000). A total of 193 samples are reported on in Table 5-12.
TEQ concentrations were calculated using the WHO 1998 TEF scheme, with zero values
used for non-detects. When no congeners were detected in the sample, the value was
reported as zero. Fish were sampled for the Ontario Sports Fish Program. This program
monitors sport fish in the Ontario fresh water lakes and issues consumption advisories
through its biannual guide. Because of this purpose, the fish sampled are typically larger
and from suspect areas in order to obtain positive results and therefore be able to set
consumption advisories (Reiner et al., 1995). An initial set of data on CDD/CDFs from
this program were reported on in Reiner et al. (1995). Sampling reported there occurred
between 1991 and 1994. Kolic et al. (2000) examined the data taken between 1996 and
1998 for purposes of studying the relationship between dioxin-like PCBs and CDD/CDFs in
the fish. They observed PCB TEQ concentrations substantially greater that CDD/CDF TEQ
concentrations in most locations, as is evident from Table 5-12. Over all lakes, they
observe an average ratio of 6.5 (omitting the large value of 86 from the Welland River, as
well as all circumstances when CDD/CDF TEQ concentrations are zero).
Also noteworthy is that the concentrations reported for these fish are substantially
higher than the fish concentrations used for background exposure calculations in Chapter
5. CDD/CDF concentrations for marine and freshwater sources were 1.0 and 0.26 pg/g
whole weight TEQDF-WH098, respectively. PCB concentrations were 1.2 and 0.25 pg/g
whole weight TEQP-WH098, respectively. In contrast, the overall concentrations for this
Great Lakes data set were 5.4 and 26.6 TEQDF-WH098/g whole weight for CDD/CDFs and
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PCBs, respectively. As observed by Kolic et al. (2000), CDD/CDF concentrations
appeared to be declining since their earlier reporting in 1995. At that time, Reiner et al.
(1995) reported on 198 samples, and the average I-TEQDF concentration was 11.5 pg/g
whole weight.
Hong et al. (1994) analyzed PCBs in human milk from Mohawk and control women
to evaluate the potential effect that relatively high levels of environmental contamination
may have had on the body burdens of lactating Mohawk women in New York. PCBs were
found to be present in fish and wildlife in the vicinity of the Mohawk Reservation, and the
Mohawk people formerly depended on local fish and wildlife for food. However, no
significant differences were observed between the mean total dioxin-like PCB levels in milk
from 30 Mohawk women and the 20 control women. The mean PCB concentrations for
these women were 49 ppb and 55 ppb, respectively. The age of the mother, the length
of the nursing period, and the number of breastfed children were found to influence PCB
levels in human milk. Older women, mothers of first born children, and smokers had
higher levels of PCBs. PCB levels were also higher at the onset of lactation and in earlier
samples during a breastfeeding session.
Dewailly et al. (1994) observed elevated levels of dioxin-like PCBs in the blood of
fishermen on the north shore of the Gulf of the St. Lawrence River who consume large
amounts of seafood. Of the 185 study samples, the 10 samples with the highest total
PCB levels were analyzed for dioxin-like PCBs. Samples from Red Cross blood donors in
Ontario served as controls. Dioxin-like PCB levels were 20 times higher among the 10
highly exposed fishermen than among the controls (Table 5-13). Based on these results
of the 10 highest samples, Dewailly et al. (1994) estimated that for the entire fishing
population studied, dioxin-like PCB levels would be eight to ten times higher than the
control group. Dewailly et al. (1994) also observed elevated levels of dioxin-like PCBs in
the breast milk of Inuit women of Arctic Quebec. The principal source of protein for the
Inuit people is fish and sea mammal consumption. Breast milk samples were collected
from 109 Inuit women within the first 3 days after delivery and analyzed for di-ortho-
dioxin-like PCBs during 1989 and 1990. Subsets of 35 and 40 randomly selected
samples were analyzed for mono-ortho dioxin-like and non-ortho dioxin-like PCBs,
respectively. Samples from 96 Caucasian women from Quebec served as controls. The
levels of non-ortho dioxin-like PCBs for Inuit women ranged from 24.7 to 220.9 ppt.
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These values were three to seven times higher than those observed in the control group.
For mono-ortho and di-ortho dioxin-like PCBS, the levels among the Inuit women were
three to ten times higher than in the control group.
Humphrey et al. (2000) reported on an elevation in the PCB concentrations in
serum of humans consuming Great Lakes fish. They described a careful identification of
"sport fish eaters" and "non-sport fish eaters," or controls, from 11 Lake Michigan
shoreline communities during the years 1979 and 1982. Sport fish eaters were defined as
those individuals who consumed 26 or more pounds of sport-caught fish annually, while
non-sport fish eaters consumed less than 6 pounds of sport-caught fish annually. This
cohort was revisited a second time in 1992 for a study of individuals over the age of 50.
Blood sampling occurred in 1993-1995 for 101 fish-eaters and 78 controls. These
samples were measured to 90 PCB congeners, including dioxin-like PCB congeners 77,
105, 118, 1 23, 1 57, 1 69, and 1 80. They found that sport fishers had significantly
higher PCB concentrations as compared to controls. The mean concentration of total
PCBs (sum of the 90 measured) in the fish-eaters was 14.26 ppb whole weight, while for
the controls, the concentration was 4.56 ppb. They found that 22 of the congeners
explained most of the concentration, and dioxin-like PCBs 105, 118, and 180 were among
those 22. The whole weight concentrations (ppb) of these three PCBs in sport and
control fishers were 2.00 (fishers) vs. 0.79 (control) for PCB 180; 0.26 vs. 0.02 ppb for
PCB 105; and 0.83 vs. 0.06 for PCB 118.
5.4. LOCALIZED IMPACTS
Data have been collected that demonstrate that localized impacts may occur from
emissions of dioxins from incinerators and other potential sources. "Localized impacts"
are defined as measurements of CDD/CDFs in environmental (air, soil) or biotic
(vegetation, animal tissue) samples near incinerators or other sources that show elevation
above typical background levels for the area being studied. Therefore, "impacts," as used
below, refer to elevation above background. These localized impacts may result in
elevated exposure among some members of the population. Most of the data on localized
impacts originate from studies conducted outside the United States, specifically from the
European countries of England, Switzerland, Germany, Austria, The Netherlands, Belgium,
and France. Data collected include concentrations of dioxins in air and soil, biota including
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grass and cow's milk, as well as human blood and hair samples. This section reviews
several of these studies, primarily discussing results in terms of TEQs from CDD/CDFs
only. Following a review of the studies, the principal findings with regard to localized
impacts are summarized.
Goldman et al. (2000) compared serum concentrations of CDD/CDFs among
residents of 2 homes where contaminated home-produced eggs and beef were consumed
to residents of a similar rural area that did not consume home-produced eggs and beef.
The contaminated eggs and beef originated from a residence located near the site of a
1987 fire at a wood preservative plant. The chicken eggs had an I-TEQDF mean
concentration of 10 pg l-TEQDF/g; beef fat contained 27 pg l-TEQDF/g. These
concentrations are 10- to 100-fold higher than the results observed in samples of
commercial foods. The soil near the home where contaminated eggs were observed
contained CDD/CDF levels ranging from 30 to 40 pg l-TEQDF/g, and the soil CDD/CDF
profile was similar to that observed in the eggs. Serum samples were collected from 9
individuals residing in homes where contaminated eggs and beef were consumed. I-TEQDF
concentrations in serum were 26.7 ppt for the 4 individuals who had consumed eggs from
the contaminated site over a 2-year period and 63.7 ppt for the 5 individuals who had
consumed both eggs and beef from the contaminated site for up to 15 years, compared to
17.0 ppt for the comparison group.
Beck et al. (1990) sampled milk from a rural and an industrial area in Germany, and
from dairies near a metals reclamation plant in Austria. Beck et al. (1990) observed
average lipid-based concentration of 0.9 pg l-TEQDF/g in rural, background milk, 2.5 pg
l-TEQDF/g in "industrial milk," and 9.6 pg l-TEQDF/g in the milk obtained from dairies near
the metals reclamation plant. The dairy nearest the metals reclamation plant was located
about a kilometer in the downwind direction and had the highest milk concentration (i.e.,
14pg l-TEQDF/g).
The Austrian metals reclamation plant described above has also been studied for
impacts to air, soil, vegetation, and human blood by another research team (Riss et al.,
1990; Riss, 1993). The plant was located in a rural Alpine river valley in Tyrol, Austria, in
a mostly agriculture area. Although emissions data were unavailable, air concentrations
measured near the incinerator were 1.2 to 2.3 pg l-TEQDF/m3 (Riss, 1993). These data
suggest very high emissions, because typical urban air concentrations are approximately
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0.10 pg/m3 in Europe as well as in the United States, and rural air concentrations are
typically less than 0.05 pg l-TEQDF/m3. (See Chapter 3.) Soil concentrations averaged
420 pg l-TEQDF/g at the site of the incinerator, 170 pg/g within 200 meters of plant, and
46 ppt about 2 km in the downwind direction (Riss et al., 1990). This compares with
typical urban soil concentrations of approximately 10 to 20 pg l-TEQDF/g in both Europe
and the United States and rural soil concentrations of less than 5 pg l-TEQDF/g. (See
Chapter 3.)
A dairy farm was located between 1,400 and 2,100 meters from the same metals
reclamation site in the downwind direction, and members of that farming family consumed
milk from their own cows. Samples of the cows milk ranged from 20.1 to 69.5 pg
l-TEQDF/g on a lipid basis. Given a general background level of milk in the low to sub ppt
level on a lipid basis, it is clear that the milk showed elevated dioxin levels. (See Chapter
3.) Samples in the grass and hay from that farm were also elevated at 13 to 36 pg
l-TEQDF/g dry weight. This compares to typical grass samples found in rural areas at the
low to sub ppt levels (Reed et al., 1990; Kjeller et al., 1991; 1996). Blood samples from
two farmers who consumed this milk were also elevated. Their blood CDD/CDF
concentrations were 152 and 946 pg l-TEQDF/g on a lipid basis. Subsequent samples from
three additional family members were also slightly elevated above typical levels at 41, 66,
and 77 pg TEQP-WH094/g lipid.
The Austrian samples described above were taken in the late 1980s, before
emission controls and other practices (i.e, removal of some plastics) were undertaken to
reduce emissions from these plants. Riss (1993) reported on reductions in both cow's
milk and fodder from this nearby farm in the early 1990s and speculated that they
resulted from reductions in incinerator emissions. CDD/CDF concentrations in cows' milk
dropped steadily from a high in 1987/88 samplings, averaging 49 pg l-TEQDF/g fat, to an
average of 5 pg l-TEQDF/g fat in the 1992/93 sampling. Grass concentrations similarly
dropped from 33 pg l-TEQDF/g dry weight to 4 pg l-TEQDF/g dry weight between the two
sample dates. This trend demonstrates an important expectation with regard to
environmental responses to reductions in emissions from tall industrial stacks.
Specifically, vegetation appears to respond immediately to reduced air concentrations, and
if dairy cows are being fed with vegetation that has reduced concentrations, cow's milk
should similarly respond in a rapid manner. Fries and Paustenbach (1990) stated that a
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steady state is reached in cow's milk with a constant dietary input of dioxins after about
30 to 60 days. Therefore, reductions in emissions will result in both a reduction in
vegetation and cow's milk concentrations almost simultaneously.
Another study was conducted in Austria by Moche and Thanner (1997). The study
evaluated ambient air patterns and CDD/CDF concentrations in a vicinity of steel
production plants in Leoben/Donawitz. Samples were collected from sites in the
immediate vicinity of the production plants, in an area that was expected to be impacted
by the production plants, and in an area that was shielded by mountains in the northwest.
Sampling occurred over four periods to address the potential influence of the summer and
winter fluctuations in CDD/CDF concentration. The CDD/CDF concentrations in these
samples were compared to previous data collected in the three Austrian conurbations
Graz, Linz, and Wien. The previous data suggested average summer levels of CDD/CDFs
in the range of 20 to 40 fg l-TEQDF/Nm3 and winter levels in the range of 50 to 220 fg I-
TEQDF/Nm3. The data collected at Leoben/Donawitz indicated higher ambient air levels of
CDD/CDF concentrations. Only the levels in the area shielded by mountains fall within the
levels of the previously reported data. In addition, the CDD/CDF profiles of the
Leoben/Donawitz sites indicated a high contribution of the lower chlorinated CDFs (tetra-
through hexachlorinated CDFs as the most abundant). The patterns were in good
agreement with emission profiles of metallurgical processes reported by Hagenmaier et al.
(1994) (Moche and Thanner, 1997).
Liem et al. (1991) reported on the analysis of over 200 samples of cow's milk that
were taken in various regions in The Netherlands, including some that were near municipal
solid waste incinerators and metals reclamations plants, and some identified as
background sites. Background levels ranged from 0.7 to 2.5 pg l-TEQDF/g lipid. The
highest levels were found approximately 2 km from the largest municipal solid waste
incinerator identified at the time, with concentrations ranging from 2.8 to 12.6 pg I-
TEQDF/g lipid. Higher than background levels were also found in samplings near other
incinerators. The researchers did a principal component analysis on congener profiles in
the milk samples to determine if there were any discernable differences among groupings
of samples. Liem et al. (1991) observed a distinct pattern for samples around the metals
reclamation plant compared to samples around municipal solid waste facilities. A higher
CDF/CDD ratio was found around the metals reclamation plant (i.e., higher furan
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concentrations were in the milk near the metals reclamation plant than near the municipal
solid waste incinerator). Liem et al. (1991) speculated that metals reclamation plants
process cables that contain PVC, and according to Christmann et al. (1989), furans are
predominantly formed in the combustion of PVC. Subsequently, the higher levels of
furans would be taken up into vegetation and then into cow's milk. Liem et al. (1991)
also found distinct patterns in samples associated with other facilities, as characterized by
the relative amounts of lower and higher chlorinated congeners. Two of the incinerators
were closed in April of 1990, and a marked decrease in sample concentrations associated
with these two incinerators was noted between the February and August 1990 sampling.
This supports the expectation described above regarding the response of vegetation and
milk to changes in nearby source emissions.
A limited sample from six cows in Switzerland showed similarly elevated CDD/CDFs
in association with incinerators or manufacturing sites. Higher CDD/CDF concentrations
were observed in milk samples that were within 1,000 meters of an incinerator (two
samples) and those that were within 1,000 meters of a production site for various
chlorinated samples (one sample) than samples from a background farm (one sample) and
from local dairies that pooled milk from several farms (two samples) (Rappe et al., 1987).
Insufficient information was available in this report to calculate I-TEQDF concentrations.
De Fre and Wevers (1998) evaluated paired CDD/CDF deposition and cow's milk
data from several locations in Belgium to evaluate the relationship between deposition
rates and milk levels, and the potential impact that elevated deposition rates may have on
local milk supplies. CDD/CDF deposition ranged from approximately 2 ng l-TEQDF/m2/year
to 45 ng l-TEQDF/m2/year, and CDD/CDF concentrations in milk fat ranged from
approximately 1 pg l-TEQDF/g to 1 9 pg l-TEQDF/g. The correlation coefficient (R) for
CDD/CDF deposition rates and milk fat concentrations was 0.69. The results of a
regression analysis using these data indicated that milk fat concentrations of l-TEQDFs
could be predicted from deposition rates using the equation y = 0.3332x, where y is the
milk fat concentration of CDD/CDFs in units of pg TEQDF/g and x is the CDD/CDF
deposition rate in units of ng TEQDF/m2/y.
In France, the Ministry of Agriculture and Fisheries investigated CDD/CDF
concentrations in cow's milk sampled from farms in a downwind direction within 11 km,
but mostly within 5 km, of 26 industrial facilities (Defour et al., 1998). These industries
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included: steel manufacturing, secondary lead and aluminum smelting, copper refining,
chemical and oil refining industries, electricity production, and municipal waste
incinerators. Of the 49 milk samples analyzed, 46 samples had CDD/CDF concentrations
that were less than 3 pg l-TEQDF/g fat with an average of 1.53 pg l-TEQDF/g on milk fat
basis. One milk sample collected from a site near a chemistry industry was found to
contain 3 to 5 pg l-TEQDF/g fat, and two milk samples collected 250 m and 1 km
downwind of incinerators had concentrations higher than 5 pg l-TEQDF/g fat. The average
concentration in milk and diary products in France assessed through a 1996 survey
conducted by the Ministry of Agriculture and Fisheries was 1.33 pg l-TEQDF/g fat (Defour
et al., 1998).
Abraham et al. (1998) reported on the levels of CDD/CDFs in the human milk of 10
mothers who lived within a radius of 8 km of llsenburg, Germany. The town was
identified as an area highly contaminated with CDD/CDFs reportedly resulting from
emissions from a copper plant. At the time of sample collection (i.e., 1997) the plant had
been closed for approximately 6 years, Abraham et al. (1998) compared the findings to
the results of a previous study of human milk levels conducted in 1990/1991 when the
plant was still in operation. The 1990/1991 human milk samples contained a mean I-
TEQDF of 59 ppt, lipid based (n = 9). The 1997 human milk samples contained a mean I-
TEQDF of 41 ppt, lipid based (n = 10). Abraham et al. (1998) documents that this
decrease in CDD/CDFs is lower than the decline reported in general background
concentrations in human milk from Western Germany in recent years. These values are
somewhat higher than the values reported in Chapter 4 for the general population of the
United States.
An extensive study was undertaken in the Pontypool environment of South Wales
(Ball et al., 1993; Ball et al., 1994a; Ball et al., 1994b; Ball et al., 1995). Evidence of the
impact of emissions from waste incineration at Rechem International Ltd. (a chemical
company) prompted extensive investigations into impacts from emissions of PCBs and
CDD/CDFs to nearby and regional media including soil, grass, water, air, fruit/vegetables,
cow's milk, duck meat, and eggs from chicken and ducks. The region has a combination
of residential and industrial uses, with very little agricultural uses. The greatest impact
was found at a residence adjacent to the site, located only about 100 meters away. The
soil at Rechem International averaged 810 pg l-TEQDF/g (n=4), while at this nearby
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residence, the concentration averaged 66 pg l-TEQDF/g (n = 8). Other areas evaluated
ranged from 4 to 24 pg l-TEQDF/g. Data were not available on CDD/CDF emissions, but air
measurements at this residence suggested high emission rates. For five air samples taken
at the residence, air concentrations ranged from 1.6 to 14.8 pg l-TEQDF/m3. This
compares to air concentrations ranging from 0.02 to 0.68 pg l-TEQDF/m3 taken from a site
about 2,500 meters away in the same direction from the Rechem site. The researchers
also compared these air concentrations to average air concentrations ranging from 0.21 to
0.67 pg l-TEQDF/m3 in four other UK urban areas. Concentrations of CDDs/CDFs in grass
were found to be elevated at the same residence, but described as more typical for other
grass sampling sites. Perhaps most importantly, samples of duck and bantam eggs from
this residence showed concentrations that exceeded other duck and bantam egg samples
in the area by a factor of 10. Duck and bantam egg concentrations in the area, but not at
this residence, were described as typical of background. Duck meat at the impacted
residence was not described as elevated compared to duck meat from nearby settings.
Sampling of sediments in a nearby reservoir did not indicate elevated concentrations of
dioxins or PCBs. There was no sampling of human blood or tissue. However, a simple
exposure exercise showed that consumption of duck eggs, duck meat, apples, inhalation,
and incidental soil ingestion at this impacted residence would result a daily intake of 165
pg l-TEQDF/day, compared to a background intake from these pathways of 43.2 pg
l-TEQDF/day (consumption rates described as typical derived from consumption data from
the Ministry of Food and Fisheries in the UK).
Foxall et al. (1997) also reported geographical variations in environmental levels
and human exposure to CDD/CDFs and PCBs of the above study. The data indicated a
particular impact in a 200-meter wide strip of land around the boundary of the incineration
plant owned by Rechem International Ltd. This location is predominantly downwind from
the incinerator and there had been evidence suggesting that fugitive emissions from the
plant contributed to the environmental impacts. Marked differences were noted between
the CDD/CDF and PCB content of samples (i.e., air, soil, and foods) collected at the
impacted site and those collected at rural background locations. Intakes of CDD/CDFs (pg
l-TEQDF/day) and PCBs (//g/day) were estimated using mean daily food consumption rates,
inhalation and soil ingestion rates of 20 m3/day and 100 mg/day, respectively, and the
median concentrations of CDD/CDFs and PCBs found in the samples. These estimates
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indicated that exposure to CDD/CDFs and PCBs at the impacted site was much higher
than for background levels and the main contributors to these higher levels were residues
in bantam and duck eggs. The estimated intake of CDD/CDFs from ingestion of bantam or
duck eggs at the impacted site were 204 pg l-TEQDF/day and 103 pg l-TEQDF/day,
respectively; levels that are substantially higher than the average UK dietary intake of 88
pg l-TEQDF/day from all food sources. Based on a body mass of 60 kg, these egg intakes
(i.e., 3.4 and 1.7 pg l-TEQDF/kg body mass/day) would represent 34 and 17 percent of the
WHO (World Health Organization) TDI (Total Dietary Intake) value of 10 pg l-TEQDF/kg
body mass. Similarly, the corresponding PCB intake of 7.3 and 6.3 /vg/day would
represent 73 and 63 percent, respectively, of an average dietary intake (10//g/day) of
PCBs.
Lovett et al. (1998) performed additional analysis of chicken, bantam, and duck
eggs; and also duck meat collected from the vicinity of the Rechem incinerator and
compared the results to PCB and CDD/F levels of comparable foodstuffs collected from
rural areas in the same Welsh district. Poultry produced at the impacted residence
displayed a congener profile with noticeable variations compared to those collected from
nearby rural sites. A prominence of higher chlorinated congeners in the egg and duck
meat samples for the residence located near the incinerator was observed. Analysis of 46
PCB congeners resulted in a median fresh mass total PCB concentration in duck eggs of
1 91 /vg/kg (n = 2), 341 /vg/kg in bantam eggs (n = 2), and 43 /vg/kg in duck meat (n = 2)
from samples collected in the impacted area. Observations from rural areas showed fresh
mass based total PCB concentrations of 14/vg/kg for duck eggs (n = 6), 22/vg/kg for
bantam eggs(n = 4), and 25 //g/kg for duck meat (n = 6).
A second location in the United Kingdom, the Derbyshire area in central England,
has shown elevations in cow's milk and other animal tissues. Initially, samples of cow's
milk were taken by the Ministry of Agriculture, Fisheries, and Food (MAFF) from individual
farm tanks on 11 farms in 1990. When 2 of the samples showed high concentrations of
40 and 42 ng l-TEQDF/kg fat (the other 9 showed more typical concentrations in the 1.1 to
7.1 ng l-TEQDF/kg fat), the sampling was expanded to 30 farms. These original two
farms, plus an additional farm, continued to show high concentrations in the milk. Testing
continued in milk through 1994. Milk concentrations dropped at one farm, but overall
concentrations appeared to remain high (i.e., 29 ng l-TEQDF/kg) for the most recently
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reported sampling in July of 1994 (Harrison et al., 1996). As a result of these findings,
MAFF tested animal tissue from the three farms. Calves from one of the three farms had
extremely elevated levels of dioxins and furans, with concentrations ranging from 2.5 to
6.9 ng l-TEQDF/kg whole weight (i.e., not lipid basis) in muscle tissue (MAFF, 1992a).
This compares, for example, with I-TEQDF concentrations approximately 0.20 ng I-
TEQDF/kg in the United States beef supply (from the national study on beef back fat,
assuming 19 percent fat in whole beef (Winters et al., 1996). Egg samples were taken
from one of the three farms and a second "free range" supplier. The concentrations found
were reported as 2.2 and 2.1 ng l-TEQDF/kg in whole eggs. A second sample from one of
the farms taken a year later showed a lower concentration of 0.8 ng l-TEQDF/kg whole
weight. These would appear to be elevated, considering that eggs found in a background
setting in Mississippi (Cooper et al., 1995), had concentrations less than 0.10 ng I-
TEQDF/kg whole weight.
MAFF (1992b) also sampled leafy herbage (grass, hay, etc.) from the three farms
described above. Concentrations ranged from about 2 to 14 ng l-TEQDF/kg dry weight.
This appears elevated, considering that samplings of background grass in England showed
0.89 ng l-TEQDF/kg dry weight, as reported in 1991 (Kjeller et al., 1991) and 0.57 ng
l-TEQ/kg dry weight in 1996 (Kjeller et al., 1996). The evidence suggests that the
impacts were due to nearby industrial emissions from a fuel plant and chemical waste
incineration. Her Majesty's Inspectorate of Pollution (HMIP) conducted additional studies
to evaluate this possibility. These included stack testing of the Coalite Fuels, Ltd. and the
Coalite Chemicals, Ltd. (which were adjacent to one of the farms and near the other two),
air dispersion modeling, and soil monitoring. No results were available to evaluate the
stack testing, but the air dispersion modeling predicted that the three impacted farms
would be in the sectors having the highest air concentrations. The soil sampling on the
three farms showed concentrations ranging from 10 to 90 ppt. This can be considered
elevated above typical rural background and in the range or even higher than typical urban
concentrations. Specifically, this compares with typical urban soil concentrations of 10 to
20 pg l-TEQDF/g in both Europe and the United States, and rural soil concentrations
typically less than 5 pg l-TEQDF/g. (See Chapter 3.) Also, these concentrations are similar
to concentrations found near incinerators emitting very high concentrations of dioxins in
Tyrol, Austria, as described above, and in Columbus, Ohio, as described below. The
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National Rivers Authority sampled sediment upstream and downstream of the effluent
discharge pipe of the Coalite Chemicals, Ltd site. Samples collected about 1.5 km
downstream of the discharge site had CDD/CDF levels that were 1,000 times greater than
background samples collected 1.5 km upstream of the discharge point (these studies
reported in MAFF, 1992a).
Another interesting finding associated with the samplings of foods and
environmental media in the Derbyshire area were the congener profiles. Compared to
background milk samples, the samples from the three impacted farms had proportionally
higher concentrations of lower chlorinated dioxins, particularly 2,3,7,8-TCDD and
1,2,3,6,7,8-HxCDD. The background samples of milk tended to be dominated by OCDD
(MAFF, 1992b). Blood samples were also collected from residents of these three
impacted farms and analyzed for CDD/CDFs (Startin et al., 1994). The I-TEQDF
concentrations for the 10 individual blood samples ranged from 49 pg/g to 291 pg/g on a
lipid basis. In contrast, the two control samples had I-TEQDF concentrations of 16 pg/g
and 26 pg/g on a lipid basis. The Derbyshire samples were dominated by OCDD, followed
by 1,2,3,4,6,7,8-HpCDD, 2,3,7,8-TCDD, and 1,2,3,6,7,8-HxCDD.
Sandalls et al. (1998) analyzed soil concentrations around the site of a chemical
waste incinerator near Bolsover, Derbyshire, United Kingdom. At each of 46 sites, five
surface soil samples were collected, at varying depths up to a depth of 5 centimeters,
every 1 square meter. All 46 sample sites had total TCDD concentrations exceeding
background concentrations. Higher concentrations of TCDD were observed at locations
closer to the incinerator and there was a strong correlation between the TCDD
concentration in a given quadrant and the amount of time that the wind was blowing in
that direction. At four quadrants around the site, TCDD soil concentrations were reported
as 603 ppt for the northeast (approximately 42 percent of the total deposition); 315 ppt
for the southeast (22 percent of the total); 269 ppt for the southwest (19 percent of the
total); and 244 ppt (1 7 percent of the total) for the northwest. The results of the spatial
distribution of CDD/CDFs implicated the incinerator as the likely source, and the
correlation between deposition and wind direction suggested that these compounds
reached the ground via the atmosphere. Also, 42 of the 46 sample sites showed similar
CDD/CDF congener ratios to the flue gas of the waste incinerator. Soil concentrations
were well in excess of background concentrations, up to 5 kilometers around the site.
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Ohta et al. (1997) studied levels of CDD/CDF and non-ortho coplanar PCBs in soil
at a high cancer-rate area close to a batch-type municipal solid waste (MSW) incinerator in
Japan. Sixty-one soil samples were collected around the MSW incinerator. Among them,
52 samples were radially collected within 2 km from the center of the MSW incinerator,
and 9 samples were collected across the high cancer-rate area. High concentrations of
CDD/CDFs and coplanar PCBs were observed in all the soil samples from the leeward side
of the MSW incinerator. Total concentrations ranged from 5,303 to 32,167 pg/g; mean
= 13,934 pg/g. On the other hand, all but one sample on the windward site showed high
contamination. Among the 61 samples analyzed, the total concentration was greater than
2,000 pg/g in 45 of the 61 samples and the TEQDFP-WH094 concentration was over 10
pg/g in 39 of the 61 samples. In addition, the levels of CDD/CDFs and coplanar PCBs at a
distance of 0 to 1.1 km from the MSW incinerator was compared with that of the area
1.1 to 2.0 km from the MSW incinerator. The area closer to the incinerator contained a
higher ratio of samples with contamination over 5,000 pg/g (63.2 percent) than in the
area further away from the MSW incinerator (38.5 percent). Similarly, the area closer to
the incinerator had a higher percentage of samples with CDD/CDF/PCB levels over 30 pg
TEQDFP-WH094/g (26.3 percent) than the area further away from the incinerator (15.3
percent).
Miyata et al. (1998) collected blood samples from residents living within 2 km from
a batch-type municipal solid waste incinerator in Japan where soil concentrations of
CDD/CDFs and PCBs were shown to be elevated. Eighteen blood samples were collected
from 13 men, aged 23 to 63 years old (average age = 45 years), and 5 women, aged 30
to 72 years old (average age = 46 years) in March 1996. The results indicated that the
average lipid-based TEQDFP-WH094 concentrations found in blood samples ranged from 34
pg/g to 200 pg/g with a mean of 81 pg/g for men, and from 22 pg/g to 463 pg/g with a
mean of 149 pg/g for women. These mean TEQDFP-WH094 values are higher than those
reported for the general population of various countries (the mean value estimated in
Chapter 4 of this document is 55 pg/g).
Local impacts around a waste-to-energy municipal solid waste incinerator in
Columbus, Ohio, were undertaken by the Ohio Environmental Protection Agency, and the
U.S. Environmental Protection Agency (Lorber et al., 1998). This incinerator operated
between 1983 and 1994. A stack test was taken in 1992, and when the results were
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extrapolated to typical operation of the incinerator, annual emissions were calculated at
985 g l-TEQ/yr. This is a very high emission rate, and compares to total emissions from
several European countries. It is about one-tenth of the national emissions estimated for
all United States sources. (See Volume 1.) Process modifications were undertaken in the
winter of 1993/94. A stack test was conducted which indicated that annual emissions
were reduced to 267 g l-TEQDF/yr. The U.S. EPA undertook a soil testing program in
December of 1995. Results showed a definite impact to soils at the site of the
incinerator, with an average concentration of 356 pg l-TEQDF/g (n = 4). Of the four
samples collected, three of the samples averaged 458 pg l-TEQDF/g and the fourth was
much lower at 50 g l-TEQDF/g. Just offsite in the downwind direction, a cluster of four
samples within 1,000 meters also showed some elevation of CDD/CDFs with an average
concentration of 49 pg l-TEQDF/g. Fourteen additional samples, generally within 2 miles of
the site, averaged 10 pg l-TEQDF/g. Three soil samples at a background site 28 miles
away in the upwind direction averaged 1 pg l-TEQDF/g. These latter two clusters of urban
and background samples have concentrations that are typical of urban and background
situations. The urban results suggests that, despite large emissions from this source, soil
impacts above typical levels appeared to be restricted to within 1,000 meters of the
incinerator.
The Ohio EPA conducted air monitoring in 1994 and 1995 (OEPA, 1994; Lorber et
al., 1998). Monitoring in 1994 occurred after process modifications were undertaken to
reduce dioxin emissions. A stack test conducted just prior to the air sampling showed
reductions of 75 percent from the levels measured in 1992. Wind rose data were taken
on an hourly basis during the 1994 sampling. This showed that two samples from a
sampler located about 2 miles away were in the downwind direction during the 48-hour
sampling period. Eight other samples from four samplers (which were between 1 and 2
miles from the incinerator) were clearly not in the downwind direction. The two
downwind samples averaged 0.26 pg l-TEQ/m3 CDD/CDFs, while the eight upwind
samples averaged 0.05 pg l-TEQDF/m3. The incinerator shut down in December 1994.
Five samples taken in 1995 showed an average of 0.05 pg l-TEQDF/m3 CDD/CDFs. This
air sampling suggests the following: (1) the typical background urban air CDD/CDF
concentration in Columbus is probably around 0.05 pg l-TEQDF/m3; and (2) when the
incinerator is emitting dioxins typical of the rate measured in the 1994 stack test (not the
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1992 stack test), air concentrations about 2 miles away are higher, perhaps by a factor of
5. Other media, including vegetation, agricultural products, or human blood were not
sampled.
Schecter and Papke (1998) examined CDD/CDFs and PCBs 77, 126, and 169 in
blood sampled from 10 residents living near a PCB manufacturing facility in Alabama in
1997, and compared these concentrations to a pooled sample from a control group
representing 100 adults. The results showed that, for the 10 residents, total lipid-based
CDD/CDF concentrations ranged from 825 ppt to 6,422 ppt. The corresponding total
CDD/CDF concentrations from the pooled control sample was 1,112 ppt. Total PCB
concentrations ranged from 240 ppt to 5,216 ppt for the 10 nearby residents, compared
to 1,112 ppt for the pooled control sample. PCB-77 was only detected in the blood of 5
of the 10 residents, ranging from 41 ppt to 71 3 ppt. PCB-1 26 concentrations ranged
from 104 ppt to 4,050 ppt in residents, compared to 48 ppt found in the control sample.
PCB-1 69 concentrations ranged from 136 ppt to 2,807 ppt for the 10 residents,
compared to 35 ppt for the control sample. In terms of TEQ, the total I-TEQD and I-TEQF
concentrations ranged from 16.3 ppt to 38.9 ppt, and from 6.7 ppt to 131 ppt,
respectively, compared with 18.5 ppt and 8.3 ppt from the control blood. TEQP-WH094s
ranged from 34 ppt to 360 ppt compared to 32 ppt for the control blood.
There were also studies conducted in Asia to address localized impacts.
Luksemburg et al. (1997) reported, in a preliminary study, that high levels of CDD/CDFs
were observed in soil and sediment samples collected inside and outside a sodium
pentachlorophenate plant in Tianjin, China. The plant is situated in a wetland with rivers
emptying into the nearby Pacific Ocean and close to several large housing developments.
Human hair samples collected from barber shops in the housing developments near the
plant were also collected and CDD/CDFs were detected in these samples. The I-TEQDF
concentrations in soil ranged from 1 5 ppt at a site upstream of the plant to 740,000 ppt
within the plant, and was 1,800 to 2,200 ppt at sites outside the plant. The I-TEQDF
concentrations in sediment ranged from 1 50 ppt at a site 50 km away from the site to
110,000 ppt in a drainage canal located just southwest of the plant. Hair samples
contained I-TEQDF concentrations ranging from 1 2 to 120 ppt. According to Luksemburg
et al. (1997), "the isomer profiles of all the samples were consistent with the
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pentachlorophenol sources." However, it should be noted that no background information
on the test subjects (time of residence, health records, etc.) were collected in this study.
The major findings and conclusions based on this review of localized sources
include:
• Localized impacts, meaning elevated concentrations of CDD/CDFs above
background, have been found in the vicinity of some CDD/CDF sources.
• Localized impacts appear to be limited to an area within 5 km of an
incinerator source, perhaps only within 2 to 3 km of the source, and in some
cases, only within a few hundred meters of the source. One study noted
elevations in grass, cow's milk, and human blood on a farm located 2 km
from an incinerator presumed to be emitting high amounts of CDD/CDFs.
Not all of the studies described in the literature discussed distance from the
source, as the surveyed areas were simply identified as "industrial."
• Several studies continued environmental samplings after efforts were made
to reduce emissions or after the sources were shut down. In these cases,
reductions in CDD/CDF concentrations were noted for various media
including, cow's milk, vegetation, and air. As discussed below, vegetation
has been found to respond rapidly to reductions in air concentrations, and
the time to reach steady state in cow's milk given a steady input of
CDD/CDFs is also relatively short. In other cases, such as in the
accumulation of CDD/CDFs in soils or in body fat, the benefits may not be
as immediate. Soil and body fat are reservoirs in which the residence time
of these compounds are measured on the order of years.
The available data reviewed above suggest that measurable impacts near
incinerators only occur if the incinerator emits very high amounts of dioxins, in contrast to
emissions that are known to be within regulatory limits. However, two key descriptors
here, including "measurable impacts" and "very high amounts of emissions" cannot be
rigorously defined. The data suggest that "measurable" impacts can be defined as
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elevations in dioxin concentrations in environmental or biotic media on the order of 5-10
times higher than typical background. "Very high amounts" of releases is less well
defined. The Columbus incinerator is the only incinerator reviewed above for which
emission data were available, and the stack test of 1992 showed emissions that were
about 200 times higher than the 1995 proposed regulatory limit for solid waste
incinerators of 30 ng of total dioxins per m3. By comparison of environmental media
sampling, one could surmise that the metals reclamation plant in Tyrol, Austria, and the
incinerators in the Derbyshire area of Central England, if not others noted above, were also
emitting unusually high amounts of dioxins.
Also, it is important to understand that elevations in air, soil, vegetation, and
animal products do not automatically translate to higher exposure levels. This document
(and several other efforts worldwide) have concluded that the bulk of exposure to dioxins
occurs via the diet, and specifically animal fats. Higher exposure to an individual would
only result if an individual subsisted on animal food products from animals raised near
incinerators (meaning also that the animal's diet was comprised of vegetation grown
where the animal is raised), and perhaps only incinerators emitting high amounts of
dioxins. As described above, there are limited human tissue data for individuals where
localized environmental contamination has been demonstrated. In one study in Austria of
a farming family consuming home-grown milk near the metals reclamation plant, both the
milk and the blood of the family were shown to have elevated levels of dioxins. In the
vicinity of a municipal solid waste incinerator in Japan where CDD/CDF and PCB
concentrations in soil were elevated, nearby residents also had elevated blood levels of
CDD/CDF/PCBs. In the U.S., there have been no studies to evaluate the prevalence of
subsistence behavior near sources, in general, and near high emitting incinerators, in
particular. Even if there is a sparsity of subsistence behaviors near sources in the U.S., it
is reasonable to assume that animal fats produced near high emitting incinerators would
likely have elevated CDD/CDF levels and be consumed. If incinerators are meeting
regulation limits, they would not be high emitters, and the likelihood of localized impacts
would be small.
In addition to the localized contamination resulting from incinerator emissions, as
described above, there have been several incidents involving contamination of commercial
food supplies from natural and accidental sources in various parts of the world. For
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example, ball clay was found to be the source of elevated CDD/CDF levels in animal food
products in the United States (Ferrario et al., 2000). Ball clay from a mine in Mississippi,
which was used as an anti-caking agent in soy-based animal feeds, was found to be the
source of contamination after poultry and catfish samples collected in the same region of
the United States were found to have concentrations of CDD/CDFs that were significantly
elevated above background concentrations. The incident, which occurred in 1998,
involved less than 5 percent of the national poultry production. Subsequently, the use of
ball clay in animal feeds was discontinued. In Germany, dairy products were found to be
contaminated in 1998 (Malisch, 1998). The concentration of CDD/CDFs in milk was 1.38
pg l-TEQDF/g fat (N=43) in 1998, compared to 0.62 pg l-TEQDF/g fat (N = 76) in 1997.
After intense investigation, contaminated citrus pulp obtained from Brazil, which was used
as a feed ingredient for dairy cows in certain regions of Germany in 1998, was found to
be the source of contamination of the food supply. In 1999, similar contamination of the
commercial food supply occurred in Belgium when 500 tons of animal feed was
inadvertently contaminated with approximately 50 kg of PCBs and 1 g of dioxins in
transformer oil (Van Larebeke et al., 2001). The feed was delivered to poultry farms (and
to a lesser extent rabbit, cow, and pig breeding facilities), primarily in Belgium. This
contaminated feed represented a "limited percentage" of the feed produced in Belgium,
but was delivered to hundreds of farms in the country. The mean TEQDFP-WH094
concentrations were 170 pg/g fat in poultry, 2.3 pg/g fat in eggs, and 2,320 pg/g fat in
animal feed. Once discovered, animal products with excessive levels were destroyed,
including approximately 2 million chickens.
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5.5. CIGARETTE SMOKERS
As discussed in Volume 1, cigarette smoking has been found to be a source of
CDD/CDFs. As a result, individuals who smoke cigarettes, and nonsmokers who are
exposed to second-hand smoke, may experience higher levels of exposure to dioxin-like
compounds than the general population. Matsueda et al. (1994) reported that the mean
I-TEQDF content of a pack of U.S. cigarettes was 8.6 pg. This estimate is based on
analytical data from seven brands of U.S. cigarettes. Assuming that a pack of cigarettes
contains 20 cigarettes, the I-TEQDF content of a single cigarette would be 0.43 pg. This
value represents about half of the I-TEQDF value reported for a mainstream cigarette smoke
from a Swedish brand of cigarettes (Lofroth and Zebuhr, 1992) and is about five times
higher than the I-TEQDF level in mainstream smoke from German cigarettes (Ball et al.,
1990). The daily intake of CDD/CDFs by smokers can be estimated by multiplying the
CDD/CDF content of a single cigarette by the mean number of cigarettes smoked per day
by current smokers. According to U.S. EPA (1992), 25.5 percent of the adult U.S.
population were smokers in 1990. The average daily number of cigarettes smoked by this
population was 19.1. Thus, mean CDD/CDF exposures via cigarette smoking are
estimated to be 8.2 pg l-TEQDF/day for smokers. This level of exposure represents over
10 percent of the average daily background dose of CDD/CDFs from soil, air, water, and
foods, as described in Chapter 4. The use of data on the total I-TEQDF content of a
cigarette from Matsueda et al. (1994) results in uncertainties as to the estimate of
exposure to smokers because the approach assumes that all of the dioxin in the unburned
cigarette is inhaled. It is likely that some of the dioxins are released with the sidestream
smoke rather than being inhaled. It is also possible that dioxins are destroyed and/or
formed during the combustion process. Thus, it is unclear if these factors would lead to a
net increase or decrease in the amount of dioxins inhaled. However, as described above,
the I-TEQDF value reported by Matsueda et al. (1994) is less than that in mainstream (i.e.,
inhaled) smoke reported by Lofroth and Zebuhr (1992) and greater than that of Ball et al.
(1990) and provides the best estimate of CDD/CDFs in cigarettes to which smokers may
be exposed.
Nonsmokers may also be exposed to CDD/CDFs from environmental tobacco
smoke. Although the data on the frequency, magnitude, and duration of exposure to
environmental tobacco smoke are limited, an idea of the magnitude of exposure to
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CDD/CDFs can be gained by assuming that nonsmokers receive a fraction of the CDD/CDF
TEQ received by smokers. Based on data for nicotine, the dose to nonsmokers exposed
to environmental tobacco smoke is estimated to be 0.1 to 0.7 percent that of smokers
(U.S. EPA, 1992). For 4-aminobiphenyl, nonsmokers exposed to environmental tobacco
smoke were estimated to receive a dose that was 10 to 20 percent that of smokers (U.S.
EPA, 1992). Assuming that nonsmokers receive 0.1 to 20 percent of the dose of
CDD/CDFs from second-hand smoke that smokers receive, the estimated daily dose of
CDD/CDFs for nonsmokers would range from 0.008 pg l-TEQDF/day to 1.6 pg l-TEQDF/day.
It should be noted, however, that individual exposure to sidestream smoke is highly
variable, depending on a person's proximity to smokers, how often they are near smokers,
and the ventilation rate in these areas.
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Table 5-1. Concentrations of CDDs, CDFs, and Dioxin-Like PCBs in Blood
(lipid based) of a Breast-Fed and a Formula-Fed
Infant at the Age of 11 and 25 Months
Compound
(cone, in pg/g fat)
2,3,7,8-T4CDF
2,3,7,8-T4CDD
1,2,3,7,8-P5CDF
2,3,4,7,8-P5CDF
1,2,3,7,8-P5CDD
1,2, 3,4,7, 8H6CDF
1,2,3,6,7,8-H6CDF
2, 3,4,6,7, 8-H6CDF
1,2,3,4,7,8-H6CDD
1,2, 3,6,7, 8-H6CDD
1,2,3,7,8,9-H6CDD
1,2, 3,4,6,7, 8-H7CDF
1,2,3,4,6,7,8-H7CDD
OCDF
OCDD
TEQDF-WH098
«LD = 0.5*LD)
PCB 77
PCB 126
PCB 169
TEQP-WH098
Age (Months)
Breast-Fed Infant
11
<2.7*
3.7
<1.2
23.1
11.1
9.8
8.1
<3.4
7.8
43.0
7.1
13.1
24.3
<5.0
148.7
29.2
23 (m)
287
270
31.4
25
<2.5
4.1
n.d. (1.4)
29.7
15.2
12.2
10.2
<3.0
9.1
51.7
8.1
n.a.
29.7
n.a.
204.0
36.8
20 (m)
n.a.
183
1.8
Formula-Fed Infant
11
<3.0
<1.0
<1.2
1.5
<1.0
<2.2
<1.0
<2.3
n.d (1.1)
2.5
n.d. (1.2)
<5.8
8.8
<5.0
79.3
2.4
26 (m)
24
7
7
25
<2.5
<1.0
n.d. (2.5)
<2.5
n.d. (1.8)
<2.5
<2.5
<2.5
n.d. (2.8)
<5.4
<4.5
<6.0
<10.0
n.a.
70.0
2.3
20 (m)
n.a.
11
0.1
* for values reported as "<" a value (2.7, e.g.), % the concentration was used for TEQ calculations.
n.a. = not analyzed
n.d. = not detected (limit of detection)
(m) = maximum value, due to possible contribution of a contaminant
Source: Abraham et al. (1995).
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Table 5-2. Concentrations of CDDs and CDFs in Adipose Tissue
(lipid based) of Stillborn, Formula-Fed, and Breast-Fed Infants
Compound
(cone, in pg/g fat)
2,3,7,8-TCDD
1,2,3,7,8-PCDD
1,2, 3,4,7, 8-HxCDD
1,2,3,6,7,8-HxCDD
1,2,3,7,8,9-HxCDD
1,2,3,4,6,7,8-HpCDD
OCDD
2,3,7,8-TCDF
1,2,3,7,8,-PCDF
2,3,4,7,8-PCDF
1,2, 3,4,7, 8-HxCDF
1,2,3,6,7,8-HxCDF
2, 3,4,6,7, 8-HxCDF
1,2,3,7,8,9-HxCDF
1,2, 3,4,6,7, 8-HpCDF
1,2,3,4,7,8,9-HpCDF
OCDF
TEQDF-WH098
Stillborn
(n = 3)
1.6
3.4
2.5
8.8
1.3
12.9
51.2
1.4
0.2
9.2
3.7
2.4
1.0
0.1
3.6
0.4
2.1
11.9
Formula-Fed
(n = 8)
0.4
1.1
1.0
4.0
0.7
5.0
29.1
1.9
1.0
3.1
1.7
1.0
0.2
0.1
1.6
0.1
1.8
4.3
Breast-Fed
(n = 9)
1.7
4.9
4.0
19.9
3.7
25.2
91.6
1.1
0.5
10.6
3.5
2.8
1.1
0.1
3.8
0.1
1.6
15.9
Note: Average congener concentrations calculated assuming non-detects equal to % detection limit.
Source: Kreuzer et al. (1 997).
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Table 5-3. Parameters Used for Modeling the Impact of Nursing on Body Burden and
Body Lipid Concentrations of TEQs from Infancy to Adulthood
Time After
Birth
At birth
1 month
2 months
3 months
4 months
5 months
6 months
7 months
8 months
9 months
10 months
1 1 months
1 year
2 years
5 years
1 1 years
1 8 years
34 years
55 years
TEQDFP-WH09S
Concentration
in Breast Milk
Fat (pg/g)
25
22.9
20.8
18.8
16.7
14.6
12.5
11.5
10.4
9.4
8.3
7.3
6.3
--
--
--
--
--
--
Body
Weight
(kg)
3.3
4.3
4.6
6.0
6.7
7.4
7.9
8.4
8.8
9.2
9.4
9.8
11.3
13.3
19.7
41.1
65.1
71.5
73.8
Lipid
Fraction
0.14
0.16
0.18
0.20
0.22
0.23
0.25
0.25
0.24
0.24
0.23
0.23
0.23
0.20
0.15
0.15
0.13
0.21
0.27
Half-life
(yrs)
0.40
0.50
0.60
0.70
0.75
0.80
1.00
1.00
1.05
1.08
1.10
1.12
1.14
1.39
2.12
3.60
5.33
7.70
9.76
Administered Dose of Dioxin TEQDFP- WH09S, pg/daya
Formula
Only
50
50
50
50
50
50
50
50
50
50
50
50
50
50
54
65
66
66
66
6-week BF
800
733
667 / 50
50
50
50
50
50
50
50
50
50
50
50
54
65
66
66
66
6 Months BF
800
733
667
600
533
467
400
50
50
50
50
50
50
50
54
65
66
66
66
1 Year BF
800
733
667
600
533
467
400
367
333
300
267
233
200
50
54
65
66
66
66
2 Year BF
800
733
667
600
533
467
400
367
333
300
267
233
200
200
54
65
66
66
66
a Dose = TEQDFP-WH09S concentration in milk fat (pg/g) x lipid fraction in milk (0.04) x ingestion rate of milk (800 g/day).
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Table 5-4. Model Validation Data and Results (all concentrations in ppt TEQDF-WH098 lipid basis)
Description
1st Mother, 1st child
1st Mother, 2nd child
2nd Mother, 1st child
2nd Mother, 2nd child
3rd Mother, only child
4th Mother, only child
Average, pg/g
TEQDF-WH098
Observed Data
Milk TEQDF-
WHOgg/month
23.5 12
13.7 12
26.5 12
18.3 12
13.7 12
12.7 12
Milk TEQDF-
WH098/month
14.0 / 11
12.7 /5
15.2 / 11
13.1 /6
NA
13.0/6
Child TEQDF-
WHOgg/month
34.7 / 11
11.9/11
44.2 / 12
18.87 12
5.0 / 13
26.5 / 12
23.5
Weeks
Breast-Fed
26
29
30
32
7
30
Model Predictions
Child TEQDF-
WHOgg @
Selected k(t)*
34
27
36
27
10
21
26
Child TEQDF-
WHOgg @
Long k(t)
51
39
56
41
16
32
39
* Predicted child TEQDF-WH098 at the time of measurement (between 11 and 13 months) using the more rapid dissipation rate, k(t),
selected for this model in comparison to the slower k(t) (longer half-life of 7 years) shown in the last column.
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Table 5-5. Results of PK Modeling for Formula Feeding and 4 Breast Feeding Scenarios
Description of Model Output
Peak concentration,
pg TEQDFP-WH098/g lipid
Time after birth of peak
AUC1 after 1 year
AUC after 10 years
AUC after 70 years
AUC (bf) / AUC (formula)2 - 1 yr
AUC (bf) /AUC (formula) - 10yr
AUC (bf) / AUC (formula) - 70 yr
Formula
13.0
9 yr
2,168
39,433
27,5419
—
—
—
6-wk BF
34.1
6 wk
5,989
46,516
282,654
2.8
1.2
1.03
6-mo BF
44.3
9 wk
12,129
62,696
299,304
5.6
1.6
1.09
1-yr BF
44.3
9 wk
13,645
73,183
310,210
6.3
1.9
1.13
2-yr BF
44.3
9 wk
13,645
86,370
324,202
6.3
2.2
1.18
1 AUC = measure of accumulated exposure defined as, "area under the curve", equal to lipid-
concentration, ppt TEQDFP-WH098 * days. For example, a lifetime at 10 ppt TEQDFP-WH098 lipid
would yield an AUC of 10 ppt TEQDFP-WH098 * 70 years * 365 d/yr = 255,500 ppt TEQDFP-WH098-
day.
2 AUC (bf) / AUC (formula) = ratio comparing the accumulated exposure difference between the
breast-feeding scenario and the formula. A result of 6.0 means that the accumulated exposure for
the breast-feeding scenario being evaluated is 6 times more than a formula only scenario.
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Table 5-6. Sensitivity Analysis Testing of PK Model for Breast-Milk Impacts3
Description of Model Output
Peak concentration,
pg TEQDFP-WH098/g lipid
Time after birth of peak
AUC1 (sa) / AUC (6-mo)2 - 1 yr
AUC (sa) /AUC (6-mo) - 10yr
AUC (sa) / AUC (6-mo) - 70 yr
6-mo
baseline
44.3
9 wk
—
—
—
Dissipation
Tests
Sc
#1
49.8
9 wk
1.3
2.2
1.4
Sc
#2
44.3
9 wk
1.0
0.8
0.7
Dose Tests
Sc
#3
48.1
9 wk
1.2
1.1
1.03
Sc
#4
28.5
9 wk
0.6
0.8
0.96
Extremes
Sc #5
54.0
10 wk
1.6
2.6
1.5
Sc #6
28.5
9 wk
0.6
0.7
0.6
1 AUC = measure of accumulated exposure defined as, "area under the curve", equal to lipid-
concentration, ppt TEQDFP-WH098 * days. For example, a lifetime at 10 ppt TEQDFP-WH098 lipid
would yield an AUC of 10 ppt TEQDFP-WH098 * 70 years * 365 d/yr = 255,500 ppt TEQDFP-WH098-
day.
2 AUC (sa) / AUC (6-mo) = ratio comparing the accumulated exposure difference between the
sensitivity analysis scenario and the 6-month breast-feeding baseline scenario. A result of 6.0
means that the accumulated exposure for the sensitivity analysis scenario being evaluated is 6 times
more than the 6-month baseline scenario.
3 Scenario definitions:
Sc #1: Use of the Pinsky and Lorber (1998) lipid-based function for dissipation rate
Sc #2: Use of the Kreuzer, et al. (1997) modeled dissipation rate
Sc #3: Assumption of mother's milk concentration not declining from 25 ppt TEQDFP-WH098
lipid
Sc #4: Assumption of mother's milk concentration beginning at 15 ppt TEQDFP-WH098,
declining to 7.5 ppt TEQDFP-WH098 after 6 months.
Sc #5: Use of Pinsky and Lorber (1998) lipid-based function for dissipation rate and the
assumption of mother's milk concentration not declining from 25 ppt TEQDFP-WH098 lipid.
Sc #6: Use of Kreuzer, et al. (1997) modeled dissipation rate and the assumption of the
mother's milk concentration beginning at 15 ppt TEQDFP-WH098, declining to 7.5 ppt TEQDFP-
WH098 after 6 months.
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Table 5-7. Estimated CDD/CDF/PCB Exposures for Adult Subsistence Fishermen
Media
Soil ingestion
Soil dermal contact
Freshwater fish ingestion
Marine fish ingestion
Inhalation
Water ingestion
Milk ingestion
Dairy ingestion
Vegetable fat ingestion
Cone. TEQDFP-WH098a
11.6 ppte
1 1.6 ppt
2.2 ppt9
0.51 ppt9
0.12 pg/m3
0.00056 ppq
0.027 ppt
0.18 ppt
0.093 ppte
Contact Rate"
50 mg/day
12 g/dayf
59 to 170 g/day
12.5 g/day
13.3 m3/day
1 .4 L/day
175 g/day
55 g/day
17 g/day
Total
Daily Intake
(pg/kg-day)c
8.2 x 10'3
1.9 x 10'3
1.9 to 5.3 x 10+0
9.1 x 10'2
2.3 x 10'2
1.1 x 10'5
6.8 x 10'2
1.4x 10'1
2.2 x 10'2
2.2 to 5.7 x 10 + 0d
Values from Table 3-64.
Values for adult soil ingestion, inhalation, water ingestion, and subsistence fish ingestion from
Exposure Factors Handbook (U.S. EPA, 1997). Contact rates for milk, dairy, and vegetable fats
are based on data from USDA (1995).
Daily intake (mg/kg-day) = [Contact rate (g/day; m3/day; L/day; mg/day) x Cone. TEQ x Unit
Conversion (soil unit conversion = 10"3, all other media no unit conversion needed)/Body Weight
(kg)] or Contact rate (g/kg-day) x Cone. TEQ x Unit Conversion.
Approximately equivalent to 77 to 186 pg/day, assuming an adult body weight of 70 kg.
Calculated by setting nondetects to zero.
Calculated as the surface area of the body that contacts the soil (5,700 cm2/day) x the rate that
soil adheres to the skin (0.07 mg/cm2) x the fraction of CDD/CDFs absorbed through the skin
(0.03); exposure factors based on recommendations in U.S. EPA (1999) for an adult resident,
which assumes that the lower legs, forearms, hands, and head are exposed to the soil.
This concentration is a species-specific ingestion-weighted average value.
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Table 5-8. Levels of Different PCB Congeners in Blood Samples from Three Groups
of Men with Different Fish Consumption Habits
Congener
(UIPAC)
Fish Intake
None
Plasma
(n = 9)
Lipid
(n=8)
Moderate
Plasma
(n = 14)
Lipid
(n = 7
High
Plasma
(n = 14)
Lipid
(n = 11)
Non-ortho-PCBs
77 (pg/g)a
126(pg/g)
169 (pg/g)
0.04
(0.01-0.09)
0.73
(0.3-1.2)
0.65
(1.3-1.5)
15
(3-38)
220
(100-450)
200
(100-340)
0.1b
(9.03-0.2)
1.05
(0.6-2.4)
0.86
(0.4-1.7)
41b
(26-62)
400b
(210-650)
250
(170-360)
0.2b-c
(0.1-0.5)
2 8b,c
(1.2-4.9)
1.80b-c
(0.3-3.6)
50b
(15-140)
790b-c
(380-1400)
570b-c
(210-1200)
Mono-ortho-PCBs
105 (ng/g)
118 (ng/g)
156(ng/g)d
157 (ng/g)d
0.02
(0-0.03)
0.12
(0.05-0.21)
0.13
(0.05-0.34)
0.02
(0.01-0.05
5
(0-13)
41
(17-92)
40
(19-68)
6.6
(2.8-11)
0.04
(0.02-0.07)
0.21
(0.12-0.43)
0.14
(0.07-0.28)
0.02
(0.01-0.05)
14b
(9-20)
76
(45-120)
44
(30-64)
7.8
(5.4-11)
0.14b-c
(0.04-0.3)
0.58b-c
(0.21-1.00)
0.3b-c
(0.05-0.7)
0.06b-c
(0.01-0.14)
39b-c
(18-77)
1 60b-c
(84-300)
90b-c
(36-180)
18b,c
(7.4-39)
Di-ortho- and other PC Us
180 (ng/g)e
1
400
1
400
2
600
Notes: Means and ranges indicated on plasma and lipid basis.
a Near the detection limit.
b p <.05, compared with group "none."
c p<.05, compared with group "moderate."
d Quantified, using single-response factors.
e CB-180 quantified from two fractions, concentrations thus estimated.
Source: Asplund et al. (1994).
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Table 5-9. Mean TEQ Levels in Pooled Serum Samples
Cornwall
Sports Fishers
<38 years, lower
higher
38 years, lower
higher
> 50 years, higher
Nonfish Eaters
<38 years
38-50 years
>50 years
Mississauga
Sports Fishers
<38 years
38-50 years
>50 years
Nonfish Eaters
<38 years
38-50 years
>50 years
I-TEQDF
(ppt, lipid basis)
20.8
22.2
28.4
31.4
33.5
24.7
29.8
36.8
32.4
40.1
41.2
34.0
29.1
34.3
TEQP-WH094
(ppt, lipid basis)
-
3.6
3.1
9.5
17.3
2.6
6.8
9.7
-
-
—
-
-
-
Source: Adapted from Cole et al. (1995).
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Table 5-10. Mean CDD/CDF Levels in Serum of Consumers of Great Lakes Sport Fish (ppt, lipid adjusted)
All Sport Fish
Consumer
Subjects (ppt)
(n = 31)a
Lake Michigan
Participants
(ppt)
(n = 9)
Lake Huron
Participants
(ppt)
(n-11)
Lake Erie
Participants
(ppt)
(n = 11)
Comparison
Group3
(ppt)
(n = 70)
ODD Congeners
2,3,7,8-TCDD
1, 2,3,7, 8-PeCDD
1,2, 3,4,7, 8-HxCDD
1, 2,3,6,7, 8-HxCDD
1,2,3,7, 8, 9-HxCDD
1, 2,3,4,6,7, 8-HpCDD
1, 2,3,4,6,7, 9-HpCDD
OCDD
Total
Total I-TEQD
5.6
10.4
8.4
126
7.0
134
111
1,062
27.5
4.7
9.8
11.4
120
8.7
144
ND
783
1,087
25.8
10.5
16
8.4
142
5.5
153
ND
918
1,258
36
4.9
5.8
5.5
115
5.8
95.9
623
844
20.7
2.8
5.5
9.0
70.8
8.4
124
4.4
971
1,188
15.5
CDF Congeners
2,3,7,8-TeCDF
1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
1,2, 3,4,7, 8-HxCDF
1,2,3,6,7,8-HxCDF
1,2,3,7,8,9-HxCDF
2,3,4,6,7,8-HxCDF
1,2,3,4,6,7,8-HpCDF
1,2,3,4,7,8,9-HpCDF
OCDF
Total
Total I-TEQF
2.2
2.0
17.7
12.7
9.0
ND
5.1
20.0
ND
58.2
11.9
2.4
ND
20.4
11.6
8.0
ND
6.0
22.1
ND
ND
70.8
13.2
2.1
1.7
22.8
16.0
10.5
ND
4.8
22.9
ND
79.3
14.8
ND
10.4
10.2
7.7
ND
8.0
15.2
ND
ND
48.3
7.8
2.1
1.6
5.5
8.0
5.3
1.8
3.8
21.3
NA
6.9
87.3
4.9
a One individual was excluded from the data summary due to unusually high occupational/environmental
exposures.
b Comparison group is from a 1991 unpublished NCEH/CDC data set of a Jacksonville, Arkansas, population
of 70 individuals.
Source: Anderson et al. (1 998).
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Table 5-11. Mean PCB Levels in Serum of Consumers of Great Lakes Sport Fish (ppt, lipid adjusted)
All Sport Fish
Consumer
Subjects8 (ppt)
(n = 31)
Lake Michigan
Participants
(PPt)
(n = 9)
Lake Huron
Participants
(PPt)
(n-11)
Lake Erie
Participants
(PPt)
(n = 11)
Comparison
Group"
(PPt)
(n = 70)
Coplanar PCB Congeners
77
81
126
169
Coplanar PCB Total
I-TEQP-WH094
14.6
13.5
148
80.8
228
17.4
16.5
17.4
261
113
340
26
14.2
13.2
187
84.2
282
23
13.3
28
48.4
75.4
4.8
12.6
8.6
18.4
17.9
57.4
1.8
Congener-Specific PCBs
28
52
56
58
74
99
101
1.5
118
130
138
146
153
156
157
187
170
172
177
178
180
183
187
189
193
194
195
201
203/196
206
209
Total
0.08
0.01
0.02
0.04
0.3
0.4
ND
0.1
0.4
0.1
0.8
0.2
1.1
0.02
0.1
0.03
0.1
0.02
0.04
0.07
0.4
0.1
0.3
ND
0.03
0.09
0.04
0.2
0.09
0.07
0.02
5.2
0.08
ND
0.06
0.08
0.6
0.7
0.01
0.2
0.8
0.2
1.3
0.3
1.7
0.04
0.2
0.07
0.2
0.05
0.09
0.13
0.8
0.2
0.4
ND
0.08
0.1
0.07
0.3
0.2
0.08
0.03
8.6
0.1
0.01
ND
0.04
0.4
0.5
ND
0.1
0.5
0.1
0.8
0.2
1.1
0.02
0.1
0.03
0.2
0.03
0.06
0.08
0.4
0.1
0.3
ND
0.02
0.1
0.06
0.2
0.11
0.08
0.04
5.7
0.08
ND
ND
0.01
0.2
0.1
ND
0.02
0.08
0.02
0.4
0.04
0.6
ND
0.08
ND
0.07
ND
ND
0.03
0.2
0.03
0.08
ND
ND
0.05
ND
0.09
0.03
0.04
ND
2.2
ND
ND
ND
ND
0.009
ND
ND
0.4
0.03
NA
0.4
ND
0.4
NA
NA
ND
ND
ND
ND
ND
0.4
ND
0.04
NA
NA
0.004
ND
0.04
0.007
ND
NA
1.2
a One individual was excluded from the data summary due to unusually high occupational/environmental exposures.
b Comparison group from a 1996 unpublished data set of 41 non-Great Lake sport fish consumers analyzed by the
Wisconsin State Laboratory of Hygiene.
Source: Anderson et al. (1998).
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Table 5.12. Average PCB and CDD/F TEQ-WH098 Concentrations (all concentrations in pg/g whole weight)
Main
Water
Body
Lake
Superior
Lake
Huron
Lake Erie
Niagara
River
Lake
Ontario
Northern
Areas
Location
Black Bay
Jackfish Bay
Peninsula Harbour
Algoma Area
Algoma-Agawa Bay
Goulais Bay
Manitoulin Island
Nottawassaga River
Nottawasaga River
Tobermory
Oliphant/Fishing Is.
Oliphant/Fishing Is.
Bruce Cty
Grand Bend
Western Basin
Niagara River Bar
Niagara River Bar
Niagara River Bar
Niagara River
Niagara River
Niagara River
Welland River
Hamilton Harbour
Hamilton Harbour
Bronte Creek
Port Credit
Credit River
Credit River
Don River
Whitby Harbour
Whitby/Pickering
Cobourg
Trent River
Trent River - 4
Upper Bay of Quinte
L. Bay of Quinte
Cataraqui River
Mattagami River
Cochrane
Species
Lake Trout (5)
Lake Trout (5)
Lake Trout (5)
Lake Trout (5)
L. Whitefish (5)
L. Whitefish (5)
L. Whitefish (5)
Chinook (5)
Rainbow Trout (5)
L. Trout (5)
L. Trout (5)
L. Whitefish (5)
Carp (5)
L. Trout (5)
Ch. Catfish (5)
Lake Trout (5)
Chinook (5)
Brown Trout (5)
White Perch (5)
Rainbow Trout (5)
White Bass (4)
Carp (5)
Carp (5)
Ch. Catfish (5)
Brown Trout (5)
Lake Trout (4)
Brown Trout (5)
Chinook (5)
White Sucker (5)
Carp (5)
Chinook (5)
L. Trout (5)
Chinook (5)
L. Whitefish (5)
Whitefish (5)
L. Trout (5)
Carp (5)
White Sucker (5)
White Sucker (5)
TEQpWH098
9.4
13
9.5
83
4.5
4.1
1.4
14
6.9
21
12
4.4
31
22
55
79
52
21
6.2
22
1.6
11
25
58
56
72
41
39
16
27
29
67
182
52
8.7
110
57
21
21
TEQnr
WH098
0.68
5.3
2.8
23
5.4
1.8
0
0.94
0
3.5
0.75
2.7
2.1
0.76
8.1
22
12
4.6
3.2
4.8
0
0.13
1.9
6.7
8.3
19
10
8.7
4.4
40
6.8
26
59
5.8
7.0
29
4.7
0
0
TEQDPF-WH098
14
2.5
3.3
3.7
0.83
2.3
>1.4
15
>6.9
5.9
17
1.7
14
29
6.8
3.5
4.4
4.5
1.9
4.6
>1.6
86
13
8.7
6.7
3.8
4.1
4.5
3.6
0.66
4.3
2.6
3.1
9.0
1.2
3.8
12
>21
>21
Source: Kolic et al. (2000).
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Table 5-13. Comparison Between Mean PCB Levels in Fish-eating Populations and Controls
PCBs
126
169
118
170
180
TOTAL TEQP-WH094
Fishermen
Mean
Concentration
(ppt, lipid basis)
1540
1010
568
539
1776
-
TEQP-WH094
(ppt, lipid basis)
154
10.1
56.8
53.9
17.76
292.6
Controls
Mean
Concentration
(ppt, lipid basis)
48
29
25.4
27.7
48.2
-
TEQP-WH094
(ppt, lipid basis)
4.8
0.29
2.54
2.77
0.48
10.9
Source: Adapted from Dewailly et al. (1994).
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0.4T
0.4 -,
I 0.3 H
o
ro
2 0.2-
Q.
•c 0.1-1
o
00
0.0
10 20 30 40
50 60
70
Age, yrs
Age, yrs
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Selected half-life
•Lipid-based half-life, Lorber & Pinsky (1998)
Modeled half-life, Kreuzer, et al (1997)
Figure 5-2. Comparison of the selected half-life of TEQs in the body with two options that were
available in the literature for 2,3,7,8-TCDD.
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(A)
O
LJJ
c
o
.
T3
O
m
Formula only
1-year
6-week
2-years
6-month
Figure 5-3. Demonstration of the model for evaluating impacts on lipid concentrations (A) and body
burdens (B) of infants resulting from various nursing scenarios during a lifetime.
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50 T
(A)
Formula only 6-week
1-year 2-years
6-month
(B)
Formula only
1-year
6-week
2-years
6-month
Figure 5-4. Demonstration of the model for evaluating impacts on lipid concentrations (A) and body
burdens (B) of infants resulting from various nursing scenarios during the first 10 years of life.
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(A)
10
• - ~
i
51234
Low Impact Scenario
i
.
i i
i i
5 6 7 8 9 10
Age, yrs
High Impact Scenario
•6-Month Scenario
(B)
10-
n
~"~-S _„. ^_
II II I
10
20
30 40
Age, yrs
50
60
70
Low Impact Scenario
•6-Month Scenario
High Impact Scenario
Figure 5-5. Results of sensitivity analysis showing the difference when making modeling assumptions
that lead to a high impact to the infant (high impact scenario) and to a low impact to the infant (low
impact scenario) as compared to the baseline scenario for a 6-month breast-feeding scenario (6-month
scenario).
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6. TEMPORAL TRENDS
6.1. INTRODUCTION
Small amounts of dioxin-like compounds may be formed during natural fires,
suggesting that these compounds may have always been present in the environment.
However, it is generally believed that greater amounts of these compounds have been
produced and released into the environment in association with human industrial and
combustion practices. As a result, environmental levels are likely to be higher in modern
times than in earlier times. The trend of increasing levels may now be reversing (i.e.,
releases and environmental levels may be gradually decreasing), however, with changes in
industrial practices. As discussed in Volume 1, the potential for environmental releases of
dioxin-like compounds has been reduced as a result of the switch to unleaded automobile
fuels and associated use of catalytic converters and reduction in halogenated scavenger
fuel additives (remaining uses of leaded fuel include chain saws, logging machinery, and
mowers), process changes at pulp and paper mills, improved emission controls for
incinerators, and reductions in the manufacture and use of chlorinated phenolic
intermediates and products.
This chapter describes trends in the levels of dioxin-like compounds that have been
observed in various environmental media and foods, as well as evidence of downward
trends in exposure to dioxin-like compounds in humans. The downward trend in human
exposure is supported by a modeling exercise that reconstructs the most likely past doses
of dioxin-like compounds contributing to observed body burdens. Reviews of several
studies and the modeling exercise are followed by several observations with regard to
temporal trends of dioxin-like compounds.
6.2. SEDIMENT CORE STUDIES OF TEMPORAL TRENDS
Questions regarding the contribution made by natural sources to the overall
environmental burden of CDDs and CDFs can be partially addressed using the results from
analyses of the temporal distribution of CDD/CDFs in sediment core samples. Sediment
cores provide a historical record of contaminant inputs into the environment and have
been used by several researchers to study temporal trends in CDD/CDF deposition.
Studies at various sites in the United States and Europe suggest that environmental
DRAFT-DO NOT QUOTE OR CITE 6-1 December 2003
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concentrations of CDD/CDFs began to increase rapidly in the 1930s and peaked around
the 1970s.
Czuczwa and Hites (1984) analyzed sediment core samples taken in Lake Huron by
the University of Michigan's Great Lakes Research Station. Sedimentation rates within the
core samples were determined using Cs-137 and Pb-210 techniques. Those rates were
used as a basis for relating depth of core sample to time of deposition. CDD/CDFs were
detected in the core samples with no appreciable degradation over time. The most
abundant CDD/CDFs were OCDDs and HpCDD/CDFs. Analysis of sample depth showed
that the concentration of CDD/CDFs increased steadily beginning in approximately 1940
and leveled off around 1960. Correlations were observed over time between the levels of
CDDs and CDFs in the sediment cores and the total volume of synthetic chloro-aromatics
produced by the petrochemical industry in the United States. However, coal consumption
did not show a good correlation with CDD/CDF concentrations over time. Czuczwa and
Hites (1984) concluded that the history of sedimentation rates of CDD/CDFs in core
samples from Lake Huron reflected of atmospheric deposition from the combustion of
synthetic chloro-aromatics.
In a similar study, Czuczwa et al. (1985a) reported on the temporal variability of
CDD/CDFs in sediment core samples taken from a wilderness lake, located in an
uninhabited and undeveloped island (Siskiwit Lake, Isle Royale) in Lake Superior. The only
mechanism of contaminant input into the lake was believed to be atmospheric transport
and deposition. The historical record of CDD/CDF concentration in the core samples
showed that CDD/CDFs were virtually absent from the sediments until around 1940. All
CDD/CDF homologue groups were detected in sediment samples near the surface, with
HpCDDs and OCDDs accounting for the highest percentage of total CDD/CDFs.
Comparisons were made between the congener profiles found in the lake sediments and
congener profiles found in urban air particles. A correlation coefficient of 0.997 was
observed, leading to the conclusion that CDD/CDFs entered the lake system from aerial
transport and deposition.
Smith et al. (1992, 1993) analyzed sediment core layers from Green Lake, located
near Syracuse, New York, to determine temporal trends in the deposition of CDDs and
CDFs since the beginning of the industrial era (circa 1860). This deep lake (200-foot
depth) is thought to be affected only by atmospheric deposition because no industrial
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inputs are present and motorboats are not allowed. Relatively low concentrations of
CDDs and CDFs (10 pg/g or less) were observed in sediments deposited from 1860 to
1930. However, concentrations increased rapidly thereafter, reaching a peak in the mid-
1960s when total CDD concentrations exceeded 1,300 pg/g and total CDF concentrations
exceeded 250 pg/kg. The concentrations of CDDs and CDFs have declined rapidly since
the mid-1960s and, in 1986-1990, were measured at 750 pg/g as total CDD/CDF. In the
most recent samples, HpCDD and OCDD dominated the mixture of CDD/CDFs. This
observation is consistent with that of Czuczwa et al. (1985a). The authors speculated
that the decline in CDD/CDF concentrations over time may be due to the switch to
unleaded fuels for vehicles.
Sediment cores from the Hudson River were analyzed by Smith et al. (1995). The
results indicated that the subsurface sediment layers, dated between 1950 and 1980, had
the highest concentrations of CDD/CDFs. OCDD, HpCDF, and OCDF accounted for the
highest percentage of total CDD/CDFs in these cores. Pearson et al. (1995) studied
sediment cores collected from the Great Lakes (i.e., Lakes Superior, Michigan, and
Ontario) and remote inland lakes. The researchers calculated CDD/CDF accumulation rates
as the product of concentration (pg/g) and sedimentation rate (g/cm2-yr). The results of
this study indicated that CDD/CDF accumulation began in the 1930s-1940s and peaked
in the early to mid-1970s. Lake Ontario (350 to 575 pg/cm2-yr) and Lake Michigan (25 to
100 pg/cm2-yr) were found to have higher CDD/CDF accumulation rates than Lake
Superior and the remote inland lakes (5 to 10 pg/cm2-yr). CDD/CDF profiles for these
lakes indicated that OCDD dominated. However, the homologue profile for Lake Ontario
differed from the other lakes, leading the authors to speculate that different
nonatmospheric sources were responsible for the CDD/CDFs found in this lake.
MacDonald et al. (1992) observed similar temporal results in Canada. MacDonald et al.
(1992) observed that OCDD and PCB-77 concentrations in sediments collected from the
Strait of Georgia, British Columbia, began to increase in about 1940, reaching a maximum
in about 1970. However, 2,3,7,8-TCDF concentrations did not begin to increase until
about 1965 as a result of discharges of chlorine bleach effluent from local pulp mills.
Lebeuf et al. (1995) observed decreasing dioxin-like PCB (i.e., PCBs 77, 126, and
169) trends in sediments from the Lower Estuary and Gulf of St. Lawrence, Canada. Two
sediment cores were collected from sites approximately 50 km apart and sliced into 25
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samples. PCB concentrations were found to increase with increasing depth. These
results indicate that recent inputs to this water body have decreased substantially (Lebeuf
et al., 1995).
Rappe et al. (1997) analyzed sediment core samples from five lakes in southern
Mississippi. The sediment cores were collected from five man-made recreational lakes
with no known industrial point source of CDD/CDFs and low atmospheric deposition rates.
Cores were subdivided into sections to evaluate temporal trends in deposition of
CDD/CDFs. No observable trend for levels of CDDs, homologues, or l-TEQs correlating to
the age of the strata could be identified.
Recently, EPA/DOE conducted a time-trend study of dioxin-like compounds in
sediment cores (Cleverly et al., 1996; Versar, 1996). Cores from 11 lakes/reservoirs were
collected, sectioned, and dated using 137Cs and 210Pb dating techniques, and analyzed for
CDD/CDFs and PCBs. The lakes were located in various geographic locations throughout
the United States (10 within the continental United States and 1 in Arctic/Alaska) and
were selected to represent background conditions (i.e., no known CDD/CDF sources). For
several of the lakes, dated samples were available for time periods ranging from the 1700s
to the present. The results of the study indicated that CDD/CDF and PCB inputs to U.S.
lakes have increased over time, with significant increases occurring after the 1930s. This
is consistent with the findings of other researchers. In general, a minimum of one order of
magnitude increase in concentration occurred from the pre- to post-1 930s timeframe.
With few exceptions, this observation was consistent for all 2,3,7,8-substituted CDD/CDF
congeners, CDD/CDF homologue groups, and PCBs. The observed temporal trends were
consistent across lakes (except the Arctic/Alaska lake), especially for lakes within the
same geographic region. For some lakes, a downward trend appeared to exist for the
most recent periods. The point of inflection for this downward trend varied across lakes,
but appeared to occur between the 1950s and 1970s. Figure 6-1 depicts the changes in
concentration over time for Beaver Lake, Washington. The data also indicate that the
CDD/CDF and PCB profiles in these lakes were similar across all periods and across all
lakes. This may suggest that the relative congener-specific inputs from the atmosphere
have remained consistent over time and are similar across all geographic regions.
Relationships also were observed between CDD/CDF and PCB trends and indicators of
anthropogenic activities (i.e., PCP production, leaded gasoline sales, carbon monoxide
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emissions, and PCB releases) that may be associated with production of CDD/CDFs or
PCBs. However, it should be noted that correlations between these variables do not
necessarily reflect causal relationships.
Several studies have also evaluated sediment cores from European lakes. Czuczwa
et al. (1985b) studied temporal trends in three Swiss lakes (Lakes Zurich, Lugano, and
Baldegg). No CDD/CDFs were detected in the sediments prior to 1945, but increasing
levels were observed in subsequent time periods. The most abundant congeners found in
the uppermost sediment samples were OCDD, HpCDD/CDFs, and OCDF. Using
sedimentation rates and CDD/CDF surface sediment concentrations, the estimated
accumulation rates were 300 pg/cm2-yr for Lake Zurich, 270 pg/cm2-yr for Lake Lugano,
and 190 pg/cm2-yr for Lake Baldegg. The authors noted that the similarities in congener
profiles and fluxes of CDD/CDF in Switzerland and the United States may be indicative of
similar sources of CDD/CDFs. Similar trends were noted by Beurskens et al. (1993,
1994). These researchers evaluated sediment cores from Lake Ketelmeer, a sedimentation
area of the Rhine River in The Netherlands. CDD/CDF concentrations were shown to
increase after the 1940s and peak between 1960 and 1980. Similar results were
obtained for coplanar PCBs.
Sediment cores from two lakes in the Black Forest region of Germany were
analyzed for temporal trends in CDD/CDF deposition by Schramm et al. (1994).
CDD/CDFs were found to have increased by a factor of 13 since the 1930s. Recently,
Hagenmaier and Walczok (1996) evaluated CDD/CDF levels in dated sediment core
samples collected from Lake Constance, Germany. Cores were collected in December
1995 and April 1996. Based on a preliminary dating scheme, the results indicated that
the I-TEQDF concentrations for these samples began increasing around 1940, reached their
peak around 1970 to 1975, and then began decreasing. The homologue profiles for the
1940s sediments were similar to those in recent deposition samples.
Using dated sediment core analyses, Alcock et al. (1997a) reported that CDD/CDF
concentrations in a remote lake in Scotland began increasing in the 1860s and 1870s and
peaked in the 1950s and 1960s. According to Alcock et al. (1997a), concentrations
appear to have decreased in recent years. Brzuzy and Hites (1995) reported on changes
over time in the CDD/CDF homologue profile for Lake Windermere in the United Kingdom.
CDFs accounted for a significant fraction of the total CDD/CDF concentration in sediment
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core sections dated 1946-1950. In contrast, sections dated 1988-1992 had a
significantly lower fraction of CDFs, and the profile was dominated by OCDD and HpCDD.
These results provide evidence that the sources of CDD/CDF deposition have changed
over time, with CDFs accounting for a much higher percentage of inputs during the earlier
period.
Sediment core samples from the Baltic Proper, near Sweden, showed detectable
levels of CDD/CDFs dating back as early as 1882 (Kjeller and Rappe, 1994). CDD/CDFs
increased slowly in the sediment strata dated between 1882 (92 pg/g) and 1962 (233
pg/g) and then increased rapidly in the 1970s. Total CDD/CDFs were estimated to be 520
pg/g in 1970 and 1,803 pg/g in 1978. CDD/CDFs decreased to a concentration of 1,454
pg/g in the most recent layer, dated 1985. DeWit et al. (1990) also observed that
CDD/CDF concentrations were higher in deeper sediment layers from the Baltic Sea.
I-TEQDF concentrations in surface sediments (i.e., most recently deposited) were
approximately 20 times higher than in sediments collected at depths of 22 to 28 cm.
Sediment cores were collected to a depth of 5-18 m at three locations near Osaka
Bay, Japan, and analyzed for CDD/CDFs (Sakai et al., 1998). CDD/CDFs found at Yodo
River, which is influenced by urban activities, increased slowly from 1980, reached a peak
in 1993, and then dropped to a lower concentration before stabilizing. Comparison
between the southern and northern parts of Lake Biwa, representing areas more and less
affected by human activities, respectively, showed that CDD/CDF concentrations at the
southern part of Lake Biwa began increasing around 1955 and increased dramatically in
1964, reaching a peak in 1973. At the northern part of Lake Biwa, CDD/CDF
concentrations increased dramatically in the late 1960s and continued to increase until the
1980s, when they leveled off. Homologue profiles for the sediment core sampled from
the northern part of Lake Biwa showed that OCDD was the dominant congener since
1842, followed by TCDD after 1935. The concentrations of OCDD and TCDD continued
to increase after 1935. The sources of these two congeners were suspected to be
herbicides, preservatives, and municipal solid waste incinerators (Sakai et al., 1998).
CDD/CDFs have also been detected in remote Arctic sediment core samples, but at
very low concentrations (Tan et al., 1993; Vartiainen et al., 1995). Tan et al. (1993)
analyzed a sediment core collected from Wonder Lake, Alaska. With the exception of
1,2,3,4,6,7,8-HpCDF and OCDF, which had concentrations of 1 3 and 1 5 pg/g,
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respectively, in all sediment sections dated between 1590 and 1790, CDD/CDF
concentrations were less than 8 pg/g. The authors suggest that these results support de
novo synthesis of CDD/CDFs. Vartiainen et al. (1995) found total CDD/CDF levels of
2.29 pg/g in sediments dated 1890 and 55 pg/g in sediments dated 1994. OCDD
dominated in these samples, followed by the hepta- and hexa-chlorinated congeners.
The results of these sediment core studies provide evidence that deposition of
dioxin-like compounds began increasing dramatically after the 1930s and continued
throughout the 1960s. Decreases appear to have occurred only during the most recent
periods. In all of these studies, the higher chlorinated compounds dominated the
homologue profiles. These observations are consistent among cores collected in various
locations throughout the United States and Europe. CDD/CDFs have been observed both
in relatively remote lakes, as well as in lakes close to industrialized areas. This suggests
that atmospheric transport and deposition may be an important mechanism of entry into
these lakes.
6.3. TEMPORAL TRENDS IN SOIL, VEGETATION, AND AIR
Temporal trends in CDD/CDF and PCB deposition have also been studied in other
types of environmental media including soil, vegetation, and air samples. Kjeller et al.
(1991) analyzed archived soil samples dated from 1846 to 1986 from semirural plots in
the United Kingdom. Herbage samples dated from 1891 to 1988 that originated from a
grassland area were also tested for CDD/CDFs (Kjeller et al., 1991, 1996). All CDD/CDF
homologue groups were detected in soil and herbage samples from all time periods, and
the CDD/CDF concentrations increased over time beginning at about 1900 (Kjeller et al.,
1991). The concentrations of total CDDs in soil increased from 31 to 92 pg/g between
1893 and 1986. CDD/CDFs in the vegetation samples remained essentially constant
between 1861 and 1945, increased to peak levels in the early 1960s, declined, and then
reached a second peak in the late 1970s (Kjeller et al., 1996). CDD/CDFs were about 7
to 8 times higher in the 1960s to 1980s than in the sample from 1891 to 1900. In the
most recent sample (1991 to 1993), total CDD/CDFs declined to levels similar to those
observed in pre-1946 samples (Kjeller et al., 1996).
In a similar study, Alcock et al. (1997b) presented evidence that CDD/CDFs were
present in UK soils before the widespread development of chloroaromatics (around
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1930s). A previously unopened bottle of soil, collected in 1881 from Rothamsted
Experimental Station as part of an agricultural experiment, was analyzed for CDD/CDFs.
The soil sample contained 0.7 ppt I-TEQDF, with OCDD, 1,2,3,4,6,7,8-HpCDD, and
1,2,3,4,6,7,8-HpCDF as the dominant congeners. Great care was taken to avoid
contamination with modern air and dust for the initial analysis, and the sample was
subsequently exposed to laboratory air for 32 days to determine whether current air
concentrations would alter the CDD/CDF concentrations detected in the archived soil. The
results indicated that such exposure did not alter the soil concentration of CDD/CDF.
Modern soil samples collected from the same field plot were found to contain 1.4 ppt I-
TEQDF. The authors speculated that the increase was presumably a result of cumulative
atmospheric deposition of CDD/CDFs. The results also indicated that although the soils
from 1881 had lower concentrations of CDD/CDFs, they contained similar congener
profiles of CDD/CDF as modern soil. This may indicate long-term persistence of
CDD/CDFs in soil, and similarities in source inputs over time (Alcock et al., 1997b).
Alcock et al. (1997a) also summarized temporal trends in sediments, archived vegetation,
soil, food groups, and direct air measurements from the United Kingdom. They suggested
that concentrations in UK media were the highest between the 1950s and the 1970s and
were directly related to human activities. Since the 1970s, CDD/CDF concentrations have
shown a consistent decline. According to Alcock et al. (1997b), current herbage
concentrations are similar to pre-1946 levels, and ambient air monitoring data from
London and Manchester indicate that CDD/CDF air concentration have steadily declined
since the 1970s. Archived soil, herbage, and air samples collected in the United Kingdom
between 1942 and 1992 were used to evaluate changes in PCB emissions over time
(Harner et al., 1995). The concentrations of PCB congeners 28, 52, 138, and 153 in
these samples rose from near zero in 1935 to a maximum in the late 1960s and then fell
steadily to their present levels.
Hiester et al. (1995) observed a decrease of CDD/CDF concentrations in Germany's
ambient air over a 6-year period. Ambient air samples were collected over 12 sampling
intervals from four sites in the heavily industrialized Rhine-Ruhr region of Germany during
1987-1988 and 1993-1994, and analyzed for CDD/CDFs. Total l-TEQDFs for these sites
ranged from 0.13 pg/m3 to 0.33 pg/m3 during 1987-1988, and from 0.04 pg/m3 to 0.12
pg/m3 during 1993-1994. Reductions in CDD/CDF l-TEQDFs at these sites ranged from 46
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to 69 percent over the 6-year period (i.e, from 0.22 pg/m3 to 0.13 pg/m3 at Dortmund,
and from 0.13 pg/m3 to 0.04 pg/m3 at Koln). These reductions were attributed to
abatement actions taken since 1989 (Hiester et al., 1995).
6.4. TEMPORAL TRENDS IN WILDLIFE
Temporal trends in CDD/CDFs and PCBs have also been studied in wildlife,
including fish and bird eggs. Hebert et al. (1994) analyzed pooled herring gull eggs for
CDDs annually between 1981 and 1991. The eggs were collected from colonies in the
Great Lakes and the Gulf of St. Lawrence River. Analyses results indicate that CDD levels
declined between 1981 and 1984, but that CDD levels have remained relatively constant
since 1984. DeWit et al. (1994) evaluated temporal trends in the levels of CDD/CDFs and
coplanar PCBs in the biota of Sweden. Guillemot eggs were collected from the Island of
St. Karlso in the Baltic Proper (Sweden) between 1969 and 1992, and pike samples were
collected from Lake Storvindeln in Lapland, Sweden, between 1968 and 1992. During
these time periods, the concentrations of CDD/CDFs and PCBs decreased in both species.
Roos et al. (1998) confirmed that PCB concentrations decreased significantly in the Baltic
Sea between 1989 and 1997 at an annual rate of 2-4 percent by analyzing samples from
54 juvenile grey seals caught off the Swedish coast. Roos et al. (1998) also found that
the PCB concentrations in herring, cod, and guillemot eggs caught from the Baltic Sea
during the period 1969-1996 decreased over time by 9-10 percent annually. Decreases
in PCBs since the 1970s were also observed in fish from Finland (Korhonen et al., 1995).
Pike samples from both inland lakes and coastal areas of Finland had significantly lower
total PCB concentrations in 1994 (1.7 to 2.1 //g/g) than in 1971 (>7 to 10//g/g).
U.S. EPA (1994a) reported a decline in PCB concentrations in lake trout from Lake
Michigan since the late 1970s; however, PCB concentrations currently appear to be
approaching equilibrium in the Great Lakes system. U.S. EPA (1994a) attributed the
decline in tissue concentrations to reductions in pollutant loadings to the water column.
The levels of PCBs in coho salmon also declined during the early 1980s, but have
remained relatively constant since that time. According to U.S. EPA (1994a), the leveling
off of PCB concentrations in the Great Lakes is related to "(1) historically contaminated
sediments; (2) tributaries inputs resulting from point sources, spills, and runoff from both
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urban and rural areas, and resuspension from contaminated sediments; and (3)
atmospheric deposition of pollutants."
Hilbert et al. (1997) analyzed cod livers collected from Danish waters between
1973 and 1996 for total PCBs (Aroclor 1260). The results indicated that PCB levels in
cod livers decreased during the past three decades. Total PCBs ranged from
approximately 4 to 8 mg/kg (fresh weight) in 1973 at five locations in Danish waters to
less than 1 mg/kg (fresh weight) in 1996 in those same waters.
Huestis et al. (1997) examined the temporal and age-related trends of CDD/CDFs
and coplanar PCBs in Lake Ontario lake trout. Archived samples of 4-year-old lake trout,
collected between 1977 and 1993 from the eastern basin of Lake Ontario at Main Duck
Island (MDI), were analyzed for CDD/CDF/PCBs. Three- to 9-year-old trout were collected
from the western end of the basin at Port Credit. The results of the temporal trends
analysis indicated that CDD/CDF/PCB concentrations were at their highest levels in 1977
and lowest in 1987. The total TEQDFP-WH094 was 583 ppt in 1977, compared to 124 ppt
in 1993. The most important contributor to the total TEQDFP-WH094 was PCB 126. This
PCB congener contributed between 40 to 50 percent of the total TEQDFp-WH094,
depending on the year examined. The contribution of 2,3,7,8-TCDD accounted for 15 to
20 percent of the total TEQDFP-WH094. CDD/CDF contaminant profiles for 1977, 1982,
and 1991 suggest an increase of the proportion of 2,3,7,8-TCDF as the proportion of
2,3,7,8-TCDD decreases. The study also evaluated age-related effects of CDD/CDF/PCB
concentrations. The results of the analyses of 3- to 9-year-old fish indicate that as the
age of the fish increases, the level of contamination also increases.
Boumphrey et al. (1998) examined 10 gannet eggs taken from Ailsa Craig, a colony
in the northern part of the Irish Sea, every 2 years from 1977 to 1992 to analyze temporal
trends in total PCB and PCB congener concentrations. The results indicated that the
average total PCB concentration was 6.08/vg/g in 1977 and 2.5/vg/g in 1992 with an
annual rate of decline of approximately 0.25 /vg/g. Individual PCB congeners declined at
various rates, which resulted in different congener profiles between 1977 and 1992.
6.5. TEMPORAL TRENDS IN FOOD PRODUCTS
Recent studies have evaluated trends in concentrations of dioxin-like compounds in
food by analyzing levels of PCBs and CDD/CDFs in food products from different time
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periods. The United Kingdom's Ministry of Agriculture, Fisheries, and Food (MAFF)
compared the levels of CDD/CDFs found in commercially available cows' milk in 1990 to
the levels in samples collected in 1995 (MAFF, 1997a). The 1995 samples were
collected from 12 locations in England and corresponded, where possible, to the 1990
sampling locations. Analysis of the 1995 samples was performed on a pool of 105 pints
of full fat milk from each location. As shown in Table 6-1, the lipid-based I-TEQDF
concentrations in 1990 ranged from 1.1 to 3.3 ppt, while the 1995 I-TEQDF levels
decreased to between 0.67 and 1.4 ppt. Table 6-1 also reports PCB levels for the 1995
samples. Lipid-based TEQP-WH094 levels ranged from 0.75 to 2.2 ppt. No PCB analysis
for the 1990 cows' milk samples was presented.
Furst and Wilmers (1995) compared the levels of CDD/CDFs found in German dairy
products in 1990 to the levels in 120 dairy samples collected in 1994. Over the 4-year
period, mean I-TEQDF concentration in milk fat decreased by almost 25 percent from 1.35
ppt to 1.02 ppt. Similar reductions were noted in human milk fat (Furst and Wilmers,
1995).
To examine trends in CDD/CDF and PCB concentration in American food products,
Winters et al. (1998) analyzed 14 preserved food samples from various decades of the
20th century for 7 dioxin-like coplanar PCBs and the 17 2,3,7,8-substituted dioxin and
furan congeners. The authors compared the concentrations found in historical samples to
the current dioxin concentrations observed in the national food surveys for beef (Winters
et al., 1996a; 1996b), pork (Lorber et al., 1997), poultry (Ferrario et al., 1997), and milk
(Lorber et al., 1998). As shown in Table 6-2, all 10 samples, dated from 1957 to 1982,
had I-TEQDF concentrations higher than the current mean concentrations (when nondetects
were set to one-half the limit of detection). Similarly, mean TEQP-WH094 concentrations
were higher than current mean concentrations for 12 of the 13 samples taken between
1945 and 1983. If these samples are indicative of past CDD/CDF concentrations,
normalized I-TEQDF results suggest CDD/CDF levels 2 to 3 times higher, and PCB levels
over 10 times higher during the 1950s, 1960s, and 1970s than current concentrations.
As shown in Figures 6-2 and 6-3, I-TEQDF and TEQP-WH094 concentrations in food
products began to increase with the 1957 sample and continued to increase throughout
the 1960s and 1970s. The peak concentration was observed in the 1968 poultry sample.
This trend in CDD/CDF and PCB concentrations in food products is consistent with the
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pattern observed in sediment cores, as discussed in Section 6.2 (Cleverly et al., 1996;
Versar, 1996; Smith et al., 1992, 1993, 1995; Czuczwa et al., 1984, 1985a).
6.6. TEMPORAL TRENDS IN HUMAN EXPOSURE
Several studies have examined trends in the dietary intake of CDD/CDFs and PCBs.
In 1995-1996, MAFF reported on its analysis of Total Diet Study samples collected in
1982 and 1992 from 24 locations in the United Kingdom (UK) (MAFF, 1995, 1996,
1997b). Of the 11 food groups examined, 10 groups represented the major dietary
contribution to intake of dioxins and PCBs, and 1 group represented bread because it is a
dietary staple. The results indicated that total dietary intake of PCBs and CDD/CDFs by
consumers in the United Kingdom decreased dramatically between 1982 and 1992.
Dietary intake of CDD/CDFs and PCBs for specific food items was estimated by
multiplying the TEQ concentration of CDD/CDFs and PCBs in the food item (calculated by
setting nondetects to the limit of detection) by the average daily intake for that food item,
as estimated in the UK's National Food Consumption Survey. Total dietary intake was
calculated by summing the dietary intakes for all food groups. The estimated upper bound
dietary intakes of CDD/CDFs and PCBs by the average adult UK consumer in 1982 and
1992 are presented in Table 6-3. The total dietary intake of CDD/CDFs was estimated to
be 240-pg/day I-TEQDF in 1982 and 69-pg/day I-TEQDF in 1992. The total dietary intake of
PCBs decreased from 156-pg/day TEQP-WH094 in 1982 to 46-pg/day in 1992 (MAFF,
1997b). Harrison et al. (1998) also reported on composite human milk samples collected
as part of the MAFF study. Lipid-based CDD/CDF I-TEQ levels in Birmingham, England,
were 37 ppt in 1987-1988 and 21 ppt in 1993-1994. I-TEQ levels were 29 ppt in
Glasglow in 1987-1988 and 21 ppt in 1993-1994.
Liem et al. (1997) analyzed duplicate portions of 24-hour diet samples collected in
The Netherlands in 1978, 1984 to 1985, and 1994. This study was conducted to
estimate the dietary intake of CDD/CDFs and PCBs in the Dutch population 18 years of
age and older to evaluate trends in dietary exposures. Dietary intake was estimated by
combining the results of the chemical analyses of foods with data on consumption rates
from the Dutch National Food Consumption Survey. Liem et al. (1997) reported a
significant reduction of CDDs and CDFs in the diet over the three time periods. The mean
daily dietary intake of CDD/CDF decreased from 4.2 pg/kg I-TEQDF in 1978 to 1.8 pg/kg I-
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TEQDF in 1984-1985 and 0.5 pg/kg I-TEQDF in 1994. When PCBs were included in the
TEQ calculation, the daily dietary intake was 11 pg/kg TEQDFP-WH094 in 1978, 4.2 pg/kg
TEQDFP-WH094 in 1984-1985, and 1.4 pg/kg TEQDFP-WH094 in 1994. The percentage
decrease in 1994 samples was consistent for all the measured CDD/CDF/PCBs. The
results of this study suggest that a reduction in dietary ingestion of CDD/CDFs and PCBs
occurred in The Netherlands beginning in the late 1970s.
In a study similar to the United Kingdom's MAFF (1995, 1996, 1997b) study,
FUrst and Wilmers (1997) found that CDD/CDF dietary levels also dropped in Germany in
recent years. Several hundred food samples were randomly collected and analyzed for
CDD/CDFs during 1989 and 1995. Fish products showed the greatest decline in
CDD/CDF food concentrations over this period. Significant decreases also were noted for
meats. Samples of more than 300 dairy products were collected in 1990 and 1994 from
several dairies in North Rhine and Westphalia and analyzed for CDD/CDFs. The results
indicate that from 1990 to 1994, CDD/CDF levels in cows' milk decreased approximately
25 percent. To test whether the reduction of CDD/CDF concentrations in food had a
positive effect on human exposure, dietary intake of CDD/CDFs was estimated using the
results of the food sample analysis described above and standard food consumption data.
This analysis indicated that in the past few years, CDD/CDF intake by humans decreased
by approximately 50 percent. The current average daily intake is estimated to be 69.6 pg
I-TEQDF compared to a daily average intake of 127.3 pg I-TEQDF in 1990. This decrease in
daily intake was also reflected in a decrease in breastmilk concentrations (Furst and
Wilmers, 1997). A study of more than 1,000 individual breastmilk samples from the
North Rhine-Westphalia region showed a decrease from 34 ppt I-TEQDF in milk fat in 1989
to 14.2 ppt I-TEQDF in milk fat in 1996. This represents a 60 percent reduction since
1989.
6.7. TEMPORAL TRENDS IN HUMAN BODY BURDENS OF DIOXIN-LIKE COMPOUNDS
IN THE UNITED STATES
Long-term, nationally representative environmental monitoring for dioxin-like
compounds has not been conducted. However, this section reviews various dioxin body
burden monitoring studies which have been conducted in the United States in past
decades. Studies which sampled either blood or adipose tissue were compiled; studies on
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other matrices such as breast milk were not included. Also, this compilation only included
dioxin and furan congeners; dioxin-like PCBs were not included. Other characteristics of
studies included in this compilation are:
1. All or a toxicologically significant subset of the 1 7 dioxin-like dioxins and furans
were measured and individual-specific or overall study average concentrations of
these congeners were available (this was needed in order to calculate TEQDF -
WH098 concentrations).
2. All results were reported on a lipid-basis.
3. Although not possible for all studies, data were obtained which had reported
detection limits so that average congener concentrations could be derived assuming
non-detects (NDs) equal one-half detection limit (1/2 DL). When detection limits
were not supplied, NDs were assumed to equal zero. In only one study from the
1970s (Kang et al., 1991; U.S. EPA, 1990), NDs were reported as zero and
detection limits were not supplied. In this case, it made no difference in the
calculation of TEQDF -WH098 concentrations.
4. All measurements were on background populations. The first study described
below included Vietnam Veterans, non-Vietnam Veterans, and civilians. All three
groups were included in this analysis as the concentrations in the three were
indistinguishable - the civilian population had, in fact, the highest concentrations.
Another reason to include all three populations was that this was the only study
available with the full suite of congener concentrations in the 1970s. Two other
studies (Schecter et al., 1989; Schecter et al., 1993) were comprised of blood
samples taken from Vietnam Veterans only. In both of these, small subsets of
individuals appeared to have elevated concentrations of 2,3,7,8-TCDD and TEQ.
Congener-specific averages, not including these individuals, could be developed
with the information in the articles, so these amended profiles were included in
this compilation.
5. Only studies which sampled adult populations were included; data on children or
infants were not included. Studies on "average adult populations" which included
demographic information were comprised of approximately the same number of
males and females and the average age was usually in the 40s. Studies on
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Vietnam Veterans were all male populations and the maximum age was in the 40s,
and one study that sampled breast adipose tissue was on females alone.
Twelve studies were found for this compilation. A summary of the results from
these studies is shown in Table 6-4. Table 6-5 provides the congener-specific average
concentrations from each of the years in this compilation. Figure 6-4 shows the TEQ
concentrations collected from these studies as a function of year. Figure 6-5 shows the
age trend for three studies. This trend refers to the common finding for surveys in the
1980s and 1990s that older individuals had higher dioxin concentrations as compared to
younger individuals. Following now are study-by-study summaries.
Study 1: Dioxins and Dibenzofurans in adipose tissue of US Vietnam veterans and controls
This study was undertaken by the U.S. Department of Veterans Affairs and the
U.S. EPA (Kang et al. 1991; U.S. EPA, 1990). They used EPA's National Human Adipose
Tissue Survey (NHATS) Repository to collect samples. In the repository at the time of the
study were approximately 8,000 tissue samples, with up to a 1000 a year collected
annually starting in 1970. The target population for NHATS was all non-institutionalized
persons in the US. Due to the invasive nature of adipose tissue sampling, the population
was actually derived from individuals who died from external causes (90%) and surgical
patients (10%). From that repository, they were able to identify samples from 494 males
who potentially served in Vietnam, those born between 1936 and 1954. These 494
samples were taken between 1972 and 1981. Searching through military records, they
were able to identify 134 veterans from this potential population of 494, 40 of whom
served in Vietnam. They selected all of these 40, as well as 80 of the remaining 94
veterans who had not served in Vietnam (these 80 selected randomly from the population
of 94). They matched each of the 40 Veterans with 2 civilians in terms of age and sex
from the potential population of 494. The final population of this study was, therefore,
200 individuals: 40 Veterans serving in Vietnam, 80 non-Vietnam Veterans and 80
civilians. Five samples were discarded as inappropriate (not enough lipid, records of
individuals not certain with regard to military status, etc.), for a final sample count of 195.
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While a comprehensive set of data, this was not a good background representation
because the oldest individual was no older than 45 years old (the last year sampled,
1981, minus the earliest birth date, 1936), and they were all males. Still, it was the only
data set from these years which measured all the 17 CDD/CDF congeners, and results
suggest that these individuals were not influenced by unique high exposures.
The results from these three sample sets were indistinguishable - TEQDF -WH098
concentrations were 71.9, 65.4, and 72.0 pg/g lipid for the Vietnam Veterans, the non-
Vietnam Veterans, and the civilians, respectively. The 2,3,7,8-TCDD concentrations
followed the same trend, with concentrations of 13.4, 12.5, and 15.8 pg/d lipid for the
same three groups. For this reason, these study populations were merged for further
analysis here. These full study summaries can be considered to represent the years 1972
to 1981, for this limited male population. It would be desirable to compile concentrations
by sampling year, to see if there was a temporal trend in the data. However, the full
sample-specific study results in U.S. EPA (1990) did not indicate the year in which each
of the samples were taken. This information was retrieved from EPA's files by personal
communication (personal communication, J. Remmers, Office of Prevention, Pesticides,
and Toxic Substances, U.S. EPA, to M. Lorber, Office of Research and Development, U.S.
EPA, 2000), which allowed for the year-by-year compilations shown in Table 6-4. Only
178 of the 195 samples could be identified by year of sample collection.
As seen in Table 6-4, there appears to be a clear trend, with concentrations (in ppt)
declining from the 80s to the 50s between 1972 and 1981. The 5 samples averaging
129 ppt TEQ in 1976 appear to be anomalous. There were other samples with
concentrations above 100 ppt, such as a sample at 151 ppt TEQDF -WH098 from 1981, at
166 ppt TEQDF -WH098 from 1978, and at 179 ppt TEQDF -WH098 from 1973, but 4 of 5
samples in 1976 ranged from 99 to 215 ppt TEQDF -WH098. There is no reason to believe
that the high average in 1976 is indicative of any exposure for that year.
Data from 1976 notwithstanding, these data are the earliest that could be found
and seem to clearly suggest a declining trend of TEQs throughout the 1970s, starting as
high as in the 80s ppt TEQ lipid during the early 1970s, and declining to the 50s ppt TEQ
lipid by the late 1970s.
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Study 2. Control samples taken to compare to individuals exposed to a PCB transformer
fire in a building in upstate New York
Schecter et al. (1986) sampled intra-abdominal and subcutaneous adipose tissue in
five individuals in Binghamton, during 1983 and 1984 (these samples were assumed to be
taken in 1983 for tabular and figure display). One individual had been exposed to fumes
from a PCB transformer fire in an office building, but that individual is not included here.
Two of the remaining 4 individuals had died from unknown causes - two types of adipose
tissue were sampled and averaged for purposes here. Subcutaneous adipose tissue
samples were taken from an additional two controls. No information was available as to
the age and sex of these four individuals. Schecter et al. (1986) reported the
concentrations of 14 of 17 of the CDD/CDF congeners; 1,2,3,7,8-PCDF; 2,3,4,6,7,8-
HxCDF; and 1,2,3,7,8,9-HxCDF were not reported. The average of the four control
samples was 34 ppt TEQDF -WH098 lipid.
Study 3. Background population of St. Louis, Missouri
Graham et al. (1986) reported on the analysis of adipose tissues from autopsy
patients who had died suddenly or violently out of the hospital during 1985. It was
unclear as to whether to include the results from this study in this compilation, since only
these 6 CDD/CDF congeners were measured: 2,3,7,8-TCDD; 1,2,3,7,8-PCDD;
1,2,3,6,7,8-HxCDD; 1,2,3,4,6,7,8-HpCDD; OCDD; and 2,3,4,7,8-PCDF. However, a
comparison between these results and the results from the previous two studies suggests
that these six congeners comprise over 90% of total TEQDF -WH098. For this reason,
TEQDF -WH098 concentrations were calculated and included in this compilation. Graham et
al. (1986) also listed age and sex in the tabular summary of results. Of the 35 samples,
16 were male and 19 were female, and the age range was from 21 to 88 years, with an
average age of 43. The average (congener limited) TEQDF -WH098 concentration from this
population was 47 ppt lipid. Figure 6-5a shows the results from this study, with
concentrations displayed as a function of age. There is a clear age trend, with
concentrations ranging from less than 20 ppt TEQDF -WH098 lipid for the youngest
sampled individual to over 100 ppt TEQDF -WH098 lipid for the oldest sampled individual.
The highest sample was about 140 ppt TEQDF -WH098 lipid for a 60 year-old individual.
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Study 4: Four background individuals from Atlanta, Georgia
Patterson et al. (1994) reported on the analysis of mono- and di-ortho substituted
polychlorinated biphenyls, dioxins, and furans in serum and adipose tissue samples
collected in Atlanta. The results included from this study for the present purposes were
adipose tissue samples from four individuals who had died suddenly. These four included
2 men and 2 women, ages 19, 25, 35, and 55 years. Patterson et al. (1994) reported on
1 3 of 17 congeners, not evaluating 2 hexa- and 1 hepta-furan congeners. As above, the
reported results comprised the bulk of the TEQ. Therefore, these data were included in
the current compilation. The average TEQ concentration from this small set was 31 ppt
TEQDF -WH098 lipid.
Study 5: NHATS 1987
The National Human Adipose Tissue Survey of 1987, FY87 NHATS, was
undertaken, in part, for comparison with a very similarly designed survey in 1982, FY82
NHATS. For both surveys, composite samples of adipose tissue from individuals
characteristic of average background conditions were analyzed for the full suite of dioxin
and furan congeners. FY82 NHATS was not included in this temporal trend compilation
because there appears to have been unexplained high findings of 1,2,3,7,8-PCDD. Table
6-6 compares average congener concentrations of selected dioxin congeners from FY82
NHATS and FY87 NHATS for the 1 5-44 year age group. As seen in Table 6-6, all dioxin
congeners had reasonably comparable concentrations, except 1,2,3,7,8-PCDD. The
finding of 125.0 ppt for 1,2,3,7,8-PCDD is much higher than results for that congener for
any of the studies found here, even the highest ones in the 1970s. The overall average
1,2,3,7,8-PCDD concentration for FY82 NHATS at 73.6 is itself significantly higher than
ever found anywhere. For that reason, FY82 NHATS was not included in this compilation.
Results from 38 of the 48 composite samples from NHATS '87 were used. These
were results for the adult population, described in the age groups: 15-44 and >45 years.
The 10 composites representing the <15 year age group were not included. The 38
samples represented 666 individuals (the 48 samples represent 865 individuals).
According to the 1980 census, these two age groups comprised 46 and 31 % of the
population, respectively. Average congener concentrations for these age groups
presented in U.S. EPA (1991) were weighted to calculate the adult average concentrations
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shown in Table 6-5. Again, not all congeners were reported, but those that were reported
comprised over 90% of TEQ concentrations, as suggested by other studies which had
results for all congeners. The overall adult average concentration was 37 ppt TEQDF -
WH098 lipid. Like other studies, there appears to be an age trend, with the older set
having an average of 53 ppt TEQDF -WH098, while the younger age range had an average
that was almost half that at 28 ppt TEQDF -WH098.
Study 6. Vietnam Veterans in Massachusetts in 1988
Schecter et al. (1989) reported on a study in which blood was taken from 28
Vietnam Veterans from the state of Massachusetts. The year in which the sampling
occurred was not provided. Therefore, it is assumed to have occurred one year prior to
the year of study publication. Of the 28 individuals sampled, it appeared as though 2
individuals had been exposed to Agent Orange in Vietnam or to another source of 2,3,7,8-
TCDD. The concentration of this congener in their blood was 34 and 29 ppt lipid, and the
TEQDF -WH098 concentrations were 54 and 62 ppt lipid. The other congeners in the blood
of these two individuals did not appear elevated. For the other 26 individuals, the average
2,3,7,8-TCDD concentration was 4.8 ppt and the average TEQDF -WH098 concentration
was 29.6 ppt lipid.
Study 7: San Francisco and Los Angeles residents in the late 1980s
This study was undertaken by the California Air Resources Board for the purpose of
determining a preliminary estimate of the body burden levels of CDD/CDFs in the California
population, so that future efforts evaluating the impact of specific air sources could use
the knowledge gained, both in terms of survey design, implementation, and analysis, as
well as the background levels found (Kramer et al., 1989). A total of 57 adipose tissue
samples, from an initial target population of 60, were taken from surgical patients who
were in surgery for reasons other than cancer. Samples were selected based on the
following stratification variables: 1) age - three age groups of 12-34, 35-49, and >50
years were sampled, 2) location - San Francisco and Los Angeles were targeted, 3) sex -
half of the samples were sought from each sex. The average age of the respondents was
about 50 years. The average concentration found was 31 ppt TEQDF -WH098 lipid. Figure
6-5b shows the results as a function of age. Again, a trend is suggested showing higher
DRAFT-DO NOT QUOTE OR CITE 6-19 December 2003
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concentrations with age. However, the trend is not nearly as well defined as in the
Missouri study. Concentrations ranged from < 10 ppt TEQDF -WH098 lipid for the
youngest individuals sampled (about 21 years old) to approximately 80 ppt TEQDF -WH098
lipid for an individual over 60 years old.
Study 8: Pooled blood sample from blood bank donors in upstate New York
In a survey article describing various sampling efforts in the United States and
around the world, Schecter (1991) presented the results of a pooled blood sample
comprised of the blood from 100 individuals. This same profile was identified in Schecter
et al. (1991) as the control population for an AIDS study, and having originated from a
Syracuse Red Cross Blood Bank. Schecter et al. (1991) claims that this control population
contained samples collected from approximately an equal number of males and females.
No additional demographic information was available for these individuals. The average
concentration reported was somewhat higher at 50 ppt TEQDF -WH098 lipid, compared to
other values presented for the 1980s in this compilation.
Study 9: Fifty Michigan Vietnam Veterans
Blood samples from 50 Michigan Vietnam Veterans were collected by Schecter et
al. (1993) in 1991. The purpose of this collection was to compare impacts of exposure to
Agent Orange in these veterans to levels of dioxin in Vietnamese exposed to Agent
Orange. The average age of the 50 veterans at the time of sampling was 48 years, with a
range of 41 to 66 years. Levels of 2,3,7,8-TCDD and other dioxins were elevated in 6 of
these 50 Vietnam veterans. The average TEQDF -WH098 concentration in these 6 veterans
was 85 ppt lipid. The average TEQDF -WH098 concentration in the other 44 veterans was
much lower at 29.1 ppt lipid. The difference in 2,3,7,8-TCDD concentration was more
striking: 46 ppt in the 6 veterans versus 4.1 ppt in the other 44 veterans. The average of
the 44 veterans was used for this compilation.
Study 10: Pooled blood sample from Binghamton
Schecter et al. (1997) reported on the result of a pooled blood sample representing
100 adult men and women. The samples were collected at a Binghamton hospital
laboratory from specimens ready to be discarded after having been used for routine
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medical purposes. The average TEQ concentration from this pooled sample was 32 ppt
TEQDF -WH098 lipid.
Study 11: Blood samples from 6 study sites around the country
This is the extensive compilation prepared by the Centers for Disease Control (CDC,
2000) that forms the basis of this Reassessment's representation of current background
body burdens in the United States. Details on this compilation are provided in Chapter 4,
Section 4.3.2 and are not included here. Briefly, these were background populations from
site-specific studies. Sites and populations were: Manchester, Missouri (n = 61); Times
Beach, Missouri (n = 67); Jacksonville, Arkansas (n = 57); Oregon (n = 9); Wisconsin
(n = 93); and North Carolina (n = 29). The average TEQDF -WH098 concentration is 20
ppt lipid. The age of the individuals sampled were available for 214 individuals located in
Missouri, Arkansas, and North Carolina. The average age was 45 years, with a range of
20-70 years. The concentrations of these individuals are graphed in Figure 6-5c. While
an age trend is evident, it does appear as though that the trend is much less marked, as
compared to earlier years.
Study 12. Breast adipose tissue in San Francisco
Petreas et al. (2000) presented a summary of results of analyses of breast tissue
samples taken from women undergoing breast surgery during 1998. These 45 individuals
comprised the control group in a breast cancer case-control study in the San Francisco
Bay area. Specific results from each individual were not available in Petreas et al. (2000),
but were supplied by personal communication (personal communication from M. Petreas,
Hazardous Material Laboratory, California EPA, to M. Lorber, Office of Research and
Development, U.S. EPA, 2000). The average age of these women was 45 years, with a
range of 28-67 years. The average TEQDF -WH098 concentration was 25 ppt lipid.
Two important trends that can be ascertained from these data are:
1) While certainly not a statistically collected set of data, these studies suggest a
downward trend in average adult body burdens of CDD/CDFs from the 1970s
through the 1990s. The adult concentrations appear to be in the range of 70 - 90
ppt TEQDF -WH098 in the early 1970s; in the range of 30 - 50 ppt TEQDF -WH098 in
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the 1980s; below 30 ppt in the 1990s; and perhaps approximately 20 ppt by the
year 2000.
2) An age trend that has been identified by others in the literature is displayed is
discussed here and displayed in Figure 6-5. Specifically, older individuals appear to
have higher body burdens as compared to younger individuals. Based on an
examination of three studies in the 1980s (NHATS FY87 and the two studies
graphed in Figure 6-5), and one from the 1990s (Figure 6-5c), one might speculate
that the age trend is less pronounced through the 1990s. This is probably due to a
dose trend discussed earlier in Section 6.7 (i.e., that doses to CDD/CDFs, as
exemplified by 2,3,7,8-TCDD, were possibly higher in the 1960s and 70s,
declining to current background levels). With lower exposures through the 1990s,
older individuals could be in a period of depuration - body burdens are actually
declining as they age. For example, individuals who were in their 30s and 40s in
the 1970s and 80s may have had concentrations above 40 ppt TEQDF -WH098, but
may have had concentrations less than 30 ppt in the 1990s, as lower background
doses could not sustain the higher body burdens.
6.8. ADDITIONAL EVIDENCE OF TEMPORAL TRENDS IN BODY BURDENS
Schecter (1991) analyzed liver tissues estimated to be 100 to 400 years old
recovered from the frozen bodies of two Native American (Eskimo) women. The women
died in their igloo in Point Barrow, Alaska, when they were trapped and frozen by an ice
overflow. Oil was used for cooking and heating, and ventilation was poor. One woman
had soot-laden lungs. The results indicated that dioxin levels were much lower in these
ancient tissues than in livers of people currently living in industrial areas. Tong et al.
(1990), as cited in Schecter (1991), found a lipid-based total I-TEQDF level of 0.24 ppt in
one of the ancient liver samples. I-TEQ levels (i.e., CDD, CDF, and total) were
nondetectable in the other ancient sample. Analysis of two liver samples from modern
times showed lipid-based total I-TEQDF levels of 13.3 ppt (Ryan et al., 1986, as cited in
Schecter, 1991).
Recently, Pa'pke et al. (1997) analyzed 180 whole blood samples collected in
Germany in 1996 for CDD/CDFs. The samples were taken only from individuals who had
no known exposure to CDD/CDFs (i.e., their only exposure would be through food
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ingestion). The results of this study were compared to a similar study conducted in 1994.
The results indicated that concentrations declined from 19.1 ppt I-TEQDF in blood lipids in
1994 to 16.5 ppt I-TEQDF in blood lipids in 1996. These results suggest human exposure
to CDD/CDFs have decreased over the past several years.
Wittsiepe et al. (1998) measured CDD/CDFs in 507 blood samples collected in
Germany between 1991 and 1996. The samples were intended to represent the general
population (i.e., the individuals were not exposed to dioxin-like compounds as a result of
occupational or accidental contacts). The results indicated that blood levels of CDD/CDFs
declined significantly between 1991 and 1996, with the more recent concentrations
representing approximately one-half the earlier concentrations. The mean lipid-based total
CDD/CDF concentrations were 71.8 pg/g in 1 991 and 334.4 pg/g in 1 996 (Table 6-7).
Liem et al. (1996) detected downward trends in the levels of CDD/CDFs in human
breastmilk between 1987-1988 and 1992-1993. Breastmilk samples were collected
from women in 11 countries as part of a World Health Organization (WHO)-coordinated
exposure study. Protocols were developed to ensure that the samples collected during the
two periods were comparable. The protocols included criteria for selection of donors,
sampling areas, etc. The samples were analyzed for the 17 CDD/CDF congeners, as well
as 6 marker PCBs (IUPAC numbers 28, 51, 101, 138, 153, and 180). The results
indicated that CDD/CDF levels are decreasing in some countries (Table 6-8). Liem et al.
(1996) estimated an overall annual CDD/CDF decrease of 7.2 percent, based on the data
from those countries.
In a similar study, Schecter et al. (1997) assessed CDD/CDF concentrations in
blood and breastmilk samples collected in Germany and the United States during two time
periods. More than 100 blood samples were collected in Germany during the years 1989
and 1994 from "persons for whom there was concern about dioxin exposure but abnormal
dioxin blood levels were not found." American blood samples were collected in
1984-1989 (male veterans with no dioxin abnormalities) and 1996. The German milk
samples were collected in Westphalia in 1991 and 1995, and American samples were two
pooled samples from 1988 (Binghamton, NY, and Los Angeles, CA), five individual
analyses from the period 1995-1996 (Binghamton, NY), and one pooled sample from
1997 (Binghamton, NY). The results of the study indicated that from 1989 to 1994, the
CDD/CDF concentrations in German blood declined from 43 ppt I-TEQDF to 19 ppt I-TEQDF.
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CDD/CDF concentrations in United States blood samples declined from 28 ppt I-TEQDF in
1984-1989 to 25 ppt I-TEQDF in 1996. The results of the German breastmilk analyses
indicated a decline from 23 ppt I-TEQDF in 1991 to 16 ppt I-TEQDF in 1995. American milk
samples showed a reduction from 17 ppt I-TEQDF in 1988 to 9 ppt I-TEQDF in 1995-1997.
Kiviranta et al. (1998) measured the concentrations of the 17 toxic CDD/CDF
congeners and 6 PCB congeners (IUPAC 28, 52, 101, 138, 153, and 180) in human milk
samples from primiparae mothers in Finland. Samples were collected from women in both
rural and urban areas between 1992 and 1994 and compared to data from 1987 (Table
6-9). Total lipid-based CDD/CDF concentrations declined from 339 ppt (n = 37) to 217
ppt (n = 28) in rural areas between 1987 and 1992-1994; urban concentrations were
similar: 375 ppt (n = 47) and 381 ppt (n = 14) for the two sample periods, respectively. I-
TEQDF values decreased from 20.1 ppt to 13.6 ppt in rural areas, and 26.3 ppt to 19.9
ppt for urban areas between 1987 and 1992-1994. Total lipid-based PCB concentrations
declined from 396 ng/g (n = 37) to 198 ng/g (n = 28) in rural areas, and from 496 ng/g
(n =47) to 296 ng/g (n = 14) in urban areas for the same two sample periods.
6.9. A MODELING EFFORT TO RECONSTRUCT PAST DOSES OF 2,3,7,8-TCDD
Previous sections in this chapter describe evidence supporting temporal trends in
environmental concentrations and human exposure to CDD/CDF/PCBs. Levels of dioxin-
like compounds appeared to increase in the environment starting from the 1930s through
the 1960s, and loadings began to decline perhaps starting in the 1970s to the present.
Recent evidence collected on animal food products in the United States (Winters et al.,
1998), combined with body burden data, are the best evidence that human exposures to
dioxins may have followed the same trends. (See Sections 6.5 and 6.6.) This section
describes a third way of evaluating past exposures to dioxins. Pinsky and Lorber (1998)
described an effort to statistically reconstruct the pattern of past human exposure to the
most toxic dioxin congener, 2,3,7,8-TCDD (abbreviated TCDD), through use of a simple
pharmacokinetic (PK) model that included a time-varying TCDD exposure dose. This
section summarizes the procedure and presents some key results from this modeling
exercise. The original reference (Pinsky and Lorber, 1998) should be obtained for further
detail.
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A first-order, one-compartment PK model was used to compute an individual's
body lipids' TCDD concentration over time. Key inputs for that model include: (1) a time-
varying dose of TCDD (expressed in units of pg/kg-day), (2) a fraction of dose absorbed
into the body lipid compartment (assumed to be constant), (3) the volume of the body
lipid compartment (assumed to be time varying), and (4) a rate of TCDD loss from the lipid
compartment (modeled as a function of the percent of body fat). To calculate the rate of
TCDD loss, a model was needed to predict how body lipid volumes vary over time, in
addition to a model of how overall body weight varied over time.
In this modeling exercise, all inputs were fixed except the time-varying dose of
TCDD. Using Bayesian statistical approaches, the dose was "calibrated" to best fit a set
of data on TCDD concentration in body lipids. These data, shown in Table 6-10, were
obtained from studies that focused on persons with no known direct exposure to dioxins
and, as such, measured background exposure levels. In terms of this modeling exercise,
the most important data from this set, were from the 1970s, suggesting that body lipid
concentrations of TCDD were above 10 ppt during those years (VA/U.S. EPA, 1988).
Current data from the 1980s into the 1990s show TCDD concentrations below 10 ppt
(U.S. EPA, 1991; Michalek et al., 1998; Andrews et al., 1989).
The feature of the Bayesian approach that is most relevant to this calibration
modeling exercise was the use of constraints on the input functions. In other words,
much of the evidence described earlier in this chapter suggests an expected trend on the
dose function that was being calibrated in this modeling exercise (i.e., that the TCDD dose
may have increased from the 1930s to the 1970s and declined thereafter). An
examination of the existing trend data suggests, specifically, the following for the current
dose modeling purposes: (1) a peak in environmental levels appears to have occurred in
the 1960s or 1970s, (2) early century levels are from 2 to >33 times lower than the
peak, (3) late 1980s levels are from 1 to 20 times lower than the peak, and (4) late 1980s
levels are higher than early century levels; in all cases, the ratio of peak to 1980s levels is
lower than the ratio of peak to early century levels. Also, and importantly for this
modeling exercise, the estimate of TCDD exposure dose based on the 1994 release of this
dioxin reassessment document (U.S. EPA, 1994b) was 0.17 pg TCDD/kg-day. Using
these trends, the following Bayesian "plausibility criteria" were established for calibration
modeling purposes:
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1. A range of 0.0 to 0.50 pg TCDD/kg-day for the exposure dose in 1 990.
The same plausible range was used for the 1900 dose.
2. Ranges of 2 to 200 for the ratio of peak to 1900 dose, and 1 to 100 for
peak to 1990 dose.
3. Peak year set between 1945 and 1980.
Finally, to ensure a smooth exposure curve, a limit of 20 percent was set on the rate of
decrease from the peak exposure level going forward or backward 1 year.
Pinsky and Lorber (1998) detailed how well the calibrated doses duplicated the
measured body burdens shown in Table 6-10. In general, several slightly different
calibrated dose functions fit the data equally well. An example of a family of similar dose
curves is shown in Figure 6-6. In that figure, the dose appears to increase from the
1940s through the 1960s, then begins to drop through the 1970s, with a baseline level
being reached by the 1980s. This qualitatively fits some of the trend data described
previously. However, while the calibrated model fits the data reasonably well, it does not
fit the data perfectly. Obviously, the lack of a perfect model fit beginning in the 1970s
could be partially attributed to the lack of observed body concentrations of 2,3,7,8-TCDD.
As seen in Figure 6-6, the dose curves appear to converge after the 1970s, when there
were observed data, while the dose curves seem to diverge prior to 1970, when there
were no data on which to base the calibration. Also, some data were inconsistent,
particularly the National Human Adipose Tissue Study (NHATS) data from 1982 and 1987
(U.S. EPA, 1991). Specifically, in a comparison of the NHATS 1982 and 1987 data, the
mean TCDD concentration increased considerably from 1982 to 1987 in the oldest age
group (45+), but decreased considerably in the two younger age groups. Further, the
NHATS 1982 data do not display the trend of increasing TCDD concentrations by age that
is seen in most other studies done in the 1980s (mean was 6.9 pg/g in the 15-44 age
group and 5.5 pg/g in the 45+ age group), while the age trend in NHATS 1987 data
(mean of 4.4 pg/g in the 15-44 age group versus 9.4 pg/g in the 45+ age group) seems
exaggerated. These trends are difficult to explain with the current modeling structure;
subsequently, all models with a good data to model fit overpredicted the 1982 mean and
underpredicted the 1987 mean in the highest age group. More data from prior to and
during the 1970s would have provided a much more useful database from which to
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calibrate the model; however, no other TCDD body concentration data could be found
from those time periods.
Despite the lack of a perfect fit of the model to the data, several informative
findings resulted from this exercise.
1. The model calibration exercise was regenerated using two changes to the initial
Bayesian constraints on the shape of the exposure/dose curve. One change
essentially dropped all constraints in order to test whether the imposition of the
constraints restricted how well a calibrated dose curve could fit the data. It was
found that a best-fit solution for exposure with no constraints provided only an
insignificant improvement. The second change was to constrain the exposure
dose, making it constant over time. Results showed that the temporally varying
dose provided a significantly improved fit to the data, as compared to the constant
dose. Further, the best-fit constant dose in this exercise was 0.35 pg TCDD/kg-
day, compared with the current average adult dose of 0.17 pg TCDD/kg-day, as
determined by the previous version of this dioxin reassessment (U.S. EPA, 1994b),
and with the revised 0.09 pg TCDD/kg-day of this current reassessment. The
result provides strong evidence that past doses were, in fact, higher than current
doses.
2. The exposure/dose curves in Figure 6-6 suggest that dioxin exposure followed a
sharp bell curve, with a precipitous drop to a flat baseline in dose before 1980.
This drop is counterintuitive and probably more the result of the simplicity of the
pharmacokinetic model than a real-world trend. However, it may be reasonable to
treat some generalizations from these calibrated dose curves as reasonable
hypotheses. For example, the late 1960s were estimated as years of peak TCDD
exposure, an observation that coincides with peaks found in sediment core studies.
The estimates derived suggest that TCDD exposures may have been 20 times
higher during the 1960s than the 1980s. Over a 10-year peak period in the 1960s
and early 1970s, daily exposures could have been as high as 1.5 to 2.0 pg/kg-day,
possibly dropping to as low as 0.10 pg/kg-day and below into the 1980s. Without
body burden data, it may be difficult to go much further with the model results.
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3. In addition to an exposure dose, the results of this exercise also include temporal
body burden levels, as described by body lipid concentrations of TCDD. An
example of these results is shown in Figure 6-7. The "specimen year" on the x-
axis refers simply to the year in which a cross-section of the population can be
examined. For example, in 1986, young individuals have a body burden of about 2
pg/g lipid; whereas older individuals have a body burden exceeding 5 pg/g lipid.
The modeled build-up of TCDD in an individual's body can be ascertained by
following a curve corresponding to the individual's birth year, shown on the z-axis,
and progressing to the left. This figure displays two important trends: (a) body
burdens in general tend to be dropping (Schecter, 1991; U.S. EPA, 1991; MAFF,
1995), and (b) body burdens are higher in older individuals than younger individuals
(Orban et al., 1994; Andrews et al., 1989; Van der Molen et al., 1996). As seen
from specimen years after approximately 1970, body concentrations in individuals
of all ages appear to be dropping. At the same time, the cross section of all birth-
year populations after about 1970 suggests that concentrations are higher in older
individuals. Interestingly, this trend may not have been present in the U.S.
population in the mid-1970s and earlier. In the mid-1970s, peak body
concentrations appear to have peaked for individuals in their 20s (roughly), with a
constant body burden for older individuals. In the 1960s and earlier, differences in
body burden do not appear to be a function of age.
4. This model also had a breastmilk feeding component. To search for the best-fit
exposure/dose curve, it was arbitrarily assumed that half the population was breast
fed and the other half was bottle fed. Therefore, the average concentration for all
individuals was calculated as the midpoint of body lipid concentrations modeled
with and without breast-feeding. Breast-fed infants were exposed to milk
concentrations modeled to occur in 25-year-old females (where breastmilk lipid
concentrations were assumed to equal body lipid concentrations). Infants were
assumed to be breast fed for 4 months, and their consumption of breastmilk lipids
was 26 g/day. Bottle-fed infants were assumed to be exposed to the general
exposure dose, which as can be expected, turned out to be much lower than the
breast-feeding dose. Model predictions were compared to a limited subset of the
available body burden data, specifically to the under-15 age groups for the two
DRAFT-DO NOT QUOTE OR CITE 6-28 December 2003
-------
NHATS data sets, where the mean concentrations were 4.2 pg/g lipid (NHATS
1987) and 2.0 pg/g lipid (NHATS 1982). For the under-15 age group in 1982, the
predicted mean concentrations in one possible solution were 3.8 pg/g in breast-fed
children versus 0.3 pg/g in bottle-fed children; in 1987, the expected means in this
age group were 1.8 pg/g for breast-fed and 0.2 pg/g for bottle-fed individuals.
Assuming a 50 percent breast-fed rate, predictions yielded averages of 2.0 pg/g
and 1.0 pg/g for the 1982 and 1987 NHATS, respectively. Two observations
could be made. First, the modeling exercise shows the impact of breast-feeding
since modeled predictions of bottle-fed body concentrations were much lower than
breast-fed body concentrations (i.e., 0.3 and 0.2 pg/g lipid for bottle fed versus
3.8 and 1.8 pg/g lipid for breast fed). Therefore, if the low body burden found for
bottle-fed infants reflects reality, then the NHATS data showing 4.2 and 2.0 pg/mL
also show the influence of breast-feeding on the body burden of children. Second,
both the model and the data show a drop in the body concentrations of the under-
15 age group between 1987 and 1982, suggesting a trend toward declining
exposures through the 1980s.
DRAFT-DO NOT QUOTE OR CITE 6-29 December 2003
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Table 6-1. Lipid Based Concentrations of CDD/CDFs and PCBs in Samples of
Pooled Retail Milk Purchased in the United Kingdom
During 1990 and 1995
Location
Bristol
Cambridge
Carlisle
Central London
Commuter London0
Crewe
Exeter
Northallerton
Norwich
Nottingham
Slough
Worchester
Concentrations (ppt) TEQ (lipid)
CDD/CDFs
(1990)a
2.0
1.4
1.1-1.4
1.4-3.3
1.7-2.1
—
1.1-3.3
—
—
—
CDD/CDFs
(1995)
0.92
0.84
0.67
0.99
0.88
1.4
1.3
1.1
1.1
1.1
0.80
1.0
PCBs
(1995)
2.2
1.5
0.75
1.7
1.7
2.1
1.7
1.7
2.1
2.3
1.6
2.3
CDD/CDF/PCBsb
(1995/1996)
3.1
2.3
1.4
2.7
2.6
3.5
2.9
2.8
3.2
3.5
2.4
3.4
Note: l-TEFDFs were used in calculating l-TEQDFs; TEFP-WH094s were used in calculating
TEQP-WH094s.
ppt = parts per trillion
a Some locations were sampled twice in 1990. Samples were also purchased from
Beverley, Leeds, and Preston in 1990. Fat contents were not measured for the 1990
samples, and the whole milk concentrations were converted assuming 4 percent fat
content, which is typical for UK whole milk.
b The combined concentrations of CDD/CDFs and PCBs were calculated before rounding.
c The individual pints forming this pool were purchased in outer London and the home
counties.
Source: MAFF (1997a).
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Table 6-2. CDD/F and PCB TEQ Concentrations and
Percent Differences from Current TEQ Levels
Description
1 908 Beef ration
1 945 Beef and pork
1 957 Dried cream
1968 Bacon bar
1968 Deviled ham
1971 Beef
1 971 Bacon wafer
1 977 Raw chicken
1977 Cooked chicken
1979 Pork slices
1 980 Beef steak
1982 Ham slice
1983 Beef in bbq
1983 Turkey with gravy
I-TEQDF, pg/g
Lipid
0.34 (0.15)
0.98 (0.75)
2.05 (0.81)
3.01 (2.94)
3.73 (3.71)
1.36 (0.02)
1.75 (1.62)
1.29 (1.18)
1.33 (1.20)
1.46 (1.20)
0.94 (0.73)
1.36 (1.04)
0.50 (0.03)
0.55 (0.23)
TEQP-WH094,
pg/g Lipid
0.07 (0.07)
0.36 (0.36)
3.56 (3.54)
1.05 (1.05)
0.61 (0.61)
2.48 (1.98)
1.98 (1.98)
2.72 (2.72)
2.83 (2.83)
0.04 (0.04)
0.93 (0.93)
0.07 (0.07)
0.79 (0.79)
0.32 (0.31)
Percent of
Current I-
TEQDF Levels3
38 (42)
89 (197)
244 (96)
231 (638)
287 (805)
153 (7)
135 (352)
202 (287)
209 (292)
112 (262)
106 (207)
105 (227)
56 (8)
85 (57)
Percent of Current
TEQP-WH094
Levels3
15 (15)
140 (146)
827 (824)
1747 (2620)
1019 (1529)
540 (540)
3301 (4952)
970 (970)
1009 (1009)
72 (105)
203 (203)
119 (178)
171 (171)
113 (113)
Note: (results assume ND = 1/2 LOD; results calculated at ND = 0 shown in parenthesis)
pg/g = picograms per gram
a Current CDD/CDF/PCB levels are the levels observed in national food surveys for beef (Winters
et al., 1996a, 1996b), pork (Lorber et al., 1997), poultry (Ferrario et al., 1997), and milk (Lorber
et al., 1998).
Source: Winters et al. (1 998).
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December 2003
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Table 6-3. Estimated Upper Bound Dietary Intakes of CDD/CDFs and PCBs
by the Average UK Consumer in 1982 and 1992
Food Group
Bread
Other cereal products
Carcass meat
Offals (internal organs)
Meat products
Poultry
Fish
Oils and fats
Eggs
Milk
Milk products
TOTAL
CDD/CDF Intake (mean)
(pg l-TEQDF/person/day)a
1982
3
14
16
3
15
8
7
38
22
48
66
240
1992
4
17
4
1
3
2
3
6
3
17
9
69
PCB Intake (mean)
(pg TEQP-WH094/
person/day)15
1982
2
13
10
0.3
8
4
13
40
5
28
34
156
1992
2
3
3
0.2
3
1
6
8
2
11
7
46
Note: Estimated total dietary intakes were calculated before rounding.
a MAFF (1995).
b Adapted from MAFF (1997b).
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December 2003
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Table 6-4. Summary of Studies with Body Burden Data of Dioxins and Furans
Year
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1983
1985
1986
1987
1988
1988
1989
1991
1996
1996
1998
Mean TEQ,
pg/g lipid
87
89
70
68
129
69
69
67
55
54
34
47
31
overall: 37
15-44: 27.5
> 45: 53.4
30
37
50
29
32
20
25
N
7
14
14
14
5
17
17
22
42
26
4
35
4
666
26
57
100
44
100
316
45
Ages
(years)
18-45
NA
X = 43
(21-88)
X = 34
(19-55)
NA
NA
X~50
(12-88)
NA
X = 48
(41-66)
NA
X = 45
(20-70)
X = 45
(28-67)
Study #; Reference; Location
1; Kang et al., 1991; U.S. EPA, 1990.
Vietnam Veterans, non-Vietnam Veterans, and
Civilians.
2; Schecter et al., 1986; Binghamton
3; Graham et al., 1986; St. Louis
4; Patterson et al., 1994; Atlanta
5; Orban et al., 1987; U.S. EPA, 1991; NHATS
'87
6; Schecter et al., 1989; Massachusetts
7; Stanley et al., 1989; San Francisco and Los
Angeles
8; Schecter et al., 1991; Syracuse
9; Schecter et al., 1993; Michigan Vietnam
veterans
10; Schecter et al., 1997; Binghamton
1 1 ; CDC, 2000; background populations from
site-specific studies in MO, OR, WS, AK, and
NC.
12; Petreas et al., 2000; San Francisco
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December 2003
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Table 6-5. Average Congener Concentrations for Body Burden Studies of Dioxins and Furans
(columns are study number and sampling year; all results in pg/g lipid)
Congener
2378-D
12378-D
123478-D
123678-D
123789-D
1234678-D
OCDD
2378-F
12378-F
23478-F
123478-F
123678-F
234678-F
123789-F
1 234678-F
1234789-F
OCDF
WHO- TEQ
1; 1972
22.4
22.1
NA1
170.6
18.5
277.0
1273.4
3.8
1.2
30.9
27.6
11.0
3.7
0 (ND)
52.7
1.9
3.7
87
1; 1973
20.2
24.4
NA1
189.4
24.7
448.1
1768.0
3.2
0.6
25.9
29.3
13.9
4.8
0.2
57.4
1.5
5.9
89
1; 1974
14.5
20.1
NA1
168.4
18.1
250.5
1291.4
2.3
0.2
19.5
24.5
11.1
3.1
0 (ND)
44.2
0.8
4.2
70
1; 1975
15.3
18.8
NA1
148.9
17.1
221.5
939.4
1.6
0.3
23.8
17.1
8.7
2.6
<0.1
31.3
0.6
3.7
68
1; 1976
26.8
38.4
NA1
304.6
36.2
588.2
2790.0
4.2
1.3
32.8
34.1
17.0
6.6
0 (ND)
49.7
1.7
2.9
129
1; 1977
11.8
18.1
NA1
152.9
16.5
246.1
1154.7
1.1
0.1
24.4
20.7
11.1
3.4
0.1
35.8
0.8
2.8
69
1; 1978
11.6
18.1
NA1
175.0
18.6
308.8
1325.1
1.3
0.2
25.3
24.3
11.5
3.5
0 (ND)
37.2
1.5
1.9
69
1; 1979
12.5
18.2
NA1
154.9
17.1
265.7
1294.1
1.2
0.1
25.0
20.8
10.9
3.1
<0.1
32.3
1.1
1.8
67
1; 1980
11.0
14.1
NA1
133.4
14.6
214.8
1004.2
1.9
0.3
18.0
16.6
8.6
2.6
<0.1
30.6
0.7
1.6
55
1; 1981
11.9
16.0
NA1
124.5
12.8
178.4
810.4
1.3
0.2
15.6
14.6
7.5
1.7
0.1
23.7
0.4
1.8
54
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December 2003
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Table 6-5. Average Congener Concentrations for Body Burden Studies of Dioxins and Furans
(columns are study number and sampling year; all results in pg/g lipid) (continued)
Congener
2378-D
12378-D
123478-D
123678-D
123789-D
1234678-D
OCDD
2378-F
12378-F
23478-F
123478-F
123678-F
234678-F
123789-F
1 234678-F
1234789-F
OCDF
WHO- TEQ
2; 1983
6.7
10.3
NA
55.9
8.0
88.2
579.5
1.3
NA
13.9
13.4
8.5
NA
NA
16.0
9.1
5.8
34
3; 1985
8.4
19.2
NA
107.5
NA
253.3
1273.3
NA
NA
12.0
NA
NA
NA
NA
NA
NA
NA
47
4; 1986
4.4
11.6
5.1
94.2
16.9
55.6
446
1.1
NA
3.7
3.7
5.8
NA
NA
12.0
NA
NA
31
5; 1987
6.4
12.9
NA
90.7
13.3
129.7
876.4
1.9
NA
12.0
NA
7.0
NA
NA
NA
NA
NA
37
6; 1988
4.8
7.8
9.1
64.1
13.3
126.3
1054.6
NA
NA
9.1
13.1
7.3
1.9
NA
26.0
NA
NA
30
7; 1988
6.2
12.7
14.0
70.1
12.8
124.8
700.7
2.7
1.0
8.0
8.8
5.4
2.2
0.6
11.0
60.1
1.2
37
8; 1989
5.2
21.0
13.0
84.0
15.0
187.0
1174.0
3.1
2.8
13.0
15.0
14.0
3.6
ND (1.2)
36.0
ND (1.8)
4.2
50
9; 1991
4.1
8.3
NA
76.2
10.9
108.8
731.0
2.1
1.2
8.5
10.5
5.5
2.9
2.7
19.0
3.2
9.9
29
10; 1996
4.2
9.8
10.6
67.9
10.7
116.5
879.8
ND (2.00)
ND (1.9)
9.3
14.0
7.9
ND (4.1)
4
13.9
4.9
ND (5.00)
32
11; 1996
2.1
5.2
6.2
73.1
7.1
79.2
664.0
0.7
0.8
6.2
6.5
5.3
6.2
0.7
13.2
1.3
2.1
20
12; 1998
3.9
6.3
NA
57.6
NA
68.6
528.3
NA
NA
10.5
5.9
4.1
NA
NA
NA
NA
NA
25
1 For these data, 123478-HxCDD and 123678-HxCDD were measured together and reported as, 123478/678-HsCDD.
DRAFT-DO NOT QUOTE OR CITE 6-43 December 2003
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Table 6-6. Comparison of the 15-44 Age Group Average Concentration of
Selected Congeners from NHATS FY82 and NHATS FY87
Congener
2,3,7,8-TCDD
1,2,3,7,8-PCDD
1,2,3,4,6,7,8-HpCDD
OCDD
NHATS FY82
6.87
125.0
1 14.0
760.0
NHATS FY87
4.33
9.48
99.8
726.0
Source: U.S. EPA (1991).
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December 2003
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Table 6-7. Trends in Blood CDD/CDF Levels in a German Population, 1991-1996
Year
Number of samples
Mean age (yrs)
Fat content (mg/g)
Mean total CDD/CDF
concentration (pg/g)
1991
95
44.7
5.7
718.4
1992
157
42.4
5.7
703.2
1993
17
40.5
6.0
534.5
1994
74
46.5
6.0
376.7
1995
69
45.2
5.7
431.6
1996
95
37.7
5.1
373.1
Source: Wittsiepe et al. (1 998).
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December 2003
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Table 6-8. Comparison of Results from the First and Second Round of
WHO-Coordinated Human Milk Study
Country
Austria
Belgium
Canada
Croatia
Denmark
Finland
Germany
Hungary
Netherlands
Norwayd
United
Kingdom
Area
Vienna (urban)
Tulln (rural)
Brabant Wallou
Liege
Brussels
All Provinces 1981
All Provinces 1982
Maritimes
Quebec
Ontario6
Prairies
British Columbia
Kirk
Zagreb
Several Regions/Cities
Helsinki
Kuopio
Berlin
North Rhine-Westphalia
Budapest
Scentes
Rural Area
Urban Area
All Regions
Troms0 (coastal)
Hamar (rural)
Skien/Porsgrumm (ind)
Birmingham
Glasgow
CDDs and CDFs (pg l-TEQDF/g)
1987/88'
17.1
18.6
33.7
40.2
38.8
15.6
18.1
17.6
19.4
23.0
12.0
1 1.8
17.8
18.0
15.5
32.0
31.6
9.1
1 1.3
37.4
39.6
34.2
18.9
15.0
19.4
37.0
29.1
n
54
51
19
34
76
31
23
14
41
42
38
31
40
79
100
50
13
13
10
1 1
10
10
1992/93
10.7
10.9
20.8
27.1
26.6
28.6
14.5
10.8
13.4
18.1
14.6
15.7
8.4
13.5
15.2
21.5
12.0
16.5
20. 7e
8.5
7.8
22.4
10.1
9.3
12.5
17.9
15.2
n
13
21
8
20
6
200
100
20
20
20
20
20
10
13
48
10
24
10
20
10
17
10
10
10
20
23
V [Marker PCBs] (ng/g)
1987/88
558
609
500°
450°
830°
150
203
762
416
392
272
562°
507°
533°
n
12
21
14
41
10
38
31
143
10
10
96
10
10
8
1992/93
381
303
275
306
260
212
112
86
137
128
58
70
218
219
209
189
133
375
61
45
253
273 (536°)
265 (4c83c)
302 (468°)
129
131
n
13
21
8
20
6
200
100
20
20
20
20
20
10
13
48
10
24
10
20
10
17
10
10
10
20
23
NOTE: Results are expressed on a fat basis. £ (marker PCBs) and l-TEQDFs are calculated assuming non-detect values are equal to zero.
a Calculated using Nordic TEF-model.
b Ontario-1988 denotes proportional mean of two pooled samples analyzed in the first round.
c Analyzed using packed column technique.
d To compare results between first and second round, samples from 1992/93 have been reanalyzed using (old) packed column technique (Becher
and Skare, personal communication).
e Dioxin levels in human milk samples from North Rhine-Westphalia collected in 1992 as reported by Furst (1993).
Source: Liem et al. (1996).
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December 2003
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Table 6-9. Comparison of CDD/CDF Concentrations in Human Milk
from Finland in 1987 and 1992-1994
Selected Congeners
2,3,7,8-TCDF
2,3,7,8-TCDD
2,3,4,7,8-PeCDF
1,2,3,7,8-PeCDD
1,2,3,6,7,8-HxCDD
OCDD
^COD/CDF
I-TEQDF
Cone, pg/g fat
Urban Area
n = 14
1.93 ± 0.74***
2.66 ± 1.46
16.3 ± 7.0*
6.22 ± 2.16*
33.2 + 8.94
230 ± 80.9***
381 ± 120***
19.9 ± 7.42*
n 1992-1994
Rural Area
n = 28
0.49 ± 0.44
1.71 ± 0.68
10.4 ± 4.65
4.36 ± 1.56
26.9 + 8.16
126 ± 55.7
217 ± 76.6
13.6 ± 4.57
Cone, pg/g
Urban Area
n = 47
2.98 ± 2.89
3.37 ± 1.85
20.1 ± 12.5
9.78 ± 4.87XXX
48.2 + 15.8XXX
187 ± 83.6
375 ± 132
26.3 ± 11.9
fat in 1987
Rural Area
n = 37
6.75 ± 4.29XXX
2.50 ± 1.25X
13.1 ± 5.41
7.53 ± 3.23XXX
41.5 + 15.3XXX
171 ± 71.5XXX
339 ± 108XXX
20.1 ± 6.54XXX
Note:
Asterisks indicate a statistically significant difference between urban and rural areas in 1992-1994.
p<0.01
p<0.005
p<0.001
* *
* * *
x indicates a statistically significant difference between 1987 and 1992-1994 results
p<0.01
p< 0.005
p< 0.001
Source: Kiviranta et al. (1998).
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December 2003
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Table 6-10. Mean Human Lipid TCDD Concentrations Reported in Various U.S. Studies
Study (Reference)
Andrews et al.
(1989)
Air Force (Michalek
et al., 1997)
NHATS 82
(U.S. EPA, 1991)
NHATS 87
(U.S. EPA, 1991)
VA/EPA
(VA/U.S. EPA,1988)
Age/Gender
Group
18-29, both
30-39, both
40-49, both
50-59, both
60-79, both
35-39, male
40-44, male
45-49, male
50-54, male
55-59, male
60-64, male
65-69, male
0-14, both
15-44, both
45 + , both
0-14, both
15-44, both
45 + , both
20-36, male
23-39, male
26-42, male
29-45, male
Sample
Size
14
30
25
22
37
168
280
165
232
142
33
35
178
312
273
146
318
401
27
29
57
82
Year
1986
1986
1986
1986
1986
1987
1987
1987
1987
1987
1987
1987
1982
1982
1982
1987
1987
1987
1971-1973
1974-1976
1977-1979
1980-1982
TCDD
Mean,
pg/g
4.0
5.9
5.5
8.0
9.5
3.8
4.0
4.6
4.7
4.8
5.0
6.2
4.2
6.9
5.5
2.0
4.4
9.4
19.8
17.3
11.6
12.6
Standard
Error of
the Mean
0.95
0.65
0.71
0.76
0.59
0.23
0.18
0.23
0.20
0.25
0.52
0.51
0.69
0.87
0.84
0.82
0.52
0.41
1.2
1.2
1.2
1.2
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December 2003
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Sediment Levels, Beaver Lake, Olympic Peninsula, WA
Nondetects = 1/2 LOD
1974.4 1964 1955 1946 1932 1921
1884
iTotalCDDs DTotal CDFs DTotal CDD/Fs
Figure 6-1. CDD/CDF Levels in Sediment, Beaver Lake, Washington
DRAFT-DO NOT QUOTE OR CITE
6-49
December 2003
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.0.
D)
D)
CL 2
c
o
•*=
ro
0
O
c
O 1
O '
I
I
1900
1920
1940
1960
1980
2000
Year
[Current CDD/CDF Concentrations (pg l-TEQ/g lipid): beef=0.89; chicken=0.64; pork=1.30; milk=0.84 ||
Figure 6-2. I-TEQDF Concentrations of Historical Food Samples from the U.S. (results calculated at ND
= 1/2 LOD)
Source: Adapted from Winters et al. (1998).
T3
^g_
.0) 3
c
o
•fs
ro
'c
0) -1
o '
A
A
A
A
A
Al A
1900
1920
1940
1960
1980
2000
Year
| Current CDD/CDF Concentrations (pg TEQ-WHO94/g lipid): beef=0.46; chicken=0.28; pork=0.06; mill^0.43|
Figure 6-3. TEQP-WH094 Concentrations of Historical Food Samples from the U.S. (results calculated
at ND = 1/2 LOD)
Source: Adapted from Winters et al. (1998).
DRAFT-DO NOT QUOTE OR CITE
6-50
December 2003
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140
120
2 100
Q.
o> 80
°: GO
a
£ 40
20
0
1960
1970
1980
Year
1990
2000
Figure 6-4. Average Adult Population TEQDF -WH098 Concentrations
as a Function of Year (all results in ppt lipid).
DRAFT-DO NOT QUOTE OR CITE
6-51
December 2003
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(a) Missouri, 1985 (n = 35),
average = 47.0 ppt lipid
(Graham et al., 1986)
(b) California, 1988 (n = 57),
average = 31.0 ppt lipid
(Orban et al., 1989)
(c) CDC data, 1995-1997 (n - 216),
average = 18.3 pg/g lipid
(CDC, 2000)
160
140
1. 120
ra 10°
g 80
60 H
40
20 H
0
O
p
• *
20 40 60 80 100
Age
•o
|5.
"01
Q.
cT
ii i
IOU
140
120
80
60
40
20
n
•
*•* **
* *» « J •
* * * *^ ^t* *
*
20 40 60 80 100
Age
IOU
140
1120-
^100-
g 80-
0 60
P 40 -
20
0
(
* » * «*
**£^8^4^*
) 20 40 60 80 1C
Age
Figure 6-5. Age Trend Relationships for Three Studies.
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6-52
December 2003
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e
pg/kg-day
3.5-
3.0'
2.5'
2.0'
1.5'
1.0-
0.5-
0.01
-2logProb(YI8)
1950
Year
-147.9 148.5
. 149.1 149.4
— — 148.9
Figure 6-6. Examples of Temporal Exposure Curves for 2,3,7,8-TCDD, e(t) in
Units of pg/kg-day.
Source: Pinsky and Lorber (1998).
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December 2003
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TCDD
pg/g lipid
Birth Year
1966
1956
1946
1983
Specimen Year
Figure 6-7. Predicted Mean TCDD Lipid Concentrations (pg/g) in Males by Birth
Year and Specimen Year Derived Using e(t) Curve Labeled A in Figure
6-6
Source: Pinsky and Lorber (1998).
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December 2003
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APPENDIX A. ENVIRONMENTAL CHEMISTRY
The tables in this appendix are discussed in Chapter 2. References listed at the end
of each table are included in the reference list at the end of Chapter 2. Following are the
tables included in this Appendix:
Table A-1. Physical and Chemical Properties For the Dioxin, Furan,
and PCB Congeners A-2
Table A-2. Rankings For the Physical and Chemical Property
Literature A-17
DRAFT-DO NOT QUOTE OR CITE A-1 December 2003
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Table A-1. P-Chem Properties for the Dioxin, Furan, and PCB Congeners
Congener
CAS No.
Water Solubility
Value3'0
(mg/l)
Temp.
(°C)
Ref.
Vapor Pressure
Value3'"
(mm Hg)
Temp.
(°C)
Ref.
Henry's Law Constant
Value3
(atm-m3/mol)
Ref.
Log Kow
Value3
Ref.
Log Koc
Value3
Ref.
Tetrachlorodibenzo-p-dioxins (MW = 321 .98)
1,2,3,4-TCDD
30746-58-8
1,2,3,6-TCDD
71669-25-5
1,2,3,7-TCDD
67028-18-6
1,2,3,8-TCDD
53555-02-5
1,2,3,9-TCDD
71669-26-6
1,2,4,6-TCDD
71669-27-7
1,2,4,7-TCDD
71669-28-8
1,2,4,8-TCDD
71669-29-9
1,2,4,9-TCDD
71665-99-1
1,2,6,7-TCDD
40581-90-6
1,2,6,8-TCDD
67323-56-2
1,2,6,9-TCDD
40581-91-7
3.88E-04-
6.30E-04
[5.50E-04]
2.80E-04-
7.30E-04
[4.20E-04]
25
[25]
20-26
[20]
5,38,46,51,53
[53]
6,53
[6,53]
1.81E-08-
4.80E-08
[4.80E-08]
(7.50E-09)-
(5.3E-08)
[7.50E-09]
25
[25]
25
[25]
9,33,53
[9,53]
9,21,52,5
3
[21,53]
1.99E-05-
(3.70E-05)
[1.99E-05]
(7.57E-06)
5,51,53
[51]
9,53
(5. SOl-
IS. 97)
[6.60]
6.86
(5. SOl-
IS. 81)
[6.90]
6.48
6.39
6.10
6.25
6.25
6.10
6.43
5,7,8,10,
26,29,53
[53]
10
5,7,8,10,
26,53
[53]
10
10
10
10
10
10
10
4.26-
6.55
45,53
DRAFT-DO NOT QUOTE OR CITE
A-2
December 2003
-------
Table A-1 (continued)
Congener
CAS No.
1,2,7,8-TCDD
34816-53-0
1,2,7,9-TCDD
71669-23-3
1,2,8,9-TCDD
62470-54-6
1,3,6,8-TCDD
33423-92-6
1,3,6,9-TCDD
71669-24-4
1,3,7,8-TCDD
50585-46-1
1,3,7,9-TCDD
62470-53-5
1,4,6,9-TCDD
40581-93-9
1,4,7,8-TCDD
40581-94-0
2,3,7,8-TCDD
1746-01-6
Congener Group
Average
Water Solubility
Value3'0
(mg/l)
1.68E-04-
(6.0E-04)
[3.2E-04]
7.91E-06-
4.83E-04
[1.93E-05]
(3.3E-04)
Temp.
(°C)
20-25
[20]
17-25
[25]
25
Ref.
6,7,38,53
[7,53]
1,2,4,5,27,
36,50,53
[4,53]
20
Vapor Pressure
Value3'"
(mm Hg)
(5.25E-09)-
4.03E-06
[5.25E-09]
(6.3E-09)
7.40E-10-
3.38E-08
[1.50E-09]
(1.4E-08)
Temp.
(°C)
20-25
[25]
25
25
[25]
25
Ref.
7,9,53
[9,53]
9
2,3,5,9,3
4,53
[9,53]
20
Henry's Law Constant
Value3
(atm-m3/mol)
(5.92E-06)-
6.81E-05
[6.95E-06]
(2.07E-08)-
(1.02E-04)
[3.29E-05]
(1.7E-05)
Ref.
5,7,53
[53]
2,3,5,53
[53]
20
Log Kow
Value3
6.38
6.86
(5.5H9.43)
[7.10]
6.25
6.30
6.39-7.06
[6.39]
6.38
6.39
5.38-8.93
[6.80]
(6.5)
Ref.
10
10
5,7,8,10,
26,53
[5,53]
10
10
8,10
[10]
10
10
2,4,5,8,10,
50,53
[53]
20
Log Koc
Value3
2.05-
(6.74)
3.06-
8.50
[6.4-
6.66]
Ref.
53
32,47,48,
49,50,53,57
[50,57]
DRAFT-DO NOT QUOTE OR CITE
A-3
December 2003
-------
Table A-1 (continued)
Congener
CAS No.
Water Solubility
Value3'0
(mg/l)
Temp.
(°C)
Ref.
Vapor Pressure
Value3'"
(mm Hg)
Temp.
(°C)
Ref.
Henry's Law Constant
Value3
(atm-m3/mol)
Ref.
Log Kow
Value3
Ref.
Log Koc
Value3
Ref.
Pentachlorodibenzo-p-dioxins (MW = 356.42)
1,2,3,4,6-PeCDD
67028-19-7
1,2,3,4,7-PeCDD
39227-61-7
1,2,3,6,7-PeCDD
71925-15-0
1,2,3,6,8-PeCDD
71925-16-1
1,2,3,6,9-PeCDD
82291-34-7
1,2,3,7,8-PeCDD
40321-76-4
1,2,3,7,9-PeCDD
71925-17-2
1, 2,3,8, 9-PeCDD
71925-18-3
1,2,4,6,7-PeCDD
82291-35-8
1,2,4,6,8-PeCDD
71998-76-0
1,2,4,6,9-PeCDD
82291-36-9
1,2,4,7,8-PeCDD
58802-08-7
9.55E-05-
8.16E-03
[1.18E-04]
20-26
[20]
5,6,38,53
[6,53]
(6.60E-10)-
7.5E-09
[6.60E-10]
(4.4E-10)-
9.48E-10
[4.40E-10]
(5.8E-10)
25
[25]
25
[25]
25
5,9,53
[9,53]
9,33
[9]
9
(2.63E-06)
5,53
6.30
6.60-
(10.05)
[7.40]
6.74
6.53
6.24
6.64
6.40
6.60
6.20
10
5,7,8,10,
26,53
[53]
10
10
10
10
10
10
10
4.85-
6.38
45,53
DRAFT-DO NOT QUOTE OR CITE
A-4
December 2003
-------
Table A-1 (continued)
Congener
CAS No.
1, 2,4,7, 9-PeCDD
82291-37-0
1, 2,4,8, 9-PeCDD
82291-38-1
Congener Group
Average
Water Solubility
Value3'0
(mg/l)
(1.18E-04)
Temp.
(°C)
20
Ref.
20
Vapor Pressure
Value3'"
(mm Hg)
(5.6E-10)
Temp.
(°C)
25
Ref.
20
Henry's Law Constant
Value3
(atm-m3/mol)
(2.6E-06)
Ref.
20
Log Kow
Value3
(6.6)
Ref.
20
Log Koc
Value3
Ref.
Hexachlorodibenzo-p-dioxins (MW = 390.87)
1,2,3,4,6,7-
HxCDD
58200-66-1
1,2,3,4,6,8-
HxCDD
58200-67-2
1,2,3,4,6,9-
HxCDD
58200-68-3
1,2,3,4,7,8-
HxCDD
39227-28-6
1,2,3,6,7,8-
HxCDD
57653-85-7
1,2,3,6,7,9-
HxCDD
64461-98-9
1,2,3,6,8,9-
HxCDD
58200-69-4
4.00E-06-
6.44E-06
[4.42E-06]
21-26
[25]
5,6,53
[6,53]
(3.8E-11)-
1.01E-10
[3.8E-11]
(3.6E-11)
25
[25]
25
5,9,33,
53
[9,53]
9
(1.07E-05)-
(4.46E-05)
[1.07E-05]
5,19,53
[53]
6.85
7.79-10.44
[7.80]
7.59
7.59
10
7,8,26,53
[53]
10
10
5.02-
(7.10)
45,53
DRAFT-DO NOT QUOTE OR CITE
A-5
December 2003
-------
Table A-1 (continued)
Congener
CAS No.
1,2,3,7,8,9-
HxCDD
19408-74-3
1,2,4,6,7,9-
HxCDD
39227-62-8
1,2,4,6,8,9-
HxCDD
58802-09-8
Congener Group
Average
Water Solubility
Value3'0
(mg/l)
(4.4E-06)
Temp.
(°C)
25
Ref.
20
Vapor Pressure
Value3'"
(mm Hg)
(4.9E-11)
(5.1E-11)
(4.4E-11)
Temp.
(°C)
25
25
25
Ref.
9
9
20
Henry's Law Constant
Value3
(atm-m3/mol)
(1.1E-05)
Ref.
20
Log Kow
Value3
6.85
6.85
(7.3)
Ref.
10
10
20
Log Koc
Value3
Ref.
Heptachlorodibenzo-p-dioxins (MW = 425.31)
1,2,3,4,6,7,8-
HpCDD
35822-46-9
1,2,3,4,6,7,9-
HpCDD
58200-70-7
Congener Group
Average
2.3E-06-
2.56E-06
[2.4E-06]
(2.4E-06)
20-26
[20]
20
5,6,53
[6,53]
20
(5.6E-12)-
(2.40E-10)
[5.6E-12]
(5.6E-12)
25
[25]
25
9,33,53
[9,53]
20
(1.31E-06)-
(1.26E-05)
[1.26E-05]
(1.3E-05)
5,53
[53]
20
7.92-
(11.98)
[8.00]
(8.0)
7,8,10,26,
53
[53]
20
5.47-
(7.80)
53
Octachlorodibenzo-p-dioxin (MW = 460.76)
1,2,3,4,6,7,8,9-
OCDD
3268-87-9
7.4E-08-
7.36E-06
[7.4E-08]
20-25
[25]
5,6,7,53
[5,53]
(8.25E-13)-
6.54E-08
[8.25E-13]
20-25
7,9,33,
53
[9,53]
(6.75E-06)
5,53
(7.5H13)
[8.20]
5,7,8,26,
29,53
[5,53]
5.92-
(7.90)
53
Tetrachlorodibenzofurans (MW = 305.98)
1,2,3,4-TCDF
30402-14-3
(3.1E-08)
25
21
6.17
10
DRAFT-DO NOT QUOTE OR CITE
A-6
December 2003
-------
Table A-1 (continued)
Congener
CAS No.
1,2,3,6-TCDF
83704-21-6
1,2,3,7-TCDF
83704-22-7
1,2,3,8-TCDF
62615-08-1
1,2,3,9-TCDF
83704-23-8
1,2,4,6-TCDF
71998-73-7
1,2,4,7-TCDF
83719-40-8
1,2,4,8-TCDF
64126-87-0
1,2,4,9-TCDF
83704-24-9
1,2,6,7-TCDF
83704-25-0
1,2,6,8-TCDF
83710-07-0
1,2,6,9-TCDF
70648-18-9
1,2,7,8-TCDF
58802-20-3
1,2,7,9-TCDF
83704-26-1
Water Solubility
Value3'0
(mg/l)
Temp.
(°C)
Ref.
Vapor Pressure
Value3'"
(mm Hg)
(3.2E-08)
(2.1E-08)
(2.2E-08)
(2.0E-08)
(1.8E-08)
(2.5E-08)
Temp.
(°C)
25
25
25
25
25
25
Ref.
21
21
21
21
21
21
Henry's Law Constant
Value3
(atm-m3/mol)
Ref.
Log Kow
Value3
6.15
6.15
6.06
6.31
6.25
6.23
6.25
Ref.
10
10
10
10
10
10
10
Log Koc
Value3
Ref.
DRAFT-DO NOT QUOTE OR CITE
A-7
December 2003
-------
Table A-1 (continued)
Congener
CAS No.
1,2,8,9-TCDF
70648-22-5
1,3,4,6-TCDF
83704-27-2
1,3,4,7-TCDF
70648-16-7
1,3,4,8-TCDF
92341-04-3
1,3,4,9-TCDF
83704-28-3
1,3,6,7-TCDF
57117-36-9
1,3,6,8-TCDF
71998-72-6
1,3,6,9-TCDF
83690-98-6
1,3,7,8-TCDF
57117-35-8
1,3,7,9-TCDF
64560-17-4
1,4,6,7-TCDF
66794-59-0
1,4,6,8-TCDF
82911-58-8
1,4,6,9-TCDF
70648-19-0
Water Solubility
Value3'0
(mg/l)
Temp.
(°C)
Ref.
Vapor Pressure
Value3'"
(mm Hg)
(2.8E-08)
(2.7E-08)
(1.9E-08)
(2.6E-08)
Temp.
(°C)
25
25
25
25
Ref.
21
21
21
21
Henry's Law Constant
Value3
(atm-m3/mol)
Ref.
Log Kow
Value3
6.31
6.23
6.13
5.89
6.37
6.34
6.34
6.15
5.60
Ref.
10
10
10
10
10
10
10
10
10
Log Koc
Value3
Ref.
DRAFT-DO NOT QUOTE OR CITE
A-8
December 2003
-------
Table A-1 (continued)
Congener
CAS No.
1,4,7,8-TCDF
83704-29-4
1,6,7,8-TCDF
83704-33-0
2,3,4,6-TCDF
83704-30-7
2,3,4,7-TCDF
83704-31-8
2,3,4,8-TCDF
83704-32-9
2,3,6,7-TCDF
57117-39-2
2,3,6,8-TCDF
57117-37-0
2,3,7,8-TCDF
51207-31-9
2,4,6,7-TCDF
57117-38-1
2,4,6,8-TCDF
58802-19-0
3,4,6,7-TCDF
57117-40-5
Congener Group
Average
Water Solubility
Value3'0
(mg/l)
4.19E-04
(4.2E-04)
Temp.
(°C)
22.7
22.7
Ref.
11
20
Vapor Pressure
Value3'"
(mm Hg)
(4.0E-08)
(2.9E-08)
(2.8E-08)
(2.1E-08)
(2.0E-08)
8.96E-09-
(1.5E-08)
[1.5E-08]
(3.3E-08)
(2.0E-08)
(2.5E-08)
Temp.
(°C)
25
25
25
25
25
25
[25]
25
25
25
Ref.
21
21
21
21
21
21,33,53
[21,53]
21
21
20
Henry's Law Constant
Value3
(atm-m3/mol)
(1.44E-05)-
(1.48E-05)
[1.44E-05]
(1.4E-05)
Ref.
53,54
[53]
20
Log Kow
Value3
6.17
6.11
6.06
6.31
6.73
5.82-6.53
[6.1]
6.25
6.17
(6.2)
Ref.
10
10
10
10
10
8,10,53
[53]
10
10
20
Log Koc
Value3
(5.20)-
(7.50)
Ref.
53
Pentachlorodibenzofurans (MW = 340.42)
DRAFT-DO NOT QUOTE OR CITE
A-9
December 2003
-------
Table A-1 (continued)
Congener
CAS No.
1,2,3,4,6-PeCDF
83704-47-6
1,2,3,4,7-PeCDF
83704-48-7
1,2,3,4,8-PeCDF
67517-48-0
1,2,3,4,9-PeCDF
83704-49-8
1,2,3,6,7-PeCDF
57117-42-7
1,2,3,6,8-PeCDF
83704-51-2
1,2,3,6,9-PeCDF
83704-52-3
1,2,3,7,8-PeCDF
57117-41-6
1,2,3,7,9-PeCDF
83704-53-4
1,2,3,8,9-PeCDF
83704-54-5
1,2,4,6,7-PeCDF
83704-50-1
1,2,4,6,8-PeCDF
69698-57-3
1,2,4,6,9-PeCDF
70648-24-7
Water Solubility
Value3'0
(mg/l)
Temp.
(°C)
Ref.
Vapor Pressure
Value3'"
(mm Hg)
(2.7E-09)
(3.6E-09)
(2.2E-09)
(1.7E-09)-
2.72E-09
[1.7E-09]
(3.5E-09)
(2.3E-09)
Temp.
(°C)
25
25
25
25
25
25
Ref.
21
21
21
21,33
[21]
21
21
Henry's Law Constant
Value3
(atm-m3/mol)
Ref.
Log Kow
Value3
6.53
6.79
6.26
6.33
6.79
6.27
6.34
6.59
Ref.
10
10
10
10
10
10
10
10
Log Koc
Value3
Ref.
DRAFT-DO NOT QUOTE OR CITE
A-10
December 2003
-------
Table A-1 (continued)
Congener
CAS No.
1,2,4,7,8-PeCDF
58802-15-6
1,2,4,7,9-PeCDF
71998-74-8
1,2,4,8,9-PeCDF
70648-23-6
1,2,6,7,8-PeCDF
69433-00-7
1,2,6,7,9-PeCDF
70872-82-1
1,3,4,6,7-PeCDF
83704-36-3
1,3,4,6,8-PeCDF
83704-55-6
1,3,4,6,9-PeCDF
70648-15-6
1,3,4,7,8-PeCDF
58802-16-7
1,3,4,7,9-PeCDF
70648-20-3
1,3,6,7,8-PeCDF
70648-21-4
1,4,6,7,8-PeCDF
83704-35-2
2,3,4,6,7-PeCDF
57117-43-8
Water Solubility
Value3'0
(mg/l)
Temp.
(°C)
Ref.
Vapor Pressure
Value3'"
(mm Hg)
(1.5E-09)
(2.6E-09)
(1.9E-09)
(2.7E-09)
(4.3E-09)
(2.4E-09)
Temp.
(°C)
25
25
25
25
25
25
Ref.
21
21
21
21
21
21
Henry's Law Constant
Value3
(atm-m3/mol)
Ref.
Log Kow
Value3
6.26
6.19
6.42
6.51
6.19
6.24
6.34
6.33
6.53
6.47
Ref.
10
10
10
10
10
10
10
10
10
10
Log Koc
Value3
Ref.
DRAFT-DO NOT QUOTE OR CITE
A-11
December 2003
-------
Table A-1 (continued)
Congener
CAS No.
2,3,4,6,8-PeCDF
67481-22-5
2,3,4,7,8-PeCDF
57117-31-4
Congener Group
Average
Water Solubility
Value3'0
(mg/l)
2.36E-04
(2.4E-04)
Temp.
(°C)
22.7
22.7
Ref.
11
20
Vapor Pressure
Value3'"
(mm Hg)
(1.9E-09)
(2.63E-09)-
3.29E-09
[2.63E-09]
(2.7E-09)
Temp.
(°C)
25
25
[25]
25
Ref.
21
21,33
[53]
20
Henry's Law Constant
Value3
(atm-m3/mol)
(4.98E-06)
(5.0E-06)
Ref.
53
20
Log Kow
Value3
6.59
6.92-I7.82)
[6.5]
(6.4)
Ref.
10
10,53
[53]
20
Log Koc
Value3
5.59-
(7.40)
Ref.
53
DRAFT-DO NOT QUOTE OR CITE
A-1 2
December 2003
-------
Table A-1 (continued)
Congener
CAS No.
Water Solubility
Value3'0
(mg/l)
Temp.
(°C)
Ref.
Vapor Pressure
Value3'"
(mm Hg)
Temp.
(°C)
Ref.
Henry's Law Constant
Value3
(atm-m3/mol)
Ref.
Log Kow
Value3
Ref.
Log Koc
Value3
Ref.
Hexachlorodibenzofurans (MW = 374.87)
1,2,3,4,6,7-
HxCDF
79060-60-9
1,2,3,4,6,8-
HxCDF
69698-60-8
1,2,3,4,6,9-
HxCDF
91538-83-9
1,2,3,4,7,8-
HxCDF
70648-26-9
1,2,3,4,7,9-
HxCDF
91538-84-0
1,2,3,4,8,9-
HxCDF
92341-07-6
1,2,3,6,7,8-
HxCDF
57117-44-9
1,2,3,6,7,9-
HxCDF
92341-06-5
1,2,3,6,8,9-
HxCDF
75198-38-8
8.25E-06
1.77E-05
22.7
22.7
11
11
(2.4E-10)
(2.2E-10)
(4.1E-10)
(2.4E-10)-
(6.7E-10)
[2.4E-10]
(2.8E-10)
(2.2E-10) -
(6.68E-10)
[2.2E-10]
(3.4E-10)
25
25
25
25
[25]
25
25
[25]
25
21
21
21
21,52,53
[21,53]
21
21,53
[21,53]
21
(1.43E-05)
(6.1E-06)-
(7.31E-06)
[7.31E-06]
19
19,53
[53]
(7.0)
53
(7.40)
53
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A-1 3
December 2003
-------
Table A-1 (continued)
Congener
CAS No.
1,2,3,7,8,9-
HxCDF
72918-21-9
1,2,4,6,7,8-
HxCDF
67562-40-7
1,2,4,6,7,9-
HxCDF
75627-02-0
1,2,4,6,8,9-
HxCDF
69698-59-5
1,3,4,6,7,8-
HxCDF
71998-75-9
1,3,4,6,7,9-
HxCDF
92341-05-4
2,3,4,6,7,8-
HxCDF
60851-34-5
Congener Group
Average
Water Solubility
Value3'0
(mg/l)
(1.3E-05)
Temp.
(°C)
22.7
Ref.
20
Vapor Pressure
Value3'"
(mm Hg)
(1.8E-10)
(2.6E-10)
(5.7E-10)
(1.8E-10)
(2.3E-10)
(2.0E-10)
(2.8E-10)
Temp.
(°C)
25
25
25
25
25
25
25
Ref.
21
21
21
21
21
21
20
Henry's Law Constant
Value3
(atm-m3/mol)
(1.1E-05)
Ref.
20
Log Kow
Value3
(7.0)
Ref.
20
Log Koc
Value3
Ref.
Heptachlorodibenzofurans (MW = 409.31)
1,2,3,4,6,7,8-
HpCDF
67562-39-4
1.35E-06
22.7
11
(3.5E-11)-
1.33E-10
[3.5E-11]
25
[25]
21,33
[21,53]
(1.41E-05)-
(5.3E-05)
[1.41E-05]
19,53
[53]
7.92-I9.25)
[7.4]
10,53
[53]
6.00-
(7.90)
53
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A-1 4
December 2003
-------
Table A-1 (continued)
Congener
CAS No.
1,2,3,4,6,7,9-
HpCDF
70648-25-8
1,2,3,4,6,8,9-
HpCDF
69698-58-4
1,2,3,4,7,8,9-
HpCDF
55673-89-7
Congener Group
Average
Water Solubility
Value3'0
(mg/l)
(1.4E-06)
Temp.
(°C)
22.7
Ref.
20
Vapor Pressure
Value3'"
(mm Hg)
(5.8E-11)
(4.7E-11)-
1.07E-10
[4.7E-11]
(4.7E-11)
Temp.
(°C)
25
25
[25]
25
Ref.
21
21,33
[21,53]
20
Henry's Law Constant
Value3
(atm-m3/mol)
(1.4E-05)
Ref.
20
Log Kow
Value3
(7.4)
Ref.
20
Log Koc
Value3
5.00-
(6.70)
Ref.
53
Octachlorodibenzofurans (MW = 444.76)
1,2,3,4,6,7,8,9-
OCDF
39001-02-0
(1.16E-06)
25
11
(3.75E-12)
25
21
(1.88E-06)
19
(7.0)-(13)
[8.0]
8,26,29,53
[53]
6.00-
(7.40)
53
Tetrachloro-PCB (MW = 291.99)
3,3',4,4'-TCB
32598-13-3
3,4,4',5-TCB
70362-60-4 (81)
(5.5E-04)-
(1.7E-01)
[1.03E-03]
2.92E-03
25
25
12,13,17,28,3
5,38,40,44,56
[56]
17
1.37E-07-
4.47E-07
[4.47E-07]
(7.85E-07)
25
25
13,18,56
[56]
18
1.70E-05-
(1.0E-04)
[1.70E-05]
1.28E-04
13,35,39,
56
[56]
41
5.62-6.77
[6.5]
(6.36)
8,15,31,55
,56
[56]
15
(4.41)-
(5.75)
56
Pentachloro-PCB (MW = 326.44)
2,3,3',4,4'-PeCB
32598-14-4
(1.9E-03)-
(1.1E-02)
[1.90E-03]
25
17,35,37,56
[35]
(8.28E-07)
25
18
(6.0E-05)-
(9.93E-05)
[9.93E-05]
16,35
[35]
(6.3H6.6)
[6.0]
15,37,55,
56
[56]
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A-1 5
December 2003
-------
Table A-1 (continued)
Congener
CAS No.
2,3,4,4',5-PeCB
74472-37-0
2,3',4,4',5-PeCB
31508-00-6
3,3',4,4',5-PeCB
57465-28-8
2',3,4,4',5-PeCB
65510-44-3
Water Solubility
Value3'0
(mg/l)
(2.58E-03)-
(1.1E-02)
[2.58E-03]
(1.59E-03)-
(1.1E-02)
[1.59E-03]
(1.03E-03)
(1.64E-03)
Temp.
(°C)
20-25
[20]
20-25
[20]
25
25
Ref.
17,35,37,41
[41]
17,35,37,41
[41]
17
17
Vapor Pressure
Value3'"
(mm Hg)
(4.18E-07)-
(1.43E-04)
[4.18E-07]
(3.14E-07)-
(1.38E-06)
[3.14E-07]
(2.19E-07)-
(3.72E-07)
[2.96E-07]
(7.62E-07)-
(9.95E-07)
[8.78E-07]
Temp.
(°C)
20-25
[20]
20-25
[20]
25
25
Ref.
18,41
[41]
18,41
[41]
18
[18]
18
[18]
Henry's Law Constant
Value3
(atm-m3/mol)
6.90E-05-
(1.4E-04)
[6.90E-05]
8.50E-05-
4.0E-04
[8.50E-05]
(5.4E-05)-
(8.2E-05)
[5.40E-05]
(1.74E-04)-
(2.62E-04)
[1.74E-04]
Ref.
16,35,41
[41]
16,35,41,
43
[41]
16,35
[35]
16,35
[35]
Log Kow
Value3
(6.3H6.65)
[6.65]
(6.21-7.12
[7.12]
(6.26)-
(6.89)
[6.89]
(6.74)
Ref.
15,37
[15]
15,31,37,
55
[31]
15,37
[15]
15
Log Koc
Value3
(5.7)
Ref.
42
Hexachloro-PCB (MW = 360.88)
2, 3, 3', 4,4', 5-
HxCB
38380-08-4
2, 3, 3', 4,4', 5'-
HxCB
69782-90-7
2,3', 4, 4', 5,5'-
HxCB
52663-72-6
3,3',4,4',5,5'-
HxCB
32774-16-6
(4.10E-04)-
(2.4E-03)
[4.10E-04]
(3.61E-04)
(3.61E-04)-
(2.44E-03)
[3.61E-04]
(3.61E-05)-
(2.5E-03)
[3.61E-05]
20-25
[20]
25
25
25
17,37,41
[41]
17
17,37
[17]
17,35,37
[17]
(1.47E-07)
(5.47E-08)-
(1.08E-07)
[5.47E-08]
(1.46E-07)-
(1.95E-07)
[1.95E-07]
(8.1E-09)-
(3.5E-07)
[1.81E-07]d
25
25
25
25
18
18
18
56
(2.2E-05)
-8.7E-04
[8.70E-04]
(6.6E-05)
-5.8E-04
[5.80E-04]
(1.1E-04)-
(1.2E-04)
[1.10E-04]
(1.6E-05)-
(6.5E-05)
[6.52E-05]
16,35,43
[43]
16,35,43
[43]
16,35
[35]
16,35,56
[35]
(6.64)-
(7.16)
[7.16]
7.18-17.20)
[7.19]
(6.64)-
(7.27)
[7.09]
(6.641-7.46
[7.46]
14,15,37
[14]
14,15
[14]
14,15,37
[14]
14,15,37,
56
[14]
(6.60)
56
Heptachloro-PCB (MW = 396.33)
2,3, 3', 4, 4', 5,5'-
HpCB
39635-31-9
(4.5E-05)-
(5.3E-04)
[6.26E-05]
25
17,35,37
[17]
(1.19E-08)-
(1.73E-08)
[1.31E-08]
25
18
(6.6E-05)
35
(7. 0)-(7.71)
[7.71]
15,37
[15]
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A-1 6
December 2003
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Table A-1 (continued)
Congener
CAS No.
2, 2', 3, 3', 4, 4', 5-
HpCB
35065-30-6
2, 2', 3, 4, 4', 5,5'-
HpCB
35069-29-3
Water Solubility
Value3'0
(mg/l)
(1.19E-04)-
(5.21E-04)
[2.27E-04]
(2.25E-04)-
(4.40E-04)
[4.40E-04]
Temp.
(°C)
20-25
[20]
20-25
[20]
Ref.
17,35,37,41
[41]
17,35,37,41
[41]
Vapor Pressure
Value3'"
(mm Hg)
(6.46E-09)-
8.60E-09
[6.46E-09]
3.39E-08-
(2.72E-07)
[2.72E-07]
Temp.
(°C)
20-25
[25]
20-25
[25]
Ref.
18,41
[41]
18,41
[41]
Henry's Law Constant
Value3
(atm-m3/mol)
(1.50E-05)-
(8.73E-05)
[1.50E-05]
(3.20E-05)-
(1.07E-04)
[3.20E-05]
Ref.
35,41
[41]
35,41
[41]
Log Kow
Value3
(7.03)-
(7.27)
[7.27]
(6.70)-
(7.36)
[7.36]
Ref.
15,37,42,
56
[15]
15,37,42,
56
[15]
Log Koc
Value3
5.5-6.8
5.1-7.4
Ref.
42,56
42,56
Footnote References
a Values are presented as they appeared in the referenced article. Values in ( ) are either estimated or are calculated/extrapolated from experimental values.
b [R] is the ranking of the value from the cited reference.
c For several PCB congeners, subcooled liquid values were converted to solid values using the melting points presented in this table and the conversion methodology presented in Eitzer and Hites (1988)
and Mackay et al. (1 992).
d The selected vapor pressure value for PCB-1 69 is the midpoint of the range of calculated values.
In (PSC/PS) = 6.79 (Tm-T)/T
where: Psc = subcooled value
Ps = solid value
Tm = melting point (°K)
T = ambient temperature (°K)
1. Marple et al. (1986a)
2. USEPA (1990)
3. Podoll et al. (1986)
4. Marple et al. (1986b)
5. Shiu et al. (1988)
6. Friesen et al. (1985)
7. Webster et al. (1985)
8. Burkhard and Kuehl (1986)
9. Rordorf (1987)
10. Sijm et al. (1989)
1 1. Friesen et al. (1990b)
12. Dickhut et al. (1986)
20. Average of all selected and single values within a congener group
21. Rordorf (1989)
22. Dulin et al. (1986)
23. Choudhry and Webster (1987)
24. Choudhry and Webster (1989)
25. Choudhry et al. (1990)
26. Sarna et al. (1984)
27. Adams and Elaine (1986)
28. Mackay et al. (1980)
29. Doucette and Andren (1988a)
30. Orth et al. (1989)
31. Rapaport and Eisenreich (1984)
39. Dunnivant et al. (1988)
40. Opperhuizen et al. (1988)
41. Murphy et al. (1987)
42. EPRI (1990)
43. Murphy et al. (1983)
44. Yalkowsky et al. (1983)
45. Webster et al. (1986)
46. Doucette and Andren (1988b)
47. Walters and Guiseppi-Elie (1988)
48. Jackson et al. (1986)
49. Puri et al. (1989)
50. Marple et al. (1987)
DRAFT-DO NOT QUOTE OR CITE
A-1 7
December 2003
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Table A-1 (continued)
13. Dunnivant and Elzerman (1988)
14. Risby et al. (1990)
15. Hawker and Connell (1988)
1 6. Sabljic and Gusten (1 989)
1 7. Abramowitz and Yalkowsky (1 990)
18. Foreman and Bidleman (1985)
19. Calculated by the VP/WS ratio technique
32. Lodge and Cook (1989)
33. Eitzer and Hites (1988)
34. Rordorf (1985)
35. Dunnivant et al. (1992)
36. Lodge (1989)
37. Patil (1991)
38. Nirmalakhandan and Speece (1989)
51. Santl et al. (1994)
52. Rordorf et al. (1990)
53. Mackay et al. (1992a)
54. Eitzer and Hites (1989)
55. Sacan and Inel (1995)
56. Mackay, et al. (1992b)
57. Walters et al. (1989)
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A-1 8
December 2003
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Table A-1 (continued)
Table A-2. Rankings for the P-Chem Property Literature
Reference
Number
1
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
Ranking3
Water
Solubility
2
2
2
2
2
1
1
5
5
2
Vapor
Pressure
2
2
2 &4
4
2 &4
5
2 & 4
Henry's
Constant
4
4
2
2
5
5
5
Log
KQW
1
1
4
4
1 & 2
3
2 & 5
5
5
Log
Koc
5
Photo
Quantum
Yield
2
2
2
2
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A-1 9
December 2003
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Table A-2 (continued)
Reference
Number
1
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
Ranking3
Water
Solubility
2
5
5
2
5
5
2
2
5
2
1
2
Vapor
Pressure
4
4
2
4
Henry's
Constant
5
2
4
4
2
Log
KQW
5
4
5
1
Log
Koc
1
4
2
1
1
2
1
Photo
Quantum
Yield
2
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A-20
December 2003
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Table A-2 (continued)
Reference
Number
1
54
55
57
Ranking3
Water
Solubility
2
Vapor
Pressure
Henry's
Constant
4
Log
KQW
4
Log
Koc
1
Photo
Quantum
Yield
Footnote References
P-chem properties with two ranks indicates that some of the values for the chemicals in the reference had been
verified by another laboratory, and some values have not been verified yet. It also indicates that more than one
methodology may have been used in the reference to determine the p-chem property.
1. Marple et al. (1986a)
3. Podoll et al. (1986)
4. Marple et al. (1986b)
5. Shiu et al. (1988)
6. Friesen et al. (1985)
7. Webster et al. (1985)
8. Burkhard and Kuehl (1986)
9. Rordorf (1987)
10. Sijm et al. (1989)
11. Friesen et al. (1990)
12. Dickhut et al. (1986)
1 3. Dunnivant and Elzerman (1 988)
14. Risby et al. (1990)
1 5. Hawker and Connell (1 988)
1 6. Sabljic and Gunsten (1 989)
1 7. Abramowitz and Yalkowsky (1 990)
18. Foreman and Bidleman (1985)
1 9. Calculated by the VP/WS ratio technique
20. Average of all literature values
(measured and calculated) within a congener group.
21. Rordorf (1989)
22. Dulin et al. (1986)
23. Choudhry and Webster (1 987)
24. Choudhry and Webster (1 989)
25. Choudhry et al. (1990)
26. Sarna et al. (1984)
27. Adams and Elaine (1986)
28. Mackay et al. (1980)
29. Doucette and Andren (1 988)
30. Orth et al. (1989)
31. Rapaport and Eisenreich (1 984)
32. Lodge and Cook (1989)
33. Eitzer and Hites (1988)
34. Rordorf (1985)
35. Dunnivant et al. (1992)
36. Lodge (1989)
37. Patil (1991)
38. Nirmalakhandan and Speece (1989)
39. Dunnivant et al. (1988)
40. Opperhuizen et al. (1988)
41. Murphy et al. (1987)
42. EPRI (199)
43. Murphy et al. (1983)
44. Yalkowsky et al. (1983)
45. Webster et al. (1986)
46. Doucette and Andren (1988)
47. Walters and Guiseppi-Elie (1988)
48. Jackson et al. (1986)
49. Puri et al. (1989)
50. Marple et al. (1987)
51. Santl et al. (1994)
52. Rordorf et al. (1990)
53. Mackay et al. (1992a)
54. Eitzer and Hites (1989)
55. Sacan and Inel (1995)
56. Mackay et al. (1992b)
57. Walters et al. (1989)
DRAFT-DO NOT QUOTE OR CITE
A-21
December 2003
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APPENDIX B. ENVIRONMENTAL CONCENTRATIONS
The tables in this appendix are discussed in Chapter 5. References listed at the
end of each table are included in the reference list at the end of Chapter 4. Following are
the tables included in this Appendix:
Table B-1.
Table B-2.
Table B-3.
Table B-4.
Table B-5.
Table B-6.
Table B-7.
Table B-8.
Table B-9.
Table B-10.
Table B-11.
Table B-1 2.
Table B-1 3.
Table B-14.
Table B-1 5.
Table B-1 6.
Environmetnal Levels of Dioxins in Air (pg/m3) B-3
Environmental Levels of Dibenzofurans in Air (pg/m3) B-1 8
Environmental Levels of PCBs in Air (pg/m3) B-34
Environmental Levels of Dioxins in Soil (ppt) B-35
Environmental Levels of Dibenzofurans in Soil (ppt) B-46
Environmental Levels of Dioxins in Water (ppq) B-58
Environmental Levels of Dibenzofurans in Water (ppq) B-60
Environmental Levels of Dioxins in Sediments (ppt) B-62
Environmental Levels of Dibenzofurans in Sediments (ppt) . . . B-75
Environmental Levels of PCBs in Sediment (ppt) B-89
Environmetnal Levels of Dioxins in Fish (ppt) B-92
Environmental Levels of Dibenzofurans in Fish (ppt) B-1 23
Environmental Levels of PCBs in Fish (ppt) B-1 64
Level of Dioxins in Food Products (ppt) B-1 72
Levels of Dibenzofurans in Food Products (ppt) B-211
Environmental Levels of PCBs in Food (ppt) B-258
DRAFT-DO NOT QUOTE OR CITE
B-1
December 2003
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Table B-l. Environmental Levels of Dioxins in Air (pg/m3)
Chemical
Number
samples
Number
positive
samples
Concentration
range
Cone.
mean
Location
Location
description
Sample
year
Ref.
no.
Comments
Tetrachlorodibenzo-p-dioxins (MW = 32 1 . 98)
2,3,7,8-TCDD
3
3
NR
1
7
28
NR
NR
1
2
2
3
2
1
1
3
1
2
2
1
1
1
1
7
1
0
0
NR
0
NR
2
NR
NR
1
2
2
0
0
0
0
0
0
0
0
1
1
1
1
0
0
ND(0. 01-0.02)
ND(0.01-0.02)
ND(O.Ol)
ND(0.0095)
ND-0.05
ND-0.004
ND(0.02)
ND(0.03)
0.0004
0.02-0.06
0.02-0.08
ND(0.012-0.2)
ND(0.24-0.82)
ND(0.15)
ND(0.058)
ND(0.04-0.15)
ND(0.06)
ND(0.05-0.18)
ND(0.04-0.21)
0.0004
0.0007
0.0002
0.0001
ND(0.004-0.023)
ND(0.030)
NA
NA
NA
NA
0.01
0.002
NA
NA
0.0004
0.04
0.05
NA
NA
NA
NA
NA
NA
NA
NA
0.0004
0.0007
0.0002
0.0001
NA
NA
Niagra Falls, NY
Niagra Falls, NY
Greenbay, WI
Los Angeles, CA
Bridgeport, CT
Wallingford, CT
Rutland, VT
Durham, NC
Stockholm, Sweden
Hamburg, Germany
Hamburg, Germany
Akron, OH
Columbus, OH
Columbus, OH
Waldo, OH
Albany, NY
Binghamton, NY
Utica, NY
Niagara Falls, NY
Stockholm
Stockholm
Stockholm
Stockholm
Reseda, CA
Commerce, CA
Urban
Industrial
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Industrial
Industrial
Urban
Rural
Urban
Urban
Urban
Industrial
Urban
Suburban
Rural
Coastal
Urban
Urban
86-87
86-87
NR
87
87-88
88
NR
NR
89
NR
NR
87
87
87
87
87-88
88
88
87
89
89
89
89
87-89
87
1
1
2
3
4
5
6
6
7
8
8
9
9
9
9
10
10
10
10
11
11
11
11
12
12
downwind industrial complex
composite of 2-7 samples
urban air & inside traffic tunnel
downwind incinerator & industrial complex
near incinerators
near incinerators
next to interstate highway
background site
mostly residential
near freeway
DRAFT-DO NOT QUOTE OR CITE
B-3
December 2003
-------
Table B-l. Environmental Levels of Dioxins in Air (pg/m3) (continued)
Chemical
2,3,7,8-TCDD
(continued)
TCDDs
Number
samples
6
5
7
4
2
2
20
4
14
3
2
16
53
16
16
7
27
1
2
2
3
2
1
1
3
1
Number
positive
samples
0
0
0
1
0
1
13
1
4
0
2
NR
53
3
12
NR
20
1
2
2
1
0
0
0
0
0
Concentration
range
ND(0.0026-0.048)
ND(0.0106-0.045)
ND(0.0070-0.051)
ND-0.034
ND(0.022-0.039)
ND-0.0086
ND-0.003
ND-0.001
ND-0.026
ND
0.012-0.026
NR
0.0001-0.003
ND-0.18
ND-10.12
ND-0.54
ND-0.07
0.05
0.10-0.22
0.21-1.5
ND-0.18
ND(0.24-0.82)
ND(0.15)
ND(0.058)
ND(0.04-0.15)
ND(0.06)
Cone.
mean
NA
NA
NA
0.017
NA
0.0079
0.00123
0.000375
0.0048
NA
0.019
0.0078
0.00076
0.04
0.99
0.20
0.03
0.05
0.16
0.86
0.12
NA
NA
NA
NA
NA
Location
North Long Beach, CA
San Bernadino, CA
El Toro, CA
Cal Transit, CA
Carson, CA
West Long Beach, CA
Connecticut
Connecticut
Franklin County, OH
Franklin County, OH
Franklin County, OH
Phoenix, AZ
Various U.S. Sites
Niagra Falls, NY
Niagra Falls, NY
Bridgeport, CT
Wallingford, CT
Stockholm, Sweden
Hamburg, Germany
Hamburg, Germany
Akron, OH
Columbus, OH
Columbus, OH
Waldo, OH
Albany, NY
Binghamton, NY
Location
description
Urban
Urban
Urban
Urban
Industrial
Urban
Urban
Rural
Urban
Background
Industrial
Urban
Rural
Urban
Industrial
Urban
Urban
Urban
Urban
Urban
Industrial
Industrial
Urban
Rural
Urban
Urban
Sample
year
87-89
87-89
87-88
88-89
88-89
88-89
93-94
93-94
95
95
95
94
98-99
86-87
86-87
87-88
88
89
NR
NR
87
87
87
87
87-88
88
Ref.
no.
12
12
12
12
12
12
13
13
14
14
14
16
17
1
1
4
5
7
8
8
9
9
9
9
10
10
Comments
mostly residential
mostly residential
mostly residential
near highway
on site at gas cooking equipment manufacturer
mostly residential area
near resource recovery facilities
near resource recovery facilities
near waste to energy facility
close proximity to a heavily traveled road
background
downwind industrial complex
composite of 2-7 samples
urban air & inside traffic tunnel
downwind incinerator & industrial complex
near incinerators
near incinerators
next to interstate highway
background Site
DRAFT-DO NOT QUOTE OR CITE
B-4
December 2003
-------
Table B-l. Environmental Levels of Dioxins in Air (pg/m3) (continued)
Chemical
Number
samples
2
Number
positive
samples
0
Concentration
range
ND(0.05-0.18)
Cone.
mean
NA
Location
Utica, NY
Location
description
Urban
Sample
year
88
Ref.
no.
10
Comments
DRAFT-DO NOT QUOTE OR CITE
B-5
December 2003
-------
Table B-l. Environmental Levels of Dioxins in Air (pg/m3) (continued)
Chemical
TCDDs (continued)
Number
samples
2
1
1
1
1
7
1
6
5
7
4
2
2
20
4
16
Number
positive
samples
0
1
1
1
1
0
0
0
0
0
1
1
1
20
4
NR
Concentration
range
ND(0.04-0.21)
0.05
0.026
0.031
0.0057
ND(0.0050-0.046)
ND(0.030)
ND(0.0026-0.075)
ND(0.0106-0.093)
ND(0.0090-0.051)
ND-0.280
ND-0.0230
ND-0.0402
0.007-0.121
0.007-0.032
NR
Cone.
mean
NA
0.05
0.026
0.031
0.0057
NA
NA
NA
NA
NA
0.0788
0.0170
0.0237
0.0457
0.017
0.53
Location
Niagara Falls, NY
Stockholm
Stockholm
Stockholm
Stockholm
Reseda, CA
Commerce, CA
North Long Beach,CA
San Bernadino, CA
El Toro, CA
Cak Transit, CA
Carson, CA
West Long Beach, CA
Connecticut
Connecticut
Phoenix, AZ
Location
description
Industrial
Urban
Suburban
Rural
Coastal
Urban
Urban
Urban
Urban
Urban
Urban
Industrial
Urban
Urban
Rural
Urban
Sample
year
87
89
89
89
89
87-89
87
87-89
87-89
87-88
88-89
88-89
88-89
93-94
93-94
94
Ref.
no.
10
11
11
11
11
12
12
12
12
12
12
12
12
13
13
16
Comments
mostly residential
near freeway
mostly residential
mostly residential
mostly residential
near highway
on site at gas cooking equipment manufacturer
mostly residential
near resource recovery facilities
near resource recovery facilities
close proximity to a heavily traveled road
Pentachlorodibenzo-p-dioxins (MW=356.42)
1,2,3,7,8-PeCDD
3
3
NR
1
7
28
NR
NR
1
0
1
NR
0
NR
9
NR
NR
1
ND(0. 01-0.02)
ND-0.49
ND(0.02)
ND(0.039)
ND-0.07
ND-0.02
ND(0.03)
ND(O.Ol)
0.006
NA
0.17
NA
NA
0.02
0.006
NA
NA
0.006
Niagra Falls, NY
Niagra Falls, NY
Greenbay, WI
Los Angeles, CA
Bridgeport, CT
Wallingford, CT
Rutland, VT
Durham, NC
Stockholm, Sweden
Urban
Industrial
Urban
Urban
Urban
Urban
Urban
Urban
Urban
86-87
86-87
NR
87
87-88
88
NR
NR
89
1
1
2
3
4
5
6
6
7
downwind industrial complex
composite of 2-7 samples
DRAFT-DO NOT QUOTE OR CITE
B-6
December 2003
-------
Table B-l. Environmental Levels of Dioxins in Air (pg/m3) (continued)
Chemical
Number
samples
2
Number
positive
samples
1
Concentration
range
ND-0.28
Cone.
mean
0.14
Location
Hamburg, Germany
Location
description
Urban
Sample
year
NR
Ref.
no.
8
Comments
urban air & inside traffic tunnel
DRAFT-DO NOT QUOTE OR CITE
B-7
December 2003
-------
Table B-l. Environmental Levels of Dioxins in Air (pg/m3) (continued)
Chemical
1,2,3,7,8-PeCDD
(continued)
Number
samples
2
3
2
1
1
1
1
1
1
1
7
1
6
5
7
4
2
2
20
4
14
3
2
16
53
Number
positive
samples
2
0
0
0
0
1
1
1
1
1
1
1
0
0
0
0
0
0
19
3
8
1
2
NR
53
Concentration
range
0.22-0.60
ND(0. 034-0. 27)
ND(0.047-0.06)
ND(0.082)
ND(0.033)
0.006
0.0038
0.0014
0.0007
0.0004
ND-0.14
0.120
ND(0.0060-0.095)
ND(0.048-0.93)
ND(0.0062-0.058)
ND(0.0126-0.081)
ND(0.0090-0.054)
ND(0. 043-0.088)
ND-0.014
ND-0.004
ND-0.021
ND-0.01112
0.042-0.082
NR
0.00043-0.023
Cone.
mean
0.41
NA
NA
NA
NA
0.006
0.0038
0.0014
0.0007
0.0004
0.0332
0.120
NA
NA
NA
NA
NA
NA
0.00633
0.00213
0.0103
0.0052
0.0619
0.051
0.0043
Location
Hamburg, Germany
Akron, OH
Columbus, OH
Columbus, OH
Waldo, OH
Stockholm
Stockholm
Stockholm
Stockholm
Bloomington, IN
Reseda, CA
Commerce, CA
North Long Beach, CA
San Bernadino, CA
El Toro, CA
Cal Transit, CA
Carson, CA
West Long Beach, CA
Connecticut
Connecticut
Franklin County, OH
Franklin County, OH
Franklin County, OH
Phoenix, AZ
Various U.S. Sites
Location
description
Urban
Industrial
Industrial
Urban
Rural
Urban
Suburban
Rural
Coastal
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Industrial
Urban
Urban
Rural
Urban
Background
Industrial
Urban
Rural
Sample
year
NR
87
87
87
87
89
89
89
89
86
87-89
87
87-89
87-89
87-88
88-89
88-89
88-89
93-94
93-94
95
95
95
94
98-99
Ref.
no.
8
9
9
9
9
11
11
11
11
15
12
12
12
12
12
12
12
12
13
13
14
14
14
16
17
Comments
downwind incinerator & industrial complex
near incinerators
near incinerators
next to interstate highway
background site
mostly residential
near freeway
mostly residential
mostly residential
mostly residential
near highway
on site at gas cooking equipment manufacturer
mostly residential
near resource recovery facilities
near resource recovery facilities
near waste to energy facility
close proximity to a heavily traveled road
background
DRAFT-DO NOT QUOTE OR CITE
B-8
December 2003
-------
Table B-l. Environmental Levels of Dioxins in Air (pg/m3) (continued)
Chemical
PeCDDs
Number
samples
16
16
7
28
1
2
2
3
2
1
1
3
1
2
2
1
1
1
1
7
1
6
5
7
4
2
Number
positive
samples
1
11
NR
21
1
2
2
i
0
0
0
0
0
0
0
1
1
1
1
2
1
2
0
0
1
1
Concentration
range
ND-0.05
ND-11.16
0.01-0.66
ND-0.15
0.11
0.07-1.3
2.4-5.0
ND-0.10
ND(0.47-0.06)
ND(0.082)
ND(0.033)
ND(0.04-0.21)
ND(O.ll)
ND(0.07-0.34)
ND(0.07-0.34)
0.110
0.079
0.04
0.019
ND-0.89
0.57
ND-0.81
ND(0.063-0.93)
ND(0.0062-0.047)
ND-0.18
ND-0.042
Cone.
mean
0.02
1.04
0.24
0.05
0.11
0.68
3.70
0.097
NA
NA
NA
NA
NA
NA
NA
0.110
0.079
0.04
0.019
0.143
0.57
0.150
NA
NA
0.0618
0.0345
Location
Niagra Falls, NY
Niagra Falls, NY
Bridgeport, CT
Wallingford, CT
Stockholm, Sweden
Hamburg, Germany
Hamburg, Germany
Akron, OH
Columbus, OH
Columbus, OH
Waldo, OH
Albany, NY
Binghamton, NY
Utica, NY
Niagara, NY
Stockholm
Stockholm
Stockholm
Stockholm
Reseda, CA
Commerce, CA
North Long Beach, CA
San Bernadino, CA
El Toro, CA
Cal Transit, CA
Carson, CA
Location
description
Urban
Industrial
Urban
Urban
Urban
Urban
Urban
Industrial
Industrial
Urban
Rural
Urban
Urban
Urban
Industrial
Urban
Suburban
Rural
Coastal
Urban
Urban
Urban
Urban
Urban
Urban
Industrial
Sample
year
86-87
86-87
87-88
88
89
NR
NR
87
87
87
87
87-88
88
88
87
89
89
89
89
87-89
87
87-89
87-89
87-88
88-89
88-89
Ref.
no.
1
1
4
5
7
8
8
9
9
9
9
10
10
10
10
11
11
11
11
12
12
12
12
12
12
12
Comments
downwind industrial complex
composite of 2-7 samples
urban air & inside traffic tunnel
downwind incinerator & industrial complex
near incinerators
near incinerators
next to interstate highway
background site
mostly residential
near freeway
mostly residential
mostly residential
mostly residential
near highway
on site at gas cooking equipment manufacturer
DRAFT-DO NOT QUOTE OR CITE
B-9
December 2003
-------
Table B-l. Environmental Levels of Dioxins in Air (pg/m3) (continued)
Chemical
PeCDDs (continued)
Number
samples
2
20
4
16
Number
positive
samples
0
19
4
NR
Concentration
range
ND(0. 045-0.088)
ND-0.224
0.009-0.043
NR
Cone.
mean
NA
0.0801
0.0253
0.57
Location
West Long Beach, CA
Connecticut
Connecticut
Phoenix, AZ
Location
description
Urban
Urban
Rural
Urban
Sample
year
88-89
93-94
93-94
94
Ref.
no.
12
13
13
16
Comments
mostly residential
near resource recovery facilities
near resource recovery facilities
close proximity to a heavily traveled road
Hexachlorodibenzo-p-dioxins (MW=390.87)
1,2,3,4,7,8-HxCDD
3
3
NR
1
7
28
NR
NR
1
2
2
3
2
1
1
1
1
1
1
1
7
0
3
NR
0
NR
8
NR
NR
1
0
2
3
0
0
1
1
1
1
1
1
3
ND(0. 01-0.02)
0.04-0.64
NR
ND(0.076)
ND-0.08
ND-0.03
NR
NR
0.004
ND(0.08-0.17)
0.19-1.0
0.032-0.055
ND(0. 028-0.039)
ND(0.032)
0.031
0.004
0.0028
0.0012
0.0006
0.0023
ND-0.20
NA
0.24
0.01
NA
0.03
0.01
0.05
0.01
0.004
NA
0.60
0.041
NA
NA
0.031
0.004
0.0028
0.0012
0.0006
0.0023
0.0588
Niagra Falls, NY
Niagra Falls, NY
Greenbay, WI
Los Angeles, CA
Bridgeport, CT
Wallingford, CT
Rutland, VT
Durham, NC
Stockholm, Sweden
Hamburg, Germany
Hamburg, Germany
Akron, OH
Columbus, OH
Columbus, OH
Waldo, OH
Stockholm
Stockholm
Stockholm
Stockholm
Bloomington, IN
Reseda, CA
Urban
Industrial
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Industrial
Industrial
Urban
Rural
Urban
Suburban
Rural
Coastal
Urban
Urban
86-87
86-87
NR
87
87-88
88
NR
NR
89
NR
NR
87
87
87
87
89
89
89
89
86
87-89
1
1
2
3
4
5
6
6
7
8
8
9
9
9
9
11
11
11
11
15
12
downwind industrial complex
composite of 2-7 samples
urban air & inside traffic tunnel
downwind incinerator & industrial complex
near incinerators
near incinerators
next to interstate highway
background site
mostly residential
DRAFT-DO NOT QUOTE OR CITE
B-10
December 2003
-------
Table B-l. Environmental Levels of Dioxins in Air (pg/m3) (continued)
Chemical
1,2,3,4,7,8-HxCDD
(continued)
1,2,3,6,7,8-HxCDD
Number
samples
1
6
5
7
4
2
2
20
4
14
3
2
16
53
3
3
NR
1
7
28
NR
NR
1
2
2
3
Number
positive
samples
1
1
1
0
0
0
0
20
3
8
1
2
NR
53
1
3
NR
0
NR
22
NR
NR
1
2
2
3
Concentration
range
0.12
ND-0.14
ND-0.043
ND(0. 012-0. 10)
ND(0.0078-0.074)
ND(0.015-0.025)
ND(0.038-0.043)
0.001-0.022
ND-0.007
ND-0.04
ND-0.0152
0.053-0.109
NR
0.00058-0.027
ND-0.03
0.05-1.06
NR
ND(0.083)
ND-0.13
ND-0.06
NR
NR
0.008
0.23-0.66
0.71-2.2
0.052-0.053
Cone.
mean
0.12
0.0402
0.0406
NA
NA
NA
NA
0.0088
0.00338
0.0142
0.0079
0.081
0.096
0.0054
0.02
0.39
0.02
NA
0.04
0.02
0.07
0.01
0.008
0.44
1.46
0.053
Location
Commerce, CA
North Long Beach, CA
San Bernadino, CA
El Toro, CA
Cal Transit, CA
Carson, CA
West Long Beach, CA
Connecticut
Connecticut
Franklin County, OH
Franklin County, OH
Franklin County, OH
Phoenix, AZ
Various U.S. Sites
Niagra Falls, NY
Niagra Falls, NY
Greenbay, WI
Los Angeles, CA
Bridgeport, CT
Wallingford, CT
Rutland, VT
Durham, NC
Stockholm, Sweden
Hamburg, Germany
Hamburg, Germany
Akron, OH
Location
description
Urban
Urban
Urban
Urban
Urban
Industrial
Urban
Urban
Rural
Urban
Background
Industrial
Urban
Rural
Urban
Industrial
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Industrial
Sample
year
87
87-89
87-89
87-88
88-89
88-89
88-89
93-94
93-94
95
95
95
94
98-99
86-87
86-87
NR
87
87-88
88
NR
NR
89
NR
NR
87
Ref.
no.
12
12
12
12
12
12
12
13
13
14
14
14
16
17
1
1
2
3
4
5
6
6
7
8
8
9
Comments
near freeway
mostly residential
mostly residential
mostly residential
near highway
on site at gas cooking equipment manufacturer
mostly residential
near resource recovery facilities
near resource recovery facilities
near waste to energy facility
close proximity to a heavily traveled road
background
downwind industrial complex
composite of 2-7 samples
urban air & inside traffic tunnel
downwind incinerator & industrial complex
near incinerators
DRAFT-DO NOT QUOTE OR CITE
B-11
December 2003
-------
Table B-l. Environmental Levels of Dioxins in Air (pg/m3) (continued)
Chemical
1,2,3,6,7,8-HxCDD
(continued)
1,2,3,7,8,9-HxCDD
Number
samples
2
1
1
1
1
1
1
1
7
1
6
5
7
4
2
2
20
4
14
3
2
16
53
3
3
NR
Number
positive
samples
1
0
1
1
1
1
1
1
3
1
1
1
0
1
0
0
20
4
12
1
2
NR
53
1
2
NR
Concentration
range
ND-0.078
ND(0.032)
0.025
0.0076
0.0062
0.0023
0.0009
0.0029
ND-0.35
0.25
ND-0.39
ND-0.15
ND(0.0070-0.097)
ND-0.065
ND(0.015-0.025)
ND(0.019-0.032)
0.002-0.034
0.002-0.010
ND-0.05
ND-0.0191
0.06-0.13
NR
0.0011-0.044
ND-0.03
ND-0.11
NR
Cone.
mean
0.046
NA
0.025
0.0076
0.0062
0.0023
0.0009
0.0029
0.0801
0.25
0.0833
0.0586
NA
0.0383
NA
NA
0.0145
0.005
0.026
0.0093
0.095
0.22
0.0097
0.02
0.06
0.02
Location
Columbus, OH
Columbus, OH
Waldo, OH
Stockholm
Stockholm
Stockholm
Stockholm
Bloomington, IN
Reseda, CA
Commerce, CA
North Long Beach, CA
San Bernadino, CA
El Toro, CA
Cal Transit, CA
Carson, CA
West Long Beach, CA
Connecticut
Connecticut
Franklin County, OH
Franklin County, OH
Franklin County, OH
Phoenix, AZ
Various U.S. Sites
Niagra Falls, NY
Niagra Falls, NY
Greenbay, WI
Location
description
Industrial
Urban
Rural
Urban
Suburban
Rural
Coastal
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Industrial
Urban
Urban
Rural
Urban
Background
Industrial
Urban
Rural
Urban
Industrial
Urban
Sample
year
87
87
87
89
89
89
89
86
87-89
87
87-89
87-89
87-88
88-89
88-89
88-89
93-94
93-94
95
95
95
94
98-99
86-87
86-87
NR
Ref.
no.
9
9
9
11
11
11
11
15
12
12
12
12
12
12
12
12
13
13
14
14
14
16
17
1
1
2
Comments
near incinerators
next to interstate highway
background site
mostly residential
near freeway
mostly residential
mostly residential
mostly residential
near highway
on site at gas cooking equipment manufacturer
mostly residential
near resource recovery facilities
near resource recovery facilities
near waste to energy facility
close proximity to a heavily traveled road
background
downwind industrial complex
DRAFT-DO NOT QUOTE OR CITE
B-12
December 2003
-------
Table B-l. Environmental Levels of Dioxins in Air (pg/m3) (continued)
Chemical
1,2,3,7,8,9-HxCDD
(continued)
Number
samples
1
7
28
NR
NR
1
2
2
3
2
1
1
1
1
1
1
1
7
1
6
5
7
4
2
2
20
Number
positive
samples
0
NR
18
NR
NR
0
0
2
3
1
0
1
1
1
1
1
1
3
1
1
1
0
0
0
0
20
Concentration
range
ND(0.086)
ND-0.25
ND-0.07
NR
ND(O.Ol)
ND(O.OOl)
ND(0.08-0.17)
0.36-5.2
0.017-0.050
ND-0.064
ND(0.032)
0.025
0.0065
0.0052
0.0018
0.0013
0.0013
ND-0.35
0.27
ND-0.35
ND-0.10
ND(0. 009-0. 12)
ND(0.023-0.074)
ND(0.015-0.019)
ND(0.019-0.040)
0.002-0.027
Cone.
mean
NA
0.08
0.03
0.05
NA
NA
NA
2.78
0.031
0.039
NA
0.025
0.0065
0.0052
0.0018
0.0013
0.0013
0.1198
0.27
0.0758
0.0499
NA
NA
NA
NA
0.0123
Location
Los Angeles, CA
Bridgeport, CT
Wallingford, CT
Rutland, VT
Durham, NC
Stockholm, Sweden
Hamburg, Germany
Hamburg, Germany
Akron, OH
Columbus, OH
Columbus, OH
Waldo, OH
Stockholm
Stockholm
Stockholm
Stockholm
Bloomington, IN
Reseda, CA
Commerce, CA
North Long Beach, CA
San Bernadino, CA
El Toro, CA
Cal Transit, CA
Carson, CA
West Long Beach, CA
Connecticut
Location
description
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Industrial
Industrial
Urban
Rural
Urban
Suburban
Rural
Coastal
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Industrial
Urban
Urban
Sample
year
87
87-88
88
NR
NR
89
NR
NR
87
87
87
87
89
89
89
89
86
87-89
87
87-89
87-89
87-88
88-89
88-89
88-89
93-94
Ref.
no.
3
4
5
6
6
7
8
8
9
9
9
9
11
11
11
11
15
12
12
12
12
12
12
12
12
13
Comments
composite of 2-7 samples
urban air & inside traffic tunnel
downwind incinerator & industrial complex
near incinerators
near incinerators
next to interstate highway
background site
mostly residential
near freeway
mostly residential
mostly residential
mostly residential
near highway
on site at gas cooking equipment manufacturer
mostly residential
near resource recovery facilities
DRAFT-DO NOT QUOTE OR CITE
B-13
December 2003
-------
Table B-l. Environmental Levels of Dioxins in Air (pg/m3) (continued)
Chemical
1,2,3,7,8,9-HxCDD
(continued)
HxCDDs
Number
samples
4
14
3
2
16
53
16
16
7
28
1
2
2
3
2
1
1
3
1
2
2
1
1
1
1
7
Number
positive
samples
3
11
1
2
NR
53
11
12
NR
27
1
2
2
3
2
1
1
1
0
1
1
1
1
1
1
7
Concentration
range
ND-0.011
ND-0.046
ND-0.023
0.06-0.11
NR
0.00046-0.041
ND-0.23
ND-12.16
ND-2.17
ND-0.68
0.10
0.74-2.7
5.3-24
0.6-0.63
0.43-0.78
0.15
0.33
ND(0.34)-0.13
ND(0.16)
ND(0.55)-0.1
ND(0.11)-0.17
0.096
0.082
0.03
0.014
0.062-3.0
Cone.
mean
0.00463
0.025
0.0135
0.09
0.18
0.0093
0.08
1.69
0.72
0.26
0.10
1.72
14.6
0.62
0.60
0.15
0.33
0.125
NA
0.188
0.112
0.096
0.082
0.03
0.014
0.988
Location
Connecticut
Franklin County, OH
Franklin County, OH
Franklin County, OH
Phoenix, AZ
Various U.S. Sites
Niagra Falls, NY
Niagra Falls, NY
Bridgeport, CT
Wallingford, CT
Stockholm, Sweden
Hamburg, Germany
Hamburg, Germany
Akron, OH
Columbus, OH
Columbus, OH
Waldo, OH
Albany, NY
Binghamton, NY
Utica, NY
Niagara Falls, NY
Stockholm
Stockholm
Stockholm
Stockholm
Reseda, CA
Location
description
Rural
Urban
Background
Industrial
Urban
Rural
Urban
Industrial
Urban
Urban
Urban
Urban
Urban
Industrial
Industrial
Urban
Rural
Urban
Urban
Urban
Industrial
Urban
Suburban
Rural
Coastal
Urban
Sample
year
93-94
95
95
95
94
98-99
86-87
86-87
87-88
88
89
NR
NR
87
87
87
87
NR
NR
NR
NR
89
89
89
89
87-89
Ref.
no.
13
14
14
14
16
17
1
1
4
5
7
8
8
9
9
9
9
10
10
10
10
11
11
11
11
12
Comments
near resource recovery facilities
near waste to energy facility
close proximity to a heavily traveled road
background
downwind industrial complex
composite of 2-7 samples
urban air & inside traffic tunnel
downwind incinerator & industrial complex
near incinerators
near incinerators
next to interstate highway
background site
mostly residential
DRAFT-DO NOT QUOTE OR CITE
B-14
December 2003
-------
Table B-l. Environmental Levels of Dioxins in Air (pg/m3) (continued)
Chemical
HxCDDs (continued)
Number
samples
1
6
5
7
4
2
2
20
4
16
Number
positive
samples
1
4
2
4
2
1
2
20
4
NR
Concentration
range
2.00
ND-3.2
ND-0.77
ND-0.11
ND-0.27
ND-0.14
0.16-0.32
0.017-0.471
0.020-0.122
NR
Cone.
mean
2.00
0.640
0.197
0.0424
0.153
0.10
0.241
0.177
0.0615
2.41
Location
Commerce, CA
North Long Beach, CA
San Bernadino, CA
El Toro, CA
Cal Transit, CA
Carson, CA
West Long Beach, CA
Connecticut
Connecticut
Phoenix, AZ
Location
description
Urban
Urban
Urban
Urban
Urban
Industrial
Urban
Urban
Rural
Urban
Sample
year
87
87-89
87-89
87-88
88-89
88-89
88-89
93-94
93-94
94
Ref.
no.
12
12
12
12
12
12
12
13
13
16
Comments
near freeway
mostly residential
mostly residential
mostly residential
near highway
on site at gas cooking equipment manufacturer
mostly residential
near resource recovery facilities
near resource recovery facilities
close proximity to a heavily traveled road
Heptachlorodibenzo-p-dioxins (MW =425.31)
1,2,3,4,6,7,8-HpCDD
3
3
NR
1
7
28
NR
NR
1
3
2
1
1
1
1
3
2
NR
1
NR
23
NR
NR
1
3
2
1
1
1
1
0.34-0.51
ND-5.43
NR
0.25
0.02-1.07
ND-0.73
NR
NR
0.10
0.52-0.57
0.26-0.52
0.32
0.24
0.1
0.091
0.41
2.0
0.11
0.25
0.48
0.29
0.41
0.04
NA
0.54
0.39
0.32
0.24
0.1
0.091
Niagra Falls, NY
Niagra Falls, NY
Greenbay, WI
Los Angeles, CA
Bridgeport, CT
Wallingford, CT
Rutland, VT
Durham, NC
Stockholm, Sweden
Akron, OH
Columbus, OH
Columbus, OH
Waldo, OH
Stockholm
Stockholm
Urban
Industrial
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Industrial
Industrial
Urban
Rural
Urban
Suburban
86-87
86-87
NR
87
87-88
88
NR
NR
89
87
87
87
87
89
89
1
1
2
3
4
5
6
6
7
9
9
9
9
11
11
downwind industrial complex
composite of 2-7 samples
near incinerators
near incinerators
next to interstate highway
background site
DRAFT-DO NOT QUOTE OR CITE
B-15
December 2003
-------
Table B-l. Environmental Levels of Dioxins in Air (pg/m3) (continued)
Chemical
1,2,3,4,6,7,8-HpCDD
(continued)
HpCDDs
Number
samples
1
1
1
7
1
6
5
7
4
2
2
20
4
14
3
2
16
53
16
15
7
28
1
2
2
3
Number
positive
samples
1
1
1
7
1
6
5
4
4
2
2
20
4
14
3
2
NR
53
14
15
NR
26
1
2
2
3
Concentration
range
0.027
0.012
0.0051
0.11-8.40
2.70
0.21-3.50
0.21-1.20
ND-0.26
0.41-0.87
0.19-0.22
0.30-0.40
0.019-0.314
0.016-0.159
0.11-0.40
0.197-0.265
0.4-0.87
NR
0.012-0.63
ND-0.86
0.24-9.78
0.02-2.19
ND-1.48
0.20
0.6-3.4
5.3-15
1.00-1.10
Cone.
mean
0.027
0.012
0.0051
2.44
2.70
0.795
0.582
0.138
0.540
0.205
0.351
0.140
0.0673
0.23
0.2273
0.63
2.95
0.13
0.44
2.60
1.02
0.61
0.20
2.0
10.2
1.07
Location
Stockholm
Stockholm
Bloomington, IN
Reseda, CA
Commerce, CA
North Long Beach, CA
San Bernadino, CA
El Toro, CA
Cal. Transit, CA
Carson, CA
West Long Beach, CA
Connecticut
Connecticut
Franklin County, OH
Franklin County, OH
Franklin County, OH
Phoenix, AZ
Various U.S. Sites
Niagra Falls, NY
Niagra Falls, NY
Bridgeport, CT
Wallingford, CT
Stockholm, Sweden
Hamburg, Germany
Hamburg, Germany
Akron, OH
Location
description
Rural
Coastal
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Industrial
Urban
Urban
Rural
Urban
Background
Industrial
Urban
Rural
Urban
Industrial
Urban
Urban
Urban
Urban
Urban
Industrial
Sample
year
89
89
86
87-89
87
87-89
87-89
87-88
88-89
88-89
88-89
93-94
93-94
95
95
95
94
98-99
86-87
86-87
87-88
88
89
NR
NR
87
Ref.
no.
11
11
15
12
12
12
12
12
12
12
12
13
13
14
14
14
16
17
1
1
4
5
7
8
8
9
Comments
mostly residential
near freeway
mostly residential
mostly residential
mostly residential
near highway
on site at gas cooking equipment manufacturer
mostly residential
near resource recovery facilities
near resource recovery facilities
near waste to energy facility
close proximity to a heavily traveled road
background
downwind industrial complex
composite of 2-7 samples
urban air & inside traffic tunnel
downwind incinerator & industrial complex
near incinerators
DRAFT-DO NOT QUOTE OR CITE
B-16
December 2003
-------
Table B-l. Environmental Levels of Dioxins in Air (pg/m3) (continued)
Chemical
HpCDDs (continued)
Number
samples
2
1
1
3
1
2
2
1
1
1
1
7
1
6
5
7
4
2
2
20
4
16
Number
positive
samples
2
1
1
2
1
1
2
1
1
1
1
7
1
6
5
4
4
2
2
20
4
NR
Concentration
range
0.41-1.00
0.56
0.48
0.28-0.69
0.48
ND(0.77)-0.3
0.49-0.56
0.2
0.19
0.062
0.03
0.24-8.90
5.30
0.24-7.20
0.43-1.60
ND-0.46
0.66-1.96
0.46-0.48
0.70-0.78
0.039-0.628
0.033-0.317
NR
Cone.
mean
0.70
0.56
0.48
0.44
0.48
0.342
0.525
0.2
0.19
0.062
0.03
4.94
5.30
1.61
1.06
0.246
1.07
0.467
0.739
0.279
0.136
5.39
Location
Columbus, OH
Columbus, OH
Waldo, OH
Albany, NY
Binghamton, NY
Utica, NY
Niagara Falls, NY
Stockholm
Stockholm
Stockholm
Stockholm
Reseda, CA
Commerce, CA
North Long Beach, CA
San Bernadino, CA
El Toro, CA
Cal. Transit, CA
Carson, CA
West Long Beach, CA
Connecticut
Connecticut
Phoenix, AZ
Location
description
Industrial
Urban
Rural
Urban
Urban
Urban
Industrial
Urban
Suburban
Rural
Coastal
Urban
Urban
Urban
Urban
Urban
Urban
Industrial
Urban
Urban
Rural
Urban
Sample
year
87
87
87
NR
NR
NR
NR
89
89
89
89
87-89
87
87-89
87-89
87-88
88-89
88-89
88-89
93-94
93-94
94
Ref.
no.
9
9
9
10
10
10
10
11
11
11
11
12
12
12
12
12
12
12
12
13
13
16
Comments
near incinerators
next to interstate highway
background site
mostly residential
near freeway
mostly residential
mostly residential
mostly residential
near highway
on site at gas cooking equipment manufacturer
mostly residential
near resource recovery facilities
near resource recovery facilities
close proximity to a heavily traveled road
Octachlorodibenzo-p-dioxin (MW = 460 . 76)
1,2,3,4,6,7,8,9-OCDD
16
14
NR
15
14
NR
ND-5.79
0.39-8.88
NR
1.14
2.94
0.30
Niagra Falls, NY
Niagra Falls, NY
Greenbav, WI
Urban
Industrial
Urban
86-87
86-87
NR
1
1
2
downwind industrial complex
DRAFT-DO NOT QUOTE OR CITE
B-17
December 2003
-------
Table B-l. Environmental Levels of Dioxins in Air (pg/m3) (continued)
Chemical
1,2,3,4,6,7,8,9-OCDD
(continued)
Number
samples
1
7
16
NR
NR
1
2
2
3
2
1
1
3
1
2
2
1
1
1
1
6
5
5
7
4
2
Number
positive
samples
1
NR
15
NR
NR
1
2
2
3
2
1
1
2
i
2
2
1
1
1
1
6
5
5
6
4
2
Concentration
range
1.9
0.17-5.55
ND-29.5
NR
NR
0.23
0.37-6.4
7.4-40
1.00-1.20
0.51-1.10
0.96
0.50
0.6-3.16
1.35
0.84-1.58
1.4-1.6
0.23
0.23
0.068
0.041
0.43-17.0
0.68-1.90
0.93-8.60
ND-2.16
1.80-3.73
0.48-1.60
Cone.
mean
1.9
2.10
5.53
1.10
0.13
0.23
3.38
23.7
1.13
0.80
0.96
0.50
1.53
1.35
1.21
1.5
0.23
0.23
0.068
0.041
5.36
1.42
3.08
1.05
2.37
1.04
Location
Los Angeles, CA
Bridgeport, CT
Wallingford, CT
Rutland, VT
Durham, NC
Stockholm, Sweden
Hamburg, Germany
Hamburg, Germany
Akron, OH
Columbus, OH
Columbus, OH
Waldo, OH
Albany, NY
Binghamton, NY
Utica, NY
Niagara Falls, NY
Stockholm
Stockholm
Stockholm
Stockholm
Reseda, CA
North Long Beach, CA
San Bernadino, CA
El Toro, CA
Cal. Transit, CA
Carson, CA
Location
description
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Industrial
Industrial
Urban
Rural
Urban
Urban
Urban
Insdustrial
Urban
Suburban
Rural
Coastal
Urban
Urban
Urban
Urban
Urban
Industrial
Sample
year
87
87-88
88
NR
NR
89
NR
NR
87
87
87
87
NR
NR
NR
NR
89
89
89
89
87-89
87-89
87-89
87-88
88-89
88-89
Ref.
no.
3
4
5
6
6
7
8
8
9
9
9
9
10
10
10
10
11
11
11
11
12
12
12
12
12
12
Comments
composite of 2-7 samples
urban air & inside traffic tunnel
downwind incinerator & industrial complex
near incinerators
near incinerators
next to interstate highway
background site
mostly residential
near freeway
mostly residential
mostly residential
near highway
on site at gas cooking equipment manufacturer
DRAFT-DO NOT QUOTE OR CITE
B-18
December 2003
-------
Table B-l. Environmental Levels of Dioxins in Air (pg/m3) (continued)
Chemical
Number
samples
2
20
4
14
3
2
16
53
Number
positive
samples
2
20
4
14
3
2
NR
53
Concentration
range
2.05-3.83
0.072-0.839
0.056-0.451
0.431-1.56
0.634-1.14
1.17-2.36
NR
0.058-1.7
Cone.
mean
2.94
0.436
0.215
0.952
0.9037
1.77
9.55
0.44
Location
West Long Beach, CA
Connecticut
Connecticut
Franklin County, OH
Franklin County, OH
Franklin County, OH
Phoenix, AZ
Various U.S. Sites
Location
description
Urban
Urban
Rural
Urban
Background
Industrial
Urban
Rural
Sample
year
88-89
93-94
93-94
95
95
95
94
98-99
Ref.
no.
12
13
13
14
14
14
16
17
Comments
mostly residential
near resource recovery facilities
near resource recovery facilities
near waste to energy facility
close proximity to a heavily traveled road
background
Footnote References
NOTES: Summary statistics provided in or derived from references; when reference did not compute mean, it was computed using one-half the detection limit for non-detects;
NA = Not applicable;
ND = Non-detect;
NR = Not reported.
Sources:
1. Smith etal. (1989)
2. Harlessetal. (1990)
3. Maisel and Hunt (1990)
4. Hunt and Maisel (1990)
5. CDEP(1988)
6. Harlessetal. (1991)
7. Nafetal. (1990)
8. Rappe and Kjeller (1987)
9. Edgerton et al. (1989)
10. Smith et al. (1990)
11. Bromanet al. (1991)
12. Hunt etal. (1990)
13. CDEP (1995)
14. OEPA (1995)
15. Eitzer and Hites (1989)
16. Hunt etal. (1997)
17. Cleverly et al. (2000)
DRAFT-DO NOT QUOTE OR CITE
B-19
December 2003
-------
Table B-2. Environmental Levels of Dibenzofurans in Air (pg/m3)
Chemical
Number
samples
Number
positive
samples
Concentration
range
Cone.
mean
Location
range
Location
description
Sample
year
Ref.
no.
Comments
Tetrachlorodibenzofiirans (MW = 305 . 98)
2,3,7,8-TCDF
3
3
NR
1
7
28
NR
NR
2
2
3
2
1
1
3
1
2
2
7
1
6
5
7
4
2
2
20
3
3
NR
1
NR
22
NR
NR
2
2
3
2
0
1
3
1
2
0
4
1
4
4
3
2
1
2
20
0.04-0.14
0.28-3.81
NR
0.02
ND-0.20
ND-0.10
ND(O.l)
NR
0.04-0.72
0.18-0.38
0.19-0.20
0.32-0.49
ND(0.13)
0.13
0.5-1.24
0.18
1.07-1.23
ND(0.04-0.2)
ND-0.046
0.11
ND-0.039
ND-0.091
ND-0.027
ND-0.21
ND-0.024
0.019-0.48
0.002-0.024
0.09
1.47
0.03
0.02
0.08
0.04
NA
0.03
0.38
0.28
0.20
0.40
NA
0.13
0.85
0.18
1.15
NA
0.0271
0.11
0.0198
0.0383
0.0146
0.0687
0.0137
0.250
0.0093
Niagra Falls, NY
Niagra Falls, NY
Greenbay, WI
Los Angeles, CA
Bridgeport, CT
Wallingford, CT
Rutland, VT
Durham, NC
Hamburg, Germany
Hamburg, Germany
Akron, OH
Columbus, OH
Columbus, OH
Waldo, OH
Albany, NY
Binghamton, NY
Utica, NY
Niagra Falls, NY
Reseda, CA
Commerce, CA
North Long Beach, CA
San Bernadino, CA
El Toro, CA
Cal. Transit, CA
Carson, CA
West Long Beach, CA
Connecticut
Urban
Industrial
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Industrial
Industrial
Industrial
Urban
Rural
Urban
Urban
Urban
Industrial
Urban
Urban
Urban
Urban
Urban
Urban
Industrial
Urban
Urban
86-87
86-87
NR
87
87-88
88
NR
NR
NR
NR
87
87
87
87
87-89
88
88
87
87-89
87
87-89
87-89
87-88
88-89
88-89
88-89
93-94
1
1
2
3
4
5
6
6
7
7
8
8
8
8
9
9
9
9
10
10
10
10
10
10
10
10
11
downwind industrial complex
composite of 2-7 samples
urban air & inside traffic tunnel
downwind incinerator & industrial complex
near incinerators
near incinerators
next to interstate highway
background site
mostly residential
near freeway
mostly residential
mostly residential
mostly residential
near highway
on site at gas cooking equipment manufacturer
mostly residential
near resource recovery facilities
DRAFT-DO NOT QUOTE OR CITE
B-20
December 2003
-------
Table B-2. Environmental Levels of Dibenzofurans in Air (pg/m3) (continued)
Chemical
2,3,7,8,-TCDF
(continued)
TCDFs
Number
samples
4
14
3
2
16
53
16
16
7
28
1
2
2
3
2
1
1
3
1
2
2
1
1
1
1
7
1
6
Number
positive
samples
4
12
0
2
NR
53
10
16
NR
26
1
2
2
3
2
0
1
3
1
2
2
1
1
1
1
5
1
4
Concentration
range
0.002-0.004
ND-0.034
ND
0.032-0.069
NR
0.00021-0.0057
ND-0.66
0.18-17.4
ND-2.29
ND-0.86
0.33
0.36-6.2
3.3-4.9
0.99-1.50
1.90-3.80
ND(0.13)
0.89
2.08-5.46
0.94
5.87-8.81
1.10-1.20
0.33
0.20
0.08
0.048
ND-1.10
1.40
ND-1.00
Cone.
mean
0.00325
0.014
NA
0.051
0.033
0.0017
0.23
3.25
0.86
0.38
0.33
3.28
4.1
1.23
2.85
NA
0.89
3.64
0.94
7.34
1.15
0.33
0.20
0.08
0.048
0.275
1.40
0.432
Location
range
Connecticut
Franklin County, OH
Franklin County, OH
Franklin County, OH
Phoenix, AZ
Various U.S. Sites
Niagra Falls, NY
Niagra Falls, NY
Bridgeport, CT
Wallingford, CT
Stockholm, Sweden
Hamburg, Germany
Hamburg, Germany
Akron, OH
Columbus, OH
Columbus, OH
Waldo, OH
Albany, NY
Binghamton, NY
Utica, NY
Niagra Falls, NY
Stockholm
Stockholm
Stockholm
Stockholm
Reseda, CA
Commerce, CA
North Long Beach, CA
Location
description
Rural
Urban
Rural
Industrial
Urban
Rural
Urban
Industrial
Urban
Urban
Urban
Urban
Industrial
Industrial
Industrial
Urban
Rural
Urban
Urban
Urban
Industrial
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Sample
year
93-94
95
95
95
94
98-99
86-87
86-87
87-88
88
89
NR
NR
87
87
87
87
87-88
88
88
87
89
89
89
89
87-89
87
87-89
Ref.
no.
11
12
12
12
16
17
1
1
4
5
13
7
7
8
8
8
8
9
9
9
9
14
14
14
14
10
10
10
Comments
near resource recovery facilities
near waste to energy facility
close proximity to a heavily traveled road
background
downwind industrial complex
composite of 2-7 samples
urban air & inside traffic tunnel
downwind incinerator & industrial complex
near incinerators
near incinerators
next to interstate highway
background site
mostly residential
near freeway
mostly residential
DRAFT-DO NOT QUOTE OR CITE
B-21
December 2003
-------
Table B-2. Environmental Levels of Dibenzofurans in Air (pg/m3) (continued)
Chemical
TCDFs (continued)
Number
samples
5
7
4
2
2
20
4
16
Number
positive
samples
5
4
2
2
2
20
4
NR
Concentration
range
0.024-0.98
ND-0.32
ND-0.87
0.089-0.32
0.15-0.48
0.086-0.465
0.090-0.134
NR
Cone.
mean
0.430
0.147
0.418
0.206
0.316
0.264
0.108
1.57
Location
range
San Bernadino, CA
El Toro, CA
Cal. Transit, CA
Carson, CA
West Long Beach, CA
Connecticut
Connecticut
Phoenix, AZ
Location
description
Urban
Urban
Urban
Industrial
Urban
Urban
Rural
Urban
Sample
year
87-89
87-88
88-89
88-89
88-89
93-94
93-94
94
Ref.
no.
10
10
10
10
10
11
11
16
Comments
mostly residential
mostly residential
near freeway
on site at gas cooking equipment manufacturer
mostly residential
near resource recovery facilities
near resource recovery facilities
close proximity to a heavily traveled road
1,2,3,7,8-PeCDF (MW=340.42)
1,2,3,7,8-PeCDF
3
3
NR
1
7
28
NR
NR
3
2
1
1
7
1
6
5
7
4
2
0
3
NR
1
NR
9
NR
NR
3
2
0
1
1
1
1
1
2
i
0
ND(O.Ol)
0.03-0.61
NR
0.08
ND-0.10
ND-0.02
NR
NR
0.026-0.033
0.032-0.057
ND(0.036)
0.021
ND-0.14
0.092
ND-0.13
ND-1.90
ND-0.077
ND-0.053
ND(O.OIO-O.OIS)
NA
0.25
0.05
0.08
0.03
0.01
0.03
0.01
0.029
0.044
NA
0.021
0.0327
0.0920
0.0383
0.399
0.0349
0.0346
NA
Niagra Falls, NY
Niagra Falls, NY
Greenbay, WI
Los Angeles, CA
Bridgeport, CT
Wallingford, CT
Rutland, VT
Durham, NC
Akron, OH
Columbus, OH
Columbus, OH
Waldo, OH
Reseda, CA
Commerce, CA
North Long Beach, CA
San Bernadino, CA
El Toro, CA
Cal Transit, CA
Carson, CA
Urban
Industrial
Urban
Urban
Urban
Urban
Urban
Urban
Industrial
Industrial
Urban
Rural
Urban
Urban
Urban
Urban
Urban
Urban
Industrial
86-87
86-87
NR
87
87-88
88
NR
NR
87
87
87
87
87-89
87
87-89
87-89
87-89
88-89
88-89
1
1
2
3
4
5
6
6
8
8
8
8
10
10
10
10
10
10
10
downwind industrial complex
composite of 2-7 samples
near incinerators
near incinerators
next to interstate highway
background site
mostly residential
near freeway
mostly residential
mostly residential
mostly residential
near highway
On site at gas cooking equipment manufacturer
DRAFT-DO NOT QUOTE OR CITE
B-22
December 2003
-------
Table B-2. Environmental Levels of Dibenzofurans in Air (pg/m3) (continued)
Chemical
Number
samples
2
20
4
14
3
Number
positive
samples
1
20
4
12
1
Concentration
range
ND-0.022
0.003-0.019
0.003-0.004
ND-0.046
ND-0.01354
Cone.
mean
0.0161
0.00925
0.00325
0.023
0.0065
Location
range
West Long Beach, CA
Connecticut
Connecticut
Franklin County, OH
Franklin Countv, OH
Location
description
Urban
Urban
Rural
Urban
Rural
Sample
year
88-89
93-94
93-94
95
95
Ref.
no.
10
11
11
12
12
Comments
mostly residential
near resource recovery facilities
near resource recovery facilities
DRAFT-DO NOT QUOTE OR CITE
B-23
December 2003
-------
Table B-2. Environmental Levels of Dibenzofurans in Air (pg/m3) (continued)
Chemical
1,2,3,7,8-PeCDF
(continued)
2,3,4,7,8-PeCDF
Number
samples
2
16
53
3
3
NR
1
7
28
NR
NR
1
1
2
3
2
1
1
1
1
1
1
7
1
6
5
7
4
Number
positive
samples
2
NR
53
0
2
NR
1
NR
16
NR
NR
1
1
2
3
1
0
0
1
1
1
1
1
1
1
1
2
1
Concentration
range
0.08-0.16
NR
0.00033-0.0073
ND(0.01-0.02)
ND-1.92
NR
0.08
ND-0.16
ND-0.04
NR
NR
0.02
0.04
0.43-1.2
0.032-0.042
ND-0.089
ND(0.036)
ND(0.033)
0.018
0.0078
0.0021
0.0012
ND-0.11
0.0890
ND-0.13
ND-0.10
ND-0.08
ND-0.15
Cone.
mean
0.12
0.052
0.0019
NA
0.68
0.04
0.08
0.05
0.02
0.20
0.01
0.02
0.04
0.82
0.036
0.050
NA
NA
0.018
0.0078
0.0021
0.0012
0.0295
0.0890
0.0393
0.0379
0.0363
0.0516
Location
range
Franklin County, OH
Phoenix, AZ
Various U.S. Sites
Niagra Falls, NY
Niagra Falls, NY
Greenbay, WI
Los Angeles, CA
Bridgeport, CT
Wallingford, CT
Rutland, VT
Durham, NC
Stockholm, Sweden
Hamburg, Germany
Hamburg, Germany
Akron, OH
Columbus, OH
Columbus, OH
Waldo, OH
Stockholm
Stockholm
Stockholm
Stockholm
Reseda, CA
Commerce, CA
North Long Beach, CA
San Bernadino, CA
El Toro, CA
Cal. Transit, CA
Location
description
Industrial
Urban
Rural
Urban
Industrial
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Industrial
Industrial
Industrial
Urban
Rural
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Sample
year
95
94
98-99
86-87
86-87
NR
87
87-88
88
NR
NR
89
NR
NR
87
87
87
87
89
89
89
89
87-89
87
87-89
87-89
87-88
88-89
Ref.
no.
12
16
17
1
1
2
3
4
5
6
6
13
7
7
8
8
8
8
14
14
14
14
10
10
10
10
10
10
Comments
near waste to energy facility
close proximity to a heavily traveled road
background
downwind industrial complex
composite of 2-7 samples
urban air & inside traffic tunnel
downwind incinerator & industrial complex
near incinerators
near incinerators
next to interstate highway
background site
mostly residential
near freeway
mostly residential
mostly residential
mostly residential
near highway
DRAFT-DO NOT QUOTE OR CITE
B-24
December 2003
-------
Table B-2. Environmental Levels of Dibenzofurans in Air (pg/m3) (continued)
Chemical
2,3,4,7,8-PeCDF
(continued)
PeCDFs
Number
samples
2
2
20
4
14
3
2
16
53
16
16
7
28
1
2
2
3
2
1
1
3
1
2
2
1
1
1
1
Number
positive
samples
0
0
20
4
13
2
2
NR
53
8
15
NR
28
1
2
2
3
2
0
1
3
1
2
2
1
1
1
1
Concentration
range
ND(0.010-0.012)
ND(0.010-0.012)
0.003-0.038
0.004-0.006
ND-0.055
ND-0.01236
0.11-0.23
NR
0.00051-0.011
ND-0.41
ND-12.4
ND-1.77
0.04-0.71
0.17
0.51-4.1
5-10
0.53-0.66
0.69-1.30
ND(0.036)
0.50
1.24-3.26
0.25
2.71-3.61
0.31-0.39
0.170
0.094
0.046
0.026
Cone.
mean
NA
NA
0.0159
0.00475
0.025
0.0095
0.168
0.11
0.0032
0.12
2.08
0.57
0.29
0.17
2.30
7.5
0.59
1.00
NA
0.50
1.96
0.25
3.16
0.35
0.17
0.094
0.046
0.026
Location
range
Carson, CA
West Long Beach, CA
Connecticut
Connecticut
Franklin County, OH
Franklin County, OH
Franklin County, OH
Phoenix, AZ
Various U.S. Sites
Niagra Falls, NY
Niagra Falls, NY
Bridgeport, CT
Wallingford, CT
Stockholm, Sweden
Hamburg, Germany
Hamburg, Germany
Akron, OH
Columbus, OH
Columbus, OH
Waldo, OH
Albany, NY
Binghamton, NY
Utica, NY
Niagra Falls, NY
Stockholm
Stockholm
Stockholm
Stockholm
Location
description
Industrial
Urban
Urban
Rural
Urban
Rural
Industrial
Urban
Rural
Urban
Industrial
Urban
Urban
Urban
Urban
Industrial
Industrial
Industrial
Urban
Rural
Urban
Urban
Urban
Industrial
Urban
Urban
Urban
Urban
Sample
year
88-89
88-89
93-94
93-94
95
95
95
94
98-99
86-87
86-87
87-88
88
89
NR
NR
87
87
87
87
87-88
88
88
87
89
89
89
89
Ref.
no.
10
10
11
11
12
12
12
16
17
1
1
4
5
13
7
7
8
8
8
8
9
9
9
9
14
14
14
14
Comments
on site at gas cooking equipment manufacturer
mostly residential
near resource recovery facilities
near resource recovery facilities
near waste to energy facility
close proximity to a heavily traveled road
background
downwind industrial complex
composite of 2-7 samples
urban air & inside traffic tunnel
downwind incinerator & industrial complex
near incinerators
near incinerators
next to interstate highway
background site
DRAFT-DO NOT QUOTE OR CITE
B-25
December 2003
-------
Table B-2. Environmental Levels of Dibenzofurans in Air (pg/m3) (continued)
Chemical
PeCDFs (continued)
Number
samples
7
1
6
5
7
4
2
2
20
4
16
Number
positive
samples
5
1
4
2
4
2
2
2
20
4
NR
Concentration
range
ND-1.90
1.00
ND-1.50
ND-2.70
ND-0.57
ND-1.60
0.25-0.28
0.23-0.24
0.057-0.451
0.057-0.078
NR
Cone.
mean
0.402
1.00
0.450
0.582
0.247
0.524
0.265
0.235
0.199
0.0663
1.43
Location
range
Reseda, CA
Commerce, CA
North Long Beach, CA
San Bernadino, CA
El Toro, CA
Cal. Transit, CA
Carson, CA
West Long Beach, CA
Connecticut
Connecticut
Phoenix, AZ
Location
description
Urban
Urban
Urban
Urban
Urban
Urban
Industrial
Urban
Urban
Rural
Urban
Sample
year
87-89
87
87-89
87-89
87-88
88-89
88-89
88-89
93-94
93-94
94
Ref.
no.
10
10
10
10
10
10
10
10
11
11
16
Comments
mostly residential
near freeway
mostly residential
mostly residential
mostly residential
near highway
on site at gas cooking equipment manufacturer
mostly residential
near resource recovery facilities
near resource recovery facilities
close proximity to a heavily traveled road
Hexachlorodibenzofurans (MW=374.87)
1,2,3,4,7,8-HxCDF
3
3
NR
1
7
28
NR
NR
3
2
1
1
7
1
6
5
1
2
NR
1
NR
21
NR
NR
3
2
0
1
1
1
1
2
ND-0.06
ND-0.22
NR
0.15
ND-0.41
ND-0.13
NR
NR
0.053-0.10
0.060-0.27
ND(0.034)
0.098
ND-0.27
0.180
ND-0.25
ND-0.18
0.02
0.11
0.03
0.15
0.11
0.05
0.04
0.02
0.083
0.16
NA
0.098
0.0586
0.180
0.0574
0.0821
Niagra Falls, NY
Niagra Falls, NY
Greenbay, WI
Los Angeles, CA
Bridgeport, CT
Wallingford, CT
Rutland, VT
Durham, NC
Akron, OH
Columbus, OH
Columbus, OH
Waldo, OH
Reseda, CA
Commerce, CA
North Long Beach, CA
San Bernadino, CA
Urban
Industrial
Urban
Urban
Urban
Urban
Urban
Urban
Industrial
Industrial
Urban
Rural
Urban
Urban
Urban
Urban
86-87
86-87
NR
87
87-88
88
NR
NR
87
87
87
87
87-89
87
87-89
87-89
1
1
2
3
4
5
6
6
8
8
8
8
10
10
10
10
downwind industrial complex
composite of 2-7 samples
near incinerators
near incinerators
next to interstate highway
background site
mostly residential
near freeway
mostly residential
mostly residential
DRAFT-DO NOT QUOTE OR CITE
B-26
December 2003
-------
Table B-2. Environmental Levels of Dibenzofurans in Air (pg/m3) (continued)
Chemical
Number
samples
7
4
2
2
20
Number
positive
samples
2
0
0
0
20
Concentration
range
ND-0.15
ND(0. 039-0. 15)
ND(0.038-0.054)
ND(0.029-0.085)
0.004-0.054
Cone.
mean
0.0499
NA
NA
NA
0.0198
Location
range
El Toro, CA
Cal. Transit, CA
Carson, CA
West Long Beach, CA
Connecticut
Location
description
Urban
Urban
Industrial
Urban
Urban
Sample
year
87-88
88-89
88-89
88-89
93-94
Ref.
no.
10
10
10
10
11
Comments
mostly residential
near highway
on site at gas cooking equipment manufacturer
mostly residential
near resource recovery facilities
DRAFT-DO NOT QUOTE OR CITE
B-27
December 2003
-------
Table B-2. Environmental Levels of Dibenzofurans in Air (pg/m3) (continued)
Chemical
1,2,3,4,7,8-HxCDF
(continued)
1,2,3,6,7,8-HxCDF
Number
samples
4
14
3
2
16
53
3
3
NR
1
7
28
NR
NR
1
2
2
3
2
1
1
1
1
1
1
7
1
6
Number
positive
samples
4
13
1
2
NR
53
1
3
NR
1
NR
17
NR
NR
1
2
2
3
2
0
1
1
1
1
1
1
1
1
Concentration
range
0.004-0.008
ND-0.134
ND-0.0261
0.123-0.286
NR
0.00071-0.016
ND-0.02
0.05-1.17
NR
0.25
ND-0.15
ND-0.07
NR
NR
0.008
0.03-0.15
0.24-1.4
0.048-0.092
0.092-0.19
ND(0.034)
0.014
0.0078
0.0059
0.0024
0.0014
ND-0.80
0.41
ND-0.48
Cone.
mean
0.006
0.049
0.0134
0.205
0.15
0.0038
0.01
0.45
0.03
NA
0.04
0.03
0.03
0.02
0.008
0.09
0.82
0.065
0.14
NA
0.014
0.0078
0.0059
0.0024
0.0014
0.130
0.410
0.0931
Location
range
Connecticut
Franklin County, OH
Franklin County, OH
Franklin County, OH
Phoenix, AZ
Various U.S. Sites
Niagra Falls, NY
Niagra Falls, NY
Greenbay, WI
Los Angeles, CA
Bridgeport, CT
Wallingford, CT
Rutland, VT
Durham, NC
Stockholm, Sweden
Hamburg, Germany
Hamburg, Germany
Akron, OH
Columbus, OH
Columbus, OH
Waldo, OH
Stockholm
Stockholm
Stockholm
Stockholm
Reseda, CA
Commerce, CA
North Long Beach, CA
Location
description
Rural
Urban
Rural
Industrial
Urban
Rural
Urban
Industrial
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Industrial
Industrial
Industrial
Urban
Rural
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Sample
year
93-94
95
95
95
94
98-99
86-87
86-87
NR
87
87-88
88
NR
NR
89
NR
NR
87
87
87
87
89
89
89
89
87-89
87
87-89
Ref.
no.
11
12
12
12
16
17
1
1
2
3
4
5
6
6
13
7
7
8
8
8
8
14
14
14
14
10
10
10
Comments
near resource recovery facilities
near waste to energy facility
close proximity to a heavily traveled road
background
downwind industrial complex
composite of 2-7 samples
urban air & inside traffic tunnel
downwind incinerator & industrial complex
near incinerators
near incinerators
next to interstate highway
background site
mostly residential
near freeway
mostly residential
DRAFT-DO NOT QUOTE OR CITE
B-28
December 2003
-------
Table B-2. Environmental Levels of Dibenzofurans in Air (pg/m3) (continued)
Chemical
1,2,3,6,7,8-HxCDF
(continued)
1,2,3,7,8,9-HxCDF
Number
samples
5
7
4
2
2
20
4
14
3
2
16
53
3
3
NR
1
7
28
NR
NR
1
2
2
3
2
1
1
1
Number
positive
samples
1
2
0
0
0
20
4
14
1
2
NR
53
0
1
NR
0
NR
1
NR
NR
1
0
1
3
2
0
1
1
Concentration
range
ND-0.37
ND-0.25
ND(0. 031-0. 092)
ND(0. 030-0. 036)
ND(0.060-0.070)
0.003-0.038
0.003-0.007
0.0132-0.115
ND-0.0243
0.199-0.405
NR
0.00062-0.011
ND(0.01-0.02)
ND-0.1
NR
ND(0.08)
ND-0.02
ND-0.003
ND(0.03)
ND(O.Ol)
0.0008
ND(0. 01-0.05)
ND-0.33
0.020-0.039
0.038-0.12
ND(0.034)
0.097
0.0008
Cone.
mean
0.0977
0.0689
NA
NA
NA
0.0161
0.005
0.057
0.0156
0.302
0.13
0.0035
NA
0.04
0.01
NA
0.01
0.003
NA
NA
0.0008
NA
0.17
0.032
0.079
NA
0.097
0.0008
Location
range
San Bernadino, CA
El Toro, CA
Cal. Transit, CA
Carson, CA
West Long Beach, CA
Connecticut
Connecticut
Franklin County, OH
Franklin County, OH
Franklin County, OH
Phoenix, AZ
Various U.S. Sites
Niagra Falls, NY
Niagra Falls, NY
Greenbay, WI
Los Angeles, CA
Bridgeport, CT
Wallingford, CT
Rutland, VT
Durham, NC
Stockholm, Sweden
Hamburg, Germany
Hamburg, Germany
Akron, OH
Columbus, OH
Columbus, OH
Waldo, OH
Stockholm
Location
description
Urban
Urban
Urban
Industrial
Urban
Urban
Rural
Urban
Ruran
Industrial
Urban
Rural
Urban
Industrial
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Industrial
Industrial
Industrial
Urban
Rural
Urban
Sample
year
87-89
87-88
88-89
88-89
88-89
93-94
93-94
95
95
95
94
98-99
86-87
86-87
NR
87
87-88
88
NR
NR
89
NR
NR
87
87
87
87
89
Ref.
no.
10
10
10
10
10
11
11
12
12
12
16
17
1
1
2
3
4
5
6
6
13
7
7
8
8
8
8
14
Comments
mostly residential
mostly residential
near highway
on site at gas cooking equipment manufacturer
mostly residential
near resource recovery facilities
near resource recovery facilities
near waste to energy facility
close proximity to a heavily traveled road
background
downwind industrial complex
composite of 2-7 samples
urban air & inside traffic tunnel
downwind incinerator & industrial complex
near incinerators
near incinerators
next to interstate highway
background site
DRAFT-DO NOT QUOTE OR CITE
B-29
December 2003
-------
Table B-2. Environmental Levels of Dibenzofurans in Air (pg/m3) (continued)
Chemical
1,2,3,7,8,9-HxCDF
(continued)
2,3,4,6,7,8-HxCDF
Number
samples
1
1
1
1
7
1
6
5
7
4
2
2
20
4
14
3
2
16
53
3
3
NR
1
7
28
NR
NR
1
Number
positive
samples
1
1
1
1
0
0
0
0
0
0
0
0
19
3
1
0
0
NR
53
1
2
NR
0
NR
19
NR
NR
1
Concentration
range
0.0006
0.0003
0.0004
0.0001
ND(0. 0040-0.075)
ND(O.ll)
ND(0.010-0.043)
ND(0. 033-0.21)
ND(0.015-0.083)
ND(0.0032-0.068)
ND(0.0054-0.014)
ND(0.040-0.086)
ND-0.020
ND-0.004
ND-0.00528
ND
ND
NR
0.00018-0.0061
ND-0.04
ND-2.17
NR
ND(0.08)
ND-0.30
ND-0.10
ND(0.03)
ND(O.Ol)
0.005
Cone.
mean
0.0006
0.0003
0.0004
0.0001
NA
NA
NA
NA
NA
NA
NA
NA
0.0072
0.00213
0.003
NA
NA
0.21
0.0014
0.02
0.76
ND(O.Ol)
NA
0.09
0.04
NA
NA
0.005
Location
range
Stockholm
Stockholm
Stockholm
Bloomington, IN
Reseda, CA
Commerce, CA
North Long Beach, CA
San Bernadino, CA
El Toro, CA
Cal. Transit, CA
Carson, CA
West Long Beach, CA
Connecticut
Connecticut
Franklin County, OH
Franklin County, OH
Franklin County, OH
Phoenix, AZ
Various U.S. Sites
Niagra Falls, NY
Niagra Falls, NY
Greenbay, WI
Los Angeles, CA
Bridgeport, CT
Wallingford, CT
Rutland, VT
Durham, NC
Stockholm, Sweden
Location
description
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Industrial
Urban
Urban
Rural
Urban
Rural
Industrial
Urban
Rural
Urban
Industrial
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Sample
year
89
89
89
86
87-89
87
87-89
87-89
87-88
88-89
88-89
88-89
93-94
93-94
95
95
95
94
98-99
86-87
86-87
NR
87
87-88
88
NR
NR
89
Ref.
no.
14
14
14
15
10
10
10
10
10
10
10
10
11
11
12
12
12
16
17
1
1
2
3
4
5
6
6
13
Comments
mostly residential
near freeway
mostly residential
mostly residential
mostly residential
near highway
on site at gas cooking equipment manufacturer
mostly residential
near resource recovery facilities
near resource recovery facilities
near waste to energy facility
close proximity to a heavily traveled road
background
downwind industrial complex
composite of 2-7 samples
DRAFT-DO NOT QUOTE OR CITE
B-30
December 2003
-------
Table B-2. Environmental Levels of Dibenzofurans in Air (pg/m3) (continued)
Chemical
2,3,4,6,7,8-HxCDF
(continued)
HxCDFs
Number
samples
2
2
3
2
1
1
1
1
1
1
1
7
1
6
5
7
4
2
2
20
4
14
3
2
16
53
16
16
Number
positive
samples
1
2
0
0
0
0
1
1
1
1
1
1
1
1
1
0
0
0
0
20
4
13
1
2
NR
53
11
15
Concentration
range
ND-0.05
0.21-0.8
ND(0.005-0.036)
ND(0.012-0.028)
ND(0.034)
ND(.008)
0.0054
0.0063
0.002
0.0009
0.0016
ND-0.28
0.180
ND-0.19
ND-0.16
ND(0.018-0.078)
ND(0.039-0.103)
ND(0.014-0.021)
ND(0.035-0.086)
0.003-0.056
0.004-0.009
ND-0.08
ND-0.0174
0.122-0.255
NR
0.00072-0.015
ND-0.58
ND-10.2
Cone.
mean
0.03
0.50
NA
NA
NA
NA
0.0054
0.0063
0.002
0.009
0.0016
0.0551
0.180
0.0474
0.0584
NA
NA
NA
NA
0.0215
0.00625
0.0304
0.0092
0.189
0.078
0.0044
0.14
1.96
Location
range
Hamburg, Germany
Hamburg, Germany
Akron, OH
Columbus, OH
Columbus, OH
Waldo, OH
Stockholm
Stockholm
Stockholm
Stockholm
Bloomington, IN
Reseda, CA
Commerce, CA
North Long Beach, CA
San Bernadino, CA
El Toro, CA
Cal. Transit, CA
Carson, CA
West Long Beach, CA
Connecticut
Connecticut
Franklin County, OH
Franklin County, OH
Franklin County, OH
Phoenix, AZ
Various U.S. Sites
Niagra Falls, NY
Niagra Falls, NY
Location
description
Urban
Industrial
Industrial
Industrial
Urban
Rural
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Industrial
Urban
Urban
Rural
Urban
Rural
Industrial
Urban
Rural
Urban
Industrial
Sample
year
NR
NR
87
87
87
87
89
89
89
89
86
87-89
87
87-89
87-89
87-88
88-89
88-89
88-89
93-94
93-94
95
95
95
94
98-99
86-87
86-87
Ref.
no.
7
7
8
8
8
8
14
14
14
14
15
10
10
10
10
10
10
10
10
11
11
12
12
12
16
17
1
1
Comments
urban air & inside traffic tunnel
downwind incinerator & industrial complex
near incinerators
near incinerators
next to interstate highway
background site
mostly residential
near freeway
mostly residential
mostly residential
mostly residential
near highway
on site at gas cooking equipment manufacturer
mostly residential
near resource recovery facilities
near resource recovery facilities
near waste to energy facility
close proximity to a heavily traveled road
background
downwind industrial complex
DRAFT-DO NOT QUOTE OR CITE
B-31
December 2003
-------
Table B-2. Environmental Levels of Dibenzofurans in Air (pg/m3) (continued)
Chemical
Number
samples
7
28
1
2
2
3
2
Number
positive
samples
NR
28
1
2
2
3
2
Concentration
range
ND-2.15
0.03-1.57
0.08
0.18-1.1
2.2-9.5
0.56-0.70
0.37-1.20
Cone.
mean
0.58
0.49
0.08
0.64
5.85
0.62
0.78
Location
range
Bridgeport, CT
Wallingford, CT
Stockholm, Sweden
Hamburg, Germany
Hamburg, Germany
Akron, OH
Columbus, OH
Location
description
Urban
Urban
Urban
Urban
Industrial
Industrial
Industrial
Sample
year
87-88
88
89
87-88
88
87
87
Ref.
no.
4
5
13
7
7
8
8
Comments
composite of 2-7 samples
urban air & inside traffic tunnel
downwind incinerator & industrial complex
near incinerators
near incinerators
DRAFT-DO NOT QUOTE OR CITE
B-32
December 2003
-------
Table B-2. Environmental Levels of Dibenzofurans in Air (pg/m3) (continued)
Chemical
HxCDFs (continued)
Number
samples
1
1
3
1
2
2
1
1
1
1
7
1
6
5
7
4
2
2
20
4
16
Number
positive
samples
1
1
3
0
1
2
i
i
i
i
5
1
4
4
4
3
2
1
20
4
NR
Concentration
range
0.10
0.51
0.19-0.37
ND(0.09)
ND(0.26)-0.46
0.12-0.18
0.078
0.062
0.029
0.015
ND-2.00
1.1
ND-1.70
ND-0.90
ND-0.40
ND-0.84
0.19-0.27
ND-0.35
0.038-0.426
0.041-0.078
NR
Cone.
mean
0.10
0.51
0.31
NA
0.295
0.15
0.078
0.062
0.029
0.015
0.415
1.10
0.452
0.606
0.162
0.0341
0.0230
0.200
0.181
0.055
3.45
Location
range
Columbus, OH
Waldo, OH
Albany, NY
Binghamton, NY
Utica, NY
Niagra Falls, NY
Stockholm
Stockholm
Stockholm
Stockholm
Reseda, CA
Commerce, CA
North Long Beach, CA
San Bernadino, CA
El Toro, CA
Cal. Transit, CA
Carson, CA
West Long Beach, CA
Connecticut
Connecticut
Phoenix, AZ
Location
description
Urban
Rural
Urban
Urban
Urban
Industrial
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Industrial
Urban
Urban
Rural
Urban
Sample
year
87
87
87-88
88
88
87
89
89
89
89
87-89
87
87-89
87-89
87-88
88-89
88-89
88-89
93-94
93-94
94
Ref.
no.
8
8
9
9
9
9
14
14
14
14
10
10
10
10
10
10
10
10
11
11
16
Comments
next to interstate highway
background site
mostly residential
near freeway
mostly residential
mostly residential
mostly residential
near highway
on site at gas cooking equipment manufacturer
mostly residential
near resource recovery facilities
near resource recovery facilities
close proximity to a heavily traveled road
Heptachlorodibenzofurans (MW = 409. 3 1)
1,2,3,4,6,7,8-HpCDF
3
3
NR
1
7
28
1
3
NR
0
NR
22
ND-0.15
0.26-5.43
NR
ND(0.2)
ND-0.54
ND-0.80
0.05
2.08
0.08
NA
0.21
0.26
Niagra Falls, NY
Niagra Falls, NY
Greenbay, WI
Los Angeles, CA
Bridgeport, CT
Wallingford, CT
Urban
Industrial
Urban
Urban
Urban
Urban
86-87
86-87
NR
87
87-88
88
1
1
2
3
4
5
downwind industrial complex
composite of 2-7 samples
DRAFT-DO NOT QUOTE OR CITE
B-33
December 2003
-------
Table B-2. Environmental Levels of Dibenzofurans in Air (pg/m3) (continued)
Chemical
1,2,3,4,6,7,8-HpCDF
(continued)
1,2,3,4,7,8,9-HpCDF
Number
samples
NR
NR
1
3
2
1
1
1
1
1
1
1
7
1
6
5
7
4
2
2
20
4
14
3
2
16
53
NR
Number
positive
samples
NR
NR
1
3
2
1
1
1
1
1
1
1
3
1
4
3
1
3
1
1
20
4
13
2
2
NR
53
NR
Concentration
range
NR
NR
0.09
0.22-0.25
0.20-0.47
0.087
0.22
0.087
0.055
0.028
0.011
0.0035
ND-1.20
0.820
ND-1.10
ND-0.47
ND-0.13
ND-1.58
ND-0.20
ND-0.13
0.012-0.164
0.012-0.035
ND-0.429
ND-0.1
0.609-1.27
NR
0.0037-0.064
NR
Cone.
mean
0.12
0.02
0.09
0.24
0.34
0.087
0.22
0.087
0.055
0.028
0.011
0.0035
0.211
0.820
0.276
0.254
0.0746
0.497
0.110
0.0733
0.0709
0.0228
0.165
0.0612
0.94
0.76
0.02
0.01
Location
range
Rutland, VT
Durham, NC
Stockholm, Sweden
Akron, OH
Columbus, OH
Columbus, OH
Waldo, OH
Stockholm
Stockholm
Stockholm
Stockholm
Bloomington
Reseda, CA
Commerce, CA
North Long Beach, CA
San Bernadino, CA
El Toro, CA
Cal. Transit, CA
Carson, CA
West Long Beach, CA
Connecticut
Connecticut
Franklin County, OH
Franklin County, OH
Franklin County, OH
Phoenix, AZ
Various U.S. Sites
Greenbay, WI
Location
description
Urban
Urban
Urban
Industrial
Industrial
Urban
Rural
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Industrial
Urban
Urban
Rural
Urban
Rural
Industrial
Urban
Rural
Urban
Sample
year
NR
NR
89
87
87
87
87
89
89
89
89
86
87-89
87
87-89
87-89
87-88
88-89
88-89
88-89
93-94
93-94
95
95
95
94
98-99
NR
Ref.
no.
6
6
13
8
8
8
8
14
14
14
14
15
10
10
10
10
10
10
10
10
11
11
12
12
12
16
17
2
Comments
near incinerators
near incinerators
next to interstate highway
background site
mostly residential
near freeway
mostly residential
mostly residential
mostly residential
near highway
on site at gas cooking equipment manufacturer
mostly residential
near resource recovery facilities
near resource recovery facilities
near waste to energy facility
close proximity to a heavily traveled road
background
DRAFT-DO NOT QUOTE OR CITE
B-34
December 2003
-------
Table B-2. Environmental Levels of Dibenzofurans in Air (pg/m3) (continued)
Chemical
Number
samples
1
7
28
NR
Number
positive
samples
0
NR
12
NR
Concentration
range
ND(0.02)
ND-0.07
ND-0.30
ND(O.Ol)
Cone.
mean
NA
0.03
0.03
NA
Location
range
Los Angeles, CA
Bridgeport, CT
Wallingford, CT
Rutland, VT
Location
description
Urban
Urban
Urban
Urban
Sample
year
87
87-88
88
NR
Ref.
no.
3
4
5
6
Comments
composite of 2-7 samples
DRAFT-DO NOT QUOTE OR CITE
B-35
December 2003
-------
Table B-2. Environmental Levels of Dibenzofurans in Air (pg/m3) (continued)
Chemical
1,2,3,4,7,8,9-HpCDF
(continued)
HpCDFs
Number
samples
NR
1
3
2
1
1
1
1
1
1
1
7
1
6
5
7
4
2
2
20
4
14
3
2
16
53
16
16
Number
positive
samples
NR
0
1
0
0
1
1
1
1
1
1
0
1
1
0
0
2
0
0
19
4
13
2
2
NR
53
12
12
Concentration
range
ND(O.Ol)
ND(0.001)
ND-0.031
ND(0.015-0.028)
ND(0.013)
0.019
0.004
0.0033
0.0014
0.0003
0.0001
ND(0.0062-0.11)
0.0920
ND-0.11
ND(0.030-0.12)
ND(0.017-0.10)
ND-0.14
ND(0. 024-0. 040)
ND(0.015-0.043)
ND-0.029
0.001-0.006
ND-0.09
ND-0.0188
0.082-0.18
NR
ND(0.000091)-0.011
ND-0.43
ND-8.76
Cone.
mean
NA
NA
0.020
NA
NA
0.019
0.004
0.0033
0.0014
0.0003
0.0001
NA
0.0920
0.0397
NA
NA
0.0606
NA
NA
0.0104
0.0035
0.032
0.0143
0.13
0.10
0.0025
0.13
1.67
Location
range
Durham, NC
Stockholm, Sweden
Akron, OH
Columbus, OH
Columbus, OH
Waldo, OH
Stockholm
Stockholm
Stockholm
Stockholm
Bloomington, IN
Reseda, CA
Commerce, CA
North Long Beach, CA
San Bernadino, CA
El Toro, CA
Cal. Transit, CA
Carson, CA
West Long Beach, CA
Connecticut
Connecticut
Franklin County, OH
Franklin County, OH
Franklin County, OH
Phoenix, AZ
Various U.S. Sites
Niagra Falls, NY
Niagra Falls, NY
Location
description
Urban
Urban
Industrial
Industrial
Urban
Rural
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Industrial
Urban
Urban
Rural
Urban
Rural
Industrial
Urban
Rural
Urban
Industrial
Sample
year
NR
89
87
87
87
87
89
89
89
89
86
87-89
87
87-89
87-89
87-88
88-89
88-89
88-89
93-94
93-94
95
95
95
94
98-99
86-87
86-87
Ref.
no.
6
13
8
8
8
8
14
14
14
14
15
10
10
10
10
10
10
10
10
11
11
12
12
12
16
17
1
1
Comments
near incinerators
near incinerators
next to interstate highway
background site
mostly residential
near freeway
mostly residential
mostly residential
mostly residential
near highway
on site at gas cooking equipment manufacturer
mostly residential
near resource recovery facilities
near resource recovery facilities
near waste to energy facility
close proximity to a heavily traveled road
background
downwind industrial complex
DRAFT-DO NOT QUOTE OR CITE
B-36
December 2003
-------
Table B-2. Environmental Levels of Dibenzofurans in Air (pg/m3) (continued)
Chemical
HpCDFs (continued)
Number
samples
7
28
1
2
2
3
2
1
1
3
1
2
2
1
1
1
1
7
1
6
5
7
4
2
2
20
4
16
Number
positive
samples
NR
26
1
2
2
3
2
1
1
1
0
1
2
1
1
1
1
5
1
4
3
1
3
1
1
20
4
NR
Concentration
range
ND-1.0
ND-1.58
0.11
0.1-1.2
2-5
0.37-0.39
0.26-0.64
0.15
0.29
ND-0.65
ND(0.14)
ND(0.41)-0.07
0.13-0.26
0.11
0.081
0.036
0.015
ND-1.40
1.80
ND-1.20
ND-0.66
ND-0.13
ND-2.25
ND-0.33
ND-0.30
0.022-0.314
0.020-0.070
NR
Cone.
mean
0.37
0.47
0.11
0.65
3.5
0.38
0.45
0.15
0.29
0.312
NA
0.138
0.195
0.11
0.081
0.036
0.015
0.288
1.80
0.327
0.822
0.0860
0.724
0.174
0.161
0.128
0.042
1.33
Location
range
Bridgeport, CT
Wallingford, CT
Stockholm, Sweden
Hamburg, Germany
Hamburg, Germany
Akron, OH
Columbus, OH
Columbus, OH
Waldo, OH
Albany, NY
Binghamton, NY
Utica, NY
Niagra Falls, NY
Stockholm
Stockholm
Stockholm
Stockholm
Reseda, CA
Commerce, CA
North Long Beach, CA
San Bernadino, CA
El Toro, CA
Cal. Transit, CA
Carson, CA
West Long Beach, CA
Connecticut
Connecticut
Phoenix, AZ
Location
description
Urban
Urban
Urban
Urban
Industrial
Industrial
Industrial
Urban
Rural
Urban
Urban
Urban
Industrial
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Industrial
Urban
Urban
Rural
Urban
Sample
year
87-88
88
89
NR
NR
87
87
87
87
87-88
88
88
87
89
89
89
89
87-89
87
87-89
87-89
87-88
88-89
88-89
88-89
93-94
93-94
94
Ref.
no.
4
5
13
7
7
8
8
8
8
9
9
9
9
14
14
14
14
10
10
10
10
10
10
10
10
11
11
16
Comments
composite of 2-7 samples
urban air & inside traffic tunnel
downwind incinerator & industrial complex
near incinerators
near incinerators
next to interstate highway
background site
mostly residential
near freeway
mostly residential
mostly residential
mostly residential
near highway
on site at gas cooking equipment manufacturer
mostly residential
near resource recovery facilities
near resource recovery facilities
close proximity to a heavily traveled road
DRAFT-DO NOT QUOTE OR CITE
B-37
December 2003
-------
Table B-2. Environmental Levels of Dibenzofurans in Air (pg/m3) (continued)
Chemical
Number
samples
Number
positive
samples
Concentration
range
Cone.
mean
Location
range
Location
description
Sample
year
Ref.
no.
Comments
Octachlorodibenzofiirans (MW =444. 76)
1,2,3,4,6,7,8,9-OCDF
16
15
NR
1
7
28
NR
NR
1
2
2
3
2
1
1
3
1
2
2
1
1
1
1
6
11
8
NR
1
NR
18
NR
NR
1
0
2
3
1
0
1
1
0
0
2
i
i
i
i
3
ND-0.22
ND-3.38
NR
0.06
ND-0.56
ND-0.70
NR
NR
0.02
ND(0. 11-1.0)
0.78-7.0
0.17-0.19
ND-0.21
ND(0.16)
0.077
ND(0.93)-0.3
ND(0.3)
ND(0.12-1.10)
0.12-0.18
0.016
0.0072
0.003
0.0009
ND-0.13
0.09
0.62
0.07
0.06
0.21
0.21
0.14
0.03
0.02
NA
3.89
0.18
0.18
NA
0.077
0.312
NA
NA
0.15
0.016
0.0072
0.003
0.0009
0.116
Niagra Falls, NY
Niagra Falls, NY
Greenbay, WI
Los Angeles, CA
Bridgeport, CT
Wallingford, CT
Rutland, VT
Durham, NC
Stockholm, Sweden
Hamburg, Germany
Hamburg, Germany
Akron, OH
Columbus, OH
Columbus, OH
Waldo, OH
Albany, NY
Binghamton, NY
Utica, NY
Niagra Falls, NY
Stockholm
Stockholm
Stockholm
Stockholm
Reseda, CA
Urban
Industrial
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Industrial
Industrial
Industrial
Urban
Rural
Urban
Urban
Urban
Industrial
Urban
Urban
Urban
Urban
Urban
86-87
86-87
NR
87
87-88
88
NR
NR
89
NR
NR
87
87
87
87
87-88
88
88
87
89
89
89
89
87-89
1
1
2
3
4
5
6
6
13
7
7
8
8
8
8
9
9
9
9
14
14
14
14
10
downwind industrial complex
composite of 2-7 samples
urban air & inside traffic tunnel
downwind incinerator & industrial complex
near incinerators
near incinerators
next to interstate highway
background site
mostly residential
DRAFT-DO NOT QUOTE OR CITE
B-38
December 2003
-------
Chemical
1,2,3,4,6,7,8,9-OCDF
(continued)
Number
samples
5
5
7
4
2
2
20
4
14
3
2
16
53
Number
positive
samples
2
3
4
2
i
2
20
4
14
3
2
NR
53
Concentration
range
ND-0.43
ND-0.29
ND-0.13
ND-2.17
ND-0.12
0.32-0.43
0.009-0.179
0.011-0.028
0.038-0.366
0.0403-0.0899
0.261-0.56
NR
0.0022-0.052
Cone.
mean
0.152
0.162
0.0756
0.662
0.164
0.374
0.0551
0.0193
0.14
0.0668
0.411
0.344
0.017
Location
range
North Long Beach, CA
San Bernadino, CA
El Toro, CA
Cal. Transit, CA
Carson, CA
West Long Beach, CA
Connecticut
Connecticut
Franklin County, OH
Franklin County, OH
Franklin County, OH
Phoenix, AZ
Various U.S. Sites
Location
description
Urban
Urban
Urban
Urban
Industrial
Urban
Urban
Rural
Urban
Rural
Industrial
Urban
Rural
Sample
year
87-89
87-89
87-88
88-89
88-89
88-89
93-94
93-94
95
95
95
94
98-99
Ref.
no.
10
10
10
10
10
10
11
11
12
12
12
16
17
Comments
mostly residential
mostly residential
mostly residential
near highway
on site at gas cooking equipment manufacturer
mostly residential
near resource recovery facilities
near resource recovery facilities
near waste-to-energy facility
close proximity to a heavily traveled road
background
Footnote References
NOTES: Summary statistics provided in or derived from references; when reference did not compute mean, it was computed using one-half the detection limit for non-detects;
NA = Not available;
NR = Not reported;
ND = Non-detect.
Sources:
1. Smith etal. (1989)
2. Harlessetal. (1990)
3. Maisel and Hunt (1990)
4. Hunt and Maisel (1990)
5. CDEP(1988)
6. Harless et al. (1991)
7. Rappe and Kjeller (1987)
8. Edgerton et al. (1989)
9. Smith etal. (1990)
10. Hunt etal. (1990)
11. CDEP(1995)
12. OEPA (1995)
13. Nafetal. (1990)
14. Broman et al. (1991)
15. Eitzer and Hites (1989)
16. Hunt etal. (1997)
17. Cleverly et al. (2000)
DRAFT-DO NOT QUOTE OR CITE
B-39
December 2003
-------
Table B-3. Environmental Levels of PCBs in Air (pg/m3)
IUPAC
Number Chemical
Number
samples
Number
positive
samples
Concentration
range
Cone.
mean
Location
Location
description
Sample
year
Ref.
no.
Comments
Tetrachloro-PCB
77 3,3'4,4'-TeCB
53
52
0.0051-0.14
0.054
Various U.S. Sites
Rural
98-99
2
Pentachloro-PCB
118 2,3',4,4',5-PeCB
114 2,3,4,4',5-PeCB
105 2,3,3',4,4'-PeCB
118 2,3',4,4',5-PeCB
105 2,3,3',4,4'-PeCB
126 3,3',4,4',5-PeCB
143
143
143
53
53
53
143
143
143
50
50
52
NR
NR
NR
0.1-4
0.039-1.3
0.00088-0.04
2.3
1.2
0.16
0.82
0.3
0.0062
Egbert, ON
Egbert, ON
Egbert, ON
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
Rural
Rural
Rural
Rural
Rural
Rural
88-89
88-89
88-89
98-99
98-99
98-99
1
1
1
2
2
2
Hexachloro-PCB
156 2,3,3',4,4',5-HxCB
156 2,3,3',4,4',5-HxCB
157 2,3,3',4,4',5'-HxCB
169 3,3',4,4',5,5'-HxCB
143
53
53
53
143
50
50
52
NR
0.0075-0.24
0.0016-0.051
ND(0.00006)-0.0036
0.07
0.05
0.011
0.00068
Egbert, ON
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
Rural
Rural
Rural
Rural
88-89
98-99
98-99
98-99
1
2
2
2
HeptacWoro-PCB
1892,3,3',4,4',5,5'-HpCB
143
143
NR
0.01
Egbert, ON
Rural
88-89
i
Footnote References
NOTES: Summary statistics provided in or derived from references; when reference did not compute mean, it was computed using one-half the detection limit for non-detects;
NA = not available;
NR = not reported;
ND = Non-detect.
Sources:
1. Hoff et al. (1992)
2. Cleverly etal. (2000)
DRAFT-DO NOT QUOTE OR CITE
B-34
December 2003
-------
Table B-4. Environmental Levels of Dioxins in Soil (ppt)
Chemical
Number
samples
Number
positive
samples
Concentration
range
Cone.
mean
Location
Location
description
Sample
year
Ref.
no.
Comments
Tetrachlorodibenzo-p-dioxins (MW=321 .98)
2,3,7,8-TCDD
23
62
13
22
4
NR
33
11
20
8
4
12
19
3
2
65
NR
NR
NR
NR
13
5
6
53
23
59
1
6
0
NR
33
9
13
4
0
12
6
0
2
NR
NR
NR
NR
NR
0
0
0
0
10-36000
ND-270
ND-2
ND-5
ND(l.O)
NR
22-52000
ND-590
ND-9.4
ND-3.1
ND
1-7
ND-4.2
ND(0.2-2.0)
2.4-0.84
ND-2.1
200000
2800
900
874000
ND
ND
ND
ND
2133
55
<1
1
NA
2
4300
145
2
2
NA
3
1
NA
1.62
<0.5
NR
NR
NR
NR
NA
NA
NA
NA
Midland, MI
Midland, MI
Henry, IL
Middletown, OH
MN
Finland
Midland, MI
Midland, MI
US
Sweden
Elk River, MN
England
England
Various parts of Europe
Various parts of Europe
British Isles
Muggenburger st.
Hamburg , Germany
Kirchsteinbek,
Hamburg , Germany
Ochsenwerder
Landscheideweg,
Hamburg , Germany
Moorefleeter Brack
Hamburg , Germany
Salzburg, Austria
Salzburg, Austria
Salzburg, Austria
British Columbia, Canada
Industrial
Residential
Residential
Residential
Pristine
Industrial
Industrial
Industrial
Industrial
Urban
Rural
Residential
Urban
Rural
Industrial
Background
Industrial
Industrial
Contaminated site
Contaminated site
Urban
Industrial
Rural
Background
1983
1983
1984
1983
1983
NR
NR
NR
NR
1989
1988
1990
NR
NR
NR
NR
1985
1985
1985
1985
1990/91
1990/91
1990/91
NR
1
1
1
1
1
2
3
3
3
4
5
6
7
8
8
9
10
10
10
10
11
11
11
12
Urban area
Near Stockholm
Agriculture
Rural
Maximum contents reported
Maximum contents reported
Maximum contents reported
Maximum contents reported
DRAFT-DO NOT QUOTE OR CITE
B-35
December 2003
-------
Table B-4. Environmental Levels of Dioxins in Soils (ppt) (continued)
Chemical
2,3,7,8-TCDD
(continued)
TCDDs
Number
samples
31
47
14
7
9
11
3
4
18
34
8
162
1
7
5
3
NR
11
12
43
29
8
4
12
19
2
1
65
Number
positive
samples
8
9
NR
NR
NR
NR
2
4
15
15
3
161
1
5
0
0
NR
NR
NR
0
NR
8
0
12
19
2
i
NR
Concentration
range
ND-85
ND-550
ND(0.2)
ND(0.2)
0.5-3.0
ND-4.0
ND-0.57
4.2-56.99
ND-10.91
0.02-7.96
ND-1.1
0.0011-20
320
ND-290
ND(l.O)
ND(l.O)
NR
ND-7
ND-430
ND
ND-1200
37-217
ND
17-120
9-160
11.2-55.5
3.2
ND-69
Cone.
mean
5.18
15.44
NA
NA
1.4
92
0.393
28.520
2.273
0.61
0.28
0.61
320
109
NA
NA
89
<1
69
NA
69
98
NA
42
65
33.4
3.2
9.4
Location
British Columbia, Canada
British Columbia, Canada
Western Germany
Western Germany
Western Germany
Western Germany
Ohio
Ohio
Ohio
Connecticut
Yarmouth, ME
Denver Front Range
Midland, MI
Midland, MI
Middletown, OH
MN
Finland
Canada
Canada
Canada
Canada
Sweden
Elk River, MN
England
England
Various parts of Europe
Various parts of Europe
British Isles
Location
description
Near Impacted site
Impacted site
Background
Background
Background
Background
Background
Impacted site
Urban
Urban
Background
Various Land Uses
Industrial
Residential
Residential
Pristine
Industrial
Urban
Urban
Rural
Urban
Urban
Agriculture
Residential
Urban
Industrial
Rural
Background
Sample
year
NR
NR
NR
NR
NR
NR
1995
1995
1995
1987/90
1996
99-00
1983
1983
1983
1983
NR
1983
1987
83-88
83-88
1989
1988
1990
NR
NR
NR
NR
Ref.
no.
12
12
13
13
13
13
14
14
14
17
18
19
1
1
1
1
2
15
15
15
15
4
5
6
7
8
8
10
Comments
Plowland
Grassland
Deciduous forest
Coniferous forest
Near waste-to-energy facility
Pre-operational
background
Near Incinerator
Near Incinerator
Near Stockholm
Rural
DRAFT-DO NOT QUOTE OR CITE
B-36
December 2003
-------
Table B-4. Environmental Levels of Dioxins in Soils (ppt) (continued)
Chemical
TCDDs (continued)
Number
samples
30
20
47
13
5
6
53
31
47
14
7
9
11
34
8
Number
positive
samples
0
0
11
2
4
1
19
11
19
NR
NR
NR
NR
28
5
Concentration
range
ND
ND
ND-430
0-1.6
ND-9.8
ND-1.8
ND-240
ND-1100
ND-11000
0.4-3.4
0.9-6.9
16.1-235.8
19-392.1
0.09-33.3
ND-5.17
Cone.
mean
NA
NA
40.3
0.75
4.8
0.3
12.48
71.93
1009.58
2
2.8
70.5
92
3.63
1.95
Location
Ontario and U.S. Midwestern
States
Ontario and U.S. Midwestern
States
Ontario and U.S. Midwestern
States.
Salzburg, Austria
Salzburg, Austria
Salzburg, Austria
British Columbia, Canada
British Columbia, Canada
British Columbia, Canada
Western Germany
Western Germany
Western Germany
Western Germany
Connecticut
Yarmouth, ME
Location
description
Rural
Industrial
Urban
Urban
Industrial
Rural
Background
Near Impacted site
Impacted site
Background
Background
Background
Background
Urban
Background
Sample
year
NR
NR
NR
1990/91
1990/91
1990/91
NR
NR
NR
NR
NR
NR
NR
1987/90
1996
Ref.
no.
16
16
16
11
11
11
12
12
12
13
13
13
13
17
18
Comments
Plowland
Grassland
Deciduous forest
Coniferous forest
Pre-operational
Pentachlorodibenzo-p-dioxins (MW = 356 . 42)
1,2,3,7,8-PeCDD
NR
8
4
19
3
2
65
13
5
6
53
NR
7
0
7
0
2
NR
2
i
0
3
NR
2.6-18.3
ND
ND-11
ND(0.1-2)
18-34
ND-2.4
1.1-4.6
ND-1.9
ND
ND-4.4
15
10
NA
2
NA
26
<0.5
0.4
0.4
NA
0.16
Finland
Sweden
Elk River, MN
England
various parts of Europe
various parts of Europe
British Isles
Salzburg, Austria
Salzburg, Austria
Salzburg, Austria
British Columbia, Canada
Industrial
Urban
Rural
Urban
Rural
Industrial
Background
Urban
Industrial
Rural
Background
NR
1989
1988
NR
NR
NR
NR
1990/91
1990/91
1990/91
NR
2
4
5
7
8
8
9
11
11
11
12
Near Stockholm
Agriculture
DRAFT-DO NOT QUOTE OR CITE
B-37
December 2003
-------
Table B-4. Environmental Levels of Dioxins in Soils (ppt) (continued)
Chemical
1,2,3,7,8-PeCDD
(continued)
PeCDDs
Number
samples
31
47
14
7
9
11
3
4
18
34
8
162
1
7
5
3
NR
11
12
43
29
8
4
12
19
1
2
65
Number
positive
samples
5
17
NR
NR
NR
NR
0
4
18
34
4
160
1
2
0
0
NR
NR
NR
0
NR
8
1
12
19
1
2
NR
Concentration
range
ND-670
ND-410
ND(0.4)
0.4-0.4
1.1-29.1
ND-8.9
ND
21.52-393.18
3.2-24.62
0.04-15.0
ND-0.98
0.0017-54
240
ND-120
ND(10)
ND(4.0)
NR
ND-580
ND-540
ND
ND-130
46-476
ND-38
4-50
6-190
4.6
220-270
ND-46
Cone.
mean
45.94
40.28
NA
0.4
8.3
5.1
NA
180.008
6.584
1.74
0.43
1.4
240
31
NA
NA
900
53
81
NA
12.8
159
10
20
69
4.6
245
6.6
Location
British Columbia, Canada
British Columbia, Canada
Western Germany
Western Germany
Western Germany
Western Germany
Ohio
Ohio
Ohio
Connecticut
Yarmouth, ME
Denver Front Range
Midland, MI
Midland, MI
Middletown, OH
MN
Finland
Canada
Canada
Canada
Canada
Sweden
Elk River, MN
England
England
various parts of Europe
various parts of Europe
British Isles
Location
description
Near Impacted site
Impacted site
Background
Background
Background
Background
Background
Impacted site
Urban
Urban
Background
Various Land Uses
Industrial
Residential
Residential
Pristine
Industrial
Urban
Urban
Rural
Urban
Urban
Rural
Residential
Urban
Rural
Industrial
Background
Sample
year
NR
NR
NR
NR
NR
NR
1995
1995
1995
1987/90
1996
99-00
1983
1983
1983
1983
NR
1983
1987
83-88
83-88
1989
1988
1990
NR
NR
NR
NR
Ref.
no.
12
12
13
13
13
13
14
14
14
17
18
19
1
1
1
1
2
15
15
15
15
4
5
6
7
8
8
9
Comments
Plowland
Grassland
Deciduous forest
Coniferous forest
Near waste-to-energy facility
Pre-operational
background
Near Incinerator
Near Incinerator
Near Stockholm
Agriculture
Rural
DRAFT-DO NOT QUOTE OR CITE
B-38
December 2003
-------
Table B-4. Environmental Levels of Dioxins in Soils (ppt) (continued)
Chemical
PeCDDs (continued)
Number
samples
47
30
20
13
5
6
53
31
47
14
7
9
11
35
8
Number
positive
samples
7
0
0
7
4
5
16
8
25
NR
NR
NR
NR
31
6
Concentration
range
ND-580
ND
ND
0.8-36.3
ND-15.6
ND-4.9
ND-190
ND-4700
ND-9100
1.2-5.3
0.9-23.8
19.5-285.2
32.6-192.4
0.16-108
ND-5.7
Cone.
mean
38.4
NA
NA
4.2
7.3
0.8
8.96
358.61
1049.05
2.3
6.1
98.1
91.1
11.73
3.0
Location
Ontario and U.S. Midwestern
States
Ontario and U.S. Midwestern
States
Ontario and U.S. Midwestern
States
Salzburg, Austria
Salzburg, Austria
Salzburg, Austria
British Columbia, Canada
British Columbia, Canada
British Columbia, Canada
Western Germany
Western Germany
Western Germany
Western Germany
Connecticut
Yarmouth, ME
Location
description
Urban
Rural
Industrial
Urban
Industrial
Rural
Background
Near Impacted site
Impacted site
Background
Background
Background
Background
Urban
Background
Sample
year
NR
NR
NR
1990/91
1990/91
1990/91
NR
NR
NR
NR
NR
NR
NR
1987/90
1996
Ref.
no.
16
16
16
11
11
11
12
12
12
13
13
13
13
17
18
Comments
Plowland
Grassland
Deciduous forest
Coniferous forest
Pre-operational
Hexachlorodibenzo-p-dioxins (MW = 390 . 87)
1,2,3,4,7,8-HxCDD
NR
8
4
3
2
13
5
6
53
31
47
NR
8
0
0
2
8
1
2
3
8
17
NR
4.3-8.0
ND
ND(0.1-2)
13-28
ND-3.2
ND-0.8
0.7-1.1
ND-6.7
ND-420
ND-490
<2
6
NA
NA
21
0.8
0.2
0.3
0.2
37.14
56.99
Finland
Sweden
Elk River, MN
various parts of Europe
various parts of Europe
Salzburg, Austria
Salzurg, Austria
Salzburg, Austria
British Columbia, Canada
British Columbia, Canada
British Columbia, Canada
Industrial
Urban
Rural
Rural
Industrial
Urban
Industrial
Rural
Background
Near Impacted site
Impacted site
NR
1989
1988
NR
NR
1990/91
1990/91
1990/91
NR
NR
NR
2
4
5
8
8
11
11
11
12
12
12
Near Stockholm
Agriculture
DRAFT-DO NOT QUOTE OR CITE
B-39
December 2003
-------
Table B-4. Environmental Levels of Dioxins in Soils (ppt) (continued)
Chemical
1,2,3,4,7,8-HxCDD
(continued)
1,2,3,6,7,8-HxCDD
Number
samples
14
7
9
11
3
4
18
35
8
162
NR
8
4
3
2
13
5
6
53
31
47
14
7
9
11
3
4
18
35
Number
positive
samples
NR
NR
NR
NR
1
4
18
35
7
161
NR
8
1
0
2
9
2
2
28
16
34
NR
NR
NR
NR
3
4
18
34
Concentration
range
0.8-1.4
ND(0.8)
1.5-20.9
2.1-14.0
ND-0.74
20.61-297.59
2.2-21.94
0.08-8.49
ND-2.6
0.0049-65
NR
3.3-32.2
ND-14
ND(0.1-2)
19-64
ND-5.6
ND-2.3
1.1-1.9
ND-185
ND-10000
ND-8600
1.1-1.8
1.4-2.9
3.1-49.4
3.7-28.8
0.52-1.39
18.15-295.49
1.2-40.17
0.25-17.3
Cone.
mean
1.2
NA
6.5
5.8
0.350
142.287
6.144
1.29
1.22
2.5
2100
12
4
NA
42
1.7
1.2
0.5
13.13
777.70
680.58
1.5
1.9
12.4
11.1
0.817
137.798
10.940
2.66
Location
Western Germany
Western Germany
Western Germany
Western Germany
Ohio
Ohio
Ohio
Connecticut
Yarmouth, ME
Denver Front Range
Finland
Sweden
Elk River, MN
various parts of Europe
various parts of Europe
Salzburg, Austria
Salzburg, Austria
Salzburg, Austria
British Columbia, Canada
British Columbia, Canada
British Columbia, Canada
Western Germany
Western Germany
Western Germany
Western Germany
Ohio
Ohio
Ohio
Connecticut
Location
description
Background
Background
Background
Background
Background
Impacted site
Urban
Urban
Background
Various Land Uses
Industrial
Urban
Rural
Rural
Industrial
Urban
Industrial
Rural
Background
Near Impacted site
Impacted site
Background
Background
Background
Background
Background
Impacted site
Urban
Urban
Sample
year
NR
NR
NR
NR
1995
1995
1995
1987/90
1996
99-00
NR
1989
1988
NR
NR
1990/91
1990/91
1990/91
NR
NR
NR
NR
NR
NR
NR
1995
1995
1995
1987/90
Ref.
no.
13
13
13
13
14
14
14
17
18
19
2
4
5
8
8
11
11
11
12
12
12
13
13
13
13
14
14
14
17
Comments
Plowland
Grassland
Deciduous forest
Coniferous forest
Near waste-to-energy facility
Pre-operational
background
Near Stockholm
Agriculture
Plowland
Grassland
Deciduous forest
Coniferous forest
Near waste-to-energy facility
Pre-operational
DRAFT-DO NOT QUOTE OR CITE
B-40
December 2003
-------
Table B-4. Environmental Levels of Dioxins in Soils (ppt) (continued)
Chemical
1,2,3,6,7,8-HxCDD
(continued)
1,2,3,7,8,9-HxCDD
HxCDDs
Number
samples
8
162
NR
8
4
3
2
13
5
6
53
31
47
14
7
9
11
3
4
18
35
8
162
1
8
5
3
NR
11
Number
positive
samples
8
161
NR
7
2
0
2
7
2
1
16
13
28
NR
NR
NR
NR
3
4
18
34
8
161
1
6
1
0
NR
NA
Concentration
range
1.3-5.8
0.0044-200
NR
ND-16.6
ND-9.9
ND(0.1-2)
6.2-19
ND-4.6
1.6-2.5
ND-2.1
ND-55.5
ND-1200
ND-2700
1.6-2.4
1.7-1.7
3.6-82.0
5.3-54.3
0.75-2.11
96.1-422.85
1.47-36.59
0.28-15.70
0.97-3.10
0.0047-120
4000
ND-410
ND-72
ND(5.0)
NR
ND-170
Cone.
mean
2.89
6.5
700
8
5
NA
13
1.1
1.1
0.4
3.42
99.24
228.74
2.0
1.7
19.1
16.2
1.23
201.608
10.839
2.75
2.41
4
4000
151
14
NA
7200
15
Location
Yarmouth, ME
Denver Front Range
Finland
Sweden
Elk River, MN
various parts of Europe
various parts of Europe
Salzburg, Austria
Salzburg, Austria
Salzburg, Austria
British Columbia, Canada
British Columbia, Canada
British Columbia, Canada
Western Germany
Western Germany
Western Germany
Western Germany
Ohio
Ohio
Ohio
Connecticut
Yarmouth, ME
Denver Front Range
Midland, MI
Midland, MI
Middletown, OH
MN
Finland
Canada
Location
description
Background
Various Land Uses
Industrial
Urban
Rural
Rural
Industrial
Urban
Industrial
Rural
Background
Near Impacted site
Impacted site
Background
Background
Background
Background
Background
Impacted site
Urban
Urban
Background
Various Land Uses
Industrial
Residential
Residential
Pristine
Industrial
Urban
Sample
year
1996
99-00
NR
1989
1988
NR
NR
1990/91
1990/91
1990/91
NR
NR
NR
NR
NR
NR
NR
1995
1995
1995
1987/90
1996
99-00
1983
1983
1983
1983
NR
1983
Ref.
no.
18
19
2
4
5
8
8
11
11
11
12
12
12
13
13
13
13
14
14
14
17
18
19
1
1
1
1
2
15
Comments
background
Near Stockholm
Agriculture
Plowland
Grassland
Deciduous forest
Coniferous forest
Near waste-to-energy facility
Pre-operational
background
Near Incinerator
DRAFT-DO NOT QUOTE OR CITE
B-41
December 2003
-------
Table B-4. Environmental Levels of Dioxins in Soils (ppt) (continued)
Chemical
Number
samples
12
43
Number
positive
samples
NA
0
Concentration
range
ND-70
ND
Cone.
mean
9
NA
Location
Canada
Canada
Location
description
Urban
Rural
Sample
year
1987
83-88
Ref.
no.
15
15
Comments
Near Incinerator
DRAFT-DO NOT QUOTE OR CITE
B-42
December 2003
-------
Table B-4. Environmental Levels of Dioxins in Soils (ppt) (continued)
Chemical
HxCDDs (continued)
Number
samples
29
8
4
12
19
1
2
65
47
30
20
13
5
6
53
31
47
14
7
9
11
35
7
Number
positive
samples
0
8
4
12
19
1
2
NR
13
0
8
13
5
5
37
18
39
NR
NR
NR
NR
34
7
Concentration
range
ND
43-349
12-99
8-43
23-340
4.7
200-330
2.8-165
ND-410
ND
ND-240
4.8-53
20.3-48.0
ND-44.3
ND-1250
ND-29000
ND-36000
2-13.1
3.9-26.0
39.9-901.0
55.7-397.0
0.29-170.0
9.4-30
Cone.
mean
NA
156
48
23
154
4.7
265
38
38.12
NA
44.1
18.8
30.3
12.0
82.46
2794.7
3393.51
7.0
14.9
202.0
156.2
28.7
19.6
Location
Canada
Sweden
Elk River, MN
England
England
Various parts of Europe
Various parts of Europe
British Isles
Ontario and U.S. Midwestern
States
Ontario and U.S. Midwestern
States
Canada and U.S.A.
Salzburg, Austria
Salzburg, Austria
Salzburg, Austria
British Columbia, Canada
British Columbia, Canada
British Columbia, Canada
Western Germany
Western Germany
Western Germany
Western Germany
Connecticut
Yarmouth, ME
Location
description
Urban
Urban
Rural
Residential
Urban
Rural
Industrial
Background
Urban
Rural
Industrial
Urban
Industrial
Rural
Background
Near Impacted site
Impacted site
Background
Background
Background
Background
Urban
Background
Sample
year
83-88
1989
1988
1990
NR
NR
NR
NR
NR
NR
NR
1990/91
1990/91
1990/91
NR
NR
NR
NR
NR
NR
NR
1987/90
1996
Ref.
no.
15
4
5
6
7
8
8
9
16
16
16
11
11
11
12
12
12
13
13
13
13
17
18
Comments
Near Stockholm
Agriculture
Rural
Plowland
Grassland
Deciduous forest
Coniferous forest
Pre-operational
Heptachlorodibenzo-p-dioxins (MW = 425 .31)
1,2,3,4,6,7,8-HpCDD
NR
8
4
13
NR
8
4
13
NR
43-492
37-360
5.9-121.8
4700
144
194
30.3
Finland
Sweden
Elk River, MN
Salzburg, Austria
Industrial
Urban
Rural
Urban
NR
1989
1988
1990/91
2
4
5
11
Near Stockholm
Agriculture
DRAFT-DO NOT QUOTE OR CITE
B-43
December 2003
-------
Table B-4. Environmental Levels of Dioxins in Soils (ppt) (continued)
Chemical
1,2,3,4,6,7,8-HpCDD
(continued)
1,2,3,4,6,7,9-HpCDD
HpCDDs
Number
samples
5
6
53
31
47
14
7
9
11
3
4
18
35
8
160
NR
1
8
5
3
11
12
43
29
8
4
12
19
3
Number
positive
samples
5
6
43
22
42
NR
NR
NR
NR
3
4
18
35
8
161
NR
1
88
5
3
NR
NR
0
NR
8
4
12
19
1
Concentration
range
13.6-92.0
1.8-24.6
ND-2100
ND-37000
ND-530000
4.1-21.9
7.1-34.8
22.8-398.7
35.9-271.7
9.4-31.6
139.24-1508.86
11.69-902.55
2.34-270
12.9-160
0.36-3,700
NR
75000
150-2400
23-200
25-91
ND-390
ND-300
ND
ND-1100
83-904
62-640
20-130
77-5500
ND-17
Cone.
mean
30.6
8.1
141.3
3589.22
16877.14
9.1
14.6
120.7
109.0
17.773
765.160
190.081
55.3
68.1
170
7100
75000
813
113
54
90
43
NA
93
277
346
64
817
9.0
Location
Salzburg, Austria
Salzburg, Austria
British Columbia, Canada
British Columbia, Canada
British Columbia, Canada
Western Germany
Western Germany
Western Germany
Western Germany
Ohio
Ohio
Ohio
Connecticut
Yarmouth, ME
Denver Front Range
Finland
Midland, MI
Midland, MI
Middletown, OH
MN
Canada
Canada
Canada
Canada
Sweden
Elk River, MN
England
England
Various parts of Europe
Location
description
Industrial
Rural
Background
Near Impacted site
Impacted site
Background
Background
Background
Background
Background
Impacted site
Urban
Urban
Background
Various Land Uses
Industrial
Industrial
Residential
Residential
Pristine
Urban
Urban
Rural
Urban
Urban
Rural
Residential
Urban
Rural
Sample
year
1990/91
1990/91
NR
NR
NR
NR
NR
NR
NR
1995
1995
1995
1987/90
1996
99-00
NR
1983
1983
1983
1983
1983
1987
83-88
83-88
1989
1988
1990
NR
NR
Ref.
no.
11
11
12
12
12
13
13
13
13
14
14
14
17
18
19
2
1
1
1
1
15
15
15
15
4
5
6
7
8
Comments
Plowland
Grassland
Deciduous forest
Coniferous forest
Near waste-to-energy facility
Pre-operational
background
Near Incinerator
Near Incinerator
Near Stockholm
Agriculture
Rural
DRAFT-DO NOT QUOTE OR CITE
B-44
December 2003
-------
Table B-4. Environmental Levels of Dioxins in Soils (ppt) (continued)
Chemical
HpCDDs (continued)
Number
samples
2
65
30
47
20
13
5
6
53
31
47
14
7
9
11
35
8
Number
positive
samples
2
NR
3
25
19
13
5
6
43
22
42
NR
NR
NR
NR
35
8
Concentration
range
370-1600
7.5-234
ND-91
ND-2400
ND-5000
10.3-197.1
24.0-166.0
3.5-45.3
ND-3350
ND-5200
ND-760000
3.2-26.3
11.1-62.9
45.0-1151.6
74.2-522.7
4.59-568
19.5-240
Cone.
mean
985
66
5.4
212
1197
51.1
57.3
15.0
233.29
5460.29
25253.48
14.2
25.7
275.3
219.9
121
118
Location
Various parts of Europe
British Isles
Ontario and U.S. Midwestern
States.
Ontario and U.S. Midwestern
States.
Canada and U.S.A.
Salzburg, Austria
Salzburg, Austria
Salzburg, Austria
British Columbia, Canada
British Columbia, Canada
British Columbia, Canada
Western Germany
Western Germany
Western Germany
Western Germany
Connecticut
Yarmouth, ME
Location
description
Industrial
Background
Rural
Urban
Industrial
Urban
Industrial
Rural
Background
Near Impacted site
Impacted site
Background
Background
Background
Background
Urban
Background
Sample
year
NR
NR
NR
NR
NR
1990/91
1990/91
1990/91
NR
NR
NR
NR
NR
NR
NR
1987/90
1996
Ref.
no.
8
9
16
16
16
11
11
11
12
12
12
13
13
13
13
17
18
Comments
Plowland
Grassland
Deciduous forest
Coniferous forest
Pre-operational
Octachlorodibenzo-p-dioxin (MW=460.76)
1,2,3,4,6,7,8,9-OCDD
1
8
5
3
NR
11
12
43
29
8
1
8
5
3
NR
NR
NR
NR
NR
8
375000
330-12000
170-10600
92-200
NR
ND-3500
ND-1500
ND-100
ND-16000
113-2659
375000
3914
2418
140
6200
663
570
38
2464
687
Midland, MI
Midland, MI
Middletown, OH
MN
Finland
Canada
Canada
Canada
Canada
Sweden
Industrial
Residential
Residential
Pristine
Industrial
Urban
Urban
Rural
Urban
Urban
1983
1983
1983
1983
NR
1983
1987
83-88
83-88
1989
1
1
1
1
2
15
15
15
15
4
Near Incinerator
Near Incinerator
Near Stockholm
DRAFT-DO NOT QUOTE OR CITE
B-45
December 2003
-------
Table B-4. Environmental Levels of Dioxins in Soils (ppt) (continued)
Chemical
1,2,3,4,6,7,8,9-OCDD
(continued)
Number
samples
4
12
19
1
2
65
30
47
20
3
4
18
35
8
162
Number
positive
samples
4
12
19
1
2
NR
17
38
20
3
4
18
35
8
161
Concentration
range
340-3300
20-150
176-99000
14
140-160
29-832
44-810
ND-12000
15-26000
75.76-298.32
653.25-1973.490
76.61-6995.43
20.4-4,970
145-4,000
0.14-18,000
Cone.
mean
1655
58
9980
14
160
191
67.33
1599
3442
160.893
1495.390
1560.161
814.3
967
900
Location
Elk River, MN
England
England
Various parts of Europe
Various parts of Europe
British Isles
Ontario and U.S. Midwestern
States.
Ontario and U.S. Midwestern
States.
Canada and U.S.A.
Ohio
Ohio
Ohio
Connecticut
Yarmouth, ME
Denver Front Range
Location
description
Rural
Residential
Urban
Rural
Industrial
Background
Rural
Urban
Industrial
Background
Impacted site
Urban
Urban
Background
Various Land Uses
Sample
year
1988
1990
NR
NR
NR
NR
NR
NR
NR
1995
1995
1995
1987/90
1996
99-00
Ref.
no.
5
6
7
8
8
9
16
16
16
14
14
14
17
18
19
Comments
Agriculture
Near waste-to-energy facility
Pre-operational
background
Footnote References
NOTES: Summary statistics provided in or derived from references; when reference did not compute mean, it was computed using one-half the detection limit for non-detects if congener-specific detection limits were given; when
detection limits were not given, zero was used for nondetects.
NA = Not applicable.
ND = Non-detect.
NR = Not reported.
Detection limits varied by study and as was different for different compounds, but generally were 1 to 5 ng/kg (ppt). Descriptions provided were those given by reference or surmised from study description when not given.
Sources:
1. U.S. EPA (1985)
2. Kitunen and Salkinoja-Salonen (1990)
3. Nestrick et al. (1986)
4. Bromanetal. (1990)
5. Reedetal. (1990)
6. Stenhouse and Badsha (1990)
7. Greaser et al. (1990)
8. Rappe and Kjeller (1987)
9. Greaser et al. (1989)
10. Sievers and Friesel (1989)
11. Boosetal. (1992)
12. BC Environment (1995)
13. Retard etal. (1994)
14. U.S. EPA (1996)
15. Pearson et al. (1990)
16. Birmingham (1990)
17. MRI (1992)
18. Tewhey Associates (1997)
19. U.S. EPA Region 8 (2000)
DRAFT-DO NOT QUOTE OR CITE
B-46
December 2003
-------
Table B-5. Environmental Levels of Dibenzofurans in Soil (ppt)
Chemical
Number
samples
Number
positive
samples
Concentration
range
Cone.
mean
Location
Location
description
Sample
year
Ref.
no.
Comments
Tetrachlorodibenzofiirans (MW =305. 98)
2,3,7,8-TCDF
2,3,4,8/2,3,7,8-TCDF
TCDFs
2
8
5
3
NR
4
12
13
5
6
53
31
47
14
7
9
11
3
4
18
34
8
162
8
1
1
NR
11
12
2
3
2
0
NR
0
12
7
3
1
28
16
23
NR
NR
NR
NR
0
4
18
33
4
161
8
0
0
NR
NR
0
27-450
ND-15
ND-6
ND(l.O)
NR
ND
3-50
ND-6.1
ND-12.0
ND-1.9
ND-32
ND-520
ND-550
0.7-3.4
0.7-3.6
7.2-67.8
10.0-60.6
ND
11.4-184.66
0.67-16.19
0.08-15.1
ND-1.2
0.00067-8.4
8.4-57.5
ND(2.0)
ND(l.O)
NR
ND-71
ND
238
5
2
NA
<2
NA
17
1.4
4.1
0.3
3.21
47.86
60.68
1.8
2.2
25.4
27.9
NA
85.892
4.118
2.29
0.38
0.38
22
NA
NA
300
10
NA
Midland, MI
Midland, MI
Middletown, OH
MN
Finland
Elk River, MN
England
Salzurg, Austria
Salzburg, Austria
Salzburg, Austria
British Columbia, Canada
British Columbia, Canada
British Columbia, Canada
West Germany
West Germany
West Germany
West Germany
Ohio
Ohio
Ohio
Connecticut
Yarmouth, ME
Denver Front Range
Sweden
Midland, MI
MN
Finland
Canada
Canada
Industrial
Residential
Residential
Pristine
Industrial
Rural
Residential
Urban
Industrial
Rural
Background
Near Impacted site
Impacted site
Background
Background
Background
Background
Background
Impacted site
Urban
Urban
Background
Various Land Uses
Urban
Residential
Pristine
Industrial
Urban
Urban
1983
1983
1983
1983
NR
1988
1990
1990/91
1990/91
1990/91
1995
1995
1995
NR
NR
NR
NR
1995
1995
1995
1987/90
1996
99-00
1989
1983
1983
NR
1983
1987
1
1
1
1
2
3
4
5
5
5
6
6
6
7
7
7
7
8
8
8
15
16
17
9
1
1
2
10
10
Agriculture
Rural
Plowland
Grassland
Deciduous forest
Coniferous forest
Near waste-to-energy facility
Pre-operational
background
Near Stockholm
Near Incinerator
Near Incinerator
DRAFT-DO NOT QUOTE OR CITE
B-47
December 2003
-------
Table B-5. Environmental Levels of Dibenzofurans in Soil (ppt) (continued)
Chemical
TCDFs (continued)
Number
samples
43
29
8
4
12
19
3
2
65
30
47
20
13
5
6
53
31
47
14
7
9
11
34
8
Number
positive
samples
1
NR
8
1
12
19
3
2
NR
0
13
3
8
4
3
32
17
34
NR
NR
NR
NR
32
6
Concentration
range
ND-280
ND-120
109-454
ND-1.2
20-300
29-950
7.7-11
320-370
ND-237
ND
ND-120
ND-1850
ND-44.3
ND-59.4
ND-36.7
ND-260
ND-9300
ND-3200
4.6-28.8
1.9-34.8
90.7-959.5
134.4-1602.2
0.12-202
ND-15
Cone.
mean
6.5
15
237
<1
102
232
9.3
345
25
NA
11.87
152.5
13.9
36.9
9.0
17.13
538.93
374.58
15.7
15.7
338.2
431.4
23.36
5.4
Location
Canada
Canada
Sweden
Elk River, MN
England
England
Various parts of Europe
Various parts of Europe
British Isles
Ontario and U.S.
Midwestern states
Ontario and U.S.
Midwestern states
Ontario and U.S.
Midwestern states
Salzburg, Austria
Salzburg, Austria
Salzburg, Austria
British Columbia, Canada
British Columbia, Canada
British Columbia, Canada
West Germany
West Germany
West Germany
West Germany
Connecticut
Yarmouth, ME
Location
description
Rural
Urban
Urban
Rural
Residential
Urban
Rural
Industrial
Background
Rural
Urban
Industrial
Urban
Industrial
Rural
Background
Near Impacted site
Impacted site
Background
Background
Background
Background
Urban
Background
Sample
year
83-88
83-88
1989
1988
1990
NR
NR
NR
NR
NR
NR
NR
1990/91
1990/91
1990/91
1995
1995
1995
NR
NR
NR
NR
1987/90
1996
Ref.
no.
10
10
9
3
4
11
12
12
13
14
14
14
5
5
5
6
6
6
7
7
7
7
15
16
Comments
Near Stockholm
Agriculture
Rural
Plowland
Grassland
Deciduous forest
Coniferous forest
Pre-operational
Pentachlorodibenzofurans (MW=340.42)
1,2,3,7,8-PeCDF
4
12
0
12
ND
1-10
NA
3
Elk River, MN
England
Rural
Residential
1988
1990
3
4
Agriculture
Rural
DRAFT-DO NOT QUOTE OR CITE
B-48
December 2003
-------
Table B-5. Environmental Levels of Dibenzofurans in Soil (ppt) (continued)
Chemical
1,2,3,7,8-PeCDF (continued)
2,3,4,7,8-PeCDF
Number
samples
53
31
47
3
4
18
34
8
162
NR
8
4
12
3
2
13
5
6
53
31
47
14
7
9
11
3
4
18
34
Number
positive
samples
3
9
9
0
4
17
34
4
161
NR
8
0
12
3
2
11
5
5
1
9
16
NR
NR
NR
NR
1
4
17
33
Concentration
range
ND-7.10
ND-140
ND-160
ND
20.11-298.89
ND-18.51
0.04-15.0
ND-0.75
0.0032-30
NR
3.1-26.5
ND
1-5
0.6-1
23-65
ND-3.5
2.8-11.1
ND-2.0
ND-9.3
ND-270
ND-210
0.7-3.1
1.2-5.3
5.6-85.9
8.1-96.6
ND-0.17
26.55-434.37
ND-26.61
0.11-17.0
Cone.
mean
0.31
15.67
17.66
NA
139.577
5.504
1.74
0.26
0.82
580
11
NA
2
0.8
44
1.6
5.0
0.7
0.18
27.44
23.33
1.7
2.6
30.2
32.1
0.210
199.942
7.562
2.36
Location
British Columbia, Canada
British Columbia, Canada
British Columbia, Canada
Ohio
Ohio
Ohio
Connecticut
Yarmouth, ME
Denver Front Range
Finland
Sweden
Elk River, MN
England
various parts of Europe
various parts of Europe
Salzburg, Austria
Salzburg, Austria
Salzburg, Austria
British Columbia, Canada
British Columbia, Canada
British Columbia, Canada
West Germany
West Germany
West Germany
West Germany
Ohio
Ohio
Ohio
Connecticut
Location
description
Background
Near Impacted site
Impacted site
Background
Impacted site
Urban
Urban
Background
Various Land Uses
Industrial
Urban
Rural
Residential
Rural
Industrial
Urban
Industrial
Rural
Background
Near Impacted site
Impacted site
Background
Background
Background
Background
Background
Impacted Site
Urban
Urban
Sample
year
1995
1995
1995
1995
1995
1995
1987/90
1996
99-00
NR
1989
1988
1990
NR
NR
1990/91
1990/91
1990/91
1995
1995
1995
NR
NR
NR
NR
1995
1995
1995
87-90
Ref.
no.
6
6
6
8
8
8
15
16
17
2
9
3
4
12
12
5
5
5
6
6
6
7
7
7
7
8
8
8
15
Comments
Near waste-to-energy facility
Pre-operational
background
Near Stockholm
Agriculture
Rural
Plowland
Grassland
Deciduous forest
Coniferous forest
Near waste-to-energy facility
Pre-operational
DRAFT-DO NOT QUOTE OR CITE
B-49
December 2003
-------
Table B-5. Environmental Levels of Dibenzofurans in Soil (ppt) (continued)
Chemical
2,3,4,7,8-PeCDF (continued)
1, 2,3,7,8/1, 2,3,4,8-PeCDF
PeCDFs
Number
samples
8
162
NR
8
13
5
6
14
7
9
11
1
8
5
3
NR
11
12
43
29
8
4
12
19
2
3
65
47
Number
positive
samples
4
161
NR
8
13
5
5
NR
NR
NR
NR
1
2
0
0
NR
0
0
0
NR
8
3
12
19
2
3
NR
4
Concentration
range
ND-1.2
ND(0.0026)-64
NR
7.4-32.1
1.5-6.7
2.3-13.4
ND-4.9
0.5-3.4
0.9-5.0
5.9-93.0
10.5-107.8
900
ND-110
ND(8)
ND(9)
NR
ND
ND
ND
ND-160
36-457
18-45
6-70
19-830
200-450
6.7-14
ND-185
ND-110
Cone.
mean
0.51
1.7
82
16
3.2
31.0
2.3
1.8
2.7
36.2
36.2
900
19
NA
NA
27000
NA
NA
NA
35
182
26
31
189
325
11.2
23
3.5
Location
Yarmouth, ME
Denver Front Range
Finland
Sweden
Salzburg, Austria
Salzburg, Austria
Salzburg, Austria
West Germany
West Germany
West Germany
West Germany
Midland, MI
Midland, MI
Middletown, OH
MN
Finland
Canada
Canada
Canada
Canada
Sweden
Elk River, MN
England
England
Various parts of Europe
Various parts of Europe
British Isles
Ontario and U.S.
Midwestern states
Location
description
Impacted site
Various Land Uses
Industrial
Urban
Urban
Industrial
Rural
Background
Background
Background
Background
Industrial
Residential
Residential
Pristine
Industrial
Urban
Urban
Rural
Urban
Urban
Rural
Residential
Urban
Industrial
Rural
Background
Urban
Sample
year
1996
99-00
NR
1989
1990/91
1990/91
1990/91
NR
NR
NR
NR
1983
1983
1983
1983
NR
1983
1987
83-88
83-88
1989
1988
1990
NR
NR
NR
NR
NR
Ref.
no.
16
17
2
9
5
5
5
7
7
7
7
1
1
1
1
2
10
10
10
10
9
3
4
11
12
12
13
14
Comments
Near pole yard facility
background
Near Stockholm
Plowland
Grassland
Deciduous forest
Coniferous forest
Near Incinerator
Near Incinerator
Near Stockholm
Agriculture
Rural
DRAFT-DO NOT QUOTE OR CITE
B-50
December 2003
-------
Table B-5. Environmental Levels of Dibenzofurans in Soil (ppt) (continued)
Chemical
Number
samples
20
Number
positive
samples
5
Concentration
range
ND-285
Cone.
mean
21.5
Location
Ontario and U.S.
Midwestern states
Location
description
Industrial
Sample
year
NR
Ref.
no.
14
Comments
DRAFT-DO NOT QUOTE OR CITE
B-51
December 2003
-------
Table B-5. Environmental Levels of Dibenzofurans in Soil (ppt) (continued)
Chemical
PeCDFs (continued)
Number
samples
30
13
5
6
53
31
47
14
7
9
11
34
8
Number
positive
samples
0
13
5
5
27
17
30
NR
NR
NR
NR
34
7
Concentration
range
ND
3.0-67.0
22.0-64.1
ND-20.5
ND-635
ND-23000
ND-8000
1.4-29.5
4.6-38.1
74.0-973.7
91.8-1167.8
0.59-343.0
ND-16
Cone.
mean
NA
23.8
46.6
9.9
22.93
1991.63
767.86
11.6
18.4
362.8
400.1
42.97
8.81
Location
Ontario and U.S.
Midwestern states
Salzburg, Austria
Salzburg, Austria
Salzburg, Austria
British Columbia, Canada
British Columbia, Canada
British Columbia, Canada
West Germany
West Germany
West Germany
West Germany
Connecticut
Yarmouth, ME
Location
description
Rural
Urban
Industrial
Rural
Background
Near Impacted site
Impacted site
Background
Background
Background
Background
Urban
Background
Sample
year
NR
1990/91
1990/91
1990/91
1995
1995
1995
NR
NR
NR
NR
1987/90
1996
Ref.
no.
14
5
5
5
6
6
6
7
7
7
7
15
16
Comments
Plowland
Grassland
Deciduous Forest
Coniferous Forest
Pre-operational
Hexachlorodibenzofurans (MW =374. 87)
1,2,3,4,7,8/1,2,3,4,7,9-HxCDF
1,2,3,4,7,8/1,2,3,4,7,9-HxCDF
1,2,3,4,7,8-HxCDF
NR
8
13
5
6
14
7
9
11
4
53
31
47
3
NR
8
13
5
4
NR
NR
NR
NR
0
6
9
23
1
NR
6.5-29.1
1.0-11.3
3.2-14.0
ND-5.1
0.9-3.3
1.0-4.8
3.7-129.0
5.4-88.6
ND
ND-16
ND-900
ND-2800
ND-0.23
920
16
4.4
7.2
2.1
1.7
2.6
35.1
24.5
NA
1.09
99.55
141.22
0.187
Finland
Sweden
Salzburg, Austria
Salzburg, Austria
Salzburg, Austria
West Germany
West Germany
West Germany
West Germany
Elk River, MN
British Columbia, Canada
British Columbia, Canada
British Columbia, Canada
Ohio
Industrial
Urban
Urban
Industrial
Rural
Background
Background
Background
Background
Rural
Background
Near Impacted site
Impacted site
Background
NR
1989
1990/91
1990/91
1990/91
NR
NR
NR
NR
1988
1995
1995
1995
1995
2
9
5
5
5
7
7
7
7
3
6
6
6
8
Near Stockholm
Plowland
Grassland
Deciduous Forest
Coniferous Forest
Agriculture
DRAFT-DO NOT QUOTE OR CITE
B-52
December 2003
-------
Table B-5. Environmental Levels of Dibenzofurans in Soil (ppt) (continued)
Chemical
1,2,3,4,7,8-HxCDF
(continued)
1,2,3,6,7,8-HxCDF
1,2,3,7,8,9-HxCDF
Number
samples
4
18
34
8
162
NR
8
4
13
5
6
53
31
47
14
7
9
11
3
4
18
35
8
162
NR
8
4
13
5
Number
positive
samples
4
15
35
5
161
NR
8
0
13
5
4
4
9
20
NR
NR
NR
NR
3
4
17
35
5
161
NR
4
0
0
1
Concentration
range
37-420
ND-34
0.21-49.0
ND-1.6
0.0079-77
NR
7.7-28.9
ND
0.6-6.2
2.5-6.9
ND-3.2
ND-15
ND-1400
ND-1100
0.7-2.4
0.7-3.7
3.3-83.4
5.4-77.4
0.47-0.55
31.68-437.19
ND-28.13
0.20-17.8
ND-1.1
0.037-48
NR
ND-3.8
ND
ND
ND-4.2
Cone.
mean
196.750
8.059
4.84
0.75
2.3
<2
14
NA
2.7
4.3
1.2
0.62
144.73
89.42
1.4
1.9
26.0
21.2
0.523
209.110
8.120
2.32
0.60
1.5
<2
1
NA
NA
0.2
Location
Ohio
Ohio
Connecticut
Yarmouth, ME
Denver Front Range
Finland
Sweden
Elk River, MN
Salzburg, Austria
Salzburg, Austria
Salzburg, Austria
British Columbia, Canada
British Columbia, Canada
British Columbia, Canada
West Germany
West Germany
West Germany
West Germany
Ohio
Ohio
Ohio
Connecticut
Yarmouth, ME
Denver Front Range
Finland
Sweden
Elk River, MN
Salzburg, Austria
Salzburg, Austria
Location
description
Impacted site
Urban
Urban
Background
Various Land Uses
Industrial
Urban
Rural
Urban
Industrial
Rural
Background
Near Impacted site
Impacted site
Background
Background
Background
Background
Background
Impacted site
Urban
Urban
Background
Various Land Uses
Industrial
Urban
Rural
Urban
Industrial
Sample
year
1995
1995
1987/90
1996
99-00
NR
1989
1988
1990/91
1990/91
1990/91
1995
1995
1995
NR
NR
NR
NR
1995
1995
1995
1987/90
1996
99-00
NR
1989
1988
1990/91
1990/91
Ref.
no.
8
8
15
16
17
2
9
3
5
5
5
6
6
6
7
7
7
7
8
8
8
15
16
17
2
9
3
5
5
Comments
Near waste-to-energy facility
Pre-operational
background
Near Stockholm
Agriculture
Plowland
Grassland
Deciduous Forest
Coniferous Forest
Near waste-to-energy facility
Pre-operational
background
Near Stockholm
Agriculture
DRAFT-DO NOT QUOTE OR CITE
B-53
December 2003
-------
Table B-5. Environmental Levels of Dibenzofurans in Soil (ppt) (continued)
Chemical
1,2,3,7,8,9-HxCDF
(continued)
2,3,4,6,7,8-HxCDF
Number
samples
6
53
31
47
14
7
9
11
3
4
18
35
8
162
NR
8
4
30
13
5
6
53
31
47
14
7
9
11
3
Number
positive
samples
1
3
5
7
NR
NR
NR
NR
0
4
6
18
1
161
NR
7
1
0
11
3
4
1
7
14
NR
NR
NR
NR
3
Concentration
range
ND-2.8
ND-13
ND-40
ND-300
0.5-0.9
0.7-1.8
1.0-27.1
ND-16.3
ND
1.79-24.16
ND-1.98
0.04-4.02
ND-0.55
0.075-30
NR
ND-21.5
ND-7.1
ND
ND-9.6
ND-5.5
ND-4.1
ND-9
ND-1400
ND-520
0.7-2.8
1.0-3.7
2.1-53.8
4.1-62.5
0.47-0.89
Cone.
mean
0.5
0.36
3.35
12.11
0.7
1.1
7.6
4.4
NA
11.550
0.506
0.56
0.07
0.92
<2
8
2
NA
3.0
2.7
1.2
0.17
111.82
35.7
1.3
2.2
18.5
17.2
0.637
Location
Salzurg, Austria
British Columbia, Canada
British Columbia, Canada
British Columbia, Canada
West Germany
West Germany
West Germany
West Germany
Ohio
Ohio
Ohio
Connecticut
Yarmouth, ME
Denver Front Range
Finland
Sweden
Elk River, MN
Ontario and U.S.
Midwestern states
Salzburg, Austria
Salzburg, Austria
Salzburg, Austria
British Columbia, Canada
British Columbia, Canada
British Columbia, Canada
West Germany
West Germany
West Germany
West Germany
Ohio
Location
description
Rural
Background
Near Impacted site
Impacted site
Background
Background
Background
Background
Background
Impacted site
Urban
Urban
Background
Various Land Uses
Industrial
Urban
Rural
Rural
Urban
Industrial
Rural
Background
Near Impacted site
Impacted site
Background
Background
Background
Background
Background
Sample
year
1990/91
1995
1995
1995
NR
NR
NR
NR
1995
1995
1995
1987/90
1996
99-00
NR
1989
1988
NR
1990/91
1990/91
1990/91
1995
1995
1995
NR
NR
NR
NR
1995
Ref.
no.
5
6
6
6
7
7
7
7
8
8
8
15
16
17
2
9
3
14
5
5
5
6
6
6
7
7
7
7
8
Comments
Plowland
Grassland
Deciduous Forest
Coniferous Forest
Near waste-to-energy facility
Pre-operational
background
Agriculture
Plowland
Grassland
Deciduous Forest
Coniferous Forest
DRAFT-DO NOT QUOTE OR CITE
B-54
December 2003
-------
Table B-5. Environmental Levels of Dibenzofurans in Soil (ppt) (continued)
Chemical
2,3,4,6,7,8-HxCDF (continued)
HxCDFs
Number
samples
4
18
35
8
162
1
8
5
3
NR
11
12
43
29
8
4
12
19
3
2
65
30
47
20
13
5
6
Number
positive
samples
4
18
35
7
161
1
3
0
0
NR
0
0
0
NR
8
4
12
19
3
2
NR
0
6
7
13
5
4
Concentration
range
26.13-327.62
1.21-20.79
0.21-18.70
ND-2.0
0.0086-87
3100
ND-260
ND(8.0)
ND(9.0)
NR
ND
ND
ND
ND-120
53-308
7-150
6-50
17-660
11-16
270-1900
4.3-212
ND
ND-260
ND-420
8.5-61.9
13.0-73.4
ND-32.7
Cone.
mean
156.660
6.990
2.54
1.43
2.6
3100
62
NA
NA
110000
NA
NA
NA
9
145
66
24
156
13
1085
41
NA
12.1
62.3
25.5
38.3
10.4
Location
Ohio
Ohio
Connecticut
Yarmouth, ME
Denver Front Range
Midland, MI
Midland, MI
Middletown, OH
MN
Finland
Canada
Canada
Canada
Canada
Sweden
Elk River, MN
England
England
Various parts of Europe
Various parts of Europe
British Isles
Ontario and U.S.
Midwestern states
Ontario and U.S.
Midwestern states
Ontario and U.S.
Midwestern states
Salzburg, Austria
Salzburg, Austria
Salzburg, Austria
Location
description
Impacted site
Urban
Urban
Background
Various Land Uses
Industrial
Residential
Residential
Pristine
Industrial
Urban
Urban
Rural
Urban
Urban
Rural
Residential
Urban
Rural
Industrial
Background
Rural
Urban
Industrial
Urban
Industrial
Rural
Sample
year
1995
1995
1987/90
1996
99-00
1983
1983
1983
1983
NR
1983
1987
83-88
83-88
1989
1988
1990
NR
NR
NR
NR
NR
NR
1990/91
1990/91
1990/91
Ref.
no.
8
8
15
16
17
1
1
1
1
2
10
10
10
10
9
3
4
11
12
12
13
14
14
14
5
5
5
Comments
Near waste-to-energy facility
Pre-operational
background
Near Incinerator
Near Incinerator
Near Stockholm
Agriculture
Rural
DRAFT-DO NOT QUOTE OR CITE
B-55
December 2003
-------
Table B-5. Environmental Levels of Dibenzofurans in Soil (ppt) (continued)
Chemical
HxCDFs (continued)
Number
samples
53
31
47
14
7
9
11
35
7
Number
positive
samples
32
18
38
NR
NR
NR
NR
35
7
Concentration
range
ND-610
ND-79000
ND-97000
5.6-21.1
4.2-31.7
28.2-699.4
45.6-655.3
0.72-373.0
5.5-2.5
Cone.
mean
50.65
7387.93
6361.66
10.7
15.2
229.0
169.5
40.99
16.0
Location
British Columbia, Canada
British Columbia, Canada
British Columbia, Canada
West Germany
West Germany
West Germany
West Germany
Connecticut
Yarmouth, ME
Location
description
Background
Near Impacted site
Impacted site
Background
Background
Background
Background
Urban
Background
Sample
year
1995
1995
1995
NR
NR
NR
NR
1987/90
1996
Ref.
no.
6
6
6
7
7
7
7
15
16
Comments
Plowland
Grassland
Deciduous Forest
Coniferous Forest
Pre-operational
Heptachlorodibenzofurans (MW = 409. 3 1)
1,2,3,4,6,7,8-HpCDF
NR
8
4
13
5
6
53
31
47
14
7
9
11
3
4
18
35
8
162
NR
8
4
13
5
6
30
19
34
NR
NR
NR
NR
3
4
18
35
8
161
NR
31-134
11-80
5.7-38.8
8.2-85
3.1-13.5
ND-300
ND-43000
ND-71000
3.2-24.7
4.6-33.9
24.7-697.0
23.3-645.9
3.3-6.46
106.75-1340.6
6.11-138.76
1.01-105.0
5.8-26
0.083-450
190000
73
47
17.1
28.7
7.2
42.26
3334.85
4107.19
9.5
13.1
183.6
139.9
4.060
640.950
41.747
17.0
14.2
26
Finland
Sweden
Elk River, MN
Salzburg, Austria
Salzburg, Austria
Salzburg, Austria
British Columbia, Canada
British Columbia, Canada
British Columbia, Canada
West Germany
West Germany
West Germany
West Germany
Ohio
Ohio
Ohio
Connecticut
Yarmouth, ME
Denver Front Range
Industrial
Urban
Rural
Urban
Industrial
Rural
Background
Near Impacted site
Impacted site
Background
Background
Background
Background
Background
Impacted site
Urban
Urban
Background
Various Land Uses
NR
1989
1988
1990/91
1990/91
1990/91
1995
1995
1995
NR
NR
NR
NR
1995
1995
1995
1987/90
1996
99-00
2
9
3
5
5
5
6
6
6
7
7
7
7
8
8
8
15
16
17
Near Stockholm
Agriculture
Plowland
Grassland
Deciduous Forest
Coniferous Forest
Near waste-to-energy facility
Pre-operational
background
DRAFT-DO NOT QUOTE OR CITE
B-56
December 2003
-------
Table B-5. Environmental Levels of Dibenzofurans in Soil (ppt) (continued)
Chemical
1,2,3,4,7,8,9-HpCDF
1,2,3,4,7,8,9-HpCDF
(continued)
HpCDFs
Number
samples
NR
8
4
13
5
6
53
31
47
14
7
9
11
3
4
18
35
8
162
1
NR
8
5
3
11
12
43
29
8
Number
positive
samples
NR
2
0
11
2
4
6
8
22
NR
NR
NR
NR
1
4
16
33
6
161
1
NR
6
1
0
NR
0
0
NR
8
Concentration
range
NR
ND-6.3
ND
ND-4.2
ND-4.1
ND-1.7
ND-45
ND-900
ND-7100
0.4-1.6
0.8-2.8
2.3-63.4
1.6-50.3
ND-0.35
9.3-123.81
ND-15.05
0.03-9.70
0.98-2.1
0.0052-27
15400
NR
ND-820
ND-43
ND(9.0)
ND-180
ND
ND
ND-410
31-187
Cone.
mean
<5
1
NA
1.3
2.6
0.7
1.59
81.76
236.27
1.0
1.7
15.8
10.3
0.267
57.888
3.815
1.21
1.01
2.1
15400
140000
300
9
NA
30
NA
NA
29
81
Location
Finland
Sweden
Elk River, MN
Salzburg, Austria
Salzburg, Austria
Salzburg, Austria
British Columbia, Canada
British Columbia, Canada
British Columbia, Canada
West Germany
West Germany
West Germany
West Germany
Ohio
Ohio
Ohio
Connecticut
Yarmouth, ME
Denver Front Range
Midland, MI
Finland
Midland, MI
Middletown, OH
MN
Canada
Canada
Canada
Canada
Sweden
Location
description
Industrial
Urban
Rural
Urban
Industrial
Rural
Background
Near Impacted site
Impacted site
Background
Background
Background
Background
Background
Impacted site
Urban
Urban
Background
Various Land Uses
Industrial
Industrial
Residential
Residential
Pristine
Urban
Urban
Rural
Urban
Urban
Sample
year
NR
1989
1988
1990/91
1990/91
1990/91
1995
1995
1995
NR
NR
NR
NR
1995
1995
1995
1987/90
1996
99-00
1983
NR
1983
1983
1983
1983
1987
83-88
83-88
1989
Ref.
no.
2
9
3
5
5
5
6
6
6
7
7
7
7
8
8
8
15
16
17
1
2
1
1
1
10
10
10
10
9
Comments
Near Stockholm
Agriculture
Plowland
Grassland
Deciduous forest
Coniferous forest
Near waste-to-energy facility
Pre-operational
background
Near Incinerator
Near Incinerator
Near Stockholm
DRAFT-DO NOT QUOTE OR CITE
B-57
December 2003
-------
Table B-5. Environmental Levels of Dibenzofurans in Soil (ppt) (continued)
Chemical
HpCDFs (continued)
Number
samples
4
12
19
3
2
65
47
20
30
13
5
6
53
31
47
14
7
9
11
35
8
Number
positive
samples
4
12
19
3
2
NR
10
15
0
13
5
6
33
19
36
NR
NR
NR
NR
35
8
Concentration
range
30-260
4-59
16-458
14-22
260-4500
1.5-138
ND-820
ND-3750
ND
7.8-61.6
10.8-85.0
5.5-19.2
ND-890
ND- 130000
ND-580000
3.2-31.2
4.6-44.4
33.7-922.9
32.4-876.6
1.66-188.0
12.9-82
Cone.
mean
100
20
152
18
2380
26
60.2
550
NA
27.1
34.2
9.8
112.99
9330.42
20235.95
12.9
17.5
245.5
187.4
33.21
39.4
Location
Elk River, MN
England
England
Various parts of Europe
Various parts of Europe
British Isles
Ontario and U.S.
Midwestern states
Ontario and U.S.
Midwestern states
Ontario and U.S.
Midwestern states
Salzburg, Austria
Salzburg, Austria
Salzburg, Austria
British Columbia, Canada
British Columbia, Canada
British Columbia, Canada
West Germany
West Germany
West Germany
West Germany
Connecticut
Yarmouth, ME
Location
description
Rural
Residential
Urban
Rural
Industrial
Background
Urban
Industrial
Rural
Urban
Industrial
Rural
Background
Near Impacted Site
Impacted site
Background
Background
Background
Background
Urban
Background
Sample
year
1988
1990
NR
NR
NR
NR
NR
NR
NR
1990/91
1990/91
1990/91
1995
1995
1995
NR
NR
NR
NR
1987/90
1996
Ref.
no.
3
4
11
12
12
13
14
14
14
5
5
5
6
6
6
7
7
7
7
15
16
Comments
Agriculture
Rural
Plowland
Grassland
Deciduous forest
Coniferous
Pre-operational
Octachlorodibenzofurans (MW =444. 76)
1,2,3,4,6,7,8,9-OCDF
1
8
5
3
NR
1
6
1
0
NR
8600
ND-660
ND-50
ND(10)
NR
8600
240
10
NA
3000
Midland, MI
Midland, MI
Middletown, OH
MN
Finland
Industrial
Residential
Residential
Pristine
Industrial
1983
1983
1983
1983
NR
1
1
1
1
2
DRAFT-DO NOT QUOTE OR CITE
B-58
December 2003
-------
Table B-5. Environmental Levels of Dibenzofurans in Soil (ppt) (continued)
Chemical
Number
samples
11
12
Number
positive
samples
NR
NR
Concentration
range
ND-33
ND-230
Cone.
mean
4
43
Location
Canada
Canada
Location
description
Urban
Urban
Sample
year
1983
1987
Ref.
no.
10
10
Comments
Near Incinerator
Near Incinerator
DRAFT-DO NOT QUOTE OR CITE
B-59
December 2003
-------
Table B-5. Environmental Levels of Dibenzofurans in Soil (ppt) (continued)
Chemical
1,2,3,4,6,7,8,9-OCDF
(continued)
Number
samples
43
29
8
4
12
19
1
65
47
20
30
2
3
4
18
35
8
162
Number
positive
samples
0
NR
1
3
12
19
1
NR
15
15
0
2
3
4
18
35
8
161
Concentration
range
ND
ND-600
ND-19.0
60-270
10-90
7-1100
5.7
ND-144
ND-660
ND-5200
ND
68-71
4.7-18.02
44.74-307.96
3.53-199.37
20.4-4,970
13-95
0.32-1,500
Cone.
mean
NA
50
2
113
30
196
5.7
27
60
632
NA
69.5
10.717
184.495
44.282
814.3
42.3
72
Location
Canada
Canada
Sweden
Elk River, MN
England
England
Various parts of Europe
British Isles
Canada and USA
Canada and USA
Canada and USA
Various parts of Europe
Ohio
Ohio
Ohio
Connecticut
Yarmouth, ME
Denver Front Range
Location
description
Rural
Urban
Urban
Rural
Residential
Urban
Rural
Background
Urban
Industrial
Rural
Industrial
Background
Impacted site
Urban
Urban
Background
Various Land Uses
Sample
year
83-88
83-88
1989
1988
1990
NR
NR
NR
NR
NR
NR
NR
1995
1995
1995
1987/90
1996
99-00
Ref.
no.
10
10
9
3
4
11
12
13
14
14
14
12
8
8
8
15
16
17
Comments
Near Stockholm
Agriculture
Rural
Near waste-to-energy facility
Pre-operational
background
Footnote References
NOTES: Summary statistics provided in or derived from references; when reference did not compute mean, it was computed using one-half the detection limit for non-detects if congener-specific detection limits were given; when
detection limits were not given, zero was used for nondetects.
NA = Not available.
NR = Not reported.
ND = Non-detect.
Detection limits varied by study and were different for different compounds, but generally were 1 to 5 ng/kg (ppt). Descriptions provided were those given by reference or surmised from study description when not given.
Sources: 1. U.S. EPA (1985)
2. Kitunen and Salkinoja-Salonen (1990)
3. Reedetal. (1990)
4. Stenhouse and Badsha (1990)
5. Boos et al. (1992)
6. BC Environment (1995)
7. Retard et al. (1994)
9. Bromanetal. (1990)
10. Pearson etal. (1990)
11. Greaser etal. (1990)
12. Rappe and Kjeller (1987)
13. Greaser etal. (1989)
14. Birmingham (1990)
15. MRI (1992)
17. U.S. EPA Region 8 (2000)
DRAFT-DO NOT QUOTE OR CITE
B-60
December 2003
-------
Table B-5. Environmental Levels of Dibenzofurans in Soil (ppt) (continued)
8. U.S. EPA (1996) 16. Tewhey Associates (1997)
DRAFT-DO NOT QUOTE OR CITE B-61 December 2003
-------
Table B-6. Environmental Levels of Dioxins in Water (ppq)
Chemical
Number
samples
Number
positive
samples
Concentration
range
Cone.
mean
Location
Location
description
Samp.
year
Ref.
no.
Comments
Tetrachlorodibenzo-p-dioxins (MW = 32 1 . 98)
2,3,7,8-TCDD
TCDDs
1
2
185
22
1
2
0
0
1
0
1
2
ND(O.T)
ND(.02-.024)
ND-40
ND(.4-2.6)
1.7
.05-. 084
NA
NA
2.70
NA
1.7
.067
Lockport, New York
Eman River, Sweden
Ontario, Canada
New York State
Lockport, New York
Eman River, Sweden
NR
PCB contaminated
NR
NR
NR
PCB contaminated
88
NR
83-89
86-88
88
NR
1
2
3
1
1
2
raw surface drinking water
raw surface drinking water
raw surface drinking water
treated surface drinking water
raw surface drinking water
raw surface drinking water
Pentachlorodibenzo-p-dioxins (MW=356.42)
1,2,3,7,8-PeCDD
PeCDDs
1
2
1
22
2
0
0
0
0
2
ND(l.O)
ND(.025-.039)
ND(l.O)
ND(1. 2-7.4)
.067-. 12
NA
NA
NA
NA
.094
Lockport, New York
Eman River, Sweden
Lockport, New York
New York State
Eman River, Sweden
NR
PCB contaminated
NR
NR
PCB contaminated
88
NR
88
86-88
NR
1
2
1
1
2
raw surface drinking water
raw surface drinking water
raw surface drinking water
treated surface drinking water
raw surface drinking water
Hexachlorodibenzo-p-dioxins (MW=390.87)
1,2,3,4,7,8-HxCDD
1,2,3,6,7,8-HxCDD
1,2,3,7,8,9-HxCDD
1
2
1
2
1
2
0
1
0
1
0
1
ND(1.8)
ND-.054
ND(1.5)
ND-.12
ND(1.5)
ND-.075
NA
.027
NA
.06
NA
.038
Lockport, New York
Eman River, Sweden
Lockport, New York
Eman River, Sweden
Lockport, New York
Eman River, Sweden
NR
PCB contaminated
NR
PCB contaminated
NR
PCB contaminated
88
NR
88
NR
88
NR
1
2
1
2
1
2
raw surface drinking water
raw surface drinking water
raw surface drinking water
raw surface drinking water
raw surface drinking water
raw surface drinking water
DRAFT-DO NOT QUOTE OR CITE
B-62
December 2003
-------
Table B-6. Environmental Levels of Dioxins in Water (ppq) (continued)
Chemical
HxCDDs
Number
samples
1
22
2
Number
positive
samples
0
0
2
Concentration
range
ND(1.5)
ND(. 4-4.7)
.13-. 67
Cone.
mean
NA
NA
.4
Location
Lockport, New York
New York State
Eman River, Sweden
Location
description
NR
NR
PCB contaminated
Samp.
year
88
86-88
NR
Ref.
no.
1
1
2
Comments
raw surface drinking water
treated surface drinking water
raw surface drinking water
Heptachlorodibenzo-p-dioxins (MW = 425 . 3 1)
1,2,3,4,6,7,8-HpCDD
HpCDDs
1
2
1
22
2
0
2
0
0
2
ND(2.8)
.15-. 30
ND(2.8)
ND(.4-6.8)
.17-. 64
NA
.22
NA
NA
.40
Lockport, New York
Eman River, Sweden
Lockport, New York
New York State
Eman River, Sweden
NR
PCB contaminated
NR
NR
PCB contaminated
88
NR
88
86-88
NR
1
2
1
1
2
raw surface drinking water
raw surface drinking water
raw surface drinking water
treated surface drinking water
raw surface drinking water
Octachlorodibenzo-p-dioxin (MW = 460 . 76)
1,2,3,4,6,7,8,9-OCDD
185
214
32
4
ND-175
ND-46
10.6
3.16
Ontario, Canada
Ontario, Canada
NR
NR
83-89
83-89
3
3
raw surface drinking water
treated surface drinking water
Footnote References
NOTES: Summary statistics provided in or derived from references; when reference did not compute mean, one-half the limit of detection was used in non-detects. Therefore, it is possible to have mean concentrations greater
than the range (e.g., reported detection limit for nondetects greater than the positive sample).
NA = not applicable;
ND = non-detected (limit of detection);
NR = not reported;
Descriptions provided were those given by reference or surmised from study description when not given.
Sources:!. Meyer et al. (1989)
2. Rappe et al. (1989a)
3. Jobb et al. (1990)
DRAFT-DO NOT QUOTE OR CITE
B-63
December 2003
-------
Table B-7. Environmental Levels of Dibenzofurans in Water (ppq)
Chemical
Number
samples
Number
positive
samples
Concentration
range
Cone.
mean
Location
Location
description
Samp.
year
Ref.
no.
Comments
Tetrachlorodibenzofiirans (MW = 305 . 98)
2,3,7,8-TCDF
TCDFs
2
1
22
1
2
2
0
1
1
2
.022-. 026
ND(O.T)
ND-2.6
18
.21-. 23
.024
NA
0.12
18
0.22
Eman River, Sweden
Lockport, New York
New York State
Lockport, New York
Eman River, Sweden
PCB contaminated
NR
NR
NR
PCB contaminated
NR
88
86-88
88
NR
1
2
2
2
1
raw surface drinking water
raw surface drinking water
treated surface drinking water
raw surface drinking water
raw surface drinking water
Pentachlorodibenzofiirans (MW = 340 . 42)
1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
1,2,3,4, 8/1,2,3,7,8-PeCDF
PeCDFs
1
2
1
2
1
22
2
1
2
0
2
1
0
2
2.0
.014-. 019
ND(l.O)
.013-. 025
27
ND(0.3-4.0)
.13-. 21
2.0
.016
NA
.019
27
NA
.17
Lockport, New York
Eman River, Sweden
Lockport, New York
Eman River, Sweden
Lockport, New York
New York State
Eman River, Sweden
NR
PCB contaminated
NR
PCB contaminated
NR
NR
PCB contaminated
88
NR
88
NR
88
86-88
NR
2
1
2
1
2
2
1
raw surface drinking water
raw surface drinking water
raw surface drinking water
raw surface drinking water
raw surface drinking water
treated surface drinking water
raw surface drinking water
HexacWorodibenzofiirans (MW=374.87)
1,2,3,4,7,8-HxCDF
1,2,3,6,7,8-HxCDF
2,3,4,6,7,8-HxCDF
1,2,3,7,8,9-HxCDF
1
1
2
1
2
1
2
1
1
2
0
1
0
1
39
9.2
.019-. 025
ND(1.3)
ND-.027
ND(1.2)
ND-.022
39
9.2
.022
NA
.014
NA
.011
Lockport, New York
Lockport, New York
Eman River, Sweden
Lockport, New York
Eman River, Sweden
Lockport, New York
Eman River, Sweden
NR
NR
PCB contaminated
NR
PCB contaminated
NR
PCB contaminated
88
88
NR
88
NR
88
NR
2
2
1
2
1
2
1
raw surface drinking water
raw surface drinking water
raw surface drinking water
raw surface drinking water
raw surface drinking water
raw surface drinking water
raw surface drinking water
DRAFT-DO NOT QUOTE OR CITE
B-64
December 2003
-------
Table B-7. Environmental Levels of Dibenzofurans in Water (ppq) (continued)
Chemical
1,2,3,4,7,9/1,2,3,4,7,8-HxCDF
HxCDFs
Number
samples
2
1
22
2
Number
positive
samples
2
1
0
2
Concentration
range
.021-. 026
85
ND(.3-4.4)
.17-. 19
Cone.
mean
.024
NA
NA
.18
Location
Eman River, Sweden
Lockport, New York
New York State
Eman River, Sweden
Location
description
PCB contaminated
NR
NR
PCB contaminated
Samp.
year
NR
88
86-88
NR
Ref.
no.
1
2
2
1
Comments
raw surface drinking water
raw surface drinking water
treated surface drinking water
raw surface drinking water
HeptacWorodibenzofiirans (MW=409.31)
1,2,3,4,6,7,8-HpCDF
1,2,3,4,7,8,9-HpCDF
HpCDFs
1
2
2
1
22
2
1
2
2
1
0
2
210
.083-. 13
.03-. 058
210
ND(.8-6.6)
.18-. 35
210
.11
.044
210
NA
.26
Lockport, New York
Eman River, Sweden
Eman River, Sweden
Lockport, New York
New York State
Eman River, Sweden
NR
PCB contaminated
PCB contaminated
NR
NR
PCB contaminated
88
NR
NR
88
86-88
NR
2
1
1
2
2
1
raw surface drinking water
raw surface drinking water
raw surface drinking water
raw surface drinking water
treated surface drinking water
raw surface drinking water
Octachlorodibenzofiirans (MW =444. 76)
1,2,3,4,6,7,8,9-OCDF
22
1
2
2
1
2
ND-0.8
230
.15-. 36
2.45
230
.26
New York State
Lockport, New York
Eman River, Sweden
NR
NR
PCB contaminated
86-88
88
NR
2
2
1
treated surface drinking water
raw surface drinking water
raw surface drinking water
Footnote References
NOTES: Summary statistics provided in or derived from references; when reference did not compute mean, one-half the detection limit was used for non-detects. Therefore, it is possible to have mean concentrations greater than
the range (e.g., reported detection limit for nondetects greater than the positive sample).
NA = not applicable;
ND = non-detected (limit of detection);
NR = not reported.
Descriptions provided were those given by reference or surmised from study description when not given.
Sources: 1. Rappe et al. (1989a)
2. Meyer et al. (1989)
DRAFT-DO NOT QUOTE OR CITE
B-65
December 2003
-------
Table B-8. Environmental Levels of Dioxins in Sediments (ppt)
Chemical
Number
samples
Number
positive
samples
Concentration
range
Cone.
mean
Wt.
basis
Location
Location
description
Sample
year
Ref.
no.
Comments3
Tetrachlorodibenzo-p-dioxins (MW = 32 1 . 98)
2,3,7,8-TCDD
18
9
4
2
1
1
2
3
3
1
2
1
1
12
4
4
3
2
4
0
8
4
2
i
i
2
i
3
1
1
1
0
6
0
0
2
2
4
ND
ND-730
75-2500
190-1200
680
150
660-1100
ND-7600
390-2900
93
ND-7600
21,000
ND
ND-57
ND
ND
ND-2.4
1.0-1.4
1.9-26
NA
236
1769
695
680
150
880
367
1227
93
3800
21000
NA
13
NA
NA
1.5
1.2
9.6
Dry
Dry
Dry
Dry
Dry
Dry
Dry
Dry
Dry
Dry
Dry
Dry
Dry
Dry
Dry
NR
NR
Dry
Dry
South Central Finland
Newark, NJ
Newark, NJ
Newark, NJ
Newark, NJ
Newark, NJ
Newark, NJ
Newark, NJ
Newark, NJ
Newark, NJ
Newark, NJ
Newark, NJ
Long Island Sound
New England
Seattle, WA
Central Minnesota
Stockholm Sweden
Baltic Sea
Issesund Sweden
Various
Industrial
Industrial
Industrial
Industrial
Industrial
Industrial
Industrial
Industrial
Industrial
Industrial
Industrial
Reference Site
Industrial
Industrial
Rural
Various
Reference Site
Industrial
88/89
85/86
85/86
85/86
85/86
85/86
85/86
85/86
85/86
85/86
85/86
85/86
NR
NR
NR
NR
NR
NR
NR
1
2
2
2
2
2
2
2
2
2
2
2
3
3
3
4
5
6
6
A,B
0-2", A,C
2-4", A,C
4-8", A,C
12-16", A,C
20-24", A,C
24-28", A,C
28-32", A,C
32-36", A,C
40-44", A,C
48-52", A,C
108-111", A,C
A
A/3 sites
A
A
A
A
A/papermill
DRAFT-DO NOT QUOTE OR CITE
B-66
December 2003
-------
Table B-8. Environmental Levels of Dioxins in Sediments (ppt) (continued)
Chemical
2,3,7,8-TCDD (continued)
Number
samples
4
1
1
5
4
6
5
61
12
14
7
11
162
19
5
Number
positive
samples
4
1
1
5
1
6
3
54
0
2
1
6
125
10
0
Concentration
range
0.03-0.11
0.04
0.01
3.4-1,500
ND-3
1.2-32
ND-110
NR
ND
ND-2.7
ND-1.1
ND-0.78
0.04-23.10
ND-510
ND
Cone.
mean
0.06
0.04
0.01
375
1.19
12.0
28.1
0.30
NA
0.23
0.16
0.27
2.30
NR
ND
Wt.
basis
Dry
Dry
Dry
NR
NR
NR
NR
Dry
Dry
Dry
Dry
Dry
Dry
Dry
Dry
Location
Fjord between Denmark,
Sweden, and Norway
Fjord between Denmark,
Sweden, and Norway
Fjord between Demark,
Sweden, and Norway
Hamburg Germany
Dala River, Sweden
Lake Vattern, Sweden
Lake Vanern, Sweden
Southern Mississippi
British Columbia
British Columbia
British Columbia
Various U.S. Lakes
Connecticut
L. Passaic River & Newark
Bay
Southern Mississippi Lakes
Location
description
Industrial
Industrial
Industrial
Urban
Industrial
Industrial
Industrial
NR
Background
Near Impacted
Sites
Near Impacted
Sites
Background
Urban
Industrial
Pristine
Sample
year
87
87
87
NR
88
88
88
94
NR
NR
NR
87-90
90
95-96
Ref.
no.
7
7
7
8
9
9
9
10
11
11
11
12
17
18
20
Comments8
0-1 cm, E
4-6 cm, E
9-13 cm, E
0-1 cm
0-1 cm
0-1 cm
Chemical
Sources
Combustion
Sources
Pre-operational
Upper stratum of
sediment core
DRAFT-DO NOT QUOTE OR CITE
B-67
December 2003
-------
Table B-8. Environmental Levels of Dioxins in Sediments (ppt) (continued)
Chemical
TCDDs
Number
samples
18
1
1
1
4
25
12
3
4
2
4
1
1
5
12
14
Number
positive
samples
14
1
1
0
0
0
7
3
4
2
4
1
1
5
0
4
Concentration
range
ND- 1,400
26
12
ND
ND
ND
ND-44
21-69
21-66
19-35
1.4-6.7
13
5.0
80-1,700
ND
ND-210.0
Cone.
mean
372
26
12
NA
NA
NA
17
38
45
27
4.2
13
5.0
564
NA
26.0
Wt.
basis
Dry
Dry
Dry
Dry
NR
NR
NR
NR
Dry
Dry
Dry
Dry
Dry
NR
Dry
Dry
Location
South Central Finland
Siskiwit Lake, Isle Royale,
Lake Michigan
Siskiwit Lake, Isle Royale,
Lake Michigan
Siskiwit Lake, Isle Royale,
Lake Michigan
Central Minnesota
Ontario Canada
NY/Mass
Stockholm Sweden
Iggesund Sweden
Baltic Sea
Fjord between Denmark,
Sweden, and Norway
Fjord between Denmark,
Sweden, and Norway
Fjord between Denmark,
Sweden, and Norway
Hamburg Germany
British Columbia
British Columbia
Location
description
Various
Pristine
Pristine
Pristine
Rural
Industrial
NR
Various
Industrial
Reference Site
Industrial
Industrial
Industrial
Urban
Background
Near Impacted
Sites
Sample
year
88/89
NR
NR
NR
NR
88
NR
NR
NR
NR
87
87
87
NR
NR
NR
Ref.
no.
1
13
13
13
4
14
15
5
6
6
7
7
7
8
11
11
Comments8
AB
0-0.5 cm, A,D
5-6 cm, A,D
8-9 cm, A,D
A/4 sites
A/25 sites
A
A
A/paper mill
A
0-2 cm, E
4-6 cm, E
9-13 cm, E
Chemical
Sources
DRAFT-DO NOT QUOTE OR CITE
B-68
December 2003
-------
Table B-8. Environmental Levels of Dioxins in Sediments (ppt) (continued)
Chemical
TCDDs (continued)
Number
samples
7
11
162
5
Number
positive
samples
2
10
150
0
Concentration
range
ND-32.0
ND-21.2
0.07-48.7
ND
Cone.
mean
5.3
6.45
9.34
ND
Wt.
basis
Dry
Dry
Dry
Dry
Location
British Columbia
Various U.S. Lakes
Connecticut
Southern Mississippi Lakes
Location
description
Near Impacted
Sites
Background
Urban
Pristine
Sample
year
NR
87-90
95-96
Ref.
no.
11
12
17
20
Comments8
Combustion
Sources
Pre-operational
Upper stratum of
sediment core
Pentachlorodibenzo-p-dioxins (MW=356.42)
1,2,3,7,8-PeCDD
4
6
5
12
14
7
11
161
19
5
3
6
4
0
3
1
6
154
2
2
ND-25
7.4-95
ND-100
ND
ND-30.0
ND-6.6
ND-3.91
0.03-48.7
ND-22
ND-0.18
7.68
44.1
49.6
NA
4.2
0.94
0.99
3.49
NR
NR
NR
NR
NR
Dry
Dry
Dry
Dry
Dry
Dry
Dry
Dala River, Sweden
Lake Vattern, Sweden
Lake Vanern, Sweden
British Columbia
British Columbia
British Columbia
Various U.S. Lakes
Connecticut
L. Passaic River & Newark
Bay
Southern Mississippi Lakes
Industrial
Industrial
Industrial
Background
Near Impacted
Sites
Near Impacted
Sites
Background
Urban
Industrial
Pristine
88
88
88
NR
NR
NR
87-90
90
95-96
9
9
9
11
11
11
12
17
18
20
0-1 cm
0-1 cm
0-1 cm
Chemical
Sources
Combustion
Sources
Pre-operational
Upper stratum of
sediment core
DRAFT-DO NOT QUOTE OR CITE
B-69
December 2003
-------
Table B-8. Environmental Levels of Dioxins in Sediments (ppt) (continued)
Chemical
PeCDDs
Number
samples
1
1
1
4
25
12
3
4
2
4
1
1
5
12
14
7
Number
positive
samples
1
1
0
0
0
7
3
4
2
4
1
1
5
0
5
1
Concentration
range
12
11
ND
ND
ND
ND-235
86-230
52-500
52-100
1.7-41
6.6
15
260-2,700
ND
ND-400.0
ND-86.0
Cone.
mean
12
11
NA
NA
NA
50
138
209
76
19
6.6
15
1,112
NA
79.8
12.3
Wt.
basis
Dry
Dry
Dry
NR
NR
NR
NR
Dry
Dry
Dry
Dry
Dry
NR
Dry
Dry
Dry
Location
Siskiwit Lake, Isle Royale,
Lake Michigan
Siskiwit Lake, Isle Royale,
Lake Michigan
Siskiwit Lake, Isle Royale,
Lake Michigan
Central Minnesota
Ontario Canada
NY/Mass
Stockholm Sweden
Iggesund Sweden
Baltic Sea
Fjord between Denmark,
Sweden, and Norway
Fjord between Denmark,
Sweden, and Norway
Fjord between Denmark,
Sweden, and Norway
Hamburg Germany
British Columbia
British Columbia
British Columbia
Location
description
Pristine
Pristine
Pristine
Rural
Industrial
NR
Various
Industrial
Reference Site
Industrial
Industrial
Industrial
Urban
Background
Near Impacted
Sites
Near Impacted
Sites
Sample
year
NR
NR
NR
NR
88
NR
NR
NR
NR
87
87
87
NR
NR
NR
NR
Ref.
no.
13
13
13
4
14
15
5
6
6
7
7
7
8
11
11
11
Comments8
0-0.5 cm, A,D
5-6 cm, A,D
8-9 cm, A,D
A/4 sites
A/25 sites
A
A
A/paper mill
A
0-2 cm, E
4-6 cm, E
9-13 cm, E
Chemical
Sources
Combustion
Sources
DRAFT-DO NOT QUOTE OR CITE
B-70
December 2003
-------
Table B-8. Environmental Levels of Dioxins in Sediments (ppt) (continued)
Chemical
PeCDDs (continued)
Number
samples
11
162
5
Number
positive
samples
10
158
5
Concentration
range
ND-28.7
0.03-327.0
0.11-4.9
Cone.
mean
9.1
28.85
1.5
Wt.
basis
Dry
Dry
Dry
Location
Various U.S. Lakes
Connecticut
Southern Mississippi Lakes
Location
description
Background
Urban
Pristine
Sample
year
87-90
95-96
Ref.
no.
12
17
20
Comments8
Pre-operational
Upper stratum of
sediment core
Hexachlorodibenzo-p-dioxins (MW=390.87)
1,2,3,4,7,8-HxCDD
1,2,3,6,7,8-HxCDD
4
6
5
12
14
7
11
166
19
5
4
6
5
3
6
5
1
4
1
10
149
4
2
4
6
5
ND-19
6.1-33
14-94
ND-7.9
ND-82.0
ND-11.0
ND-6.87
0.05-31.60
ND-9.6
ND-0.49
4.9-120
21-450
36-600
6.68
20.5
36.0
0.66
7.9
1.6
1.8
4.24
NR
NR
36.4
188
236
NR
NR
NR
Dry
Dry
Dry
Dry
Dry
Dry
Dry
NR
NR
NR
Dala River, Sweden
Lake Vattern, Sweden
Lake Vanern, Sweden
British Columbia
British Columbia
British Columbia
Various U.S. Lakes
Connecticut
L. Passaic River & Newark
Bay
Southern Mississippi Lakes
Dala River, Sweden
Lake Vattern, Sweden
Lake Vanern, Sweden
Industrial
Industrial
Industrial
Background
Near Impacted
Sites
Near Impacted
Sites
Background
Urban
Industrial
Pristine
Industrial
Industrial
Industrial
88
88
88
NR
NR
NR
87-90
90
95-96
88
88
88
9
9
9
11
11
11
12
17
18
20
9
9
9
0-1 cm
0-1 cm
0-1 cm
Chemical
Sources
Combustion
Sources
Pre-operational
Upper stratum of
sediment core
0-1 cm
0-1 cm
0-1 cm
DRAFT-DO NOT QUOTE OR CITE
B-71
December 2003
-------
Table B-8. Environmental Levels of Dioxins in Sediments (ppt) (continued)
Chemical
1,2,3,6,7,8-HxCDD
(continued)
1,2,3,7,8,9-HxCDD
Number
samples
12
14
7
11
166
19
5
4
6
5
12
14
7
11
166
19
5
Number
positive
samples
2
9
3
10
163
10
4
4
6
5
1
6
3
10
164
4
5
Concentration
range
ND-67.0
ND-540.0
ND-220.0
ND-21.6
0.10-107.0
ND-46
ND-1.7
3.9-51
18-200
18-330
ND-17.5
ND-290.0
ND-39.0
ND-14.55
0.09-98.3
ND-25
0.28-5.9
Cone.
mean
11.2
127.2
51.07
4.7
15.26
NR
NR
18.1
95.5
149
1.5
47.9
12.1
4.04
11.24
NR
2.5
Wt.
basis
Dry
Dry
Dry
Dry
Dry
Dry
Dry
NR
NR
NR
Dry
Dry
Dry
Dry
Dry
Dry
Dry
Location
British Columbia
British Columbia
British Columbia
Various U.S. Lakes
Connecticut
L. Passaic River & Newark
Bay
Southern Mississippi Lakes
Dala River, Sweden
Lake Vattern, Sweden
Lake Vanern, Sweden
British Columbia
British Columbia
British Columbia
Various U.S. Lakes
Connecticut
L. Passaic River & Newark
Bay
Southern Mississippi Lakes
Location
description
Background
Near Impacted
Sites
Near Impacted
Sites
Background
Urban
Industrial
Pristine
Industrial
Industrial
Industrial
Background
Near Impacted
Sites
Near Impacted
Sites
Background
Urban
Industrial
Pristine
Sample
year
NR
NR
NR
87-90
90
95-96
88
88
88
NR
NR
NR
87-90
90
95-96
Ref.
no.
11
11
11
12
17
18
20
9
9
9
11
11
11
12
17
18
20
Comments8
__
Chemical
Sources
Combustion
Sources
Pre-operational
Upper stratum of
sediment core
0-1 cm
0-1 cm
0-1 cm
Chemical
Sources
Combustion
Sources
Pre-operational
Upper stratum of
sediment core
DRAFT-DO NOT QUOTE OR CITE
B-72
December 2003
-------
Table B-8. Environmental Levels of Dioxins in Sediments (ppt) (continued)
Chemical
HxCDDs
Number
samples
1
1
1
4
25
12
3
2
4
4
1
1
5
12
14
7
Number
positive
samples
1
1
0
2
6
10
3
2
4
4
1
1
5
4
9
3
Concentration
range
10
8
ND
ND-14
ND-5,700
ND-1,335
16-49
120-170
130-1,900
2.3-27.0
16
14
580-7,500
ND-355.0
ND-3500
ND-1000
Cone.
mean
10
8
NA
5.2
1,157
399
28
145
608
14
16
14
2,744
55.9
678.4
236.4
Wt.
basis
Dry
Dry
Dry
NR
NR
NR
NR
Dry
Dry
Dry
Dry
Dry
NR
Dry
Dry
Dry
Location
Siskiwit Lake, Isle Royale,
Lake Michigan
Siskiwit Lake, Isle Royale,
Lake Michigan
Siskiwit Lake, Isle Royale,
Lake Michigan
Central Minnesota
Ontario Canada
NY/Mass
Stockholm Sweden
Baltic Sea
Iggesund Sweden
Fjord between Denmark,
Sweden, and Norway
Fjord between Denmark,
Sweden, and Norway
Fjord between Denmark,
Sweden, and Norway
Hamburg Germany
British Columbia
British Columbia
British Columbia
Location
description
Pristine
Pristine
Pristine
Rural
Industrial
NR
Various
Reference Site
Industrial
Industrial
Industrial
Industrial
Urban
Background
Near Impacted
Sites
Near Impacted
Sites
Sample
year
NR
NR
NR
NR
88
NR
NR
NR
NR
87
87
87
NR
NR
NR
NR
Ref.
no.
13
13
13
4
14
15
5
6
6
7
7
7
8
11
11
11
Comments8
0-0.5 cm, A,D
5-6 cm, A,D
8-9 cm, A,D
A/4 sites
A/25 sites
A
A
A
A/paper mill
0-2 cm, E
4-6 cm, E
9-13 cm, E
Chemical
Sources
Combustion
Sources
DRAFT-DO NOT QUOTE OR CITE
B-73
December 2003
-------
Table B-8. Environmental Levels of Dioxins in Sediments (ppt) (continued)
Chemical
HxCDDs (continued)
Number
samples
11
166
5
Number
positive
samples
10
165
5
Concentration
range
ND-157
0.09-950.0
2.4-46
Cone.
mean
43.3
130.2
20
Wt.
basis
Dry
Dry
Dry
Location
Various U.S. Lakes
Connecticut
Southern Mississippi Lakes
Location
description
Background
Urban
Pristine
Sample
year
87-90
95-96
Ref.
no.
12
17
20
Comments8
Pre-operational
Upper stratum of
sediment core
Heptachlorodibenzo-p-dioxins (MW = 425 . 3 1)
1,2,3,4,6,7,8-HpCDD
HpCDDs
12
14
7
11
166
19
5
4
25
12
3
2
4
5
11
3
10
166
19
5
4
20
11
3
2
4
ND-810.0
ND-5100.0
ND-850.0
ND-431
0.94-2,680
28-2,100
11-130
7.3-110
ND-320,000
ND-18,950
880-5,700
79-210
90-340
116.5
811.9
185.1
100.6
306.9
NR
59
71
51,680
4,168
2,233
145
190
Dry
Dry
Dry
Dry
Dry
Dry
Dry
NR
NR
NR
NR
Dry
Dry
British Columbia
British Columbia
British Columbia
Various U.S. Lakes
Connecticut
L. Passaic River & Newark
Bay
Southern Mississippi Lakes
Central Minnesota
Ontario Canada
NY/Mass
Stockholm Sweden
Baltic Sea
Iggesund Sweden
Background
Near Impacted
Sites
Near Impacted
Sites
Background
Urban
Industrial
Pristine
Rural
Industrial
NR
Various
Reference Site
Industrial
NR
NR
NR
87-90
90
95-96
NR
88
NR
NR
NR
NR
11
11
11
12
17
18
20
4
14
15
5
6
6
__
Chemical
Sources
Combustion
Sources
Pre-operational
Upper stratum of
sediment core
A/4 sites
A/25 sites
A
A
A
A/paper mill
DRAFT-DO NOT QUOTE OR CITE
B-74
December 2003
-------
Table B-8. Environmental Levels of Dioxins in Sediments (ppt) (continued)
Chemical
HpCDDs (continued)
Number
samples
4
1
1
5
12
14
7
11
166
5
Number
positive
samples
4
1
1
5
5
1
3
10
166
5
Concentration
range
2.2-19
7.2
7.1
1,300-8,600
ND-1500
ND-8000
ND-1500
ND-828
1.77-5,820
17-300
Cone.
mean
12.4
7.2
7.1
4,040
210.8
1329
325.7
199.0
647.0
134
Wt.
basis
Dry
Dry
Dry
NR
Dry
Dry
Dry
Dry
Dry
Dry
Location
Fjord between Denmark,
Sweden, and Norway
Fjord between Denmark,
Sweden, and Norway
Fjord between Denmark,
Sweden, and Norway
Hamburg Germany
British Columbia
British Columbia
Brisith Columbia
Various U.S. Lakes
Connecticut
Southern Mississippi Lakes
Location
description
Industrial
Industrial
Industrial
Urban
Background
Near Impacted
Sites
Near Impacted
Sites
Background
Urban
Pristine
Sample
year
87
87
87
NR
NR
NR
NR
87-90
95-96
Ref.
no.
7
7
7
8
11
11
11
12
17
20
Comments8
0-2 cm, E
4-6 cm, E
9-13 cm, E
Chemical
Sources
Combustion
Sources
Pre-operational
Upper stratum of
sediment core
Octachlorodibenzo-p-dioxin (MW = 460 . 76)
1,2,3,4,6,7,8,9-OCDD
18
9
4
2
1
1
3
9
4
2
1
1
ND-42
3,100-14,000
5,300-23,000
10,000-31,000
7,500
5,900
6.1
8,100
14,100
20,500
7,500
5,900
Dry
Dry
Dry
Dry
Dry
Dry
South Central Finland
Newark, NJ
Newark, NJ
Newark, NJ
Newark, NJ
Newark, NJ
Various
Industrial
Industrial
Industrial
Industrial
Industrial
88/89
85/86
85/86
85/86
85/86
85/86
1
2
2
2
2
2
A,B
0-2", A,C
2-4", A,C
4-8", A,C
12-16", A,C
20-24", A,C
DRAFT-DO NOT QUOTE OR CITE
B-75
December 2003
-------
Table B-8. Environmental Levels of Dioxins in Sediments (ppt) (continued)
Chemical
1,2,3,4,6,7,8,9-OCDD
(continued)
Number
samples
2
2
3
1
2
1
1
1
1
4
25
12
7
3
2
4
4
Number
positive
samples
2
2
3
1
2
1
1
1
1
4
20
12
7
3
2
4
4
Concentration
range
11,000-19,000
5,500-22,000
5,600-42,000
5,500
4,400-24,000
38,000
560
390
54
450-600
ND-980,000
1,990-15,500
12-250
260-3,100
89-250
96-330
3.6-45
Cone.
mean
15,000
13,800
17,800
5,500
14,200
38,000
560
390
54
518
141,420
8,201
145
1,290
170
194
24
Wt.
basis
Dry
Dry
Dry
Dry
Dry
Dry
Dry
Dry
Dry
NR
NR
NR
Dry
NR
Dry
Dry
Dry
Location
Newark, NJ
Newark, NJ
Newark, NJ
Newark, NJ
Newark, NJ
Newark, NJ
Siskiwit Lake, Isle Royale,
Lake Superior
Siskiwit Lake, Isle Royale,
Lake Superior
Siskiwit Lake, Isle Royale,
Lake Superior
Central Minnesota
Ontario Canada
NY/Mass
Jackfish Bay, Lake Superior
Stockholm Sweden
Baltic Sea
Iggesund Sweden
Fjord between Denmark,
Sweden, and Norway
Location
description
Industrial
Industrial
Industrial
Industrial
Industrial
Industrial
Pristine
Pristine
Pristine
Rural
Industrial
NR
Various
Various
Reference Site
Industrial
Industrial
Sample
year
85/86
85/86
85/86
85/86
85/86
85/86
NR
NR
NR
NR
88
NR
NR
NR
NR
NR
87
Ref.
no.
2
2
2
2
2
2
13
13
13
4
14
15
16
5
6
6
7
Comments8
24-28", A,C
28-32", A,C
32-36", A,C
40-44", A,C
48-52", A,C
108-111", A,C
0-0.5 cm, A,D
5-6 cm, A,D
8-9 cm, A,D
A/4 sites
A/25 sites
A
Papermill,
atmospheric
contamination
A
A
A/paper mill
0-2 cm, E
DRAFT-DO NOT QUOTE OR CITE
B-76
December 2003
-------
Table B-8. Environmental Levels of Dioxins in Sediments (ppt) (continued)
Chemical
1,2,3,4,6,7,8,9-OCDD
(continued)
Number
samples
1
1
5
4
6
5
12
14
7
11
166
19
10
5
Number
positive
samples
1
1
5
4
6
5
8
12
4
11
166
19
10
5
Concentration
range
10
6.9
2,800-15,000
180-16,830
800-2200
1320-6090
ND-4600
ND-23000
ND-2700
3.0-990
7.70-9,170
310-17,000
1,600-39,000
150-7,200
Cone.
mean
10
6.9
7,560
5195
1775
3040
622.1
3079
540.5
400.4
2,131
NR
NR
2,560
Wt.
basis
Dry
Dry
NR
NR
NR
NR
Dry
Dry
Dry
Dry
Dry
Dry
Dry
Dry
Location
Fjord between Denmark,
Sweden, and Norway
Fjord between Denmark,
Sweden, and Norway
Hamburg Germany
Dala River, Sweden
Lake Vattern, Sweden
Lake Vanern, Sweden
Brisith Columbia
British Columbia
British Columbia
Various U.S. Lakes
Connecticut
L. Passaic River & Newark
Bay
Southern Mississippi Lakes
Southern Mississippi Lakes
Location
description
Industrial
Industrial
Urban
Industrial
Industrial
Industrial
Background
Near Impacted
Sites
Near Impacted
Sites
Background
Urban
Industrial
Pristine
Pristine
Sample
year
87
87
NR
88
88
88
NR
NR
NR
—
87-90
90
95-96
95-96
Ref.
no.
7
7
8
9
9
9
11
11
11
12
17
18
19
20
Comments8
4-6 cm, E
9-13 cm, E
0-1 cm
0-1 cm
0-1 cm
Chemical
Sources
Combustion
Sources
—
Pre-operational
Sedimentation
area
Upper stratum of
sediment core
'Key: A LOD (Ref 1) = 20 to 50 ppt, LOD (Ref 2) = 22 to 60 ppt, LOD (Ref 3) = 0.4 ppt, LOD (Ref 4) = 0.7 to 12.0 ppt, LOD (Ref 5) = 0.61 to 4.1 ppt, LOD (Ref 8) = 10 to 500 ppt, LOD (Ref 10) = 3 ppt,
LOD (Ref 12) = 1 to 20 ppt, LOD (Ref 13) = 1 to 6.6 ppt.
B Dry surface sediments from 18 lakes.
C Industry produced 2,4,5-Trichlorophenate (2,4,5-T precursor).
D No anthropogenic inputs into drainage basin—only atmospheric sources into lake.
E Mg-Production Facility.
DRAFT-DO NOT QUOTE OR CITE
B-77
December 2003
-------
Notes
NR = Not Reported
NA = Not Applicable
ND = Not Detected
ppt = Parts per trillion
Sources:
Table B-8. Environmental Levels of Dioxins in Sediments (ppt) (continued)
1. Koistinenetal. (1990)
2. Bopp etal. (1991)
3. Norwood etal. (1989)
4. Reed etal. (1990)
5. Rappe and Kjeller (1987)
6. Rappe et al. (1989b)
7. Oehmeetal. (1989)
8. Gotz and Schumacher (1990)
9. Kjeller etal. (1990)
10. Fiedler etal. (1995)
11. B.C. Environment (1995)
12. Cleverly et al. (1996)
13. Czuczwa et al. (1984)
14. McKeeetal. (1990)
15. Petty et al. (1982)
16. Sherman et al. (1990)
17. MRI (1992)
18. Wenning etal. (1992)
19. Rappe etal. (1997a)
20. Rappe etal. (1997b)
DRAFT-DO NOT QUOTE OR CITE
B-78
December 2003
-------
Table B-9. Environmental Levels of Dibenzofurans in Sediment (ppt)
Chemical
Number
samples
Number
positive
samples
Concentration
range
Cone.
mean
Weight
basis
Location
Location
description
Sample
year
Ref.
no.
Comments3
Tetrachlorodibenzofiirans (MW = 305 . 98)
2,3,7,8-TCDF
18
9
4
2
1
1
2
3
3
1
2
1
1
12
4
4
2
4
4
0
9
4
2
1
1
2
2
3
1
2
1
1
12
0
1
2
4
4
ND(20-50)
24-490
150-1,400
580-1,200
370
300
300-390
ND-530
190-730
140
80-3,100
4,500
15
8.8-1,400
ND(0.7-12)
ND-0.31
8.3-14
11-210
0.7-9.6
NA
232
855
890
370
300
345
243
370
140
1,590
4,500
15
566
NA
0.08
11
72
5.2
Dry
Dry
Dry
Dry
Dry
Dry
Dry
Dry
Dry
Dry
Dry
Dry
Dry
Dry
Dry
NR
Dry
Dry
Dry
South Central Finland
Newark, NJ
Newark, NJ
Newark, NJ
Newark, NJ
Newark, NJ
Newark, NJ
Newark, NJ
Newark, NJ
Newark, NJ
Newark, NJ
Newark, NJ
Long Island Sound
New England
Seattle, WA
Central Minnesota
Baltic Sea
Iggesund Sweden
Fjord between Sweden,
Norway and Denmark
Various
Industrial
Industrial
Industrial
Industrial
Industrial
Industrial
Industrial
Industrial
Industrial
Industrial
Industrial
Reference Site
Industrial
Industrial
Rural
Reference Site
Industrial
Industrial
88/89
85/86
85/86
85/86
85/86
85/86
85/86
85/86
85/86
85/86
85/86
85/86
NR
NR
NR
NR
NR
NR
87
1
2
2
2
2
2
2
2
2
2
2
2
3
3
3
4
5
5
6
A,B
0-2", A,C
2-4", A,C
4-8", A,C
12-16", A,C
20-24", A,C
24-28", A,C
28-32", A,C
32-36", A,C
40-44", A,C
48-52", A,C
108-111", A,C
A
A/3 sites
A
A/4 sites
A
A/papermill
0-2cm, E
DRAFT-DO NOT QUOTE OR CITE
B-79
December 2003
-------
Table B-9. Environmental Levels of Dibenzofurans in Sediment (ppt) (continued)
Chemical
2,3,7,8-TCDF (continued)
TCDFs
Number
samples
1
1
4
6
5
12
14
7
11
142
19
5
1
1
1
4
Number
positive
samples
1
1
4
6
5
1
3
2
8
141
19
3
1
1
0
2
Concentration
range
13
5.2
4.9-170
41-320
54-810
ND-17
ND-3.0
ND-12.0
ND-8.44
0.03-101
2.8-480
ND-0.18
15
18
ND(0.4)
ND-0.54
Cone.
mean
13
5.2
46.5
176
241
1.42
3.8
3.0
1.95
13.56
NR
NR
15
18
NA
0.21
Weight
basis
Dry
Dry
NR
NR
NR
Dry
Dry
Dry
Dry
Dry
Dry
Dry
Dry
Dry
Dry
NR
Location
Fjord between Sweden,
Norway and Denmark
Fjord between Sweden,
Norway and Denmark
Dala River, Sweden
Lake Vattern, Sweden
Lake Vanern, Sweden
British Columbia
British Columbia
British Columbia
Various U.S. Lakes
Connecticut
L. Passaic River &
Newark Bay
Southern Mississippi
Lakes
Siskiwit Lake, Isle
Royale, Lake Superior
Siskiwit Lake, Isle
Royale, Lake Superior
Siskiwit Lake, Isle
Royale, Lake Superior
Central Minnesota
Location
description
Industrial
Industrial
Industrial
Industrial
Industrial
Background
Near Impacted Sites
Near Impacted Sites
Background
Urban
Industrial
Pristine
Pristine
Pristine
Pristine
Rural
Sample
year
87
87
88
88
88
NR
NR
NR
87-90
90
95-96
NR
NR
NR
NR
Ref.
no.
6
6
7
7
7
8
8
8
9
16
17
18
10
10
10
4
Comments3
4-6cm, E
9- 13cm, E
0-1 cm
0-1 cm
0-1 cm
Chemical
Sources
Combustion
Sources
Pre-operational
Upper stratum of
sediment core
0-0. 5cm, A,D
5-6cm, A,D
8-9cm, A,D
A/4 sites
DRAFT-DO NOT QUOTE OR CITE
B-80
December 2003
-------
Table B-9. Environmental Levels of Dibenzofurans in Sediment (ppt) (continued)
Chemical
TCDFs (continued)
Number
samples
25
12
7
3
2
4
4
1
1
5
12
14
7
11
142
5
Number
positive
samples
0
11
7
3
2
4
4
1
1
5
1
5
2
11
141
3
Concentration
range
ND(10-700)
ND-200
2.4-6,223
120-290
87-130
79-360
6.7-54
63
23
170-1070
ND-17.0
ND-88
ND-47.0
1.68-91.7
0.03-290
ND-0.18
Cone.
mean
NA
58
1,260
187
109
180
30
63
23
526
1.42
15.9
9.9
25.7
62.78
NR
Weight
basis
NR
NR
Dry
NR
Dry
Dry
Dry
Dry
Dry
NR
Dry
Dry
Dry
Dry
Dry
Dry
Location
Ontario Canada
NY/MASS
Jackfish Bay, Lake
Superior
Stockholm Sweden
Baltic Sea
Iggesund Sweden
Fjord between Sweden,
Norway and Denmark
Fjord between Sweden,
Norway and Denmark
Fjord between Sweden,
Norway and Denmark
Hamburg Germany
British Columbia
British Columbia
British Columbia
Various U.S. Lakes
Connecticut
Southern Mississippi
Lakes
Location
description
Industrial
NR
Various
Various
Reference Site
Industrial
Industrial
Industrial
Industrial
Urban
Background
Near Impacted Sites
Near Impacted Sites
Background
Urban
Pristine
Sample
year
88
NR
NR
NR
NR
NR
87
87
87
NR
NR
NR
NR
87-90
95-96
Ref.
no.
11
12
13
14
5
5
6
6
6
15
8
8
8
9
16
18
Comments3
A/25 sites
A
Papermill/
atmospheric
contamination
A
A
A/papermill
0-2cm, E
4-6cm, E
9- 13cm, E
Chemical
Sources
Combustion
Sources
Pre-operational
Upper stratum of
sediment core
DRAFT-DO NOT QUOTE OR CITE
B-81
December 2003
-------
Table B-9. Environmental Levels of Dibenzofurans in Sediment (ppt) (continued)
Chemical
Number
samples
Number
positive
samples
Concentration
range
Cone.
mean
Weight
basis
Location
Location
description
Sample
year
Ref.
no.
Comments3
Pentachlorodibenzofiirans (MW=340.42)
1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
4
6
5
12
14
7
11
163
19
5
4
6
5
12
14
7
4
6
5
0
2
1
8
160
6
2
4
6
5
0
2
1
5.6-110
27-120
50-300
ND
ND-8.8
ND-4.4
ND-3.1
0.01-37.80
ND-36
ND-0.3
7.8-99
25-110
36-250
ND
ND-14.0
ND-7.5
34.4
74.3
120
NA
0.68
0.63
0.94
4.98
NR
NR
34.7
74.2
108
NA
1.06
1.07
NR
NR
NR
Dry
Dry
Dry
Dry
Dry
Dry
Dry
NR
NR
NR
Dry
Dry
Dry
Dala River, Sweden
Lake Vattern, Sweden
Lake Vanern, Sweden
British Columbia
British Columbia
British Columbia
Various U.S. Lakes
Connecticut
L. Passaic River &
Newark Bay
Southern Mississippi
Lakes
Dala River, Sweden
Lake Vattern, Sweden
Lake Vanern, Sweden
British Columbia
British Columbia
British Columbia
Industrial
Industrial
Industrial
Background
Near Impacted Sites
Near Impacted Sites
Background
Urban
Industrial
Pristine
Industrial
Industrial
Industrial
Background
Near Impacted Sites
Near Impacted Sites
88
88
88
NR
NR
NR
87-90
90
95-96
88
88
88
NR
NR
NR
7
7
7
8
8
8
9
16
17
18
7
7
7
8
8
8
0-1 cm
0-1 cm
0-1 cm
Chemical
Sources
Combustion
Sources
Pre-operational
Upper stratum of
sediment core
0-1 cm
0-1 cm
0-1 cm
Chemical
Sources
Combustion
Sources
DRAFT-DO NOT QUOTE OR CITE
B-82
December 2003
-------
Table B-9. Environmental Levels of Dibenzofurans in Sediment (ppt) (continued)
Chemical
2,3,4,7,8-PeCDF
(continued)
PeCDFs
Number
samples
11
163
19
5
1
1
1
4
25
12
3
2
4
4
1
1
Number
positive
samples
9
158
11
2
1
1
0
2
0
9
3
2
4
4
1
1
Concentration
range
ND-5.3
0.01-49.50
ND-420
ND-0.078
5.0
2.0
ND(0.4)
ND-25
ND(10-700)
ND-193
130-260
66-125
48-58
7.7-81
24
44
Cone.
mean
1.52
5.36
NR
NR
5.0
2.0
NA
7.4
NA
64
177
96
55
47
24
44
Weight
basis
Dry
Dry
Dry
Dry
Dry
Dry
Dry
NR
NR
NR
NR
Dry
Dry
Dry
Dry
Dry
Location
Various U.S. Lakes
Connecticut
L. Passaic River &
Newark Bay
Southern Mississippi
Lakes
Siskiwit Lake, Isle
Royale, Lake Michigan
Siskiwit Lake, Isle
Royale, Lake Michigan
Siskiwit Lake, Isle
Royale, Lake Michigan
Central Minnesota
Ontario Canada
NY/MASS
Stockholm Sweden
Baltic Sea
Iggesund Sweden
Fjord between Sweden,
Norway and Denmark
Fjord between Sweden,
Norway and Denmark
Fjord between Sweden,
Norway and Denmark
Location
description
Background
Urban
Industrial
Pristine
Pristine
Pristine
Pristine
Rural
Industrial
NR
Various
Reference Site
Industrial
Industrial
Industrial
Industrial
Sample
year
87-90
90
95-96
NR
NR
NR
NR
88
NR
NR
NR
NR
87
87
87
Ref.
no.
9
16
17
18
10
10
10
4
11
12
14
5
5
6
6
6
Comments3
Pre-operational
Upper stratum of
sediment core
0-0.5cm A,D
5-6cm A,D
8-9cm A,D
A/4 sites
A/25 sites
A
A
A
A/papermill
0-2cm, E
4-6cm, E
9- 13cm, E
DRAFT-DO NOT QUOTE OR CITE
B-83
December 2003
-------
Table B-9. Environmental Levels of Dibenzofurans in Sediment (ppt) (continued)
Chemical
PeCDFs (continued)
Number
samples
5
12
14
7
11
163
5
Number
positive
samples
5
3
8
2
11
163
2
Concentration
range
1,300-5,200
ND-86.0
ND-710.0
ND-310.0
0.384-66.8
0.20-775.0
ND-0.52
Cone.
mean
2,980
14.3
134.4
53.5
17.8
103.0
NR
Weight
basis
NR
Dry
Dry
Dry
Dry
Dry
Dry
Location
Hamburg Germany
British Columbia
British Columbia
British Columbia
Various U.S. Lakes
Connecticut
Southern Mississippi
Lakes
Location
description
Urban
Background
Near Impacted Sites
Near Impacted Sites
Background
Urban
Pristine
Sample
year
NR
NR
NR
NR
87-90
95-96
Ref.
no.
15
8
8
8
9
16
18
Comments3
Chemical
Sources
Combustion
Sources
Pre-operational
Upper stratum of
sediment core
Hexachlorodibenzoiurans (MW=374.87)
1,2,3,4,7,8-HxCDF
1,2,3,6,7,8-HxCDF
4
6
5
12
14
7
11
166
19
5
4
4
6
5
1
5
2
10
163
10
1
4
9.3-120
28-170
32-460
ND-13.0
ND-82.0
ND-30.0
ND-4.67
0.03-193.0
ND-410
ND-0.11
3.7-73
44.3
89.5
163
1.08
12.3
5.26
1.87
16.12
NR
NR
26.7
NR
NR
NR
Dry
Dry
Dry
Dry
Dry
Dry
Dry
NR
Dala River, Sweden
Lake Vattern, Sweden
Lake Vanern, Sweden
British Columbia
British Columbia
British Columbia
Various U.S. Lakes
Connecticut
L. Passaic River &
Newark Bay
Southern Mississippi
Lakes
Dala River, Sweden
Industrial
Industrial
Industrial
Background
Near Impacted Sites
Near Impacted Sites
Background
Urban
Industrial
Pristine
Industrial
88
88
88
NR
NR
NR
87-90
90
95-96
88
7
7
7
8
8
8
9
16
17
18
7
0-1 cm
0-1 cm
0-1 cm
Chemical
Sources
Combustion
Sources
Pre-operational
Upper stratum of
sediment core
0-1 cm
DRAFT-DO NOT QUOTE OR CITE
B-84
December 2003
-------
Table B-9. Environmental Levels of Dibenzofurans in Sediment (ppt) (continued)
Chemical
1,2,3,7,8,9-HxCDF
Number
samples
6
5
12
14
7
11
166
19
5
4
6
5
12
14
7
11
Number
positive
samples
6
5
1
3
1
0
165
8
0
3
2
2
0
2
2
1
Concentration
range
15-110
25-140
ND-12.0
ND-98.0
ND-24.0
ND
0.01-66.70
ND-73
ND
ND(2)-25
ND-4.4
ND-14
ND
ND-40.0
ND-30.0
ND-0.20
Cone.
mean
64.7
73.4
1.00
10.2
3.4
NA
6.95
NR
NR
9.38
1.58
5.68
NA
2.9
5.4
0.25
Weight
basis
NR
NR
Dry
Dry
Dry
Dry
Dry
Dry
Dry
NR
NR
NR
Dry
Dry
Dry
Dry
Location
Lake Vattern, Sweden
Lake Vanern, Sweden
British Columbia
British Columbia
British Columbia
Various U.S. Lakes
Connecticut
L. Passaic River &
Newark Bay
Southern Mississippi
Lakes
Dala River, Sweden
Lake Vattern, Sweden
Lake Vanern, Sweden
British Columbia
British Columbia
British Columbia
Various U.S. Lakes
Location
description
Industrial
Industrial
Background
Near Impacted Sites
Near Impacted Sites
Background
Urban
Industrial
Pristine
Industrial
Industrial
Industrial
Background
Near Impacted Sites
Near Impacted Sites
Background
Sample
year
88
88
NR
NR
NR
87-90
90
95-96
88
88
88
NR
NR
NR
Ref.
no.
7
7
8
8
8
9
16
17
18
7
7
7
8
8
8
9
Comments3
0-1 cm
0-1 cm
Chemical
Sources
Combustion
Sources
Pre-operational
Upper stratum of
sediment core
0-1 cm
0-1 cm
0-1 cm
Chemical
Sources
Combustion
Sources
DRAFT-DO NOT QUOTE OR CITE
B-85
December 2003
-------
Table B-9. Environmental Levels of Dibenzofurans in Sediment (ppt) (continued)
Chemical
1,2,3,7,8,9-HxCDF
(continued)
2,3,4,6,7,8-HxCDF
HxCDF
Number
samples
166
19
5
4
6
5
12
14
7
11
166
19
5
1
1
1
Number
positive
samples
84
10
0
4
6
5
0
0
0
10
164
0
0
1
1
0
Concentration
range
0.01-10.30
ND-29
ND
1.8-78
32-130
36-110
ND
ND
ND
ND-4.43
0.11-71.10
ND
ND
2.0
2.0
ND(0.4)
Cone.
mean
1.26
NR
NR
26.2
77.7
70.8
NA
NA
NA
1.68
8.41
NR
NR
2.0
2.0
NA
Weight
basis
Dry
Dry
Dry
NR
NR
NR
Dry
Dry
Dry
Dry
Dry
Dry
Dry
Dry
Dry
Dry
Location
Connecticut
L. Passaic River &
Newark Bay
Southern Mississippi
Lakes
Dala River, Sweden
Lake Vattern, Sweden
Lake Vanern, Sweden
British Columbia
British Columbia
British Columbia
Various U.S. Lakes
Connecticut
L. Passaic River &
Newark Bay
Southern Mississippi
Lakes
Siskiwit Lake, Isle
Royale, Lake Michigan
Siskiwit Lake, Isle
Royale, Lake Michigan
Siskiwit Lake, Isle
Royale, Lake Michigan
Location
description
Urban
Industrial
Pristine
Industrial
Industrial
Industrial
Background
Near Impacted Sites
Near Impacted Sites
Background
Urban
Industrial
Pristine
Pristine
Pristine
Pristine
Sample
year
87-90
90
95-96
88
88
88
NR
NR
NR
87-90
90
95-96
NR
NR
NR
Ref.
no.
16
17
18
7
7
7
8
8
8
9
16
17
18
10
10
10
Comments3
Pre-operational
Upper stratum of
sediment core
0-1 cm
0-1 cm
0-1 cm
Chemical
Sources
Combustion
Sources
Pre-operational
Upper stratum of
sediment core
0-0. 5cm, A,D
5-6cm, A,D
8-9cm, A,D
DRAFT-DO NOT QUOTE OR CITE
B-86
December 2003
-------
Table B-9. Environmental Levels of Dibenzofurans in Sediment (ppt) (continued)
Chemical
HxCDF (continued)
Number
samples
4
25
12
3
2
4
4
1
1
5
12
14
7
11
166
5
Number
positive
samples
1
17
10
3
2
4
4
1
1
5
3
10
2
10
166
2
Concentration
range
ND-12
ND-6,500
ND-377
92-250
78-150
59-150
23-283
166
131
930-8,600
ND-405.0
ND-3300
ND-1700
ND-145
0.44-908.0
ND-1.8
Cone.
mean
3.0
1,339
133
187
114
104
148
166
131
4,106
59.3
840.7
347.1
38.0
132.0
NR
Weight
basis
NR
NR
NR
NR
Dry
Dry
Dry
Dry
Dry
NR
Dry
Dry
Dry
Dry
Dry
Dry
Location
Central Minnesota
Ontario Canada
NY/MASS
Stockholm Sweden
Baltic Sea
Iggesund Sweden
Fjord between Sweden,
Norway and Denmark
Fjord between Sweden,
Norway and Denmark
Fjord between Sweden,
Norway and Denmark
Hamburg Germany
British Columbia
British Columbia
British Columbia
Various U.S. Lakes
Connecticut
Southern Mississippi
Lakes
Location
description
Rural
Industrial
NR
Various
Reference Site
Industrial
Industrial
Industrial
Industrial
Urban
Background
Near Impacted Sites
Near Impacted Sites
Background
Urban
Pristine
Sample
year
NR
88
NR
NR
NR
NR
87
87
87
NR
NR
NR
NR
87-90
95-96
Ref.
no.
4
11
12
14
5
5
6
6
6
15
8
8
8
9
16
18
Comments3
A/4 sites
A/25 sites
A
A
A
A/papermill
0-2cm, E
4-6cm, E
9- 13cm, E
Chemical
Sources
Combustion
Sources
Pre-operational
Upper stratum of
sediment core
DRAFT-DO NOT QUOTE OR CITE
B-87
December 2003
-------
Table B-9. Environmental Levels of Dibenzofurans in Sediment (ppt) (continued)
Chemical
Number
samples
Number
positive
samples
Concentration
range
Cone.
mean
Weight
basis
Location
Location
description
Sample
year
Ref.
no.
Comments3
Heptachlorodibenzofiirans (MW =409.31)
1,2,3,4,6,7,8-HpCDF
1,2,3,4,7,8,9-HpCDF
8
12
10
12
14
7
11
166
19
5
4
6
5
12
14
8
12
10
3
11
3
1
166
18
5
3
6
4
1
3
59-2180
130-1030
21-8400
ND-310.0
ND-2800
ND-700
ND-0.14
0.23-1,148
ND- 1,800
0.33-3.0
ND-42
4.3-91
ND-260
ND-7.7
ND-59.0
558
584
1764
43.97
604.0
134.1
33.2
93.58
NR
1.2
14.2
33.2
78.3
0.64
8.2
NR
NR
NR
Dry
Dry
Dry
Dry
Dry
Dry
Dry
NR
NR
NR
Dry
Dry
Dala River, Sweden
Lake Vattern, Sweden
Lake Vanern, Sweden
British Columbia
British Columbia
British Columbia
Various U.S. Lakes
Connecticut
L. Passaic River &
Newark Bay
Southern Mississippi
Lakes
Dala River, Sweden
Lake Vattern, Sweden
Lake Vanern, Sweden
British Columbia
British Columbia
Industrial
Industrial
Industrial
Background
Near Impacted Sites
Near Impacted Sites
Background
Urban
Industrial
Pristine
Industrial
Industrial
Industrial
Background
Near Impacted Sites
88
88
88
NR
NR
NR
87-90
90
95-96
88
88
88
NR
NR
7
7
7
8
8
8
9
16
17
18
7
7
7
8
8
0-1 cm
0-1 cm
0-1 cm
Chemical
Sources
Combustion
Sources
Pre-operational
Upper stratum of
sediment core
0-1 cm
0-1 cm
0-1 cm
Chemical
Sources
DRAFT-DO NOT QUOTE OR CITE
B-88
December 2003
-------
Table B-9. Environmental Levels of Dibenzofurans in Sediment (ppt) (continued)
Chemical
1,2,3,4,7,8,9-HpCDF
(continued)
HpCDFs
Number
samples
7
11
166
19
5
4
25
12
3
2
4
4
1
1
5
12
Number
positive
samples
1
8
158
7
0
3
20
10
3
2
4
4
1
1
5
3
Concentration
range
ND-17.0
ND-8.9
0.07-205.0
ND-48
ND
ND-30
ND-53,000
ND-2,436
190-1,500
79-180
11-410
20-158
43
192
560-4,300
ND-790.0
Cone.
mean
2.43
2.13
6.52
NR
NR
16
11,715
1,039
997
130
178
100
43
192
2,358
115.6
Weight
basis
Dry
Dry
Dry
Dry
Dry
NR
NR
NR
NR
Dry
Dry
Dry
Dry
Dry
NR
Dry
Location
British Columbia
Various U.S. Lakes
Connecticut
L. Passaic River &
Newark Bay
Southern Mississippi
Lakes
Central Minnesota
Ontario Canada
NY/MASS
Stockholm Sweden
Baltic Sea
Iggesund Sweden
Fjord between Sweden,
Norway and Denmark
Fjord between Sweden,
Norway and Denmark
Fjord between Sweden,
Norway and Denmark
Hamburg Germany
British Columbia
Location
description
Near Impacted Sites
Background
Urban
Industrial
Pristine
Rural
Industrial
NR
Various
Reference Site
Industrial
Industrial
Industrial
Industrial
Urban
Background
Sample
year
NR
87-90
90
95-96
NR
88
NR
NR
NR
NR
87
87
87
NR
NR
Ref.
no.
8
9
16
17
18
4
11
12
14
5
5
6
6
6
15
8
Comments3
Combustion
Sources
Pre-operational
Upper stratum of
sediment core
A/4 sites
A/25 sites
A
A
A
A/papermill
0-2cm, E
4-6cm, E
9- 13cm, E
DRAFT-DO NOT QUOTE OR CITE
B-89
December 2003
-------
Table B-9. Environmental Levels of Dibenzofurans in Sediment (ppt) (continued)
Chemical
HpCDFs
(continued)
Number
samples
14
7
11
166
5
Number
positive
samples
11
3
10
166
5
Concentration
range
ND-9900
ND-2000
ND-349
0.56-1,270
0.39-5.9
Cone.
mean
1772
3743
82.8
190.2
2.1
Weight
basis
Dry
Dry
Dry
Dry
Dry
Location
British Columbia
British Columbia
Various U.S. Lakes
Connecticut
Southern Mississippi
Lakes
Location
description
Near Impacted Sites
Near Impacted Sites
Background
Urban
Pristine
Sample
year
NR
NR
87-90
95-96
Ref.
no.
8
8
9
16
18
Comments3
Chemical
Sources
Combustion
Sources
Pre-operational
Upper stratum of
sediment core
Octachlorodibenzoiurans (MW=444.76)
1,2,3,4,6,7,8,9-OCDF
1,2,3,4,6,7,8,9-OCDF
(continued)
18
1
1
1
4
25
12
3
2
4
4
1
3
1
1
1
1
21
11
1
1
2
4
1
ND-160
4
3.2
1.1
ND-23
ND-400,000
ND-1,010
ND-39
ND-3.8
ND-15
58-151
43
14
4
3.2
1.1
5.8
34,912
460
14
1.9
5
96
43
Dry
Dry
Dry
Dry
NR
NR
NR
NR
Dry
Dry
Dry
Dry
South Central Finland
Siskiwit Lake, Isle
Royale, Lake Michigan
Siskiwit Lake, Isle
Royale, Lake Michigan
Siskiwit Lake, Isle
Royale, Lake Michigan
Central Minnesota
Ontario Canada
NY/MASS
Stockholm Sweden
Baltic Sea
Iggesund Sweden
Fjord between Sweden,
Norway and Denmark
Fjord between Sweden,
Norway and Denmark
Various
Pristine
Pristine
Pristine
Rural
Industrial
NR
Various
Reference Site
Industrial
Industrial
Industrial
88/89
NR
NR
NR
NR
88
NR
NR
NR
NR
87
87
1
10
10
10
4
11
12
14
5
5
6
6
A,B
0-0. 5cm, A,D
5-6cm, A,D
8-9cm, A,D
A/4 sites
A/25 sites
A
A
A
A/Papermill
0-2cm, E
4-6cm, E
DRAFT-DO NOT QUOTE OR CITE
B-90
December 2003
-------
Table B-9. Environmental Levels of Dibenzofurans in Sediment (ppt) (continued)
Chemical
Number
samples
1
5
4
6
5
12
14
7
11
166
19
5
Number
positive
samples
1
5
4
6
5
3
10
3
9
166
18
4
Concentration
range
192
660-5,200
150-4250
170-1310
230-79,250
ND-330.0
ND-3200
ND-520.0
ND-385
7.70-9,170
ND-2,400
ND-4.8
Cone.
mean
192
2,712
1212
602
19,356
31.7
639.0
100.7
103.6
2,131
NR
NR
Weight
basis
Dry
NR
NR
NR
NR
Dry
Dry
Dry
Dry
Dry
Dry
Dry
Location
Fjord between Sweden,
Norway and Denmark
Hamburg Germany
Dala River, Sweden
Lake Vattern, Sweden
Lake Vanern, Sweden
British Columbia
British Columbia
British Columbia
Various U.S. Lakes
Connecticut
L. Passaic River &
Newark Bay
Southern Mississippi
Lakes
Location
description
Industrial
Urban
Industrial
Industrial
Industrial
Background
Near Impacted Sites
Near Impacted Sites
Background
Urban
Industrial
Pristine
Sample
year
87
NR
88
88
88
NR
NR
NR
—
87-90
90
95-96
Ref.
no.
6
15
7
7
7
8
8
8
9
16
17
18
Comments3
9- 13cm, E
0-1 cm
0-1 cm
0-1 cm
Chemical
Sources
Combustion
Sources
—
Pre-operational
Upper stratum of
sediment core
1 Key: A LOD (Ref. 1) = 20 to 50 ppt, LOD (Ref. 2) = 32 ppt, LOD (Ref. 3) = 0.4 ppt, LOD (Ref. 4) = 0.7 to 12.0 ppt, LOD (Ref. 5) = 0.61 to 4.1 ppt, LOD (Ref.
= 3 ppt, LOD (ref. 12) = 1 to 20 ppt, LOD (Ref. 13) = 1 to 6.6 ppt.
B Dry surface sediments from 18 lakes.
C Industry produced 2,4,5 trichlorophenate (2,4,5T precursor).
D No anthropogenic inputs into drainage basin — only atmospheric sources into lake.
E Mg-production facility.
= 10 to 700 ppt, LOD (Ref. 10)
DRAFT-DO NOT QUOTE OR CITE
B-91
December 2003
-------
Notes
NR = Not Reported
NA = Not Applicable
ND = Not Detected
ppt = Parts per trillion
Table B-9. Environmental Levels of Dibenzofurans in Sediment (ppt) (continued)
Sources:
1. Koistinenetal. (1990) 10.
2. Boppet al. (1991) 11.
3. Norwood etal. (1989) 12.
4. Reed etal. (1990) 13.
5. Rappeetal. (1989b) 14.
6. Oehmeetal. (1989) 15.
7. Kjeller etal. (1990) 16.
8. BC Environment (1995) 17.
9. Cleverly etal. (1996) 18.
Czuczwa et al. (1984)
McKeeetal. (1990)
Petty et al. (1982)
Sherman et al. (1990)
Rappe and Kjeller (1987)
Gotz and Schumacher (1990)
MRI (1992)
Wenning et al. (1992)
Rappeetal. (1997b)
DRAFT-DO NOT QUOTE OR CITE
B-92
December 2003
-------
Table B-10. Environmental Levels of PCBs in Sediment (ppt)
IUPAC
number Chemical
Number
samples
Number
positive
samples
Concentration
range
Cone.
mean
Wt. basis
Location
Location
description
Sample
year
Ref.
no.
Comments3
Tetrachloro-PCB (MW=291.99)
77 3,3',4,4'-TCB
81 3,4,4',5-TCB
18
8
5
NR
11
8
NR
13
8
5
NR
11
3
NR
ND-550
500-360,000
5,000-27.5M
NR
3.16-93.3
ND-90,000
NR
138
64,250
9.47M
ND(500)
26.6
26,880
ND(500)
Dry
Dry
NR
Dry
Dry
Dry
Dry
South Central Finland
Eastern Wisconsin
Waukegan, Illinois
Green Bay, Lake
Michigan
Various U.S. Lakes
Eastern Wisconsin
Green Bay, Lake
Michigan
Various
Industrial
Urban
Various
Background
Industrial
Various
88/89
88/89
78
NR
88/89
NR
1
2
3
4
5
2
4
A,B
A,D
5 sites
A
A,D
A
PentacWoro-PCB (MW=326.44)
126 3,3',4,4',5-PeCB
105 2,3,3',4,4'-PeCB
18
8
NR
11
10
8
38
5
1
2
NR
9
10
8
NR
5
ND-110
ND-10,000
NR
ND-18.6
52-120
6,000-
490,000
NR
102,000-
131M
6.1
2,380
ND(500)
4.48
96.4
85,120
10,000
35.14M
Dry
Dry
Dry
Dry
Dry
Dry
Dry
NR
South Central Finland
Eastern Wisconsin
Green Bay, Lake
Michigan
Various U.S. Lakes
South Central Finland
Eastern Wisconsin
Lake Ontario
Waukegan, Illinois
Various
Industrial
Various
Background
Various
Industrial
Various
Urban
88/89
88/89
NR
88/89
88/89
81
78
1
2
4
5
1
2
6
3
A,B
A,D
A
A,B,C
A,D
Bottom Sediment
5 Locations
DRAFT-DO NOT QUOTE OR CITE
B-93
December 2003
-------
Table B-10. Environmental Levels of PCBs in Sediment (ppt) (continued)
IUPAC
number Chemical
105 2,3,3',4,4'-PeCB
(continued)
114 2,3,4,4',5-PeCB
118 2,3',4,4',5-PeCB
Number
samples
NR
11
8
NR
8
38
NR
2
11
Number
positive
samples
NR
11
1
NR
8
NR
NR
0
11
Concentration
range
NR
18.3-446.0
ND-1 10,000
NR
86,000-
1.48M
NR
NR
0.01
53.4-1350
Cone.
mean
5,800
123.2
13,750
1,000
351,620
15,000
11,000
0.01
323.2
Wt. basis
Dry
Dry
Dry
Dry
Dry
Dry
Dry
Dry
Dry
Location
Green Bay, Lake
Michigan
Various U.S. Lakes
Eastern Wisconsin
Green Bay, Lake
Michigan
Eastern Wisconsin
Lake Ontario
Green Bay, Lake
Michigan
Alicante, Spain
Various U.S. Lakes
Location
description
Various
Background
Industrial
Various
Industrial
Various
Various
Coastal
Background
Sample
year
NR
88/89
NR
88/89
81
NR
89/90
Ref.
no.
4
5
2
4
2
6
4
7
5
Comments3
A
A,D
A
A,D
Bottom Sediment
A
Hexachloro-PCB (MW=360.88)
156 2,3,3',4,4',5-HxCB
157 1,2,3',4,4',5'HxCB
167 2,3',4,4',5,5'-HxCB
38
NR
11
11
8
NR
NR
NR
11
11
2
NR
NR
NR
4.6-203
1.3-53.2
ND-80,000
NR
2,100
1,700
45.9
12.2
15,500
ND(500)
Dry
Dry
Dry
Dry
Dry
Dry
Lake Ontario
Green Bay, Lake
Michigan
Various U.S. Lakes
Various U.S. Lakes
Eastern Wisconsin
Green Bay, Lake
Michigan
Various
Various
Background
Background
Industrial
Various
81
NR
88/89
NR
6
4
5
5
2
4
Bottom Sediment
A
A,D
A
DRAFT-DO NOT QUOTE OR CITE
B-94
December 2003
-------
Table B-10. Environmental Levels of PCBs in Sediment (ppt) (continued)
IUPAC
number Chemical
169 3,3',4,4',5,5'-HxCB
Number
samples
18
8
NR
11
Number
positive
samples
0
3
NR
10
Concentration
range
ND(20-50)
ND-19,000
NR
ND-2.47
Cone.
mean
NA
4,800
ND(500)
0.94
Wt. basis
Dry
Dry
Dry
Dry
Location
South Central Finland
Eastern Wisconsin
Green Bay, Lake
Michigan
Various U.S. Lakes
Location
description
Various
Industrial
Various
Background
Sample
year
88/89
88/89
NR
Ref.
no.
1
2
4
5
Comments3
A,B
A,D
A
HeptacWoro-PCB (MW=396.33)
189 2,3,3',4,4',5,5'HpCB
NR
NR
NR
ND(500)
Dry
Green Bay, Lake
Michigan
Various
NR
4
A
1 Key: A LOD (Ref. 1) = 20 to 50 ppt, LOD (Ref. 6) = 1,000 ppt, LOD (Ref. 16)=500 ppt.
B Dry Surface Sediments from 18 lakes.
C All collected samples not analyzed.
D Superfund/Michigan "Area of Concern" Site.
NOTES:
NR = Not Reported
NA = Not Applicable
ND = Not Detected
ppt = Parts per trillion
Sources:
1. Koistinen et al. (1990)
2. Sonzogni et al. (1991)
3. Huckinsetal. (1988)
4. Smith et al. (1990)
5. Cleverly etal. (1996)
6. Oliver and Niimi (1988)
7. Prats et al. (1992)
DRAFT-DO NOT QUOTE OR CITE
B-95
December 2003
-------
Table B-ll. Environmental Levels of Dioxins in Fish (ppt)
Chemical
Fish
species8
Tissue
Number
samples
Number
positive
samples
Concentration
range
Cone.
mean
Wt.
basis
Location6
Location
description
Samp.
year
Ref.
no.
Comments
Tetrachlorodibenzo-p-dioxins (MW = 32 1 . 98)
2,3,7,8-TCDD
Eel
Eel
Trout
Grayling
Barbel
Carp
Chub
Eel
Bream
Perch
Herring
Herring
Herring
Salmon
Salmon
Perch
Artie Char
Carp
Carp
Blue Crab
Lobster
Liver
Fillet
Fillet
Fillet
Fillet
Fillet
Fillet
Fillet
Whole
Whole
Whole
Muscle
Muscle
NR
NR
Whole
Whole
Meat
Meat
6
5
1
1
1
1
1
5
14
3
1
2
2
2
2
3
5
3
3
2
2
6
5
1
1
1
1
0
5
14
3
0
0
0
2
2
3
5
0
0
2
2
1.2-9.1
2.4-3.3
1.4
3.8
5.1
2.5
ND(2.3)
0.9-1.5
1.4-94.4
1.8-8.1
ND(O.l)
ND(O.l)
ND(O.l)
4.6-19.0
0.2-0.3
2.6-19
6.5-25
ND(6.6)
ND(6.6)
106-116
4.7-6.3
3.32
3.04
1.4
3.8
5.1
2.5
NA
1.3
18.0
5.9
NA
NA
NA
11.8
0.25
11.5
14.3
NA
NA
111
5.5
NR
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fresh
Fresh
Fresh
Fresh
Fresh
Fresh
Fresh
Fresh
Fresh
NR
NR
Wet
Wet
Various, Netherlands
Rhine River, Germany
Neckar River, Germany
Neckar River, Germany
Neckar River, Germany
Neckar River, Germany
Neckar River, Germany
Neckar River, Germany
Hamburg, Germany
Hamburg, Germany
Atlantic Coast, Sweden
Baltic Sea, Sweden
Gulf of Bothnia, Sweden
Gulf of Bothnia, Sweden
Gulf of Bothnia, Sweden
Gulf of Bothnia, Sweden
Lake Vattern, Sweden
Mississippi River, MN
Lake Orono, MN
Passaic River, NJ
New York Bight
NR
NR
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Pristine
Urban
Industrial
NR
NR
NR
NR
Industrial
Industrial
Urban
Dump Site
NR
88
88
88
88
88
88
87-88
84
84
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
1
2
2
2
2
2
2
2
3
3
4
4
4
4
4
4
4
5
5
6
6
one sample near dump site
up & downstream Basal
composite sample
composite sample
composite 5-10 fish
composite 5-10 fish
composite 5-10 fish
wild salmon
hatched salmon
caught near pulp mill
Elk River power station
Elk River power station
former sewage sludge
DRAFT-DO NOT QUOTE OR CITE
B-96
December 2003
-------
Table B-ll. Environmental Levels of Dioxins in Fish (ppt) (continued)
Chemical
2,3,7,8-TCDD (continued)
Fish
species8
Str. Bass
Lake Trout
Lake Trout
Lake Trout
Walleye
Walleye
Lake Trout
Lake Trout
Br. Trout
Br. Trout
Rb. Trout
Rb. Trout
Lake Trout
Lake Trout
Coho
Salmon
Coho
Salmon
Cod
Haddock
P. Flounder
Plaice
Flounder
Tissue
Fillets
Whole
Whole
Whole
Whole
Whole
Whole
Whole
Whole
Fillets
Whole
Fillets
Whole
Fillets
Whole
Fillets
Fillets
Fillets
Fillets
Fillets
Fillets
Number
samples
2
1
1
3
1
1
1
10
1
1
1
1
1
1
1
1
4
1
1
1
1
Number
positive
samples
2
1
1
3
1
1
1
10
1
1
1
1
1
1
1
1
0
0
0
0
0
Concentration
range
83.9-734
1.0
8.6
3.5-5.8
1.8
6.6
48.9
3.0-8.7
6
5
14
5
18
8
20
6
ND(l.O)
ND(0.2)
ND(0.2)
ND(0.5)
ND(0.5)
Cone.
mean
409
1.0
8.6
4.4
1.8
6.6
48.9
4.2
6
5
14
5
18
8
20
6
NA
NA
NA
NA
NA
Wt.
basis
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
NR
NR
NR
NR
NR
NR
NR
NR
Fresh
Fresh
Fresh
Fresh
Fresh
Location6
Newark Bay, NJ
Lake Superior
Lake Huron
Lake Michigan
Lake Erie
Lake St. Clair
Lake Ontario
Lake Michigan
Lake Ontario
Lake Ontario
Lake Ontario
Lake Ontario
Lake Ontario
Lake Ontario
Lake Ontario
Lake Ontario
Various, Sweden
Various, Sweden
Various, Sweden
Various, Sweden
Various, Sweden
Location
description
Urban
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
Industrial
Industrial
Industrial
Industrial
Industrial
Samp.
year
NR
84
84
84
84
84
84
84
NR
NR
NR
NR
NR
NR
NR
NR
88
88
88
88
88
Ref.
no.
6
7
7
7
7
7
7
7
8
8
8
8
8
8
8
8
9
9
9
9
9
Comments
mean 5 samples
mean 5 samples
range 3 sample sites
mean 5 samples
mean 5 samples
mean 5 samples
composite 3 samples
composite 3 samples
composite 3 samples
composite 3 samples
composite 3 samples
composite 3 samples
composite 3 samples
composite 3 samples
composite 10 samples
composite 10 samples
composite 10 samples
composite 10 samples
DRAFT-DO NOT QUOTE OR CITE
B-97
December 2003
-------
Table B-ll. Environmental Levels of Dioxins in Fish (ppt) (continued)
Chemical
2,3,7,8-TCDD (continued)
2,3,7,8-TCDD (continued)
Fish
species8
Eel
Mussel
Shrimp
Cod
Carp
Pike
Pike
Various0
Sucker
Lm Bass
Rock Bass
Rock Bass
Sm Bass
Redeye Bass
Carp
not available
Brown Trout
Brown Trout
Rainbow
Trout
Tissue
Fillets
Muscle
Muscle
Fillets
Whole
Muscle
Muscle
Mixed"
Whole
Fillet
Whole
Fillet
Fillet
Fillet
Whole
Whole
Whole
Fillet
Fillet
Number
samples
4
3
2
6
2
8
1
314
15
4
2
1
2
1
5
2
1
2
2
Number
positive
samples
1
0
0
NR
2
8
1
220
6
0
0
0
0
0
2
0
0
0
2
Concentration
range
ND-1.4
ND(0.5)
ND(2.0)
ND-3.8
3-28
40-833
78
NR
ND-0.85
ND(0.74-
1.39)
ND(0.96-
1.49)
ND(1.17)
ND(0.99-
1.10)
ND(l.OO)
ND-0.46
ND(1.15-
2.35)
ND(0.27)
ND(0.34-
0.75)
1.87-2.26
Cone.
mean
0.35
NA
NA
NR
16
186
78
6.84
0.45
NA
NA
NA
NA
NA
0.48
NA
NA
NA
2.06
Wt.
basis
Fresh
Fresh
Fresh
Fresh
NR
Fat
Fat
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Location6
Various, Sweden
Grenlandsfiord, Sweden
Grenlandsfiord , Sweden
Frierfiord, Sweden
Lake Huron
Lake Vanern, Sweden
Hedesunda Bay, Sweden
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Location
description
Industrial
Industrial
Industrial
Industrial
NR
Industrial
Industrial
Various
Background
Background
Background
Background
Background
Background
Background
Background
Background
Background
Background
Samp.
year
87-88
87
88
87
NR
88
88
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
Ref.
no.
9
9
9
9
10
11
11
12
12
12
12
12
12
12
12
12
12
12
12
Comments
only cone, range given
samples composite 3-5 fish
samples composite 2-5 fish
composite 5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
DRAFT-DO NOT QUOTE OR CITE
B-98
December 2003
-------
Table B-ll. Environmental Levels of Dioxins in Fish (ppt) (continued)
Chemical
2,3,7,8-TCDD (continued)
Fish
species8
Brook Trout
Gray
Redhorse
Black
Redhorse
Golden
Redhorse
Longear
Sunfish
Walleye
Chain
Pickerel
Black
Crappie
Sea Catfish
North
Hogsucker
Summer
Flounder
Dolly
Varden
Composite
Bottom
Winter
Flounder
Bluefish
Tissue
Fillet
Whole
Whole
Whole
Whole
Fillet
Fillet
Fillet
Fillet
Whole
Whole
Whole
Whole
Whole
Whole
Number
samples
1
1
1
1
1
3
3
1
1
1
1
2
1
2
1
Number
positive
samples
0
0
0
1
0
0
0
0
0
0
0
0
0
1
1
Concentration
range
ND(0.58)
ND(0.20)
ND(0.99)
0.31
ND(0.30)
ND(0.10-
1.00)
ND(0.10-
0.11)
ND(0.16)
ND(l.OO)
ND(l.OO)
ND(1.02)
ND(1.04-
1.11)
ND(l.Ol)
ND-1.20
0.75
Cone.
mean
NA
NA
NA
0.31
NA
NA
NA
NA
NA
NA
NA
NA
NA
1.02
0.75
Wt.
basis
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Location6
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Location
description
Background
Background
Background
Background
Background
Background
Background
Background
Background
Background
Background
Background
Background
Background
Background
Samp.
year
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
Ref.
no.
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
Comments
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
DRAFT-DO NOT QUOTE OR CITE
B-99
December 2003
-------
Table B-ll. Environmental Levels of Dioxins in Fish (ppt) (continued)
Chemical
2,3,7,8-TCDD (continued)
Fish
species8
White
Catfish
Crayfish
Oyster
Blue Crab
Blue Crab
Crawfish
Crawfish
Catfish
Mullet
Spanish
Mackerel
Herring
Herring
Cod
Redfish
Plaice
Mackerel
Herring
Cod
Skate
Coley
Fish
Tissue
Whole
Whole
Whole
Body
Claw
Head
Tail
Fillet
Fillet
Fillet
Fillet
NR
NR
NR
Whole
Whole
Whole
Whole
Whole
Whole
Whole
Number
samples
1
1
3
3
3
2
2
3
2
1
1
1
1
1
3
1
1
1
1
1
1
Number
positive
samples
1
0
3
3
3
2
1
3
1
1
1
1
1
1
3
1
1
1
0
1
1
Concentration
range
0.75
ND(0.99)
7.2-12
4.3-6.4
3.2-4.9
1.3-1.4
ND-2
2.5-8.8
ND-10
7.1
1.4
4.7
23
2.8
0.13-0.18
0.07
0.19
0.05
ND(0.16)
0.06
0.09
Cone.
mean
0.75
NA
8.8
5.5
4.1
1.35
1.53
5.86
5.1
NA
1.4
4.7
23
2.8
0.16
0.07
0.19
0.05
NA
0.06
0.09
Wt.
basis
Wet
Wet
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fresh
Fat
Fat
Fat
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Location6
Various, US
Various, US
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
Baltic Sea, Sweden
Berlin, W. Germany
Berlin, W. Germany
Berlin, W. Germany
Norwich, UK
Norwich, UK
Norwich, UK
Norwich, UK
Norwich, UK
Norwich, UK
Port Talbot, UK
Location
description
Background
Background
NR
NR
NR
NR
NR
Farm raised
NR
NR
NR
Urban
Urban
Urban
NR
NR
NR
NR
NR
NR
Urban/
Industrial
Samp.
year
86-89
86-89
94
94
94
94
94
94
94
94
88
NR
NR
NR
88
88
88
88
88
88
88
Ref.
no.
12
12
13
13
13
13
13
13
13
13
14
15
15
15
16
16
16
16
16
16
17
Comments
samples composite 3-5 fish
samples composite 3-5 fish
purchased at supermarket
purchased at supermarket
purchased at supermarket
purchased at supermarket
purchased at supermarket
purchased at supermarket
purchased at supermarket
purchased at supermarket
sample composite 12 fillets
purchased at different shops
purchased at different shops
purchased at different shops
purchased at retail outlet
purchased at retail outlet
purchased at retail outlet
purchased at retail outlet
purchased at retail outlet
purchased at retail outlet
DRAFT-DO NOT QUOTE OR CITE
B-100
December 2003
-------
Table B-ll. Environmental Levels of Dioxins in Fish (ppt) (continued)
Chemical
TCDDs
TCDDs (continued)
Fish
species8
Ocean Fish
Fresh Fish
Fish
Fish&
Seafood
Freshwater
Fish
Bream
Perch
Carp
Carp
Blue Crab
Lobster
Str. Bass
Br. Trout
Br. Trout
Rb. Trout
Rb. Trout
Lake Trout
Lake Trout
Coho
Salmon
Coho
Salmon
Tissue
Mixed
Mixed
NR
NR
NR
NR
NR
Whole
Whole
Meat
Meat
Fillets
Whole
Fillets
Whole
Fillets
Whole
Fillets
Whole
Fillets
Number
samples
13
10
138
8
NR
13
2
3
3
2
2
2
1
1
1
1
1
1
1
1
Number
positive
samples
NR
NR
NR
NR
nR
13
2
1
0
2
2
2
1
1
1
1
1
1
1
1
Concentration
range
NR
NR
0-246
NR
nR
2.5-102
9.0-10.5
ND-3.9
ND(6.6)
118-150
6.6-8.3
85.4-734
11
9
29
11
32
12
22
9
Cone.
mean
2.3
3.09
5.45
1.2
1.89
17.6
9.8
1.3
NA
134
7.4
410
11
9
29
11
32
12
22
9
Wt.
basis
Fat
Fat
Fat
Fat
Fat
Fresh
Fresh
NR
NR
Wet
Wet
Wet
NR
NR
NR
NR
NR
NR
NR
NR
Location6
Various U.S. Sites
Various U.S. Sites
Germany
Catalonia, Spain
Russia
Hamburg, Germany
Hamburg, Germany
Mississippi River, MN
Lake Orono, MN
Passaic River, NJ
New York Bight
Newark Bay, NJ
Lake Ontario
Lake Ontario
Lake Ontario
Lake Ontario
Lake Ontario
Lake Ontario
Lake Ontario
Lake Ontario
Location
description
Urban
Urban
NR
NR
NR
Urban
Urban
Industrial
Industrial
Urban
Dump Site
Urban
NR
NR
NR
NR
NR
NR
NR
NR
Samp.
year
NR
NR
93-96
99
96
84
84
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
Ref.
no.
18
18
19
20
21
3
3
5
5
6
6
6
8
8
8
8
8
8
8
8
Comments
purchased at supermarket
purchased at supermarket
official food inspection
samples
purchased in supermarkets
purchased in supermarkets
Elk River power station
Elk River power station
former sewage sludge
composite 3 samples
composite 3 samples
composite 3 samples
composite 3 samples
composite 3 samples
composite 3 samples
composite 3 samples
composite 3 samples
DRAFT-DO NOT QUOTE OR CITE
B-101
December 2003
-------
Table B-ll. Environmental Levels of Dioxins in Fish (ppt) (continued)
Chemical
Fish
species8
Oyster
Blue Crab
Blue Crab
Crawfish
Crawfish
Spanish
Mackerel
Ocean Fish
Fresh Fish
Tissue
Whole
Body
Claw
Head
Tail
Fillet
Mixed
Mixed
Number
samples
3
3
3
2
2
1
13
10
Number
positive
samples
3
3
3
2
2
1
NR
NR
Concentration
range
120-240
39-87
24-62
16-45
4.2-38
9.5
NR
NR
Cone.
mean
163.33
59
37.67
30.5
21.1
9.5
4.06
4.44
Wt.
basis
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Location6
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
Various U.S. Sites
Various U.S. Sites
Location
description
NR
NR
NR
NR
NR
NR
Urban
Urban
Samp.
year
94
94
94
94
94
94
NR
NR
Ref
no.
13
13
13
13
13
13
18
18
Comments
purchased at supermarket
purchased at supermarket
purchased at supermarket
purchased at supermarket
purchased at supermarket
purchased at supermarket
purchased at supermarket
purchased at supermarket
Pentachlorodibenzo-p-dioxins (MW=356.42)
1,2,3,7,8-PeCDD
1,2,3,7,8-PeCDD
(continued)
Herring
Herring
Herring
Carp
Pike
Pike
Sucker
Lm Bass
Rock Bass
Rock Bass
Sm Bass
Whole
Whole
Whole
Whole
Muscle
Muscle
Whole
Fillet
Whole
Fillet
Fillet
1
2
2
2
8
1
15
4
2
1
2
1
2
2
2
8
1
2
0
0
0
0
0.6
1.1-2.8
2.0-4.7
2-11
70-250
39
ND-0.54
ND(0.75-
2.18)
ND(2.74-
2.99)
ND(1.85)
ND(0.92-
0.95)
0.6
1.95
3.35
6
129
39
0.72
NA
NA
NA
NA
Fresh
Fresh
Fresh
NR
Fat
Fat
Wet
Wet
Wet
Wet
Wet
Atlantic Coast, Sweden
Baltic Sea, Sweden
Gulf of Bothnia, Sweden
Lake Huron
Lake Vanern, Sweden
Hedesunda Bay, Sweden
Various, US
Various, US
Various, US
Various, US
Various, US
Pristine
Urban
Industrial
NR
Industrial
Industrial
Background
Background
Background
Background
Background
NR
NR
NR
NR
88
88
86-89
86-89
86-89
86-89
86-89
4
4
4
10
11
11
12
12
12
12
12
composite 5-10 fish
composite 5-10 fish
composite 5-10 fish
samples composite 3-5 fish
samples composite 2-5 fish
composite 5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
DRAFT-DO NOT QUOTE OR CITE
B-102
December 2003
-------
Table B-ll. Environmental Levels of Dioxins in Fish (ppt) (continued)
Chemical
1,2,3,7,8-PeCDD
(continued)
Fish
species8
Redeye Bass
Carp
not available
Brown Trout
Brown Trout
Rainbow
Trout
Brook Trout
Gray
Redhorse
Black
Redhorse
Golden
Redhorse
Longear
Sunfish
Walleye
Chain
Pickerel
Black
Crappie
Sea Catfish
North
Hogsucker
Tissue
Fillet
Whole
Whole
Whole
Fillet
Fillet
Fillet
Whole
Whole
Whole
Whole
Fillet
Fillet
Fillet
Fillet
Whole
Number
samples
1
5
2
1
2
2
1
1
1
1
1
3
3
1
1
1
Number
positive
samples
0
1
0
0
0
0
0
0
0
1
0
0
0
0
0
0
Concentration
range
ND(0.92)
ND-0.38
ND(1.94-
5.33)
ND(0.97)
ND(0.48-
0.70)
ND(1.11-
1.50)
ND(1.75)
ND(0.62)
ND(l.lO)
0.57
ND(1.40)
ND(0.37-
0.92)
ND(0.19-
0.49)
ND(0.20)
ND(0.92)
ND(0.97)
Cone.
mean
NA
0.80
NA
NA
NA
NA
NA
NA
NA
0.57
NA
NA
NA
NA
NA
NA
Wt.
basis
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Location6
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Location
description
Background
Background
Background
Background
Background
Background
Background
Background
Background
Background
Background
Background
Background
Background
Background
Background
Samp.
year
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
Ref.
no.
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
Comments
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
DRAFT-DO NOT QUOTE OR CITE
B-103
December 2003
-------
Table B-ll. Environmental Levels of Dioxins in Fish (ppt) (continued)
Chemical
1,2,3,7,8-PeCDD
(continued)
Fish
species8
Summer
Flounder
Dolly
Varden
Composite
Bottom
Winter
Flounder
Bluefish
White
Catfish
Crayfish
Oyster
Blue Crab
Blue Crab
Crawfish
Crawfish
Catfish
Mullet
Spanish
Mackerel
Herring
Herring
Cod
Redfish
Tissue
Whole
Whole
Whole
Whole
Whole
Whole
Whole
Whole
Body
Claw
Head
Tail
Fillet
Fillet
Fillet
Fillet
NR
NR
NR
Number
samples
1
2
1
2
1
1
1
3
3
3
2
2
3
2
1
1
1
1
1
Number
positive
samples
0
0
0
0
0
0
0
3
3
3
2
2
3
2
1
1
1
1
1
Concentration
range
ND(1.21)
ND(0.92-
0.95)
ND(0.95)
ND(1.20-
1.24)
ND(1.61)
ND(l.Ol)
ND(0.92)
6.3-14
16-26
10-10
4.3-4.9
2.6-6.3
8.2-19
0.63-5.6
17
3.5
12
1.3
6.5
Cone.
mean
NA
NA
NA
NA
NA
NA
NA
9.77
21.33
10
4.6
4.45
14.73
3.12
17
3.5
12
1.3
6.5
Wt.
basis
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fresh
Fat
Fat
Fat
Location6
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
Baltic Sea, Sweden
Berlin, W. Germany
Berlin, W. Germany
Berlin, W. Germany
Location
description
Background
Background
Background
Background
Background
Background
Background
NR
NR
NR
NR
NR
Farm raised
NR
NR
NR
Urban
Urban
Urban
Samp.
year
86-89
86-89
86-89
86-89
86-89
86-89
86-89
94
94
94
94
94
94
94
94
88
NR
NR
NR
Ref.
no.
12
12
12
12
12
12
12
13
13
13
13
13
13
13
13
14
15
15
15
Comments
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
purchased at supermarket
purchased at supermarket
purchased at supermarket
purchased at supermarket
purchased at supermarket
purchased at supermarket
purchased at supermarket
purchased at supermarket
sample composite 12 fillets
purchased at different shops
purchased at different shops
purchased at different shops
DRAFT-DO NOT QUOTE OR CITE
B-104
December 2003
-------
Table B-ll. Environmental Levels of Dioxins in Fish (ppt) (continued)
Chemical
Fish
species8
Plaice
Mackerel
Herring
Cod
Skate
Coley
Fish
Ocean Fish
Fresh Fish
Fish
Fish&
Seafood
Freshwater
Fish
Tissue
Whole
Whole
Whole
Whole
Whole
Whole
Whole
Mixed
Mixed
NR
NR
NR
Number
samples
3
1
1
1
1
1
1
13
10
138
8
NR
Number
positive
samples
3
1
1
0
1
0
1
0
NR
NR
NR
NR
Concentration
range
0.10-0.38
0.10
0.60
ND(0.06)
0.07
ND(0.04)
0.18
ND
NR
0-247
NR
NR
Cone.
mean
0.24
0.10
0.60
NA
0.07
NA
0.18
NA
5.2
6.2
0.8
3.4
Wt.
basis
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Fat
Fat
Fat
Fat
Fat
Location6
Norwich, UK
Norwich, UK
Norwich, UK
Norwich, UK
Norwich, UK
Norwich, UK
Port Talbot, UK
Various U.S. Sites
Various U.S. Sites
Germany
Catalonia, Spain
Russia
Location
description
NR
NR
NR
NR
NR
NR
Urban/
Industrial
Urban
Urban
NR
NR
NR
Samp.
year
88
88
88
88
88
88
88
NR
NR
93-96
96
96
Ref.
no.
16
16
16
16
16
16
17
18
18
19
20
21
Comments
purchased at retail outlet
purchased at retail outlet
purchased at retail outlet
purchased at retail outlet
purchased at retail outlet
purchased at retail outlet
purchased at supermarket
purchased at supermarket
official food inspection
samples
purchased at supermarkets
purchased at supermarkets
DRAFT-DO NOT QUOTE OR CITE
B-105
December 2003
-------
Table B-ll. Environmental Levels of Dioxins in Fish (ppt) (continued)
Chemical
PeCDDs
Fish
species8
Bream
Perch
Carp
Carp
Blue Crab
Lobster
Str. Bass
Br. Trout
Br. Trout
Rb. Trout
Rb. Trout
Lake Trout
Lake Trout
Coho
Salmon
Coho
Salmon
Ocean Fish
Fresh Fish
Various0
Oyster
Blue Crab
Blue Crab
Tissue
Whole
Whole
Meat
Meat
Fillets
Whole
Fillets
Whole
Fillets
Whole
Fillets
Whole
Fillets
Mixed"
Whole
Body
Claw
Number
samples
13
2
3
3
2
2
2
1
1
1
1
1
1
1
1
13
10
34
3
3
3
Number
positive
samples
13
2
3
0
2
2
2
1
1
1
1
1
1
1
1
0
NR
34
3
3
3
Concentration
range
3.2-27.8
9.8-29.8
3.5-4.5
ND(6.6)
17.2-18.0
10.0-11.0
5.2-10.6
8
6
31
15
39
9
6
5
ND
NR
0.15-2.67
140-350
440-680
250-560
Cone.
mean
12.1
19.8
3.9
NA
17.6
10.5
7.9
8
6
31
15
39
9
6
5
NA
5.2
0.77
216.67
556.67
390
Wt.
basis
Fresh
Fresh
NR
NR
Wet
Wet
Wet
NR
NR
NR
NR
NR
NR
NR
NR
Fat
Fat
Wet
Fat
Fat
Fat
Location6
Hamburg, Germany
Hamburg, Germany
Mississippi River, MN
Lake Orono, MN
Passaic River, NJ
New York Bight
Newark Bay, NJ
Lake Ontario
Lake Ontario
Lake Ontario
Lake Ontario
Lake Ontario
Lake Ontario
Lake Ontario
Lake Ontario
Various U.S. Sites
Various U.S. Sites
Various, US
S. Mississippi
S. Mississippi
S. Mississippi
Location
description
Urban
Urban
Industrial
Industrial
Urban
Dump Site
Urban
NR
NR
NR
NR
NR
NR
NR
NR
Background'
NR
NR
NR
Samp.
year
84
84
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
86-89
94
94
94
Ref.
no.
3
3
5
5
6
6
6
8
8
8
8
8
8
8
8
9
9
12
13
13
13
Comments
Elk River power station
Elk River power station
former sewage sludge
composite 3 samples
composite 3 samples
composite 3 samples
composite 3 samples
composite 3 samples
composite 3 samples
composite 3 samples
composite 3 samples
samples composite 3-5 fish
purchased at supermarket
purchased at supermarket
purchased at supermarket
DRAFT-DO NOT QUOTE OR CITE
B-106
December 2003
-------
Table B-ll. Environmental Levels of Dioxins in Fish (ppt) (continued)
Chemical
PeCDDs (continued)
Fish
species8
Crawfish
Crawfish
Spanish
Mackerel
Tissue
Head
Tail
Fillet
Number
samples
2
2
1
Number
positive
samples
2
2
1
Concentration
range
43-47
14-31
24
Cone.
mean
45
22.5
24
Wt.
basis
Fat
Fat
Fat
Location6
S. Mississippi
S. Mississippi
S. Mississippi
Location
description
NR
NR
NR
Samp.
year
94
94
94
Ref
no.
13
13
13
Comments
purchased at supermarket
purchased at supermarket
purchased at supermarket
Hexachlorodibenzo-p-dioxins (MW=390.8)
1,2,3,4,7,8-HxCDD
1,2,3,4,7,8-HxCDD
(continued)
Herring
Herring
Herring
Carp
Pike
Pike
Sucker
Lm Bass
Rock Bass
Rock Bass
Sm Bass
Redeye Bass
Carp
not available
Brown Trout
Brown Trout
Whole
Whole
Whole
Whole
Muscle
Muscle
Whole
Fillet
Whole
Fillet
Fillet
Fillet
Whole
Whole
Whole
Fillet
1
2
2
2
8
1
15
4
2
1
2
1
5
2
1
2
0
2
0
2
8
1
2
0
0
0
0
0
1
0
0
0
ND(0.2)
0.2-0.3
ND(0.2)
3-5
6.7-22
11
ND-0.24
ND(0.90-
2.47)
ND(2.47-
2.87)
ND(1.25)
ND(2.47)
ND(2.47)
ND-0.70
ND(1.50-
3.31)
ND(1.96)
ND(0.65-
0.72)
NA
0.25
NA
4
12.8
11
0.81
NA
NA
NA
NA
NA
1.08
NA
NA
NA
Fresh
Fresh
Fresh
NR
Fat
Fat
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Atlantic Coast, Sweden
Baltic Sea, Sweden
Gulf of Bothnia, Sweden
Lake Huron
Lake Vanern, Sweden
Hedesunda Bay, Sweden
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Pristine
Urban
Industrial
NR
Industrial
Industrial
Background
Background
Background
Background
Background
Background
Background
Background
Background
Background
NR
NR
NR
NR
88
88
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
4
4
4
10
11
11
12
12
12
12
12
12
12
12
12
12
composite 5-10 fish
composite 5-10 fish
composite 5-10 fish
samples composite 3-5 fish
samples composite 2-5 fish
composite 5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
DRAFT-DO NOT QUOTE OR CITE
B-107
December 2003
-------
Table B-ll. Environmental Levels of Dioxins in Fish (ppt) (continued)
Chemical
1,2,3,4,7,8-HxCDD
(continued)
Fish
species8
Rainbow
Trout
Brook Trout
Gray
Redhorse
Black
Redhorse
Golden
Redhorse
Long ear
Sunfish
Walleye
Chain
Pickerel
Black
Crappie
Sea Catfish
North
Hogsucker
Summer
Flounder
Dolly
Varden
Composite
Bottom
Winter
Flounder
Tissue
Fillet
Fillet
Whole
Whole
Whole
Whole
Fillet
Fillet
Fillet
Fillet
Whole
Whole
Whole
Whole
Whole
Number
samples
2
1
1
1
1
1
3
3
1
1
1
1
2
1
2
Number
positive
samples
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Concentration
range
ND(0.89-
1.04)
ND(1.45)
ND(l.lO)
ND(2.46)
ND(2.56)
ND(1.13)
ND(0.60-
2.47)
ND(0.22-
1.04)
ND(0.22)
ND(2.47)
ND(2.47)
ND(2.47)
ND(2.46)
ND(2.47)
ND(2.47)
Cone.
mean
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
Wt.
basis
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Location6
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Location
description
Background
Background
Background
Background
Background
Background
Background
Background
Background
Background
Background
Background
Background
Background
Background
Samp.
year
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
Ref.
no.
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
Comments
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
DRAFT-DO NOT QUOTE OR CITE
B-108
December 2003
-------
Table B-ll. Environmental Levels of Dioxins in Fish (ppt) (continued)
Chemical
1,2,3,4,7,8-HxCDD
(continued)
Fish
species8
Bluefish
White
Catfish
Crayfish
Various0
Oyster
Blue Crab
Blue Crab
Crawfish
Crawfish
Catfish
Mullet
Spanish
Mackerel
Herring
Herring
Cod
Redfish
Fish
Fish
Ocean Fish
Fresh Fish
Tissue
Whole
Whole
Whole
Mixed"
Whole
Body
Claw
Head
Tail
Fillet
Fillet
Fillet
Fillet
NR
NR
NR
Whole
Whole
Mixed
Mixed
Number
samples
1
1
1
314
3
3
3
2
2
3
2
1
1
1
1
1
1
1
13
10
Number
positive
samples
0
0
0
100
3
3
3
2
1
3
0
1
1
1
1
1
1
1
0
NR
Concentration
range
ND(2.47)
ND(2.47)
ND(2.47)
NR
4.2-14
16-26
7.2-7.5
3-4.1
ND-3.6
7.9-19
ND-(0.26-
1.25)
9.0
0.47
1.2
0.01
0.5
0.14
0.06
ND
NR
Cone.
mean
NA
NA
NA
1.67
7.87
22.33
7.37
3.55
2.55
15.3
0.76
9.0
0.47
1.2
0.01
0.5
0.14
0.06
NA
3.01
Wt.
basis
Wet
Wet
Wet
Wet
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fresh
Fat
Fat
Fat
Wet
Wet
Fat
Fat
Location6
Various, US
Various, US
Various, US
Various, US
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
Baltic Sea, Sweden
Berlin, W. Germany
Berlin, W. Germany
Berlin, W. Germany
Port Talbot, UK
Stonehaven, UK
Various U.S. Sites
Various U.S. Sites
Location
description
Background
Background
Background
Various
NR
NR
NR
NR
NR
Farm raised
NR
NR
NR
Urban
Urban
Urban
Urban/
Industrial
Rural
Urban
Urban
Samp.
year
86-89
86-89
86-89
86-89
94
94
94
94
94
94
94
94
88
NR
NR
NR
88
91
NR
NR
Ref.
no.
12
12
12
12
13
13
13
13
13
13
13
13
14
15
15
15
17
17
18
18
Comments
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
purchased at supermarket
purchased at supermarket
purchased at supermarket
purchased at supermarket
purchased at supermarket
purchased at supermarket
purchased at supermarket
purchased at supermarket
sample composite 12 fillets
purchased at different shops
purchased at different shops
purchased at different shops
purchased at supermarket
purchased at supermarket
DRAFT-DO NOT QUOTE OR CITE
B-109
December 2003
-------
Table B-ll. Environmental Levels of Dioxins in Fish (ppt) (continued)
Chemical
1,2,3,6,7,8-HxCDD
1,2,3,6,7,8-HxCDD
(continued)
Fish
species8
Fish
Fish&
Seafood
Freshwater
Fish
Herring
Herring
Herring
Pike
Pike
Sucker
Lm Bass
Rock Bass
Rock Bass
Sm Bass
Redeye Bass
Carp
not available
Brown Trout
Tissue
NR
NR
NR
Whole
Whole
Whole
Muscle
Muscle
Whole
Fillet
Whole
Fillet
Fillet
Fillet
Whole
Whole
Whole
Number
samples
138
8
NR
1
2
2
8
1
15
4
2
1
2
1
5
2
1
Number
positive
samples
NR
NR
NR
0
1
2
8
1
4
0
0
0
0
0
3
0
0
Concentration
range
0-19.5
NR
NR
ND(0.2)
ND-2.4
2.2-8.1
30-100
22
ND-0.46
ND(0.90-
2.40)
ND(2.87-
3.71)
ND(3.49)
ND(1.84-
1.85)
ND(1.85)
ND-3.57
ND(2.25-
5.00)
ND(1.96)
Cone.
mean
0.80
0.40
5.71
NA
1.25
5.15
50.5
22
0.97
NA
NA
NA
NA
NA
1.63
NA
NA
Wt.
basis
Fat
Fat
Fat
Fresh
Fresh
Fresh
Fat
Fat
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Location6
Germany
Catalonia, Spain
Russia
Atlantic Coast, Sweden
Baltic Sea, Sweden
Gulf of Bothnia, Sweden
Lake Vanern, Sweden
Hedesunda Bay, Sweden
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Location
description
NR
NR
NR
Pristine
Urban
Industrial
Industrial
Industrial
Background
Background
Background
Background
Background
Background
Background
Background
Background
Samp.
year
93-96
96
96
NR
NR
NR
88
88
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
Ref.
no.
19
20
21
4
4
4
11
11
12
12
12
12
12
12
12
12
12
Comments
official food inspection
samples
purchased at supermarkets
purchased at supermarkets
composite 5-10 fish
composite 5-10 fish
composite 5-10 fish
samples composite 2-5 fish
composite 5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
DRAFT-DO NOT QUOTE OR CITE
B-110
December 2003
-------
Table B-ll. Environmental Levels of Dioxins in Fish (ppt) (continued)
Chemical
1,2,3,6,7,8-HxCDD
(continued)
Fish
species8
Brown Trout
Rainbow
Trout
Brook Trout
Gray
Redhorse
Black
Redhorse
Golden
Redhorse
Longear
Sunfish
Walleye
Chain
Pickerel
Black
Crappie
Sea Catfish
North
Hogsucker
Summer
Flounder
Dolly
Varden
Composite
Bottom
Tissue
Fillet
Fillet
Fillet
Whole
Whole
Whole
Whole
Fillet
Fillet
Fillet
Fillet
Whole
Whole
Whole
Whole
Number
samples
2
2
1
1
1
1
1
3
3
1
1
1
1
2
1
Number
positive
samples
0
0
0
0
0
1
0
0
0
0
0
0
1
0
0
Concentration
range
ND(0.65-
0.72)
ND(1.74-
1.78)
ND(1.45)
ND(l.lO)
ND(1.84)
1.36
ND(1.13)
ND(0.60-
1.85)
ND(0.87-
1.04)
ND(0.22)
ND(1.85)
ND(1.85)
0.67
ND(1.84)
ND(1.85)
Cone.
mean
NA
NA
NA
NA
NA
1.36
NA
NA
NA
NA
NA
NA
0.67
NA
NA
Wt.
basis
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Location6
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Location
description
Background
Background
Background
Background
Background
Background
Background
Background
Background
Background
Background
Background
Background
Background
Background
Samp.
year
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
Ref.
no.
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
Comments
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
DRAFT-DO NOT QUOTE OR CITE
B-111
December 2003
-------
Table B-ll. Environmental Levels of Dioxins in Fish (ppt) (continued)
Chemical
1,2,3,6,7,8-HxCDD
(continued)
Fish
species8
Winter
Flounder
Bluefish
White
Catfish
Crayfish
Various0
Oyster
Blue Crab
Blue Crab
Crawfish
Crawfish
Catfish
Mullett
Spanish
Mackerel
Herring
Herring
Cod
Redfish
Ocean Fish
Fresh Fish
Fish
Tissue
Whole
Whole
Whole
Whole
Mixed"
Whole
Body
Claw
Head
Tail
Fillet
Fillet
Fillet
Fillet
NR
NR
NR
Mixed
Mixed
NR
Number
samples
2
1
1
1
314
3
3
3
2
2
3
2
1
1
1
1
1
13
10
138
Number
positive
samples
1
0
1
0
217
3
3
3
2
1
3
0
1
1
1
1
1
ND
NR
NR
Concentration
range
ND-0.40
ND(1.84)
0.68
ND(1.85)
NR
9.6-31
26-40
13-15
5.9-9.3
ND-5.4
11-26
ND(0.026-
1.25)
47
1.8
5.8
17
8.4
ND
NR
0-101
Cone.
mean
0.67
NA
0.68
NA
4.29
17.53
34.33
14
7.6
3.43
20.33
0.75
47
1.8
5.8
17
8.4
NA
5.31
3.8
Wt.
basis
Wet
Wet
Wet
Wet
Wet
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fresh
Fat
Fat
Fat
Fat
Fat
Fat
Location6
Various, US
Various, US
Various, US
Various, US
Various, US
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
Baltic Sea, Sweden
Berlin, W. Germany
Berlin, W. Germany
Berlin, W. Germany
Various U.S. Sites
Various U.S. Sites
Germany
Location
description
Background
Background
Background
Background
Various
NR
NR
NR
NR
NR
Farm raised
NR
NR
NR
Urban
Urban
Urban
Urban
Urban
NR
Samp.
year
86-89
86-89
86-89
86-89
86-89
94
94
94
94
94
94
94
94
88
NR
NR
NR
NR
NR
93-96
Ref.
no.
12
12
12
12
12
13
13
13
13
13
13
13
13
14
15
15
15
18
18
19
Comments
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
purchased at supermarket
purchased at supermarket
purchased at supermarket
purchased at supermarket
purchased at supermarket
purchased at supermarket
purchased at supermarket
purchased at supermarket
sample composite 12 fillets
purchased at different shops
purchased at different shops
purchased at different shops
purchased at supermarket
purchased at supermarket
official food inspection
samples
DRAFT-DO NOT QUOTE OR CITE
B-112
December 2003
-------
Table B-ll. Environmental Levels of Dioxins in Fish (ppt) (continued)
Chemical
1,2,3,7,8,9-HxCDD
1,2,3,7,8,9-HxCDD
(continued)
Fish
species8
Fish&
Seafood
Freshwater
Fish
Herring
Herring
Herring
Pike
Pike
Sucker
Lm Bass
Rock Bass
Rock Bass
Sm Bass
Redeye Bass
Carp
not available
Brown Trout
Brown Trout
Tissue
NR
NR
Whole
Whole
Whole
Muscle
Muscle
Whole
Fillet
Whole
Fillet
Fillet
Fillet
Whole
Whole
Whole
Fillet
Number
samples
8
NR
1
2
2
8
1
15
4
2
1
2
1
5
2
1
2
Number
positive
samples
NR
NR
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Concentration
range
NR
NR
ND(0.2)
ND(0.2)
ND(0.2)
ND(3-11)
ND(6)
ND(0.60-
3.37)
ND(0.90-
2.40)
ND(2.47-
2.87)
ND(1.25)
ND(1.38)
ND(1.38)
ND(1.15-
2.69)
ND(1.50-
2.48)
ND(1.96)
ND(0.65-
0.72)
Cone.
mean
1.5
4.3
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
Wt.
basis
Fat
Fat
Fresh
Fresh
Fresh
Fat
Fat
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Location6
Catalonia, Spain
Russia
Atlantic Coast, Sweden
Baltic Sea, Sweden
Gulf of Bothnia, Sweden
Lake Vanern, Sweden
Hedesunda Bay, Sweden
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Location
description
NR
NR
Pristine
Urban
Industrial
Industrial
Industrial
Background
Background
Background
Background
Background
Background
Background
Background
Background
Background
Samp.
year
96
96
NR
NR
NR
88
88
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
Ref.
no.
20
21
4
4
4
11
11
12
12
12
12
12
12
12
12
12
12
Comments
purchased at supermarkets
purchased at supermarkets
composite 5-10 fish
composite 5-10 fish
composite 5-10 fish
samples composite 2-5 fish
composite 5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
DRAFT-DO NOT QUOTE OR CITE
B-113
December 2003
-------
Table B-ll. Environmental Levels of Dioxins in Fish (ppt) (continued)
Chemical
1,2,3,7,8,9-HxCDD
(continued)
Fish
species8
Rainbow
Trout
Brook Trout
Gray
Redhorse
Black
Redhorse
Golden
Redhorse
Long ear
Sunfish
Walleye
Chain
Pickerel
Black
Crappie
Sea Catfish
North
Hogsucker
Summer
Flounder
Dolly
Varden
Composite
Bottom
Winter
Flounder
Tissue
Fillet
Fillet
Whole
Whole
Whole
Whole
Fillet
Fillet
Fillet
Fillet
Whole
Whole
Whole
Whole
Whole
Number
samples
2
1
1
1
1
1
3
3
1
1
1
1
2
1
2
Number
positive
samples
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
Concentration
range
ND(0.89-
1.04)
ND(1.45)
ND(l.lO)
ND(1.37)
ND(1.38)
ND(1.13)
ND(0.60-
1.38)
ND(0.22-
1.04)
ND(0.22)
ND(1.38)
ND(1.38)
0.34
ND(1.37-
1.38)
ND(1.38)
ND(1.38)
Cone.
mean
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
0.34
NA
NA
NA
Wt.
basis
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Location6
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Location
description
Background
Background
Background
Background
Background
Background
Background
Background
Background
Background
Background
Background
Background
Background
Background
Samp.
year
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
Ref.
no.
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
Comments
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
DRAFT-DO NOT QUOTE OR CITE
B-114
December 2003
-------
Table B-ll. Environmental Levels of Dioxins in Fish (ppt) (continued)
Chemical
1,2,3,7,8,9-HxCDD
(continued)
Fish
species8
Bluefish
White
Catfish
Crayfish
Various0
Oyster
Blue Crab
Blue Crab
Crawfish
Crawfish
Catfish
Mullett
Spanish
Mackerel
Herring
Herring
Cod
Redfish
Plaice
Mackerel
Herring
Cod
Tissue
Whole
Whole
Whole
Mixed"
Whole
Body
Claw
Head
Tail
Fillet
Fillet
Fillet
Fillet
NR
NR
NR
Whole
Whole
Whole
Whole
Number
samples
1
1
1
314
3
3
3
2
2
3
2
1
1
1
1
1
3
1
1
1
Number
positive
samples
0
0
0
119
3
3
3
2
1
3
0
1
1
1
1
1
3
1
1
0
Concentration
range
ND(1.38)
ND(1.38)
ND(1.38)
NR
8.2-20
30-40
16-19
6-8.1
ND-10
5-20
ND(0.21-
1.00)
13
0.25
1.0
5.2
1.3
0.02-0.05
0.02
0.07
ND(0.02)
Cone.
mean
NA
NA
NA
1.15
12.6
34.67
17.67
7.05
5.6
14
0.6
13
0.25
1.0
5.2
1.3
0.04
0.02
0.07
NA
Wt.
basis
Wet
Wet
Wet
Wet
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fresh
Fat
Fat
Fat
Wet
Wet
Wet
Wet
Location6
Various, US
Various, US
Various, US
Various, US
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
Baltic Sea, Sweden
Berlin, W. Germany
Berlin, W. Germany
Berlin, W. Germany
Norwich, UK
Norwich, UK
Norwich, UK
Norwich, UK
Location
description
Background
Background
Background
Various
NR
NR
NR
NR
NR
Farm raised
NR
NR
NR
Urban
Urban
Urban
NR
NR
NR
NR
Samp.
year
86-89
86-89
86-89
86-89
94
94
94
94
94
94
94
94
88
NR
NR
NR
88
88
88
88
Ref.
no.
12
12
12
12
13
13
13
13
13
13
13
13
14
15
15
15
16
16
16
16
Comments
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
purchased at supermarket
purchased at supermarket
purchased at supermarket
purchased at supermarket
purchased at supermarket
purchased at supermarket
purchased at supermarket
purchased at supermarket
sample composite 12 fillets
purchased at different shops
purchased at different shops
purchased at different shops
purchased at retail outlet
purchased at retail outlet
purchased at retail outlet
purchased at retail outlet
DRAFT-DO NOT QUOTE OR CITE
B-115
December 2003
-------
Table B-ll. Environmental Levels of Dioxins in Fish (ppt) (continued)
Chemical
HxCDDs
HxCDDs (continued)
Fish
species8
Skate
Coley
Fish
Ocean Fish
Fresh Fish
Fish
Fish&
Seafood
Freshwater
Fish
Bream
Perch
Carp
Carp
Blue Crab
Lobster
Str. Bass
Br. Trout
Br. Trout
Rb. Trout
Rb. Trout
Lake Trout
Tissue
Whole
Whole
Whole
Mixed
Mixed
NR
NR
NR
Whole
Whole
Meat
Meat
Fillets
Whole
Fillets
Whole
Fillets
Whole
Number
samples
1
1
1
13
10
138
8
NR
13
2
3
3
2
2
2
1
1
1
1
1
Number
positive
samples
1
0
1
0
NR
NR
NR
NR
13
2
3
1
2
2
2
1
1
1
1
1
Concentration
range
0.04
ND(0.04)
0.04
ND
NR
0-24
NR
NR
4.3-46.4
18.6-21.5
2.3-11
ND-3.0
0.3-1.5
3.0-3.4
0.6-0.7
20
25
67
37
114
Cone.
mean
0.04
NA
0.04
NA
4.11
1.1
1.2
4.3
17.8
20.0
6.9
1.0
0.9
3.2
0.65
20
25
67
37
114
Wt.
basis
Wet
Wet
Wet
Fat
Fat
Fat
Fat
Fat
Fresh
Fresh
NR
NR
Wet
Wet
Wet
NR
NR
NR
NR
NR
Location6
Norwich, UK
Norwich, UK
Port Talbot, UK
Various U.S. Sites
Various U.S. Sites
Germany
Catalonia, Spain
Russia
Hamburg, Germany
Hamburg, Germany
Mississippi River, MN
Lake Orono, MN
Passaic River, NJ
New York Bight
Newark Bay, NJ
Lake Ontario
Lake Ontario
Lake Ontario
Lake Ontario
Lake Ontario
Location
description
NR
NR
Urban/
Industrial
Urban
Urban
NR
NR
NR
Urban
Urban
Industrial
Industrial
Urban
Dump Site
Urban
NR
NR
NR
NR
NR
Samp.
year
88
88
88
NR
NR
93-96
96
96
84
84
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
Ref.
no.
16
16
17
18
18
19
20
21
3
3
5
5
6
6
6
8
8
8
8
8
Comments
purchased at retail outlet
purchased at retail outlet
purchased at supermarket
purchased at supermarket
official food inspection
samples
purchased at supermarkets
purchased at supermarkets
Elk River power station
Elk River power station
former sewage sludge
composite 3 samples
composite 3 samples
composite 3 samples
composite 3 samples
composite 3 samples
DRAFT-DO NOT QUOTE OR CITE
B-116
December 2003
-------
Table B-ll. Environmental Levels of Dioxins in Fish (ppt) (continued)
Chemical
Fish
species8
Lake Trout
Coho
Salmon
Coho
Salmon
Oyster
Blue Crab
Blue Crab
Crawfish
Crawfish
Spanish
Mackerel
Ocean Fish
Fresh Fish
Tissue
Fillets
Whole
Fillets
Whole
Body
Claw
Head
Tail
Fillet
Mixed
Mixed
Number
samples
1
1
1
3
3
3
2
2
1
13
10
Number
positive
samples
1
1
1
3
3
3
2
2
1
0
NR
Concentration
range
27
16
22
230-630
1500-2400
790-950
120-140
44-100
77
ND
NR
Cone.
mean
27
16
22
383.33
1966.67
880
130
72
77
NA
12.5
Wt.
basis
NR
NR
NR
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Location6
Lake Ontario
Lake Ontario
Lake Ontario
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
Various U.S. Sites
Various U.S. Sites
Location
description
NR
NR
NR
NR
NR
NR
NR
NR
NR
Urban
Urban
Samp.
year
NR
NR
NR
94
94
94
94
94
94
NR
NR
Ref
no.
8
8
8
13
13
13
13
13
13
18
18
Comments
composite 3 samples
composite 3 samples
composite 3 samples
purchased at supermarket
purchased at supermarket
purchased at supermarket
purchased at supermarket
purchased at supermarket
purchased at supermarket
purchased at supermarket
purchased at supermarket
Heptachlorodibenzo-p-dioxins (MW = 425 . 3 1)
1,2,3,4,6,7,8-HpCDD
1,2,3,4,6,7,8-HpCDD
(continued)
Herring
Herring
Herring
Carp
Sucker
Lm Bass
Rock Bass
Rock Bass
Whole
Whole
Whole
Whole
Whole
Fillet
Whole
Fillet
1
2
2
2
15
4
2
1
0
1
0
2
8
1
0
0
ND(0.2)
ND-0.6
ND(0.2)
3-4
ND-8.16
ND-0.75
ND(12.94-
19.27)
ND(10.70)
NA
0.35
NA
3.5
2.59
1.54
NA
NA
Fresh
Fresh
Fresh
NR
Wet
Wet
Wet
Wet
Atlantic Coast, Sweden
Baltic Sea, Sweden
Gulf of Bothnia, Sweden
Lake Huron
Various, US
Various, US
Various, US
Various, US
Pristine
Urban
Industrial
NR
Background
Background
Background
Background
NR
NR
NR
NR
86-89
86-89
86-89
86-89
4
4
4
10
12
12
12
12
composite 5-10 fish
composite 5-10 fish
composite 5-10 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
DRAFT-DO NOT QUOTE OR CITE
B-117
December 2003
-------
Table B-ll. Environmental Levels of Dioxins in Fish (ppt) (continued)
Chemical
1,2,3,4,6,7,8-HpCDD
(continued)
Fish
species8
Sm Bass
Redeye Bass
Carp
not available
Brown Trout
Brown Trout
Rainbow
Trout
Brook Trout
Gray
Redhorse
Black
Redhorse
Golden
Redhorse
Longear
Sunfish
Walleye
Chain
Pickerel
Black
Crappie
Sea Catfish
North
Hogsucker
Tissue
Fillet
Fillet
Whole
Whole
Whole
Fillet
Fillet
Fillet
Whole
Whole
Whole
Whole
Fillet
Fillet
Fillet
Fillet
Whole
Number
samples
2
1
5
1
1
2
2
1
1
1
1
1
2
3
1
1
1
Number
positive
samples
1
0
3
0
0
0
2
1
0
0
1
0
0
0
0
1
1
Concentration
range
ND-0.23
ND(1.26)
ND-7.14
ND(6.96)
ND(4.91)
ND(1.31-
4.45)
1.67-2.21
1.80
ND(3.10)
ND(4.43)
3.23
ND(7.36)
ND(0.75-
0.86)
ND(3.08-
5.09)
ND(2.29)
0.51
0.74
Cone.
mean
0.11
NA
7.18
NA
NA
NA
1.94
1.80
NA
NA
3.23
NA
NA
NA
NA
0.51
0.74
Wt.
basis
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Location6
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Location
description
Background
Background
Background
Background
Background
Background
Background
Background
Background
Background
Background
Background
Background
Background
Background
Background
Background
Samp.
year
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
Ref.
no.
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
Comments
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
DRAFT-DO NOT QUOTE OR CITE
B-118
December 2003
-------
Table B-ll. Environmental Levels of Dioxins in Fish (ppt) (continued)
Chemical
1,2,3,4,6,7,8-HpCDD
(continued)
Fish
species8
Summer
Flounder
Dolly
Varden
Composite
Bottom
Winter
Flounder
Bluefish
White
Catfish
Crayfish
Various0
Oyster
Blue Crab
Blue Crab
Crawfish
Crawfish
Catfish
Mullett
Spanish
Mackerel
Herring
Herring
Cod
Tissue
Whole
Whole
Whole
Whole
Whole
Whole
Whole
Mixed"
Whole
Body
Claw
Head
Tail
Fillet
Fillet
Fillet
Fillet
NR
NR
Number
samples
1
2
1
2
1
1
1
314
3
3
3
2
2
3
2
1
1
1
1
Number
positive
samples
1
2
1
2
0
1
1
279
3
3
3
2
2
3
2
1
1
1
1
Concentration
range
3.06
0.77-0.79
2.18
0.61-0.80
ND(4.10)
0.89
0.49
NR
27-90
110-140
64-70
22-29
19-33
53-170
1.2-7.2
52
0.45
3.6
10
Cone.
mean
3.06
0.78
2.18
0.71
NA
0.89
0.49
10.5
48.67
126.67
67.33
25.5
26
121
4.2
52
0.45
3.6
10
Wt.
basis
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fresh
Fat
Fat
Location6
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
Baltic Sea, Sweden
Berlin, W. Germany
Berlin, W. Germany
Location
description
Background
Background
Background
Background
Background
Background
Background
Various
NR
NR
NR
NR
NR
Farm raised
NR
NR
NR
Urban
Urban
Samp.
year
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
94
94
94
94
94
94
94
94
88
NR
NR
Ref.
no.
12
12
12
12
12
12
12
12
13
13
13
13
13
13
13
13
14
15
15
Comments
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
purchased at supermarket
purchased at supermarket
purchased at supermarket
purchased at supermarket
purchased at supermarket
purchased at supermarket
purchased at supermarket
purchased at supermarket
sample composite 12 fillets
purchased at different shops
purchased at different shops
DRAFT-DO NOT QUOTE OR CITE
B-119
December 2003
-------
Table B-ll. Environmental Levels of Dioxins in Fish (ppt) (continued)
Chemical
HpCDDs
Fish
species8
Redfish
Plaice
Mackerel
Herring
Cod
Skate
Coley
Fish
Fish
Ocean Fish
Fresh Fish
Fish
Fish&
Seafood
Freshwater
Fish
Bream
Perch
Carp
Carp
Blue Crab
Lobster
Tissue
NR
Whole
Whole
Whole
Whole
Whole
Whole
Whole
Whole
Mixed
Mixed
NR
NR
NR
Whole
Whole
Meat
Meat
Number
samples
1
3
1
1
1
1
1
1
1
13
10
138
8
NR
13
2
3
3
2
2
Number
positive
samples
1
3
1
1
0
1
1
1
1
NR
NR
NR
NR
NR
13
2
3
2
0
1
Concentration
range
3.0
0.13-0.34
0.48
0.47
ND(0.44)
0.26
0.21
0.52
0.28
NR
NR
0.33-64
NR
NR
1.5-14.4
3.5-7.1
15-22
ND-11
ND(l.l)
ND-8.5
Cone.
mean
3.0
0.22
0.48
0.47
NA
0.26
0.21
0.52
0.28
11.7
23.5
4.1
96.2
3.1
6.7
5.3
19.3
7.0
NA
4.25
Wt.
basis
Fat
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Fat
Fat
Fat
Fat
Fat
Fresh
Fresh
NR
NR
Wet
Wet
Location6
Berlin, W. Germany
Norwich, UK
Norwich, UK
Norwich, UK
Norwich, UK
Norwich, UK
Norwich, UK
Port Talbot, UK
Stonehaven, UK
Various U.S. Sites
Various U.S. Sites
Germany
Catalonia, Spain
Russia
Hamburg, Germany
Hamburg, Germany
Mississippi River, MN
Lake Orono, MN
Passaic River, NJ
New York Bight
Location
description
Urban
NR
NR
NR
NR
NR
NR
Urban/
Industrial
Rural
Urban
Urban
NR
NR
NR
Urban
Urban
Industrial
Industrial
Urban
Dump Site
Samp.
year
NR
88
88
88
88
88
88
88
91
NR
NR
93-96
96
96
84
84
NR
NR
NR
NR
Ref.
no.
15
16
16
16
16
16
16
17
17
18
18
19
20
21
3
3
5
5
6
6
Comments
purchased at different shops
purchased at retail outlet
purchased at retail outlet
purchased at retail outlet
purchased at retail outlet
purchased at retail outlet
purchased at retail outlet
purchased at supermarket
purchased at supermarket
official food inspection
samples
purchased at supermarkets
purchased at supermarkets
Elk River power station
Elk River power station
former sewage sludge
DRAFT-DO NOT QUOTE OR CITE
B-120
December 2003
-------
Table B-ll. Environmental Levels of Dioxins in Fish (ppt) (continued)
Chemical
HpCDDs (continued)
Fish
species8
Str. Bass
Br. Trout
Br. Trout
Rb. Trout
Rb. Trout
Lake Trout
Lake Trout
Coho
Salmon
Coho
Salmon
Oyster
Blue Crab
Blue Crab
Crawfish
Crawfish
Spanish
Mackerel
Ocean Fish
Fresh Fish
Tissue
Fillets
Whole
Fillets
Whole
Fillets
Whole
Fillets
Whole
Fillets
Whole
Body
Claw
Head
Tail
Fillet
Mixed
Mixed
Number
samples
2
1
1
1
1
1
1
1
1
3
3
3
2
2
1
13
10
Number
positive
samples
2
1
1
1
0
1
0
1
1
3
3
3
2
2
1
NR
NR
Concentration
range
4.0-11.4
7
9
12
ND(7)
16
ND(7)
30
50
84-290
380-500
260-290
85-120
70-130
54
NR
NR
Cone.
mean
7.7
7
9
12
NA
16
NA
30
50
156.33
456.67
273.33
102.5
100
54
11.7
23.5
Wt.
basis
Wet
NR
NR
NR
NR
NR
NR
NR
NR
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Location6
Newark Bay, NJ
Lake Ontario
Lake Ontario
Lake Ontario
Lake Ontario
Lake Ontario
Lake Ontario
Lake Ontario
Lake Ontario
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
Various U.S. Sites
Various U.S. Sites
Location
description
Urban
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
Urban
Urban
Samp.
year
NR
NR
NR
NR
NR
NR
NR
NR
NR
94
94
94
94
94
94
NR
NR
Ref.
no.
6
8
8
8
8
8
8
8
8
13
13
13
13
13
13
18
18
Comments
composite 3 samples
composite 3 samples
composite 3 samples
composite 3 samples
composite 3 samples
composite 3 samples
composite 3 samples
composite 3 samples
purchased at supermarket
purchased at supermarket
purchased at supermarket
purchased at supermarket
purchased at supermarket
purchased at supermarket
purchased at supermarket
purchased at supermarket
Octachlorodibenzo-p-dioxin (MW = 460 . 76)
1,2,3,4,6,7,8,9-OCDD
Eel
Trout
Grayling
Fillet
Fillet
Fillet
5
1
1
5
0
1
28-60
ND(5.0)
47
44.4
NA
47
Fat
Fat
Fat
Rhine River, Germany
Neckar River, Germany
Neckar River, Germany
NR
Urban
Urban
88
88
88
2
2
2
up & downstream Basal
composite sample
DRAFT-DO NOT QUOTE OR CITE
B-121
December 2003
-------
Table B-ll. Environmental Levels of Dioxins in Fish (ppt) (continued)
Chemical
1,2,3,4,6,7,8,9-OCDD
(continued)
Fish
species8
Barbel
Carp
Chub
Eel
Bream
Perch
Herring
Herring
Herring
Salmon
Salmon
Perch
Carp
Carp
Blue Crab
Lobster
Str. Bass
Lake Trout
Lake Trout
Lake Trout
Walleye
Walleye
Tissue
Fillet
Fillet
Fillet
Fillet
Whole
Whole
Whole
Muscle
Muscle
NR
Whole
Whole
Meat
Meat
Fillets
Whole
Whole
Whole
Whole
Whole
Number
samples
1
1
1
5
14
3
1
2
2
2
2
3
3
3
2
2
2
1
1
3
1
1
Number
positive
samples
1
1
1
5
14
3
1
1
1
1
2
3
3
3
2
2
2
1
1
3
1
1
Concentration
range
9.0
23
15
25-40
1.4-5.1
2.3-10.5
1.1
ND-0.7
ND-0.3
ND-1.5
0.8-1.9
0.6-0.8
56-62
35-43
34.3-78.8
6.3-10.9
5.1-49.5
1.0
0.7
1.1-2.5
2.8
1.8
Cone.
mean
9
23
15
30
2.5
5.2
1.1
0.4
0.2
0.75
1.35
0.73
59
39
56.6
8.6
27.3
1.0
0.7
1.8
2.8
1.8
Wt.
basis
Fat
Fat
Fat
Fat
Fresh
Fresh
Fresh
Fresh
Fresh
Fresh
Fresh
Fresh
NR
NR
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Location6
Neckar River, Germany
Neckar River, Germany
Neckar River, Germany
Neckar River, Germany
Hamburg, Germany
Hamburg, Germany
Atlantic Coast, Sweden
Baltic Sea, Sweden
Gulf of Bothnia, Sweden
Gulf of Bothnia, Sweden
Gulf of Bothnia, Sweden
Gulf of Bothnia, Sweden
Mississippi River, MN
Lake Orono, MN
Passaic River, NJ
New York Bight
Newark Bay, NJ
Lake Superior
Lake Huron
Lake Michigan
Lake Erie
Lake St. Clair
Location
description
Urban
Urban
Urban
Urban
Urban
Urban
Pristine
Urban
Industrial
NR
NR
NR
Industrial
Industrial
Urban
Dump Site
Urban
NR
NR
NR
NR
NR
Samp.
year
88
88
88
87-88
84
84
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
84
84
84
84
84
Ref.
no.
2
2
2
2
3
3
4
4
4
4
4
4
5
5
7
6
6
7
7
7
7
7
Comments
composite sample
composite 5-10 fish
composite 5-10 fish
composite 5-10 fish
wild salmon
hatched salmon
caught near pulp mill
Elk River power station
Elk River power station
former sewage sludge
mean 5 samples
mean 5 samples
range 3 sample sites
mean 5 samples
mean 5 samples
DRAFT-DO NOT QUOTE OR CITE
B-122
December 2003
-------
Table B-ll. Environmental Levels of Dioxins in Fish (ppt) (continued)
Chemical
1,2,3,4,6,7,8,9-OCDD
(continued)
Fish
species8
Lake Trout
Lake Trout
Br. Trout
Br. Trout
Rb. Trout
Rb. Trout
Lake Trout
Lake Trout
Coho
Salmon
Coho
Salmon
Cod
Haddock
P. Flounder
Plaice
Flounder
Eel
Mussel
Shrimp
Cod
Carp
Pike
Tissue
Whole
Whole
Whole
Fillets
Whole
Fillets
Whole
Fillets
Whole
Fillets
Fillets
Fillets
Fillets
Fillets
Fillets
Fillets
Muscle
Muscle
Fillets
Whole
Muscle
Number
samples
1
10
1
1
1
1
1
1
1
1
4
1
1
1
1
4
3
2
6
2
8
Number
positive
samples
1
10
0
1
0
0
1
1
1
1
3
0
1
1
1
3
3
1
NR
2
8
Concentration
range
1.2
0.8-3.7
ND(7)
11
ND(7)
ND(7)
89
28
160
280
ND-11
ND(3.6)
3.4
424
2.4
ND-770
12-140
ND-18
0.63-2.2
3-5
10-17
Cone.
mean
1.2
1.6
NA
11
NA
NA
89
28
160
280
4.95
NA
3.4
424
2.4
204
62.0
9.0
NR
4
14.75
Wt.
basis
Wet
Wet
NR
NR
NR
NR
NR
NR
NR
NR
Fresh
Fresh
Fresh
Fresh
Fresh
Fresh
Fresh
Fresh
Fresh
NR
Fat
Location6
Lake Ontario
Lake Michigan
Lake Ontario
Lake Ontario
Lake Ontario
Lake Ontario
Lake Ontario
Lake Ontario
Lake Ontario
Lake Ontario
Various, Sweden
Various, Sweden
Various, Sweden
Various, Sweden
Various, Sweden
Various, Sweden
Grenlandsfiord, Sweden
Grenlandsfiord, Sweden
Frierfiord, Sweden
Lake Huron
Lake Vanern, Sweden
Location
description
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
Industrial
Industrial
Industrial
Industrial
Industrial
Industrial
Industrial
Industrial
Industrial
NR
Industrial
Samp.
year
84
84
NR
NR
NR
NR
NR
NR
NR
NR
88
88
88
88
88
87-88
87
88
87
NR
88
Ref.
no.
7
7
8
8
8
8
8
8
8
8
9
9
9
9
9
9
9
9
9
10
11
Comments
mean 5 samples
composite 3 samples
composite 3 samples
composite 3 samples
composite 3 samples
composite 3 samples
composite 3 samples
composite 3 samples
composite 3 samples
composite 10 samples
composite 10 samples
composite 10 samples
composite 10 samples
only cone, range given
samples composite 3-5 fish
samples composite 2-5 fish
DRAFT-DO NOT QUOTE OR CITE
B-123
December 2003
-------
Table B-ll. Environmental Levels of Dioxins in Fish (ppt) (continued)
Chemical
1,2,3,4,6,7,8,9-OCDD
(continued)
Fish
species8
Pike
Herring
Herring
Cod
Redfish
Plaice
Mackerel
Herring
Cod
Skate
Coley
Fish
Fish
Ocean Fish
Fresh Fish
Oyster
Blue Crab
Blue Crab
Crawfish
Crawfish
Catfish
Mullett
Tissue
Muscle
Fillet
NR
NR
NR
Whole
Whole
Whole
Whole
Whole
Whole
Whole
Whole
Mixed
Mixed
Whole
Body
Claw
Head
Tail
Fillet
Fillet
Number
samples
1
1
1
1
1
3
1
1
1
1
1
1
1
13
10
3
3
3
2
2
3
2
Number
positive
samples
1
1
1
1
1
3
1
1
1
1
1
1
1
NR
NR
3
3
3
2
2
3
2
Concentration
range
22
0.34
19
83
11
1.40-3.20
4.81
3.4
2.79
1.36
2.25
4.0
1.6
NR
NR
250-1100
240-470
250-400
66-80
210-240
400-1100
8.7-32
Cone.
mean
22
0.34
19
83
11
2.12
4.81
3.4
2.79
1.36
2.25
4.0
1.6
31.6
122
536.67
320
303.33
73
225
800
20.35
Wt.
basis
Fat
Fresh
Fat
Fat
Fat
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Location6
Hedesunda Bay, Sweden
Baltic Sea, Sweden
Berlin, W. Germany
Berlin, W. Germany
Berlin, W. Germany
Norwich, UK
Norwich, UK
Norwich, UK
Norwich, UK
Norwich, UK
Norwich, UK
Port Talbot, UK
Stonehaven, UK
Various U.S. Sites
Various U.S. Sites
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
Location
description
Industrial
NR
Urban
Urban
Urban
NR
NR
NR
NR
NR
NR
Urban/
Industrial
Rural
Urban
Urban
NR
NR
NR
NR
NR
Farm raised
NR
Samp.
year
88
88
NR
NR
NR
88
88
88
88
88
88
88
91
NR
NR
94
94
94
94
94
94
94
Ref.
no.
11
14
15
15
15
16
16
16
16
16
16
17
17
18
18
13
13
13
13
13
13
13
Comments
composite 5 fish
sample composite 12 fillets
purchased at different shops
purchased at different shops
purchased at different shops
purchased at retail outlet
purchased at retail outlet
purchased at retail outlet
purchased at retail outlet
purchased at retail outlet
purchased at retail outlet
purchased at supermarket
purchased at supermarket
purchased at supermarket
purchased at supermarket
purchased at supermarket
purchased at supermarket
purchased at supermarket
purchased at supermarket
purchased at supermarket
DRAFT-DO NOT QUOTE OR CITE
B-124
December 2003
-------
Table B-ll. Environmental Levels of Dioxins in Fish (ppt) (continued)
Chemical
Fish
species8
Spanish
Mackerel
Fish
Fish&
Seafood
Freshwater
Fish
Tissue
Fillet
NR
NR
NR
Number
samples
1
138
8
NR
Number
positive
samples
1
NR
NR
NR
Concentration
range
76
2-426
NR
NR
Cone.
mean
76
20.5
126.9
5.94
Wt.
basis
Fat
Fat
Fat
Fat
Location6
S. Mississippi
Germany
Catalonia, Spain
Russia
Location
description
NR
NR
NR
NR
Samp.
year
94
93-96
96
96
Ref.
no.
13
19
20
21
Comments
purchased at supermarket
official food inspection
samples
purchased at supermarkets
purchased at supermarkets
Footnote References
' Ch. = Channel; Y. = Yellow; Sm. M. = Small Mouth; Str. = Striped; Br. = Brown; Rb. = Rainbow; P. = Pole.
b Various, Netherlands = samples taken from six locations around Ijsselmeer Lake; Various, Michigan = samples taken from Tittabawassee River, Grand River, Saginaw River, Saginaw Bay, and Lake Michigan; Various,
Sweden = samples taken from Grenlandsfjord and Frierfjord; Various US = samples taken from 314 sites across the US, including industrial and background sites.
c Species were taken from both bottom feeders and open water feeders, and then composited.
d Whole fish samples and fillet samples were combined during analysis.
DRAFT-DO NOT QUOTE OR CITE
B-125
December 2003
-------
Table B-ll. Environmental Levels of Dioxins in Fish (ppt) (continued)
NOTES: Summary statistics provided in or derived from references; when reference did not compute mean, it was computed using one-half the detection limit for non-detects;
NA = not applicable;
ND = non-detected (limit of detection);
NR = not reported;
Descriptions provided were those given by reference or surmised from study description when not given;
One half the detection limit was used in calculating means. Therefore, it is possible to have mean concentrations greater than the range (e.g., reported detection limit for nondetects greater than the positive sample).
Sources: 1.
2.
3.
4.
5.
9.
10
11
Van den Berg (1987)
Frommberger (1991)
Gotz and Schumacher (1990)
Rappe et al. (1989b)
Reedetal. (1990)
Rappe etal. (1991)
DeVault et al. (1989)
Niimi and Oliver (1989a)
Oehmeetal. (1989)
Stalling etal. (1983)
Kjeller et al. (1990)
12. U.S. EPA (1992)
13. Fiedler etal. (1997)
14. DeWit etal. (1990)
15. Beck et al. (1989)
16. Startin et al. (1990)
17. MAFF (1992)
18. Schecter et al. (1996)
19. Malisch (1998)
20. Domingo etal. (1999)
21. Amirovaetal. (1997)
DRAFT-DO NOT QUOTE OR CITE
B-126
December 2003
-------
Table B-12. Environmental Levels of Dibenzofurans in Fish (ppt)
Chemical
Fish
species8
Tissue
Number
samples
Number
positive
samples
Concentration
range
Cone.
mean
Wt.
basis
Location6
Location
description
Samp.
year
Ref
no.
Comments
Tetrachlorodibenzofiirans (MW = 305 . 98)
2,3,7,8-TCDF
Eel
Eel
Br. Trout
Grayling
Barbel
Carp
Chub
Eel
Herring
Herring
Herring
Salmon
Salmon
Perch
Artie Char
Carp
Carp
Carp
Catfish
Liver
Fillets
Fillets
Fillets
Fillets
Fillets
Fillets
Fillets
Whole
Whole
Whole
Muscle
Muscle
NR
NR
Whole
Whole
Fillets
Fillets
6
5
1
1
1
1
1
5
1
2
2
2
2
3
5
3
3
1
1
0
5
1
1
1
1
1
5
1
2
2
2
2
3
5
3
3
1
1
ND
2.1-12
45
108
57
58
128
0.9-2.0
1.7
5.3-5.5
3.0-6.2
28-35
7.8-9.0
2.1-8.7
21-75
1.8-3.0
1.0-1.3
49
6
NA
6.98
45
108
57
58
128
1.35
1.7
5.4
4.6
31.5
8.4
5.4
55
2.6
1.1
49
6
NR
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fresh
Fresh
Fresh
Fresh
Fresh
Fresh
Fresh
NR
NR
NR
NR
Various, Netherlands
Rhine River, Germany
Neckar River, Germany
Neckar River, Germany
Neckar River, Germany
Neckar River, Germany
Neckar River, Germany
Neckar River, Germany
Atlantic Coast, Sweden
Baltic Sea, Sweden
Gulf of Bothnia, Sweden
Gulf of Bothnia, Sweden
Gulf of Bothnia, Sweden
Gulf of Bothnia, Sweden
Lake Vattern, Sweden
Mississippi River, MN
Lake Orono, MN
NR
Saginaw River
NR
NR
Urban
Urban
Urban
Urban
Urban
Urban
Pristine
Urban
Industrial
NR
NR
NR
NR
Industrial
Industrial
NR
NR
NR
88
88
88
88
88
88
87-88
NR
NR
NR
NR
NR
NR
NR
NR
NR
78
84
1
2
2
2
2
2
2
2
4
4
4
4
4
4
4
5
5
6
6
one sample near dump site
up & downstream Basal
composite sample
composite sample
composite 2-5 fish
composite 2-5 fish
composite 2-5 fish
wild salmon
hatched salmon
caught near pulp mill
Elk river power station
Elk river power station
contamimated site
contaminated site
DRAFT-DO NOT QUOTE OR CITE
B-127
December 2003
-------
Table B-12. Environmental Levels of Dibenzofurans in Fish (ppt) (continued)
Chemical
2,3,7,8-TCDF (continued)
Fish
species8
Smk. Chub
Str. Bass
Lg. M. Bass
Lake Trout
Blue Crab
Lobster
Str. Bass
Lake Trout
Lake Trout
Lake Trout
Walleye
Walleye
Lake Trout
Lake Trout
Br. Trout
Br. Trout
Rb. Trout
Rb. Trout
Lake Trout
Lake Trout
Coho Salmon
Tissue
Fillets
Fillets
Fillets
Fillets
Meat
Meat
Fillets
Whole
Whole
Whole
Whole
Whole
Whole
Whole
Whole
Fillets
Whole
Fillets
Whole
Fillets
Whole
Number
samples
1
5
1
3
2
2
2
1
1
3
1
1
1
10
1
1
1
1
1
1
1
Number
positive
samples
1
5
1
3
2
2
2
1
1
3
1
1
1
10
1
1
1
1
1
1
1
Concentration
range
3
7-93
10
11-56
11.0-15.5
3.5-4.1
51.9-85.5
14.8
22.8
34.8-42.3
11.3
24.8
18.5
27.0-56.0
11
8
19
6
15
6
20
Cone.
mean
3
28.0
10
31.7
13.2
3.8
68.7
14.8
22.8
39.5
11.3
24.8
18.5
38.4
11
8
19
6
15
6
20
Wt.
basis
NR
NR
NR
NR
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
NR
NR
NR
NR
NR
NR
NR
Location6
NR
Hudson River
Hudson River
Lake Superior
Passaic River, NJ
New York Bight
Newark Bay, NJ
Lake Superior
Lake Huron
Lake Michigan
Lake Erie
Lake St. Clair
Lake Ontario
Lake Michigan
Lake Ontario
Lake Ontario
Lake Ontario
Lake Ontario
Lake Ontario
Lake Ontario
Lake Ontario
Location
description
NR
NR
NR
NR
Urban
Dump Site
Urban
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
Samp.
year
85
85
85
85
NR
NR
NR
84
84
84
84
84
84
84
NR
NR
NR
NR
NR
NR
NR
Ref.
no.
6
6
6
6
7
7
7
8
8
8
8
8
8
8
9
9
9
9
9
9
9
Comments
contaminated site
contaminated site
contaminated site
contaminted site
former sewage sludge
mean 5 samples
mean 5 samples
range 3 sample sites
mean 5 samples
mean 5 samples
mean 5 samples
composite 3 samples
composite 3 samples
composite 3 samples
composite 3 samples
composite 3 samples
composite 3 samples
composite 3 samples
DRAFT-DO NOT QUOTE OR CITE
B-128
December 2003
-------
Table B-12. Environmental Levels of Dibenzofurans in Fish (ppt) (continued)
Chemical
2,3,7,8-TCDF (continued)
Fish
species8
Coho Salmon
Cod
Haddock
P. Flounder
Plaice
Flounder
Eel
Mussel
Shrimp
Cod
Carp
Pike
Pike
Various0
Sucker
Lm Bass
Rock Bass
Rock Bass
Sm Bass
Redeye Bass
Tissue
Fillets
Fillets
Fillets
Fillets
Fillets
Fillets
Fillets
Muscle
Muscle
Fillets
Whole
Muscle
Muscle
Mixed"
Whole
Fillet
Whole
Fillet
Fillet
Fillet
Number
samples
1
4
1
1
1
1
4
3
2
6
2
8
1
314
15
4
2
1
2
1
Number
positive
samples
1
4
1
1
1
1
3
3
2
NR
2
8
1
279
12
0
1
0
2
0
Concentration
range
6
0.2-1.4
0.75
0.28
1.4
1.2
ND-68
16.1-61
6.1-37
0.49-14.3
11
330-3000
430
NR
ND-6.21
ND(0.23-
0.59)
ND-0.86
ND(1.95)
0.19-0.30
ND(0.49)
Cone.
mean
6
0.62
0.75
0.28
1.4
1.2
17.1
32.4
21.6
NR
NA
774
430
13.6
1.96
NA
1.24
NA
0.25
NA
Wt.
basis
NR
Fresh
Fresh
Fresh
Fresh
Fresh
Fresh
Fresh
Fresh
Fresh
NR
Fat
Fat
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Location6
Lake Ontario
Various, Sweden
Various, Sweden
Various, Sweden
Various, Sweden
Various, Sweden
Various, Sweden
Grenlandsfiord, Sweden
Grenlandsfiord, Sweden
Frierfiord, Sweden
Lake Huron
Lake Vanern, Sweden
Hedesunda Bay, Sweden
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Location
description
NR
Industrial
Industrial
Industrial
Industrial
Industrial
Industrial
Industrial
Industrial
Industrial
NR
Industrial
Industrial
Various
Background
Background
Background
Background
Background
Background
Samp.
year
NR
88
88
88
88
88
88
87
88
87
NR
88
88
86-89
86-89
86-89
86-89
86-89
86-89
86-89
Ref.
no.
9
10
10
10
10
10
10
10
10
10
11
12
12
13
13
13
13
13
13
13
Comments
composite 3 samples
composite 10 samples
composite 10 samples
composite 10 samples
composite 10 samples
only cone, range given
samples composite 3-5 fish
samples composite 2-5 fish
composite 5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
DRAFT-DO NOT QUOTE OR CITE
B-129
December 2003
-------
Table B-12. Environmental Levels of Dibenzofurans in Fish (ppt) (continued)
Chemical
2,3,7,8-TCDF (continued)
Fish
species8
Carp
not available
Brown Trout
Brown Trout
Rainbow
Trout
Brook Trout
Gray
Redhorse
Black
Redhorse
Golden
Redhorse
Longear
Sunfish
Walleye
Chain Pickerel
Black Crappie
Sea Catfish
North
Hogsucker
Summer
Flounder
Tissue
Whole
Whole
Whole
Fillet
Fillet
Fillet
Whole
Whole
Whole
Whole
Fillet
Fillet
Fillet
Fillet
Whole
Whole
Number
samples
5
2
1
2
2
1
1
1
1
1
3
3
1
1
1
1
Number
positive
samples
4
1
0
0
2
0
0
1
1
0
1
0
0
0
0
0
Concentration
range
ND-1.36
ND-0.29
ND(0.20)
ND(0.20)
0.75-0.90
ND(0.48)
ND(0.42)
2.30
1.77
ND(0.41)
ND-0.86
ND(0.20-
0.24)
ND(0.20)
ND(0.49)
ND(0.84)
ND(0.71)
Cone.
mean
0.96
0.37
NA
NA
0.83
NA
NA
2.30
1.77
NA
0.41
NA
NA
NA
NA
NA
Wt.
basis
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Location6
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Location
description
Background
Background
Background
Background
Background
Background
Background
Background
Background
Background
Background
Background
Background
Background
Background
Background
Samp.
year
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
Ref.
no.
13
13
13
13
13
13
13
13
13
13
13
13
13
13
13
13
Comments
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
DRAFT-DO NOT QUOTE OR CITE
B-130
December 2003
-------
Table B-12. Environmental Levels of Dibenzofurans in Fish (ppt) (continued)
Chemical
2,3,7,8-TCDF (continued)
Fish
species8
Dolly Varden
Composite
Bottom
Winter
Flounder
Bluefish
White Catfish
Crayfish
Oyster
Blue Crab
Blue Crab
Crawfish
Crawfish
Catfish
Mullet
Spanish
Mackerel
Herring
Herring
Cod
Redfish
Plaice
Tissue
Whole
Whole
Whole
Whole
Whole
Whole
Whole
Body
Claw
Head
Tail
Fillet
Fillet
Fillet
Fillet
NR
NR
NR
Whole
Number
samples
2
1
2
1
1
1
3
3
3
2
2
3
2
1
1
1
1
1
3
Number
positive
samples
2
1
2
1
1
0
3
3
3
2
2
3
2
1
1
1
1
1
3
Concentration
range
0.37
0.86
13.30-13.73
1.93
1.14
ND(0.57)
14-48
26-51
9.8-15
5.1-5.4
2.8-9.5
0.64-0.99
1.1-7.8
11
7.6
57
98
78
0.90-1.86
Cone.
mean
0.37
0.86
13.52
1.93
1.14
NA
29.67
34.67
13.6
5.25
6.15
0.84
4.45
NA
7.6
57
98
78
1.32
Wt.
basis
Wet
Wet
Wet
Wet
Wet
Wet
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fresh
Fat
Fat
Fat
Wet
Location6
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
Baltic Sea, Sweden
Berlin, W. Germany
Berlin, W. Germany
Berlin, W. Germany
Norwich, UK
Location
description
Background
Background
Background
Background
Background
Background
NR
NR
NR
NR
NR
Farm raised
NR
NR
NR
Urban
Urban
Urban
NR
Samp.
year
86-89
86-89
86-89
86-89
86-89
86-89
94
94
94
94
94
94
94
94
88
NR
NR
NR
88
Ref.
no.
13
13
13
13
13
13
14
14
14
14
14
14
14
14
15
16
16
16
17
Comments
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
purchased at supermarket
purchased at supermarket
purchased at supermarket
purchased at supermarket
purchased at supermarket
purchased at supermarket
purchased at supermarket
purchased at supermarket
sample composite 12 fillets
purchased at different shops
purchased at different shops
purchased at different shops
purchased at retail outlet
DRAFT-DO NOT QUOTE OR CITE
B-131
December 2003
-------
Table B-12. Environmental Levels of Dibenzofurans in Fish (ppt) (continued)
Chemical
2,3,7,8-TCDF (continued)
TCDFs
TCDFs (continued)
Fish
species8
Mackeral
Herring
Cod
Skate
Coley
Fish
Fish
Ocean Fish
Fresh Fish
Fish
Fish&
Seafood
Freshwater
Fish
Bream
Perch
Carp
Carp
Blue Crab
Lobster
Str. Bass
Br. Trout
Tissue
Whole
Whole
Whole
Whole
Whole
Whole
Whole
Mixed
Mixed
NR
NR
NR
NR
NR
Whole
Whole
Meat
Meat
Fillets
Whole
Number
samples
1
1
1
1
1
1
1
13
10
138
8
NR
13
2
3
3
2
2
2
1
Number
positive
samples
1
1
1
1
1
1
1
0
NR
NR
NR
NR
13
2
3
3
2
2
2
1
Concentration
range
2.61
2.47
0.22
0.31
0.14
0.6
0.29
ND
NR
0.18-42.81
NR
NR
7.8-86.5
10.7-41.1
2.4-4.0
1.0-1.3
133-164
23.0-31.2
77.2-108
11
Cone.
mean
2.61
2.47
0.22
0.31
0.14
0.6
0.29
NA
14.4
66.5
11.1
9.98
44.3
25.9
3.1
1.2
149
27.1
92.4
11
Wt.
basis
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Fat
Fat
Fat
Fat
Fat
Fresh
Fresh
NR
NR
Wet
Wet
Wet
NR
Location6
Norwich, UK
Norwich, UK
Norwich, UK
Norwich, UK
Norwich, UK
Port Talbot, UK
Stonehaven, UK
Various U.S. Sites
Various U.S. Sites
Germany
Catalonia, Spain
Russia
Hamburg, Germany
Hamburg, Germany
Mississippi River, MN
Lake Orono, MN
Passaic River, NJ
New York Bight
Newark Bay, NJ
Lake Ontario
Location
description
NR
NR
NR
NR
NR
Urban/
Industrial
Rural
Urban
Urban
NR
NR
NR
Urban
Urban
Industrial
Industrial
Urban
Dump Site
Urban
NR
Samp.
year
88
88
88
88
88
1988
1991
NR
NR
93-96
96
96
84
84
NR
NR
NR
NR
NR
NR
Ref.
no.
17
17
17
17
17
18
18
19
19
20
21
22
3
3
5
5
7
7
7
9
Comments
purchased at retail outlet
purchased at retail outlet
purchased at retail outlet
purchased at retail outlet
purchased at retail outlet
purchased at supermarket
purchased at supermarket
official food inspection
samples
purchased in supermarkets
purchased in supermarkets
Elk river power station
Elk river power station
former sewage sludge
composite 3 samples
DRAFT-DO NOT QUOTE OR CITE
B-132
December 2003
-------
Table B-12. Environmental Levels of Dibenzofurans in Fish (ppt) (continued)
Chemical
TCDFs
other than
2,3,7,8-TCDF
Fish
species8
Br. Trout
Rb. Trout
Rb. Trout
Lake Trout
Lake Trout
Coho Salmon
Coho Salmon
Oyster
Blue Crab
Blue Crab
Crawfish
Crawfish
Spanish
Mackerel
Ocean Fish
Fresh Fish
Eel
Grayling
Barbel
Carp
Chub
Eel
Tissue
Fillets
Whole
Fillets
Whole
Fillets
Whole
Fillets
Whole
Body
Claw
Head
Tail
Fillet
Mixed
Mixed
Fillets
Fillets
Fillets
Fillets
Fillets
Fillets
Number
samples
1
1
1
1
1
1
1
3
3
3
2
2
1
13
10
5
1
1
1
1
1
Number
positive
samples
1
1
1
1
1
1
1
3
3
3
2
2
1
NR
NR
5
1
1
1
1
1
Concentration
range
8
19
6
18
6
20
6
96-170
69-82
49-96
47-93
34-120
15
NR
NR
4.6-13
142
77
14
17
1.5
Cone.
mean
8
19
6
18
6
20
6
122
74.67
66
70
78.50
15
17.8
40.4
8.8
142
77
14
17
1.5
Wt.
basis
NR
NR
NR
NR
NR
NR
NR
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Location6
Lake Ontario
Lake Ontario
Lake Ontario
Lake Ontario
Lake Ontario
Lake Ontario
Lake Ontario
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
Various U.S. Sites
Various U.S. Sites
Rhine River, Germany
Neckar River, Germany
Neckar River, Germany
Neckar River, Germany
Neckar River, Germany
Neckar River, Germany
Location
description
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
Urban
Urban
NR
Urban
Urban
Urban
Urban
Urban
Samp.
year
NR
NR
NR
NR
NR
NR
NR
94
94
94
94
94
94
NR
NR
88
88
88
88
88
88
Ref.
no.
9
9
9
9
9
9
9
14
14
14
14
14
14
19
19
2
2
2
2
2
2
Comments
composite 3 samples
composite 3 samples
composite 3 samples
composite 3 samples
composite 3 samples
composite 3 samples
composite 3 samples
purchased at supermarket
purchased at supermarket
purchased at supermarket
purchased at supermarket
purchased at supermarket
purchased at supermarket
purchased at supermarket
purchased at supermarket
up & downstream Basal
sample composite 5 chubs
sample composite 2 eels
DRAFT-DO NOT QUOTE OR CITE
B-133
December 2003
-------
Table B-12. Environmental Levels of Dibenzofurans in Fish (ppt) (continued)
Chemical
Fish
species8
Tissue
Number
samples
Number
positive
samples
Concentration
range
Cone.
mean
Wt.
basis
Location6
Location
description
Samp.
year
Ref.
no.
Comments
Pentachlorodibenzofurans (MW=340.42)
1,2,3,7,8-PeCDF
Herring
Herring
Herring
Carp
Pike
Pike
Various0
Sucker
Lm Bass
Rock Bass
Rock Bass
Sm Bass
Redeye Bass
Carp
not available
Brown Trout
Brown Trout
Whole
Whole
Whole
Whole
Muscle
Muscle
Mixed"
Whole
Fillet
Whole
Fillet
Fillet
Fillet
Whole
Whole
Whole
Fillet
1
2
2
2
8
1
314
15
4
2
1
2
1
5
2
1
2
1
2
2
2
8
1
151
1
0
0
0
1
0
0
1
0
0
0.4
1.4-2.5
0.8-0.9
1-5
43-140
39
NR
ND-0.62
ND(0.33-
0.78)
ND(0.76-
0.82)
ND(0.59)
ND-0.33
ND(0.78)
ND(0.56-
0.80)
ND-0.92
ND(0.36)
ND(0.20-
0.35)
0.4
1.95
0.85
3
73.2
39
1.71
0.33
NA
NA
NA
0.16
NA
NA
0.69
NA
NA
Fresh
Fresh
Fresh
NR
Fat
Fat
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Atlantic Coast, Sweden
Baltic Sea, Sweden
Gulf of Bothnia, Sweden
Lake Huron
Lake Vanern, Sweden
Hedesunda Bay, Sweden
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Pristine
Urban
Industrial
NR
Industrial
Industrial
Various
Background
Background
Background
Background
Background
Background
Background
Background
Background
Background
NR
NR
NR
NR
88
88
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
4
4
4
11
12
12
13
13
13
13
13
13
13
13
13
13
13
composite 2-5 fish
composite 2-5 fish
composite 2-5 fish
samples composite 3-5 fish
samples composite 2-5 fish
composite 5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
DRAFT-DO NOT QUOTE OR CITE
B-134
December 2003
-------
Table B-12. Environmental Levels of Dibenzofurans in Fish (ppt) (continued)
Chemical
1,2,3,7,8-PeCDF
(continued)
Fish
species8
Rainbow
Trout
Brook Trout
Gray
Redhorse
Black
Redhorse
Golden
Redhorse
Longear
Sunfish
Walleye
Chain Pickerel
Black Crappie
Sea Catfish
North
Hogsucker
Summer
Flounder
Dolly Varden
Composite
Bottom
Winter
Flounder
Tissue
Fillet
Fillet
Whole
Whole
Whole
Whole
Fillet
Fillet
Fillet
Fillet
Whole
Whole
Whole
Whole
Whole
Number
samples
2
1
1
1
1
1
3
3
1
1
1
1
2
1
2
Number
positive
samples
1
0
0
0
0
0
0
0
0
0
0
0
0
0
2
Concentration
range
ND-0.47
ND(0.90)
ND(0.25)
ND(0.77)
ND(0.78)
ND(0.52)
ND(0.20-
0.78)
ND(0.19-
0.28)
ND(0.20)
ND(0.78)
ND(0.78)
ND(0.77)
ND(0.77)
ND(0.78)
1.74-1.90
Cone.
mean
0.51
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
1.82
Wt.
basis
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Location6
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Location
description
Background
Background
Background
Background
Background
Background
Background
Background
Background
Background
Background
Background
Background
Background
Background
Samp.
year
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
Ref.
no.
13
13
13
13
13
13
13
13
13
13
13
13
13
13
13
Comments
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
DRAFT-DO NOT QUOTE OR CITE
B-135
December 2003
-------
Table B-12. Environmental Levels of Dibenzofurans in Fish (ppt) (continued)
Chemical
1,2,3,7,8-PeCDF
(continued)
Fish
species8
Bluefish
White Catfish
Crayfish
Various0
Oyster
Blue Crab
Blue Crab
Crawfish
Crawfish
Catfish
Mullet
Spanish
Mackerel
Herring
Herring
Cod
Redfish
Plaice
Mackeral
Herring
Cod
Tissue
Whole
Whole
Whole
Mixed"
Whole
Body
Claw
Head
Tail
Fillet
Fillet
Fillet
Fillet
NR
NR
NR
Whole
Whole
Whole
Whole
Number
samples
1
1
1
34
3
3
3
2
2
3
2
1
1
1
1
1
3
1
1
1
Number
positive
samples
1
1
0
34
3
3
3
2
2
3
2
1
1
1
1
1
3
1
1
1
Concentration
range
1.06
0.73
ND(0.78)
0.1-1.90
1.2-1.4
1.3-2.9
0.69-1.6
1.2-2
1.6-3.2
0.11-0.21
0.52-1.3
3.5
4.2
16
48
31
0.14-0.23
0.08
0.47
0.05
Cone.
mean
1.06
0.73
NA
0.43
1.3
2.03
1.2
1.6
2.4
0.15
0.91
3.5
4.2
16
48
31
0.18
0.08
0.47
0.05
Wt.
basis
Wet
Wet
Wet
Wet
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fresh
Fat
Fat
Fat
Wet
Wet
Wet
Wet
Location6
Various, US
Various, US
Various, US
Various, US
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
Baltic Sea, Sweden
Berlin, W. Germany
Berlin, W. Germany
Berlin, W. Germany
Norwich, UK
Norwich, UK
Norwich, UK
Norwich, UK
Location
description
Background
Background
Background
Background'
NR
NR
NR
NR
NR
Farm raised
NR
NR
Urban
Urban
Urban
NR
NR
NR
NR
Samp.
year
86-89
86-89
86-89
86-89
94
94
94
94
94
94
94
94
88
NR
NR
NR
88
88
88
88
Ref.
no.
13
13
13
13
14
14
14
14
14
14
14
14
15
16
16
16
17
17
17
17
Comments
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
purchased at supermarket
purchased at supermarket
purchased at supermarket
purchased at supermarket
purchased at supermarket
purchased at supermarket
purchased at supermarket
purchased at supermarket
sample composite 12 fillets
purchased at different shops
purchased at different shops
purchased at different shops
purchased at retail outlet
purchased at retail outlet
purchased at retail outlet
purchased at retail outlet
DRAFT-DO NOT QUOTE OR CITE
B-136
December 2003
-------
Table B-12. Environmental Levels of Dibenzofurans in Fish (ppt) (continued)
Chemical
1,2,3,7,8-PeCDF
(continued)
2,3,4,7,8-PeCDF
2,3,4,7,8-PeCDF
Fish
species8
Skate
Coley
Fish
Fish
Ocean Fish
Fresh Fish
Fish
Fish&
Seafood
Freshwater
Fish
Herring
Herring
Herring
Carp
Pike
Pike
Sucker
Lm Bass
Rock Bass
Rock Bass
Tissue
Whole
Whole
Whole
Whole
Mixed
Mixed
NR
NR
NR
Whole
Whole
Whole
Whole
Muscle
Muscle
Whole
Fillet
Whole
Fillet
Number
samples
1
1
8
1
13
10
138
8
NR
1
2
2
2
8
1
15
4
2
1
Number
positive
samples
1
1
NR
1
0
0
NR
NR
NR
1
2
2
2
8
1
6
0
0
0
Concentration
range
0.07
0.07
0.05-0.47
0.08
ND
ND
0-268
NR
NR
3.0
6.8-19.0
8.8-8.9
4-11
120-290
110
ND-1.36
ND(0.33-
0.85)
ND(0.76-
0.82)
ND(0.59)
Cone.
mean
0.07
0.07
0.16
0.08
NA
NA
7.3
3.7
2.46
NA
12.9
8.85
7.5
189
NA
0.46
NA
NA
NA
Wt.
basis
Wet
Wet
Wet
Wet
Fat
Fat
Fat
Fat
Fat
Fresh
Fresh
Fresh
NR
Fat
Fat
Wet
Wet
Wet
Wet
Location6
Norwich, UK
Norwich, UK
Norwich, UK
Port Talbot, UK
Various U.S. Sites
Various U.S. Sites
Germany
Catalonia, Spain
Russia
Atlantic Coast, Sweden
Baltic Sea, Sweden
Gulf of Bothnia, Sweden
Lake Huron
Lake Vanern, Sweden
Hedesunda Bay, Sweden
Various, US
Various, US
Various, US
Various, US
Location
description
NR
NR
NR
Urban/
Industrial
Urban
Urban
NR
NR
NR
Pristine
Urban
Industrial
NR
Industrial
Industrial
Background
Background
Background
Background
Samp.
year
88
88
88
88
NR
NR
93-96
96
96
NR
NR
NR
NR
88
88
86-89
86-89
86-89
86-89
Ref.
no.
17
17
18
18
19
19
20
21
22
4
4
4
11
12
12
13
13
13
13
Comments
purchased at retail outlet
purchased at retail outlet
purchased at supermarket
purchased at supermarket
official food inspection
samples
purchased in supermarkets
purchased in supermarkets
composite 2-5 fish
composite 2-5 fish
composite 2-5 fish
samples composite 3-5 fish
samples composite 2-5 fish
composite 5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
DRAFT-DO NOT QUOTE OR CITE
B-137
December 2003
-------
Table B-12. Environmental Levels of Dibenzofurans in Fish (ppt) (continued)
Chemical
(continued)
Fish
species8
Sm Bass
Redeye Bass
Carp
not available
Brown Trout
Brown Trout
Rainbow
Trout
Brook Trout
Gray
Redhorse
Black
Redhorse
Golden
Redhorse
Longear
Sunfish
Walleye
Chain Pickerel
Black Crappie
Sea Catfish
North
Hogsucker
Tissue
Fillet
Fillet
Whole
Whole
Whole
Fillet
Fillet
Fillet
Whole
Whole
Whole
Whole
Fillet
Fillet
Fillet
Fillet
Whole
Number
samples
2
1
5
2
1
2
2
1
1
1
1
1
3
3
1
1
1
Number
positive
samples
0
0
2
1
0
0
2
0
0
0
0
0
0
0
0
0
0
Concentration
range
ND(0.85)
ND(0.85)
ND-0.34
ND-1.33
ND(0.36)
ND(0.20-
0.35)
0.70
ND(0.90)
ND(0.25)
ND(0.95)
ND(0.92)
ND(0.52)
ND(0.20-
0.85)
ND(0.19-
0.28)
ND(0.20)
ND(0.85)
ND(0.85)
Cone.
mean
NA
NA
0.35
0.82
NA
NA
0.70
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
Wt.
basis
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Location6
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Location
description
Background
Background
Background
Background
Background
Background
Background
Background
Background
Background
Background
Background
Background
Background
Background
Background
Background
Samp.
year
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
Ref.
no.
13
13
13
13
13
13
13
13
13
13
13
13
13
13
13
13
13
Comments
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
DRAFT-DO NOT QUOTE OR CITE
B-138
December 2003
-------
Table B-12. Environmental Levels of Dibenzofurans in Fish (ppt) (continued)
Chemical
2,3,4,7,8-PeCDF
(continued)
2,3,4,7,8-PeCDF
Fish
species8
Summer
Flounder
Dolly Varden
Composite
Bottom
Winter
Flounder
Bluefish
White Catfish
Crayfish
Various0
Oyster
Blue Crab
Blue Crab
Crawfish
Crawfish
Catfish
Mullet
Spanish
Mackerel
Herring
Herring
Cod
Redfish
Tissue
Whole
Whole
Whole
Whole
Whole
Whole
Whole
Mixed"
Whole
Body
Claw
Head
Tail
Fillet
Fillet
Fillet
Fillet
NR
NR
NR
Number
samples
1
2
1
2
1
1
1
314
3
3
3
2
2
3
2
1
1
1
1
1
Number
positive
samples
0
0
0
2
1
1
0
201
3
3
3
2
2
3
2
1
1
1
1
1
Concentration
range
ND(0.85)
ND(0.85)
ND(0.85)
0.64-0.70
0.93
1.39
ND(0.85)
NR
4.3-5.8
7-9
2.8-5.9
1.4-2.3
2.1-2.4
0.26-0.31
0.62-4.6
5.1
17.0
29
3.1
25
Cone.
mean
NA
NA
NA
0.67
0.93
1.39
NA
3.06
5.2
8.17
4.27
1.85
2.25
0.28
2.61
5.1
17.0
29
3.1
25
Wt.
basis
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fresh
Fat
Fat
Fat
Location6
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
Baltic Sea, Sweden
Berlin, W. Germany
Berlin, W. Germany
Berlin, W. Germany
Location
description
Background
Background
Background
Background
Background
Background
Background
Various
NR
NR
NR
NR
NR
Farm raised
NR
NR
Urban
Urban
Urban
Samp.
year
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
94
94
94
94
94
94
94
94
88
NR
NR
NR
Ref.
no.
13
13
13
13
13
13
13
13
14
14
14
14
14
14
14
14
15
16
16
16
Comments
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
purchased at supermarket
purchased at supermarket
purchased at supermarket
purchased at supermarket
purchased at supermarket
purchased at supermarket
purchased at supermarket
purchased at supermarket
sample composite 12 fillets
purchased at different shops
purchased at different shops
purchased at different shops
DRAFT-DO NOT QUOTE OR CITE
B-139
December 2003
-------
Table B-12. Environmental Levels of Dibenzofurans in Fish (ppt) (continued)
Chemical
(continued)
PeCDFs
PeCDFs (continued)
Fish
species8
Plaice
Mackeral
Herring
Cod
Skate
Coley
Fish
Fish
Ocean Fish
Fresh Fish
Fish
Fish&
Seafood
Freshwater
Fish
Bream
Perch
Carp
Carp
Blue Crab
Lobster
Str. Bass
Tissue
Whole
Whole
Whole
Whole
Whole
Whole
Whole
Whole
Mixed
Mixed
NR
NR
NR
NR
NR
Whole
Whole
Meat
Meat
Fillets
Number
samples
3
1
1
1
1
1
1
1
13
10
138
8
NR
13
2
3
3
2
2
2
Number
positive
samples
3
1
1
1
0
1
1
1
0
NR
NR
NR
NR
13
2
3
2
2
2
2
Concentration
range
0.39-1.58
0.37
1.96
0.03
ND(0.04)
0.04
0.68
0.07
ND
NR
0.12-669
NR
NR
13.9-114
62.3-153
15-45
ND-13
89.7-94.1
29.8-37.3
34.3-82.6
Cone.
mean
0.95
0.37
1.96
0.03
NA
0.04
0.68
0.07
NA
7.56
14.4
4.4
3.77
65.4
108
26.3
9.0
91.9
33.6
58.4
Wt.
basis
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Fat
Fat
Fat
Fat
Fat
Fresh
Fresh
NR
NR
Wet
Wet
Wet
Location6
Norwich, UK
Norwich, UK
Norwich, UK
Norwich, UK
Norwich, UK
Norwich, UK
Port Talbot, UK
Stonehaven, UK
Various U.S. Sites
Various U.S. Sites
Germany
Catalonia, Spain
Russia
Hamburg, Germany
Hamburg, Germany
Mississippi River, MN
Lake Orono, MN
Passaic River, NJ
New York Bight
Newark Bay, NJ
Location
description
NR
NR
NR
NR
NR
NR
Urban/
Industrial
Rural
Urban
Urban
NR
NR
NR
Urban
Urban
Industrial
Industrial
Urban
Dump Site
Urban
Samp.
year
88
88
88
88
88
88
88
88-91
NR
NR
93-96
96
96
84
84
NR
NR
NR
NR
NR
Ref.
no.
17
17
17
17
17
17
18
18
19
19
20
21
22
3
3
5
5
7
7
7
Comments
purchased at retail outlet
purchased at retail outlet
purchased at retail outlet
purchased at retail outlet
purchased at retail outlet
purchased at retail outlet
purchased at supermarket
purchased at supermarket
official food inspection
samples
purchased in supermarkets
purchased in supermarkets
Elk river power station
Elk river power station
former sewage sludge
DRAFT-DO NOT QUOTE OR CITE
B-140
December 2003
-------
Table B-12. Environmental Levels of Dibenzofurans in Fish (ppt) (continued)
Chemical
PeCDFs
other than
1,2,3,7,8-PeCDF
and
2,3,4,7,8-PeCDF
Fish
species8
Br. Trout
Br. Trout
Rb. Trout
Rb. Trout
Lake Trout
Lake Trout
Coho Salmon
Coho Salmon
Oyster
Blue Crab
Blue Crab
Crawfish
Crawfish
Spanish
Mackerel
Ocean Fish
Fresh Fish
Grayling
Barbel
Carp
Tissue
Whole
Fillets
Whole
Fillets
Whole
Fillets
Whole
Fillets
Whole
Body
Claw
Head
Tail
Fillet
Mixed
Mixed
Fillets
Fillets
Fillets
Number
samples
1
1
1
1
1
1
1
1
3
3
3
2
2
1
13
10
1
1
1
Number
positive
samples
0
1
1
1
1
1
1
0
3
3
3
2
2
1
NR
NR
1
1
1
Concentration
range
ND(7)
3
8
7
39
8
13
ND(7)
56-66
110-200
43-110
29-54
20-39
15
NR
NR
22
21
17
Cone.
mean
NA
3
8
7
39
8
13
NA
59.67
146.67
71.33
41.50
29.5
15
8.75
21.7
22
21
17
Wt.
basis
NR
NR
NR
NR
NR
NR
NR
NR
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Location6
Lake Ontario
Lake Ontario
Lake Ontario
Lake Ontario
Lake Ontario
Lake Ontario
Lake Ontario
Lake Ontario
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
Various U.S. Sites
Various U.S. Sites
Neckar River, Germany
Neckar River, Germany
Neckar River, Germany
Location
description
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
Urban
Urban
Urban
Urban
Urban
Samp.
year
NR
NR
NR
NR
NR
NR
NR
NR
94
94
94
94
94
94
NR
NR
88
88
88
Ref.
no.
9
9
9
9
9
9
9
9
14
14
14
14
14
14
19
19
2
2
2
Comments
composite 3 samples
composite 3 samples
composite 3 samples
composite 3 samples
composite 3 samples
composite 3 samples
composite 3 samples
composite 3 samples
purchased at supermarket
purchased at supermarket
purchased at supermarket
purchased at supermarket
purchased at supermarket
purchased at supermarket
purchased at supermarket
purchased at supermarket
Hexachlorodibenzofurans (MW=374.87)
1,2,3,4,7,8-HxCDF
Herring
Herring
Whole
Whole
1
2
1
2
0.2
0.4-0.7
0.2
0.55
Fresh
Fresh
Atlantic Coast, Sweden
Baltic Sea, Sweden
Pristine
Urban
NR
NR
4
4
composite 2-5 fish
composite 2-5 fish
DRAFT-DO NOT QUOTE OR CITE
B-141
December 2003
-------
Table B-12. Environmental Levels of Dibenzofurans in Fish (ppt) (continued)
Chemical
1,2,3,4,7,8-HxCDF
(continued)
Fish
species8
Herring
Carp
Pike
Pike
Sucker
Lm Bass
Rock Bass
Rock Bass
Sm Bass
Redeye Bass
Carp
not available
Brown Trout
Brown Trout
Rainbow
Trout
Brook Trout
Gray
Redhorse
Black
Redhorse
Tissue
Whole
Whole
Muscle
Muscle
Whole
Fillet
Whole
Fillet
Fillet
Fillet
Whole
Whole
Whole
Fillet
Fillet
Fillet
Whole
Whole
Number
samples
2
2
8
1
15
4
2
1
2
1
5
2
1
2
2
1
1
1
Number
positive
samples
2
2
8
1
0
0
0
0
0
0
1
1
0
0
0
0
0
0
Concentration
range
0.3
2-5
10-33
11
ND(0.33-
2.84)
ND(0.41-
2.84)
ND(1.13-
1.30)
ND(0.72)
ND(2.83-
2.84)
ND(2.84)
ND-0.40
ND-1.24
ND(0.96)
ND(0.23-
0.42)
ND(0.45-
0.58)
ND(1.46)
ND(0.52)
ND(2.82)
Cone.
mean
NA
3.5
14.4
11
NA
NA
NA
NA
NA
NA
0.98
0.81
NA
NA
NA
NA
NA
NA
Wt.
basis
Fresh
NR
Fat
Fat
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Location6
Gulf of Bothnia, Sweden
Lake Huron
Lake Vanern, Sweden
Hedesunda Bay, Sweden
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Location
description
Industrial
NR
Industrial
Industrial
Background
Background
Background
Background
Background
Background
Background
Background
Background
Background
Background
Background
Background
Background
Samp.
year
NR
NR
88
88
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
Ref.
no.
4
11
12
12
13
13
13
13
13
13
13
13
13
13
13
13
13
13
Comments
composite 2-5 fish
samples composite 3-5 fish
samples composite 2-5 fish
composite 5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
DRAFT-DO NOT QUOTE OR CITE
B-142
December 2003
-------
Table B-12. Environmental Levels of Dibenzofurans in Fish (ppt) (continued)
Chemical
1,2,3,4,7,8-HxCDF
(continued)
Fish
species8
Golden
Redhorse
Longear
Sunfish
Walleye
Chain Pickerel
Black Crappie
Sea Catfish
North
Hogsucker
Summer
Flounder
Dolly Varden
Composite
Bottom
Winter
Flounder
Bluefish
White Catfish
Crayfish
Various0
Oyster
Blue Crab
Blue Crab
Tissue
Whole
Whole
Fillet
Fillet
Fillet
Fillet
Whole
Whole
Whole
Whole
Whole
Whole
Whole
Whole
Mixed"
Whole
Body
Claw
Number
samples
1
1
3
3
1
1
1
1
2
1
2
1
1
1
314
3
3
3
Number
positive
samples
1
0
0
0
0
0
0
0
0
0
0
0
0
0
132
1
3
3
Concentration
range
1.18
ND(0.78)
ND(0.21-
2.84)
ND(0.20-
0.42)
ND(0.20)
ND(2.84)
ND(2.84)
ND(2.83)
ND(2.83)
ND(2.84)
ND(2.83-
2.84)
ND(2.83)
ND(2.84)
ND(2.84)
NR
ND-0.41
5.8-9.7
3-4.1
Cone.
mean
1.18
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
2.35
0.22
7.5
3.53
Wt.
basis
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Fat
Fat
Fat
Location6
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
S. Mississippi
S. Mississippi
S. Mississippi
Location
description
Background
Background
Background
Background
Background
Background
Background
Background
Background
Background
Background
Background
Background
Background
Various
NR
NR
NR
Samp.
year
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
94
94
94
Ref.
no.
13
13
13
13
13
13
13
13
13
13
13
13
13
13
13
14
14
14
Comments
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
purchased at supermarket
purchased at supermarket
purchased at supermarket
DRAFT-DO NOT QUOTE OR CITE
B-143
December 2003
-------
Table B-12. Environmental Levels of Dibenzofurans in Fish (ppt) (continued)
Chemical
Fish
species8
Crawfish
Crawfish
Catfish
Mullet
Spanish
Mackerel
Herring
Cod
Redfish
Plaice
Mackeral
Herring
Cod
Skate
Coley
Fish
Ocean Fish
Tissue
Head
Tail
Fillet
Fillet
Fillet
NR
NR
NR
Whole
Whole
Whole
Whole
Whole
Whole
Whole
Mixed
Number
samples
2
2
3
2
1
1
1
1
3
1
1
1
1
1
1
13
Number
positive
samples
2
1
1
1
1
1
1
1
3
0
1
0
1
1
1
0
Concentration
range
0.91-4.7
ND-1.9
ND-0.39
ND-7.6
2.5
3.0
6.9
3.5
0.05-0.16
ND(0.02)
0.12
ND(O.l)
0.04
0.03
0.06
ND
Cone.
mean
2.81
1.53
0.25
3.94
2.5
3.0
6.9
3.5
0.11
NA
0.12
NA
0.04
0.03
0.06
NA
Wt.
basis
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Fat
Location6
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
Berlin, W. Germany
Berlin, W. Germany
Berlin, W. Germany
Norwich, UK
Norwich, UK
Norwich, UK
Norwich, UK
Norwich, UK
Norwich, UK
Port Talbot, UK
Various U.S. Sites
Location
description
NR
NR
Farm raised
NR
NR
Urban
Urban
Urban
NR
NR
NR
NR
NR
NR
Urban/
Industrial
Urban
Samp.
year
94
94
94
94
94
NR
NR
NR
88
88
88
88
88
88
88
NR
Ref.
no.
14
14
14
14
14
16
16
16
17
17
17
17
17
17
18
19
Comments
purchased at supermarket
purchased at supermarket
purchased at supermarket
purchased at supermarket
purchased at supermarket
purchased at different shops
purchased at different shops
purchased at different shops
purchased at retail outlet
purchased at retail outlet
purchased at retail outlet
purchased at retail outlet
purchased at retail outlet
purchased at retail outlet
purchased at supermarket
DRAFT-DO NOT QUOTE OR CITE
B-144
December 2003
-------
Table B-12. Environmental Levels of Dibenzofurans in Fish (ppt) (continued)
Chemical
1,2,3,4,7,8-HxCDF
(continued)
1,2,3,6,7,8-HxCDF
Fish
species8
Fresh Fish
Fish
Fish&
Seafood
Freshwater
Fish
Herring
Herring
Herring
Pike
Pike
Sucker
Lm Bass
Rock Bass
Rock Bass
Sm Bass
Redeye Bass
Carp
not available
Tissue
Mixed
NR
NR
NR
Whole
Whole
Whole
Muscle
Muscle
Whole
Fillet
Whole
Fillet
Fillet
Fillet
Whole
Whole
Number
samples
10
138
8
NR
1
2
2
8
1
15
4
2
1
2
1
5
2
Number
positive
samples
0
NR
NR
NR
1
2
2
8
1
0
0
0
0
0
0
0
1
Concentration
range
ND
0.11-62
NR
NR
0.1
0.4-0.8
0.2
5.6-22
5.6
ND(0.26-
2.85)
ND(0.41-
2.85)
ND(1.13-
1.30)
ND(0.72)
ND(2.84-
2.85)
ND(2.85)
ND(0.52-
2.85)
ND-1.35
Cone.
mean
NA
2.8
2.2
4.66
0.1
0.6
NA
11.9
5.6
NA
NA
NA
NA
NA
NA
NA
0.87
Wt.
basis
Fat
Fat
Fat
Fat
Fresh
Fresh
Fresh
Fat
Fat
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Location6
Various U.S. Sites
Germany
Catalonia, Spain
Russia
Atlantic Coast, Sweden
Baltic Sea, Sweden
Gulf of Bothnia, Sweden
Lake Vanern, Sweden
Hedesunda Bay, Sweden
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Location
description
Urban
NR
NR
NR
Pristine
Urban
Industrial
Industrial
Industrial
Background
Background
Background
Background
Background
Background
Background
Background
Samp.
year
NR
93-96
96
96
NR
NR
NR
88
88
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
Ref.
no.
19
20
21
22
4
4
4
12
12
13
13
13
13
13
13
13
13
Comments
purchased at supermarket
official food inspection
samples
purchased in supermarkets
purchased in supermarkets
composite 2-5 fish
composite 2-5 fish
composite 2-5 fish
samples composite 2-5 fish
composite 5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
DRAFT-DO NOT QUOTE OR CITE
B-145
December 2003
-------
Table B-12. Environmental Levels of Dibenzofurans in Fish (ppt) (continued)
Chemical
1,2,3,6,7,8-HxCDF
(continued)
Fish
species8
Brown Trout
Brown Trout
Rainbow
Trout
Brook Trout
Gray
Redhorse
Black
Redhorse
Golden
Redhorse
Longear
Sunfish
Walleye
Chain Pickerel
Black Crappie
Sea Catfish
North
Hogsucker
Summer
Flounder
Dolly Varden
Composite
Bottom
Tissue
Whole
Fillet
Fillet
Fillet
Whole
Whole
Whole
Whole
Fillet
Fillet
Fillet
Fillet
Whole
Whole
Whole
Whole
Number
samples
1
2
2
1
1
1
1
1
3
3
1
1
1
1
2
1
Number
positive
samples
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Concentration
range
ND(0.96)
ND(0.23-
0.42)
ND(0.45-
0.58)
ND(1.46)
ND(0.52)
ND(2.83)
ND(2.85)
ND(0.52)
ND(0.21-
2.85)
ND(0.20-
0.42)
ND(0.20)
ND(2.85)
ND(2.85)
ND(2.84)
ND(2.84)
ND(2.85)
Cone.
mean
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
Wt.
basis
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Location6
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Location
description
Background
Background
Background
Background
Background
Background
Background
Background
Background
Background
Background
Background
Background
Background
Background
Background
Samp.
year
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
Ref.
no.
13
13
13
13
13
13
13
13
13
13
13
13
13
13
13
13
Comments
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
DRAFT-DO NOT QUOTE OR CITE
B-146
December 2003
-------
Table B-12. Environmental Levels of Dibenzofurans in Fish (ppt) (continued)
Chemical
1,2,3,6,7,8-HxCDF
(continued)
1,2,3,6,7,8-HxCDF
Fish
species8
Winter
Flounder
Bluefish
White Catfish
Crayfish
Various0
Oyster
Blue Crab
Blue Crab
Crawfish
Crawfish
Catfish
Mullet
Spanish
Mackerel
Herring
Herring
Cod
Redfish
Plaice
Mackeral
Herring
Cod
Tissue
Whole
Whole
Whole
Whole
Mixed"
Whole
Body
Claw
Head
Tail
Fillet
Fillet
Fillet
Fillet
NR
NR
NR
Whole
Whole
Whole
Whole
Number
samples
2
1
1
1
314
3
3
3
2
2
3
2
1
1
1
1
1
3
1
1
1
Number
positive
samples
0
0
0
0
66
3
3
3
2
0
1
0
1
1
1
1
1
3
1
1
0
Concentration
range
ND(2.85)
ND(2.84)
ND(2.85)
ND(2.85)
NR
0.29-0.79
2.8-3.6
1.6-2.1
0.63-1.8
ND(0.7-1.05)
ND-0.18
ND(0.24-1.1)
2.1
1.7
4.2
13
6.0
0.02-0.06
0.06
0.16
ND(O.l)
Cone.
mean
NA
NA
NA
NA
1.74
0.47
3.13
1.9
1.22
0.88
0.02
0.67
2.1
1.7
4.2
13
6.0
0.04
0.06
0.16
NA
Wt.
basis
Wet
Wet
Wet
Wet
Wet
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fresh
Fat
Fat
Fat
Wet
Wet
Wet
Wet
Location6
Various, US
Various, US
Various, US
Various, US
Various, US
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
Baltic Sea, Sweden
Berlin, W. Germany
Berlin, W. Germany
Berlin, W. Germany
Norwich, UK
Norwich, UK
Norwich, UK
Norwich, UK
Location
description
Background
Background
Background
Background
Various
NR
NR
NR
NR
NR
Farm raised
NR
NR
NR
Urban
Urban
Urban
NR
NR
NR
NR
Samp.
year
86-89
86-89
86-89
86-89
86-89
94
94
94
94
94
94
94
94
88
NR
NR
NR
88
88
88
88
Ref.
no.
13
13
13
13
13
14
14
14
14
14
14
14
14
15
16
16
16
17
17
17
17
Comments
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
purchased at supermarket
purchased at supermarket
purchased at supermarket
purchased at supermarket
purchased at supermarket
purchased at supermarket
purchased at supermarket
purchased at supermarket
sample composite 12 fillets
purchased at different shops
purchased at different shops
purchased at different shops
purchased at retail outlet
purchased at retail outlet
purchased at retail outlet
purchased at retail outlet
DRAFT-DO NOT QUOTE OR CITE
B-147
December 2003
-------
Table B-12. Environmental Levels of Dibenzofurans in Fish (ppt) (continued)
Chemical
(continued)
1,2,3,7,8,9-HxCDF
1,2,3,7,8,9-HxCDF
(continued)
Fish
species8
Skate
Coley
Fish
Ocean Fish
Fresh Fish
Fish
Fish&
Seafood
Freshwater
Fish
Pike
Pike
Sucker
Lm Bass
Rock Bass
Rock Bass
Sm Bass
Redeye Bass
Carp
Tissue
Whole
Whole
Whole
Mixed
Mixed
NR
NR
NR
Muscle
Muscle
Whole
Fillet
Whole
Fillet
Fillet
Fillet
Whole
Number
samples
1
1
1
13
10
138
8
NR
8
1
15
4
2
1
2
1
5
Number
positive
samples
1
1
1
0
0
NR
NR
NR
0
0
0
0
0
0
0
0
0
Concentration
range
0.05
0.06
0.04
ND
ND
0-36.5
NR
NR
ND(3-6)
ND(6)
ND(0.26-
2.78)
ND(0.41-
2.78)
ND(1.13-
1.30)
ND(0.72)
ND(2.77-
2.78)
ND(2.78)
ND(0.52-
2.78)
Cone.
mean
0.05
0.06
0.04
NA
NA
2.1
0.7
1.69
NA
NA
NA
NA
NA
NA
NA
NA
NA
Wt.
basis
Wet
Wet
Wet
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Location6
Norwich, UK
Norwich, UK
Port Talbot, UK
Various U.S. Sites
Various U.S. Sites
Germany
Catalonia, Spain
Russia
Lake Vanern, Sweden
Hedesunda Bay, Sweden
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Location
description
NR
NR
Urban/
Industrial
Urban
Urban
NR
NR
NR
Industrial
Industrial
Background
Background
Background
Background
Background
Background
Background
Samp.
year
88
88
88
NR
NR
93-96
96
96
88
88
86-89
86-89
86-89
86-89
86-89
86-89
86-89
Ref.
no.
17
17
18
19
19
20
21
22
12
12
13
13
13
13
13
13
13
Comments
purchased at retail outlet
purchased at retail outlet
purchased at supermarket
purchased at supermarket
official foor inspection
samples
purchased in supermarkets
purchased in supermarkets
samples composite 2-5 fish
composite 5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
DRAFT-DO NOT QUOTE OR CITE
B-148
December 2003
-------
Table B-12. Environmental Levels of Dibenzofurans in Fish (ppt) (continued)
Chemical
1,2,3,7,8,9-HxCDF
Fish
species8
not available
Brown Trout
Brown Trout
Rainbow
Trout
Brook Trout
Gray
Redhorse
lack Redhorse
Golden
Redhorse
Long ear
Sunfish
Walleye
Chain Pickerel
Black Crappie
Sea Catfish
North
Hogsucker
Summer
Flounder
Dolly Varden
Tissue
Whole
Whole
Fillet
Fillet
Fillet
Whole
Whole
Whole
Whole
Fillet
Fillet
Fillet
Fillet
Whole
Whole
Whole
Number
samples
2
1
2
2
1
1
1
1
1
3
3
1
1
1
1
2
Number
positive
samples
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Concentration
range
ND(0.77-
0.78)
ND(0.96)
ND(0.23-
0.42)
ND(0.39-
0.45)
ND(1.46)
ND(0.52)
ND(2.76)
ND(2.78)
ND(0.52)
ND(0.21-
2.78)
ND(0.20-
0.42)
ND(0.20)
ND(2.78)
ND(2.78)
ND(2.77)
ND(2.77)
Cone.
mean
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
Wt.
basis
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Location6
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Location
description
Background
Background
Background
Background
Background
Background
Background
Background
Background
Background
Background
Background
Background
Background
Background
Background
Samp.
year
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
Ref.
no.
13
13
13
13
13
13
13
13
13
13
13
13
13
13
13
13
Comments
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
DRAFT-DO NOT QUOTE OR CITE
B-149
December 2003
-------
Table B-12. Environmental Levels of Dibenzofurans in Fish (ppt) (continued)
Chemical
(continued)
1,2,3,7,8,9-HxCDF
(continued)
Fish
species8
Composite
Bottom
Winter
Flounder
Bluefish
White Catfish
Crayfish
Various0
Oyster
Blue Crab
Blue Crab
Crawfish
Crawfish
Catfish
Mullet
Spanish
Mackerel
Herring
Plaice
Mackeral
Herring
Cod
Tissue
Whole
Whole
Whole
Whole
Whole
Mixed"
Whole
Body
Claw
Head
Tail
Fillet
Fillet
Fillet
Fillet
Whole
Whole
Whole
Whole
Number
samples
1
2
1
1
1
314
3
3
3
2
2
3
2
1
1
3
1
1
1
Number
positive
samples
0
0
0
0
0
3
0
0
0
2
0
0
0
1
0
1
0
0
0
Concentration
range
ND(2.78)
ND(2.77-
2.78)
ND(2.77)
ND(2.78)
ND(2.78)
NR
ND(0.15-
0.22)
ND(0.09-
0.13)
ND(0.6-0.9)
0.7-0.31
ND(0.85-1.4)
ND(0.05-
0.08)
ND(0.34-1.6)
ND(0.22)
ND(0.04)
ND-0.02
ND(0.04)
ND(0.05)
ND(O.l)
Cone.
mean
NA
NA
NA
NA
NA
1.22
0.17
0.11
0.75
0.51
1.13
0.04
0.97
0.22
NA
0.015
NA
NA
NA
Wt.
basis
Wet
Wet
Wet
Wet
Wet
Wet
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fresh
Wet
Wet
Wet
Wet
Location6
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
Baltic Sea, Sweden
Norwich, UK
Norwich, UK
Norwich, UK
Norwich, UK
Location
description
Background
Background
Background
Background
Background
Various
NR
NR
NR
NR
NR
Farm raised
NR
NR
NR
NR
NR
NR
NR
Samp.
year
86-89
86-89
86-89
86-89
86-89
86-89
94
94
94
94
94
94
94
94
88
88
88
88
88
Ref.
no.
13
13
13
13
13
13
14
14
14
14
14
14
14
14
15
17
17
17
17
Comments
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
purchased at supermarket
purchased at supermarket
purchased at supermarket
purchased at supermarket
purchased at supermarket
purchased at supermarket
purchased at supermarket
purchased at supermarket
sample composite 12 fillets
purchased at retail outlet
purchased at retail outlet
purchased at retail outlet
purchased at retail outlet
DRAFT-DO NOT QUOTE OR CITE
B-150
December 2003
-------
Table B-12. Environmental Levels of Dibenzofurans in Fish (ppt) (continued)
Chemical
2,3,4,6,7,8-HxCDF
2,3,4,6,7,8-HxCDF
(continued)
Fish
species8
Skate
Coley
Ocean Fish
Fresh Fish
Fish
Fish&
Seafood
Freshwater
Fish
Herring
Herring
Herring
Pike
Pike
Sucker
Lm Bass
Rock Bass
Rock Bass
Sm Bass
Redeye Bass
Carp
Tissue
Whole
Whole
Mixed
Mixed
NR
NR
NR
Whole
Whole
Whole
Muscle
Muscle
Whole
Fillet
Whole
Fillet
Fillet
Fillet
Whole
Number
samples
1
1
13
10
138
8
NR
1
2
2
8
1
15
4
2
1
2
1
5
Number
positive
samples
0
0
0
0
NR
NR
NR
0
2
2
7
0
0
0
0
0
0
0
1
Concentration
range
ND(0.05)
ND(0.04)
ND
ND
0-0.5
NR
NR
ND(0.2)
0.4-0.8
0.3
ND(3-17)
ND(6)
ND(0.26-
1.96)
ND(0.41-
1.96)
ND(1.13-
1.30)
ND(0.72)
ND(1.96)
ND(1.96)
ND-0.92
Cone.
mean
NA
NA
NA
NA
0.01
0.5
2.74
NA
0.6
NA
7.96
NA
NA
NA
NA
NA
NA
NA
0.76
Wt.
basis
Wet
Wet
Fat
Fat
Fat
Fat
Fat
Fresh
Fresh
Fresh
Fat
Fat
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Location6
Norwich, UK
Norwich, UK
Various U.S. Sites
Various U.S. Sites
Germany
Catalonia, Spain
Russia
Atlantic Coast, Sweden
Baltic Sea, Sweden
Gulf of Bothnia, Sweden
Lake Vanern, Sweden
Hedesunda Bay, Sweden
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Location
description
NR
NR
Urban
Urban
NR
NR
NR
Pristine
Urban
Industrial
Industrial
Industrial
Background
Background
Background
Background
Background
Background
Background
Samp.
year
88
88
NR
NR
93-96
96
96
NR
NR
NR
88
88
86-89
86-89
86-89
86-89
86-89
86-89
86-89
Ref.
no.
17
17
19
19
20
21
22
4
4
4
12
12
13
13
13
13
13
13
13
Comments
purchased at retail outlet
purchased at retail outlet
purchased at supermarket
purchased at supermarket
official foor inspection
samples
purchased in supermarkets
purchased in supermarkets
composite 2-5 fish
composite 2-5 fish
composite 2-5 fish
samples composite 2-5 fish
composite 5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
DRAFT-DO NOT QUOTE OR CITE
B-151
December 2003
-------
Table B-12. Environmental Levels of Dibenzofurans in Fish (ppt) (continued)
Chemical
2,3,4,6,7,8-HxCDF
(continued)
Fish
species8
not available
Brown Trout
Brown Trout
Rainbow
Trout
Brook Trout
Gray
Redhorse
Black
Redhorse
Golden
Redhorse
Longear
Sunfish
Walleye
Chain Pickerel
Black Crappie
Sea Catfish
North
Hogsucker
Summer
Flounder
Dolly Varden
Tissue
Whole
Whole
Fillet
Fillet
Fillet
Whole
Whole
Whole
Whole
Fillet
Fillet
Fillet
Fillet
Whole
Whole
Whole
Number
samples
2
1
2
2
1
1
1
1
1
3
3
1
1
1
1
2
Number
positive
samples
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
Concentration
range
ND(0.78-
2.72)
ND(0.96)
ND(0.23-
0.42)
ND(0.45-
0.58)
ND(1.46)
ND(0.52)
ND(1.95)
1.25
ND(0.52)
ND(0.21-
1.96)
ND(0.20-
0.42)
ND(0.20)
ND(1.96)
ND(1.97)
ND(1.96)
ND(1.95-
1.96)
Cone.
mean
NA
NA
NA
NA
NA
NA
NA
1.25
NA
NA
NA
NA
NA
NA
NA
NA
Wt.
basis
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Location6
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Location
description
Background
Background
Background
Background
Background
Background
Background
Background
Background
Background
Background
Background
Background
Background
Background
Background
Samp.
year
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
Ref.
no.
13
13
13
13
13
13
13
13
13
13
13
13
13
13
13
13
Comments
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
DRAFT-DO NOT QUOTE OR CITE
B-152
December 2003
-------
Table B-12. Environmental Levels of Dibenzofurans in Fish (ppt) (continued)
Chemical
2,3,4,6,7,8-HxCDF
(continued)
Fish
species8
Composite
Bottom
Winter
Flounder
Bluefish
White Catfish
Crayfish
Various0
Oyster
Blue Crab
Blue Crab
Crawfish
Crawfish
Catfish
Mullet
Spanish
Mackerel
Herring
Herring
Cod
Redfish
Plaice
Mackeral
Tissue
Whole
Whole
Whole
Whole
Whole
Mixed"
Whole
Body
Claw
Head
Tail
Fillet
Fillet
Fillet
Fillet
NR
NR
NR
Whole
Whole
Number
samples
1
2
1
1
1
314
3
3
3
2
2
3
2
1
1
1
1
1
3
1
Number
positive
samples
0
0
0
0
0
100
3
3
0
2
0
0
0
1
1
1
1
1
3
1
Concentration
range
ND(1.96)
ND(1.96)
ND(1.96)
ND(1.96)
ND(1.96)
NR
0.59-0.65
2.2-2.6
ND(0.6-0.85)
0.38-1.8
ND(0.85-
1.35)
ND(0.04-
0.07)
ND(0.3-1.4)
1.2
3.9
3.6
8.2
7.2
0.04-0.13
0.03
Cone.
mean
NA
NA
NA
NA
NA
1.24
0.72
2.33
0.73
1.09
1.1
0.056
0.85
1.2
3.9
3.6
8.2
7.2
0.08
0.03
Wt.
basis
Wet
Wet
Wet
Wet
Wet
Wet
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fresh
Fat
Fat
Fat
Wet
Wet
Location6
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
Baltic Sea, Sweden
Berlin, W. Germany
Berlin, W. Germany
Berlin, W. Germany
Norwich, UK
Norwich, UK
Location
description
Background
Background
Background
Background
Background
Various
NR
NR
NR
NR
NR
Farm raised
NR
NR
NR
Urban
Urban
Urban
NR
NR
Samp.
year
86-89
86-89
86-89
86-89
86-89
86-89
94
94
94
94
94
94
94
94
88
NR
NR
NR
88
88
Ref.
no.
13
13
13
13
13
13
14
14
14
14
14
14
14
14
15
16
16
16
17
17
Comments
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
purchased at supermarket
purchased at supermarket
purchased at supermarket
purchased at supermarket
purchased at supermarket
purchased at supermarket
purchased at supermarket
purchased at supermarket
sample composite 12 fillets
purchased at different shops
purchased at different shops
purchased at different shops
purchased at retail outlet
purchased at retail outlet
DRAFT-DO NOT QUOTE OR CITE
B-153
December 2003
-------
Table B-12. Environmental Levels of Dibenzofurans in Fish (ppt) (continued)
Chemical
HxCDFs
HxCDFs (continued)
Fish
species8
Herring
Cod
Skate
Coley
Fish
Ocean Fish
Fresh Fish
Fish
Fish&
Seafood
Freshwater
Fish
Bream
Perch
Carp
Carp
Blue Crab
Lobster
Str. Bass
Br. Trout
Br. Trout
Rb. Trout
Tissue
Whole
Whole
Whole
Whole
Whole
Mixed
Mixed
NR
NR
NR
NR
NR
Whole
Whole
Meat
Meat
Fillets
Whole
Fillets
Whole
Number
samples
1
1
1
1
1
13
10
138
8
NR
13
2
3
3
2
2
2
1
1
1
Number
positive
samples
1
0
1
1
1
0
0
NR
0
NR
13
2
2
3
2
2
2
1
0
1
Concentration
range
0.15
ND(O.l)
0.04
0.03
0.07
ND
ND
0-29
ND
NR
5.7-47.4
21.9-53.8
ND-24
2.7-5.1
9.3-9.4
7.7-7.9
2.0-4.4
2
ND(7)
8
Cone.
mean
0.15
NA
0.04
0.03
0.07
NA
NA
1.8
NA
2.31
25.2
37.8
10.0
3.5
9.35
7.8
3.2
2
NA
8
Wt.
basis
Wet
Wet
Wet
Wet
Wet
Fat
Fat
Fat
Fat
Fat
Fresh
Fresh
NR
NR
Wet
Wet
Wet
NR
NR
NR
Location6
Norwich, UK
Norwich, UK
Norwich, UK
Norwich, UK
Port Talbot, UK
Various U.S. Sites
Various U.S. Sites
Germany
Catalonia, Spain
Russia
Hamburg, Germany
Hamburg, Germany
Mississippi River, MN
Lake Orono, MN
Passaic River, NJ
New York Bight
Newark Bay, NJ
Lake Ontario
Lake Ontario
Lake Ontario
Location
description
NR
NR
NR
NR
Urban/
Industrial
Urban
Urban
NR
NR
NR
Urban
Urban
Industrial
Industrial
Urban
Dump Site
Urban
NR
NR
NR
Samp.
year
88
88
88
88
88
NR
NR
93-96
96
96
84
84
NR
NR
NR
NR
NR
NR
NR
NR
Ref.
no.
17
17
17
17
18
19
19
20
21
22
3
3
5
5
7
7
7
9
9
9
Comments
purchased at retail outlet
purchased at retail outlet
purchased at retail outlet
purchased at retail outlet
purchased at supermarket
purchased at supermarket
official food inspection
samples
purchased in supermarkets
purchased in supermarkets
Elk river power station
Elk river power station
former sewage sludge
composite 3 samples
composite 3 samples
composite 3 samples
DRAFT-DO NOT QUOTE OR CITE
B-154
December 2003
-------
Table B-12. Environmental Levels of Dibenzofurans in Fish (ppt) (continued)
Chemical
non-2,3,7,8-HxCDFs
Fish
species8
Rb. Trout
Lake Trout
Lake Trout
Coho Salmon
Coho Salmon
Oyster
Blue Crab
Blue Crab
Crawfish
Crawfish
Spanish
Mackerel
Ocean Fish
Fresh Fish
Barbel
Tissue
Fillets
Whole
Fillets
Whole
Fillets
Whole
Body
Claw
Head
Tail
Fillet
Mixed
Mixed
Fillets
Number
samples
1
1
1
1
1
3
3
3
2
2
1
13
10
1
Number
positive
samples
1
1
0
0
0
3
3
3
2
2
1
0
0
1
Concentration
range
2
16
ND(7)
ND(7)
ND(7)
6.6-22
86-150
35-73
8.9-63
7.5-53
11
ND
ND
2.1
Cone.
mean
2
16
NA
NA
NA
13.53
115.33
49
35.95
30.25
NA
NA
NA
2.1
Wt.
basis
NR
NR
NR
NR
NR
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Location6
Lake Ontario
Lake Ontario
Lake Ontario
Lake Ontario
Lake Ontario
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
Various U.S. Sites
Various U.S. Sites
Neckar River, Germany
Location
description
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
Urban
Urban
Urban
Samp.
year
NR
NR
NR
NR
NR
94
94
94
94
94
94
NR
NR
88
Ref
no.
9
9
9
9
9
14
14
14
14
14
14
19
19
2
Comments
composite 3 samples
composite 3 samples
composite 3 samples
composite 3 samples
composite 3 samples
purchased at supermarket
purchased at supermarket
purchased at supermarket
purchased at supermarket
purchased at supermarket
purchased at supermarket
purchased at supermarket
purchased at supermarket
Heptachlorodibenzofiirans (MW = 409 . 3 1)
1,2,3,4,6,7,8-HpCDF
1,2,3,4,6,7,8-HpCDF
(continued)
Herring
Herring
Herring
Carp
Pike
Pike
Sucker
Whole
Whole
Whole
Whole
Muscle
Muscle
Whole
1
2
2
2
16
2
15
0
2
1
2
1
0
3
ND(0.2)
0.8-1.2
ND-0.9
3-4
ND(3-7)-17
ND(6-11)
ND-1.88
NA
1.0
0.5
3.5
3.41
NA
0.96
Fresh
Fresh
Fresh
NR
Fat
Fat
Wet
Atlantic Coast, Sweden
Baltic Sea, Sweden
Gulf of Bothnia, Sweden
Lake Huron
Lake Vanern, Sweden
Hedesunda Bay, Sweden
Various, US
Pristine
Urban
Industrial
NR
Industrial
Industrial
Background
NR
NR
NR
NR
88
88
86-89
4
4
4
11
12
12
13
composite 2-5 fish
composite 2-5 fish
composite 2-5 fish
samples composite 3-5 fish
samples composite 2-5 fish
composite 5 fish
samples composite 3-5 fish
DRAFT-DO NOT QUOTE OR CITE
B-155
December 2003
-------
Table B-12. Environmental Levels of Dibenzofurans in Fish (ppt) (continued)
Chemical
Fish
species8
Lm Bass
Rock Bass
Rock Bass
Sm Bass
Redeye Bass
Carp
not available
Brown Trout
Rainbow
Trout
Brook Trout
Gray
Redhorse
Black
Redhorse
Golden
Redhorse
Tissue
Fillet
Whole
Fillet
Fillet
Fillet
Whole
Whole
Fillet
Fillet
Fillet
Whole
Whole
Whole
Number
samples
2
2
1
2
1
5
2
2
2
1
1
1
1
Number
positive
samples
0
0
0
0
0
3
1
0
1
0
0
0
1
Concentration
range
ND(1.44-
1.45)
ND(6.61-
8.89)
ND(1.40)
ND(1.45)
ND(1.45)
ND-1.31
ND-1.13
ND(0.77-
0.80)
ND-0.48
ND(4.13)
ND(2.75)
ND(2.25)
1.25
Cone.
mean
NA
NA
NA
NA
NA
0.71
2.21
NA
0.56
NA
NA
NA
1.25
Wt.
basis
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Location6
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Location
description
Background
Background
Background
Background
Background
Background
Background
Background
Background
Background
Background
Background
Background
Samp.
year
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
Ref.
no.
13
13
13
13
13
13
13
13
13
13
13
13
13
Comments
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
DRAFT-DO NOT QUOTE OR CITE
B-156
December 2003
-------
Table B-12. Environmental Levels of Dibenzofurans in Fish (ppt) (continued)
Chemical
1,2,3,4,6,7,8-HpCDF
(continued)
Fish
species8
Longear
Sunfish
Walleye
Chain Pickerel
Black Crappie
Sea Catfish
North
Hogsucker
Summer
Flounder
Dolly Varden
Composite
Bottom
Winter
Flounder
Bluefish
White Catfish
Crayfish
Various0
Oyster
Blue Crab
Tissue
Whole
Fillet
Fillet
Fillet
Fillet
Whole
Whole
Whole
Whole
Whole
Whole
Whole
Whole
Mixed"
Whole
Body
Number
samples
1
3
3
1
1
1
1
2
1
2
1
1
1
314
3
3
Number
positive
samples
0
0
0
0
0
1
0
0
1
0
0
0
0
170
3
3
Concentration
range
ND(3.11)
ND(0.71-
1.45)
ND(0.27-
1.28)
ND(0.35)
ND(1.45)
0.27
ND(1.45)
ND(1.44-
1.45)
0.23
ND(1.45)
ND(1.78)
ND(1.45)
ND(1.45)
NR
1.3-1.8
6-7.7
Cone.
mean
NA
NA
NA
NA
NA
0.27
NA
NA
0.23
NA
NA
NA
NA
1.91
1.5
6.83
Wt.
basis
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Fat
Fat
Location6
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
S. Mississippi
S. Mississippi
Location
description
Background
Background
Background
Background
Background
Background
Background
Background
Background
Background
Background
Background
Background
Various
NR
NR
Samp.
year
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
94
94
Ref.
no.
13
13
13
13
13
13
13
13
13
13
13
13
13
13
14
14
Comments
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
purchased at supermarket
purchased at supermarket
DRAFT-DO NOT QUOTE OR CITE
B-157
December 2003
-------
Table B-12. Environmental Levels of Dibenzofurans in Fish (ppt) (continued)
Chemical
1,2,3,4,6,7,8-HpCDF
(continued)
1,2,3,4,6,7,8-HpCDF
(continued)
Fish
species8
Blue Crab
Crawfish
Crawfish
Catfish
Mullet
Spanish
Mackerel
Herring
Herring
Cod
Redfish
Plaice
Mackeral
Herring
Cod
Skate
Coley
Fish
Fish
Ocean Fish
Fresh Fish
Fish
Tissue
Claw
Head
Tail
Fillet
Fillet
Fillet
Fillet
NR
NR
NR
Whole
Whole
Whole
Whole
Whole
Whole
Whole
Whole
Mixed
Mixed
NR
Number
samples
3
2
2
3
2
1
1
1
1
1
3
1
1
1
1
1
1
1
13
10
138
Number
positive
samples
3
2
2
1
2
1
1
1
1
1
3
1
1
0
1
1
1
1
0
0
NR
Concentration
range
4.4-6.2
1-7.3
4.9-9.9
ND-42
1.5-3.1
2.1
0.38
1.6
10
1.5
0.04-0.10
0.07
0.14
ND(0.2)
0.06
0.05
0.10
0.08
ND
ND
0-14.1
Cone.
mean
5.13
4.15
7.4
0.18
2.3
2.1
0.38
1.6
10
1.5
0.07
0.07
0.14
NA
0.06
0.05
0.10
0.08
NA
NA
1.3
Wt.
basis
Fat
Fat
Fat
Fat
Fat
Fat
Fresh
Fat
Fat
Fat
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Fat
Fat
Fat
Location6
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
Baltic Sea, Sweden
Berlin, W. Germany
Berlin, W. Germany
Berlin, W. Germany
Norwich, UK
Norwich, UK
Norwich, UK
Norwich, UK
Norwich, UK
Norwich, UK
Port Talbot, UK
Stonehaven, UK
Various U.S. Sites
Various U.S. Sites
Germany
Location
description
NR
NR
NR
Farm raised
NR
NR
NR
Urban
Urban
Urban
NR
NR
NR
NR
NR
NR
Urban/
Industrial
Rural
Urban
Urban
NR
Samp.
year
94
94
94
94
94
94
88
NR
NR
NR
88
88
88
88
88
88
88
91
NR
NR
93-96
Ref.
no.
14
14
14
14
14
14
15
16
16
16
17
17
17
17
17
17
18
18
19
19
20
Comments
purchased at supermarket
purchased at supermarket
purchased at supermarket
purchased at supermarket
purchased at supermarket
purchased at supermarket
sample composite 12 fillets
purchased at different shops
purchased at different shops
purchased at different shops
purchased at retail outlet
purchased at retail outlet
purchased at retail outlet
purchased at retail outlet
purchased at retail outlet
purchased at retail outlet
purchased at supermarket
purchased at supermarket
official food inspection
samples
DRAFT-DO NOT QUOTE OR CITE
B-158
December 2003
-------
Table B-12. Environmental Levels of Dibenzofurans in Fish (ppt) (continued)
Chemical
1,2,3,4,7,8,9-HpCDF
1,2,3,4,7,8,9-HpCDF
Fish
species8
Fish&
Seafood
Freshwater
Fish
Pike
Pike
Sucker
Lm Bass
Rock Bass
Rock Bass
Sm Bass
Redeye Bass
Carp
not available
Brown Trout
Rainbow
Trout
Brook Trout
Gray
Redhorse
Tissue
NR
NR
Muscle
Muscle
Whole
Fillet
Whole
Fillet
Fillet
Fillet
Whole
Whole
Fillet
Fillet
Fillet
Whole
Number
samples
8
NR
8
1
15
2
2
1
2
1
3
2
2
2
1
1
Number
positive
samples
NR
NR
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Concentration
range
NR
NR
ND(3-11)
ND(6)
ND(0.71-
4.23)
ND(2.61-
2.26)
ND(2.64-
5.92)
ND(1.40)
ND(2.61-
2.62)
ND(2.62)
ND(2.61-
2.62)
ND(1.12-
1.64)
ND(0.77-
0.80)
ND(0.63-
0.91)
ND(4.13)
ND(2.75)
Cone.
mean
2.0
6.74
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
Wt.
basis
Fat
Fat
Fat
Fat
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Location6
Catalonia, Spain
Russia
Lake Vanern, Sweden
Hedesunda Bay, Sweden
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Location
description
NR
NR
Industrial
Industrial
Background
Background
Background
Background
Background
Background
Background
Background
Background
Background
Background
Background
Samp.
year
96
96
88
88
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
Ref.
no.
21
22
12
12
13
13
13
13
13
13
13
13
13
13
13
13
Comments
purchased in supermarkets
purchased in supermarkets
samples composite 2-5 fish
composite 5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
DRAFT-DO NOT QUOTE OR CITE
B-159
December 2003
-------
Table B-12. Environmental Levels of Dibenzofurans in Fish (ppt) (continued)
Chemical
(continued)
1,2,3,4,7,8,9-HpCDF
(continued)
Fish
species8
Black
Redhorse
Golden
Redhorse
Long ear
Sunfish
Walleye
Chain Pickerel
Black Crappie
Sea Catfish
North
Hogsucker
Summer
Flounder
Dolly Varden
Composite
Bottom
Winter
Flounder
Bluefish
White Catfish
Crayfish
Various0
Oyster
Tissue
Whole
Whole
Whole
Fillet
Fillet
Fillet
Fillet
Whole
Whole
Whole
Whole
Whole
Whole
Whole
Whole
Mixed"
Whole
Number
samples
1
1
1
3
3
1
1
1
1
2
1
2
1
1
1
314
3
Number
positive
samples
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
13
0
Concentration
range
ND(3.82)
ND(2.62)
ND(3.11)
ND(0.71-
2.62)
ND(0.27-
1.28)
ND(0.23)
ND(2.62)
ND(2.62)
ND(2.61)
ND(2.61)
ND(2.62)
ND(2.62)
ND(3.12)
ND(2.62)
ND(2.62)
NR
ND(0.15-
0.21)
Cone.
mean
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
1.24
0.17
Wt.
basis
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Fat
Location6
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
Various, US
S. Mississippi
Location
description
Background
Background
Background
Background
Background
Background
Background
Background
Background
Background
Background
Background
Background
Background
Background
Various
NR
Samp.
year
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
86-89
94
Ref.
no.
13
13
13
13
13
13
13
13
13
13
13
13
13
13
13
13
14
Comments
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
samples composite 3-5 fish
purchased at supermarket
DRAFT-DO NOT QUOTE OR CITE
B-160
December 2003
-------
Table B-12. Environmental Levels of Dibenzofurans in Fish (ppt) (continued)
Chemical
Fish
species8
Blue Crab
Blue Crab
Crawfish
Crawfish
Catfish
Mullet
Spanish
Mackerel
Herring
Plaice
Mackeral
Herring
Cod
Skate
Coley
Fish
Ocean Fish
Fresh Fish
Tissue
Body
Claw
Head
Tail
Fillet
Fillet
Fillet
Fillet
Whole
Whole
Whole
Whole
Whole
Whole
Whole
Mixed
Mixed
Number
samples
3
3
2
2
3
2
1
1
3
1
1
1
1
1
1
13
10
Number
positive
samples
1
0
2
0
0
0
0
0
1
0
0
0
0
0
0
0
0
Concentration
range
ND-0.79
ND(0.65-
0.95)
0.19-0.47
ND(0.95-1.4)
ND(0.07-
0.09)
ND(0.33-1.5)
ND(0.44)
ND(0.04)
ND-0.02
ND(0.08)
ND(0.06)
ND(0.2)
ND(0.13)
ND(O.IO)
<0.01
ND
ND
Cone.
mean
0.33
0.8
0.28
1.18
0.08
0.92
0.44
NA
0.03
NA
NA
NA
NA
NA
<0.01
NA
NA
Wt.
basis
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fresh
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Fat
Fat
Location6
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
Baltic Sea, Sweden
Norwich, UK
Norwich, UK
Norwich, UK
Norwich, UK
Norwich, UK
Norwich, UK
Port Talbot, UK
Various U.S. Sites
Various U.S. Sites
Location
description
NR
NR
NR
NR
Farm raised
NR
NR
NR
NR
NR
NR
NR
NR
NR
Urban/
Industrial
Urban
Urban
Samp.
year
94
94
94
94
94
94
94
88
88
88
88
88
88
88
88
NR
NR
Ref.
no.
14
14
14
14
14
14
14
15
17
17
17
17
17
17
18
19
19
Comments
purchased at supermarket
purchased at supermarket
purchased at supermarket
purchased at supermarket
purchased at supermarket
purchased at supermarket
purchased at supermarket
sample composite 12 fillets
purchased at retail outlet
purchased at retail outlet
purchased at retail outlet
purchased at retail outlet
purchased at retail outlet
purchased at retail outlet
purchased at supermarket
purchased at supermarket
DRAFT-DO NOT QUOTE OR CITE
B-161
December 2003
-------
Table B-12. Environmental Levels of Dibenzofurans in Fish (ppt) (continued)
Chemical
1,2,3,4,7,8,9-HpCDF
(continued)
HpCDFs
HpCDFs (continued)
Fish
species8
Fish
Fish&
Seafood
Freshwater
Fish
Bream
Perch
Carp
Carp
Blue Crab
Lobster
Str. Bass
Br. Trout
Br. Trout
Rb. Trout
Rb. Trout
Lake Trout
Lake Trout
Coho Salmon
Coho Salmon
Oyster
Blue Crab
Blue Crab
Tissue
NR
NR
NR
NR
NR
Whole
Whole
Meat
Meat
Fillets
Whole
Fillets
Whole
Fillets
Whole
Fillets
Whole
Fillets
Whole
Body
Claw
Number
samples
138
8
NR
13
2
3
3
2
2
2
1
1
1
1
1
1
1
1
3
3
3
Number
positive
samples
NR
NR
NR
13
2
1
0
2
0
2
0
0
1
0
1
0
0
0
3
3
3
Concentration
range
0-1.1
NR
NR
1.8-6.0
5.0-10.1
ND-14
ND(6.6)
2.8-3.5
ND(0.9)
1.3-2.4
ND(7)
ND(7)
1
ND(7)
1
ND(7)
ND(7)
ND(7)
2.3-3.2
8.1-12
4.6-6.6
Cone.
mean
0.08
0.3
2.26
3.6
7.6
4.7
NA
3.15
NA
1.8
NA
NA
1
NA
1
NA
NA
NA
2.87
9.53
5.43
Wt.
basis
Fat
Fat
Fat
Fresh
Fresh
NR
NR
Wet
Wet
Wet
NR
NR
NR
NR
NR
NR
NR
NR
Fat
Fat
Fat
Location6
Germany
Catalonia, Spain
Russia
Hamburg, Germany
Hamburg, Germany
Mississippi River, MN
Lake Orono, MN
Passaic River, NJ
New York Bight
Newark Bay, NJ
Lake Ontario
Lake Ontario
Lake Ontario
Lake Ontario
Lake Ontario
Lake Ontario
Lake Ontario
Lake Ontario
S. Mississippi
S. Mississippi
S. Mississippi
Location
description
NR
NR
NR
Urban
Urban
Industrial
Industrial
Urban
Dump Site
Urban
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
Samp.
year
93-96
96
96
84
84
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
94
94
94
Ref.
no.
20
21
22
3
3
5
5
7
7
7
9
9
9
9
9
9
9
9
14
14
14
Comments
official foor inspection
samples
purchased in supermarkets
purchased in supermarkets
Elk river power station
Elk river power station
former sewage sludge
composite 3 samples
composite 3 samples
composite 3 samples
composite 3 samples
composite 3 samples
composite 3 samples
composite 3 samples
composite 3 samples
purchased at supermarket
purchased at supermarket
purchased at supermarket
DRAFT-DO NOT QUOTE OR CITE
B-162
December 2003
-------
Table B-12. Environmental Levels of Dibenzofurans in Fish (ppt) (continued)
Chemical
Fish
species8
Crawfish
Crawfish
Spanish
Mackerel
Ocean Fish
Fresh Fish
Tissue
Head
Tail
Fillet
Mixed
Mixed
Number
samples
2
2
1
13
10
Number
positive
samples
2
2
1
0
0
Concentration
range
1.1-7.8
5.2-14
3.3
ND
ND
Cone.
mean
4.45
9.6
3.3
NA
NA
Wt.
basis
Fat
Fat
Fat
Fat
Fat
Location6
S. Mississippi
S. Mississippi
S. Mississippi
Various U.S. Sites
Various U.S. Sites
Location
description
NR
NR
NR
Urban
Urban
Samp.
year
94
94
94
NR
NR
Ref
no.
14
14
14
19
19
Comments
purchased at supermarket
purchased at supermarket
purchased at supermarket
purchased at supermarket
purchased at supermarket
Octachlorodibenzofiirans (MW=444.76)
1,2,3,4,6,7,8,9-OCDF
1,2,3,4,6,7,8,9-OCDF
(continued)
Bream
Perch
Herring
Herring
Herring
Salmon
Salmon
Perch
Carp
Carp
Blue Crab
Lobster
Str. Bass
Lake Trout
Lake Trout
Lake Trout
NR
NR
Whole
Whole
Whole
Muscle
Muscle
NR
Whole
Whole
Meat
Meat
Fillets
Whole
Whole
Whole
14
3
1
2
2
2
2
3
3
3
2
2
2
1
1
3
10
3
0
1
0
0
0
1
0
3
0
0
0
1
1
3
ND-3.1
1.1-8.3
ND(0.2)
ND-0.3
ND(0.2)
ND(2.0)
ND(0.5)
ND-1.7
ND(6.6)
ND(6.6)
ND(8.3)
ND(8.4)
ND(3.1)
0.4
0.1
0.3-1.0
1.2
3.7
NA
0.2
NA
NA
NA
0.57
NA
NA
NA
NA
NA
0.4
0.1
0.85
Fresh
Fresh
Fresh
Fresh
Fresh
Fresh
Fresh
Fresh
NR
NR
Wet
Wet
Wet
Wet
Wet
Wet
Hamburg, Germany
Hamburg, Germany
Atlantic Coast, Sweden
Baltic Sea, Sweden
Gulf of Bothnia, Sweden
Gulf of Bothnia, Sweden
Gulf of Bothnia, Sweden
Gulf of Bothnia, Sweden
Mississippi River, MN
Lake Orono, MN
Passaic River, NJ
New York Bight
Newark Bay, NJ
Lake Superior
Lake Huron
Lake Michigan
Urban
Urban
Pristine
Urban
Industrial
NR
NR
NR
Industrial
Industrial
Urban
Dump Site
Urban
NR
NR
NR
84
84
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
84
84
84
3
3
4
4
4
4
4
4
5
5
7
7
7
8
8
8
composite 2-5 fish
composite 2-5 fish
composite 2-5 fish
wild salmon
hatched salmon
caught near pulp mill
Elk river power station
Elk river power station
former sewage sludge
mean 5 samples
mean 5 samples
range 3 sample sites
DRAFT-DO NOT QUOTE OR CITE
B-163
December 2003
-------
Table B-12. Environmental Levels of Dibenzofurans in Fish (ppt) (continued)
Chemical
1,2,3,4,6,7,8,9-OCDF
(continued)
Fish
species8
Walleye
Walleye
Lake Trout
Br. Trout
Rb. Trout
Rb. Trout
Lake Trout
Lake Trout
Coho Salmon
Coho Salmon
Cod
Haddock
P. Flounder
Plaice
Flounder
Eel
Mussel
Shrimp
Cod
Fresh Fish
Carp
Pike
Tissue
Whole
Whole
Whole
Fillets
Whole
Fillets
Whole
Fillets
Whole
Fillets
Fillets
Fillets
Fillets
Fillets
Fillets
Fillets
Muscle
Muscle
Fillets
Whole
Muscle
Number
samples
1
1
1
1
1
1
1
1
1
1
4
1
1
1
1
4
3
2
6
10
2
8
Number
positive
samples
1
1
1
0
0
0
1
0
0
0
4
1
1
1
1
4
3
2
NR
0
2
0
Concentration
range
0.9
0.4
0.4
ND(7)
ND(7)
ND(7)
2
ND(7)
ND(7)
ND(7)
3.4-9.6
4.3
4.7
41
5.6
31-581
13.4-933
2.3-41
ND-21
ND
4-8
ND(3-11)
Cone.
mean
0.9
0.4
0.4
NA
NA
NA
2
NA
NA
NA
6.3
4.3
4.7
41
5.6
205
339
21.6
NR
NA
6
NA
Wt.
basis
Wet
Wet
Wet
NR
NR
NR
NR
NR
NR
NR
Fresh
Fresh
Fresh
Fresh
Fresh
Fresh
Fresh
Fresh
Fresh
Fat
NR
Fat
Location6
Lake Erie
Lake St. Clair
Lake Ontario
Lake Ontario
Lake Ontario
Lake Ontario
Lake Ontario
Lake Ontario
Lake Ontario
Lake Ontario
Various, Sweden
Various, Sweden
Various, Sweden
Various, Sweden
Various, Sweden
Various, Sweden
Grenlandsfiord, Sweden
Grenlandsfiord, Sweden
Frierfiord, Sweden
Various U.S. Sites
Lake Huron
Lake Vanern, Sweden
Location
description
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
Industrial
Industrial
Industrial
Industrial
Industrial
Industrial
Industrial
Industrial
Industrial
NR
Industrial
Samp.
year
84
84
84
NR
NR
NR
NR
NR
NR
NR
88
88
88
88
88
88
87
88
87
NR
NR
88
Ref.
no.
8
8
8
9
9
9
9
9
9
9
10
10
10
10
10
10
10
10
10
10
11
12
Comments
mean 5 samples
mean 5 samples
mean 5 samples
composite 3 samples
composite 3 samples
composite 3 samples
composite 3 samples
composite 3 samples
composite 3 samples
composite 3 samples
composite 10 samples
composite 10 samples
composite 10 samples
composite 10 samples
only cone, range given
samples composite 3-5 fish
samples composite 2-5 fish
DRAFT-DO NOT QUOTE OR CITE
B-164
December 2003
-------
Table B-12. Environmental Levels of Dibenzofurans in Fish (ppt) (continued)
Chemical
Fish
species8
Pike
Oyster
Blue Crab
Blue Crab
Crawfish
Crawfish
Catfish
Mullet
Spanish
Mackerel
Herring
Herring
Cod
Redfish
Plaice
Mackeral
Herring
Cod
Tissue
Muscle
Whole
Body
Claw
Head
Tail
Fillet
Fillet
Fillet
Fillet
NR
NR
NR
Whole
Whole
Whole
Whole
Number
samples
1
3
3
3
2
2
3
2
1
1
1
1
1
3
1
1
1
Number
positive
samples
0
3
2
0
0
2
0
2
1
0
1
1
1
3
1
1
1
Concentration
range
ND(ll)
3.1-4.9
ND-0.77
ND(1.2-1.95)
ND(0.27-
0.38)
7.7-57
ND(0.08-
0.12)
1.5-6.6
1.8
ND(0.07)
1.4
2.1
0.3
0.08-0.23
0.20
0.19
0.26
Cone.
mean
NA
4.00
0.5
1.52
0.16
32.35
0.96
4.05
1.8
NA
1.4
2.1
0.3
0.16
0.20
0.19
0.26
Wt.
basis
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fresh
Fat
Fat
Fat
Wet
Wet
Wet
Wet
Location6
Hedesunda Bay, Sweden
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
Baltic Sea, Sweden
Berlin, W. Germany
Berlin, W. Germany
Berlin, W. Germany
Norwich, UK
Norwich, UK
Norwich, UK
Norwich, UK
Location
description
Industrial
NR
NR
NR
NR
NR
Farm raised
NR
NR
NR
Urban
Urban
Urban
NR
NR
NR
NR
Samp.
year
88
94
94
94
94
94
94
94
94
88
NR
NR
NR
88
88
88
88
Ref.
no.
12
14
14
14
14
14
14
14
14
15
16
16
16
17
17
17
17
Comments
composite 5 fish
purchased at supermarket
purchased at supermarket
purchased at supermarket
purchased at supermarket
purchased at supermarket
purchased at supermarket
purchased at supermarket
purchased at supermarket
sample composite 12 fillets
purchased at different shops
purchased at different shops
purchased at different shops
purchased at retail outlet
purchased at retail outlet
purchased at retail outlet
purchased at retail outlet
DRAFT-DO NOT QUOTE OR CITE
B-165
December 2003
-------
Table B-12. Environmental Levels of Dibenzofurans in Fish (ppt) (continued)
Chemical
1,2,3,4,6,7,8,9-OCDF
(continued)
Fish
species8
Skate
Coley
Fish
Fish
Ocean Fish
Fish
Fish&
Seafood
Freshwater
Fish
Tissue
Whole
Whole
Whole
Whole
Mixed
NR
NR
NR
Number
samples
1
1
1
1
13
138
8
NR
Number
positive
samples
1
0
1
1
0
NR
NR
NR
Concentration
range
0.14
ND(0.30)
0.14
0.20
ND
0-46
NR
NR
Cone.
mean
0.14
NA
0.14
0.02
NA
1.2
8.8
6.63
Wt.
basis
Wet
Wet
Wet
Wet
Fat
Fat
Fat
Fat
Location6
Norwich, UK
Norwich, UK
Port Talbot, UK
Stonehaven, UK
Various U.S. Sites
Germany
Catalonia, Spain
Russia
Location
description
NR
NR
Urban/
Industrial
Rural
Urban
NR
NR
NR
Samp.
year
88
88
88
91
NR
93-96
96
96
Ref.
no.
17
17
18
18
19
20
21
22
Comments
purchased at retail outlet
purchased at retail outlet
purchased at supermarket
official food inspection
samples
purchased in supermarkets
purchased in supermarkets
Footnote References
a Br. = Brown; Smk. = Smoked; Str. = Striped; Lg. M. = Large Mouth; Rb. = Rainbow; P. = Pole; Y. = Yellow.
b Various, Netherlands = samples taken from six locations around Usselmeer Lake; Various, Sweden = samples taken from Grenlandsfjord and Frierfjord; Various US = samples taken from 314 sites across the US,
including industrial and background sites.
c Species were taken from both bottom feeders and open water feeders, and then composited.
d Whole fish samples and fillet samples were combined during analysis.
NOTES: Summary statistics provided in or derived from references; when reference did not compute mean, it was computed using one-half the detection limit for non-detects;
NA = not applicable;
ND = non-detected (limit of detection);
NR = not reported;
Descriptions provided were those given by reference or surmised from study description when not given;
One half the detection limit was used in calculating means. Therefore, it is possible to have mean concentrations greater than the range (e.g., reported detection limit for non detects greater than the positive sample).
DRAFT-DO NOT QUOTE OR CITE
B-166
December 2003
-------
Table B-12. Environmental Levels of Dibenzofurans in Fish (ppt) (continued)
Sources: 1. Van den Berg (1987)
2. Frommberger (1991)
3. Gotz and Schumacher (1990)
4. Rappe et al. (1989b)
5. Reed et al. (1990)
6. Gardner and White (1990)
7. Rappe et al. (1991)
8. DeVault etal. (1989)
9. Niimi and Oliver (1989a)
10. Oehmeetal. (1989)
11. Stalling etal. (1983)
12. Kjeller et al. (1990)
13. U.S. EPA (1992)
14. Fiedler etal. (1997)
15. deWit etal. (1990)
16. Beck et al. (1989)
17. Startin et al. (1990)
18. MAFF (1992)
19. Schecter et al. (1996)
20. Malisch (1998)
21. Domingo etal. (1999)
22. Amirovaetal. (1997)
DRAFT-DO NOT QUOTE OR CITE
B-167
December 2003
-------
Table B-13. Environmental Levels of PCBs in Fish (ppt)
Chemical
(IUPAC number)
Fish
species3
Tissue
Number
samples
Number
positive
samples
Concentration
range
Cone.
mean
Wt.
basis
Location
Location
description
Samp.
year
Ref.
no.
Comments
Tetrachloro-PCB (MW=291.99)
3,3',4,4'-TCB
(77)
Baltic Herring
Rainbow
Trout
Other Fish
Freshwater
Fish
Ocean Fish
Fresh Fish
Blk. Bullhead
Lg. M. Bass
Blk. Crappie
Wh. Sucker
Coho Salmon
Wh. Crappie
Y. Perch
Br. Trout
Br. Trout
Lake Trout
Lake Trout
Rb. Trout
Rb. Trout
NR
NR
NR
NR
Mixed
Mixed
Whole
Whole
Whole
Whole
Whole
Whole
Whole
Whole
Fillets
Whole
Fillets
Whole
Fillets
6
4
4
NR
NR
NR
1
1
1
1
1
1
1
1
1
1
1
2
2
NR
NR
NR
NR
NR
0
1
1
1
1
1
1
1
1
1
1
1
2
1
33-136
80-150
5.6-153
NR
NR
ND
89,000
86,000
43,000
50,000
2,000
24,000
23,000
5,000
2,000
18,000
8,000
6000-11,000
ND-4,000
97
100
53
36
6.19
NA
89,000
86,000
43,000
50,000
2,000
24,000
23,000
5,000
2,000
18,000
8,000
8,500
2,000
Wet
Wet
Wet
Wet
Wet
Wet
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
Finland
Finland
Finland
Canada
Various U.S. Locations
Various U.S. Locations
Waukegan Harbor, IL
Waukegan Harbor, IL
Waukegan Harbor, IL
Waukegan Harbor, IL
Waukegan Harbor, IL
Waukegan Harbor, IL
Waukegan Harbor, IL
Vineland, Lake Ontario
Vineland, Lake Ontario
Port Credit, Lake Ontario
Port Credit, Lake Ontario
Lake Ontario
Lake Ontario
Urban
Urban
Urban
Urban
Urban
Urban
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
86-88
95
95
78
78
78
78
78
78
78
NR
NR
NR
NR
NR
NR
1
1
1
2
3
3
4
4
4
4
4
4
4
5
5
5
5
5
5
3 samples of white fish
and one pike perch
composite 6 samples
composite 3 samples
composite 6 samples
composite 5 samples
composite 10 samples
composite 10 samples
composite 10 samples
composite 10 samples
DRAFT-DO NOT QUOTE OR CITE
B-168
December 2003
-------
Table B-13. Environmental Levels of PCBs in Fish (ppt) (continued)
Chemical
(IUPAC number)
3,3',4,4'-TCB
(continued)
3,4,4',5-TCB
(81)
Fish
species3
Coho Salmon
Coho Salmon
Carp
White Catfish
Channel
Catfish
White Catfish
Channel
Catfish
Yellowfm
Goby
Yellowfm
Goby
Staghorn
Sculp in
Diamond
Turbot
Starry
Flounder
Br. Trout
Br. Trout
Lake Trout
Lake Trout
Rb. Trout
Rb. Trout
Tissue
Whole
Fillets
Whole
Whole
Whole
Whole
Whole
Whole
Whole
Whole
Whole
Whole
Whole
Fillets
Whole
Fillets
Whole
Fillets
Number
samples
2
2
1
3
2
1
1
1
1
1
1
1
1
1
1
1
2
2
Number
positive
samples
2
2
0
3
2
1
1
1
1
1
1
1
1
1
1
1
2
1
Concentration
range
8000-10,000
3,000-5,000
ND(5,000)
38.6-120.6
14.2-1,095
40.47
25.99
54.04
54.92
38.62
78.91
80.64
24,000
10,000
90,000
38,000
13,000-30,000
ND-9,000
Cone.
mean
9,000
4,000
NA
89.3
554.7
40.47
25.99
54.04
54.92
38.62
78.91
80.64
24,000
10,000
90,000
38,000
21,500
4,500
Wt.
basis
NR
NR
NR
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
NR
NR
NR
NR
NR
NR
Location
Lake Ontario
Lake Ontario
Saginaw Bay
San Joaquin River
Antioch
Sacramento River
Sacramento River
Sacramento River
San Pablo Bay
San Pablo Bay
San Pablo Bay
San Pablo Bay
Vineland, Lake Ontario
Vineland, Lake Ontario
Port Credit, Lake Ontario
Port Credit, Lake Ontario
Lake Ontario
Lake Ontario
Location
description
NR
NR
NR
Industrial
Industrial
Industrial
Industrial
Industrial
Industrial
Industrial
Industrial
Industrial
NR
NR
NR
NR
NR
NR
Samp.
year
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
Ref.
no.
5
5
6
9
9
9
9
9
9
9
9
9
5
5
5
5
5
5
Comments
composite 10 samples
composite 10 samples
composite 10 samples
composite 10 samples
DRAFT-DO NOT QUOTE OR CITE
B-169
December 2003
-------
Table B-13. Environmental Levels of PCBs in Fish (ppt) (continued)
Chemical
(IUPAC number)
3,4,4',5-TCB
(81)
(continued)
TCBs
Fish
species3
Coho Salmon
Coho Salmon
Carp
Various0
Tissue
Whole
Fillets
Whole
Mixed"
Number
samples
2
2
1
362
Number
positive
samples
2
2
1
263
Concentration
range
2,000-26,000
8,000-14,000
17,000
NR
Cone.
mean
14,000
11,000
NA
696,240
Wt.
basis
NR
NR
NR
Wet
Location
Lake Ontario
Lake Ontario
Saginaw Bay
Various, US"
Location
description
NR
NR
NR
Various
Samp.
year
NR
NR
NR
86-89
Ref.
no.
5
5
6
7
Comments
samples composite 3-5
fish
Pentachloro-PCB (MW=326.44)
3,3',4,4',5-PeCB
(126)
Baltic Herring
Rainbow
Trout
Other Fish
Freshwater
Fish
Ocean Fish
Fresh Fish
Carp
White Catfish
Channel
Catfish
White Catfish
Channel
Catfish
Yellowfm
Goby
NR
NR
NR
NR
Mixed
Mixed
Whole
Whole
Whole
Whole
Whole
Whole
6
4
4
NR
NR
NR
1
3
2
1
1
1
NR
NR
NR
NR
NR
0
0
3
2
1
1
1
7.4-26
5.2-35
2.3-28
NR
NR
ND
ND(5,000)
14.1-66.1
17.7-211
25.3
21.4
12.33
17
17
11
8
0.83
NA
NA
36.9
114.2
25.3
21.4
12.33
Wet
Wet
Wet
Wet
Wet
Wet
NR
Wet
Wet
Wet
Wet
Wet
Finland
Finland
Finland
Canada
Various U.S. Locations
Various U.S. Locations
Saginaw Bay
San Joaquin River
Antioch
Sacramento River
Sacramento River
Sacramento River
Urban
Urban
Urban
Urban
Urban
Urban
NR
Industrial
Industrial
Industrial
Industrial
Industrial
NR
NR
NR
86-88
95
95
NR
NR
NR
NR
NR
NR
1
1
1
2
3
3
6
9
9
9
9
9
3 samples of white fish
and one pike perch
DRAFT-DO NOT QUOTE OR CITE
B-170
December 2003
-------
Table B-13. Environmental Levels of PCBs in Fish (ppt) (continued)
Chemical
(IUPAC number)
3,3',4,4',5-PeCB
(126)
(continued)
2,3,3',4,4'-PeCB
(105)
Fish
species3
Yellowfin
Goby
Staghorn
Sculpin
Diamond
Turbot
Starry
Flounder
Baltic Herring
Rainbow
Trout
Other Fish
Ocean Fish
Fresh Fish
Blk. Bullhead
Lg. M. Bass
Blk. Crappie
Wh. Sucker
Coho Salmon
Wh. Crappie
Y. Perch
Small Smelt
Large Smelt
Salmonids
Tissue
Whole
Whole
Whole
Whole
NR
NR
NR
Mixed
Mixed
Whole
Whole
Whole
Whole
Whole
Whole
Whole
Whole
Whole
Whole
Number
samples
1
1
1
1
6
4
4
NR
NR
1
1
1
1
1
1
1
1
1
1
Number
positive
samples
1
1
1
1
NR
NR
NR
NR
0
1
1
1
1
1
1
1
1
1
1
Concentration
range
15.52
17.36
61.37
27.67
960-2,700
410-2,100
113-1,100
NR
ND
352,000
290,000
114,000
483,000
45,000
242,000
80,000
15,000
38,000
110,000
Cone.
mean
15.52
17.36
61.37
27.67
1,700
1,200
400
120
NA
352,000
290,000
114,000
483,000
45,000
242,000
80,000
15,000
38,000
110,000
Wt.
basis
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
NR
NR
NR
NR
NR
NR
NR
Wet
Wet
Wet
Location
San Pablo Bay
San Pablo Bay
San Pablo Bay
San Pablo Bay
Finland
Finland
Finland
Various U.S. Locations
Various U.S. Locations
Waukegan Harbor, IL
Waukegan Harbor, IL
Waukegan Harbor, IL
Waukegan Harbor, IL
Waukegan Harbor, IL
Waukegan Harbor, IL
Waukegan Harbor, IL
Port Credit, Lake Ontario
Vineland, Lake Ontario
Lake Ontario
Location
description
Industrial
Industrial
Industrial
Industrial
Urban
Urban
Urban
Urban
Urban
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
Samp.
year
NR
NR
NR
NR
NR
NR
NR
95
95
78
78
78
78
78
78
78
86
82
81-82
Ref.
no.
9
9
9
9
1
1
1
3
3
4
4
4
4
4
4
4
8
8
8
Comments
3 samples of white fish
and one pike perch
composite 6 samples
composite 3 samples
composite 6 samples
composite 5 samples
composite 48 samples
composite 20 samples
composite 60 samples
DRAFT-DO NOT QUOTE OR CITE
B-171
December 2003
-------
Table B-13. Environmental Levels of PCBs in Fish (ppt) (continued)
Chemical
(IUPAC number)
2,3,3',4,4'-PeCB
(105)
(continued)
2,3,4,4', 5-PeCB
(114)
2,3',4,4',5-PeCB
(118)
Fish
species3
Br. Trout
Br. Trout
Lake Trout
Lake Trout
Rb. Trout
Rb. Trout
Coho Salmon
Coho Salmon
Carp
Ocean Fish
Fresh Fish
Carp
Ocean Fish
Fresh Fish
Small Smelt
Large Smelt
Salmonids
Br. Trout
Br. Trout
Lake Trout
Lake Trout
Rb. Trout
Tissue
Whole
Fillets
Whole
Fillets
Whole
Fillets
Whole
Fillets
Whole
Mixed
Mixed
Whole
Mixed
Mixed
Whole
Whole
Whole
Whole
Fillets
Whole
Fillets
Whole
Number
samples
1
1
1
1
2
2
2
2
1
NR
NR
1
NR
NR
1
1
1
1
1
1
1
2
Number
positive
samples
1
1
1
1
2
2
2
2
1
0
NR
1
NR
NR
1
1
1
1
1
1
1
2
Concentration
range
55,000
24,000
253,000
101,000
34,000-138,000
6,000-50,000
48,000-121,000
19,000-56,000
427,000
ND
NR
57,000
NR
NR
37,000
87,000
250,000
133,000
60,000
634,000
242,000
80,000-310,000
Cone.
mean
55,000
24,000
253,000
101,000
86,000
28,000
84,500
37,500
427,000
NA
250
57,000
320
1,800
37,000
87,000
250,000
133,000
60,000
634,000
242,000
195,000
Wt.
basis
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
Wet
Wet
Wet
NR
NR
NR
NR
NR
Location
Vineland, Lake Ontario
Vineland, Lake Ontario
Port Credit, Lake Ontario
Port Credit, Lake Ontario
Lake Ontario
Lake Ontario
Lake Ontario
Lake Ontario
Saginaw Bay
Various U.S. Locations
Various U.S. Locations
Saginaw Bay
Various U.S. Locations
Various U.S. Locations
Port Credit, Lake Ontario
Vineland, Lake Ontario
Lake Ontario
Vineland, Lake Ontario
Vineland, Lake Ontario
Port Credit, Lake Ontario
Port Credit, Lake Ontario
Lake Ontario
Location
description
NR
NR
NR
NR
NR
NR
NR
NR
NR
Urban
Urban
NR
Urban
Urban
NR
NR
NR
NR
NR
NR
NR
NR
Samp.
year
NR
NR
NR
NR
NR
NR
NR
NR
NR
95
95
NR
95
95
86
82
81-82
NR
NR
NR
NR
NR
Ref.
no.
5
5
5
5
5
5
5
5
6
3
3
6
3
3
8
8
8
5
5
5
5
5
Comments
composite 10 samples
composite 10 samples
composite 10 samples
composite 10 samples
composite 48 samples
composite 20 samples
composite 60 samples
composite 10 samples
composite 10 samples
composite 10 samples
composite 10 samples
DRAFT-DO NOT QUOTE OR CITE
B-172
December 2003
-------
Table B-13. Environmental Levels of PCBs in Fish (ppt) (continued)
Chemical
(IUPAC number)
2,3',4,4',5-PeCB
(118)
(continued)
PeCBs
Fish
species3
Rb. Trout
Coho Salmon
Coho Salmon
Carp
Various0
Tissue
Fillets
Whole
Fillets
Whole
Mixed"
Number
samples
2
2
2
1
362
Number
positive
samples
2
2
2
1
314
Concentration
range
16,000-115,000
100,000-271,000
39,000-136,000
1.35x 106
NR
Cone.
mean
65,500
185,500
87,500
1.35x 106
564,700
Wt.
basis
NR
NR
NR
NR
Wet
Location
Lake Ontario
Lake Ontario
Lake Ontario
Saginaw Bay
Various, US"
Location
description
NR
NR
NR
NR
Various
Samp.
year
NR
NR
NR
NR
86-89
Ref.
no.
5
5
5
6
7
Comments
composite 3-5 fish
Hexachloro-PCB (MW=360.88)
3,3',4,4',5,5'-HxCB
(169)
Baltic Herring
Rainbow
Trout
Other Fish
Freshwater
Fish
Ocean Fish
Fresh Fish
Carp
White Catfish
Channel
Catfish
White Catfish
Channel
Catfish
Yellowfm
Goby
NR
NR
NR
NR
Mixed
Mixed
Whole
Whole
Whole
Whole
Whole
Whole
6
4
4
NR
NR
NR
1
3
2
1
1
1
NR
NR
NR
NR
NR
NR
0
3
2
1
1
1
ND-12
ND-7.4
0.6-6.5
NR
NR
NR
ND(5,000)
1.28-4.42
1.4-6.1
2.23
1.64
1.19
4.5
3.9
1.9
<1
0.2
0.94
NA
2.82
3.7
2.23
1.64
1.19
Wet
Wet
Wet
Wet
Wet
Wet
NR
Wet
Wet
Wet
Wet
Wet
Finland
Finland
Finland
Canada
Various U.S. Locations
Various U.S. Locations
Saginaw Bay
San Joaquin River
Antioch
Sacramento River
Sacramento River
Sacramento River
Urban
Urban
Urban
Urban
Urban
Urban
NR
Industrial
Industrial
Industrial
Industrial
Industrial
NR
NR
NR
86-88
95
95
NR
NR
NR
NR
NR
NR
1
1
1
2
3
3
6
9
9
9
9
9
3 samples of white fish
and one pike perch
DRAFT-DO NOT QUOTE OR CITE
B-173
December 2003
-------
Table B-13. Environmental Levels of PCBs in Fish (ppt) (continued)
Chemical
(IUPAC number)
3,3',4,4',5,5'-HxCB
(169)
(continued)
2,3,3',4,4',5-HxCB
(156)
2,3,3',4,4',5'-HxCB
(157)
2,3',4,4',5,5'-HxCB
(167)
HxCBs
Fish
species3
Yellowfin
Goby
Staghorn
Sculp in
Diamond
Turbot
Starry
Flounder
Small Smelt
Large Smelt
Salmonids
Carp
Carp
Carp
Various0
Tissue
Whole
Whole
Whole
Whole
Whole
Whole
Whole
Whole
Whole
Whole
Mixed"
Number
samples
1
1
1
1
1
1
1
1
1
1
362
Number
positive
samples
1
1
1
1
1
1
1
1
1
1
321
Concentration
range
1.63
1.96
9.89
3.50
2,700
6,100
34,000
79,000
76,000
77,000
NR
Cone.
mean
1.63
1.96
9.89
3.50
2,700
6,100
34,000
79,000
76,000
77,000
355,930
Wt.
basis
Wet
Wet
Wet
Wet
Wet
Wet
Wet
NR
NR
NR
Wet
Location
San Pablo Bay
San Pablo Bay
San Pablo Bay
San Pablo Bay
Port Credit, Lake Ontario
Vineland, Lake Ontario
Lake Ontario
Saginaw Bay
Saginaw Bay
Saginaw Bay
Various, US"
Location
description
Industrial
Industrial
Industrial
Industrial
NR
NR
NR
NR
NR
NR
Various
Samp.
year
NR
NR
NR
NR
86
82
81-82
NR
NR
NR
86-89
Ref.
no.
9
9
9
9
8
8
8
6
6
6
7
Comments
composite 48 samples
composite 20 samples
composite 60 samples
composite 3-5 fish
Heptachloro-PCB (MW=396.33)
2,2',3,4,4',5,5-HpCB
(180)
2,3,3',4,4',5,5'-HpCB
(189)
HpCBs
Ocean Fish
Fresh Fish
Carp
Various0
Mixed
Mixed
Whole
Mixed"
NR
NR
1
362
NR
NR
1
250
NR
NR
29,000
NR
170
810
29,000
96,700
NR
NR
NR
Wet
Various U.S. Locations
Various U.S. Locations
Saginaw Bay
Various, US"
Urban
Urban
NR
Various
95
95
NR
86-89
3
3
6
7
composite 3-5 fish
DRAFT-DO NOT QUOTE OR CITE
B-174
December 2003
-------
Table B-13. Environmental Levels of PCBs in Fish (ppt) (continued)
Footnote References
Blk. = Black; Lg. M. = Large Mouth; Wh. = White; Y. = Yellow; Br. = Brown; Rb. = Rainbow
b US = samples taken from 362 sites across the US, including industrial and background sites.
c Species were taken from both bottom feeders and open water feeders, and then composited.
d Whole fish samples and fillet samples were combined for analysis.
NOTES: Summary statistics provided in or derived from references; when reference did not compute composite, it was computed using one-half the detection limit for non-detects;
NA = not applicable;
ND = non-detected (limit of detection);
NR = not reported;
Descriptions provided were those given by reference or surmised from study description when not given.
Sources: 1. Himberg (1993)
2. Mes and Weber (1989)
3. Schecter et al. (1996)
4. Huckinsetal. (1988)
5. Niimi and Oliver (1989b)
6. Smith et al. (1990)
7. U.S. EPA (1991)
8. Oliver and Niimi (1988)
9. Petreas (1991)
DRAFT-DO NOT QUOTE OR CITE B-175 December 2003
-------
Table B-14. Levels of Dioxins in Food Products (ppt)
Chemical
Sample
Type'
Number
Samples
Number
Positive
Samples
Concentration
Range
Cone.
Mean"
Wt.
Basis
Location
Location
Description
Sample
Year
Ref.
No.
Comments
Tetrachlorodibenzo-p-dioxins (MW=321 .98)
2,3,7,8-TCDD
Food basket
Milk
Milk
Milk
Butter
Beef fat
Pork
Sheep fat
Chicken
Eggs
Milk
Beef
Pork
Milk
Cheese
Butter
Beef
Veal
Pork
Sheep
3
2
4
1
1
1
1
1
1
1
1
3
3
10
10
5
3
4
3
2
0
0
3
1
1
1
1
1
1
1
1
3
0
NR
0
0
0
0
0
0
ND(0. 1-0.4)
ND(.012-.013)
ND-0.049
0.2
0.08
0.6
0.03
0.01
0.3
0.2
0.0018
0.017-0.062
ND(0.006)
ND-1.9
ND(0.5)
ND(0.5)
ND(0.5)
ND(0.5)
ND(0.5)
ND(0.5)
NA
NA
0.027
0.2
0.08
0.6
0.03
0.01
0.3
0.2
0.0018
0.032
NA
0.4
NA
NA
NA
NA
NA
NA
Fresh
Whole
Whole
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Whole
Whole
Whole
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Sweden
Switzerland
Switzerland
W. Germany
Berlin, W. Germany
Berlin, W. Germany
Berlin, W. Germany
Berlin, W. Germany
Berlin, W. Germany
Berlin, W. Germany
U.S.
U.S.
U.S.
W. Germany
W. Germany
W. Germany
W. Germany
W. Germany
W. Germany
W. Germany
Urban
Background
Industrial
Urban
Urban
Urban
Urban
Urban
Urban
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
1
2
2
3
3
3
3
3
3
3
4
4
4
5
5
5
5
5
5
5
near incinerators
composite from 8 trucks
from slaughterhouse
from slaughterhouse
samples not randomly selected
DRAFT-DO NOT QUOTE OR CITE
B-176
December 2003
-------
Table B-14. Levels of Dioxins in Food Products (ppt) (continued)
Chemical
2,3,7,8-TCDD
(continued)
Sample
Type'
Chicken
Canned meat
Lard
Milk
Cow cream
Beef
Cheese w/butter
Beef fat
Pork
Butter
Swiss cheese
Sausage
Pork sticks
Pork fat
Chicken fat
Milk
Cottage cheese
Soft blue cheese
Heavy cream
cheese
Soft cream cheese
Number
Samples
2
2
4
7
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
Number
Positive
Samples
0
0
0
NR
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
1
Concentration
Range
ND(0.5)
ND(0.5)
ND(0.5)
ND-0.013
ND(0.15)
ND(0.03)
ND(0.36)
ND(0.34)
ND(0.72)
ND(0.53)
ND(0.03)
ND(0.57)
ND(0.34)
ND(0.99)
ND(0.95)
0.12"
ND(0.003)
ND(0.05)
ND(0.04)
0.04
Cone.
Mean"
NA
NA
NA
0.009C
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
0.12"
NA
NA
NA
0.04
Wt.
Basis
Fat
Fat
Fat
Whole
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Whole
Wet
Wet
Wet
Wet
Location
W. Germany
W. Germany
W. Germany
England & Wales
USSR
USSR
USSR
USSR
USSR
USSR
USSR
Moscow, USSR
South Vietnam
South Vietnam
South Vietnam
Vermont, U.S.
New York, NY
New York, NY
New York, NY
New York, NY
Location
Description
Rural
Background
Sample
Year
NR
NR
NR
1989
88-89
88-89
88-89
88-89
88-89
88-89
88-89
88-89
NR
NR
NR
87-88
1990
1990
1990
1990
Ref.
No.
5
5
5
6
7
7
7
7
7
7
7
7
7
7
7
8
9
9
9
9
Comments
DRAFT-DO NOT QUOTE OR CITE
B-177
December 2003
-------
Table B-14. Levels of Dioxins in Food Products (ppt) (continued)
Chemical
2,3,7,8-TCDD
(continued)
2,3,7,8-TCDD
Sample
Type'
American cheese
Beef
Beef
Pork
Pork
Chicken
Chicken
Eggs
Eggs
Beef
Pork
Chicken
Beef
Carcass Meat
Poultry
Meat Products
Milk Products
Butter
Cheddar Cheese
Reduced Fat
Cheese
Fats & Oils
Number
Samples
1
5
3
5
3
5
3
5
3
3
1
1
63
2
2
1
2
4
1
1
1
Number
Positive
Samples
1
0
0
0
0
2
1
0
0
3
1
1
11
1
2
1
1
NR
1
0
1
Concentration
Range
0.07
ND(0. 12-0.41)
ND(0. 16-0.40)
ND(0.07-0.52)
ND(0.39-0.49)
ND-0.43
ND-1.67
ND(0.01-0.03)
ND(0.01-0.03)
0.005-0.028
0.013
0.011
ND-0.74
ND-0.28
0.09-0.11
0.05
ND-0.02
0.14-0.16
0.05
ND
0.21
Cone.
Mean"
0.07
NA
NA
NA
NA
0.23
0.70
NA
NA
0.017
0.013
0.011
0.05
0.15
0.1
0.05
0.035
0.15
0.05
NA
0.21
Wt.
Basis
Wet
Fat
Fat
Fat
Fat
Fat
Fat
Whole
Whole
Wet
Wet
Wet
Fat
Whole
Whole
Whole
Whole
Whole
Whole
Whole
Whole
Location
New York, NY
Los Angeles
San Francisco
Los Angeles
San Francisco
Los Angeles
San Francisco
Los Angeles
San Francisco
New York, NY
New York, NY
New York, NY
Various U.S. Sites
United Kingdom
United Kingdom
United Kingdom
United Kingdom
United Kingdom
United Kingdom
United Kingdom
United Kingdom
Location
Description
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Federally-Inspected
Slaughter Houses
Sample
Year
1990
NR
NR
NR
NR
NR
NR
NR
NR
1990
1990
1990
1994
88-91
88-91
88-91
88-91
88-91
88-91
88-91
88-91
Ref.
No.
9
10
10
10
10
10
10
10
10
11
11
11
12
13
13
13
13
13
13
13
13
Comments
composite 6 samples
composite 6 samples
composite 6 samples
composite 6 samples
composite 6 samples
composite 6 samples
composite 6 samples
composite 6 samples
statistically-based survey
DRAFT-DO NOT QUOTE OR CITE
B-178
December 2003
-------
Table B-14. Levels of Dioxins in Food Products (ppt) (continued)
Chemical
(continued)
Sample
Type'
Eggs
Green Vegetables
Potatoes
Fresh Fruit
Milk
Beef
Chicken
Pork
Hamburger
Pizza
Fried Chicken
Milk
Eggs
Beef
Pork
Milk
Cheddar Cheese
Butter
Eggs
Chicken
Number
Samples
2
2
1
2
27
9
7
7
1
1
1
20
20
20
20
3
3
3
3
3
Number
Positive
Samples
2
0
0
0
NR
NR
NR
NR
0
0
0
0
NR
0
0
3
3
3
0
3
Concentration
Range
0.05
ND
ND
ND
ND-0.25
ND
ND
ND
ND
ND
ND
ND
ND-0.064
ND
ND
0.06-0.07
0.05-0.07
0.05-0.09
ND(0.03-0.07)
0.15-0.19
Cone.
Mean"
0.05
NA
NA
NA
<0.2
NA
NA
NA
NA
NA
NA
NA
0.004
NA
NA
0.06
0.06
0.06
0.04
0.16
Wt.
Basis
Whole
Whole
Whole
Whole
Fat
Fat
Fat
Fat
Whole
Whole
Whole
Whole
Whole
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Location
United Kingdom
United Kingdom
United Kingdom
United Kingdom
Germany
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
U.S.
U.S.
U.S.
Finland
Finland
Finland
Finland
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
Location
Description
NR
NR
NR
NR
NR
Sample
Year
88-91
88-91
88-91
88-91
1992
NR
NR
NR
NR
NR
NR
1991
1991
1991
1991
1994
1994
1994
1994
1994
Ref.
No.
13
13
13
13
14
15
15
15
16
16
16
17
17
17
17
18
18
18
18
18
Comments
fast food
fast food
fast food
purchased from grocery store
purchased from grocery store
purchased from grocery store
purchased from grocery store
purchased from grocery store
DRAFT-DO NOT QUOTE OR CITE
B-179
December 2003
-------
Table B-14. Levels of Dioxins in Food Products (ppt) (continued)
Chemical
2,3,7,8-TCDD
(continued)
Sample
Type'
Chicken Liver
Ground Beef
Sausage
Milk
Pork
Young Chicken
Light Fowl
Heavy Fowl
Young Turkeys
Eggs
Milk
Butter
Eggs
Meat
Beef
Smoked Sausage
Chicken
Pork
Goose Fat
Duck Fat
Number
Samples
3
3
3
8
78
39
12
12
15
3
538
222
218
107
NR
NR
NR
NR
NR
NR
Number
Positive
Samples
3
3
3
8
3
26
3
11
11
0
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
Concentration
Range
0.18-0.45
0.04-0.05
0.04-0.11
NR
NR
NR
NR
NR
NR
NR
0- 1.2
0 - 0.26
0-3.4
0.04 - 0.85
NR
NR
NR
NR
NR
NR
Cone.
Mean"
0.29
0.04
0.07
0.07
0.10
0.16
0.05
0.43
0.24
0.02
0.71
0.64
2.1
0.61
0.36
0.26
0.26
0.08
0.21
0.26
Wt.
Basis
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Whole
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Location
S. Mississippi
S. Mississippi
S. Mississippi
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
Germany
Germany
Germany
Germany
Russia
Russia
Russia
Russia
Russia
Russia
Location
Description
NR
NR
NR
Background
Background
Background
Background
Background
Background
Sample
Year
1994
1994
1994
1996
1995
1996
1996
1996
1996
1995
93-96
93-96
93-96
93-96
1996
1996
1996
1996
1996
1996
Ref.
No.
18
18
18
21
22
23
23
23
23
24
25
25
25
25
26
26
26
26
26
26
Comments
purchased from grocery store
purchased from grocery store
purchased from grocery store
official food inspection
official food inspection
official food inspection
official food inspection
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
DRAFT-DO NOT QUOTE OR CITE
B-180
December 2003
-------
Table B-14. Levels of Dioxins in Food Products (ppt) (continued)
Chemical
2,3,7,8-TCDD
(continued)
TCDD
Sample
Type'
Chicken Fat
Vegetables
Pulses & Cereals
Fruits
Meat
Eggs
Milk & Dairy
Fats & Oils
Butter
Beef
Chicken
Pork
Eggs
Butter
Number
Samples
NR
2
4
2
7
2
6
4
21
9
7
7
3
18
Number
Positive
Samples
NR
NR
NR
NR
NR
NR
NR
NR
15
0
0
0
0
11
Concentration
Range
NR
NR
NR
NR
NR
NR
NR
NR
ND- 1.1
ND
ND
ND
NR
ND- 1.5
Cone.
Mean"
0.23
0.1
ND
ND
0.1
0.1
ND
0.4
NR
NA
NA
NA
0.03
NR
Wt.
Basis
Fat
Dry
Dry
Dry
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Whole
Fat
Location
Russia
Spain
Spain
Spain
Spain
Spain
Spain
Spain
Spain
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
Spain
Location
Description
Sample
Year
1996
1996
1996
1996
1996
1996
1996
1996
NR
NR
NR
NR
1995
NR
Ref.
No.
26
27
27
27
27
27
27
27
28
15
15
15
24
28
Comments
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
Pentachlorodibenzo-p-dioxins (MW = 326.44)
1,2,3,7,8-PeCDD
Food basket
Milk
Milk
Milk
Butter
3
2
4
1
1
0
0
2
1
1
ND(0.2-0.8)
ND(0.04-0.06)
ND-0.25
0.7
0.41
NA
NA
0.12
0.7
0.41
Fresh
Whole
Whole
Fat
Fat
Stockholm, Sweden
Switzerland
Switzerland
W. Germany
Berlin, W. Germany
Urban
Background
Industrial
Urban
NR
NR
NR
NR
NR
1
2
2
3
3
near incinerators
composite from 8 trucks
DRAFT-DO NOT QUOTE OR CITE
B-181
December 2003
-------
Table B-14. Levels of Dioxins in Food Products (ppt) (continued)
Chemical
1,2,3,7,8-PeCDD
(continued)
Sample
Type'
Beef fat
Pork
Sheep fat
Chicken
Eggs
Milk
Cheese
Butter
Beef
Veal
Pork
Sheep
Chicken
Canned meat
Lard
Milk
Cow cream
Beef
Cheese w/butter
Beef fat
Number
Samples
1
1
1
1
1
10
10
5
3
4
3
2
2
2
4
7
1
1
1
1
Number
Positive
Samples
1
1
1
1
1
NR
NR
0
3
4
0
0
2
1
0
7
0
0
0
0
Concentration
Range
0.8
0.12
0.5
0.7
0.4
ND-2.5
ND-0.8
ND(0.5)
0.5-4.6
2.5-3.4
ND(0.5)
ND(0.5)
0.9-1.2
ND-0.9
ND(0.5)
0.012-0.023
ND(0.07)
ND(0.02)
ND(0.18)
ND(0.17)
Cone.
Mean"
0.8
0.12
0.5
0.7
0.4
1.2
0.6
NA
1.7
3.1
NA
NA
1.0
0.6
NA
0.016C
NA
NA
NA
NA
Wt.
Basis
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Whole
Wet
Wet
Wet
Wet
Location
Berlin, W. Germany
Berlin, W. Germany
Berlin, W. Germany
Berlin, W. Germany
Berlin, W. Germany
W. Germany
W. Germany
W. Germany
W. Germany
W. Germany
W. Germany
W. Germany
W. Germany
W. Germany
W. Germany
England & Wales
USSR
USSR
USSR
USSR
Location
Description
Urban
Urban
Urban
Urban
Urban
Rural
Sample
Year
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
1989
88-89
88-89
88-89
88-89
Ref.
No.
3
3
3
3
3
5
5
5
5
5
5
5
5
5
5
6
7
7
7
7
Comments
from slaughterhouse
from slaughterhouse
samples not randomly selected
DRAFT-DO NOT QUOTE OR CITE
B-182
December 2003
-------
Table B-14. Levels of Dioxins in Food Products (ppt) (continued)
Chemical
1,2,3,7,8-PeCDD
(continued)
Sample
Type'
Pork
Butter
Swiss cheese
Sausage
Pork sticks
Pork fat
Chicken fat
Cottage cheese
Soft blue cheese
Heavy cream
cheese
Soft cream cheese
American cheese
Beef
Beef
Pork
Pork
Chicken
Chicken
Eggs
Eggs
Number
Samples
1
1
1
1
1
1
1
1
1
1
1
1
5
3
5
3
5
3
5
3
Number
Positive
Samples
0
0
0
0
1
1
1
1
1
1
1
1
0
0
0
0
0
0
0
0
Concentration
Range
ND(0.36)
ND(0.26)
ND(0.02)
ND(0.29)
0.17
0.50
0.95
0.01
0.2
0.11
0.11
0.12
ND(0.40-17.50)
ND(0.49-1.09)
ND(1. 00-4.36)
ND(1. 94-2.70)
ND(0.19-2.19)
ND(0.44-7.40)
ND(0.06-0.40)
ND(0.04-0.06)
Cone.
Mean"
NA
NA
NA
NA
0.17
0.50
0.95
0.01
0.2
0.11
0.11
0.12
NA
NA
NA
NA
NA
NA
NA
NA
Wt.
Basis
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Fat
Fat
Fat
Fat
Fat
Fat
Whole
Whole
Location
USSR
USSR
USSR
Moscow, USSR
South Vietnam
South Vietnam
South Vietnam
New York, NY
New York, NY
New York, NY
New York, NY
New York, NY
Los Angeles
San Francisco
Los Angeles
San Francisco
Los Angeles
San Francisco
Los Angeles
San Francisco
Location
Description
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Sample
Year
88-89
88-89
88-89
88-89
NR
NR
NR
1990
1990
1990
1990
1990
NR
NR
NR
NR
NR
NR
NR
NR
Ref.
No.
7
7
7
7
7
7
7
9
9
9
9
9
10
10
10
10
10
10
10
10
Comments
composite 6 samples
composite 6 samples
composite 6 samples
composite 6 samples
composite 6 samples
composite 6 samples
composite 6 samples
composite 6 samples
DRAFT-DO NOT QUOTE OR CITE
B-183
December 2003
-------
Table B-14. Levels of Dioxins in Food Products (ppt) (continued)
Chemical
1,2,3,7,8-PeCDD
(continued)
Sample
Type'
Beef
Pork
Chicken
Beef
Carcass Meat
Offals
Poultry
Meat Products
Milk Products
Butter
Cheddar Cheese
Reduced Fat
Cheese
Fats & Oils
Eggs
Green Vegetables
Potatoes
Fresh Fruit
Milk
Milk
Number
Samples
3
1
1
63
2
1
2
2
2
4
1
1
1
2
1
1
1
9
27
Number
Positive
Samples
3
1
0
2
1
0
2
1
1
NR
1
1
1
2
0
0
0
9
NR
Concentration
Range
0.01 - 0.208
0.041
ND(0.011)
ND-3.04
ND-0.41
ND
0.09-0.11
ND-0.07
ND-0.04
ND-0.48
0.04
0.05
0.29
0.06-0.09
ND
ND
ND
0.34-0.76
0.22-0.56
Cone.
Mean"
0.093
0.041
NA
0.35
0.22
NA
0.1
0.048
0.06
0.32
0.04
0.05
0.29
0.075
NA
NA
NA
0.46
0.37
Wt.
Basis
Wet
Wet
Wet
Fat
Whole
Whole
Whole
Whole
Whole
Whole
Whole
Whole
Whole
Whole
Whole
Whole
Whole
Fat
Fat
Location
New York, NY
New York, NY
New York, NY
Various U.S. Sites
United Kingdom
United Kingdom
United Kingdom
United Kingdom
United Kingdom
United Kingdom
United Kingdom
United Kingdom
United Kingdom
United Kingdom
United Kingdom
United Kingdom
United Kingdom
Switzerland
Germany
Location
Description
Federally-Inspected
Slaughter Houses
Sample
Year
1990
1990
1990
1994
88-91
88-91
88-91
88-91
88-91
88-91
88-91
88-91
88-91
88-91
88-91
88-91
88-91
1990
1992
Ref.
No.
11
11
11
12
13
13
13
13
13
13
13
13
13
13
13
13
13
19
14
Comments
statistically-based survey
DRAFT-DO NOT QUOTE OR CITE
B-184
December 2003
-------
Table B-14. Levels of Dioxins in Food Products (ppt) (continued)
Chemical
1,2,3,7,8-PeCDD
(continued)
Sample
Type'
Beef
Chicken
Pork
Hamburger
Pizza
Fried Chicken
Milk
Eggs
Beef
Pork
Milk
Cheddar Cheese
Butter
Eggs
Chicken
Chicken Liver
Ground Beef
Sausage
Milk
Pork
Number
Samples
9
7
7
1
1
1
20
20
20
20
3
3
3
3
3
3
3
3
8
78
Number
Positive
Samples
0
0
0
0
0
0
NR
NR
NR
0
3
3
3
3
3
3
3
2
8
3
Concentration
Range
ND
ND
ND
ND
ND
ND
ND-40
ND-0.168
ND-0.78
<0.2
0.28-0.43
0.03-0.37
0.34-0.49
0.13-0.14
0.18-0.25
0.27-0.46
0.18-0.35
ND-0.21
NR
NR
Cone.
Mean"
NA
NA
NA
NA
NA
NA
<10
0.041
<0.5
<0.2
0.38
0.25
0.4
0.14
0.22
0.35
0.25
0.15
0.32
0.45
Wt.
Basis
Fat
Fat
Fat
Whole
Whole
Whole
Whole
Whole
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Location
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
U.S.
U.S.
U.S.
Finland
Finland
Finland
Finland
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
Various U.S. Sites
Various U.S. Sites
Location
Description
NR
NR
NR
NR
NR
NR
NR
NR
Background
Background
Sample
Year
NR
NR
NR
NR
NR
NR
1991
1991
1991
1991
1994
1994
1994
1994
1994
1994
1994
1994
1996
1995
Ref.
No.
15
15
15
16
16
16
17
17
17
17
18
18
18
18
18
18
18
18
21
22
Comments
fast food
fast food
fast food
purchased from grocery store
purchased from grocery store
purchased from grocery store
purchased from grocery store
purchased from grocery store
purchased from grocery store
purchased from grocery store
purchased from grocery store
DRAFT-DO NOT QUOTE OR CITE
B-185
December 2003
-------
Table B-14. Levels of Dioxins in Food Products (ppt) (continued)
Chemical
1,2,3,7,8-PeCDD
(continued)
Sample
Type'
Young Chicken
Light Fowl
Heavy Fowl
Young Turkeys
Eggs
Milk
Butter
Eggs
Meat
Beef
Smoked Sausage
Chicken
Pork
Goose Fat
Duck Fat
Chicken Fat
Vegetables
Pulses & Cereals
Fruits
Meat
Number
Samples
39
12
12
15
3
538
222
218
107
NR
NR
NR
NR
NR
NR
NR
2
4
2
7
Number
Positive
Samples
8
0
4
5
0
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
Concentration
Range
NR
NR
NR
NR
NR
0- 1.3
0-0.48
0-8.2
0.03 - 2.4
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
Cone.
Mean"
0.24
0.15
0.32
0.32
0.12
0.25
0.21
0.68
0.3
ND
0.41
ND
0.20
0.47
ND
ND
0.1
ND
ND
ND
Wt.
Basis
Fat
Fat
Fat
Fat
Whole
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Dry
Dry
Dry
Fat
Location
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
Germany
Germany
Germany
Germany
Russia
Russia
Russia
Russia
Russia
Russia
Russia
Spain
Spain
Spain
Spain
Location
Description
Background
Background
Background
Background
Sample
Year
1996
1996
1996
1996
1995
93-96
93-96
93-96
93-96
1996
1996
1996
1996
1996
1996
1996
1996
1996
1996
1996
Ref.
No.
23
23
23
23
24
25
25
25
25
26
26
26
26
26
26
26
27
27
27
27
Comments
official food inspection
official food inspection
official food inspection
official food inspection
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
DRAFT-DO NOT QUOTE OR CITE
B-186
December 2003
-------
Table B-14. Levels of Dioxins in Food Products (ppt) (continued)
Chemical
1,2,3,7,8-PeCDD
(continued)
PeCDDs
Sample
Type'
Eggs
Milk & Dairy
Fats & Oils
Butter
Cottage cheese
Soft blue cheese
Heavy cream
cheese
Soft cream cheese
American cheese
Beef
Chicken
Pork
Eggs
Butter
Number
Samples
2
6
4
21
1
1
1
1
1
9
7
7
3
21
Number
Positive
Samples
NR
NR
NR
19
1
1
1
1
1
0
0
0
0
19
Concentration
Range
NR
NR
NR
ND- 1.08
0.6
14
5
4
4
ND
ND
ND
NR
ND- 1.23
Cone.
Mean"
0.1
ND
0.1
NR
0.6
14
5
4
4
NA
NA
NA
0.12
NR
Wt.
Basis
Fat
Fat
Fat
Fat
Wet
Wet
Wet
Wet
Wet
Fat
Fat
Fat
Whole
Fat
Location
Spain
Spain
Spain
Spain
New York, NY
New York, NY
New York, NY
New York, NY
New York, NY
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
Spain
Location
Description
Sample
Year
1996
1996
1996
NR
1990
1990
1990
1990
1990
NR
NR
NR
1995
NR
Ref.
No.
27
27
27
28
9
9
9
9
9
15
15
15
24
28
Comments
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
HexacWorodibenzo-p-dioxins (MW = 390.87)
1,2,3,4,7,8-HxCDD
Food basket
Milk
Milk
3
2
4
0
1
3
ND(0.4-1.6)
ND-0.068
ND-0.23
NA
0.049
0.14
Fresh
Whole
Whole
Stockholm, Sweden
Switzerland
Switzerland
Urban
Background
Industrial
NR
NR
NR
1
2
2
near incinerators
DRAFT-DO NOT QUOTE OR CITE
B-187
December 2003
-------
Table B-14. Levels of Dioxins in Food Products (ppt) (continued)
Chemical
1,2,3,4,7,8-HxCDD
(continued)
Sample
Type'
Milk
Butter
Beef fat
Pork
Sheep fat
Chicken
Eggs
Milk
Cheese
Butter
Beef
Veal
Pork
Sheep
Chicken
Canned meat
Lard
Cow cream
Beef
Cheese w/butter
Beef fat
Number
Samples
1
1
1
1
1
1
1
10
10
5
3
4
3
2
2
2
4
1
1
1
1
Number
Positive
Samples
1
1
1
1
1
1
1
NR
10
0
3
4
0
2
1
1
0
0
0
0
0
Concentration
Range
0.3
0.15
0.6
0.21
0.3
0.5
1.3
ND-2.0
0.2-0.4
ND(0.5)
0.5-4.6
1.1-3.0
ND(0.5)
0.7-1.0
ND-0.8
ND-2.1
ND(0.5)
ND(0.07)
ND(0.02)
ND(0.18)
ND(0.17)
Cone.
Mean"
0.3
0.15
0.6
0.21
0.3
0.5
1.3
0.8
0.3
NA
1.9
1.9
NA
0.8
0.6
1.0
NA
NA
NA
NA
NA
Wt.
Basis
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Wet
Wet
Wet
Wet
Location
W. Germany
Berlin, W. Germany
Berlin, W. Germany
Berlin, W. Germany
Berlin, W. Germany
Berlin, W. Germany
Berlin, W. Germany
W. Germany
W. Germany
W. Germany
W. Germany
W. Germany
W. Germany
W. Germany
W. Germany
W. Germany
W. Germany
USSR
USSR
USSR
USSR
Location
Description
Urban
Urban
Urban
Urban
Urban
Urban
Sample
Year
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
88-89
88-89
88-89
88-89
Ref.
No.
3
3
3
3
3
3
3
5
5
5
5
5
5
5
5
5
5
7
7
7
7
Comments
composite from 8 trucks
from slaughterhouse
from slaughterhouse
samples not randomly selected
DRAFT-DO NOT QUOTE OR CITE
B-188
December 2003
-------
Table B-14. Levels of Dioxins in Food Products (ppt) (continued)
Chemical
1,2,3,4,7,8-HxCDD
(continued)
Sample
Type'
Pork
Butter
Swiss cheese
Sausage
Pork sticks
Pork fat
Chicken fat
Cottage cheese
Soft blue cheese
Heavy cream
cheese
Soft cream cheese
American cheese
Eggs
Eggs
Chicken
Beef
Carcass Meat
Offals
Poultry
Number
Samples
1
1
1
1
1
1
1
1
1
1
1
1
5
3
1
63
2
2
2
Number
Positive
Samples
0
0
1
0
1
1
1
1
1
1
1
1
0
0
0
8
2
2
2
Concentration
Range
ND(0.36)
ND(0.26)
0.012
ND(0.29)
0.24
0.60
0.48
0.02
0.29
0.07
0.14
0.017
ND(0. 12-0.67)
ND(0.08-0.25)
ND(0.017)
ND-4.69
0.32-1.1
0.31-0.37
0.45-0.82
Cone.
Mean"
NA
NA
0.012
NA
NA
NA
NA
0.02
0.29
0.07
0.14
0.017
NA
NA
NA
0.64
0.71
0.34
0.64
Wt.
Basis
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Whole
Whole
Wet
Fat
Whole
Whole
Whole
Location
USSR
USSR
USSR
Moscow, USSR
South Vietnam
South Vietnam
South Vietnam
New York, NY
New York, NY
New York, NY
New York, NY
New York, NY
Los Angeles
San Fransisco
New York, NY
Various U.S. Sites
United Kingdom
United Kingdom
United Kingdom
Location
Description
Urban
Urban
Federally-Inspected
Slaughter Houses
Sample
Year
88-89
88-89
88-89
88-89
NR
NR
NR
1990
1990
1990
1990
1990
NR
NR
1990
1994
88-91
88-91
88-91
Ref.
No.
7
7
7
7
7
7
7
9
9
9
9
9
10
10
11
12
13
13
13
Comments
composite 6 samples
composite 6 samples
statistically-based survey
DRAFT-DO NOT QUOTE OR CITE
B-189
December 2003
-------
Table B-14. Levels of Dioxins in Food Products (ppt) (continued)
Chemical
1,2,3,4,7,8-HxCDD
(continued)
Sample
Type'
Meat Products
Milk Products
Butter
Cheddar Cheese
Reduced Fat
Cheese
Fats & Oils
Eggs
Green Vegetables
Other Vegetables
Potatoes
Fresh Fruit
Milk
Milk
Beef
Chicken
Pork
Hamburger
Pizza
Fried Chicken
Milk
Number
Samples
2
2
4
1
1
2
2
2
2
2
1
9
27
9
7
7
1
1
1
20
Number
Positive
Samples
2
1
NR
1
1
1
2
2
2
1
0
9
NR
0
0
0
0
0
0
0
Concentration
Range
0.17-0.24
ND-0.11
0.7-0.94
0.1
0.12
ND-0.41
0.22-0.29
0.01-0.02
0.11-0.24
ND-0.12
ND
0.16-0.29
ND-0.53
ND
ND
ND
ND
ND
ND
ND
Cone.
Mean"
0.21
0.14
0.82
0.1
0.12
0.31
0.26
0.015
0.175
0.07
NA
0.21
0.29
NA
NA
NA
NA
NA
NA
NA
Wt.
Basis
Whole
Whole
Whole
Whole
Whole
Whole
Whole
Whole
Whole
Whole
Whole
Fat
Fat
Fat
Fat
Fat
Whole
Whole
Whole
Whole
Location
United Kingdom
United Kingdom
United Kingdom
United Kingdom
United Kingdom
United Kingdom
United Kingdom
United Kingdom
United Kingdom
United Kingdom
United Kingdom
Switzerland
Germany
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
U.S.
U.S.
U.S.
Finland
Location
Description
Sample
Year
88-91
88-91
88-91
88-91
88-91
88-91
88-91
88-91
88-91
88-91
88-91
1990
1992
NR
NR
NR
NR
NR
NR
1991
Ref.
No.
13
13
13
13
13
13
13
13
13
13
13
19
14
15
15
15
16
16
16
17
Comments
fast food
fast food
fast food
DRAFT-DO NOT QUOTE OR CITE
B-190
December 2003
-------
Table B-14. Levels of Dioxins in Food Products (ppt) (continued)
Chemical
1,2,3,4,7,8-HxCDD
(continued)
Sample
Type'
Beef
Pork
Eggs
Milk
Cheddar Cheese
Butter
Eggs
Chicken
Chicken Liver
Ground Beef
Sausage
Milk
Pork
Young Chicken
Light Fowl
Heavy Fowl
Young Turkeys
Eggs
Milk
Butter
Number
Samples
20
20
20
3
3
3
3
3
3
3
3
8
78
39
12
12
15
3
538
222
Number
Positive
Samples
NR
0
0
3
3
3
1
2
3
3
3
8
12
4
0
3
2
0
NR
NR
Concentration
Range
ND-1.52
ND
ND
0.09-0.36
0.22-0.28
0.34-0.51
ND-0.18
ND-0.19
0.19-0.39
0.15-0.47
0.26-1.2
NR
NR
NR
NR
NR
NR
NR
0- 1.1
0-0.48
Cone.
Mean"
<0.5
NA
NA
0.26
0.26
0.43
0.10
0.15
0.29
0.27
0.59
0.39
0.52
0.18
0.15
0.24
0.16
0.12
0.17
0.14
Wt.
Basis
Fat
Fat
Whole
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Whole
Fat
Fat
Location
Finland
Finland
Finland
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
Germany
Germany
Location
Description
NR
NR
NR
NR
NR
NR
NR
NR
Background
Background
Background
Background
Background
Background
Sample
Year
1991
1991
1991
1994
1994
1994
1994
1994
1994
1994
1994
1996
1995
1996
1996
1996
1996
1995
93-96
93-96
Ref.
No.
17
17
17
18
18
18
18
18
18
18
18
21
22
23
23
23
23
24
25
25
Comments
purchased from grocery store
purchased from grocery store
purchased from grocery store
purchased from grocery store
purchased from grocery store
purchased from grocery store
purchased from grocery store
purchased from grocery store
official food inspection
official food inspection
DRAFT-DO NOT QUOTE OR CITE
B-191
December 2003
-------
Table B-14. Levels of Dioxins in Food Products (ppt) (continued)
Chemical
1,2,3,4,7,8-HxCDD
(continued)
1,2,3,6,7,8-HxCDD
Sample
Type'
Eggs
Meat
Beef
Smoked Sausage
Chicken
Pork
Goose Fat
Duck Fat
Chicken Fat
Vegetables
Pulses & Cereals
Fruits
Meat
Eggs
Milk & Dairy
Fats & Oils
Butter
Food basket
Milk
Milk
Number
Samples
218
107
NR
NR
NR
NR
NR
NR
NR
2
4
2
7
2
6
4
21
3
2
4
Number
Positive
Samples
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
20
0
1
3
Concentration
Range
0-8.4
0.03 - 2.2
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
ND - 0.63
ND(0.4-1.6)
ND-0.068
ND-0.29
Cone.
Mean"
0.56
0.23
ND
0.44
0.65
0.1
0.19
0.08
ND
0.4
0.3
ND
0.4
0.3
0.1
0.2
NR
NA
0.049
0.18
Wt.
Basis
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Dry
Dry
Dry
Fat
Fat
Fat
Fat
Fat
Fresh
Whole
Whole
Location
Germany
Germany
Russia
Russia
Russia
Russia
Russia
Russia
Russia
Spain
Spain
Spain
Spain
Spain
Spain
Spain
Spain
Stockholm, Sweden
Switzerland
Switzerland
Location
Description
Urban
Background
Industrial
Sample
Year
93-96
93-96
1996
1996
1996
1996
1996
1996
1996
1996
1996
1996
1996
1996
1996
1996
NR
NR
NR
NR
Ref.
No.
25
25
26
26
26
26
26
26
26
27
27
27
27
27
27
27
28
1
2
2
Comments
official food inspection
official food inspection
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
near incinerators
DRAFT-DO NOT QUOTE OR CITE
B-192
December 2003
-------
Table B-14. Levels of Dioxins in Food Products (ppt) (continued)
Chemical
1,2,3,6,7,8-HxCDD
(continued)
Sample
Type'
Milk
Butter
Beef fat
Pork
Sheep fat
Chicken
Eggs
Milk
Cheese
Butter
Beef
Veal
Pork
Sheep
Chicken
Canned meat
Lard
Cow cream
Beef
Cheese w/butter
Number
Samples
1
1
1
1
1
1
1
10
10
5
3
4
3
2
2
2
4
1
1
1
Number
Positive
Samples
1
1
1
1
1
1
1
10
10
NR
3
4
0
2
2
2
NR
1
1
1
Concentration
Range
1.1
0.95
1.9
0.29
1.5
2.8
1.4
0.5-10.0
0.4-1.2
ND-1.0
1.3-6.0
3.3-8.0
ND(0.5)
2.3-3.7
1.7-1.8
0.9-7.4
ND-0.6
0.135
0.018
0.288
Cone.
Mean"
1.1
0.95
1.9
0.29
1.5
2.8
1.4
4.0
0.8
0.7
3.2
5.3
NA
3.0
1.8
3.2
0.3
0.135
0.018
0.288
Wt.
Basis
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Wet
Wet
Wet
Location
W. Germany
Berlin, W. Germany
Berlin, W. Germany
Berlin, W. Germany
Berlin, W. Germany
Berlin, W. Germany
Berlin, W. Germany
W. Germany
W. Germany
W. Germany
W. Germany
W. Germany
W. Germany
W. Germany
W. Germany
W. Germany
W. Germany
USSR
USSR
USSR
Location
Description
Urban
Urban
Urban
Urban
Urban
Urban
Sample
Year
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
88-89
88-89
88-89
Ref.
No.
3
3
3
3
3
3
3
5
5
5
5
5
5
5
5
5
5
7
7
7
Comments
composite from 8 trucks
from slaughterhouse
from slaughterhouse
samples not randomly selected
DRAFT-DO NOT QUOTE OR CITE
B-193
December 2003
-------
Table B-14. Levels of Dioxins in Food Products (ppt) (continued)
Chemical
1,2,3,6,7,8-HxCDD
(continued)
Sample
Type'
Beef fat
Pork
Butter
Swiss cheese
Sausage
Pork sticks
Pork fat
Chicken fat
Cottage cheese
Soft blue cheese
Heavy cream
cheese
Soft cream cheese
American cheese
Beef
Beef
Pork
Pork
Chicken
Chicken
Eggs
Number
Samples
1
1
1
1
1
1
1
1
1
1
1
1
1
5
3
5
3
5
3
5
Number
Positive
Samples
1
0
1
1
0
1
1
1
1
1
1
1
1
0
0
0
0
1
1
0
Concentration
Range
0.340
ND(0.36)
0.318
0.048
ND(0.29)
0.47
1.69
3.8
0.07
1.72
0.7
0.58
0.38
ND(0.74-2.72)
ND(1. 64-4.08)
ND(0.64-3.60)
ND(1. 06-2.92)
ND-2.14
ND-4.30
ND(0. 10-0.56)
Cone.
Mean"
0.340
NA
0.318
0.048
NA
0.47
1.69
3.8
0.07
1.72
0.7
0.58
0.38
NA
NA
NA
NA
0.98
1.84
NA
Wt.
Basis
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Fat
Fat
Fat
Fat
Fat
Fat
Whole
Location
USSR
USSR
USSR
USSR
Moscow, USSR
South Vietnam
South Vietnam
South Vietnam
New York, NY
New York, NY
New York, NY
New York, NY
New York, NY
Los Angeles
San Francisco
Los Angeles
San Francisco
Los Angeles
San Francisco
Los Angeles
Location
Description
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Sample
Year
88-89
88-89
88-89
88-89
88-89
NR
NR
NR
1990
1990
1990
1990
1990
NR
NR
NR
NR
NR
NR
NR
Ref.
No.
7
7
7
7
7
7
7
7
9
9
9
9
9
10
10
10
10
10
10
10
Comments
composite 6 samples
composite 6 samples
composite 6 samples
composite 6 samples
composite 6 samples
composite 6 samples
composite 6 samples
DRAFT-DO NOT QUOTE OR CITE
B-194
December 2003
-------
Table B-14. Levels of Dioxins in Food Products (ppt) (continued)
Chemical
1,2,3,6,7,8-HxCDD
(continued)
Sample
Type'
Eggs
Beef
Pork
Chicken
Beef
Milk
Milk
Beef
Chicken
Pork
Hamburger
Pizza
Fried Chicken
Milk
Beef
Pork
Eggs
Milk
Cheddar Cheese
Butter
Number
Samples
3
3
1
1
63
9
27
9
7
7
1
1
1
20
20
20
20
3
3
3
Number
Positive
Samples
0
3
1
1
21
9
NR
NR
ND
NR
1
1
0
NR
NR
0
NR
3
3
3
Concentration
Range
ND(0.07-0.21)
0.03-1.981
0.282
0.04
ND-12.46
0.37-0.63
0.44-0.84
NR
ND
NR
0.22
0.149
ND
ND-69
ND-1.52
ND
ND-0.472
0.68-2
1.5-1.8
1.7-2.3
Cone.
Mean"
NA
0.84
0.282
0.04
1.42
0.49
0.55
4.92
NA
1.81
0.22
0.149
NA
47
<0.5
NA
0.119
1.46
1.67
1.9
Wt.
Basis
Whole
Wet
Wet
Wet
Fat
Fat
Fat
Fat
Fat
Fat
Whole
Whole
Whole
Whole
Fat
Fat
Whole
Fat
Fat
Fat
Location
San Francisco
New York, NY
New York, NY
New York, NY
Various U.S. Sites
Switzerland
Germany
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
U.S.
U.S.
U.S.
Finland
Finland
Finland
Finland
S. Mississippi
S. Mississippi
S. Mississippi
Location
Description
Urban
Federally-Inspected
Slaughter Houses
NR
NR
NR
Sample
Year
NR
1990
1990
1990
1994
1990
1992
NR
NR
NR
NR
NR
NR
1991
1991
1991
1991
1994
1994
1994
Ref.
No.
10
11
11
11
12
19
14
15
15
15
16
16
16
17
17
17
17
18
18
18
Comments
composite 6 samples
statistically-based survey
fast food
fast food
fast food
purchased from grocery store
purchased from grocery store
purchased from grocery store
DRAFT-DO NOT QUOTE OR CITE
B-195
December 2003
-------
Table B-14. Levels of Dioxins in Food Products (ppt) (continued)
Chemical
1,2,3,6,7,8-HxCDD
(continued)
Sample
Type'
Eggs
Chicken
Chicken Liver
Ground Beef
Sausage
Milk
Pork
Young Chicken
Light Fowl
Heavy Fowl
Young Turkeys
Eggs
Milk
Butter
Eggs
Meat
Beef
Smoked Sausage
Chicken
Pork
Number
Samples
3
3
3
3
3
8
78
39
12
12
15
3
538
222
218
107
NR
NR
NR
NR
Number
Positive
Samples
3
3
3
3
3
8
29
24
8
11
13
0
NR
NR
NR
NR
NR
NR
NR
NR
Concentration
Range
0.16-0.39
0.29-1.3
0.42-0.6
0.95-3.4
0.63-1.3
NR
NR
NR
NR
NR
NR
NR
0-2.6
0- 1.1
0.09 - 35
0.04 -5.4
NR
NR
NR
NR
Cone.
Mean"
0.26
0.7
0.5
2.02
0.97
1.87
1.10
0.39
0.34
0.71
0.79
0.16
0.44
0.36
1.7
0.42
ND
0.36
ND
0.11
Wt.
Basis
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Whole
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Location
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
Germany
Germany
Germany
Germany
Russia
Russia
Russia
Russia
Location
Description
NR
NR
NR
NR
NR
Background
Background
Background
Background
Background
Background
Sample
Year
1994
1994
1994
1994
1994
1996
1995
1996
1996
1996
1996
1995
93-96
93-96
93-96
93-96
1996
1996
1996
1996
Ref.
No.
18
18
18
18
18
21
22
23
23
23
23
24
25
25
25
25
26
26
26
26
Comments
purchased from grocery store
purchased from grocery store
purchased from grocery store
purchased from grocery store
purchased from grocery store
official food inspection
official food inspection
official food inspection
official food inspection
supermarkets
supermarkets
supermarkets
supermarkets
DRAFT-DO NOT QUOTE OR CITE
B-196
December 2003
-------
Table B-14. Levels of Dioxins in Food Products (ppt) (continued)
Chemical
1,2,3,6,7,8-HxCDD
(continued)
1,2,3,7,8,9-HxCDD
Sample
Type'
Goose Fat
Duck Fat
Chicken Fat
Vegetables
Pulses & Cereals
Fruits
Meat
Eggs
Milk & Dairy
Fats & Oils
Butter
Food basket
Milk
Milk
Milk
Butter
Beef fat
Pork
Sheep fat
Chicken
Number
Samples
NR
NR
NR
2
4
2
7
2
6
4
21
3
2
4
1
1
1
1
1
1
Number
Positive
Samples
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
20
0
1
3
1
1
1
1
1
1
Concentration
Range
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
ND- 1.33
ND(0.5-1.6)
ND-0.068
ND-0.17
0.4
0.26
0.6
0.06
0.4
0.6
Cone.
Mean"
0.24
0.16
0.59
0.8
0.8
ND
1.1
1.7
0.6
0.3
NR
NA
0.049
0.10
0.4
0.26
0.6
0.06
0.4
0.6
Wt.
Basis
Fat
Fat
Fat
Dry
Dry
Dry
Fat
Fat
Fat
Fat
Fat
Fresh
Whole
Whole
Fat
Fat
Fat
Fat
Fat
Fat
Location
Russia
Russia
Russia
Spain
Spain
Spain
Spain
Spain
Spain
Spain
Spain
Stockholm, Sweden
Switzerland
Switzerland
W. Germany
Berlin, W. Germany
Berlin, W. Germany
Berlin, W. Germany
Berlin, W. Germany
Berlin, W. Germany
Location
Description
Urban
Background
Industrial
Urban
Urban
Urban
Urban
Urban
Sample
Year
1996
1996
1996
1996
1996
1996
1996
1996
1996
1996
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
Ref.
No.
26
26
26
27
27
27
27
27
27
27
28
1
2
2
3
3
3
3
3
3
Comments
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
near incinerators
composite from 8 trucks
from slaughterhouse
from slaughterhouse
DRAFT-DO NOT QUOTE OR CITE
B-197
December 2003
-------
Table B-14. Levels of Dioxins in Food Products (ppt) (continued)
Chemical
1,2,3,7,8,9-HxCDD
(continued)
Sample
Type'
Eggs
Milk
Cheese
Butter
Beef
Veal
Pork
Sheep
Chicken
Canned meat
Lard
Milk
Cow cream
Beef
Cheese w/butter
Beef fat
Pork
Butter
Swiss cheese
Sausage
Number
Samples
1
10
10
5
3
4
3
2
2
2
4
7
1
1
1
1
1
1
1
1
Number
Positive
Samples
1
NR
10
0
3
4
0
NR
2
NR
0
NR
0
0
0
0
0
0
1
0
Concentration
Range
0.5
ND-3.0
0.3-0.9
ND(0.5)
0.7-4.5
1.2-3.0
ND(0.5)
ND-1.1
0.5-0.6
ND-2.7
ND(0.5)
ND-0.018
ND(0.07)
ND(0.02)
ND(0.18)
ND(0.17)
ND(0.36)
ND(0.26)
0.012
ND(0.29)
Cone.
Mean"
0.5
0.8
0.5
NA
2.0
1.8
NA
0.7
0.6
1.2
NA
0.010°
NA
NA
NA
NA
NA
NA
0.012
NA
Wt.
Basis
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Whole
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Location
Berlin, W. Germany
W. Germany
W. Germany
W. Germany
W. Germany
W. Germany
W. Germany
W. Germany
W. Germany
W. Germany
W. Germany
England & Wales
USSR
USSR
USSR
USSR
USSR
USSR
USSR
Moscow, USSR
Location
Description
Urban
Rural
Sample
Year
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
1989
88-89
88-89
88-89
88-89
88-89
88-89
88-89
88-89
Ref.
No.
3
5
5
5
5
5
5
5
5
5
5
6
7
7
7
7
7
7
7
7
Comments
samples not randomly selected
DRAFT-DO NOT QUOTE OR CITE
B-198
December 2003
-------
Table B-14. Levels of Dioxins in Food Products (ppt) (continued)
Chemical
1,2,3,7,8,9-HxCDD
(continued)
Sample
Type'
Pork sticks
Pork fat
Chicken fat
Cottage cheese
Soft blue cheese
Heavy cream
cheese
Soft cream cheese
American cheese
Beef
Beef
Pork
Pork
Chicken
Chicken
Beef
Pork
Chicken
Beef
Milk
Number
Samples
1
1
1
1
1
1
1
1
5
3
5
3
5
3
3
1
1
63
9
Number
Positive
Samples
1
1
1
1
1
1
1
1
0
0
0
0
1
1
3
1
0
9
9
Concentration
Range
0.10
0.40
0.67
0.02
0.29
0.14
0.14
0.19
ND(0.74-2.72)
ND(1.64-4.08)
ND(0.64-3.6)
ND(1. 06-2.92)
ND-2.14
ND-4.30
0.011-0.616
0.044
ND(0.014)
ND-3.68
0.19-0.46
Cone.
Mean"
0.10
0.40
0.67
0.02
0.29
0.14
0.14
0.19
NA
NA
NA
NA
NA
NA
0.238
0.044
NA
0.53
0.27
Wt.
Basis
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Fat
Fat
Fat
Fat
Fat
Fat
Wet
Wet
Wet
Fat
Fat
Location
South Vietnam
South Vietnam
South Vietnam
New York, NY
New York, NY
New York, NY
New York, NY
New York, NY
Los Angeles, CA
San Francisco, CA
Los Angeles,
San Francisco, CA
Los Angeles, CA
San Francisco, CA
New York
New York, NY
New York, NY
Various U.S. Sites
Switzerland
Location
Description
Federally-Inspected
Slaughter Houses
Sample
Year
NR
NR
NR
1990
1990
1990
1990
1990
NR
NR
NR
NR
NR
NR
1990
1990
1990
1994
1990
Ref.
No.
7
7
7
9
9
9
9
9
10
10
10
10
10
10
11
11
11
12
19
Comments
statistically-based survey
DRAFT-DO NOT QUOTE OR CITE
B-199
December 2003
-------
Table B-14. Levels of Dioxins in Food Products (ppt) (continued)
Chemical
1,2,3,7,8,9-HxCDD
(continued)
Sample
Type'
Milk
Beef
Chicken
Pork
Hamburger
Pizza
Fried Chicken
Milk
Eggs
Beef
Pork
Milk
Cheddar Cheese
Butter
Eggs
Chicken
Chicken Liver
Ground Beef
Sausage
Milk
Number
Samples
27
9
7
7
1
1
1
20
20
20
20
3
3
3
3
3
3
3
3
8
Number
Positive
Samples
NR
NR
0
0
0
0
0
NR
NR
0
0
3
3
3
2
3
3
3
3
8
Concentration
Range
ND-0.47
NR
ND
ND
ND
ND
ND
ND-67
ND-0.436
ND
ND
0.12-0.58
0.32-0.46
0.33-0.57
ND-0.1
0.11-0.17
0.16-0.29
0.13-0.68
0.12-0.19
NR
Cone.
Mean"
0.24
1.04
NA
NA
NA
NA
NA
28
0.082
NA
NA
0.4
0.37
0.43
0.07
0.14
0.21
0.35
0.16
0.55
Wt.
Basis
Fat
Fat
Fat
Fat
Whole
Whole
Whole
Whole
Whole
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Location
Germany
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
U.S.
U.S.
U.S.
Finland
Finland
Finland
Finland
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
Various U.S. Sites
Location
Description
NR
NR
NR
NR
NR
NR
NR
NR
Background
Sample
Year
1992
NR
NR
NR
NR
NR
NR
1991
1991
1991
1991
1994
1994
1994
1994
1994
1994
1994
1994
1996
Ref.
No.
14
15
15
15
16
16
16
17
17
17
17
18
18
18
18
18
18
18
18
21
Comments
fast food
fast food
fast food
purchased from grocery store
purchased from grocery store
purchased from grocery store
purchased from grocery store
purchased from grocery store
purchased from grocery store
purchased from grocery store
purchased from grocery store
DRAFT-DO NOT QUOTE OR CITE
B-200
December 2003
-------
Table B-14. Levels of Dioxins in Food Products (ppt) (continued)
Chemical
1,2,3,7,8,9-HxCDD
(continued)
Sample
Type'
Pork
Young Chicken
Light Fowl
Heavy Fowl
Young Turkeys
Eggs
Milk
Butter
Eggs
Meat
Beef
Smoked Sausage
Chicken
Pork
Goose Fat
Duck Fat
Chicken Fat
Vegetables
Pulses & Cereals
Fruits
Number
Samples
78
39
12
12
15
3
538
222
218
107
NR
NR
NR
NR
NR
NR
NR
2
4
2
Number
Positive
Samples
5
12
1
5
3
0
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
Concentration
Range
NR
NR
NR
NR
NR
NR
0-0.82
0-0.57
0- 16
0.03- 1.9
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
Cone.
Mean"
0.47
0.39
0.15
0.60
0.17
0.12
0.18
0.15
0.82
0.20
ND
0.71
ND
0.06
0.15
0.08
0.7
0.5
0.3
ND
Wt.
Basis
Fat
Fat
Fat
Fat
Fat
Whole
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Dry
Dry
Drv
Location
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
Germany
Germany
Germany
Germany
Russia
Russia
Russia
Russia
Russia
Russia
Russia
Spain
Spain
Spain
Location
Description
Background
Background
Background
Background
Background
Sample
Year
1995
1996
1996
1996
1996
1995
93-96
93-96
93-96
93-96
1996
1996
1996
1996
1996
1996
1996
1996
1996
1996
Ref.
No.
22
23
23
23
23
24
25
25
25
25
26
26
26
26
26
26
26
27
27
27
Comments
official food inspection
official food inspection
official food inspection
official food inspection
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
DRAFT-DO NOT QUOTE OR CITE
B-201
December 2003
-------
Table B-14. Levels of Dioxins in Food Products (ppt) (continued)
Chemical
1,2,3,7,8,9-HxCDD
(continued)
HxCDDs
Sample
Type'
Meat
Eggs
Milk & Dairy
Fats & Oils
Butter
Chicken fat
Beef
Chicken
Pork
Eggs
Butter
Number
Samples
7
2
6
4
21
26
9
7
7
3
20
Number
Positive
Samples
NR
NR
NR
NR
19
13
NR
NR
NR
NR
18
Concentration
Range
NR
NR
NR
NR
ND - 0.67
ND-67
NR
NR
NR
NR
ND - 2.45
Cone.
Mean"
0.3
0.6
0.1
ND
NR
27
6.23
2.39
1.93
0.17
NR
Wt.
Basis
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Whole
Fat
Location
Spain
Spain
Spain
Spain
Spain
Canada
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
Spain
Location
Description
Sample
Year
1996
1996
1996
1996
NR
1980
NR
NR
NR
1995
NR
Ref.
No.
27
27
27
27
28
20
15
15
15
24
28
Comments
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
Heptachlorodibenzo-p-dioxins (MW = 425.31)
1,2,3,4,6,7,8-HpCDD
Food basket
Milk
Milk
Milk
Butter
Beef fat
Pork
Sheep fat
3
2
4
1
1
1
1
1
0
2
4
1
1
1
1
1
ND(0.4-1.7)
0.064
0.066-0.42
2
1.5
18
2.1
15
NA
0.064
0.21
2
1.5
18
2.1
15
Fresh
Whole
Whole
Fat
Fat
Fat
Fat
Fat
Stockholm, Sweden
Switzerland
Switzerland
W. Germany
Berlin, W. Germany
Berlin, W. Germany
Berlin, W. Germany
Berlin, W. Germany
Urban
Background
Industrial
Urban
Urban
Urban
Urban
NR
NR
NR
NR
NR
NR
NR
NR
1
2
2
3
3
3
3
3
near incinerators
composite from 8 trucks
from slaughterhouse
from slaughterhouse
DRAFT-DO NOT QUOTE OR CITE
B-202
December 2003
-------
Table B-14. Levels of Dioxins in Food Products (ppt) (continued)
Chemical
1,2,3,4,6,7,8-HpCDD
(continued)
Sample
Type'
Chicken
Eggs
Milk
Cheese
Butter
Beef
Veal
Pork
Sheep
Chicken
Canned meat
Lard
Milk
Cow cream
Beef
Cheese w/butter
Beef fat
Pork
Butter
Swiss cheese
Number
Samples
1
1
10
10
5
3
4
3
2
2
2
4
7
1
1
1
1
1
1
1
Number
Positive
Samples
1
1
10
10
5
3
4
NR
2
2
2
4
NR
1
1
1
0
1
1
1
Concentration
Range
6.0
0.4
1.0-29.0
1.2-4.0
0.5-5.0
1.8-6.7
2.6-43.7
ND-1.6
10.9-11.8
4.5-5.0
2.5-33.0
2.0-3.0
ND-0.066
0.450
0.156
0.720
ND(0.17)
1.44
0.530
0.240
Cone.
Mean"
6.0
0.4
6.2
2.3
1.7
3.9
14.4
0.7
11.4
4.5
13.2
2.8
0.046C
0.450
0.156
0.720
NA
1.44
0.530
0.240
Wt.
Basis
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Whole
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Location
Berlin, W. Germany
Berlin, W. Germany
W. Germany
W. Germany
W. Germany
W. Germany
W. Germany
W. Germany
W. Germany
W. Germany
W. Germany
W. Germany
England & Wales
USSR
USSR
USSR
USSR
USSR
USSR
USSR
Location
Description
Urban
Urban
Rural
Sample
Year
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
1989
88-89
88-89
88-89
88-89
88-89
88-89
88-89
Ref.
No.
3
3
5
5
5
5
5
5
5
5
5
5
6
7
7
7
7
7
7
7
Comments
samples not randomly selected
DRAFT-DO NOT QUOTE OR CITE
B-203
December 2003
-------
Table B-14. Levels of Dioxins in Food Products (ppt) (continued)
Chemical
1,2,3,4,6,7,8-HpCDD
(continued)
Sample
Type'
Sausage
Pork sticks
Pork fat
Chicken fat
Cottage cheese
Soft blue cheese
Heavy cream
cheese
Soft cream cheese
American cheese
Beef
Beef
Pork
Pork
Chicken
Chicken
Eggs
Eggs
Beef
Pork
Number
Samples
1
1
1
1
1
1
1
1
1
5
3
5
3
5
3
5
3
3
1
Number
Positive
Samples
1
1
1
1
1
1
1
1
1
4
3
5
3
4
3
0
0
3
1
Concentration
Range
0.57
2.84
7.44
13.3
0.18
5.88
2.11
1.51
1.13
ND-6.71
4.56-8.95
3.32-45.50
3.04-15.30
ND-35.20
1.10-11.40
ND(0. 10-0.42)
ND(0.08-0.24)
0.117-12.065
8.197
Cone.
Mean"
0.57
2.84
7.44
13.3
0.18
5.88
2.11
1.51
1.13
4.48
6.28
14.74
10.15
9.64
4.62
NA
NA
4.45
8.2
Wt.
Basis
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Fat
Fat
Fat
Fat
Fat
Fat
Whole
Whole
Wet
Wet
Location
Moscow, USSR
South Vietnam
South Vietnam
South Vietnam
New York, NY
New York, NY
New York, NY
New York, NY
New York, NY
Los Angeles
San Francisco
Los Angeles
San Francisco
Los Angeles
San Francisco
Los Angeles
San Francisco
New York, NY
New York, NY
Location
Description
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Sample
Year
88-89
NR
NR
NR
1990
1990
1990
1990
1990
NR
NR
NR
NR
NR
NR
NR
NR
1990
1990
Ref.
No.
7
7
7
7
9
9
9
9
9
10
10
10
10
10
10
10
10
11
11
Comments
composite 6 samples
composite 6 samples
composite 6 samples
composite 6 samples
composite 6 samples
composite 6 samples
composite 6 samples
composite 6 samples
DRAFT-DO NOT QUOTE OR CITE
B-204
December 2003
-------
Table B-14. Levels of Dioxins in Food Products (ppt) (continued)
Chemical
1,2,3,4,6,7,8-HpCDD
(continued)
Sample
Type'
Chicken
Beef
Carcass Meat
Offals
Poultry
Meat Products
Milk Products
Butter
Cheddar Cheese
Reduced Fat
Cheese
Fats & Oils
Eggs
Green Vegetables
Other Vegetables
Potatoes
Fresh Fruit
Milk
Milk
Beef
Number
Samples
1
63
2
2
2
2
2
4
1
1
2
2
2
2
2
2
9
27
9
Number
Positive
Samples
1
45
2
2
2
2
2
NR
1
1
2
2
2
2
2
2
9
NR
NR
Concentration
Range
0.133
ND-47.56
0.8-7.7
2.6-8.1
1.3-3.7
1.2-2.2
0.21-0.60
0.98-2.9
0.47
0.48
2.1-4.5
0.74-0.95
0.12-0.13
0.44-1.0
0.18-1.2
1.3-3.6
0.61-1.25
0.45-1.00
NR
Cone.
Mean"
0.13
4.48
4.25
5.35
2.5
1.7
0.4
1.9
0.47
0.48
3.3
0.85
0.125
0.72
0.69
2.45
0.98
0.66
20.9
Wt.
Basis
Wet
Fat
Whole
Whole
Whole
Whole
Whole
Whole
Whole
Whole
Whole
Whole
Whole
Whole
Whole
Whole
Fat
Fat
Fat
Location
New York, NY
Various U.S. Sites
United Kingdom
United Kingdom
United Kingdom
United Kingdom
United Kingdom
United Kingdom
United Kingdom
United Kingdom
United Kingdom
United Kingdom
United Kingdom
United Kingdom
United Kingdom
United Kingdom
Switzerland
Germany
Various U.S. Sites
Location
Description
Federally-Inspected
Slaughter Houses
Sample
Year
1990
1994
88-91
88-91
88-91
88-91
88-91
88-91
88-91
88-91
88-91
88-91
88-91
88-91
88-91
88-91
1990
1992
NR
Ref.
No.
11
12
13
13
13
13
13
13
13
13
13
13
13
13
13
13
19
14
15
Comments
statistically-based survey
DRAFT-DO NOT QUOTE OR CITE
B-205
December 2003
-------
Table B-14. Levels of Dioxins in Food Products (ppt) (continued)
Chemical
1,2,3,4,6,7,8-HpCDD
(continued)
Sample
Type'
Chicken
Pork
Hamburger
Pizza
Fried Chicken
Milk
Eggs
Beef
Pork
Milk
Cheddar Cheese
Butter
Eggs
Chicken
Chicken Liver
Ground Beef
Sausage
Milk
Pork
Young Chicken
Number
Samples
7
7
1
1
1
20
20
20
20
3
3
3
3
3
3
3
3
8
78
39
Number
Positive
Samples
NR
NR
1
1
1
NR
NR
NR
NR
3
3
3
3
3
3
3
3
8
43
39
Concentration
Range
NR
NR
0.862
0.936
0.454
12-82
0.002-0.876
ND-10
ND-4.13
1.4-4.9
4.1-4.9
4.5-6.9
1.2-1.8
1.5-8.2
5.3-6.8
2.9-12
8.8-15
NR
NR
NR
Cone.
Mean"
8.09
17.1
0.862
0.936
0.454
53
0.184
1.5
0.63
3.67
4.53
5.4
1.4
4.37
5.97
6.97
11.6
5.03
10.2
1.53
Wt.
Basis
Fat
Fat
Whole
Whole
Whole
Whole
Whole
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Location
Various U.S. Sites
Various U.S. Sites
U.S.
U.S.
U.S.
Finland
Finland
Finland
Finland
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
Location
Description
NR
NR
NR
NR
NR
NR
NR
NR
Background
Background
Background
Sample
Year
NR
NR
NR
NR
NR
1991
1991
1991
1991
1994
1994
1994
1994
1994
1994
1994
1994
1996
1995
1996
Ref.
No.
15
15
16
16
16
17
17
17
17
18
18
18
18
18
18
18
18
21
22
23
Comments
fast food
fast food
fast food
purchased from grocery store
purchased from grocery store
purchased from grocery store
purchased from grocery store
purchased from grocery store
purchased from grocery store
purchased from grocery store
purchased from grocery store
DRAFT-DO NOT QUOTE OR CITE
B-206
December 2003
-------
Table B-14. Levels of Dioxins in Food Products (ppt) (continued)
Chemical
1,2,3,4,6,7,8-HpCDD
(continued)
Sample
Type'
Light Fowl
Heavy Fowl
Young Turkeys
Eggs
Milk
Butter
Eggs
Meat
Beef
Smoked Sausage
Chicken
Pork
Goose Fat
Duck Fat
Chicken Fat
Vegetables
Pulses & Cereals
Fruits
Meat
Eggs
Number
Samples
12
12
15
3
538
222
218
107
NR
NR
NR
NR
NR
NR
NR
2
4
2
7
2
Number
Positive
Samples
12
11
13
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
Concentration
Range
NR
NR
NR
NR
0- 12
0.22-2.1
0.55 - 277
0.18-50
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
Cone.
Mean"
0.93
2.04
0.54
2.74
0.89
0.69
10.2
2.3
0.18
0.75
1.04
0.43
0.71
0.06
0.93
10.3
5.6
0.2
16.6
10.2
Wt.
Basis
Fat
Fat
Fat
Whole
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Dry
Dry
Dry
Fat
Fat
Location
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
Germany
Germany
Germany
Germany
Russia
Russia
Russia
Russia
Russia
Russia
Russia
Spain
Spain
Spain
Spain
Spain
Location
Description
Background
Background
Background
Sample
Year
1996
1996
1996
1995
93-96
93-96
93-96
93-96
1996
1996
1996
1996
1996
1996
1996
1996
1996
1996
1996
1996
Ref.
No.
23
23
23
24
25
25
25
25
26
26
26
26
26
26
26
27
27
27
27
27
Comments
official food inspection
official food inspection
official food inspection
official food inspection
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
DRAFT-DO NOT QUOTE OR CITE
B-207
December 2003
-------
Table B-14. Levels of Dioxins in Food Products (ppt) (continued)
Chemical
1,2,3,4,6,7,8-HpCDD
(continued)
HpCDDs
Sample
Type'
Milk & Dairy
Fats & Oils
Butter
Chicken fat
Beef
Chicken
Pork
Eggs
Butter
Number
Samples
6
4
21
26
9
7
7
3
21
Number
Positive
Samples
NR
NR
21
16
NR
NR
NR
NR
21
Concentration
Range
NR
NR
0.58-3.09
ND-142
NR
NR
NR
NR
0.58-5.51
Cone.
Mean"
2.6
1.9
1.32
52
20.9
8.09
17.1
6.04
1.65
Wt.
Basis
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Whole
Fat
Location
Spain
Spain
Spain
Canada
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
Spain
Location
Description
Sample
Year
1996
1996
NR
1980
NR
NR
NR
1995
NR
Ref.
No.
27
27
28
20
15
15
15
24
28
Comments
supermarkets
supermarkets
supermarkets
supermarkets
OctacWorodibenzo-p-dioxin (MW=460.76)
1,2,3,4,6,7,8,9-OCDD
Food basket
Milk
Milk
Milk
Butter
Beef fat
Pork
Sheep fat
Chicken
Eggs
3
2
4
1
1
1
1
1
1
1
3
2
4
1
1
1
1
1
1
1
1.0-2.1
0.12-0.16
0.16-0.59
10
3.4
25
19
68
52
12
1.47
0.14
0.32
10
3.4
25
19
68
52
12
Fresh
Whole
Whole
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Stockholm, Sweden
Switzerland
Switzerland
W. Germany
Berlin, W. Germany
Berlin, W. Germany
Berlin, W. Germany
Berlin, W. Germany
Berlin, W. Germany
Berlin, W. Germany
Urban
Background
Industrial
Urban
Urban
Urban
Urban
Urban
Urban
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
1
2
2
3
3
3
3
3
3
3
near incinerators
composite from 8 trucks
from slaughterhouse
from slaughterhouse
DRAFT-DO NOT QUOTE OR CITE
B-208
December 2003
-------
Table B-14. Levels of Dioxins in Food Products (ppt) (continued)
Chemical
1,2,3,4,6,7,8,9-OCDD
(continued)
Sample
Type'
Milk
Cheese
Butter
Beef
Veal
Pork
Sheep
Chicken
Canned meat
Lard
Chicken fat
Milk
Cow cream
Beef
Cheese w/butter
Beef fat
Pork
Butter
Swiss cheese
Sausage
Number
Samples
10
10
5
3
4
3
2
2
2
4
26
7
1
1
1
1
1
1
1
1
Number
Positive
Samples
10
10
5
3
4
3
2
2
2
4
12
7
1
1
1
1
1
1
1
1
Concentration
Range
4.3-25.0
5.0-17.0
2.0-35.0
4.7-6.0
3.1-69.0
5.4-12.3
14.4-24.4
14.0-19.0
17.0-122
10.0-23.0
ND-238
0.215-0.323
3.15
0.63
5.40
3.40
11.5
9.01
0.660
5.70
Cone.
Mean"
11.0
10.5
11.6
5.4
22.3
8.2
19.3
16.5
53.0
16.0
90
0.230C
3.15
0.63
5.40
3.40
11.5
9.01
0.660
5.70
Wt.
Basis
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Whole
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Location
W. Germany
W. Germany
W. Germany
W. Germany
W. Germany
W. Germany
W. Germany
W. Germany
W. Germany
W. Germany
Canada
England & Wales
USSR
USSR
USSR
USSR
USSR
USSR
USSR
Moscow, USSR
Location
Description
Rural
Sample
Year
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
1980
1989
88-89
88-89
88-89
88-89
88-89
88-89
88-89
88-89
Ref.
No.
5
5
5
5
5
5
5
5
5
5
20
6
7
7
7
7
7
7
7
7
Comments
samples not randomly selected
DRAFT-DO NOT QUOTE OR CITE
B-209
December 2003
-------
Table B-14. Levels of Dioxins in Food Products (ppt) (continued)
Chemical
1,2,3,4,6,7,8,9-OCDD
(continued)
Sample
Type'
Pork sticks
Pork fat
Chicken fat
Swiss cheese
Sausage
Pork sticks
Pork fat
Chicken fat
Cottage cheese
Soft blue cheese
Heavy cream
cheese
Soft cream cheese
American cheese
Beef
Beef
Pork
Pork
Chicken
Chicken
Eggs
Number
Samples
1
1
1
1
1
1
1
1
1
1
1
1
1
5
3
5
3
5
3
5
Number
Positive
Samples
1
1
1
1
1
1
1
1
1
1
1
1
1
4
3
5
3
4
3
0
Concentration
Range
9.46
29.8
22.8
0.66
5.7
9.46
29.8
22.8
0.34
5.93
1.54
1.5
1.6
ND-11.40
8.03-11.90
13.70-254.00
24.90-125.00
ND-64.00
2.61-96.20
ND(0.80-1.60)
Cone.
Mean"
9.46
29.8
22.8
0.66
5.7
9.46
29.8
22.8
0.34
5.93
1.54
1.5
1.6
8.58
9.43
77.20
72.43
28.97
35.01
NA
Wt.
Basis
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Fat
Fat
Fat
Fat
Fat
Fat
Whole
Location
South Vietnam
South Vietnam
South Vietnam
USSR
Moscow, USSR
South Vietnam
South Vietnam
South Vietnam
New York, NY
New York, NY
New York, NY
New York, NY
New York, NY
Los Angeles
San Francisco
Los Angeles
San Francisco
Los Angeles
San Francisco
Los Angeles
Location
Description
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Sample
Year
NR
NR
NR
88-89
88-89
NR
NR
NR
1990
1990
1990
1990
1990
NR
NR
NR
NR
NR
NR
NR
Ref.
No.
7
7
7
7
7
7
7
7
9
9
9
9
9
10
10
10
10
10
10
10
Comments
composite 6 samples
composite 6 samples
composite 6 samples
composite 6 samples
composite 6 samples
composite 6 samples
composite 6 samples
DRAFT-DO NOT QUOTE OR CITE
B-210
December 2003
-------
Table B-14. Levels of Dioxins in Food Products (ppt) (continued)
Chemical
1,2,3,4,6,7,8,9-OCDD
(continued)
Sample
Type'
Eggs
Beef
Pork
Chicken
Beef
Carcass Meat
Offals
Poultry
Meat Products
Milk Products
Butter
Cheddar Cheese
Reduced Fat
Cheese
Milk
Milk
Beef
Chicken
Pork
Hamburger
Number
Samples
3
3
1
1
63
2
1
2
2
2
4
1
1
9
27
9
7
7
1
Number
Positive
Samples
1
3
1
1
13
2
1
2
2
2
NR
1
1
9
NR
NR
NR
NR
1
Concentration
Range
ND-1.30
0.414-15.825
50.742
0.74
ND-71.84
4.6-13
21
2.8-4.2
13-20
1.9-3.1
1.9-5.5
2.4
1.8
1.79-3.06
0.41-1.05
NR
NR
NR
1.59
Cone.
Mean"
0.63
6.167
50.7
0.74
4.78
8.8
21
3.5
16.5
2.5
3.7
2.4
1.8
2.5
0.63
32.7
20.2
87.1
1.59
Wt.
Basis
Whole
Wet
Wet
Wet
Fat
Whole
Whole
Whole
Whole
Whole
Whole
Whole
Whole
Fat
Fat
Fat
Fat
Fat
Whole
Location
San Francisco
New York, NY
New York, NY
New York, NY
Various U.S. Sites
United Kingdom
United Kingdom
United Kingdom
United Kingdom
United Kingdom
United Kingdom
United Kingdom
United Kingdom
Switzerland
Germany
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
U.S.
Location
Description
Urban
Federally-Inspected
Slaughter Houses
Sample
Year
NR
1990
1990
1990
1994
88-91
88-91
88-91
88-91
88-91
88-91
88-91
88-91
1990
1992
NR
NR
NR
NR
Ref.
No.
10
11
11
11
12
13
13
13
13
13
13
13
13
19
14
15
15
15
16
Comments
composite 6 samples
statistically-based survey
fast food
DRAFT-DO NOT QUOTE OR CITE
B-211
December 2003
-------
Table B-14. Levels of Dioxins in Food Products (ppt) (continued)
Chemical
1,2,3,4,6,7,8,9-OCDD
(continued)
Sample
Type'
Pizza
Fried Chicken
Milk
Eggs
Beef
Pork
Milk
Cheddar Cheese
Butter
Eggs
Chicken
Chicken Liver
Ground Beef
Sausage
Milk
Pork
Young Chicken
Light Fowl
Heavy Fowl
Young Turkeys
Number
Samples
1
1
20
20
20
20
3
3
3
3
3
3
3
3
8
78
39
12
12
15
Number
Positive
Samples
1
1
NR
NR
NR
NR
3
3
3
3
3
3
3
3
8
49
38
12
12
11
Concentration
Range
4.04
2.55
67-181
0.087-2.38
ND-1.0
ND-8.9
2.8-5.7
3.7-3.9
5.1-6.7
7.3-20
17-54
18-53
4.4-20
56-92
NR
NR
NR
NR
NR
NR
Cone.
Mean"
4.04
2.55
120
0.677
3.0
2.53
4.13
3.77
5.67
14.10
35
40.33
11.33
75.67
4.89
52.8
5.31
2.07
7.67
0.78
Wt.
Basis
Whole
Whole
Whole
Whole
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Location
U.S.
U.S.
Finland
Finland
Finland
Finland
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
Location
Description
NR
NR
NR
NR
NR
NR
NR
NR
Background
Background
Background
Background
Background
Background
Sample
Year
NR
NR
1991
1991
1991
1991
1994
1994
1994
1994
1994
1994
1994
1994
1996
1995
1996
1996
1996
1996
Ref.
No.
16
16
17
17
17
17
18
18
18
18
18
18
18
18
21
22
23
23
23
23
Comments
fast food
fast food
purchased from grocery store
purchased from grocery store
purchased from grocery store
purchased from grocery store
purchased from grocery store
purchased from grocery store
purchased from grocery store
purchased from grocery store
DRAFT-DO NOT QUOTE OR CITE
B-212
December 2003
-------
Table B-14. Levels of Dioxins in Food Products (ppt) (continued)
Chemical
1,2,3,4,6,7,8,9-OCDD
(continued)
1,2,3,4,6,7,8,9-OCDD
Sample
Type'
Eggs
Milk
Butter
Eggs
Meat
Beef
Smoked Sausage
Chicken
Pork
Goose Fat
Duck Fat
Chicken Fat
Vegetables
Pulses & Cereals
Fruits
Meat
Eggs
Milk & Dairy
Fats & Oils
Butter
Number
Samples
3
538
222
218
107
NR
NR
NR
NR
NR
NR
NR
2
4
2
7
2
6
4
21
Number
Positive
Samples
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
21
Concentration
Range
NR
0.09- 13
0.75-4.8
2.3 - 1924
1.1 -213
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
1.35-26.6
Cone.
Mean"
33.10
1.8
1.4
72
11
1.58
2.78
2.49
2.6
4.4
3.26
2.63
111.5
51.7
1.7
224.6
61.6
26.7
17.8
6.57
Wt.
Basis
Whole
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Dry
Dry
Dry
Fat
Fat
Fat
Fat
Fat
Location
Various U.S. Sites
Germany
Germany
Germany
Germany
Russia
Russia
Russia
Russia
Russia
Russia
Russia
Spain
Spain
Spain
Spain
Spain
Spain
Spain
Spain
Location
Description
Sample
Year
1995
93-96
93-96
93-96
93-96
1996
1996
1996
1996
1996
1996
1996
1996
1996
1996
1996
1996
1996
1996
NR
Ref.
No.
24
25
25
25
25
26
26
26
26
26
26
26
27
27
27
27
27
27
27
28
Comments
official food inspection
official food inspection
official food inspection
official food inspection
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
DRAFT-DO NOT QUOTE OR CITE
B-213
December 2003
-------
Table B-14. Levels of Dioxins in Food Products (ppt) (continued)
Footnote references
Samples were obtained from grocery stores unless stated otherwise. Milk samples were obtained from dairies or transport trucks. No cooked samples from the references were used.
For ND values 1/2 LOD was used in calculating the mean. Therefore, it is possible to have mean concentrations greater than the range (e.g., reported detection limit for nondetects
greater than the positive sample).
For ND values the detection limit was used in calculating the mean.
Value in reference was reported as total 2,3,7,8-TCDD toxic equivalents.
NR = not reported
NA = not applicable
Sources: 1. De Wit et al. (1990)
2. Rappe et al. (1987)
3. Beck et al. (1989)
4. LaFleur et al. (1990)
5. Furst et al. (1990)
6. Startin et al. (1990)
7. Schecter et al. (1990)
8. U.S. EPA (1991)
9. Schecter et al. (1992)
10. Stanley and Bauer (1989)
11. Schecter etal. (1993)
12. Winters etal. (1994)
13. MAFF (1992)
14. Mayer (1995)
15. Schecter et al. (1996)
16. Schecter et al. (1995)
17. Vartiainen and Hallikainen (1994)
18. Fiedler etal. (1997)
19. Schmid and Schlatter (1992)
20. Ryan etal. (1985)
21. Lorberetal. (1998)
22. Lorberetal. (1997)
23. Ferrarioetal. (1997)
24. Schecter et al. (1997)
25. Malisch (1998)
26. Amirovaetal. (1997)
27. Domingo et al. (1999)
28. Ramos etal. (1999)
DRAFT-DO NOT QUOTE OR CITE
B-214
December 2003
-------
Table B-15. Levels of Dibenzofurans in Food Products (ppt)
Chemical
Sample
type3
Number
samples
Number
positive
samples
Concentration
range
Cone.
meanb
Wt.
basis
Location
Location
description
Sample
year
Ref.
no.
Comments
Tetrachlorodibenzofiirans (MW = 305 . 98)
2,3,7,8-TCDF
Food basket
Milk
Milk
Milk
Butter
Beef fat
Pork
Sheep fat
Chicken
Eggs
Milk
Beef
Pork
Milk
Cheese
Butter
Beef
Veal
Pork
Sheep
Chicken
3
2
4
1
1
1
1
1
1
1
1
3
3
10
10
5
3
4
3
2
2
3
2
4
1
1
1
1
1
1
1
0
1
3
NR
NR
0
0
NR
0
0
2
0.1-0.4
0.021-0.028
0.022-0.035
0.7
0.15
0.3
0.11
0.6
2.1
1.1
ND(O.OOl)
ND-0.005
0.013-0.020
ND-10.0
ND-2.6
ND(0.3)
ND(0.3)
ND-0.5
ND(0.3)
ND(0.3)
1.2-4.0
2.3
0.024
0.029
0.7
0.15
0.3
0.11
0.6
2.1
1.1
NA
0.0032
0.015
4.1
1.1
NA
NA
0.2
NA
NA
2.6
Fresh
Whole
Whole
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Whole
Whole
Whole
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Sweden
Switzerland
Switzerland
W. Germany
Berlin, W. Germany
Berlin, W. Germany
Berlin, W. Germany
Berlin, W. Germany
Berlin, W. Germany
Berlin, W. Germany
U.S.
U.S.
U.S.
W. Germany
W. Germany
W. Germany
W. Germany
W. Germany
W. Germany
W. Germany
W. Germany
Urban
Background
Industrial
Urban
Urban
Urban
Urban
Urban
Urban
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
1
2
2
3
3
3
3
3
3
3
4
4
4
5
5
5
5
5
5
5
5
near incinerators
composite from 8 trucks
from slaughterhouse
from slaughterhouse
samples not randomly selected
DRAFT-DO NOT QUOTE OR CITE
B-215
December 2003
-------
Table B-15. Levels of Dibenzofurans in Food Products (ppt) (continued)
Chemical
2,3,7,8-TCDF
(continued)
Sample
type8
Canned meat
Lard
Milk
Cow cream
Beef
Cheese w/butter
Beef fat
Pork
Butter
Swiss cheese
Sausage
Pork sticks
Pork fat
Chicken fat
Cottage cheese
Soft blue cheese
Heavy Cream cheese
Soft cream cheese
American cheese
Beef
Beef
Pork
Number
samples
2
4
7
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
5
3
5
Number
positive
samples
0
NR
NR
1
1
1
0
1
1
1
0
1
1
1
1
1
1
1
1
1
2
0
Concentration
range
ND(0.3)
ND-1.0
ND-0.011
0.285
0.027
0.108
ND(0.17)
0.288
0.212
0.021
ND(0.17)
0.34
0.50
1.9
0.02
0.15
0.07
0.07
0.1
ND-0.84
ND-1.56
ND(0. 22-0.49)
Cone.
meanb
NA
0.5
0.008C
0.285
0.027
0.108
NA
0.288
0.212
0.021
NA
0.34
0.50
1.9
0.02
0.15
0.07
0.07
0.1
0.28
0.78
NA
Wt.
basis
Fat
Fat
Whole
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Fat
Fat
Fat
Location
W. Germany
W. Germany
England & Wales
USSR
USSR
USSR
USSR
USSR
USSR
USSR
Moscow, USSR
South Vietnam
South Vietnam
South Vietnam
New York, NY
New York, NY
New York, NY
New York, NY
New York, NY
Los Angeles
San Francisco
Los Angeles
Location
description
Rural
Urban
Urban
Urban
Sample
year
NR
NR
89
88-89
88-89
88-89
88-89
88-89
88-89
88-89
88-89
NR
NR
NR
1990
1990
1990
1990
1990
NR
NR
NR
Ref.
no.
5
5
6
7
7
7
7
7
7
7
7
7
7
7
8
8
8
8
8
9
9
9
Comments
composite 6 samples
composite 6 samples
composite 6 samples
DRAFT-DO NOT QUOTE OR CITE
B-216
December 2003
-------
Table B-15. Levels of Dibenzofurans in Food Products (ppt) (continued)
Chemical
Sample
type3
Pork
Number
samples
3
Number
positive
samples
0
Concentration
range
ND(0.35-0.54)
Cone.
meanb
NA
Wt.
basis
Fat
Location
San Francisco
Location
description
Urban
Sample
year
NR
Ref.
no.
9
Comments
composite 6 samples
DRAFT-DO NOT QUOTE OR CITE
B-217
December 2003
-------
Table B-15. Levels of Dibenzofurans in Food Products (ppt) (continued)
Chemical
2,3,7,8-TCDF
(continued)
Sample
type8
Chicken
Chicken
Eggs
Eggs
Beef
Pork
Chicken
Beef
Fats & Oils
Eggs
Green Vegetables
Other Vegetables
Potatoes
Fresh Fruit
Carcass Meat
Offals
Poultry
Meat Products
Milk Products
Butter
Cheddar Cheese
Reduced Fat Cheese
Number
samples
5
3
5
3
3
1
1
63
1
2
2
2
1
1
2
2
2
2
2
4
1
1
Number
positive
samples
0
1
0
1
3
1
1
0
1
2
2
1
1
1
2
2
2
2
1
0
1
0
Concentration
range
ND(0. 19-0.58)
ND-0.67
ND(0. 01-0.03)
ND-0.01
0.01-0.051
0.065
0.032
ND
0.24
0.10-0.11
0.02
ND-0.13
0.03
0.1
0.13-0.32
0.06
0.15-0.22
0.07-0.14
ND-0.02
ND
0.04
ND
Cone.
meanb
NA
0.33
NA
0.01
0.029
0.065
0.032
NA
0.24
0.15
0.02
0.14
0.03
0.1
0.23
0.06
0.19
0.11
0.04
NA
0.04
NA
Wt.
basis
Fat
Fat
Whole
Whole
Wet
Wet
Wet
Fat
Whole
Whole
Whole
Whole
Whole
Whole
Whole
Whole
Whole
Whole
Whole
Whole
Whole
Whole
Location
Los Angeles
San Francisco
Los Angeles
San Francisco
New York, NY
New York, NY
New York, NY
Various U.S. Sites
United Kingdom
United Kingdom
United Kingdom
United Kingdom
United Kingdom
United Kingdom
United Kingdom
United Kingdom
United Kingdom
United Kingdom
United Kingdom
United Kingdom
United Kingdom
United Kingdom
Location
description
Urban
Urban
Urban
Urban
Federally-Inspected
Slaughter Houses
Sample
year
NR
NR
NR
NR
1990
1990
1990
1994
88-91
88-91
88-91
88-91
88-91
88-91
88-91
88-91
88-91
88-91
88-91
88-91
88-91
88-91
Ref.
no.
9
9
9
9
10
10
10
11
12
12
12
12
12
12
12
12
12
12
12
12
12
12
Comments
composite 6 samples
composite 6 samples
composite 6 samples
composite 6 samples
statistically-based survey
DRAFT-DO NOT QUOTE OR CITE
B-218
December 2003
-------
Table B-15. Levels of Dibenzofurans in Food Products (ppt) (continued)
Chemical
2,3,7,8-TCDF
(continued)
Sample
type8
Milk
Milk
Beef
Chicken
Pork
Hamburger
Pizza
Fried Chicken
Milk
Eggs
Beef
Pork
Milk
Cheddar Cheese
Butter
Eggs
Chicken
Chicken Liver
Ground Beef
Sausage
Milk
Pork
Number
samples
9
27
9
7
7
1
1
1
20
20
20
20
3
3
3
3
3
3
3
3
8
78
Number
positive
samples
9
0
NR
0
0
0
0
1
NR
NR
NR
NR
3
3
3
3
3
3
3
3
8
1
Concentration
range
0.18-0.31
ND
NR
ND
NR
ND
ND
0.0734
ND-90
ND-0.346
ND-1.42
ND-3.02
0.09-0.14
0.04-0.08
0.04-0.07
0.08-0.11
0.74-0.83
0.4-0.99
0.06-0.08
0.06-0.25
NR
NR
Cone.
meanb
0.24
NA
0.488
NA
1.97
NA
NA
0.0734
19
0.064
<0.5
0.74
0.12
0.06
0.06
0.10
0.77
0.78
0.07
0.17
0.08
0.09
Wt.
basis
Fat
Fat
Fat
Fat
Fat
Whole
Whole
Whole
Whole
Whole
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Location
Switzerland
Germany
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
U.S.
U.S.
U.S.
Finland
Finland
Finland
Finland
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
Various U.S. Sites
Various U.S. Sites
Location
description
NR
NR
NR
NR
NR
NR
NR
NR
Background
Background
Sample
year
1990
1992
NR
NR
NR
NR
NR
NR
1991
1991
1991
1991
1994
1994
1994
1994
1994
1994
1994
1994
1996
1995
Ref.
no.
13
14
15
15
15
16
16
16
17
17
17
17
18
18
18
18
18
18
18
18
19
20
Comments
fast food
fast food
fast food
purchased from a grocery store
purchased from a grocery store
purchased from a grocery store
purchased from a grocery store
purchased from a grocery store
purchased from a grocery store
purchased from a grocery store
purchased from a grocery store
DRAFT-DO NOT QUOTE OR CITE
B-219
December 2003
-------
Table B-15. Levels of Dibenzofurans in Food Products (ppt) (continued)
Chemical
2,3,7,8-TCDF
(continued)
Sample
type8
Young Chickens
Light Fowl
Heavy Fowl
Young Turkeys
Eggs
Milk
Butter
Eggs
Meat
Beef
Smoked Sausage
Chicken
Pork
Goose Fat
Duck Fat
Chicken Fat
Vegetables
Pulses & Cereals
Fruits
Meats
Eggs
Milk & Dairy
Number
samples
39
12
12
15
3
538
222
218
107
NR
NR
NR
NR
NR
NR
NR
2
4
2
7
2
6
Number
positive
samples
35
9
11
12
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
Concentration
range
NR
NR
NR
NR
NR
0-0.62
0-0.66
0-32
0.04-3.0
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
Cone.
meanb
0.28
0.25
0.48
0.57
0.03
0.08
0.04
1.5
0.30
0.35
0.64
1.32
0.30
0.89
0.46
0.25
0.6
0.1
1.0
1.8
2.1
2.1
Wt.
basis
Fat
Fat
Fat
Fat
Whole
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Dry
Dry
Dry
Fat
Fat
Fat
Location
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
Germany
Germany
Germany
Germany
Russia
Russia
Russia
Russia
Russia
Russia
Russia
Spain
Spain
Spain
Spain
Spain
Spain
Location
description
Background
Background
Background
Background
Sample
year
1996
1996
1996
1996
1995
93-96
93-96
93-96
93-96
1996
1996
1996
1996
1996
1996
1996
1996
1996
1996
1996
1996
1996
Ref.
no.
21
21
21
21
22
23
23
23
23
24
24
24
24
24
24
24
25
25
25
25
25
25
Comments
official food inspection
official food inspection
official food inspection
official food inspection
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
DRAFT-DO NOT QUOTE OR CITE
B-220
December 2003
-------
Table B-15. Levels of Dibenzofurans in Food Products (ppt) (continued)
Chemical
Sample
type3
Fats & Oils
Butter
Number
samples
4
21
Number
positive
samples
NR
21
Concentration
range
NR
ND-0.75
Cone.
meanb
0.3
NR
Wt.
basis
Fat
Fat
Location
Spain
Spain
Location
description
Sample
year
1996
NR
Ref.
no.
25
26
Comments
supermarkets
supermarkets
DRAFT-DO NOT QUOTE OR CITE
B-221
December 2003
-------
Table B-15. Levels of Dibenzofurans in Food Products (ppt) (continued)
Chemical
TCDF
Sample
type8
Beef
Chicken
Pork
Eggs
Butter
Number
samples
9
7
7
3
21
Number
positive
samples
NR
NR
NR
NR
21
Concentration
range
NR
NR
NR
NR
ND-3.10
Cone.
meanb
0.949
42.2
4.7
0.11
1.1
Wt.
basis
Fat
Fat
Fat
Whole
Fat
Location
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
Spain
Location
description
Sample
year
NR
NR
NR
1995
1996
Ref.
no.
15
15
15
22
26
Comments
supermarkets
Pentachlorodibenzofurans (MW=340.42)
1,2,3,7,8-PeCDF
Food basket
Milk
Milk
Milk
Butter
Beef fat
Pork
Sheep fat
Chicken
Eggs
Milk
Cheese
Butter
Beef
Veal
Pork
3
2
4
1
1
1
1
1
1
1
10
10
5
3
4
3
0
2
4
1
1
1
1
1
1
1
NR
NR
0
0
0
0
ND(0. 1-0.4)
0.020-0.021
0.020-0.036
0.2
0.09
0.01
0.01
0.01
0.01
0.6
ND-1.3
ND-0.3
ND(0.3)
ND(0.3)
ND(0.3)
ND(0.3)
NA
0.020
0.028
0.2
0.09
0.01
0.01
0.01
0.01
0.6
0.3
0.2
NA
NA
NA
NA
Fresh
Whole
Whole
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Sweden
Switzerland
Switzerland
W. Germany
Berlin, W. Germany
Berlin, W. Germany
Berlin, W. Germany
Berlin, W. Germany
Berlin, W. Germany
Berlin, W. Germany
W. Germany
W. Germany
W. Germany
W. Germany
W. Germany
W. Germany
Urban
Background
Industrial
Urban
Urban
Urban
Urban
Urban
Urban
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
1
2
2
3
3
3
3
3
3
3
5
5
5
5
5
5
near incinerators
composite from 8 trucks
from slaughterhouse
from slaughterhouse
samples not randomly selected
DRAFT-DO NOT QUOTE OR CITE
B-222
December 2003
-------
Table B-15. Levels of Dibenzofurans in Food Products (ppt) (continued)
Chemical
1,2,3,7,8-PeCDF
(continued)
Sample
type8
Sheep
Chicken
Canned meat
Lard
Milk
Cow cream
Beef
Cheese w/butter
Beef fat
Pork
Butter
Swiss cheese
Sausage
Pork sticks
Pork fat
Chicken fat
Cottage cheese
Soft blue cheese
Heavy Cream cheese
Soft cream cheese
American cheese
Beef
Number
samples
2
2
2
4
7
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
5
Number
positive
samples
0
1
0
NR
0
1
1
0
0
1
1
1
0
1
1
1
0
0
0
1
0
0
Concentration
range
ND(0.3)
ND-1.2
ND(0.3)
ND-0.3
ND(.002-.017)
0.165
0.006
ND(0.07)
ND(0.17)
0.144
0.212
0.006
ND(0.17)
0.14
0.20
0.48
ND(0.003)
ND(0.05)
ND(0.04)
0.04
ND(0.05)
ND(0.25-0.86)
Cone.
meanb
NA
0.7
NA
0.2
0.005C
0.165
0.006
NA
NA
0.144
0.212
0.006
NA
0.14
0.20
0.48
NA
NA
NA
0.04
NA
NA
Wt.
basis
Fat
Fat
Fat
Fat
Whole
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Fat
Location
W. Germany
W. Germany
W. Germany
W. Germany
England & Wales
USSR
USSR
USSR
USSR
USSR
USSR
USSR
Moscow, USSR
South Vietnam
South Vietnam
South Vietnam
New York, NY
New York, NY
New York, NY
New York, NY
New York, NY
Los Angeles
Location
description
Rural
Urban
Sample
year
NR
NR
NR
NR
89
88-89
88-89
88-89
88-89
88-89
88-89
88-89
88-89
NR
NR
NR
1990
1990
1990
1990
1990
NR
Ref.
no.
5
5
5
5
6
7
7
7
7
7
7
7
7
7
7
7
8
8
8
8
8
9
Comments
composite 6 samples
DRAFT-DO NOT QUOTE OR CITE
B-223
December 2003
-------
Table B-15. Levels of Dibenzofurans in Food Products (ppt) (continued)
Chemical
1,2,3,7,8-PeCDF
(continued)
Sample
type8
Beef
Pork
Pork
Chicken
Chicken
Eggs
Eggs
Beef
Pork
Chicken
Beef
Pork
Chicken
Beef
Fats & Oils
Eggs
Green Vegetables
Other Vegetables
Potatoes
Fresh Fruit
Carcass Meat
Offals
Number
samples
3
5
3
5
3
5
3
3
1
1
3
1
1
63
1
2
1
1
2
2
2
1
Number
positive
samples
0
0
0
0
0
0
0
1
1
0
1
1
0
0
1
2
0
0
0
0
2
1
Concentration
range
ND(0.10-1.44)
ND(0.37-1.40)
ND(0.28-0.58)
ND(0. 16-0.67)
ND(0.12-0.15)
ND(0.03-0.10)
ND(0.01-0.02)
ND-0.01
0.009
ND(0.006)
ND-0.01
0.009
ND(0.006)
ND
0.11
0.01-0.03
ND
ND
ND
ND
0.01-0.24
0.02
Cone.
meanb
NA
NA
NA
NA
NA
NA
NA
NA
0.009
NA
NA
0.009
NA
NA
0.11
0.02
NA
NA
NA
NA
0.125
0.02
Wt.
basis
Fat
Fat
Fat
Fat
Fat
Whole
Whole
Wet
Wet
Wet
Wet
Wet
Wet
Fat
Whole
Whole
Whole
Whole
Whole
Whole
Whole
Whole
Location
San Francisco
Los Angeles
San Francisco
Los Angeles
San Francisco
Los Angeles
San Francisco
New York, NY
New York, NY
New York, NY
New York, NY
New York, NY
New York, NY
Various U.S. Sites
United Kingdom
United Kingdom
United Kingdom
United Kingdom
United Kingdom
United Kingdom
United Kingdom
United Kingdom
Location
description
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Federally-Inspected
Slaughter Houses
Sample
year
NR
NR
NR
NR
NR
NR
NR
1990
1990
1990
1990
1990
1990
1994
88-91
88-91
88-91
88-91
88-91
88-91
88-91
88-91
Ref.
no.
9
9
9
9
9
9
9
10
10
10
10
10
10
11
12
12
12
12
12
12
12
12
Comments
composite 6 samples
composite 6 samples
composite 6 samples
composite 6 samples
composite 6 samples
composite 6 samples
composite 6 samples
statistically-based survey
DRAFT-DO NOT QUOTE OR CITE
B-224
December 2003
-------
Table B-15. Levels of Dibenzofurans in Food Products (ppt) (continued)
Chemical
1,2,3,7,8-PeCDF
(continued)
Sample
type8
Poultry
Meat Products
Milk Products
Butter
Cheddar Cheese
Reduced Fat Cheese
Milk
Milk
Beef
Chicken
Pork
Hamburger
Pizza
Fried Chicken
Milk
Eggs
Beef
Pork
Milk
Cheddar Cheese
Butter
Eggs
Number
samples
2
2
2
4
1
1
9
27
9
7
7
1
1
1
20
20
20
20
3
3
3
3
Number
positive
samples
2
1
0
NR
1
1
9
0
0
0
0
0
0
0
NR
NR
0
0
0
3
1
0
Concentration
range
0.03-0.04
ND-0.01
ND
ND-0.04
0.01
0.01
0.06-0.16
ND
ND
ND
ND
ND
ND
ND
ND-28
ND-0.254
ND
ND
ND(0.02)
0.02-0.03
ND-0.05
ND(0.02)
Cone.
meanb
0.035
0.0125
NA
0.035
0.01
0.01
0.09
NA
NA
NA
NA
NA
NA
NA
<10
0.061
NA
NA
0.02
0.03
0.02
0.02
Wt.
basis
Whole
Whole
Whole
Whole
Whole
Whole
Fat
Fat
Fat
Fat
Fat
Whole
Whole
Whole
Whole
Whole
Fat
Fat
Fat
Fat
Fat
Fat
Location
United Kingdom
United Kingdom
United Kingdom
United Kingdom
United Kingdom
United Kingdom
Switzerland
Germany
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
U.S.
U.S.
U.S.
Finland
Finland
Finland
Finland
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
Location
description
NR
NR
NR
NR
Sample
year
88-91
88-91
88-91
88-91
88-91
88-91
1990
1992
NR
NR
NR
NR
NR
NR
1991
1991
1991
1991
1994
1994
1994
1994
Ref.
no.
12
12
12
12
12
12
13
14
15
15
15
16
16
16
17
17
17
17
18
18
18
18
Comments
fast food
fast food
fast food
purchased from a grocery store
purchased from a grocery store
purchased from a grocery store
purchased from a grocery store
DRAFT-DO NOT QUOTE OR CITE
B-225
December 2003
-------
Table B-15. Levels of Dibenzofurans in Food Products (ppt) (continued)
Chemical
1,2,3,7,8-PeCDF
(continued)
Sample
type8
Chicken
Chicken Liver
Ground Beef
Sausage
Milk
Pork
Young Chickens
Light Fowl
Heavy Fowl
Young Turkeys
Eggs
Milk
Butter
Eggs
Meat
Beef
Smoked Sausage
Chicken
Pork
Goose Fat
Duck Fat
Chicken Fat
Number
samples
3
3
3
3
8
78
39
12
12
15
3
538
222
218
107
NR
NR
NR
NR
NR
NR
NR
Number
positive
samples
3
3
3
3
0
0
5
1
2
4
0
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
Concentration
range
0.14-0.25
0.09-0.14
0.01-004
0.03-0.08
NR
NR
NR
NR
NR
NR
NR
0-0.32
0-0.66
0-9.5
0-1.4
NR
NR
NR
NR
NR
NR
NR
Cone.
meanb
0.20
0.12
0.02
0.05
0.05
0.45
0.21
0.18
0.14
0.36
0.12
0.04
0.03
0.65
0.11
0.31
0.55
1.62
0.27
0.56
0.44
0.98
Wt.
basis
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Whole
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Location
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
Germany
Germany
Germany
Germany
Russia
Russia
Russia
Russia
Russia
Russia
Russia
Location
description
NR
NR
NR
NR
Background
Background
Background
Background
Background
Background
Sample
year
1994
1994
1994
1994
1996
1995
1996
1996
1996
1996
1995
93-96
93-96
93-96
93-96
1996
1996
1996
1996
1996
1996
1996
Ref.
no.
18
18
18
18
19
20
21
21
21
21
22
23
23
23
23
24
24
24
24
24
24
24
Comments
purchased from a grocery store
purchased from a grocery store
purchased from a grocery store
purchased from a grocery store
official food inspection
official food inspection
official food inspection
official food inspection
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
DRAFT-DO NOT QUOTE OR CITE
B-226
December 2003
-------
Table B-15. Levels of Dibenzofurans in Food Products (ppt) (continued)
Chemical
Sample
type8
Vegetables
Pulses & Cereals
Fruits
Meats
Eggs
Milk & Dairy
Fats & Oils
Butter
Number
samples
2
4
2
7
2
6
4
21
Number
positive
samples
NR
NR
NR
NR
NR
NR
NR
21
Concentration
range
NR
NR
NR
NR
NR
NR
NR
ND-0.32
Cone.
meanb
0.6
0.2
0.6
0.9
1.0
1.5
0.4
NR
Wt.
basis
Dry
Dry
Dry
Fat
Fat
Fat
Fat
Fat
Location
Spain
Spain
Spain
Spain
Spain
Spain
Spain
Spain
Location
description
Sample
year
1996
1996
1996
1996
1996
1996
1996
NR
Ref.
no.
25
25
25
25
25
25
25
26
Comments
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
DRAFT-DO NOT QUOTE OR CITE
B-227
December 2003
-------
Table B-15. Levels of Dibenzofurans in Food Products (ppt) (continued)
Chemical
2,3,4,7,8-PeCDF
Sample
type8
Food basket
Milk
Milk
Milk
Butter
Beef fat
Pork
Sheep fat
Chicken
Eggs
Milk
Cheese
Butter
Beef
Veal
Pork
Sheep
Chicken
Canned meat
Lard
Milk
Cow cream
Number
samples
3
2
4
1
1
1
1
1
1
1
10
10
5
3
4
3
2
2
2
4
7
1
Number
positive
samples
0
2
4
1
1
1
1
1
1
1
10
10
NR
3
4
0
2
2
2
NR
7
1
Concentration
range
ND(0. 1-0.5)
0.069-0.084
0.066-0.43
1.4
0.45
1.5
0.08
0.9
1.5
0.8
1.7-4.6
0.9-2.5
ND-2.0
1.7-3.9
6.5-8.2
ND(0.3)
0.7-2.8
0.7-2.0
0.3-1.3
ND-0.4
0.028-0.038
1.10
Cone.
meanb
NA
0.076
0.24
1.4
0.45
1.5
0.08
0.9
1.5
0.8
2.7
1.7
1.1
2.7
7.4
NA
1.7
1.3
0.8
0.3
0.032C
1.10
Wt.
basis
Fresh
Whole
Whole
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Whole
Wet
Location
Sweden
Switzerland
Switzerland
W. Germany
Berlin, W. Germany
Berlin, W. Germany
Berlin, W. Germany
Berlin, W. Germany
Berlin, W. Germany
Berlin, W. Germany
W. Germany
W. Germany
W. Germany
W. Germany
W. Germany
W. Germany
W. Germany
W. Germany
W. Germany
W. Germany
England & Wales
USSR
Location
description
Urban
Background
Industrial
Urban
Urban
Urban
Urban
Urban
Urban
Rural
Sample
year
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
89
88-89
Ref.
no.
1
2
2
3
3
3
3
3
3
3
5
5
5
5
5
5
5
5
5
5
6
7
Comments
near incinerators
composite from 8 trucks
from slaughterhouse
from slaughterhouse
samples not randomly selected
DRAFT-DO NOT QUOTE OR CITE
B-228
December 2003
-------
Table B-15. Levels of Dibenzofurans in Food Products (ppt) (continued)
Chemical
2,3,4,7,8-PeCDF
(continued)
Sample
type8
Beef
Cheese w/butter
Beef fat
Pork
Butter
Swiss cheese
Sausage
Pork sticks
Pork fat
Cottage cheese
Soft blue cheese
Heavy Cream cheese
Soft cream cheese
American cheese
Beef
Beef
Pork
Pork
Chicken
Chicken
Eggs
Eggs
Number
samples
1
1
1
1
1
1
1
1
1
1
1
1
1
1
5
3
5
3
5
3
5
3
Number
positive
samples
1
1
1
1
1
1
1
1
1
1
1
1
1
1
0
0
0
0
0
0
0
0
Concentration
range
0.177
0.108
0.204
0.504
1.43
0.015
0.171
0.27
0.99
0.02
0.25
0.14
0.18
0.07
ND(0.22-0.78)
ND(0.28-1.31)
ND(0.33-1.28)
ND(0.26-0.53)
ND(0.15-0.60)
ND(0.11-0.14)
ND(0.01-0.07)
ND(0.02-0.02)
Cone.
meanb
0.177
0.108
0.204
0.504
1.43
0.015
0.171
0.27
0.99
0.02
0.25
0.14
0.18
0.07
NA
NA
NA
NA
NA
NA
NA
NA
Wt.
basis
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Fat
Fat
Fat
Fat
Fat
Fat
Whole
Whole
Location
USSR
USSR
USSR
USSR
USSR
USSR
Moscow, USSR
South Vietnam
South Vietnam
New York, NY
New York, NY
New York, NY
New York, NY
New York, NY
Los Angeles
San Francisco
Los Angeles
San Francisco
Los Angeles
San Francisco
Los Angeles
San Francisco
Location
description
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Sample
year
88-89
88-89
88-89
88-89
88-89
88-89
88-89
NR
NR
1990
1990
1990
1990
1990
NR
NR
NR
NR
NR
NR
NR
NR
Ref.
no.
7
7
7
7
7
7
7
7
7
8
8
8
8
8
9
9
9
9
9
9
9
9
Comments
composite 6 samples
composite 6 samples
composite 6 samples
composite 6 samples
composite 6 samples
composite 6 samples
composite 6 samples
composite 6 samples
DRAFT-DO NOT QUOTE OR CITE
B-229
December 2003
-------
Table B-15. Levels of Dibenzofurans in Food Products (ppt) (continued)
Chemical
2,3,4,7,8-PeCDF
(continued)
Sample
type8
Beef
Pork
Chicken
Beef
Carcass Meat
Offals
Poultry
Meat Products
Milk Products
Butter
Cheddar Cheese
Reduced Fat Cheese
Fats & Oils
Eggs
Green Vegetables
Other Vegetables
Potatoes
Fresh Fruit
Milk
Milk
Beef
Chicken
Number
samples
3
1
1
63
2
2
2
2
2
4
1
1
2
2
1
2
1
2
9
27
9
7
Number
positive
samples
3
1
1
4
2
2
2
2
2
NR
1
1
1
1
0
1
1
1
9
NR
0
NR
Concentration
range
0.03-1.783
0.039
0.01
ND-1.09
0.18-0.39
0.09-0.71
0.05-0.08
0.09-0.12
0.04-0.15
0.78-0.99
0.1
0.07
ND-0.55
0.05-0.10
ND
ND-0.04
0.02
ND-0.02
0.69-1.54
0.4-0.88
ND
NR
Cone.
meanb
0.626
0.039
0.01
0.36
0.29
0.40
0.065
0.105
0.095
0.89
0.1
0.07
0.37
0.075
NA
0.0275
0.02
0.0175
1.13
0.66
NA
2.6
Wt.
basis
Wet
Wet
Wet
Fat
Whole
Whole
Whole
Whole
Whole
Whole
Whole
Whole
Whole
Whole
Whole
Whole
Whole
Whole
Fat
Fat
Fat
Fat
Location
New York, NY
New York, NY
New York, NY
Various U.S. Sites
United Kingdom
United Kingdom
United Kingdom
United Kingdom
United Kingdom
United Kingdom
United Kingdom
United Kingdom
United Kingdom
United Kingdom
United Kingdom
United Kingdom
United Kingdom
United Kingdom
Switzerland
Germany
Various U.S. Sites
Various U.S. Sites
Location
description
Federally-Inspected
Slaughter Houses
Rural
Sample
year
1990
1990
1990
1994
88-91
88-91
88-91
88-91
88-91
88-91
88-91
88-91
88-91
88-91
88-91
88-91
88-91
88-91
1990
1992
NR
NR
Ref.
no.
10
10
10
11
12
12
12
12
12
12
12
12
12
12
12
12
12
12
13
14
15
15
Comments
statistically-based survey
DRAFT-DO NOT QUOTE OR CITE
B-230
December 2003
-------
Table B-15. Levels of Dibenzofurans in Food Products (ppt) (continued)
Chemical
2,3,4,7,8-PeCDF
(continued)
Sample
type8
Pork
Hamburger
Pizza
Fried Chicken
Milk
Eggs
Beef
Pork
Milk
Cheddar Cheese
Butter
Eggs
Chicken
Chicken Liver
Ground Beef
Sausage
Milk
Pork
Young Chickens
Light Fowl
Heavy Fowl
Young Turkeys
Number
samples
7
1
1
1
20
20
20
20
3
3
3
3
3
3
3
3
8
78
39
12
12
15
Number
positive
samples
0
0
0
0
NR
NR
0
NR
3
3
3
1
3
3
3
3
8
7
5
3
4
9
Concentration
range
ND
ND
ND
ND
ND-81
ND-0.112
ND
NR
0.1-0.08
0.23-0.36
0.17-0.34
ND-0.08
0.18-0.24
0.23-0.35
0.17-0.26
0.08-0.18
NR
NR
NR
NR
NR
NR
Cone.
meanb
NA
NA
NA
NA
14
0.016
NA
0.3
0.15
0.31
0.25
0.04
0.22
0.27
0.21
0.12
0.28
0.56
0.25
0.22
0.18
0.53
Wt.
basis
Fat
Whole
Whole
Whole
Whole
Whole
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Location
Various U.S. Sites
U.S.
U.S.
U.S.
Finland
Finland
Finland
Finland
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
Location
description
NR
NR
NR
NR
NR
NR
NR
NR
Background
Background
Background
Background
Background
Background
Sample
year
NR
NR
NR
NR
1991
1991
1991
1991
1994
1994
1994
1994
1994
1994
1994
1994
1996
1995
1996
1996
1996
1996
Ref.
no.
15
16
16
16
17
17
17
17
18
18
18
18
18
18
18
18
19
20
21
21
21
21
Comments
fast food
fast food
fast food
purchased from a grocery store
purchased from a grocery store
purchased from a grocery store
purchased from a grocery store
purchased from a grocery store
purchased from a grocery store
purchased from a grocery store
purchased from a grocery store
DRAFT-DO NOT QUOTE OR CITE
B-231
December 2003
-------
Table B-15. Levels of Dibenzofurans in Food Products (ppt) (continued)
Chemical
2,3,4,7,8-PeCDF
(continued)
PeCDFs
Sample
type8
Eggs
Milk
Butter
Eggs
Meat
Beef
Smoked Sausage
Chicken
Pork
Goose Fat
Duck Fat
Chicken Fat
Vegetables
Pulses & Cereals
Fruits
Meats
Eggs
Milk & Dairy
Fats & Oils
Butter
Cottage cheese
Soft blue cheese
Number
samples
3
538
222
218
107
NR
NR
NR
NR
NR
NR
NR
2
4
2
7
2
6
4
21
1
1
Number
positive
samples
0
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
20
1
1
Concentration
range
NR
0.22-3.1
0.18-2.5
0.09-17
0.02-2.0
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
ND-2.06
0.3
5
Cone.
meanb
0.12
0.62
0.59
1.1
0.40
0.48
0.51
0.68
0.39
0.71
0.63
1.3
0.7
0.2
0.5
0.7
0.5
1.5
0.1
0.86
0.03
5
Wt.
basis
Whole
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Dry
Dry
Dry
Fat
Fat
Fat
Fat
Fat
Wet
Wet
Location
Various U.S. Sites
Germany
Germany
Germany
Germany
Russia
Russia
Russia
Russia
Russia
Russia
Russia
Spain
Spain
Spain
Spain
Spain
Spain
Spain
Spain
New York, NY
New York, NY
Location
description
Sample
year
1995
93-96
93-96
93-96
93-96
1996
1996
1996
1996
1996
1996
1996
1996
1996
1996
1996
1996
1996
1996
NR
1990
1990
Ref.
no.
22
23
23
23
23
24
24
24
24
24
24
24
25
25
25
25
25
25
25
26
8
8
Comments
official food inspection
official food inspection
official food inspection
official food inspection
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
DRAFT-DO NOT QUOTE OR CITE
B-232
December 2003
-------
Table B-15. Levels of Dibenzofurans in Food Products (ppt) (continued)
Chemical
Sample
type8
Heavy Cream cheese
Soft cream cheese
American cheese
Beef
Chicken
Pork
Eggs
Butter
Number
samples
1
1
1
9
7
7
3
21
Number
positive
samples
1
1
1
0
NR
0
0
21
Concentration
range
2
2
2
ND
NR
ND
NR
0.50-9.58
Cone.
meanb
2
2
2
NA
30.8
NA
0.12
1.72
Wt.
basis
Wet
Wet
Wet
Fat
Fat
Fat
Whole
Fat
Location
New York, NY
New York, NY
New York, NY
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
Spain
Location
description
Sample
year
1990
1990
1990
NR
NR
NR
1996
NR
Ref.
no.
8
8
8
15
15
15
22
26
Comments
supermarkets
Hexachlorodibenzofiirans (MW=374.87)
1,2,3,4,7,8-HxCDF
Milk
Milk
Milk
Butter
Beef fat
Pork
Sheep fat
Chicken
Eggs
Milk
Cheese
Butter
Beef
2
4
1
1
1
1
1
1
1
10
10
5
3
2
4
1
1
1
1
1
1
1
10
NR
NR
NR
0.017-0.020
0.026-0.13
0.9
0.43
0.8
0.15
0.9
0.6
0.4
0.7-3.0
ND-2.6
ND-1.0
ND-1.1
0.018
0.075
0.9
0.43
0.8
0.15
0.9
0.6
0.4
1.7
0.8
0.7
0.7
Whole
Whole
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Switzerland
Switzerland
W. Germany
Berlin, W. Germany
Berlin, W. Germany
Berlin, W. Germany
Berlin, W. Germany
Berlin, W. Germany
Berlin, W. Germany
W. Germany
W. Germany
W. Germany
W. Germany
Background
Industrial
Urban
Urban
Urban
Urban
Urban
Urban
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
2
2
3
3
3
3
3
3
3
5
5
5
5
near incinerators
composite from 8 trucks
from slaughterhouse
from slaughterhouse
samples not randomly selected
DRAFT-DO NOT QUOTE OR CITE
B-233
December 2003
-------
Table B-15. Levels of Dibenzofurans in Food Products (ppt) (continued)
Chemical
1,2,3,4,7,8-HxCDF
(continued)
Sample
type8
Veal
Pork
Sheep
Chicken
Canned meat
Lard
Milk
Cow cream
Beef
Cheese w/butter
Beef fat
Pork
Butter
Swiss cheese
Sausage
Pork sticks
Pork fat
Chicken fat
Cottage cheese
Soft blue cheese
Heavy Cream cheese
Soft cream cheese
Number
samples
4
3
2
2
2
4
7
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
Number
positive
samples
4
0
1
1
2
0
7
1
1
1
0
1
1
1
1
1
1
1
1
1
1
1
Concentration
range
1.8-6.0
ND(0.3)
ND-1.4
ND-1.9
0.6-0.9
ND(0.3)
0.013-0.026
1.05
0.141
0.108
ND(0.17)
0.432
2.01
0.009
0.171
0.27
0.79
0.48
0.06
0.93
0.47
0.43
Cone.
meanb
2.9
NA
0.8
1.0
0.8
NA
0.017C
1.05
0.141
0.108
NA
0.432
2.01
0.009
0.171
0.27
0.79
0.48
0.06
0.93
0.47
0.43
Wt.
basis
Fat
Fat
Fat
Fat
Fat
Fat
Whole
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Location
W. Germany
W. Germany
W. Germany
W. Germany
W. Germany
W. Germany
England & Wales
USSR
USSR
USSR
USSR
USSR
USSR
USSR
Moscow, USSR
South Vietnam
South Vietnam
South Vietnam
New York, NY
New York, NY
New York, NY
New York, NY
Location
description
Rural
Sample
year
NR
NR
NR
NR
NR
NR
89
88-89
88-89
88-89
88-89
88-89
88-89
88-89
88-89
NR
NR
NR
1990
1990
1990
1990
Ref.
no.
5
5
5
5
5
5
6
7
7
7
7
7
7
7
7
7
7
7
8
8
8
8
Comments
DRAFT-DO NOT QUOTE OR CITE
B-234
December 2003
-------
Table B-15. Levels of Dibenzofurans in Food Products (ppt) (continued)
Chemical
1,2,3,4,7,8-HxCDF
(continued)
Sample
type8
American cheese
Beef
Beef
Pork
Pork
Chicken
Chicken
Eggs
Eggs
Beef
Pork
Chicken
Beef
Carcass Meat
Offals
Poultry
Meat Products
Milk Products
Butter
Cheddar Cheese
Reduced Fat Cheese
Fats & Oils
Number
samples
1
5
3
5
3
5
3
5
3
3
1
1
63
2
2
2
2
2
4
1
1
2
Number
positive
samples
1
0
0
0
0
0
0
0
0
3
1
1
8
2
1
2
2
1
NR
1
1
1
Concentration
range
0.36
ND(0.38-1.19)
ND(0.35-0.79)
ND(0.49-3.40)
ND(0.40-3.33)
ND(0.35-0.59)
ND(0.51-0.71)
ND(0. 14-0.25)
ND(0.04-0.09)
0.066-4.846
0.108
0.009
ND-4.29
0.1-0.37
ND-0.40
0.06-0.08
0.06-0.14
ND-0.02
0.43-0.48
0.5
0.5
ND-0.21
Cone.
meanb
0.36
NA
NA
NA
NA
NA
NA
NA
NA
1.69
0.108
0.009
0.55
0.235
0.22
0.07
0.1
0.0125
0.455
0.5
0.5
0.17
Wt.
basis
Wet
Fat
Fat
Fat
Fat
Fat
Fat
Whole
Whole
Wet
Wet
Wet
Fat
Whole
Whole
Whole
Whole
Whole
Whole
Whole
Whole
Whole
Location
New York, NY
Los Angeles
San Francisco
Los Angeles
San Francisco
Los Angeles
San Francisco
Los Angeles
San Francisco
New York, NY
New York, NY
New York, NY
Various U.S. Sites
United Kingdom
United Kingdom
United Kingdom
United Kingdom
United Kingdom
United Kingdom
United Kingdom
United Kingdom
United Kingdom
Location
description
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Federally-Inspected
Slaughter Houses
Rural
Sample
year
1990
NR
NR
NR
NR
NR
NR
NR
NR
1990
1990
1990
1994
88-91
88-91
88-91
88-91
88-91
88-91
88-91
88-91
88-91
Ref.
no.
8
9
9
9
9
9
9
9
9
10
10
10
11
12
12
12
12
12
12
12
12
12
Comments
composite 6 samples
composite 6 samples
composite 6 samples
composite 6 samples
composite 6 samples
composite 6 samples
composite 6 samples
composite 6 samples
statistically-based survey
DRAFT-DO NOT QUOTE OR CITE
B-235
December 2003
-------
Table B-15. Levels of Dibenzofurans in Food Products (ppt) (continued)
Chemical
1,2,3,4,7,8-HxCDF
(continued)
Sample
type8
Eggs
Green Vegetables
Other Vegetables
Potatoes
Fresh Fruit
Milk
Milk
Beef
Chicken
Pork
Hamburger
Pizza
Fried Chicken
Milk
Eggs
Beef
Pork
Milk
Cheddar Cheese
Butter
Eggs
Chicken
Number
samples
2
2
2
1
2
9
27
9
7
7
1
1
1
20
20
20
20
3
3
3
3
3
Number
positive
samples
2
1
1
1
1
9
NR
0
0
0
0
0
0
NR
NR
NR
NR
3
3
3
1
3
Concentration
range
0.04-0.09
ND-0.01
ND-0.05
0.01
ND-0.03
0.42-0.73
0.22-0.47
ND
ND
ND
ND
ND
ND
ND-63
ND-0.302
ND
ND-0.9
0.12-0.35
0.45-0.48
0.28-0.57
ND-0.14
0.21-0.22
Cone.
meanb
0.065
0.0075
0.04
0.01
0.02
0.56
0.31
NA
NA
NA
NA
NA
NA
21
0.083
NA
0.27
0.25
0.52
0.46
0.07
0.21
Wt.
basis
Whole
Whole
Whole
Whole
Whole
Fat
Fat
Fat
Fat
Fat
Whole
Whole
Whole
Whole
Whole
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Location
United Kingdom
United Kingdom
United Kingdom
United Kingdom
United Kingdom
Switzerland
Germany
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
U.S.
U.S.
U.S.
Finland
Finland
Finland
Finland
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
Location
description
NR
NR
NR
NR
NR
Sample
year
88-91
88-91
88-91
88-91
88-91
1990
1992
NR
NR
NR
NR
NR
NR
1991
1991
1991
1991
1994
1994
1994
1994
1994
Ref.
no.
12
12
12
12
12
13
14
15
15
15
16
16
16
17
17
17
17
18
18
18
18
18
Comments
fast food
fast food
fast food
purchased from a grocery store
purchased from a grocery store
purchased from a grocery store
purchased from a grocery store
purchased from a grocery store
DRAFT-DO NOT QUOTE OR CITE
B-236
December 2003
-------
Table B-15. Levels of Dibenzofurans in Food Products (ppt) (continued)
Chemical
Sample
type8
Chicken Liver
Ground Beef
Sausage
Milk
Pork
Young Chickens
Light Fowl
Heavy Fowl
Young Turkeys
Eggs
Milk
Butter
Eggs
Meat
Beef
Number
samples
3
3
3
8
78
39
12
12
15
3
538
222
218
107
NR
Number
positive
samples
3
3
3
8
15
5
2
3
8
0
NR
NR
NR
NR
NR
Concentration
range
0.32-0.5
0.38-0.81
0.14-1.2
NR
NR
NR
NR
NR
NR
NR
0.10-1.9
0.06-0.76
0.08-10
0.03-2.1
NR
Cone.
meanb
0.4
0.55
0.62
0.39
0.98
0.23
0.16
0.17
0.32
0.67
0.32
0.29
0.91
0.28
ND
Wt.
basis
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Whole
Fat
Fat
Fat
Fat
Fat
Location
S. Mississippi
S. Mississippi
S. Mississippi
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
Germany
Germany
Germany
Germany
Russia
Location
description
NR
NR
NR
Background
Background
Background
Background
Background
Background
Sample
year
1994
1994
1994
1996
1995
1996
1996
1996
1996
1995
93-96
93-96
93-96
93-96
1996
Ref.
no.
18
18
18
19
20
21
21
21
21
22
23
23
23
23
24
Comments
purchased from a grocery store
purchased from a grocery store
purchased from a grocery store
official food inspection
official food inspection
official food inspection
official food inspection
supermarkets
DRAFT-DO NOT QUOTE OR CITE
B-237
December 2003
-------
Table B-15. Levels of Dibenzofurans in Food Products (ppt) (continued)
Chemical
1,2,3,4,7,8-HxCDF
(continued)
1,2,3,6,7,8-HxCDF
Sample
type8
Smoked Sausage
Chicken
Pork
Goose Fat
Duck Fat
Chicken Fat
Vegetables
Pulses & Cereals
Fruits
Meats
Eggs
Milk & Dairy
Fats & Oils
Butter
Food basket
Milk
Milk
Milk
Butter
Beef fat
Pork
Sheep fat
Number
samples
NR
NR
NR
NR
NR
NR
2
4
2
7
2
6
4
21
3
2
4
1
1
1
1
1
Number
positive
samples
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
21
0
2
4
1
1
1
1
1
Concentration
range
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
ND-0.93
ND(0. 1-0.8)
0.021-0.028
0.018-0.19
0.8
0.44
0.6
0.07
1.2
Cone.
meanb
0.7
1.24
0.28
0.47
0.49
0.74
3.3
1.0
0.5
2.1
2.0
2.0
0.6
NR
NA
0.024
0.090
0.8
0.44
0.6
0.07
1.2
Wt.
basis
Fat
Fat
Fat
Fat
Fat
Fat
Dry
Dry
Dry
Fat
Fat
Fat
Fat
Fat
Fresh
Whole
Whole
Fat
Fat
Fat
Fat
Fat
Location
Russia
Russia
Russia
Russia
Russia
Russia
Spain
Spain
Spain
Spain
Spain
Spain
Spain
Spain
Sweden
Switzerland
Switzerland
W. Germany
Berlin, W. Germany
Berlin, W. Germany
Berlin, W. Germany
Berlin, W. Germany
Location
description
Urban
Background
Industrial
Urban
Urban
Urban
Urban
Sample
year
1996
1996
1996
1996
1996
1996
1996
1996
1996
1996
1996
1996
1996
NR
NR
NR
NR
NR
NR
NR
NR
NR
Ref.
no.
24
24
24
24
24
24
25
25
25
25
25
25
25
26
1
2
2
3
3
3
3
3
Comments
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
near incinerators
composite from 8 trucks
from slaughterhouse
from slaughterhouse
DRAFT-DO NOT QUOTE OR CITE
B-238
December 2003
-------
Table B-15. Levels of Dibenzofurans in Food Products (ppt) (continued)
Chemical
1,2,3,6,7,8-HxCDF
(continued)
Sample
type8
Chicken
Eggs
Milk
Cheese
Butter
Beef
Veal
Pork
Sheep
Chicken
Canned meat
Lard
Milk
Cow cream
Beef
Cheese w/butter
Beef fat
Pork
Butter
Swiss cheese
Sausage
Pork sticks
Number
samples
1
1
10
10
5
3
4
3
2
2
2
4
7
1
1
1
1
1
1
1
1
1
Number
positive
samples
1
1
10
NR
NR
NR
4
0
1
2
1
0
7
1
1
1
0
1
1
1
1
1
Concentration
range
0.4
0.3
0.5-2.0
ND-0.9
ND-1.0
ND-0.9
1.3-5.0
ND(0.3)
ND-1.0
0.3-0.8
ND-0.7
ND(0.3)
0.009-0.017
0.240
0.030
0.108
ND(0.17)
0.144
0.424
0.009
0.171
0.14
Cone.
meanb
0.4
0.3
1.1
0.5
0.7
0.5
2.4
NA
0.6
0.5
0.5
NA
0.012C
0.240
0.030
0.108
NA
0.144
0.424
0.009
0.171
0.14
Wt.
basis
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Whole
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Location
Berlin, W. Germany
Berlin, W. Germany
W. Germany
W. Germany
W. Germany
W. Germany
W. Germany
W. Germany
W. Germany
W. Germany
W. Germany
W. Germany
England & Wales
USSR
USSR
USSR
USSR
USSR
USSR
USSR
Moscow, USSR
South Vietnam
Location
description
Urban
Urban
Rural
Sample
year
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
89
88-89
88-89
88-89
88-89
88-89
88-89
88-89
88-89
NR
Ref.
no.
3
3
5
5
5
5
5
5
5
5
5
5
6
7
7
7
7
7
7
7
7
7
Comments
samples not randomly selected
DRAFT-DO NOT QUOTE OR CITE
B-239
December 2003
-------
Table B-15. Levels of Dibenzofurans in Food Products (ppt) (continued)
Chemical
1,2,3,6,7,8-HxCDF
(continued)
Sample
type8
Pork fat
Chicken fat
Cottage cheese
Soft blue cheese
Heavy Cream cheese
Soft cream cheese
American cheese
Beef
Beef
Pork
Pork
Chicken
Chicken
Eggs
Eggs
Beef
Pork
Chicken
Beef
Carcass Meat
Offals
Poultry
Number
samples
1
1
1
1
1
1
1
5
3
5
3
5
3
5
3
3
1
1
63
2
2
2
Number
positive
samples
1
1
1
1
1
1
1
0
0
0
0
0
0
0
0
3
1
1
7
2
1
2
Concentration
range
0.40
0.38
0.02
0.34
0.14
0.18
0.1
ND(0.37-1.17)
ND(0. 35-0.77)
ND(0. 48-0.81)
ND(0. 39-0.84)
ND(0. 35-0.56)
ND(0.14-0.70)
ND(0.14-0.31)
ND(0.04-0.05)
ND-0.199
0.031
0.008
ND-1.96
0.05-0.36
ND-0.42
0.03-0.05
Cone.
meanb
0.40
0.38
0.02
0.34
0.14
0.18
0.1
NA
NA
NA
NA
NA
NA
NA
NA
NA
0.031
0.008
0.40
0.205
0.23
0.04
Wt.
basis
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Fat
Fat
Fat
Fat
Fat
Fat
Whole
Whole
Wet
Wet
Wet
Fat
Whole
Whole
Whole
Location
South Vietnam
South Vietnam
New York, NY
New York, NY
New York, NY
New York, NY
New York, NY
Los Angeles
San Francisco
Los Angeles
San Francisco
Los Angeles
San Francisco
Los Angeles
San Francisco
New York
New York
New York
Various U.S. Sites
United Kingdom
United Kingdom
United Kingdom
Location
description
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Urban
1990
Federally-Inspected
Slaughter Houses
Sample
year
NR
NR
1990
1990
1990
1990
1990
NR
NR
NR
NR
NR
NR
NR
NR
1990
1990
1990
1994
88-91
88-91
88-91
Ref.
no.
7
7
8
8
8
8
8
9
9
9
9
9
9
9
9
10
10
10
11
12
12
12
Comments
composite 6 samples
composite 6 samples
composite 6 samples
composite 6 samples
composite 6 samples
composite 6 samples
composite 6 samples
composite 6 samples
statistically-based survey
DRAFT-DO NOT QUOTE OR CITE
B-240
December 2003
-------
Table B-15. Levels of Dibenzofurans in Food Products (ppt) (continued)
Chemical
Sample
type8
Meat Products
Milk Products
Butter
Reduced Fat Cheese
Fats & Oils
Number
samples
2
1
4
1
2
Number
positive
samples
2
1
NR
1
1
Concentration
range
0.02-0.08
0.02
0.4-0.5
0.04
ND-0.2
Cone.
meanb
0.05
0.02
0.45
0.04
0.148
Wt.
basis
Whole
Whole
Whole
Whole
Whole
Location
United Kingdom
United Kingdom
United Kingdom
United Kingdom
United Kingdom
Location
description
Rural
Sample
year
88-91
88-91
88-91
88-91
88-91
Ref.
no.
12
12
12
12
12
Comments
DRAFT-DO NOT QUOTE OR CITE
B-241
December 2003
-------
Table B-15. Levels of Dibenzofurans in Food Products (ppt) (continued)
Chemical
1,2,3,6,7,8-HxCDF
(continued)
Sample
type8
Eggs
Green Vegetables
Other Vegetables
Potatoes
Fresh Fruit
Milk
Milk
Beef
Chicken
Pork
Hamburger
Pizza
Fried Chicken
Milk
Eggs
Beef
Pork
Milk
Cheddar Cheese
Butter
Eggs
Chicken
Number
samples
2
1
1
1
2
9
27
9
7
7
1
1
1
20
20
20
20
3
3
3
3
3
Number
positive
samples
2
0
0
0
1
9
NR
0
0
0
0
0
0
NR
NR
0
NR
3
3
3
1
3
Concentration
range
0.02-0.05
ND
ND
ND
ND-0.01
0.35-0.81
0.20-0.54
ND
ND
ND
ND
ND
ND
ND-65
ND-0.295
ND
ND-1.0
0.12-0.34
0.26-0.39
0.28-0.42
ND-0.08
0.05-0.08
Cone.
meanb
0.035
NA
NA
NA
0.008
0.51
0.26
NA
NA
NA
NA
NA
NA
22
0.087
NA
0.28
0.23
0.32
0.34
0.04
0.07
Wt.
basis
Whole
Whole
Whole
Whole
Whole
Fat
Fat
Fat
Fat
Fat
Whole
Whole
Whole
Whole
Whole
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Location
United Kingdom
United Kingdom
United Kingdom
United Kingdom
United Kingdom
Switzerland
Germany
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
U.S.
U.S.
U.S.
Finland
Finland
Finland
Finland
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
Location
description
NR
NR
NR
NR
NR
Sample
year
88-91
88-91
88-91
88-91
88-91
1990
1992
NR
NR
NR
NR
NR
NR
1991
1991
1991
1991
1994
1994
1994
1994
1994
Ref.
no.
12
12
12
12
12
13
14
15
15
15
16
16
16
17
17
17
17
18
18
18
18
18
Comments
fast food
fast food
fast food
purchased from a grocery store
purchased from a grocery store
purchased from a grocery store
purchased from a grocery store
purchased from a grocery store
DRAFT-DO NOT QUOTE OR CITE
B-242
December 2003
-------
Table B-15. Levels of Dibenzofurans in Food Products (ppt) (continued)
Chemical
Sample
type3
Chicken Liver
Number
samples
3
Number
positive
samples
3
Concentration
range
0.09-0.22
Cone.
meanb
0.17
Wt.
basis
Fat
Location
S. Mississippi
Location
description
NR
Sample
year
1994
Ref.
no.
18
Comments
purchased from a grocery store
DRAFT-DO NOT QUOTE OR CITE
B-243
December 2003
-------
Table B-15. Levels of Dibenzofurans in Food Products (ppt) (continued)
Chemical
1,2,3,6,7,8-HxCDF
(continued)
Sample
type8
Ground Beef
Sausage
Milk
Pork
Young Chickens
Light Fowl
Heavy Fowl
Young Turkeys
Eggs
Milk
Butter
Eggs
Meat
Beef
Smoked Sausage
Chicken
Pork
Goose Fat
Duck Fat
Chicken Fat
Vegetables
Pulses & Cereals
Number
samples
3
3
8
78
39
12
12
15
3
538
222
218
107
NR
NR
NR
NR
NR
NR
NR
2
4
Number
positive
samples
3
3
8
12
5
2
1
1
0
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
Concentration
range
0.2-0.39
0.08-0.21
NR
NR
NR
NR
NR
NR
NR
0-1.3
0.06-0.69
0.05-5.5
0.02-1.7
NR
NR
NR
NR
NR
NR
NR
NR
NR
Cone.
meanb
0.28
0.16
0.25
0.58
0.20
0.15
0.15
0.17
0.77
0.26
0.25
0.53
0.17
ND
0.38
0.81
0.17
0.25
0.24
0.51
0.9
0.5
Wt.
basis
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Whole
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Dry
Drv
Location
S. Mississippi
S. Mississippi
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
Germany
Germany
Germany
Germany
Russia
Russia
Russia
Russia
Russia
Russia
Russia
Spain
Spain
Location
description
NR
NR
Background
Background
Background
Background
Background
Background
Sample
year
1994
1994
1996
1995
1996
1996
1996
1996
1995
93-96
93-96
93-96
93-96
1996
1996
1996
1996
1996
1996
1996
1996
1996
Ref.
no.
18
18
19
20
21
21
21
21
22
23
23
23
23
24
24
24
24
24
24
24
25
25
Comments
purchased from a grocery store
purchased from a grocery store
official food inspection
official food inspection
official food inspection
official food inspection
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
DRAFT-DO NOT QUOTE OR CITE
B-244
December 2003
-------
Table B-15. Levels of Dibenzofurans in Food Products (ppt) (continued)
Chemical
1,2,3,6,7,8-HxCDF
(continued)
1,2,3,7,8,9-HxCDF
Sample
type8
Fruits
Meats
Eggs
Milk & Dairy
Fats & Oils
Butter
Food basket
Milk
Cow cream
Beef
Cheese w/butter
Beef fat
Pork
Butter
Swiss cheese
Sausage
Pork sticks
Pork fat
Chicken fat
Cottage cheese
Soft blue cheese
Heavy Cream cheese
Number
samples
2
7
2
6
4
21
3
7
1
1
1
1
1
1
1
1
1
1
1
1
1
1
Number
positive
samples
NR
NR
NR
NR
NR
21
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Concentration
range
NR
NR
NR
NR
NR
ND-0.94
ND(0. 1-0.3)
ND(.002-.012)
ND(0.04)
ND(O.Ol)
ND(0. 1 1)
ND(0.17)
ND(0.14)
ND(0.16)
ND(O.Ol)
ND(O.ll)
ND(O.l)
ND(0.2)
ND(0.19)
ND(0.006)
ND(O.l)
ND(0.04)
Cone.
meanb
0.4
0.7
0.4
0.8
0.4
NR
NA
0.008C
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
Wt.
basis
Dry
Fat
Fat
Fat
Fat
Fat
Fresh
Whole
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Location
Spain
Spain
Spain
Spain
Spain
Spain
Sweden
England & Wales
USSR
USSR
USSR
USSR
USSR
USSR
USSR
Moscow, USSR
South Vietnam
South Vietnam
South Vietnam
New York, NY
New York, NY
New York, NY
Location
description
Urban
Rural
Sample
year
1996
1996
1996
1996
1996
NR
NR
89
88-89
88-89
88-89
88-89
88-89
88-89
88-89
88-89
NR
NR
NR
1990
1990
1990
Ref.
no.
25
25
25
25
25
26
1
6
7
7
7
7
7
7
7
7
7
7
7
8
8
8
Comments
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
DRAFT-DO NOT QUOTE OR CITE
B-245
December 2003
-------
Table B-15. Levels of Dibenzofurans in Food Products (ppt) (continued)
Chemical
1,2,3,7,8,9-HxCDF
(continued)
Sample
type8
Soft cream cheese
American cheese
Beef
Beef
Pork
Pork
Chicken
Chicken
Eggs
Eggs
Beef
Pork
Chicken
Beef
Carcass Meat
Offals
Butter
Fats & Oils
Eggs
Milk
Milk
Beef
Number
samples
1
1
5
3
5
3
5
3
5
3
3
1
1
63
1
1
4
2
1
9
27
9
Number
positive
samples
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
NR
0
0
9
0
0
Concentration
range
ND(0.04)
ND(0.05)
ND(0.48-1.51)
ND(0.45-1.01)
ND(0.62-1.06)
ND(0.51-1.09)
ND(0.45-0.75)
ND(0. 18-0.91)
ND(0. 18-0.58)
ND(0.05-0.06)
ND(0.002-0.01)
ND(0.007)
ND(0.012)
ND
0.36
0.01
ND-0.02
ND
ND
0.02-0.06
ND
ND
Cone.
meanb
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
0.36
0.01
0.02
NA
NA
0.03
NA
NA
Wt.
basis
Wet
Wet
Fat
Fat
Fat
Fat
Fat
Fat
Whole
Whole
Wet
Wet
Wet
Fat
Whole
Whole
Whole
Whole
Whole
Fat
Fat
Fat
Location
New York, NY
New York, NY
Los Angeles
San Francisco
Los Angeles
San Francisco
Los Angeles
San Francisco
Los Angeles
San Francisco
New York, NY
New York, NY
New York, NY
Various U.S. Sites
United Kingdom
United Kingdom
United Kingdom
United Kingdom
United Kingdom
Switzerland
Germany
Various U.S. Sites
Location
description
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Federally-Inspected
Slaughter Houses
Rural
Sample
year
1990
1990
NR
NR
NR
NR
NR
NR
NR
NR
1990
1990
1990
1994
88-91
88-91
88-91
88-91
88-91
1990
1992
NR
Ref.
no.
8
8
9
9
9
9
9
9
9
9
10
10
10
11
12
12
12
12
12
13
14
15
Comments
composite 6 samples
composite 6 samples
composite 6 samples
composite 6 samples
composite 6 samples
composite 6 samples
composite 6 samples
composite 6 samples
statistically-based survey
DRAFT-DO NOT QUOTE OR CITE
B-246
December 2003
-------
Table B-15. Levels of Dibenzofurans in Food Products (ppt) (continued)
Chemical
Sample
type8
Chicken
Pork
Hamburger
Pizza
Fried Chicken
Number
samples
7
7
1
1
1
Number
positive
samples
0
0
0
0
0
Concentration
range
ND
ND
ND
ND
ND
Cone.
meanb
NA
NA
NA
NA
NA
Wt.
basis
Fat
Fat
Whole
Whole
Whole
Location
Various U.S. Sites
Various U.S. Sites
U.S.
U.S.
U.S.
Location
description
Sample
year
NR
NR
NR
NR
NR
Ref.
no.
15
15
16
16
16
Comments
fast food
fast food
fast food
DRAFT-DO NOT QUOTE OR CITE
B-247
December 2003
-------
Table B-15. Levels of Dibenzofurans in Food Products (ppt) (continued)
Chemical
1,2,3,7,8,9-HxCDF
(continued)
Sample
type8
Milk
Eggs
Beef
Pork
Milk
Cheddar Cheese
Butter
Eggs
Chicken
Chicken Liver
Ground Beef
Sausage
Milk
Pork
Young Chickens
Light Fowl
Heavy Fowl
Young Turkeys
Eggs
Milk
Butter
Eggs
Number
samples
20
20
20
20
3
3
3
3
3
3
3
3
8
78
39
12
12
15
3
538
222
218
Number
positive
samples
0
0
0
0
0
0
0
0
1
0
3
3
0
0
0
0
0
0
0
NR
NR
NR
Concentration
range
ND
ND
ND
ND
ND(0.03)
ND(0.01-0.02)
ND(0. 01-0.02)
ND(0. 03-0.04)
ND-0.07
ND(0. 03-0.04)
0.01-0.02
0.06-0.15
NR
NR
NR
NR
NR
NR
NR
0-0.11
0-0.29
0-0.62
Cone.
meanb
NA
NA
NA
NA
0.03
0.01
0.02
0.03
0.05
0.04
0.02
0.1
0.05
0.45
0.15
0.15
0.15
0.15
0.63
0.01
0.01
0.03
Wt.
basis
Whole
Whole
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Whole
Fat
Fat
Fat
Location
Finland
Finland
Finland
Finland
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
Germany
Germany
Germany
Location
description
NR
NR
NR
NR
NR
NR
NR
NR
Background
Background
Background
Background
Background
Background
Sample
year
1991
1991
1991
1991
1994
1994
1994
1994
1994
1994
1994
1994
1996
1995
1996
1996
1996
1996
1995
93-96
93-96
93-96
Ref.
no.
17
17
17
17
18
18
18
18
18
18
18
18
19
20
21
21
21
21
22
23
23
23
Comments
purchased from a grocery store
purchased from a grocery store
purchased from a grocery store
purchased from a grocery store
purchased from a grocery store
purchased from a grocery store
purchased from a grocery store
purchased from a grocery store
official food inspection
official food inspection
official food inspection
DRAFT-DO NOT QUOTE OR CITE
B-248
December 2003
-------
Table B-15. Levels of Dibenzofurans in Food Products (ppt) (continued)
Chemical
1,2,3,7,8,9-HxCDF
(continued)
2,3,4,6,7,8-HxCDF
Sample
type8
Meat
Beef
Smoked Sausage
Chicken
Pork
Goose Fat
Duck Fat
Chicken Fat
Vegetables
Pulses & Cereals
Fruits
Meats
Eggs
Milk & Dairy
Fats & Oils
Butter
Food basket
Milk
Milk
Milk
Butter
Beef fat
Number
samples
107
NR
NR
NR
NR
NR
NR
NR
2
4
2
7
2
6
4
21
3
2
4
1
1
1
Number
positive
samples
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
21
0
1
4
1
1
1
Concentration
range
0-0.12
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
ND-3.1
ND(0. 1-0.5)
ND-0.020
0.018-0.28
0.7
0.31
1.3
Cone.
meanb
0.02
ND
0.42
0.94
0.16
0.20
0.17
0.44
0.9
0.3
0.06
0.1
0.1
0.2
ND
NR
NA
0.015
0.12
0.7
0.31
1.3
Wt.
basis
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Dry
Dry
Dry
Fat
Fat
Fat
Fat
Fat
Fresh
Whole
Whole
Fat
Fat
Fat
Location
Germany
Russia
Russia
Russia
Russia
Russia
Russia
Russia
Spain
Spain
Spain
Spain
Spain
Spain
Spain
Spain
Sweden
Switzerland
Switzerland
W. Germany
Berlin, W. Germany
Berlin, W. Germany
Location
description
Urban
Background
Industrial
Urban
Urban
Sample
year
93-96
1996
1996
1996
1996
1996
1996
1996
1996
1996
1996
1996
1996
1996
1996
NR
NR
NR
NR
NR
NR
NR
Ref.
no.
23
24
24
24
24
24
24
24
25
25
25
25
25
25
25
26
1
2
2
3
3
3
Comments
official food inspection
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
near incinerators
composite from 8 trucks
from slaughterhouse
DRAFT-DO NOT QUOTE OR CITE
B-249
December 2003
-------
Table B-15. Levels of Dibenzofurans in Food Products (ppt) (continued)
Chemical
2,3,4,6,7,8-HxCDF
(continued)
Sample
type8
Pork
Sheep fat
Chicken
Eggs
Milk
Cheese
Butter
Beef
Veal
Pork
Sheep
Chicken
Canned meat
Lard
Milk
Cow cream
Beef
Cheese w/butter
Beef fat
Pork
Butter
Swiss cheese
Number
samples
1
1
1
1
10
10
5
3
4
3
2
2
2
4
7
1
1
1
1
1
1
1
Number
positive
samples
1
1
1
1
10
NR
NR
NR
4
0
1
1
1
0
7
1
1
1
0
1
1
1
Concentration
range
0.05
1.5
0.3
1.7
0.7-2.2
ND-1.1
ND-1.0
ND-1.5
1.7-5.0
ND(0.3)
ND-0.9
ND-1.2
ND-0.6
ND(0.3)
0.007-0.017
0.060
0.009
0.072
ND(0.17)
0.144
0.159
0.006
Cone.
meanb
0.05
1.5
0.3
1.7
1.3
0.7
0.7
0.9
2.8
NA
0.5
0.7
0.4
NA
0.012C
0.060
0.009
0.072
NA
0.144
0.159
0.006
Wt.
basis
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Whole
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Location
Berlin, W. Germany
Berlin, W. Germany
Berlin, W. Germany
Berlin, W. Germany
W. Germany
W. Germany
W. Germany
W. Germany
W. Germany
W. Germany
W. Germany
W. Germany
W. Germany
W. Germany
England & Wales
USSR
USSR
USSR
USSR
USSR
USSR
USSR
Location
description
Urban
Urban
Urban
Urban
Rural
Sample
year
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
89
88-89
88-89
88-89
88-89
88-89
88-89
88-89
Ref.
no.
3
3
3
3
5
5
5
5
5
5
5
5
5
5
6
7
7
7
7
7
7
7
Comments
from slaughterhouse
samples not randomly selected
DRAFT-DO NOT QUOTE OR CITE
B-250
December 2003
-------
Table B-15. Levels of Dibenzofurans in Food Products (ppt) (continued)
Chemical
Sample
type8
Sausage
Pork sticks
Pork fat
Chicken fat
Cottage cheese
Soft blue cheese
Number
samples
1
1
1
1
1
1
Number
positive
samples
1
1
1
1
1
1
Concentration
range
0.114
0.10
0.20
0.19
0.01
0.15
Cone.
meanb
0.114
0.10
0.20
0.19
0.01
0.15
Wt.
basis
Wet
Wet
Wet
Wet
Wet
Wet
Location
Moscow, USSR
South Vietnam
South Vietnam
South Vietnam
New York, NY
New York, NY
Location
description
Sample
year
88-89
NR
NR
NR
1990
1990
Ref.
no.
7
7
7
7
8
8
Comments
DRAFT-DO NOT QUOTE OR CITE
B-251
December 2003
-------
Table B-15. Levels of Dibenzofurans in Food Products (ppt) (continued)
Chemical
2,3,4,6,7,8-HxCDF
(continued)
Sample
type8
Heavy Cream cheese
Soft cream cheese
American cheese
Beef
Beef
Pork
Pork
Chicken
Chicken
Eggs
Eggs
Beef
Pork
Chicken
Beef
Carcass Meat
Offals
Poultry
Meat Products
Milk Products
Butter
Cheddar Cheese
Number
samples
1
1
1
5
3
5
3
5
3
5
3
3
1
1
63
2
1
2
2
2
4
1
Number
positive
samples
1
1
1
0
0
0
0
0
0
0
0
3
1
0
5
2
1
1
0
1
NR
1
Concentration
range
0.11
0.14
0.07
ND(0.44-1.39)
ND(0.41-0.92)
ND(0.57-0.97)
ND(0.47-1.00)
ND(0.41-0.69)
ND(0.17-0.84)
ND(0.16-0.52)
ND(0.04-0.05)
0.01-0.177
0.019
ND(O.Ol)
ND-1.75
0.07-0.38
0.42
ND-0.05
ND
ND-0.01
0.38-0.47
0.04
Cone.
meanb
0.11
0.14
0.07
NA
NA
NA
NA
NA
NA
NA
NA
0.075
0.019
NA
0.39
0.225
0.42
0.035
NA
0.02
0.43
0.04
Wt.
basis
Wet
Wet
Wet
Fat
Fat
Fat
Fat
Fat
Fat
Whole
Whole
Wet
Wet
Wet
Fat
Whole
Whole
Whole
Whole
Whole
Whole
Whole
Location
New York, NY
New York, NY
New York, NY
Los Angeles
San Francisco
Los Angeles
San Francisco
Los Angeles
San Francisco
Los Angeles
San Francisco
New York, NY
New York, NY
New York, Ny
Various U.S. Sites
United Kingdom
United Kingdom
United Kingdom
United Kingdom
United Kingdom
United Kingdom
United Kingdom
Location
description
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Federally-Inspected
Slaughter Houses
Sample
year
1990
1990
1990
NR
NR
NR
NR
NR
NR
NR
NR
1990
1990
1990
1994
88-91
88-91
88-91
88-91
88-91
88-91
88-91
Ref.
no.
8
8
8
9
9
9
9
9
9
9
9
10
10
10
11
12
12
12
12
12
12
12
Comments
composite 6 samples
composite 6 samples
composite 6 samples
composite 6 samples
composite 6 samples
composite 6 samples
composite 6 samples
composite 6 samples
statistically-based survey
DRAFT-DO NOT QUOTE OR CITE
B-252
December 2003
-------
Table B-15. Levels of Dibenzofurans in Food Products (ppt) (continued)
Chemical
2,3,4,6,7,8-HxCDF
(continued)
Sample
type8
Reduced Fat Cheese
Fats & Oils
Eggs
Green Vegetables
Potatoes
Milk
Milk
Beef
Chicken
Pork
Hamburger
Pizza
Fried Chicken
Milk
Eggs
Beef
Pork
Milk
Cheddar Cheese
Butter
Eggs
Chicken
Number
samples
1
2
2
1
1
9
27
9
7
7
1
1
1
20
20
20
20
3
3
3
3
3
Number
positive
samples
1
0
2
0
0
9
NR
0
0
0
0
0
0
NR
NR
NR
NR
3
3
3
1
2
Concentration
range
0.03
ND
0.02-0.04
ND
ND
0.39-0.98
ND-0.33
ND
ND
ND
ND
ND
ND
ND-69
ND-0.4
ND
ND
0.11-0.28
0.09-0.4
0.18-0.25
ND-0.06
ND-0.08
Cone.
meanb
0.03
NA
0.03
NA
NA
0.61
0.22
NA
NA
NA
NA
NA
NA
20
0.127
NA
NA
0.19
0.21
0.22
0.04
0.07
Wt.
basis
Whole
Whole
Whole
Whole
Whole
Fat
Fat
Fat
Fat
Fat
Whole
Whole
Whole
Whole
Whole
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Location
United Kingdom
United Kingdom
United Kingdom
United Kingdom
United Kingdom
Switzerland
Germany
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
U.S.
U.S.
U.S.
Finland
Finland
Finland
Finland
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
Location
description
Rural
NR
NR
NR
NR
NR
Sample
year
88-91
88-91
88-91
88-91
88-91
1990
1992
NR
NR
NR
NR
NR
NR
1991
1991
1991
1991
1994
1994
1994
1994
1994
Ref.
no.
12
12
12
12
12
13
14
15
15
15
16
16
16
17
17
17
17
18
18
18
18
18
Comments
fast food
fast food
fast food
purchased from a grocery store
purchased from a grocery store
purchased from a grocery store
purchased from a grocery store
purchased from a grocery store
DRAFT-DO NOT QUOTE OR CITE
B-253
December 2003
-------
Table B-15. Levels of Dibenzofurans in Food Products (ppt) (continued)
Chemical
Sample
type3
Chicken Liver
Number
samples
3
Number
positive
samples
2
Concentration
range
ND-0.29
Cone.
meanb
0.2
Wt.
basis
Fat
Location
S. Mississippi
Location
description
NR
Sample
year
1994
Ref.
no.
18
Comments
purchased from a grocery store
DRAFT-DO NOT QUOTE OR CITE
B-254
December 2003
-------
Table B-15. Levels of Dibenzofurans in Food Products (ppt) (continued)
Chemical
2,3,4,6,7,8-HxCDF
(continued)
Sample
type8
Ground Beef
Sausage
Milk
Pork
Young Chickens
Light Fowl
Heavy Fowl
Young Turkeys
Eggs
Milk
Butter
Eggs
Meat
Beef
Smoked Sausage
Chicken
Pork
Goose Fat
Duck Fat
Chicken Fat
Vegetables
Pulses & Cereals
Number
samples
3
3
8
78
39
12
12
15
3
538
222
218
107
NR
NR
NR
NR
NR
NR
NR
2
4
Number
positive
samples
3
3
8
9
6
2
2
2
0
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
Concentration
range
0.18-0.3
0.08-0.45
NR
NR
NR
NR
NR
NR
NR
0-1.4
0-0.63
0.04-5.8
0.02-0.72
NR
NR
NR
NR
NR
NR
NR
NR
NR
Cone.
meanb
0.24
0.22
0.28
0.57
0.21
0.14
0.15
0.15
0.56
0.24
0.23
0.47
0.15
ND
0.66
1.22
0.19
0.28
0.22
0.62
ND
ND
Wt.
basis
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Whole
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Dry
Drv
Location
S. Mississippi
S. Mississippi
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
Germany
Germany
Germany
Germany
Russia
Russia
Russia
Russia
Russia
Russia
Russia
Spain
Spain
Location
description
NR
NR
Background
Background
Background
Background
Background
Background
Sample
year
1994
1994
1996
1995
1996
1996
1996
1996
1995
93-96
93-96
93-96
93-96
1996
1996
1996
1996
1996
1996
1996
1996
1996
Ref.
no.
18
18
19
20
21
21
21
21
22
23
23
23
23
24
24
24
24
24
24
24
25
25
Comments
purchased from a grocery store
purchased from a grocery store
official food inspection
official food inspection
official food inspection
official food inspection
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
DRAFT-DO NOT QUOTE OR CITE
B-255
December 2003
-------
Table B-15. Levels of Dibenzofurans in Food Products (ppt) (continued)
Chemical
2,3,4,6,7,8-HxCDF
(continued)
HxCDF
Sample
type8
Fruits
Meats
Eggs
Milk & Dairy
Fats & Oils
Butter
Beef
Chicken
Pork
Eggs
Butter
Number
samples
2
7
2
6
4
21
9
7
7
3
21
Number
positive
samples
NR
NR
NR
NR
NR
21
NR
0
0
0
21
Concentration
range
NR
NR
NR
NR
NR
ND-1.27
NR
ND
ND
NR
ND-5.56
Cone.
meanb
0.05
ND
ND
ND
ND
NR
1.13
NA
NA
0.62
NR
Wt.
basis
Dry
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Whole
Dry
Location
Spain
Spain
Spain
Spain
Spain
Spain
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
Spain
Location
description
Sample
year
1996
1996
1996
1996
1996
NR
NR
NR
NR
1995
NR
Ref.
no.
25
25
25
25
25
26
15
15
15
22
26
Comments
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
HeptacWorodibenzoiuran(MW = 409. 3 1)
1,2,3,4,6,7,8-HpCDF
Food basket
Milk
Milk
Milk
Butter
Beef fat
Pork
Sheep fat
Chicken
Eggs
3
2
4
1
1
1
1
1
1
1
0
1
3
1
1
1
1
1
1
1
ND(0.2-0.9)
ND-0.12
ND-0.49
0.5
0.34
2.2
1.1
8.1
0.8
0.6
NA
0.08
0.25
0.5
0.34
2.2
1.1
8.1
0.8
0.6
Fresh
Whole
Whole
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Sweden
Switzerland
Switzerland
W. Germany
Berlin, W. Germany
Berlin, W. Germany
Berlin, W. Germany
Berlin, W. Germany
Berlin, W. Germany
Berlin, W. Germany
Urban
Background
Industrial
Urban
Urban
Urban
Urban
Urban
Urban
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
1
2
2
3
3
3
3
3
3
3
near incinerators
composite from 8 trucks
from slaughterhouse
from slaughterhouse
DRAFT-DO NOT QUOTE OR CITE
B-256
December 2003
-------
Table B-15. Levels of Dibenzofurans in Food Products (ppt) (continued)
Chemical
1,2,3,4,6,7,8-HpCDF
(continued)
Sample
type8
Milk
Cheese
Butter
Beef
Veal
Pork
Sheep
Chicken
Canned meat
Lard
Milk
Cow cream
Beef
Cheese w/butter
Beef fat
Pork
Butter
Swiss cheese
Sausage
Pork sticks
Pork fat
Chicken fat
Number
samples
10
10
5
3
4
3
2
2
2
4
7
1
1
1
1
1
1
1
1
1
1
1
Number
positive
samples
10
NR
NR
NR
4
0
2
2
2
NR
0
0
0
0
0
0
0
0
0
1
1
1
Concentration
range
0.2-6.0
ND-1.1
ND-1.0
ND-5.1
0.7-4.0
ND(0.3)
0.6-1.4
0.5-0.5
0.9-1.9
ND-0.5
ND(.007-.067)
ND(0.07)
ND(0.02)
ND(0.18)
ND(0.17)
ND(0.36)
ND(0.26)
ND(0.02)
ND(0.29)
0.10
0.40
0.38
Cone.
meanb
1.5
0.5
0.3
2.0
1.7
NA
1.0
0.5
1.2
0.2
0.020°
NA
NA
NA
NA
NA
NA
NA
NA
0.10
0.40
0.38
Wt.
basis
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Whole
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Location
W. Germany
W. Germany
W. Germany
W. Germany
W. Germany
W. Germany
W. Germany
W. Germany
W. Germany
W. Germany
England & Wales
USSR
USSR
USSR
USSR
USSR
USSR
USSR
Moscow, USSR
South Vietnam
South Vietnam
South Vietnam
Location
description
Rural
Sample
year
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
89
88-89
88-89
88-89
88-89
88-89
88-89
88-89
88-89
NR
NR
NR
Ref.
no.
5
5
5
5
5
5
5
5
5
5
6
7
7
7
7
7
7
7
7
7
7
7
Comments
samples not randomly selected
DRAFT-DO NOT QUOTE OR CITE
B-257
December 2003
-------
Table B-15. Levels of Dibenzofurans in Food Products (ppt) (continued)
Chemical
1,2,3,4,6,7,8-HpCDF
(continued)
Sample
type8
Cottage cheese
Soft blue cheese
Heavy Cream cheese
Soft cream cheese
American cheese
Beef
Beef
Pork
Pork
Chicken
Chicken
Eggs
Eggs
Beef
Pork
Chicken
Beef
Carcass Meat
Offals
Poultry
Meat Products
Milk Products
Number
samples
1
1
1
1
1
5
3
5
3
5
3
5
3
3
1
1
63
2
2
2
2
2
Number
positive
samples
1
1
1
1
1
3
1
4
3
5
1
0
1
3
1
1
14
2
2
2
2
1
Concentration
range
0.1
1.76
0.6
0.58
0.52
ND-1.15
ND-0.67
ND-10.60
2.09-5.68
1.57-24.60
ND-1.01
ND(0. 06-0.77)
ND-0.07
0.018-2.702
1.251
0.024
ND-10.11
0.12-0.5
2.5-5.2
0.72-1.5
0.1-0.34
ND-0.04
Cone.
meanb
0.1
1.76
0.6
0.58
0.52
0.96
0.74
4.05
3.55
7.00
0.51
NA
0.05
1
1.251
0.024
1.00
0.31
3.9
1.1
0.22
0.098
Wt.
basis
Wet
Wet
Wet
Wet
Wet
Fat
Fat
Fat
Fat
Fat
Fat
Whole
Whole
Wet
Wet
Wet
Fat
Whole
Whole
Whole
Whole
Whole
Location
New York, NY
New York, NY
New York, NY
New York, NY
New York, NY
Los Angeles
San Francisco
Los Angeles
San Francisco
Los Angeles
San Francisco
Los Angeles
San Francisco
New York, NY
New York, NY
New York, NY
Various U.S. Sites
United Kingdom
United Kingdom
United Kingdom
United Kingdom
United Kingdom
Location
description
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Federally-Inspected
Slaughter Houses
Sample
year
1990
1990
1990
1990
1990
NR
NR
NR
NR
NR
NR
NR
NR
1990
1990
1990
1994
88-91
88-91
88-91
88-91
88-91
Ref.
no.
8
8
8
8
8
9
9
9
9
9
9
9
9
10
10
10
11
12
12
12
12
12
Comments
composite 6 samples
composite 6 samples
composite 6 samples
composite 6 samples
composite 6 samples
composite 6 samples
composite 6 samples
composite 6 samples
statistically-based survey
DRAFT-DO NOT QUOTE OR CITE
B-258
December 2003
-------
Table B-15. Levels of Dibenzofurans in Food Products (ppt) (continued)
Chemical
1,2,3,4,6,7,8-HpCDF
(continued)
Sample
type8
Butter
Cheddar Cheese
Reduced Fat Cheese
Fats & Oils
Eggs
Green Vegetables
Other Vegetables
Potatoes
Fresh Fruit
Milk
Milk
Beef
Chicken
Pork
Hamburger
Pizza
Fried Chicken
Milk
Eggs
Beef
Pork
Milk
Number
samples
4
1
1
2
2
2
2
2
2
9
27
9
7
7
1
1
1
20
20
20
20
3
Number
positive
samples
NR
1
1
1
2
2
2
2
2
9
NR
0
0
0
0
0
0
NR
NR
NR
NR
0
Concentration
range
0.32-0.33
0.10
0.06
ND-0.61
0.10-0.12
0.03-0.04
0.16-0.26
0.03-0.15
0.03-0.09
0.20-0.53
ND-0.23
ND
ND
ND
ND
ND
ND
ND-390
ND-0.282
ND
ND
ND(0.29-1.00)
Cone.
meanb
0.325
0.10
0.06
0.55
0.11
0.035
0.21
0.09
0.06
0.36
<0.02
NA
NA
NA
NA
NA
NA
50
0.069
NA
NA
0.73
Wt.
basis
Whole
Whole
Whole
Whole
Whole
Whole
Whole
Whole
Whole
Fat
Fat
Fat
Fat
Fat
Whole
Whole
Whole
Whole
Whole
Fat
Fat
Fat
Location
United Kingdom
United Kingdom
United Kingdom
United Kingdom
United Kingdom
United Kingdom
United Kingdom
United Kingdom
United Kingdom
Switzerland
Germany
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
U.S.
U.S.
U.S.
Finland
Finland
Finland
Finland
S. Mississippi
Location
description
NR
Sample
year
88-91
88-91
88-91
88-91
88-91
88-91
88-91
88-91
88-91
1990
1992
NR
NR
NR
NR
NR
NR
1991
1991
1991
1991
1994
Ref.
no.
12
12
12
12
12
12
12
12
12
13
14
15
15
15
16
16
16
17
17
17
17
18
Comments
fast food
fast food
fast food
purchased from a grocery store
DRAFT-DO NOT QUOTE OR CITE
B-259
December 2003
-------
Table B-15. Levels of Dibenzofurans in Food Products (ppt) (continued)
Chemical
1,2,3,4,6,7,8-HpCDF
(continued)
Sample
type8
Cheddar Cheese
Butter
Eggs
Chicken
Chicken Liver
Ground Beef
Sausage
Milk
Pork
Young Chickens
Light Fowl
Heavy Fowl
Young Turkeys
Eggs
Milk
Butter
Eggs
Meat
Beef
Smoked Sausage
Chicken
Pork
Number
samples
3
3
3
3
3
3
3
8
78
39
12
12
15
3
538
222
218
107
NR
NR
NR
NR
Number
positive
samples
3
3
3
3
3
3
3
8
45
19
4
4
2
NR
NR
NR
NR
NR
NR
NR
NR
NR
Concentration
range
0.55-0.93
0.74-1.3
0.18-0.33
0.21-2.9
0.63-0.99
0.88-2.7
3.3-4.3
NR
NR
NR
NR
NR
NR
NR
0-2.9
0.02-0.45
0.06-36
0.03-7.2
NR
NR
NR
NR
Cone.
meanb
0.68
1.01
0.25
1.25
0.78
1.63
3.83
0.83
3.56
0.27
0.15
0.20
0.15
0.28
0.20
0.17
1.5
0.45
1.32
0.87
1.92
0.42
Wt.
basis
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Whole
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Location
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
Germany
Germany
Germany
Germany
Russia
Russia
Russia
Russia
Location
description
NR
NR
NR
NR
NR
NR
NR
Background
Background
Background
Background
Background
Background
Sample
year
1994
1994
1994
1994
1994
1994
1994
1996
1995
1996
1996
1996
1996
1995
93-96
93-96
93-96
93-96
1996
1996
1996
1996
Ref.
no.
18
18
18
18
18
18
18
19
20
21
21
21
21
22
23
23
23
23
24
24
24
24
Comments
purchased from a grocery store
purchased from a grocery store
purchased from a grocery store
purchased from a grocery store
purchased from a grocery store
purchased from a grocery store
purchased from a grocery store
official food inspection
official food inspection
official food inspection
official food inspection
supermarkets
supermarkets
supermarkets
supermarkets
DRAFT-DO NOT QUOTE OR CITE
B-260
December 2003
-------
Table B-15. Levels of Dibenzofurans in Food Products (ppt) (continued)
Chemical
Sample
type8
Goose Fat
Duck Fat
Chicken Fat
Vegetables
Pulses & Cereals
Fruits
Meats
Eggs
Milk & Dairy
Fats & Oils
Butter
Number
samples
NR
NR
NR
2
4
2
7
2
6
4
21
Number
positive
samples
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
21
Concentration
range
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
ND-1.33
Cone.
meanb
0.54
0.47
1.15
23.5
5.6
0.2
1.9
0.9
1.1
0.8
NR
Wt.
basis
Fat
Fat
Fat
Dry
Dry
Dry
Fat
Fat
Fat
Fat
Fat
Location
Russia
Russia
Russia
Spain
Spain
Spain
Spain
Spain
Spain
Spain
Spain
Location
description
Sample
year
1996
1996
1996
1996
1996
1996
1996
1996
1996
1996
NR
Ref.
no.
24
24
24
25
25
25
25
25
25
25
26
Comments
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
DRAFT-DO NOT QUOTE OR CITE
B-261
December 2003
-------
Table B-15. Levels of Dibenzofurans in Food Products (ppt) (continued)
Chemical
1,2,3,4,7,8,9-HpCDF
Sample
type8
Food basket
Milk
Cow cream
Beef
Cheese w/butter
Beef fat
Pork
Butter
Swiss cheese
Sausage
Pork sticks
Pork fat
Chicken fat
Cottage cheese
Soft blue cheese
Heavy Cream cheese
Soft cream cheese
American cheese
Beef
Beef
Pork
Pork
Number
samples
3
7
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
5
3
5
3
Number
positive
samples
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
Concentration
range
ND(0.3-1.6)
ND(0.01-.067)
ND(0.07)
ND(0.02)
ND(0.18)
ND(0.17)
ND(0.36)
ND(0.26)
ND(0.02)
ND(0.29)
ND(0.07)
ND(0.3)
ND(0.19)
ND(0.03)
ND(0.34)
0.14
ND(0.18)
ND(0.12)
ND(0.37-3.28)
ND(0.78-2.37)
ND(2.22-5.40)
ND(1.63-3.12)
Cone.
meanb
NA
0.026C
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
0.14
NA
NA
NA
NA
NA
NA
Wt.
basis
Fresh
Whole
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Fat
Fat
Fat
Fat
Location
Sweden
England & Wales
USSR
USSR
USSR
USSR
USSR
USSR
USSR
Moscow, USSR
South Vietnam
South Vietnam
South Vietnam
New York, NY
New York, NY
New York, NY
New York, NY
New York, NY
Los Angeles
San Francisco
Los Angeles
San Francisco
Location
description
Urban
Rural
Urban
Urban
Urban
Urban
Sample
year
NR
1989
88-89
88-89
88-89
88-89
88-89
88-89
88-89
88-89
NR
NR
NR
1990
1990
1990
1990
1990
NR
NR
NR
NR
Ref.
no.
1
6
7
7
7
7
7
7
7
7
7
7
7
8
8
8
8
8
9
9
9
9
Comments
composite 6 samples
composite 6 samples
composite 6 samples
composite 6 samples
DRAFT-DO NOT QUOTE OR CITE
B-262
December 2003
-------
Table B-15. Levels of Dibenzofurans in Food Products (ppt) (continued)
Chemical
1,2,3,4,7,8,9-HpCDF
(continued)
Sample
type8
Chicken
Chicken
Eggs
Eggs
Beef
Pork
Chicken
Beef
Carcass Meat
Offals
Poultry
Meat Products
Milk Products
Butter
Cheddar Cheese
Reduced Fat Cheese
Fats & Oils
Other Vegetables
Potatoes
Milk
Milk
Beef
Number
samples
5
3
5
3
3
1
1
63
1
2
2
1
1
2
1
1
1
1
2
9
27
9
Number
positive
samples
0
0
0
0
2
1
0
0
1
2
2
1
0
0
0
1
0
0
0
9
0
0
Concentration
range
ND(0.47-4.10)
ND(0.45-0.75)
ND(0.09-1.10)
ND(0.04-0.18)
ND-0.118
0.097
ND(O.Ol)
ND
0.41
0.1-0.13
0.02-0.1
0.03
ND
ND
ND
0.02
ND
ND
ND
0.04-0.09
ND
ND
Cone.
meanb
NA
NA
NA
NA
NA
0.097
NA
NA
0.41
0.115
0.06
0.03
NA
NA
NA
0.02
NA
NA
NA
0.05
NA
NA
Wt.
basis
Fat
Fat
Whole
Whole
Wet
Wet
Wet
Fat
Whole
Whole
Whole
Whole
Whole
Whole
Whole
Whole
Whole
Whole
Whole
Fat
Fat
Fat
Location
Los Angeles
San Francisco
Los Angeles
San Francisco
New York, NY
New York, NY
New York, NY
Various U.S. Sites
United Kingdom
United Kingdom
United Kingdom
United Kingdom
United Kingdom
United Kingdom
United Kingdom
United Kingdom
United Kingdom
United Kingdom
United Kingdom
Switzerland
Germany
Various U.S. Sites
Location
description
Urban
Urban
Urban
Urban
Federally-Inspected
Slaughter Houses
Sample
year
NR
NR
NR
NR
1990
1990
1990
1994
88-91
88-91
88-91
88-91
88-91
88-91
88-91
88-91
88-91
88-91
88-91
1990
1992
NR
Ref.
no.
9
9
9
9
10
10
10
11
12
12
12
12
12
12
12
12
12
12
12
13
14
15
Comments
composite 6 samples
composite 6 samples
composite 6 samples
composite 6 samples
statistically-based survey
DRAFT-DO NOT QUOTE OR CITE
B-263
December 2003
-------
Table B-15. Levels of Dibenzofurans in Food Products (ppt) (continued)
Chemical
1,2,3,4,7,8,9-HpCDF
(continued)
Sample
type8
Chicken
Pork
Hamburger
Pizza
Fried Chicken
Milk
Eggs
Beef
Pork
Milk
Cheddar Cheese
Butter
Eggs
Chicken
Chicken Liver
Ground Beef
Sausage
Milk
Pork
Young Chickens
Light Fowl
Heavv Fowl
Number
samples
7
7
1
1
1
20
20
20
20
3
3
3
3
3
3
3
3
8
78
39
12
12
Number
positive
samples
0
0
0
0
0
NR
0
0
0
0
3
1
0
2
3
3
3
0
10
4
0
0
Concentration
range
ND
ND
ND
ND
ND
ND-97
ND
ND
ND
ND(0. 03-0.04)
0.04-0.09
ND-0.07
ND(0.03-0.07)
ND-0.04
0.1-0.15
0.04-0.09
0.06-0.2
NR
NR
NR
NR
NR
Cone.
meanb
NA
NA
NA
NA
NA
25
NA
NA
NA
0.03
0.05
0.04
0.04
0.04
0.13
0.07
0.12
0.05
0.57
0.17
0.15
0.15
Wt.
basis
Fat
Fat
Whole
Whole
Whole
Whole
Whole
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Location
Various U.S. Sites
Various U.S. Sites
U.S.
U.S.
U.S.
Finland
Finland
Finland
Finland
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
Location
description
NR
NR
NR
NR
NR
NR
NR
NR
Background
Background
Background
Background
Background
Sample
year
NR
NR
NR
NR
NR
1991
1991
1991
1991
1994
1994
1994
1994
1994
1994
1994
1994
1996
1995
1996
1996
1996
Ref.
no.
15
15
16
16
16
17
17
17
17
18
18
18
18
18
18
18
18
19
20
21
21
21
Comments
fast food
fast food
fast food
purchased from a grocery store
purchased from a grocery store
purchased from a grocery store
purchased from a grocery store
purchased from a grocery store
purchased from a grocery store
purchased from a grocery store
purchased from a grocery store
DRAFT-DO NOT QUOTE OR CITE
B-264
December 2003
-------
Table B-15. Levels of Dibenzofurans in Food Products (ppt) (continued)
Chemical
1,2,3,4,7,8,9-HpCDF
(continued)
HpCDF
Sample
type8
Young Turkeys
Eggs
Milk
Butter
Eggs
Meat
Beef
Smoked Sausage
Chicken
Pork
Goose Fat
Duck Fat
Chicken Fat
Vegetables
Pulses & Cereals
Fruits
Meats
Eggs
Milk & Dairy
Fats & Oils
Butter
Beef
Number
samples
15
3
538
222
218
107
NR
NR
NR
NR
NR
NR
NR
2
4
2
7
2
6
4
21
9
Number
positive
samples
0
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
21
0
Concentration
range
NR
NR
0-0.61
0-0.21
0-5.8
0-0.71
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
ND-0.63
ND
Cone.
meanb
0.15
0.12
0.02
0.02
0.19
0.06
ND
0.54
0.74
0.13
0.15
0.13
0.48
2.2
0.6
0.2
0.5
0.5
0.4
ND
NR
NA
Wt.
basis
Fat
Whole
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Dry
Dry
Dry
Fat
Fat
Fat
Fat
Fat
Fat
Location
Various U.S. Sites
Various U.S. Sites
Germany
Germany
Germany
Germany
Russia
Russia
Russia
Russia
Russia
Russia
Russia
Spain
Spain
Spain
Spain
Spain
Spain
Spain
Spain
Various U.S. Sites
Location
description
Background
Sample
year
1996
1995
93-96
93-96
93-96
93-96
1996
1996
1996
1996
1996
1996
1996
1996
1996
1996
1996
1996
1996
1996
NR
NR
Ref.
no.
21
22
23
23
23
23
24
24
24
24
24
24
24
25
25
25
25
25
25
25
26
15
Comments
official food inspection
official food inspection
official food inspection
official food inspection
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
DRAFT-DO NOT QUOTE OR CITE
B-265
December 2003
-------
Table B-15. Levels of Dibenzofurans in Food Products (ppt) (continued)
Chemical
Sample
type8
Chicken
Pork
Eggs
Butter
Number
samples
7
7
3
21
Number
positive
samples
0
0
NR
21
Concentration
range
ND
ND
NR
0.36-8.85
Cone.
meanb
NA
NA
1.1
1.72
Wt.
basis
Fat
Fat
Whole
Fat
Location
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
Spain
Location
description
Sample
year
NR
NR
1995
NR
Ref.
no.
15
15
22
26
Comments
supermarkets
DRAFT-DO NOT QUOTE OR CITE
B-266
December 2003
-------
Table B-15. Levels of Dibenzofurans in Food Products (ppt) (continued)
Chemical
Sample
type8
Number
samples
Number
positive
samples
Concentration
range
Cone.
meanb
Wt.
basis
Location
Location
description
Sample
year
Ref.
no.
Comments
Octachlorodibenzofiirans (MW=444.76)
1,2,3,4,6,7,8,9-OCDF
Food basket
Milk
Milk
Milk
Butter
Beef fat
Pork
Sheep fat
Chicken
Eggs
Milk
Cheese
Butter
Beef
Veal
Pork
Sheep
Chicken
Canned meat
Lard
Milk
3
2
4
1
1
1
1
1
1
1
10
10
5
3
4
3
2
2
2
4
1
0
1
1
1
1
1
1
1
1
1
NR
10
0
0
NR
0
0
2
2
0
7
ND(0.4-2.1)
ND-0.20
ND-0.52
1
0.25
0.2
0.41
0.3
0.6
0.2
ND-4.3
0.4-4.2
ND(0.3)
ND(0.3)
ND-5.0
ND(0.3)
ND(0.3)
0.6-1.5
0.3-2.7
ND(0.3)
0.023-0.071
NA
0.12
0.19
1
0.25
0.2
0.41
0.3
0.6
0.2
1.2
1.2
NA
NA
1.4
NA
NA
1.0
1.3
NA
0.041C
Fresh
Whole
Whole
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Whole
Sweden
Switzerland
Switzerland
W. Germany
Berlin, W. Germany
Berlin, W. Germany
Berlin, W. Germany
Berlin, W. Germany
Berlin, W. Germany
Berlin, W. Germany
W. Germany
W. Germany
W. Germany
W. Germany
W. Germany
W. Germany
W. Germany
W. Germany
W. Germany
W. Germany
England & Wales
Urban
Background
Industrial
Urban
Urban
Urban
Urban
Urban
Urban
Rural
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
89
1
2
2
3
3
3
3
3
3
3
5
5
5
5
5
5
5
5
5
5
6
near incinerators
composite from 8 trucks
from slaughterhouse
from slaughterhouse
samples not randomly selected
DRAFT-DO NOT QUOTE OR CITE
B-267
December 2003
-------
Table B-15. Levels of Dibenzofurans in Food Products (ppt) (continued)
Chemical
1,2,3,4,6,7,8,9-OCDF
(continued)
Sample
type8
Cow cream
Beef
Cheese w/butter
Beef fat
Pork
Butter
Swiss cheese
Sausage
Pork sticks
Pork fat
Chicken fat
Cottage cheese
Soft blue cheese
Heavy Cream cheese
Soft cream cheese
American cheese
Beef
Beef
Pork
Pork
Chicken
Chicken
Number
samples
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
5
3
5
3
5
3
Number
positive
samples
0
0
0
0
0
0
0
0
1
0
1
1
1
1
1
1
0
0
4
1
2
0
Concentration
range
ND(0.07)
ND(0.02)
ND(0.18)
ND(0.17)
ND(0.36)
ND(0.26)
ND(0.02)
ND(0.29)
0.10
ND(0.3)
0.57
0.06
1.08
0.29
0.29
0.3
ND(0.48-5.31)
ND(0.45-2.15)
ND-9.36
ND-1.89
ND-26.00
ND(0.64-0.77)
Cone.
meanb
NA
NA
NA
NA
NA
NA
NA
NA
0.10
NA
0.57
0.06
1.08
0.29
0.29
0.03
NA
NA
2.90
1.20
6.64
NA
Wt.
basis
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Wet
Fat
Fat
Fat
Fat
Fat
Fat
Location
USSR
USSR
USSR
USSR
USSR
USSR
USSR
Moscow, USSR
South Vietnam
South Vietnam
South Vietnam
New York, NY
New York, NY
New York, NY
New York, NY
New York, NY
Los Angeles
San Francisco
Los Angeles
San Francisco
Los Angeles
San Francisco
Location
description
Urban
Urban
Urban
Urban
Urban
Urban
Sample
year
88-89
88-89
88-89
88-89
88-89
88-89
88-89
88-89
NR
NR
NR
1990
1990
1990
1990
1990
NR
NR
NR
NR
NR
NR
Ref.
no.
7
7
7
7
7
7
7
7
7
7
7
8
8
8
8
8
9
9
9
9
9
9
Comments
composite 6 samples
composite 6 samples
composite 6 samples
composite 6 samples
composite 6 samples
composite 6 samples
DRAFT-DO NOT QUOTE OR CITE
B-268
December 2003
-------
Table B-15. Levels of Dibenzofurans in Food Products (ppt) (continued)
Chemical
1,2,3,4,6,7,8,9-OCDF
(continued)
Sample
type8
Eggs
Eggs
Beef
Pork
Chicken
Beef
Carcass Meat
Offals
Poultry
Meat Products
Milk Products
Butter
Cheddar Cheese
Reduced Fat Cheese
Fats & Oils
Eggs
Green Vegetables
Other Vegetables
Potatoes
Fresh Fruit
Milk
Milk
Number
samples
5
3
3
1
1
63
2
1
2
2
2
4
1
1
2
2
2
2
2
2
9
27
Number
positive
samples
0
0
3
1
1
0
2
1
2
2
1
NR
1
1
1
2
2
2
2
2
9
NR
Concentration
range
ND(0.10-1.30)
ND(0.05-0.21)
0.018-1.073
0.821
0.034
ND
0.16-11.0
1.2
0.51-0.52
1.5-3.8
ND-0.46
ND-0.16
0.1
0.07
ND-2.3
0.03-0.05
0.04
0.17-0.27
0.13-0.47
0.15-0.18
0.10-0.32
ND-0.29
Cone.
meanb
NA
NA
0.381
0.821
0.034
NA
5.58
1.2
0.515
2.65
0.275
0.103
0.1
0.07
1.34
0.04
0.04
0.22
0.30
0.165
0.18
<0.02
Wt.
basis
Whole
Whole
Wet
Wet
Wet
Fat
Whole
Whole
Whole
Whole
Whole
Whole
Whole
Whole
Whole
Whole
Whole
Whole
Whole
Whole
Fat
Fat
Location
Los Angeles
San Francisco
New York, NY
New York, NY
New York, NY
Various U.S. Sites
United Kingdom
United Kingdom
United Kingdom
United Kingdom
United Kingdom
United Kingdom
United Kingdom
United Kingdom
United Kingdom
United Kingdom
United Kingdom
United Kingdom
United Kingdom
United Kingdom
Switzerland
Germany
Location
description
Urban
Urban
Federally-Inspected
Slaughter Houses
Sample
year
NR
NR
1990
1990
1990
1994
88-91
88-91
88-91
88-91
88-91
88-91
88-91
88-91
88-91
88-91
88-91
88-91
88-91
88-91
1990
1992
Ref.
no.
9
9
10
10
10
11
12
12
12
12
12
12
12
12
12
12
12
12
12
12
13
14
Comments
composite 6 samples
composite 6 samples
statistically-based survey
DRAFT-DO NOT QUOTE OR CITE
B-269
December 2003
-------
Table B-15. Levels of Dibenzofurans in Food Products (ppt) (continued)
Chemical
1,2,3,4,6,7,8,9-OCDF
(continued)
Sample
type8
Beef
Chicken
Pork
Hamburger
Pizza
Fried Chicken
Milk
Eggs
Beef
Pork
Milk
Cheddar Cheese
Butter
Eggs
Chicken
Chicken Liver
Ground Beef
Sausage
Milk
Pork
Young Chickens
Light Fowl
Number
samples
9
7
7
1
1
1
20
20
20
20
3
3
3
3
3
3
3
3
8
78
39
12
Number
positive
samples
0
0
0
0
0
1
NR
NR
0
0
0
3
3
3
3
3
3
3
0
41
5
0
Concentration
range
ND
ND
ND
ND
ND
0.385
ND-38
ND-0.105
ND
ND
ND(0. 06-0.07)
0.18-0.59
0.23-0.61
0.25-0.42
0.3-1.5
0.39-1.3
0.26-0.86
1.2-2.3
NR
NR
NR
NR
Cone.
meanb
NA
NA
NA
NA
NA
0.385
16
0.032
NA
NA
0.06
0.36
0.42
0.31
0.68
0.83
0.51
1.63
0.05
2.30
0.34
0.29
Wt.
basis
Fat
Fat
Fat
Whole
Whole
Whole
Whole
Whole
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Location
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
U.S.
U.S.
U.S.
Finland
Finland
Finland
Finland
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
S. Mississippi
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
Location
description
NR
NR
NR
NR
NR
NR
NR
NR
Background
Background
Background
Background
Sample
year
NR
NR
NR
NR
NR
NR
1991
1991
1991
1991
1994
1994
1994
1994
1994
1994
1994
1994
1996
1995
1996
1996
Ref.
no.
15
15
15
16
16
16
17
17
17
17
18
18
18
18
18
18
18
18
19
20
21
21
Comments
fast food
fast food
fast food
purchased from a grocery store
purchased from a grocery store
purchased from a grocery store
purchased from a grocery store
purchased from a grocery store
purchased from a grocery store
purchased from a grocery store
purchased from a grocery store
DRAFT-DO NOT QUOTE OR CITE
B-270
December 2003
-------
Table B-15. Levels of Dibenzofurans in Food Products (ppt) (continued)
Chemical
1,2,3,4,6,7,8,9-OCDF
(continued)
Sample
type8
Heavy Fowl
Young Turkeys
Eggs
Milk
Butter
Eggs
Meat
Beef
Smoked Sausage
Chicken
Pork
Goose Fat
Duck Fat
Chicken Fat
Vegetables
Pulses & Cereals
Fruits
Meats
Eggs
Milk & Dairy
Fats & Oils
Butter
Number
samples
12
15
3
538
222
218
107
NR
NR
NR
NR
NR
NR
NR
2
4
2
7
2
6
4
21
Number
positive
samples
1
0
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
16
Concentration
range
NR
NR
NR
0-2.5
0-0.67
0-46
0.05-6.0
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
ND-3.18
Cone.
meanb
0.31
0.29
1.34
0.11
0.09
1.8
0.32
2.5
1.99
1.57
0.48
0.83
0.52
1.07
119.5
42.5
0.8
8.2
3.7
3.5
1.8
NR
Wt.
basis
Fat
Fat
Whole
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Fat
Dry
Dry
Dry
Fat
Fat
Fat
Fat
Fat
Location
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
Germany
Germany
Germany
Germany
Russia
Russia
Russia
Russia
Russia
Russia
Russia
Spain
Spain
Spain
Spain
Spain
Spain
Spain
Spain
Location
description
Background
Background
Sample
year
1996
1996
1995
93-96
93-96
93-96
93-96
1996
1996
1996
1996
1996
1996
1996
1996
1996
1996
1996
1996
1996
1996
NR
Ref.
no.
21
21
22
23
23
23
23
24
24
24
24
24
24
24
25
25
25
25
25
25
25
26
Comments
official food inspection
official food inspection
official food inspection
official food inspection
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
supermarkets
DRAFT-DO NOT QUOTE OR CITE
B-271
December 2003
-------
Table B-15. Levels of Dibenzofurans in Food Products (ppt) (continued)
Footnote references
Samples were obtained from grocery stores unless stated otherwise. Milk samples were obtained from dairies or transport trucks. No cooked samples from the references were used.
For ND values 1/2 LOD was used in calculating the mean. Therefore, it is possible to have mean concentrations greater than the range (e.g., reported detection limit for nondetects
greater than the positive sample).
For ND values the detection limit was used in calculating the mean.
NR = not reported.
NA = not applicable.
Sources: 1. De Wit et al. (1990)
2. Rappe et al. (1987)
3. Becketal. (1989)
4. LaFleur etal. (1990)
5. Furst etal. (1990)
6. Startinetal. (1990)
7. Schecter etal. (1990)
8. Schecter etal. (1992)
9. Stanley and Bauer (1989)
10. Schecter et al. (1993)
11. Winters etal. (1994)
12. MAFF (1992)
13. Schmid and Schlatter (1992)
14. Mayer (1995)
15. Schecter et al. (1996)
16. Schecter et al. (1995)
17. Vartiainen and Hallikainen (1994)
18. Fiedler etal. (1997)
19. Lorber etal. (1998)
20. Lorber etal. (1997)
21. Ferrario et al. (1997)
22. Schecter et al. (1997)
23. Malisch (1998)
24. Amirovaetal. (1997)
25. Domingo etal. (1999)
DRAFT-DO NOT QUOTE OR CITE
B-272
December 2003
-------
Table B-16. Environmental Levels of PCBs in Food (ppt)
IUPAC
number Chemical
Sample
Type
Number
samples
Number
positive
samples
Concentration
range
Cone.
mean
Location
Location description
Sample
year
Ref.
no.
Comments
Tetrachloro-PCB (MW=291.99)
77 3,3',4,4'-TCB
Beef
Pork
Poultry
Inner
Organs
Eggs
Fish Liver
Oil
Milk
Milk
Milk
Milk
Beef
Butter
Canned
Fish
Cheese
Cream
Eggs
6
3
2
5
2
2
17
5
7
10
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
0.6-38
1.0-24
NR
ND-7.9
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
13
13
8.2
3.2
4.1
2,700
4.0
3.3
8.9
8.4
1
6
8
7
<1
1
Finland
Finland
Finland
Finland
Finland
Finland
The Netherlands
The Netherlands
The Netherlands
The Netherlands
Canada
Canada
Canada
Canada
Canada
Canada
Urban
Urban
Urban
Urban
Urban
Urban
Agricultural
Industrial
Near Waste
Incinerator
Outlying Land of
Main Rivers
Urban
Urban
Urban
Urban
Urban
Urban
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
86-88
86-88
86-88
86-88
86-88
86-88
1
1
1
1
1
1
2
2
2
2
3
3
3
3
3
3
DRAFT-DO NOT QUOTE OR CITE
B-258
December 2003
-------
Table B-16 Environmental Levels of PCBs in Food Products (ppt) (continued)
IUPAC
number Chemical
77 3,3',4,4'-TCB
(continued)
Sample
Type
Organ
Meats
Poultry
Beef
Chicken
Pork
Beef Back
Fat
Milk
Pork
Young
Chickens
Light Fowl
Heavy
Fowl
Young
Turkeys
Butter
Number
samples
NR
NR
NR
NR
NR
63
8
78
39
12
12
15
21
Number
positive
samples
NR
NR
NR
NR
NR
12
8
13
39
12
12
12
21
Concentration
range
NR
NR
NR
NR
NR
ND-7.97
NR
NR
NR
NR
NR
NR
0.03-1.83
Cone.
mean
2
2
3.83
10.7
10.6
0.98
10.6
1.57
9.3
12.2
10.6
5.6
0.41
Location
Canada
Canada
Various U.S.
Locations
Various U.S.
Locations
Various U.S.
Locations
Various U.S.
Locations
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
Spain
Location description
Urban
Urban
Urban
Urban
Urban
U.S. Slaughterhouses
Background
Background
Background
Background
Background
Background
Sample
year
86-88
86-88
95
95
95
94
96
95
96
96
96
96
NR
Ref.
no.
3
3
4
4
4
5
7
8
9
9
9
9
11
Comments
supermarket
DRAFT-DO NOT QUOTE OR CITE
B-259
December 2003
-------
Table B-16 Environmental Levels of PCBs in Food Products (ppt) (continued)
IUPAC
number Chemical
Sample
Type
Number
samples
Number
positive
samples
Concentration
range
Cone.
mean
Location
Location description
Sample
year
Ref.
no.
Comments
Pentachloro-PCB (MW=326.44)
105 2,3,3',4,4'-PeCB
105 2,3,3',4,4'-PeCB
Steak
Roast
Beef
Ground
Beef
Pork
Poultry
Eggs
Cream
Ice cream
Yogurt
Cheese
Cottage
cheese
Processed
cheese
Butter
Rainbow
Trout
Beef
Pork
Poultry
Inner
Organs
1
3
2
1
4
4
3
4
1
5
3
5
4
4
6
3
2
5
1
3
2
1
4
4
3
4
1
5
3
5
4
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
410-2,100
5.3-38
11-47
NR
8.1-11
19
10
32
54
25
34
17
16
10
61
13
49
116
1,200
22
24
68
45
Canada
Canada
Canada
Canada
Canada
Canada
Canada
Canada
Canada
Canada
Canada
Canada
Canada
Finland
Finland
Finland
Finland
Finland
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Urban
86-88
86-88
86-88
86-88
86-88
86-88
86-88
86-88
86-88
86-88
86-88
86-88
86-88
NR
NR
NR
NR
NR
6
6
6
6
6
6
6
6
6
6
6
6
6
1
1
1
1
1
DRAFT-DO NOT QUOTE OR CITE
B-260
December 2003
-------
Table B-16 Environmental Levels of PCBs in Food Products (ppt) (continued)
IUPAC
number Chemical
(continued)
105 2,3,3',4,4'-PeCB
(continued)
Sample
Type
Eggs
Fish Liver
Oil
Beef
Chicken
Pork
Hot Dog/
Bologna
Eggs
Vegan Diet
Butter
Cheese
Beef Back
Fat
Milk
Pork
Young
Chickens
Light Fowl
Heavy
Fowl
Number
samples
2
2
NR
NR
NR
NR
NR
NR
NR
NR
63
8
78
39
12
12
Number
positive
samples
NR
NR
0
NR
0
NR
0
0
NR
0
55
8
15
39
12
12
Concentration
range
NR
NR
ND
NR
ND
NR
ND
ND
NR
ND
ND-437
NR
NR
NR
NR
NR
Cone.
mean
98
30,000
NA
78
NA
400
NA
NA
220
NA
92.7
170.3
33.4
132
171
165
Location
Finland
Finland
Various U.S.
Locations
Various U.S.
Locations
Various U.S.
Locations
Various U.S.
Locations
Various U.S.
Locations
Various U.S.
Locations
Various U.S.
Locations
Various U.S.
Locations
Various U.S.
Locations
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
Location description
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Urban
U.S. Slaughterhouses
Background
Background
Background
Background
Background
Sample
year
NR
NR
95
95
95
95
95
95
95
95
94
96
95
96
96
96
Ref.
no.
1
1
4
4
4
4
4
4
4
4
5
7
8
9
9
9
Comments
DRAFT-DO NOT QUOTE OR CITE
B-261
December 2003
-------
Table B-16 Environmental Levels of PCBs in Food Products (ppt) (continued)
IUPAC
number Chemical
114 2,3,4,4',5-PeCB
Sample
Type
Young
Turkeys
Eggs
Butter
Beef
Chicken
Pork
Hot Dog/
Bologna
Eggs
Vegan Diet
Butter
Cheese
Eggs
Number
samples
15
3
21
NR
NR
NR
NR
NR
NR
NR
NR
3
Number
positive
samples
12
0
21
0
0
0
0
0
0
0
0
0
Concentration
range
NR
ND
0.02-0.31
ND
ND
ND
ND
ND
ND
ND
ND
ND
Cone.
mean
307
NA
0.098
NA
NA
NA
NA
NA
NA
NA
NA
NA
Location
Various U.S. Sites
Various U.S. Sites
Spain
Various U.S.
Locations
Various U.S.
Locations
Various U.S.
Locations
Various U.S.
Locations
Various U.S.
Locations
Various U.S.
Locations
Various U.S.
Locations
Various U.S.
Locations
Various U.S. Sites
Location description
Background
Background
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Sample
year
96
95
NR
95
95
95
95
95
95
95
95
95
Ref.
no.
9
10
11
4
4
4
4
4
4
4
4
10
Comments
supermarket
DRAFT-DO NOT QUOTE OR CITE
B-262
December 2003
-------
Table B-16 Environmental Levels of PCBs in Food Products (ppt) (continued)
IUPAC
number Chemical
118 2,3',4,4',5-PeCB
118 "2,3',4,4',5-PeCB
Sample
Type
Steak
Roast
Beef
Ground
Beef
Pork
Poultry
Eggs
Margarine
Cream
Ice cream
Yogurt
Cheese
Cottage
cheese
Processed
cheese
Butter
Beef
Chicken
Pork
Hot Dog/
Bologna
Number
samples
5
5
5
4
4
5
1
5.00
5
5
5
5
5
5
NR
NR
NR
NR
Number
positive
samples
5
5
5
4
4
5
1
5
5
5
5
5
5
5
NR
NR
0
NR
Concentration
range
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
ND
NR
Cone.
mean
56
38
119
55
81
99
19
72
53
24
251
33
184
487
94
200
NA
1,100
Location
Canada
Canada
Canada
Canada
Canada
Canada
Canada
Canada
Canada
Canada
Canada
Canada
Canada
Canada
Various U.S.
Locations
Various U.S.
Locations
Various U.S.
Locations
Various U.S.
Locations
Location description
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Sample
year
86-88
86-88
86-88
86-88
86-88
86-88
86-88
86-88
86-88
86-88
86-88
86-88
86-88
86-88
95
95
95
95
Ref.
no.
6
6
6
6
6
6
6
6
6
6
6
6
6
6
4
4
4
4
Comments
DRAFT-DO NOT QUOTE OR CITE
B-263
December 2003
-------
Table B-16 Environmental Levels of PCBs in Food Products (ppt) (continued)
IUPAC
number Chemical
(continued)
Sample
Type
Eggs
Vegan Diet
Butter
Cheese
Beef Back
Fat
Milk
Pork
Young
Chickens
Light Fowl
Heavy
Fowl
Young
Turkeys
Eggs
Butter
Number
samples
NR
NR
NR
NR
63
8
78
39
12
12
15
3
21
Number
positive
samples
NR
NR
NR
NR
63
8
24
39
12
12
12
NR
21
Concentration
range
NR
NR
NR
NR
61-2,294
NR
NR
NR
NR
NR
NR
NR
0.05-1.17
Cone.
mean
64
15
930
240
448.6
685.3
95.5
522
599
663
1,116
64
0.37
Location
Various U.S.
Locations
Various U.S.
Locations
Various U.S.
Locations
Various U.S.
Locations
Various U.S.
Locations
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
Spain
Location description
Urban
Urban
Urban
Urban
U.S. Slaughterhouses
Background
Background
Background
Background
Background
Background
Background
Sample
year
95
95
95
95
94
96
95
96
96
96
96
95
NR
Ref.
no.
4
4
4
4
5
7
8
9
9
9
9
10
11
Comments
supermarket
DRAFT-DO NOT QUOTE OR CITE
B-264
December 2003
-------
Table B-16 Environmental Levels of PCBs in Food Products (ppt) (continued)
IUPAC
number Chemical
126 3,3',4,4',5-PeCB
126 3,3',4,4',5-PeCB
Sample
Type
Beef
Pork
Poultry
Inner
Organs
Eggs
Fish Liver
Oil
Milk
Milk
Milk
Milk
Beef
Butter
Canned
Fish
Cheese
Cream
Eggs
Organ
Meats
Poultry
Beef
Number
samples
6
3
2
5
2
2
17
5
7
10
NR
NR
NR
NR
NR
NR
NR
NR
NR
Number
positive
samples
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
Concentration
range
0.3-7.3
0.5-3.7
NR
0.4-6.0
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
Cone.
mean
3.2
1.5
1.2
2.6
2.9
620
16.5
20.5
35.8
30.3
1
12
3
3
<1
1
1
<1
0.39
Location
Finland
Finland
Finland
Finland
Finland
Finland
The Netherlands
The Netherlands
The Netherlands
The Netherlands
Canada
Canada
Canada
Canada
Canada
Canada
Canada
Canada
Various U.S.
Locations
Location description
Urban
Urban
Urban
Urban
Urban
Urban
Agricultural
Industrial
Near Waste
Incinerator
Outlying Land of
Main Rivers
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Sample
year
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
86-88
86-88
86-88
86-88
86-88
86-88
86-88
86-88
95
Ref.
no.
1
1
1
1
1
1
2
2
2
2
3
3
3
3
3
3
3
3
4
Comments
DRAFT-DO NOT QUOTE OR CITE
B-265
December 2003
-------
Table B-16 Environmental Levels of PCBs in Food Products (ppt) (continued)
IUPAC
number Chemical
(continued)
126 3,3',4,4',5-PeCB
Sample
Type
Chicken
Pork
Hot Dog/
Bologna
Eggs
Vegan Diet
Butter
Cheese
Milk
Ice Cream
Beef Back
Fat
Milk
Pork
Young
Chickens
Light Fowl
Heavy
Fowl
Number
samples
NR
NR
NR
NR
NR
NR
NR
NR
Nr
63
8
78
39
12
12
Number
positive
samples
NR
NR
NR
NR
0
NR
NR
NR
NR
63
8
24
39
12
12
Concentration
range
NR
NR
NR
NR
ND
NR
NR
NR
NR
0.7-21.2
NR
NR
NR
NR
NR
Cone.
mean
0.38
0.10
0.71
0.29
NA
3.38
1.04
0.16
0.86
4.1
3.6
0.33
1.8
1.6
2.2
Location
Various U.S.
Locations
Various U.S.
Locations
Various U.S.
Locations
Various U.S.
Locations
Various U.S.
Locations
Various U.S.
Locations
Various U.S.
Locations
Various U.S.
Locations
Various U.S.
Locations
Various U.S.
Locations
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
Location description
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Urban
U.S. Slaughterhouses
Background
Background
Background
Background
Background
Sample
year
95
95
95
95
95
95
95
95
95
94
96
95
96
96
96
Ref.
no.
4
4
4
4
4
4
4
4
4
5
7
8
9
9
9
Comments
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B-266
December 2003
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Table B-16 Environmental Levels of PCBs in Food Products (ppt) (continued)
IUPAC
number Chemical
(continued)
Sample
Type
Young
Turkeys
Eggs
Butter
Number
samples
15
3
21
Number
positive
samples
12
NR
16
Concentration
range
NR
NR
ND-0.62
Cone.
mean
4.4
0.29
NR
Location
Various U.S. Sites
Various U.S. Sites
Spain
Location description
Background
Background
Sample
year
96
95
NR
Ref.
no.
9
10
11
Comments
supermarket
Hexachloro-PCB (MW=360.88)
156 2,3,3',4,4',5-HxCB
156 2,3,3',4,4',5-HxCB
(Continued)
Steak
Ground
Beef
Pork
Poultry
Eggs
Cream
Ice cream
Yogurt
Cheese
Cottage
cheese
Processed
cheese
Butter
Beef Back
Fat
Milk
Pork
2
4
2
3
3
2
2
1
5
1
5
5
63
8
78
2
4
2
3
3
2
2
1
5
1
5
5
63
8
30
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
4.9-426
NR
NR
8
14
9
16
24
9
9
7
28
13
22
63
60.7
60.1
21.6
Canada
Canada
Canada
Canada
Canada
Canada
Canada
Canada
Canada
Canada
Canada
Canada
Various U.S.
Locations
Various U.S. Sites
Various U.S. Sites
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Background
Background
86-88
86-88
86-88
86-88
86-88
86-88
86-88
86-88
86-88
86-88
86-88
86-88
94
96
95
6
6
6
6
6
6
6
6
6
6
6
6
5
7
8
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B-267
December 2003
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Table B-16 Environmental Levels of PCBs in Food Products (ppt) (continued)
IUPAC
number Chemical
157 2,3,3',4,4',5'-HxCB
167 2,3',4,4',5,5'-HxCB
Sample
Type
Young
Chickens
Light Fowl
Heavy
Fowl
Young
Turkeys
Butter
Beef Back
Fat
Milk
Pork
Young
Chickens
Light Fowl
Heavy
Fowl
Young
Turkeys
Butter
Number
samples
39
12
12
15
21
63
8
78
39
12
12
15
21
Number
positive
samples
39
11
12
12
15
62
8
32
39
11
12
12
20
Concentration
range
NR
NR
NR
NR
ND-0.21
ND-91.7
NR
NR
NR
NR
NR
NR
ND-0.49
Cone.
mean
41
58
54
108
NR
13.8
13.8
5.1
10.5
12.5
13.3
26.2
NR
Location
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
Spain
Various U.S.
Locations
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
Spain
Location description
Background
Background
Background
Background
Urban
Background
Background
Background
Background
Background
Background
Sample
year
96
96
96
96
NR
94
96
95
96
96
96
96
NR
Ref.
no.
9
9
9
9
11
5
7
8
9
9
9
9
11
Comments
supermarket
supermarkets
DRAFT-DO NOT QUOTE OR CITE
B-268
December 2003
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Table B-16 Environmental Levels of PCBs in Food Products (ppt) (continued)
IUPAC
number Chemical
169 3,3',4,4',5,5'-HxCB
169 3,3',4,4',5,5'-HxCB
Sample
Type
Beef
Pork
Poultry
Inner
Organs
Eggs
Fish Liver
Oil
Milk
Milk
Milk
Milk
Beef
Butter
Canned
Fish
Cheese
Cream
Eggs
Organ
Meats
Poultry
Beef
Number
samples
6
3
2
5
2
2
17
5
7
10
NR
NR
NR
NR
NR
NR
NR
NR
NR
Number
positive
samples
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
Concentration
range
ND-1.0
ND-2.2
ND
ND-0.6
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
Cone.
mean
0.5
0.8
NA
0.3
0.1
130
2.6
4.0
8.4
4.2
<1
6
<1
1
<1
1
1
<1
0.12
Location
Finland
Finland
Finland
Finland
Finland
Finland
The Netherlands
The Netherlands
The Netherlands
The Netherlands
Canada
Canada
Canada
Canada
Canada
Canada
Canada
Canada
Various U.S.
Locations
Location description
Urban
Urban
Urban
Urban
Urban
Urban
Agricultural
Industrial
Near Waste
Incinerator
Outlying Land of
Main Rivers
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Sample
year
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
86-88
86-88
86-88
86-88
86-88
86-88
86-88
86-88
95
Ref.
no.
1
1
1
1
1
1
2
2
2
2
3
3
3
3
3
3
3
3
4
Comments
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B-269
December 2003
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Table B-16 Environmental Levels of PCBs in Food Products (ppt) (continued)
IUPAC
number Chemical
(continued)
Sample
Type
Chicken
Pork
Hot Dog/
Bologna
Eggs
Vegan Diet
Butter
Cheese
Milk
Ice Cream
Beef Back
Fat
Milk
Pork
Young
Chickens
Number
samples
NR
NR
NR
NR
NR
NR
NR
NR
NR
63
8
78
39
Number
positive
samples
0
NR
0
0
0
NR
0
0
0
59
8
29
31
Concentration
range
ND
NR
ND
ND
ND
NR
ND
ND
ND
ND-2.4
NR
NR
NR
Cone.
mean
NA
0.14
NA
NA
NA
0.39
NA
NA
NA
0.70
0.5
0.26
0.20
Location
Various U.S.
Locations
Various U.S.
Locations
Various U.S.
Locations
Various U.S.
Locations
Various U.S.
Locations
Various U.S.
Locations
Various U.S.
Locations
Various U.S.
Locations
Various U.S.
Locations
Various U.S.
Locations
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
Location description
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Urban
U.S. Slaughterhouses
Background
Background
Background
Sample
year
95
95
95
95
95
95
95
95
95
94
96
95
96
Ref.
no.
4
4
4
4
4
4
4
4
4
5
7
8
9
Comments
DRAFT-DO NOT QUOTE OR CITE
B-270
December 2003
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Table B-16 Environmental Levels of PCBs in Food Products (ppt) (continued)
IUPAC
number Chemical
169 3,3',4,4',5,5'-HxCB
(continued)
Sample
Type
Light Fowl
Heavy
Fowl
Young
Turkeys
Eggs
Butter
Number
samples
12
12
15
3
21
Number
positive
samples
8
11
12
0
0
Concentration
range
NR
NR
NR
NR
ND
Cone.
mean
0.20
0.40
0.60
0.10
NA
Location
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
Various U.S. Sites
Spain
Location description
Background
Background
Background
Background
Sample
year
96
96
96
95
NR
Ref.
no.
9
9
9
10
11
Comments
supermarket
Heptachloro-PCB (MW=396.33)
170 2,2',3,3',4,4',5-HpCB
180 2,2',3,4,4',5,5-HpCB
Butter
Beef
Chicken
Pork
Hot Dog/
Bologna
Eggs
Vegan Diet
Butter
Cheese
21
NR
NR
NR
NR
NR
NR
NR
NR
21
NR
NR
0
NR
NR
NR
NR
NR
0.07-0.27
NR
NR
ND
NR
NR
NR
NR
NR
0.16
280
230
NA
140
16
7
250
32
Spain
Various U.S.
Locations
Various U.S.
Locations
Various U.S.
Locations
Various U.S.
Locations
Various U.S.
Locations
Various U.S.
Locations
Various U.S.
Locations
Various U.S.
Locations
__
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Urban
NR
95
95
95
95
95
95
95
95
11
4
4
4
4
4
4
4
4
supermarkets
DRAFT-DO NOT QUOTE OR CITE
B-271
December 2003
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Table B-16 Environmental Levels of PCBs in Food Products (ppt) (continued)
IUPAC
number Chemical
180 2,2',3,4,4',5,5-HpCB
(continued)
189 2,3,3',4,4',5,5'-HpCB
Sample
Type
Eggs
Butter
Roast beef
Cream
Butter
Number
samples
3
21
1
1
1
Number
positive
samples
NR
21
1
1
1
Concentration
range
NR
0.12-1.26
NR
NR
NR
Cone.
mean
16
0.61
10
10
7
Location
Various U.S. Sites
Spain
Canada
Canada
Canada
Location description
__
Urban
Urban
Urban
Sample
year
95
NR
86-88
86-88
86-88
Ref.
no.
10
11
6
6
6
Comments
supermarkets
Footnote References
NR= Not Reported
Sources:
1. Himberg (1993)
2. Van Rhijr et al. (1993)
3. Mes and Weber (1989)
4. Schecter etal. (1996)
5. Winters etal. (1996)
6. Mes et al. (1991)
7. Lorber et al. (1998)
8. Lorber etal. (1997)
9. Ferrario et al. (1997)
10. Schecter et al. (1997)
11. Ramos etal. (1999)
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December 2003
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APPENDIX C. BIOAVAILABILITY OF DIOXIN
Table C-1. Summary of Data on the Bioavailability of
2,3,7,8-TCDD Following Ingestion of Environmental
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APPENDIX C. BIOAVAILABILITY OF DIOXINS
C.I Bioavailability Data
Umbreit et al. (1985, 1986a,b) conducted experiments in guinea pigs,
administering 2,3,7,8-TCDD in corn oil, 2,3,7,8-TCDD added to chemically
decontaminated soil, or soil from two industrial sites in Newark, New Jersey (a
manufacturing site and a salvage site) contaminated with CDDs. 2,3,7,8-TCDD was the
principal lower chlorinated isomer (dioxin or furan) present in the soil from the
manufacturing site (for which a chemical analysis was presented). Soil from the
manufacturing site was found to have 1,500 to 2,500 ppb 2,3,7,8-TCDD under soxhlet
extraction; release under ambient temperature manual solvent extraction was much lower,
reported as ">2.5 ppb." The soil from the salvage site was reported as approximately
180 ppb 2,3,7,8-TCDD under soxhlet extraction.
In this study, groups of two or four male and two or four female guinea pigs
received single gavage doses of the test materials and were observed until death or
sacrifice at 60 days. 2,3,7,8-TCDD in corn oil or in recontaminated soil (6 g/kg in both)
proved highly toxic, without similar toxicity being observed in animals treated with up to
twice this dose of 2,3,7,8-TCDD in the soil from the manufacturing site. The limited data
on 2,3,7,8-TCDD levels in the liver showed much higher levels following administration of
recontaminated soil versus contaminated soil from the manufacturing site.
Umbreit et al. (1986a) thus demonstrated that gavaged 2,3,7,8-TCDD containing
soil from the manufacturing site was substantially less toxic than equivalent doses of
2,3,7,8-TCDD in corn oil. However, quantitative comparison of the effective doses in this
study is difficult. Approaches to a quantitative comparison are outlined below.
(1) Guinea pigs receiving 12 ug/kg 2,3,7,8-TCDD in contaminated
soil experienced no deaths, while five out of eight guinea pigs
receiving 6 ug/kg 2,3,7,8-TCDD in corn oil died, with no
groups tested having lower doses in corn oil. Other authors
have provided data on the toxic effects of 2,3,7,8-TCDD in
corn oil which could aid in the comparison.
McConnell et al. (1984) observed one out of six animals dying at
1 ug/kg and six out of six animals dying at 3 ug/kg. Silkworth et al. (1982)
observed three out of six animals dying at 2.5 ug/kg and no deaths out of
six at 0.5 ug/kg. Comparing these data directly with the Umbreit et al.
results would suggest that the 2,3,7,8-TCDD in the Newark manufacturing
site soil was less effective, by a factor of 10 or greater, in producing
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toxicity than 2,3,7,8-TCDD in corn oil.
(2) Umbreit et al. reported a "slightly reduced" weight gain in guinea pigs
receiving 6 ug/kg of 2,3,7,8-TCDD in Newark manufacturing site soil, and a
"greater reduction" at the 12 ug/kg dose. No other signs of toxicity were
noted in these groups. The animals receiving 6 ug/kg 2,3,7,8-TCDD in corn
oil, in contrast, exhibited a marked loss of body weight and showed toxicity
and mortality. Silkworth et al. (1982) also provided data on weights of
guinea pigs receiving 2,3,7,8-TCDD in corn oil. Those receiving 2.5 ug/kg
exhibited a marked reduction in weight gain among three out of six
survivors, while those receiving 0.5 ug/kg showed a weight gain comparable
to vehicle controls. Comparison of this weight data with that of Umbreit et
al. suggests that the 2,3,7,8-TCDD in corn oil was more than 5 times but
less than 25 times as potent as 2,3,7,8-TCDD in the Newark soil. This
comparison assumes that the effect of the Newark manufacturing site soil
on weight gain was due to 2,3,7,8-TCDD as opposed to other compounds
in the soil. Numerous other dioxin and furan compounds and other
chemicals have been identified in this soil (Umbreit et al., 1987a). It has not
been established that 2,3,7,8-TCDD is the sole or prime source of toxicity in
the soil.
(3) Umbreit et al. presented liver concentrations of 2,3,7,8-TCDD after death or
sacrifice at 60 days following gavage. Much lower concentrations of
2,3,7,8-TCDD were found in the livers of animals receiving soil from the
manufacturing site compared with those receiving the dose in corn oil.
There are, however, two factors that limit the conclusions than can be
drawn from this comparison.
First, the corn oil group experienced major toxicity and weight loss, particularly
complete loss of body fat. These changes may have affected the partitioning of 2,3,7,8-
TCDD within the body, leading to a higher concentration in the livers of the animals
experiencing toxicity. Second, the animals gavaged with corn oil died early-half were
dead by 26 days, while all of the guinea pigs treated with soil survived to 60 days (with
the exception of one gavage death). The U.S. EPA (1985c) reported a half-life for
2,3,7,8-TCDD elimination of 30 _+_ 6 or 22 to 43 days from two studies in guinea pigs.
Additionally, the U.S. EPA (1985c) stated that elimination in the guinea pig may follow
zero-order kinetics. Differences in elimination due to differences in periods of survival are
likely to have affected the relative quantities of 2,3,7,8-TCDD found in the livers of the
test groups.
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Perhaps a more appropriate comparison can be made with the four animals
receiving 0.32 ug/kg of 2,3,7,8-TCDD in contaminated soil from the Newark salvage site.
These animals experienced no reported toxic signs (weight data not presented) and
survived the full 60-day experiment. Approximately 6% of the gavage dose was found in
the liver of these animals, while only about 0.06% of the gavage dose was found in the
livers of guinea pigs in the 12 ug/kg group receiving the Newark manufacturing site soil.
This would suggest that the 2,3,7,8-TCDD in the manufacturing site soil was 100 times
less bioavailable. However, given the different doses used and the fact that only a single
pooled sample was analyzed for 2,3,7,8-TCDD in each group, caution must be used in
interpreting this comparison.
The 2,3,7,8-TCDD in soil from the salvage site was substantially bioavailable,
based on the single liver tissue analysis. Approximately 6% of the administered dose was
recovered from the livers of these animals at 60 days. This can be compared with data on
hamsters given 2,3,7,8-TCDD in corn oil by McConnell et al. (1984), where approximately
8% of the 2,3,7,8-TCDD could be recovered in the 1 ug/kg dose group among survivors
at 30 days.
McConnell et al. (1984) treated Hartley guinea pigs (2.5 weeks old) with single
gavage doses of either 2,3,7,8-TCDD or dioxin contaminated soil from two sites in
Missouri. The 2,3,7,8-TCDD concentrations from the two sites were reported at 700 and
880 ppb respectively; total tetrachlorodibenzofurans (TCDF) concentrations in the soil
were 40 to 80 ppb, and polychlorinated biphenyls (PCB) concentrations were 3 to 4 ppm.
Taking into account the relative toxicities, the authors concluded that toxicity from the
other compounds was likely to be small compared with that from 2,3,7,8-TCDD. Livers
were analyzed for 2,3,7,8-TCDD at death or sacrifice at 30 days following treatment.
Treatment deaths occurred between 5 and 21 days post-gavage.
Guinea pigs that died exhibited severe loss of body fat, markedly reduced thymus
and testicle size, and adrenal hemorrhage. No adverse affects were noted in animals
treated with decontaminated soil. For 2,3,7,8-TCDD in corn oil and for both contaminated
soils, there were clear dose-responses in mortality. The calculated LD50 values for the two
soil types were lower than the LD50 for 2,3,7,8-TCDD in corn oil by a factor of three to
four.
There was a dose-response between the liver concentration of 2,3,7,8-TCDD and
the gavage dose; the details of this relationship are complex. Animals dying during the
experiment had liver concentrations a factor of 1.4 to 3.2 higher than animals in the
same dose groups who survived 30 days. This observation makes quantification of the
dose-response relationships difficult (all or most of the animals in the low-dose groups
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survived the experiment, while all of the animals in the high-dose groups died). When the
liver concentrations of 2,3,7,8-TCDD in animals dying early at the middle and high-dose
groups are compared, there appears to be a greater-than-linear increase in liver
concentration with dose for the Times Beach and Minker Stout soil groups, with a 3.3-fold
increase in dose producing a 10- to 13-fold increase in liver concentration.
Liver concentrations of animals in the different dosing groups can best be
compared among groups that experienced similar mortality.
(1) Animals in dose groups in which all animals died within 30 days: 2,3,7,8-
TCDD in corn oil, approximately 20% of the administered dose was in the
liver. For the soil-treated groups, 13% and 11 % of the doses, respectively,
were in the liver. Comparison of these data suggest that 2,3,7,8-TCDD was
approximately twice as available through corn oil as through soil.
(2) Animals surviving the 30-day experiment (in groups where at least 4 out of
6 survived): For 2,3,7,8-TCDD in corn oil, 7.5% of the administered dose
was in the liver. For soil-treated animals < 3.6, 1.3, < 4.2, and 2.0% of
the doses, respectively, were in the liver. Comparison here would suggest
that 2,3,7,8-TCDD was approximately four times as available through corn
oil as through soil.
The authors note that the differences in liver concentrations observed in the study
may reflect varying partitioning of the 2,3,7,8-TCDD among internal organs, since dying
animals suffered major loss of body weight and fat content. In addition, surviving animals
would have had greater opportunity to metabolize and excrete 2,3,7,8-TCDD due to a
longer lifetime.
Umbreit et al. (1986a) reported additional chemical analyses of the Times Beach
soil. Soxhlet extraction of the Times Beach soil yielded a similar quantity of 2,3,7,8-
TCDD to the solvent extraction reported by McConnell et al. (1984). This is in contrast to
the Newark manufacturing site soil used in the Umbreit et al. (1987a) experiments, where
only a small fraction of soxhlet-extractable 2,3,7,8-TCDD was extractable by the solvent
extraction methodology used by McConnell et al. (1984).
McConnell et al. (1984) also reported an experiment in which groups of six
Sprague-Dawley rats were given single gavage doses of 2,3,7,8-TCDD in corn oil or
dioxin-contaminated soil from the Minker site. Induction of aryl hydrocarbon hydroxylase
(AHH) in the rat livers was measured at sacrifice 6 days after dosing. Experimental doses
ranged from 0.4 to 5.0 ug/kg 2,3,7,8-TCDD. Measured AHH induction was similar for
groups receiving 2,3,7,8-TCDD in corn oil or receiving contaminated soil containing nearly
equal doses of 2,3,7,8-TCDD. For example (based on the rate of formation of 3-
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hydroxybenzo[a]pyrene), AHH activity was measured at 1,269 pmole min"1 mg"1 for the
group receiving 5 ug/kg 2,3,7,8-TCDD in corn oil and at 1,230 pmole min"1 mg"1 for the
group receiving 5.5 ug/kg 2,3,7,8-TCDD in contaminated soil. For the five dose groups,
the AHH activity for the soil group ranged from 50% to 110% of the activity in the corn
oil group.
The McConnell et al. (1984) rat data indicate that the bioavailability of 2,3,7,8-
TCDD from the Minker site soil was at least 50% of that of equivalent doses of 2,3,7,8-
TCDD in corn oil.
Lucier et al. (1986) provided additional information on the induction of hepatic
enzymes in rats by the 2,3,7,8-TCDD contaminated soil from the Minker site tested by
McConnell et al. (1984). AHH induction was similar for the groups of rats receiving
2,3,7,8-TCDD in corn oil and contaminated soil (within a factor of two) over a broader
range of doses (0.015 ug/kg to 5 ug/kg) than reported by McConnell et al. (1984). In a
second enzyme assay using the same animals, UDP glucuronyltransferase activity was
found to be slightly higher in groups receiving 2,3,7,8-TCDD in corn oil than groups
receiving equal doses in contaminated soil.
Liver concentrations of 2,3,7,8-TCDD for the rats were also reported. For the corn
oil vehicle the liver concentrations were 40.8 _+_ 6.5 ppb at the 5 ug/kg dose and 7.6 _+_
2.5 ppb at the 1 ug/kg dose. Assuming that the liver comprises 4.0% of body weight,
the retention rates for the 5 and 1 ug/kg doses were 33% and 30%, respectively. In rats
receiving 2,3,7,8-TCDD in contaminated soil, the 5.5 ug/kg group had liver concentrations
of 20.3 _+_ 1 2.9 ppb, and the 1.1 ug/kg group had concentrations of 1.8 _+ 0.3. Thus,
retention rates for the 5.5 and 1.1 ug/kg groups are estimated at 14% and 7%,
respectively. These data indicate that liver retention in the soil group was 20% to 40% of
that in the corn oil vehicle groups.
Umbreit et al (1986b) report additional studies of mortality in guinea pigs treated
with soil containing 2,3,7,8-TCDD from Newark (manufacturing site) and Missouri (Times
Beach) previously tested by Umbreit et al (1985, 1986a) and McConnell et al. (1984),
respectively. Guinea pigs received a single gavage dose of a soil suspension and were
observed for 60 days. After autopsy, deaths were classified as whether or not they
appeared to be due to TCDD toxicity. Substantial mortality (25% overall) from conditions
not attributed to TCDD was observed across all groups.
The data for both the Newark and Missouri sites are similar in trend for the
previous data on these sites; and clearly indicate the greater toxicity of the Newark soil for
given equal administered doses of 2,3,7,8-TCDD. With larger groups of guinea pig
studied, a toxicity-related death was observed in both the 5 and 10 mg/kg dose groups for
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Newark soil while no deaths were observed in corresponding dose groups (6 and 12
mg/kg) with fewer animals in Umbreit et al. (1986a).
Comparing groups within this study, similar mortality (1 or 2 deaths in 10 to 16
animals) was seen in both the 5 and 10 ug/kg Newark groups and the 1 and 3 ug/kg
Missouri groups. These results suggest that the toxicity of these materials differs by an
order of magnitude or less. As noted above, the degree to which toxicity from these soils
can be attributed to 2,3,7,8-TCDD in the presence of numerous other related toxic
compounds is not known. 2,3,7,8-TCDD tissue concentrations were not reported in this
work.
In another comparative study Umbreit et al. (1987b) compared the Newark
manufacturing site and Times Beach soils in the induction of aryl hydrocarbon hydroxylase
(AHH) in rats. While the use of only single dose levels prevents detailed analysis, the two
soils proved quite similar in their ability to induce AHH. The explanation for the difference
in this finding from those observed in the toxicity studies discussed above is not clear, but
may relate to the presence of other toxic and/or AHH inducing compounds.
Umbreit et al. (1987a) report a reproductive toxicity study with soils from the
Newark manufacturing site and salvage yard previously studied by Umbreit et al. (1986a).
Female mice were treated thrice weekly with soil from these sites, with treatment
continuing through fertilization to weaning of pups. The total doses of 2,3,7,8-TCDD
received by the mice were 720 ug/kg in manufacturing site soil, and 86 ug/kg in salvage
yard soil. A corn oil vehicle group and a recontaminated soil group received a total of 225
ug/kg.
Deaths in animals showing "classic signs" of TCDD toxicity were observed in the
corn oil and recontaminated soil groups, and indicate appreciable bioavailability of 2,3,7,8-
TCDD. Deaths were also observed in animals receiving manufacturing site soil but the
authors did not observe "classic signs" of TCDD toxicity. Fewer live pups born and fewer
pups surviving until weaning were observed in the manufacturing site soil group compared
with those receiving decontaminated soil. TCDD completely blocked reproduction in the
corn oil and recontaminated soil groups. The results of this study demonstrate acute and
reproductive effects occurred in animals receiving manufacturing site soil. However, these
effects were of a lesser magnitude than those seen in animals treated with 2,3,7,8-TCDD
in corn oil at a dose three fold lower. The authors note the presence of substantial
quantities of other toxic substances in the manufacturing site soil (chemical analyses
presented). No toxic effects were noted in animals treated with salvage site soil, who
received a much smaller 2,3,7,8-TCDD dose. The data does not allow a quantitative
evaluation of the bioavailability of 2,3,7,8-TCDD.
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Kaminski et al. (1985) and Silkworth et al. (1982) reported the results of a series
of studies on the toxicity of soot containing dioxin and furan compounds from a fire
involving transformer fluid containing PCBs. Hartley guinea pigs (500 to 600 g) received
single oral doses of soot in an aqueous vehicle, a soxhlet extract of the soot in the same
vehicle, or 2,3,7,8-TCDD in either an aqueous vehicle or corn oil.
The soot was reported to contain 2.8 to 2.9 ppm 2,3,7,8-TCDD and 124 to 273
ppm 2,3,7,8-TCDF. The total polychlorinated dibenzofuran content was estimated at
5,000 ppm. Animal weights and mortality were recorded for 42 days, at which point the
survivors were sacrificed and LD50 values were calculated. Blood chemistry and a
pathologic examination were performed at sacrifice.
Silkworth et al. (1982) noted that the LD50s for contaminated soot and soot extract
were similar at 410 and 327 equivalent ug/kg, indicating that the matrix had only a small
effect on toxicity. If expressed in terms of the content of 2,3,7,8-TCDD, the LD50 from
soot is 2.5 ug/kg, which is a factor of seven below the LD50 for 2,3,7,8-TCDD in an
aqueous vehicle, suggesting that other compounds contributed to the toxicity of the soot
and soot extract.
The authors stated that they adopted an aqueous vehicle in these experiments
because it was nontoxic and provided a stable suspension of soot; they regarded this
vehicle as more appropriate for modeling of human exposure conditions than an oil
vehicle. The data from these experiments also demonstrate that use of an oil vehicle
leads to substantially greater 2,3,7,8-TCDD toxicity than does an aqueous vehicle.
Comparison of mortality and weight loss in groups of female guinea pigs receiving
500 ug/kg of soot or the equivalent amount of soot extract suggests that the extract may
be somewhat more toxic; however, all six animals died in the 1,000 ug/kg soot group,
while four out of five died in the 500 ug/kg extract group. Taken together, these data
indicate that the soxhlet extract of soot in an aqueous vehicle was between one and two
times as toxic as the soot itself. It is likely that a larger difference in toxicity would have
been observed if the soot extract was in an oil vehicle.
Van den Berg et al. (1983) fed small groups of male Wistar rats fly ash from a
municipal incinerator (pretreated with HCI) containing dioxins and furans, a soxhlet extract
of the fly ash, or a purified extract of the ash that was obtained using column
chromatography. 2,3,7,8-TCDD was present as 3.3% of the TCDD isomer group in the
fly ash extract. (The authors did not specify whether this reference was to crude or
purified extract.) 2,3,7,8-TCDF was present as 17.9% of the tetra-CDF isomer group in
the extract. The rats were fed 2 g/d fly ash mixed with diet or the residual from 2 ml_/d
extract after the extract was mixed with diet and the solvent was evaporated. The animals
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were exposed to the treated diet for 19 days, and then sacrificed, and the liver tissue was
analyzed for the presence of dioxins and furans.
Approximately 1 % of the 2,3,7,8-TCDD dose from fly ash was retained in the liver,
and approximately 4% of the dose of this isomer from fly ash extract was so retained.
The corresponding percentages for 2,3,7,8-TCDF are 0.3 and 1.0. Data on the retention
of isomer groups in adipose tissue were presented for the extract-treated groups but not
for the fly ash-treated group. The concentrations of the various isomers in adipose tissue
are comparable to, or less than, the concentrations in liver tissue.
The U.S. EPA (1985b) reported a half-life for elimination of 2,3,7,8-TCDD in the rat
of 20 days at high dose. If a similar half-life is assumed in this experiment, the quantities
of 2,3,7,8-TCDD in the animals at the end of the 19-day feeding experiment would be
significantly less than the absorbed dose, but still of the same order of magnitude.
However, the recovery percentages in this study are low for both the fly ash and fly ash
extract groups in comparison with other studies in which 2,3,7,8-TCDD was administered
to rats. Fries and Marrow (1975) fed rats diets containing 7 or 20 ppb of 2,3,7,8-TCDD
for a period of up to 42 days. After 14 days of feeding, the rat livers contained an
average of 32% of the cumulative administered dose; at 28 days, 21 % of the dose; and
at 42 days, 18% of the dose. Thus, in the van den Berg et al. (1983) study, the liver
retention of 2,3,7,8-TCDD for the fly ash extract group is a factor of five to eight below
what could be anticipated for the Fries and Marrow (1975) data, and the liver retention in
the van den Berg et al. (1983) group fed soot is a factor of 20 to 30 lower than that seen
by Fries and Marrow (1975). Data from Kociba et al. (1976), Rose et al. (1976), and
Kociba et al. (1978) lead to similar conclusions to those from the Fries and Marrow (1975)
data regarding the fraction of cumulative 2,3,7,8-TCDD dose retained in the rat liver.
An explanation of the low level of recovery for the animals receiving the soxhlet
extract of soot is not apparent. It is possible that the presence of multiple compounds
affected absorption or metabolism in the rats fed soot and soot extract.
A second approach to the van den Berg et al. (1983) data is to compare the ratios
of liver concentrations for dioxins in fly-ash-treated animals to the concentrations in
extract-treated animals. These ratios, based on measurements in small numbers of
animals, indicate a substantial bioavailability of dioxin and furan compounds from the
tested fly ash. This availability varied among the different isomers with the value of 0.3
for 2,3,7,8-TCDD, indicating that this isomer was three times as available from fly ash
extract as from fly ash.
Van den Berg et al. (1985) fed fly ash (pre-treated with HCI) to Wistar rats, guinea
pigs, and Syrian golden hamsters. Fly ash was mixed with standard laboratory diet at
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2.5% by weight, and animals were allowed to eat ad libitum. The amount of fly ash
consumed by each group of five rodents was determined by the authors. For each
species there were three groups of animals each fed fly ash for approximately 32 days
(group I), 60 days (group II), or 94 days (group III). Concentrations of dioxin and furan
isomer groups in the food were presented, and include 1.4 ng/g TCDD compounds and
2.1 ng/g TCDF compounds.
The authors presented calculated recovery percentages for the cumulative dose of
specific isomers in the rodent liver. For 2,3,7,8-TCDD in guinea pigs, 3.7%, 0.9%, and
1.4% of the administered dose was recovered in the liver in groups I, II, and III,
respectively. The 32-day (group I) recovery percentage is somewhat higher than seen in
the lower dose groups receiving 2,3,7,8-TCDD contaminated soil in McConnell et al.
(1984). The value in hamsters was approximately 2% (only reported for group II), and
analytical problems prevented this determination in rats. No other TCDD compounds were
quantified. Similarly, for 2,3,7,8-TCDF, guinea pigs showed retention of 4.7%, 2.2%,
2.5% of the administered dose in groups I, II, and III, respectively. For both 2,3,7,8-
TCDD and 2,3,7,8-TCDF the recovery percentages in guinea pigs at 32 days were
approximately a factor of 4 to 15 higher than that observed in the van den Berg et al.
(1983) study in rats.
Other TCDD compounds that were present showed comparable or somewhat lower
retention, averaging 1 % to 2% over the animals groups. No TCDD or TCDF compounds
were detected in hamster liver or analyzed for in rat liver. Higher chlorinated congeners
most typically showed retention in the range of 2% to 5% in rat liver and 1 % to 3% in
guinea pig liver, with the exception of 2,3,4,7,8-PeCDF (9.8%, 8.3%, and 11.3% in the
hamster groups). Few other compounds were found in hamster liver, but 2,3,4,7,8-
PeCDF was found with a recovery of 5% to 8% and 2,3,4,7,8-HxCDD was found at 3%
to 7%.
As with other experiments in which the retention of dioxins in the liver has been
determined, these percentages place a lower bound on the bioavailability of the dioxins
but, because not all dioxin is localized in the liver, do not permit bioavailability to be
estimated without knowledge of the elimination of the administered dose over time and
the quantity of dioxins in the remainder of the organism. No positive control group
receiving 2,3,7,8-TCDD was included for comparison.
Poiger and Schlatter (1980) conducted several experiments in Sprague-Dawley rats
(180 to 220 g) in which liver concentrations of tritium label from 2,3,7,8-TCDD were
determined using various doses and vehicles. All experiments consisted of a single gastric
intubation of 2,3,7,8-TCDD-containing material, followed by animal sacrifice at
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predetermined times. The doses used were well below the LD50 in the rat (the maximum
dose applied was 5 ug/kg), and no deaths or toxic effects were reported.
In a preliminary experiment, rats were treated with 14.7 ng/rat 2,3,7,8-TCDD in
ethanol. The results indicate substantial localization of 2,3,7,8-TCDD in the rat liver, with
a decrease of a factor of two in the fraction of the dose in the liver between 1 and 4
days. Poiger and Schlatter (1980) conducted all further studies with sacrifice at 24 hours
to maximize the recovery of 2,3,7,8-TCDD from the liver.
In a second experiment, the authors administered 2,3,7,8-TCDD doses in ethanol
ranging from 15 to 1,070 ng/rat to groups of six rats. They found a graded increase in
percentage retained in the liver from 37% _+_ 1 % at the 1 5 ng dose to 51 % _+_ 4% at 280
ng. At the high-dose point, the percentage may have fallen (42% _+_ 10% at 1,070 ng).
In a further experiment, 2,3,7,8-TCDD was administered at low dose in a series of
vehicles. These data demonstrate that administration of 2,3,7,8-TCDD in soil reduced the
retention of the dose in the liver to 66%, or 44% of the retention seen with 2,3,7,8-
TCDD
in ethanol. The lower value, 44%, was obtained for soil that was aged for 8 days at
30-40 °C following addition of 2,3,7,8-TCDD. This observation is consistent with the
findings of other studies reported here that 2,3,7,8-TCDD from environmental soil
(naturally aged) was generally less available than 2,3,7,8-TCDD freshly added to clean
samples of these soils. The aqueous suspension of 2,3,7,8-TCDD in activated carbon
showed little evidence of bioavailability; this is supported by the authors' measurements
showing that 2,3,7,8-TCDD was only slightly extractable from the activated carbon matrix
by various solvents. In contrast, 58% to 70% of 2,3,7,8-TCDD could be recovered from
soil samples by washing with hexane/acetone (4:1 v/v).
Poiger and Schlatter (1980) also presented results from several skin application
experiments with TCDD-containing materials using rats and rabbits (not reviewed here).
Bonaccorsi et al. (1984) reported the results of a study of gut absorption of
2,3,7,8-TCDD from soil taken from the Seveso, Italy accident site. Soil containing 81 _+.
8 ppb 2,3,7,8-TCDD from the "highly contaminated" area in Seveso was administered to
albino male rabbits (2.6 _+_ 0.3 kg) in daily gavage doses for seven days. Additional
samples of clean soil were spiked with 2,3,7,8-TCDD in the laboratory to yield 10 and 40
ppb contamination levels and were administered to rabbits following the same protocol.
For comparison, rabbits were also treated with 2,3,7,8-TCDD in solution in acetone-
vegetable oil (1:6) or alcohol-water (1:1). Rabbits were sacrificed on the day after
treatment stopped and liver concentrations of 2,3,7,8-TCDD were measured. The authors
did not remark on the presence or absence of toxicity in the treated rabbits. EPA (1985a)
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reports values for the single dose LD50 of 2,3,7,8-TCDD in rabbits of 11 5 and 275 ug/kg.
The total doses received by the rabbits in this study were approximately 54, 107, and
215 ug/kg over seven days. Based on this comparison, there is a likelihood that toxic
effects occurred in the Bonaccorsi work, and as noted above, toxicity has the potential to
affect the tissue concentrations of 2,3,7,8-TCDD. For this reason, the most appropriate
comparisons among these data are between groups showing similar liver concentrations of
2,3,7,8-TCDD, which may then be inferred to have experienced similar toxic effects.
That this method of comparison is desirable can also be seen from the Bonaccorsi
et al. (1984) data, where both solvent vehicle groups and the spiked soil groups show an
increase of the fraction of the dose in the liver at the higher administered doses.
However, it should be mentioned that use of two different solvent vehicles complicates
interpretation. Similar liver concentrations of 2,3,7,8-TCDD were seen in the 40 ug/d
solvent vehicle and 80 ug/d Seveso soil groups. Comparing the percentage of liver
retention in these two groups indicates absorption from Seveso soil was 40% of that from
the solvent vehicle. Using the same approach, comparison of the 80 ug/d solvent vehicle
and 160 ug/d Seveso soil groups indicates that absorption from the soil was 41 % of that
from the solvent.
The same approach can be used to compare absorption from the solvent vehicle
and from the spiked soil. In this case the 40 ug/d solvent vehicle group had the liver
concentrations closest to either the 40 or 80 ug/d spiked soil groups. Comparison of the
percentage of dose in the liver indicates absorption from spiked soil is 68-73% of that
from the solvent vehicle. Bonaccorsi et al. (1984) conducted work with either aged or
non-aged spiked soil but do not present data to allow a comparison of these groups.
Shu et al. (1987, as cited by Leung and Paustenbach, 1987) studied 2,3,7,8-TCDD
from the Missouri site tested by McConnell et al. (1984). Their paper reports an oral
bioavailability of approximately 43% in the rat dosed with environmentally contaminated
soil from Times Beach, Missouri. This figure did not change significantly over a 500-fold
dose range of 2 to 1450 ng 2,3,7,8-TCDD per kg of body weight for soil contaminated
with approximately 2, 30 or 60 ppb of 2,3,7,8-TCDD. The data from this study is not
now available to the Exposure Assessment Group of EPA for review.
C.2 Summary of Bioavailability
Table C-1
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summarizes data that are pertinent to the bioavailability of 2,3,7,8-TCDD from
environmental matrices. Studies of bioavailability, which examined soil samples, soot, and
fly ash, have utilized three methodologies: measuring acute toxicity, retention of 2,3,7,8-
TCDD in the liver, and induction of hepatic enzymes.
Among the five samples of soil from contaminated sites that have been tested,
three have shown substantial bioavailability, e.g., 25% to 50%, when compared with
2,3,7,8-TCDD in corn oil gavage. A fourth soil sample was compared with 2,3,7,8-TCDD
administered in a solvent vehicle, and fell in this range. The fifth soil, tested by Umbreit
et al. (1986a,b; 1987a,b) showed bioavailability substantially less than the other soils
tested. While difficult to gauge quantitatively, dioxin from this fifth soil may be an order
of magnitude less available than from the other soils.
Additionally, three samples of soil spiked with 2,3,7,8-TCDD have been tested for
bioavailability, including one sample in which the 2,3,7,8-TCDD was incubated with soil at
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an elevated temperature. The 2,3,7,8-TCDD added to these soil samples proved to be
highly available (e.g., 40% to 70%).
In one study, soot from a transformer fire containing dioxins and furans proved
similarly toxic to a soxhlet extract of the soot in an aqueous vehicle. However, the soot
extract may have proved more toxic if delivered in corn oil, as was 2,3,7,8-TCDD in the
soil studies. The availability of 2,3,7,8-TCDD and other dioxins and furans from
incinerator fly ash have been addressed by van den Berg et al. (1983, 1985) in extended
feeding studies. In these studies, liver retention of 2,3,7,8-TCDD from either fly ash or fly
ash extract proved low, with availability from fly ash being approximately 25% of that
from the extract.
The individual studies reviewed have a variety of limitations, as discussed in the
preceding text. A notable limitation was that some experiments were conducted using
highly toxic doses of 2,3,7,8-TCDD, so that determination of bioavailability was
complicated by wasting and early death of the test animals. It should also be noted that,
while the relative retention of 2,3,7,8-TCDD in the liver can serve as an appropriate
indication of differences in bioavailability between samples, the percentage of dose found
in the liver only places a lower bound on absorption. This is particularly relevant to
experiments where animals have been maintained for many weeks after dosing and an
undetermined quantity of 2,3,7,8-TCDD has been excreted.
Finally, toxicity data for mixtures for which both toxicity and bioavailability of
individual compounds may vary are difficult to interpret quantitatively in terms of
bioavailability.
As presented in U.S. EPA (1985c), Rose et al. (1976) determined gut absorption of
2,3,7,8-TCDD in a 1:25 mixture of acetone to corn oil (by volume) in the rat. In both
single dose and multiple dose experiments, measured absorption was approximately 85%.
Assuming that absorption from pure corn oil is similar to that from this mixture, and
assuming that absorption in other species for which data are not available is similar, the
85% factor can be applied to the data presented here to obtain an approximate range for
typical 2,3,7,8-TCDD absorption from soil. Using this factor, the estimated relative
bioavailability of 2,3,7,8-TCDD from soil is 25% to 50% and, when compared with corn
oil, provides an estimate of gut absorption of 20% to 40% of ingested 2,3,7,8-TCDD in
soil. This estimate is comparable with the 20% to 26% absorption from 2,3,7,8-TCDD
treated soil from the work of Poiger and Schlatter (1980).
Recognizing these limitations, the weight of evidence indicates that 2,3,7,8-TCDD
is often highly available from environmental materials. However, in one tested soil sample
the compound was substantially less bioavailable. While the data are too sparse to allow
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a prediction as to whether a particular environmental sample will prove more or less
bioavailable, one important suggestion has emerged. In the two samples that have proved
least bioavailable (the Umbreit et al. (1986a) manufacturing site soil sample, and 2,3,7,8-
TCDD on activated carbon tested by Poiger and Schlatter (1980)) the 2,3,7,8-TCDD was
largely resistant to solvent extraction. This was not the case for more bioavailable
materials.
Further research, using short-term experiments in which animals are handled under
identical conditions and are fed dioxins in different media, is needed for an improved
comparison of absorption between different environmental samples. Acutely toxic doses
should be avoided to ensure that tissue concentrations are directly interpretable.
Experiments studying both tissue retention and enzyme induction should prove valuable
for this research. Whole-body levels of 2,3,7,8-TCDD need to be related to liver
concentrations, and the effects of metabolism need to be addressed. The vehicle of
administration has been shown to affect acute 2,3,7,8-TCDD toxicity, and vehicle effects
need to be considered in designing experiments.
C.3 Distribution
Ryan et al. (1985) examined the distribution of 2,3,7,8-TCDD in two humans at
autopsy. On a weight basis, there were 6 ppt of TCDD in fat, 2 ppt in liver and below
levels of detection in kidney and muscle. They reported that on a per lipid basis the levels
were similar between tissues. It is important to note that one of these subjects suffered
from a fatty liver syndrome, possibly resulting in higher levels in the liver than might
normally be found in healthy individuals.
Poiger and Schlatter (1986) estimated that about 90% of the total body burden of
2,3,7,8-TCDD was sequestered in fat. Levels of 2,3,7,8-TCDD averaging 5-10 ppt have
been reported for background populations in St. Louis, MO, by Graham et al. (1986), and
in Atlanta, GA, and Utah by Patterson et al. (1986). These data are consistent with the
lipid bioconcentrations assumptions made in calculations of daily intakes (vidae supra).
Patterson et al. (1987) developed a high resolution gas chromatographic/high
resolution mass spectrometric analysis for 2,3,7,8-TCDD in human serum. A high
correlation was reported between adipose tissue and serum concentrations when adjusted
for total lipid content. The reader is referred to other documents (U.S/ EPA, 1993;
Schlatter, 1991; Schecter, 1991) for more details on the distribution and elimination.
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Biological Basis for Risk Assessment of Dioxins and Related Compounds; Gallo, M.;
Scheuplein, R.; Van Der Heijden, K. eds.; Banbury Report 35, Cold Spring Harbor
Laboratory Press.
Schlatter, C. (1991) Data on kinetics of PCDDs and PCDFs as a prerequisite for human
risk assessment. In: Biological Basis for Risk Assessment of Dioxins and Related
Compounds; Gallo, M.; Scheuplein, R.; Van Der Heijden, K. eds.; Banbury Report
35, Cold Spring Harbor Laboratory Press.
Shu, J.; Paustenbach, D.; Murray, F.J. (1988) Bioavailability of soil bound TCDD: oral
bioavailability in the rat. Fund. Appl. Toxicol. 10:648-654.
Silkworth, J.; Marti, D.; DeCapri, A.; Rej, R.; O'Keefe, P.; Kaminski, L. (1982) Acute
toxicity in guinea pigs and rabbits of soot from a polychlorinated biphenyl-
containing transformer fire. Toxicol. Appl. Pharmacol. 65:425-439.
Stanley, J.S.; Boggess, K.; Onstot, J.; Sack, T.; Remmers, J.; Breen, J.; Kutz, F.W.;
Robinson, P.; Mack, G. (1986) PCDDs and PCDFs in human adipose tissues from
the EPA FY82 NHATS repository. Chemosphere 15:1605-1612.
Umbreit, T.J.; Patel, D.; Gallo, M.A. (1985) Acute toxicity of TCDD contaminated soil
from an industrial site. Chemosphere 14:945-947.
Umbreit, T.H.; Hesse, E.J.; Gallo, M.A. (1986a) Bioavailability of dioxin in soil from a
2.4.5.-T manufacturing site. Science 232:497-499.
Umbreit, T.H.; Hesse, E.J.; Gallo, M.A. (1986b) Comparative toxicity to TCDD
C-21
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contaminated soil from Times Beach, Missouri, and Newark, New Jersey.
Chemosphere 1 5(9-1 2):21 21-21 24.
Umbreit, T.H.; Hesse, E.J.; Gallo, M.S. (1987a) Reproductive toxicity to female mice of
dioxin-contaminated soils from a 2,4,5-trichlorooxacetic acid manufacturing site.
Arch. Environmental Contamination and Toxicology 16: 461-466.
Umbreit, T.H.; Hesse, E.J.; Gallo, M.S. (1987b) Differential bioavailabilty of 2,3,7,8-
Tetrachlorodibenzo-p-dioxin from contaminated soils. American Chemical Society
Symposium Series No. 338, "Solving hazardous waste problems: Learning from
dioxin.
U.S. Environmental Protection Agency (1985a) Compilation of air pollutant emission
factors, Vol 1. Research Triangle Park, NC: Office of Air Quality Planning and
Standards.
U.S. Environmental Protection Agency (1985b) Rapid assessment of exposure to
particulate emission from surface contamination sites. Washington, DC: Office of
Solid Waste and Emergency Response. EPA-600/8-85-002.
U.S. Environmental Protection Agency (1985c) Health assessment document for
polychlorinated dibenzo-p-dioxins. Research Triangle Park, NC: Office of Air
Planning and Standards. EPA-600/8-84-014F.
U.S. Environmental Protection Agency (1993) Health assessment for 2,3,7,8-
tetrachlorodibenzo-p-dioxin (TCDD) and related compounds. Washington, DC:
Office of Health and Environmental Assessment.
van den Berg, M.; Ollie, K.; Hutzinger, 0. (1983) Uptake and selective retention in rats of
orally administered chlorinated dioxins and dibenzofurans from fly-ash and fly-ash
extract. Chemosphere 1 2:537-544.
van den Berg, M.; Vroom, A.; van Greevenbroek, M.; Olie, K.; Hutzinger, 0. (1985)
Bioavailability of PCDDs and PCDFs adsorbed on fly ash in the rat, guinea pig, and
Syrian golden hamster. Chemosphere 14:865-869.
C-22
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DO NOT CITE OR QUOTE NAS Review Draft
www.epa.gov/ncea/dioxin
Exposure and Human Health Reassessment
of 2,3,7,8-Tetrachlorodibenzo-/7-Dioxin (TCDD)
and Related Compounds
Part I: Estimating Exposure to Dioxin-Like Compounds
Volume 3: Site-Specific Assessment Procedures
Exposure Assessment and Risk Characterization Group
National Center for Environmental Assessment - Washington Office
Office of Research and Development
U.S. Environmental Protection Agency
Washington, DC
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DISCLAIMER
This document is a draft. It has not been formally released by the U.S. Environmental
Protection Agency and should not at this stage be construed to represent Agency policy. Mention
of trade names or commercial products does not constitute endorsement or recommendation for
use.
3-i December 2003
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CONTENTS
1. INTRODUCTION 1-1
1.1. BACKGROUND 1-1
1.2. DESCRIPTION OF DIOXIN-LIKE COMPOUNDS 1-2
1.3. TOXICITY EQUIVALENCY FACTORS 1-4
1.4. OVERALL COMMENTS ON THE USE OF VOLUME IV OF THE DIOXIN
EXPOSURE DOCUMENT 1-8
1.5. EXECUTIVE SUMMARY OF VOLUME IE 1-11
REFERENCES FOR CHAPTER 1 1-45
2. ESTIMATING EXPOSURES AND RISKS 2-1
2.1. INTRODUCTION 2-1
2.2. EXPOSURE EQUATION 2-2
2.3. CANCER AND NON-CANCER RISK ASSESSMENT 2-4
2.3.1. Background Exposure Dose and Body Burden 2-6
2.3.2. The Increment Over Background Concept for Non-Cancer
Risk Assessment 2-13
2.3.3. Traditional Agency Cancer Risk Assessment Procedures 2-15
2.3.4. Interpretation of Cancer and Non-Cancer Risk Assessment
Results for Dioxin 2-18
2.4. THE TOXIC EQUIVALENCY PROCEDURE 2-19
2.5. PROCEDURE FOR ESTIMATING EXPOSURE 2-20
2.6. STRATEGY FOR DEVISING EXPOSURE SCENARIOS 2-22
2.7. EXPOSURE PATHWAYS AND PARAMETERS 2-26
2.7.1. Soil Related Exposures 2-26
2.7.1.1. Soil Ingestion 2-26
2.7.1.2. Soil Dermal Contact 2-28
2.7.2. Vapor and Dust Inhalation 2-30
2.7.3. Water Ingestion 2-32
2.7.4. Ingestion of Terrestrial Food Products 2-32
2.7.4.1. Derivation of the Contact Fractions for Beef, Milk, Chicken, Eggs,
Vegetables, andFruits 2-35
2.7.4.2. Beef Ingestion 2-37
2.7.4.3. Dairy Ingestion 2-37
2.7.4.4. Chicken Ingestion 2-38
2.7.4.5. Egglngestion 2-38
2.7.4.6. Vegetable and Fruit Ingestion 2-38
2.7.5. Fish Ingestion 2-39
REFERENCES FOR CHAPTER 2 2-41
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CONTENTS (continued)
3. EVALUATING ATMOSPHERIC RELEASES OF DIOXIN-LIKE COMPOUNDS
FROM COMBUSTION SOURCES 3-1
3.1. INTRODUCTION 3-1
3.2. ESTIMATING THE EMISSIONS OF DIOXIN-LIKE COMPOUNDS
FROM ANTHROPOGENIC COMBUSTION SOURCES 3-2
3.2.1. A Strategy for Generating Emission Factors 3-4
3.2.2. Use of Homologue and Congener-Specific Profiles to Estimate
Emission Factors 3-7
3.2.2.1 Using Congener Profiles to Convert Total CDD/F 3-8
3.2.2.2 Estimating Congener-Specific Emissions when no Congener Profiles
are Available 3-8
3.2.3. Estimation of Emissions of Dioxin-Like Compounds from the
Hypothetical Incinerator 3-9
3.2.4. Estimation of the Vapor Phase/Particle Phase Partitioning of Emissions
of Dioxin-Like Compounds 3-10
3.2.4.1. Vapor Phase/Particulate Phase Inferences from Stack
Measurements 3-10
3.2.4.2. Discussion of Vapor/Particle Ratios Derived from Stack
Testing Methods 3-13
3.2.4.3. Vapor/Particle Partitioning of CDD/Fs from Ambient Air
Sampling 3-16
3.2.4.4. Discussion of the Vapor/Particle Partitioning in Ambient
Air Sampling Studies 3-23
3.2.4.5. Junge-Pankow Model of Particle/Gas Distribution in
Ambient Air 3-23
3.2.4.6. Modeling the Vapor/Particle (V/P) Distribution of CDD/Fs .. 3-26
3.2.4.7. Comparison of Measured and Modeled Vapor/Particle
Distributions for CDD/Fs 3-29
3.2.4.8. Discussion of Monitored and Modeled Results for CDD/Fs . . 3-31
3.2.4.9. Discussion of Vapor/Particle Partitioning 3-33
3.2.5. Estimation of the Concentration of Dioxin-Like Compounds in
Incineration Ash 3-34
3.3. AIR DISPERSION/DEPOSITION MODELING OF THE STACK GAS
EMISSIONS OF DIOXIN-LIKE COMPOUNDS 3-36
3.3.1. Basic Physical Principles Used to Estimate Atmospheric
Dispersion/Deposition of Stack Emissions 3-37
3.3.2. Estimation of Dry Surface Deposition Flux 3-39
3.3.3. Estimation of the Particle Size Distribution in the Stack Emissions . . . 3-42
3.3.4. Estimation of Wet Deposition Flux 3-44
3.3.5. Using ISCST3 to Model Emissions of Particles and Vapors 3-46
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CONTENTS (continued)
3.4. RESULTS OF THE AIR DISPERSION MODELING OF CONGENER-
SPECIFIC EMISSIONS FROM THE HYPOTHETICAL ORGANIC
WASTE INCINERATOR 3-47
3.5. REVIEW OF PROCEDURES FOR ESTIMATING SITE-SPECIFIC
IMPACTS FROM A STACK EMISSION SOURCE 3-50
REFERENCES FOR CHAPTER 3 3-52
4. ESTIMATING EXPOSURE MEDIA CONCENTRATIONS 4-1
4.1. INTRODUCTION 4-1
4.2. BACKGROUND FOR SOLUTION ALGORITHMS 4-1
4.3. ALGORITHMS FOR THE SOIL CONTAMINATION SOURCE
CATEGORY 4-7
4.3.1. Surface Water and Sediment Contamination 4-8
4.3.2. Exposure Site Soil Concentrations 4-23
4.3.3. Vapor- and Particle-Phase Air Concentrations 4-31
4.3.4. Biota Concentrations 4-41
4.3.4.1. Fish Concentrations 4-41
4.3.4.2. Vegetation Concentrations 4-49
4.3.4.3. Beef and Milk Concentrations 4-69
4.3.4.4. Chicken and Egg Concentrations 4-78
4.3.5. Specific Cases of Soil Contamination 4-84
4.3.5.1. Landfills Receiving Ash from Municipal Waste
Incinerators 4-85
4.3.5.2. Land Application of Sludge from Pulp and Paper Mills 4-92
4.3.5.3. Sites Studied in the National Dioxin Study 4-95
4.4. ALGORITHMS FOR THE STACK EMISSION SOURCE CATEGORY .... 4-97
4.4.1. Steady-State Soil Concentrations 4-99
4.4.2 Surface Water Impacts 4-100
4.5. ALGORITHMS FOR THE EFFLUENT DISCHARGE SOURCE
CATEGORY 4-106
4.5.1. The Simple Dilution Model 4-108
REFERENCES FOR CHAPTER 4 4-116
3-iv December 2003
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CONTENTS (continued)
5. DEMONSTRATION OF METHODOLOGY 5-1
5.1. INTRODUCTION 5-1
5.2. STRATEGIES FOR DEVISING EXPOSURE SCENARIOS 5-2
5.3. EXAMPLE EXPOSURE SCENARIOS 5-8
5.4. EXAMPLE COMPOUNDS 5-12
5.5. SOURCE TERMS 5-13
5.6. RESULTS 5-19
5.6.1. Observations Concerning Exposure Media Concentrations 5-20
5.6.2. Observations Concerning LADD Exposure Estimates 5-27
5.7. HEALTH RISK DEMONSTRATIONS 5-30
REFERENCES FOR CHAPTER 5 5-34
6. USER CONSIDERATIONS 6-1
6.1. INTRODUCTION 6-1
6.2. CATEGORIZATION OF METHODOLOGY PARAMETERS 6-1
6.3. SENSITIVITY ANALYSIS 6-7
6.3.1. Limitations of the Sensitivity Analysis Exercises 6-7
6.3.2. Methodology Description and Parameter Assignments 6-10
6.3.3. Results 6-23
6.3.3.1. Estimation of Vapor-Phase and Particle-phase Air
Concentrations Distant from a Site of Soil Contamination . . . 6-23
6.3.3.2. Estimation of Soil Erosion Impacts to Nearby Sites of
Exposure 6-25
6.3.3.3. Estimation of Soil Erosion Impacts to Nearby Surface
Water Bodies 6-27
6.3.3.4. Vapor-Phase Transfers and Particle-Phase Depositions to
Above Ground Vegetation 6-29
6.3.3.5. Estimation of Below Ground Vegetation Concentrations 6-33
6.3.3.6. Beef Fat Concentration Estimation in the Soil Contamination
and Stack Emission Source Categories 6-34
6.3.3.7. Impact of Distance from the Stack Emission Source on
Concentrations in Soil, Vegetables, and Beef Fat 6-37
6.3.3.8. Water and Fish Concentrations Resulting from Effluent
Discharges 6-38
6.3.3.9. Water and Fish Concentrations Resulting from Stack
Emissions 6-39
6.3.4. Key Trends from the Sensitivity Analysis Testing 6-40
6.4. MASS BALANCE CONSIDERATIONS 6-42
REFERENCES FOR CHAPTER 6 6-48
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CONTENTS (continued)
7. MODEL COMPARISONS AND MODEL VALIDATIONS 7-1
7.1. INTRODUCTION 7-1
7.2. MODEL COMPARISON EXERCISES 7-3
7.2.1. Evaluation of Alternative Air-to-Leaf Modeling Approaches 7-3
7.2.1.1. The FieldData 7-4
7.2.1.2. Model Descriptions and Application to the FieldData 7-4
7.2.1.3. Results and Discussion of the Air-to-Leaf Model Comparison
Exercise 7-9
7.2.1.4. Literature Comparisons ofAir-to-Plant Modeling Approaches
7-14
7.2.2. An Alternate Modeling Approach for Estimating Water Concentrations
Given a Steady Input Load from Overland Sources 7-16
7.2.3. Estimating Fish Tissue Concentrations Based on Water Column
Concentrations Rather than Bottom Sediment Concentrations 7-19
7.2.4. Other Modeling Approaches and Considerations for Air Concentrations
Resulting from Soil Volatilization 7-24
7.2.5. Alternate Models for Estimating Plant Concentrations from Soil
Concentrations 7-37
7.2.6. Alternate Modeling Approaches for Estimating Beef and Milk
Concentrations 7-40
7.2.7. An Alternate Approach to Vapor/Particle Partitioning in the Air 7-49
7.3. MODEL VALIDATION EXERCISES 7-54
7.3.1. The Impact of Dioxin Soil Contamination to Nearby Soils 7-54
7.3.2. Soil Concentrations and Concurrent Concentrations in Bottom Sediments
and Fish 7-57
7.3.3. Other Bottom Sediment Concentration Data 7-61
7.3.4. Data on Water Concentrations of Dioxin-Like Compounds 7-63
7.3.5. Data on Fish Concentrations in the Literature 7-64
7.3.6. Impact of Pulp and Paper Mill Effluent Discharges on Fish Tissue
Concentrations 7-67
7.3.7. Air Dispersion and Soil Concentration Modeling Around an Incinerator
Known to be Emitting Large Amounts of Dioxins 7-73
7.3.7.1. Modeling Procedures 7-75
7.3.7.2. Results and Discussions 7-82
7.3.7.3. Discussion and Concluding Remarks 7-88
7.3.8. Air-to-Soil and Soil-to-Air Modeling 7-90
7.3.9. Transfer of Dioxins From Soils to Below Ground Vegetables 7-93
7.3.10. Impacts of Contaminated Soils to Vegetation 7-95
7.3.11. Comparison of Measured and Modeled Vapor/Particle Distributions for
Semivolatile Compounds Other Than Dioxin 7-100
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CONTENTS (continued)
7.3.12. An Update of the Air-to-Beef Model Validation Exercise 7-103
7.3.13. Expansion of the Terrestrial Food Chain Model for Dioxins and
Applications to other Foodstuffs in the United Kingdom 7-113
7.3.14. Beef and Milk Fat Concentrations when Soil is the Source of
Contamination 7-114
REFERENCES FOR CHAPTER 7 7-116
UNCERTAINTY 8-1
8.1. INTRODUCTION 8-1
8.2. A DISCUSSION OF UNCERTAINTY ISSUES ASSOCIATED WITH THE
USE OF ISCST3 FOR TRANSPORT AND DISPERSION OF STACK
EMITTED CONTAMINANTS 8-3
8.3. UNCERTAINTIES AND VARIABILITIES WITH CHEMICAL-SPECIFIC
MODEL PARAMETERS AND ASSUMPTIONS 8-7
8.4. UNCERTAINTIES ASSOCIATED WITH EXPOSURE PATHWAYS 8-11
8.4.1. Lifetime, Body Weights, and Exposure Durations 8-12
8.4.2. Soil Ingestion Exposure 8-13
8.4.3. Soil Dermal Contact Pathway 8-16
8.4.4 Water Ingestion 8-18
8.4.5. Fish Ingestion Exposure 8-19
8.4.6. Vapor and Particle Phase Inhalation Exposures 8-22
8.4.7. Fruit and Vegetable Ingestion 8-26
8.4.8. Ingestion of Terrestrial Animal Food Products Including Beef, Milk,
Chicken, and Eggs 8-30
8.5. USE OF PROBABILISTIC TECHNIQUES FOR ASSESSING EXPOSURE
TO DIOXIN-LIKE COMPOUNDS 8-33
REFERENCES FOR CHAPTER 8 8-38
3-vii December 2003
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TABLES
Table 1-1. The TEF scheme for I-TEQDF 1-12
Table 1-2. The TEF scheme for dioxin-like coplanar PCBs, as determined by the
World Health Organization in 1994 1-13
Table 1-3. The TEF scheme for TEQDFP-WHO98 1-14
Table 2-1. Summary of exposure pathway parameters selected for the demonstration
scenarios of Chapter 5 2-45
Table 2-2. Percent weight losses from preparation of various meats 2-48
Table 3-1. The number of dioxin-like and total congeners within dioxin, furan, and
coplanar PCB homologue groups 3-61
Table 3-2. Emission factors and average emissions used for the hypothetical incinerator . 3-62
Table 3-3. Percent distribution of dioxins and furans between vapor-phase (V) and
particulate-phase (P) as interpreted by various stack sampling methods
(4-D = tetraCDD; 4-F = tetraCDF) 3-63
Table 3-4. Review of air monitoring data on the percentage of measured dioxins and
furans which are in the particle phase (4-D = tetraCDD; 4-F = tetraCDF) .... 3-65
Table 3-5. Values of 6, VT, and TSP in different air regimes 3-66
Table 3-6. Data for calculation of the liquid subcooled vapor pressure, p°L, at 20 °C, and
final p°L for the dioxin-like congeners 3-67
Table 3-7. Particle fractions, cj), in four airsheds at 20°C for the dioxin-like congeners . . . 3-68
Table 3-8. Regression parameters slope m and intercept b for Equation (3-5),
Log Kp = m Log p°L + b, based on field measurements of particle/gas
distributions for CDD/Fs 3-69
Table 3-9. Comparison of monitored and modeled particulate percentage for CDD/F
homologues at 20°C 3-70
Table 3-10. Factors that influence the dry deposition removal rate in the atmosphere 3-71
Table 3-11. A summary of dry deposition velocities for particles 3-72
Table 3-12. Generalized particle size distribution (|am), and proportion of available
surface area, in particulate emissions from incineration 3-73
Table 3-13. Unit wet deposition scavenging coefficients per particle diameter category
(micrometers) used in the example ISCST3 analysis, expressed as
l/(sec-mm/hr) 3-74
Table 3-14. Emission of CDD/Fs (g/sec) from the hypothetical incinerator 3-75
Table 3-15. Modeling parameters used in the ISCST3 modeling of CDD/F emissions
from the hypothetical incinerator 3-76
Table 3-16. Predicted average vapor-phase concentrations of CDD/Fs (pg/m3; columns
are downwind distance in km) 3-77
Table 3-17. Predicted average particle-phase concentrations of CDD/Fs (pg/m3; columns
are downwind distance in km) 3-78
3-viii
December 2003
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TABLES (continued)
Table 3-18. Predicted annual dry deposition of particle-bound CDD/Fs (pg/m2-yr; columns
are downwind distance in km) 3-79
Table 3-19. Predicted annual wet deposition of particle-bound CDDs/Fs (pg/m2-yr;
columns are downwind distance in km) 3-80
Table 4-1. Available Biota to Sediment Accumulation Factors, BSAF, for dioxin-like
compounds 4-130
Table 4-2. Available Biota to Sediment Accumulation Factors, BSAF, for PCBs 4-134
Table 4-3. Data and parameters used to determine the part of the plant concentration
which was due to the deposition of particle bound dioxins (see below table
for definition of columns) 4-137
Table 4-4. Development of the Bvpa using data of Welsch-Pausch, et al (1995) compared
against the Bvpa as developed in EPA (1994) (see below table for column
definitions) 4-138
Table 4-5. Ratios of dioxins and furans in milk fat (MF) and body fat (BF) to
concentrations in diets of farm animals 4-139
Table 4-6. Ratios of PCBs in milk fat (MF) and body fat (BF) to concentrations in
diets of lactating cows 4-141
Table 4-7. BCFs for liver, adipose, thigh meat, and eggs calculated from the Cal-EPA
experiments 4-142
Table 4-8. Chicken and egg BCFs for Aroclor mixtures 4-143
Table 4-9. Ranges of concentrations of PCDDs, PCDFs, and PCBs in municipal waste
combustor ash (results in ng/g or ppb; ND = Not detected; NR = not reported;
Tr = trace; DL between 0.01 and 0.1 ng/g) 4-144
Table 5-1. Fate and transport parameters for the dioxin-like congeners demonstrated in
this chapter 5-33
Table 5-2. Summary of key source terms for the background scenarios, 1 and 2 5-35
Table 5-3. Summary of key source terms for Scenarios 4 and 5, the stack emission
demonstration scenarios 5-36
Table 5-4. WHO98-TEQDF environmental and exposure media concentrations for the
background conditions scenarios, #1 and #2, and the stack emissions
demonstration scenarios, #4 and #5 5-37
Table 5-5. Environmental and exposure media concentrations for 2,3,7,8-TCDD
("dioxin"), 2,3,4,7,8-PCDF ("furan") and 2,3,3',4,4',5,5'-HPCB (PCB) for
the soil contamination demonstration, scenario #3, and the effluent discharge
demonstration, scenario #6 (NA = not applicable) 5-38
Table 5-6. Individual congener and Toxic Equivalent (WHO98-TEQDF) concentrations for
predicted beef concentration for the background high scenario, scenario # 2,
and the stack emission high scenario, scenario 5 5-39
3-ix
December 2003
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TABLES (continued)
Table 5-7. Lifetime average daily doses, LADD, of Toxic Equivalents (TEQs), for the
background scenarios, #1 and #2, and for the stack emission scenarios, #4
and #5 5-40
Table 5-8. Lifetime average daily doses, LADD, for 2,3,7,8-TCDD ("dioxin"),
2,3,4,7,8-PCDF ("furan") and 2,3,3',4,4',5,5'-HPCB (PCB) for the soil
contamination demonstration, scenario #3, and the effluent discharge
demonstration, scenario #6 5-42
Table 5-9. Lifetime Average Daily Doses, LADD, of Toxic Equivalents (WHO98-TEQDF)
for exposure pathways evaluated outside of the scenarios for background
conditions and stack emissions 5-43
Table 5-10. Lifetime Average Daily Doses, LADD, of 2,3,7,8-TCDD ("dioxin"),
2,3,4,7,8-PCDF ("furan") and 2,3,3',4,4',5,5'-HPCB ("PCB") for exposure
pathways evaluated outside of the scenarios for the soil contamination and
the effluent discharge settings 5-44
Table 5-11. Relative magnitude of all exposure pathways evaluated for the background
setting and the stack emission, high exposure scenario setting (see table
bottom for notes) 5-45
Table 6-1. Parameters used to estimate exposure media concentrations for this
assessment 6-51
Table 6-2. Contribution of above ground vegetation concentrations of 2,3,7,8-TCDD
from air-to-leaf transfers and particulate depositions 6-59
Table 7-1. Observed data for the air-to-plant model comparison exercise 7-128
Table 7-2. Model results comparing the EPA vapor transfer model and the Vapor
Deposition Model with the field data for 2,3,7,8-TCDD (concentrations
in pg/g dry weight) 7-129
Table 7-3. Model parameters used in the Hwang and the alternate volatilization
models tested in this comparison exercise 7-130
Table 7-4. Results of model volatilization comparison exercise 7-131
Table 7-5. Comparison of the derivation of the fraction of sorbed dioxin congener
based on the octanol air partition coefficient, Koa, or based on the
sub-cooled liquid vapor pressure, as done for this document as described
in Chapter 3 7-132
Table 7-6. Summary of off-site soil contamination from Tier 1 and 2 sites of the
National Dioxin Study 7-133
Table 7-7. Description of soil, sediment, and fish sampling program of dioxin-like
compounds undertaken by the Connecticut Department of Environmental
Protection 7-134
Table 7-8. Frequency of non-detects and detection limits for soil, sediment, and
fish, for three congeners in the Connecticut Department of Environmental
Protection data set 7-138
3-x
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TABLES (continued)
Table 7-9. Results for Connecticut Department of Environmental Protection
sampling, including soil, sediment and fish concentrations, and the key
concentration ratios of sediment to soil and the Biota Sediment Accumulation
Factor (BSAF) ratio 7-139
Table 7-10. Model parameters and results for effluent discharge model validation testing 7-142
Table 7-11. ISCST3 and soil model input assumptions and parameters 7-148
Table 7-12. Comparison of observed and modeled total CDD/F concentration
increments at the urban monitoring stations 7-149
Table 7-13. Comparison of observed and modeled homologue and TEQ concentrations
at station SE-3 using on-site meteorological data for model input 7-150
Table 7-14. Results of ISCST3 deposition and soil prediction modeling, comparing
measured concentrations for clusters of soil samples with modeled
concentrations assuming either the 1992 or the 1994 stack tests 7-151
Table 7-15. Results of the air-to-soil and soil-to-air model testing 7-152
Table 7-16. Data and results of the soil to below ground vegetable validation exercise .. 7-153
Table 7-17. Summary of plant concentration versus soil concentration data for
2,3,7,8-TCDD 7-154
Table 7-18. Parameters for the empirical relationship relating the sub-cooled liquid
vapor pressure, p°L, to the particle/gas partition coefficient, Kp, of
semivolatile organic compounds (SOC) 7-158
Table 7-19. Summary of modeling changes from the 1994 air-to-beef model
validation exercise to the present update 7-159
Table 7-20. Comparison of air concentration profiles used in the 1994 air-to-beef
model validation compared against the current air profiles 7-160
Table 7-21. Comparison of predicted leafy vegetation samples of the current,
revised validation exercise with the previous predictions of leafy
vegetations and several observations in the literature (units are pg/g
dry weight) 7-161
Table 7-22. Results of the 1994 air-to-beef model validation exercise compared
against results from the current air-to-beef model validation exercises 7-162
Table 8-1. Uncertainties associated with the lifetime, body weight, and exposure
duration parameters 8-42
Table 8-2. Uncertainties associated with the soil ingestion pathway 8-43
Table 8-3. Uncertainties associated with the dermal exposure pathway 8-44
Table 8-4. Uncertainties associated with the water ingestion pathway 8-45
Table 8-5. Uncertainties associated with the fish ingestion pathway 8-46
Table 8-6. Uncertainties and sensitivities associated with estimating vapor and
particle-phase air concentrations from contaminated soils 8-47
Table 8-7. Uncertainties associated with vegetable/ fruit ingestion exposure algorithms . 8-49
Table 8-8. Uncertainties associated with the terrestrial animal food pathways 8-50
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TABLES (continued)
Table 8-9. Distributions for a Monte Carlo exercise which developed soil cleanup
levels at residential and industrial sites 8-51
Table 8-10. Summary of Monte Carlo distributions used in a fish consumption
assessment 8-52
Table 8-11. Summary of Monte Carlo distributions used in food chain study 8-53
Table 8-12. Summary of parameter distributions used for modeling terrestrial fruits and
vegetables for human consumption in a Monte Carlo exercise 8-54
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FIGURES
Figure 1-1. Chemical structure of 2,3,7,8-TCDD and related compounds 1-15
Figure 2-1. Predicted distributions of and average WHO98-TEQDF concentrations within an
adult population for four years: 1965, 1985, 1995, and 2030 2-49
Figure 3-1. Example of a congener and a homologue profile from a sewage sludge
incinerator 3-81
Figure 3-2. The relationships between the log of liquid sub-cooled vapor pressure, pL°,0
and the particle-gas partition coefficient, Kp, (figure (a)), and between pL°
and modeled (as indicated by "J-P" in figure (b)) and measured percent
particulate-phase in the ambient air (measurements from Eitzer &
Kites (1989)) 3-82
Figure 3-3. Comparison of measured paniculate percentages of PCDD/F on a homolog
basis to predictions of the Junge-Pankow model as a function of the
sub-cooled liquid vapor pressure, p°L, of the homolog groups 3-83
Figure 4-1. Diagram of the fate, transport, and transfer relationships for the
soil contamination source category 4-145
Figure 4-2. Diagram of the fate, transport, and transfer relationships for the stack
emission source category 4-146
Figure 4-3. Diagram of the fate, transport, and transfer relationships for the effluent
discharge source category 4-147
Figure 4-4. Watershed delivery ratio, SDW, as a function of watershed size 4-148
Figure 6-1. Results of sensitivity analysis of algorithms estimating exposure site vapor
phase air concentrations resulting from a distant contaminated soil site 6-60
Figure 6-2. Results of sensitivity analysis of algorithms estimating exposure site particle
phase air concentrations resulting from a distant contaminated soil site 6-61
Figure 6-3. Results of sensitivity analysis of algorithms estimating exposure site soil
concentrations resulting from erosion from a site of soil contamination 6-62
Figure 6-4. Results of sensitivity analysis of algorithms estimating surface water impacts,
including sediment, water, and fish concentrations, resulting from a site of
soil contamination 6-63
Figure 6-5. Results of sensitivity analysis of algorithms estimate above ground
vegetation concentrations due to vapor phase transfers 6-64
Figure 6-6. Results of sensitivity of algorithms estimating above ground vegetation
concentrations from deposition of particle-bound dioxins 6-65
Figure 6-7. Impact of vapor/particle partitioning on vegetation concentrations in the
stack emission source category 6-66
Figure 6-8. Results of sensitivity analysis of algorithms estimating below ground
vegetable concentrations in the soil contamination source category 6-67
Figure 6-9. Results of sensitivity analysis of algorithms estimating beef fat
concentrations in the soil contamination source category 6-68
Figure 6-10. Results of sensitivity analysis of algorithms estimating beef fat
concentrations in the stack emission source category 6-69
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FIGURES (continued)
Figure 6-11. Impact of distance from the stack emission source to soil, vegetable, and
beef fat concentrations 6-70
Figure 6-12. Results of sensitivity analysis of algorithms estimating surface water
and fish concentrations resulting from effluent discharges 6-71
Figure 6-13. Results of sensitivity analysis of algorithms estimating surface water
and fish concentrations resulting from stack emissions 6-72
Figure 7-1. Comparison of observed and predicted grass concentrations of dioxin
and furan congeners for the EPA and the scavenging models at the rural
site. The perfect match of observed and predicted is shown in the dashed
observed = predicted line 7-163
Figure 7-2. Comparison of observed and predicted grass concentrations of dioxin
and furan congeners for the EPA and the scavenging models at the
industrial site. The perfect match of observed and predicted is shown
in the dashed observed = predicted line 7-164
Figure 7-3. The observed scavenging coefficient (grass concentration over air
concentration) calculated from the rural site data 7-165
Figure 7-4. Comparison of observed and predicted deposition at the rural and
industrial sites. The perfect match of observed and predicted is shown
in the dashed observed = predicted line 7-166
Figure 7-5. Schematic of effluent discharge model showing all parameter inputs
and observed fish concentrations 7-167
Figure 7-6. Comparison of predicted and observed fish tissue concentrations for
validation of the effluent discharge model 7-168
Figure 7-7. Site map showing locations of soil and air samples in the vicinity
of the Columbus Municipal Solid Waste-To-Energy (CMWSTE,
abbreviated WTE above) Facility 7-169
Figure 7-8. Isoline figures of predicted air concentrations overlain by measured
air concentrations of TCDD, OCDD, and TEQ (pg/m3) when using
the "on-site" meteorological data set (sub-figures a, b, and c) and
when using the "airport" meteorological data set (sub-figures d, e, and f) ... 7-170
Figure 7-9. Isoline figures of predicted soil concentrations of TCDD, OCDD,
and TEQ (sub-figures a, d, g) compared against isoline figures of
measured soil concentrations using the 1992 stack emission test
(sub-figures b, e, and h) and the 1994 stack emission test (sub-figures
c, f, and i) 7-171
Figure 7-10. Comparison of measured and predicted particulate percentages of
PAHs in urban and rural air 7-173
Figure 7-11. Comparison of measured and predicted parti culate percentages of
PCBs and organochlorine pesticides in urban and rural air 7-174
Figure 7-12. Overview of model to predict beef concentrations from air concentrations . . 7-175
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1. BACKGROUND AND SUMMARY
1.1. BACKGROUND
This reassessment is comprised of three reports:
Part 1. Estimating Exposure to Dioxin-Like Compounds (EPA, 2000a) (which expanded
upon a 1988 draft exposure report titled, Estimating Exposure to 2,3,7,8-TCDD [EPA,
1988]);
Part 2. Health Assessment of 2,3,7,8-Tetrachlorodibenzo-p-Dioxin (TCDD) and Related
Compounds (EPA, 1994; EPA, 2000b); and
Part 3. Dioxin: Integrated Summary and Risk Characterization for 2,3,7,8-
Tetrachlorodibenzo-p-Dioxin (TCDD) and Related Compounds (EPA, 2000c).
Throughout the remainder of this document, these three parts as a whole will be abbreviated as
the Reassessment Documents, and the individual parts will be referred to as the Exposure
Reassessment Document, the Health Reassessment Document, and the Risk Characterization.
The Exposure Reassessment Document has expanded to three volumes, as discussed below.
Volumes 1 and 2 of the Exposure Reassessment Document are summarized in Section 4 of the
Risk Characterization.
The process for developing the Reassessment Documents has been open and
participatory. Each of the documents has been developed in collaboration with scientists from
inside and outside the Federal Government. Each document has undergone extensive internal
and external review, including review by EPA's Science Advisory Board (SAB). In September
1994, drafts of each document were made available for public review and comment. This
included a 150-day comment period and 11 public meetings around the country to receive oral
and written comments. These comments, along with those of the SAB (EPA, 1995a), have been
considered in the drafting of this final document. The Dose-Response Chapter of the Health
Document underwent peer review in 1997 (EPA, 1997a); an earlier version of the Integrated
Summary and Risk Characterization underwent development and review in 1997 and 1998, and
comments have been incorporated. In 1998, EPA released a workshop review version of the
sources inventory (EPA, 1998), one of the three volumes of the Exposure Reassessment
Document. In addition, as requested by the SAB, a chapter on Toxic Equivalency has been
developed and underwent external peer review in parallel with the Integrated Summary and Risk
Characterization in July, 2000. The November, 2000, review by the SAB of the Dose-Response
Chapter, the Toxic Equivalency Chapter and the Integrated Summary and Risk Characterization
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was the final step in this open and participatory process of reassessment. The full set of
background documents and the integrative summary and risk characterization replace the
previous dioxin assessments as the scientific basis for EPA decision-making.
The final Exposure Reassessment Document reflects changes made as a result of both
review comments and analyses of a variety of other types of information that has come available.
These include relevant information obtained from published peer-reviewed literature, EPA
program offices, and other Federal agencies. This version of the Exposure Reassessment
Document is current in this regard through 2000.
The purpose of the Exposure Reassessment Document is threefold: 1) to inventory the
known sources of release of dioxins into the environment, 2) to develop an understanding of
dioxins in the environment, including fate and transport properties, environmental and exposure
media concentrations, background as well as elevated exposures, and temporal trends in
exposure, and 3) provide site-specific procedures for evaluating the incremental exposures due to
specific sources of dioxin-like compounds. Following this structure, the Exposure Reassessment
Document is presented in three volumes:
Volume I - Sources of Dioxin-Like Compounds in the United States
This volume presents a comprehensive review of known sources of environmental
releases of dioxin-like compounds in the United States. It includes an inventory of
known source activity in terms of estimates of annual releases of dioxin-like compounds
into the U.S. environment (i.e., air, water and land). This inventory is specific for two
reference years, 1987 and 1995. From these data, it is possible to compare and contrast
releases of dioxin-like compounds among the sources and between the reference years.
Volume II - Properties, Environmental Levels, and Background Exposures
This volume presents and evaluates information on the physical-chemical properties,
environmental fate, environmental and exposure media levels, background and elevated
human exposures, and temporal trends of dioxin-like compounds in the U.S. environment
during the 20th century.
Volume III - Site-Specific Assessment Procedures
This volume presents procedures for evaluating the incremental impact from sources of
dioxin release into the environment. The sources covered include contaminated soils,
stack emissions, and point discharges into surface water. This volume includes sections
on: exposure parameters and exposure scenario development; stack emissions and
atmospheric transport modeling; aquatic and terrestrial fate, and food chain modeling;
demonstration of methodologies; and uncertainty evaluations including exercises on
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sensitivity analysis and model validation, review of Monte Carlo assessments conducted
for dioxin-like compounds, and other discussions.
The primary technical resource supporting the development of the inventory of sources of
dioxin-like compounds discussed in Volume I (above) is the Database of Sources of
Environmental Releases of Dioxin-Like Compounds in the United States (EPA/600/C-01/012.
March, 2001). This database includes congener-specific CDD and CDF emissions data extracted
from original engineering test reports. It has been published independently from the
Reassessment and is available on Compact Disk -Read only Memory (CD-ROM), without cost,
from EPA's National Service Center for Environmental Publications (NSCEP) in Cincinnati,
Ohio (telephone: 1-800-490-9198, or 513-489-8190; fax: 513-489-8695). In addition, it can be
downloaded from the web page of the National Center for Environmental Assessment,
www.epa.gov/ncea/dioxin.htm.
1.2. DEFINITION OF DIOXIN-LIKE COMPOUNDS
This assessment addresses specific compounds in the following chemical classes:
polychlorinated dibenzo-^-dioxins (PCDDs or CDDs), polychlorinated dibenzofurans (PCDFs or
CDFs), polybrominated dibenzo-p-dioxins (PBDDs or BDDs), polybrominated dibenzofurans
(PBDFs or BDFs), and polychlorinated biphenyls (PCBs), and describes this subset of chemicals
as "dioxin-like." Dioxin-like refers to the fact that these compounds have similar chemical
structure, similar physical-chemical properties, and invoke a common battery of toxic responses.
Because of their hydrophobic nature and resistance towards metabolism, these chemicals persist
and bioaccumulate in fatty tissues of animals and humans. The CDDs include 75 individual
compounds; CDFs include 135 different compounds. These individual compounds are referred
to technically as congeners. Likewise, the BDDs include 75 different congeners and the BDFs
include an additional 135 congeners. Only 7 of the 75 congeners of CDDs, or of BDDs, are
thought to have dioxin-like toxicity; these are ones with chlorine/bromine substitutions in, at a
minimum, the 2, 3, 7, and 8 positions. Only 10 of the 135 possible congeners of CDFs or of
BDFs are thought to have dioxin-like toxicity; these also are ones with substitutions in the 2, 3,
7, and 8 positions. This suggests that 17 individual CDDs/CDFs, and an additional 17
BDDs/BDFs, exhibit dioxin-like toxicity. The database on many of the brominated compounds
regarding dioxin-like activity has been less extensively evaluated, and these compounds have not
been explicitly considered in this assessment.
There are 209 PCB congeners. Only 12 of the 209 congeners are thought to have dioxin-
like toxicity; these are PCBs with 4 or more lateral chlorines with 1 or no substitution in the
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ortho position. These compounds are sometimes referred to as coplanar, meaning that they can
assume a flat configuration with rings in the same plane. Similarly configured polybrominated
biphenyls (PBBs) are likely to have similar properties. However, the database on these
compounds with regard to dioxin-like activity has been less extensively evaluated, and these
compounds have not been explicitly considered in this assessment. Mixed chlorinated and
brominated congeners of dioxins, furans, and biphenyls also exist, increasing the number of
compounds potentially considered dioxin-like within the definitions of this assessment. The
physical/chemical properties of each congener vary according to the degree and position of
chlorine and/or bromine substitution. Very little is known about occurrence and toxicity of the
mixed (chlorinated and brominated) dioxin, furan, and biphenyl congeners. Again, these
compounds have not been explicitly considered in this assessment. Generally speaking, this
assessment focuses on the 17 CDDs/CDFs and a few of the coplanar PCBs that are frequently
encountered in source characterization or environmental samples. While recognizing that other
"dioxin-like" compounds exist in the chemical classes discussed above (e.g., brominated or
chlorinated/brominated congeners) or in other chemical classes (e.g., halogenated naphthalenes
or benzenes, azo- or azoxybenzenes), the evaluation of less than two dozen chlorinated
congeners is generally considered sufficient to characterize environmental "dioxin."
The chlorinated dibenzodioxins and dibenzofurans are tricyclic aromatic compounds with
similar physical and chemical properties. Certain of the PCBs (the so-called coplanar or mono-
ortho coplanar congeners) are also structurally and conformationally similar. The most widely
studied of this general class of compounds is 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD). This
compound, often called simply "dioxin," represents the reference compound for this class of
compounds. The structure of TCDD and several related compounds is shown in Figure 1-1 .
Although sometimes confusing, the term "dioxin" is often also used to refer to the complex
mixtures of TCDD and related compounds emitted from sources, or found in the environment or
in biological samples. It can also be used to refer to the total TCDD "equivalents" found in a
sample. This concept of toxic equivalency is discussed below.
1.3. TOXIC EQUIVALENCY FACTORS
CDDs, CDFs, and PCBs are commonly found as complex mixtures when detected in
environmental media and biological tissues, or when measured as environmental releases from
specific sources. Humans are likely to be exposed to variable distributions of CDDs, CDFs, and
dioxin-like PCB congeners that vary by source and pathway of exposures. This complicates the
human health risk assessment that may be associated with exposures to variable mixtures of
dioxin-like compounds. In order to address this problem, the concept of toxic equivalency has
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been considered and discussed by the scientific community, and TEFs have been developed and
introduced to facilitate risk assessment of exposure to these chemical mixtures.
On the most basic level, TEFs compare the potential toxicity of each dioxin-like
compound comprising the mixture to the well-studied and understood toxicity of TCDD, the
most toxic member of the group. The background and historical perspective regarding this
procedure is described in detail in Part II, Chapter 9, Section 9.1, 9.2, and in Agency documents
(EPA 1987, 1989a,b, 1991). This procedure involves assigning individual TEFs to the 2,3,7,8-
substituted CDD/CDF congeners and "dioxin-like" PCBs. To accomplish this, scientists have
reviewed the toxicological databases along with considerations of chemical structure,
persistence, and resistance to metabolism, and have agreed to ascribe specific, "order of
magnitude" TEFs for each dioxin-like congener relative to TCDD, which is assigned a TEF of
1.0. The other congeners have TEF values ranging from 1.0 to 0.00001. Thus, these TEFs are
the result of scientific judgment of a panel of experts using all of the available data and are
selected to account for uncertainties in the available data and to avoid underestimating risk. In
this sense, they can be described as "public health conservative" values. To apply this TEF
concept, the TEF of each congener present in a mixture is multiplied by the respective mass
concentration and the products are summed to represent the 2,3,7,8-TCDD Toxic Equivalence
(TEQ) of the mixture, as determined by Equation (1-1):
The TEF values for PCDDs and PCDFs were originally adopted by international convention
(EPA, 1989a). Subsequent to the development of the first international TEFs for CDD/CDFs,
these values were further reviewed and/or revised and TEFs were also developed for PCBs
(Ahlborg et al., 1994; van den Berg et al., 1998). A problem arises in that past and present
quantitative exposure and risk assessments may not have clearly identified which of three TEF
schemes was used to estimate the TEQ. This reassessment introduces a new uniform TEQ
TEQ= 2_,.n(Congenert x TEF^ [Congener>. x TEFA+ (Congenern x TEFn 1 (1-D
nomenclature that clearly distinguishes between the different TEF schemes and identifies the
congener groups included in specific TEQ calculations. The nomenclature uses the following
abbreviations to designate which TEF scheme was used in the TEQ calculation:
1. I-TEQ refers to the International TEF scheme adopted by EPA in 1989 (EPA, 1989a).
See Table 1-1.
2. TEQ-WHO94 refers to the 1994 WHO extension of the I-TEF scheme to include 13
dioxin-like PCBs (Ahlborg et al., 1994). See Table 1-2.
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3. TEQ-WHO98 refers to the 1998 WHO update to the previously established TEFs for
dioxins, furans, and dioxin-like PCBs (van den Berg et al., 1998). See Table 1-3.
The nomenclature also uses subscripts to indicate which family of compounds is included
in any specific TEQ calculation. Under this convention, the subscript D is used to designate
dioxins, the subscript F to designate furans and the subscript P to designate PCBs. As an
example, "TEQDF-WHO98" would be used to describe a mixture for which only dioxin and furan
congeners were determined and where the TEQ was calculated using the WHO98 scheme. If
PCBs had also been determined, the nomenclature would be "TEQDFP-WHO98." Note that the
designations TEQDF-WHO94 and I-TEQDF are interchangeable, as the TEFs for dioxins and furans
are the same in each scheme. Note also that in this document, I-TEQ sometimes appears without
the D and F subscripts. This indicates that the TEQ calculation includes both dioxins and furans.
This reassessment recommends that the WHO98 TEF scheme be used to assign toxic
equivalency to complex environmental mixtures for assessment and regulatory purposes.
Sections in the Health Reassessment Document, and summarized in the Risk Characterization,
describe the mode(s) of action by which dioxin-like chemicals mediate biochemical and
toxicological actions. These data provide the scientific basis for the TEF/TEQ methodology. In
its 20-year history, the approach has evolved, and decision criteria supporting the scientific
judgment and expert opinion used in assigning TEFs has become more transparent. Numerous
states, countries, and several international organizations have evaluated and adopted this
approach to evaluating complex mixtures of dioxin and related compounds. It has become the
accepted methodology, although the need for research to explore alternative approaches is
widely endorsed. Clearly, basing risk on TCDD alone or assuming all chemicals are equally
potent to TCDD is inappropriate on the basis of available data. Although uncertainties in the use
of the TEF methodology have been identified (which are described in detail in the Health
Reassessment Document, Chapter 9, Section 9.5), one must examine the use of this method in
the broader context of the need to evaluate the potential public health impact of complex
mixtures of persistent, bioaccumulative chemicals. It can be generally concluded that the use of
TEF methodology for evaluating complex mixtures of dioxin-like compounds decreases the
overall uncertainties in the risk assessment process as compared to alternative approaches. Use
of the latest consensus values for TEFs assures that the most recent scientific information
informs this "useful, interim approach" (EPA, 1989a; Kutz et al., 1990) to dealing with complex
environmental mixtures of dioxin-like compounds. As stated by the U.S. EPA Science Advisory
Board (EPA, 1995a), "The use of the TEFs as a basis for developing an overall index of public
health risk is clearly justifiable, but its practical application depends on the reliability of the
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TEFs and the availability of representative and reliable exposure data." EPA will continue to
work with the international scientific community to update these TEF values to assure that the
most up-to-date and reliable data are used in their derivation and to evaluate their use on a
periodic basis.
A chemical is assigned a TEF value based on all the available data comparing the
chemical to either TCDD or PCB 126. In addition, there are weighting criteria that place more
emphasis on chronic and subchronic studies examining toxic endpoints (van den Berg et al.,
1998). There is a broad range in the quantity and quality of the data available for individual
congeners. For example, the TEF for PCB 126 is based on over 60 in vivo endpoints examining
responses as diverse as enzyme induction, developmental toxicity, immunotoxicity, hepatic
toxicity, alterations in hormones and tumor promotion, while the TEF for 3,4,4',5-
tetrachlorobiphenyl (PCB 81) is based on in vitro CYP1A induction and QSAR calculations.
Fortunately, PCB 81 does not significantly contribute to human TEQ exposures. There are 5
congeners that contribute approximately 80% of the total TEQ in humans: 2,3,7,8-TCDD,
1,2,3,7,8-PCDD, 1,2,3,6,7,8-HxCDD, 2,3,4,7,8-PCDF, and PCB 126 (See Part I, Volume 3 and
Section 4.4.3 of this document). With the exception of 1,2,3,6,7,8-HxCDD, the TEFs for these
chemicals are based on a number of different endpoints from multiple studies performed in
different laboratories. The TEF for 1,2,3,6,7,8-HxCDD is based on a two-year bioassay in which
rats were exposed to a mixture of 1,2,3,6,7,8-HxCDD and 1,2,3,7,8,9-HxCDD. The TEFs for
2,3,4,7,8-PCDF and PCB 126 are similar to the mean REP value for all in vivo endpoints and are
similar to their REPs for tumor promotion. The TEF for 12378-PCDD is based largely on its
REP for tumor promotion in rats. From these data, it is clear that the chemicals that contribute
approximately 80% to the total human TEQ are well studied and the assigned TEFs provide
reasonable estimates of the relative potency of these chemicals. In contrast, while there are some
chemicals in the TEF methodology which have minimal data sets to reliably assess their relative
potency, these chemicals do not contribute substantially to the human blood TEQ.
The ability of the TEF methodology to predict the biological effects of mixtures
containing dioxin-like chemicals has been evaluated in a number of experimental systems.
These studies generally demonstrate that the assumption of additivity provides a reasonable
estimate of the dioxin-like potential of a mixture (described in the Health Reassessment
Document, Chapter 9, Section 9.4). In addition, there are examples of non-additive interactions
between dioxins and non-dioxins. Both greater than additive and less than additive interactions
have been observed in these studies. In general the non-additive interactions between the
dioxins and non-dioxins have been observed at doses that are considerably higher than present
background human exposures.
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There are a number of natural chemicals that bind and activate the AhR and induce some
dioxin-like effects. It has been proposed by some scientists that these chemicals contribute
significantly to the total TEQ exposures and that these exposures far out weigh those from
PCDDs, PCDFs and PCBs (Safe, 1995). While this hypothesis is intriguing, there are several
limitations to these analyses. The in vivo data on the natural AhR ligands is limited to enzyme
induction and a single developmental study. Few, if any, toxicology studies demonstrating clear
dioxin-like toxicities have been published. The natural AhR ligands are rapidly metabolized and
result in both transient tissue concentrations and transient effects. The natural ligands also have
significant biological effects that are independent of the AhR and it is not clear as to the role of
the AhR in the biological effects of these chemicals. Clearly this issue requires further research
in order to better understand the relative potential health effect of dioxin and related chemicals as
compared to natural AhR ligands.
One of the limitations of the use of the TEF methodology in risk assessment of complex
environmental mixtures is that the risk from non-dioxin-like chemicals is not evaluated in
concert with that of dioxin-like chemicals. Another limition of the TEF methodology is their
application to non-biological samples. The fate and distribution of PCDDs, PCDFs and PCBs
are not necessarily related to their TEF. Thus, the use of the TEF for non-biological media must
be done cautiously. Future approaches to the assessment of environmental mixtures should
focus on the development of methods that will allow risks to be predicted when multiple
mechanisms are present from a variety of contaminants.
1.4. OVERALL COMMENTS ON THE USE OF VOLUME III OF THE DIOXIN
EXPOSURE DOCUMENT
Users of the dioxin exposure document should recognize the following:
1. This document does not present detailed procedures for evaluating multiple sources of
release. However, it can be used in two ways to address this issue. Incremental impacts
estimated with procedures in Volume III can be compared to background exposure estimates
which are presented in Volume II. This would be a way of comparing the incremental impact of
a specific source to an individual's total exposure otherwise. Assuming the releases from
multiple sources behave independently, it is possible to model them individually and then add
the impacts. For example, if several stack emission sources are identified and their emissions
quantified, and it is desired to evaluate the impact of all sources simultaneously, then it is
possible with ISCST3 to model each stack emission source individually and then sum the
concentrations and depositions at points of interest in the surrounding area.
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2. The demonstration of the site-specific procedures presented in this exposure document
best serve as general examples for evaluating exposures to dioxin-like compounds, rather
than specific assessments. This demonstration scenarios in Chapter 5 of this document were
not generated for purposes of supporting any specific regulation. Rather, they were only
intended to demonstrate the procedure described earlier in Chapters 2 through 4. Certainly, the
goal of developing "high end" and "central" is consistent with Agency policy, and even
assignment of many of the exposure and fate parameters can be adopted for other assessments.
Therefore, assessors may find even the specifics of the demonstration scenarios useful for other
purposes.
3. The understanding of the exposure to dioxin-like compounds continues to expand.
Despite being one of the most studied groups of organic enivronmental contaminants, new
information is generated almost daily about dioxin-like compounds. This document is
considered to be current through 2000.
Numerous parameter values are used in this document and it is important to understand
their degree of "endorsement" by EPA. The parameters can be divided into the following four
classes for purposes of addressing this issue:
1) First Order Defaults: As defaults, these parameters are independent of site specific
characteristics and can be used for any assessment. Also, as first order defaults, it is felt that the
values selected for the demonstration scenarios carry a sufficient weight of evidence from
current literature such that these values are recommended for other assessments. Several of the
chemical specific parameters, such as the Henry's Constant, H, and the organic carbon partition
coefficient, Koc, fall into this category. The qualifier above, "current literature", indicates that
new information could lead to changes in these values.
2) Second Order Defaults: Like the above category, these parameters are judged to be
independent of site specific characteristics. However, unlike the above category, the current
scientific weight of evidence is judged insufficient to describe values selected for demonstration
purposes as first order defaults. Parameters of principal note in this category are the
bioconcentration parameters specific to the chemicals, such the Biota Sediment Accumulation
Factor, or BSAF. This parameter translates a bottom sediment concentration to a fish tissue
concentration. The science is evolving for this parameter, including thought on the extent to
which BSAFs generated for one species at one site can be generalized to other sites and/or
species, the differences in BSAF between column and bottom feeders, the differences between
past and ongoing contamination, and so on. Users should carefully review the justification for
the SOD values selected for the demonstration scenarios before using the same values.
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3) Site Specific: These parameters should or can be assigned values based on site-specific
information. The information provided on their assignment for the demonstration of
methodologies in this document can be useful where site specific information is unavailable. A
key class of site specific are the source strength terms - the soil concentrations, effluent
discharge rates, and stack emission rates. Others include physical properties (organic carbon
contents of soil and sediment, climate variables, areas, distances, and volumes) and parameters
for bioconcentration algorithms (yields of vegetation, cattle raising practices, fish lipid contents).
4) Exposure Parameters: The exposure parameters have not been categorized as have the
contaminant fate and transport/transfer parameters. Assignment of these values are critical as
Lifetime Average Daily Dose (LADD) estimates are linearly related to parameter assignments -
doubling exposure duration assumptions double LADDs, and so on. Some exposure parameters
are appropriately described as first order defaults. These include: lifetime, body weights, water
ingestion rates, inhalation rates, and an exposure duration for a childhood pattern of soil
ingestion. All of the other exposure parameters are better described as either second order
defaults or site specific parameters. All exposure parameters were developed based on
information and recommendations in EPA's Exposure Factors Handbook (EPA, 1997b) and
Dermal Exposure Assessment: Principles and Applications (EPA, 1992a).
The end products of the exposure assessment procedures presented in this document are
estimates of potential dose expressed in mass (pg, ng, etc.) of dioxin-like compound/body weight
(usually kg)-day. The procedures for converting these dose estimates to risk estimates, both
cancer and non-cancer, are described in Chapter 2 and demonstrated in Chapter 5.
The scope of each chapter in Volume III is summarized below.
Chapter 2, Estimating Exposure and Risks, presents overall framework for conducting
exposure assessments. It provides procedures for identifying exposure pathways, estimating
contact rates and resulting exposure levels. Approaches for defining exposure scenarios are
presented. Procedures for converting exposure dose to lifetime cancer risk estimates are
provided, and procedures for evaluating non-cancer risk are also discussed.
Chapter 3, Evaluating Atmospheric Releases of Dioxin-Like Compounds from
Combustion Sources, provides procedures to estimate the emission rates of dioxin-like
compounds from combustion processes and further atmospheric transport modeling procedures
from the stack to the surrounding land surface. This chapter describes and demonstrates the use
of the ISCST3 model on a hypothetical incinerator and lists the associated atmospheric
dispersion and deposition estimates from that model exercise.
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Chapter 4, Estimating Exposure Media Concentrations, provides procedures for
estimating concentrations of the dioxin-like compounds in exposure media (soil, air, water,
biota) resulting from soil contamination, effluent discharges, and stack emissions.
Chapter 5, Demonstration of Methodology, develops hypothetical scenarios and
generates exposure and risk estimates to demonstrate the methodologies of this document.
Chapter 6, User Considerations, discusses key issues for users of the methodologies. All
model parameters are listed and categorized according to the scheme noted above. Sensitivity
analysis is conducted on the algorithms estimating exposure media concentrations. An exercise
on estimating the releases from a bounded area of soil contamination is presented. The purpose
of this exercise is to determine whether a reservoir of soil contamination would be depleted prior
to an assumed duration of exposure.
Chapter 7, Model Comparisons and Validations, presents extensive information aimed at
gaining confidence and establishing credibility for the use of the fate models of this assessment
to predict the fate, movement, and resulting exposure media concentrations near sources of
dioxin release. One section of this chapter presents alternate fate models, and where possible,
generates results from these models to compare with results from the models selected for this
assessment. The second major section presents several model validation exercises, where the
models are paramterized to predict exposure media concentrations, and the results are compared
with appropriate real world observations.
Chapter 8, Uncertainty, discusses the sources and possible magnitude of uncertainty in
the exposure assessment procedures. Uncertainty and variability of fate and transport, and
exposure parameters, are discussed. Monte Carlo and similar numerical methods to quantify
variability and uncertainty are discussed, and several literature examples of these types of
exercises conducted for dioxin-like compounds are summarized.
1.5. EXECUTIVE SUMMARY OF VOLUME III
Volume III describes procedures for conducting site specific exposure and risk
assessments to estimate potential dose, cancer, and non-cancer risks from exposure to dioxin-like
compounds from a nearby source of release. Sections below summarize the key issues and
results from each chapter of this volume.
1.5.1. Exposure Equation
A potential dose is defined as a daily amount of contaminant inhaled, ingested, or
otherwise coming in contact with outer surfaces of the body, averaged over an individual's body
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weight and lifetime. The general equation used to estimate potential dose normalized over body
weight and lifetime is as follows:
Lifetime Average Daily Dose (LADD) = (exposure media concentration x
contact rate x contact fraction x exposure duration ) /
(body weight x lifetime) (1-2)
This procedure is used to estimate dose in the form needed to assess cancer risks. For non-
cancer risks, an ADD term is instead derived. ADD is calculated as above except that exposure
duration and lifetime are taken out of the equation above. Each of the terms in this exposure
equation is discussed briefly below:
• Exposure media concentrations: These include the average concentrations in the
media to which individuals are exposed. Media considered in this assessment include soil, air,
water, vegetables/fruits, fish, beef, milk, and poultry.
• Contact rate: These include the ingestion rates, inhalation rates, and soil contact
rates for the exposure pathways.
• Contact fraction: This term describes the distribution of total contact between
contaminated and uncontaminated media. This assessment describes exposures which occur at
homes, so the contact rate translates to time spent at home for air, soil, and water exposures, and
fraction of total food product produced at home (vegetables/fruits, beef, milk, and poultry) or
obtained recreationally (fish) from an impacted water body. This assessment assumes time at
home fractions of 0.70 and 0.90 for central and high end scenarios, respectively, and home food
production factors, or food contact fractions, of about 0.50 and less.
• Exposure duration: This is the overall time period of exposure, mostly pertinent to
adult exposures. Central and high end durations of 9 and 30 years, respectively, are assumed in
this assessment. Another exposure duration considered in this methodology is one associated
with a childhood pattern of soil ingestion. The exposure duration in this case is 5 years.
• Body weight: For all the pathways, the human adult body weight of 70 kg is
assumed. Although the United States population average is closer to 60 kg (EPA, 1997b), the
value of 70 kg has been more traditionally used. The body weight for child soil ingestion is 17
kg.
• Lifetime: Following convention, and because cancer slope factors are derived based
on a 70-year human lifetime, the average adult lifetime assumed throughout this document is 70
years.
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1.5.2. Procedures for Evaluating Cancer and Non-Cancer Risk
Although the focus of the site-specific methodology is to estimate exposures to dioxins,
procedures are also presented and demonstrated for estimating cancer and non-cancer risk. The
usual procedure used to calculate an upper-limit incremental cancer risk is as follows:
* qi* LADD d-3)
when qx* LADD < 10"3 and where qx* is the 95% upper confidence limit of the linearized cancer
slope factor of the dose-response function (expressed in inverse units of the dose quantity, such
as kg-day/pg, or equivalently, (pg/kg-day)"1 ) and LADD is the dose (which needs to be in units
appropriate to cancel those of q^, pg/kg-day). This assessment uses the simplified qx* LADD
since the exposures and risks demonstrated are generally less than 10"3. The slope factor, q^, for
2,3,7,8-TCDD has been previously estimated by EPA as 0.000156 (pg/kg-d)'1 (EPA, 1984;
1981), but has been reevaluated as 0.001 (pg/kg-d)"1 in this Reassessment. Also, it is being
applied to a TEQ dose in this Assessment.
This selected cancer slope factor was based primarily on the meta analysis of the human
epidemiology studies where exposure was estimated from dioxin concentrations in blood in
occupationally exposed cohorts. The dose estimates used to derive the slope factor were
obtained by using a PK model to convert the blood concentrations to an administered, or
potential, dose. An administered dose is defined as the dose which contacts the body boundary
surfaces, such as the skin as in dermal exposure or the dose ingested prior to absorption. This
administered dose was derived by first calculating an absorbed dose and then dividing by 0.8 -
i.e., an absorbed dose was assumed to be 80% of an administered dose. Because the potency
factor was derived based on an administered dose, the new slope factor can be applied to an
administered dose without any adjustment for absorption as long as the absorption is
approximately 80%. Although the data are limited, this is probably a reasonable assumption for
most types of food ingestion and inhalation. For soil pathways, however, an additional
adjustment factor has to be added to account for significantly less absorption. Data suggests that
the dose of dioxins absorbed from soil ingestion is about 30% and about 3% from soil dermal
contact. For soil dermal contact, an absorbed dose is already calculated; thus the dermal contact
pathway yields an absorbed dose already. Therefore, for these two pathways, Equation (1-3)
needs an additional adjustment factor equal to 0.3/0.8, or 0.375, for soil ingestion and 1.00/0.8,
or 1.25, for soil dermal contact. A full discussion on absorption of administered dioxin through
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the various pathways can be found in Chapter 1. Disposition and Pharmacokinetics, of the
Health Reassessment Document (EPA, 2000b).
To evaluate incremental non-cancer effects in a risk assessment, EPA uses established
Reference Doses (RfDs) for most contaminants. An RfD is defined as an estimate, with
uncertainty spanning perhaps an order of magnitude, of a daily exposure of the human
population, including sensitive subgroups, that is likely to be without an appreciable risk of
deleterious non-cancer effects during a lifetime. The incremental dose from a particular source
is compared with the RfD. However, an RfD has not been established for dioxin-like
compounds. It was concluded in the Risk Characterization that setting an RfD using traditional
Agency approaches would result in an RfD that would likely be 2-3 orders of magnitude below
current background intakes and body burdens. For this reason, EPA concluded that establishing
an RfD for assessing the potential for non-cancer effects would not be helpful to risk managers.
Instead, it was suggested in the Risk Characterization that risk managers compare the increment
of exposure to a specific source with background exposures for assessing the potential for non-
cancer effects. For this reason, a "ROIE", or Ratio of Incremental Exposure, was developed.
The ROIE was defined as a ratio between the increment of daily exposure from the source in
question, the term defined by ADD as noted above, to background daily exposure. A ROIE
equaling 1.0 suggests that the increment of exposure from the source in question is equal to
background exposure.
Background exposure could be considered to be a national average background exposure,
or it could a quantity specifically developed for the site in question. The latter would be
appropriate if certain behaviors, such as subsistence fishing or farming, or certain environmental
conditions, such as a high local background of dioxin in soil or air due to industrial practices,
were present in the vicinity of the specific source being evaluated.
1.5.3. Procedure for Estimating Exposure
Before making exposure estimates, the assessor needs to gain a more complete
understanding of the exposure setting and the contamination source. The approach used for this
assessment is termed the exposure scenario approach. There are 7 steps in this approach:
Step 1. Identify Source: Three principal sources are addressed in this document:
contaminated soils, stack emissions, and effluent discharges.
Step 2. Estimate Release Rates: Estimating the release of contaminants from the
initial source is the first step towards estimating the concentration in the exposure media.
Releases from soil contamination include volatilization, and wind and soil erosion. Stack
emissions and effluent discharges are point source releases into the environment.
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Step 3. Estimate Exposure Point Concentrations: Contaminants released from soils,
emitted from stacks, or discharged into surface waters move through the environment to points
where human exposure may occur, and/or to impact environmental media to which humans are
exposed. Various fate, transport, and transfer models are used to predict exposure media
concentrations given source releases.
Step 4. Characterize Exposed Individuals and Exposure Patterns: Exposed
individuals in the scenarios of this assessment are individuals who are exposed in their home
environments. They are residents who breathe air at their residence, fish recreationally, have a
home garden, farm, and are children ages 2-6 for the soil ingestion pathway. Exposures which
occur at the workplace or other locations are not discussed in this assessment, although the
procedures could be adapted for other exposure sites. Each pathway has a set of exposure
parameters including contact rates, contact fractions, body weights, exposure durations, and a
lifetime. An individual's total exposure is the sum of the exposures from individual pathways.
Step 5. Put It Together in Terms of Exposure Scenarios: A common framework for
assessing exposure is with the use of "settings" and "scenarios." Settings are the physical aspects
of an exposure area and the scenario characterizes the behavior of the population in the setting
and determines the severity of the exposure. A wide range of exposures are possible depending
on behavior pattern assumptions. An exposure scenario framework offers the opportunity to
vary any number of assumptions and parameters to demonstrate the impact of changes to
exposure and risk estimates.
Step 6. Estimate Exposure: The end result of having followed the above 5 steps are
estimates of individual exposures to a characterized source of contamination.
Step 7. Assess Uncertainty: Uncertainties should be considered when applying
procedures in this document to a particular site. Pertinent issues explored in this assessment
include: 1) model predictions of exposure media concentrations compared to field
measurements in a series of model validation exercises, 2) similarities and differences for
alternate models for estimating exposure media concentrations, 3) sensitivity of model results to
a range of values for methodology parameters, 4) mass balance checks, and 5) qualitative and
quantitative discussions on the uncertainties with the model parameters and exposure estimates
generated for the demonstration scenarios.
1.5.4. Estimating Exposure Media Concentrations
Literally hundreds of fate and transport models have been published which differ widely
in their technical sophistication, level of spatial or temporal resolution, need for site specific
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parameterization, and so on. This makes selection of the most appropriate one for any particular
situation very difficult. For this assessment, relatively simple, screening level models are used to
model fate, transport, and transfer of dioxin-like compounds from the source to the exposure
media. Simple assumptions are often made in order to arrive at the desired result, which is
long-term average exposure media concentrations. Perhaps the most critical of the assumptions
made is that the source strength remains constant throughout the period of exposure.
It is important to understand that EPA is not endorsing the algorithms of this assessment
as the best ones for use in all dioxin assessments. They are suggested as reasonable starting
points for site-specific or general assessments. All assumptions for the models and selection of
parameter values are carefully described. If these assumptions do not apply to a particular
situation, or where assessors require more spatial or temporal resolution, more complex models
should be selected. Finally, it cannot be overemphasized that measured concentrations are
generally more reliable than modeled ones. Assessors should use measured concentrations if
available and if such measurements can be considered spatially and temporally representative for
the exposed populations.
Chapter 4 provides algorithms used to evaluate the fate, transport, and transfer of dioxin-
like compounds from contaminated soil, stack emissions, and effluent discharge. These three
sources of dioxin release are referred to as "source categories" in this document. Algorithms are
presented which link each of these sources to estimated concentrations in a number of media
which may be contaminated as a result, and are therefore potential "exposure media": 1) surface
soils, 2) surface-water associated media: suspended and bottom sediment and dissolved phase
concentrations, 3) air including the vapor phase and in particulate form, and 4) biota including
beef, milk, poultry, fruit and vegetables, and fish. The remainder of this section describes how
each potential exposure medium can be affected by each source, and the algorithms used to make
this link.
• Surface soils: Exposure to contaminated soil may be a result of direct contact with
soil on the site of the contamination, or indirectly after the contaminated soil has been
transported off the site of contamination and onto a nearby site of exposure. These cases are
termed "on-site": the site of contamination and the site of exposure are the same, and "off-site":
the site of exposure is distant from the site of contamination. In either case, soil concentrations
are specified for the contaminated source. For the off-site case, dioxins reach the site of
exposure via erosion. Mixing of contaminated and uncontaminated exposure site soil is into
either a "tilled" 20-cm depth or a "non-tilled" 2-cm depth. The tilled concentrations are used to
estimate concentrations in underground vegetables, and for outdoor dermal contact. The non-
tilled concentrations are used for indoor dermal contact events, for childhood soil ingestion in
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residential and farm settings, and for cattle soil ingestion (used in estimation of beef and milk
concentrations).
Exposure site soils can also be impacted from stack emissions due to air transport of
particle-bound dioxins from the stack to the exposure site. Deposition modeling for dioxin-laden
particles allows for estimation of tilled and non-tilled soil concentrations. When stack emissions
are the source, the nontilled depth of mixing is again assumed to be 2 cm.
A key assumption for evaluating the exposure site as a result of both off-site erosion and
stack emissions is that contaminants impact a thin layer of soil and do, in fact, dissipate; no
dissipation is assumed if the site of contamination is also the site of exposure. A soil dissipation
half-life of 25 years is assumed for all dioxin-like congeners for the shallow 2 cm depth and 100
years is assumed for the residues tilled to 20 cm.
• Surface Water: The principal assumption driving the solutions for the soil and stack
emission source categories is that the suspended and bottom sediments of water bodies originate
as watershed soils, which are subsequently eroded. For the stack emission source category, a
portion of the sediments also originates from directly-depositing dioxins. The process of erosion
transports soils within the watershed to the water body. Unit rates of erosion along with
watershed size determine the total potential amount of soil which could be delivered to the water
body. Sediment delivery ratios reduce that potential amount. A mass balance assures that soil
eroding on an annual basis becomes either suspended or bottom sediment within an annualized
volume of surface water. "Enrichment" of eroded soil is assumed, which means that eroded soil
from a contaminated source is assumed to be higher in concentration of dioxin-like compounds
than in situ, off-site soils. Once in the water body, a standard partitioning model based on the
organic carbon partition coefficient, Koc, determines the concentration of contaminant in the
water in truly dissolved form and the concentration on suspended sediments. The organic carbon
normalized concentrations of suspended and bottom sediment are assumed to be equal.
Watershed soil concentrations are model input parameters for determining the effect on surface
water from contaminated soils. For stack emissions, a total (dry + wet) deposition rate of
contaminant which represents average depositions onto the watershed is specified as an input
parameter, as well as a mixing depth representing the watershed. In this way, average watershed
soil concentrations are calculated for the stack emission source category.
For effluent discharges as sources, watershed soils are not considered. An amount of
contaminant is discharged into an annual flow volume to obtain a simple dilution concentration.
This total concentration is partitioned into a truly dissolved phase and a phase sorbed to
suspended sediments using the organic carbon partition coefficient, the Koc. Bottom sediments
are not considered for effluent discharges.
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• Soil to Air: From contaminated soils, residues become airborne via the processes of
volatilization and wind erosion. For on-site soil contamination, these vapor and particle phase
fluxes are translated to ambient air concentrations using a near-field dispersion model. For the
off-site scenario, the same approach is used to estimate ambient air exposure site concentrations,
except that a far-field dispersion model is used. These airborne reservoirs are the basis for
inhalation exposures, and are also used to estimate plant concentrations for vegetable ingestion
and in grass and feed for estimating beef and milk concentrations.
• Stack emissions and atmospheric transport modeling : Air dispersion/deposition
models consider the basic physical processes of advection, turbulent diffusion, and removal via
wet and dry deposition to estimate the atmospheric transport, resulting ambient air concentration,
and settling of particles. The ISCST3 model is used for air dispersion and deposition modeling.
Besides discussions in Chapter 3 on theoretical underpinnings and parameter assignment, further
discussions on the ISCST3 model can be found in EPA (1995b).
Application of the ISCST3 model follows these steps:
Step 1. Emission factors: The first step in the use of the ISCST3 model is to determine
"emission factors" for dioxin-like congeners. These factors are defined as the |ig (or other mass
unit) congener emitted per kg (or other mass unit) feed material combusted. Once assuming a
rate of feed material combusted in appropriate units, kg/day, these emission factors can be
translated to the units appropriate for atmospheric transport modeling, [ig/sec. This assessment
promotes the generation of specific congener emission factors, rather than emission factors for
TEQ or homologue groups. Emission factors for the demonstration of stack emission sources in
Chapter 5 were generated from actual test data from an incinerator burning organic wastes
(source otherwise unspecified). Emission estimates for this example incinerator are similar to
emissions that are known to be emitted from combustors employing sophisticated air pollution
control devices (e.g., scrubbers combined with fabric filters).
Step 2. Vapor/particle (V/P) partitioning: The second step in atmospheric transport
modeling is to determine the percent of totally emitted dioxin-like congener which is in a vapor
phase, and the percent which is in the particle phase. The partitioning of stack emissions into
these two phases was examined by reviewing stack testing data, ambient air sampling data, and a
theoretical approach developed in Bidleman (1988). From this review, it was generally
concluded that the most appropriate representation of partitioning of dioxins for purposes of fate
modeling and exposure assessing was provided by the modeling approach, and the V/P
partitioning scheme for dioxins and furans shown in Table 1-4 is the one adopted for this
Assessment.
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Step 3. Two runs of the ISCST3 model: In order to provide estimates of vapor and
particle phase concentrations of dioxin-like compounds, as well as estimates of wet/dry particle
deposition flux, it is necessary that to run the ISCST3 model twice. Both model runs should
assume a "unit emissions release rate", e.g., 1 g/s. Results from these unit runs can easily be
transformed to final outputs given the total emission rate of the congener and vapor/particle
partitioning. A vapor phase run involves turning wet/dry deposition switches to the "off
position. This inactivates a plume depletion equation that subtracts out losses in ambient air
concentration due to particle deposition. What is left are the Gaussian dispersion algorithms.
The vapor phase concentrations are used for inhalation exposures and also for vapor transfers
onto vegetation for food chain modeling. A second run of ISCST3 with wet/dry deposition
switches turned to the "on" position is considered a simulation of particle-bound contaminant.
Outputs from this run include wet and dry deposition rates, and air concentrations of
contaminants in the particulate phase. The depositions are used in soil and food chain modeling,
and the concentrations are added to the vapor phase concentrations from the first ISCST3 run to
arrive at the total air-borne reservoir for inhalation exposures.
• Biota: Simple bioconcentration/biotransfer approaches are used to estimate biota
concentrations in this assessment. Specifics for each biota considered are:
1. Fish - The soil contamination and stack emission source categories estimate the
concentration of contaminant on bottom sediments of water bodies. A fish lipid concentration is
estimated based the organic carbon normalized bottom sediment concentration and a BSAF, or
Biota Sediment Accumulation Factor. Whole fish concentrations for exposure estimation then
equal this lipid concentrations times a whole fish lipid content (or a fillet lipid content). For the
effluent discharge source category, fish lipid concentrations are estimated as a function of
organic carbon normalized concentrations and the closely related BSSAF, or Biota Suspended
Solids Accumulation Factor. This recently introduced bioaccumulation factor (EPA, 1993) is
analogous to the BSAF, and it is suggested in EPA (1993) that, as a first estimate, it take on the
same chemical-specific numerical value as the BSAF.
2. Vegetation - Concentrations in three types of vegetation are considered in this assessment:
below ground vegetables (carrots, potatoes, e.g.), above ground vegetables/fruits (tomatoes,
apples), and above ground grass and cattle feed which are required for estimation of beef and
milk concentrations. Assumptions critical to all three include: above ground vegetation is
impacted by vapor phase transfers and particle deposition - there is no root to shoot
translocation, outer portions of the vegetation are only impacted with minimal within plant
translocation, particle bound contaminants deposit onto and mix in a vegetative reservoir and are
subject to a fourteen-day dissipation half-life which represents particle washoff, and
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vegetables/fruits which have an outer protective layer (peas, citrus e.g.) are unimpacted by
dioxin-like compounds. Below ground vegetable concentrations are estimated from soil water
concentrations and a Root Concentration Factor, or RCF. Above ground concentrations in plants
due to atmospheric vapor concentrations are modeled using a "biotransfer" approach, where the
vapor concentrations are simply multiplied by an air-to-leaf transfer factor, Bvpa, and a surface
area to volume reduction factor, VG, which is equal to 1.00 for grasses and other leafy
vegetation and less than 1.00 for bulky vegetation. This Bvpa was found to be one of the most
critical parameters for not only vegetation concentration modeling (i.e., above ground
vegetations were found to be dominated by vapor transfers over particle phase depositions), but
for subsequent terrestrial animal food chain models. The Bvpa was developed for the dioxins in a
field calibration exercise.
3. Beef and Milk - Weighted average concentrations of dioxin-like compounds in the diets of
cattle raised for beef or lactating cattle are multiplied by a congener-specific bioconcentration
factor, BCF, which yields the concentrations in the fat of beef or milk. The same congener-
specific BCF is used for beef and milk. This presumes that dioxin-like compounds
bioaccumulate equally in body fat and milk fat of beef and dairy cattle. The difference between
the two food products is mostly a function of the diets presumed for beef cattle and lactating
cows. A set of BCFs for all dioxin-like congeners for this assessment were based on a set of data
on a lactating cow (i.e., dietary intakes of dioxin congeners, concentrations in milk, and other
pertinent quantities; McLachlan, et al., 1990). A later feeding experiment by Fries, et al. (1999)
found BCFs very similar to the McLachlan, et al. (1990) single cow BCFs adopted for this
assessment. Beef and dairy cattle diets are described in terms of proportions in pasture grass,
cattle feed (silage, grains), and soil. Models described above estimate concentrations in these
cattle intakes.
4. Chicken and Eggs - The algorithm to estimate the concentration of contaminant in chicken
and/or eggs is essentially the same algorithm as in beef/milk above: the concentration in the lipid
of chicken/eggs is a function of the weighted average concentration in the chicken diet
(comprised of vegetation and soil) and chicken/egg bioconcentration factors. The experiments
used to develop the chicken and egg bioconcentration factors were conducted by the Hazardous
Materials Laboratory at the California EPA (Stephens, et al. 1995). Three key differences in the
application of the chicken/egg bioconcentration model and beef/milk model were: 1) data was
available and robust enough to assign different bioconcentration factors for chicken and eggs, 2)
chickens, both layers and non-layers, were assumed to "free range" in the demonstrations of this
pathway in Chapter 5, which translated to a higher exposure to soil in their diet - 10% for free
range chickens vs. 4% for beef cattle and 2% for dairy cattle, and 3) based on information in
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Stephens, et al. (1995), chicken feed was assumed to originate from protected vegetation and was
therefore assumed to be dioxin-free.
1.5.5. Demonstration of Methodology
EPA (1992b) states, "In exposure scenario evaluation, the assessor attempts to determine
the concentrations of chemicals in a medium or location and link this information with the time
that individuals or populations contact the chemical. The set of assumptions about how this
contact takes place is an exposure scenario." These assumptions can be made many different
ways producing a wide variety of scenarios and associated exposure levels. The number of
people exposed at different levels form a distribution of exposures. Ideally assessors would
develop this entire distribution to fully describe the exposed population. Since the necessary
information for developing a population distribution is rarely available, EPA (1992b)
recommends developing a central and high end scenario to provide some idea of the possible
range of exposure levels.
1.5.5.1. Description of the Demonstration Scenarios
The basic setting for which the methodologies are demonstrated is a rural setting which
contains both farms and non-farm residences. The three principal sources of contamination, soil,
stack emissions, and effluent discharges, are assumed to exist in such a setting. "Central"
scenarios are based on typical behavior at a residence and "high end" scenarios are comprised of
a farm family that raises a portion of its own food. Key distinguishing features between the high
end and central scenarios include: 1) individuals in high end scenarios are assumed to be at their
home a greater proportion of the day than the central scenarios (which impacts assignment of
contact fraction), 2) individuals in high end scenarios are exposed to impacted beef from cattle
which they raise on their farm while these exposures are not considered for the central scenarios,
3) in contrast, individuals in the central scenario recreationally fish, 4) the exposure duration for
individuals in the high end scenario is 30 years compared to 9 years for the central scenario, and
5) certain exposure parameters, such as water ingestion rate which is 1.4 L/day for the central
scenarios and 2 L/day for the high end scenario, are different.
The example scenarios were carefully crafted to be plausible and meaningful, considering
key factors such as source strength, fate and transport parameterization, exposure parameters,
and selection of exposure pathways. However, it should be clearly understood that the purpose
of the demonstration scenarios is to provide users of this methodologies with a comprehensive
example of their application. The demonstration exposure scenarios were:
Scenarios 1 and 2: Background conditions, Residence and Farm
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Surface soils within the watershed are initialized to soil concentrations of the 17 dioxin-
like congeners (no dioxin-like PCBs) which have been found in an actual rural setting. Also, air
concentrations of the 17 congeners are initialized to air concentrations which have been found in
this same rural setting. Scenario 1 is the central residential scenario, and Scenario 2 is the high
end farming scenario. The exposure pathways for Scenario 1 are: water ingestion, air
inhalation, fish ingestion, fruit/vegetable ingestion, soil dermal contact, and soil ingestion. The
exposure pathways for Scenario 2 are: water ingestion, air inhalation, beef ingestion,
fruit/vegetable ingestion, soil dermal contact, and soil ingestion. It is noted that for a
background condition, it could be argued that all exposure is to background concentrations in
exposure media. In other words, all contact fractions would be 1.00. However, if an assessor
wished to compare the incremental impacts from a specific source of dioxin release with impacts
an individual would receive by contact with the same exposure media which has only
background concentrations of dioxins, than the assessor would assume all the same exposure
behaviors (rates of contact, contact fractions). This demonstration takes this approach.
Exposure Scenario 3: Soil Contamination, Farm
A 40,000 m2 rural farm is located 150 m (500 ft roughly) from a 40,000 m2 area of bare
soil contamination; an area that might be typical of contaminated industrial property. The
surface soil at this property is contaminated with three example dioxin-like compounds to the
same concentration of 1 part per billion (ppb). These compounds are: 2,3,7,8-TCDD, 2,3,4,7,8-
PCDF, and 2,3,3',4,4',5,5'-HPCB. The 1 ppb soil concentration is reasonable for industrial sites
of contamination of dioxin-like compounds, and generally about three orders of magnitude
higher than the concentrations of these congeners in background settings. As in the above and
all scenarios, bottom sediment in a nearby river is impacted, which impacts the water and fish.
The exposure pathways include: water ingestion, air inhalation, beef ingestion, fruit/vegetable
ingestion, soil dermal contact, and soil ingestion.
Exposure Scenarios 4 and 5: Stack Emissions, Residence and Farm
A 4,000 m2 rural residence (Scenario 4) is located 5000 meters from an incinerator, and a
40,000 m2 (Scenario 5) rural farm is located 500 meters downwind from an incinerator.
Emission data of the suite of 17 dioxin-like dioxin and furan congeners (no dioxin-like PCBs) is
available from stack testing of an actual incinerator. This allows for estimation of impacts from
each congener individually, and estimation of WHO98-TEQDF impacts. The exposure pathways
for Scenario 4 are: water ingestion, air inhalation, fish ingestion, fruit/vegetable ingestion, soil
dermal contact, and soil ingestion. The exposure pathways for Scenario 5 are: water ingestion,
air inhalation, beef ingestion, fruit/vegetable ingestion, soil dermal contact, and soil ingestion.
Exposure Scenario 6: Effluent Discharge into a River
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Exposure parameters associated with central behaviors for the water and fish ingestion
pathways were chosen to demonstrate this source category. The source strength was developed
from data on pulp and paper mill discharges of 2,3,7,8-TCDD. The discharges of the other two
example compounds are assumed to be the same for purposes of demonstration. Obviously,
however, there is less of a tie to real data for the discharge rate for these other two example
compounds.
Food pathway analyses outside of the scenario framework: The food consumption
pathways offish, milk, chicken, and eggs are demonstrated using source strength characteristics
of the three high end scenarios above: Scenarios 2 (background conditions), 3 (soil
contamination), and 5 (stack emission). These food pathways were not modeled in the scenarios
themselves. In these analyses, exposure media concentrations are calculated for each source and
the pathway exposure estimates are provided. The purpose of these external pathway analyses
was to provide further demonstration and to compare impacts from the various food pathways
where methodologies have been provided in this assessment.
1.5.5.2. Results from the Demonstration Scenarios
For brevity, only a subset of results from the demonstrations will be summarized. Table
1-5 gives the exposure media concentrations for WHO98-TEQDF for Example Scenarios #1 and
#2, demonstrating a background setting, and Scenarios #4 and #5, the central and high end
scenarios for the stack emission source category. Table 1-6 gives the estimated Lifetime
Average Daily Doses, LADDs, and the cancer risk for Scenarios #2 and #5, the high end
scenarios for the background setting and stack emission source.
Much of the differences between exposure pathways and scenarios is due to differences
in exposure media estimation. Following are some of the observations on exposure media
concentrations, LADDs, cancer and non-cancer risks for the background and stack emission
scenario demonstrations:
1) Concentrations in environmental and exposure media for the stack emission central
and high end scenario were about 3 and 2 orders of magnitude lower than the central and high
end scenarios demonstrating background conditions, respectively. For example, the background
WHO98-TEQDF air concentration was 0.021 pg/m3. In contrast, the WHO98-TEQDF air
concentration for the stack emission source was 2 orders of magnitude lower at 500 meters from
the stack, at 0.00024 pg/m3, and was lower still at 5000 meters from the stack, at 0.000085
pg/m3. This suggests that the example stack emission source, which was a single emission
source with a high level of pollution control, would contribute little to overall background
exposure levels.
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2) For both the background scenarios, 1 and 2, and the stack emission scenarios, 4 and 5,
WHO98-TEQDF soil concentrations were over an order of magnitude higher than 2,3,7,8-TCDD
concentrations. The difference in 2,3,7,8-TCDD and WHO98-TEQDF impacts to all media
mirrors the difference in stack emissions of 2,3,7,8-TCDD and stack emissions of WHO98-
TEQDF. This trend in differences between 2,3,7,8-TCDD and TEQ impacts occurs in all
exposure media estimations for both the background scenarios and the stack emission scenarios.
3) Within each demonstration scenario, there appears to be a reasonably narrow range of
predicted lipid concentrations among beef, milk, chicken, and egg fat. The difference is about a
factor of 3 to 4. The lowest concentrations are noted for the stack emission demonstration
scenarios, in the 10"3 to 10"2 pg WHO98-TEQDF/g (ppt) range, lipid basis. The background
concentrations were next highest, about two orders of magnitude higher in the 10"1 to 10° ppt
range. The average concentration of WHO98-TEQDF in lipids of terrestrial animal food products
measured in US food products, as described in Volume II of the Exposure Reassessment
Document, is similar to the background concentration predictions. This is not unexpected since
concentrations of dioxins in terrestrial food animals in the background scenarios were modeled
based on a profile of dioxins and furans found in air in an actual rural setting.
4) Table 1-6 shows the percent of total scenario exposure which is accounted for by each
pathway. The total scenario LADD was calculated simply as the sum of the pathway LADDs in
the scenario, without accounting for any differences in body absorption. As discussed above,
inhalation and food/water ingestion pathways have an absorption in the range of 80%, soil
ingestion has an absorption of 30%, and soil dermal contact has absorption already considered,
so LADD estimates are already at 100% absorption. From Table 1-6, it is seen that the beef
pathway dominates the scenarios. For the central scenarios (not summarized in this section),
which included fish ingestion but not beef or milk ingestion, the fish ingestion dominated.
Interestingly, the beef ingestion pathway LADD was over an order of magnitude higher than the
fish ingestion pathway LADD. This was more due to differences in the exposure parameters
including the ingestion and contact rates, and the differences in the lipid content of the full
product, rather than lipid concentrations themselves since the fish lipid concentrations tended to
be higher than the beef lipid concentrations for a given source.
5) Differences between analogous "central" and "high end" exposure pathway estimates
for the background demonstration scenarios, 1 and 2, were near or less than an order of
magnitude (inhalation exposure for the central background scenario and the inhalation exposure
for high end on-site scenario are analogous exposures). This is because the exposure parameters
used to distinguish typical and high end exposures, the contact rates, contact fractions, and
exposure durations, themselves did not differ significantly, and these were the only
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distinguishing features for analogous pathways in the background demonstrations. For the total
exposure, however, there was a difference of a factor of 20 between high end and central
exposure in the background demonstration scenarios. This is because the high end scenario
included consumption of beef, which was the highest exposure pathway and exceeded the fish
pathway of the central scenario by over an order of magnitude.
6) In the stack emission scenarios, placing exposed individuals either 500 or 5000 meters
away from the incinerator did significantly impact the results. The order of magnitude difference
in distance added about an order of magnitude difference in exposure media concentrations and
hence LADD estimates. Therefore, the full difference in analogous pathways between the
central and high end was closer to 2 orders of magnitude for the stack emission demonstration
scenarios.
7) The LADD of 0.093 WHO98-TEQDF/kg-day for the background high scenario is about
an order of magnitude lower than the adult background dose of 0.64 pg WHO98-TEQDF/kg-day
generated in Volume II, Chapter 4. The reasons for this difference are: 1) the Volume II
background exposure estimate was an average daily dose, ADD, not an LADD calculated in the
demonstration scenarios here. The LADD estimated in this chapter assumes 30 years of
exposure. The ADD during the exposure period would be just over twice, or 70/30, as high as
the LADD; 2) the Volume II background exposure estimate considered additional pathways
including fish, dairy ingestion (milk and otherwise), eggs, pork, and poultry. If one adds the
additional pathways for the background high scenario - milk, chicken, egg, and fish, the LADD
(and ADD) roughly doubles; 3) the exposure factors are different, with the most important
difference being that in the exposure scenarios considered in this chapter, contact fractions of
less than 1.0 were assumed - less than 0.5 for the terrestrial animal pathways, in fact.
Some of these differences between the Volume II background exposure estimate and the
LADD estimates for the background high scenario also are relevant for the procedures
demonstrated here to characterize non-cancer risk. As described earlier, a "ratio of incremental
exposure", or ROIE, is the current recommended approach for evaluating non-cancer risk. This
is defined as the ratio of the incremental dose due to the source being evaluated and the
background dose, multiplied by 100%. The background dose can be a generic US background
dose, or a site-specific dose. The generic adult background dose of WHO98-TEQDF, as noted
above, is 0.64 pg TEQ/kg-day. A background dose for the specific site being evaluated here has
not yet been developed. All of the exposures and risks displayed in previous tables assumed a
less-than-lifetime exposure, a limited set of exposure pathways, and contact fractions less than
1.0 (meaning that a fraction of their total consumption was from home-produced and impacted
food). Producing a site-specific background exposure requires an assessor to estimate the total
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exposure of an individual (or individuals) to dioxins if the nearby source were not in existence.
In that circumstance, the family would be still be consuming home produced foods. But they
would also be consuming store bought or restaurant bought foods. The "total" exposure would
include all pathways considered in the scenarios of this chapter, but other pathways as well.
For the purposes of the demonstration in Chapter 5, it was assumed that the farming
family in the background scenario consume foods at similar rates whether or not they are
consuming home produced or store bought food products, and that their exposure is
characterized by all the pathways in the formal demonstration scenarios, as well as the additional
scenarios that were demonstrated in Chapter 5, including milk, chicken, eggs, and fish. To
estimate their average background daily dose over a lifetime, the exposure duration will increase
from 30 to 70 years, and the contact fractions will all rise to 1 .00. The resulting daily exposure
is 1 . 16 pg WHO98-TEQDF/kg-day. This 1 . 16 pg WHO98-TEQDF/kg-day will be used here as the
"site-specific background dose" against which one can develop ROIEs for the incinerator source.
As discussed in Chapter 2, a pertinent issue for generation of ROIEs is the use of
LADDs or ADDs. Chapter 2 recommends the use of ADD for ROIEs, but this demonstration
will show a ROIE calculation for both LADD and ADD. The total LADD for the stack emission
high end scenario, as displayed in Table 1-7, is 1.01 * 10"3 pg WHO98-TEQDF /kg-day. The ADD
can be simply calculated as this LADD times 70/30, or 2.36 * 10'3 pg WHO98-TEQDF/kg-day.
The ROIEs are then easily calculated as:
usingLADD: [ (1.01*10-3)/(1.16) ] * 100% = 0.09%
using ADD: [ (2.36*10-3)/(1.16) ] * 100% = 0.20%
As seen by these two calculations, the ROIE is less than 1%. If the generic US background dose
of 0.64 pg WHO98-TEQDF/kg-day were used instead of the site-specific background dose of 1.16
pg WHO98-TEQDF/kg-day, the ROIEs would not be significantly different: 0.16% using LADD
and 0.37% for ADD.
1.5.6. Sensitivity Analysis
This section discusses three sensitivity analysis issues pertinent to use of the site-specific
methodologies promoted in this document: 1) the appropriate use and categorization of model
parameters, 2) a sensitivity analysis exercise on the parameters required for algorithms
estimating exposure media concentrations, and 3) the issue of mass balance with regard to the
source strength terms of the four source categories.
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1.5.6.1. Categorization and Use of Model Parameters
Table 6.1 in Chapter 6 lists all the parameters, including names, definitions, and units,
that are required for the methodologies of this assessment except the exposure parameters.
Exposure parameters are given in Table 2.1 in Chapter 2. Table 6.1 also gives four additional
pieces of information for each parameter listed. Three are numerical values which were used in
the sensitivity analysis exercises that are described below. One of those parameters is labeled
"selected", which were the ones used in the demonstration exposure scenarios. High and low
values of parameters selected for sensitivity analysis were carefully developed and might be
considered a reasonable range of values for other uses of the methodology (with obvious
exceptions such as areas of contamination, distances from contaminated to exposure site, and so
on). The fourth piece of information is a qualitative judgement on the part of the authors of this
document as to the appropriateness of using the "selected" parameter values for other
assessments. This judgement is categorized in three ways:
1) First Order Defaults: As defaults, these parameters are independent of site specific
characteristics. As first order defaults, it is felt that the values selected for the demonstration
scenarios carry a sufficient weight of evidence from current literature such that these values are
recommended for other assessments. Several of the chemical specific parameters, such as the
Henry's Constant, H, and the organic carbon partition coefficient, Koc, fall into this category.
The qualifier above, "current literature", indicates that new information could lead to changes in
these values.
2) Second Order Defaults: Like the above category, these parameters are judged to be
independent of site specific characteristics. However, unlike the above category, the current
scientific weight of evidence is judged insufficient to describe values selected for demonstration
purposes as first order defaults. Parameters of principal note in this category are the
bioconcentration parameters specific to the chemicals, such as the Biota Sediment Accumulation
Factor, or BSAF. This parameter translates a bottom sediment concentration to a fish tissue
concentration. Users should carefully review the justification for the SOD values selected for
the demonstration scenarios before using the same values.
3) Site Specific: These parameters should or can be assigned values based on site-specific
information. The information provided on their assignment for the demonstration scenarios, and
for selection of high and low values for sensitivity analysis testing, is useful for determining
alternate values for a specific site. A key class of SS parameters which are the source strength
terms - the soil concentrations, effluent discharge rates, and stack emission rates. If users are
unable to obtain site-specific information, or their use of the methodologies is for general
purposes, they should review the justification for selection of values for methodology
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demonstration, as well as information provided giving ranges of likely values for model
parameters.
The exposure parameters can be categorized as have the contaminant fate and
transport/transfer parameters. Assignment of these values are critical as LADD estimates are
linearly related to parameter assignments - doubling exposure duration assumptions double
LADDs, and so on. Some of the exposure parameters are appropriately described as first order
defaults. These include: lifetime, body weights, water ingestion rates, inhalation rates, and an
exposure duration for a childhood pattern of soil ingestion. All of the other exposure parameters
are better described as either second order defaults or site-specific. All exposure parameters
were developed based on information and recommendations in EPA's Exposure Factors
Handbook (EPA, 1997b) and Dermal Exposure Assessment: Principals and Applications (EPA,
1992c). Attaining site-specific information is recommended for exposure parameters.
1.5.6.2. Sensitivity Analysis
Sensitivity analysis was undertaken in order to evaluate the impact to exposure media
concentration estimations with changes in fate and transport/transfer model parameters. Figure
1-5 shows an example of sensitivity analysis conducted. This figure describes the impact of key
factors for the stack emission source category for determining impacts to beef. The x-axis
contains the names of the parameters evaluated. The key below the figure gives the definition of
the parameters and the values selected for the demonstration scenarios. The y-axis shows the
fractional change to the key model result, in this case, beef fat concentrations, to the changes
made in the parameter. These actual changes in model predictions are noted above and below
the bars. For example, beef fat concentrations increase by about a factor 2 with a 10-fold
increase in the soil concentration. In contrast, there appears to be almost a linear relationship
between an increase in vegetation concentration with beef fat concentration, as shown by the
sensitivity test displayed next to the soil concentration test. There, a 10-fold increase in grass
and other feed concentration resulted in a 10-fold increase in beef fat concentration. This was
not the same trend that was tested for the soil contamination source category. There, soil dioxin
provides the source term for vegetation and cattle impacts, not the air source of the stack
emission category. In the soil contamination source category, the soil-to-cattle pathway
dominates the prediction of beef fat concentration, whereas in the stack emission source
category, the air-to-plant-to-cattle dominates. The right side of Figure 1 shows how the beef fat
predictions are effected by changes in the assumptions regarding how the beef cattle are
exposed, as expressed in the exposure parameters BCSDF (beef cattle soil diet fraction), BCFDF
(beef cattle feed diet fraction), and the others listed in Figure 1-2. When....
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Following are key overall observations from the several sensitivity analysis exercises
undertaken in Chapter 6:
1) Source terms are the most critical for exposure media impacts. Source terms include
soil concentrations, stack emission rates, and effluent discharge rates. In all cases, the impact to
exposure media is linear with changes to source terms. Proximity to the source term can be
important as well, as demonstrated with differences in distance from the stack emission source.
2) Chemical-specific parameters, particularly the bioconcentration/biotransfer
parameters, are the second most critical model inputs. Some of these have lesser impacts
within the range tested, such as the organic carbon partition coefficient, Koc, for surface water
impacts. Generally, at least an order of magnitude in range in possible media concentrations is
noted with the range of chemical-specific parameter ranges tested. The impact of changes to
bioconcentration/biotransfer parameters is mostly linear. This is because these transfer factors
estimate media concentrations as a linear transfer from one media to another. For example, fish
lipid concentrations are a linear function of the organic carbon normalized concentration of
contaminants in sediments. These transfer parameters are also identified as uncertain
parameters. Tested ranges sometimes spanned over an order of magnitude for 2,3,7,8-TCDD.
3) All other parameters had less of an impact as compared to source strength and
chemical specific parameters; nearly all impacts were within an order of magnitude for the
range of tested values. Part of the reason for this trend is that there is a reasonably narrow
range for many of the non-chemical specific or source term parameters - soil properties, wind
speeds, vegetation yields, and others.
4) The sensitivity analysis exercises unearthed a dichotomy in model performance, and
likely therefore behavior in the real world, when soil is the source of dioxins as compared
to when stack emissions are the source of dioxins. The on-site soil source category was
demonstrated with a 1 ppt soil concentration of 2,3,7,8-TCDD, a concentration similar to
measured concentrations of 2,3,7,8-TCDD in rural settings. Air concentrations are estimated to
be 4*10"5 pg/m3 (vapor+particle phases summed). Atmospheric transport modeling in the
demonstration of the stack emission source category resulted in an exposure site air
concentration (vapor+particle phases summed also) at 500 meters from the stack to be 1*10"5
pg/m3. With similar air concentrations predicted to occur at the exposure site for the
demonstration of the soil and stack emission categories, one might hypothesize that all
subsequent impacts would be similar. That was not the case. The stack emission source
algorithms deposited particulates onto soil to estimate a soil concentration that was in the 10"3
ppt range for the 1-cm untilled depth and the 10"5 range for the 20-cm tilled depth. This
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compares to the 1 ppt concentration for the on-site soil source category demonstration. With
similar air concentrations but a 3+ order of magnitude difference in soil concentrations in the
demonstration of the soil and the stack emission sources, the following trends were noted: 1)
below ground vegetables had much higher concentrations for the soil source demonstration
scenario; 2) soil-related exposures (dermal contact and soil ingestion) were much higher for the
soil source demonstration scenario; 3) soil was significantly more critical in predicting beef and
milk fat concentrations in the soil source category. The following shows the relative impact of
soil versus vegetation (grass and cattle feed) for the on-site soil demonstration and the stack
emission demonstration:
Percent impact due to ingestion of:
Description Soil Grass Feed
Soil contamination, beef 90 7 3
Soil contamination, milk 87 2 11
Stack emission, beef 5 59 32
Stack emission, milk 3 15 82
Subsequently, beef and milk concentrations were almost two orders of magnitude higher for the
soil source category as compared to the stack emission source category, 4) because above ground
vegetation are driven by air concentrations, above ground vegetables/fruit and grass/cattle feed
concentrations were similar for both demonstrations.
1.5.6.3. Mass Balance
A mass balance exercise was undertaken to evaluate whether a principal of mass balance
will be violated with the models and parameters used for the demonstration of the soil source
category - that principal being that dioxin releases from a site cannot exceed the original amount
at the site (assuming no replenishment). A simplifying assumption for the soil source category
was that the soil concentration remained constant over the period of exposure - there was not a
systematic depletion of the reservoir over time due to modeled dissipation processes.
First, an estimate of the "reservoir" of 2,3,7,8-TCDD that is implied with the
demonstration parameters was made. Then, an estimate of the rate at which this reservoir
dissipated using the solution algorithms for dissipation: volatilization and wind erosion flux from
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soils, soil erosion, the soil ingestion by cattle and children, losses in runoff and leaching, the loss
via dermal contact, and the removal via harvest of below ground vegetation. The premise
examined was that, if it takes substantially more time than the exposure period to dissipate the
reservoir, then the assumption of a constant soil concentration may be suitable for purposes of
exposure assessments. On the other hand, complete dissipation within a time period less than or
even near to the period of exposure would mean that exposures and risks are being
overestimated. This analysis led to a conclusion that the reservoir modeled in the exercise above
would take more than 90 years to dissipate.
This was not a definitive exercise, by any means, but it does lend some confidence that a
principal of mass balance may not have been violated for the soil source categories, and for the
assumption of 30 years exposure duration.
1.5.7. Model Comparisons and Model Validations
Chapter 7 contains a series of tests of the fate models, including comparisons with other
available models and model validation exercises. Brief summaries of these exercises providing
an overview and qualitative statement about the results are provided below; Chapter 7 provides
all the detail and the quantitative results. Overall, model comparison and validation tests in
Chapter 7 showed that: 1) the empirical models selected for this methodology compared well
with most other models, many of them empirical as well, 2) since many of the key models of this
assessment, in particular the bioconcentration models relating a biota concentration (vegetation,
terrestrial animal lipid concentration) to an adjacent media concentration (air, animal feeds),
were developed from field data, it is not surprising that when tested against other field data, they
were shown to reproduce the field data reasonably well. It can be concluded that, with careful
parameter assignment, model predictions of environmental/exposure media concentrations of
dioxin-like compounds should be reasonably realistic for most uses. Nonetheless, it should be
understood that model testing is an ongoing process. The model comparisons and validations
summarized here are, by no means, expected to establish model validity beyond any doubt.
Users of this methodology are encouraged to subject the models to any number of tests,
validation or otherwise, as they use the models described in this document to conduct site-
specific assessments for dioxin-like compounds.
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1.5.7.1. Model Comparisons
1. Evaluation of alternative air-to-leaf modeling approaches Three empirical air-
to-leaf models for estimating grass concentrations from air concentrations are described and
tested against two field data sets. Both field data sets contained simultaneous air concentration
and grass concentration measurements of dioxin-like congeners. One set was in a rural and the
other in an industrial setting in the United Kingdom. Therefore, this test was both a model
comparison test as well as a validation of the air-to-plant modeling developed in this
methodology. A principal finding of this exercise was that the model selected for this
assessment provided the best fit of the data to the model.
2. Estimating water concentrations given a steady input load from overland sources
The WASP4 model, a substantially more complicated aquatic fate model than the one
developed in this methodology, was tested in a dynamic and a steady-state mode for Lake
Ontario (EPA, 1990a). Conditions in the steady state run were duplicated for the simple dilution
model used in the effluent discharge source category of this assessment. Results suggest that
the simple dilution model of this assessment produces reasonably similar results as the more
complicated WASP4 model.
3. Estimating fish tissue concentrations based on water column concentrations rather than
bottom sediment concentrations A water column measure of the potential for a contaminant
to accumulate in fish tissue is termed the Bioaccumulation Factor, or BAF. Bioaccumulation
refers to the net accumulation of a chemical from exposure via food and sediments as well as
water, and is calculated as the ratio of the chemical concentration in the fish to that in the water.
A ssBAF/ and a ssBAF^ (defined respectively as the steady state BAF, lipid- and total water
concentration-based, and steady state BAF, lipid- and dissolved water concentration-based) were
developed for lake trout, 2,3,7,8-TCDD, and for Lake Ontario 1987 contamination conditions
(EPA, 1990a). WASP4 model runs assuming steady loadings to Lake Ontario were duplicated
using the watershed modeling approach of this assessment, where water body concentrations
were a function of soil erosion loading, followed by simple partitioning and dilution algorithms.
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The prediction of whole fish tissue concentrations using ssBAF/ and ssBAF^ in the WASP4
modeling exercise was similar to fish concentrations predicted using the simple dilution model
and the use of the BSAF of this assessment. Differences were further studied using changes in
key modeling parameters including sediment organic carbon fraction and others.
4. Other modeling approaches and considerations for air concentrations resulting from
soil volatilization For the soil contamination source category, air concentrations result from
volatilization of vapor phase dioxins and suspension of particle-bound dioxins. Dioxins released
in these two ways disperse over the soils using simple dispersion models. Two alternate
modeling approaches for soil volatilization were tested, and one alternate air dispersion model
was tested. One of the alternate volatilization approaches was developed by Jury and co-workers
(Jury, et al., 1983, 1984a,b). If one assumes that the contaminant moves through the soil column
in only the vapor phase, a simplification of the fundamental equations used by Jury offers
another option for modeling soil volatilization. This model comparison test showed that the
volatilization model chosen for this methodology predicted an average flux over 30 years
roughly four times higher than the average flux predicted by the Jury model. The exact reason
for this four-fold difference was not ascertained, and could lie in differences in assumed
boundary conditions. In any case, it is judged that both models predict comparable volatilization
fluxes. On the other hand, the vapor diffusion model predicted volatilization rates that were 100
times less than the Jury models and about 250 times lower than the model of this methodology.
The reason for this discrepancy also could not be ascertained.
The alternate approach to estimating on-site dispersion is the "box-model" approach.
This is a simple dilution approach similar to the dilution model used to model the dispersion of
dioxins emitted from a pipe effluent discharge. Model testing showed that the box model
predicted air concentrations above a soil that was 10 times higher than the near-field dispersion
model used in this methodology and 100 times higher than the far-field solution.
5. An alternate model for estimating plant concentrations from soil concentrations For
plants grown in contaminated soils, plant concentrations are modeled as a two-step process:
vapor and particle-phase releases from soil disperse in the air and settle (particle-bound) or
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transfer (vapor-phase) to the vegetation in the models of this assessment. An alternate and
simpler approach was developed from field data on above ground vegetation concentrations
correlated to soil concentrations of contaminants and the octanol water partition coefficient in
Travis and Arms (1988). This correlation led to an empirical bioconcentration factor for
vegetation, Bv, regressed against the contaminant log Kow, and defined by the authors as the
concentration in above ground plant parts divided by the concentration in soil. The Bv calculated
for 2,3,7,8-TCDD is 0.0041. This compares with two plantsoil ratios calculated using the soil-
to-air-to-plant algorithms of this assessment: they were in the range of 10"5 for bulky vegetables
and 10"3 for leafy vegetation.
6. Alternate modeling approaches for estimating beef and milk concentrations
Webster and Connett (1990) compared five models which estimated the 2,3,7,8-TCDD content
of cow's milk from 2,3,7,8-TCDD air contamination. All five models have the same basic
framework. Particulate-bound 2,3,7,8-TCDD deposits onto the ground and vegetation, cattle
feed and pasture grass, to which the cattle are exposed. Algorithms to estimate soil
concentrations in these models are the same ones used in this approach, but the vegetation
algorithms importantly do not consider vapor phase dioxins. Model validation exercises done in
Chapter 6, and model sensitivity analysis exercises done in Chapter 5 both show that neglecting
the vapor phase will result in a significant underprediction in vegetation impacts for the lower
chlorinated dioxins.
These models use a "biotransfer factor" approach to estimating dioxin concentrations in
cow's milk. This approach converts a daily dosage of dioxin, in units of mass/day, into a dioxin
concentration. The "bioconcentration" approach in this assessment first calculates an average
dioxin concentration in the whole diet, considering fractions of the diet in vegetation and soil
and the concentrations in these diet components, and then multiplies this average concentration
by a bioconcentration factor. The most sophisticated biotransfer factor was developed by Travis
and Arms (1988), who developed a beef and a milk biotransfer factor, Bb and Bm, as a function of
the log Kow of the contaminant. Given a log Kow of 6.8 for 2,3,7,8-TCDD (assumed in this
assessment), Bb is solved for as 0.16 and Bm is solved for as 0.03. The beef/milk
bioconcentration factors of this document were developed from a data in a study by McLachlan,
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et al. (1990). This same data can be used to calculate biotransfer factors. The 2,3,7,8-TCDD Bm
from that data set is calculated at 0.01. While this 0.01 looks similar to the 0.03 calculated using
the empirical relationship developed by Travis and Arms (1988), this does not imply that the
Travis and Arms relationship is valid for dioxin-like compounds. In fact, the Travis and Arms
relationship calculates a larger Bm as log Kow increases, while the McLachlan data suggest that
Bm decreases as the degree of chlorination increases. It was concluded that the Travis and Arms
relationship is not valid for compounds with high log Kow, and definitely not valid for dioxin-
like compounds.
1.5.7.2. Model Validations
1. The impact of dioxin soil contamination to nearby soils The demonstration of the
"soil source" category in Chapter 5 suggested that the concentration at a site of exposure 100
meters away would be 40% of the concentration at the contaminated site for the 2 cm depth and
6% for the 20-cm depth. The literature contained several, somewhat anecdotal, information on
elevations of dioxin in soils near a contaminated site, and these were reviewed, including
derivation of a similar percentage when possible. One of those sites was the Dow Chemical site
in Midland, MI, and percentages derived with their data ranged from 3.5% to 15%, depending on
what soil concentration was assumed to represent contamination and what represented impacted.
2. Background soil concentration to bottom sediment concentration ratio A "sediment
enrichment ratio" of 3.00 in the model increases the concentration of dioxins in eroding soil as
compared to the concentration in the basin draining into a water body. The concentrations of
dioxins in the water body will be predicted to be higher than watershed soils, but not necessarily
three times higher. Other factors, particularly the organic carbon partition coefficient of the
dioxin compound and organic carbon fraction of the water body sediments, also influence the
prediction of water body sediment concentration. In the demonstration of background conditions
in Chapter 5, the ratio of the concentration of 2,3,7,8-TCDD in water soil to the water body
sediment was 2.8. A set of data on 2,3,7,8-TCDD in bottom sediment and background soil
concentration from several water bodies and nearby soils was used to derive several similar
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ratios. Although there was variability among the various water bodies, the average ratio derived
using all soil samples (n = 77) and all sediment concentrations (n = 346) was also 2.8.
3. Soil-to-Air and Air-to-Soil Modeling Air and soil measurements of the 17 dioxin
congeners at a rural site were available for model testing. Two tests were conducted - one
assumed air was the source of dioxins in the soil and soil concentrations were predicted based on
deposition of dioxins from the air, and the other assumed that soil was the source of dioxins in
the air and modeled the release and dispersion of dioxins. It was found that, when air dioxin was
assumed to be source of dioxin in the soil, a good match between predicted and observed soil
concentrations were found. When soil was instead assumed to be the source of dioxins in the air,
it was found that air concentrations were underpredicted by about three orders of magnitude.
This supports findings described earlier in the fate and transport overview of Volume III that
long range transport of dioxins by air is the cause for widespread dispersal of dioxins in the
environment.
4. Soil to Below Ground Vegetation A laboratory test in which carrots and
potatoes were grown in soil at various 2,3,7,8-TCDD levels provided a test for the soil-to-below
ground vegetation model. In that model, an RCF (root concentration factor) is multiplied by soil
water concentration to predict root concentration. For bulky below ground vegetables, an
additional VGbg reduced the concentration predicted only with RCF since this RCF was
developed from experiments on barley roots grown in solution. It was found that the measured
concentrations in the carrots and potatoes exceeded the predictions made using the RCF and a
VGbg initially set at 0.01. It was found that the measured peel concentrations were similar to
model predictions without the VGbg. This suggested that the dioxins translocated more than
expected into the carrots and potatoes, and on the basis of these tests, the VGbg was increased
from 0.01 to 0.25 for application of this model to below ground vegetables.
5. Paper and Pulp Mill Discharges and the Subsequent Impact to Fish Emissions of
2,3,7,8-TCDD measured in the 101 mill study (EPA, 1990b) were combined with average
streamflow data from the rivers and streams into which the paper mills discharged. Predictions
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of 2,3,7,8-TCDD in fish were compared with measurements of TCDD in fish from EPA's
National Bioaccumulation Study (EPA, 1992c). These fish were known to be downstream of the
pulp and paper mills. First, it was found that the model significantly underpredicted fish
concentrations when the mill discharged into the largest water bodies where paired data was
available - those with streamflows averaging 2.8 * 1010 L/hr, with a narrow range of 1 to 4 * 1010
L/hr. The predicted concentrations were about two orders of magnitude lower than measured. It
was speculated that there were many more sources of dioxin input to the rivers in these larger
systems; this modeling exercise assumed that the pulp mill was the only source of dioxins to
impact the fish. The predictions of TCDD in fish were much better in other paired data sets
when the streamflows averaged 5.4 * 108 L/hr, with a range of 107 to 109 L/hr. These lower
streamflows characterized 38 of 47 mills used in this model validation exercise. The average of
38 mills and 74 fish for modeled and observed fish concentrations is 7 ppt and 15 ppt,
respectively. Also of note and perhaps not ironically, the highest observed fish concentration of
143.3 ppt is matched by the highest predicted fish concentration of 89.2 ppt, for these lower
streamflow conditions. The average of 9 mills and 21 fish associated with large receiving water
bodies for modeled and observed fish concentrations is 0.7 and 5.3 ppt, respectively.
6. ISCST3 Modeling of the Release of Large Amounts of Dioxin from a Municipal Solid
Waste Incinerator and the Subsequent Impacts to Air and Soil Measurements of
dioxin TEQ emissions at nearly 1 kg/yr from a municipal solid waste incinerator in Columbus,
OH, in 1994 caused EPA's Region 5 to issue an Emergency Order requiring operators of this
incinerator to install MACT, Maximum Achievable Control Technology, pollution devices on an
accelerated basis. The incinerator had been in operation 11 years at this time, and a soil study
clearly showed elevations of dioxins in soils to about 3 km from the incinerator. Air
measurements taken at the same time additional stack measurements were taken were also
available, and the stack, air, soil, and obtained meteorological data allowed for a comprehensive
test of the ISCST3 model to predict short-term air concentrations, and with predicted
depositions, long-term soil concentrations. It was found that the prediction of highest 48-hour
air concentrations were generally in the same direction and magnitude as was found in the air
monitoring program. With specific congener differences, the model also showed the highest soil
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concentrations near the incinerator, with reduced concentrations in both measured and modeled
concentrations as the distance increased. An important observation was that the modeled soil
concentrations retained a similar relative profile as the stack-emitted dioxins. Differences
between the soil and stack profiles were only due to vapor/particle partitioning - there was no
modeling of air degradation (there was plume depletion due to deposition), and all congeners
were modeled to dissipate in soil at a similar half-life of 25 years. On the other hand, the
measured soil concentrations, while unambiguously and significantly elevated near the
incinerator, had a relative profile very similar to dioxin profiles found in a typical background
urban or rural soil profile, where concentrations are much lower. This shows that, while high
emissions clearly resulted in high soil concentrations near the Ohio incinerator, differential
dissipation mechanisms for the congeners in the soil and/or in the air resulted in a very typical
profile that was not modeled by ISCST3 and the soil concentration model. Future modeling
exercises for dioxins in air and depositing to soil using the ISCST3 or similar models should
focus on this disparity.
1.5.8. Uncertainty
Some discussion of the issues commonly lumped into the term "uncertainty" is needed at
the outset. The following questions capture the range of issues typically involved in uncertainty
evaluations:
(1) How certain are site specific exposure predictions that can be made with the methods?
(2) How variable are the levels of exposure among different members of an exposed local
population?
(3) How variable are exposures associated with different sources of contamination?
The emphasis in Volume III is in providing the technical tools needed to perform site-
specific exposure assessments. For the assessor focusing on a particular site, question (1) will be
of preeminent importance. Therefore, the emphasis of the uncertainty evaluation is to elucidate
those uncertainties inherent to the exposure assessment tools presented. This chapter examines
the capabilities and uncertainties associated with estimating exposure media concentrations of
the dioxin-like compounds using the fate, transport, and transfer algorithms, and also identifies
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and discusses uncertain parameters associated with human exposure patterns (contact rates and
fractions, exposure durations, etc.).
A site specific assessment will also need to address the variability of risks among
different members of the exposed population, the second key question above. The level of detail
with which this can be done depends on the assessors knowledge about the actual or likely
activities of the exposed population. In this document, one approach to evaluating this
variability is demonstrated. Separate "central" and "high end" scenario calculations are
presented to reflect different patterns of human activities within a hypothetical rural population.
A key issue with regard to intra-population variability is that it is best (if not only)
addressed within the context of a specifically identified population. If such information is
available, a powerful tool that can be used to evaluate the variability within a population is
Monte Carlo Analysis. Three recent Monte Carlo studies which have been done for exposure to
2,3,7,8-TCDD were reviewed. Assumptions on distributions of exposure patterns and fate and
transport parameter distributions are described, as are the results of their analyses.
With regard to question (3), this document does not present a detailed evaluation of how
exposure levels will vary between different sources of release of dioxin-like compounds into the
environment. While Volume III does demonstrate the methodologies developed for sources of
release of dioxin-like compounds into the environment with source strengths and environments
crafted to be plausible and meaningful, there is still a great deal of variability on both the source
strengths and on the environments into which the releases occur. For example, the frequency
with which farms and rural residences are near stack emissions of dioxin-like compounds is not
addressed. Such analysis is beyond the scope of this document.
Chapter 7 identifies some of the key uncertainties, as well as the key supporting
evidence, for the procedures and parameters associated with all the pathways. A summary of
discussions from the uncertainty evaluation is now presented. First is a summary of three
exposure parameters common to all pathways:
1. Lifetime, Body Weights, and Exposure Durations: Of these three parameters, the
exposure duration is the most uncertain. The estimates of 9 and 30 years were made in this
assessment for non-farming residents in rural settings, and farming residents in rural settings.
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An adult body weight of 70 kilograms and a lifetime of 70 years are standard assumptions for
exposure and risk and, although variability is recognized for these parameters, these variations
are not expected to add significant uncertainty in exposure estimates. The same is true for the 17
kg child body weight in the childhood exposure pattern of soil ingestion.
2. Soil Ingestion and Soil Dermal Contact: Soil ingestion for older children and
adults were not considered, which may have underestimated lifetime soil ingestion exposures.
Pica soil ingestion patterns were not evaluated in this assessment. The ingestion rates (100
mg/day for central scenarios and 600 mg/day for high end scenarios, during ages 2-6)
considering this appear reasonable. For the soil dermal contact pathway, key uncertain
parameters include the contact rate, (0.005 and 0.1 mg/cm2-event for indoor and outdoor events
for the high end farming scenario) and the absorption fraction (0.03 for dioxin-like compounds).
A major area of uncertainty for both pathways is the estimation of soil concentrations
where the source of contamination is located distant from the site of exposure. For this
assessment, this includes the off-site soil source category and the stack emission source category.
Validation exercises described above seem to suggest that prediction of soil concentrations from
airborne depositions or from soil erosion appear reasonable. Key uncertain parameters identified
include the dissipation rate (0.0693 yr"1), the mixing depth (2 cm), and the use of an enrichment
ratio (equal to 3.0) which increases the concentration of dioxin-like compound on eroded soil
relative to in-situ soil.
3. Ingestion of Water: A comparison of alternate modeling approaches for
estimating water concentrations showed similar results to the models adopted for this
assessment. There also does not appear to be a wide range of possible values for water ingestion
rate (1.4 L/day for central scenarios and 2.0 L/day for high end scenarios) and contact fraction
(0.75 for central scenarios and 0.90 for high end scenarios), and these are not expected to
introduce significant uncertainty into water ingestion exposure estimates.
4. Inhalation: The inhalation rate assumed for both central and high end scenarios was
20 m3/day. The distinction in the scenarios was in the contact fractions: central scenarios
assumed a contact fraction of 0.75 and high end scenarios had a 0.90 contact fraction. These
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fractions correspond to time at the home environment. These fractions and the inhalation rate
are not expected to add significant uncertainty in inhalation exposure estimates.
Sensitivity analysis showed air concentrations resulting from soil emissions to be
sensitive to Koc and H, and also to key source strength and delivery terms such as areas of
contamination and wind speed. Assuming these non-chemical specific parameters can be known
with reasonable certainty for site-specific applications, the most uncertainty lies with chemical
specific data.
Alternate approaches for volatilization and air dispersion tested included the
volatilization approach developed by Jury, et al. (1983, 1984a,b) and the box model for
dispersion calculations. The Jury model predicted about 1/3 as much volatilization flux (given
the selection of parameters, made equal to or most analogous to the models of this assessment) as
the Hwang, et al. (1986) model of this assessment. The box model predicted about 6 times
higher air concentrations than the near-field dispersion approach of this assessment. This
reasonable comparison lends some credibility to the models selected.
Approaches to estimate particulate phase concentrations are empirical and based on field
data. They are based on highly erodible soils but are specific to inhalable size particles, those
less than 10 |im. As such, they may overestimate inhalation exposures, but may underestimate
the total reservoir of particulates, which becomes critical for the particle deposition to vegetation
algorithms. Another area of uncertainty is the assumption that volatilized contaminants do not
become sorbed to airborne particles - this is also critical because vapor phase transfers dominate
plant concentration estimation. A final key area of uncertainty is that transported contaminants
from a contaminated to an exposure site via erosion are assumed not to volatilize or resuspend at
the exposure site or from soils between the contaminated and the exposure site - air borne
exposure site concentrations may be underestimated as a result.
5. Fruit and Vegetable Ingestion: All ingestion parameters assumed are evaluated as
reasonable for general exposure to broad categories of fruits and vegetables. However, great
variability is expected if using these procedures on a specific site where home gardening
practices can be more precisely ascertained. Concepts of below and above ground vegetations
were developed to accommodate soil to root algorithms and soil to air to vegetation algorithms.
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Protected vegetation - those with outer inedible protections such as citrus or corn - were assumed
not to be impacted by dioxin-like compounds.
A key assumption in the vegetation algorithm, that dioxin-like compounds do not
translocate from root to shoot, was verified by two experiments. Vapor-phase contributions to
vegetation dominated the contaminated soil and stack emission source categories, with one
exception. Particle depositions were more important for above ground fruit/vegetable
concentrations for the stack emission source.
Critical empirical parameters were the above and below ground correction factors, VGag
and VGbg, which were set at 0.01 and 0.25, respectively, for fruits and vegetables. The VGag was
justified based on the fact that the above-ground transfer algorithm was based on experiments for
the azalea leaf, and this leafy vegetation would differ for fruits such as apples where
translocation into the fruit would be minimal; the VGag for grass was 1.00 for this same reason.
Support for this VGag for bulky above ground vegetation came from independent experiments by
McCrady (1994). The VGbg was given a value of 0.25 based on the testing of the root transfer
algorithm on experiments on carrots and potatoes.
Important experimentally derived empirical factors describing the transfer of dioxin-like
compounds from one media to plants include the RCF, the soil to below ground vegetables
transfer factor, and Bvpa, the vapor-phase to above ground plant transfer factor. Validation
exercises described above on both of these factors lent a degree a credibility to the independent
use of these transfer factors.
6. Ingestion of Fish: The key exposure parameter for this pathway was the fish
ingestion rate. The rates assumed in the demonstration scenarios were low in comparison to
estimates given for subsistence fisherman or others who live near large water bodies where fish
are commercially caught. The justification for the lower ingestion rate for demonstration
purposes was that the setting demonstrated was described as rural, containing farms and non-
farm residences, where the emphasis is on agriculture. A relatively small watershed with a small
impacted water body was assumed. Daily ingestion rates of 8 (central) and 25 (high end) g/day
were assumed, based on data from recreational fisherman surveys summarized in EPA (1997b)
and on subsequent recommendations for these values in EPA (1997b).
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Other models for estimating fish concentration based on water column concentrations,
rather than suspended sediment concentrations, were described in EPA (1993) and demonstrated
in this assessment. Results indicated that the water column approaches would predict similar
whole fish concentrations compared with the sediment concentration approaches of this
assessment. A key uncertain parameter for estimating fish tissue concentrations is the Biota
Sediment Accumulation Factor, or BSAF, and the Biota Suspended Sediment Accumulation
Factor, or BSSAF. A range of 0.03 to 0.30 for 2,3,7,8-TCDD is hypothesized for column
feeding fish, while the Connecticut data (CDEP, 1992) and some other data on bottom feeding
fish indicate higher BSAFs ranging up to 0.86 for 2,3,7,8-TCDD. A value of 0.09 for 2,3,7,8-
TCDD for BSAF and BSSAF is used in this assessments. A "Bioequivalency Factor", or BEF,
approach is used to assign values to other dioxin-like congeners. In this approach, a relative
rating scheme provides factors to multiply the 2,3,7,8-TCDD bioconcentration parameter to get
this same factor for the other dioxin congeners. Another key parameter is the fish lipid content,
which can vary from below 0.05 to above 0.20. The model estimates a fish lipid concentration.
Multiplying fish lipid concentration by fish lipid content arrives at a whole fish concentration or
an edible fish concentration, depending on the user's assignment and characterization of the fish
lipid content variable. For this assignment, the fish lipid content was assigned a value of 0.07 for
the demonstration scenarios, based on lipid content offish in EPA's Lake Ontario study (EPA,
1990a).
7. Beef and Milk Ingestion: The rates of beef and milk fat ingestion are 2.45 and 14.0
g/day, respectively. Beef fat and milk fat contents are assumed to be 22% and 3.5%,
respectively. An additional factor of 0.55 accounts for cooking and post cooking loss of beef.
The assumptions for contact fractions for beef and milk (fractions of their total consumption that
comes from home supplies) was 0.48 and 0.21, respectively. Since exposure estimates from
these pathways are linearly related to ingestion rate and contact fraction, these are critical
exposure parameters for site specific applications.
Comparison with earlier modeling approaches showed that the current approach to
estimating beef and milk concentrations is the same as earlier approaches, although
mathematically formulated differently. Early efforts in the literature did not consider vapor
transfers to vegetation; one later assessment did include vapor transfers, and a key result in that
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assessment, as well as this one, is that vapor transfers are critical for beef impacts. Finally,
earlier assessments considered the practice of fattening beef cattle prior to slaughter by feeding
them residue-free grains. These efforts estimated over a 50% reduction in beef concentration
due to residue degradation or elimination and/or dilution with increases in body fat. The
demonstration scenarios in this assessment did not consider this practice. However, this practice
was considered in the air-to-beef food chain validation exercise. There, a 50% reduction in beef
concentrations due to feedlot fattening was assumed.
Key uncertain and variable parameters for beef/milk concentrations include: 1) the beef
and milk fat bioconcentration factor, BCF, 2) the beef cattle and dairy cow exposure
assumptions (fractions of their feed in which feed categories), 3) the assumptions concerning
vapor/particle partitioning for the stack emission source category, and 4) the air-to-leaf transfer
parameter, B^, for vapor phase contaminants.
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(TEFs) for PCBs, PCDDs, PCDFs for Humans and Wildlife. Environmental Health
Perspectives 106: 775-792.
Webster, T., P. Connett (1990). The use of bioconcentration factors in estimating the 2,3,7,8-
TCDD content of cow's milk. Chemosphere 20:779-786.
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Table 1-1. The TEF scheme for I-TEQ
:DF
Dioxin (D) Congener
2,3,7,8-TCDD
1,2,3,7,8-PeCDD
1,2,3,4,7,8-HxCDD
1,2,3,6,7,8-HxCDD
1,2,3,7,8,9-HxCDD
1,2,3,4,6,7,8-HpCDD
OCDD
TEF
1.0
0.5
0.1
0.1
0.1
0.01
0.001
Furan (F) Congener
2,3,7,8-TCDF
1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
1,2,3,4,7,8-HxCDF
1,2,3,6,7,8-HxCDF
1,2,3,7,8,9-HxCDF
2,3,4,6,7,8-HxCDF
1,2,3,4,6,7,8-HpCDF
1,2,3,4,7,8,9-HpCDF
OCDF
TEF
0.1
0.05
0.5
0.1
0.1
0.1
0.1
0.01
0.01
0.001
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Table 1-2. The TEF scheme for dioxin-like coplanar PCBs,
as determined by the World Health Organization in 1994
Chemical Structure
3,3',4,4'-TeCB
2,3,3',4,4'-PeCB
2,3,4,4',5-PeCB
2,3',4,4',5-PeCB
2',3,4,4',5-PeCB
3,3',4,4',5-PeCB
2,3,3',4,4',5-HxCB
2,3,3',4,4',5'-HxCB
2,3',4,4',5,5'-HxCB
3,3',4,4',5,5'-HxCB
2,2',3,3',4,4',5-HpCB
2,2',3,4,4',5,5'-HpCB
2,3,3',4,4',5,5'-HpCB
IUPAC Number
PCB-77
PCB-105
PCB-114
PCB-118
PCB-123
PCB-126
PCB-156
PCB-157
PCB-167
PCB-169
PCB-170
PCB-180
PCB-189
TEF
0.0005
0.0001
0.0005
0.0001
0.0001
0.1
0.0005
0.0005
0.00001
0.01
0.0001
0.00001
0.0001
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Table 1-3. The TEF scheme for TEQDFP-WHO9
Dioxin Congeners
2,3,7,8-TCDD
1,2,3,7,8-PeCDD
1,2,3,4,7,8-HxCDD
1,2,3,6,7,8-HxCDD
1,2,3,7,8,9-HxCDD
1,2,3,4,6,7,8-HpCDD
OCDD
TEF
1.0
1.0
0.1
0.1
0.1
0.01
0.0001
Furan Congeners
2,3,7,8-TCDF
1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
1,2,3,4,7,8-HxCDF
1,2,3,6,7,8-HxCDF
1,2,3,7,8,9-HxCDF
2,3,4,6,7,8-HxCDF
1,2,3,4,6,7,8-HpCDF
1,2,3,4,7,8,9-HpCDF
OCDF
TEF
0.1
0.05
0.5
0.1
0.1
0.1
0.1
0.01
0.01
0.0001
Chemical Structure
3,3',4,4'-TeCB
3,4,4',5-TCB
2,3,3',4,4'-PeCB
2,3,4,4',5-PeCB
2,3',4,4',5-PeCB
2',3,4,4',5-PeCB
3,3',4,4',5-PeCB
2,3,3',4,4',5-HxCB
2,3,3',4,4',5'-HxCB
2,3',4,4',5,5'-HxCB
3,3',4,4',5,5'-HxCB
2,3,3',4,4',5,5'-HpCB
IUPAC Number
PCB-77
PCB-81
PCB-105
PCB-114
PCB-118
PCB-123
PCB-126
PCB-156
PCB-157
PCB-167
PCB-169
PCB-189
TEF
0.0001
0.0001
0.0001
0.0005
0.0001
0.0001
0.1
0.0005
0.0005
0.00001
0.01
0.0001
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Table 1-4. Particle fractions, cj), in four airsheds at 20°C for the dioxin-like congeners.
Congener
2378-TCDD
12378-PCDD
123478-HxCDD
123678-HxCDD
123789-HxCDD
1234678-HpCDD
OCDD
2378-TCDF
12378-PCDF
23478-PCDF
123478-HxCDF
123678-HxCDF
123789-HxCDF
234678-HxCDF
1234678-HpCDF
1234789-HpCDF
OCDF
Clean
Continental
0.10
0.44
0.78
0.78
0.78
0.93
0.98
0.09
0.27
0.38
0.63
0.63
0.74
0.74
0.86
0.92
0.98
Average
Background
0.29
0.74
0.93
0.93
0.93
0.98
0.995
0.27
0.57
0.69
0.86
0.86
0.91
0.91
0.96
0.98
0.995
Background
Plus Local
Sources
0.49
0.87
0.97
0.97
0.97
0.99
0.998
0.47
0.75
0.84
0.93
0.93
0.96
0.96
0.98
0.99
0.998
Urban
0.75
0.95
0.99
0.99
0.99
0.997
0.999
0.73
0.91
0.94
0.98
0.98
0.99
0.99
0.99
0.997
0.999
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Table 1-5. WHO98-TEQDF environmental and exposure media concentrations for the
background conditions scenarios, #1 and #2, and the stack emissions demonstration scenarios, #4
and #5.
Description
Air, vapor phase, pg/m3
Air, particle phase, pg/m3
Soil, untilled, pg/g
Soil, tilled, pg/g
Soil, watershed, pg/g
Surface water, pg/L
Sediment, pg/g
fish lipid, pg/g*
leafy vegetation, pg/g dry
above ground fruit/veg, pg/g
fresh
below ground vegetables,
pg/g fresh
beef fat, pg/g
milk fat, pg/g
chicken fat, pg/g
egg fat, pg/g
Background,
Scenarios 1 and 2
2.59*10'3
1.87*10-2
1.29
0.65
1.29
2.63*1Q-3
3.37
6.33
0.45
5.74*1Q-3
1 94*1 Q-2
1.58
1.10
0.61
0.71
Emission, Central
Scenario 4
2.45*1Q-5
6.04* ID'5
4.46* ID'3
4.46* ID'4
8.91*1Q-4
3.80*1Q-5
2.39*10'3
5.64*1Q-3
1.86*1Q-3
1.20*10-5
1.63*1Q-5
4.35*10-3
3.05*10-3
2.02* 10-3
2.25*1Q-3
Emission, High
End Scenario 5
6.94*1Q-5
1.74*10-4
3.51*10-2
3.51*10-3
8.91*10-4
3.80*10-5
2.39*10-3
5.64*10-3
6.39*1Q-3
6.37*1Q-5
1.29*1Q-4
1.65*10-2
i.n*io-3
1.38*1Q-2
1.55*1Q-2
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Table 1-6. Lifetime average daily doses, LADD, and cancer risk estimates, of WHO98-TEQDF
for the high end background scenario, #2, and for the high end stack emission scenarios, #5.
Scenario/Pathway
Scenario 2 - Background High
Soil Ingestion
Soil Dermal Contact
Inhalation
Water Ingestion
Beef Ingestion
Vegetable Ingestion
Fruit Ingestion
Total
Scenario 5 - Stack Emission High
Soil Ingestion
Soil dermal contact
Inhalation
Water ingestion
Beef ingestion
Vegetable ingestion
Fruit ingestion
Total
LADD, ng/kg-
day
3.25*1Q-6
1.40*10-6
2.34*1Q-6
2.90*10-8
8.30*10-5
2.32*1Q-6
3.65*1Q-7
9.30*10-5
8.86*1Q-8
8.51*1Q-9
2.68*1Q-8
4 19*10-io
8.65*1Q-7
1.83*10-8
4.05*10-9
i.oi*io-6
Percent of total
scenario exposure
3
2
O
<1
89
2
<1
100
9
1
2
<1
86
2
<1
100
Cancer Risk
1.22*1Q-6
5.27*1Q-8
2.34*1Q-6
2.90*1Q-7
8.30*1Q-5
2.36*1Q-6
3.65*1Q-7
8.96*1Q-5
3 32*10-8
3.19*1040
2.68*1Q-8
4 19*10-io
8.65*1Q-7
1.83*1Q-8
4.05*1Q-9
9.48*1Q-7
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ci
2,3,7,8-Tetrachlorodibenzo-p-dioxin
ci
1,2,3,7,8-Pentachlorodibenzo-p-dioxin
ci
3,3',4,4',5,5'-Hexachlorobiphenyl
ci
2,3,7,8-Tetrachlorodibenzofuran
ci
2,3,4,7,8-Pentachlorodibenzofuran
ci
3,3',4,4',5-Pentachlorobiphenyl
Figure 1-1. Chemical structure of 2,3,7,8-TCDD and related compounds
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(0 'U
g
1
CO
.2
(D
2
^ :
O)
IE
1
-g
«
Q.
E
n 4
10x
10x
Cbfat = 0.002 ppt
10 Exposure Extent of /
Conditions Pasturing /
0.1x
U
09 high high /
I__|__l 1 1
y
0.3 low u
low
Soil
Ingestion
II
II
low
low
0 1x 1 Extent of
' Contamination
Cs Cgr BCF
Cfeed
Parameter Name
Bs
Exposure Scenario
Figure 1-2. Results of sensitivity analysis of algorithms estimating beef fat concentrations in the
stack emission source category.
Parameter Name
Definition
Selected
Cs
Cgr
Cfeed
Cbfat
BCF
Bs
2,3,7,8-TCDD soil concentration, pg/g
2,3,7,8-TCDD grass concentration, pg/g dry wt.
2,3,7,8-TCDD feed concentration, pg/g dry wt.
2,3,7,8-TCDD beef fat concentration, pg/g
beef/milk bioconcentration factor, unitless
bioavailability of contaminant on soil relative
to vegetation
Exposure Scenario Parameters:
BCSDF beef cattle soil diet fraction
BCFDF beef cattle feed diet fraction
BCGDF beef cattle grass diet fraction
BCGRA beef cattle fraction of contaminated grazing land
BCFOD beef cattle fraction of contaminated feed
0.001
0.0004
0.0002
0.002
5.76
0.65
0.04
0.48
0.48
1.00
1.00
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2. ESTIMATING EXPOSURES AND RISKS
2.1. INTRODUCTION
In this chapter, the framework for assessing exposure and risk to 2,3,7,8-TCDD and
related dioxin-like compounds will be described. Section 2.2 introduces the exposure equation
and its key terms. Section 2.3 describes how cancer risk and non-cancer risk estimates are
generated, given exposure estimates. Section 2.4 summarizes procedures for calculating
exposure and risk for dioxin toxic equivalents. Section 2.5 provides the overview of the
procedures used in this document. Section 2.6 describes the development of exposure scenarios
for this assessment. Finally, Section 2.7 discusses the exposure pathways and exposure
parameters chosen for this assessment.
The development of exposure assessment methods, scenarios and associated parameter
values raises many issues which are generic to all chemicals. In order to keep the scope of this
document reasonable, the decision was made to focus on issues specific to dioxin-like
compounds and to avoid evaluating generic issues. Thus, priority is given to addressing issues
such as fish bioconcentration, dermal absorption, degradation, and other chemical/physical
properties of these compounds. The approach used to address generic issues such as soil
ingestion rates, inhalation rates and other behavior parameters is based on published Agency
documents, primarily the Exposure Factors Handbook, published first in 1989 (EPA, 1989). The
current version of the Exposure Factors Handbook was published in 1997 (EPA, 1997a).
Exposure factors used in this chapter are based on this version.
Another generic issue which has been raised in connection with this document is the use
of Monte Carlo procedures to define exposure scenarios. These procedures require distributions
for the input parameters used in the assessment. Such distributions are now being addressed in
the Exposure Factors Handbook. However, this document has not demonstrated Monte Carlo
and related procedures on the scenarios developed in this chapter. Individuals outside of the
Agency have published assessments which applied Monte Carlo procedures to problems
involving dioxin-like compounds. In recognition of the high interest in this area, a general
description of this technique and summaries of assessments which applied it to dioxin-like
compounds are included in Chapter 8 of this Volume.
2.2. EXPOSURE EQUATION
This document describes procedures for conducting exposure assessments to estimate
either potential or internal dose. A potential dose is defined as a daily amount of contaminant
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inhaled, ingested, or otherwise coming in contact with outer surfaces of the body, averaged over
an individual's body weight. An internal dose is defined as the amount of the potential dose
which is absorbed into the body. Section 2.3 below discusses the relevancy of this distinction for
dioxin-like compounds.
The general equation used to estimate potential dose normalized over body weight and
lifetime is as follows:
Lifetime Average Daily Dose (LADD) = (exposure media concentration x
contact rate x contact fraction x exposure duration ) /
(body weight x lifetime) (2-1)
The LADD is used to assess cancer risks. Non-cancer risks from exposure to dioxin-like
compounds can be assessed using the Average Daily Dose, or ADD. This is calculated as in
Equation (2-1), but without the exposure duration in the numerator and the lifetime in the
denominator. Each of the terms in this exposure equation is discussed briefly below:
• D Exposure media concentrations: These include the concentrations in soil for the
dermal contact and soil ingestion exposure pathways, in the vapor and particulate
phases in air for the inhalation exposure pathway, in water for the water ingestion
pathway, and in food products such as fish, fruits and vegetables, and beef and
milk, for the food ingestion pathways. The concentrations used should represent
an average over the exposure period. Chapter 4 provides models for estimating
exposure media concentrations.
• D Contact rate: These include the ingestion rates, inhalation rates, and soil contact
rates for the exposure pathways. These quantities are generally the total amount
of food ingested, air inhaled, etc. Only a portion of this material may be
contaminated. The next term, the contact fraction, which is 1.0 or less, reduces
the total contact rate to the rate specific to the contaminated media.
• D Contact fraction: As noted, this term describes the distribution of total contact
between contaminated and uncontaminated media. For example, a contact
fraction of 0.8 for inhalation means that 80% of the air inhaled over the exposure
period contains dioxin-like compounds in vapor form or sorbed to air-borne
particulates. The contact fractions for the exposure pathways of air inhalation and
water ingestion are related to the time individuals spend at home. Other pathways
such as fish ingestion or ingestion of home grown foods are not related to time at
home. Similarly, contact fractions for individuals exposed at work places relate
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largely to time spent at the work place. EPA (1997a) discusses several time use
studies which can be used to make assumptions about time spent at home (and
outdoors at the home environment) versus time spent away from home.
Generally, these time use studies asked participants to keep 24 hour diaries of all
activities. Studies summarized were national in scope, involved large numbers of
individuals, cross-sections of populations in terms of age and other factors, and up
to 87 categories of activities. Results from different studies reviewed in EPA
(1997a) consistently indicate that the average adult spends between 68 to 73% of
time at the home environment.
Exposure duration: This is the overall time period of exposure. Values of 9
years and 30 years are used in the example scenarios described in Chapter 5. EPA
(1997a) describes several population mobility studies. A recent study by the U.S.
Bureau of the Census (1993) covered a national sample of 55,000 interviews. The
50th and 90th percentile values of values for years living in current residence were
determined to be 9.1 and 32.7 years, which are rounded here simply to 9 and 30
years. The 9 years will be used in a residential scenario and the 30 years for the
farming scenario. The fact that farmers tend to live longer in their residents than
average individuals was supported by a second study (Isreali and Nelson, 1992)
described in EPA (1997a) which categorized respondents in a number of different
ways. The average time living in current residence for individuals in farms was
17.3 years, while it was 7.8 years for individuals in rural settings and 4.6 years for
all households (Isreali and Nelson, 1992). Another exposure duration
demonstrated in Chapter 5 is one associated with a childhood pattern of soil
ingestion. The exposure duration in this case is 5 years.
Body weight: The human adult body weight of 70 kg is assumed. While the
average adult (males and females) body weight is closer to 60 kg (EPA, 1997a),
70 kg has been traditionally used by the Agency and will be used in this
assessment where appropriate. This document has chosen to use consumption rate
data that are in terms of g food consumed/kg body weight/day, rather than g food
consumed/day. Therefore, body weight drops out as an exposure factor for these
pathways; it is still required in this assessment for the inhalation, soil ingestion,
soil dermal contact, and the water ingestion pathways. The fish consumption
pathway still uses the g/day convention since most fish consumption survey data,
and the data used in this assessment, are in that form. As such, this pathway also
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uses the 70 kg body weight assumption. The average of male and female average
body weights at ages 1 through 6 was 16.6 (EPA, 1997a). This was used to
assume an average body weight of 17 kg for the childhood pattern of soil
ingestion. The 70 kg adult body weight will be used in some, but not all of the
pathways.
• D Lifetime: Following traditional assumptions, the average adult lifetime assumed
throughout this document is 70 years. Even though actuarial data indicate that the
United States average lifetime now exceeds 70 years, this convention is used to be
consistent with other Agency assessments of exposure and risk.
2.3. CANCER AND NON-CANCER RISK ASSESSMENT
The primary source of information on the health risks of the dioxin-related compounds is
the Health Document of this Reassessment, Part II of the Reassessment Documents (Part I are the
Exposure Documents and Part HI is the Risk Characterization). While that remains the principal
source of information on potential health effects resulting from dioxin exposures, some general
procedures for using exposure estimates in support of cancer and non-cancer risk assessments are
summarized here.
The Risk Characterization emphasizes that risk assessments for dioxin-like compounds
must consider background exposures when evaluating the impact of increments that are due to
specific sources:
When evaluating incremental exposures associated with specific sources, knowing the
increment relative to background may help to understand the impact of the incremental
exposure. For instance, it would be misleading to focus on incremental exposure in
evaluating the potential impact on human health when a relatively large background
body burden of dioxin already exists in the exposed population. In these circumstances,
the incremental exposure needs to be evaluated in the context of these background levels.
This has led us to suggest that perhaps the best information for a decision-maker to have
is: (1) a characterization of effects noted in the low dose range; (2) a characterization of
the range and average of "background" exposures, including a discussion ofMOE; (3) a
characterization of the incremental percent increase over background of individuals or
subpopulations of interest; and (4) guidance on when contributions from incremental
exposures adding to average "background" become significant for the decision.
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The Risk Characterization as well as the Health Document provide detailed discussions of the
effects of dioxin exposure in the low dose, or background, range, and those discussions are not
repeated here. These documents also discuss in great deal the Margin Of Exposure, MOE,
concept and the MOE associated with current background exposures. The MOE is calculated by
dividing a "point of departure" dose or exposure at the low end of the range of observation in
human or animal studies by the comparable surrogate of human exposure at a "level of interest".
These points of departure could include the human-equivalent lowest observed adverse effect
level (LOAEL), the no observed adverse effect level (NOAEL), the benchmark dose (BMD), or
the effective dose (ED) at some percentage; ED01 is the effective dose for 1% of the population.
Points of departure could also be body burden levels associated with health effects. The
comparable "level of interest" for dioxin-like compounds are defined here as terms relating to
background exposures, or perhaps, background exposures plus incremental exposures due to a
specific source being evaluated. The Risk Characterization provides this general guidance
regarding interpretation of MOEs:
Generally speaking, when considering either background exposures or incremental
exposures plus background, MOEs of 100 or more are considered adequate to rule out
the likelihood of significant effects occurring in humans based on sensitive animal
responses or results from epidemiologic studies and traditional factors used in safety
assessment. Conversely, as MOEs approach the range of observation of effects, reaching
any conclusion regarding the certainty of no harm is much more difficult.
The Risk Characterization concludes that the MOE for background exposures are well
below 100 and even below 10:
One of the difficulties in assessing the potential health risk of exposure to dioxins
is that background exposures are often a significant component of total exposure when
based on TEQ. The average levels of background intake and associated body burdens of
dioxin-like compounds in terms ofTEQs in the general population (1 pg TEQ/kgBW/day
and 5 ng TEQ/KgBW, respectively) are well within a factor of 10 of human-equivalent
levels associated with NOELS, LOAELs, BMDs, or ED01 values derived from studies in
laboratory animals exposed to TCDD or TCDD equivalents. Therefore, in many cases,
the MOE compared to background using these endpoints is a factor of 10 or less....
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The Risk Characterization provides tabular summaries of these health endpoints that show that
the MOEs for certain endpoints are even below 5. With MOEs below 10 for background
exposures alone, a calculation of an MOE based on background plus incremental exposure may
not be of benefit in a site-specific risk assessment. For example, if an MOE for a particular
toxicological endpoint of interest is calculated to be 5 for background exposures (5/1), and it
were determined that a source resulted in an incremental exposure that is 10% of background,
than a resulting MOE considering background plus incremental exposure would be about 4.5
(5/1.1). While this kind information does not appear to be of benefit to decisionmakers, it may
be useful to know that a particular source adds 10% more exposure than an individual would get
by background exposures alone. This is identified in the third recommendation provided in the
first quote above from the Risk Characterization: "...(3) a characterization of the incremental
percent increase over background of individuals or subpopulations of interest".
The first section below discusses the quantification of "background exposures" to dioxin-
like compounds, from both a dose and body burden standpoint. The second section introduces
the "increment above background", or IOB, ratio, which is a ratio that describes the percent
increase over background a specific source contributes. The IOB concept is suggested as a way
of evaluating non-cancer risk. The final section will discuss the traditional approach to
estimating cancer risk, based on an incremental exposure and a slope factor. These approaches
will be demonstrated in Chapter 5.
2.3.1. Background Exposure Doses and Body Burdens
The second volume of the Exposure Document, Volume IT: Properties. Environmental
Levels, and Background Exposures, describes United States background exposures to dioxin-like
compounds, both from the perspective of exposure doses and body burden. Combining average
adult contact rates (inhalation rate, food consumption rates, etc.) with average concentrations of
these compounds in the contact media (air, food, etc.), the current adult background dose to
dioxin-like compounds is quantified as 1.0 pg WHO98-TEQDFP/kg-day. This Volume further
provides similar background estimates for children of various ages, and these estimates are: 1-5
yrs: 3.3 pg WHO98-TEQDFP/kg-day, 6-11 yrs: 1.9 pg WHO98-TEQDFP/kg-day, and 12-19 yrs: 1.1
pg WHO98-TEQDFP/kg-day. In addition, background exposures of infants to dioxins in breast
milk is also addressed in the Volume n of the Exposure Document.
The average adult body burden of dioxin-like compounds, expressed on a lipid basis, was
estimated as 25 pg WHO98-TEQDFP/g (ppt). This was based on measurements of dioxins in blood
of specifically selected "background" populations in six site-specific studies conducted by, or
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with the assistance of, the Agency for Toxic Substances and Disease Registry between 1995 and
1997, with the blood analyzed by the Centers for Disease Control (CDC, 2000). A total of 316
adults ranging in age from 20 to 70 years were measured in these studies. The individuals
sampled were all U.S. residents with no known exposures to dioxin other than normal
background. While the samples in this data set were not collected in a manner that can be
considered statistically representative of the national population and lack wide geographic
coverage, they are judged to provide the best available indication of current tissue levels in an
average adult population in the United States. The Risk Characterization has estimated a
national background body burden of 5 pg WHO98-TEQDFP/g, on a whole weight basis.
An important issue associated with the use of this national body burden estimate is that it
represents the average for the wide range of adult ages from 20 through 70. As discussed in
Volume II, there is an age trend associated current body burden data - older individuals have
higher body burdens. An in-depth analysis of this trend in Lorber (2002) concludes that this
trend is due to two factors: 1) that exposures were higher in past, such that older individuals have
been exposed to much more dioxin during their lives compared to younger individuals, and 2)
that dioxins are long-lived in the human body, such that an individual's body burden will rise as
they age if they receive a steady input dose during their life. Lorber (2002) concluded that higher
past exposures was much more responsible for this trend than build-up over time in an
individual's body. They explored this trend using simple pharmacokinetic modeling. Figure 2-1
shows one result of their analysis. These are modeled populations distributions of body burdens
of dioxin and furan TEQ lipid-based concentrations (Lorber's examination didn't include dioxin-
like PCBs) corresponding to the hypothetical sampling years 1965, 1985, 1995, and 2030.
There are a few important observations to make regarding the information in this figure.
It shows that the average population body burdens are declining, and that by 2030, the average
body burdens of WHO98-TEQDF will be 9 pg/g lipid, or about 2.3 pg/g whole weight basis
(assuming 25% body lipids). This trend for 2030 is essentially identical to the trend of how an
individual's body burden would very slowly rise through adulthood if they are exposed to a
constant dose. This result was derived by assuming that today's dose of dioxin and furan TEQs
stayed constant throughout the early decades of the 21st century. It also simplistically modeled
dioxin TEQs as though they were a single compound with a single half-life. If one were to redo
this to include dioxin-like PCBs, again modeling TEQs as though they were a single compound,
it would show that the average adult body burden at the steady intake of 1.0 pg WHO98-
TEQDFP/kg-day would be about 13 pg WHO98-TEQDFP/g-lipid, or about 3.3 pg WHO98-TEQDFP/g
whole weight. This figures also shows that today's background body burdens (as estimated by
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the population distribution for 1995) of younger individuals is lower than the population average:
the population average is 28 ppt lipid while the concentrations for the under 30 age group is
below 20 ppt lipid. This means that if one were to conduct an analysis of the incremental impact
to specific individuals who might comprise the younger portion of an adult population, such as
women of child-bearing age, it might be more appropriate to assume a lower average background
body burden than the 25 pg WHO98-TEQDFP/g lipid that characterizes the full range of today's
population.
In site-specific assessments, the assessor has the option of using information on current
national average daily intakes or body burdens as the "background" against which to compare
incremental exposures. In this case, the assessor needs to make it clear that he is comparing
incremental exposures and/or body burdens to national background estimates. In certain
circumstances, it may be preferable to have an understanding of the "local", or "regional",
background rather than the national background. A local background dose or body burden for a
site-specific assessment, where a specific source of dioxin release is being evaluated, can be
thought of as the overall dose or body burden that an individual potentially impacted by that
source of release would experience, or would have, if the source in question were not in
existence. Local background exposures for one setting may be higher than in another because,
for example, one setting may have more sources of industrial dioxin release (other than the one
being evaluated) within it. Another site-specific consideration would be the behaviors of
exposed individuals. There may be a water body for fishing in one site, while agriculture may
dominate another site. If there is reason to consider a subsistence behavior for a particular setting
that could lead to elevated exposures, it may be appropriate to derive a local background
exposure to dioxin based on that subsistence behavior, rather than on general behaviors.
While it may be important for risk assessors or risk managers to consider local
backgrounds rather than national backgrounds, it should be emphasized that this document will
not provide guidance, such as a hierarchy of recommendations, on how to assign background
quantities for any quantitative evaluation of the incremental exposures using the increment above
background, or IOB, approach described below. Obviously, this choice can be important. If a
particular area is likely to have a higher background as compared to a national background, this
would influence the numerical value of a ratio such as an IOB ratio. For example, if local
background is twice as high as a national background, than an incremental exposure that is equal
to 10% of national background would be equal to 5% of a local background. Similarly, if an area
is thought to be have lower exposures as compared to background exposures, say half as much
for example, than an incremental exposure equal to 10% of national background would be equal
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to 20% of a local background. Risk managers could evaluate incremental exposures differently if
they are told that the incremental is 5% of background as compared to 20% of background.
Perhaps the best general advice that can be offered in this procedures document is to recommend
that a risk assessor supply the risk manager with this type of information: 1) the incremental
exposure from a specific source, 2) how that compares to the national background exposure, and
3) information on how local exposures may relate to national exposures, if this information is
available.
A local background body burden can be ascertained through monitoring. A
comprehensive discussion of monitoring approaches of human matrices (blood, adipose tissue,
mother's milk) is beyond the scope of this document. However, some key generalizations can be
noted here that are relevant to site-specific assessments for dioxin-like compounds:
1) Whether the source is in operation or not: Some site-specific assessments are
conducted to evaluate the potential impact of an industrial facility not yet operating. In that case,
sampling of the representative population that could be impacted by emissions from the facility
would be appropriate. If the facility has been operating, it becomes necessary to sample a
population that is not impacted by the facility, but one that is comparable with regard to
demographics. Dioxin body burdens are a function of many factors including age, nursing
practices, dietary preferences and possibly occupational history. Ideally, the population of
concern around a site should be characterized in terms of these factors. If the source is ongoing,
and it becomes necessary to locate and sample a different population to characterize background,
then a comparison population with similar characteristics should be chosen.
2) How to interpret differences in the study and a comparison population: Since dioxin
body burdens reflect contributions from all sources, the portion due to releases from the site of
concern cannot be definitively established. Sometimes measurements from a comparison
population can be difficult to interpret; the better the demographic match between the study and
comparison populations, the more likely it is that any differences between the groups can be
attributed to the site or source of concern.
3) Body matrix to sample: As noted above, the background body burden of 25 ppt
WHO98-TEQDFP lipid-basis was determined from sampling of blood in background populations
from several site-specific evaluations. Blood is the recommended matrix for sampling in that the
entire population can be sampled in a relatively easy, non-invasive manner. In can be costly,
however, and obtaining volunteers for a background blood sampling program can be an issue.
Mother's milk can be considered and it does have certain advantages over blood: 1) it may be
easier to obtain volunteers for a mother's milk sampling program in contrast to a blood sampling
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program, 2) mother's milk has a higher lipid content (3-4 %) than blood (<1%), and therefore
less milk would be required for analysis than blood, with a greater possibility of being able to
quantify concentrations of the lipophilic dioxin-like compounds, and 3) useful information would
be gained on potential infant impacts in addition to population exposures. However, mother's
milk concentrations should be interpreted carefully, given these considerations: 1) needless to
say, these programs monitor only half the population, and 2) proper interpretation of data for the
female population can also be an issue. Evidence has shown that breast-feeding provides an
avenue of depuration of dioxin-like compounds in the lactating female: breast milk
concentrations decline while the mother is breast-feeding and female body burdens are lower for
the second and subsequent children as compared to first children. As a matrix for screening
studies, mother's milk can be more expedient than blood monitoring. Certainly, important
implications exist when unusually high concentrations of dioxin-like compounds are found in
breast milk, and general differences between populations can easily be identified in a breast milk
monitoring program.
Other issues exist when deriving a background dose rather than a background body
burden. A background dose estimate requires two quantities: contact rates and exposure media
concentrations. Two options for contact rate assignment could be contact rates derived for a
specific site (i.e., subsistence fishing consumption rates), or national average contact rates.
National average contact rates were used to develop the 1 pg WHO98-TEQDFP/kg-day. Four
options are available for estimating exposure media concentrations:
1) National average exposure media concentrations: Combining site-specific contact
rates with national average exposure media concentrations could provide the assessor with useful
information at minimal cost, and would be quite acceptable in most cases where the assessor has
no reason to believe that local concentrations are substantially different than national average
concentrations. In other circumstances, it might be most appropriate to conclude that local
concentrations are different than national average concentrations, necessitating a different
approach for estimating concentrations.
2) Measurement of Local Exposure Media Concentrations: Measurement of
concentrations of dioxin-like compounds in all relevant exposure media associated with a site
would be the most accurate way to characterize a site-specific exposure media, but it would
prove costly and, in fact, it may not be useful if the source in question has been in existence for
several years. For example, a measurement of dioxins in air and soil in the vicinity of an
operating incinerator source would reflect the impacts of the incinerator. Measurements in
environmental/exposure media in the vicinity of an incinerator, even if it was shut down, could
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still be influenced by past emissions from the incinerator. This would be the case if measuring
soil around a closed incinerator. It would not be the case, however, for measuring air
concentrations in a vicinity around a closed incinerator. In this case, the measured air
concentrations would be appropriate reflections of background air concentrations for that site
(although small contributions to that measured air concentration could come from releases of
dioxins from soils; evidence suggests that these contributions would be small). The
measurement of exposure media concentrations is mostly feasible (but still costly) if the
assessment is prospective in nature - i.e., that it evaluates a new source not yet emitting dioxins.
Another possibility is to use existing measurements or take new measurements in a nearby area
which is matched as closely as possible to the study area, but doesn't have a similar dioxin
source present. EPA's National Dioxin Air Monitoring Network, NDAMN, has been collecting
air concentrations in background areas of the United States on a quarterly basis (4 samples/yr)
since 1998 (Cleverly, et al., 2001). These concentrations could be used as background air
concentrations for a site-specific assessment. However, the purpose of this network is to
measure background air concentrations in settings where no apparent sources are present, such as
in national parks. For this reason, NDAMN measurements might be lower than would exist in
urban or suburban settings where a source of release is being evaluated with the methodologies
of this document. Lorber, et al. (1998) showed how concentrations of dioxins in soil in
Columbus, OH, approached a local urban background about 12 kilometers away from an
incinerator. Soil or air measurements on the order of 10 kilometers or more from an air source
being evaluated, but still within a very similar urban or rural setting, might be reasonable
surrogates for background concentrations for specific sites.
3. Modeling of Exposure Media Concentrations: There are at least two ways in which
background exposure media concentrations could be estimated with the use of models alone.
One is to model the impacts from all major sources in the area being evaluated except for the
source in question and then adding impacts at points of interest. This assumes that the impacts
from all modeled sources are independent. The model ISCST3 used in this site-specific
methodology document does, in fact, have the capability of combining inputs from multiple
sources. In a complex setting, this option may be more costly and difficult than can be justified
given the uncertainty in the exercise and the output. Specifically, the assessor must consider
issues such as, have all sources been identified?, how accurate are source emission estimates?,
and so on. Another possibility, for some time in the future, is to use results from studies now
underway to develop multiple source, large-scale air dispersion/deposition models specific to
dioxin. As an example, EPA is developing an application of the RELMAP model, which is the
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model originally developed for acid rain assessments by EPA's Office of Air Quality and
Planning Standards, and was also used to predict national fate of mercury emissions in EPA
(1997b). This model takes, as input, emissions into the air from specifically defined sources in
the United States, and predicts, for any location (on a 40 km grid) in the United States, deposition
rates and air concentrations for modeled contaminants. Air concentrations and depositions could
then serve as the input for modeling impacts to the terrestrial and aquatic environment using the
steady state models discussed in this assessment. The uncertainties involved in using RELMAP
and similar models to predict impacts of dioxin sources are not well quantified at the present
time. EPA initiated a study in 2001 to compare modeled to measured air concentrations, and to
compare RELMAP outputs to outputs generated with other similar regional models. These
comparisons will help to reduce the uncertainty and to refine/calibrate these models for future
possible site-specific applications involving dioxin-like compounds. Exposure media
concentrations modeled by RELMAP or other aggregate source models would represent the
"background" concentrations. The source in question is modeled separately and any predicted
concentration would be in addition to the "background" concentration predicted by the aggregate
modeling.
4. A combination of modeling and limited monitoring: This may be a reasonable way
to estimate background exposure media concentrations for a specific site, if useful site-specific
monitoring is available. For example, if an incinerator were no longer operating, then air
concentrations measured in the vicinity of the shut down incinerator could give an estimate of the
background air concentrations for that site. These air concentrations could then be routed
through the terrestrial and aquatic fate models to predict soil and food concentrations for
background exposure estimation. Careful monitoring of soil and air concentrations in the
vicinity of the source while it is still operating is another possibility. Specifically, if several air
monitors in all directions from an incinerator source were sampled on several days, during which
time wind rose data were also taken, then one could probably identify air concentrations most
and least likely to be impacted by the source in question by a careful examination of the wind
rose data. Soil concentration data several miles away from an incinerator source, or a soil
contamination source, may be an appropriate measure of dioxin impacts to soils not including the
source in question. An example of careful monitoring around an incinerator emitting large
amounts of dioxin allowed Lorber, et al (1998) to distinguish background air and soil
concentrations from impacted air and soil. Again, these intermediate exposure media
concentrations could be used with the steady-state models described in this assessment to
estimate the full range of terrestrial and aquatic impacts.
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A demonstration of a site-specific background scenario is presented in Chapter 5.
Specifically, this scenario uses site-specific exposure factors and option 4 above to derive
exposure media concentrations. Actual air and soil measurements in a background setting are
used for the inhalation and soil related pathways. These measured concentrations are routed
through the terrestrial and aquatic food chain models to predict concentrations in food products.
Background dose estimates are calculated using these site-specific exposure factors and these
hybrid exposure media concentrations. As such, the "background doses" in this example
scenario in Chapter 5 can be thought of as specific to the region in which the source in question
exists.
2.3.2. The Increment Over Background Concept for Non-Cancer Risk Assessment
EPA's Office of Solid Waste (OSW) has evaluated the potential for non-cancer impacts
from individual sources of dioxin release based on an "increment above background", or IOB,
approach. They have recommended that assessments conducted for hazardous waste incinerators
use, on a provisional and site-specific basis, an IOB calculated as the ratio of the incremental
exposure dose divided by the background exposure dose for dioxins, and then multiplying by
100%, so that the IOB is expressed in terms of a percentage. Some of the experience using this
approach in OSW has been compiled and reported upon in Canter, et al. (1998). OSW has
termed this ratio the margin of incremental exposure, or MOIE. That terminology and acronym
will not be used here as it is similar to the MOE, or margin of exposure, which is used here and
has a specific meaning in Agency risk assessing.
The other important difference is that the IOB approach developed here will be used to
characterize the incremental increase to body burden and not to dose. The Risk Characterization
concludes that non-cancer health effects due to dioxin exposure are best correlated to body
burden and not to dose. If a dose has been occurring for a long period of time such that the body
burden approaches a steady state, than of course dose and body burden are related. A percent
increase in dose over background dose will be similar to a percent increase in body burden over
background body burden in this circumstance. If an incremental dose has been occurring for a
short amount of time, than the body burden will not rise to the level it would reach at steady state
from that incremental dose. Therefore, a percent increase over background in terms of dose
could be significantly higher than a percent increase over background in terms of body burden for
short duration exposures. For that reason, the IOB approach developed here will be focused on
body burden.
The IOB equation for body burden is given as:
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77? 7?
IOB,, = -=^- * 100%
B**
where:
IOBbb = increment over background ratios for body burden (bb), %
IBB = increment of body burden increase due to the incremental
exposure, pg/g whole weight
BB bk = background body burden, pg/g whole weight basis
To use this equation, a background body burden needs to be assigned, and an approach
needs to be developed to estimate the increment of body burden that is due to the source being
evaluated. Discussions in the previous section address the assignment of the background body
burden. As discussed above, issues to consider include: whether to use a national or a local
background body burden, whether to consider the background body burden for younger adults
rather than the full range of adult ages (younger adults would have a lower background body
burden), whether to consider specific populations such as women of child-bearing age (which
again might imply a lower concentration as compared to a full population average), and so on.
The focus on the procedures in this volume are on predicting exposure media
concentrations and estimating site-specific intake doses. A simple procedure is recommended
here to estimate body burdens given intake doses to dioxin-like compounds. Specifically, the
simple one-compartment, first-order pharmacokinetic model is recommended. The non steady-
state form of this model to predict body burden from a constant intake dose is given by:
dCT
L —- = D - kLCT (2-3)
dt L
where:
CL = concentration in lipid, pg/g;
L = lipid mass, g
= BW * Flipid (= body weight * fraction lipid)
D = steady-state absorbed dose, pg/day (* 365 d/yr = pg/yr)
k = first-order dissipation rate constant, I/day (or 1/yr, if D is in 1/yr units)
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0.693/t1/2
t1/2 = half-life, days (or years)
time period of interest, most often the full duration of exposure
The closed form solution for Equation (2-4) is given as:
CL(f) - C^e-x* + -.(! - e-*) (2.4)
The first term on the right hand side of Equation (2-4) describes the decline in an initial body
burden. If using Equation (2-4) to solve for the full body burden after a period of exposure
denoted by "t", than one has to also assign a value of "D" equal to the incremental dose plus the
ongoing background dose times an absorption fraction. However, if using this solution to
estimate the incremental body burden due to the incremental dose only, than the first term drops
out and the equation for solving IBB is:
a, -
(25)
where the term "D" has been replaced by ADD * AF, where ADD is the average daily
incremental dose, and AF is the absorption fraction. The Risk Characterization has assumed a
value of 0.8 for the absorption fraction, 70 kg for the body weight (BW) and 7.1 years for the
half-life (t1/2), when using this model for WHO98-TEQDFP in a backwards mode to estimate a
steady-state dose that is associated with a body burden. For site-specific assessments, the
assessor can assign values to these parameters based on whether they intend to model individual
dioxin-like compounds (which could influence the assignment of t1/2 or A) and/or whether they
intend to consider different sexes and/or stages of life (which could influence the assignment of
BW).
2.3.3. Traditional Agency Cancer Risk Assessment Procedures
The usual procedure used to calculate an upper-limit incremental cancer risk is based on
an assessment of lifetime average daily dose, LADD, and a slope factor, as follows:
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R = i-e^ SF*LADD (2-6)
where SF is the slope factor, or more precisely, the 95% upper confidence limit of the linearized
cancer slope factor of the dose-response function (expressed in units of the probability divided by
dose, or probability/[mg/kg-day], which is most often simply shortened to [mg/kg-day]"1) and
LADD is the dose (which needs to be in units appropriate to cancel those of SF, mg/kg-day).
The estimated R in Equation (2-6) can be understood as the 95% upper confidence probability of
incurring cancer during a lifetime as a result of the exposure defined by LADD; the true
probability may be less than the quantity R, and may even be zero. The simplified form of the
risk equation shown above is reasonably accurate for risks less than 10"2. This assessment uses
the simplified SF * LADD form of this equation since the exposures and risks demonstrated are
generally less than 10"3. The slope factor, SF, for 2,3,7,8-TCDD has been previously estimated
by EPA as 1.56*105 [mg/kg-day]'1 (EPA, 1984; 1981), but has been reevaluated as 1.0*106
[mg/kg-day]"1 in this Reassessment. Also, it has been recommended in the Risk Characterization
that it be applied to a TEQ dose, in addition to a 2,3,7,8-TCDD dose. As detailed in the Risk
Characterization, a current estimate of lifetime cancer risk from background exposures to dioxin-
like compounds is about 10"3. This is calculated simply as the background exposure dose of 1 pg
WHO98- TEQDFP/kg-day times the slope factor of 1*10"3 (converted to [pg WHO98-TEQDFP/kg-
day]"1 to be consistent with the dose units).
Similar to the estimation of lifetime cancer risk demonstrated above showing a risk of
10"3 due to background exposure to dioxin-like compounds, assessors can also estimate the
incremental cancer risk in these probability terms. The LADD of Equation (2-6) would simply
be LADDSS. These LADDs can be for individual exposure pathways assessed, or all exposure
pathways combined.
When using Equation (2-6) for estimating incremental cancer risk, the assessor needs to
be aware that adjustments may be required for particular pathways. Adjustments are necessary
for the soil-related pathways, soil ingestion and soil dermal contact. The selected cancer slope
factor was based primarily on the analysis of the human epidemiology studies where exposure
was estimated from dioxin concentrations in blood in occupationally exposed cohorts. The dose
estimates used to derive the slope factor were obtained by using a pharmacokinetic model to
convert the blood concentrations to an administered, or potential, dose. An administered dose is
defined as the dose which contacts the body boundary surfaces, such as the skin as in dermal
exposure or the dose ingested prior to absorption. This administered dose was derived by first
calculating an absorbed dose and then dividing by 0.8 - i.e., an absorbed dose was assumed to be
80% of an administered dose. Because the slope factor was derived based on an administered
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dose, the new slope factor can be applied to an administered dose without any adjustment for
absorption as long as the absorption is approximately 80%. Although the data are limited, this is
probably a reasonable assumption for most types of food ingestion and inhalation.
The absorption from ingested soil or dermal exposure from soil, however, is likely to be
less than 80%. Studies have shown that the bioavailability of dioxin from ingested soil is
variable depending on properties such as organic carbon content and is best determined on a site
specific basis. A full discussion on absorption of administered dioxin through the various
pathways can be found in Chapter 1. Disposition andPharmacokmetics, of the Health
Document (Part II of these Dioxin Reassessment Documents). Assuming that 30% of dioxin in
ingested soil is absorbed, then the slope factor should be multiplied by 30/80 or 0.375 before
combining it with the ingested dose to compute risk. For the soil dermal pathway, the absorption
of dioxin through the skin has been estimated to range from 0.5 to 3.0% (EPA, 1992a), with
assessments typically (conservatively) assuming 3.0%. This assessment adopts the 3% value to
calculate the dose of dioxins absorbed through soil dermal contact. As will be described below,
this absorption fraction is already considered in the calculation of dioxin dose through soil
dermal contact, and the calculated dose is already an absorbed dose. As such, the correction
factor is a little different for soil dermal contact - its purpose is to convert a 100% absorbed dose
into a dose comparable to other administered doses to which the SF is applied. Specifically, it
needs to be adjusted downward to be equivalent to, for example, an inhaled administered dose
which is 100% as calculated by the procedures here, but it only 80% (or thereabouts) when
absorbed. Therefore, the dermal dose, needs to be adjusted upward by a factor of 1.00/0.8, or
1.25, to be used with the SF. The adjustments for the soil pathways to estimate cancer risk are
given as:
R = SF LADD AF (2-7}
s s s \-^- ')
where: Rs = lifetime excess cancer risk from soil pathways, ingestion
and dermal contact
SF = cancer slope factor, (mg/kg-day)"1
LADDS = administered dose from soil ingestion and soil dermal
contact, mg/kg-day
AFS = absorption correction factor for the soil pathways, 0.375 for
soil ingestion and 1.25 for soil dermal contact
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An assessor can easily apply the JOB concept for cancer risk: simply divide the
incremental cancer risk by an appropriate background cancer risk, and then multiply by 100% As
noted above and discussed in the Risk Characterization, the current average background cancer
risk is 10"3. One can use this national average cancer risk estimate or derive a site-specific
background cancer risk by determining a site-specific LADD and multiplying it by the slope
factor. For example, if the incremental cancer risk is 10"5, and the national background cancer
risk of 10"3 is used, then the JOB for cancer risk is estimated as 1%.
2.3.4. Interpretation of Cancer and Non-Cancer Risk Assessment Results for Dioxins
Chapter 5 will demonstrate the procedures for estimating incremental cancer risk and for
characterizing non-cancer risk for dioxin-like compounds using the JOB approach. This
demonstration will include generation of site-specific background estimates of dose and body
burden that are distinct from the national averages above, and the generation of the JOB and
incremental cancer risk estimates. However, no interpretations for these results will be provided.
Interpretations of risk assessment results is where risk management takes over, and it is beyond
the scope of this document to provide risk management guidance for assessing incremental
impacts of the dioxin-like compounds from specific sources of release.
Still, some general comments can be provided here as background. For cancer risk for
compounds other than the dioxin-like compounds, EPA has based regulatory actions on a wide
spectrum of levels, generally in the range of 10"6 to 10"4 lifetime cancer risk. Estimated lifetime
individual risks below 10"6 have rarely been found to be sufficient basis for action, while in most
cases levels above 10"4 result in some form of action, although not necessarily regulation. As
described above, the current background cancer risk from exposure to dioxin-like compounds is
at 10"3. In performing a site-specific assessment for a specific source of dioxin release, an
assessor should assume that an individual's exposure and resulting lifetime cancer risk to dioxin-
like compounds are already at 10"3 (or a different site-specific lifetime cancer risk) and that the
source adds to this impact. For example, if assessing impacts to farmers who live near an
incinerator and consume a portion of their agricultural produce, it must be assumed that their
produce has dioxin concentrations already at background levels and that the incinerator
emissions add to this concentration. The challenge for risk managers in such a circumstance is to
determine what level of incremental lifetime cancer risk posed by the specific source warrants
concern, when the background lifetime cancer risk is already at a level near where, for other
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contaminants and other circumstances, EPA has historically judged that some form of action may
be warranted.
Similar issues arise when trying to put the site-specific JOB in perspective. As discussed
above, the MOE for background exposures for non-cancer effects is already in a range (<10) that
is of concern. An incremental exposure will decrease this margin.
In both cancer and non-cancer risk assessment, therefore, traditional rules-of-thumb may
not be of immediate use to the risk assessor, because background levels translate to concern as
defined by traditional Agency methods. The Risk Characterization recognizes the difficulty
posed by this circumstance, and suggests that the Agency take a broad, long-term view of dioxin
risk management:
In this case, a strategy might be to bring average "background" exposures down and to
also focus on larger incremental exposures or highly susceptible populations. This
would be a strategy that would parallel the Agency 's approach to control lead exposures.
Other parallel science and management issues between dioxin-like compounds and lead
are under discussion within the Agency. Providing guidance on the how to judge the
significance of incremental increases to background using the MOE approach is beyond
the science scope of the reassessment and will have to be addressed elsewhere by EPA.
2.4. THE TOXIC EQUIVALENCY PROCEDURE
Assessments very rarely focus on 2,3,7,8-TCDD alone, but rather on the suite of 17
dioxin and furan dioxin-like congeners, and also the 12 dioxin-like coplanar PCBs. Chapter 1
describes the "toxicity equivalency factor", or TEF, approach to handling mixtures of dioxin-like
compounds. The TEF for a congener of interest is a measure of the potency of that congener
divided by the potency of 2,3,7,8-TCDD. As shown in Table 1-1 in Chapter 1, the TEFs for
2,3,7,8-TCDD and 1,2,3, 7,8-PCDD are 1 and all other dioxin-like compounds have TEFs less
than 1. The combined risk resulting from exposure to a mixture of dioxin-like compounds can be
computed using the TEFs and assuming that the risks are additive. This assessment recommends
that assessors model the fate of all the congeners individually until the estimate of the exposure
media concentration is made. At that point, the Toxic Equivalent exposure media concentration,
CTC, can be calculated as:
CTEQ = I>TEFiCi (2-8)
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where C; is the concentration of the individual congener. LADDs and ADDs can then be
estimated for a TEQ concentration. Cancer risk on a TEQ basis can be assessed using the qt* for
2,3,7,8-TCDD in combination with the TEQ-based LADD. Non-cancer risk procedures are
similarly driven by TEQ body burdens or increments above background.
2.5. PROCEDURE FOR ESTIMATING EXPOSURE
Section 2.2 described the exposure equation as it applies to dioxin and dioxin-like
compounds. Before making exposure estimates, the assessor needs to gain a more complete
understanding of the exposure setting and be prepared to estimate exposure media
concentrations. The purpose of this section is to provide guidance for the procedures followed in
this assessment to define such settings and estimate exposure media concentrations. The
approach used here is termed the exposure scenario approach. Brief descriptions of the steps and
associated document chapters are presented below.
Step 1. Identify Source
Three principal sources are addressed in this document. The first, identified as "soil
contamination", is called a source in that the starting point of the assessment is a bounded area of
soil contamination. Of course, the ultimate source for soil contamination is some unidentified
cause for the soil to become contaminated. For exposure and risk assessment purposes, the cause
for contamination is not relevant except to assume that the cause is not ongoing and that the
impact of the "initial" levels is what is being evaluated. For contaminated soils, exposures could
occur on the site of contamination or distant from the site of contamination. Examples of on-site
exposures include exposures to workers on Superfund or similar sites, or special circumstances
such as Times Beach where residential properties become contaminated. A common example of
an off-site impact would be impacts to residents who live near a Superfund (or similar) site
whose property becomes contaminated due to erosion and whose air contains elevated levels of
the contaminant due to dust erosion or volatilization followed by atmospheric transport. The
second principal source is called "stack emissions." Unlike the soil source, the contamination is
assumed to be on-going. Stack emissions in particulate form are assumed to deposit onto the soil
and vegetation at the site of exposure, and emissions in vapor form result in air-borne
concentrations which transfer into vegetation at sites of exposure. It is noted that individuals
working at the site where stack emissions occur are also exposed. The procedures in this
document only apply to residents who are not associated with the site where stack emissions
occur. The third principal source is called "effluent discharges". Such discharges represent point
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source inputs to surface water bodies. Like the stack emission source, impacts to surface water
bodies are assumed to be ongoing during the period of exposure. Unlike either of the above two
sources, only the impacts to water and fish are considered for this source category.
Step 2. Estimate Release Rates
Estimating the release of contaminants from the initial source is the first step towards
estimating the concentration in the exposure media. Releases from soil contamination include
volatilization, and wind and soil erosion. Chapter 4 on estimating exposure media concentrations
describes fate and transport modeling procedures for estimating soil releases. Stack emissions
and effluent discharges are point source releases into the environment. Background on stack
emissions including details on modeling from the stack to a site of exposure are provided in
Chapter 3.
Step 3. Estimate Exposure Point Concentrations
Contaminants released from soils, emitted from stacks, or discharged into surface waters
move through the environment to points where human exposure may occur. Contaminated soil
that is near but not at the site of exposure is assumed to slowly erode and contaminate the
exposure site soil, but to a level lower than the level at the contaminated site. The only time
when the source concentrations equal the exposure concentrations is for the soil pathways, soil
ingestion and dermal contact, when the soil contamination is at the site of exposure. Chapter 3
describes the use of the ISCST3 Model to estimate dispersion of stack plumes to predict air-
borne concentrations at the site of exposure as well as deposition rates of dioxins sorbed to stack
emitted particulates. Chapter 4 describes how soil and vegetation concentrations are estimated
given contaminant concentrations and deposition rates, and also how release rates from soil
initially contaminated translate to exposure point concentrations. Chapter 4 also describes a
simple dilution model which translates effluent discharges into surface water and fish tissue
concentrations.
Step 4. Characterize Exposed Individuals and Exposure Patterns
The patterns of exposure are described in Sections 2.6 and 2.7 of this Chapter. Exposed
individuals in the scenarios of this assessment are individuals who are exposed in their home
environments. They are adult residents who also recreationally fish, have a home garden, farm,
and are children ages 2-6 for the soil ingestion pathway. Each of these pathways is evaluated
separately. Although it is unlikely that individuals would experience all of these pathways
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simultaneously, quite often exposure assessments do add exposures across pathways. In this
document, the doses across pathways are added. Exposure pathways evaluated, which have
generally been alluded to in discussions above, include inhalation, ingestion, and soil dermal
contact. Each pathway has the set of parameters including contact rates, contact fractions, body
weights, and lifetime. These parameters were defined earlier in Section 2.2.
Step 5. Put It Together in Terms of Exposure Scenarios
A common framework for assessing exposure is with the use of "settings" and
"scenarios." Settings are the physical aspects of an exposure area and the scenario characterizes
the behavior of the population in the setting and determines the severity of the exposure. A wide
range of exposures are possible depending on behavior pattern assumptions. An exposure
scenario framework offers the opportunity to vary any number of assumptions and parameters to
demonstrate the impact of changes to exposure and risk estimates. Exposure estimates for six
example scenarios are demonstrated in Chapter 5.
Step 6. Estimate Exposure and Risk
Section 2.2 described the basic equation that estimates exposure for every assumed
pathway in an exposure scenario. Chapter 5 demonstrates the methodology on six example
scenarios, which includes the generation of exposure estimates for ten different exposure
pathways and the suite of 17 dioxins and furans, and one dioxin-like PCB.
Step 7. Assess Uncertainty
Chapter 7 provides a discussion on model validation and provides several exercises where
the models of this assessment were validated with real world data. Chapter 8 addresses the issue
of uncertainty more generally, identifying possible sources of uncertainty associated with this
methodology. These uncertainties should be considered when applying this procedures to a
particular site. Chapter 6 on User Considerations includes discussions on other pertinent topics
such as sensitivity of model results to parameter selection, and judgements on use of the
parameters selected for the demonstration scenarios for other applications.
2.6. STRATEGY FOR DEVISING EXPOSURE SCENARIOS
EPA (1992b) states, "In exposure scenario evaluation, the assessor attempts to determine
the concentrations of chemicals in a medium or location and link this information with the time
that individuals or populations contact the chemical. The set of assumptions about how this
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contact takes place is an exposure scenario." These assumptions can be made many different
ways producing a wide variety of scenarios and associated exposure levels. The number of
people exposed at different levels form a distribution of exposures. Ideally, assessors would
develop this entire distribution to fully describe the exposed population. Such distributions could
be defined using Monte Carlo techniques if sufficient input data are available. However, as
discussed in Section 2.1 above, generic issues surrounding use of Monte Carlo are not evaluated
here. Discussions of how other assessors have applied Monte Carlo to problems involving
dioxin-like compounds are presented in Chapter 8. Since the necessary information for
developing a population distribution is rarely available, EPA (1992b) recommends developing a
central and high end scenario to provide some idea of the possible range of exposure levels.
Since that set of guidelines, an additional set of guidelines had been developed (Browner, 1995)
which reaffirms this recommendation for central and high end risk descriptors, and adds other
recommendation to identify "highly susceptible" or "highly exposed" subpopulations. There
have also been instances where an EPA program office has provided guidelines which specify,
almost in cookbook fashion, the make-up of exposure scenarios for evaluating a source. EPA's
Office of Solid Waste and Emergency Response provided such guidance for evaluation of
hazardous waste incinerators (EPA, 1994b). US EPA's Region 6 has more recently published
similar guidance for hazardous waste incinerators (EPA, 1998).
This section will illustrate the concept of central and high end scenario development as it
could be applied to specific sources of release of the dioxin-like compounds. In addition, this
section identifies the exposure pathways which are relevant to these compounds, and provides
background and justification for the exposure parameters which were selected for the
demonstrations in Chapter 5.
For any physical setting, a wide variety of exposure scenarios are possible. The range of
exposure levels results from a number of different factors including individual behavior patterns,
proximity of individuals to the source of contamination, the fate characteristics of the
contaminant, and others. In order to illustrate the possible range, the assessor should try to
characterize a central and high end scenario. The general strategy recommended here for
defining these scenarios is to first identify and quantify the source of contamination. Next, the
assessor should determine the geographic area that is impacted by this source. The contaminant
levels are likely to vary widely over this area. Select locations of interest within this area such as
the location of the nearest exposed individual or most heavily populated area. For each of these
locations, identify behavior patterns which characterize central and high end exposure patterns.
Central scenarios correspond to average or median levels and high end scenarios are defined as
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levels above the 90th percentile but within the actual range of exposure levels (EPA, 1992b;
Browner, 1995).
Statistical data are rarely available to precisely define such scenarios. Instead, judgement
is usually required to identify behavior patterns meeting these criteria. For example, most rural
areas probably include both farming and nonfarming residents. Farmers who grow or raise a
portion of their own food could be selected to represent the high end scenario and those living in
typical residential areas could represent the central scenario. Alternatively, if more detail is
desired, central and high end scenarios could be defined for both segments of the population, i.e.,
farmers and residents. For each scenario, determine relevant exposure pathways and assign
values for exposure parameters such as contact rate, exposure duration, and so on, which
represent a central and/or high end pattern for the type of receptor. Finally, compute the
associated exposure level. The resulting range of exposure levels for each location can be used
to illustrate the possible range of exposures.
Reference has been made in this chapter to the example scenarios found in Chapter 5,
Demonstration of Methodology. Three "source categories", categories of contamination sources
described in Chapter 4, are demonstrated in Chapter 5. The stack emission source is assumed to
expose a relatively large population in a rural area containing residences and farms. For this
source, both central and high end scenarios are defined in the manner outlined above.
Specifically, a central scenario is based on typical behavior at a residence and the high end is
based on a farm family that raises a portion of its own food. For the soil contamination and
effluent discharge sources, only one scenario each will be defined and demonstrated. The soil
contamination category will be demonstrated with a high end scenario - a farm is located near the
site of contamination. Soil on the farm becomes impacted through the process of soil erosion.
Other individuals within a community can also be impacted by a site of high soil contamination.
Such individuals would include those visiting or trespassing on the site, volatilized residues can
reach their residences, they may obtain water and fish from a nearby impacted water body, and so
on. As such, alternate scenarios demonstrating the impact of a site of soil contamination could
be developed. For the sake of brevity, and also considering that those residing nearest the
contaminated are most impacted, only a high end scenario is developed for the soil contamination
source category. The effluent discharge source category is unique in that only the pathways of
water ingestion and fish ingestion are considered. For this category, fish and water ingestion
patterns will be those adopted for the central scenarios. Again, other patterns offish and water
ingestion could be evaluated for this source category. As a matter of brevity again, only central
patterns of behavior with regard to fish and water ingestion are demonstrated.
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Two other scenarios, a central and a high end scenario, will be developed for
"background" conditions. In this special circumstance demonstrated in Chapter 5, a unique point
source such as a tall stack or an effluent pipe, or diffuse source in the case of a large area of soil
contamination, is not considered. Rather, all the soil in a hypothetical setting contains dioxins at
background levels. Similarly, the air concentration in the hypothetical setting will be initialized
to background levels of dioxins. This demonstration will use fate and transport algorithms that
have been developed for both the soil contamination source category as well as the stack
emission source category. Further details of this structure are described in Chapter 5.
Finally, Chapter 5 will demonstrate some of the pathways outside the scenario structure.
These include the chicken and the egg ingestion pathways. These were not included in the
central or high scenario because it seems somewhat unlikely to assume that individuals living on
farms in rural settings would generate quantities of several food items simultaneously on the
farm or homestead. In a real setting, it may be plausible that there are farmers who raise cattle
and use a portion of their stock to supply the family with beef and/or milk, farms or residences
where chickens are raised that provide the family with eggs and/or chickens, and so on, but it is
unlikely that a single farm would raise two or more distinct food animals. Also, if a farm is
producing foods that are consumed locally, than a high end scenario could be comprised of
individuals who don't live on farms but who do consume local foods. In this document, the high
end scenario is defined as a farm raising only one type of terrestrial animal which is used to
supply the family with terrestrial animal food products; specifically, the farm family raises cattle
and consumes beef and milk from their own stock.
The methodologies used to estimate exposure media concentrations are described in
Chapter 4 as screening level in their technical sophistication, but site specific in their application.
Defining populations that are typical of central and/or high end exposures is clearly a site specific
exercise. Assessors need to make the kinds of assumptions discussed here for their own source
and populations of concern. Many acceptable ways could be used to define central and high end
scenarios. The approach used here was done for demonstration purposes only. On the other
hand, the example scenarios in Chapter 5 were carefully crafted to be plausible and meaningful,
considering key factors such as source strength, fate and transport parameterization, exposure
parameters, and selection of exposure pathways.
Key source strength terms were carefully developed and defined. These include soil
concentrations, effluent discharge rates, and stack emission rates. For the demonstration of the
soil contamination category, for example, the concentration of 2,3,7,8-TCDD and two other
example compounds were set at 1 ppb, which was a typical concentration of 2,3,7,8-TCDD
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found in Superfund-like sites studied in the National Dioxin Study (EPA, 1987). Concentrations
in soil in the demonstration of background conditions were characterized as typical of
background levels and were based on concentrations found in rural settings. Researchers
investigating concentrations of 2,3,7,8-TCDD in "background" or "rural" settings have typically
found it in the ppt range or not detected it (with a detection limit generally less than 1 ppt), which
contrasts the 1 ppb assumed for 2,3,7,8-TCDD in the demonstration of the soil contamination
source category. Introductory sections of Chapter 5 provide a more complete description of the
example scenarios.
2.7. EXPOSURE PATHWAYS AND PARAMETERS
The dioxin-like compounds have been found primarily in air, soil, sediment and biota and
to a lesser extent in water. Thus, the most likely exposure pathways are:
• D Ingestion of soil, water, terrestrial animal food products (beef, pork, chicken,
eggs, dairy products), fish, fruit, and vegetables
• D Dermal contact with soil
• D Inhalation of particulates and vapors.
The following sections describe the selection of central and high end exposure parameters for
these pathways. Table 2-1 summarizes all the exposure parameters selected to represent the
central and high end demonstration scenarios of Chapter 5.
2.7.1. Soil Related Exposures
The two soil related exposures which will be demonstrated in this assessment include a
childhood pattern of soil ingestion and an adult pattern of soil dermal contact. The soil ingestion
pathway will involve the assignment of central and high ingestion quantities in units of mg/day
and it will assume a child body weight of 17 kg. This contrasts the food pathways described
below in Section 2.7.4, where the contact rate is defined in units of g/kg/day. Parameters for the
soil dermal contact pathway were developed using the Exposure Factors Handbook (EPA,
1997a). Further details on this pathway can be found in EPA's Dermal Exposure Assessment:
Principals and Applications (EPA, 1992a).
2.7.1.1. Soil Ingestion
Soil ingestion occurs commonly among children during activities such as mouthing of
toys and other objects, nonsanitary eating habits, and inadvertent hand-to-mouth transfers. In
addition to normal soil ingestion activities, some individuals exhibit behavior known as pica
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which involves intentional soil ingestion. Soil ingestion rates associated with pica are probably
much higher. Very limited values for pica patterns have been reported in the literature. Based on
some limited data, EPA (1997'a) reports that a 5 to 10 g/day for deliberate soil ingestion rates for
children may not be unreasonable. This dioxin assessment considers only normal soil ingestion
among children.
To a lesser extent, soil ingestion also occurs among adults from activities such as hand-to-
mouth transfer when eating sandwiches or smoking. However, the data to estimate the adult rate
of soil ingestion is scarce. EPA (1997a) notes that the data available on adult soil ingestion is
consistent with a 50 mg/day assumption often used by EPA program offices to model this
pathway. Adult soil ingestion is not demonstrated in this assessment. Paustenbach (1987) and
Sheenan et al. (1991) have suggested calculating exposures for this pathway (as well as dermal
contact and inhalation) separately over three to four age periods to reflect major changes in body
weight, surface area and inhalation rates. In general, exposure assessments can be refined by
estimating exposures separately over each year of age that is of interest and summing to get the
total. Age specific data for body weight, surface area and inhalation rate are presented in EPA
(1989, 1992b, and 1997a). These procedures are not presented here, but readers interested in
refining exposure estimates are encouraged to check the above references for further guidance.
Based on the review of literature, EPA (1997a) suggests that a child soil ingestion rate of
100 mg/day appears to represent a central estimate of the mean for children under 6 years old.
This value will be adopted for the central scenarios. Of the studies which were considered
appropriate in EPA (1997a), upper percentile values ranged from 106 mg/day to 1,432 mg/day
with an average of 383 mg/day for soil ingestion and 587 mg/day for soil and dust ingestion. On
this basis, a value of 600 mg/day will be used for the high end exposure.
In cooler climates where the children may have little exposure to outdoor soils for a
significant portion of time, it may be appropriate to develop a time-weighted average daily soil
ingestion rate based on indoor and outdoor behaviors. Hawley (1985), for example, assumed that
young children ingest 100 mg of housedust per day while spending all their time indoors during
the winter months, that they ingest 250 mg/day while playing outdoors during the summer
months, and that they ingest 50 mg/day while playing indoors during the summer. His time-
weighted annual average, considering this different summer and winter patterns, was calculated
at 150 mg/day.
For the soil ingestion pathway, contact fraction refers to the portion of ingestion soil
which is contaminated. For the residential setting, the assumption is made here that all soil
ingestion by children occurs in and around the home, and that all the soil at the home is
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contaminated. Thus, a value of 1 has been adopted in the example scenarios presented in
Chapter 5. In situations where the contaminated area is located remote from where children live,
and children have some access to these areas (if the areas are parks or playgrounds, e.g.), lower
fractions would be appropriate.
Another issue for the soil ingestion, and the soil dermal contact pathway discussed below,
is the concentration of dioxin-like compounds to which individuals are exposed. As described
later in Chapter 4, two "source categories" discussed in this document include stack emission
sources and off-site soil contamination (i.e., a site of soil contamination distant from the site of
exposure). Dioxins arrive at the site of exposure either from the air, as in the stack emission
source, or via overland erosion, as in the off-site soil contamination scenario. Residues of dioxin
mix into either a shallow (2 cm) depth of soil which is "untilled", or a 20-cm depth which is
"tilled" (by gardening or farming). Obviously, tilled concentrations are lower than untilled
concentrations. Given that ingestion is only modeled for children who are likely not too heavily
involved in gardening or farming, it is assumed that their contact is with untilled soils, and the
higher concentration associated with these soils is used.
2.7.1.2. Soil Dermal Contact
The total annual dermal contact to soil alone, expressed in mg/yr, is the product of three
terms: the contact rate per soil contact event, the surface area of contact, and the number of
dermal contact events per year. The soil contact rate is also known as the soil adherence rate to
reflect that it is not only the amount of soil contacted, but the amount of contact that adheres.
Current guidance in the Exposure Factors Handbook (EPA, 1997a) suggest that soil dermal
exposure can be divided into components for indoor and outdoor exposures; for indoor
exposures, the medium might be better described as "indoor dust", and for outdoor exposures,
the medium would be soil. The adherence rate is also a function of the body part - with hands
usually having the highest adherence, with other exposed parts such as arms and legs usually
having lower adherences. The Exposure Factors Handbook provides details on these factors. A
summary pertinent to the applications demonstrated here is:
• Contact rate: <0.002 to >20 mg/cm2-event. The very high adherence rates were
found for the scenario described as, "kids-in-mud", and was from data on children
playing by a lakeshore. The lower range was found for an indoor Tae Kwon Do
setting. Adherences for a day-care setting ranged from 0.03 for arms and legs to
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0.1 for hands. Outdoor adherences for gardeners ranged from 0.005 for legs to
0.02 for arms to 0.2 for hands.
• Body surface area: Average adult and child body surface areas are in the
neighborhood of 20,000 and 6500 cm2 (for a child ages 2-5). The selection of the
surface area considered for a dermal contact event depends on the nature of the
event and the assumptions about which part(s) of the body are involved in that
event. For a child ages 2-5, hands comprise about 5.5% of total body surface area,
arms about 13%, and legs about 25%. For adults, hands are similarly about 5%,
arms are about 13%, and legs about 32%.
• Event frequency: This is a factor more based on exposure assessment judgement,
rather than data, as in the above two factors. Obviously, climate, activity, age, and
similar factors play into assignment of this parameter. This factor can be
expressed in terms of events per time period, usually events/yr. For consistency in
use in the exposure equation, it is usually transformed to an event/day basis.
All other pathways assessed here estimate an "administered" or "potential" dose, which is
defined as the dose which comes in contact with the body. For this pathway, however, an
"absorbed" dose will be estimated. This is because for all other pathways, a significant amount
of the dose that comes in contact with the body is absorbed - about 80% for the inhalation and
ingestion pathways (except for soil ingestion, when the fraction absorbed is more like 30%). An
absorption fraction, or AF, is added in the calculation of a LADD for the dermal contact pathway.
EPA (1992a) reviews the data to conclude that the absorption of dioxin through the skin has
been estimated to range from 0.5 to 3.0%. This document will adopt the conservative estimate of
3.0%, or an AF of 0.03, to give the following for LADD for the soil dermal contact pathway:
C CR SA EF AF ED 10"6
LADD, = — (2-9)
dc AT EW
where:
LADDdc = lifetime average daily dose due to soil dermal contact, mg/kg-day
Cs = concentration in soil, mg/kg
CR = contact, or adherence, rate, mg/cm2-event
S A = surface area of contact, cm2
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EF = event frequency, events/day
AF = absorption fraction, 0.03
ED = exposure duration, yr
AT = averaging time, 70 yr for LADD
BW = body weight, kg
10"6 = units conversion factor, kg/mg
In this document, soil dermal contact exposures are demonstrated only for an adult for a
"gardening" central tendency pathway and a "farming" high end pathway. Indoor activities for
both the central and high end scenarios will assume Tae Kwon do-like activities which translate
to a contact rate of 0.005 mg/cm2-event, with dermal contact only with the hands which translates
to 1,000 cm2 (20,000 cm2 total surface area * 5% surface area for hands) and 1 event per day (or
365 times per year). Outdoor dermal contact will be modeled on a gardening scenario for the
central scenarios and a farming scenario for the high end pathways. The gardening scenarios
assume 0.03 mg/cm2 for contact rate, 10,000 cm2 surface area contact which assumes hands,
arms, and legs, and 0.27 events/day (or 100 events/yr). In comparison to outdoor gardening, the
farming scenario assumes a greater adherence at 0.1 mg/cm2, a smaller surface area of 3600 cm2
which assumes hands and arms only, more frequent events at 0.96 events/day (or 350 events per
year).
Another difference between indoor and outdoor activities in this assessment is the
concentration to which the individuals are exposed. As described in the previous section on soil
ingestion, tilled (lower in concentration) and untilled (higher) soil concentrations are derived for
the stack emission and off-site soil contamination sources. In this assessment, outdoor dermal
contact events are assumed to occur in association with tilled soils (gardening or farming), while
indoor contact events are assumed to occur in association with untilled soils.
2.7.2. Vapor and Dust Inhalation
EPA (1997a) describes the derivation of ventilation rates, which include assumptions
regarding number of hours in sleep, hours inactive, and hours in various levels of activity.
Several inhalation studies are reviewed. The final recommendation for continuous exposure
assessments in which specific activity patterns are not known is 13.3 m3/day based on Layton
(1993). A rate of 13.0 m3/day (rounded for simplicity) will be adopted in this assessment. EPA
(1997a) suggests that a value of 20 m3/day represents an upper percentile estimate among adults,
and this will be adopted in this assessment as a high end.
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The contact fraction for this pathway is equal to the fraction of total inhaled air which is
contaminated. Thus it relates largely to percent of time spent in the contaminated area. For the
demonstration scenarios in Chapter 5, the contaminated area is the home environment.
Therefore, information on the time spent at home versus away from home is pertinent. In EPA
(1997a), several activity pattern studies are reviewed. Two of the studies reviewed contained
information on time spent at home and away from home, and both studies had very similar
results for time spent in the home environment. Robinson and Thomas (1991) reviewed and
compared data from the 1987-88 California Air Resources Board (CARB) time activity study
with a similar 1985 national study titled, "American's Use of Time." In an average day
comprising 1440 minutes, Robinson and Thomas (1991) found an average of 954 minutes at
home from the national study, or 66% of the time, compared to 892 minutes (62%) for
Californians. Sexton and Ryan (1987) reviewed the procedures for assessing inhalation
exposures, and in so doing, reviewed two previous studies on time use including information on
time at home and away from home. One study showed 69% of the time at home and the second
study showed 71%. Based on these, a central estimate for the fraction of time spent at home will
be 70%.
EPA (1997a) did not make specific recommendations for a high end contact fraction.
Such a fraction should be relevant to the population defined as high end, which in this
assessment is a subsistence farm. It seems reasonable to assume that a rural farming family may
have more time at home as compared to the general population. Without rigorous justification, a
high end contact fraction will be assumed to be 90%.
The two key parameters discussed above, contact rate and contact fraction, when
multiplied together, will yield a contact rate for contact with the contaminated media. For the
central scenario, the two key quantities are 13.0 m3/day and 0.70, which yield a contact rate with
impacted air of 9.1. For the high end scenario, this calculation is 20.0 m3/day times 0.90, which
is 18 m3/day. Therefore, the difference between central and high end from behavior assumptions
alone is about a factor of two.
An additional assumption needs to be made for the vapor and dust inhalation pathways.
This pertains to an assumption concerning the differences in air quality between indoor and
outdoor conditions. Algorithms for both particulate and vapor-phase air-borne concentrations of
contaminants are specific to outdoor air. Hawley (1985) assumed, based on several other studies
in which measurements were made, that the concentration of suspended particulate matter in
indoor air is equal to 75% of that outside. Also, his report stated that most household dust is
outdoor dust that is transported into the house, and that only a small percentage is developed
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from sources within. He then concluded that 80% of the indoor dust is identical in contaminant
content to outdoor soil. Refinements to the concentration of contaminants on indoor versus
outdoor dust should have a minor effect on exposure estimates. A similar trend is assumed for
air-borne vapor phase concentrations. For this reason, differences between indoor and outdoor
concentrations are not specifically considered, or equivalently, no distinctions are made for
outdoor and indoor air quality.
2.7.3. Water Ingestion
The water ingestion rate of 2 L/day has been traditionally assumed for exposure through
drinking water. However, EPA (1997a), after review of several literature sources, concludes that
2 L/day may be more appropriately described as a 84% value, or a value for high end exposure
estimates (the 90% value was actually calculated to be 2.34 L/day). For this reason, a water
ingestion rate of 2 L/day is assumed only for the high end exposure estimates. Since the high end
scenario includes a farm and the farming family, it is also argued that farm labor requirements
justify the higher rate of water ingestion. EPA (1997a) recommends a rate of 1.4 L/day as
representative of average adult tap water drinking water consumption. This is the rate used for
central scenarios in Chapter 5. The difference in central and high end tendencies is also modeled
using the contact fraction. Again, this fraction is based on the time spent at home. The value of
0.70 is used to model the central estimate, for the residence setting, and the value of 0.90 is used
to model the high end estimate, for the farm setting.
2.7.4. Ingestion of Terrestrial Food Products
This section discusses the consumption rates used in the beef, dairy, chicken, eggs, and
vegetables/fruits ingestion pathways. All these pathways are similar in that the food products
originate from the land. The high end demonstration scenario in Chapter 5 is a farming family
which home produces a portion of its beef consumption. In site-specific assessments, home
production of foods is often a scenario of concern, particularly if the home producers are located
near the source of contaminant release. An exposure pattern similar to home
production/consumption of foods is the consumption of locally produced foods. This would be
relevant for individuals who live in a setting where food is produced, a rural setting for example,
who do not produce any of their own food but who rely on local foods, such as from farmer's
markets.
In order to estimate the contact rates for consumption of home produced foods, analysis of
the USDA National Food Consumption Survey (abbreviated NFCS; USDA, 1992) of 1987-88 as
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conducted in EPA (1997a) will be used in this dioxin exposure reassessment. Specifically, EPA
(1997a) used the household component of this survey. This component collects information over
a 7-day period on the socioeconomic and demographic characteristics of households, and the
types, values, and sources of foods consumed. Like the use of any survey data, use of this data
must be understood by users. EPA (1997a) took from the household survey this information: 1)
whether or not the food product was used in the house that week, 2) whether or not the food
product used that week was home produced, 3) the quantity (mass, such as pounds or kilograms)
of food consumed (home produced or not) in the house that week, 4) the number, age, and body
weight of individuals in the household, and 5) the number of weekly meals consumed by each
family member. If the household reported consumption that week, than EPA could calculate an
individual consumption rate for "consumers only". To do this, EPA (1997a) then assumed that
all individuals in the household consumed some of the product, and the amount of individual
consumption was based on average serving sizes for each individual (different as a function of
the age of the individual) and number of meals consumed by each individual. Then, dividing by
7 (as in days of the week) and the body weights of the individuals, they derived consumer only
consumption rates in terms of g/kg/d.
EPA (1997a) also looked at a second major USDA food consumption survey - the
Continuing Survey of Food Intakes by Individuals (CSFII). This measured the food consumption
patterns of all individuals throughout a 3-day period, whether the food was eaten at home or not,
and did not have questions on home production practices. EPA (1997a) used this survey to
derive general population per capita consumption rates (see next paragraph for definition of per
capita).
In order to understand how the EPA (1997a) analysis of USDA data was used for this
dioxin exposure document, it is important first to understand three types of generic food
consumption rates: 1) Per Capita Consumption Rate: This is an average consumption rate and
includes all consumers as well as nonconsumers. 2) Consumption Rate for Consumers Only:
This is the consumption rate calculated only for individuals who report consumption of the food
product in question. A per capita consumption rate can be calculated as a product of the
consumption rate for consumers only and the ratio of those reporting consumption and all
respondents including those who reported consumption plus those who didn't report
consumption (or equivalently, reported nonconsumption):
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where Cpc is the per capita consumption rate, Cco is the consumer only consumption rate, Nc is the
number of consumers, and Nnc is the number of non-consumers, and 3) Consumption rates, Per
Capita or Consumer Only, for Consumers of Home Produced Products: Home
producers/consumers are a critical subpopulation, as noted earlier. EPA (1997a) was able to
estimate consumer only consumption rates for home producers/consumers for a variety of food
products from the household survey of the NFCS. These consumer only home
producer/consumer rates are used in the demonstrations of Chapter 5.
The use of the consumer only consumption rate for home produced foods from the
household survey of the USD A NFCS represents a significant departure from earlier versions of
the dioxin exposure reassessments (EPA, 1992c; EPA, 1994a). It is the most appropriate type of
rate to use when the scenario is a farm where individuals consume the food they produce - that is
precisely what the definition of these rates are. Unlike earlier consumption rates, body weights
are not used as the consumption rates are derived over the range of individuals including infants
and children. It is also important to note that the consumer only consumption rates for the food
products appear significantly higher than other consumption rates generated in EPA (1997a). For
example, the general population per capita consumption rate for beef using the CSFn was 0.83
g/kg-day. The consumer only consumption rate for home produced beef was 2.45 g/kg/day. The
following describes some of the differences in these two beef ingestion rates, 0.83 and 2.45
g/kg/day:
1) The household survey reports on total food product brought into the household, and this data
is used directly for the calculation of consumers only consumption rates in EPA (1997a). Their
calculation does not include bone and other wastage, trimmable fat, cooking loss, or uneaten
foods. By contrast, intake rates developed from data in the 1-day individual consumption survey
are defined "as eaten" meaning that these rates do account for cooking loss, wastage, and uneaten
food. As will be described below, cooking and post cooking losses will be accounted for the
meat product consumption rates developed from household data. The appropriate factor for beef
is calculated to be 0.55. While the household data does not account for cooking and post
cooking loss (without the correction introduced below), it does not include food eaten away from
home, which could lead to underestimation of total consumption rates.
2) The 0.83 g/kg/day rate derived from the CSFn is a per capita consumption, meaning that it is
calculated considering non-consumers as well as consumers. The percent consuming beef,
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according to the CSFII, is 91%. Therfore, solving for the consumers only rate, using Equation
(2-8) above, is 0.91 g/kg/day (0.83 g/kg/day divided by 0.91).
3) It is likely that home producing consumers - ie., only those who reported eating home
produced beef which is what the 2.45 g/kg-day rate is - eat a fair amount of their home-produced
beef during the weeks in which they reported this behavior. Other weeks they may also eat beef
that was not home produced, but it might be smaller amounts so that their overall consumption
rate of meat is likely to be lower than is reflected in these home producing consumers only rates.
No data was available in EPA (1997a) to evaluate this speculation, but it seems reasonable (isn't
it true that when the tomatoes in the home garden are ripe, the family will eat more fresh salad
than usual?).
4) Two other differences between the CSFII and the household component of the NFCS which
could lead to over or underestimation of actual behaviors include three-day versus week-long
information (unclear which direction this would lead to) and individual recall versus head of
household recall (again unclear).
To account for cooking and post cooking losses for the meat products which are home
produced, information was obtained from USDA (1975). This data is summarized in Table 2-2.
This assessment uses the home producing consumers only consumption rate for home
produced beef, milk, chicken, eggs, vegetables, and fruits. The following section describes how
the "contact fraction" for these consumption rates is calculated. As has been defined, this
describes the fraction of total food category consumption which is home produced. Following a
description of this parameter for the dioxin reassessment, sections will describe the derivation of
consumers only consumption rates for the various food products.
2.7.4.1. Derivation of the Contact Fractions for Beef, Milk, Chicken, Eggs, Vegetables, and
Fruits
The home producer consumer-only consumption rate needs to be adjusted downward by
consideration of the contact fraction. The average "consumer only" consumption rate for home
producers developed from NFCS data will include weeks where only a little of the product in
question is eaten and weeks when much of the product is eaten. Most importantly, however, this
calculated "average" will only be an average during which consumption of home produced foods
actually occurs. It is probably not reasonable to assume that average consumption weeks occur
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52 weeks a year; there will be weeks where no consumption of the home produced product
occurs. In other words, the contact fraction, or CF, will be less than 1.00.
Means to assign values to CF for site-specific applications will require judgement on the
part of assessors. If localized information is available for a site-specific assessment, this could be
used. For example, climate or local agronomic practices could assist in the assignment of a CF.
One could assume, for example, that home production of dairy does not occur from December
through February (13 weeks), and use this information to reduce the consumers only dairy
consumption by 25% (13 weeks/52 weeks); i.e., the CF = 0.75. This assumes that a home
producer of dairy products is producing at least some dairy during all weeks between February
and December. EPA (1997a) did break out the household consumption survey data into seasons
and it is clear that there was less consumption of home-produced food products during the winter
as compared to the summer. But what is still true regardless of any refinement of this type is that
the consumers only analysis of the USDA household survey is still only a reporting of
households which did consume during the week.
EPA (1997a) developed data from the NFCS which will be used to assign values of CF
for home produced foods. Based on questionnaire responses, EPA (1997a) was able to
determine the fractions of total food intake that is home-produced; that is, of all the food product
consumed by respondents in the survey, what fraction of that product was home-produced. For
example, 3.8% of all beef consumed is home produced. This 3.8% includes, however, both those
who raise cattle and then consume it (actual home producers) and those who took part in the
survey, but did not raise any animals. Therefore, this 3.8% is not what is needed for current
purposes. What is instead needed is the percentage (or fraction) of total beef consumed by
people who raise cattle, that comes from their own stock. This could not be ascertained perfectly
from the household survey because there was not this direct a question: "Knowing that you raise
cattle, what percent of your home consumption is from your own stock?" Still, there were
questions that were asked that are close to this. There was the question, "During the past year
(1986), did anyone in the household produce any animal products such as milk, eggs, meat, or
poultry for home use in your household?" The households which answered yes to this question
form the universe of home producers of animal food products. These individuals were also asked
questions about whether they consumed home-produced foods the week of the survey. For beef,
48% of households in this universe of home producers said that they did eat home-produced beef
that week, while 52% said they did not. Therefore, 0.48 becomes the contact fraction for beef.
Other contact fractions that were ascertained using this intersection of questions include: for
diary - 0.21, for poultry - 0.15, and for eggs - 0.21. For fruits and vegetables, the universe of
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home producers was ascertained by those who answered yes to the question of whether there was
gardening in the household. Responses of yes for consumption that week of the category
"exposed vegetables" - 0.233, and "root vegetables" - 0.106, was averaged to yield a 0.17 that is
used in this assessment as the CF for vegetables. The CF for fruits in this assessment is 0.12,
based on a yes response to consumption that week of home produced "exposed fruits".
2.7.4.2. Beef Ingestion
The high end farming scenarios include this pathway for purposes of demonstration.
Other terrestrial animal consumption pathways are demonstrated separately. EPA (1997a)
calculated a consumers only overall (over all ages and regions of the US) average consumption
rate for home produced beef of 2.45 g/kg/day. Applying the CF of 0.478 for beef as described
above leads to an average consumption rate for home produced beef of 1.17 g/kg/day. This is
further adjusted with the use of a preparation term. As shown in Table 2-2, the percent losses
during cooking and after cooking is 27 and 24%, respectively. Therefore, the average
consumption rate with these considerations is now estimated as, 1.17 g/kg/day * 0.73 * 0.76 =
0.65 g/kg/day.
Consumption rates of terrestrial animal food products are expressed in terms of fat
ingested per day for two reasons. One, dioxin-like compounds tend to partition strongly toward
lipids and virtually all of such compounds will be found in the fat portion of animal food
products. Two, the algorithms to estimate concentrations in these food products estimated fat
concentrations and not whole product concentrations. EPA (1997a) reports that USDA's
Agricultural Handbook Number 8 (USDA, 1979-1984) lists the fat content of cooked beef,
including lean and fat, as 21.54. This will be rounded to 22% in this assessment. Therefore, the
ingestion of beef fat for individuals in the high end farming scenarios of Chapter 5 will be 0.143
g/kg/day (0.65 * 0.22).
2.7.4.3. Dairy Ingestion
As noted above, this pathway will be demonstrated in Chapter 5, but outside of any
defined scenario. EPA (1997a) calculated a consumers only overall average consumption rate for
home produced dairy of 14.0 g/kg/day. Applying the CF of 0.207 for dairy as described above
leads to a final long term consumption rate of 2.90 g/kg/day. EPA (1997a) reports that USDA's
Agricultural Handbook Number 8 (USD A, 1979-1984) lists the fat content of whole milk of
3.16. This will be rounded to 3% in this assessment. Therefore, the ingestion of dairy fat for this
pathway demonstrated in Chapter 5 will be 0.087 g/kg/day.
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2.7.4.4. Chicken Ingestion
Like the diary ingestion pathway, the chicken ingestion pathway will be demonstrated
outside of a defined scenario. Furthermore, it will be assumed that the chickens are allowed to
free range. A full discussion of predicting the concentration of dioxins in chickens and eggs,
including free range chickens, is given in Chapter 4. EPA (1997a) calculates a consumers only
consumption rate for home produced chickens of 1.57 g/kg/day. As described above, a CF of
0.151 will be adopted for home-produced chicken consumption leading to a total of 0.24
g/kg/day. As with beef, a preparation factor will be added, and as seen in Table 2-2, this factor
will be equal to 0.70 * 0.70, or 0.49. The final consumption rate for chicken is estimated as 0.12
g/kg/day. EPA (1997a) reports that USDA's Agricultural Handbook Number 8 (USDA, 1979-
1984) lists the fat content of meat and skin cooked chicken is 13.6. This will be rounded to 14%
in this assessment. Therefore, the ingestion of chicken fat for this pathway demonstrated in
Chapter 5 will be 0.010 g/kg/day.
2.7.4.5. Egg Ingestion
Like the diary and chicken ingestion pathways, the egg ingestion pathway will be
demonstrated outside of a defined scenario. EPA (1997a) calculates a consumers only
consumption rate for home produced eggs of 0.73 g/kg/day. As described above, a CF of 0.214
will be adopted for home-produced egg consumption leading to a total of 0.156 g/kg/day. EPA
(1997a) reports that USDA's Agricultural Handbook Number 8 (USDA, 1979-1984) lists the fat
content of eggs as 8.35%. This will be rounded to 8% in this assessment. This is the fat content
of the entire egg, as the fat content of the yolk (which has all the egg fat) is about 30%.
Therefore, the ingestion of egg fat for this pathway demonstrated in Chapter 5 will be 0.012
g/kg/day.
2.7.4.6. Vegetable and Fruit Ingestion
EPA (1997a) analyzed data from the NFCS household survey to estimate the amount of
homegrown "exposed above ground vegetables/fruits" and "root vegetables". Protected
vegetables/fruits, as opposed to exposed, were defined as vegetables/fruits which have outer
protective coverings which are removed prior to consumption such as peas or oranges. No root
vegetables were considered to be protected although, of course, it is common to consume some
below ground vegetables such as carrots or potatoes after removal of the skin. The overall
consumers only average of home grown fruits, above ground vegetables, and root vegetables was
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1.49,1.52, and 1.16 g/kg/d. These data are in fresh weight. These numbers were somewhat
influenced by higher consumption rates for younger children of lesser body weight, as the
consumption rate for three age ranges less than 5.0 ranged from 1.28 g/kg/day to 5.75 g/kg/day.
The fruit and vegetable pathways will be included in both the central and high end scenarios of
Chapter 5, and it will be assumed that the behaviors in the central and high end scenarios do not
differ. The CF for both above and below ground vegetables will be 0.173 and for fruit (above
ground only) will be 0.101, leading to final fresh weight consumption rates of 0.15 g/kg-day for
fruit, 0.26 g/kg-day for above ground vegetables, and 0.20 g/kg-day for below ground vegetables.
2.7.5. Fish Ingestion
The procedure to estimate exposure to fish from consumption offish caught in
recreational pursuit will be different than that of the terrestrial food products. This is done
because it is most common in exposure assessments to model exposure to fish in terms of a g/day
consumption rate rather than a g/kg/day rate, and a wealth of data in the g/day format has been
generated from consumption and creel surveys. Therefore, the LADD equation will be used with
a separate consumption rate in g/day and an assumed body weight of 70 kg.
EPA (1997a) extensively reviewed the literature on fish consumption. Their analysis of
available surveys led them to make recommendations on studies which they considered valid for
generation offish consumption rates. They also categorized available consumption rates from
these surveys into: 1) General Population, 2) Recreational Marine Anglers, 3) Recreational
Freshwater Anglers, and 4) Native American Freshwater Anglers.
The demonstration scenarios of Chapter 5 assume a rural setting which includes farms and
non-farm residents. Also, it is assumed that a major river system cuts through the watershed
which is used for recreational fishing and drinking. It is assumed to be a freshwater system, and
therefore, the "recreational freshwater angler" consumption rates will be used in this assessment.
Three studies were deemed valid for characterizing recreational freshwater angler consumption
rates, and these include the mailed questionnaire studies of Ebert et al. (1993) and West, et al.
(1989) and the diary study of Connelly, et al. (1996). The arithmetic mean consumption rate
from these four studies are 5, 12, 17, and 5, respectively. The 95th percentile consumption rates
from these studies were 13 (Ebert et al, 1993), 39 (West, et al., 1989), and 18 g/day (from
Connelly, et al., 1996). From these data, EPA (1997a) recommended a mean and a 95th
percentile value for recreational anglers of 8 and 25 g/day. The central scenarios including a fish
consumption pathway will assume the 8 g/day rate. The high end scenarios, which were defined
as a subsistence beef farm, will not include a fish consumption pathway. This is done for
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demonstration purposes - it certainly is plausible for a farming family to partake in recreational
fishing. A high end fish consumption pathway will be demonstrated outside of a scenario,
however, as with other pathways. The high end pathway demonstration will use the 25 g/day
rate as recommended in EPA (1997'a).
It should be noted that these consumption rates are for all recreational freshwater fishers,
not only those who consumed the fish they caught. Therefore, they could be underestimates for
the purposes here - modeling individuals who consume fish they reacreationally catch. It is also
noted that these rates are probably best described as, "as eaten" since the respondents were asked
to estimate the weight of the fish they consumed, and assistance the respondents were given
(choices, photos, etc.) were based on as consumed weights by those conducting the surveys.
Because these rates are, "as eaten", there is no preparation factor required. Finally, it is assumed
that the fish is 7% lipid. There were no specific recommendations in EPA (1997a) regarding this
factor; there were tables showing the percent fat for various fish species. The 7% value selected
appears to be a reasonable mid-range from those tables, and it was also the percent assumed in
previous versions of this dioxin exposure assessment (EPA, 1994a).
As a point of comparison, the general population recommended mean consumption rate
was 20.1 g/day (14.1 marine fish and 6.0 freshwater/estuarine fish). The 95th percentile
recommended rate was 63 g/day. These were described as long term average consumption rates.
For a Native American subsistence population, EPA (1997a) recommends a value of 70 g/day for
mean intake offish, and 170 g/day for the 95th percentile intake.
The current Exposure Factors Handbook (EPA, 1997a) and its predecessor (EPA, 1989)
both emphasize the importance of obtaining site-specific information on the exposure
parameters, not the least of which is the fish consumption rate. For smaller water bodies, EPA
(1989) recommends that surveys of local fisherman would obtain the most appropriate fish
consumption information. Alternately, EPA (1989) recommends using judgement regarding how
many fish meals per year an individual could obtain from the contaminated waters and assuming
meal sizes of 100 to 200 g. This was the approach adopted in the earlier draft of the Dioxin
Exposure Assessment document (EPA, 1994a). With that approach, it was assumed that a
"central" pattern of consumption offish from an impacted water body led to 3 meals/person/year,
and that a "high end" pattern led to 10 fish meals/person/year. With a meal size of 150 g (the
current Exposure Factors Handbook recommends a mean fish meal size of 129 g, and a 95th
percentile fish meal size of 326 g), this led to consumption rates of 1.2 g/day as the central
estimate and 4.1 g/day as the high end estimate.
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Browner, C. (1995) Memorandum from C. Browner, Administrator, EPA, to Assistant
Administrators, Associate Administrators, Regional Administrators, General Counsel,
and Inspector General dated 3/21/95 and titled, "EPA Risk Characterization Program".
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CDC. (2000) Personal communication from D. Patterson, CDC, Atlanta, GA to M. Lorber, U.S.
EPA, Washington, DC. April, 2000.
Connelly, N.A.; Knuth, B.A.; Brown, T.L. (1996) Sportfish consumption patterns of Lake
Ontario anglers and the relationship to health advisories. North American Journal of
Fisheries Management 16: 90-101.
Cleverly, D.H.; Winters, D.; Ferrario, J.; Schaum, J.; Riggs, K.; Hartford, P.; Joseph, D.;
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(NDAMN): Measurements of CDDs, CDFs and coplanar PCBs at 15 rural and 6 national
park areas of the United States: June 1998-December 1999. Organohalogen Compounds
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Dilberto, J.J., Jackson, J.A., and Birnbaum, L.S. (1996) Comparison of 2,3,7,8-
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parenteral exposures to rats. Toxicology and Applied Pharmacology 138: 158-168.
Ebert, E.; Harrington, N.; Boyle, K.; Knight, J.; Keenan, R. (1993) Estimating consumption of
freshwater fish among Maine anglers. North American Journal of Fisheries Management
13:737-745.
Farland, W.H. (1987) Memorandum titled, "Absorption fraction when calculating upper-limit
risks due to dioxin exposure", dated September 2, 1987, to Michael Callahan, Exposure
Assessment Group, Washington, DC. from William Farland, U.S. Environmental
Protection Agency, Office of Health and Environmental Assessment, Washington, D.C.
Fries, G.F.; Marrow, G.S. (1975) Retention and excretion of 2,3,7,8-tetrachlorodibenzo-p-
dioxin (TCDD) by rats. Journal of Agriculture and Food Chemistry 23: 265-269.
Hawley, J.K. (1985) Assessment of health risk from exposure to contaminated soil. Risk
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Isreali, M.; Nelson, C.B. (1992) Distribution of expected time of residence for U.S. households.
Risk Analysis 12(1): 65-72.
Layton, D.W. (1993) Metabolically consistent breathing rates for use in dose assessments.
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Lorber, M (2002) A pharmacokinetic model for estimating exposure of Americans to dioxin-like
compounds in the past, present, and future. In press, in Science of the Total
Environment.
Lorber, M; Pinsky, P.; Gehring, P.; Braverman, C.; Winters, D.; Sovocool, W. (1998)
Relationships between dioxins in soil, air, ash, and emissions from a municipal solid
waste incinerator emitting large amounts of dioxins. Chemosphere 37:2173-2197.
Paustenbach, DJ. (1987) Assessing the potential human health hazards of dioxin contaminated
soils. Commun. Toxicol. 1:185-220.
Robinson, J.P. Thomas, J. (1991) Time spent in activities, locations, and microenvironments: a
California-National Comparison Project report. Las Vegas, NV: U.S. Environmental
Protection Agency, Environmental Monitoring Systems Laboratory.
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following single and repeated oral doses to the rat. Toxicology and Applied
Pharmacology 36: 209-226.
Sexton, K; Ryan, P.B. (1987) Assessment of human exposure to air pollution: methods,
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the automobile and public health: research opportunities for quantifying risk.
Washington, DC: National Academy of Sciences Press.
Sheenan, P.J.; Meyer, D.M.; Sauer, M.M.; Paustenbach, DJ. (1991) Assessment of the human
health risks posed by exposure to chromium-contaminated soils. Journal of Toxicology
and Environmental Health 34: 161-201.
U.S. Bureau of the Census. (1993) Geographical mobility: March 1991 to March 1992. Current
population reports p. 20-473.
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U.S. Department of Agriculture. (1992) USD A, Human Nutrition Information Service. Food
and nutrient intakes by individuals in the United States, 1 day, 1987-88: Nationwide Food
Consumption Survey 1987-88, NFCS Rpt. No. 87-1-1.
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U.S. Department of Agriculture. (1979-1984) Agricultural Handbook No. 8.
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trichlorophenoxy)acetic acid (2,4,5-T), (2,4,5-trichlorophenoxy)propionic acid (silvex),
and the 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD). Office of Health and
Environmental Assessment. EPA-600/6-81-003. NTIS PBS 1-234825.
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Applications. Office of Health and Environmental Assessment EPA/600/8-91/01 IB.
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Compounds. Office of Research and Development. August 1992 Workshop Review
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Related Compounds. External Review Draft. EPA/600/BP-92/001a-c, and, Estimating
Exposure to Dioxin-Like Compounds. Volume I. Executive Summary. Volume U.
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for Environmental Assessment, Washington D.C. EPA/600/P-95/002F(a-c)
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U.S. Environmental Protection Agency. (1998) Human Health Risk Assessment Protocol for
Hazardous Waste Combustion Facilities. Office of Solid Waste and Emergency
Response. EPA 530-D-98-001A,B,C. July.
West, P.C.; Fly, M.J.; Marans, R.; Larkin, F. (1989) Michican sport anglers fish consumption
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Department of Management and Budget Contract No. 87-20141.
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Table 2-1. Summary of exposure pathway parameters selected for the demonstration scenarios
of Chapter 5.
Pathway
Description
Soil Ingestion
Central
High End
Absorption fraction
Soil Dermal Contact
Central
CR, mg/cm2-ev
SA, cm2
EF, ev/day
High End
CR, mg/cm2-ev
SA, cm2
EF, ev/day
Absorption fraction
Vapor/Dust Inhalation
Central
High End
Water Ingestion
Central
High End
Contact
Rates
100 mg/d
600 mg/d
0.30
Indoor
0.005
1,000
1
0.005
1,000
1
0.03
Outdoor
0.03
10,000
0.27
0.1
3600
0.96
0.03
13 m3/day
20 m3/day
1.4L/day
2.0 L/day
Contact
Fractions
1.0
1.0
1.00
1.00
0.70
0.90
0.70
0.90
Comments
Only pathway specific to an age-
range; 2 to 6 year-old children
assumed. Absorption fraction
converts potential to absorbed
dose for risk estimation.
Unlike other pathways, daily
contact is not assumed; approach
instead estimates exposure in
terms of contact/event *
events/yr; central pattern based
on behavior of non-farming
adults, high end behavior based
on farming pattern. Absorption
fraction converts potential to
absorbed dose for risk estimation.
Indoor/outdoor air quality
assumed equal; central contact
fraction is based on average at-
home time from time use surveys
The more traditional 2.0 L/day
was evaluated in EPA (1995) as
an high end rather than a central
value; 1 .4 L/day recommended
instead for central assumptions.
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Table 2-1. (continued)
Pathway
Description
Contact
Rates
Contact
Fractions
Comments
Terrestrial Food
Products
All contact rates for the terrestrial food products, including animal
food and vegetable/fruit, will be: 1) expressed in terms of g/kg/day
thereby not requiring a 70 kg body assumption in the denominator of
the LADD equation, and 2) based on the EPA (1997a) analysis of the
household part of the USD A National Food Consumption Survey.
Fish consumption will be handled in the more traditional way using a
g/day fish consumption rate and an assumption of a 70 kg body
weight. Central scenarios include fish pathway and veg/fruit
ingestion pathway, but no other terrestrial food production; high end
scenarios include beef/dairy and veg/fruit ingestion pathway, but no
other food production including fish consumption. Chicken and egg
pathways will be demonstrated outside the context of an exposure
scenario.
Beef Fat Ingestion
Central
High End
NA
2.45 g whole/kg/d
NA
0.478
2.45 g whole/kg/d is transformed
to g fat/kg/d basis assuming 22%
fat; additional factor of 0.55
accounts for cooking and post
cooking loss
Milk Fat Ingestion
Central
High End
NA
14. Og whole/kg/d
NA
0.207
14.0 g whole/kg/d is transformed
to g fat/kg/d basis assuming 3%
fat
Chicken Fat Ingestion
Central
High End
NA
0.97 g whole/kg/d
NA
0.151
0.97 g whole/kg/d is transformed
to g fat/kg/d basis assuming 14%
fat; additional factor of 0.49
accounts for cooking and post
cooking loss
Egg Fat Ingestion
Central
High End
NA
0.73 g whole/kg/d
NA
0.214
0.73 g whole/kg/d is transformed
to g fat/kg/d basis assuming 8%
fat
Fruit Ingestion
Above ground exp.
Central
High End
1.47gfresh/kg/d
1.47gfresh/kg/d
0.101
0.101
Central and high end behaviors
assumed for residence and farm
scenarios, respectively; difference
is modeled with contact fractions
only.
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Table 2-1. (continued)
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Pathway
Description
Vegetable Ingestion
Above ground exp.
Central
High End
Root vegetables
Central
High End
Fish Ingestion
Central
High End
Contact
Rates
1.52gfresh/kg/d
1.52gfresh/kg/d
1.16gfresh/kg/d
1.16gfresh/kg/d
8.0 g/day
25.0 g/day
Contact
Fractions
0.173
0.173
0.173
0.173
1.00
1.00
Comments
Central and high end behaviors
assumed for residence and farm
scenarios, respectively; both
scenarios assume similar
behaviors; differences are in other
pathways and parameters.
Based on "freshwater recreational
angler surveys". Fat content is
assumed to be 7%.
Exposure Duration, Body Weight, Lifetime: Based on mobility data (EPA, 1997a), a
duration of 9 years was assumed for the central residence scenario, and a high end duration
was assumed to be 30 years. The childhood soil ingestion pathway had a duration of 5 years.
As noted above, a body weight assumption was not used for terrestrial food product pathways.
For all others, a body weight of 70 kg was used except for childhood soil ingestion, which
used a 17 kg body weight. In all cases, a 70 year lifetime was assumed.
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Table 2-2. Percent weight losses from preparation of various meats.
Meat Type
Beef
Chicken
Lamb
Pork
Turkey
Veal
Mean Net Cooking Loss (%)a
27
30
29
28
31
30
Mean Net Post Cooking Loss (%)b
24
30
34
36
28
25
a Includes dripping and volatile losses during cooking. Averaged over various cuts and
preparation methods.
b Includes losses from cutting, shrinkage, excess fat, bones, scraps, and juices. Averaged
over various cuts and preparation methods.
Source: USDA(1975).
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80
-60
O
30
20
10 £_".
0
Sample Avg
Year TEQ
77
49
28
-1995
2030
20 25 30 35 40 45 50 55 60 65
Age within a sampled population, yrs
70
Figure 2-1. Predicted distributions of and average WHO98-TEQDF concentrations within an adult
population for four years: 1965, 1985, 1995, and 2030.
Source: Lorber (2002)
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3. EVALUATING ATMOSPHERIC RELEASES OF DIOXIN-LIKE
COMPOUNDS FROM COMBUSTION SOURCES
3.1. INTRODUCTION
Since the late 1970's, it has become well established that the combustion of certain fuels
containing both organic material and chlorides can form polychlorinated dibenzo-p-dioxins
(CDDs) and polychlorinated dibenzofurans (CDFs). This discovery has prompted world-wide
research to identify combustion sources, to characterize the conditions favoring the formation of
CDD/Fs within the combustion process, and to characterize the emission of dioxin-like
compounds to the air from the stack of the process.
The purpose of this chapter is to provide site-specific procedures for evaluating the
emission of dioxin-like compounds from stationary combustion sources. The first step is to
characterize stack emissions in terms of mass of CDD/F congener released, and then to partition
that release into a vapor and a particle phase. Using atmospheric transport modeling, these
releases are translated to ambient air vapor and particle phase concentrations, and wet and dry
particulate deposition amounts, in the vicinity of the release. This chapter demonstrates these
procedures on a hypothetical incinerator using an air dispersion model called the Industrial
Source Complex 3, or ISC3. The short-term version of that model, ISCST3, is used in this
document. A second purpose of this chapter, therefore, is to provide the background and
justification for the model inputs and key parameters for ISCST3. The final results for this
model simulation are vapor and particle phase concentrations, and particulate deposition
amounts of the specific dioxin-like congeners, which are then used for the demonstration of the
stack emission source category in Chapter 5 of this Volume.
This chapter is structured as follows:
• Section 3.2 describes the generation of CDD/F congener-specific emission factors.
These factors are defined as the mass of congener emitted per mass of feed material combusted.
Subsections within Section 3.2 discuss: 1) a hierarchy of preferred options for generating such
emission factors, starting with site-specific stack testing for specific congeners and ending with
engineering evaluations when no other data is available, 2) an approach to estimating congener-
specific emission factors using congener profiles generated for a source when only total dioxin
(sum of homologue group CDD and CDF concentrations) emissions are available; 3) an
approach for estimating congener-specific emission rates from CDD and CDF homologue group
emissions data if congener profiles are not available; 4) the emission factors for the example
incinerator demonstrated in Chapter 5, and assuming a feed rate into the example incinerator,
emissions expressed on a mass per time basis (which is required for transport modeling), 5)
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partitioning of emissions into a vapor and a particle phase for atmospheric transport modeling,
and 6) a procedure to estimate the mass released and concentrations for a related emission of a
combustor, that of ash.
• Section 3.3 describes a general air modeling procedure for evaluating the fate and
transport of dioxin-like compounds emitted from stacks. The discussion presents a general
review of dispersion theory, a general review of dry particle deposition, and a general review of
the wet deposition algorithm employed in this analysis. EPA's ISCST3 air dispersion and
deposition model is reviewed. Wherever pertinent, Section 3.3 describes the assumptions,
equations, and parameter values that were used in the demonstration of methodologies in
Chapter 5 of this Volume.
• Sections 3.2 and 3.3 summarized input data and assumptions (emission rates,
vapor/particle partitioning assumptions, etc.) that were made for the demonstration of the
methodologies for evaluating stack emissions in Chapter 5 of this Volume. Section 3.4 supplies
all other key assumptions for the stack emission demonstration, such as stack height and exit
temperatures, meteorological data, and others. This Section also provides the final results from
the ISCST3 modeling, including vapor phase air concentrations at various distances in the
predominant wind direction, and dry and wet deposition fluxes, also at various distances in the
predominant wind direction.
• Section 3.5 closes out the chapter by summarizing critical aspects for making site
specific evaluations of stack emission sources.
3.2. ESTIMATING THE EMISSIONS OF DIOXIN-LIKE COMPOUNDS FROM
ANTHROPOGENIC COMBUSTION SOURCES
Estimating the emission factor is the first step in assessing a specific stack emission
source of dioxin-like compound release. For this assessment, an emission factor is defined as the
total mass (in vapor and paniculate forms) of dioxin-like compound emitted per mass of feed
material combusted. An emission factor is a representative value that attempts to relate the
quantity of a pollutant released to the atmosphere with an activity associated with the release of
that pollutant. Such factors facilitate estimation of emissions from various sources of air
pollution. In most cases, these factors are averages of all available data of acceptable quality,
and are generally assumed to be representative of long-term averages for all facilities in the same
source category.
The general equation for emission estimation is:
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Eyr = Ayr*EF (3-1)
where:
Eyr = emission dioxin/yr; e.g., g/yr
Ayr = annual activity level of the subject source; e.g. kg/yr
EF = emission factor, mass dioxin emitted/ unit of activity level; e.g., ng/kg
Inherent in Equation (3-1) is the assumption that long-term emissions of dioxin-like compounds
from a facility is best represented by the mean emission factor. Emission factors are usually
developed from measurements of dioxin releases that are associated with normal operating
conditions, not upsets nor industrial accidents.
This assessment recommends the generation of emission factors for individual dioxin-
like congeners for a site-specific assessment. Volume I, Sources of Dioxin-Like Compounds in
the United States, contains congener-specific CDD/F emission factors for specific types or
classes of combustion processes. The emission factors were developed from stack gas emission
measurements at tested facilities, usually as a result of compliance testing under the Clean Air
Act, or in conjunction with the issuance of a State operating permit. The actual facility-specific
emission rates and the derivation of emission factors representative of classes of combustors are
documented in Database of Sources of Environmental Releases of Dioxin-Like Compounds in
the United States (EPA/600/C-01/012. March, 2001). This database includes congener-specific
CDD and CDF emissions data extracted from original engineering test reports. It has been
published independently from the Reassessment and is available on Compact Disk-Read only
Memory (CD-ROM), without cost, from EPA's National Service Center for Environmental
Publications (NSCEP) in Cincinnati, Ohio (telephone: 1-800-490-9198, or 513-489-8190; fax:
513-489-8695). Summary files from the database will be available for downloading from the
Web page of the National Center for Environmental Assessment, www.epa.gov/ncea/dioxin.htm.
Instructions on how to order and obtain the CD-ROM will also be available on the Web page.
This compilation of emission factors is the result of entering raw data of stack emissions from
engineering reports of tested facilities data into a series of spreads-sheets. Facilities are
classified and sub-classified according to similarity of process, design, types of materials
processes or combusted and air pollution control equipment. These emissions factors are current
through 1995. However, because most combustion processes are generally configured as they
were in 1995, EPA believes the database, in most respects, is current with state-of-the-art
technologies. This also applies to air pollution control equipment. EPA will, however, provide a
periodic update to the database. Currently, EPA is updating this database to be current with the
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year 2000. These updates should reflect changes in the improvements in the combustion-related
technology and air pollution control equipment with the passage of time.
When evaluating potential human exposure to dioxin-like compounds using an Air
Quality Model, EPA has traditionally converted the stack gas concentrations and emissions of
CDD/F mixtures into an equivalent concentration of 2,3,7,8-TCDD, or TEQ (Cleverly, et al.,
1989, 1991; EPA, 1987; Mukerjee and Cleverly, 1987), when deriving an emission factor. The
fate, transport, and transfer parameters of 2,3,7,8-TCDD were applied to model the
environmental fate of this TEQ mixture. For the site-specific procedures in this document,
individual dioxin congeners are modeled from source to receptor. Only at the interface to human
exposure, e.g., ingestion, inhalation, dermal absorption, etc., are the individual congeners
recombined and converted into the toxic equivalence of 2,3,7,8-TCDD to be factored into the
quantitative risk assessment.
Section 3.2.1 presents a strategy for development of emission factors for conducting a
site-specific assessment. Section 3.2.2 describes an approach to estimating congener-specific
emission factors when all that is available are homologue group emission factors. Section 3.2.3
summarizes the emission factors for the hypothetical incinerator demonstrated in Chapter 5.
Section 3.2.4 presents an in-depth evaluation of the partitioning of emissions between a vapor
and a particle phase for further atmospheric transport modeling. This discussion includes
subsections on measurements of partitioning at the stack, measurements of partitioning in
ambient air, and the theoretical approach used in this assessment for vapor/particle partitioning.
Section 3.3.5 closes this section on emission factors by describing procedures to estimate the
mass of ash (fly and bottom) produced and the concentration of dioxin-like compounds on ash.
3.2.1. A Strategy for Generating Emission Factors
The following is a hierarchial listing of data collection options for emission factors:
A. For facilities that are built and operational, it is preferred that direct stack measurements be
used, using EPA recommended congener-specific stack monitoring and analytical protocols
(e.g., EPA Method 23 for stack and EPA Method 1613 revision b for laboratory analysis). Stack
monitoring provides concentrations and mass release rates of the pollutant, actual volume of
stack gas and temperature. Care should be taken to ensure that the emissions characterization
reflects a wide range of operating conditions and also accounts for deterioration in emissions
output of the facility over its useful life. Procedures to convert data expressed in concentrations
or mass release rates to an emission factor are as follows:
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1. Test data of emissions are first placed into common units of measurement.
English units are converted into metric, and the concentration term (mass of pollutant per
unit volume of combustion gas emitted from the stack) should be corrected to the
standard temperature and pressure on a dry gas basis, and standard percent carbon
dioxide or oxygen within the combustion gas (e.g.,12% CO2 or 7% O2). These
adjustments may be necessary if more than one test occurred for stack emissions.
2. The next step involves converting the mass emission concentration of the specific
dioxin-like congener in units of nanogram per normal cubic meter (at standard
temperature and pressure) of combustion gas corrected to 12% carbon dioxide into an
equivalent emission factor in units of grams of pollutant emitted from the stack per
kilogram of combustible material or feed (g [CDD/F]/kg feed) that was incinerated at the
facility during the duration of stack sampling. This is solved in (Equation 3-2) below. In
Equation (3-2), it is important that both the concentration of the dioxin-like compound
(Cfg) and the volume of combustion gas (Vfg) be calibrated to the identical oxygen
percent, temperature, and pressure.
EF= CfgxVfgxA
_ [ ngdioxin | fdscm(STP)] [ hr | (3-2)
~ I dscm (STP) J X I hr J X { kg combusted}
where:
EF = congener-specific CDD/F emission factor, ng dioxin/kg
Cfg = concentration in flue gas, ng dioxin/dscm
Vfg = volume of combustion gas/unit of time, dscm/hr
A = combustion activity level, kg/hr
3. As a final step, the average emission factor of each congener is derived by
summing the emission factors and dividing by the number of data points used. The
average should represent an approximation of long-term emissions (i.e., annual
emissions). Many air dispersion models require that emission factors (ng/kg) be
translated into units of amount of the pollutant emitted per second of time (ng/s).
Therefore the average emission factor must be adjusted accordingly by adjusting the units
in Equation (3-2) to a time-scale of one second.
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B. For facilities that have been constructed, but not yet operational, or are in the planning
stages of development, the following procedure is recommended:
1. Refer to the Database of Sources of Environmental Releases of Dioxin-like
Compounds in the United States (EPA, 2001). This database contains CDD/F emission
factors for a variety of combustion sources through 1995:
a) municipal solid waste incinerators
b) medical waste incinerators
c) cement kilns burning hazardous waste
d) cement kilns not burning hazardous waste
e) dedicated hazardous waste incinerators
f) industrial wood burning
g) residential wood burning
h) coal and oil-fired utility boilers
I) secondary aluminum smelters
j) iron ore sintering
k) secondary lead smelting
1) secondary copper smelting
m) kraft black liquor boilers
n) sewage sludge incinerators
o) boilers/industrial furnaces burning hazardous waste
2. Review the listing of air emission sources and combustor types. From this listing,
select the closest analogy to the subject technology in terms of design-type, kinds and
types of materials processed or combusted, and air pollution control device. Care should
be taken to assure that the subject source type and design, controls, and raw material
input are those of the source(s) analyzed to produced the emission factor. This fact
should be considered, as well as the age of the information and the user's knowledge of
technology advances.
3. After selecting the similar technology, go to the area of the Dioxin Source
Database where average and congener-specific CDD/F emissions factors have been
computed.
D. If no data exist in the National Database that is relevant to a specific facility, then the
Compilation of Air Pollution Emission Factors, Fifth Edition (EPA, 1997; and subsequent
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updates), should be used. This compilation was put together and is periodically updated by
EPA's Office of Air Quality Planning and Standards (OAQPS), and is commonly referred to as
AP-42. Care should be taken to select emission factors which were developed for technologies
that best match the facility under consideration. The basic limitation of these of these data is the
fact that emission factors are not usually reflective of specific emission control equipment. The
AP-42 emission factors can be found in one of six chapters:
Chapter 1, External Combustion Sources
Chapter 2, Solid Waste Disposal
Chapter 3, Stationary Internal Combustion Sources
Chapter 4, Evaporative Loss Sources
Chapter 5, Petroleum Industry
Chapter 6, Organic Chemical Process Industry
Emission factors presented in AP-42 are designed for estimating emissions from a large number
of sources over a wide area. They are averages of values determined at one or more individual
facilities. The individual values which are used to develop the average may vary considerably.
The use of AP-42 emission factors to estimate emissions from any one facility should be done
with great care.
3.2.2. Use of Homologue and Congener-Specific Profiles to Estimate Emission Factors
Situations may occur in which CDD/F emissions data of classes of combustor types are
reported as either homologue-groups and/or total CDDs plus CDFs. These data may, however,
be most relevant to a combustion source under evaluation. Congener-specific emissions data are
needed for the analyses of the ambient air impacts and deposition flux of dioxin-like compounds
using air dispersion and deposition models. This is because each specific congener will have
different physico-chemical properties, and this will greatly affect the modeling result. This
section presents guidance on estimating congener-specific emission rates from homologue-
specific and total CDD/F emissions data.
The preferred approach is to convert CDD/F homologue and total CDD/F emissions to
congener-specific emissions using congener profiles developed for each source. Congener
profiles are the fractional distribution of CDD/F congeners in an environmental release, in an
environmental sample, or in a biological sample. Congener-specific profiles have been
developed for known anthropogenic source activity in the U.S. as part of this assessment, and
these profiles can be reviewed at the end of each Chapter in Volume I, Sources ofDioxin-Like
Compounds in the United States. The following subsection 3.2.2.1 provides guidance for the use
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of congener profiles to estimate congener-specific emission rates when only total CDD/F
emissions data are available. In some cases no congener-specific profiles may exist for a source.
Subsection 3.2.2.2 provides guidance for assuming a congener-specific emissions if at least
homologue data are available.
3.2.2.1 Using Congener Profiles to Convert Total CDD/F
The assessor may have stack emissions data displayed as the sum of tetra through octa-
CDD and CDF congener-groups, i.e., total CDDs plus CDFs. Congener-specific emissions data
are needed for the most accurate assessment of potential air and deposition impacts near a source
with the application of an air dispersion and deposition model. Congener-profiles derived from
stack emissions data of similar combustion sources may assist this effort.
Congener profiles were determined by dividing the mean congener emission factor for
the source class by the total tetra through octa-CDD and CDF emission factor for that class. All
nondetects were treated as zero values. The result is an average fractional distribution (unitless)
of each toxic CDD/F congener. Figure 3-1 is an example of the congener and homologue profile
of typical emissions from sewage sludge incineration. In this example, the most prevalent
congener in the emissions from sewage sludge incineration is OCDD. When the congener
profile is plotted, OCDD is approximately 27% of the total CDD/F emissions. In this example,
2,3,7,8-TCDD is approximately 1% of total CDD/F emissions. Multiplication of the total
CDD/F emission factor by the fractional distribution of each congener gives an estimate of the
congener-specific emission factor for the source. For example, if it is assumed that the total
CDD/F emission factor is 500 ng/kg of waste feed for a sewage sludge incinerator emissions,
then the emission factor for OCDD is estimated from the congener profile to be 135 ng/kg (500
ng total CDD/F * 0.27). By this method, 2,3,7,8-TCDD would have an estimated emission
factor of 5 ng/kg.
3.2.2.2 Estimating Congener-Specific Emissions when no Congener Profiles are Available
In some cases the congener profile may not available for a specific source of interest.
This may be due to the general lack of emissions data for that particular source. However, the
assessor may have information on homologue emissions from a combustion source. When only
homologue emission factors are available, and no congener-specific profile exists for the source,
then rough estimates of congener specific emission factors can still be made. First, an equal
probability of occurrence of the specific congener is assumed based on relative proportionality.
For example, 2,3,7,8-TCDD is one congener out of 22 possible congeners in the TCDD
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homologue. Therefore, the probability of occurrence is assumed to be the ratio of 1/22 or 0.045.
Multiplication of a total TCDD emission factor by 0.045 gives an approximation of the emission
factor for 2,3,7,8-TCDD. Table 3-1 lists the number of dioxin-like congeners within a
homologue group and the total number of congeners within that homologue group.
3.2.3. Estimation of Emissions of Dioxin-Like Compounds from the Hypothetical
Incinerator
The emission factors for the dioxin-like compounds from the stack of the hypothetical
waste incinerator were derived from actual stack monitoring and emissions testing of an
incinerator burning a complex mixture of organic waste. The concentrations of the specific
CDD/F congeners in units of nanograms per dry standard cubic meter (at 20° C; 1 atm.; 7% O2)
were available, as was the volume of gas escaping from the stack and feed rates for the material
being combusted during the stack tests. Using procedures described in Section 3.2.1, this data
was converted to emission factors. Such factors for three test runs are shown in Table 3-2. The
fourth column is the average of these emission factors converted to g/sec units, which are the
appropriate units for the application of the ISCST3 model. The conversion assumed a constant
feed rate of 200 metric tons of feed material per day (further details on the hypothetical
incinerator are found in Section 3.5). Human exposures to the coplanar PCBs emitted from a
combustion source is not demonstrated in Chapter 5. Therefore, an estimation of congener-
specific emission factors of coplanar PCBs for the hypothetical incinerator are not provided.
In order to put the emissions from the hypothetical waste incinerator into perspective,
they can be compared with emissions from other incineration sources that are similarly
controlled, e.g., equipped with scrubbers and/or fabric filters. Such air pollution control devices
can reduce the amount of dioxin that is formed within the system by >99% prior to the release
from the stack. In this comparison, emissions typical of waste incineration were taken from
Volume 2, Chapter 3. The following types of incineration processes were used: medical waste
incineration; hazardous waste incineration; sewage sludge incineration; municipal solid waste
incineration, and tire incineration. For comparisons, all emissions factors are expressed in units
of nanograms I-TEQ emitted from the stack per kg of waste combusted, and are presented as
arithmetic mean emission factors. This should not be confused as typical of the incineration
source category, but specific only to sources having scrubbers and/or fabric filters. Volume I,
Chapter 3 of this assessment gives an overview of dioxin emissions from incineration
technologies equipped with a variety of pollution control systems. The emissions from the
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hypothetical incinerator is ranked with the other types of waste incinerators that are well
controlled with some combination of a scrubber device and/or a fabric filter, as follows:
1. Medical waste incinerator: 70 ng I-TEQ/kg waste combusted
2 .Municipal solid waste incineration: 16 ng I-TEQ/kg waste combusted.
3. Sewage sludge incineration: 6.9 ng I-TEQ/kg sludge combusted
4 Hypothetical waste incinerator: 4.5 ng I-TEQ/kg waste combusted
5. Hazardous waste incineration: 3.8 ng I-TEQ/kg waste combusted.
6. Tire incineration: 0.3 ng I-TEQ/kg tires combusted.
From these comparisons it appears that the I-TEQ emission factor derived for the
hypothetical incinerator lies well within the range of emission factors developed for well
controlled sewage sludge and hazardous waste incinerators, but considerably lower than
municipal solid waste and medical waste incinerators. The hypothetical incinerator was
arbitrarily assigned a waste combustion rate of 200,000 kg waste/day (i.e., 200 tonnes/day). This
charging rate conforms to a large medical waste incinerator, an average hazardous waste facility,
and moderate sewage sludge and municipal waste incinerators.
3.2.4. Estimation of the Vapor Phase/Particle Phase Partitioning of Emissions of Dioxin-
Like Compounds
The first step in the air modeling is the partitioning of total emissions into a vapor and a
particle state. This section will review data on partitioning at the point of stack emission, in
ambient air, and a theoretical approach to estimating the partitioning of dioxin- like compounds
in ambient air. The true vapor/particle partitioning of dioxin under different conditions has not
been directly measured, and therefore, is usually implied from these limited data or by
theoretical means.
3.2.4.1. Vapor Phase/Particulate Phase Inferences from Stack Measurements
While the available literature is weak in this area, various investigators have made
inferences on the vapor phase/particulate phase (V/P) partitioning from in-the-stack sampling of
CDD/F emissions from combustion sources. Sampling systems which have been used basically
consist of a particulate filter followed by a section designed to condense vapors in impinger
glassware surrounded by an ice bath, and a resinous material suitable for absorbing vapor phase
compounds. Depending on where the congener is distributed within the component parts of the
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sampling apparatus, the investigator reports the fraction associated with paniculate, and the
fraction found in the vapor absorbing material. In order to collect sufficient mass of particulate
for accurate analytical determination of the concentration of the recovered congener at sub-part
per trillion levels of detection, it is often necessary to sample in stack for periods of four hours or
longer. This introduces the possibility of movement of the collected dioxin sample from one part
of the sampling train to another through adsorption, desorption, particulate blow-off, or other
such phenomena as the sampling train continues to be exposed to the hot combustion gases. No
real-time sampling method currently exists to instantaneously measure the concentration and
physical state of the various CDD/F congeners in the fluid turbulence of the hot combustion
plasma characteristic of gases from combustion traveling up a cylindrical stack. For these
reasons, V/P partitioning based on stack test data is highly uncertain. Additional laboratory
research is needed that is specifically directed at identifying the physical state partitioning of
individual CDD/F congeners at the exit to the stack under varying temperature profiles and
conditions of particulate loading and acid gas concentration. Table 3-3 is a summary of the
percent distribution of CDD/Fs between the vapor-phase (V) and the particulate phase (P) as
interpreted by various stack sampling techniques employed in the measurement of the
compounds during incinerator operations.
Cavallaro, et al. (1982) performed a series of stack tests on six municipal solid waste
(MSW) incinerators in Italy. He was one of the first investigators to interpret the V/P ratio from
where the CDD/F segregated with the sampling train, e.g., the particulate filter and resinous trap.
From these data, the percent distribution of congener groups were estimated. Cavallaro observed
that the CDD/F emissions from the stack of the tested incinerators seemed to predominate in
vapor phase. He attributed this to the possibility that the relatively high temperatures of the
combustion gases during sampling (700 to 900° C) may have promoted desorption of CDD/F s
from particulate, although the sampling probe was kept at a constant 150°C.
Benfenati, et al. (1986) describes the stack testing of a modular MSW incinerator in Italy
having a combustion capacity of 1500 kg/hour. The purposes of the study were to analyze the
concentration of TCDD and TCDF at various points of the incineration process, to estimate the
vapor phase versus the particulate phase partitioning at various sampling points corresponding to
changes in temperatures, and to estimate the TCDD/F control efficiency of the pollution control
device (an electrostatic precipitator). Comparisons were made between the distribution of
TCDD/F after the secondary furnace in a region where combustion gas temperature was about
330°C, and the distribution at the stack where combustion gas temperature was 230° C.
Benfenati observed that approximately 85% of the TCDD was in the vapor phase at the exit to
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the furnace, and approximately 95% of the TCDD was in the vapor phase at the stack. It was
concluded that most of the TCDD predominated in vapor phase at the point of release from the
stack at the reported temperature of 230°C. However, Benfenati could not exclude the
possibility that the TCDD was adsorbed onto ultra fine, submicron aerosol particles.
Tiernan, et al. (1984) reported on the distribution of CDD/Fs recovered in the stack
sampling apparatus (EPA Modified Method 5) following the stack testing of a mass burn MSW
incinerator operating in Japan. In the Modified Method 5 procedure, the sampling probe is
maintained at a temperature of 120°C while the stack gases are isokinetically sampled. The
facility was equipped with a dry scrubber combined with a fabric filter as the primary pollution
control device. Tiernan observed congener-specific variability in the V/P partitioning inferred
from the sampling method. However, greater than 55% of the CDD/Fs were estimated to be in
vapor phase at the point of release to the stack. In an earlier stack test (Tiernan, et al., 1982) of
an MSW incinerator equipped with an electrostatic precipitator, Tiernan found that 45% to 89%
of the CDD/Fs were associated with particulate.
Clement, et al. (1985) stack tested a mass burn MSW incinerator operational in Canada
for the emission of CDD/Fs. Three 24-hour stack samples were taken using the EPA Modified
Method 5 train with a stack temperature of 230 - 250°C. The components of the sampling train
were analyzed separately. Clement observed that more than 95% of the total CDD/Fs detected in
the sampling train samples was found in the impingers used to condense vapor phase organic
pollutants. Interpretation of this is difficult. However, it is implied from these data that most of
the CDD/Fs prevailed in vapor phase.
Hagenmaier, et. al (1986) conducted field tests of two different stack test methods for the
accuracy, precision, and comparability of CDD/F measurements. Both instruments were
similarly constructed with a glass fiber filter for the capture of particulate-bound contaminants, a
series of water or ice-cooled impingers to promote condensation of vapor phase contaminants,
followed by an absorbing material to trap vapor phase pollutants. Eight parallel stack sampling
experiments were carried out over a three week period using the sampling trains known as the
German simple dilution method and the EPA Modified Method 5. Although the two methods
reported quite similar total concentrations of CDD/Fs, the distribution of CDD/Fs between the
heated glass filter, and ice-cooled impingers and the sorbent trap were remarkably different. In
one train, referred to as Train A by Hagenmaier, the temperature in the filter housing was 140°
C, and in the second train, Train B, the temperature was 90° C. The stack gas temperature in
both cases was 230° C. Hagenmaier found that the percentage of CDD/Fs in the glass fiber filter
was markedly greater in Train B than in Train A. Up to 93% of the PCDDs and 90% of the
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PCDFs were detected in the paniculate filter in Train B. By comparison, 73% and 58% of
PCDDs and PCDFs, respectively, were detected in the particulate filter in Train A. Although
Hagenmaier's data is used in Table 3-3, Hagenmaier theorized that this difference in the
distribution of CDD/Fs in the two sampling trains was due to the differences in the temperature
of the glass fiber filter housing.
EPA (1990a) conducted a field validation study for the EPA stack testing Method 23 (the
Modified Method 5) for the collection and retention efficiency of CDD/Fs. A carbon-13 labelled
congener was metered into the sampling probe just preceding the glass fiber filter using a
dynamic spiking system. The validation procedure involved the isokinetic sampling in the stack
of a large mass burn MSW incinerator. Sampling in situ in the stack while using a dynamic
spiking system demonstrated that most of the isotope was recovered in the filter trap and front
half of the sampling train designed to capture particulate, and a lower amount was recovered in
the XAD resin designed to capture vapor phase organic compounds. In the particular tests in
which the overall percent recovery of the dynamic spike were found to be acceptable, the XAD
resin and condenser contained about 49% of the isotope, and 51% was associated with
carbonaceous particulate.
3.2.4.2. Discussion of Vapor/Particle Ratios Derived from Stack Testing Methods
It is apparent that the stack sampling method gives inconclusive and contradictory
evidence of the V/P partitioning of CDD/Fs at the stack of incinerators. Although most of the
researchers report finding the greatest quantity of CDD/Fs captured within the resinous material
having the physical/chemical properties of absorbing vapor phase organic compounds, a few
studies have reported the opposite. What is unusual about the V/P distributions in Table 3-3 is
the lack of complete consistency despite the similarity of sampling method. Although the stack
gas temperatures may vary, the probe and housing to the sampling instrument is usually kept at a
standard temperature while traversing the hot flue gas. A more consistent pattern of V/P should
have emerged.
Hagenmaier, et al. (1986) has postulated that, depending on the temperature of the glass
fiber particulate filter housing, the CDD/Fs might desorb (volatilize) from particulate matter
trapped in the filter during the 4 hours of sampling time required of the stack sampling method.
Therefore, Hagenmaier does not believe that the distribution of CDD/Fs between the particulate
filter, the condensing impingers, and the vapor absorber gives a true indication of the V/P
partitioning of these compounds at the stack.
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Tests also have been devised by the EPA (1990a) to study the effect a change in
temperature of the glass fiber filter housing might have on the distribution of CDD/Fs in the
sampling train. During the sampling period, two sampling trains were used: one inlet to the
electrostatic precipitator (ESP), and the other placed near the outlet to the ESP. Temperatures of
the filter housing were raised from the standard 120° C to 215° C in both sampling trains. In
agreement with the observations of Hagenmaier, et al. (1986), an increase in temperature
generally resulted in a change in the distribution of the recovered 13-C labelled CDD/F
congeners. However, the temperature effect was most apparent within the sampling train inlet to
the ESP. In the inlet sampling train, the higher filter box temperature increased the relative
percentage of CDD/Fs trapped in the impingers and XAD-2 resin. An amount estimated to be in
the vapor phase, based on the segregation of the compounds within the component parts of inlet
sampling train, is as follows (with a range listed from low to high temperature): TCDD = 20 -
55% vapor; HxCDD = 10-30% vapor; OCDD = 5 - 18% vapor; HxCDF =18-58% vapor;
OCDF = 5 - 18% vapor. In the outlet sampling train (characteristic of stack emissions), this
dramatic shifting of the congeners from the filter to the XAD-2 did not occur with an increase in
temperature. Interpretation of the vapor phase partitioning in the outlet sampling train from low
to high temperatures was as follows: TCDD = 90 - 95% vapor; HxCDD = 85 - 90% vapor;
HxCDF = 90 - 95% vapor; OCDD = 75 - 90% vapor; OCDF = 78 - 90% vapor. Both these
interpretations were developed using a 500 ng CDD/F spiked congener. Notice that the vapor
phase to particle phase ratio is significantly different between the inlet and outlet sampling
trains: in the inlet train most of the CDD/F congeners seemed to predominate in the particle
phase at the standard temperature of the filter housing, whereas in the outlet train most of the
CDD/F congeners seemed to predominate in the vapor phase, as interpreted by the distribution
within the apparatus.
The temperature-dependent partitioning has recently been observed by Janssens, et al.
(1992) during field validation studies involving the sampling of operating incinerators in
Belgium. Janssens observed that the fraction of CDD/Fs collected in the heated portion of the
particulate glass filter (temperatures in the range of 250 to 300° C) showed an expected
partitioning according to the vapor pressures of the compounds. It was found that a very low
proportion of the CDD/Fs were found in the particle phase; nearly all the compounds were
detected in the vapor phase. Moreover, Janssens observed that higher temperatures seemed to
favor the vaporous state of the lower chlorinated congeners (compounds having one to five
chlorines on the aromatic ring), and the particulate phase for higher chlorinated congeners (five
to eight chlorines). This agrees well with the decrease in vapor pressures that occurs with an
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increase in chlorination, and an increase in vapor pressure that occurs with a decrease in
chlorination of CDD/Fs. Adding to the theory of Hagenmaier, et al. (1986), Janssens believed
that either the sampling apparatus was giving a true distribution of the V/P ratio of individual
congeners, or that a significant portion of the congeners were reversibly sorbed onto particulate
surfaces and could be eluded to vapor phase by the passage of the volume of sampled
combustion gas over a lengthy time interval, neither of which could be proven by his study.
Benfenati, et al. (1986) has suggested that what may be reported as vapor phase may
actually consist of nucleated aerosol particles having diameters less than 0.1 micrometers. The
impingers in the sampling method are located a few centimeters behind the heated particulate
glass fiber filter, and are bathed in an ice bath. The dramatic reduction in temperature within the
impinger glassware may cause sublimation from vapor phase to nucleation of aerosol particles.
Downstream of the impingers is the vapor absorbing material, usually XAD-2 resin. Although
this has been shown to be an excellent trap for semi-volatile organic compounds, the retention of
submicron size particles with CDD/Fs adsorbed onto the surfaces, or absorbed into the interior
spaces, cannot be ruled out or excluded as a possible explanation for investigators reporting a
preponderance of concentration both in the impingers and the vapor trap.
Complicating any meaningful interpretation of the data is the long duration of sampling
time required in the stack measurement method. In order to reach a sub-ppt level of detection of
CDD/Fs for reliable quantification of specific congeners, sampling proceeds until approximately
a five gram mass of particulate is gathered in the particulate filter. This may require in situ
placement of the sampling apparatus such that samples are taken isokinetically, and the stack
interior diameter is traversed for four or more hours. Thus the sampling instrument is
continuously exposed to the hot gas plasma over a long sampling moment. In addition the hot
gases also contain precursor compounds, chlorides, oxides of sulfur and HC1 which may have an
effect on the success of accurately sampling CDD/Fs. Although Janssens, et al. (1992),
Hagenmaier, et al. (1986), and EPA (1990a) have all but excluded the possibility that sampling
under these conditions creates results by producing CDD/Fs or destroying CDD/Fs somewhere
within the sampling train, the possibility that the method creates an illusion of the true V/P ratio
cannot be excluded.
The above discussions have indicated the variability in the data and the uncertainty with
the stack results of vapor/particle partitioning. For these reasons, these data will not be used to
infer the V/P distribution of CDD/Fs at the point of release from the stack.
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3.2.4.3. Vapor/Particle Partitioning of CDD/Fsfrom Ambient Air Sampling
The measurement of CDD/Fs in air under ambient conditions has only been achieved
since the late 1980's. The ambient air sampler which is most often used to estimate particulate
and gaseous fractions consists of a glass or quartz fiber filter followed by a sorbent trap such as
polyurethane foam (PUF), XAD resins, or a combination of the two. These are active samplers
which utilize electric pumps to draw air through the collector at approximately 0.2 - 0.6 m3/min.
This provides sample volumes of 300 - 600 m3 in a 24-h period, although longer sampling times
are not uncommon. The phase distribution is estimated from the segregation of compounds on
the filter and sorbent trap, which are assumed to capture compounds that are particulate and
gaseous, respectively. PUF is capable of collecting many semivolatile compounds with high
efficiency (Bidleman, 1987; Hart and Pankow, 1994; Pankow, 1989; Tashiro et al., 1989; Wagel,
et al., 1989), although its usefulness is limited by the vapor pressure of the compound. Examples
of compounds for which PUF can be used are the tetrachloro- and higher CDD/Fs, and three-ring
and heavier PAHs and PCBs having three or more chlorines. The more volatile members of
these classes as well as chlorobenzenes and chlorophenols are collected with greater efficiency
by XAD and similar resins (Bidleman, 1987; Hart and Pankow, 1994; Hornbuckle et al., 1993;
Patton et al., 1992; Zaranski et al., 1991). The glass fiber filters used in air samplers are rated to
collect particles of diameters >0.3 micrometer, with 99.9% collection efficiency.
Filtration samplers are subject to artifacts which include loss of organic compounds from
the particles on the filter by volatilization ("blow-off) (Eatough et al., 1993; Gundel et al., 1995;
Lane et al., 1988; Lewis et al., 1991; Subramanyam et al., 1994) and sorption of gaseous
compounds to the particle mass collected on the filter and to the filter itself (Gotham and
Bidleman, 1992; Hart and Pankow, 1994; McDow et al., 1990; Turpin et al., 1994). The
possibility of blow-off losses are recognized and discussed extensively in the literature, but there
is disagreement as to how seriously this artifact will bias estimated particle/gas distributions.
Because sampling of CDD/Fs is not instantaneous (i.e., real time measurement), but
requires 24+hour air sampling to assure a level of detection of about 0.03 pg/m3, the V/P ratios
described in this section should be considered as "operationally defined". Operationally defined
are relative and not absolute vapor phase and particle bound phase partitioning behavior within
the design contraints of the measurment method. The following is a review of ambient air
sampling data on the relative V/P partitioning of CDD/F congeners at ambient temperatures.
Table 3-4 provides a summary of the particle percentages (vapor percentage = 100 - particle
percentage) inferred from these reports.
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Oehme, et al. (1986) first described a method sensitive enough for the congener-specific
measurement of CDD/Fs at 0.1 pg/m3 levels of detection in ambient air. Such low levels of
detection introduced the possibility of taking ambient air samples in the vicinity of known
combustion sources of CDD/Fs to reliably establish an association with stack emissions. Oehme
tested the performance and reliability of an ambient air sampler consisting of a glass fiber filter
followed by a polyurethane foam plug. Ambient air was sampled over a predetermined period
after first spiking the filter with a known concentration of 13C12 labelled CDD/F standards. This
experiment was designed to determine the percent of the initial spiked labelled standard that
could be recovered from the sampler after sampling 1000 m3 of ambient air. The percent
recovery of the standard was a measure of the collection and retention efficiency of the sampler.
After collecting a sample, the particulate filter and the PUF plugs were extracted and analyzed
separately. This was done in order to establish the particle phase and vapor phase partitioning of
the CDD/F congeners. Oehme demonstrated that the sampling method was capable of a high
degree of reliability in sampling sub-part per trillion concentrations of CDD/Fs as indicated by
highly satisfactory recovery of the isotopically labelled standards in the apparatus, e.g., 88 -
102% recoveries. From the results of separately analyzing the filter and the PUF, Oehme
postulated on the typical distribution of CDD/Fs between vapor and particles in ambient air.
They suggested that TCDF and PeCDF were mainly present in the vapor phase, and HxCDD,
HxCDF as well as the less volatile isomers of HpCDF, HpCDD, OCDF, and OCDD, were
mainly present in the particle phase. Oehme took over 60 ambient air samples with this device
in rural, suburban, and urban areas of Europe.
Eitzer and Kites (1989) reported on the measurement of CDD/Fs in the ambient
atmosphere of Bloomington, Indiana while using a similarly configured ambient air sampling
method, the General Metals Works PS-1 sampler. Ambient air is drawn through a glass fiber
filter followed by a polyurethane foam plug (PUF). This was a long-term study designed to
investigate the daily and seasonal variability of the compounds in the ambient air as measured at
a single location, and to examine the vapor-phase, particulate-phase partitioning of the
chlorinated congeners under ambient conditions. Samples were taken at four different sites over
a 2-3 day sampling period until 1500 to 2400 m3 of ambient air volume had passed through the
apparatus. Sampling was conducted monthly from August, 1985 through July, 1986. The
quantitative method produced a limit of detection of the individual chlorinated congeners in the
range of ~1 femtogram/m3. Eitzer and Kites (1989) operationally defined the vapor-
phase/particle-bound phase of the chlorinated congeners as any compounds found in the PUF
plug and the glass fiber filter, respectively. The V/P ratio was subject to certain restrictions of
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the sampling method, which the authors identified as: 1. Particles smaller than 0.1 microns
would pass through the filter paper of the glass fiber paniculate filter and be absorbed into the
polyurethane foam; 2. Diurnal temperature variation could cause particle-bound CDD/Fs
collected and retained in the filter to vaporize and be "blown-off' to the PUF plug by the passage
of the sampled air stream; 3. At these relatively large sampling volumes of ambient air, it is
possible that some breakthrough on the PUF plug occurs, and a portion of the CDD/F sample is
lost. The investigators were able to rule-out the latter condition through the addition of a XAD-2
resin trap after the PUF. This was one of the first reports on the congener-specific V/P
partitioning in the ambient air under variable average ambient temperatures. Although they
could find no seasonal effect on the total concentrations of CDD/Fs, seasonal change in
temperature did affect the V/P ratio. It was noted that during the warm summer months the V/P
ratio was as great as 2:1, and during the cold winter months the V/P ratio could be <0.5. Thus, at
warm temperatures most of the lower chlorinated congeners, e.g., mono through penta-
chlorinated CDD/Fs, were mostly found in the vapor phase and the hexa - octachlorinated
congeners were mostly particulate-bound. The colder winter temperatures produced the effect of
causing the lower chlorinated species to partition more onto airborne particles. The higher
chlorinated congeners, e.g., hexa-, hepta-, and octa-CDD/Fs, mostly were found to be particle-
bound at both the warm and cold temperatures. These quantitative results of the V/P ratio of
individual congeners at three ambient air temperatures (3° C, 16 - 20° C, and >28° C) was again
reported by Kites (1991), as shown in Table 3-4. Through these analyses, Eitzer and Kites
(1989) and Kites (1991) found two dependant variables controlled the V/P ratio in ambient air:
1. the ambient air temperature; and 2. the vapor pressures of the CDD/F congeners. The authors
concluded that because the lower chlorinated compounds have higher vapor pressures, they will
be found mostly in the vapor phase, and because the higher chlorinated congeners have lower
vapor pressures, they will prevail in the ambient air bound to particulate matter.
Wagel, et al. (1989) reported on the performance of the General Metals Works PS-1
sampler for the collection and retention of CDD/Fs while sampling ambient air. This sampler
configuration consists of a quartz glass fiber filter followed by a polyurethane foam (PUF) plug,
and the investigators added an XAD-2 resin cartridge after the PUF. The addition of the XAD
was a check on whether breakthrough of any CDD/F congeners occurred from the PUF during
sampling. The PS-1 is the sampler most often used in the U.S. to quantify CDD/Fs in air under
ambient conditions. The protocol of this research was to use two samplers co-located. The
particulate filter of one sampler was spiked with 13C12-labelled CDD/F congeners while the
second sampler was used to provide background measurements of native (non-labelled) CDD/Fs.
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Both units were operated to sample ambient air for 24-hours. The average ambient temperature
during the sampling period was 24° C. Following the sampling the filter and PUF were removed
and extracted according to published procedures (Wagel, et al., 1989). Performance of the PS-1
sampler was reported as percent recovery of the labeled standards initially spiked onto the
particulate filter. The percent recovery was calculated by subtracting the background
contributions from the total detected spike concentration and dividing by the concentration of the
labeled standard initially added to the filter. The percent recoveries were reported in a range of
from 85% to 124%, with an average recovery of 102%. This indicated a high degree of
reliability in collecting and retaining CDD/Fs in the sampler during the 24-hr sampling period.
A second series of experiments were conducted to investigate the distribution of CDD/Fs
within the sampling apparatus, e.g., the particulate filter versus the PUF plug, by extracting and
analyzing the filter and PUF separately. Subject to the caveats previously discussed, the
investigators made observations regarding the V/P ratio of CDD/F congeners. It was observed
that CDD/Fs having 7-8 chlorines were mostly detected in the particulate filter, and lower
chlorinated species were mostly detected in the PUF. Wagel, et al. (1989) suggested that it was
possible that the lower chlorinated congeners volatilized from the particulate filter (somewhat
affected by the rate of flow of the sampled air volume), and then were retained by the PUF.
Furthermore, Wagel, et al. (1989) warned that if results of separately analyzing the filter and
PUF are used to derive a vapor phase and particle phase partitioning of the CDD/Fs under
ambient conditions, then this may give erroneously high estimates of the amount present in vapor
phase.
Harless and Lewis (1992) have quantitatively evaluated the performance of the General
Metals Works PS-1 sampler for the trace-level measurement of CDD/Fs in ambient air, adding to
the growing evidence that results are actual measurements and not an artifact of the sampling
method. In this study, three samplers were used in the same general vicinity, and were operated
for a 24-hour period until an air volume of 350 - 400 m3 had passed through the system. The
quartz glass fiber particulate filters of two of the samplers were then spiked with 13 C12 labeled
CDD/F congener with a known concentration after the 24-hour sampling period. The three
samplers were then operated another 24-hours. The samplers were then shut down, and the
filters and PUF plugs were removed and extracted and analyzed for CDD/Fs separately
according to prescribed procedures. A separate series of experiments involved precleaning the
glass fiber particulate filters, and adding the isotopically labeled CDD/F spike to the filter prior
to sampling for seven days until about 2660 m3 of ambient air had been sampled. Results of this
study confirmed the accuracy and reliability of the PS-1 sampler for collecting and retaining
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CDD/Fs at sub-ppt concentrations in ambient air. Performance was defined as the percent of the
initial concentration of the labelled isotope recovered in the sampling apparatus following the
operation over the predetermined sampling period. The average efficiency of recovery of the 0.8
ng 13C12-1,2,3,4,-TCDD isotope that was spiked onto the filter prior to sampling was 91%, and
similar efficiencies were observed for the recovery of the other labeled CDD/Fs. Additionally,
Harless and Lewis (1992) used the spiking system to observe the distribution of CDD/Fs in the
filter and the PUF after sampling 400 m3 of ambient air. It was observed that most of the hepta-,
and octa-CDD/Fs were retained by the glass fiber filter, indicating that these compounds were
retained and not blown off the filter, and most of the tetra-, penta-, and hexa-CDD/Fs volatilized
and were collected by the PUF plug. When partitioning was observed on a congener-specific
basis, significant differences were observed in the V/P ratio, as shown in Table 3-4.
Hunt and Maisel (1990) reported on the ambient air measurement of CDD/Fs in a
northeastern U.S. urban coastal environment during the fall and winter seasons. Isomer-specific
sampling was conducted with the General Metal Works PS-1 sampler in and around Bridgeport,
Connecticut from November, 1987 through January, 1988. Nine sampling sessions consisting of
a total of 43 ambient air samples were taken in this study. Each sampling session was conducted
either over a 24-hour or 72-hour period until about 350 m3 and 600 m3 of air volume had passed
through the sampler. Hunt and Maisel (1990) reported on the typical vapor phase/particle bound
partitioning of individual congeners during cold ambient air temperatures. The V/P ratio was
based on the results of separately analyzing the PUF plugs and the glass fiber particulate filters
for the presence of CDD/Fs. From these data, the investigators concluded that greater than 92%
of all the congeners of CDD/Fs were particulate bound (operationally defined as detected in the
particulate filter). The 2,3,7,8-TCDD isomer was not detected in any of the 43 collected samples
(reported limit of detection was 5-20 fg/m3). The particulate bound distribution (reported as a
percent of the detected concentration) for some of the other congeners were as follows: 2,3,7,8-
TCDF = 93%; 1,2,3,7,8-PeCDF = 94%; 2,3,4,7,8-PeCDF = 99%; 1,2,3,4,7,8-HxCDF = 97%;
1,2,3,4,6,7,8-HpCDF = 100%; 1,2,3,6,7,8-HxCDD = 96%; and the 1,2,3,4,6,7,8-HpCDD = 92%.
The vapor phase/ particle bound distribution observed in this study is probably controlled by the
cold January temperatures from which these observations were derived (average temperature = -
5°C).
At a later date, Hunt and Maisel (1992) conducted ambient air monitoring of CDD/Fs in
multiple locations in the warm climate of southern California for the State of California Air
Resources Board (CARB). Ambient air samplers, e.g., the General Metal Works PS-1 sampler,
were primarily placed in areas of high population density that contained known combustion
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sources of CDD/Fs, but sites were also sampled that were considered removed from the
influences of any local sources. The purpose of the study was to evaluate the congener-specific
spacial distribution of CDD/Fs in ambient air near environmental sources of the compounds, and
in remote locations, in order to provide a baseline to evaluate population exposures within the
region. Monitoring sites were established at eight locations in the South Coast Air Basin in and
around the city of Los Angeles. Nine discrete sample sets were collected from December, 1987
through March, 1989. The authors defined a sample set as consisting of five to seven stations at
which one or two co-located samplers were operated. Microscale meteorological data was
collected during sampling to include wind speed, wind direction, barometric pressure, and
temperature. One sampling site was chosen to investigate the distribution of CDD/Fs in ambient
air where average ambient temperatures ranged from 16-20°C. This was done by the usual
procedure of separately analyzing the filter and the PUF and making the assumption that what is
detected in the glass fiber filter is particulate bound, and what is trapped in the PUF is in vapor
phase. The authors noted that under these conditions, the V/P partitioning is operationally
defined by the ambient air sampling system, and therefore may not be a true indication of the
partitioning in the atmosphere. The majority of the hexa through octa CDD/F congeners were
detected in the filter, and the authors observed that they were mainly associated with particulate
matter. The authors found these observations were consistent with the V/P ratio observed by
Eitzer and Kites (1989) in warm climate conditions. In addition, the authors noted that these
observations give further evidence that vapor pressures of the specific CDD/F compounds and
ambient air temperatures strongly influence the V/P partitioning. Therefore the tetra- and penta-
CDD/Fs are expected to predominate in vapor phase during warm seasons. However, during the
cold temperatures of the winter season these congeners are expected to be primarily associated
with particulate matter in the ambient air.
Bobet, et. al. (1990) reported the results of an ambient air monitoring network operated
by Environment Canada to temporally measure CDD/Fs in the ambient air in southwestern
Ontario, Canada. The intent of the study was to monitor possible environmental impacts of a
large refuse-derived fuel municipal waste combustor operational in the City of Detroit,
Michigan. The ambient air monitoring network consisted on two stations, one in Windsor,
Ontario, and the other located in the Walpol Island Indian Reservation 18 km to the northeast of
Windsor. The former site was considered in an urban area near the expected point of maximum
impact from the stack emissions from the MWC, and the other site was considered rural, and
away from the influence of any stationary combustion source. CDD/F samples were collected
once every 24 days using an high-volume ambient air sampler consisting of a Teflon-coated
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glass fiber particulate filter and a PUF adsorbent trap. Ambient air was sampled over a 24-hour
period from July, 1987 to August, 1988 with a total sample volume of 800 - 1000 m3 of air.
From August on, the samplers were operated over a 48-hour period, and 1600 - 2000 m3 of air
passed through the sampler. Mean total concentrations of CDD/Fs were compared between the
urban and rural sites, and Bobet observed that concentrations measured at the urban site were 4 -
20 times greater than at the rural site. Additionally, the V/P partitioning of CDD/Fs (as
operationally defined by detection in the PUF verses detection in the filter) was investigated at
both sampling stations. Bobet stated that the V/P may be influenced by "blow-off of particulate
from the filter to the PUF, and/or the passage of particulate matter <0.1 microns from the filter to
the PUF, and if this is the case, then the vapor phase partitioning may be too great as interpreted
by the method. Under these circumstances the authors suggested that the V/P partitioning should
be considered as roughly representative of the vapor/particulate phases in the ambient air. On a
total concentration basis, and on a total of 12 separate ambient air samples, the investigators
found the following average percent vapor phase versus percent particle phase partitioning of the
CDD/F homologues at the Windsor, Ontario station: TCDD = not detected; PeCDD = 100% V/
0% P; HxCDD = 35% V/ 65% P; HpCDD = 18% V/ 82% P; OCDD = 0% V/100% P; TCDF =
80% V/20% P; PeCDF = 29% V/ 71% P; HxCDF = 0% V/100% P; HpCDF = 0% V/ 100% P;
OCDF = 0% V/ 100% P. At the rural Walpole Island station, no TCDD, PeCDD or TCDF -
OCDF were detected in any of the 5 separate ambient air samples. All of the detected HxCDD,
HpCDD and OCDD was found in the particulate filter indicating a V/P distribution of 0% V/
100% P for these compounds. The authors did not report the average ambient air temperature at
the two stations.
Welsch-Paulsch et al. (1995) presented results of an experiment in Bayreuth, Germany in
which grass was grown in a greenhouse and outdoors on soils having different concentrations of
CDD/Fs. The purpose of the experiment was to determine the pathways by which these
compounds accumulate in the grass. The principal finding of this study was that dry gaseous
deposition, rather than particle deposition or soil-to-plant transfer, explained the concentrations
of CDD/Fs in the grass. A subset of these data included measurements of CDD/Fs in the particle
and gas phases using a glass fiber filter - XAD resin sampler. Samples were collected over two-
week integration times during July - August 1991 when the average air temperature was 18°C.
The particulate percentages determined in the study showed a predominance of tetra- and penta-
CDD/Fs in the gas phase, approximately equal percentages of the hexa-CDD/Fs in the gas phase
and associated with particles, and most of the hepta- and octa-CDD/Fs in the particle phase.
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3.2.4.4. Discussion of the Vapor/Particle Partitioning in Ambient Air Sampling Studies
The studies that have been reviewed here indicate the following:
• The high-volume ambient air sampler consisting of a glass fiber particulate filter and
polyurethane foam absorbent trap is a reliable method for the collection and retention of CDD/Fs
in ambient air.
• Current analytical methods assure detection limits, on a congener specific basis, of
about 1-10 fg/m3.
• Experiments involving the recovery of isotopically labeled CDD/Fs within the sampler
after 24-hours operation indicate that the sampler does not create artifacts representative of either
sample losses or the synthesis of dioxin.
• Because the sampler is not artificially heated or cooled, but is allowed to operate at
existing ambient air temperatures during sampling sessions, the method can be used to imply the
vapor phase and particle bound partitioning of CDD/Fs in ambient air. This is accomplished by
separately extracting and analyzing the glass fiber filter and the polyurethane foam for the
presence of CDD/F congeners.
• However, the V/P ratio interpreted from these results is operationally defined. This
will only give an approximate indication of the V/P ratio since mass transfer between the
particulate matter on the filter and the vapor trap cannot be ruled out. The glass fiber filter will
collect particles > 0.1 microns in diameter, and therefore it is possible that aerosol particles with
smaller diameters will pass through the filter and be trapped in the polyurethane foam plug. If
this is the case, the percent observed in vapor phase will be overestimated. The method involves
ambient air sampling at a relatively high sample volume, around 300-400 m3 of air, over a 24-
hour period. It is possible that a portion of the CDD/Fs that are sorbed to particulate matter
captured by the filter may be volatilized by changes in ambient temperature, and carried with the
air flow to the PUF sorbent trap (blow-off effect). If this were to occur, the observed vapor-
phase fraction of the CDD/Fs would be an over-estimate (or equivalently, the observed
particulate fraction would be underestimated). Unfortunately there are no empirical data that
have demonstrated that any of these effects may actually occur.
3.2.4.5. Junge-Pankow Model of Particle/Gas Distribution in Ambient Air
A relationship first proposed by Junge (1977) and later reviewed and critically evaluated
by Pankow (1987) is the most widely used model for estimating the adsorption of semivolatile
compounds to aerosols:
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e
P\
c 0
(3-3)
where:
P°L
0
C
fraction of the compound adsorbed to aerosol particles
saturation liquid phase vapor pressure of the pure compound at ambient
temperature, Pa
the particle surface area per unit volume of air, cm2 aerosol/cm3 air
a constant which is related to the difference between the heat of desorption
from the particle surface, Qd, and the heat of vaporization of the
compound, Qv. The value of c is often estimated at 17.2 Pa-cm
Pankow (1987) argued that different values of Qd - Qv (and therefore c) may be appropriate for
different classes of compounds.
Although Junge (1977) did not specify the physical state of the sorbing compound in
Equation (3-3), field and laboratory studies of the particle/gas distribution of PAHs and
organochlorine compounds indicate that the process is better described by using the vapor
pressure of the subcooled liquid rather than the solid-phase vapor pressure (p°s) (Bidleman et al.,
1986; Gotham and Bidleman, 1992; Foreman and Bidleman, 1987). The two vapor pressures
are related by:
In
P°s
RT
(3-4)
where:
P°L
P°S
ASf
T
Am
T
R
liquid sub-cooled vapor pressure of the pure compund, Pa
crystalline solid phase vapor pressure of the pure compound, Pa
the entropy of fusion, J/mol-K
the melting point of the compound, K
is the ambient temperature, K
ideal gas constant, 8.314 J/mol-K.
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Values of ASf have been summarized for CDD/Fs (Rordorf, 1989) and other semivolatile
compounds (Hinckley et al., 1990). In the absence of an experimental value, an average AS/R =
6.79 is often used (Hinckley et al., 1990), and it is used in this assessment. Liquid-phase vapor
pressures have also been estimated for PCBs, PAHs, CDD/Fs and organochlorine pesticides by
capillary gas chromatography (Falconer and Bidleman, 1994; Eitzer and Kites, 1988; Eitzer and
Kites, 1989; Hinckley et al., 1990; Yamasaki et al., 1984). The hypothesis that p°L controls
sorption to aerosols is especially significant for CDD/Fs, as many of these compounds have high
melting points and thus large differences between p°L and p°s.
Estimates of 6 are given by Bidleman (1988), based on a study by Whitby (1978) of the
size distribution of accumulation mode aerosols. Whitby also estimated the average total
volume of particles per unit volume of air (VT = cm3 aerosol/cm3 air). Values of 6 and VT are
given in Table 3-5. From VT and an assumed particle density of 1.4 g/cm3, the average TSP
concentrations in urban and average background air are 98 and 42 |ig/m3. These can be
compared to average monitored TSP concentrations of 79 and 30 |ig/m3 in 46 U.S. cities and 20
rural locations in 1975 (Shah et al., 1986). Similar calculations using values of 6 yield estimates
of the aerosol specific surface area (Atsp) of 11 m2/g in urban air and 3.6 m2/g in average
background air (Bidleman, 1988). Measurements of Atsp from particles collected on glass fiber
filters were 1.9-3.1 m2/g in Pittsburgh, Pennsylvania (Corn et al., 1971) and 2.3 - 8.7 m2/g in
Portland, Oregon (Sheffield and Pankow, 1994). The latter authors noted that particles tended to
agglomerate to a greater extent on teflon membrane filters than on glass fiber filters, and that
experimentally determined specific surface areas were higher on the glass fiber filters. This
suggests that values of Atsp measured on filtered particles may be biased on the low side,
although more data are needed in this regard.
The particulate fraction can also be expressed by:
CD ( TSP
d> = - -
Cg + Cp ( TSP
where:
cj) = fraction of the compound adsorbed to aerosol particles
Cp = the concentration of semivolatile compounds associated with aerosols,
ng/|ig particles
Cg = the gas-phase concentration, ng/m3
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TSP = the total suspended particle concentration, |ig/m3
Equation (3-5) is a general relationship that applies to any experimental or model estimate of Cg
and Cp. Combining Equations (3-3) and (3-5) yields:
C c Q
Log -JL = LogKp = Log— -Log p\ (3-6)
Here and in other work (Falconer et al., 1995; Kamens et al., 1995; Hart and Pankow, 1994;
Pankow and Bidleman, 1992) Cp/Cg is referred to as the particle/gas partition coefficient, Kp
(m3/|ig). Its inverse, Cg/Cp = l/Kp, has also been used for these correlations (Gotham and
Bidleman, 1992). According to Equation (3-6), the expected slope of log Kp vs. log p°L is -1
and the intercept is related to the specific surface area of the aerosol Atsp (m2/g) = 1080/TSP.
Plots of log Kp vs. log p°L for partitioning data obtained with filtration air samplers are usually
well correlated and follow the general relationship:
Log Kp = m Log p°L + b (3.7)
It is often the case that m * -1 because of kinetic limitations and/or sampling artifacts. In these
situations the intercept b is partially dependent on the slope and cannot be used to estimate Q
(Pankow and Bidleman, 1992).
3.2.4.6. Modeling the Vapor/Particle (V/P) Distribution ofCDD/Fs
A portion of the semivolatile compounds found in ambient air appears to be freely
exchangeable between the particulate and gaseous phases. A second portion, the "non-
exchangeable" fraction, may be irreversibly sorbed or occluded by the aerosols and not in
equilibrium with the corresponding gas phase (Bidleman, 1988; Pankow, 1988; Pankow and
Bidleman, 1991, 1992). In this procedure methodology document, it is assumed that all
compounds emitted from combustion sources are freely exchangeable unless information exists
otherwise.
The first step in the modeling of the V/P distribution is to determine the sub-cooled liquid
vapor pressure for the CDD/Fs. Eitzer and Kites (1988,1989) used a capillary GC method to
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determine liquid-phase vapor pressures for 63 CDD/F congeners. These measured values will be
used to develop the V/P partitioning for the dioxin-like compounds in this assessment. The p°L
given in these references are measurements at 25°C. This section will outline a procedure for
calculating the p°L at different temperatures. The V/P calculations of this assessment assume an
ambient temperature of 20 °C, necessitating a conversion of p°L from 25 to 20°C. Finally, this
section will also show the calculation of the p°L based on the crystalline solid phase vapor
pressures, p°s. These calculated p°L will be compared against the measured p°L.
Table 3-6 lists the crystalline solid vapor pressure, p°s, at 25°C for the seventeen CDD/F
congeners having dioxin-like toxicity. These values of p°s were judged as the most appropriate
from available reportings in the literature in Chapter 2 of Volume I. They were used in
conjunction with Equation (3-4) above to calculate p°L at 25°C, also shown in Table 3-6.
Finally, Table 3-6 gives the p°L values at 25°C, which were reported in Eitzer and Kites (1988,
1989).
As seen in Table 3-6, the calculated p°L were generally less the measured p°L. On the
average, the calculated p°L was 60% of the measured p°L. This assessment will use the measured
p°L, which is thought to be preferable to using the modeled values.
These vapor pressures can be corrected to ambient, or any, temperature by the following
procedure. The relationship between the liquid sub-cooled vapor pressure and temperature (°K)
is (Hinckley, et al., 1990):
- Q
Logp°r = — + b (3-8)
L 2.303 R T ^ '
where:
p°L = sub-cooled liquid vapor pressure of the pure compund, Pa
Qv = the latent heat of vaporization, J/mol
R = ideal gas constant, 8.314 J/mol-K
T = temperature, K
b = intercept (related to entropy of vaporization)
The capillary GC method of Eitzer and Kites (1988) provides the temperature coefficient of
vapor pressure, expressed by them as the ratio of the heats of vaporization for CDD/Fs to p,p'-
DDT: Qv (CDD/Fs)/Qv (DDT). The authors determined these ratios for 14 CDD/F congeners,
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including 13 of the ones listed in Table 3-6. For the remaining 4 congeners, it was assumed that
Qv (CDD/Fs)/Qv (DDT) were the same as for other members of the same homolog group (e.g.,
the ratio for 2,3,7,8-TCDD was the same as for 1,2,3,4-TCDD). OCDF was assumed to have the
same ratio as OCDD. The value of -Qv/2.303R for p,p'-DDT is -4640 (Hinckley, et al., 1990).
This was used with the information given by Eitzer and Kites (1988) to estimate the temperature
coefficients of vapor pressure for the CDD/Fs. For example, Eitzer and Kites (1988) give the
ratio Qv (2,3,7,8-TCDF)/Qv(DDT) = 0.947. Thus the value of-Qv/2.303R for 2,3,7,8-TCDF = -
4640(0.947) = -4394. This quantity can be used as the slope of Equation (3-6) for this congener:
log p°L (2,3,7,8-TCDF) = -4394/T = b.
The next step in the procedure is to estimate the constant, b, of Equation (3-8). This can
be accomplished given the slope (as just calculated), and a known (or calculated) p°L, and the
temperature associated with that p°L. For example, the measured p°L of 2378-TCDF at 25 °C, is
1.23*1Q-4 Pa (Eitzer and Kites, 1988). Substituting -4394, T = 298 °K, and log p°L = -3.910 into
Equation (3-6) yields a value for b, 10.83. Therefore, one now has an equation for the vapor
pressure of 2378-TCDF as a function of temperature: Log p°L (2,3,7,8-TCDF) = 10.83 - 4394/T.
In this manner, values of b were derived for all the 17 dioxin-like congeners, and these are
shown in Table 3-6. It is importantly noted that all these b were developed from the measured
values of the p°L, not the modeled values calculated from the crystalline solid vapor pressure, p°s.
Therefore, extrapolations to 20 °C are defined as extrapolations from the measured and not the
modeled p°L.
At this point, it is now possible to calculate the p°L as a function of temperature for all the
congeners. For estimating the vapor particle partitioning of dioxins, it was assumed that the
ambient temperature was 20 °C. The final column in Table 3-6 lists the calculated p°L at 20°C.
Comparing the p°L at 25 and 20°C, reducing the temperature by 5°C causes the p°L to be reduced
by about a factor of 2.
The Junge-Pankow model, Equation (3-3), was used to estimate paniculate percentages
for airsheds characterized as clean continental background, average background, background
plus local sources, and urban. The particle surface area parameters (6-values) representative of
each airshed were those in Table 3-5. Liquid sub-cooled vapor pressures at 20°C, as derived
above, were used, and c = 17.2 Pa-cm was assumed. As an example, the paniculate fraction of
1,2,3,7,8-PCDF at 20°C in average background air (0 = 1.5 x 10'6) is calculated as follows:
a) p°L = 1.98*10-5 (from Table 3-6)
b) c0 = 17.2(1.5 x ID'6) = 2.58x10
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c) cj) = c0/(p°L + c0) = 2.58 xlO-5/(1.98*10-5 + 2.58 xlO'5) = 0.57
Table 3-7 shows the percentage of CDD/Fs predicted to be in the particle phase at 20°C for the
seventeen congeners having dioxin-like toxicity.
3.2.4.7. Comparison of Measured and Modeled Vapor/Particle Distributions for CDD/Fs
Two factors complicate the comparison of field measurements and predictions of the
Junge-Pankow model. One is the problem of sampling artifacts that are inherent with filter-
sorbent devices. Parallel collections of PAHs made with filtration samplers and denuders
suggest that the more volatile members of a chemical class will be partially lost from the
particles on the filter during sampling - the "blow-off" effect (Gundel et al., 1995; Lewis et al.,
1991; Subramanyam et al., 1994). A second difficulty is that very few field measurements of
both particulate and gaseous CDD/Fs have been made and presented in such a way that they can
be related to model predictions.
The study by Eitzer and Kites (1989) is the best data set for particle/gas partitioning,
since a large number of CDD/F congeners were measured on both the glass fiber filter and PUF
trap. In their paper, the particle/gas ratios of individual congeners were reported as averages for
the entire year, and the average ratios were related to the liquid-phase vapor pressures of the
congeners at 25°C. Although their study showed conclusively that vapor pressure controls the
distribution of CDD/Fs between the aerosol and gas phases, there is a problem with their method
of data treatment. The particle/gas ratio varied greatly with temperature and the average ratio for
the year may or may not have been represented by the situation at 25°C.
In his thesis, Eitzer (1989) also presented the particle/gas distributions as functions of the
sampling temperature through the equation:
A
Log K = - ^ - + b (3-9)
p 2.303 R T
where the heat of desorption, Qd, of individual congeners and the intercept b were estimated by
plotting Kp vs. 1/T for all the sampling events. In this form the data allow the particle/gas
distributions to be related to event-to-event differences in ambient air temperature.
To relate Eitzer's (1989) data to vapor pressure, his parameters of Equation (3-9) were
used to calculate log Kp for individual congeners at 20° C. The Kp values at 20°C were then
plotted against the liquid-phase vapor pressures at 20°C according to Equation (3-9) to yield:
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Log Kp = - 0.719 Log p°L - 5.53 r2 = 0.962 (3-10)
The plot of Equation (3-10) for the 17 dioxin-like CDD/Fs is shown in Figure 3-2a. As seen,
there is an excellent correlation between log Kp and log p°L. It should be noted that although
Equation (3-10) was obtained from values of Kp and p°L at 20°C, it applies at any temperature
since the variation in partitioning with temperature is accounted for by the temperature effect on
vapor pressure. The particulate fraction is related to Kp by:
K [ TSP ]
- - (3-11}
{ }
Kp [ TSP
which follows from Equation (3-5) and the definition of Kp. Comparison of the Eitzer-Hites
partitioning data to predictions of the model was done as follows:
a) Field estimates of the particulate fraction ($) were calculated from values of Kp at
20°C by Equation (3-11), scaling to TSP = 60 jig/m3 (corresponding to, "background plus
local sources", Table 3-5).
b) Model estimates were calculated from Equation (3-3), using 0 = 3.5 x 10"6
(corresponding to, "background plus local sources", Table 3-5).
Plots of measured and modeled particulate percentages vs. log p°Lat 20°C are shown in Figure 3-
2b. Although the Eitzer-Hites partitioning data show a strong dependence on vapor pressure,
their measurements fall below the model predictions.
A similar approach was used to evaluate particle/gas distributions for a subset of the
other ambient air investigations reviewed earlier. The partitioning data of Kites (1991), Hunt
and Maisel (1990, 1992) and Welsch-Paulsch et al. (1995) are given on a homolog basis in Table
3-4. However, because these studies were done at different ambient temperatures, the particulate
percentages cannot be directly compared. Therefore, all field data had to be adjusted to a
common temperature of 20°C by establishing relationships between the particle/gas distributions
and vapor pressure:
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a) Values of Kp were calculated from the parti culate percentages in Table 3-4 for CDD/Fs
which were found to a measurable extent in both the particle and gas phases (i.e., Kp
could not be calculated for the 0% and 100% parti culate data points). Since these studies
were carried out in semi-rural locations, the "background plus local sources" air regime
seemed to be the most appropriate, and a value of TSP = 60 |ig/m3 was assumed in
calculating Kp by Equation (3-1 1).
b) The resulting log Kp values were examined for outliers (only one point was omitted,
from the Kites (1991) data set), then regressed against log p°L at the ambient sampling
temperature (Equations 3-7 and 3-8). The vapor pressure of each homologue group was
taken to be the central value for the congeners in that group. Regression parameters of
Equation (3-7) are given in Table 3-8 for the various field studies.
c) Values of Kp for each homologue group at 20°C were calculated from the vapor
pressure at 20° C, using the regression parameters in Table 3-8.
d) Parti culate percentages at 20°C were calculated from Kp by Equation (3-11), assuming
TSP = 60
Thus, although the actual field measurements were done under a variety of temperatures,
they were normalized to 20°C for comparison. Figure 3-3 shows the particulate percentages
from four field studies in comparison to those predicted by the Junge-Pankow model for
Whitby's "background plus local sources" air regime. These comparisons are also given in Table
3-9. All of the field data fall substantially below the model curve. For example, the particle-
bound percentages of tetrachlorodioxins and furans, which are predicted to be 43-65% at 20°,
averaged only 11-18% with the filter-sorb ent sampler.
3.2.4.8. Discussion of Monitored and Modeled Results for CDD/Fs
The above comparisons show substantial differences between filtration sampling and the
Junge-Pankow model for estimating particulate percentages of CDD/Fs. Reasons for these
discrepancies may be related to both sampling artifacts and model uncertainties. Blow-off losses
during collection are likely to reduce the filter-retained fraction. This artifact is expected to be
the most serious for long sampling times, especially if the day -to-night temperature changes are
large. However, the Welsch-Paulsch samples, which were collected over two-week integration
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times, show only slightly lower particulate values than those of other workers which were
obtained over 24-36 h sampling periods. Aside from the Eitzer-Hites data, the measurements of
CDD/F vapor/particle distribution in Table 3-4 were based on only a few samples and it is
difficult to judge their representativeness. Daily measurements of phase distributions for PAHs
and PCBs in Chicago show up to an order of magnitude variation in Kp values, even when
normalized for vapor pressure (Gotham and Bidleman, 1995). These variations may be caused
by sampling artifacts and kinetic effects, and also by daily differences in aerosol properties.
Limitations of the Junge-Pankow model include uncertainties in the parameters c and Q.
Pankow (1987) suggested that optimal values of c might be chosen for different classes of
compounds. His reasoning was that the excess heat of desorption (Qd-Qv) appeared to be a
smaller term for organochlorine compounds than for PAHs. However, this conclusion was based
on the very limited field data available at the time Pankow's article was written. It is difficult to
extract Qd information from field data, by plots of Equation (3-7), because such plots involve
combining particle/gas distributions for individual air samples which are collected over a range
of ambient temperatures, humidities and aerosol properties. Confidence intervals around the Qd
values obtained from such plots are typically large (Pankow, 1991). Moreover, significant
differences in Qd values are obtained by analyzing Equation (3-7) plots by simple linear
regression versus regression assuming a constant y-intercept for all compounds (Pankow, 1991).
A better approach is to determine Qd in the laboratory by measuring Kp under equilibrium
conditions and at different temperatures, but few of these experiments have been carried out
(Cotham and Bidleman, 1992; Falconer and Bidleman, 1994; Storey and Pankow, 1992). The
authors of this assessment believe that there is insufficient information at this time to warrant
recommending different values of the c-parameter for different compound classes.
The 6-parameter is also subject to uncertainty. It is likely that Whitby's values of 6 do
not reflect the true surface area distribution, since they were based on the average size spectrum
of aerosols and assumed spherical particles. Other limitations of the model are the inability to
consider the kinetics of adsorption/desorption (Kamens et al., 1995; Rounds, et al., 1993) and
humidity effects (Pankow et al., 1993).
In conclusion, neither the filter-sorbent sampler nor the Junge-Pankow model necessarily
give the "correct" vapor/particle distributions. Evidence based on limited field data suggest that
the model overestimates the parti culate fraction of CDD/Fs relative to the filtration sampler, but
it can just as well be stated that the sampler underestimates the parti culate fraction relative to the
model. Further work needed to improve the state of knowledge of CDD/F partitioning between
the aerosol and gas phases includes:
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a) Comparative monitoring with filter-sorbent samplers and other speciating devices such as
denuders (Coutant et al., 1992; Eatough et al., 1993; Krieger and Kites, 1994; Lane et al., 1988;
Lewis et al., 1991; Gundel et al., 1995; Subramanyam et al., 1994; Tang et al., 1994) and
diffusion separators (Hornbuckle et al.,1995; Turpin et al., 1993).
b) Laboratory experiments to investigate the kinetics and thermodynamics of the sorption of
CDD/Fs and other semivolatile compounds sorption aerosols.
c) Improvements in modeling particle/gas distributions in ambient air.
Despite the differences in the monitored and modeled results, the Junge-Pankow model
is the recommended approach for estimating the particle/gas distribution of CDD/Fs at the
present time. In addition to reproducing the general trend in partitioning with vapor pressure
(Figures 3-2 and 3-3), the Junge-Pankow equation was used in an air-to-beef model validation
which is described in Chapter 7. That exercise used the Junge-Pankow model for partitioning
dioxins in the air. A key finding of that work was that the transfer of vapor-phase dioxins to
plants and subsequently to animals dominated the terrestrial food chain. Also, vapor/particle
partition data on other semi-volatile organic compounds are compared in Chapter 7 to model
predictions, and the match for these compounds is, in most cases (especially for PAHs), superior
to that of the CDD/Fs.
3.2.4.9. Discussion of Vapor/Particle Partitioning
This subsection has reviewed stack testing data, ambient air sampling data, and theory
rooted in basic physical chemistry that either imply, directly deduce or theoretically calculate the
V/P partitioning in the ambient air. From this review it is generally concluded that:
1. Although the sampling methods in use today to characterize stack emissions from
combustion sources accurately determine stack gas concentrations of CDD/Fs, these methods do
not provide a credible basis for assuming the vapor phase and particle bound partitioning at the
point of release. There is no consistent pattern to the interpretation of V/P based on where the
CDD/F segregates in the instrument, e.g., the glass fiber filter or the XAD resin. Factors that
mostly contributing to this are: (a) The relatively long residence time spent in situ during the
sampling of stack gases; (b) The fact that the particulate filter housing is kept at a constant
temperature, and, (c) The fact that a condensing section (consisting of glass tubing surrounded
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by an ice or chilled water bath) is usually located between the particulate trap and the vapor trap
to force condensation of vapor-phase organic constituents in the gases.
2. On the other hand, the ambient air sampling methods do give an approximate
indication of the V/P ratio that seems to be responsive to changes in temperature, and degree of
chlorination of the CDD/Fs. This is in accordance with what would be expected from their
individual vapor pressures. There is no artificial heating or cooling of any component of the
sampler. The sampler is exposed to actual temperature, pressure, and humidity of the ambient
air. This removes the possibility that the vapor phase-particle bound partitioning, operationally
defined as the compound segregating to the particulate trap and vapor trap, is actually an artifact
induced by artificial heating and cooling within the system. Therefore the methods present a
realistic picture of partitioning under variable ambient conditions. However, the method has
certain limitations that currently prevent deriving a true measurement of V/P partitioning in the
ambient air. Among these limitations are:
a. The glass fiber filter designed to capture and retain particulate matter has filter pours
down to 0.1 |im diameter. Particles less than this diameter will pass through the filter and be
retained in the polyurethane foam vapor trap downstream. If this is the case, the amount of
CDD/Fs observed to be particle bound would be underestimated, and the amount observed to be
in vapor phase would be overestimated.
b. The relatively high sampling volume passed through the system (200 to 400 m3 of air
per 24 hours) may redistribute the more volatile congeners from the filter to the absorbent trap
by a process known as 'blow-off.
3. Until sampling methods are improved and modified such that they give results that
indicate the true V/P ratio of CDD/Fs in ambient air, the theoretical construct described by
Bidleman (1988; and detailed above) is used to calculate the V/P ratio for purposes of air
dispersion and deposition modeling of emissions from the hypothetical case demonstrated in
Chapter 5. Key advantages to the theoretical approach are that the theoretical construct relies on
current adsorption theory, considers the molecular weight and the degree of halogenation of the
congeners, uses the boiling points and vapor pressures of the congeners, and uses the availability
of surface area for adsorption of atmospheric particles that correspond to a variety of ambient air
shed classifications having variable particulate matter densities.
3.2.5. Estimation of the Concentration of Dioxin-Like Compounds in Incineration Ash
The ash that is collected by the particulate matter control device preceding the stack is
known conventionally as fly ash. Fly ash is the airborne combustion residue from burning the
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fuel. Bottom ash is the ash residue that results from the combustion of the organic solids within
the combustion chamber, and usually is collected below a grate system used to convey
combustible fuels into the fire zone, or is collected at the bottom of the combustion chamber. In
general, there are many factors that may influence the formation of particulate matter known as
fly ash from the incineration of organic wastes. Among these factors are: the heating value of
the incinerated material (BTU/kg), the percent moisture in the fuel, the furnace temperature and
combustion efficiency, and the efficiency of parti culate matter capture by the air pollution
control device (Brunner, 1984; OTA, 1989). Fly ash, and not the bottom ash, contains most, if
not all, the dioxin-like congeners. This can be explained by the synthesis of dioxin that occurs
on the reactive surface of fly ash. Therefore, the following estimation of the ash generation rate,
and the concentration of dioxin-like compounds in the ash particles, will focus solely on fly ash
to the exclusion of bottom ash. Because bottom ash is mostly free of these contaminants, and is
about 10 to 100 times the mass of fly ash, the mixing of fly ash with bottom ash will dilute the
concentration of dioxin by about a factor of 10 - 100.
Estimation of the mass of fly ash generated, and concentration of dioxin-like compounds
can be determined by the following (if no actual data exists):
1. Determine the mass of fly ash generated per day at the facility. This can be estimated
from the percent control of parti culate matter (PM) of the air pollution control device (APCD)
installed at the facility. For example, if a combustor emits 0.5 kg of particulate matter per hour
of operation, then 12 kg of PM is released from the stack in one day. If PM is controlled by
99%, then this rate of emission represents one percent of the fly ash generated by the combustion
process. The amount of fly ash that is collected by the APCD would be 100 times the amount
emitted, or 1200 kg/day.
2. Estimate the congener-specific concentration of CDD/Fs contained in the collected fly
ash. This is done by assuming that what is prevented from exiting the stack is contained in the
fly ash collected by the pollution control device. If, for example, 10 picograms CDD/F is
emitted per gram of PM from the facility per day, and the APCD reduces emissions by 99%, then
100 times more CDD/F concentration, or 1000 picograms CDD/F per gram fly ash, would be in
the collected fly ash. If the concentration of dioxin in emitted fly ash and the percent control of
dioxin are known, then the concentration of dioxin in the mass of collected fly ash can be
estimated. It is important to make such estimations in order to evaluate the potential
environmental impact of ash management practices before the operation of the facility, and to
select appropriate disposal practices to preclude future adverse conditions from arising.
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3. Now estimate total mass, including fly and bottom ash, and final concentrations. If
bottom ash mass is estimated at ten times fly ash, than the total ash generated in this example
would be 1200 + 1200*10 = 13,200 kg/day. If fly and bottom ash were mixed for disposal,
which is common, than the average concentration of the total ash would be one-tenth that
estimated for fly ash.
The hypothetical example in Chapter 5 does not assess impacts associated with ash
disposal. Section 4.3.5 of Chapter 4 describes procedures for estimating impacts from ash
disposal given ash concentrations and mass generated.
3.3. AIR DISPERSION/DEPOSITION MODELING OF THE STACK GAS
EMISSIONS OF DIOXIN-LIKE COMPOUNDS
It has been customary for EPA to use air dispersion/deposition models to estimate the
atmospheric transport, the deposition flux, and the ambient air concentrations of specific
pollutants attributable to smokestack emissions from an industrial combustion source. Air
dispersion models are mathematical constructs that approximate the physical and chemical
processes occurring in the atmosphere that directly influence the dispersion of gaseous and
particulate emissions from smokestacks of stationary combustion sources. These models are
computer programs encompassing a series of partial differential and algebraic equations to
calculate the dispersion and deposition of the emissions. Concentration and deposition isopleths
of the pollutants discharged from the stack are computed at specified distances from the
smokestack. These quantities are used to estimate the magnitude of potential exposures to the
human receptor.
Numerous dispersion/deposition models have been developed. This document focuses on
the Industrial Source Complex 3 Short Term, ISCST3, dispersion model recently developed by
EPA to provide modeling outputs useful in the analysis of wet/dry deposition and ambient air
concentrations of stack emitted contaminants in all terrain settings (EPA, 1995). The ISC3 was
developed as a general replacement to the COMPDEP model first described in EPA (1990b).
Modeling enhancements of ISCST3 include more refined small particle dry and wet deposition
algorithms than used by the COMPDEP model. The ISCST3 was used to generate the results for
the hypothetical incinerator of this assessment. However, the use of ISCST3 is this assessment
is not intended to imply that ISCST3 is the only acceptable model to use in the analysis of
ambient air concentrations, and wet and dry deposition of dioxin-like compounds.
Subsection 3.3.1 below presents an overview of the dispersion and deposition algorithms
in the ISCST3 model. Subsection 3.3.2 discusses dry deposition fluxes, including pertinent
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assumptions made in the application of the ISCST3 model for the hypothetical combustor
demonstrated in Chapter 5. Subsection 3.3.3 discusses particle size distributions for emitted
particles. Subsection 3.3.4 discusses wet deposition, again noting key assumptions for the
hypothetical combustor. Subsection 3.3.5. closes the section with guidance indicating that the
ISCST3 model should be run with two simultaneous modes of operation: one mode provides
estimates of particulate concentrations in air and wet/dry particle deposition flux; the other
mode provides for the estimation of vapor phase concentrations of dioxin-like compounds in
ambient air.
3.3.1. Basic Physical Principles Used to Estimate Atmospheric Dispersion/Deposition of
Stack Emissions
Air dispersion/deposition models mathematically simulate the basic physical processes
in the atmosphere to estimate the ground-level air concentrations and deposition flux of
contaminants known to be released from the stacks/vents of stationary combustion sources.
These processes include advection, turbulent diffusion, and removal of atmospheric particles.
Advection describes the physical movement of the air contaminants by the horizontal movement
of wind. Turbulent diffusion is the "spreading" of the emissions plume with distance from the
stack due to multi-directional fluctuations in air movement. Removal refers to mechanisms
which remove emissions from the atmosphere. This can be caused by the force of gravity
exerted on the particle mass, Brownian movement of aerosol particles, and scavenging of
particles. Scavenging is the removal of particles or vapors by precipitation.
ISCST3 contains modifications of the Industrial Source Complex model (Short-Term
version; ISCLT, as described in EPA, 1986b), and COMPLEX I to incorporate algorithms to
estimate dispersion, and resulting ambient air concentrations and wet and dry deposition flux.
COMPLEX lisa second level screening model applicable to stationary combustion sources
located in complex and rolling topography (EPA, 1986a). The ISCST model was developed by
EPA to provide estimates of air concentrations and deposition rates of the stack emissions of
contaminants from industrial sources located in varied terrain (e.g., from simple to complex
terrain). Simple and complex terrain are defined as topography that is either below or above the
effective stack height of the source (Turner, 1986). To account for pollutant deposition, the
concentration algorithms in COMPLEX 1 were replaced with those from the Multiple Point
Source Algorithm with Terrain Adjustments Including Deposition and Sedimentation (MPTER-
DS) model (Rao and Sutterfield, 1982). The MPTER-DS algorithms incorporate the gradient
transfer theory described by Rao and Sutterfield (1982), and are extensions of the traditional
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Gaussian plume algorithms. The dispersion algorithms contained in the ISCST were
incorporated to analyze ground-level receptors located below the height of the emission plume
(EPA, 1986b). For a steady-state Gaussian plume, the hourly concentrations at downwind
distance x (meters) and crosswind distance y (meters) are given by:
X
QKVD
27tji.ovo.
,
(3-12)
where:
X = the ambient air concentration of the containment, |ig/m3
Q = contaminant emissions rate, g/s
K = units conversion factor
V = vertical term - accounts for the vertical distribution of the Gaussian
plume, dimensionless
D = plume depletion term relating removal by physical or chemical
processes, dimensionless
oy, oz = standard deviation of lateral and vertical concentration distribution, m
|is = mean wind speed at release height, m/s
y = crosswind distance from source to receptor, m
ISCST3 uses the generalized Briggs (1975, 1979) equation to estimate plume-rise and downwind
dispersion as a function of wind speed and atmospheric stability. A wind-profile exponent law is
used to adjust the observed mean wind speed from the measurement height to the emission
height for the plume rise and pollutant concentration calculations. The Pasquill-Gifford curves
are used to calculate lateral and vertical plume spread (EPA, 1986a). These curves are based on
Pasquill's definitions of atmospheric stability classes, e.g., extremely unstable, moderately
unstable, slightly unstable, neutral, slightly stable, and moderately stable, that correspond to
various intensities of solar radiation and wind speeds (Seinfeld, 1986). The incorporation of
these two basic models into ISCST3 permits analysis of a source located in all types of terrain.
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3.3.2. Estimation of Dry Surface Deposition Flux
The dry deposition of particle-bound contaminants is a physical atmospheric removal
process that is simulated by the ISCST3 model. Dry deposition refers to the transfer of airborne
particulate matter to the Earth's surface (including water, soil, and vegetation) whereby it is
removed from the atmosphere. Although the dry gaseous deposition of vapor-phase
contaminants is currently considered in the ISCST3 model, this feature has not been calibrated
for the estimation of the deposition flux of dioxin-like compounds into vegetation. Until the
algorithm has been verified to make reasonably accurate estimates of gaseous deposition of
dioxin-like compounds, this guidance will not incorporate examples of it's site-specific
application. The focus of the following discussion is directed to the operation of the ISCST3
model to estimate the dry deposition of dioxin-contaminated particles.
The general processes controlling the transfer of particulate from some height above the
surface through the surface layer down to the immediate vicinity of the surface are the forces of
gravity and turbulent diffusion, followed by diffusion through the laminar sub-layer (defined as a
thickness of 10"1 to 10"2cm) to the surface (Seinfeld, 1986). The term "deposition flux" is mass
concentration of a contaminant sorbed to atmospheric particulates that is delivered to the surface
per unit of time by the physical forces of gravity, atmospheric turbulence, and diffusion (Kapahi,
1991). Deposition flux is represented mathematically by Fd. The dry deposition flux, Fd, is
defined as the product of the ambient air concentration of the chemical contaminant, C0, times a
deposition velocity(m s"1) of the contaminated particles as in Equation (3-13).
(3-13)
where:
Fd = dry deposition flux of contaminants sorbed to particles, |ig/m2-sec
Vd = the particulate depostion velocity, m/sec
C0 = concentration of pollutant on settling particles, i-ig/m3
In general, Chamberlain and Chadwick (1953) first defined the depositon velocity, Vd, as the
quotient of the deposition flux, Fd, divided by the airborne concentration, C0:
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The value for Fd in Equation (3-14) has a minus sign because the downward flux is negative,
whereas the deposition velocity is positive (Sehmel,1980). By this relationship, Chamberlain
and Chadwick (1953) first introduced the concept of plume depletion, i.e., as the emission plume
is dispersed with downwind distance from the stack, the deposition flux decreases with distance
from the source.
The basic dynamics of the physics of modeling dry deposition have not changed
significantly since Sehmel's (1980) comprehensive scientific review. The factors that most
influence the predicted deposition flux can be divided as being either meteorological influences,
or the influences of the properties of the pollutant under analysis. Meteorological influences
include the friction velocity, represented as |i0, and the aerodynamic surface roughness,
represented as z0. These terms are used to describe the wind speed profile above the Earth's
surface. In most cases, the analyst uses a graphical procedure to determine values for |i0 and z0.
If the logarithm of wind speed is plotted for near neutral atmospheric stability as a function of
height from the surface, then the values for the constant z0 is fitted to a straight line on a semi-
logarithmic scale. This can be described mathematically by Equation (3-15). In most cases, the
friction velocity is a percentage of the wind speed.
In () (3-15)
where:
|i = the measured wind speed, cm/sec
H* = the friction velocity, cm/sec
z = the measured height above the surface, cm
z0 = surface roughness length, cm
A = von Karman's constant, approx. = 0.4
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As a general rule, particles greater than 30 micrometers (|im) in diameter will be removed from
the atmosphere primarily by the force of gravity, whereas particles less than 30 jim will be
removed primarily by atmospheric turbulence. The deposition flux for the smaller particles is
influenced by many factors, including: the distribution of particles by diameter and density; the
atmospheric turbulence; the friction of the ground surface and the height of the stack release of
emissions. Deposition flux is also affected by the partitioning properties of the pollutant. These
properties will determine how much of the pollutant is sorbed to the particle and how much is in
the vapor phase. A detailed list of the many factors that can affect dry deposition is shown in
Table 3-10.
The ISCST3 estimates dry deposition flux based on empirical associations developed by
Sehmel (1980) and Sehmel and Hodgson (1978) relating the deposition flux to the deposition
velocity of particles. The downward motion represented by deposition velocity is controlled by
the gravitational settling velocity, atmospheric resistance, surface resistance and the atmospheric
surface friction layer. This model assumes that a fraction of the paniculate comes into contact
with the ground surface by the combined processes of gravitational settling, atmospheric
turbulence, and Brownian diffusion. The ISCST3 model contains enhancements to calculate dry
deposition flux using a dry deposition model developed by Pleim et al. (1984). The Pleim et al.
(1984) algorithms represent Sehmel's (1980) empirical relationships for transfer resistances as a
function of particle size, density, surface roughness, and friction velocity. In the Pleim et al.
(1984) model, integrated resistances to mass transfer are computed within two layers. In the first
layer, which extends from one centimeter to one meter above the surface, atmospheric turbulence
dominates mass transfer. This is a fully turbulent region where vertical fluxes are nearly
constant, and is referred to as the aerodynamic resistance. In the second layer, which lies within
one centimeter of the surface, the resistance to mass transfer is derived from particle deposition
measurements that were taken in a wind tunnel over various surfaces using mono-dispersed
particles (Sehmel, 1980; Sehmel and Hodgson, 1978). The general approach used by ISCST3 in
the resistance methods for estimating the dry deposition velocity of particles is given by:
r +r +III
ra rd 1a1d1s
where:
Vd = the depositon velocity, cm/s
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Vg = the gravitaional settling velocity, cm/s
ra = the aerodynamic resistance, s/cm
rd = the deposition layer resistance, s/cm
Despite what is currently known about the physical and chemical processes that influence
the final deposition flux of particles released from a stationary combustion source, a more
thorough understanding of the influence of particle size on deposition velocity is needed. In
Sehmel's (1980) review of settling velocities corresponding to particle diameter it was noted that
the range of values spanned several orders of magnitude. This complicates efforts to make
generalizations of Vd by particle diameter for air modeling purposes. Although dry particle
deposition velocities have been estimated from both field studies and laboratory experiments,
derived velocities are limited and highly uncertain. This is due largely to the complex and
variable array of factors that can influence the rate of deposition (which are summarized in
Table 3-10).
In the general classification of particles, particles < 2.5 micrometers (i-im) in diameter are
referred to a "fine particles", and those > 2.5 |im are "coarse particles". Sehmel (1980) offers the
most current review of dry deposition settling velocities for a variety of depositing materials
having a broad range of particle diameters. This summary appears in Table 3-11.
For the example application of the ISCST3 model in Chapter 5, particles less than 2 |im
were represented by a 1 |im size and were calculated by ISCST3 to deposit at a velocity of <10"2
cm/sec. Particles between 2 and 10 |im were represented by a 6.78 |im size and were calculated
to deposit at a velocity of < 0.5 cm/sec. Finally, particles greater than 10 |im were represented
by a 20 |im size and were calculated to deposit at a velocity of >2.0 < 5.0 cm/sec, although the
variable ambient conditions resulted in more variable calculations. The derivation of these
particle size representations is given in the next section.
3.3.3. Estimation of the Particle Size Distribution in the Stack Emissions
A distribution of particle size and diameter of the particulate stack emissions must be
known before the ISCST3 program can predict deposition flux of the dioxin-like congeners. The
diameters of small particles comprising particulate matter in stack emissions are usually
measured in units of one millionth of a meter (micrometer, commonly called micron, abbreviated
by the letters jim). Unfortunately, few studies have been conducted that describe the distribution
of particulate matter entrained in the emissions from various combustion technologies broken
down and fractionated by particle diameter. The characterization of particulate matter by
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particle diameter will differ from one combustion process to another, and is greatly dependent on
such factors as: 1) the efficiency of various air pollution control devices to the remove particles
over a broad range of diameters from the gas stream , 2) the composition of the feed/fuel, 3) the
design of the combustion chamber, 4) the amount of air used to sustain combustion, and 5) the
temperature of combustion. Table 3-12 gives an example of a particle diameter distribution as
measured at a stack of an incinerator, and was adopted from USEPA (1980). This example
distribution will be assumed for the hypothetical incinerator.
Although the ISCST3 model can simulate up to 10 particle size categories, only three
particle sizes are assumed for the model runs of the demonstration in this assessment. These
three sizes are generalized from the data in Table 3-12:
• Category 1: < 2 jim • Category 2: >2to<10|im • Category 3: > 10 jim
After selecting the particle size distribution, it is necessary to calculate the mass emission rate of
the particulate-bound congeners of CDD/Fs by particle size category. This is accomplished by
calculating the proportion of surface area (available for adsorption of CDD/Fs) for a given
particle diameter. The ratio of the surface area to volume is proportional to the ratio of the
surface area to weight for a particle with a given radius. Multiplying this proportion times the
weight fraction of particles of a specific diameter (jim) gives a value that approximates the
amount of surface area available for chemical adsorption. The surface area to volume ratio can
be described as follows:
(a) Assume aerodynamic spherical particles.
(b) Specific surface area of a spherical particle with radius,r:
S = 4ra2
(c) Volume of spherical particle with radius, r:
V = 4/3 ur3
(d) The ratio of surface area to volume is:
S/V = 4 ur2 / (4/3 ur3)
S/V = 3/r
Dividing the surface area for each particle category by the total available surface area for all
particles gives an estimation of the fraction of total area on any size particle. Multiplication of
the emission rate of the dioxin-like congener times the fraction of available surface area will
estimate the emission rate of the pollutant per particle size. The fraction of total surface area was
computed for the three particle size categories. The fraction of total surface areas for the ranges
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of particle diameters are summed with each particle size category to represent a single fraction of
total surface area for the given particle size category, as follows:
• Particulate category 1: fraction of total surface area = 0.875
• Particulate category 2: fraction of total surface area = 0.095
• Particulate category 3: fraction of total surface area = 0.030
Thus by these assumptions, 87.5% of the emission rate of the dioxin-like congener is calculated
to be associated with particles less than < 2 jim in diameter, 9.5% of the emission is associated
with the particle size of > 2 to < 10 jim, and only 3% of the emission is associated with particles
greater than 10 jim. To assist in deposition modeling of the emissions from the hypothetical
incinerator, the particle size distribution is further simplified by assuming a median particle
diameter to represent each broad particle size category, as follows:
• Particulate category 1 = 1 jim particle diameter
• Particulate category 2 = 6.78 jim particle diameter
• Particulate category 3 = 20 jim particle diameter
3.3.4. Estimation of Wet Deposition Flux
Wet deposition occurs by precipitation (rain, hail, snow) physically washing out the
chemically contaminated particulate and vapors from the atmosphere. Vapor scavenging is not
yet well understood and is not addressed in the ISCST3 model. The remainder of this discussion
refers only to the wet deposition of particles.
Wet deposition flux depends primarily on the fraction of the time precipitation occurs
and the fraction of material removed by precipitation per unit of time by particle size. Based on
these relationships, scavenging coefficients were developed by Jindal and Heinold (1991) for
varying types and intensities of precipitation relative to different particle diameters by
incorporating the observations of Radke, et al. (1980) in a study of scavenging of aerosol
particles by precipitation. The principal assumptions made in computing wet deposition flux are:
(1) The intensity of precipitation is constant over the entire path between the source and the
receptor; (2) The precipitation originates at a level above the top of the emission plume so that
the precipitation passes vertically through the entire plume; (3) The flux is computed on the
bases of fraction of the hour precipitation occurs as determined by hourly precipitation
measurements compiled by the National Weather Service. The remaining fraction is subject
only to dry deposition processes. Thus no dry deposition occurs during hours of steady
precipitation, and dry deposition occurs between the periods of precipitation.
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Wet deposition flux is estimated by ISCST3 using a scavenging ratio approach. In this
approach, the flux of contaminant to the surface (Fw) is the product of the savenging ratio times
the contaminant concentration, as in the following equation.
Fw = A x (3-17)
where:
Fw = wet deposition flux, g/m2-sec
A = scavenging ratio, sec"1
X = contaminant air concentration value calculated from the Gaussian plume
equation, g/m3
The scavenging ratio (A) is calculated as the product of the scavenging coefficient and
precipitation rate (Scire et al., 1990), as follows:
A = A R (3-18)
where:
A = scavenging ratio, sec"1
A = scavenging coefficient, (sec-mm/hr)"1
R = precipitation rate, mm/hr
The scavenging coefficient depends on the size distribution for particles and the nature or form
of precipitation, i.e., liquid or frozen.
Across the plume, the total flux to the surface must be approximately equal to the mass
lost from the plume. ISCST3 contains a plume wet deposition depletion equation as follows:
(3-19)
where:
Q(x) = wet plume depletion factor, dimensionless
A = precipitation scavenging ratio, sec"1
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t = x/u = the plume travel time, sec
The relationship between the scavenging coefficient, A, and the particle size and precipitation
intensity was derived from the review of wet deposition studies of aerosol particles by Jindal and
Heinold (1991). Table 3-13 displays the scavenging coefficients assigned to the generalized
particle size categories and forms of precipitation (liquid rainfall, frozen) used for computing
estimates of wet deposition in the application of the ISCST3 for the demonstration scenarios in
Chapter 5.
3.3.5. Using ISCST3 to Model Emissions of Particles and Vapors
The ISCST3 model had to be run in two modes in order to provide estimates of ambient
air concentrations of vapor-phase and particle-bound dioxins, combined with estimates of
wet/dry particle deposition flux. The short-term ISCST3 model can accommodate these
estimates in a single run. The user may select any or all of the output types, e.g, air
concentration, wet deposition, dry deposition and combined wet and dry deposition, to be
generated in a single model run. Instructions for this appear in the User's Guide for the
Industrial Source Complex Models (ISC3) (EPA, 1995). To facilitate the modeling exercise, the
modeler should assume a "unit emissions release rate" of CDD/Fs from the stack, e.g., 1 g
CDD/F congener per second (1 g/s). Results from these unit runs can easily be transformed to
final outputs given assumptions on CDD/F emissions in vapor and particle forms. Two
assumptions are required. One assumption is the total emission rate of the compound, in units of
mass/time (g/s), and the second is the vapor/particle partitioning of this total emission. The two
modes are:
• Mode 1: To estimate vapor-phase concentration of the contaminant in ambient
air.
The first mode assumes that the emissions of dioxin-like compounds are gaseous, e.g.,
with the wet/dry deposition switches turned to the "off" position. This is to isolate the ambient
air concentration of the contaminant in vapor-phase from the calculation of wet and dry particle
deposition flux. This inactivates a plume depletion equation that subtracts out losses in ambient
air concentration due to particle deposition. What remains is the Gaussian dispersion algorithm
to calculate air concentrations.
With the "unitized" emission rate, one can reconstruct the actual predicted ambient air
concentration (|ig/m3) of vapors by multiplying the "actual" vapor-phase emission rate (g/s) by
the "unitized" modeling result. For example, let the actual stack gas emission rate of total (vapor
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plus particle components) contaminant be IxlO"5 g/s, and the V/P ratio (expected under ambient
conditions) be 60%V/40%P. Then the "actual" emission rate of the vapor-phase portion of the
contaminant is calculated to be 6xlO"6 g/s (IxlO"5 g/s * 0.6). If the "unitized" ambient air
concentration at the ground-level receptor is estimated by the ISCST3 model to be IxlO"8 |ig/m3
(i.e., this concentration is predicted with a unit emission rate of 1 g/s), then the "actual" predicted
air concentration at that receptor can be estimated as:
(6xlQ-6 g/s -H 1 g/s) * IxlO-8 ng/m3 =
• Mode 2: To estimate wet and dry particle deposition flux, and the ambient air
concentration of the contaminant that is particle-bound.
ISCST3 should be run with the wet/dry particle deposition switches turned to the "on"
position, and using a "unit emission rate" of 1 g/s. This second run is considered a simulation of
particle-bound contaminant only. Outputs of this run include unitized deposition rate and
unitized ambient air concentrations of particles.
Like the vapor-phase run, the "actual" deposition flux (g/m"2 -yr) and "actual" particle-
phase airborne concentrations can then be determined by multiplying the "actual" emission rate
(g/s) of the particle-bound portion of the total contaminant emissions by the "unitized" modeling
result at the ground receptor. For example, let the "actual" emission rate of the particle-bound
portion of the contaminant be 4xlO"6g/s, and the "unitized" dry deposition flux at the ground
receptor be IxlO"5 g/m2-yr. Then the "actual" predicted dry deposition flux is 4xlO"n g/m2 (4x10"
6 g/s + 1 g/s * IxlO"5 g/m2-yr). Using this same procedure, this second run provides the airborne
concentration of contaminants bound to particles (|ig/m3).
There are two meteorological preprossesors used by ISCST3 model program to access
local conditions necessary to compute model concentrations in both modes of operation:
PCRAMMET (Catalano et al., 1987) and MPRM (Irwin and Paumier, 1990). These files contain
hourly data for the wind speed, wind direction, stability class, mixing height, ambient air
temperature, precipitation, and surface friction velocity.
Inhalation exposures are estimated as the sum of vapor and particle phase concentrations.
Air-to-plant transfers require the vapor phase concentrations for vapor transfers and the particle-
phase depositions. The air-to-soil algorithm requires particle phase depositions.
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3.4. RESULTS OF THE AIR DISPERSION MODELING OF CONGENER-SPECIFIC
EMISSIONS FROM THE HYPOTHETICAL ORGANIC WASTE
INCINERATOR
The preceding subsections have presented general procedures for conducting air
modeling of the emissions of dioxin-like compounds from the stack to the ground, starting with
estimation of emission factors, vapor/particle partitioning at the stack, and proceeding to
atmospheric dispersion and deposition using EPA's ISCST3 model. Where appropriate, previous
subsections have also included discussion on the assumptions and the selection of parameters for
the hypothetical incinerator which is demonstrated in Chapter 5. For example, Section 3.2.3
provided the emission factors that were used in this demonstration. This section will provide all
other details of the hypothetical incinerator and provide the final results of the ISCST3 model
simulations.
To reiterate, the purpose of the hypothetical construct is to help readers understand how
to apply these principles to the air dispersion modeling and analysis of dioxin emissions from the
source. Therefore, generalizations should not be made on the basis of this example regarding the
magnitude of the emissions release and associated environmental impact.
A completely hypothetical incinerator was devised to serve as the example. Accordingly,
a hypothetical, but realistic, incineration technology, facility size, stack height, and geographical
location was selected. The hypothetical incineration facility has an assumed total daily capacity
of 200 metric tons of organic waste materials. The emission rates of specific congeners of
PCDD/Fs were derived from the stack testing and monitoring of emissions from a modern
incinerator of this size. These emissions are expressed in units of g/sec, and are shown for the
hypothetical incinerator in Table 3-14.
In constructing the hypothetical case, the following was defined: stack height; stack
diameter; exit velocity of the gaseous emissions from the stack; and temperature of the exhaust
gases characteristic of incineration facilities of this size. In order to access historical
meteorological data for air modeling purposes, the hypothetical facility was located in a specific
geographical area having specific meteorological conditions. To simplify the ambient air
modeling and deposition, the hypothetical organic waste incinerator was assumed to exist in a
simple terrain setting (e.g., flat terrain). By definition, simple terrain refers to an area where the
terrain features are all lower in elevation than the top of the stack of the stationary source under
analysis.
The dispersion and deposition computations performed by the ISCST3 model require
data on wind speed, wind direction, wind profile above the surface, and hourly precipitation data.
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When performing a regulatory analysis, e.g., to set air quality permit conditions, EPA's
Guideline on Air Quality Models (EPA, 1986a) recommends the use of five consecutive years of
representative meteorological data. However, in this example analysis of only one year of
meteorological data was used as compiled at the Denver-Stapleton International Airport by the
National Weather Service (NWS), because this was not intended as a regulatory analysis.
Hourly measurements of wind speed, wind direction, temperature, and precipitation were used as
a basis of computing annual average ground-level concentrations of dioxin in ambient air, and as
a basis for the estimation of the dry and wet deposition flux. The Pasquill-Gifford (P-G) stability
categories, were used as defined in the Modeling Guidelines. The specifications of stability
categories depending on wind speed, cloud cover and mixing heights were established by
Pasquill (1961), and later modified by Turner (1964). Reference is made here to Tables 9-3 and
9-4 on pages 9-21,22 of the Modeling Guidelines which gives a method for estimating P-G
Stability Categories for daytime and nighttime conditions based on surface roughness and the
wind speed profiles distributed in the United States.
To summarize, inputs for the ISCST3 model included hourly meteorological data, source
characteristics and receptor features. Hourly meteorological data requirements are the mean
wind speed, the direction from which the wind is blowing, the wind-profile exponent, the
ambient air temperature, the Pasquill stability category, the vertical potential temperature
gradient with height, the mixing layer height, and the frequency distribution of hourly
precipitation. Source input data requirements included the congener-specific mass emission rate
partitioned by vapor and particulate; the physical stack measurements, e.g., diameter, base
elevation of the stack, and exit velocity and temperature of the stack gas, and settling parameters
for particulate matter for both dry and wet deposition. Table 3-14 is a review of the congener-
specific emissions data, and Table 3-15 is a review of the modeling parameters used in the air
quality modeling of the hypothetical incinerator.
The output of the ISCST3 model for both surface deposition and ambient air impacts is a
concentration array for 160 ground-level receptors around the incinerator, e.g., 10 receptor
points along each of the 16 wind directions every 22.5° on the polar azimuth. Vapor and particle
phase concentrations are in units of grams per cubic meter of air (g/m3), and particle-bound
depositions are in units of grams per square meter of surface area per year (g/m2-yr). Results for
both ambient air and surface deposition were estimated at concentric radial distances from the
incinerator of 0.2, 0.5, 0.8, 0.9, 1.0, 2.0, 5.0, 10, 20, 30, 40, and 50 kilometers. The maximum
annual average ground-level vapor and particle-phase air concentrations of all modeled
congeners is estimated to occur 900 meters from the center of the stack. Tables 3-16 and 3-17
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display the annual average vapor-phase and particle-phase air concentrations of dioxin-like
congeners at various distances in the direction of the maximum impact. Tables 3-18 and 3-19
display the dry and wet deposition fluxes of dioxin-like compounds at various distances in the
direction of maximum impact. The maximum annual average dry deposition flux occurs 800
meters from the center of the stack, although there is no significant difference from the 900 m
distance where the maximum annual average ambient air concentration occurs. The maximum
annual average wet deposition occurs 200 meters from the center of the stack, which is what is
expected from the algorithm (refer to subsection 3.3.4. Estimation of Wet Deposition Flux).
3.5. REVIEW OF PROCEDURES FOR ESTIMATING SITE-SPECIFIC IMPACTS
FROM A STACK EMISSION SOURCE
This chapter has detailed a procedure for evaluating site-specific impacts from stack
emission sources. For purposes of demonstration, a hypothetical incinerator was defined, and
using the ISCST3 model, estimates of vapor-phase concentrations and particle phase depositions
at points around the stack were made. Three major points for estimating impacts of dioxin-like
compounds using the ISCST3 or other models are as follows:
1. Characterize the emissions on a congener-specific basis: Although much of the
information available on stack emission sources in on a TEQ or a homologue group basis, and
not a congener-specific basis, the approach in this assessment, and the recommendation made
here, is to conduct site-specific assessments using specific congener emissions. This is because
fate and transport parameters, and bioconcentration/biotransfer parameters, are different for the
various congeners. Assuming one set of such parameters for TEQ emissions can lead to a
different estimated exposure media TEQ concentration than assuming congener-specific
parameters and then, given estimated congener-specific concentrations, calculating TEQ
exposure media concentrations with the TEF scheme. Emission factors were used in this
assessment to describe source and site-specific emissions. These are defined as the mass of
contaminant emitted per mass of feed material combusted. Procedures to convert other emission
data, such as mass per time emitted or concentration emitted, are presented.
2. Estimate the vapor/particle partitioning for atmospheric transport and
deposition modeling: Vapors are dispersed assuming Gaussian plume dispersion algorithms,
and particles are transported and deposited via wet and dry deposition. The principal output of
the atmospheric transport model, ISCST3, used for further exposure analysis are the vapor and
particle phase concentrations, and the wet and dry deposition totals at sites of exposure. There is
some thought that the partitioning between the vapor and particle phases at the stack differs from
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the partitioning in ambient air. Such a difference might be due to the difference in temperature
at the stack versus temperature of ambient air. If so, then deposition and dispersion trends in the
close vicinity of the stack may differ from such trends further from the stack. Currently the data
to support such a hypothesis is lacking; the earlier review of stack vapor/particle partitioning was
inconclusive. Also, modeling approaches for such differences are unavailable. Instead, the
approach in this chapter is to assume one partitioning scheme (separate V/P partitioning for
individual congeners) for atmospheric transport and dispersion modeling. The scheme adopted
in this assessment is based on a theoretical approach described by Bidleman (1988).
3. Conduct atmospheric dispersion and deposition modeling: The ISCST3 model is
used in this assessment to estimate vapor and particle-phase concentrations, and wet and dry
deposition totals for points around the stack emission source. Key inputs are vapor phase and
particulate phase emission rates (rather than emission factor units, atmospheric transport models
require emission rates in units of mass/time, or g/sec), stack descriptors (stack height, exit
temperature, etc.), atmospheric transport parameters (particle size distributions, dry deposition
velocity), meteorological data (hourly rainfall, windspeeds, etc.), and terrain descriptions.
Procedures to translate the final model outputs of concentrations and deposition fluxes into
exposure media concentrations is given in Chapter 4, Section 4.4.
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Wagel, D.J.; Tiernan, T.O; Taylor, M.L.; Garrett, J.H.; VanNess, G.F.; Solch, J.G.; Harden, L.A.
(1989) Assessment of ambient air sampling techniques for collecting airborne
polyhalogentated dibenzo-p-dioxins (PCDD), dibenzofurans (PCDF) and biphenyls
(PCB). Chemosphere 18 (1-6): 177-184.
Welsch-Paulsch, K.; McLachlan, M.; Umlauf, G. (1995). Determination of the principal
pathways of poly chlorinated dibenzo-p-dioxins and dibenzofurans to Lolium multiflorum
(Welsh ray grass). Environmental Science and Technology 29: 189-194.
Whitby, K.T. (1978). The physical properties of sulfur aerosols. Atmospheric Environment 12:
135-159.
Yamasaki, H.; Kuwata, K; Kuge, Y. (1984). Determination of vapor pressures of poly cyclic
aromatic hydrocarbons (PAH) in the supercooled liquid phase and their adsorption on
airborne paniculate matter. Nippon Kagaku Kaishi 8, 1324-1329 (in Japanese),
Chemistry Abstracts 101: 156747p.
Zaranski, M.T.; Patton, G.W.; McConnell, L.L.; Bidleman, T.F.; Mulik, J.D. (1991).
Collection of nonpolar organic compounds from ambient air using polyurethane foam -
granular adsorbent sandwich cartridges. Analytical Chemistry 63: 1228-1232.
5-60 December 2003
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DRAFT-DO NOT QUOTE OR CITE
Table 3-1. The number of dioxin-like and total congeners within dioxin, furan, and coplanar
PCB homologue groups.
Homologue Group
n: Number of Dioxin-
Like Congeners
N: Number of Total
Congeners
1/N
I. Dioxins
Tetra-CDD
Penta-CDD
Hexa-CDD
Hepta-CDD
Octa-CDD
1
1
3
1
1
22
14
10
2
1
0.022
0.071
0.100
0.500
1.000
II. Furans
Tetra-CDF
Penta-CDF
Hexa-CDF
Hepta-CDF
Octa-CDF
1
2
4
2
1
38
28
16
4
1
0.026
0.036
0.063
0.250
1.000
III. Mono-ortho coplanar PCBs
Tetrachloro-PCBs
Pentachloro-PCBs
Hexachloro-PCBs
Heptachloro-PCBs
1
5
4
3
42
46
42
24
0.024
0.022
0.024
0.042
3-61
December 2003
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DRAFT-DO NOT QUOTE OR CITE
Table 3-2. Emission factors and average emissions used for the hypothetical incinerator.
Congener
2378-TCDD
Other TCDD
12378-PCDD
Other PCDD
123478-PCDD
123678-PCDD
123789-PCDD
Other HxCDD
1234678-HpCDD
Other HpCDD
OCDD
2378-TCDF
Other TCDF
12378-PCDF
23478-PCDF
Other PCDF
123478-HxCDF
123678-HxCDF
123789-HxCDF
234678-HxCDF
Other HxCDF
1234678-HpCDF
1234789-HpCDF
Other HpCDF
OCDF
Emission Factors, ng/kg
Testl
0.052
0.826
0.148
1.390
0.104
0.157
0.148
2.440
2.350
4.040
4.260
3.300
20.00
0.435
0.243
6.280
0.478
0.478
0.357
0.243
1.490
0.243
0.391
241.0
1.579
Test 2
0.031
0.870
0.056
0.322
0.165
0.187
0.165
0.670
0.957
1.650
1.390
2.390
15.70
0.165
0.139
4.480
0.365
0.343
0.165
0.117
0.313
0.565
0.096
2.380
0.478
Test3
0.037
0.913
0.048
0.783
0.056
0.130
0.117
1.040
0.957
2.170
30130
2.170
14.30
0.226
0.122
3.480
0.357
0.313
0.226
0.074
0.943
0.696
0.165
2.180
0.971
Emissions, g/sec
9.3*10-u
2.0* 10'9
1.9*10-10
1.9*10-9
2.5*10-10
3.6*10-10
3.3*10'9
3.2*10'9
3.3*10'9
6.0* 10'9
6.7* 10-9
6.0* 10-9
3.8*10'8
6.3*10-10
3.9*10-10
1.P10'8
9.2*10-10
8.7*10-10
5.7*10-10
3.3*10-10
2.1*10'9
1.2*10-9
5.0*10-10
5.4*10'9
2.2* 10-9
3-62
December 2003
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DRAFT-DO NOT QUOTE OR CITE
Table 3-3. Percent distribution of dioxins and furans between vapor-phase (V) and particulate-
phase (P) as interpreted by various stack sampling methods (4-D = tetraCDD; 4-F = tetraCDF).
Citation
Cavellaro et
al., 1982
Cavallaro et
al, 1982
Cavallaro et
al., 1982
Cavellaro et
al., 1982
Cavellaro et
al., 1982
Cavellaro et
al., 1982
Benfenati et
al., 1986
Tiernan et
al, 1984
Tiernan et
al, 1984
Tiernan et
al, 1984
Tiernan et
al, 1984
Clement et.
al., 1985
Clement et.
al., 1985
V/P
V
P
V
P
V
P
V
P
V
P
V
P
V
P
V
P
V
P
V
P
V
P
V
P
V
P
4-D
95
5
9
91
99
1
85
15
97
3
100
0
94
6
75
25
95
5
91
9
17
83
98
2
84
16
5-D
91
9
38
62
99
1
92
8
90
10
99
1
NR
NR
68
32
90
10
91
9
22
78
92
8
55
45
6-D
94
6
69
31
99
1
99
1
63
37
99
1
NR
NR
67
33
88
12
89
11
45
55
96
4
54
46
7-D
99
1
57
43
99
1
98
2
82
18
100
0
NR
NR
55
45
85
15
77
23
84
16
93
7
72
28
8-D
89
11
14
86
99
1
99
1
59
42
99
1
NR
NR
64
36
98
2
56
44
85
15
73
27
20
80
4-F
NR
NR
59
41
99
1
99
1
80
20
100
0
NR
NR
75
25
86
14
92
8
7
93
97
3
95
5
5-F
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
70
30
98
2
89
11
10
90
96
4
73
27
6-F
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
64
36
89
11
91
9
22
78
98
2
70
30
7-F
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
69
31
88
12
72
28
63
37
98
2
52
48
8-F
96
4
60
40
99
1
99
1
97
3
100
0
NR
NR
86
14
98
2
65
35
77
23
94
6
68
32
3-63
December 2003
-------
Table 3-3. Cont'd.
DRAFT-DO NOT QUOTE OR CITE
Citation
Clement et.
al., 1985
Hagenmaier
etal., 1986
Battelle 1988
US EPA,
1990a
Radian 1986
AVERAGE
V/P
V
P
V
P
V
P
V
P
V
P
V
P
4-D
100
0
62
38
90
10
56
44
16
84
76
24
5-D
99
1
42
58
NR
NR
42
58
16
84
70
30
6-D
98
2
25
75
NR
NR
30
70
16
84
71
29
7-D
93
7
20
80
NR
NR
26
74
20
80
73
27
8-D
95
5
20
80
NR
NR
18
82
16
84
63
37
4-F
100
0
68
32
NR
NR
62
38
16
84
76
24
5-F
100
0
55
45
NR
NR
56
44
14
84
66
34
6-F
99
1
40
60
NR
NR
45
55
17
83
64
36
7-F
99
1
25
75
NR
NR
37
63
16
84
62
38
8-F
98
2
0
100
NR
NR
21
79
17
83
73
27
3-64
December 2003
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DRAFT-DO NOT QUOTE OR CITE
Table 3-4. Review of air monitoring data on the percentage of measured dioxins and furans
which are in the particle phase (4-D = tetraCDD; 4-F = tetraCDF).
Reference
1,T=20°C
2, T=3°C
2, T=18°C
2, t=28°C
3
3
4,T=18°C
4,T=18°C
5
5
6, T=18°C
AVERAGE
4-D
23
40
8
5
21
3
NR
NR
ND
ND
10
16
5-D
37
87
28
13
20
5
NR
NR
0
ND
28
27
6-D
66
100
45
45
24
12
92
100
65
100
45
63
7-D
87
100
88
60
70
64
100
100
82
100
77
84
8-D
96
100
100
100
85
90
78
100
100
100
93
95
4-F
14
100
ND
ND
23
7
14
5
20
ND
9
24
5-F
31
60
28
0
26
12
42
43
71
ND
22
32
6-F
64
88
30
38
29
15
73
100
100
ND
48
59
7-F
87
100
93
78
59
43
100
100
100
ND
77
84
8-F
91
98
100
98
94
91
100
NR
100
ND
89
96
Notes: - For references, 1 = Eitzer & Kites (1989) and Eitzer (1989). Average distribution at
20°C based on plots of log Kp vs 1/T from their data; see Section 3.2.4.7. and Figure 3-2a).; 2 = Kites, 1991; 3 =
Harless & Lewis, 1992; 4 = Hunt, et al 1990; 5 = Bobet, et al 1990; 6 = Welsch-Pausch, et al, 1995;
- For dioxin columns, 4D = tetra dioxin congener group; 5F = penta furan congener group, etc.
- NR = not reported; ND = not detected; NR and NDs not included in average estimation
- Welsch-Pausch, et al (1995) does not include V/P data, but it was sent by authors
- Harless and Lewis (1992) do not calculate V/P, rather V/P calculated here is based on isotopically labeled CDD/F
spiked onto the filter and recovered from the filter and PUF trap.
3-65
December 2003
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DRAFT-DO NOT QUOTE OR CITE
Table 3-5. Values of 6, VT, and TSP in different air regimes.
Airshed type
Clean continental background
Average background
Background plus local sources
Urban
e
cm2 aresol/cm3 air
4.2*10-7
1.5*10-6
3.5*10'6
u*io-5
VT
cm3 aerosol/cm3 air
6.5*10-12
3.0*10-u
4.3*10-u
7.0*10-u
TSP
Hg/m3
9
42
60
98
Sources: Bidleman (1988), Whitby (1978)
3-66
December 2003
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DRAFT-DO NOT QUOTE OR CITE
Table 3-6. Data for calculation of the liquid subcooled vapor pressure, p°L, at 20 °C, and final
p°L for the dioxin-like congeners.
Congener
2378-TCDD
12378-PCDD
123478-HxCDD
123678-HxCDD
123789-HxCDD
1234678-HpCDD
OCDD
2378-TCDF
12378-PCDF
23478-PCDF
123478-HxCDF
123678-HxCDF
123789-HxCDF
234678-HxCDF
1234678-HpCDF
1234789-HpCDF
OCDF
EPA p°s
(25°C)
2.00* ID'7
5.87*10-8
5.07* 10'9
4.80* 10'9
6.53*10'9
7.47*10-10
7.10*10-10
2.00* 10-6
2.27* 10-7
3.47*10'7
3.20*10'8
2.93*10'8
3.73*10'8
2.67* 10'8
4.67* 10-9
1.43*10-8
5.00*10-10
EPA p°L
(25°C)
1.18*10-4
7.87* 10-6
1.47* 10'6
1.80*10-6
9.37*10'7
1.73*10-7
1.02* 10-7
2.00* 10-4
2.16*10-5
1.71*10-5
3.12*10-6
3.35*10-6
6.01*10-6
3.50*10-6
5.71*10-7
1.27* 10-6
i.oi*io-7
E-Hp°L
(25°C)
1.14*10-4
1.74*10-5
3.96*10-6
3.96*10-6
3.96*10-6
1.02*10-6
2.77* 10-7
1.23*10-4
3.64*10'5
2.17*10-5
8.09* 10'6
8.09* 10'6
4.99* 10'6
4.99* 10'6
2.24* 10-6
1.31*10-6
2.60* 10-7
Slope
-4417*
-4779
-5058
-5058
-5058*
-5280*
-5526
-4394
-4608
-4728
-4877
-4877
-4983
-4983
-5099
-5192
-5526*
Int.
10.88
11.28
11.57
11.57
11.57
11.73
11.99
10.83
11.02
11.20
11.27
11.27
11.42
11.42
11.46
11.54
11.96
E-Hp°L
(20°C)
6.34*10'5
9.30*10-6
2.03*10-6
2.03*10-6
2.03*10-6
5.10*10'7
1.34*10-7
6.81*10-5
1.98*10-5
1.17*10-5
4.25* 10'6
4.25* 10'6
2.58*10-6
2.58*10-6
1.14*10-6
6.58*10-7
1.24* 10-7
Column 1: p°
crystalline solid phase vapor pressure at 25°C, Pa. Assigned based on analysis of
available literature; see Volume II, Chapter 2
sub-cooled liquid vapor pressure at 25°C, Pa, calculated from p°s in Column 1 and the
use of Equation (3-2)
iquid sub-cooled vapor pressure at 25°C, as measured by Eitzer and Hites (1988, 1989)
the slope of Equation (3-6), equal to QV/2.303R
intercept b of Equation (3-6)
liquid sub-cooled vapor pressure calculated at 20°C using the slope and intercept in
Columns 4 and 5, and Equation (3-6)
* numbers without asterisks were based on ratios of Qv (CDD/Fs)/Qv (DDT) measured by Eitzer and Hitest (1989).
Numbers with asterisks were assumed to be the same as for other members of the homologue group. OCDF was
assumed to have the same ratio as OCDD.
Column 2: p°L:
Column 3: p°L:
Column 4: slope:
Column 5: b:
Column 6: p°L:
3-67
December 2003
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DRAFT-DO NOT QUOTE OR CITE
Table 3-7. Particle fractions, cj), in four airsheds at 20°C for the dioxin-like congeners.
Congener
2378-TCDD
12378-PCDD
123478-HxCDD
123678-HxCDD
123789-HxCDD
1234678-HpCDD
OCDD
2378-TCDF
12378-PCDF
23478-PCDF
123478-HxCDF
123678-HxCDF
123789-HxCDF
234678-HxCDF
1234678-HpCDF
1234789-HpCDF
OCDF
Clean
Continental
0.10
0.44
0.78
0.78
0.78
0.93
0.98
0.09
0.27
0.38
0.63
0.63
0.74
0.74
0.86
0.92
0.98
Average
Background
0.29
0.74
0.93
0.93
0.93
0.98
0.995
0.27
0.57
0.69
0.86
0.86
0.91
0.91
0.96
0.98
0.995
Background
Plus Local
Sources
0.49
0.87
0.97
0.97
0.97
0.99
0.998
0.47
0.75
0.84
0.93
0.93
0.96
0.96
0.98
0.99
0.998
Urban
0.75
0.95
0.99
0.99
0.99
0.997
0.999
0.73
0.91
0.94
0.98
0.98
0.99
0.99
0.99
0.997
0.999
5-68
December 2003
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DRAFT-DO NOT QUOTE OR CITE
Table 3-8. Regression parameters slope m and intercept b for Equation (3-5), Log Kp = m Log
p°L + b, based on field measurements of particle/gas distributions for CDD/Fs.
Reference
Eitzer and Kites (1989), congeners
Kites (1991), homologues
Hunt and Maisel (1992), homologues
Weslch-Pausch, et al. (1995), homologues
Slope, m
-0.775
-0.988
-0.620
-0.707
Intercept, b
-5.72
-6.87
-5.00
-5.73
r2
0.96
0.87
0.61
0.99
3-69
December 2003
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DRAFT-DO NOT QUOTE OR CITE
Table 3-9. Comparison of monitored and modeled particulate percentage for CDD/F
homologuesat20°C.
Homologue
TCDD
PCDD
HxCDD
HpCDD
OCDD
TCDF
PCDF
HxCDF
HpCDF
OCDF
Monitored (see below for study identification)
1
23
37
66
87
96
14
31
63
87
97
2
10
30
67
91
98
8
24
64
90
98
3
19
36
59
80
91
16
31
57
79
92
4
10
22
45
72
90
8
18
43
71
90
Modeled*
49
87
97
99
99.8
47
79
93
99
99.8
Study identification: 1 = Eitzer (1989); Eitzer and Kites (1989); 2 = Kites (1991);
3 = Hunt, et al. (1988); Hunt and Maisel (1992); 4 = Welsch-Pausch, et al. (1995)
* by Equation (3-1), 0 = 3.5*10"6 cm2 aerosol/cm3 air, c = 17.2 Pa-cm
3-70
December 2003
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DRAFT-DO NOT QUOTE OR CITE
Table 3-10. Factors that influence the dry deposition removal rate in the atmosphere.
Micrometeorological
Variables
Aerodynamic
roughness
Mass transfer of
particles
gases
Heat transfer
Momentum transfer
Atmospheric stability
Diffusion
Friction velocity
Inversion layer
Pollutant concentration
Relative humidity
Seasonal variation
Solar radiation
Surface heating
Temperature
Terrain effects
Turbulence
Wind velocity
Zero plane
Displacement Effect
Mass transfer of
particles
Gases
Heat transfer
Momentum transfer
Characteristics of
Particles
Agglomeration
Diameter
Diffusion Effects
Brownian
Eddy
Particle
Momentum
Heat
Electrostatic effects
Attraction
Repulsion
Gravity settling
Hygroscopicity
Impaction
Interception
Momentum
Physical properties
Resuspension
Solubility
Therm ophoresis
Characteristics of
Gases
Chemical activity
Diffusion effects
Brownian
Eddy
Partial pressure in
equilibrium with
the surface
Solubility
Surface Variables
Accommodation
Edudates
Trichomes
Pubescence
Wax
Biotic surface
Canopy growth
Dormant
Expanding
Senescent
Canopy structure
Areal density
Bark
Bole
Leaves
Porosity
Soils
Stem
Type
Electrostatic
properties
Water
Pollutant
Penetration of
canopy
3-71
December 2003
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DRAFT-DO NOT QUOTE OR CITE
Table 3-11. A summary of dry deposition velocities for particles.
Depositing Material
Particles
Pollen
Natural aerosol
Pb auto exhaust
Particld Diameter
(|im)
0.03-30
20
32-35
90-100
1-10
Deposition Surface
grassland
grassland
grassland
grassland
grass shard
Deposition Velocity
(cm/s)
10-3-40
4.5
9.9
20
0.8
Source: Sehmel (1980).
3-72
December 2003
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DRAFT-DO NOT QUOTE OR CITE
Table 3-12. Generalized particle size distribution (|im), and proportion of available surface
area, in particulate emissions from incineration.
Particle
Diameter
(urn)3
>15.0
12.5
8.1
5.5
3.6
2.0
1.1
0.7
<0.7
Particle
Radius
(nm)
7.50
6.25
4.05
2.75
1.80
1.00
0.55
0.40
0.40
Surface Area/
Volume
0.400
0.480
0.741
1.091
1.667
3.000
5.455
7.500
7.500
Fraction of
Total
Weight
0.128
0.105
0.104
0.073
0.103
0.105
0.082
0.076
0.224
Proportion
Available
Surface Area
0.0512
0.0504
0.0771
0.0796
0.1717
0.3150
0.4473
0.5700
1.6800
Fraction
of Total
Surface Area
0.0149
0.0146
0.0224
0.0231
0.0499
0.0915
0.1290
0.1656
0.4880
Total surface area: 3.4423 jim2
Notes: a. Geometric mean diameter in a distribution. Distribution from EPA (1980).
3-73
December 2003
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DRAFT-DO NOT QUOTE OR CITE
Table 3-13. Unit wet deposition scavenging coefficients per particle diameter category
(micrometers) used in the example ISCST3 analysis, expressed as l/(sec-mm/hr).
Form of Precipitation
Liquid (rain)
Frozen (snow)
Particle Diameter Category (|im)
1
0.43 x 1C'4
0.14x 1C'4
6.78
0.46 xlO'3
0.16 xlO'3
20
0.66 x ID'3
0.22 x ID'3
3-74
December 2003
-------
DRAFT-DO NOT QUOTE OR CITE
Table 3-14. Emission of CDD/Fs (g/sec) from the hypothetical incinerator.
Congener
2378-TCDD
Other TCDD
12378-PCDD
Other PCDD
123478-HxCDD
123789-HxCDD
123678-HxCDD
Other HxCDDs
1234678-HpCDD
Other HpCDDs
OCDD
2378-TCDF
Other TCDFs
23478-PCDF
12378-PCDF
Other PCDFs
123478-HxCDF
123678-HxCDF
123789-HxCDF
234678-HxCDF
Other HxCDFs
1234678-HpCDF
1234789-HpCDF
Other HpCDFs
OCDF
Emission rate, g/sec
9.23 * 10'11
2.00 * 10-9
1.93 * 10'10
1.91 * 10'9
2.50 * 10-10
3.63 * 10'10
3.30 * 10-10
3.19 * 10-9
3.27 * 10'9
6.03 * 10-9
6.73 * 10'9
6.03 * 10'9
3.83 * 10-8
6.33 * 10'10
3.87 * 10'10
1.09 * 10-8
9.20 * 10'10
8.70 * 10'10
5.73 * 10'10
3.33 * 10'10
2.10* 10-9
1.15 * 10'9
5.00 * 10-10
5.35 * 10-9
2.23 * 10'9
Vapor emissions,
g/sec
4.71 * 10'11
1.02 * 10-9
2.51 * 10'11
2.49 * 10'10
7.50 * 10-12
1.09 * 10'11
9.90 * 10-12
9.56 * lO'11
3.27 * 10'11
6.03 * lO'11
1.35 * 10'11
3.20 * 10'9
2.03 * 10-8
1.01 * 10'10
9.67 * lO'11
2.73 * 10'9
6.44 * 10'11
6.09 * lO'11
2.29 * 10'11
1.33 * 10'11
8.41 * lO'11
2.31 * 10'11
5.00 * lO42
5.35*10-"
4.47 * 10'12
Particulate
emissions, g/sec
4.52 * 10'11
9.00 * 10-10
1.68 * 10'10
1.66 * 10'9
2.43 * 10-10
3.52 * 10'10
3.20 * 10'10
3.09 * 10-9
3.23 * 10'9
5.97 * 10-9
6.72 * 10'9
2.84 * 10 "9
1.80 * 10-8
5.32 * 10'10
2.90 * 10-10
8.19* 10-9
8.56 * 10'10
8.09 * 10-10
5.50 * 10'10
3.20 * 10'10
2.02 * 10-9
1.13 * 10'9
4.95 * 10-10
5.29 * 10-9
2.23 * 10'9
V/P ratio
0.51/0.49
0.51/0.49
0.13/0.87
0.13/0.87
0.03/0.97
0.03/0.97
0.03/0.97
0.03/0.97
0.01/0.99
0.01/0.99
0.002/0.998
0.53/0.47
0.53/0.47
0.16/0.84
0.25/0.75
0.25/0.75
0.07/0.93
0.07/0.93
0.04/0.96
0.04/0.96
0.04/0.96
0.02/0.98
0.01/0.99
0.01/0.99
0.002/0.998
3-75
December 2003
-------
DRAFT-DO NOT QUOTE OR CITE
Table 3-15. Modeling parameters used in the ISCST3 modeling of CDD/F emissions from the
hypothetical incinerator.
Description
General
Rate of organic waste combustion
Air pollution control system
Model Terrain adjustments
Options: Stack downwash
Gradual Plume Rise
Buoyancy induced dispersion
Gravitational settling/deposition
Wet deposition
Calm winds processing option
Building wake effects
Stack height
Stack diameter
Anemometer height
Terrain
Stack temperate
Stack exit velocity
Source elevation
Z minutes
Exponents for power law wind increase with height:
Particle size categories for dry /wet deposition analysis
Category 1
Category 2
Category 3
Particle density
Fraction of CDD/F particle bound emission by particle size
category: Category 1
Category 2
Category 3
Dry deposition velocities predicted by the CARD algorithm:
1.0 |^m
6.78 urn
20.0 urn
Wet deposition scavenging coefficients
National Weather Service data
Grid System
Value
Modeled after actual organic waste
incinerator
200 metric tons per day
dry scrubber combined with a baghouse
(fabric filters) resulting in 99% reduction
ofCDDs/CDFs
Yes
No
Yes
Yes
Yes
Yes
Yes
No
30.48m
1.52m
10m
simple
400 °K
8.9 m/sec
Om
10m
0.07,0.07,0.10,0.15,0.35,0.55
< 2 \im represented by 1.0 \im diameter
>2 - < 10 [im represented by 6.78 [im
>10 [im represented by 20.0 [im
1.4 g/cm3
0.88
0.09
0.03
7.11 *10'3 cm/sec
2.87 * 10'1 cm/sec
2.47 cm/sec
Table 3-13
Denver-Stapleton Airport: 1989
Polar
3-76
December 2003
-------
DRAFT-DO NOT QUOTE OR CITE
Table 3-16. Predicted average vapor-phase concentrations of CDD/Fs (pg/m ; columns are downwind distance in km).
Congener
2378-TCDD
Other TCDD
12378-PCDD
Other PCDD
123478-HxCDD
123789-HxCDD
123678-HxCDD
Other HxCDD
1234678-HpCDD
Other HpCDD
OCDD
2378-TCDF
Other TCDF
23478-PCDF
12378-PCDF
Other PCDF
123478-HxCDF
123678-HxCDF
123789-HxCDF
234678-HxCDF
Other HxCDF
1234678-HpCDF
1234789-HpCDF
Other HpCDF
OCDF
0.2
4.34*1 Cr7
9.40*10*
2.32*10-'
2.29*10-'
6.92*10'8
1.00*10-'
9 13*10-8
8.81*10-'
3.01*10-'
5.56*10"'
1.24*10-'
2.95*10's
1.87*10-"
9.34*10"'
8.91*10''
2.52*10'5
5.94*10"'
5.61*10-'
2.11*10-'
1.23*10-'
7.76*10"'
2.13*10-'
4.61*10'8
4.93*10"'
412*10-8
0.5
7.02*10-'
1.52*10-"
3.75*10-'
3.71*10'5
1.12*10''
1.63*10"'
1.48*10"'
1.43*10'!
4.87*10"'
9.00*10"'
2.01*10"'
4.77*10""
3.03*10"3
1.51*10"'
1.44*10"'
4.07*10'"
9.60*10"'
9.08*10"'
3.42*10"'
1.99*10"'
1.25*10"'
3.44*10"'
7.46*10"'
7.97*10"'
6.66*10"'
0.8
9.14*10"'
1.98*10""
4.88*10"'
4.83*10"'
1.46*10"'
2.11*10-'
1.92*10"'
1.85*10"'
6.34*10"'
1.17*10"'
2.61*10"'
6.20*10""
3.94*10"3
1.97*10"'
1.88*10"'
5.29*10""
1.25*10"'
1.18*10"'
4.45*10"'
2.59*10"'
1.63*10"'
4.47*10''
9.70*10"'
1.04*10"'
8.67*10"'
0.9
9.19*10"'
1.99*10""
4.91*10*
4.86*10"'
1.46*10*
2.13*10*
1.93*10*
1.87*10"'
6.38*10*
1.18*10"'
2.63*10*
6.24*10""
397*10-3
1.98*10"'
1.89*10"'
5.33*10"
1.26*10"'
1.19*10"'
4.48*10*
2.60*10*
1.64*10"'
4.50*10*
9.76*10"'
1.04*10"'
8.72*10"'
1
9.10*10*
1.97*10""
4.86*10*
4.81*10"'
1.45*10*
2.11*10*
1.91*10*
1.85*10"'
6.31*10*
1.17*10"'
2.60*10*
6.18*10""
3.93*10"3
1.96*10"'
1.87*10"'
5.27*10""
1.24*10"'
1.18*10"'
4.43*10*
2.58*10*
1.63*10"'
4.46*10*
9.66*10"'
1.03*10"'
8.63*10"'
2
6.06*10*
1.31*10""
3.23*10*
3.20*10"'
9.65*10"'
1.40*10*
1.27*10*
1.23*10"'
4.20*10*
7.76*10*
1.73*10*
4.11*10""
2.61*10"3
1.30*10"'
1.24*10"'
3.51*10""
8.29*10*
7.84*10*
2.95*10*
1.72*10*
1.08*10"'
2.97*10*
6.43*10"'
6.88*10*
5.75*10"'
5
2.94*10*
5.40*10"'
1.33*10*
1.32*10"'
3.97*10"'
5.77*10"'
5.24*10"'
5.06*10*
1.73*10*
3.19*10*
7.13*10"'
1.69*10""
1.08*10"3
5.36*10*
5.12*10*
1.44*10""
3.41*10*
3.22*10*
1.21*10*
7.06*10"'
4.45*10*
1.22*10*
2.65*10"'
2.83*10*
2.36*10"'
10
1.10*10*
2.37*10"'
5.85*10"'
5.79*10*
1.74*10"'
2.54*10"'
2.30*10"'
2 22*10*
7.60*10"'
1.40*10*
3.13*10"'
7.44*10"'
4.73*10""
2.36*10*
2.25*10*
6.35*10"'
1.50*10*
1.42*10*
5.33*10"'
3.10*10"'
1.96*10*
5.37*10"'
1.16*10"'
1.24*10*
1.04*10"'
20
4.57*10"'
9.90*10*
2.44*10"'
2.42*10*
7.28*10"8
1.06*10"'
9.61*10"8
9.28*10"'
3.17*10"'
5.86*10"'
1.31*10"'
3.10*10"'
1.97*10""
9.84*10"'
9.39*10"'
2.65*10"'
6.25*10"'
5.91*10"'
2.23*10"'
1.29*10"'
8.17*10"'
2.24*10"'
4.86*10"8
5.19*10"'
434*10-8
30
2.73*10"'
5.92*10*
1.46*10"'
1.44*10*
4.35*10"8
6.32*10"8
5.74*10"8
5.54*10"'
1.89*10"'
3.50*10"'
7.81*10"8
1.85*10"'
1.18*10""
5.88*10"'
5.61*10"'
1.58*10"'
3.74*10"'
3.53*10"'
1.33*10"'
7.73*10-8
4.88*10"'
1.34*10"'
2.90*10"8
3.10*10"'
2.59*10"8
40
1.91*10"'
4.13*10*
1.02*10"'
1.01*10*
3.04*10*
4.41 *10"8
4.01 *10"8
3.87*10"'
1.32*10"'
2.44*10"'
5.45*10"8
1.30*10"'
8.23*10"'
4.10*10"'
3.92*10"'
1.10*10"'
2.61*10"'
2.47*10"'
9 29*1 0"8
5.40*10"8
3.41*10"'
9 34*1 0"8
2.03*10"8
2.17*10"'
1.81*10"8
50
1.45*10"'
3.14*10*
7.74*10*
7.66*10"'
2.31*10*
3.36*10*
3.05*W*
2.94*10"'
1.01*10"'
1.86*10"'
4.15*10*
9.85*10*
6.26*10"'
3.12*10"'
2.98*10"'
8.40*10*
1.98*10"'
1.88*10"'
7.06*10*
4.11*10*
2.59*10"'
7.10*10*
1.54*10*
1.65*10"'
1.38*10*
3-77
December 2003
-------
DRAFT-DO NOT QUOTE OR CITE
Table 3-17. Predicted average particle-phase concentrations of CDD/Fs (pg/m3; columns are downwind distance in km).
Congener
2378-TCDD
Other TCDD
12378-PCDD
Other PCDD
123478-HxCDD
123789-HxCDD
123678-HxCDD
Other HxCDD
1234678-HpCDD
Other HpCDD
OCDD
2378-TCDF
Other TCDF
23478-PCDF
12378-PCDF
Other PCDF
123478-HxCDF
123678-HxCDF
123789-HxCDF
234678-HxCDF
Other HxCDF
1234678-HpCDF
1234789-HpCDF
Other HpCDF
OCDF
0.2
4.16*10-'
8.28*10-'
1.55*10-'
1.53*10J
2.23*10-'
3.24*10"'
2.94*10''
2.84*10's
2.98*10"'
5.50*10"'
6.18*10"'
2.61*10"'
1.66*10""
4.89*10"'
2.67*10"'
7.53*10"'
7.87*10"'
7.44*10"'
5.06*10"'
2.94*10"'
1.84*10"'
1.04*10"'
4.55*10"'
4.87*10"'
2.05*10"'
0.5
6.71*10"'
1.33*10""
2.49*10"'
2.47*10""
3.59*10"'
5.22*10"'
4.74*10"'
4.58*10""
4.79*10""
8.85*10""
9.96*10""
4.20*10""
2.67*10"3
7.89*10"'
4.30*10"'
1.21*10"3
1.27*10""
1.20*10""
8.16*10"'
4.74*10"'
2.99*10""
1.68*10""
7.37*10"'
7.85*10""
3.30*10""
0.8
8.70*10"'
1.73*10""
3.24*10"'
3.20*10""
4.66*10"'
6.78*10"'
6.16*10"'
5.95*10""
6.22*10""
1.15*10"3
1.29*10"3
5.45*10""
3.47*10"3
1.02*10""
5.58*10"'
1 57*1 0"3
1.65*10""
1.56*10""
1.06*10""
6.16*10"'
3.88*10""
2.17*10""
9.52*10"'
1.05*10"3
4.29*10""
0.9
8.75*10*
1.74*10"
3.25*10"'
3.22*10"
4.69*10"'
6.82*10"'
6.19*10"'
5.98*10"
6.26*10"
1.16*10"3
1.30*10"3
5.49*10"
349*10-3
1.03*10"
5.61*10"'
1.58*10"3
1.66*10"
1.57*10"
1.06*10"
6.19*10"'
3.91*10"
2.19*10"
9.58*10"'
1.02*10"3
4.31*10"
1
8.66*10"'
1.72*10"
3.22*10"'
3.19*10"
4.64*10"'
6.75*10"'
6.13*10"'
5.92*10"
6.19*10"
1.14*10"3
1 .29*1 0"3
5.43*10"
345*10-3
1.02*10"
5.55*10"'
1.57*10"3
1.64*10"
1.55*10"
1.05*10"
6.12*10"'
3.86*10"
2.16*10"
9.47*10"'
1.01*10"3
4.27*10"
2
5.76*10"'
1.14*10"
2.14*10"'
2.12*10"
3.08*10"'
4.48*10"'
4.07*10"'
3.93*10"
4.11*10"
7.60*10"
8.55*10"
3.61*10"
2.2*10"3
6.77*10"'
3.69*10"'
1.04*10"3
1.09*10"
1.03*10"
7.00*10"'
4.07*10"'
2.57*10"
1.44*10"
6.30*10"'
6.73*10"
2.84*10"
5
2.36*10"'
4.69*10"'
8.76*10"'
8.67*10"'
1.26*10"'
1.84*10"'
1.67*10"'
1.61*10"
1.69*10"
3.11*10"
3.50*10"
1.48*10"
9.39*10"
2.77*10"'
1.51*10"'
4.27*10"
4.46*10"'
4.22*10"'
2.87*10"'
1.67*10"'
1.05*10"
5.89*10"'
2.58*10"'
2.76*10"
1.16*10"
10
1.03*10"'
2.04*10"'
3.82*10*
3.78*10"'
5.50*10*
8.00*10*
7.27*10*
7.02*10"'
7.34*10"'
1.36*10"
1.53*10"
6.44*10"'
4.09*10"
1.21*10"'
6.58*10*
1.86*10"
1.94*10"'
1.84*10"'
1.25*10"'
7.26*10*
4.58*10"'
2.57*10"'
1.12*10"'
1.20*10"
5.06*10"'
20
4.22*1 0"7
8.40*10*
1.57*10*
1.55*10"'
2.26*10*
3.2*10*
2.99*10*
2.88*10"'
3.02*10"'
5.57*10"'
6.27*10"'
2.65*10"'
1.68*10"
4.96*10*
2.71*10*
7.64*10"'
7.98*10*
7.55*10*
5.14*10*
2.99*10*
1.88*10"'
1.05*10"'
4.62*10*
4.94*10"'
2.08*10"'
30
2.49*1 0"7
4.95*10*
9.25*10"7
9.16*10*
1.33*10*
1.94*10*
1.76*10*
1.70*10"'
1.78*10"'
3.29*10"'
3.70*10"'
1.56*10"'
9.91*10"'
2.93*10*
1.60*10*
4.50*10"'
4.71*10*
4.45*10*
3.03*10*
1.76*10*
1.11*10"'
6.22*10*
2.72*10*
2.91*10"'
1.23*10"'
40
1.72*10"7
3.42*10*
6.39*10"7
6.33*10*
9. 22*1 0"7
1.34*10*
1.22*10*
1.17*10"'
1.23*10"'
2.27*10"'
2.55*10"'
1.08*10"'
6.85*10"'
2.02*10*
1.10*10*
3.11*10"'
3.25*10*
3.07*10*
2.09*10*
1.22*10*
7.67*10*
4.30*10*
1.88*10*
2.01*10"'
8.47*10*
50
1.29*10"7
2.57*10*
4.81*10"7
4.76*10*
6.94*10"7
1.01*10*
9 15*10-'
8.84*10*
9.25*10*
1.71*10"'
1.92*10"'
8.11*10*
5.15*10"'
1.52*10*
8.29*10"7
2.34*10"'
2.45*10*
2.31*10*
1.57*10*
9.15*10"7
5.77*10*
3.23*10*
1.42*10*
1.51*10"'
6.37*10*
3-78
December 2003
-------
DRAFT-DO NOT QUOTE OR CITE
Table 3-18. Predicted annual dry deposition of particle-bound CDD/Fs (pg/m2-yr; columns are downwind distance in km).
Congener
2378-TCDD
Other TCDD
12378-PCDD
Other PCDD
123478-HxCDD
123789-HxCDD
123678-HxCDD
Other HxCDD
1234678-HpCDD
Other HpCDD
OCDD
2378-TCDF
Other TCDF
23478-PCDF
12378-PCDF
Other PCDF
123478-HxCDF
123678-HxCDF
123789-HxCDF
234678-HxCDF
Other HxCDF
1234678-HpCDF
1234789-HpCDF
Other HpCDF
OCDF
0.2
0.028
0.558
0.104
1.03
0.150
0.219
0.198
1.92
2.01
3.70
4.17
1.76
11.2
0.330
0.182
5.07
0.530
.0502
0.341
0.198
1.25
0.701
0.307
3.28
1.38
0.5
0.444
8.84
1.65
16.3
2.38
3.46
3.14
30.4
31.8
58.7
66.0
27.8
177.0
5.22
2.85
80.4
8.40
7.95
5.40
3.14
19.8
11.1
4.86
52.0
21.9
0.8
0.584
11.6
2.17
21.5
3.13
4.55
4.13
39.9
41.7
77.1
86.7
36.6
232.0
6.86
3.74
106.0
11.0
10.4
7.10
4.13
26.0
14.6
6.39
68.3
28.8
0.9
0.578
11.5
2.15
21.3
3.10
4.50
4.09
39.5
41.3
76.3
85.9
36.2
230.0
6.80
3.71
105.0
10.9
10.3
7.03
4.09
25.8
14.4
6.33
67.6
28.5
1
0.561
11.2
2.09
20.6
3.01
4.37
3.97
38.3
40.1
74.1
83.3
35.2
223.0
6.60
3.60
101.0
10.6
10.0
6.82
3.97
25.0
14.0
6.14
65.6
27.6
2
0.315
6.26
1.17
11.6
1.69
2.45
2.23
21.5
22.5
41.6
46.8
19.7
125.0
3.70
2.02
57.0
5.95
5.63
3.83
2.23
14.1
7.87
3.45
36.8
15.5
5
0.097
1.93
0.360
3.56
0.519
0.754
0.685
6.61
6.92
12.8
14.4
6.07
38.6
1.14
0.621
17.5
1.83
1.73
1.18
0.685
4.32
2.42
1.06
11.3
0.477
10
0.035
0.666
0.124
1.23
0.179
0.261
0.237
2.29
2.39
4.42
4.97
2.10
13.3
0.394
0.215
6.06
0.633
0.599
0.407
0.237
1.49
0.836
0.366
3.92
0.165
20
0.010
0.207
0.039
0.393
0.059
0.081
0.074
0.711
0.744
1.37
1.55
0.652
4.12
0.122
0.067
1 .88
0.197
0.186
0.127
0.074
0.464
0.260
0.114
1.22
0.051
30
0.005
0.099
0.019
0.183
0.027
0.039
0.035
0.340
0.356
0.657
0.739
0.312
1.98
0.059
0.032
0.900
0.094
0.089
0.061
0.035
0.222
0.124
0.054
0.582
0.245
40
0.003
0.063
0.012
0.117
0.017
0.024
0.024
0.216
0.226
0.418
4.70
0.198
1.26
0.037
0.020
0.573
0.060
0.057
0.039
0.022
0.141
0.079
0.035
0.371
0.156
50
0.002
0.045
0.008
0.083
0.012
0.018
0.016
0.155
0.162
0.299
0.336
0.142
0.903
0.027
0.015
0.409
0.043
0.041
0.028
0.016
0.101
0.057
0.025
0.265
0.111
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Table 3-19. Predicted annual wet deposition of particle-bound CDDs/Fs (pg/m2-yr; columns are downwind distance in km).
Congener
2378-TCDD
Other TCDD
12378-PCDD
Other PCDD
123478-HxCDD
123789-HxCDD
123678-HxCDD
Other HxCDD
1234678-HpCDD
Other HpCDD
OCDD
2378-TCDF
Other TCDF
23478-PCDF
12378-PCDF
Other PCDF
123478-HxCDF
123678-HxCDF
123789-HxCDF
234678-HxCDF
Other HxCDF
1234678-HpCDF
1234789-HpCDF
Other HpCDF
OCDF
0.2
1.80
35.9
6.71
66.4
9.67
14.1
12.8
123.0
129.0
238.0
268.0
113.0
719.0
21.2
11.6
326.0
34.1
32.3
22.0
12.8
80.5
45.1
19.7
211.0
88.9
0.5
0.684
13.6
2.54
25.2
3.66
5.33
4.84
46.7
48.9
90.3
102.0
42.8
272.0
8.04
4.38
124.0
12.9
12.2
8.32
4.84
30.5
17.1
7.48
80.0
33.7
0.8
0.406
8.07
1.51
14.9
2.18
3.16
2.87
27.7
29.0
53.6
60.3
25.4
162.0
4.77
2.60
73.4
7.67
7.26
4.94
2.87
18.1
10.1
4.44
47.5
20.0
0.9
0.356
7.07
1.32
13.1
1.91
2.77
2.52
24.3
25.4
46.9
52.8
22.3
142.0
4.18
2.28
64.3
6.73
6.36
4.33
2.52
15.9
8.88
3.89
41.6
17.5
1
0.315
6.27
1.17
11.6
1.69
2.46
2.23
21.5
22.5
41.6
46.8
19.8
126.0
3.71
2.02
57.0
5.96
5.64
3.84
2.23
14.1
7.88
3.45
36.9
15.5
2
0.143
2.81
0.525
5.19
0.757
1.10
0.999
9.64
10.1
18.6
21.0
8.85
56.2
1.66
0.905
25.5
2.67
2.52
1.72
0.998
6.30
3.53
1.54
16.5
6.95
5
0.046
0918
0.172
1.70
0.247
0.359
0.327
3.15
3.30
6.09
6.85
2.89
18.4
0.543
0.296
8.35
0.873
0.825
0.561
0.326
2.06
1.15
0.505
5.40
2.27
10
0.019
0.369
0.069
0.683
0.099
0.144
0.131
1.27
1.33
2.45
2.76
1.16
7.39
0.218
0.119
3.36
0.351
0.332
0.226
0.131
0.828
0.463
0.203
2.17
0.914
20
0.007
0.144
0.027
0.266
0.039
0.056
0.051
0.495
0.517
0.956
10.8
0.454
2.88
0.085
0.046
1.31
0.137
0.129
0.088
0.051
0.323
0.181
0.079
0.847
0.357
30
0.004
0.081
0.015
0.150
0.022
0.032
0.029
0.278
0.291
0.538
0.605
0.255
1.62
0.048
0.026
0.737
0.077
0.073
0.049
0.029
0.182
0.102
0.045
0.476
0.201
40
0.003
0.054
0.010
0.100
0.015
0.021
0.019
0.185
0.194
0.358
0.403
0.170
1.08
0.032
0.017
0.491
0.051
0.049
0.033
0.019
0.121
0.068
0.029
0.318
0.134
50
0.002
0.036
0.007
0.067
0.010
0.014
0.013
0.124
0.129
0.239
0.269
0.113
0.721
0.021
0.012
0.327
0.034
0.032
0.022
0.013
0.081
0.045
0.020
0.212
0.089
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Ratio (congener emission factor/total CDD/CDF emission factor)
0.05 0.1 0.15 0.2 0.25
0.3
2,3,7,8-TCDD
1,2,3,7,8-PeCDD
1,2,3,4,7,8-HxCDD
1,2,3,6,7,8-HxCDD
1,2,3,7,8,9-HxCDD
1,2,3,4,6,7,8-HpCDD
1,2,3,4,6,7,8,9-OCDD
2,3,7,8-TCDF
1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
1,2,3,4,7,8-HxCDF
1,2,3,6,7,8-HxCDF
1,2,3,7,8,9-HxCDF
2,3,4,6,7,8-HxCDF
1,2,3,4,6,7,8-HpCDF
1,2,3,4,7,8,9-HpCDF
1,2,3,4,6,7,8,9-OCDF
OCDF
Ratio (congener group emission factor / total CDD/CDF emission factor)
O.I O.2 O.3 O.4
O.S
Source: Mean concentrations from Green et al. (1995}; ziondelects set equal to JTI
Figure 3-1. Example of a congener and a homologue profile from a sewage sludge incinerator
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(a)
(b)
O)
O
0
-0.5
-1
L
-1.5
-2
-2.5
-3
-3.5
100
-7 -6 -5 -4
Logp°L(Pa)
-3
80
(D
60
CO
0-40
20
J-P
-8 -7 -6 -5 -4 -3 -2
Logp°(Pa)
Figure 3-2. The relationships between the log of liquid sub-cooled vapor pressure, pL°,0 and the
particle-gas partition coefficient, Kp, (figure (a)), and between pL° and modeled (as indicated by
"J-P" in figure (b)) and measured percent particulate-phase in the ambient air (measurements
from Eitzer & Kites (1989)).
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100
80
0
«i 60
o
co
o- 40
#
20
0
J-P
-8 -7 -6 -5 -4
Logp°L(Pa)
-3
-2
KEY: E-H = Eitzer and Hites (1989); HITES = Hites (1991)
H-M = Hunt and Maisel (1990); Hunt and Maisel (1992)
W-P = Welsch-Pausch, et al. (1995)
Figure 3-3. Comparison of measured particulate percentages of PCDD/F on a homolog basis to
predictions of the lunge-Pankow model as a function of the sub-cooled liquid vapor pressure,
p°L, of the homolog groups.
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4. ESTIMATING EXPOSURE MEDIA CONCENTRATIONS
4.1. INTRODUCTION
The purpose of this chapter is twofold. First, it describes the algorithms used to
determine exposure media concentrations of the dioxin-like compounds. Discussion of the
algorithms are structured around three "source categories." These categories roughly translate to
beginning points, or origins, of contamination. The source categories are also the basis for the
example scenarios described in Chapter 5. Second, it provides information about all the model
parameters and justification for the values selected for the demonstration of methodologies in
Chapter 5. Parameter discussions appear immediately following descriptions of modeling
methodologies.
Section 4.2 provides an introduction to the type of modeling used in this assessment.
Section 4.3 describes the algorithms used for the first source category, soil contamination, where
the dioxin-like compounds occur in surface soils. The exposures could occur at the site of
contamination or the exposures could occur off the site of contamination. Section 4.3 describes
the differences in modeling for these two. Section 4.4 describes algorithms to determine
exposure media concentrations resulting from stack emissions, the second source category.
Chapter 3 laid the groundwork for this section by describing the use of air dispersion/deposition
models as applied to a point source to generate two key quantities: air-borne contaminant vapor
phase concentrations at a site of exposure, and particulate phase deposition rates. Section 4.4
describes how modeled concentrations and depositions translate to soil, vegetative, and water
concentrations. Section 4.5 concludes the chapter with a discussion of algorithms specific to the
third source category, point-source effluent discharges into surface water bodies.
Algorithms are presented which estimate exposure media concentrations for: 1) surface
soils, 2) surface water impacts: suspended and bottom sediment and dissolved phase
concentrations, 3) air including the vapor phase and in particulate form, and 4) biota including
beef, milk, fruit and vegetables, and fish.
4.2. BACKGROUND FOR SOLUTION ALGORITHMS
Literally hundreds of fate and transport models have been published which differ widely
in their technical sophistication, level of spatial or temporal resolution, need for site specific
parameterization, and so on. This can make selection of the most appropriate one for any
particular situation difficult. Relatively simple, screening level models are used to model fate,
transport, and transfer of dioxin-like compounds from the source to the exposure media in this
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assessment. Simple assumptions are often made in order to arrive at the desired result, which is
long-term average exposure media concentrations. Perhaps the most critical of the assumptions
made is the assumption that the source strength remains constant throughout the period of
exposure: the initial soil concentration of dioxin-like compound remains the same for that
exposure period, and stack emissions and effluent discharges remain steady throughout this
period.
It is important to understand that EPA is not endorsing the algorithms of this assessment
as the best ones for use in all dioxin assessments. They are suggested as reasonable starting
points for site-specific or general assessments, and as will be discussed shortly, most multi-media
exposure modeling has included similar screening level approaches. The assumptions behind
models are described carefully throughout this chapter. If these assumptions do not apply to a
particular situation, or where assessors require more spatial or temporal resolution, more
complex models should be selected. References to other models are made in this and other
sections throughout the chapter. Also, Chapter 7 compares the models of this assessment to
alternate models for several of the algorithms.
Finally, it cannot be overemphasized that measured concentrations are generally more
reliable than modeled ones. Assessors should use measured concentrations if available and if
such measurements can be considered spatially and temporally representative for the exposed
populations.
The first examples of similar multimedia compartment modeling were probably the
"fugacity" models proposed by Mackay (1979) and Mackay and Paterson (1981, 1982). Fugacity
in this context is defined as the tendency for a chemical to escape from one environmental media
compartment into another. The fugacity of a chemical present in an environmental media
compartment is modeled using common fate and transport parameters such as octanol water
partition coefficients, Henry's Constants, water solubilities, and so on. The fugacity concept is
based on the fact that at equilibrium, equal fugacities are established in all compartments of a
system. Examples of fugacity modeling include the transfer of nonionic organic chemicals
between the atmosphere and surface water (Mackay, et al., 1986), between the atmosphere and
plants (Riederer, 1990), and for food chain modeling (Travis and Hattemer-Frey, 1987). A
regional fugacity model being used in regulatory risk assessment forums is the CALTOX (DTSC,
1993) model. Mackay (1991) authored a comprehensive, though somewhat dated, text on
multimedia compartment modeling using the fugacity approach.. A key difference between
fugacity modeling and the modeling in this assessment is that for fugacity modeling, movement
between compartments is considered for both directions - from compartment A to compartment
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B and also compartment B to compartment A. The modeling in this assessment is essentially one
way - air to leaf, air to soil, vegetation to terrestrial animal, and so forth. This is thought to be
reasonable for dioxins under the circumstances for which the models are being promoted - which
is the circumstance of the source strength being constant over the period of exposure. Also,
because the persistence and adsorptive tendencies of the dioxin-like compounds, movement from
one compartment to another tends to be more one-way than it is for less persistent or more
volatile organic compounds.
One possible drawback for the fugacity approach applied to the types of source categories
discussed in this assessment is that it does not consider spatial variability of concentrations
within a compartment. For example, air concentrations vary depending on the distance from a
source of air emissions, such as a stack or a site of soil contamination. The fugacity approach
would typically treat air as a single compartment with a uniform concentration. This would be a
concern for a regional model such as the CALTOX model.
The transfer of contaminants between compartments and multimedia modeling
approaches have been extensively studied at the National Center for Intermedia Transport at the
University of California, Los Angeles. Their multimedia compartment model, MCM (Cohen and
Ryan, 1985), provides several useful algorithms for intermedia transfer factors that would have
application for dioxin-like compounds. Later on, this group introduced the spatial multimedia
compartment model (Cohen, et al., 1990), which allows for non-uniformity in some
compartments. Such a model would be more suitable for the types of source categories of this
assessment, since there is non-uniformity within a compartment as noted above in the air
compartment example.
An early approach which merged simplistic multimedia modeling with human exposure
was termed the exposure commitment method, developed by Bennett (1981). An exposure
commitment is defined as a contaminant concentration in human tissue. Exposure commitments
are calculated from transfer factors that are estimated as the ratios of the steady-state
concentrations of a contaminant in adjoining compartments of an exposure pathway. An
example of adjoining compartments is air to plants to livestock to diet. This method has been
applied to both PCBs (Bennett, 1983) and 2,3,7,8-TCDD (Jones and Bennett, 1989). These
applications have required measured concentrations of the contaminants in different
compartments in order to estimate the transfer factors. The retrospective nature of this approach
limits its usefulness for general applications.
One of the early multimedia models which also had human exposure as the endpoint, but
did not require retrospective data, was the GEOTOX model (McKone and Layton, 1986). This
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model had air (vapor and particle phases), water (surface and ground water, including bottom
sediments of surface water bodies), soil (soil gas, water, and solid subcompartments), and
biomass (eggs, milk, meat, fish, and vegetation including food crops) compartments. The most
recent evolution of this model can be found in McKone and Daniels (1991).
Multimedia modeling approaches have been extensively used to evaluate the exposure to
dioxins. Paustenbach, et al. (1992) evaluated the exposure and risk to humans from residential
and industrial soil contamination by 2,3,7,8-TCDD. Simple models were used to estimate the
concentrations of 2,3,7,8-TCDD in air-borne suspended particulates and fish that reside in nearby
streams impacted by the contaminated soil. Together with concentrations in contaminated soil,
Paustenbach evaluated human exposures via soil ingestion, dermal contact, particulate inhalation,
and fish consumption. They also used Monte Carlo techniques on exposure parameters (in
contrast to using Monte Carlo on fate and transport parameters) to determine a range of
residential and industrial soil concentrations that would result in a specified risk level. The risk
level chosen for their demonstration was 10"5, which was determined by multiplication of the
Lifetime Average Daily Doses (LADDs in mg/kg-day) and the cancer slope factor for 2,3,7,8-
TCDD of 9700 (mg/kg-day)"1 derived by Keenan, et al. (1991). Residential soil concentrations
less than 20 ppb did not pose a lifetime cancer risk greater than 10"5. For industrial sites,
concentrations in soil that could pose a 10"5 risk ranged between 131 and 582 ppb, depending on
the amount of time the industrial worker spend outdoors under typical exposure conditions.
Travis and Hattemer-Frey (1991) evaluated human exposure to 2,3,7,8-TCDD from a
broader perspective. The principal assumption of the Fugacity Food Chain model used for
Travis' human exposure assessment is that atmospheric concentrations of 2,3,7,8-TCDD can be
empirically linked to water, soil, and vegetative concentrations, which in turn are linked to
agricultural produce, meat, milk, eggs, and fish concentrations. Simple models for atmospheric
depositions onto plants, air-to-leaf transfers of vapor phase 2,3,7,8-TCDD onto plants, transfers
to cattle beef and milk, and other models, are presented. They also compared their model
predictions of exposure media concentrations to literature values, and concluded that their
approaches resulted in concentrations comparable to those found in the literature. This effort by
Travis and Hattemer-Frey is examined in more detail in Section 5.6 of Chapter 5.
Exposure to 2,3,7,8-TCDD using simplistic multimedia models has also been assessed for
specific sources. Goeden and Smith (1989) evaluated the impact to fish and subsequent human
exposure by consumption offish to dioxins and furans emitted by a resource-recovery facility.
Surface water sediment concentrations in a lake were estimated as a simple weighted average of
concentrations on three kinds of particles entering the lake: soil via erosion whose concentration
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was estimated given contaminated particle depositions onto soil (and considering mixing and soil
half-lives), deposition of background uncontaminated suspended particulates directly onto the
lake, and direct deposition of contaminated particles onto the lake. Fries and Paustenbach (1990)
also evaluated the impact of incinerator emissions of 2,3,7,8-TCDD, but they evaluated human
exposure via consumption of food crops, meat, and milk. EPA (1990d) used a simple dilution
model to evaluate the impact of pulp and paper mill effluent discharges of 2,3,7,8-TCDD and
2,3,7,8-TCDF into surface water bodies.
The air-plant interface has been the subject of numerous fugacity and related modeling
efforts for the dioxin-like compounds. This is because it is recognized that this pathway is
critical to the terrestrial animal food chain; little soil-to-plant transfer occurs for these highly
sorbed class of compounds. Trapp and Matthies (1995) present a comprehensive air/soil-to-plant
modeling system, and in their application of this approach to 2,3,7,8-TCDD, they neglect the
soil-to-above ground portion of their model. The importance of the vapor phase dioxins has been
recognized in numerous modeling efforts, particularly for the lower chlorinated congeners
(Lorber, 1995; McLachlan, et al., 1995). McLachlan, et al. (1995) developed a fugacity
approach while the empirical air-to-plant transfer factor approach for vapor-phase dioxins in
Lorber (1995) is advocated in the methodology.
This is only a cursory summary of the wealth of multimedia modeling approaches that are
available, and the application of such modeling approaches for evaluating human exposure to
dioxins. While there are many similarities and differences among the approaches, they all share
one characteristic in common - they have all been described as "screening level models".
Without attempting a definition of the qualifier, "screening level", such a qualifier for these
models seems to imply the following types of common features: assumptions of equilibrium
and/or steady state conditions between compartments, lack of substantial (if any) spatial or
temporal resolution, the use of biotransfer or bioconcentration concepts which simply relate an
environmental concentration (air or water concentration, e.g.) to a biomass concentration (plant
or fish concentrations), and so on.
A counterpoint to screening level models might be what are termed "mechanistic"
models. Such models are more theoretically sophisticated, contain more spatial and temporal
resolution, attempt to simulate actual mechanisms of fate and transport rather than depend on
empirical relationships developed from data, could involve complex food chain approaches to
model biomass concentrations (to counter the simple biotransfer or bioconcentration
approaches), and generally are highly parameterized requiring site-specific data that is often not
readily available.
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Because of the complexity of the multimedia environment, modeling of contaminant fate
in such an environment has tended to remain simple. However, there are complex models which
can be applied to smaller subsets of the multimedia environment, and which have been applied to
assessments of dioxin-like compounds. One example is the ISCST3 model, which was used in
this assessment to evaluate the impact of stack emissions of dioxin-like compounds. That model
allows for complexities of terrain, varying weather patterns, vapor/particle partitioning, etc., to be
considered. It relies on hourly meteorological data to simulate years of atmospheric transport and
deposition, and summarizes the results of its simulation as long term average concentrations and
depositions. That model is further described in Chapter 3. Another example of more complex
modeling was the use of the WASP4 model in a comprehensive evaluation of bioaccumulation of
2,3,7,8-TCDD in Lake Ontario (EPA, 1990b). That application required a substantial amount of
site-specific parameterization. Gobas, et al. (1999) describes the application of EcoFate to
2,3,7,8-TCDD and 2,3,7,8-TCDF pulp and paper mill discharges to the Fraser-Thompson River
system in British Columbia, Canada from 1988 to 1995. This is a time-dependent, multimedia
mass balance simulation of the environmental distribution and food-chain accumulation of
organic contaminants in aquatic ecosystems. In this framework, modeled river systems are
segmented, and within each segment, numerous compartments are modeled including the bed
sediment (an accessible and an inaccessible bed layer), one or several water column
compartments, the atmosphere, and one or more aquatic organism trophic levels
(algae/zooplankton, benthic filter filters and detritivores, and fish). They were able to
demonstrate a successful application of this model to predicted the two dioxin congener
concentrations in bottom sediments, rocky mountain white fish, and rainbow trout.
With the exception of the ISCST3 model, the models used for this assessment are better
described as screening level rather than mechanistic. Many of the algorithms used are the same
or very similar to the ones found in references above. Except for the effluent discharge source
category, which uses a non-spatially resolved dilution model for surface water impacts, the
algorithms do consider spatial differences between the source and site of impact or site of
exposure. For example, the algorithm estimating surface water impacts from a site of soil
contamination, while simple in its framework, does incorporate the following: the area of the
site that is contaminated, the area of the watershed which drains into the water body, the erosion
rates of the site of contamination as well as the rest of the watershed, the proximity of the site to
the water body, the concentration of the contaminant at the site of contamination as well as
within the watershed other than the contaminated site, the lipid content of the fish, and the
organic carbon fractions of the suspended and bottom sediments of the water body. Assignments
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for all these parameters impact water and fish concentrations, and it is certainly arguable that
they are all site-specific parameters. From this perspective, it could be argued that most of the
algorithms of this assessment are generally screening level in their theoretical sophistication, but
site specific in their application.
Sections in other chapters of this volume address key issues relating to the use and
credibility of the algorithms described in this chapter. Chapter 5, which demonstrates the
methodology, makes observations concerning exposure media concentrations. Chapter 6, on user
considerations for use of the models and algorithms of this assessment, discusses categorization
of model parameters and conducts sensitivity analysis exercises on key fate, transport, and
transfer algorithms. Chapter 7 discusses applications of these models in validation exercises, and
also compares the models of this assessment to others available for dioxin-like compounds.
Chapter 8 on Uncertainty has critical discussions on parameter assignment and algorithm
uncertainties.
Figures 4-1 through 4-3 are flow diagrams showing interim compartment concentrations
modeled, and principal processes modeled and assumptions made in the intermedia transfer.
Sections 4.3. through 4.5 describe the algorithms for the three source categories considered in
this assessment, and background and assignment of parameters for the demonstration scenarios
of Chapter 5.
4.3. ALGORITHMS FOR THE SOIL CONTAMINATION SOURCE CATEGORY
As earlier noted, exposures to contaminated soils can occur both at the site of the soil
contamination or away from the site of soil contamination. Examples of on-site exposure would
be worker exposures to Superfund or similar sites, or unique circumstances such as Times Beach
where soil at the site of a residence or a playground becomes contaminated. Examples of off-site
exposure include exposures at residences, farms, play or school areas, and so on, that are located
near, but not at, the site of soil contamination. There are two primary differences with regard to
modeling when exposure is on-site versus off-site. One is in the modeling of the dispersion of
dioxin residues which have been suspended via volatilization or dust suspension. Models are
presented below for on-site dispersion and off-site dispersion. The second difference is that, for
on-site exposures, the initial soil concentration is assumed to remain constant throughout the
period of exposure. For off-site exposures, the soil concentration at the site of exposure is
assumed to build up over time from an initial level of zero before the site became contaminated
to a level when exposure begins and then to a second level when exposure ends. The average
soil concentration during the period of exposure is estimated as the midpoint between the two
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latter concentrations noted. Otherwise, all other algorithms for the soil contamination source
category, including the algorithms for surface water impacts and for biota calculations, are the
same whether the exposure to the soil contamination is on-site of off-site.
Sections 4.3.1 through 4.3.4 describe the algorithms for estimating concentrations of the
dioxin-like compounds in: bottom sediment, suspended solids, and in the dissolved phase in the
water column of surface water bodies (4.3.1), exposure site soil concentrations (4.3.2.), in the air
in the vapor and particulate phases (4.3.3), and in biota including fish (4.3.4.1), home-grown
vegetables and fruit (4.3.4.2), beef and milk (4.3.4.3), and chicken and eggs (4.3.4.4). Section
4.3.5 describes some key characteristics of specific cases of soil contamination.
4.3.1. Surface Water and Sediment Contamination
The principal assumption in the algorithm estimating the impact to surface water and
surface water sediments (suspended and bottom sediments) from an area of contaminated soil is
that such an impact is correlated to surface soil concentrations at that site as well as surface soil
concentrations within a larger area draining into the water body. This drainage area is commonly
referred to as a watershed. Further, the impact to the water body is assumed to be uniform. This
tends to be more realistic for smaller water bodies as compared to large river systems. Other key
assumptions in the surface water impact algorithm are:
• Soil erosion estimates, coupled with sediment delivery ratios, can be used to describe
the impact of a contaminated site relative to other soils in the watershed which contribute
sediments to the water body;
• The sorption of dioxin-like compounds onto surface soil, suspended solids and
bottom sediments is principally a function of the contaminant's organic carbon partition
coefficient, Koc, and the organic carbon content of soils and sediments;
• The concentration of contaminants in soil eroding from a site are initially higher than
the concentrations at the site itself- it is "enriched" with contaminants. This enrichment occurs
because some processes of transport, such as wind erosion or soil erosion, favor lighter soils
(silts and clays) which have higher surface area to volume ratios (more binding sites) as well as
higher organic matter contents on the average (which also favors more binding of organic
chemicals). Other processes such as volatilization or degradation may counteract the enrichment
noted at the edge of a site - concentrations on soil entering a water body may be less than those
leaving the site;
• The concentration of contaminants in sediment suspended in the water column
exceeds the concentration in bottom sediments. Similar reasoning as the above enrichment
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argument applies: particulates which remain in suspension tend to be lighter and more enriched
with organic matter as compared to parti culates which settle to the bottom of water bodies. It
should be noted that suspended solids, in this algorithm, are simply a reservoir into which dioxin-
like compounds sorb; more complex models consider sorption onto more than one reservoir of
suspended materials including suspended particulates and dissolved organic matter;
• Suspended and bottom sediments originate principally as soil erosion; a mass balance
is maintained such that a part of the soil reaching the water body through erosion remains as
suspended particulates, and a part settles to bottom sediments.
• A steady state is achieved between concentrations in the dissolved phase in the water
column, concentrations in the sorbed phase in the water column, and concentrations in bottom
sediments;
• Volatilization out of the water body or degradation of residues in the water body are
not modeled. Neglecting these dissipation processes has the net effect of overestimating water
body impact. On the other hand, bottom sediment resuspension is not modeled. Not modeling
resuspension would have a net effect of underestimating water column impacts; and
• Estimating the average impact to the water body, rather than a localized impact which
may be the case if the contaminated soil is very near the water body, is suitable for purposes of
this assessment procedure.
Concentrations in bottom sediment are desired because fish concentrations are estimated
as a function of bottom sediment concentrations (see Section 4.3.4.1). Concentrations in
suspended solids are desired because they are used to estimate bottom sediment concentrations,
and dissolved phase concentrations are needed for estimating drinking water exposures.
The solution begins with the mass balance statement:
The mass of contaminant An amount which remains as dissolved in the water column
entering the water body = + An amount which remains sorbed to suspended materials
+ An amount which remains sorbed to particles settling to
the bottom
This can be described mathematically as:
Csed Msed (4-1)
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where:
Cswb = concentration on soil entering water body, mg/kg
ERW = total watershed annual soil erosion, kg/yr
Cwat = dissolved-phase concentration in water column, mg/L
Vwat = water body annual volume, L/yr
Cssed = concentration on suspended sediment, mg/kg
Mssed = mass of suspended sediment introduced per year, kg/yr
Csed = concentration on sediment settling to bottom, mg/kg
Msed = mass of bottom sediment introduced per year, kg/yr
Other equations based on assumptions stated above and needed for this solution are:
1) mass balance of soil is maintained:
Msed (4-2)
(4-3)
(4-4)
where:
fs = fraction of annual erosion remaining as suspended materials, unitless
2) equilibrium between sorbed and dissolved phases is maintained; suspended sediments are
enriched in comparison to bottom sediments:
SSed
s A r\
(4-5)
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ocsed
Csed = Cssed (4-6)
where:
Kdssed = soil-water partition coefficient for contaminant in suspended sediment,
L/kg
OCssed = fraction organic carbon in suspended sediment, unitless
OCsed = fraction organic carbon in bottom sediment, unitless
Now, Equations (4-2) through (4-6) can be substituted into the right hand side of Equation (4-1)
so that this side can be function a one concentration, Cssed, and one erosion amount, ERW.
Factoring out Cssed then gives:
Cssed < -L + f. ERW + = ( 1 - fs ) ERW , (4-7)
ssed
The bracketed quantity in the right hand side of Equation (4-7) can be termed cj), so that Cssed can
be solved as (Cswb ERw)/cj). Now, the numerator in this term can be expanded to describe
contaminant contributions by a site of contamination and contaminant contributions by the rest of
the watershed. Included in this solution is the assumption made above that soils eroding into
water bodies are "enriched":
= Cs SLs As E SDs + Cw SLw ( Aw - A . ) E SDw (4.8)
where:
Cswb = concentration on soil entering water body, mg/kg
ERW = total watershed erosion, kg/yr
Cs = contaminated site soil concentration of dioxin-like compound, mg/kg
E = enrichment ratio, unitless
SLS = unit soil loss from contaminated site area, kg/ha-yr
As = area of contaminated site, ha
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SDS = sediment delivery ratio for soil eroding from contaminated site to water
body, unitless
Cw = average concentration of dioxin-like compound in effective area of
watershed not including contaminated site, mg/kg
SLW = average unit soil loss for land area within watershed not including
contaminated site, kg/ha-yr
Aw = effective drainage area of watershed; the area contributing sediment which
mixes with the sediment originating from As, ha
SDW = sediment delivery ratio for watershed, unitless
Finally, the right hand side of Equation (4-8) can be termed, p, and the concentration in
suspended sediment, Cssed, is equal to p/cjx All the terms in p/cj) are input parameters or can be
solved as a function of input parameters. Other water body concentration terms, Cwat and Csed,
can now be solved using Equations (4-5) and (4-6). Note that this solution is applicable to both
stationary water bodies such as ponds or lakes and moving water bodies such as streams or
rivers. The differences in the two water systems can be expressed in the parameters, effective
watershed area, Aw, water body volume, Vwat, and organic carbon contents of suspended solids
and bottom sediments, OCssed and OCsed. Guidance on these terms and assignment of values for
the demonstration scenarios in Chapter 5 is now given.
• D Cs and Cw: These are concentrations of dioxin-like compounds in the
contaminated site soil, Cs, and the average within the effective area of the watershed, Cw. The
contaminated site concentrations drive the concentrations assumed for most exposures, and is a
principal user input. The simplest assumption for Cw is that it is 0.0. However, examination of
soil data from around the world shows that, where researchers have measured concentration in
what they described as "background" or "rural" settings, soil concentrations of CDD/Fs are in the
non-detect to low ng/kg (ppt) range. Chapter 3 of Volume II of these Dioxin Exposure
Reassessment Documents describes studies measuring dioxins in rural parts of America and
calculates a TEQDFP-WHO98 of 4 pg/g (ppt). Example Scenarios 1 and 2 in Chapter 5
demonstrate the methodologies in this chapter in what are termed "background" settings. For
these example scenarios, the 17 dioxin-like CDD/Fs are initialized to values that have been
measured in an actual background setting.
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• D E: Enrichment refers to the fact that erosion favors the lighter soil particles,
which have higher surface area to volume ratios and are higher in organic matter content.
Therefore, concentrations of organic contaminants, which are a function of organic carbon
content of sorbing media, would be expected to be higher in eroded soil as compared to in-situ
soil. While enrichment is best ascertained with sampling or site-specific expertise, generally it
has been assigned values in the range of 1 to 5 for organic matter, phosphorous, and other soil-
bound constituents of concern (EPA, 1977). The enrichment ratio would be expected to be
higher in sandy soils as compared to silty or loamy soils because the finer silt particles which
erode from a soil generally characterized as sandy are more a deviation from the norm compared
to silt particles which erode from a soil generally characterized as silty or loamy. The example
scenarios in Chapter 5 modeled mid-range agricultural loam soils (as modeled with organic
carbon fractions, soil loss parameters as discussed below). The enrichment ratio will therefore be
assigned a value of 3.0 in all circumstances.
Two data bases have been found which justify the use of the enrichment ratio in this
context. One is the data base from Connecticut which involved the sampling of surface soils and
sediments from several rural locations in Connecticut (CDEP, 1992). Enrichment ratios were
calculated from individual sites and for three dioxin-like compounds as the ratio of the
concentration measured in sediment to the concentration measured in nearby surface soil
samples. Enrichment ratios ranged from 0.33 to 12.3 with overall averages for the three dioxin-
like compounds of 1.58 (for 2,3,4,7,8-PCDF), 2.59 (2,3,7,8-TCDF), and 3.86 (2,3,7,8-TCDD).
Further details on this data are found in Chapter 7. A first set of results from another research
study on the Hudson River was described in Smith, et al. (1995). Using modeling, they
concluded that the concentration of dioxins in surficial sediment layers of the Hudson River were
principally caused by soil erosion: 76% of the total deposition of CDD/Fs to bottom sediments
were caused by surface erosion, with other causes including anthropogenic waste (sewer
outflows; 20%), and direct atmospheric input (4%). To model the concentrations in the Hudson
river surficial sediments, they used an enrichment ratio of 1.6 (defined as here as the ratio of the
sediment concentration to the surficial soil concentration), based on an earlier analysis of particle
sources of the Hudson river and its tributaries. The data presented in Smith, et al. (1995) on
surface soils and sediments also show the disparity between surface soils and sediments -
sediment concentrations are higher than surface soil concentration and sometimes substantially
higher. However, since the Hudson River watershed is fairly complex and includes important
sources other than soil erosion, it would be inappropriate to estimate enrichment ratios from the
data as was done for the Connecticut data.
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• D SLS and SLW: These are the unit soil loss, in kg/ha, from the exposure site and
the average from the effective land area draining into the surface water body. In the simplest
case, the unit losses can be considered equal. In the most complicated solution, the effective
drainage area can be broken up into "source areas", where each source area can be unique in
terms of the erosion potential, concentration of contaminant, and so on. The total contribution
equals the sum of contributions from each source area, as: £Cj*SLj*Aj*Ej*SDj for ^e right hand
side of Equation (4-8) for j number of source areas not including the exposure site. For direct
input into Equation (4-8), the terms CJ3 SLj, AJ3 EJ3 and SDj, should be determined and Cw, SLW,
Ew, and SDW should be estimated as weighted averages over all source areas, Aj. The effective
drainage area, Aw, would be the sum of all source areas, Aj.
For the example scenarios in Chapter 5 demonstrating the "background" settings, SLS and
SLW are assumed equal. This generally assumes that erosion parameters for the site of exposure
mirror the averages for the drainage area. Also, the enrichment ratio, E, is assumed to be
constant for all watershed soils. For the demonstration of the soil contamination source category,
the site of contamination is assumed to have different erosion characteristics. The following is
offered as general guidance and background for estimation of unit soil losses in this assessment.
The unit soil loss is commonly estimated using the Universal Soil Loss Equation. This
empirical equation estimates the amount of soil eroding from the edge of a field (Wischmeier and
Smith, 1965):
SL = R K LS C P (4-9)
where:
SL = average annual soil loss, Eng. tons/acre-year
R = rainfall/runoff erosivity index, t-ft/ac-yr
K = soil erodibility factor, t/ac-(unit of RF)
LS = topographical factor, unitless
C = cover and management practice, unitless
P = supporting practices factor, unitless.
Several references are available to evaluate USLE factors for agricultural and non-agricultural
settings (EPA, 1977; USDA, 1974; Wischmeier, 1972; Novotny and Chesters, 1981). For this
assessment, values for these terms will based on assumptions about contaminated sites and rural
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soils. Justification and assumptions are given below. It should be noted that more sophisticated
models are available for estimating erosion rates (i.e., CREAMS as described in Knisel, 1980),
and should be considered in actual site-specific assessments.
• D Rainfall/erosivity index, R: The R term represents the influence of precipitation
on erosion, and is derived from data on the frequency and intensity of storms. This value is
typically derived on a storm-by-storm basis, but it has been compiled regionally for the
development of average annual values (EPA, 1977). Annual values range from less than 50 for
the arid western United States to greater than 300 for the Southeast. The value used in this
assessment will be 160, which is typical of rainfall patterns seen in much of the midwestern
United States.
• D Soil erodibility, K: The soil erodibility factor reflects the influence of soil
properties on erosion, with values ranging from less than 0.05 for non-erodible sandy soils to
greater than 0.50 for highly erodible silty soils. The value used in this assessment will be 0.30,
which is typical of, for example, sandy or silty loam soils with 2% - 4% organic matter contents.
• D Length-slope factor, LS: The topographic factor reflects the influence of slope
steepness and length of the field in the direction of the erosion. Steeper slopes and longer lengths
lead to higher LS values, with a range of 0.1 for slopes less than 1.0% and lengths less than 100
ft to greater than 2.0 for slopes generally greater than 10%. The two key considerations for its
assignment, therefore, are the size of the field for which erosion estimates are being made, and
the slope of that field. The example scenarios in Chapter 5 had field sizes of 0.4 ha (1 ac) for a
rural residence, 4 ha (10 ac) for a small rural farm, and 10 ha (25 ac) for a soil contamination site.
Guidance for use of the Universal Soil Loss Equation stops short of defining appropriate sizes of
field for which unit estimates are to be derived, except that the USLE was developed for
agricultural "fields" where cover, slope, soil type, etc. are assumed to be uniform. For purposes
of estimating erosion losses in this assessment, a field of 4 ha for estimating the LS factor will be
used. In a rural watershed with agricultural and non-agricultural settings, this would be a
reasonable average area of uniformity. If square shaped, a 4 ha area translates to a side length of
200 m. For purposes of assignment of the LS factor, it will be assumed that the contaminated
site has a 2% slope. EPA (1977) (and other references as noted above) show nomagraphs giving
the LS factor as a function of slope length and slope. With a 200 meter slope length and a 2%
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slope, the LS factor is approximately 0.20. This factor will be used for all soil loss estimates
required in this assessment.
• D Support practice factor, P: The support practice factor reflects the use of
surface conditioning, dikes, or other methods to control runoff/erosion. P can be no greater than
1.0. However, values less than 1.0 should only be assigned when specific practices are employed
which are designed to reduce erosion. For the example scenarios in Chapter 5, it will be assumed
that no such practices are in place at the site of concern or throughout the watershed to control
erosion. Therefore, a value of 1.0 will be assumed.
• D Management practice factor, C: The final term in the USLE is the cover and
management practice factor, C, which primarily reflects how vegetative cover and cropping
practices, such as planting across slope rather than up and down slope, influences erosion. C
values can be no greater than 1.0, with this value appropriate for bare soils. A C value of 1.0 is
an appropriate choice for active landfills or sites of high soil contamination (like Superfund sites)
mostly devoid of vegetation. For an inactive landfill with grass cover or any area with dense
vegetative cover such as grass, a value of 0.1 or less is appropriate. Values greater than 0.1 but
less than 0.7 are appropriate for agricultural row crops, which offer more protection than bare
soil, but not as much protection as dense vegetation.
Three erosion estimates are required for scenarios demonstrated in Chapter 5. One is for
areas of high soil contamination, or the scenario demonstrating the soil contamination source
category. It will be assumed that the contaminated site is largely devoid of vegetation in this
case, and a value of 1.0 will be assumed. A second erosion estimate is needed to characterize
average unit soil loss throughout a watershed draining into a surface water body. The example
scenarios are based on a rural setting which has agricultural and non-agricultural (i.e., rural
residences) areas. The C value in this circumstance will be assumed to be 0.3. Finally, a soil
erosion estimate is needed in the algorithm transporting contaminated soil from an area of high
soil contamination to a nearby site of contamination, as part of the algorithms developed for the
soil contamination source category. In this case, the land between a site of soil contamination
and the nearby site of exposure will be assumed to be covered with dense vegetation, such as
grass. In this case, the C value will be 0.1.
As just described, three unit soil loss estimates are required for this estimates and the
difference between the three will be expressed in the C term. Multiplication of the five USLE
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terms gives unit soil loss estimates of 9.60 (with C = 1.0), 0.96 (with C = 0.1), and 2.88 (with C
= 0.3) t/ac-yr. The value of SLS and SLW for the demonstration of the background setting
scenarios in Chapter 5 is 2.88 t/ac-yr. Since Equation (4-8) and other uses of unit soil loss
estimates are needed in kg/ha-yr, these unit losses are easily converted to 21515, 2152, and 6455
kg/ha-yr.
• D As and Aw: These are the area terms, including the area of the contaminated site,
and the effective drainage area of the watershed, both in ha. The scenarios demonstrated in
Chapter 5 have assumed 0.4 ha (1 acre roughly) for exposure sites described as rural residences,
4 ha (10 acres) for farms, and 10 ha (25 acres) for the site of soil contamination. If the soil
contamination site is at the site of exposure, as in the "on-site" source category, then As should be
assigned an area equaling the site of exposure (and the concentration term, Cs, should equal the
average soil concentration over this site of exposure). If the area of contamination is away from
the site of exposure, As should equal the total area of contamination (and again Cs should equal
the average soil concentration over this area).
The total area impacting a river system has been termed a watershed. For purposes of this
assessment, an "effective" drainage area will almost always be less than the total area of a
watershed. A "watershed" includes all the land area which contributes water to a river system.
For large river systems, this area is in the order of thousands of square miles and includes several
tributaries and smaller streams feeding into the main branch of the river. Each stream and
tributary has its own sub-basin, whose sediment originates from a land area much smaller than
thousands of square miles. If the contaminated site lies within that sub-basin, that it would be
appropriate to include only the area within that sub-basin as the effective drainage area. This is
one circumstance where an "effective drainage area" would be less than a total watershed area.
Another consideration for determining the effective drainage area is the positioning of the
contaminated site with respect to the point where water is extracted for drinking and fish are
caught for consumption. If these points are significantly upstream in the river system in relation
to the contaminated site, there is no reason to conclude that sediments or water near where the
water is extracted are impacted by the contaminated site. If these withdrawal points are
downgradient of the contaminated site, then there is reason to believe that sediments and water
are impacted. However, if they are downgradient from the contaminated site but not at the
bottom of the watershed, then sediment and water quality further downgradient from the
withdrawal points is not of concern and land draining into these downgradient portions would not
be part of the "effective drainage area". One further possible consideration is how far upgradient
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in the watershed one should go when determining the size of the effective drainage area. While
sediments introduced at the furthest points may eventually work their way down to the mouth of
the watershed, this may take geologic time and not recent historic time. Therefore, sediment
quality near a site of contamination need not consider these far reaches.
For a standing water body such as a lake or a pond substantially fed by ground water
recharge, an assumption that probably should be made using the simple framework of this
assessment is that all sediments within the lake/pond are completely mixed. Therefore, the
effective area should equal all area around the lake/pond contributing sediment, and, as in the
above discussion on river systems, a part of the land area contributing sediments to streams or
rivers which may feed the standing water body.
From this discussion, it is clear that determination of an effective drainage area depends
on site specific considerations, but it will likely be less than the total watershed area. The
demonstrations in Chapter 5 assume a reasonably large watershed area draining into a river that
can support a fish population suitable for recreational fishing. The effective drainage area of this
watershed, Aw, will be assumed to be 100,000 hectares (1*109 m2, 1*103 km2, 250,000 acres, 385
mi2). Furthermore, it will be assumed that the water body in question is a river, which mainly
impacts the assignment of the total suspended solids parameter, TSS (as discussed below). This
assignment is not based on any specific sites that have been studied. As noted , it is justified as
being a reasonable size to drain into a river large enough to support recreational fishing
A useful data source for this term and the suspended sediment term below, for specific
sites in the United States, is Appendix F in Mills, et al. (1985). This appendix includes a
compilation of data from river and reservoir sediment deposition surveys, including total
drainage area, water body volumes, and rates of sediment deposition (mass/area-time). A caution
in using this and similar data bases when evaluating specific sites is that, again, these total
drainage areas are just that, total areas. Water bodies in this data base are located in the 48
conterminous states. An estimate of suspended sediment concentrations can be made using the
water volume and the sediment deposition rates from this data, and an assumption on sediment
deposition velocity. The specific weight of sediments in the water body, also supplied in this
appendix, can be used to estimate sediment deposition velocity.
• D SDS and SDW: These are the sediment delivery ratios applied to the exposure site
and the watershed as a whole. Such a ratio is required because not all the soil which erodes from
an area deposits into the receiving water body. The following delivery ratio was proposed for
construction sites (EPA, 1977):
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SDs = ( 3.28 DL )-°-22 (4-10)
where:
SDS = sediment delivery ratio for soil eroding from contaminated site to water
body, unitless
DL = distance from contaminated site to receiving water body, m
3.28 = converts m to ft (empirical equation was developed for units of ft).
Note that the sediment delivery empirical equation simplifies all land features pertinent to
erosion to a function only of length. The equation was developed to estimate sediment loads
from construction sites to nearby surface water bodies, and from distances up to 250 m (800 ft,
roughly). Without specific information on the sites from which it was developed, it is assumed
that the land area between the construction sites and the receiving water body is "average" and
this relationship can be used for applications other than construction sites.
As noted in previous bullets, the example scenarios demonstrating the background setting
assumed Cs = Cw, and SLS = SLW. The impacted water body was assumed to be 150 meters away
from the site of exposure for this demonstration setting. This distance translates to a delivery
ratio of 0.26. Site-specific conditions could result in a larger (steeper slope, e.g.) or smaller
proportion of the eroded soil being delivered to the water body than would be estimated with this
equation.
Figure 4-4 shows a watershed delivery ratio as a function of watershed size. As seen, the
ratio decreases as land area increases. The total watershed size, Aw, assumed for the example
scenarios in Chapter 5 was 1000 km2. From Figure 4-4, this translates to a watershed delivery
ratio, SDW, of 0.06. If the watershed is larger than about 2000 km2, which is the extent of
coverage of this figure from Vanoni (1975), the following empirical equation was found to
satisfactorily extend the relationship seen in Figure 4-4:
SDw = 0.6 (Aw )-°-125 (4-11)
where:
SDW = sediment delivery ratio for a watershed, unitless
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Aw = effective drainage area of watershed; the area contributing sediment which
mixes with the sediment originating from the contaminated site, m2
• D fs: As soil erodes into the water body, it will settle onto the bottom to become
bottom sediment. Part of the settled material will become resuspended because of turbulent flow.
The finest materials in eroded soil may not settle for a long time, and essentially always be in
suspension. One way to arrive at the fraction of annually eroding material which remains in
suspension ("remains in suspension" for purposes of discussion - in reality, little, if any, will
remain in suspension, but will rather deposit and resuspend) involves complex modeling. A
wealth of such models exist, such as those described in Wang (1989). The approach used here is
more simple than those in Wang (1989).
If an average level of suspended material in the water were specified, in units of mg/L,
what would be known with otherwise required parameters is the total amount of erosion reaching
the water body (as discussed above) as well as the annual water volume (discussed below). A
required parameter for this assessment will therefore be the level of suspended solids in the water
body, TSS. With this parameter and the annual water flow volume, Vwat, the total suspended load
equals, TSS (mg/L) * Vwat (L/yr). The assignment of these two terms are 10 mg/L and 4.8*10n
L/yr, leading to a total suspended load of 4.8*1012 mg/yr, or 4.8*106 kg/yr. Total erosion into the
water body, in similar units, equals, As * SLS * SDS + (Aw - As) * SLW * SDW. With parameter
assignments as discussed above, the total annual erosion equals 1.29 x 107 kg/yr. Therefore, the
fraction of total load that is suspended is 0.36 (4.8*106/1.3*107).
Given this formulation, the fs term is not a model input value, but is solved on the basis of
the other parameters noted.
• D TSS: This is the total suspended sediment in the water body. This value will be
lower for standing water bodies such as ponds or lakes as compared to streams or rivers. The
more turbulent flow in rivers will suspend sediments to a greater degree than a relatively calm
lake. A complex modeling exercise evaluating the impact of 2,3,7,8-TCDD to Lake Ontario
assumed a suspended sediment concentration of 1.2 mg/L (EPA, 1990b). For use in pond or lake
settings, an assumption of a suspended sediment concentration of 1-2 mg/L is reasonable. All
example scenarios in Chapter 5 assume that the 100,000 ha watershed drains into a river suitable
for supporting fish for consumption and water for drinking purposes. General guidance offered
for the potential for pollution problems in rivers and streams as a function of average suspended
sediment concentration are: 10 mg/L or less - no problem, 100 mg/L or less - potential problem,
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and greater than 100 mg/L - probable problem. A cutoff concentration for protection of aquatic
life is 80 mg/L (Mills, et al., 1985). The value assumed for TSS for all example scenarios in
Chapter 5 is 10 mg/L, indicating no turbidity problems and a river supportive offish for
consumption.
• D Vwat: The stream in the example scenarios will be assumed to derive its annual
flow only from the effective drainage area, Aw. Given the area of drainage, one way to estimate
annual flow volume is to multiply total drainage area (in length squared units) times a unit
surface water contribution (in length per time). The Water Atlas of the United States (Geraghty
et al., 1973) provides maps with isolines of annual average surface-water runoff, which they
define as all flow contributions to surface water bodies, including direct runoff, shallow
interflow, and ground-water recharge. The range of values shown include 5-15 in/yr throughout
the Midwest cornbelt, 15-30 in/yr in the South and Northeast, 1-5 in/yr in the desert Southwest,
and a wide range of 10-40 in/yr in the far West. For this assessment, an assumed 19 in/yr is used
to estimate the annual flow volume. Over a 100,000 hectare drainage area, total flow volume
equals 4.8 x 1011 L/yr (19 in/yr * 0.0254 m/in * 100,000 ha * 10,000 m2/ha * 1000 L/m3).
• D Kdssed: This adsorption partition coefficient describes the partitioning between
suspended sediment and the water column. For numerous applications for organic contaminants,
particularly for estimating the partitioning between soil and soil water, this partition coefficient
has been estimated as a function of the organic carbon partition coefficient and the fraction
organic carbon in the partitioning media:
Kdssed = K°c OCssed (4-12)
where:
Kdssed = soil-water partition coefficient for contaminant in suspended sediment,
L/kg
Koc = organic carbon partition coefficient for contaminants, L/kg or cm3/gm
OCssed = fraction organic carbon content of suspended sediment, unitless.
The organic carbon partition coefficient, Koc, can be a measured value or it can be
estimated. Chapter 2 of Volume n of this dioxin exposure reassessment reviewed the available
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literature on Koc for 2,3,7,8-TCDD. Based on available studies measuring the Koc, a log Koc
value of 6.6 was recommended for this congener. Recommendations could not be made for the
other dioxin-like congeners because of few measured data points, and when available, the data
sometimes showed considerable differences.
In the absence of measured values, the Koc can be estimated from a chemical's octanol
water partition coefficient, Kow. Empirical equations relating Kow to Koc are listed in Lyman,
et al. (1982). Of six different equations listed in that reference, the following derived by
Karickhoff, et al. (1979) is used to estimate the Koc for the example compounds in Chapter 5:
log Koc = log (Kow) - 0.21 (4-13)
where:
Koc = organic carbon partition coefficient, L/kg
Kow = octanol water partition coefficient, unitless
This equation was empirically developed from a limited number of hydrophobic contaminants
(n=10, R2 = 1.00). It implies that Koc is very similar to Kow for strongly sorbed compounds
such as the dioxin-like compounds. Using the log Kow of 6.8 recommended in Chapter 2,
Volume II of this assessment for 2,3,7,8-TCDD, the estimate of log Koc is 6.59. This is
essentially the same as the recommendation for log Koc of 6.6 for 2,3,7,8-TCDD based on
measurement studies. This relationship will be used to estimate the Koc for all other dioxin-like
compounds.
• D OCsed, OCssed: The organic carbon content of solids and sediments of water
bodies are generally higher than organic carbon contents of the surrounding lands. Furthermore,
organic carbon contents of suspended organic materials and solids are typically greater than those
of bottom sediments. A significant sink for strongly hydrophobic contaminants such as the
dioxin-like compounds is thought to be suspended, or non-settling, organic material. In
modeling 2,3,7,8-TCDD in Lake Ontario (EPA, 1990b) using the WASP4 model, a compartment
separate from suspended solids termed "non-settling organic matter" served as a permanent sink.
For purposes of this assessment, a single reservoir of suspended materials onto which incoming
dioxin-like compounds sorb is principally characterized by OCssed, and the values selected for
OCsed and OCssed should reflect the relative partitioning behavior of suspended and bottom
materials. As noted above, these water body carbon contents are also related to the organic
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carbon contents of surrounding soils. The model parameter, OCsb is the soil organic carbon
fraction and is required for modeling of soil contamination by dioxin-like compounds. Foth
(1978) lists the organic nitrogen content of several soil types ranging from sand and sandy loam
to clay. The range from that list is 0.0002 - 0.0024 on a fractional basis. Assuming a carbon to
nitrogen ratio of 10 (Brady, 1984; who notes that C:N ratios vary from 8 to 15, with the typical
range of 10 to 12), organic carbon ranges from 0.002 to 0.024. A soil organic carbon fraction,
OCsl, is assumed to be 0.01 for all example settings in Chapter 9, which is in the middle of this
range. The organic carbon content of bottom sediments, OCsed, will be higher at 0.03. Bottom
sediments originate as erosion from surrounding land, but also include decay of organic materials
within water bodies. The organic carbon content of suspended materials can approach 0.20, but
OCssed will be assumed to be 0.05 for the example settings in Chapter 5.
4.3.2. Exposure Site Soil Concentrations
If the soil contamination occurs at the site of exposure, than the exposure site soil
concentration is simply the soil concentration as initialized by the model user. As described in
the Section 4.3.1 above, a key assumption for the simplistic models used in this assessment is
that the source strength does not vary over the course of the exposure. Therefore, the soil
concentration as initialized remains unchanged during the exposure period. An example of a
situation where the contaminated soil is at the site of exposure is a residential property impacted
similar to the properties at Times Beach, Missouri, where residential soils were impacted by the
spraying of waste oils for dust suppression. Another example would be the analysis of worker
exposures on a Superfund site contaminated by dioxin-like compounds.
A more typical application of the methodologies for soil contamination, however, would
be the circumstance where sites of exposure are near, but not at, the site of soil contamination.
For example, residences or farms near a Superfund site would be the scenario of concern for the
contaminated soil source category. This section describes methodologies for estimating the soil
concentration at an exposure site near a contaminated soil site.
The key assumptions for the solution strategy estimating exposure site soil concentrations
resulting from an off-site soil contamination source are: 1) the exposure site soil becomes
contaminated due to erosion of contaminated soil from the source to the exposure site, 2) the
amount of soil at the exposure site does not increase, which means that soil delivered to the site
via erosion is matched by an equal amount which leaves the site, and 3) not only does soil erode
off the contaminated site en route to the exposure site, but soil between the contaminated site and
the exposure site also erodes to the exposure site.
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The second and third assumption translate to:
D1 + D2 - SR (4-14)
where:
Dj = mass of soil delivered from off-site contaminated source, kg
D2 = mass of soil delivered from land area between contaminated source and
exposure site, kg
SR = mass of soil removed from exposure site, kg.
The mass balance equation for exposure site soil concentrations can now be qualitatively
stated as (with "AC" used as shorthand for change in exposure site soil concentration over time):
(the incremental addition to C resulting from the change in erosion of
contaminated soil)
AC = (the incremental substracti on of C resulting from removal of now
contaminated soil from the exposure site)
(the incremental substraction of C resulting from dissipation of
residues at the exposure site)
This can be expressed mathematically as:
dC D. C SR C
- - kC (4-15)
^ }
dt M M
where:
Ce = the exposure site soil concentration, mg/kg
Dj = mass of soil delivered from off-site contaminated source, kg/yr
Cs = contaminated site soil concentration of dioxin-like compound, mg/kg
M = mass of soil at exposure site into which contaminant mixes, kg
SR = mass of soil removed from exposure site, kg/yr
ks = first order soil dissipation rate constant, 1/yr.
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Assuming that the contaminant concentration at the exposure site, Ce, is initially 0, Equation
(4-15) can be solved to yield:
D. C
IS
SR +
—
M
1 - e M \ (4-16)
which computes Ce as a function of time, t (in years since k is in years). This can be solved for
various increments of time starting from a time when the exposure or contaminated site initially
became contaminated, or it can be simplistically assumed that the contamination has existed at
the contaminated site for a reasonably large amount of time such that the exponential term
approaches zero. The rate at which the exponential term approaches zero is a function of the
value of k, the first-order dissipation constant. The smaller the value of k (i.e., the longer the
half-life of dioxins transported to a nearby site), the longer it will take for the system to reach
steady state. The algorithms of this assessment, such as the surface water algorithms described
above and the calculation of the biota calculations as described below, have simplistically
assumed that steady state has occurred by the time any exposure has occurred. This simplicity
will be continued here. Generally, assuming steady state will lead to conservative results - i.e.,
overall impacts over time will be less if the systems modeled have not reached steady state (or
close enough to it) by the time exposure begins. At steady state, the exponential term drops out,
and C is estimated as:
One final note on this solution is that, unlike the solution for estimating water body
impacts, there is not an assumption that the eroded soil is enriched in comparison to the soil at
the site of contamination; an enrichment ratio was used in the surface water algorithm, but not
for this algorithm. The foundation for enrichment of sediments was the arrival of finer textured
soils with higher concentrations of constituents of concerns (organic contaminants, inorganic
nutrients) at water bodies. This theory is well founded and there is data on the enrichment of
dioxin-like compounds in sediments as compared to surface soils. On the other hand, there is no
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similar evidence or foundation for assuming enrichment for dioxin-like compounds in soil when
the transport is to a nearby site instead of a more distant water body.
Equation (4-17) was used to estimate exposure site concentrations resulting from the
erosion of contaminated soil in the demonstration of the soil contamination source category in
Chapter 5. Guidance for estimation of these terms including justification for their values as
selected in the example settings are:
• D ks: For soil residues at the site of soil contamination, the assumption is made
that residues do not degrade or dissipate to the point of reducing the concentration of the "initial"
soil levels. This was partly based on information indicating generally low rates of biological or
chemical degradation for the dioxin-like compounds of this assessment, coupled with the
assumption that the soil contamination was sufficiently deep implying a reservoir of contaminant
that would remain available during a period of exposure. These assumptions are less likely to be
valid for residues which have migrated over the surface to deposit on the exposure site. The
deposition is likely to result in only a thin layer of contaminated soil. Though very small,
surface-related dissipation mechanisms such as photolysis, volatilization, or degradation, might
reduce surface soil contaminant concentrations. For these reasons, a "dissipation" rate constant is
assumed to apply to delivered contaminant, where the precise mechanisms of dissipation are not
specified, but could include transport (volatilization, erosion) and degradation (photolysis)
mechanisms.
Fries and Paustenbach (1990) suggested the use of a half-life of at least 10 years, and used
a 15 year half-life in their example scenarios on the impact of air-borne deposition of
2,3,7,8-TCDD originating from stack emissions. In a later publication, Paustenbach, et al. (1992)
reviewed the literature on the environmental half-life in soil. For surface soils, they cited the
evidence from Eglin Air Force Base (Young, 1983) suggesting a half-life of 10 to 12 years for
2,3,7,8-TCDD, and the work of Cerlesi, et al. (1989), who estimated a soil half-life of 9.1 years
for 2,3,7,8-TCDD in Seveso soil. Paustenbach, et al. (1992) also discussed the fact that the loss
of TCDD from soil is predominantly through volatilization and photodegradation of residues at
the soil surface, and that loss mechanisms are minimal below the soil surface. They suggested
that ultraviolet radiation penetrates only about the surface 0.1 cm, which implies that
photodegradation could be limited below that depth. They concluded that their review supported
the concept that TCDD probably has a half-life of 9-15 years in surface soil and 25-100 years in
subsurface soils. McLachlan, et al. (1996) reported on an analysis of soil taken from
experimental plots which had been amended with sewage sludge in 1968 and sampled in 1972,
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76, 81, 85, and 90. These archived samples were analyzed for all 17 dioxin-like CDD/Fs, and
based on an analysis of results, McLachlan and coworkers concluded that half-lives were on the
order of 20 years, with dioxin removal from the plots being mainly physical removal processes
(overland runoff, wind erosion). Furthermore, their results suggested that all congeners had been
removed at roughly the same rate, which is why they concluded that removal processes were
mainly physical and very little in-situ degradation appeared to be occurring.
This assessment assumes that residues transported from a site of soil contamination to a
site of exposure mix in a soil layer extending 2 cm. This will be discussed below. Therefore, the
reservoir of dioxin-like compounds includes a surficial component and what could be considered
a subsurface component. This suggests that the average half-life of residues within this depth
might be greater than the 9-15 year estimate for surface soils by Paustenbach, et al. (1992), but
less than the 25-100 year estimate made by these researchers. This assessment will assume a
half-life of 25 years for the 2-cm reservoir of transported dioxins, which translates to a ks of
0.0277 yr"1. Ideally, half-lives should be assigned to individual dioxin-like congeners as many of
the other contaminant-specific parameters such as Kow or bioconcentration parameters.
However, the data for 2,3,7,8-TCDD was not definitive, and based on McLachlan's findings with
sewage sludge amended soils, it appears reasonable to assign the same half-life to all congeners.
The uniform rate of degradation, ks, for all CDD/Fs, and PCBs, will be 0.0277 yf1.
• D M: The delivered contaminant mixes to a shallow depth at the exposure site.
The mixing depth depends on activities which disturb the surface, such as construction, plowing,
vehicle traffic, movement of cattle or other animals, burrowing action of animals, other
biological activity, normal leaching, and raindrop splash. Mixing depths for fallout plutonium
have been found to be 20 cm on cultivated land and 5 cm on uncultivated forest and rangeland
(Foster and Hakonson, 1987). Fries and Paustenbach (1990) suggested a depth of 15 cm for
agricultural tillage, but assumed values of 1 and 2 cm for various sensitivity tests. However, they
did not need to make a distinction between tilled and untilled situation because vegetation
(pasture grass and forage for estimating beef and milk fat concentration; above ground fruits and
vegetables for human consumption) was assumed to be impacted only by particulate deposition
and not root uptake. In another assessment on indirect impacts from incinerator emissions, EPA
(1990a) estimated vegetation concentrations as a function of particulate depositions, root uptake,
and air-to-leaf transfer from the vapor phase. Different mixing depths for untilled and tilled
concentration estimation was required. For root uptake estimation for vegetable and other crops,
the estimated soil concentrations assuming a tillage mixing depth of 20 cm. For soil
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concentrations in untilled situations, they assumed a mixing depth of 1 cm. This mixing depth of
1 cm was retained in a later addendum to this methodology (EPA, 1993a). An earlier version of
these dioxin exposure methodologies assumed a 1 cm depth for untilled soils and a 20 cm depth
for tilled soils (EPA, 1994).
The methodology of this assessment uses 2 cm for the untilled and 20 cm for the tilled
conditions for the off-site soil source category. A principal justification for the increase from the
1 cm depth of the earlier dioxin exposure assessment methodologies (EPA, 1994) to the current 2
cm depth for surface soils comes from Brzuzy and Kites (1995). Their data on soil
concentrations of dioxin congener groups as a function of depth show definite migration of
residues below the surface. Soil core results were described and concentration versus depth was
displayed for 6 cores. The cores were undisturbed, and were taken in background settings with
high vegetative cover so that the introduction of dioxins was speculated to be due only to
atmospheric depositions - not pesticides or in an oily matrix. Briefly, the results did show a
relationship between leaching and percent organic carbon - more residues near the surface soil at
higher organic carbon content. For the 6 cores displayed: 1) a peak at the 1-2 cm depth was
found in only one core, 2) there was essentially uniform concentrations for 3 cores to 5 cm with
dropoffs thereafter, and 3) in two cores which had low organic carbon, there was a steadily rising
concentration to peaks at 45 and 55 cm. The authors speculated that downward movement was
due to movement of soil particles. They also did suggest that based on their sediment core
information that deposition to these soils had been occurring for 60 years. This is not likely to be
analogous to the situation of soil eroding from a site of soil contamination both because of the
time involved and the mechanism of transport. Still, it would appear from this data that a 1 cm
depth may not be justifiable. Based on this evidence, an untilled depth assumed for the soil
contamination source will be 2.0 cm.
A tilled depth will be assumed to be 20 cm, as in earlier assessments (EPA, 1990a; EPA,
1993a; EPA, 1994). This assumption is made because tilling gardens is assumed to distribute
surface residues to the 20-cm depth. Soil concentrations for dermal contact, soil ingestion, and
pasture grass and soil intake for cattle grazing will assume a depth of 2 cm. These activities are
assumed to occur on soil which has not been tilled. As will be described in Section 4.5, tilled
and untilled depths of mixing are also required for the stack emission source category. For that
source category, the untilled and tilled mixing depths are also assumed to be 2 and 20 cm,
respectively.
Given the area of the exposure site, the mass of soil into which the eroded contaminant is
mixed can be calculated as:
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Mu - AesBsoii d (4-18)
where:
Mu = mass of soil for contaminant mixing per unit depth, kg/m
Aes = area of exposure site, m2
Bsoil = soil bulk density, kg/m3
d = depth of mixing, m
• D Dj and D2: The first step in deriving both these amounts of soil is to use the
Universal Soil Loss Equation (USLE). This approach was described above. Justification was
given for an assumption of unit soil loss from the contaminated site of 9.6 t/ac-yr in Section
4.3.1. Dj equals this unit loss times the area of contamination times a sediment delivery ratio.
The example scenario in Chapter 5 assumed that the exposure site was 150 meters from the
contaminated site, and using Equation (4-10), the sediment delivery ratio is 0.26. The unit loss
assumed for the area between the contaminated site and the exposure site is 0.96 t/ac-yr. Since
this area is adjacent to the exposure site, there is no sediment delivery, and D2 equals this unit
loss times the area between the contaminated and exposure sites.
Dj and D2 can now be expressed as:
Dl = 0.224 SZj SDl As (4_19a)
D2 - 0.224 SL2 SD2 ABLE (4-i9b)
where:
Dj 2 = mass of soil delivered from off-site contaminated source, Dl3 and from the
land area between contaminated source and exposure site, D2, kg/yr,
SLj 2 = average annual unit soil loss, Eng. tons/acre-year, equal to 9.6 t/ac-yr for
SLj and 0.96 t/ac-yr for SL2
SDj 2 = sediment delivery ratios, unitless, 0.26 for SDX (with distance =150
meters) and 1.00 for SD2
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AS/ABLE = land area of contaminated site, As, and of area between contaminated site
and exposure site, ABLE, m2
0.224 = converts t/ac-yr to kg/m2-yr.
An adjustment is made to the sediment delivery ratio, SDj, considering the size
discrepancies between the contaminated site and the exposure site. For example, if the
contaminated site is larger than the exposure site, then the amount of eroded soil delivered 150
meters downgradient would not all mix with soil at the exposure site. On the other hand, if the
contaminated site were smaller than the exposure site, than the full amount of eroded soil
delivered 150 meters downgradient would be contained within the exposure site. A simple
correction factor, equaling the ratio of a side length of the exposure site (assumed square-shaped)
and a side length of the contaminated site size (also assumed square shaped), is used to adjust the
sediment delivery ratio:
SDla = SD, CFSD (4-20)
where:
SDla = adjusted sediment delivery ratio corresponding to SDj, unitless
SDj = sediment delivery ratio reducing the amount eroding from the
contaminated site to be delivered to the exposure site, unitless
CFSD = sediment delivery correction factor, unitless
~~ AES ' /As ' it AES < As
= 1 ifAES>As
AES = area of exposure site, m2
As = area of contaminated site, m2
Similar considerations are pertinent to the land area between the contaminated and
exposure site. Remember that the algorithm assumed that some "clean" (D2) and some
"contaminated" soil (Dx) erodes onto the exposure site, and that a similar amount of soil entering
the exposure site (R, which equals Dx + D2) leaves the exposure site so as to maintain a mass
balance. The amount of clean soil eroding from upgradient sources mixing with exposure site
soil can be larger than the amount of contaminated soil if the exposure site is larger than the
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contaminated site. If the exposure site is smaller than the contaminated, and similar to the
solution for SDla above, then only the small corridor defined by the size of the exposure site
contributes clean soil. Either way (i.e., the exposure site is larger or smaller than the
contaminated site), the size of the land area contributing clean soil is defined by the size of the
exposure site. ABLE can be estimated as the product of the distance between the exposure and
contaminated site, and the side length of the exposure site:
ABLE = DLCE SLE (4-21)
where:
ABLE = land area between contaminated and exposure site, m2
DLCE = distance from contaminated site to exposure site, m
SLE = side length of exposure site, m
= (Aes)0'5
Aes = area of exposure site, m2
4.3.3. Vapor- and Particle-Phase Air Concentrations
The algorithms for estimating vapor- and particle-phase concentrations of contaminants
were presented and derived in Hwang, et al. (1986), EPA (1985b), Hwang (1987), and Turner
(1970). The algorithms, in general, entail estimation of the flux of vapor phase and particle
bound dioxin-like compounds, and then the estimation of the dispersion of fluxes to arrive at
appropriate air concentrations. The flux calculations for emissions of vapor phase and particle
bound dioxin-like compounds is the same if the soil contamination occurs at the site of exposure
or away from the site of exposure. However, the dispersion algorithms differ, depending on
whether the site of exposure is at the site of contamination or away from the site. Discussions
below begin with the procedure for calculating the soil fluxes, the volatile flux and the wind
erosion flux, of dioxin-like compounds, and then to the two different dispersion algorithms.
The procedures for estimating volatile flux were developed for soil surface and
subsurface contamination with polychlorinated biphenyls, PCBs. The models are based on the
assumptions that: 1) PCBs move through the soil primarily by vapor phase diffusion, i.e.,
leaching is not considered, 2) PCB vapor in the soil matrix reaches a local equilibrium with pore
air, 3) degradation processes for PCBs were not considered , and 4) the PCB contamination
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occurs at the surface and extends down infinitely. These assumptions are similar to the general
types of assumptions that have been made for all the algorithms estimating exposure media
concentrations in this assessment. The procedures in that PCB assessment were also used for this
assessment. Details of the derivation are presented in Hwang, et al. (1986).
The average flux rate over an exposure duration of ED can be estimated as:
FLUXy =
(2)
(DJ (C) (H) (41) (10- 6)
Kd [ (7i) (I) (ED) ]
|0.5
(4-22)
where:
FLUXV =
Dea
cs
H
Kds
ED
I
average volatilization flux rate of contaminant from soil, g/cm2-s
soil pore porosity, unitless
effective diffusivity of contaminant in air, cm2/s
contaminant concentration in soil, ppm or mg/kg
Henry's Constant of contaminant, atm m3/mol
soil/water partition coefficient, cm3/g
exposure duration, s
interim undefined term for calculation, cm2/s
Dea Eslp
Eslp + Psoil (l-Eslp) [Kds/(41 H)]
PSOii = particle bulk density of soil, g/cm3
The effective diffusivity, Dea, is solved as a function of contaminant diffusivity in air, and
soil pore porosity:
D = D
0.33
(4-23)
where:
D
D
E,
sip
effective diffusivity of contaminant in air, cm2/s
molecular diffusivity of contaminant in air, cm2/s
soil pore porosity, unitless.
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The soil adsorption partition coefficient, Kds, is given as:
Kds - Koc OCsl (4-24)
where:
Koc = contaminant organic partition coefficient, L/kg
OCsl = fraction organic carbon in soil, unitless.
It is noted in Hwang, et al. (1986) that this procedure would tend to overestimate
emissions and resulting exposures in situations involving small spills which would not involve
deep contamination. It is also noted that the average flux rate is inversely proportional to the
square root of the duration of exposure - the longer the duration of exposure, the lower will be
the average flux rate. Whereas this solution assumes an unlimited reservoir of contaminant, it is
an unsteady state solution (unlike most other solution strategies) and is essentially an average
flux rate over an amount of time defined by the exposure duration. Inherent in the solution was
the consideration that residues dissipate by volatilization at the surface layers, resulting in
contaminants diffusing upwards from deeper soil layers over time. With this longer path of
diffusion, volatilized amounts decrease, and hence the average flux over time also decreases.
The method for determining the flux of soil particles due to wind erosion for on-site
conditions was developed in EPA (1985b) based on Gillette's (1981) field measurements of
highly erodible soils. A key assumption for this solution is that the soil surface is assumed to be
exposed to the wind, uncrusted, and to consist of finely divided particles. This creates a
condition defined by EPA (1985b) as an "unlimited reservoir" and results in maximum dust
emissions due to wind only. This solution will, in essentially all cases, overestimate the amount
of dioxin-like compounds being emitted by wind erosion. This is because the soil conditions to
which it applies, uncrusted and consisting of finely divided particles, are conditions that, at the
very least, are disrupted by rainfall or anthropogenic activities (plowing, walking, etc.). The
degree of overestimation is less in the situation of most concern - mostly unattended sites of high
soil contamination. An alternate, less conservative solution, is one described as a "limited"
reservoir solution. This model is presented in EPA (1989). A major problem with this model for
application in the current framework is that it requires site-specific daily rainfalls and
windspeeds, and it should be run for a period of time, at least a year, in order to estimate average
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daily flux. To be consistent with all other methodologies of the soil contamination source
category, and to be conservative regarding dust emissions, the "unlimited" reservoir solution is
used here. This wind erosion flux for the unlimited reservoir approach is given as (EPA,
1985b):
Ee = 0.036 (\-V) (UJUt^ F(x) (4-25)
where:
Ee = total dust flux of <10 jam particle due to wind erosion, g/m2-hr
V = fraction of vegetation cover, unitless
Um = mean annual wind speed, m/s
Ut = threshold wind speed, m/s
F(x) = a function specific to this model.
EPA (1985b) provides details allowing for the application of this equation under a variety
of circumstances. Specific parameter assignments for the application of this equation to the
example scenarios in Chapter 5 are given below.
The unit dust flux is easily converted to a contaminant flux by multiplying by soil
concentration:
FLUXWE = (2.8*10-17) Cs Ee (4-26)
where:
FLUX^ = contaminant wind erosion flux from soil, g/cm2-s
Ee = total dust flux of <10 pm particle due to wind erosion, g/m2-hr
Cs = contaminant concentration in soil, ppb or ng/g
2.8*10"17 = converts ng/m2-hr to g/cm2-sec.
Like for vapor-phase dioxin-like compounds, the flux is converted to a concentration by
the use of a dispersion term. Hwang (1987) describes an algorithm for the dispersion of
contaminants emitted from an area source. Vapor- and particle-phase concentrations along the
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center (y=0.0) of this area source are used to estimate concentrations when the site of exposure is
at the site of contamination:
(2/7T)0-5 FLUX a 1010 erfje) -o.s(z/sf
vp
(4-27)
where:
Cvp
FLUX
Um
sz
z
erf
e
10
10 _
vapor- or particle-phase concentration of contaminant in air, pg/m3
average volatilization or wind erosion flux rate (FLUXV or FLUX^) of
contaminant from soil, g/cm2-s
side length of contaminated site parallel to the wind direction, m
mean annual wind speed, m/s
vertical dispersion coefficient in air, m
height of the exposed individual, m
error function
error function term, unitless
b/(2*(2*Sy>5)
side length perpendicular to the wind direction, m
horizontal dispersion coefficient in air, m
converts g/cm2-m to
The dispersion terms, Sz and Sy can be estimated using site-specific wind rose data. In the
absence of data, these terms can be estimated assuming the most common stability class, D, as:
Sy = 0.1414 X0*94
(4-28a)
Sz = 0.222 X1
0.725
(4-28b)
where:
horizontal and vertical dispersion coefficient, m
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X
distance upwind of the contaminated site, m
Estimating the dispersion and resulting exposure site concentrations of air-borne
contaminants, originating at a site of contamination which is near but not at the site of exposure,
requires a different solution than this solution for the concentrations along the center line of an
area source. A simplified solution, given as a virtual point source model, can be found in Turner
(1970). This model approximates the dispersion that occurs from an area source by using an
imaginary point source. This point is located upwind of the actual source at a distance calculated
to create a lateral dispersion at the site equal to its width:
C.
vp
2.03 FLUX Asc FREQ 1010
VD S U
(4-29)
where:
C,p
FLUX
As
FREQ
VD
Sz
Um
1010
vapor or particle phase concentration of contaminant in air, |_ig/m3
average volatilization or wind erosion flux rate (FLUXV or FLUX^) of
contaminant from soil, g/cm2-s
area of contaminated site, m2
frequency wind blows from source to receptor, unitless
virtual distance, source center to receptor, m
vertical dispersion coefficient, m
average wind speed, m/s
converts g/cm2 to |ig/m2.
The term, FREQ, in not in the original Turner (1970) solution. It has been added to this
equation to appropriately account for changing wind directions, and hence, obtain a more
accurate annual average air concentration. The vertical dispersion, Sz, is estimated as an
empirical function of the distance from the source center to receptor:
Sz = 0.222 x
0.725
- 1.7
x < 1000 m
(4-3 Oa)
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Sz = 1.260 jc°'516 - 13.0 , x > 1000 m (4-30b)
where:
Sz = vertical dispersion coefficient, m
X = actual distance from source center to receptor, m.
The virtual distance, VD, is an empirical function of the width of the contaminated area
and the actual distance from source center to receptor:
VD = 2.514 b + x (4-31)
where:
VD = virtual distance, source center to receptor, m
b = width of contaminated area perpendicular to wind direction - defined
previously as side length for assumed square-shaped contaminated area,
m
X = actual distance from source center to receptor, m.
Background on Koc and OCsl were given in Section 4.3.1. above. Guidance for other
terms in the four algorithms - volatile flux, wind erosion, on-site dispersion, off-site dispersion,
now follow.
•D Eslp. Porosity is defined as the pore space in soils occupied by air and water, and
for sandy surface soils show a range of 0.35-0.50. Medium to fine-textured soils (loams, clays,
etc.) show a higher range of 0.40-0.60 (Brady, 1984). Soil porosities in the example settings
were 0.50.
•D H: Henry's Constants were discussed in Volume 3, Chapter 2. The values of H
used for the three example compounds were: for 2,3,7,8-TCDD - 3.29*10"5 atm-m3/mole; for
2,3,4,7,8-PCDF - 4.98* 10'6 atm-m3/mole; and for 2,3,3',4,4',5,5'-HPCB - 6.65* 10'5 atm-m3/mole.
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•D Pson: Particle bulk density is defined as the mass of a volume of soil solids. This
contrasts the more common parameter, bulk density, which is the mass of a unit of dry soil,
which includes both pores and solids. Particle bulk density, Psoil, has a narrow range of 2.60 to
2.75, and for general calculation purposes, Brady (1984) recommends a value of 2.65 for average
mineral surface soils, the value used for the example settings.
•D ED: The exposure duration is simply the amount of time individuals are
exposed. Two exposure durations were used in the demonstration scenarios, 9 years for "central"
and 30 years for "high end" exposures. Used in this algorithm, and as discussed earlier, longer
exposure durations translate to lower average volatilization fluxes. This presumes a soil
concentration assumed to be uniform over depth starting at time zero, and to become depleted
over time. The selected exposure durations of 9 (2.83*108 sec) and 30 years (9.46*108 sec) was
used.
•D Dc: Molecular diffusivities in air of the example compounds could not be found
in the literature. However, diffusivities of one compound can be estimated from another with the
following (Thibodeaux, 1979):
D MW,
(4-32)
{ }
where:
Da b = Molecular diffusivities of compounds a and b, cm2/s
MWa b = Molecular weights of compounds a and b, g/mole
Thibodeaux (1979) lists the molecular diffusivity of diphenyl at 25 °C at 0.068. Given the
molecular weight of diphenyl of 154 g/mole, the diffusivities of the example compounds are:
2,3,7,8-TCDD (MW = 322) = 0.047; 2,3,4,7,8-TCDF (MW = 340) = 0.046; and 2,3,3',4,4',5,5'-
HPCB (MW = 396) = 0.043.
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•D Um: Mean annual windspeeds vary from between 2.8 and 6.3 m/s (EPA, 1985b).
An assumption of 4.0 m/s in the absence of site-specific average wind speeds was made for the
example scenarios of this assessment.
•D a, b, z, and x: Simple assumptions can be made to assign values to the length
terms above: a, b, z, and x. Assuming a square-shaped contaminated site, a equals b which
equals the square root of the area of the site. A common assumption for z, the height of the
exposed individual, is 2 m. The x term can be assumed equal to a side length (a or b), or can
equal the side length plus the distance to the exposed individual if the contamination is not
on-site and dispersion is modeled as "near field." For the residence and farm setting examples in
Chapter 5, where the contamination was on-site, the x term was equal to a side length.
• D FREQ: Where the wind blows from all directions equally, then it will blow
from one compass sector about 15% of the time. On these bases, a FREQ of 0.15 was used in the
example scenario demonstrating the soil contamination source category in Chapter 5. In most
places, however, wind direction is much less variable, and the appropriate value is best
determined with site specific information.
• D V: Chapter 5 demonstrates "background" conditions for a "residence" and a
"farm". In those settings, grass or crops are likely to substantially cover the soil, and the fraction
of vegetative cover can range from 0.5 (minimal coverage) to 0.9 (more lush coverage). For the
residence example setting in Chapter 5, V was set at 0.9 which assumes a continual grass cover
over the contaminated soil. The V for the farm settings was instead 0.5. The area of
contamination for the example farm settings was larger than the residence setting, 10 acres to 1
acre. The land where crops were grown was also contaminated; the 0.5 value for V assumes that
the cropland is totally or partially bare at some times - perhaps during spring land preparation and
fall harvest. For the demonstration of the soil contamination source category, the site of
contamination is assumed to be bare, as in an active landfill or an industrial site. With this
assumption, V is set equal to 0.0.
• D Ut: As noted above, the average windspeed, Um, is assumed to be 4.0 m/s. The
threshold wind velocity, Ut, is the wind velocity at a height of 7 m above the ground needed to
initiate soil erosion. It depends on nature of surface crust, moisture content, size distribution of
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particles, and presence of non-erodible elements. It can be estimated on the basis of the
following procedure (EPA, 1985b):
Step 1. Determine the Threshold Friction Velocity
This is the wind speed measured at the surface needed to initiate soil erosion. EPA
(1985b) shows how it can be determined as a function of soil aggregate size distribution.
However, for the "unlimited reservoir" approach for which Equation (4-25) was developed, soil
particles are assumed to be fine at 1.5 mm or less as an average. This translates to a threshold
friction velocity of 75 cm/s and less. A value of 50 cm/s might be reasonably assumed to be
representative of these types of surfaces, and was assumed for this assessment.
Step 2. Estimate the "Roughness Height"
EPA (1985b) graphically shows the roughness height for a range of possible conditions.
Included in this range are a roughness height of 0.1 cm for natural snow, 1.0 cm for a plowed
field, 2.0-4.0 cm for grassland, 4.0 cm for a wheat field or for suburban residential dwellings, and
up to 1000 cm for high rise buildings. The assumption made for the residence and farm example
settings was 4.0 cm, following the information given for a wheat field or a suburban residence.
The value assumed for the site of contamination in the demonstration of the soil contamination
source category was 1.0 cm, which assumes that the plowed field value is the most analogous to
the assumed bare soil site of contamination.
Step 3. Estimate Ratio of Threshold Wind Speed at 7 m to Friction Velocity
A chart provided by EPA (1985b) shows this ratio as a function of roughness height. For
a roughness height of 4.0 cm, this ratio is seen to be 13. For a roughness height of 1.0 cm, this
ratio is 16.5.
Step 4. Estimate Threshold Wind Speed
This is simply the product of the ratio given in step 3 above and the friction velocity.
Using values given above, 50 cm/sec * 13 = 6.5 m/sec for the residential and farm settings, and
8.25 m/sec for the site of soil contamination.
• F(x): The model-specific function, F(x), is determined by first calculating the
dimensionless ratio x, where x = 0.886 U/Um, and finding F(x) from a chart of F(x) versus x, as
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provided in EPA (1985b). For U, = 6.5 and Um = 4.0, x = 1.44 and F(x) = 1.05. For U, = 8.25
and Um = 4.0, x = 1.83 and F(x) = 0.5.
4.3.4. Biota Concentrations
This section summarizes the algorithms to estimate contaminant concentrations in fish,
vegetation (including vegetables for human consumption and pasture grass or fodder grown on
contaminated soil for beef cattle consumption), beef, and milk. As will be shown, all algorithms
are simple empirical equations which relate an environmental media concentration to a biota
concentration, using a "biotransfer" or "bioaccumulation" factor.
4.3.4.1. Fish Concentrations
The procedure and supportive data for the algorithm to estimate fish tissue concentrations
can be found in Cook, et al. (1991), in an assessment of risk of 2,3,7,8-TCDD to aquatic life and
associated wildlife (EPA, 1993b) which EPA is conducting as part of its reassessment of dioxin-
like compounds, and most recently in a technical support document for the Great Lakes Water
Quality Initiative (EPA, 1995). Of those references, only EPA (1995) contains information on
the suite of dioxin-like (and other) compounds.
Dioxin-like compounds share a high degree of hydrophobicity that increases as the degree
of chlorination increases. Cook, et al. (1991) note that this corresponds in general to an increase
in lipophilicity and an increase in ability to bind to organic carbon in sediments and to dissolved
organic matter in water. However, these tendencies are not paralleled by an increase in
bioaccumulation. Only the 2,3,7,8-chlorine-substituted congeners are substantially
bioaccumulated by fish, although large quantities of other CDD/F congeners are found in
sediments. This pattern of bioaccumulation results because of higher rates of metabolism of
CDD/Fs in fish as compared to the 2,3,7,8-chlorine-substituted congeners (EPA, 1992a; Cook, et
al., 1991, with references to Muir et al., 1986; Gobas, 1990). While the highly chlorinated
2,3,7,8-substituted congeners are very slowly accumulated, they have very slow elimination rates.
2,3,7,8-TCDD and other planar polyhalogenated aromatic hydrocarbons often have not
been detected in water from aquatic ecosystems even when biota are highly contaminated.
Because surface layers of bottom sediments are a good indicator of the relative amount of
chemical in the system over the time scale involved for bioaccumulation of super-hydrophobic
chemicals, a term known as the Biota to Sediment Accumulation Factor, or BSAF, has been
offered as a measure of site-specific bioaccumulation potential. This term was proposed to
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replace equivalent terms which were known as the Bioavailability Index, or BI (Kuehl, et al.,
1987; Cook, et al., 1991; EPA, 1990b), the Accumulation Factor, AF (Lake, et al., 1990) and the
Biota to Sediment Factor, or BSF (Parkerton, et al., 1993; Parkerton, 1991). BSAF is defined as:
BSAF = 5s* (4-33)
where:
BSAF = biota to sediment accumulation factor, unitless
Qipid = concentration of contaminant in lipid of fish, mg/kg,
Coc = concentration of contaminant in bottom sediment organic carbon, mg/kg
The surface water algorithms estimate concentration of contaminant in bottom sediments
(see Section 4.3.1 above). This concentration, Csed, can be converted to an organic carbon basis
as a function of OCsed:
Coc - (4-34)
where:
Coc = concentration of contaminant in bottom sediment organic carbon, mg/kg;
Csed = concentration of contaminant in bottom sediment, mg/kg;
OCsed = fraction organic carbon in bottom sediment, unitless
The organic carbon content of bottom sediments was assumed to 0.03; see Section 4.3.1. for the
derivation of Csed.
Since the accumulation of contaminant is assumed to occur only in fish lipid, a correction
term to estimate the whole fish tissue concentrations is needed since fish consumption in g/day
refers to whole fish consumption. The correction term is simply fUpid, and so whole fish
concentrations are simply Clipid * fUpid.
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The BSAF was developed as a measure of bioaccumulation potential rather than as a
predictor, as it is being used here. It is uncertain as to whether measured BSAFs are generally
applicable to other water bodies. Efforts are underway to evaluate the general applicability of
BSAFs (P. Cook, Duluth Environmental Research Laboratory, US EPA, 6201 Congdon
Boulevard, Duluth, MN 55804, personal communication). Using the BSAF approach as a
predictive tool greatly underplays the complexity of the processes transferring contaminants from
aquatic ecosystems to aquatic organisms. EPA (1993b and 1995) provides a comprehensive
discussion on aquatic impacts and processes for dioxin-like and related compounds. Following
are some of the key issues to consider:
1) Resident vs. Migratory Species: Parkerton (1991) applied a bioenergetics-based
bioaccumulation model in an attempt to duplicate BSAFs for 2,3,7,8-TCDD found for carp and
blue crabs in the Passaic River, New Jersey. He showed nearly a ten-fold difference in
2,3,7,8-TCDD BSAF calculated from data for resident species as compared to migratory species
in the Passaic River. This would be expected for fish which also reside part of the time in
relatively clean water bodies; migration would enable depuration of residues from fish. The
possibility that migration patterns might explain some of the results for fish concentrations of
2,3,7,8-TCDD in the Lake Ontario bioaccumulation study was also raised (EPA, 1990b). That
assessment also discussed a related issue of concern - to consider lakewide average sediment
concentrations or concentrations near where sampled fish were captured in calculating the BSAF.
Even within a large lake, more sedentary populations offish may be impacted by localized
contamination.
2) Past history of contamination: If contamination of surface water bodies with
hydrophobic compounds like the dioxin-like compounds has occurred principally in the past,
then it can be expected that most of the contamination occurs in or near the bottom sediment
layer and not within the water column. Furthermore, if inputs to water bodies are declining or
low in comparison to past loadings, then sediments would be undergoing depuration - residue
levels would be declining, and the system may not be equilibrium. EPA (1990b) noted that very
low BAF*s (defined as a fish to sediment ratio not including the sediment organic carbon and
fish lipid considerations of BSAFs) and BSAFs for 2,3,7,8-TCDD in Lake Ontario contrasts
higher BAF*s for other hydrophobic compounds such as DDE or PCBs. An explanation offered
is that loadings to the Lake may be declining, such that there is a substantial disequilibrium
between sediments, water, fish, and their prey. One parameter required in the bioenergetics
model Parkerton (1991) used (referred to in the above bullet) was a ratio of contaminant
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concentration in bottom sediment to that in suspended sediment, rs/rw. In modeling exercises on
the Passaic River, he found closer agreement between measured and predicted BSAFs with this
ratio equal to 10 in contrast to 1, the only two values tested; a ratio often means that the
concentration of contaminant in bottom sediment is ten times higher than it is in the suspended
sediment. BSAFs predicted by the model were developed as the ratios in modeled fish lipid
concentrations divided by modeled bottom sediment organic carbon normalized concentrations.
Measured BSAFs used actual Passaic River fish lipid and bottom sediment concentrations of
2,3,7,8-TCDD. BSAFs predicted with this ratio equal to 1 were roughly 4 times as high as
measured BSAFs, and BSAFs found with rs/rw equal to 10 were twice as high as measured. A
related result of his modeling exercise was that, at best fit between modeled and measured
BSAFs where the rs/rw was 10, dietary exposures explained over 50% of the BSAFs for carp and
85% in blue crabs, in contrast to water column exposures. He speculates that prey organisms
consist of benthic animals which ingest contaminated bottom sediment. If the food chain begins
near bottom sediments, and if food chain exposures are a principal explanation for fish tissue
dioxin concentrations, than it follows that a model would perform better when bottom sediment
concentrations drive fish tissue concentrations rather than water column concentrations, or
equivalently, when rs/rw = 10. Finally, he notes that 2,3,7,8-TCDD contamination in Passaic
river largely occurred as a result of historical loadings. The picture that emerges from
Parkerton's modeling is as follows: sediments are serving as an internal source of contaminants
due to past historical loadings, and the water column is in disequilibrium with bottom sediments
and driven only by depuration of bottom sediment concentrations. The bioaccumulation of these
compounds in carp and blue crabs appears to be mediated by trophic transfer via the benthic
foodweb. In both the Lake Ontario and Passaic River studies, concentrations of 2,3,7,8-TCDD
were higher in deeper bottom sediments as compared to surficial bottom sediments - this implies
historical loadings and possibly depuration of surficial residues.
This issue is non-trivial for the methodology of this assessment, since an assumption for
deriving suspended and bottom sediment concentrations is that the contamination is ongoing, and
that the hypothetical water body may be closer to a state of equilibrium as compared to situations
where contamination was principally in the past. The BSAF assumed for 2,3,7,8-TCDD in the
demonstration scenarios of 0.09 is more in line with data from EPA (1990b) on Lake Ontario and
from Parkerton (1991) from data in Passaic River, then with other data (presented later) where
historical loadings are not as clear a principal source of bottom sediment contamination. The
issue of ongoing versus historical contamination should be considered when assigning site-
specific BSAFs.
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3) Variations among fish species, particularly column feeder vs. bottom feeder:
Feeding habits, age, migratory patterns, and lipid contents (including lipid content of edible vs.
inedible fish tissues) are just a few of the interacting factors which determine a site-specific
BSAF as a function offish species. Bottom-feeders such as carp or catfish can have BSAF
values that could be an order of magnitude higher as compared to column feeders. A calculated
2,3,7,8-TCDD BSAF for carp in Connecticut of 0.86 compares to the 2,3,7,8-TCDD BSAF of
0.03-0.07 calculated for trout by EPA (see Table 4-1). The demonstration of this approach in
Chapter 5 assigns a single BSAF to each of the three example contaminants, and the assignment
is expected to be more like BSAFs for column feeders. Although not unlike other simplifications
of this assessment, such approaches are recognized as oversimplifications.
4) Study designs to obtain BSAFs: Although there is some evidence that BSAFs
specific to a contaminant may be applicable to other aquatic settings, data to evaluate such a
hypothesis is still sparse. Even data sets that do exist need to be carefully evaluated before
deriving BSAFs. Such an evaluation should consider sediment as well as fish species data.
Critical factors for sediment sampling include location, number, depth of sampling, variability,
availability of organic carbon fraction information, and so on. Similar issues are pertinent for
fish sampling and analysis.
Following now are guidance for the terms required for estimating fish tissue
concentrations.
• D BSAF: Table 4-1 summarizes literature from which biota sediment accumulation
factors for dioxin and furan congeners could be developed. Only six sets of data were found in
the literature. The data reported in EPA (1995) for Great Lakes Trout, from the Wisconsin River
(Kuehl, et al. 1987), and that from 1 lake in Sweden (Kjeller, et al. 1990) both show decreasing
BSAF with increasing chlorination. The BSAF of 2.94 for 2,3,7,8-TCDD determined from a
lake in Sweden should be questioned since it is more than an order of magnitude different than
any of the other data. Causes for this discrepancy could be manifold. Some observations from
Kjeller, et al. (1990) might shed some light on this result. Although sediment data was from
three water bodies, 8 of the 9 Pike samples (pike samples were composites of 2-5 fish from one
location in the water body) were from one of the water bodies. This is why only data from the
one water body was summarized in Table 4-1. This water body, Lake Vanern, was clearly the
most contaminated of the three water bodies studied. A paper mill was located at the northern
part of this lake and the authors concluded that discharges from this mill impacted the lake. The
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average of 2,3,7,8-TCDD and 2,3,7,8-TCDF organic carbon normalized concentrations for five
sediment samples from this lake was 297 pg/g; the analogous average concentration for 10
samples taken from another lake, Lake Vattern (6 samples), and a river, Dala (4 samples), was 65
pg/g. A similar disparity between Lake Vanern and the other water bodies is found with the
penta-CDD/F concentrations: 205 pg/g vs. 108 pg/g, with similar comparisons for the hexa-,
hepta, and octa-CDD/CDF. The sediment and corresponding pike sample nearest this mill had
the highest concentrations reported - pike samples were given as 3000 and 833 pg/g lipid
normalized 2,3,7,8-TCDF and 2,3,7,8-TCDF (a composite from 5 pike taken at this sampling
station), respectively, and sediment was 1800 and 244 pg/g organic carbon normalized for
2,3,7,8-TCDF and 2,3,7,8-TCDD. Note the BSAF for 2,3,7,8-TCDD implied from this data
point is 3.41. Another consideration for high BSAFs might be the source of contamination.
Speculation from the Lake Ontario and Passaic River field data was that contamination
principally occurred in the past, whereas in the Swedish data, contamination appears to have been
ongoing at the time of sampling. This might be one indication that BSAFs for aquatic systems
where contamination is ongoing might be greater than from systems where the contamination is
primarily historical.
The Swedish data also illustrates some of the complexities of interpreting literature data.
First, the sediment data was expressed concentrations normalized to "sediment contents of
organic material" (sic). This was interpreted as organic matter normalized, not organic carbon
normalized. Parkerton (1991) assumed that organic carbon was 45% of organic matter to derive
BSAFs when organic carbon data was unavailable; following this lead, organic matter
normalized concentrations in Kjeller, et al. (1990) were divided by 0.45 to arrive at organic
carbon normalized concentrations. Also, there was not an exact match in "sites" between
sediment samples and fish samples; these sites were physical locations within the large lake
where samples were taken. There were five sites where sediment samples were taken, and five
sites where composited pike samples were taken in Lake Vanern. However, one of the sediment
and one of the pike samples were from unique sites; only four sites had both sediment and pike
samples. The results in Table 4-1 were derived using average sediment and pike concentrations
from only these four sites. Another way to have derived BSAFs would be to average all lake
sediment and pike concentrations; since there may be some relationship between sediment and
pike concentrations based on lake location, it was decided to include only the four sites with both
fish and sediment samples. Finally, there were two sets of results listed for 1,2,3,4,6,7,8-HpCDF
as though there were two unique sets of analyses for the same congener; this is why there are two
entries for this congener in Table 4-1.
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A complete discussion of the data generated by the Connecticut Department of
Environmental Protection (CDEP, 1992) is included in Chapter 7. Generally, water bodies tested
were mostly in rural/suburban settings rather than urban settings. Concentrations of 2,3,7,8-
TCDD in surface soils and bottom sediments were in the low ppt level, indicating background
impacts. BSAFs generated with that data ranged from 0.24 to 0.85 for TEQs, 2,3,7,8-TCDD,
2,3,7,8-TCDF, and 2,3,4,7,8-PCDF.
EPA (1995) published BSAFs for lake trout for the 17 dioxin-like congeners based on a
reanalysis of the Lake Ontario sediment and lake trout samples originally taken in 1987-88 and
reported on first in EPA (1990). It is not clear how many of the samples were reanalyzed and
why the 2378-TCDD lake trout BSAF was different in the 1995 report, 0.059, as compared to the
1990 report, 0.07. EPA (1995) also discussed the concept of the "bioequivalency factor", or
BEF, which is defined as the ratio between the BSAF of each non 2378-TCDD dioxin-like
congener to the BSAF of 2378-TCDD. These BEFs are listed in Table 4-1. They can be used to
assign congener-specific BSAFs if all that is known for a specific site is the 2378-TCDD BSAF.
Excluding the Swedish data, there are 43 reported BSAFs for dioxin-like congeners in
Table 4-1. These range from 0.00065 to 0.93, with lower BSAFs associated with higher
chlorinated congeners. A BSAF of 0.09 will be assumed for 2,3,7,8-TCDD in the demonstration
scenarios in Chapter 5. The BSAFs for the other dioxin-like congeners will be assigned based on
the BEF for that congener. For example, the BSAF of 2,3,4,7,8-PCDF will be equal to 0.14,
which is estimated as the BEF for 2,3,4,7,8-PCDF, 1.6, times the BSAF for 2,3,7,8-TCDD.
EPA (1990b) estimated BSAFs for PCBs and other selected chemicals (DDE, HCB, etc.)
for Lake Ontario from several data sets. Later, EPA (1995) reported on BSAFs for subsets of the
dioxin-like congeners from three data sets: Oliver and Niimi (1988), a reanalysis of their own
sediment and late trout samples from the 1988 Lake Ontario study, and the EPA Green Bay/Fox
River Mass Balance Study. Parkerton, et al. (1993) summarizes BSAFs for PCBs and other
compounds from other water bodies using other data sets. A selected summary of BSAFs for
PCBs taken from the two EPA references and the Parkerton, et al. (1993) reference is given in
Table 4-2.
Two trends are apparent. First, the BSAFs for PCBs appear to exceed those of the dioxin
and furan congeners by an order of magnitude and more. Second, and from limited data, it would
appear that BSAFs increase from dichloro- through hexa- or perhaps hepta-chloro PCBs, and
then decrease thereafter. An assignment of a BSAF for 2,3,3',4,4',5,5'-HPCB, which is
abbreviated as PCB 189 in the IUPAC system, is not apparent from the data summary below.
Two data points from EPA place the value for that congener at 0.71 and 3.45. The data point
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from Siskiwit for the single heptachloro-PCB, which was 2,2',3,4',5,5',6-HPCB, was estimated by
Parkerton, et al. (1993) as 12.5. The BSAF for the heptachloro-PCB congener group for flounder
from New Bedford Harbor estimated by Parkerton, et al. (1993) was 0.84, with BSAFs for
lobster and crab as 1.29 and 2.74, respectively. A value of 2.10 is assigned to
2,3,3',4,4',5,5'-HPCB for the example scenarios in Chapter 5, which is the midpoint of the two
EPA data points specific to this congener
It should be noted that these assignments are based on data on vertebrate rather than
invertebrate aquatic species. It is generally recognized that invertebrates do not possess the
enzymatic capability to metabolize hydrophobic compounds as effectively as higher chordates.
As a result, invertebrate species such as mussels, clams, oysters, shrimp, crabs and lobsters may
have BSAF values much higher than those observed for fish. Parkerton (1991) and Parkerton, et
al. (1993) reviewed the literature to estimate BSAFs of 1 to 5 for species including grass shrimp,
sandworms, deposit feeding clams, and blue mussel for CDD/Fs and PCBs.
Finally, it should be noted that all bioconcentration or biotransfer parameters, such as the
BSAF, are qualified as second order defaults for purposes of general use. Section 6.2. of Chapter
6 discusses the use of parameter values selected for the demonstration scenarios, including a
categorization of parameters. Second order defaults are defined there as parameters which are
theoretical and not site-specific, but whose values are uncertain in the published literature. The
parameter values in this category should be considered carefully by users of the methodology.
• D f]ipid: Lipid contents of edible fish species have been compiled in EPA (1995), and for
the sake of brevity, this compilation will not be repeated here. Generally, lipid contents can range
from 0.05 to greater than 0.20 (5% - 20%). BSAFs are typically developed on the basis of whole
fish lipid content, so estimates of whole fish concentrations should be made with a whole fish
lipid content. Parkerton, et al. (1993) cautions, however, that lipid contents of edible portions of
fish may be lower than lipid contents of some of the fish portions that were sampled and used to
develop BSAFs. Non-edible high lipid content portions include, for example, liver and
hepatopancreas. Parkerton, et al. (1993) develops the parameter, p, which is defined as the ratio
of the lipid content of the edible portion and the sampled tissue. To demonstrate the impact of
this ratio, Parkerton used data from Niimi and Oliver (1989) which included PCB and other
halocarbon compound concentration in whole fish and fillets offish taken from the Great Lakes.
The p (defined here as the ratio of lipid in fillet to lipid of whole fish) for these fish, which
included brown trout, lake trout, rainbow trout, and coho salmon, ranged from 0.22 to 0.51. The
ratio of fillet to contaminant concentrations ranged from 0.20 to 0.54.
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In the context of the current model, concentrations in fish for estimating exposure are
estimated as the product of: organic carbon normalized bottom sediment concentrations * BSAF
* fUpid. BSAFs (in theory) are independent offish tissue being sampled - they are ratios of the
organic carbon normalized concentration and fish lipid concentration. Users should be aware,
however, that the fUpid value assigned should correspond to the fish concentration of interest - that
could be whole fish if the model is used in validation exercises or edible fish if the model is used
for exposure assessment. Cook, et al. (1990) and EPA (1993b) assumed a lipid content of 0.07
for fish in discussions of BSAF and related methodologies for estimating bioaccumulation of
2,3,7,8-TCDD in aquatic ecosystems. This assessment will also assume a fUpid of 0.07, and since
its use in this context is in exposure assessment, this value could be thought of as a edible portion
lipid fraction.
4.3.4.2. Vegetation Concentrations
Vegetation concentrations are required for the estimation of exposure to homegrown
fruits and vegetables, and also for the beef and dairy food chain algorithms. Three principal
assumptions are made to estimate vegetative concentrations:
• D Outer surfaces of bulky below ground vegetation are impacted by soils which
contain dioxin-like compounds. Inner portions are less impacted than outer portions, although
data has shown some within plant translocation into below ground vegetables.
• D Translocation of dioxin-like compounds from roots to above ground portions of
plants are negligible compared to other mechanisms which impact above ground portions of
plants. As such, translocation into above ground portions will be assumed to be zero.
• D Unlike below ground vegetables, data has shown very little impact to inner
portions of above ground bulky vegetation. Therefore, it will be assumed that outer and not inner
portions of above ground bulky vegetation are impacted.
Concentration of contaminants in below ground vegetation is only required for vegetables
(carrots, potatoes, e.g.) grown underground. The basis for the below ground algorithm is the
experiments of Briggs, et al. (1982) on uptake of contaminants into barley roots from growth
solution, and their elaboration of a Root Concentration Factor. The below ground concentration
is given by:
C RCF VG,
* (4-35)
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where:
Cbgv = fresh weight concentration of below ground vegetables, pg/g
Cs = contaminant concentration in soil, ppt or pg/g
Kds = soil-water partition coefficient, L/kg
Koc*OCsl
Koc = contaminant organic partition coefficient, L/kg
OCsl = fraction organic carbon in soil, unitless.
RCF = root concentration factor equaling the ratio of the contaminant
concentration in roots (fresh weight basis) and the concentration in soil
water, unitless
VGbg = empirical correction factor for below ground vegetation which accounts
for the differences in the barley roots for which the RCF was derived and
bulky below ground vegetables, unitless
Two processes, air-borne vapor phase absorption and air-borne particle deposition, are
assumed to contribute to above ground vegetation concentrations:
Cabv = Cvpa + Cppa (4-36)
where:
Cabv = concentration in above-ground vegetation, expressed on a dry weight
basis, pg/g or ppt
Cyp., = contribution of concentration due to vapor-phase absorption or airborne
contaminants, pg/g or ppt
Cppa = contribution of concentration due to wet plus dry deposition
of contaminated particulates onto plant matter, pg/g or ppt
The basis for a vapor-phase transfer factor for various airborne contaminants, including
1,2,3,4-TCDD, from the atmosphere to vegetation was developed by Bacci, et al. (1990, 1992),
with amendments suggested by McCrady and Maggard (1993), and McCrady (1994). The final
values selected for the 17 dioxin-like CDD/Fs were developed by calibration (Lorber, 1995), and
then field validated (Lorber and Pinsky, 1999), as described below. Bacci and coworkers
conducted laboratory growth chamber experiments on the vapor-phase transfer of 14 organic
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compounds from air to azalea leaves, and developed a generalized model to predict the vapor-
phase bioconcentration factor based on a contaminant Henry's Constant, H, and octanol water
partition coefficient, Kow. A similar experiment by McCrady and Maggard (1993) conducted for
2,3,7,8-TCDD vapor transfer to grass leaves suggested that the Bacci empirical algorithm to
would greatly overestimate the transfer factor for 2,3,7,8-TCDD. The development of the
biotransfer factor for vapor-phase dioxins, termed Bvpa in this assessment, was based on field
data. Further details on these experiments are in the section below on this critical parameter.
The algorithm estimating plant concentrations as a function of vapor-phase air concentrations is:
B C VG
-^- - * (4-37)
where:
C^ = plant concentration due to vapor-phase absorption or airborne
contaminants, pg/g or ppt, dry weight basis
Bvpa = mass-based air-to-leaf biotransfer factor, unitless [(pg contaminant/g plant
dry)/(pg contaminant/g air)]
Cv = vapor-phase concentration of contaminant in air, pg/m3
VGag = empirical correction factor which reduces vegetative concentrations
considering that Bvpa was developed for transfer of air-borne contaminants
into leaves rather than into bulky above ground vegetation
da = density of air, g/m3, 1190
Several exposure efforts for 2,3,7,8-TCDD (Fries and Paustenbach, 1990; Stevens and
Gerbec, 1988; Connett and Webster, 1987; Travis and Hattemer-Frey, 1991), have modeled the
accumulation of residues in vegetative matter (grass, feed, vegetables) resulting from deposition
of contaminated particulates. Key components of their approach, as well as the one for this
assessment, include:
• D Vegetative concentrations result from particulate deposition onto plant surfaces.
• D Vegetative dry matter yield is the reservoir for depositing contaminants; this
reservoir varies according to crop.
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• D Not all particulate deposition reaches the plant, some goes directly to the ground
surface; the "interception fraction", less than 1.0, reduces the total deposition rate. This fraction
can be related to the percent ground that is covered by the vegetation.
• D Weathering processes, such as wind or rainfall, remove residues that have
deposited onto plant surfaces via particle deposition, and this process is reasonably modeled as a
first-order exponential loss with an associated weathering dissipation rate. All the above
references have justified a dissipation rate derived from a half-life of 14 days (based principally
on field measurements described in Baes, et al. (1984)); this is the value used for all dioxin-like
compounds in this assessment as well. As well, a portion of particles depositing as wet
deposition are not retained on the vegetation after the rainfall. A retention factor reduces total
wet deposition considering this.
• D Vegetative concentrations may not reach steady state because of harvesting or
grazing, but a steady state algorithm is used.
The steady state solution for plant concentrations attributed to wet plus dry particle
deposition is:
ppa
1000 k
(4-38)
where:
CPPa =
FP =
kw =
1/1000 =
plant concentration due to settling of contaminated particulates onto plant
matter, pg/g or ppt, dry weight basis
unit contaminant wet plus dry deposition rate onto plant surfaces, pg/m2-yr
first-order weathering dissipation constant, 1/yr
dry matter yield of crop j, kg/m2
converts pg/kg to pg/g
The unit contaminant wet plus dry deposition rate, F, is given as:
9
(4-39)
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where:
Fp = unit contaminant wet plus dry deposition rate onto plant surfaces, pg/m2-yr
Cp = air-borne particulate phase contaminant concentration, pg/m3
Vd = deposition velocity, m/yr
Ij = fraction of particulates intercepted by crop j during deposition, unitless
RN = annual rainfall, m/yr
Rw = fraction of particles retained on vegetation after rainfall, unitless
Wp = volumetric washout factor for particulates, unitless
Following is brief guidance on assignment of values to the terms in Equations (4-35) to
(4-39).
• D Cs and Kds: This is the soil concentration and soil/water partition coefficient,
respectively. The soil concentration is specified for the soil contamination source category.
When the soil contamination is distinct from the site of exposure, the soil concentration at the
site of exposure is modeled. As discussed in Section 4.3.2 above, two soil concentrations
including one for a no-till and one for a tilled situation, are modeled in this situation. For
modeling below ground vegetable concentration, the tilled concentration is required. The soil
partition coefficient is a function of the contaminant organic carbon partition coefficient, Koc,
and the soil organic carbon fraction, OCsl, as discussed above in Section 4.3.1. Division of Cs by
Kds results in the equilibrium soluble phase concentration of the contaminant, in mg/L.
• D RCF: Briggs, et al. (1982) conducted experiments measuring the uptake of
several compounds into barley roots from growth solution. He developed the following
relationship for lipophilic compounds tested (lipophilic compounds were identified as those
tested that had log Kow 2.0 and higher (n=7, r=0.981):
log RCF = 0.77 log (Kow) - 1.52 (4-40)
where:
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RCF = root concentration factor equaling the ratio of the contaminant
concentration in roots (fresh weight basis) and the concentration in soil
water, unitless
Kow = contaminant octanol water partition coefficient, unitless
Since the highest log Kow of the seven for which this relationship was derived is 4.6, and the
lowest log Kow for the dioxin-like compounds is above 6.0, this relationship may not hold for
the dioxin-like compounds. However, a validation exercise described in Chapter 7 where
predictions of dioxin-like compounds in carrots are compared with observations shows this
factor to adequately perform for this class of compounds.
Briggs' experiments were conducted in growth solution. Therefore, the RCF is most
appropriately applied to soil water in field settings. This is why the Cs was divided by Kds in
Equation (4-35).
• D VG^: This correction factor and the one used to correct for air-to-leaf transfer of
contaminants, VGag, are based on a similar hypothesis. That hypothesis for VGbg is that the
uptake of lipophilic compounds into the roots of this experiments is due to sorption onto root
solids. High root concentrations were not due to translocation to within portions of the root
hairs. Direct use of the RCF for estimating concentrations in bulky below ground vegetation
would overestimate concentrations since available data suggests some but little translocation to
inner parts of below ground bulky vegetation for the dioxin-like compounds. The experiments of
Muller, et al. (1994) showed that the outer portions (peel) of carrots had higher concentrations of
dioxin congener groups as compared to inner portions (cortex and stele). However, they also
found concentrations within the carrot, suggesting some within plant translocation. Their data is
examined below and used to assign a value for VGbg in this assessment.
One set of important soil-to-plant transfer experiments, however, did suggest that certain
plants would sorb and translocate dioxins to a significant extent. Hulster, et al. (1994) found that
dioxins were sorbed by the roots of vegetables of the cucumber family and made their weight
into the above ground portions of these plants. They were unable to explain why this happened
and noted that it was a distinctly different trend then they found in several of their other soil-to-
plant transfer experiments. For this assessment, it will be assumed that the vegetables of the
cucumber family are an anomaly and not typical of vegetation for human or animal consumption.
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VGbg converts an outer portion, or skin, concentration, into a whole plant concentration.
If inner portions were entirely free of residue, the correction factor could be estimated as the ratio
of the mass of the outer portion to mass of the entire vegetable:
MASS,.
VG = £^1_ (4_41)
bg Mxl 99 ^ '
MA^ vegetable
where:
VGbg = below ground vegetation correction factor, unitless
MASSskin = mass of a thin (skin) layer of below ground vegetables, kg/m2
MASSvegetable = mass of the entire vegetable, kg/m2
Simplifying assumptions are now made to demonstrate this ratio for a carrot and a potato. First,
it will be assumed that the density of the skin and of the vegetable as a whole are the same, so the
above can become a skin to whole vegetable volume ratio. The thickness of the skin will be
assumed to be same as the thickness of the barley root for which the RCF was developed.
Without the barley root thickness in Briggs, et al. (1982), what will instead be assumed is that the
skin thickness is equal to 0.03 cm. This was the thickness of a leaf from broad-leaved trees
assumed by Riederer (1990) in modeling the atmospheric transfer of contaminants to trees. The
shape of a carrot can be assumed to be a cone. The volume of a cone is given as (/n;/3)r2l, where r
is a radius of the base and 1 is length. Assuming a carrot base radius of 1 cm and a length of 15
cm, the volume is 16 cm3. The curved surface area of a cone is given as: 7T:r(r2 + 12)'/2, which
equals 47 cm2, given the r and 1 assumptions. The volume of the cone surface area is 47 cm2 *
0.03 cm, or 1.41 cm3. The skin to whole plant ratio for this carrot is 0.09 (1.41/16). A similar
exercise is done for a potato, assuming a spherical shape with a radius of 3 cm. The volume is
given as 4/3:r;r3, or 113 cm3. The surface area of a sphere is 4:i;r2, or 113 cm2, and the volume of
this surface area is 3.39 cm3. The skin to whole plant ratio for the potato is 0.03.
This exercise indicates lower bounds for such an empirical parameter. For exposure
assessments, other factors which reduce vegetative concentrations should also be considered.
Additional reductions in concentration result from peeling, cooking, or cleaning, for example.
Wipf, et al. (1982) found that 67% of unwashed carrot residues of 2,3,7,8-TCDD came out in
wash water, and 29% was in the peels. A peeled, washed carrot correction factor might instead
be, 0.09*0.04, or 0.004 (0.09 from above; 0.04 = 100% - 67% - 29%). A 96% reduction in the
estimated VGbg for the potato (the potato is cleaned and the skin is not eaten; additional
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reductions possibly when cooking the potato) would equal 0.001. In a site-specific application,
the type of vegetation, preparation, and so on, should be considered.
As mentioned above, the Muller, et al. (1994) data did show concentrations in the inner
parts of the carrot. They presented concentrations for the cortex and stele, as well as the carrot
peel. One interesting trend with this data is that the cortex and stele concentrations are virtually
the same with both the control and the contaminated soil. Also, the cortex and stele
concentrations for the control carrots are slightly lower, but essentially similar to the peel
concentrations for the control group. The authors of this research observed a lack of change in
cortex and stele concentrations from the control to the contaminated soil, but offered no
explanation. This suggests that the empirical parameter VGbg should not be a constant but should
vary, being higher at lower soil concentrations. With a lack of data to develop an algorithm for a
varying VGbg, it remains a constant for this assessment.
This data was examined to estimate the VGbg. As noted above, a lower bound assignment
of VGbg for the hypothetical carrot was 0.09, and for the potato, was 0.03. This means that if
there were absolutely no within plant translocation of dioxin residues - all residues were only in
the skin of the carrot and potato - than the VGbg would be 0.09 and 0.03. Muller, et al. (1994) did
not include whole carrot concentrations or the necessary information to calculate whole carrot
concentrations accurately. Therefore, simple assumptions will be made to do such. It will be
assumed that the whole carrot concentration is estimated as 1 part peel concentration, 7 parts
cortex concentration, and 2 parts stele concentration. The VGbg is calculated as the whole carrot
concentration divided by the peel concentration. Using this procedure, it is found that the VGbg
for the control soil varied between 0.38 and 1.00 for the 10 congener groups, with an average of
0.60, and that it varied between 0.14 and 0.39 for the contaminated soil, with an average of 0.24.
These estimates are obviously higher than 0.09, indicating, as the data shows, that within plant
translocation does occur for carrots. This would suggest a VGbg of between 0.24 and 0.60, absent
any consideration of washing or peeling (actually the full range of 0.14 and 1.00, considering
differences among congener groups).
Data was sought to do a similar exercise for potatoes. Hulster and Marschner (1991)
present concentrations found in potato tubers, both peeled and unpeeled; no data for
concentration in peels alone is presented. Like the Muller, et al. (1994) data, simple assumptions
need to be made to estimate the peel concentration in order to estimate the VGbg for this data set.
As derived above, it will be assumed that the peel is 3% of the entire potato volume. With this
assumption, VGbgs of between 0.03 and 0.05 are calculated for potatoes from the data of Hulster
and Marschner (1991). This suggests that, unlike carrots, much of the concentration in the
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potato, even at low soil concentrations, resides in the peel. Like the carrot, some within plant
translocation is indicated for potates.
Another difference between the carrot data in Muller et al. (1994) and the potato data in
Hulster and Marscher (1991) is the potential impact of peeling. A ratio of whole vegetable to
peeled vegetable was estimated for both these sets of data. For the Muller data on carrots grown
in control soils, where the inner carrot concentrations were not substantially different than outer
carrot concentrations, the estimated ratio was 0.91, meaning that peeling would not effect the full
concentration in these carrots. It should be pointed out that the control soil Toxic Equivalent
concentration, 5 ppt, is typical of soils that are considered "background" or "typical". The
impact of peeling was more pronounced for the contaminated soil, with an estimated ratio of
0.61. For the potatoes, peeling made a larger difference. The average peeled to unpeeled potato
concentration ratio was about 0.20.
For the assessment in this document, "underground vegetables" are not any more defined
than just that. Given the data examined in this section, a VGbg of 0.25 will be assigned. This
considers that a peeled carrot grown in typical soils could have a VGbg of 0.60 according to the
interpretation of the Muller, et al. (1994) data, whereas a peeled potato could have a VGbg less
than 0.01.
• D Cv, Cp: The vapor (Cv) and particle (Cp) phase concentrations of contaminant in
air, Ca, used in this algorithm is estimated using procedures described in Section 4.3.3 above.
• D Bvpa: The first version of the air-to-leaf transfer factor used in the dioxin
reassessment document (EPA, 1992b) used an empirical algorithm developed by Bacci and
coworkers (Bacci, et al. 1990, 1992) who studied the vapor transfer of 14 organic chemicals, one
of which was 1234-TCDD (not one of the 17 toxic congeners, but with similar fate properties to
2378-TCDD), to azalea leaves. These chamber experiments did not consider the effect of
photodegradation on the transfer of the organic chemicals to the azalea leaves. The empirical
relationship these researchers developed determined the volumetric transfer factor, Bvol, as a
function of the contaminant Henry's Constant and log Kow:
log Bvol - 1.065 log Kow - log (-^-) - 1.654 (4.42)
Ki L
where:
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Bvol = Bacci volumetric air-to-leaf biotransfer factor, unitless [(|_ig contaminant/L
of wet leaf)/(|ag contaminant/L air)]
Kow = contaminant octanol water partition coefficient, unitless
H = contaminant Henry's Constant, atm-m3/mol.
Rj = ideal gas constant, 8.205 x 10"5 atm-m3/mol-K
T = temperature, 298. IK
-1.654= empirical constant
Then, McCrady and Maggard(1993) conducted chamber experiments on the transfer of 2378-
TCDD vapors to grass leaves considering photodegradation. These experiments showed that the
transfer of 2378-TCDD to grass leaves was about 40 times less than the transfer as calculated by
the Bacci empirical algorithm. In addition to not considering photodegradation, McCrady and
Maggard considered the difference in plant species between azalea leaves and grass leaves to be
important in explaining the lower rate of transfer in their experiments as compared to Bacci's
experiments. McCrady and Maggard's results were used in the next version of the air-to-plant
model and the assignment of values to Bvpa (EPA, 1994). Specifically, the transfer factor for each
dioxin congener was estimated using the Bacci algorithm, but this time all final results were
divided by 40. Therefore, the Bvpa of the original model was reduced by a factor of 40 for all 17
dioxin and furan congeners for the second version of the air-to-leaf model.
Two key presumptions are inherent in using the Bacci algorithm divided by 40: 1) that
the Bacci algorithm is generally appropriate for the dioxin congeners, despite the fact that the
physical/chemical properties of the dioxin congeners are generally outside the range of the 14
organic chemicals used by Bacci, and 2) that the factor of 40 derived from one experiment on
2378-TCDD applies to all dioxin-like compounds.
The data of Welsh-Pausch, et al. (1995) will be used to develop an air-to-leaf transfer
factor for the third version of the vapor phase air-to-leaf transfer factor described here, and also
presented in Lorber (1995). Their data includes grass concentrations and air concentrations.
Grass was grown in two flower boxes, with different agricultural soils in each box, beginning in
May of 1991. The grass was cut back on July 17 in that year. On August 9, the grass was cut
back again and the yield from the two boxes from the growth between July 17 and August 9
averaged 3900 g/m2 fresh weight. This grass was analyzed for concentrations of dioxin congener
groups, and these concentrations were provided on a fresh weight basis (i.e., pg/g fresh weight).
On July 18, the first of two week long air samples were taken. These samples were taken very
near the boxed grass. The samples were measured for the dioxin congener groups, not the
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individual congeners. Representative congener group air concentrations were then determined as
the average of the concentrations from the two samples. The concentration of dioxins in air
remained relatively constant during the experimental period.
This data was used by McLachlan, et al. (1995) to verify an air-to-plant model based on
fugacity concepts. Besides being a different model, McLachlan's modeling differed from the use
of the data in this document in that it used to data to validate the model. Here, the data is used to
calibrate a Bvpa. This was accomplished in the following 3-step procedure:
Step 1: Partition the total concentrations measured into a vapor and a particle phase.
McLachlan, et al. (1995) had used the glass fiber filter/XAD trap 2-stage high volume air
samplers to estimate vapor and particle fractions from the total reservoir. This apparatus will
yield "operationally defined" vapor and particle fractions. The vapor fraction estimated this way
will be larger than the vapor fraction estimated using the Junge model as applied and described
by Bidleman (1988). The Bidleman model will be used to partition the total air concentration of
the Welsh-Pausch, et al. (1995) experiments into a vapor and a particle phase. Whether the
Bidleman model is more "correct" than the measurements of the 2-stage sampler is an ongoing
technical issue. A full discussion of the issue for dioxins can be found in Chapter 3. Also,
model comparisons of predicted and observed vapor/particle partitioning for other organic
contaminants, including PCBs, PAHs, and organochlorine pesticides can be found in Chapter 7.
Table 4-3 shows the vapor/particle partitioning as developed using the Bidleman model and used
here compared against the measured vapor/particle partitioning from the air sampling apparatus.
As an example of how the measured and the modeled vapor fractions are different, the air
sampling apparatus measures 72% of the PCDD congener group to be in the vapor phase,
whereas the Bidleman approach estimates that 13% of the PCDD congener group is in the vapor
phase.
The vapor and particle fractions for the congener groups are modeled assuming an air
temperature of 20 °C, and a particle density in air corresponding to a condition which Bidleman
described as "background plus local sources". The average air temperature between July 17 and
August 9 of 1991 was 18 °C (McLachlan et al., 1995), and the "background plus local sources"
designation also appears most appropriate for the university city of Bayreuth, described as a
typical background situation for this area of Europe by Welsh-Pausch, et al. (1995). Two
alternate options instead of background plus local sources appear less relevant for Bayreuth: an
"urban" condition (higher particle densities) and a "background" condition (lower particle
densities).
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Step 2: Model the deposition of particle-bound dioxins to the grass, and subtract out the
resulting modeled grass concentration from the total concentration. Since an air-to-leaf vapor
phase transfer factor is sought in this exercise, what is needed is the grass concentration due only
to vapor transfers. The particle-bound impact to vegetation cannot be measured directly.
Therefore, a model will be applied to estimate that part of total grass concentration of dioxin that
was due to particle bound depositions. The model that will be applied was described earlier and
is shown in Equation (4-38).
Step 3: What is now available after accomplishment of the above two steps is a concentration of
vapor phase dioxins in air and a concentration of dioxins in grass due to vapor phase transfers.
With appropriate conversions, the air-to-leaf transfer factor is now simply calculated as the
vapor-impacted grass concentration divided by the vapor phase air concentration.
As noted, Equation (4-38) is used to estimate the particle deposition impact to the grass.
The deposition F can be estimated as the particle bound fraction times a deposition velocity. The
velocity of deposition will be assumed to be 0.002 m/sec, which was the velocity of dry
deposition of dioxins as measured by Koester and Kites (1992). Wet deposition was not
considered for this brief exercise; McLachlan, et al. (1995) indicates that only a small amount of
rain fell during this time and that results implied that rain washed off the grass leaves. The
interception fraction will be assumed to be 0.59, based on information provided in Baes, et al.
(1984). A first-order weathering rate of 0.0495 day"1, corresponding to a 14-day half-life, is used
in this model. The experiment occurred between July 17 and August 9, so a time t of 24 days is
assumed. Assuming 15% dry matter in grass, the fresh weight yield of 3900 g/m2 translates to a
yield of 0.585 kg/m2dry. The final fresh weight concentrations due to particle depositions and
vapor transfers are shown in Table 4-3.
Also shown in Table 4-3 in the last column is the percent of total plant concentration that
is estimated to be due to vapor transfers. Except for the octa congeners, it would appear that the
grass is mostly impacted by vapor transfers. This, of course, is contingent on the validity of the
particle impact model.
The volumetric air-to-leaf transfer factor, referred to as Bvol, is defined as the volumetric
concentration of dioxins in grass due to vapor phase transfers divided by the volumetric
concentration of dioxins in air. Appropriate units expressing this ratio are: [pg dioxin/m3
grass]/[pg dioxin/m3 air]. The air concentrations are already in the appropriate units. The grass
concentrations as given in Table 4-3 are in units of ng dioxins per kg plant fresh weight; these
concentrations need to be converted into a volumetric basis. To do the conversion, the
denominator in this grass concentration needs to be converted to a volumetric basis. McCrady
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and Maggard (1993) use a volumetric factor of 0.77 kg fresh leaf/L volume. Two other
conversions necessary are a conversion from L to m3 and a conversion from ng to pg. The final
volumetric plant concentration in appropriate units is given as:
r
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arundinacea L.\ and the grass in the Welsh-Pausch, et al. (1995) experiments, Welsh Ray Grass
(Lolium multiflorum). The climate might have different in Bayreuth, leading to more
photodegradative loss in the Welsh-Pausch data. Certainly the experimental designs were
different. McCrady and Maggard (1993) used a 2-stage chamber experimental design, including
an uptake phase in which the grass was not exposed to sunlight, and a release phase where the
grass was kept in sunlight. Uptake and release were occurring simultaneously and in sunlight in
the Welsh-Pausch, et al. (1995) experiments. Certainly it seems possible that the net transfer
rates might have been lower in the McCrady and Maggard (1993) experiments had the uptake
and release phases both occurred in sunlight.
It should be noted that all bioconcentration or biotransfer parameters, such as the Bvpa, are
qualified as second order defaults for purposes of general use. Section 6.2. of Chapter 6
discusses the use of parameter values selected for the demonstration scenarios, including a
categorization of parameters. Second order defaults are defined there as parameters which are
theoretical and not site specific, but whose values are uncertain in the published literature. The
parameter values in this category should be considered carefully by users of the methodology.
However, a model testing exercise published in Lorber and Pinsky (1999) and described in
Chapter 7 lends credibility to the use of these CDD/F Bvpa for general purposes.
• D VGag: The same discussion for this correction factor for below ground vegetation
applies here. Fruits such as apples, pears, plums, figs, peaches, and so on, can be approximated
by spheres, and upper bound estimates of correction factors would be less than 0.05. Peeling,
cooking, and cleaning further reduces residues. The VGag for unspecified above ground fruits
and vegetables in this assessment is assumed to be 0.01. Like VGbg, this value is assigned
considering that it should be less than estimated just based on surface volume to whole fruit
volume ratios.
Two other VGag values are required for this assessment. One is for pasture grass and the
other for other vegetation consumed by cattle. Both are required to estimate concentrations in
these vegetation consumed by cattle in order to estimate beef and milk concentrations. A VGag
value of 1.0 was used to estimate pasture grass concentrations since there is a direct analogy
between the grass in the Welsch-Pausch, et al. (1995) experiments for which the Bvpa was
developed and pasture grass of the cattle diet. However, VG should be less than the other
general category of cattle vegetation defined in this assessment, "hay/silage/grain". Pasture grass
is considered as a separate diet category because it is a principal component of the cattle diet and
most subject to impact by dioxin-like compounds because it is leafy, whereas other cattle
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vegetation are lumped together in this second category. As described below, this second general
category of non-grass cattle vegetation include some thin leafy (hay) as well as bulky (corn silage
and other grains) vegetation to consider. A volume ratio of outer surface to whole surface area to
volume vegetation could be used to assign a value to VG, if specific assumptions concerning
proportions of each type of vegetative cattle intake were made. An appropriate assumption for a
fully protected vegetation such as grain would be zero. Silage can be considered part protected
and part leafy. Since specific assumptions concerning hay/silage/grain intake are not being made
for this exercise, a simple assumption that VG equals 0.50 for hay/silage/grain is instead made,
without rigorous justification.
The only experimental evidence that a VGag for vapor transfers of dioxin-like compounds
is justified came in a study by McCrady (1994). McCrady experimentally determined uptake rate
constants, termed kl3 for vapor phase 2,3,7,8-TCDD uptake into several vegetation including
kale, grass, pepper, spruce needles, apple, tomato, and azalea leaves. Recall that the similar
experimental design of both McCrady and Maggard (1993), and Bacci, et al. (1990; 1992),
included an initial phase where vegetation in experimental chambers were exposed to the vapor-
phase organic chemicals. The uptake which occurs during this initial phase is described with the
rate constant, kt. A second "elimination" phase then occurs where organic vapors are removed
from the chambers and the chemicals allowed to volatilize or otherwise dissipate from the
vegetation. The rate constant for this phase is termed k2. A steady state bioconcentration factor
(i.e., B^), is then estimated as kj/k^ The uptake rate constants from air to the whole vegetation
estimated in the McCrady (1994) experiments demonstrate the concept behind the VG parameter.
The uptake rate for an apple divided by the uptake rate for the grass leaf was 0.02 (where uptake
rates were from air to whole vegetation on a dry weight basis). For the tomato and pepper, the
same ratios were 0.03 and 0.08. The VGag is 0.01 for fruits and vegetables in this assessment, but
note above that the simple exercise with a conical carrot and spherical potato estimated a surface
volume to whole fruit volume ratio of 0.09 (carrot) to 0.03 (potato); a value of 0.01 for fruits and
vegetable empirically considers factors such as washing or peeling which would reduce
exposures. McCrady (1994) then went on to normalize his uptake rates on a surface area basis
instead of a mass basis; i.e., air to vegetative surface area instead of air to vegetative mass. Then,
the uptake rates were substantially more similar, with the ratio of the apple uptake rate to the
grass being 1.6 instead of 0.02; i.e., the apple uptake rate was 1.6 times higher than that of grass,
instead of 1/50 as much when estimated on an air to dry weight mass basis. The ratios for tomato
and pepper were 1.2 and 2.2, respectively. McCrady (1994) concludes, "The results of our
experiments have demonstrated that the exposed surface area of plant tissue is an important
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consideration when estimating the uptake of 2,3,7,8-TCDD from airborne sources of vapor-phase
2,3,7,8-TCDD. The surface area to volume ratio (or surface area to fresh weight ratio) of
different plant species can be used to normalize uptake rate constants for different plant species."
McCrady cautions, however, that uptake rates are only part of the bioconcentration factor
estimation, and he is unsure of the impact of surface area and volume differences on the
elimination phase constant, k2 (personal communication, J. McCrady, US EPA, ERL-Corvallis,
Corvallis, OR 97333). Still, his experiments do appear to justify the use of a VG parameter since
the B^ were developed on an air to whole plant mass basis, and his results are consistent with a
VGag of 0.01 for fruits and vegetables.
• D kw: Fries and Paustenbach (1990) note that this approach may overestimate
concentrations because crops can be harvested or pastures grazed before the plant concentrations
reach steady state, and that a kw based on a weathering half-life of 14 days may be too long given
experimental results of Baes, et al. (1984) which showed a range of 2-34 days, and a median
value of 10 days. On the other hand, Umlauf and McLachlan (1994) discuss the phenomena of
the transfer of semivolatile organic compounds (SOCs) from depositing particles onto the leaf -
the particles themselves may weather off the leaves with a half-life of 14 days, but the SOCs
would transfer from the particle and not weather off the leaves. Umlauf and McLachlan (1994)
could not find information on the transfer of SOCs from particles to leaves, but they speculated
that the lipid covering of the cuticle would facilitate a rapid particle to leaf transfer. In their
modeling of several SOCs to spruce leaves, they assumed that the SOCs completely transferred
from the particles to the leaves in both wet and dry particle-bound deposition with no subsequent
loss of contaminant via weathering or degradation. This is equivalent to assuming an infinite
half-life rather than a 14-day half-life. Umlauf and McLachlan (1994) found reasonable model
performance when comparing their modeling results with measurements of several SOCs
including some PCBs on spruce leaves. Stevens and Gerbec (1988) used a 14-day half-life but
did not assume a steady-state concentration is necessarily reached in vegetation. They considered
harvest intervals by including the exponential term, (l-e"kt), and assigning values of t based on
harvest intervals of different crops. This assessment uses a kw of 18.02 yr"1, which is equivalent
to a half-life of 14 days. However, based on the discussions of Umlauf and McLachlan (1994),
this is recognized as an important area of uncertainty. If their consideration of the transfer of
SOCs from particles to leaves is valid, than an assignment of a kw of 18.02 yr"1 will significantly
underestimate the impact of dry and wet deposition to vegetation.
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• D Ij and YJ: Interception values and crop yields were determined in the afore-
mentioned assessments based on geographic-specific crop yield data provided in Baes, et al.,
(1984) and the following types of crop-specific relationships estimating interception fraction
based on yield (Y), also presented in Baes, et al., (1984):
corn silage: 1=1- e-°'768Y
hay/grasses: 1=1- e"2'88Y
lettuce: I=1.e-o.o68Y
Judgments by Fries and Paustenbach (1990) on high, medium, and low yields of silage, hay, and
pasture grass, and the use of the first two interception equations above (the first for silage, and
the second for hay and grass), can give some guidance on interception fractions and yields for
these crops:
Yield
(kg/m2)
0.30 (low)
0.90 (med)
1.35 (high)
0.25 (low)
0.45 (med)
1.30 (high)
0.05 (low)
0.1 5 (med)
0.35 (high)
Intercept
Fraction
0.20
0.50
0.64
0.51
0.73
0.98
0.13
0.35
0.64
corn silage
hay
grass
This information can be used for cattle intake of vegetation, and the resulting beef and milk
concentrations. The medium values for grass, 0.15 kg/m2 yield and 0.35 interception, were used
for the example setting in Chapter 5. An average of the medium values for hay and silage, 0.63
kg/m3 yield and 0.62 interception, were used for the second category of cattle vegetation for
Chapter 5, the hay/silage/grain category.
Stevens and Gerbec (1988), using yields obtained from the Minnesota State Agricultural
Office, derived the following yield and interception estimates, respectively, for vegetables for
human consumption in their assessment: lettuce - 8.6,0.72; tomatoes - 12.0,0.55; and beans -
2.7,0.18. Average yields and interception fractions from their exercise: 7.8 kg/m2 and 0.48, were
used in the example setting in Chapter 5. These vegetable yields are fresh weight, so they need
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to be converted to a dry weight basis in order to estimate a Cppa appropriate for use in Equation
(4-38). Since vegetables are generally 80 ->90% water, a fresh to dry weight conversion factor
of 0.15 was used, resulting in an average vegetable dry matter yield of 1.17 (7.8 * 0.15). This
was used in the example settings in Chapter 5.
•D Vd: Particles settle to the ground surface and plant surfaces due to the forces of
gravity. Gravitational settling velocity is a function of particle size, with more rapid settling
occurring with larger particles. The algorithm used to estimate the concentration of
contaminated particulates in air estimates the suspension of particles less than and equal to 10
[am, which is commonly referred to as inhalable size particles. Seinfeld (1986) listed a
gravitational deposition velocity of 1 cm/sec for 10 pm size particles. This deposition velocity
will be used in this assessment, and in units of m/yr, this equals 315,360 m/y.
• D RN: Geraghty, et al. (1973) provides a map showing isolines average annual
rainfall throughout the United States. This map shows low rates of 5 to 20 inches/year in the
desert Southwest, moderate rates of 25 to 40 in/yr in the Midwest cornbelt, 40 to 60 in/yr in the
South, and so on. The example scenarios of Chapter 5 were described as rural, with land in
agricultural and non-agricultural settings. A rate of 1 m/yr (39 in/yr) will be used in the example
scenarios.
• D Rw: It is assumed that dry depositions fully adhere to plant surfaces; the
weathering constant, kw, models the loss of the vegetative reservoir of particle bound
contaminants due to wind, rain, or other weathering process. However, it is not clear that wet
deposition should also be assumed to fully adhere during a wet deposition event. Hence, the Rw
parameter, or fraction of wet deposition adhering, was introduced. Prior modeling efforts of the
impact of depositions of dioxin-like compounds to vegetation are unclear with regard to wet
deposition. Stevens and Gerbec (1988), Fries and Paustenbach (1990), Webster and Connett
(1990), and Travis and Hattemer-Frey (1991) all model particle deposition impacts of 2,3,7,8-
TCDD to vegetation in air-to-beef/milk modeling. None of them discuss the distinction in wet
and dry deposition, and model "total deposition" impacts, describing total as wet and dry
deposition, total deposition, or simply as deposition. On the other hand, McKone and Ryan
(1989) reduce the wet deposition portion of total deposition. They promote use of a "b", which
they define as the fraction of material retained on vegetation from wet deposition. They
recommend a value between 0.1 and 0.3.
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The clearest indication of the fate of wet deposition of particles can be found in Hoffman,
et al. (1992). In that field study, simulated rain containing soluble radionuclides and insoluble
particles labeled with radionuclides was applied to pasture-type vegetation under conditions
similar to those found during convective storms. The fraction of the labeled particles found to
remain on the vegetation after the rainfall varied from 0.24 to 0.37. Nine values comprised this
range, including particle sizes of 3, 9, and 25 [am, and cover described as clover, fescue, and
mixed (a site with old field vegetation including fescue, grasses, weeds, and wild flowers).
Based on this work, the Rw will be assumed to be 0.30 for all vegetation and dioxin congeners of
this assessment.
•D Wp: Washout ratios are generally defined as the concentration of contaminant in
rain to the concentration of contaminant in air. Concentrations of contaminants in air and rain
water can be derived as a mass of contaminant divided by a mass of air/water or a volume of
air/water. Mackay, et al. (1986) shows that volume-based washout ratios (mass of contaminant
mixing in m3 air or water, e.g.) exceed mass-based washout ratios (mass of contaminant mixing
in kg of air or water) by a factor of 815, which is the ratio of water and air densities. The
washout ratio used in this assessment is a volumetric ratio based on methodologies described by
Bidleman (1988). Using a volumetric ratio then allows for direct use of contaminant
concentrations estimated in this methodology since they are already on a i-ig/m3 volume basis.
Bidleman (1988) defines the overall washout ratio as: (mass contaminant/volume rain) ^
(mass contaminant)/(volume air). Bidleman (1988) also discusses that fact that overall washout
includes both wet deposition of particulates and scouring of contaminants in the vapor phase. He
includes methodologies for estimating the vapor/particulate ratios for semi-volatile organic
compounds (abbreviated SOCs) and also for estimating the washout ratios for vapors. However,
he claims that if H is sufficiently high, vapor dissolution in droplets is negligible and only the
particulate fraction is removed by wet deposition. He claims this to be the situation for n-
alkanes, PCBs, chlordane, DDT, and 2,3,7,8-TCDD. Developing overall washout ratios for these
and several other SOCs, he estimates that vapor scouring accounts for 1% of the overall washout
ratio for 2,3,7,8-TCDD. For PCBs, he estimates similar percentages of 2, 4, and 28% for
Aroclors 1260, 1254, and 1248, respectively. Based on this work, it will be assumed that vapor
scouring of the dioxin-like compounds is small in comparison to wet deposition and the washout
ratio for this assessment will only be applied to the air-borne particulate concentration of dioxin-
like compounds.
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Bidleman (1988) does not provide a chemical or site-specific equation which estimates
the particle-phase washout ratio (which he does for the vapor-phase washout ratio). Rather, he
summarizes available data and concludes that there is a wide range of the particle-phase washout
ratio, Wp, for SOC: between 2xl03 and IxlO6. He claims that a typical range is 10s to 106, and
uses a Wp of 2 x 105 in his exercises to estimate the overall washout ratio for several SOCs.
Koester and Kites (1992) list vapor and particle scavenging ratios for congener groups of
dioxin-like compounds. To derive these ratios, they used air concentrations for congener groups
that were taken at one time period in Bloomington and Indianapolis, Indiana, and rainfall
depositions of these compounds at these sites measured during a second period of time. Using
the Bidleman vapor/particle partitioning model used in this assessment, they estimate the
vapor/split for the air concentrations. With these observations and models, they conclude that the
overall washout ratio (sum of vapor and particle ratios) ranges from 104 to 10s, which contrasts
the typical range of 10s to 106 noted above from Bidleman (1988). Also, their calculations
indicate that vapor scavenging of dioxin-like compounds is comparable to particle scavenging,
also in contrast to the Bidleman analysis summarized above. However, they did not state
whether their washout ratios were volume or mass-based. If they were mass-based, then a
conversion to volume based would put them in the 107 to 108 range, which seems improbable
given the Bidleman summary above. Therefore, it will be assumed that they are volume-based,
and they are appropriate to use for this assessment. Since no clear trend for particle washout
ratios with regard to the degree of chlorination increased appears in Koester and Kites' data, the
midpoint of their calculated range, 5 * 104, will be used for all example compounds in this
assessment.
As a final note, the multiplication of the above terms, Wp * Cpa * RN * Rw, does result in
wet deposition in appropriate units of |_ig/m2-yr, although that is not immediately obvious. First,
multiplication of Wp, in (|_ig contaminant/m3 rain)-^(|_ig/m3 air), and Cpa, in |_ig contaminant/m3 air,
leaves a partial term in units of \ig contaminant/m3 rain. Then, multiplication of this partial term
times annual rainfall rate, thought of as m3/m2-yr instead of m/yr, gives the final quantity in the
appropriate units.
When calculating concentrations in below ground fruit and vegetables using Equation (4-
35), Cbgv is on a fresh weight basis since the RCF developed by Briggs, et al. (1982) is on a fresh
weight basis, and no correction for estimating exposures is necessary. However, Cabv as
estimated in Equation (4-36) is on a dry weight basis, and should be multiplied by a dry weight to
fresh weight conversion factor when applied to above ground fruits and vegetables. A reasonable
estimate for this parameter for fruits and vegetables is 0.15 (which assumes 85% water), which
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was used in this assessment. When using Equation (4-36) to estimate Cabv for the beef and milk
food chain algorithm, a conversion to fresh weight is not required, however, since the algorithms
were developed assuming dry weight concentrations.
4.3.4.3. Beef and Milk Concentrations
The algorithm to estimate the concentration of contaminant in beef and/or milk was based
on methods developed by Fries and Paustenbach (1990). They developed the beef
bioconcentration factor for 2,3,7,8-TCDD, which is defined as the ratio of the concentration of
the contaminant in beef fat to the concentration in the dry matter dietary intake of the beef cattle.
They discussed bioavailability, which, as they define it, is the fraction of ingested contaminant
which is absorbed into the body. It depends on the vehicle of ingestion - dioxin in corn oil has a
bioavailability in the range of 0.7 to 0.8, in rodent feed it has an estimate of 0.5, while in soil it
has a range of 0.3 to 0.4. They emphasized the importance of the differences in diet between
cattle raised for beef and those which are lactating in explaining different food product
concentrations. Although there is likely to be some difference in the bioconcentration tendencies
for lactating and non-lactating cattle, Fries and Paustenbach in fact used the same
bioconcentration for beef fat and milk fat, and the same will be done here.
The concentration in the fat of cattle products is given as:
Cfat = BCFFF(DFSBSACS + DFg ACg + DFfAC) (4-45)
where:
Cfat = concentration in beef fat or milk fat, pg/g
BCF = bioconcentration ratio of contaminant as determined from cattle vegetative
intake (pasture grass or feed), unitless
FF = feedlot factor for beef fat calculation, < 1 for beef fat and =1 for milk fat,
unitless
DFS = fraction of cattle diet that is soil, unitless
Bs = bioavailability of contaminant on the soil vehicle relative to the vegetative
vehicle, unitless
ACS = average contaminant soil concentration, pg/g
DFg = fraction of cattle diet that is pasture grass, unitless
ACg = average concentration of contaminant on pasture grass, pg/g
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DFf = fraction of cattle diet that is feed, unitless
ACf = average concentration of contaminant in feed, pg/g
The following is offered as brief guidance to these terms and also the justification for the values
selected in the example Scenarios in Chapter 5.
• D Beef/milk bioconcentration factor, BCF: Fries and Paustenbach (1990)
developed the concept of a beef/milk bioconcentration ratio and applied it to 2,3,7,8-TCDD.
BCF is defined as the concentration of contaminant in fat of cattle products (i.e., dairy and beef)
divided by the concentration in dry matter intake. One key difference in the dietary intake of
cattle raised for beef versus cattle raised for dairy is that cattle raised for beef tend to be pastured
more than dairy cattle and be more exposed to contaminated soil, whereas lactating cattle are
more often fed high quality feed, including grains which are expected to be substantially residue
free since they are a protected vegetation. Another key difference is that the dioxins in lactating
cows generally will reach a steady state in the fat of the animal (muscle fat and milk fat) much
more rapidly than in non-lactating cattle because the excretion of milk will result in a large
excretion of dioxins. For non-lactating cattle, excretion only occurs in feces and urine. Based on
modeling, McLachlan (1994) speculates that it would take approximately 6 years for dioxins to
reach steady state in non-lactating cattle because of the slow excretion rate through feces and
urine, and that at steady state, concentrations in the muscle fat of non-lactating cattle would be
much higher than in milk. However, dioxin concentrations in beef fat are generally found to be
similar to that in milk fat. McLachlan (1994) explains it this way: "...it will take 6 yr to approach
this steady state, much longer than the 1.5 yr that beef cattle typically live. During this time, the
animal is also growing, continuously diluting its contaminant reservoir. As a result, the
contaminant levels in commercial beef fat are generally not much higher than in milk fat." For
this reason, the BCFs that are developed below based mostly on milk fat will be applied to both
milk and beef fat.
Based on literature studies of cattle consuming feed contaminated with dioxin-like
compounds, Fries and Paustenbach (1990) calculated a BCF of between 4 and 6, and assumed a
value of 5.0 for 2,3,7,8-TCDD. Being developed directly from data of cattle ingesting
contaminated feed, this BCF value of 5.0 already considers the bioavailability of the
experimental contaminated feed. It will be assumed that the bioavailability of the cattle
vegetation in this assessment equals that of the experimental feed. Therefore, a value of 5.0 can
go directly into Equation (4-45) when applied to concentrations in grass and pasture. However,
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this value should not be applied to soil, since it has been shown that TCDD on soil is less
bioavailable than TCDD on other vehicles. This is why a Bs appears in Equation (4-45) - it
adjusts BCF when applied to a soil concentration. The value of Bs is described below. The Fries
and Paustenbach (1990) literature review is reproduced in Table 4-5, which additionally shows
results generated based on information in McLachlan, et al. (1990) and Fries, et al. (1999).
Although the BCF of 5 determined by Fries and Paustenbach (1990) for 2,3,7,8-TCDD
appears high based on the literature for this compound, Fries and Paustenbach (1990) discuss
how short duration feeding trials (the 21 days of Jensen and Hummel (1982) and the 28 days of
Jensen, et al. (1981)) do not result in steady state bioconcentration ratios. Extrapolating the data
to a point where steady state is speculated to be reached, Fries and Paustenbach (1990) developed
the arguments for the range of 4 to 6 for 2,3,7,8-TCDD. The second example compound in
Chapter 5 was 2,3,4,7,8-PCDF. Fries and Paustenbach (1990) observed that bioconcentration
ratios for PCDDs and PCDFs decreased significantly as chlorination increased, although their
literature seems to imply that this effect is most pronounced for hepta- and octa- PCDDs and
PCDFs. They could not locate data in the literature for penta-PCDDs or PCDFs.
McLachlan, et al. (1990) was the first study where BCFs for cow milk could be generated
for furan congeners. They conducted a mass balance of dioxin and furan congeners in a lactating
cow. They carefully accounted for 16 of the 17 dioxin and furan congeners of toxicity
equivalency to 2,3,7,8-TCDD in the intake of a lactating cow in food, air, and water, and
measured amounts in feces, urine, and milk, while attributing the rest of the intake to a
compartment they termed, storage/degradation/experimental error. They obtained data well into
steady state, and provided information necessary to estimate milk BCFs including: information
appropriate to estimate the dry matter intake by the cattle; ng/day congeners in feed, water, and
air; L/day milk production (density assumed to be 0.9 g/cm3); and percent fat in milk. The data
for estimating the dry matter intake of the cattle was expressed in two ways. One, the wet weight
of feed materials was given, and assuming a dry weight fraction (the actual dry weight fraction
was not given), one could estimate a dry matter intake. Two, it was stated that the feces flux of 5
kg/day was 30% of the dry weight intake. Therefore, the dry matter intake is calculated at 16.7
kg/day. However, in a later report on this same data set (McLachlan, 1993), it was stated that the
feces flux from this experimental animal of 7 kg/day was 33% of dry matter intake. In a personal
communication (letter, M. McLachlan to M. Lorber dated 4/2/95), the author confirmed that the
correct total dry matter intake was 21 kg/day, and this value was used in the generation of BCFs
shown in Table 4-5.
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Fries, et al. (1999) conducted a feeding experiment where four cows were fed PCP-
contaminated wood. Specifically, a 3 g/day dose of ground PCP-treated wood as administered to
each cow for 58 days. The mixed feed was sampled, and a sample of each cow's milk was
obtained on days 28, 42, and 56. Feed amounts were carefully controlled to not only these cows
but to other cows at the USDA agricultural research facility. Bulk milk was obtained from cows
who were not treated, and the concentrations from these samples served not only as controls for
the laboratory, but also were used to calculate a limited set of BCFs for background settings.
BCFs were calculated when both feed and milk were higher than background. The results for the
cows fed the wood as well as for background cows are shown in Table 4-5. As seen, there is
reasonable agreement between the cows fed the contaminated wood and the background cows,
with the exception, perhaps, of two of the furan congeners, and there is also good agreement
between these BCFs and those developed from the data of McLachlan, et al. (1990). Also, the
BCF for 2,3,7,8-TCDD was highest in this experiment at 7.1 as compared to other BCFs
calculated for 2,3,7,8-TCDD
The McLachlan data will be used to assign BCFs for the demonstrations in Chapter 5.
The BCF value for 2,3,7,8-TCDD value is 5.73 and the BCF for 2,3,4,7,8-PCDF is 4.13, in the
demonstration scenarios which include a dioxin, a furan, and a PCB. For the demonstration of
the incinerator, the suite of dioxin-like compounds are demonstrated, and the full BCF set
developed by McLachlan and coworkers will be used.
A review of the literature for PCBs is given in Table 4-6. McLachlan and coworkers
measured a suite of PCBs in his lactating cow mass balance experiments and reported the results
in McLachlan (1993). However, unlike the dioxin results which showed a favorable mass
balance result (i.e., most of the mass of the dioxin intake by the cow could be accounted for in
the feces, urine, and milk - the remainder attributed to storage, metabolism, or experimental
error), the PCBs results showed that up to 40% more PCBs were being excreted through urine,
feces, and milk, than was taken up by the cattle. McLachlan (1993) discounted the possibility that
the cow was losing body fat which could explain the result, and instead speculated that there was
secondary contamination of the feed through binder twine that had been treated with recycled oil
(i.e., the amount of PCB intake by the cow was underestimated). In any case, this data could not
be used to generate BCFs for PCBs.
Although PCBs, dioxins, and furans are related compounds in terms of environmental
fate characteristics, a difference in bioaccumulation potential is noted with higher degrees of
chlorination, based on the study of Tuinstra, et al. (1981). Their work implies increasing
bioaccumulation potential as the degree of chlorination increases. They developed BCF values
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(defined in the same manner as in this assessment) for a suite of congeners identified to occur in
Aroclor 1260 administered to lactating cows. Therefore, their data allowed for a partial
examination on congener bioaccumulation patterns. The results given in Table 4-6 are
interpreted from the data supplied in Tuinstra, et al. (1981). Tuinstra determined the identity and
percentage of specific congeners which comprise Aroclor 1260. He was able to identify 36
congeners, but could only quantify 27 of them (because of the unavailability of standards for 9
congeners). These 27 comprised 81%, by mass, of the Aroclor 1260. Tuinstra was able to
estimate BCF values for most, but not all, of the identified congeners - for 23 of the 27 congeners
they identified (which equaled 77% of the congeners, by mass, of Aroclor 1260). As seen in
Table 4-6, the average congener-group BCF value increases from about 2 to 4 going from hexa-
to nanochloro-PCBs. However, there was a wide range of measured BCF values for specific
congeners. In the heptagroup, for example, Tuinstra estimated BCF values between 0.4 and 5.2.
Fries, et al. (1973) showed increasing BCF values in milk fat at 20, 40, and 60 days for Aroclor
1254 up to a value of 4.8 at day 60. The body fat BCF value at 60 days, the only time such a
measurement was taken, was 3.4. The trend of having a higher BCF value for milk fat as
compared to body fat for lactating cows was also noted by Willett, et al. (1987). They fed
lactating cattle Aroclor 1254 sorbed to ground corn. In three sequential periods of 60 days, they
fed the cattle 10, 100, and then 1000 mg/day of Aroclor 1254. Given their average daily dry
matter intake of 19.5 kg during the experiment, the concentration during each of those 3 periods
was 0.51, 5.13, and 51.28 mg/kg (ppm). However, milk and body fat concentrations of PCBs
were given after 60, 120, and 180 days, so that for estimation of the BCF value, what is needed is
average concentration of Aroclor intake after these periods. These averages are 0.51, 2.82, and
18.97 mg/kg. Given the reported concentrations of PCBs in milk and body fat after these
experimental periods, BCF values were estimated and given in Table 4-6. Two studies, that of
Willett and Liu (1982) and Perry, et al. (1981), contained data from which estimates of BCF
could be made, except that these studies did not report daily dry matter intake. An estimate of
19.5 kg/day was assumed for lactating cattle for these studies, which was the experimental dry
matter intake noted by Willett, et al. (1987). Willett and Liu (1982) dosed cattle for only 20
days, and arrived at the lowest noted BCF value for Aroclor 1254, 1.2. The trend of increasing
BCF value over time of dosing was noted by Fries, et al. (1973). Willett, et al. (1990) conclude
that steady state is reached after about 60 days, so that estimates of BCF made from experiments
less than 60 days may not reflect steady state conditions. Perry, et al. (1981) had a high BCF
value, 4.2, despite the dosing period being only 32 days. This would appear to be the result of
having a high concentration in the diet. Similarly high BCF values with corresponding high
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concentration in dosed intake were noted in Fries, et al. (1973) and Willett, et al. (1987). It
should be noted that the concentrations in body fat in the studies of Willett and Liu (1982) and
Perry, et al. (1981) were corrected as recommended by Willett, et al. (1990) in estimating BCF
values.
Five trends for PCBs were discussed above: 1) that steady state is reached after
approximately 60 days, 2) that higher BCF values appear to result with higher concentrations in
feed, 3) that BCF values for milk fat may exceed those of body fat for lactating cows (this also
seems true for dioxins/furans; see Table 4-5), 4) that the BCF values tend to increase with
increasing chlorination of PCB congener groups, and 5) that this fourth trend is based on a
limited set of data and much variability exists within specific congener groups.
Generally there is a sparsity and inconsistency in the data which would allow for
definitive estimation of BCF values for the example heptachloro-PCB example compound in
Chapter 5, 2,3,3',4,4',5,5'-PCB. Most of the data noted is for Aroclor 1254, and this data implies
BCF values between 1.2 and 4.8. Based on the results from Tuinstra, et al. (1981) for the
average of eight heptachloro-PCBs, a BCF value of 2.3 will be assigned to 2,3,3',4,4',5,5'-PCB.
It should be noted that all bioconcentration or biotransfer parameters, such as the BCF,
are qualified as second order defaults for purposes of general use. Section 6.2. of Chapter 6
discusses the use of parameter values selected for the demonstration scenarios, including a
categorization of parameters. Second order defaults are defined there as parameters which are
theoretical and not site specific, but whose values are uncertain in the published literature. The
parameter values in this category should be considered carefully by users of the methodology.
• D FF: Fries and Paustenbach (1990) summarize pertinent literature to conclude
that cattle raised for beef are not slaughtered without an intervening period of high-level grain
feeding. Agricultural statistics (USDA, 1992) show that 32.9 million cattle were slaughtered in
1991. Of this number, 6.1 million were cows and bulls that likely did not go through a feedlot
prior to slaughter. Quarterly statistics from 1991 show that at any time, cattle and calves on feed
for slaughter range from 10 to 12 million. Fries uses these statistics to conclude that 75 to 80%
of the total beef supply is from animals that went through a feedlot finishing process, and that the
portion of beef that did not go through a feedlot process are (generally speaking) those 6.1
million cows and bulls (personal communication, G. Fries, USDA Agricultural Research Service,
Beltsville, Maryland, 20705). He suggests that a representative feedlot finishing process would
include a length of 120 days and diet consisting of 20% corn silage and 80% grain. The grains
can be assumed to be residue-free, since grains are protected and, as discussed above, little
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within plant translocation of outer contamination can be assumed. Also, the ears of the corn
silage are in the same category, leaving only the stalks and leaves of the corn silage impacted by
atmospheric transfers of dioxin-like compounds.
A feedlot finishing process is important to consider if assessing beef impacts in a site-
specific assessment. However, data could not be found in the literature which measured the
impact of this process to beef concentrations. Such impacts could occur as the result of increased
weight gain from substantially residue-free feeds. Fries and Paustenbach (1990) and Stevens and
Gerbec (1988) modeled the impact of a residue-free grain-only diet for four months prior to
slaughter. Based on within-cow dilution and depuration considerations, both efforts estimated
that the feedlot process would reduce beef concentrations by about one-half. This translates to an
assignment of 0.5 for FF. This was the assumption used in the air-to-beef food chain validation
exercise described in Chapter 7.
The demonstration scenarios of Chapter 5 assume that farming families slaughter a
portion of their cattle for home consumption, and that they do not put their cattle through any
special diet before slaughter. Therefore, an FF = 1 is assumed for the demonstration scenarios in
Chapter 5.
With the use of an FF, it is noted that the quantity in the parenthetical of Equation (4-45)
above describes the cattle diet prior to slaughter.
• D Soil bioavailability, Bs: This parameter reduces the bioconcentration ratio, F,
considering that soil is a less efficient vehicle of transfer compared to feed. Remember that the
values of BCF appropriate for Equation (4-45) already consider bioavailability and were
developed from experimental data placing the BCF of 2,3,7,8-TCDD in the 4 to 6 range. Fries
and Paustenbach (1990) reviewed several studies on the oral bioavailability of TCDD in soil in
the diet of rats, and concluded that soil is a less efficient vehicle of transfer as compared to rat
feed. If the same is true for cattle - that soil is less efficient than their feed - than the BCF value
must be reduced when applied to soil ingestion. Most studies reviewed by Fries and Paustenbach
used corn oil as the positive control, since there is a high absorption of TCDD in rats when corn
oil is used as the vehicle, with 70-83% of the administered TCDD dose absorbed. Their
literature review on rat data showed that the bioavailability of TCDD in soil was between 0.4 and
0.5 that in corn oil, or 0.3 to 0.4 overall. The literature implied a range of 0.5 to 0.6 of TCDD in
standard rat feed is absorbed, and although few studies were available, a similar 50% absorption
rate of TCDD in cattle feed was noted. They concluded, therefore, that the rat data was a
reasonable surrogate for cattle. The Bs can be thought of as the ratio of BCF values between soil
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and feed, or, (BCFsoil)/(BCFfeed). If the difference in BCFsoil and BCFfeed is explained solely by
bioavailability differences, then the ratio of overall bioavailability of soil to feed should equal
this ratio. As described above for rat data, the overall bioavailability of soil was 0.3-0.4, and the
overall bioavailability of feed was 0.5-0.6. The ratio of overall bioavailabilities is, therefore,
(0.3-0.4)7(0.5-0.6). If the argument that this ratio equals the ratio of BCFs is valid, than this
would lead to a Bs of 0.5 to 0.8. This implies that absorption of TCDD when soil is the vehicle is
50 to 80% of what it would be if feed were the vehicle. These assumptions and implications are
made for this assessment, and the soil bioavailability term, Bs, used for all example compounds
in Chapter 5 is 0.65.
• D Soil diet fraction, DFS: Fries and Paustenbach (1990) report that soil intake by
cattle feeding on pasture varies between 2 and 18% of total dry matter intake, depending on
whether the grazing area is lush or not. The soil diet fraction would be lower for cattle which are
barn-fed with minimal opportunity for contaminated soil intake. Cattle raised for milk are rarely
pastured, so one possible assumption for estimating milk fat concentrations would be a DFS of
0.0. Fries and Paustenbach (1990) assumed between 0 and 2% of the dry matter intake by
lactating cattle was soil in various sensitivity tests. Since cattle raised for beef are commonly
pastured, a conservative assumption would be a high DFS of 0.15 (15%), although a more
reasonable assumption which would consider grazing in lush conditions and/or a portion of diet
in feed or supplemental feed leads to DFS less than 0.10. Fries and Paustenbach (1990) assumed
DFS of between 0 and 0.08 for beef cattle in various sensitivity tests. The example settings in
Chapter 5 assume 0.02 (2%) for lactating cattle, and 0.04 (4%) for beef cattle.
• D Feed and grass diet fractions, DFf and DFg: The sum of the three diet
fractions, DFS + DFf + DFg must equal 1.0. Setting DFS equal to 0.02 (2%) for lactating cattle
assumed that they are pastured to some extent or could be taking in residues of soil sticking to
home grown feed. Assuming lactating cattle graze a small amount of time, the DFg for lactating
cattle will be 0.08 (8%). This assessment simplifies the definitions of dairy and beef cattle diets
by defining non-pasture grass vegetation simply as "feed". Feeds include hay, silage, grain, or
other supplements. While dairy cattle are lactating, 90% of their dietary intake is assumed to be
in this general category. Beef cattle spend a significant amount of time pasturing. However,
their diet is supplemented with hays, silages, and grains, and particularly so in colder climates
where they need to be housed during the winter. In this assessment, the simple assumption that
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they ingest equal proportions of pasture grass and feeds is made. Therefore, with 4% soil
ingestion, DFf and DFg are both 48% for beef cattle.
• D Average contaminant soil concentration, ACS: The simplest assumption for
ACS would be that it equals the initial level of contamination, Cs. However, this would be too
high if the cattle also graze in uncontaminated areas. Where cattle have random access to all
portions of a grazing area with contaminated and uncontaminated portions, a ratio of the spatial
average of the contaminated area to the total area should be multiplied by Cs to estimate ACS. If
cattle spend more time in certain areas, these areas should be weighted proportionally higher.
Different assumptions for determining ACS might also be in order when using Equation (4-45) to
estimate milk fat as compared to beef fat concentrations. Lactating cattle, if pastured, might
graze on different areas than beef cattle. After determining a spatial average based on current
conditions, a second consideration might be given to temporal changes. If soil levels are
expected to change over time (due to changes in source strength or other factors) then the
concentrations should be averaged over the exposure duration as well. The example scenarios in
Chapter 5 where beef and milk exposures were estimated were termed "farms". The
methodologies in this chapter were used to estimate the average soil concentration over the entire
farm property. Assuming the cattle are raised on the farm property, than 100% of their intake of
soil comes from the farm. This means that the average soil concentration, ACS in Equation
(4-45), is equal to the level of contamination given as the initial level, or determined as average
for the farm based on fate and transport algorithms.
• D Average feed and pasture grass concentration, ACf and ACg: The
concentration of contaminant in pasture grass or feed is equal to Cabv as calculated in Equation
(4-36). As described earlier, pasture grass or feed grown on-site can be impacted by air-to-plant
vapor phase transfer and particulate deposition. Refinements noted above include the empirical
parameter VGag equals 1.00 when applying the air-to-leaf transfer algorithm to pasture grass and
0.50 when applied to cattle feed. A refinement noted here, and like ACS above, is that an
assumption needs to be made about the fraction of feed or fraction of pasture grass that is
impacted by contamination. Part of the feed diet could come from outside sources and not be
contaminated, and part of the grazing area could be far from a localized area of soil
contamination, making it less impacted by contaminated particulates or vapors. The simplest
assumption is that the entire vegetative diet of the cattle includes pasture grass and feed impacted
by the contaminated soil, in which case ACf and ACg would equal Cabv. For the sake of
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simplicity and consistency, the assumption made for ACS was also made for ACf and ACg in the
example Scenarios in Chapter 5. That is, the grass and feed intakes of beef and dairy cattle
originate within the farm property and concentrations in grass and feed are a function of the soil
concentrations within the farm property; ACf and ACg are equal to Cabv as calculated in Equation
(4-36). For site-specific situations, ACf and ACg should be estimated as Cabv reduced according
to assumptions on quality of cattle feed, and impacts of air-borne contaminants on grazing land
and cattle feed grown at the site where cattle are raised.
There is one final but critical note on solving for beef and milk concentrations given a
solution for Cfat as in Equation (4-45). Human daily ingestion amounts are typically expressed in
whole product rather than the fat portion of product. Whole milk is 4% fat, meaning that the Cfat
needs to be multiplied by 0.04 to get whole milk concentration. Similarly, beef is generally 18-
22% fat, meaning that the Cfat needs to be multiplied by 0.18-0.22 to get whole beef
concentration. However, the ingestion rates in this assessment for beef and milk were developed
on a fat basis, so no adjustment is necessary.
4.3.4.4. Chicken and Egg Concentrations
The algorithm to estimate the concentration of contaminant in chicken and/or eggs is the
same algorithm as in beef/milk above. The experiments used to develop the chicken and egg
bioconcentration factors were conducted by the Hazardous Materials Laboratory at the California
EPA (Stephens, et al. 1995), and they will be described below. To review, the equation used to
calculate the concentration of dioxin-like compounds in chicken and egg fat is:
fat
= (BCFDFSBSAC) + (BCFDFgACg) + (BCF DFfAC (4-46)
where:
Cfat = concentration in chicken fat or egg fat, pg/g
BCF = bioconcentration ratio of contaminant developed for chicken vegetative
intake, unitless
DFS = fraction of chicken diet that is soil, unitless
Bs = bioavailability of contaminant on the soil vehicle relative to the vegetative
vehicle, unitless
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ACS = average contaminant soil concentration, pg/g
DFg = fraction of chicken diet that is incidental vegetation while free ranging,
unitless
ACg = average concentration of contaminant on free range vegetation, pg/g
DFf = fraction of chicken diet that is feed, unitless
ACf = average concentration of contaminant in feed, pg/g.
The scenario of principal concern for chicken and egg contamination is the free range chicken
scenario. This is the scenario where chickens are allowed to graze during all, or a portion, of
their lives. The feed they consume could be spread out on the ground or in troughs. When they
are free ranging but not eating their formulated feed from troughs, they still may forage for
vegetation or earthworms which would give them considerable exposure to soil and vegetation,
not to mention the earthworms which could be a source of dioxin-like compounds (earthworm
exposure is not considered in this model). The percentage of all chickens raised for meat or eggs
in this manner is a very small percentage of the total; most chickens are raised in commercial
settings where their cages are raised and they have little exposure to soil or vegetation. When
raised this way, their exposure to dioxins is only through the feed, and measurements of dioxin in
chicken feed have shown little or no dioxin residues (Chang, et al, 1989; Stephens, et al, 1995).
Chang, et al. (1989) sampled eggs from three sources: from foraging chickens that were raised
near a contaminated PCP site in Oroville, CA (see the discussion of BCF below for a discussion
of this site), from foraging chickens that were raised 4.5 km away in the neighboring town of
Palermo, and from a grocery store, which were considered the control eggs. They found that the
eggs from the foraging chickens near the site contained the highest concentrations of dioxin and
furan congener groups (individual congeners were not reported in Chang, et al., 1989), with eggs
from Palermo containing concentrations of dioxins and furans about 3 to 5 times less, and the
commercial eggs being one to two orders of magnitude less than the eggs from near the PCP site.
The demonstration of this pathway in Chapter 5 is a free range scenario. The following is
offered as brief guidance to the parameters in Equation (4-46) above and also the justification for
the values selected in the example Scenarios in Chapter 5.
• Chicken/egg bioconcentration factor, BCF: In 1987, an explosion and resulting
fire occurred at the Koppers Wood Preservative Treatment plant in Oroville, CA. Subsequent
environmental studies found soil concentrations of the tetra through octa congener groups up to
the ppm level adjacent to the fire site, and impacts to foraging chickens were also found, as was
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discussed above (Chang, et al, 1989). The concern over this site led Stephens, Petreas,
Hayward, and colleagues at the Hazardous Materials Laboratory (HML) of the California
Environmental Protection Agency (Cal-EPA) to begin an investigation to test the hypothesis that
grazing chickens would bioaccumulate dioxins. The first phase of their study, the laboratory
phase, involved dosing chickens with feed mixed with soils of varying known levels of dioxins
and furans. The completed results of this study have been published (Stephens, et al. 1995), with
earlier publications available on the design and some interim results (Stephens, et al., 1992;
Petreas, et al., 1991). Briefly, the study design was as follows. White Leghorn (Babcock D 300)
chickens were randomly assigned to three groups with 22 individual chickens per group. The
control group was fed a formulated laying-bird diet containing 10% uncontaminated soil - i.e.,
soil from a rural background setting with low levels of dioxins and furans. The TEQ
concentration on this uncontaminated soil was less than 0.5 ppt. The "low" group was fed the
same formulated diet to which they mixed soil to obtain a 9 (feed): 1 (soil) ratio. The decision to
have soil be 10% of the diet was based on discussions they had with agricultural experts on the
amount of soil a free range chicken would take in. The soil had a CDD/F concentration of 42 ppt
I-TEQ. It was taken from the backyard of a residence near the Koppers site, and had a similar
sandy loam texture as the uncontaminated soil. The "high" group was fed the same formulated
diet containing 10% of the same backyard soil but this time with several of congeners spiked in
the soil to much higher levels. The concentration on this spiked soil rose to 459 ppt I-TEQ.
Analysis by Stephens, et al. (1995) suggested that this spiking did not affect the bioavailability of
the spiked congeners in the soil. Analysis of the feed before the introduction of any soils showed
non-detects (with detection limits near or below 1 ppt, mostly at 0.1 ppt) for all congeners. Once
the soils and feed were mixed, the I-TEQ concentrations in the feed/soil mixture for the
"control", "low", and "high" groups were 0.6, 3.2, and 35.8 ppt, respectively (counting non-
detects at half-detection). Eggs were collected every 5 days during the first month and every 10
days thereafter. Chickens were culled on days 10, 20, 41, 80, 164, 188, 210 and 278. Samples
were taken of blood, feces, liver, adipose, and thigh muscle. Stephens, et al. (1995) reported on
the analysis of liver, adipose, thigh muscle and egg for the suite of 17 dioxin-like compounds at
various time intervals.
They also developed bioconcentration factors for these four tissues and for both feeding
regimes, "low" and "high". To calculate the BCF, they averaged the tissue concentrations for
days 80 and 164 when the animals had apparently reached a steady state, and for eggs at days 80,
160, and 178. The same will be done for the calculation of BCFs in this assessment. However,
the BCFs for this assessment will be different than those published by Stephens, et al. (1995).
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Their BCFs were defined as the concentration in the whole tissue on a wet weight basis divided
by the concentration in the feed/soil mixture. Furthermore, although the concentration in the
soil/feed mixture was determined and reported, Stephens, et al. (1995) surmised that the
concentrations in the soil alone were more reliable than the concentrations measured in the
feed/soil mixture. Therefore, the concentrations to which the chickens were exposed was
calculated as the soil concentration divided by 10. The difference for this assessment is that the
BCFs will be defined as the concentration in the tissue on a lipid basis divided by the
concentration in soil, rather than the feed/soil mixture. The calculation for BCFs will also
involve the use of the soil bioavailability term, Bs, as shown above in Equation (4-46). The
procedure to calculate BCFs from the data is now explained.
Assuming that the chickens were not exposed to vegetation and that the feed
concentrations were equal to 0.0 in the Cal-EPA experiments, Equation (4-46) reduces to the
following:
Cfat - BCFDFsBsACs (4-47)
The congener-specific BCFs were then easily calculated assuming that the DFS is equal to 0.10
and, the Bs is equal to 0.65, as it was above for the beef/milk bioconcentration algorithm, and
directly using the soil concentrations as reported in Stephens, et al. (1995). Table 4-7 shows the
final BCFs calculated in this manner from the Cal-EPA data. Stephens, et al. (1995) made the
following three pertinent observations regarding this data and the development of the BCFs: 1)
the highest concentrations were found in the liver, implying that mechanisms other than lipid
solubility operate in that organ, 2) the soil had a high organic matter content, which the authors
suggested would result in a lower observed bioavailability of the dioxins in the chickens than
could have been observed had the soil organic matter content been lower and perhaps more
typical of agricultural soils, and 3) the BCFs were higher in the "high" exposure group as
compared to the "low" exposure group.
With regard to the first observation, the higher BCFs for the liver as compared to the
other three tissues is apparent from Table 4-7 for both the low and high group. This is not
relevant for exposure unless, of course, one were predicting concentrations in chicken livers for
human consumption. In that case, one could use the liver-specific BCFs in Table 4-7. With
regard to the second observation, this is important in that it provides some justification for the Bs
of Equation (4-47) which was assigned a value of 0.65. The BCFs in Table 4-7 should, therefore,
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be interpreted as BCFs specific to non-soil chicken intakes which are assumed to include chicken
feeds and incidental vegetation in the model used in this assessment. This makes the chicken/egg
BCFs analogous to the beef/milk BCFs. With regard to the third observation, Stephens, et al.
(1995) tested the observation statistically and found that the BCFs of 3 congeners - 12378-
PCDD, 234678-HxCDF, and 1234678-HpCDF - were significantly different at the p < 0.05 level
and that the BCFs of 2 congeners - 2378-TCDF and 1234789-HpCDF - were significantly
different at the p < 0.025 level. Even without statistical significance, it does appear that the trend
of higher BCFs for the high exposure regime holds up for the other congeners as well.
Other interesting trends to observe in this data qualitatively (without statistical rigor) are
related to the bioconcentration differences between adipose, thigh, and egg within the low group
alone and/or compared against the high group. One important observation is that, in the low
group, the dioxins concentrate more in the adipose lipid as compared to the thigh lipid, but this
difference is much smaller in the high group. This might suggest that the dioxins deposit first, or
preferentially, in adipose tissue rather than muscle tissue at low doses. Another trend is that, at
the low dose, the dioxins concentrate in the lipid of the thigh and the egg about equally, but in the
high group, the dioxins concentrate more in the thigh lipid as compared to the egg lipid.
In real world settings, soil concentrations to which chickens are exposed are expected to
be more like the "low" exposure as compared the "high" exposure. Because of this, the BCFs for
the low exposure group will be used in this assessment. For site-specific assessments where the
soils are highly contaminated, and the assessor has justification for assuming that chickens are
grazing on the highly contaminated soil, it would be appropriate to use the BCFs for the "high"
group. The BCFs developed for the thigh and egg will be used for the calculation of
concentrations in chicken meat and eggs, respectively. This is a different strategy than for the
calculation of dioxin concentrations in beef or milk. There, a single set of BCFs were used to
calculate concentrations in the fat of beef or milk. Here, the data appears robust enough to
support separate BCFs. BCFs for two congeners in the low group could not be reliably
calculated because one or both of the measurements (i.e., the soil/feed measurement and/or the
tissue measurement) were below the quantification limit. These congeners are 2378-TCDD and
234678-HxCDF. For 2378-TCDD, it is noted that for the high exposure group, the 2378-TCDD
BCF is about 1.3 times that of the 12378-PCDD BCF for both the thigh and egg. Based on this
observation, the BCF for 2378-TCDD for thigh and egg will be 8.8 (i.e., 6.8*1.3) and 7.8
(6.0* 1.3), respectively. The BCFs for the three HxCDFs for which a BCF could be estimated
appear to be within a factor of 2-3 of each other. Based on this observation, the BCF for 234789-
HxCDF for the thigh and egg will be estimated as an average for the three HxCDFs for these
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tissues for which the BCF was derived. This calculation results in a 234789-HxCDF BCF of 4.1
for thigh and 6.2 for egg.
The only data that could be found for developing PCB BCFs was from a study by Fries, et
al. (1977), and this data was only pertinent for Aroclor mixtures. Fries, et al. (1977) fed caged
White Leghorn hens, 41 weeks of age, various PCB Aroclor mixtures at either 2 or 20 ppm in
their standard breeder diet. Data was taken at nine weeks, after establishment of steady state, for
body fat and egg concentrations of the 6 different Aroclor mixtures. In order to calculate
concentrations, Fries et al. (1977) assumed that the eggs weighed 50 grams and that the body fat
was 10% of the full weight of body tissue. BCFs for these Aroclors were calculated in this
assessment as the ratio of the egg fat concentration to the feed concentration, for egg BCFs, and
the body fat concentration to the feed concentration. Fries, et al. (1977) had provided full egg
concentrations, and these were converted to egg fat concentrations by assuming that lipid is 10%
of the full egg weight. The assumption of Fries, et al. (1977) that the fat content of his tested
tissues was 10% will be retained here. The BCFs for the Aroclors are shown in Table 4-8.
A noteworthy trend for this data, as observed by Fries, et al. (1977), is that the PCBs
appear to be retained at greater amounts as the degree of chlorination continues but then drops off
at the higher chlorinated congeners. This is apparent in Table 4-8, as the BCFs increase through
Aroclor 1248, but then drop off for Aroclor 1254 for the low dose and at 1268 for the high dose.
The BCFs for the eggs continue to increase suggesting that excretion through eggs continues to
be a mechanism for removal through the high chlorinated congeners. It is unclear that the BCFs
from this study are pertinent to the modeling of this assessment, not only because they are on
Aroclors and not on the dioxin-like PCB congeners, but also because soil concentrations are
unlikely to reach the 2-20 ppm level for individual dioxin-like PCBs. Still, it is the only data
that could be found. Without rigorous justification, it will be assumed that the BCFs for the low
dose of Aroclor 1254, 6.5 for egg fat and 7.4 for body far, are appropriate for the dioxin-like PCB
demonstrated in Chapter 5, 2,3,3',4,4',5,5'-PCB.
It should be noted that all bioconcentration or biotransfer parameters, such as the BCF,
are qualified as second order defaults for purposes of general use. Section 6.2. of Chapter 6
discusses the use of parameter values selected for the demonstration scenarios, including a
categorization of parameters. Second order defaults are defined there as parameters which are
theoretical and not site specific, but whose values are uncertain in the published literature. The
parameter values in this category should be considered carefully by users of the methodology.
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• D Soil bioavailability Bs; Feed, grass, and soil diet fractions, DFf, DFg, and DFS; and
average feed, grass, and soil concentrations, ACP ACg, ACS: The soil bioavailability is
assumed to be 0.65, as it was for the beef/milk algorithm. It is important for users to understand
that the BCFs were developed with this assumption. If other users believe it is more appropriate
to assume that the BCFs as directly derived from the Stephens, et al. (1995) experiments are
equally appropriate for soils, incidental vegetation, and feeds, than the BCFs need to be adjusted
downward by multiplying them by 0.65. If doing this, users should then not apply a Bs in the
calculation of chicken and egg concentrations for a free range or other scenario which has the
chickens exposed to soil. Following on the lead of the Cal-EPA experiments, it will be assumed
that soil is 10% of a free range chicken diet, and DFS will be equal to 0.10. Since the chickens
are free ranging, it also seems reasonable that they should exposed to some incidental vegetation.
No guidance could be found on the assignment of DFg, but it does seem reasonable that the
chicken would ingest less incidental vegetation than soil, particularly if their exposure to soil
occurs when the chickens are feeding and their feed is spread out on a certain patch of ground
worn bare to soil. On that basis, DFg will be assigned a value of 0.05, and DFf will be 0.85. The
soil concentration, ACS, could be modeled as in the case of erosion from a site of contamination
to a site of exposure, or as in the case of stack emission deposition. For the latter case, the soil
concentration assumed will be the untilled soil concentration. The soil concentration could also
be specified, as in the case of soil contamination which is on-site. The incidental vegetation will
be assumed to be leafy, as in grass, and the concentration in the grass will be modeled as grass
would be modeled in the context of the source category. Based on the measurements of Chang,
et al. (1989) and Stephens, et al, (1995), it will be assumed that the concentration on chicken feed
will be zero.
4.3.5. Specific Cases of Soil Contamination
This section provides background information on specific sites of soil contamination
which have been studied for the presence and impact of dioxin-like compounds. These include
landfills used for disposal of ash from municipal waste combustion facilities, the disposal of
sludge from pulp and paper mills, and sites of soil contamination typified by the sites monitored
in the National Dioxin Study (in many cases, Superfund sites or sites that were in some stage of
being considered for inclusion on the NPL list at the time of the study; EPA, 1987). Discussion
of these particular sites does not imply that they represent the bulk of such sites nationally, or that
they are discussed here based on any critical environmental or exposure rational. They are
discussed because they present unique issues for emissions and fate and transport of dioxin-like
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compounds from sites of soil contamination, and because they have been studied. Issues
discussed below are pertinent for other types of off-site soil contamination sites.
4.3.5.1. Landfills Receiving Ash from Municipal Waste Incinerators
Particular issues regarding landfills receiving ash include: the impact of soil cover on
releases, the ash concentrations, the size of such landfills, the quantity of ash generated by
incinerators, and the fugitive emissions that result from ash management. Key sources providing
information for this section include a methodology document describing approaches to
estimating environmental releases and exposures to ash (EPA, 1991), and a contractor report
applying these types of methodologies using site-specific data from several ash landfills (MRI,
1990). Each of the identified topics will be discussed in turn.
• Landfill Cover: Whether or not ash is covered once it is disposed of at the landfill
is critical in determining releases and subsequent exposures. Currently, practices at operating
landfills vary from no coverage after disposal on active portions of the landfill to daily coverage
of disposed ash. MRI (1990) visited six facilities disposing ash, including ash monofills and
municipal solid waste landfills. In one of the facilities, an ash monofill located at the site of the
combustor, the disposal area encompassed 15 acres and did not use daily cover until final
elevation was reached. At that time, a clean cover of 2 feet of soil would be applied. At a
second facility, located at the site of the combustor but landfilling municipal solid waste as well
as ash, ash was used for different purposes, including a subbase roadbed material, as soil
substitute for earth work, and as a daily cover for MSW receipts. An assumption of bare surfaces
(i.e., no vegetation) during the period of landfilling activity, with concentrations of dioxin-like
compounds equal to concentrations in the ash would appear to be appropriate assumptions for
practices at these two landfills.
Where daily cover is employed, however, appropriate assumptions are not
straightforward. Of the remaining four sites studied by MRI, two employed daily covers ("clean
cover material" in one case and a "HOPE liner" in the other, sic), and daily coverage practices
were not discussed for two sites. Approaches described for airborne emissions and erosion
losses would have to be modified when daily cover is applied. First, losses of contaminants via
overland soil or wind erosion could not be expected to occur when cover (soil cover or
otherwise) is in place, although the active part of the landfill would be subject to erosion during
an operating day. Even in that case, however, site-specific practices might include little or no ash
disposal during periods of soil-erosion-producing storms. Depending on site-specific practices,
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one might estimate annual erosion losses using methodologies described in this assessment, and
then empirically reduce erosions losses based on these practices and scientific judgement.
Air emissions from active portions of the landfill, as in wind erosion and volatilization,
also are obviously impacted by cover practices. These emissions would occur during the actual
disposal. Wind erosion and volatilization fluxes could be estimated as given in earlier sections,
and then reduced by two-thirds, which might correspond to an assumption of disposal during 1/3
of a day or a year, etc. When covered by soil or a synthetic cover, wind erosion losses would not
occur. However, buried residues may diffuse through layers of clean soil and be released via
volatilization.
Estimates of volatilization release via diffusion through clean cover have been made. A
rigorous approach for such estimates is detailed in Hwang, et al. (1986). Use of this approach
requires a computer to iteratively solve a partial differential equation, expressed in terms of a
Fourier series. It can be shown, with these equations, that the vapor emission rate through such a
cover will not reach steady state for hundreds of years. Hwang's approach was applied to an
earlier assessment for 2,3,7,8-TCDD (EPA, 1988b). Calculations were performed for
2,3,7,8-TCDD contamination with a thickness of contamination of 8 ft, and clean caps ranging
from 10 to 25 cm. The results of this exercise suggest that the average emission rate of a 70-year
period are 1/4 to 1/5 of what they would be without the cap. Based on this exercise, a simple
assumption might be made that a clean cap will reduce the average emission rate calculated
without a clean cap by 80%. However, these results are not consistent with those described in
Jury, et al. (1990). The analytical solution developed by Jury was demonstrated on 35 organic
compounds. One exercise conducted by Jury was to estimate the cover thickness required to
restrict volatilization to less than 0.7% of the mass incorporated in soil. For 2,3,7,8-TCDD, the
thickness was estimated at 0.7 cm for a sandy soil and 0.2 cm for a clayey soil. This appears to
contradict the work of Hwang since it shows an essentially insignificant loss for a cap much less
thick than the 10-25 cm cap in the exercises using Hwang's approach. However, Jury's approach
allows for assumptions on degradation of the buried compound. For that exercise, Jury assumed
that the half-life for 2,3,7,8-TCDD was 1 year. This is a very rapid degradation rate, given
information that the dioxin-like compounds resist degradation, particularly when not exposed to
sunlight. On the other hand, the Hwang model assumes no degradation loss, and as such, the
generalization from his exercise might be an overestimate. Hwang's exercise might also have
overestimated since it assumed a rather thick 8-ft layer of subsoil contamination. From these
arguments, it would appear that neither exercise appropriately evaluated the difference in
volatilization in a no cover versus a cover situation.
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The above discussions concerned flux calculations when cover practices are used. One
set of adjustments discussed reduced a total potential flux of volatilized or wind eroded losses
based on a portion of the time that the ash would be uncovered. A second discussion indicated
that some loss via volatilization might be modeled with a clean cap. In any case, it is clear that
cover practices will reduce losses. Cover practices must be considered when evaluating the
exposure to ash disposed of in landfills.
• Ash Concentrations: A key consideration, of course, in modeling transport of
dioxin-like compounds from an ash landfill is the concentration on the ash. Ash concentrations
of dioxin-like compounds have been found to vary widely, from non-detect (generally less than
0.1 ppb) to the hundreds and thousands of part per billion. Table 4-9 appears in EPA (1991) and
summarizes concentrations of dioxin-like compounds and PCBs found in fly, bottom, and
combined ash. These data are a summary of 19 references, ranging in publication date from 1974
to 1990. It should be noted that, except for 2,3,7,8-TCDD and 2,3,7,8-TCDF, results listed are
for congener groupings defined by degree chlorination.
• Size of Landfill and Amount of Ash Landfilled: The size of the landfill and
the amount of ash applied daily or over time are both required for estimating exposures nearby.
These can both be obtained from site-specific observations. Amounts of daily disposed ash are
required to estimate fugitive particulate emissions, as will be discussed shortly. Amounts of
daily or ultimate disposal are also tied to landfill size, or the portion of a landfill that is active on
a daily basis. One common practice is to fill cells of a landfill one at a time, and once filled, to
cover with a 2-ft (or so) layer of clean soil. The appropriate size in this case is the average size
of a landfill cell. If daily coverage is applied, than the size for modeling purposes corresponds to
the area over which daily coverage occurs. This can also vary depending on the depth of disposal
during a day. A six-inch daily coverage, for example, would take twice as much space as a 1-ft
depth of daily disposal. If the intent of a day's disposal is to cover over the entire area of an
active cell, then depth of coverage need not be considered in determining landfill size.
Determination of landfill size (or the size of the active portion of the landfill) may be
required in the absence of site-specific information, such as in the planning stages for a new
incinerator. This is where details on landfill management need to be determined. One important
detail, as already noted, is the amount of ash generated for daily disposal. Cook (1991) assumes
that bottom and fly ash combined comprise about 11% of total receipts on a volume basis.
However, a relationship between ash generated and solid waste received by an incinerator on a
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mass basis is more useful for estimating daily disposal amounts. In an EPA (1990f) report on ash
characterization, ash mass was estimated as an average of 29.5% of municipal solid waste
received in five facilities studies, with a narrow range of 25-35%. This mass was estimated on a
wet weight basis. Ash is wetted when exiting the incinerator, and water comprises 20-30% of the
total weight at that point. If the ash is immediately trucked for landfill disposal, its total weight
includes the weight of this quench water. Often ash is stored at the incinerator site in piles prior
to disposal, that storage ranging from hours to days. In this circumstance, much of the quench
water would have drained off or evaporated, and then the total weight hauled would be closer to
a dry weight estimate. In summary, the amount of ash generated to be disposed of a daily basis
can be estimated as: the daily receipt of municipal solid waste (tons) * a wet weight ash fraction
(0.25-0.35) * a wet to dry weight conversion if appropriate (wet weight * 0.80, e.g.).
• Fugitive Particulate Emissions: Fugitive emissions can occur from the time ash
exits the incinerator for temporary storage at the facility site (or immediate loading onto trucks
for disposal) until ultimate disposal. Approaches to estimate fugitive releases from incinerator
ash management are described in EPA (1991), and will be summarized here.
As noted, ash can be wet when exiting the quench tank. If stored at the facility site prior
to disposal in a landfill, leaching from piles can occur. Because dioxin-like compounds are
strongly hydrophobic, however, the impact of leaching is unlikely to occur much beyond the soil
beneath and near the storage piles. If loaded onto trucks when very wet, leaking onto roadways
may also occur. If these storage piles are left uncovered, they would of course be subject to
erosion losses, which might move residues further from the piles than just leaching of water from
the piles.
Of more concern than water-borne losses due to ash management are fugitive emissions
of dry ash. Wind erosion, which can occur from open storage piles or uncovered portions of the
landfill, is a fugitive emission that has been discussed for soil contamination. Specific practices
in the management of ash can also result in fugitive emissions. Such practices include: 1)
loading onto and dumping out of trucks, 2) truck transport from the incinerator facility to the
landfill site, 3) truck or other traffic over paved or unpaved roadways at the incinerator site, at the
landfill site, or other roadways containing contaminated dust, and 4) spreading and compacting
of ash at the landfill site. A set of empirical emission factor equations for estimating fugitive
particulate emissions, called "AP-42" equations, have been developed by EPA's Office of Air
Quality Planning and Standards (EPA, 1985a; EPA, 1988a). Specifics on applying these
equations for ash management are described in EPA (1991). An example of their application
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using site-specific information for ash management is detailed in MRI (1990). An abbreviated
listing of emission factor equations that have been used in these two publications are:
• Vehicular traffic over unpaved roadways. Dust on the surfaces of roads, both
unpaved and paved, can become suspended due to vehicular traffic. When these roadways are
near ash storage piles or within the landfill, that dust can become contaminated. The emission
factor equation for emissions from unpaved roadways is:
M "5 -
(4-48)
where:
Eup = emission flux for unpaved surfaces, kg/VKt (VKt equals vehicle kilometer
traveled)
kunp = particle size multiplier specific to the unpaved road emission flux
equation, unitless
scu = silt content of unpaved roadway, %
Vs = vehicle speed, km/hr
W = vehicle weight, kg
nw = number of wheels per vehicle, unitless
NP = number of days with >0.25 mm precipitation per year, unitless
• Emissions off trucks in transit. Although no emission factor equations have
specifically been developed for trucks while in transit from the incinerator facility to the landfill,
such emissions can occur if the ash is dry, and partially or completely uncovered. The following
equation for estimating emissions from open storage piles has been suggested for use in
estimating fugitive emissions from trucks in transit (EPA, 1991; the emission factor equation
from EPA, 1985a). Note that use of this equation will require specific management assumptions
in order to estimate the number of uncovered hectares per day: the number of trucks in use per
day, the surface area of trucks, the percent of uncovered area if a tarpaulin is used, the moisture
content of ash, and so on.
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,-, ! „ ,^ , /\ ,365-^
E = 1.9 ( ) M-) ( ) (4-49)
' 1.5 15 365 ( }
where:
Et = particulates emitted from trucks in transit, kg/day/hectare
sea = silt content material of ash, %
NP = number of days with >0.25 mm precipitation per year
f = percentage of time that the unobstructed wind speed exceeds 5.4 m/s.
• Loading and unloading. The unloading operations at the disposal site may result in
the release of fugitive dust. The following emission factor equation provides emission factors for
kilograms of particulate emitted per megagram (metric ton, or 1000 kg) of soil loaded and
unloaded:
= 0-0016 ^()13(r14 (4-50)
where:
Elu = emission factor for loading and unloading, kg dust/MT ash
kunl = particle size multiplier, unitless
Um = wind speed, m/s
MC = material moisture content, %.
• Spreading and compacting of ash at the landfill. An emission factor specifically for
ash spreading and compacting has not been developed. However, emission factor equations for
similar applications have been applied for estimating fugitive emissions due to spreading and
compacting. MRI (1990) used an AP-42 emission factor developed for dozer moving of
overburden in western surface coal mines. Kellermeyer and Ziemer (1989) assumed that the
spreading and compaction of ash was analogous to vehicular transport on unpaved surfaces, and
used the emission factor for that process. A third possible assumption is that the processes of
spreading and compacting are analogous to agricultural tillage. That emission factor equation
for agricultural tillage is:
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Eat - 5-38 ** ^°'6 (4-51)
where:
Eat = emission factor for agricultural tillage, kg/ha
kat = particle size multiplier, unitless
scs = silt content of soil, %.
When applying such equations, there are further key issues to consider. These include:
• Concentrations on fugitive ash emissions: When such an emission occurs from ash
surfaces, such as from storage piles, off trucks in transit, in spreading and compacting, and so on,
than there is a good argument to assume that such concentrations on such emissions are
"enriched" in comparison to an ash average. The argument here is similar to the argument for
enrichment assumed for eroded soils: processes resulting in fugitive air emissions favor lighter
particles with more surface area and hence more sites for binding. No data could be found to
assign a value to an ash enrichment ratio. MRI (1990) did, however, take data on municipal
waste combustor facility roadway dust, and based on that data and statistical evaluations,
speculated that fly ash constituted the principal source of lead and cadmium found on paved
surfaces. Since fly ash is finer than bottom or combined ash, one hypothesis for this finding is
that fugitive emissions from ash management at the combustor site transported these finer
particles to roadway surfaces. This is not to imply, however, that concentrations in dust
suspended from roadways due to traffic should be higher in concentration than concentrations in
ash - this enrichment concept only applies to ash surfaces themselves. Rather, the concentration
on roadway suspended dust should be lower than on the ash. This is because contaminated dust
on roadways mixes with clean dust from other sources. As noted, MRI (1990) did take roadway
dust samples, and their data appears to place such a dilution factor (concentration on roadway
dust divided by concentration on ash) in the range of 0. 1 to 0.3 . Specifically, they took
parti culate samples from landfill haul routes while at the same time taking samples of incinerator
ash being delivered for disposal the same day. Each paired sample (roadway paniculate and ash),
were measured for four metals: As, Cd, Cr, and Pb. Several paired samples were taken on both
paved and unpaved haul routes. Ratios were then generated for roadway particulate metal
concentrations over ash metal concentrations. Results were: As - paved and unpaved ratios were
similar and consistently near 0.1 (roadside particulate concentrations of As were 10% of ash
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concentrations of As), Cd - paved and unpaved ratios were similar and ranged between 0.0 and
0.4, Cr - paved ratios ranged from 0.3 to 0.6, while unpaved had a wide range of 0.3 to 2.0, Pb -
paved and unpaved ratios were similar between 0.0 and 0.2. For analogous situations - daily
deliveries of contaminated ash - one might assume a dilution factor in the 0.1-0.2 range.
• Selection of values for emission factor equations: As noted, all these equations are
empirical equations. They were developed from data on sites where such emissions occur, such
as strip mining sites. EPA (1988a) describes the range of conditions over which such equations
were developed. What is meant by "conditions" are such factors as the range of vehicle weights
in the data set, the range in number of wheels on such vehicles, and so on. Application of these
equations for situations not included within these ranges should be done cautiously. Very critical
also is the selection of the particle size multiplier variable, k. These values range from about
0.10 to no higher than 1.0. Lower k values are used to estimate emissions of the smallest sized
particles; generally particles less than 5 \im in diameter. Higher k values are used to estimate
emissions of all sized particles less than a higher diameter, usually either 15 or 30 [am. If these
equations are used to only estimate particulate inhalation exposures, than the k value
corresponding to 10 |_im sized particles, or inhalable sized particles, should be used. When used
to estimate total emissions, than the highest k value listed should be used. Such estimations are
appropriate when also evaluating impacts to off-site soils or vegetation.
• Controls for fugitive emissions: All these equations were developed when no fugitive
emission controls were in place. Common controls for roadway dust suppression include wetting
or use of a chemical dust suppressant. Ash transported in trucks is commonly wetted and/or a
tarpaulin is used to control emissions off trucks. There is no guidance or data on the
effectiveness of such controls, but they must be considered. In demonstrating these procedures,
EPA (1991) assumed that controls on emissions resulted in 90% reductions in potential
emissions. If a control is known to be in place and used on a regular basis, than this percent
reduction is probably a reasonable starting assumption.
4.3.5.2. Land Application of Sludge from Pulp and Paper Mills
This discussion focuses on an assessment on the land application of sludge from bleached
kraft and sulfite pulp and paper mills (EPA, 1990e). Focusing on this source of sludge does not
imply that pulp and paper mills produce more sludge than other industries, or that sludge from
pulp and paper mills contains more dioxin-like compounds than other sludges. However, it is
known that dioxin-like compounds are found in pulp and paper mill sludges. Also, because of
the 104-mill study in 1988, much information is available on the content and disposal of this
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sludge (further information on the 104-mill study can be found in EPA (1990c,d)). Some of the
issues briefly discussed below for pulp and paper mill sludges would also pertain to sludges
containing dioxin-like compounds from other sources.
EPA (1990e) described frequency distributions of concentrations of 2,3,7,8-TCDD and
2,3,7,8-TCDF for 79 mills reporting this information and also broke out the data based on
disposal option. Although EPA (1990e) used the disposal option breakout of concentrations in
their assessment of the impacts of the various options, it is not felt that the disposal option of
choice is based on concentration. Over all options, the median (50% percentile as given in EPA
(1990e)) and maximum 2,3,7,8-TCDD concentrations found in sludges were 51 and 3800 ng/kg
(ppt), respectively. The median and maximum 2,3,7,8-TCDF concentrations found were 158 and
17100 ppt.
Fate and transport for contaminants is sludge is dependent on disposal means. Of the
approximate 2.5 million metric tons of pulp and paper mill sludge generated annually (as
estimated in the 1988 104-mill study), five principal options for disposal were noted: landfilling
(44% of all sludge disposed), surface impoundments (24%), land application (12%), incineration
(12%), and distribution and marketing (8%). Impacts by incineration were not discussed in EPA
(1990e) and are not discussed in this section. Key issues pertaining to each disposal issue are
now discussed.
• Landfilling: The issue of coverage as discussed above for ash landfills is relevant for
any landfill. However, fugitive particulate emissions during sludge handling and disposal is not
an issue as it was for disposal of ash from incinerators due to the differences in moisture content.
Sludge is much higher in moisture at the time it is disposed of in comparison to ash - with
moisture contents as high as 90%.
• Surface Impoundments: It was assumed in EPA (1990e) that sludge disposed of in
surface impoundments have a higher moisture content as compared to sludge disposed of in
landfills. Surface impoundments were located at the mill site, explaining the assumption for a
higher moisture content. A surface impoundment in the EPA (1990e) assessment was defined as
a facility in which the sludges are stored or disposed on land without a cover layer of soil. For
this type of management, soil cover would not be an issue. Concentrations would be those
measured in the sludge. Also, vegetative cover would be expected to be minimal, which would
influence parameters associated with soil erosion.
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• Land Application: Twelve percent of all sludge produced annually was land
applied. Four of the 104 mills applied the sludge to forest land, two mills land applied the sludge
to agricultural land, and two mills used the sludge for abandoned mine reclamation. The high
organic matter content (EPA (1990e) assumed a 25% organic carbon fraction in sludge) and high
fraction of clay-sized particles make sludge an attractive soil amender. Sludge is either applied
to the land surface with or without incorporation. When not incorporated, sludge can be assumed
to replace surface soils and concentrations would be those in the sludge. When incorporated, soil
concentrations can be estimated simply as (in mg/kg): (mass of contaminant added, mg)/(mass of
sludge added, kg + mass of soil in mixing zone, kg). One key issue when incorporated is the
number of years of such treatments. Most of the land application uses of paper and pulp mill
sludges reported in EPA (1990e) made applications in only one year. As easily seen in the above
suggested equation, higher concentrations result with more years of incorporation. The other key
issue with incorporation, of course, is the depth of incorporation. For agricultural applications,
the depth of incorporation assumed in EPA (1990e) was 15 cm, similar to the 20 cm
incorporation assumed for home vegetable gardening in this assessment. For silvicultural uses,
the assumption in EPA (1990e) was 2.5 cm, which corresponds to some but minimal mixing.
For abandoned mine reclamation, the assumption was 0 cm incorporation. Routes of exposure
might also vary from focuses in this document depending on land application choice. When
applied to agricultural land, impacts to food crops would demand particular attention (the
procedures in this assessment were demonstrated with home grown vegetables, although of
course impacts to food crops are critical when agricultural field soils are impacted by dioxin-like
compounds). When applied to forest land, ecological impacts might warrant particular attention,
as was discussed and demonstrated in EPA (1990e). A final issue to consider when land
applying sludge to land is a rate of dissipation/degradation of dioxin-like compounds. Landfills
and surface impoundments have ongoing surface applications and over time, the total depth of
applications in the range of meters, so an assumption of a constant source strength over a period
of exposure, as was assumed in this assessment for soil contamination sources, is reasonable.
However, if only a few centimeters of surface soil are impacted, which might be the case for
single applications to land and/or surface applications with no incorporation, an assumption of
dissipation may be warranted. EPA (1990e) assumed no degradation of 2,3,7,8-TCDD in their
assessment of impacts from land applications.
• Distribution and Marketing Uses: The volume of sludge distributed and marketed
was approximately 8% of the total amount of sludge generated for the 104-mill study. For this
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use, sludge was composted and then sold as a soil amendment in residential, agricultural, and
commercial settings. More attention to the dermal contact pathway appears appropriate for this
usage. Site-specific factors, and the values for these factors used in EPA (1990e), include: 1)
depth of incorporation - 0, 15 and 25 cm in assumptions characterized as high, best, and low
estimates, 2) garden size - 0.016 and 0.022 hectares characterized as low/best estimate and high,
and referencing a national gardening survey, 3) rate of application - between 5 and 20 dry metric
tons per hectare references a USDA publication on use of sewage sludge compost for soil
improvement and plant growth, and 4) years of using such compost - 20 without specific
reference. The years of application is needed for estimating soil concentrations during and after
the period of exposure, using a simple ratio as discussed above in land application.
4.3.5.3. Sites Studied in the National Dioxin Study
The National Dioxin Study (EPA, 1987) focused on sites of known or suspected
contamination of soil by 2,3,7,8-TCDD. There were 7 "Tiers" of investigation, with roughly
decreasing expectations of finding 2,3,7,8-TCDD. Tiers 1 and 2 included 2,4,5-TCP production
and associated disposal sites (Tier 1) and sites where 2,4,5-TCP was used as a precursor in the
manufacture of pesticidal products and associated disposal sites (Tier 2). These tiers had the
highest expectation for finding 2,3,7,8-TCDD. There were originally thought to be 450 sites that
would fall in Tiers 1 and 2, but after investigation, only 100 sites were included for study. Some
were downgraded into Tier 3. Of the 100 sites studied, 20 were on or were proposed for
inclusion in the Superfund National Priorities List. Tiers 3 and 5 were associated with 2,4,5-TCP
formulation (Tier 3) and use (Tier 5). Tier 6 were organic chemical or pesticide manufacturing
facilities were 2,3,7,8-TCDD was suspected of being present. Tier 4 included combustion
sources and are not discussed further in this section. Tier 7, basically an examination of
background areas, are also not discussed here.
Issues that are identified as important in fate and transport modeling for this subcategory
of off-site sources include concentrations, the possibility of ground water contamination, and
site-specific characterization. These are discussed in turn.
• Concentrations: Only 11 of the 100 Tier 1 and Tier 2 sites were eventually
classified as requiring "no further action" because 2,3,7,8-TCDD soil concentrations were very
low, < 1 ppb, or not detected (with detection limits generally at 1.00 ppb). Where it was
detected, a general trend was to find very high concentrations where 2,4,5-TCP production
wastes were stored or disposed of, with much lower concentrations at soils near these particular
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areas. At hot spots, concentrations were as high as 2,000 parts per million, but generally soil
concentrations were in the parts per billion. It was this parts per billion generalization that led to
the assignment of a 1 ppb soil concentration for the demonstration of the off-site source category
in Chapter 5. There were findings in the low ppb range for Tiers 3, 5, and 6, but at much lower
frequency and no findings higher than the tens of ppb range. For exposure assessments, the
characterization of soil concentrations in a site containing hot spots has to be carefully
considered. For site evaluations and proposed options for remediation, an areally weighted
average might be considered, although this could dilute loss estimates depending on the area
chosen - choosing a large area corresponding to property lines might, for example, lead to an
"average" concentration orders of magnitude lower than concentrations found in hot spots. One
approach which should be considered is a "hot spot" impact compared to an areally averaged
impact. It should also be remembered that removal of highly contaminated soils is a common
practice and another option for evaluation would be a concentration assuming hot spots are
removed.
• Potential for Ground Water Contamination: PCBs have been found in ground
water in sites associated with dielectric fluids of transformers. Oils can migrate through soils as
a separate immiscible phase and reach ground water, which has been the common explanation for
PCB impacts to ground water. Ground water contamination by 2,3,7,8-TCDD has very rarely
been found in ground water, although it has been released to the environment in an oil matrix.
The Times Beach area of Missouri is the principal example of this release, where waste oils
containing 2,3,7,8-TCDD were used for dust control. Ground water sampling did occur in many
of the Tier 1 and 2 National Dioxin Study sites, but the results were mostly non-detects. One
occurrence at 0.18 ppt was noted for the Hyde Park site of Hooker Chemical in Niagara, NY, and
a high of 1.8 ppb was found in an on-site monitoring well at National Industrial Environmental
Services in Furley, KS. There were, however, numerous high occurrences in sub-soil samples in
hot spot areas, in bottom sediments of evaporation lagoons, and so on, in the hundreds of ppb
range.
There have been some limited experimentation showing different patterns of 2,3,7,8-
TCDD migration in soils in the presence of solvents or in an oily matrix. Palusky, et al. (1986)
studied the mobility of 2,3,7,8-TCDD in soils associated with each of 6 solvents. Migration was
found to be higher with aromatic solvents and chloroform in comparison to saturated
hydrocarbons and methanol. They speculated that the extent of migration related to the solubility
of 2,3,7,8-TCDD in the solvent. Puri, et al. (1989) studied the migration potential of 2,3,7,8-
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TCDD in soil, water, and waste oil mixtures. Over time, they observed a reversible sorption
pattern of TCDD, and concluded that a carrier medium with a significant amount of waste oil
would play a dominant role in the movement of TCDD through soils.
• Site-specific Characterization: In the case of landfills or sludge land application
sites, the assignment of a soil concentration and an area can be made with some reasonableness.
Such is not the case with the industrial contamination sites such as those studied in the National
Dioxin Study, as briefly discussed above in the concentration bullet. Most of the sites studied in
the National Dioxin Study were in the order of tens of hectares to below ten hectares. On the
other hand, the Dow Chemical site in Midland, Michigan is described as a site 607 ha in size
(Nestrick, et al, 1986). That area corresponds to the size of the property, and the many soil
sampling sites within that area were described as "background". Several of the pesticide
formulator sites studied in Tier 3 were 2 hectares or less in size. Many of the them were
extensively or partially paved with buildings, which complicate fate and transport modeling.
Some of the Tier 5 sites of 2,4,5-TCP use were agricultural fields, which are less complicated to
describe. However, two sites were described as 2500 acres in size, which again is very large and
makes assignment of an average soil concentration non-trivial. Other use sites were described as
railyards and railroad rights of way. While estimates of loss into air could be made in
complicated sites such as these, use of soil erosion modeling becomes very complicated if not
undoable with paved areas, buildings, drainage ditches, roads, and the like.
4.4. ALGORITHMS FOR THE STACK EMISSION SOURCE CATEGORY
Contaminants emitted from incinerator stacks are transported in air and deposit on the
exposure site, water bodies that may be used for drinking or fishing purposes, and on surrounding
land. Chapter 3 describes the application of the ISCST3 model to obtain vapor-phase air
concentrations and deposition rates of particles at a specified distance from an example stack
emission source. These quantities are assumed to be given for purposes of discussion in this
section; further discussion of the air transport modeling is given in Chapter 3.
Estimating soil concentrations based on particulate depositions follows a similar
approach as estimating exposure site soil concentrations resulting from erosion of contaminated
soil from off-site areas of contamination. Section 4.4.1. describes how soil concentrations are
estimated given total (wet plus dry) deposition rates. Surface water impacts are assumed to result
from direct deposition onto surface water bodies as well as erosion from the impacted effective
drainage area. This solution is an extension of the solution given in Section 4.3.1. for the soil
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contamination source category, and is given in Section 4.5.2. Following now are bullet
summaries for similarities and small refinements to algorithms previously discussed:
• Air impacts: The atmospheric transport modeling described in Chapter 3 was
comprised of two computer simulations: one which considered that emissions
were in a vapor form and were transported as such, and one which considered that
emissions were in particle form and likewise were transported as such. The result
of the vapor-phase runs was a unitized ambient air concentration at various
distances up to 50 km in all directions from the stack. Only the results in the
predominant wind direction were used in this demonstration. The result of the
particle-phase runs were an ambient reservoir of air-borne contaminants sorbed to
particulates (used only for inhalation exposures), and wet and dry deposition unit
rates also at various distances up to 50 km. By "unitized", what is meant is that
emissions for the vapor or particle runs can be thought of as "1" mass/time (g/sec)
emissions. Results for all distances are linear with respect to this emission rate;
that is, if the rate of vapor contaminant determined to be emitted is "5", than
ambient air concentrations at any location are 5 times what they are when "1" is
assumed to be emitted. The same holds true for emissions in the particle phase.
Chapter 3 developed a framework for assigning a vapor and a particle fraction for
specific dioxin congeners. For example, 2,3,7,8-TCDD was assumed to have a
vapor fraction of 0.51 (51% was in vapor form) and a particle fraction of 0.49.
The final model results for air concentrations, and dry and wet deposition rates for
all congeners, starting from these unit model runs and then incorporating
congener-specific emission rates and vapor/particle splits, are given in Tables 3-
12 to 3-17. The vapor-phase air concentrations were used to model vapor phase
transfers in the vegetative bioconcentration algorithms. They were also used,
summed with the simulated reservoir of particle-bound contaminants, to estimate
the total reservoir of contaminant available for inhalation exposures.
• Vegetative impacts: The rates of wet and dry deposition modeled by ISCST3
were used to determine vegetative impacts. The model for particle deposition
impacts to vegetation is described in Section 4.3.4.2 above. Of course, this above
section solves for dry deposition as a reservoir times a dry deposition velocity (for
dry deposition), and as a reservoir times rainfall and a washout factor (for wet
deposition); such a solution is not required for the stack emission source category
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since the deposition totals are estimated by the ISCST3 model. Other parameters
for the vegetative model - the Bvpa (air-to-leaf vapor transfer factor), the Rw
(fraction of wet deposition retained on vegetation surfaces), crop yields and
interceptions, and the vegetative washout factor, kw, are used for the stack
emission source category.
• Biota concentrations: The algorithm estimating concentration in fish tissue
based on bottom sediment concentrations is the same as in previous source
categories. Modeled rates of contaminant deposition on particles onto the
exposure site are used to estimate a "tilled" and an "untilled" soil concentration, as
described below in Section 4.4.1. Underground vegetable concentrations are a
function of tilled soil concentrations. The soil concentration used for cattle soil
ingestion is untilled. Beef and milk concentrations are again a function of
vegetative and soil concentrations, diet fractions, and bioconcentration and
bioavailability factors as described in Section 4.3.4.3.
4.4.1. Steady-State Soil Concentrations
Chapter 3 describes the use of the ISCST3 Model to estimate the particulate phase
deposition rates at the exposure site. This total deposition rate, F, includes both dry and wet
deposition, and is used to estimate the soil concentrations. The deposition of contaminated
particulates from the air is assumed to be somewhat analogous to the process of eroding
contaminated soil from an off-site source depositing on an exposure site. Specifically, the
following assumptions are also made: 1) only a thin layer of soil becomes contaminated, 2) this
layer is either "untilled" or "tilled", depending on surface activities, and 3) surface residues are
assumed to dissipate with a half-life of 25 years corresponding to a first order decay rate of
0.0277 yr"1. Considerations of upgradient erosion and exposure site soil removal are not made.
Depositions occur over the exposure site and surrounding land area on an on-going basis. It
might be said that upgradient soil concentrations are similar to exposure site concentrations at all
times. Like the soil source categories, a tilled mixing depth of 20 cm, and an untilled mixing
depth of 2 cm is assumed for this source category. The qualitative mass balance statement
(similar to the one made above in Section 4.3.2, with AC equaling change in exposure site soil
concentrations over time) can now be made as:
(the incremental addition to C resulting from the change in
deposition of stack emitted particulates)
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AC = (the incremental substraction of C resulting from
degradation of residues at the exposure site)
This is mathematically stated as:
f - £ - *
where:
C = the exposure site soil concentration, mg/kg
F = deposition rate of contaminant on particles, mg/yr
M = mass of soil at exposure site into which contaminant mixes, kg
k = first order dissipation rate constant, 1/yr.
The solution to this equation is:
C - — ( 1 - e'1*) (4-53)
kM ( }
which computes C as function of time, t. Similar to the assumption made above in Section 4.3.2,
the steady state solution for C is simply F/kM. The deposition rates supplied by the ISCST3
model are in units of g/m2-yr, so a conversion to mg/yr requires a multiplication by the land area
of the exposure site and a multiplication of 1000 mg/g. Procedures to estimate M are given
above in Section 4.3.2.
4.4.2 Surface Water Impacts
The solution for stack emission impacts to surface water bodies is an extension of the
solution for the soil contamination source category described in Section 4.3.1. Stack emissions
deposit onto soils within the effective drainage area to result in an average basin-wide soil
concentration. Soil erosion then delivers contaminants to surface waters as in Section 4.3.1.
Stack emissions also directly deposit onto and impact the surface water body as well. All the
assumptions laid out at the beginning of Section 4.3.1 apply here as well. New quantities needed
for this solution include: a rate of contaminant deposition onto soils of the effective drainage area
used to estimate average soil concentrations (such concentrations are estimated using the
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approach given in Section 4.4. 1 . above), a rate of contaminant deposition onto the water body,
and a rate of parti culate matter deposition onto the water body.
Equations (4-1) through (4-8) are now displayed again with these additions.
DEPc - Cwat V*at + Cssed Mssed + Csed Msed (4-54)
where:
Cswb = concentration on soil entering water body, mg/kg
ERW = total watershed annual soil erosion, kg/yr
DEPC = total annual direct deposition of contaminant, mg/yr
Cwat = dissolved-phase concentration in water column, mg/L
Vwat = water body annual volume, L/yr
Cssed = concentration on suspended sediment, mg/kg
Mssed = mass of suspended sediment introduced per year, kg/yr
Csed = concentration on sediment settling to bottom, mg/kg
Msed = mass of bottom sediment introduced per year, kg/yr
Mass balance and equilibrium equations continue:
ERw + DEPp = Mssed + Msed (4-55)
= f, ER* + fsd DEPP (4-56)
- fsd ) DEPp (4-57)
(4-58)
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(4-59)
where:
DEPp = total annual direct deposition of particulate matter, kg/yr
fs = fraction of annual erosion remaining as suspended materials, unitless
fsd = fraction of annual deposition remaining as suspended material, unitless
Kdssed = soil-water partition coefficient for contaminant in suspended sediment,
L/kg
OCssed = fraction organic carbon in suspended sediment, unitless
OCsed = fraction organic carbon in bottom sediment, unitless
Substituting again as in Equation (4-7):
V OC
DEPc = Cssed [ -- + /, ERw + fsd DEPp +
[ ( 1 - /, ) ERw + ( 1 - fsd ) DEPp ] ] (4-60)
As before, the bracketed quantity in the right hand side of Equation (4-60) can be termed cj), so
that Cssed can be solved as (Cswb ERW + DEPc)/cj). The numerator in this term can be expanded to
describe contaminant contributions by the effective drainage area which has received depositions,
the first quantity in the numerator, and to describe direct depositions, the second quantity:
DEPc - Cw SLw Aw E SDw + RDEPc Awat 1000
where:
Cswb = concentration on soil entering water body, mg/kg
ERW = total watershed erosion, kg/yr
DEPC = annual deposition of contaminant on water body, mg/yr
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E = enrichment ratio, unitless
Cw = average soil concentration of dioxin-like compound in effective area of
watershed, mg/kg
SLW = average unit soil loss for land area within watershed, kg/ha-yr
Aw = effective drainage area of watershed, ha
SDW = sediment delivery ratio for watershed, unitless
RDEPC = rate of contaminant deposition, g/m2-yr
Awat = area of water body, m2
1000 = converts g to mg
Again as before, the right hand side of Equation (4-61) can be termed, p, and the concentration in
suspended sediment, Cssed, is equal to p/cjx Other water body concentration terms, Cwat and Csed,
can now be solved using Equations (4-58) and (4-59). Guidance on these terms and assignment
of values for the demonstration scenarios in Chapter 5 is now given.
• Cswb and ERW: Equation (4-61) shows all the terms necessary to arrive at an estimate
of the annual contaminant entry into the water body via erosion, the Cswb * ERW term. Section
4.4.1 describes the algorithm to estimate soil concentrations given a deposition rate of
contaminant. One deposition rate will be chosen to represent average deposition rates over the
effective drainage area of the watershed (the effective drainage area is termed Aw). This rate will
be the rate given in ISCST3 modeling at 5.0 kilometers. Tables 3-15 and 3-16 (Chapter 3)
display wet and dry deposition rates for this distance. These rates are added to arrive at total
deposition, shown in Table 3-17. Second, a representative mixing depth to characterize average
watershed soil concentrations needs to be selected. Previous algorithms used a mixing depth of
20 cm for tillage activities, specifically home gardening, and 2 cm for non-tilled soil
concentrations. For the sake of demonstration, it will be assumed that a representative watershed
depth will equal 10 cm, which might be interpreted as an average of tilled and untilled lands
within the effective drainage area. The values for SLW (6455 kg/ha-yr), Aw (100,000 ha), ER (3),
and SDW (0.06) were all given and discussed in Section 4.3.1. and will not be repeated here.
• DEPC: The second quantity of Equation (4-61) describes the annual input to the
surface water body that comes from direct deposition. This term is RDEPC * Awat * 1000, where
RDEPC is the rate of contaminant deposition onto the water body (g/m2-yr) , Awat is the area of the
water body (m2) , and 1000 converts g to mg. The rate of contaminant deposition at 5 km will
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also be used to describe direct deposition impact to the surface water body. Depositions nearer to
the emission source will be greater and depositions further from the emission source will be less.
The area of the water body has not been required for any other reason, and one will now be
given. First, the effective drainage area of 100,000 ha is reasonably large and has resulted in a
river with an annual flow volume of 4.8*10n L/yr (4.8*108 m3/yr). This volume is also equal to
the average cross sectional area of the river (m2) times stream velocity (m/yr). Assuming a
stream velocity of 4.73 * 106 m/yr (15 cm/sec; /^ ft/sec), which is reasonable for a river, the cross
sectional area is solved as 100 m2. An average 5 m depth and 20 m width appear reasonable.
This width times the stream length would give stream surface area, Awat. Assuming a rectangular
shaped watershed, dimensions of 500 ha wide by 2,000 ha long (to arrive at the 100,000 ha
effective drainage) seem reasonable. This length of 2,000 ha translates to 200,000 meters, and
the full surface area of the stream is 4*106 m2. This will be the value assumed for Awat.
• DEPp: The rate of particulate deposition onto the lake is required to achieve a mass
balance of all annual soil erosion + particle deposition contributions to water body solids. The
rate of paniculate matter emitting from the stack and arriving at downwind locations was not
supplied in Chapter 3. Goeden and Smith (1989) modeled the impacts of a resource recovery
facility to a local water body. In their analysis, they assumed that the stack emitted 4.63 g/s
particulate matter and that the annual deposition of stack-emitted paniculate matter to the nearby
impacted water body was 0.03 g/m2-yr. This deposition rate will be adopted for this assessment.
Now, with the surface area as solved for above at 2*106 m2, the total particle deposition, DEPp is
60 kg/yr.
• fs and fsd: These are the fractions of total erosion and depositing particles remaining
as suspended materials within a year. As discussed in the solution for the contaminated soil
source category in Section 4.3.1, fs was solved for as: a value for total suspended solid, TSS of 10
mg/L, multiplied by a total flow volume Vwat of 4.8 x 1011 L/yr, divided by the total erosion into
the water body, 1.29*107 kg/yr. This resulted in an fs of 0.36. Note that this implies a total
suspended load of 480,000 kg/yr. It could be assumed that the minuscule 60 kg/yr of particles
directly depositing onto the stream remain in suspension during the year, on the basis of being
smaller in size than eroded soil. This assumption will, in fact, be made, but it will be supported
as follows.
In a quiescent water body, settling occurs through gravity and can be expressed in terms
of Stokes Law:
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V,= ( * / 18ji ) ( p, - p ) d2 (4-62)
where:
Vs = Stokes settling velocity, cm/sec
g = acceleration of gravity, 980 cm/sec2
[i = absolute viscosity of water, g/cm-sec (poise)
0.089 g/cm-sec @ 25 C
ps = particle density, g/cm3
p = density of water, 1 g/cm3
d = particle diameter, cm
For purposes of this discussion, a reasonable assignment of particle density of is 2.5
g/cm3 for depositing particles or eroding soil. Therefore, making substitutions, the right hand
side of Equation (4-62) reduces to 918 d2.
Now, assumptions for the particle sizes of eroding soil and depositing particles can be
made to arrive at a ratio of settling velocities, Vssoil/Vspart. The basis for assigning an enrichment
ratio for delivery of contaminants via soil erosion was that fine-sized particles were the ones
eventually reaching the water body via erosion. Lick (1982) states that a major fraction of the
sediments (suspended and bottom) in the Great Lakes are fine grained, silts and clays, and that
data from Lake Erie indicates that 90% of the sediments are of this category. Brady (1984)
shows USDA's classification of soils according to particle size, and gives a range of 0.0002 to
0.005 cm for silt sized particles and less than 0.0002 for clay size particles. The following
assumptions are made to arrive at a representative diameter for particles in eroded soil: eroded
soil is comprised of a 50/50 split of these two sized particles, silt-sized particles are, on the
average 0.0026 cm in diameter, and clay size particles are 0.0001 cm in diameter. With these
assumptions, the average particle size for eroding soil is 0.0014 cm. The settling velocity for a
0.0014 cm particle is 1.8 x 10"3 cm/sec. In Section 3.4.3, Chapter 3, the argument was developed
that 87.5% of the total emission rate of dioxin-like congeners would be associated with particles
less than 2 [am. The basis of this argument was a surface area to volume ratio, with smaller
particle sizes having significantly larger ratios. This does not mean that 87.5% of the 1 kg/yr
depositing particles are of this size. However, for this discussion, the size of depositing particles
will be assumed to be 2 pm (2 x 10"4 cm), since these size particles deliver most of the dioxin-
like compounds to the water body (and the ultimate purpose of this exercise is to determine a
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value for the fraction of depositing particles which remain suspended and impact suspended
sediment concentrations). The settling velocity, Vspart, is estimated as 3.7 x 10"5 cm/sec.
The ratio Vssoil/Vspart is about 50. Said another way and with all the assumptions and
simplifications made above, depositing particles will remain in suspension 50 times longer than
eroding soil in a quiescent water body.
Given this high a difference in settling velocities, it seems reasonable to assume fsb equals
1.0. The fraction of soil erosion remaining in suspension, fs, will be estimated given TSS, Vwat,
etc., as before (see Section 4.3.1), only DEPp (the total amount of depositing particles, in kg/yr)
will comprise a given increment of suspended materials when solving for fs.
• Vwat, OCssed, OCsed, and Kdssed: These have all been discussed in Section 4.3.1. The
values for these parameters in the demonstration scenarios in Chapter 5 are: Vwat = 4.8 x 1011
L/yr, OCssed = 0.05, OCsed = 0.03, and Kdssed = OCsed * Koc, where Koc is the organic partition
coefficient of the contaminant.
4.5. ALGORITHMS FOR THE EFFLUENT DISCHARGE SOURCE CATEGORY
Dioxin-like compounds can be released to waterways via various types of effluent
discharges such as discharges from municipal waste water treatment facilities and pulp and paper
mills using chlorine bleaching. These emissions have declined substantially in recent years,
especially from pulp and paper mills. Since the procedures for considering point source
discharges to waterways are somewhat different than those associated with the nonpoint source
procedures for soil contamination and stack emissions, they are covered separately in this
section. This source category is also different from others in that effluent discharges into surface
water bodies are assumed only to impact fish and water.
The approach used in this report is an extension of the "simple dilution" model described
in the Superfund Exposure Assessment Manual (EPA, 1988c). Other models are available which
offer more spatial and temporal resolution than the model described here. One such model is the
EXposure Analysis Modeling System, or EXAMS (Burns, et al., 1982, and Burns and Cline,
1985). The EXAMS and a simple dilution model were both applied in an assessment of effluent
discharges from pulp and paper mills (EPA, 1990d). In this assessment, 98 of the 104 pulp and
paper mills were modeled with both models using site-specific information (water body flow
rates from STORET for all but 6 of the mills, effluent flow rates and contaminant discharges,
etc.). Three key quantities - one model result and two model parameters - led to a range of
exposure conditions for humans consuming fish impacted by discharges from these pulp and
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paper mills: a water column concentration, a bioconcentration factor (BCF) applied to the water
column concentration to get fish tissue concentration, and a fish ingestion rate. The simple
dilution model was used to estimate total water concentrations - i.e., mg TCDD total/L water.
The EXAMS model was used to estimate dissolved phase water column concentration - i.e., mg
TCDD dissolved in water column/L water. Then, with each set of water concentrations, two sets
of exposure estimates (a low and a high estimate, in one sense) were generated - one with a BCF
of 5,000 and a fish ingestion rate of 6.5 g/day, and one with a BCF of 50,000 and a fish ingestion
rate of 30 g/day. Note that in deriving the range of results in that exercise, the BCF was applied
to both a total and a dissolved phase water concentrations. EPA (1993b) and EPA (1995) discuss
several bioconcentration/bioaccumulation empirical parameters for 2,3,7,8-TCDD, and makes
the clear distinction for those which are to be applied to a total water concentration versus those
applied to a concentration in the dissolved phase. The dilution and EXAMS model study
indicated that the simple dilution model generally estimated higher water column contaminant
concentrations compared to the EXAMS model, although this trend was not consistent among all
water bodies modeled. The results from both models were comparable when the receiving water
body had relatively low suspended solids concentration.
One key limitation of the EXAMS and the simple dilution model for use with dioxin-like
compounds in aquatic systems is that they do not account for sediment transport processes. The
EXAMS model was designed to determine the fate of transport of contaminants in the dissolved
phase. Another spatially and temporally resolved model for this source category is the Water
Analysis Simulation Package, the most up-to-date version termed WASP4 (Ambrose, et al.,
1988). This model does include sediment processes and has been applied in a comprehensive
evaluation of 2,3,7,8-TCDD bioaccumulation in Lake Ontario (EPA, 1990b). It requires
extensive site-specific parameterization, but should be considered for more detailed site-specific
evaluations of strongly hydrophobic and bioaccumulating contaminants such as the dioxin-like
compounds.
The dilution model described below will be demonstrated in Chapter 5 with a set of data
developed using site-specific data from the 104 pulp and paper mills of the 104-mill study. As
will be discussed below, a hypothetical effluent discharge will have characteristics developed as
the average of key characteristics from the 104 mill study. These key data include: flow rates of
the receiving water bodies, suspended solids concentration in these receiving water bodies,
effluent discharge flow rates, suspended solids in the effluent discharges, organic carbon content
of solids in the effluent stream, and discharges of 2,3,7,8-TCDD.
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4.5.1. The Simple Dilution Model
The principal assumption for the simple dilution model is that contaminants released into
a water body uniformly mix and equilibrate with the surrounding water in an area near the
effluent discharge point. This area is commonly referred to as a "mixing zone". For application
of this model with dioxin-like compounds, what is desired is a concentration on the suspended
solids in this mixing zone. Multiplication of the organic carbon normalized concentration on
suspended solids and a Biota Suspended Solids Accumulation Factor, or BSSAF, will result in a
concentration of contaminant in fish lipids. This is defined similarly to the BSAF used for other
source categories of this assessment, except that the organic carbon normalized concentration is
that of suspended solids rather than of bottom sediments.
The BSSAF is one of several empirical factors discussed for estimating the impact to fish
in water bodies impacted by 2,3,7,8-TCDD (EPA, 1993b). Others include the BSAF, total and
dissolved phase bioconcentration factors (BCFs), and total and dissolved phase bioaccumulation
factors (BAFs). BAFs and similar to BSAFs and BSSAFs in that all three reflect total exposure
offish to contaminant, including water column, sediment, and food chain exposures. The BCFs
reflect water column exposures only. EPA (1993b) states that there is currently no data available
on organic carbon normalized concentrations of dioxin-like compounds on suspended solids,
hence no basis to compare BSAF and BSSAF. This assessment assumes a similar numerical
assignment of BSSAFs and BSAFs.
The total water concentration in a simple dilution model is:
MASS
(4'63)
where:
Ctot = total water concentration, mg/L
MASSC = mass of contaminant in discharge, mg/hr
Qu = harmonic mean flow at a point just upstream of effluent discharge, L/hr
Qe = effluent flow, L/hr
Dissolved phase and suspended sediment concentrations are then estimated using an approach
developed by Mills, et al. (1985) and others:
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'tot
(4-64)
TSSmii ID-6)
Cssed = KdmiX Cwat (4-65)
where:
Cwat = dissolved-phase water concentration of contaminant, mg/L
Ctot = total water column concentration, sorbed + dissolved, mg/kg (note:
mg/kg is essentially equal to mg/L since 1 L « 1 kg)
= suspended sediment-water partition coefficient for contaminant in
mixing zone, L/kg
= total suspended solids in water column in mixing zone, mg/L
Cssed = concentration of dioxin-like compounds on suspended sediments,
mg/kg
10"6 = converts mg/L to kg/L
The total suspended solids concentration in the mixing zone is a function of the suspended solids
just upstream of the discharge point and the suspended solids introduced in the effluent stream:
__ U Qu + TSSe Qe
TSSmix - - - - (4-66)
where:
TSS^x = adjusted total suspended solids concentration, mg/L
TSSU = total suspended solids concentration at a point just upstream of effluent
discharge, mg/L
TSSe = total suspended solids concentration in effluent discharge, mg/L
Qu?Qe = upstream harmonic mean flow and effluent discharge flow rates, L/hr
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The suspended solids partition coefficient in the mixing zone is a function of the organic
carbon partition coefficient of the contaminant and the organic carbon fraction of suspended
solids:
- KocOCmix (4-67)
where:
Kdj^ = suspended sediment-water partition coefficient in the mixing zone, L/kg
Koc = compound specific organic carbon partition coefficient, L/kg
OCmix = organic carbon content of suspended sediments in the mixing zone,
unitless
This organic carbon content can be solved as the weighted average concentrations of the organic
carbon contents of the suspended solids in the effluent discharge and the suspended solids of the
receiving water body:
oc - TSS" Q" OC» + TSSe Qe OCe
mix TSS O + TSS O ( '
U '^U Z 'Z'B
where:
OCmix = organic carbon content of suspended solids in mixing zone,
unitless
TSSU = total suspended solids concentration at a point just upstream of
effluent discharge, mg/L
TSSe = total suspended solids concentration in effluent discharge, mg/L
Qu,Qe = upstream harmonic mean flow and effluent discharge flow rates,
L/hr
OCU, OCe = organic carbon contents of suspended solids upstream of the
discharge point and within effluent discharge stream
Fish lipid concentrations for this solution are then given as:
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C, ., = BSSAF
hP,d
(4-69)
where:
Mipid
BSSAF
c
^
fish lipid concentration, mg/kg
biota suspended solids accumulation factor, unitless
concentration of dioxin-like compounds on suspended sediments,
mg/kg
organic carbon content of suspended sediments, unitless
Finally, whole fish concentrations are simply this lipid concentrations times a fraction offish
lipid, or Clipid * flipid.
The harmonic mean flow, Qe, is distinct from the long term average flow. To understand
the difference, the following discussion is offered. Assume, for this discussion, that the effluent
flow, Qe, is much lower than the receiving water body flow, Q;, and therefore it can be neglected.
The daily average total water concentration to which a fish is exposed, Q, is then a function of
the daily mass of chemical released, MASS;, divided by the average flow for that day, Q;:
C, =
MASS
(4-70)
Bioconcentration factors are multiplied by an average concentration, Cavg, over n days. Assuming
the loading is constant, MASS; = MASS, then the average concentration is given as:
C
1 MASS
avg
(4-71)
This is equivalent to:
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MASS
-— (4-72)
where QH is the harmonic mean flow defined by:
QH
1 1
~ E77 (4-73)
This is different from the arithmetic average flow, Q (i.e., mean flow):
(4-74)
To see how these numbers would differ, consider 10 daily average flows of 1, 1, 1, 1, 2, 2, 3, 4,
7, 10. The QH is calculated as 1.72, whereas the Qavg is calculated as 3.20. The difference is
further illustrated by considering the effect on the calculation of the concentration to which one
would apply the bioconcentration factor. If the daily load were 10.0, then the sequence of water
concentrations would be 10, 10, 10, 10, 5, 5, 3.3, 2, 1.4, and 1. The average concentration is
5.77, and this is the appropriate concentration to use with a bioconcentration factor. If the daily
load often were divided by the QH of 1.72, one would arrive at this correct concentration, but if
the daily load were divided by the average flow of 3.2, the incorrectly calculated average
concentration would be 3.1.
The key model parameter for the effluent discharge model is the BSSAF. A value of 0.09
for 2,3,7,8-TCDD was assumed for BSAF based on data from Lake Ontario. One important
difference between the Lake Ontario ecosystem and the effluent discharge source category is that
the impact to Lake Ontario is thought to be principally historical (EPA, 1990b), while for the
effluent source category, the impact is, by definition, ongoing. This difference may translate to
differences in assignment of BSSAF as compared to BSAF. Consider two aquatic settings where
bottom sediments are found to have equal concentrations of dioxin-like compounds - one in
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which contamination is ongoing and one in which contamination is primarily in the past. For the
aquatic setting where contamination occurred in the past, water column and suspended sediment
concentrations would be lower as compared to the aquatic setting where contamination is
ongoing, because water column impacts are only a function of depuration of bottom sediments
for the historically impacted water body. It is certainly arguable that exposure of aquatic
organisms is greater in the ecosystem where impacts are ongoing, as compared to a system where
impacts are historical, when bottom sediment concentrations are equal in the two systems. Now
recall the assumption made for the soil contamination and stack emission source categories (in
both cases the water body impact is ongoing) concerning the relationship between suspended and
bottom sediments - that the organic carbon normalized concentrations are equal. If this is a valid
assumption for a system with ongoing impacts, and if in fact fish are relatively more exposed
when impacts are ongoing rather than historical, then this argues that a BSSAF for an ongoing
contamination setting should be greater in numerical value than a BSAF for a setting where
contamination was historical.
However, no data could be found to support such a hypothesis, and there would be no
numerical basis for an assumed difference between BSAF and BSSAF. For this reason, the
values assumed for BSSAF and BSAF are equal for this assessment. It should be noted that all
bioconcentration or biotransfer parameters, such as the BSSAF, are qualified as second order
defaults for purposes of general use. Section 6.2. of Chapter 6 discusses the use of parameter
values selected for the demonstration scenarios, including a categorization of parameters.
Second order defaults are defined there as parameters which are theoretical and not site specific,
but whose values are uncertain in the published literature. The parameter values in this category
should be considered carefully by users of the methodology.
The effluent discharge solution algorithm was evaluated using data and information from
the 104 pulp and paper mill study (EPA, 1990c), which measured discharges of 2,3,7,8-TCDD
from 104 mills in 1988, and from the National Study of Chemical Residues in Fish (NSCRF;
EPA, 1992a), which measured fish tissue concentrations of 2,3,7,8-TCDD at points downstream
from several of these mils. A third modeling study (EPA, 1990d) collected critical data for this
modeling evaluation, such as harmonic mean flows downstream of the mills. Finally, the
National Council for Air and Stream Improvement (NCASI) provided details on their assessment
of this data, which was used here. Importantly, this information included linking specific fish
samples to specific mills. A full description of this modeling evaluation is in Chapter 7, Section
7.2.3.6.
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There was a dichotomy of model performance as a function of the size of the receiving
water body. For most of the mills, the receiving water bodies had harmonic mean flows around
108 L/hr, with a range of 107 to 109 L/hr. A small number of mills, however, discharged into
more substantial receiving water bodies which had an average flow of 5 x 1010 L/hr. Comparing
model predictions offish tissue concentrations for mills discharging into the smaller water
bodies, it was found that the model tended to underpredict fish tissue concentrations - the average
predicted whole fish concentration was near 7 ppt, whereas the average observed whole fish
concentration was near 15 ppt. The same was not true for the large receiving water bodies. In
that case, the average whole fish tissue concentration observed was an order of magnitude or
more higher than predicted whole fish concentration. No precise explanation could be given for
this result. The most likely explanation is that, for these large water bodies, there were other
sources of dioxin releases. This comparative exercise did assume inherently that the effluent
discharge was the sole source offish tissue concentrations of 2,3,7,8-TCDD.
It was noted that, for the smaller receiving water bodies, an increase in the assumed
BSSAF of 0.09 (which was the value of BSAF assumed otherwise in this assessment) to 0.20
resulted in an average model prediction offish tissue concentration of near 15 ppt, essentially the
same as the observed fish concentration. This could be some empirical evidence for the
argument developed above - that the BSSAF for a system with ongoing impacts should be greater
in numerical value than a BSAF developed from data on an ecosystem where impacts were
primarily historical.
In any case, parameters for the demonstration scenario in Chapter 5 for this source
category were derived from 104-mill data. Data from only 77 of the mills was used for the
following parameter developments. Mills not included are: 1) the ten mills discharging into the
largest water bodies, 2) 9 mills for which EPA (1990d) was unable to derive harmonic mean
flows from STORET data, and 3) 8 mills for which data on total suspended solids content in the
effluent stream was unavailable from EPA (1990c; actually 11 mills did not suspended solids
data, but three were in other categories deleted).
Values of model parameters for the demonstration are now summarized:
• TSSU, TSSe: The average upstream total suspended solids term from the 77 mills,
TSSU, was 9.5 mg/L. The average suspended solids concentration within the effluent streams
from the 77 mills was 70 mg/L.
• OCU, OCe: No information was available on the organic carbon content of the
suspended solids upstream of the effluent discharge point. A value of 0.05 was assigned, which
was the value assigned for other source categories. No data as well could be found for the
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organic carbon content of the effluent solids. However, such solids are essentially biosolids from
biological treatments of mill sludges. The organic carbon content of such solids is expected to be
much higher than 0.05. The value recommended for OCe was 0.36 (Steven Hinton, PhD., P.E.,
National Council of the Paper Industry for Air and Stream Improvement, Inc.; Department of
Civil Engineering, Tufts University, Medford, MA 02155). This was based on an average
proportion of carbon in algal biomass of 0.36 given in Morel (1983).
• Qm Qe: Flow values for the receiving water and effluent stream were summarized in
EPA (1990d). The average effluent flow rate, Qe, for the 77 mills was 4.10 * 106 L/hr, and the
average harmonic mean flow for the receiving water body, Qu, was 4.65 * 108 L/hr.
• Koc, BSSAF, fjipid: Values of Koc and fUpid are the same ones which have been used
for the other source categories. As discussed in the introduction to this section, the Biota
Suspended Solids Accumulation Factor, BSSAF, will be assumed to be the same as the Biota
Sediment Accumulation, BSAF. This value is 0.09 for 2,3,7,8-TCDD.
• MASSC: The mass of 2,3,7,8-TCDD exiting from the 77 mills averaged 0.197 mg/hr.
However, this data was pertinent for 1988. Since then, pulp and paper mills have reduced the
discharge of dioxin-like compounds in their effluents by altering the pulp bleaching processes.
Gillespie (1992) reports that data on effluent quality from all 104 mills demonstrate reductions in
discharges of 2,3,7,8-TCDD of 84%. On this basis, the value of MASSC for all three example
compounds will be 0.0315 mg/hr (16% of 0.197 mg/hr).
Using these parameters in the simple dilution model for 2,3,7,8-TCDD results in the
following:
1) If the mass loadings of 2,3,7,8-TCDD are assumed to be fully sorbed to solids in the
effluent discharge, and not to exist in the soluble phase in the discharge, than the concentration of
2,3,7,8-TCDD on discharging effluent solids is 1.1*10'4 mg/kg, or 110 ppt.
2) The total suspended solids concentration in the mixing zone, TSS^, equals 10.0 mg/L.
The organic carbon content of suspended solids in the mixing zone, OC^, is estimated as 0.069.
It is seen how the effluent discharge influences these two key quantities: the unadjusted TSSU was
given as 9.5 mg/L, and the unadjusted OCU was 0.05.
3) The overall suspended solids concentration of 2,3,7,8-TCDD in the mixing zone after
mixing and equilibrating with surrounding water, Cssed, was 4.5 ppt. This compares to the
concentration that might be on the effluent solids of 110 ppt, indicating more than an order of
magnitude reduction in concentration by mixing with solids of the receiving water body, and
partitioning into the water column.
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Table 4-1. Available Biota to Sediment Accumulation Factors, BSAF, for dioxin-like compounds.
Reference/Congener
Kuehl, etal., 1987
2378-TCDD
2378-TCDF
12378-PCDD
1234/678-HxCDD
123678-HxCDF
1234678-HpCDD
1234678-HpCDF
US EPA, 1990b
2378-TCDD
2378-TCDD
2378-TCDD
2378-TCDD
2378-TCDD
Parkerton, 1991
2378-TCDD
2378-TCDD
2378-TCDD
Fish Species
Carp
Brown Trout
Lake Trout
Smallmouth Bass
White Perch
Yellow Perch
Resident Fish
Migratory fish
Blue Crab
Water Body
Wisconsin
River
Lake
Ontario
Passaic
River
# Sed. Samples
# Fish Samples
1/1
55/81
55/81
55/14
55/38
55/77
61/11
61/15
61/14
BSAF
0.27
0.06
0.06
0.035
0.037
0.0048
0.0033
0.03
0.07
0.05
0.20
0.03
0.081
0.009
0.055
Comments
Laboratory flow through experiment using
Wisconsin River sediment and Lake
Superior water; BSAFs determined from
one "representative" sediment sample and
one "composited" fish sample; sediment
organic carbon and fish lipid contents
given in article; no other details provided.
Comprehensive field study on
bioaccumulation of 2378-TCDD in Lake
Ontario; BSAFs are estimated given 55
sediment samples and specific number of
fish samples as noted; report evaluates
matching fish with sediment data from
sites where fish were caught
7 "resident" fish species were best
represented by carp; "migratory" species
were eel and striped bass; TCDD
contamination attributed to historical
industrial input, particularly a 2,4,5-T plant
operation from 1940s to 60s.
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Table 4-1. (Cont'd)
DRAFT-DO NOT QUOTE OR CITE
Reference/Congener
Kjeller, etal, 1990
2378-TCDD
12378-PCDD
123478-HxCDD
123678-HxCDD
123789-HxCDD
OCDD
2378-TCDF
1234/78-PCDF
23478-HxCDF
123478/9-HxCDF
123678-HxCDF
123789-HxCDF
234678-HxCDF
1234678-HpCDF
1234789-HpCDF
1234678-HpCDF
OCDF
Fish Species
Pike
Water Body
Lake
Vanern
# Sed. Samples
# Fish Samples
4/6
4/6
4/6
4/6
4/6
4/6
4/6
4/6
4/6
4/6
4/6
4/6
4/6
4/6
4/6
4/6
4/6
BSAF
2.94
1.03
0.17
0.086
0.018
0.002
1.40
0.25
0.71
0.036
0.065
0.27
0.047
0.0009
0.023
0.006
0.0001
Comments
Results presented at right derived from data
in Kjeller, et al (1990); data includes
sediment samples from four sites in Lake
Vanern and 6 composited (2-5 fish in
composite) pike associated with the four
sites; pike concentrations reported in article
on a lipid basis; Lake Vanern is near a paper
mill.
4-131
December 2003
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Table 4-1. (Cont'd)
DRAFT-DO NOT QUOTE OR CITE
Reference/Congener
US EPA, 1993b
2378-TCDD
2378-TCDD
2378-TCDD
2378-TCDD
2378-TCDD
2378-TCDD
2378-TCDD
CDEP, 1992
2378-TCDD
2378-TCDF
23478-PCDF
I-TEQ
Fish Species
Smelt
Sculpin
Herring Gull
Bullhead
Sandworm
Clam
Shrimp
See "Comment"
Water Body
different
bodies
23 different
bodies
# Sed. Samples
# Fish Samples
NA
346/521
346/521
346/521
346/521
BSAF
0.04
0.12
0.43
0.05
0.48
0.93
0.73
0.86
0.25
0.47
0.24
Comments
Results compiled in EPA (1993) for 2378-
TCDD; details found in these studies:
Batterman, et al. (1989)
Batterman, et al. (1989)
EPA (1990b) and Braune, Norstrom (1989)
Cook (unpublished) as listed in EPA (1993b)
Rubinstein, etal. (1983)
Rubinstein, etal. (1983)
Rubinstein, et al. (1983)
Data supplied by Connecticut DEP;
complete data description in Chapter 7 of
this assessment; study designed to evaluate
newly operating resource recover facilities.
BSAFs are calculated based on 521 fish
samples including the species: carp, channel
catfish, white catfish, white sucker, brown
bullhead, yellow perch
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December 2003
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Table 4-1. (Cont'd)
DRAFT-DO NOT QUOTE OR CITE
Reference/Congener
US EPA, 1995
2378-TCDD
12378-PCDD
123478-HxCDD
123678-HxCDD
123489-HxCDD
1234678-HpCDD
OCDD
2378-TCDF
12378-PCDF
23478-PCDF
123478-HxCDF
123678-HxCDF
123789-HxCDF
234678-HxCDF
1234678-HpCDF
1234789-HpCDF
OCDF
Fish Species
Lake Trout
Water Body
Lake
Ontario
# Sed. Samples
# Fish Samples
NA
BSAF
0.059
0.054
0.018
0.0073
0.0081
0.0031
.00074
0.047
0.013
0.095
0.0045
0.011
0.037
0.04
.00065
0.023
.00099
BEF
1.00
0.92
0.31
0.12
0.14
0.051
0.012
0.80
0.22
1.6
0.076
0.19
0.67
0.63
0.011
0.39
0.016
Comments
EPA (1995) claims that the Lake
Ontario data from EPA (1990b)
was reanalyzed for the suite of
dioxin-like congeners. It is not
clear why the 2378-TCDD for
lake trout was 0.059 in this study
but listed as 0.07 for lake trout in
EPA(1990b). It is also not clear
how many samples offish and
sediment were reanalyzed for the
data on the right. Also listed to
the left are the "bioequivalency
factors", or BEFs, as listed in
EPA (1995) and as developed
from this data set. See text for
more detail.
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Table 4-2. Available Biota to Sediment Accumulation Factors, BSAF, for PCBs.
Reference/C ongener
EPA(1990b)
PCB
EPA (1995)
Total PCBs
PCB 105
PCB 118
PCB 156
PCB 180
EPA (1995)
PCB 77
PCB 105
PCB 118
PCB 126
PCB 167
PCB 180
PCB 189
Fish Species
trout, salmon,
perch, bass
Trout
Trout
Water Body
Lake Ontario
Lake Ontario
Lake Ontario
BSAF
1.40,0.77,
0.52, 0.86,
3.35, 1.42
1.85
2.70
4.09
3.97
3.78
0.29
4.49
1.72
3.21
0.69
3.26
0.71
Comments
These BSAF were compiled in EPA (1990b) from
several data sources, years of study, and fish species.
The summary provided was not specific in terms of
BSAFs associated with specific fish species. It is
assumed that "PCBs" described in EPA (1990b)
meant "total PCBs". There was one BSAF of 0.58
derived in EPA (1990b) for Aroclor 1254
EPA (1995) used the data of Oliver and Niimi (1988)
to derive the BSAFs listed for total PCBs and the
PCB congeners. EPA (1995) also lists BSAFs for
numerous other non-dioxin-like PCB congeners.
EPA (1995) derived these BSAFs based on their
reanalysis of sediment and trout data from the
original Lake Ontario study (1990b). EPA (1995)
also lists BSAFs for numerous other non-dioxin-like
PCB congeners.
4-134
December 2003
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Table 4-2. (cont'd)
DRAFT-DO NOT QUOTE OR CITE
Ref erence/C ongener
EPA (1995)
PCB77
PCB 105
PCB 118
PCB 167
PCB 180
PCB 189
Parkerton, etal(1993)
trichloro-PCB
tetrachloro-PCB
pentachloro-PCB
hexachloro-PCB
heptachloro-PCB
octachloro-PCB
Parkerton, etal( 1993)
Total PCBs
Fish Species
Brown Trout
lake trout,
whitefish
Three species
of marine fish
Water Body
Green Bay, WI
Si ski wit Lake
Rio de La Plata,
Argentina
BSAF
4.12
5.35
4.96
16.0
10.96
3.45
0.45-2.6
0.71-1.3
3.4-9.4
2.9-20.8
12.5
2.2-12.7
4.40
Comments
EPA (1995) derived these BSAFs from data on lake
trout from the Green Bay. The study from which
these data came from was described as EPA's Green
Bay/Fox River Mass Balance Study which involved
extensive sampling of water, sediment and fish in
1989. No further citation was provided in EPA
(1995) for this study.
Compiled by Parkerton, et al. (1993) from data in
Swackhammer, et al. (1988) and Swackhammer and
Kites (1988); Parkerton presents data for individual
congeners - summary at left aggregates by
chlorination and includes both fish species; only one
data point presented by heptachloro-PCB.
Determined by Parkerton, et al (1993) from
Columbo, et al (1990) on total PCBs. Columbo
reference has data on PCBs 5-8, 14, 19, 28-31, 52,
101, 110, 138, 153, 180.
4-135
December 2003
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Table 4-2. (cont'd)
DRAFT-DO NOT QUOTE OR CITE
Ref erence/C ongener
Parkerton, etal(1993)
dichloro-PCB
trichloro-PCB
tetrachloro-PCB
pentachloro-PCB
hexachloro-PCB
heptachloro-PCB
octachloro-PCB
nonachloro-PCB
Fish Species
flounder,
lobster, crab
Water Body
New Bedford
Harbor
BSAF
0.11-0.59
0.26-0.65
0.65-1.02
1.05-2.08
1.29-4.00
0.84-2.74
0.23-1.17
0.02-0.38
Comments
Compiled by Parkerton, et al. (1993) from data in
BOS (1990); summary at left is the range of values
for PCB congener groups, and averaged across the
noted species.
4-136
December 2003
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DRAFT-DO NOT QUOTE OR CITE
Table 4-3. Data and parameters used to determine the part of the plant concentration which was
due to the deposition of particle bound dioxins (see below table for definition of columns).
#1
C14DD
C15DD
C16DD
C17DD
OCDD
C14DF
C15DF
C16DF
C17DF
OCDF
#2
0.51/0.49
(0.90/0.10)
0.13/0.87
(0.72/0.28)
0.03/0.97
(0.55/0.45)
0.01/0.99
(0.23/0.77)
0.002/0.998
(0.07/0.93)
0.53/0.47
(0.91/0.09)
0.20/0.80
(0.78/0.22)
0.06/0.94
(0.52/0.48)
0.02/0.98
(0.23/0.77)
0.002/0.998
(0.11/0.89)
#3
0.029
0.029
0.053
0.088
0.163
0.190
0.104
0.082
0.057
0.026
#4
0.0141
0.0251
0.0513
0.0873
0.1629
0.0887
0.0827
0.0766
0.0559
0.0259
#5
0.007
0.013
0.027
0.046
0.084
0.046
0.043
0.040
0.029
0.014
#6
0.13
0.13
0.14
0.13
0.19
0.63
0.25
0.15
0.14
0.029
#7
0.123
0.117
0.113
0.085
0.106
0.584
0.208
0.110
0.111
0.016
#8
94
90
81
65
56
93
83
74
79
54
Column Definition:
#1 - Congener Group
#2 - Vapor/Particle Fractions. Ratios in parenthesis were the fractions measured in the 2-stage
air sampling equipment. See text for more detail.
#3 - Total air concentration, pg/m3
#4 - Particle bound air concentration, pg/m3
#5 - Plant concentration calculated to be due to particle deposition, ng/kg fresh
#6 - Total plant concentration, ng/kg fresh
#7 - Plant concentration calculated to be due to vapor transfer, estimated as Column 6 -
Column 5, ng/kg fresh
#8 - Percent of plant concentration due to vapor transfers.
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Table 4-4. Development of the Bvpa using data of Welsch-Pausch, et al (1995) compared against
the Bvpa as developed in EPA (1994) (see below table for column definitions).
# 1
C14DD
C15DD
C16DD
C17DD
OCDD
C14DF
C15DF
C16DF
C17DF
OCDF
#2
94400
90100
87300
65200
81300
450000
159000
84900
85400
12000
#3
0.0149
0.0039
0.0017
0.00074
0.00036
0.101
0.0168
0.0054
0.00106
0.000054
#4
6.35*106
2.32*107
5.05*107
8.83*108
2.25*108
4.44*106
9.47*106
1.57*107
8.05*108
2.21*108
#5
6.55*104
2.39*105
5.20*105
9.10*106
2.36*106
4.57*104
9.75*104
1.62*105
8.30*106
2.28*106
#6
1.0*105
6.3*105
6.9*105-
2.3*106
1.0*107
2.4*109
1.5*105
3.8*105 -
5.3*105
5.9*105 -
1.4*106
6.8*105
1.7*108
Column Definitions:
#1 -
#2 -
#3 -
#4 -
#5 -
#6 -
Congener
Vapor phase volumetric grass concentration, pg/m3
Vapor phase volumetric air concentration, pg/m3
Bvol calculated from the data of Welsh-Pausch, et al (1995)
Bvpa calculated from the data of Welsh-Pausch, et al (1995)
Bvpa as developed in EPA (1994). These Bvpa were calculated for the individual dioxin-
like congeners. Where a range is presented, such as for C16DF, this was the range for the
dioxin-like congeners in the congener group.
4-138
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Table 4-5. Ratios of dioxins and furans in milk fat (MF) and body fat (BF) to concentrations
in diets of farm animals.
Animal
Goats
Cows
Cows
Steers
Heifers
Cow
Days
56
21
70
28
160
92
Compound
2378-TCDD
2378-TCDD
123678-HxCDD
1234678-HpCDD
OCDD
2378-TCDD
123678-HxCDD
1234678-HpCDD
OCDD
1234678-HpCDD
OCDF
2378-TCDD
12378-PCDD
123478-HxCDD
123678-HxCDD
123789-HxCDD
1234679-HpCDD
1234678-HpCDD
OCDD
234/78-TCDF
1234/78-PCDF
23478-PCDF
123478/9-HxCDF
123678-HxCDF
234678-HxCDF
1234678-HpCDF
1234789-HpCDF
OCDF
I-TEQ
BF:Die
t
-
-
3.9
0.4
0.1
3.5
2.1
0.2
0.05
0.3
0.1
-
-
-
-
-
-
-
-
-
-
-
-
-
MF:Diet
2.8
4.4
5.7
0.6
0.1
-
-
_
5.76
5.55
2.69
2.32
2.99
0.27
0.48
0.69
1.25
0.97
4.13
3.12
2.67
2.37
0.55
1.32
0.27
3.33
Reference
Arstillaetal. (1981)
Jensen & Hummel (1982)
Firestone, et al (1979)
Jensen, etal (1981)
Parker, et al ( 1980)
McLachlan, et al. (1990)1
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Table 4-5. (Cont'd)
Animal
Cows
4 cows
and three
times
during
milking
for each
cow
Days
28,
42,
56
Compound
2378-TCDD
12378-PCDD
123478-HxCDD
123678-HxCDD
123789-HxCDD
1234678-HpCDD
OCDD
23478-PCDF
123478-HxCDF
123678-HxCDF
234678-HxCDF
1234678-HpCDF
1234789-HpCDF
OCDF
BF:Die
t
MF:Diet
7.1
5.0
3.1(3.6)
3.7(3.2)
2.6 (2.2)
0.68 (0.3)
0.08 (0.05)
3.5(3.5)
3.0(1.1)
3.1 (2.2)
1.9(1.6)
0.72(0.3)
0.87
0.07(0.01)
Reference
Fries, etal. (1999)2
1 McLachlan, et al. (1990) was not a dosed feeding study; the single cow studied was given
normal rationing. The first sample was taken Feb 16, 1989, two months after the last calving on
Dec. 22, to maximize the possibility that steady state had been reached. The 92 days listed was
from Dec. 22 until the last sample on Mar. 24.
2Fries, et al. (1999) were derived from a study where ground up PCP-treated wood was added to
the cows' feed. Results are presented only for congeners where concentrations were above
background for both milk and feed. Comparison with BCFs derived for other cows which were
fed a normal diet (without PCP-treated wood) setting were comparable to the feeding
experiment; these background BCFs are shown in parenthesis
Source: Fries and Paustenbach (1990), McLachlan, et al. (1990), and Fries, et al. (1999)
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Table 4-6. Ratios of PCBs in milk fat (MF) and body fat (BF) to concentrations in diets of lactating cowsa.
Animal
Lactating
Cows
Lactating
Cows
Lactating
Cows
Lactating
Cows
Lactating
Cows
Days
20
40
60
56
60
120
180
20
32
Compound
Aroclor 1254
Aroclor 1254
Aroclor 1254
dichloro-PCBs
tetrachloro-PCBs
pentachloro-PCBs
hexachloro-PCBs
heptachloro-PCBs
octachloro-PCBs
nanochloro-PCBs
Aroclor 1254
Aroclor 1254
Aroclor 1254
Aroclor 1254
Aroclor 1254
Concentration in
diet, ppm
12.1
12.1
12.1
0.05
0.001
0.003
0.009
0.010
0.005
0.001
0.51
2.82
18.97
2.56
10.25
BF:diet
-
-
3.4
-
-
-
-
-
-
-
2.8
2.4
3.7
-
-
MF:diet
3.1
4.4
4.8
0.4
5.9
1.2
2.2
2.3
3.8
4.0
3.7
3.9
4.8
1.2
4.2
Reference and Comments
Fries, etal (1973)
Tuinstra et al (1981), data is:
average of 2 congeners
one congener
average of 2 congeners
average of 7 congeners
average of 8 congeners
average of 5 congeners
one congener
Willett, et al. (1987)
values at left reflect different
average intake over 3 periods.
Willett and Liu (1982)
Perry, etal (1981)
see text for full details of noted studies.
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Table 4-7. BCFs for liver, adipose, thigh meat, and eggs calculated from the Cal-EPA experiments.
Congener
2378-TCDD
12378-PCDD
123478-HxCDD
123678-HxCDD
123789-HxCDD
1234678-HpCDD
OCDD
2378-TCDF
12378-PCDF
23478-PCDF
123478-HxCDF
123678-HxCDF
123789-HxCDF
234678-HxCDF
1234678-HpCDF
1234789-HpCDF
OCDD
Low Exposure Group
Liver
NA
13.7
6.7
10.7
6.1
7.6
3.1
8.1
36.2
16.2
13.8
10.9
NA
8.2
4.2
3.8
1.9
Adipose
NA
11.7
7.6
11.0
5.1
2.7
0.6
2.7
111
13.1
11.9
11.7
NA
4.8
2.4
1.8
0.5
Thigh
NA
6.8
3.6
5.6
2.4
1.4
0.3
3.1
18.0
7.4
4.8
5.3
NA
2.1
1.0
0.9
0.3
Egg
NA
6.0
5.4
10.2
4.5
4.8
4.3
2.7
20.5
7.8
7.4
8.2
NA
3.0
3.1
2.2
1.4
High Exposure Group
Liver
63.4
44.3
37.3
26.8
16.0
22.3
14.9
42.6
NA
59.8
46.8
37.8
NA
33.4
15.3
26.5
11.4
Adipose
28.8
23.7
18.3
12.5
7.4
4.1
0.7
18.0
NA
26.7
15.1
16.9
NA
8.3
3.3
5.2
0.8
Thigh
28.7
21.5
15.9
9.9
5.4
3.3
0.4
21.9
NA
28.4
13.4
14.2
NA
6.8
2.7
4.2
0.1
Egg
11.3
8.9
8.5
7.0
4.4
4.8
1.8
6.8
NA
10.6
8.5
8.5
NA
5.1
3.9
4.6
1.5
NA = described as "not applicable" for derivation of BCFs by Stephens, et al. (1995) because one or both measurements were below the quantification limit.
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Table 4-8. Chicken and egg BCFs for Aroclor mixtures.
Aroclor Mixture
1221
1232
1242
1248
1254
1268
Low Exposure Group
Egg
NA
NA
3.5
3.5
6.5
NA
Body Fat
NA
NA
2.9
9.8
7.4
NA
High Exposure Group
Egg
0.4
1.2
7.2
4.7
5.7
10.8
Body Fat
1.2
2.6
4.5
4.5
6.2
2.6
NA = data was not taken for the low, 2 ppm, concentration exposure for these Aroclors
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Table 4-9. Ranges of concentrations of PCDDs, PCDFs, and PCBs in municipal waste
combustor ash (results in ng/g or ppb; ND = Not detected; NR = not reported; Tr
= trace; DL between 0.01 and 0.1 ng/g).
Constituent
I. DIOXINS
MCDD
DCDD
T3CDD
T4CDD
PCDD
H6CDD
H7CDD
OCDD
2378-TCDD
Total PCDD
II. FURANS
MCDF
DCDF
T3CDF
T4CDF
PCDF
H6CDF
H7CDF
OCDF
2378-TCDF
Total PCDF
Fly ash
2.0
0.4 - 200
1.1-82
ND-250
ND-722
ND- 5,565
ND - 3,030
ND-3,152
ND-330
5 - 10,883
41
ND-90
0.7-550
ND-410
ND- 1800
Tr- 2,353
Tr - 887
ND-398
0.05-5.4
3.73 - 2,396
Combined
ash
ND
ND-120
ND-33
0.14-14
0.07-50
0.07-78
0.07-120
0.07-89
0.02-0.78
6.2-350
1.1
ND-42
ND-14
2.3-9
1.6-37
1.2-35
0.62-36
0.18-8.4
0.41-12
6.14-153.9
Bottom ash
NR
NR
NR
<0. 04-4 10
ND-800
ND-1000
ND-290
ND-55
<0.04-6.7
ND-2800
NR
NR
NR
10.1-350
0.07-430
ND-920
ND-210
ND-11
ND-13
ND-1600
Constituent
III. PCBs
Mono CB
DiCB
TriCB
Tetra CB
Penta CB
Hexa CB
Hepta CB
Octa CB
Nona CB
Deca Cb
Total PCB
Fly ash
0.29-9.5
0.13-9.9
ND-110
0.5-140
0.8-225
0.45-65
ND-0.1
ND-1.2
ND
ND
ND-360
Combined
ash
ND
0.13-1.35
0.35-14.3
16.5
ND
ND-39
ND
ND
ND
ND
ND-32.15
Bottom
ash
ND-1.3
ND-5.5
ND-80
ND-47
ND-48
NR
NR
NR
NR
NR
ND-180
Source: EPA (1991)
4-144
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Wind erosion
Volatilization
Far-field dispersion
Air:
Vapor and Particulate
Phases
SOIL
SOURCE
Erosion
Watershed
dilution
Enrichment
Above-ground\
Vegetables and)
Erosion
Surface soil
mixing
Dissipation
Bioaccumulation
Beef & dairy cattle diet
Wet + dry particle deposition
Particle washoff
Air-to-leaf vapor phase
transfers
Pasture Grass
Cattle Feed
Soil-to-root transfers
Steady state between 3 compartments
Below-ground ^v
Vegetables and j
Fruits J
Bottom\
Sediments )
Equilibrium
partitioning
Organic carbon normalized
concentrations are equal
Biota Sediment
Accumulation
Factor
Figure 4-1. Diagram of the fate, transport, and transfer relationships for the
soil contamination source category.
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Dispersion/deposition
modeling
Vapor/particle
partitioning
Emission factors
Vapor, Particle-phase
Concentrations
Dry and Wet Depositions
Above-ground
Vegetables and
Fruits
Particle washoff
Air-to-leaf vapor phase transfers
Beef and
Milk
Deposition
modeling
Soil mixing
Dissipation
Pasture Grass
Cattle Feed
STACK
EMISSIONS
Bioaccumulaton
Beef & dairy cattle diet
assumptions
Exposure
Site Soils
Below-ground
Vegetables and
Fruits
Deposition
modeling
Soil-to-root transfers
Watershed
Soils
Mass balance maintained
Steady state between 3 compartments
Suspended
Solids
Dissolved
Phase
Bottom
Sediments
Deposition
modeling
Soil mixing
Dissipation
Erosion
Sediment delivery
Enrichment
Equilibrium
partitioning
Organic carbon normalized
concentrations are equal
Biota-Sediment
Accumulation
Factor
Figure 4-2. Diagram of the fate, transport, and transfer relationships for the stack emission source category.
4-146 December 2003
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EFFLUENT
DISCHARGE
Total Water
Concentration
Biota Suspended Solids
Accumulation Factor
Figure 4-3. Diagram of the fate, transport, and transfer relationships
for the effluent discharge source category.
4-147
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1000'
PMAMA6C ARE*
Source: Vanoni, 1975
Figure 4-4. Watershed delivery ratio, SDW, as a function of
watershed size.
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5. DEMONSTRATION OF METHODOLOGY
5.1. INTRODUCTION
This document has provided methodologies and background information for conducting
site-specific exposure assessments for dioxin-like compounds. Chapter 2 summarized an overall
exposure assessment framework, Chapter 3 described mechanisms of formation of dioxin-like
compounds in stack emissions and the fate and transport modeling of releases from the stack to a
site of exposure, and Chapter 4 provided methodologies to estimate exposure media
concentrations for three sources of contamination, which were termed source categories.
The purpose of this chapter is to put all this information together and demonstrate the
methodologies that have been developed. For this demonstration, exposure scenarios are
developed which are associated with the three source categories. These categories were defined
in Chapter 4, and are:
• D Soil Contamination: The source of contamination is soil. The
contaminated soil could occur at the site of exposure, such as in worker
exposure scenarios at Superfund sites or contaminated soil at a residence,
or the contaminated soil could occur distant from the site of exposure,
such as a residence near a Superfund site.
• D Stack emissions: Exposed individuals reside in the vicinity of the site where
stack emissions occur and are exposed to resulting air-borne contaminants, and
soil and vegetation on their property is impacted by deposition of contaminated
particulates.
• D Effluent discharge: A discharge of dioxin-like compounds in effluents impacts
surface water and fish. Exposure occurs through consumption of the impacted
fish and water.
An additional and important scenario is developed which merges the fate and transport
algorithms of the soil contamination and emission source categories. This scenario is called,
"background conditions". Further details on the structure and fate algorithms of the background
conditions scenario are provided in Sections 5.3 through 5.5 below.
The demonstration in this chapter is structured around what are termed exposure
scenarios. As defined in Chapter 2, an exposure scenario includes a description of the physical
setting of the source of contamination and the site of exposure, behavior of exposed individuals,
and exposure pathways. Chapter 2 also described the objective of exposure assessors to
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determine "central" and "high end" exposure scenarios. This objective was an important one for
this demonstration, and the strategy to design such scenarios is detailed in Section 5.2 below.
For the soil contamination and the effluent discharge source categories, three dioxin-like
compounds are demonstrated for each exposure scenario, including 2,3,7,8-TCDD, 2,3,4,7,8-
PCDF, and 2,3,3',4,4',5,5'-HPCB. For the stack emission source and the background conditions
demonstrations, a different approach is taken with regard to compounds demonstrated. The 17
dioxin and furan compounds of non-zero toxicity are demonstrated; no dioxin-like PCBs are
demonstrated. Exposure media concentration results are developed for all 17 congeners. As
well, a "toxic equivalent" exposure media concentration, or TEQ concentration, is calculated as
the sum of the individual congener concentrations multiplied the congener's Toxicity
Equivalency Factor, or TEF. As described in Chapter 1, TEQ concentrations are determined
using the WHO 1998 scheme, and the TEQs of this chapter are therefore further identified as
WHO98-TEQDF. Final exposure estimates (Lifetime Average Daily Doses, or LADDs) are
developed based on the WHO98-TEQDF exposure media concentrations for these demonstrations.
Section 5.2 describes the strategy for development of the demonstration exposure
scenarios. Section 5.3 gives a complete summary of the demonstration scenarios. Section 5.4
provides some detail on the example compounds demonstrated. Section 5.5 describes the source
strength terms for the scenarios. Section 5.6 summarizes the results for all scenarios, which are
exposure media concentrations for all exposure pathways, and exposure estimates which are
Lifetime Average Daily Doses (LADDs) for all pathways. Also, several observations and
additional analyses are provided in Section 5.6.
5.2. STRATEGIES FOR DEVISING EXPOSURE SCENARIOS
Chapter 2 of this document described procedures to assess individual exposures to known
sources of contamination. Central and high end exposure patterns, and exposure parameters
consistent with these definitions were proposed in that chapter. The demonstration in this
chapter attempts to merge procedures for estimating individual exposures to known sources of
contamination and current thoughts on devising central and high end exposure scenarios.
An exposure assessor's first task in determining patterns of exposure is to fully
characterize the exposed population in relation to the source of contamination. If the extent of
contamination can be characterized, then the exposed population would be limited to those
within the geographically bounded area. An example of this situation might be an area impacted
by stack emissions. Chapter 3 demonstrated the use of ISCST3 atmospheric dispersion model to
predict ambient air concentrations and depositions rates for all points surrounding the stack.
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Results listed in Tables 3-16 through 3-19 were only for the prevailing wind direction. As can be
seen on these tables, the points of maximum impact were within 1 km of the stack. By
overlaying the concentration isopleths onto a population density map, the exposed population can
be identified. If the extent of contamination is not as clearly defined, such as extent of impact of
nonpoint source pollution (impacts from use of agricultural pesticides, e.g.) or the compound is
found ubiquitously without a clearly defined source, then the emphasis shifts from geographical
bounding to understanding ambient concentrations, exposure pathways and patterns of behavior
in general populations. The background conditions scenarios do, in fact, focus on the
development of realistic ambient concentrations for its source strength terms.
After identifying the exposed population, the next task is to develop an understanding of
the continuum of exposures. The exposures faced by the 10 percent of the population most
exposed has been defined as high end exposures. Those faced by the middle of the continuum
are called central exposures. Another important estimate of exposure level is a bounding
exposure, which is defined as a level above that of the most exposed individual in a population.
Arriving at such an understanding can be more of an art than a science. One consideration is the
proximity of individuals within an exposed population to the source of contamination. For the
incinerator example discussed above, one might begin an analysis by assuming that bounding or
high end exposures occur within a kilometer from the stack, in the prevailing wind direction.
Another important consideration is the relative contribution of different exposure pathways to an
individual's total exposure. While individuals residing at this distance from the incinerator might
experience the highest inhalation exposures, they may not experience other exposure pathways
associated with contaminated soil on their property - such as consumption of home grown
vegetables, dermal contact, or soil ingestion. Families with home gardens and individuals who
regularly work in those gardens may reside over a kilometer from the incinerator and possibly be
more exposed because of their behavior patterns. Screening tools, such as the algorithms of this
assessment which are amenable to spreadsheet analysis (except for the ISCST3 modeling), can be
used in an iterative mode to evaluate the interplay of such complex factors. When applied to a
real world situation, information should be sought as to the makeup and behavior patterns of an
exposed population.
The third principle task for evaluating impacts on an exposed population is to understand
the relationship between impacts attributed to the source in question and the background
exposures faced by the identified population. Assessors should attempt to answer the following
question for dioxin-like compounds: What exposures to dioxin-like compounds would the
identified populations have if the source in question were not in existence? The exposures faced
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by the identified in the absence of the source in question can be termed "background" or
"cumulative" exposures; cumulative in two senses: that the exposures faced by the identified
population result from the cumulative impacts of all sources in their environment and from all
pathways. Chapter 2 describes approaches to determining background exposures to dioxin-like
compounds, and this chapter demonstrates one way to evaluate background/cumulative
exposures for a specific site.
The demonstration in this chapter attempts to be consistent with the goal of
quantifying central and high end exposures, and properly considering background exposures.
However, it is not exhaustive in its analysis, nor should it be construed as a case study with
widely applicable results. All the scenario definitions, parameter values, and so on, were
construed to be plausible and reasonable, and to demonstrate the application of a site-specific
methodology, not to set any regulatory precedent.
Following are bullet summaries of key features of the structure and intent of the
demonstrations.
• Exposed populations: Exposed individuals are assumed to reside in a rural
setting. Exposures occur in the home environment, in contrast to the work
environment or other environments away from home (parks, etc.). The
presumption is made that the sources of contamination of this assessment can
occur in rural settings in the United States. It is further assumed that the behavior
patterns associated with the exposure pathways can exist in rural settings. Several
of these behaviors characterized as high end relate to individuals on farms as
compared to behaviors characterized as central for individuals not on farms. The
exposed population for this demonstration, therefore, consists of rural individuals
in farming and non-farming residences.
• Plausibility of source strength terms: The objective to determine plausible
levels of source strength contamination was an important one for this
demonstration. Sources demonstrated include small areas of soils with
concentrations that have been found in industrial sites, stack emissions with
emission rates typical of facilities containing state-of-the-art emission controls,
and effluent discharges where characteristics of the effluent stream including rate
of contaminant discharges were developed from recent data from pulp and paper
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mills. Also, the background conditions scenarios include watershed soils and air
concentrations which have been measured in an actual rural background setting.
Section 5.5 describes the source terms in detail.
Proximity to sources of contamination: The background scenarios use soil and
air concentrations which have been measured in an actual background setting.
Therefore, proximity to the source of contamination is not an issue. The effluent
discharge source category is unique from the others in that soils or air are not
impacted by the source. Only the surface water body into which the effluent is
discharged is impacted. The only exposure pathways considered for this source
category are drinking water and fish ingestion. Like the demonstration of
background conditions, proximity is not an issue for this source category because
the simple dilution model does not model fish and water concentrations as a
function of distance from the source. It is felt that the water movement of a river
or stream receiving the effluent discharge allows for sufficient mixing such that
the simplistic dilution approach is reasonable for the dioxin-like compounds. As
well, fish are not stationary so that a relationship between distance to source and
where the fish are caught would be hard to develop or defend. Individuals in the
effluent discharge demonstration simply exhibit behaviors associated with an
impacted water body - they fish and they consume the water. Proximity to a stack
emitting dioxin-like compounds was identified as an important determinant for
identifying the continuum of exposures. Assuming there is a uniform distribution
of exposure-related behaviors among exposed populations, i.e., their behavior
patterns are not a function of where they live in relation to the stack, the most
exposed individuals will be those exhibiting high end exposure behavior nearest
the stack. This was the assumption made for purposes of this demonstration. A
set of high end exposure behaviors and pathways were demonstrated for
individuals residing 500 meters east of the stack, and a set of central exposure
pathways were demonstrated for individuals residing 5000 meters east of the
stack. The highest ambient air concentrations, and dry and wet deposition rates
were simulated to occur at 200 to 1000 meters downwind, justifying 500 meters as
an appropriate point for assuming high end impacts. Tables 3-16 through 3-19
listing concentrations and depositions rates as a function show that air
concentrations and dry depositions rates at 5000 meters are only about half of
what they are at 500 meters, although wet deposition rates are about 20 times
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higher at 500 meters as compared to 5000 meters. Without rigorous justification,
the model output (concentrations and deposition rates) at 500 and 5000 meters
was felt to appropriately characterize high end and central exposures. The site of
contamination in the demonstration of the soil contamination source category is
10 acres in size and has concentrations that have been found in industrial sites. A
working hypothesis is made that the population most exposed are those residing
very near the site. Their soil is assumed to become contaminated over time due to
the process of erosion; these processes normally do not carry contaminants long
distances across land, particularly land developed with residences or where
erosion is interrupted with ditches or surface water bodies. People from the
surrounding community can be impacted by visiting or trespassing on the
contaminated land, volatilized residues may reach their home environments, they
may obtain water and fish from impacted water bodies, and so on. It seems
reasonable to assume that those residing near these sites comprise the principally
exposed individuals, or equivalently, the individuals experiencing the high end or
bounding exposures associated with these areas of soil contamination. The soil
contamination source category will be demonstrated with a single, high end
scenario. The exposure site is assumed to be located 150 meters downgradient
from the site of soil contamination.
Central and high end exposure patterns: Chapter 2 described the exposure
pathways that are considered in this methodology, and justified assignment of key
exposure parameters (contact rates and contact fractions, exposure durations, and
so on) as central or high end estimates. The bullet above discussing exposed
populations indicated that several of the behavior assumptions were specific to
individuals on farms, and that these behavior patterns were evaluated as "high
end". "Central" behavior patterns were those for individuals residing in a non-
farm residence. High end behaviors assumed to be different for individuals on
farms versus central behaviors for individuals not on farms include, for example,
residing on larger tracts of land (10 acres assumed for farmers; 1 acre assumed for
non-farmers) and ingestion of home produced and impacted beef. Other patterns
of behavior modeled as central and high end are not specifically associated with
farming and not farming, but are assumed to be plausible for individuals in rural
settings. These include home gardening for fruit and vegetables, inhalation
exposures, children that ingest soil, and the use of impacted surface water bodies
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for drinking and fish to be ingested. Finally, a set of additional exposure
pathways are modeled which are outside the scenarios altogether. These include
the pathways of milk ingestion, chicken, and egg ingestion. Like the beef
ingestion pathways, these pathways involve home production of the food
products. Since an objective of the scenario development was to be realistic, it
was felt that a scenario involving home production of the four animal food
products: beef, milk, chicken, and eggs (not to mention fruits and vegetables), was
highly unlikely. However, a scenario involving production of at least one animal
terrestrial food product is more realistic.
Consideration of background exposures: Background scenarios are devised
which include the same exposure pathways and exposure parameters as the
source-specific scenarios. The difference is that the exposure media
concentrations are "background" or "cumulative" as contrasted to concentrations
that result only from the source being modeled. Specifically, the background
scenarios use, as input, "background" air and soil concentrations, and model all
subsequent terrestrial and aquatic impacts. By structuring the background
scenarios in this way, the key question, "What would be the exposure of
individuals if the source in question did not exist?" is most specifically answered.
Chapter 2 described a second approach to the issue of background exposures, and
that was the comparison of source/scenario specific exposures to a generic
background exposure. Volume II of this assessment has estimated that a general
background exposure to WHO98-TEQDFP is about 60 pg/day. One could, therefore,
compare any source-specific estimates to this overall background estimate.
Further detail on the specifics of the background scenarios is given below.
Realism of modeled concentrations: The air and soil concentrations of the
background scenarios are, by definition, realistic since they were derived from
actual measured concentrations from a background site. For all other exposure
media of the background scenarios, and in all other scenarios, the exposure media
concentrations were modeled. The realism of modeled exposure media
concentrations is dependent on the appropriateness of the models used for such
estimations and the assignment of parameter values for those models. One way to
arrive at a judgement as to the realism of estimated concentrations is to compare
predictions with observations. To the extent possible (i.e., given the availability
of appropriate data), model predictions of exposure media concentrations are
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compared with occurrence data in Chapter 7, which describes several model
validation exercises. As is shown, predictions fell within the realm of observed
data. Chapter 4 describes the justification of all model parameter values. Many
of the parameters are specific to the contaminants. Some contaminant properties
were estimated as empirical functions of contaminant-specific parameters: the
root concentration factor, RCF, was estimated as a function of the octanol water
partition coefficient, Kow, for example. Other parameters were measured values,
such as the vapor pressure or some of the bioconcentration factors. For non-
contaminant parameters such as soil and sediment properties, patterns of cattle
ingestion of soil (and other bioaccumulation/biotransfer parameters), and many
others, selected values were carefully described and crafted to be plausible.
5.3. EXAMPLE EXPOSURE SCENARIOS
As noted above, all exposures occur in a rural setting. Exposure pathways were those
which could be associated with places of residence in contrast to the work place or other places
of exposure. The example scenarios are structured so that some (but not all) of the behaviors
associated with high end exposures are included in the "high end" scenarios and all the central
behaviors are in the scenarios characterized as "central". To summarize, the components which
distinguished the high end exposure scenarios in contrast to the central scenarios include:
• D Individuals in the central scenarios lived in their homes and were exposed to the
source of contamination for only 9 years, in contrast to individuals in the high end
scenarios, who were exposed for 30 years (except for the exposure pathway of soil
ingestion, where the individuals are assumed to be children ages 2-6, and in both
the central and high end scenarios, the exposure duration is 5 years).
• D Individuals in the central scenarios lived on properties 1 acre in size, whereas
individuals in the high end scenarios lived on properties 10 acres in size.
• D Individuals in the high end scenario associated with the stack emission source
category lived 500 meters from the incinerator, whereas individuals in central
scenario lived 5000 meters from the incinerator.
• D Individuals in high end scenarios obtained a portion of their beef from home-
raised cattle stocks - such individuals are obviously farmers. Consumption of
terrestrial animal food products were not assessed for non-farming rural
individuals, representing the central scenarios.
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• D On the other hand, farming individuals in the high end scenarios were not
assumed to recreationally fish. A fish pathway was included only in the central
scenarios.
• D Ninety percent of the inhaled air and ingested water by the high end individuals
were assumed to be contaminated, whereas only 70% of these exposures were
with impacted media for the central individuals. This is based on time at home
versus time away from home assumptions for central versus high end individuals.
Also, individuals in high end scenarios were assumed to consume 2.0 L/day of
water and breathe 20 m3/day of air as compared to 1.4 L/day and 13 m3/day for
individuals in central scenarios.
• D Both the central and high end scenarios included a fruit/vegetable ingestion
pathway. Although patterns of home production and consumption of fruits and
vegetables differ within a population, average behaviors for individuals who home
produce fruit and vegetables was assumed for both the central and high end
scenarios in this assessment.
• D The rates of ingestion of soil by children were higher for the high end individuals
than the central individuals.
These are the distinguishing features for the central and high end exposure scenarios. For
the sake of convenience mainly, all the scenarios defined below as high end are called "farms",
and all central scenarios are called "residences". In addition to the scenarios, high end behaviors
including fish, milk, chicken and egg consumption are separately modeled for the background
conditions farm setting, the stack emission high end farm setting, and the soil contamination farm
setting.
Again, the reason for separating these four pathways from the scenarios is that it is
important for assessors to develop scenarios which combine a series of behaviors which are
plausible to occur simultaneously in a real world setting. If such a strategy is followed, than the
assessor is able to sum the exposures over all pathways to arrive at a total scenario exposure. It
does not seem reasonably common in the real world that a single farm would include home
production of several terrestrial animal food products (along with recreational fishing), which is
why such a scenario is not developed in this assessment. In an exhaustive site-specific analysis,
one might begin by evaluating all possible pathways, further evaluating pathways of most
exposure, and then determining what pathways occur simultaneously for identified individuals in
the exposed population. Only then can be the assessor begin to define a continuum of exposures.
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The following bullets describe six exposure scenarios that are demonstrated. The
numbering scheme and titles will be referenced for the remainder of this chapter:
Exposure Scenarios 1 and 2: Background conditions, Residence and Farm
Surface soils within the watershed are initialized to soil concentrations of the 17 dioxin-
like congeners (no dioxin-like PCBs) which have been found in an actual rural setting. Also, air
concentrations of the 17 congeners are initialized to air concentrations which have been found in
this same rural setting. More details on this setting are provided in Section 5.5 below. Scenario
1 is the central residential scenario, and Scenario 2 is the high end farming scenario. Bottom
sediment in a nearby river becomes impacted by long term erosion and atmospheric deposition.
Water and fish in that stream are subsequently impacted. The exposure pathways for Scenario 1
are: water ingestion, air inhalation, fish ingestion, fruit/vegetable ingestion, soil dermal contact,
and soil ingestion. The exposure pathways for Scenario 2 are: water ingestion, air inhalation, beef
ingestion, fruit/vegetable ingestion, soil dermal contact, and soil ingestion. It is noted that for a
background condition, it could be argued that all exposure is to background concentrations in
exposure media. In other words, all contact fractions would be 1.00. However, if an assessor
wished to compare the incremental impacts from a specific source of dioxin release with impacts
an individual would receive by contact with the same exposure media which has only
background concentrations of dioxins, than the assessor would assume all the same exposure
behaviors (rates of contact, contact fractions). This demonstration takes this approach. When
evaluating non-cancer risks using a margin-of-exposure approach, which is also demonstrated in
this chapter, it is most appropriate, however, to compare incremental impacts with all
background impacts, not only the same source-specific incremental impacts. This is discussed in
more detail in Section 5.7, which demonstrates cancer and non-cancer risk assessments.
Exposure Scenario 3: Soil Contamination, Farm
A 40,000 m2 rural farm is located 150 m (500 ft roughly) from a 40,000 m2 area of bare
soil contamination; an area that might be typical of contaminated industrial property. The
surface soil at this property is contaminated with three example dioxin-like compounds to the
same concentration of 1 part per billion (ppb). These compounds are: 2,3,7,8-TCDD, 2,3,4,7,8-
PCDF, and 2,3,3',4,4',5,5'-HPCB. The 1 ppb soil concentration is reasonable for industrial sites
of contamination of dioxin-like compounds, and generally about three orders of magnitude
higher than the concentrations of these congeners in background settings. As in the above and all
scenarios, bottom sediment in a nearby river is impacted, which impacts the water and fish. The
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exposure pathways include: water ingestion, air inhalation, beef ingestion, fruit/vegetable
ingestion, soil dermal contact, and soil ingestion.
Exposure Scenarios 4 and 5: Stack Emissions, Residence and Farm
A 4,000 m2 rural residence (Scenario 4) is located 5000 meters from an incinerator, and a
40,000 m2 (Scenario 5) rural farm is located 500 meters downwind from an incinerator.
Emission data of the suite of 17 dioxin-like dioxin and furan congeners (no dioxin-like PCBs) is
available from stack testing of an actual incinerator. This allows for estimation of impacts from
each congener individually, and estimation of WHO98-TEQDF impacts. The modeling of the
transport of these contaminants from the stack to the site of exposure and other points in the
watershed used the ISCST3 model. Details on the stack emission source for this demonstration
and the ISCST3 model application are found in Chapter 3. A nearby impacted river provides
drinking water and fish for recreational fishing. The exposure pathways for Scenario 4 are:
water ingestion, air inhalation, fish ingestion, fruit/vegetable ingestion, soil dermal contact, and
soil ingestion. The exposure pathways for Scenario 5 are: water ingestion, air inhalation, beef
ingestion, fruit/vegetable ingestion, soil dermal contact, and soil ingestion.
Exposure Scenario 6: Effluent Discharge into a River
As has been discussed, this source category is different from others in that the air, soil,
and vegetation at a site are not impacted. Rather, only surface water impacts are considered.
Therefore, central and high end behaviors associated with places of residence are less pertinent
for this source category. Exposure parameters associated with central behaviors for the water and
fish ingestion pathways were chosen to demonstrate this source category. The source strength
was developed from data on pulp and paper mill discharges of 2,3,7,8-TCDD; more detail on this
source strength term development is provided in Section 5.5 below. The discharges of the other
two example compounds are assumed to be the same for purposes of demonstration. Obviously,
however, there is less of a tie to real data for the discharge rate for these other two example
compounds. Also noteworthy for this source category as compared to the others is the size of the
surface water body into which discharges occur. The other source categories all were
demonstrated on water bodies with annual flow rates of 4.8 * 1011 L/yr. The river size into which
the example effluent was discharged was developed from data from the 104 pulp and paper mill
study (as discussed in Section 5.5 below). This river size was 4 * 1012 L/yr, one order of
magnitude larger than the river of the other scenarios.
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Food pathway analyses outside of the scenario framework: The food consumption
pathways offish, milk, chicken, and eggs are demonstrated using source strength characteristics
of the three high end scenarios above: Scenarios 2 (background conditions), 3 (soil
contamination), and 5 (stack emission). These food pathways were not modeled in the scenarios
themselves. In these analyses, exposure media concentrations are calculated for each source and
the pathway exposure estimates are provided. The purpose of these external pathway analyses
was to provide further demonstration and to compare impacts from the various food pathways
where methodologies have been provided in this assessment.
5.4. EXAMPLE COMPOUNDS
Three compounds were demonstrated for the soil contamination source and for the
effluent discharge source category. For purposes of illustration, one compound was arbitrarily
selected from each of the major classes of dioxin-like compounds. They are: 2,3,7,8-
tetrachlorodibenzo-p-dioxin, 2,3,4,7,8-pentachlorodibenzofuran, and 2,3,3',4,4',5,5'-heptachloro-
PCB. For the remainder of this chapter, these compounds will be abbreviated as 2,3,7,8-TCDD,
2,3,4,7,8-PCDF, and 2,3,3',4,4',5,5'-HPCB.
These compounds demonstrate a range of expected results because of the variability of
their key fate and transport parameters. The log octanol water partition coefficients (log Kow)
for 2,3,7,8-TCDD, 2,3,4,7,8-PCDF, and 2,3,3',4,4',5,5'-HPCB were 6.80, 6.50, and 7.71,
respectively. Whereas the span of reported log Kow ranged from less than 6.00 to greater than
8.00, only a few reported values were at these extremes. Increasing log Kow translates to the
following trends: tighter sorption to soils and sediments and less releases into air and water, less
accumulation in plants and in cattle products (beef, milk), and more accumulation in fish. The
Henry's Constants for the three compounds span the range of reported values, with the value of
the PCB compound the highest of all reported at 6.6 * 10"5 atm-m3/mole. There were few values
less than the 4.98 * 10'6 atm-m3/mole reported for 2,3,4,7,8-PCDF. Higher Henry's Constants
translate to greater amounts of volatilization flux. The fate parameters for these three
compounds and the 15 other dioxin and furan congeners are provided in Table 5-1.
For the background conditions and the stack emission demonstrations, Scenarios 1, 2, 4,
and 5, a different approach was taken. All 17 of the dioxin-like dioxin and furan congeners were
modeled. The ISCST3 modeling exercise described in Chapter 3 allowed for the generation of
deposition amounts (wet and dry) and ambient air concentrations of all 17 congeners at sites of
exposure for the demonstration of the stack emission source. For the background conditions
demonstration, air concentrations were taken from an actual rural site (see Section 5.5). The dry
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depositions of particle-bound congeners were estimated as the particle-bound air concentration
times a deposition velocity. Based on the measurements of Koester and Kites (1992), this
deposition velocity was assumed to be 0.2 cm/sec. Also based on Koester and Kites (1992), who
measured wet and dry deposition and showed these two quantities to be roughly equal for
settings in Indiana, wet deposition was set equal to dry deposition.
The individual deposition rates and air concentrations for the 17 congeners in Scenarios
1, 2, 4, and 5 were used to model the exposure media concentrations for each congener
individually with unique fate and bioaccumulation parameters. The exposure media
concentrations include: air, soil, fruit/vegetables, water, fish, and the terrestrial animal food
products including beef, milk, chicken, and eggs. A final WHO98-TEQDF exposure media
concentration was estimated using the 1998 WHO TEFs (Van der Berg, 1998):
CTE = ^TEFiCi (5-1)
where:
CTCQ = Toxic Equivalent concentration
TEF; = Toxicity Equivalency Factor for congener i
C; = concentration of congener i
The final results which are displayed for these scenarios are the WHO98-TEQDF results only.
5.5. SOURCE TERMS
This section describes the source terms for the example scenarios. The source terms for
the demonstration of background conditions, Scenarios 1 and 2, include both the initial air
concentrations and the initial soil concentrations. The source terms for the soil contamination
source demonstration, Scenario 3, include the area of contamination and soil concentrations. The
source terms for the stack emission scenarios, 4 and 5, are the emission rates of contaminants
from the stacks. These are described in Chapter 3. What will be detailed here, instead, are the
deposition rates, air concentrations, and predicted soil concentrations at the site of exposure. In
this way, scenarios 4 and 5 can be compared to the background scenarios, 1 and 2. The source
term for the effluent discharge example scenario is the rate of discharge of dioxin-like
compounds. This is briefly discussed in this section, with reference to a more detailed discussion
in Chapter 7. Following now are discussions on these terms for all scenarios.
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Scenarios 1 and 2
The 1994, the Ohio EPA (OEPA) conducted air monitoring in the city of Columbus in
order to evaluate the impact of the Columbus Municipal Solid Waste Incinerator (MSWI). This
incinerator operated between June, 1983 and December, 1994. Air samples were taken in March
and April, 1994, at 6 sites in the city in Columbus and in a background site 28 miles southwest of
Columbus. This background site is in the upwind direction from the facility. The air
concentrations were higher in the urban air of Columbus as compared to the air concentrations in
the background site: the average I-TEQ (I is short for "international"; see Chapter 1 for a
discussion of the WHO TEFs versus the I TEFs) air concentration from 10 samples (6 sites, 2
sample dates, but only 5 sites sampled each sample date) in Columbus was 0.092 pg I-TEQ/m3 as
compared to 0.023 pg I-TEQ/m3 from 2 samples (1 site, 2 sample dates) at the background site.
The Ohio EPA visited these same sites in April, 1995, to measure air concentrations once the
incinerator was no longer operating. The average air concentration from the 6 urban sites (all 6
sites sampled in 1995) was 0.046 pg I-TEQ/m3 as compared to the background site of 0.018
pg/m3. Further details on the 1994 sampling can be found in OEPA (1994) and details on the
1995 sampling can be found in OEPA (1995), and an overall summary of all sampling, including
soil sampling, can be found in Lorber, et al. (1998). For the demonstration of background
conditions, concentrations of the 17 dioxin-like congeners from the three sample dates at the
rural site will be averaged to give the air concentration source terms.
The I-TEQ results discussed above were calculated assuming non-detects were equal to /^
the detection limit. Typically, non-detects are either assumed to be 0.0 or 1A detection limit. For
TEQ concentrations, assumptions on the treatment of the detection limit can be an important
issue if concentrations are consistently less than the detection limit and/or quantified
concentrations are near the detection limit. For many samples of the OEPA sampling at
Columbus, it turned out that I-TEQ concentrations did not differ significantly assuming non-
detects equal 0.0 or non-detects equal 1A detection limit. For example, for the 10 Columbus
samples in Mar/Apr of 1994, the average I-TEQ concentration would be 0.088 pg/m3 at ND equal
to 0.0 instead of 0.092 pg/m3 at /^ detection limit. Likewise, for most of the congeners, the
assumption on handling of non-detects is not critical as most of the samples were positively
quantified, and/or the concentrations were sufficiently high such that assumptions on the values
used for non-detects was not critical.
This was not the case, however, for 2,3,7,8-TCDD, the most toxic of congeners. For six
sites and three sampling dates in the city of Columbus, or 16 data points (5 sites sampled for 2
dates, 6 sites sampled for one date), 6 were positive ranging from 0.0027 to 0.0262 pg/m3. With
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non-detect equal to 0.0, the average of these 16 data points was 0.0048 pg/m3; with non-detect
equal to 1A detection limit, the average concentration was 0.0065 pg/m3. Although seemingly
small, this kind of difference can be important in the calculation of TEQ media concentrations.
There were no positive occurrences of 2,3,7,8-TCDD in the three dates of sampling in the rural
site.
The detection limit for 2,3,7,8-TCDD varied by sampling, but was always in the narrow
range of 0.0043 to 0.0074 pg/m3 at the rural site. At /^ the detection limit for the three rural
samples, the 2,3,7,8-TCDD average concentration would be 0.0029 pg/m3, but the range of
possible concentrations would be 0.00 (ND=0) to 0.0058 pg/m3 (ND=1/2 DL).
An examination of the available quantified concentrations at the rural site and in
Columbus suggests that assuming 1A detection limit for 2,3,7,8-TCDD would overestimate the air
concentration of this congener in the rural site. Concentrations were more available for the penta
dioxin congener, 1,2,3,7,8-PCDD, which are now examined to lend some insight about the
difference in concentrations between Columbus and the rural site for the lower chlorinated
congeners. To estimate the "true" 2,3,7,8-TCDD concentration, it will be assumed that the
difference in the urban 1,2,3,7,8-PCDD concentration and the rural 1,2,3,7,8-PCDD
concentration is assumed to be similar to the difference in the urban and rural 2,3,7,8-TCDD
concentration. Of 16 samples of 1,2,3,7,8-PCDD in Columbus, 10 were quantified. The average
concentrations at non-detect equal 0.0 and non-detect equal 1A detection limit for these 10
samples were 0.0151 and 0.0159 pg/m3, respectively. One of 3 rural samples was quantified,
leading to averages of 0.0037 and 0.0045 pg/m3 at non-detect equal 0.0 and non-detect equal /^
detection limit. This would suggest that the rural concentration of 1,2,3,7,8-PCDD is about 1/4
that of the urban concentration (i.e., 0.0037/0.0151 = 0.245, and 0.0045/0.0159 = 0.28).
For 2,3,7,8-TCDD, where the urban concentration ranges from 0.0048 to 0.0065 pg/m3,
the "true" rural concentration is speculated to range from 0.0012 (0.0048/4) to 0.0016 (0.0065/4)
pg/m3, somewhat smaller than the 0.0029 pg/m3 by the traditional non-detect equal to /^ detection
limit method. For this example, the rural concentration of 2,3,7,8-TCDD will be assigned a
value of 0.0014 pg/m3, the midpoint of the hypothesized range.
All other air concentrations were calculated as the average of the three air samples,
assuming /^ the detection limit for non-detects. The WHO98-TEQDF air concentration for this
profile was 0.021 pg/m3.
In 1995, a soil sampling program was undertaken to evaluate the soils in the vicinity of
the Columbus MSWI. This program was sponsored by the EPA with participation of the Agency
for Toxic Substances Disease Registry (ATSDR), Ohio EPA and the Ohio Department of
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Agriculture, and other state and local agencies. The purpose of the study was to determine
whether the soils in the vicinity and also distant from the incinerator were impacted by the
operation of the incinerator. Twenty-five samples were available for analysis, including 22 in the
city of Columbus, and 3 in the same rural site 28 miles upwind of Columbus where air
concentrations were taken. A full discussion of the soil sampling program can be found in EPA
(1996), and an overview can be found in Lorber, et al. (1998).
This background scenario will, however, take advantage of the samples which were
taken in the background setting. The soil concentration at the background site will be calculated
as the average of the three background samples. The final WHO98-TEQDF soil concentration for
the background scenarios was 1.3 ppt. This soil sampling program took soil samples to a depth
of 7.5 cm. Therefore, the concentrations as analyzed will be used to represent the "untilled" soil
concentration. They will also be used to represent watershed soils for calculation of water body
impacts. The question exists as to whether they should also be used to represent the tilled
concentrations for the high end farming scenarios and for calculation of below ground vegetable
concentrations. Brzuzy and Kites (1995) reported on the concentrations of dioxin in soil profiles
from undisturbed background locations. Measuring the concentration in 2 cm increments, they
generally found uniform concentrations to a depth of about 5 cm, with dropoffs thereafter. For
two sandy soils, they found increasing concentrations which peaked at approximately 30 and 40
cm. Based on this information, the soil concentrations from the rural site used here will be
divided by 2 to estimate tilled soil concentrations. A division by 3 to estimate the average
concentration over the approximate 20 cm depth of the tilled soil depth for other scenarios in this
assessment (stack emissions, soil contamination) would assume no dioxins exist below 7.5 cm in
background soils. This was not found in the Brzuzy and Kites (1995) data. That is why the tilled
concentration is calculated as the 7.5 cm concentration divided by 2.0 rather than 3.0. Recall that
tilled soil concentrations are used to estimate concentrations in below ground vegetables, as well
as in the dermal contact pathways, which assume gardening or farming as the cause for soil
contact.
In summary, the background scenarios 1 and 2 use air concentrations averaged from three
points in time and soil concentrations corresponding to the air samples from an actual rural
setting.
It has been stated earlier in this chapter that the fate algorithms for this demonstration of
background conditions would merge the fate and transport algorithms for the contaminated soil
source with the stack emission source. In particular, the following will be done. First, deposition
to soils will not be evaluated; soil concentrations will be supplied as source terms and are not
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assumed to change over the course of the time period of the demonstration, as in the soil
contamination source category. In the same vein, air concentrations are not assumed to be
impacted by soil emissions; the air concentrations will be assumed to be constant and supplied as
source terms as in the stack emission source category. Above ground vegetative impacts will be
evaluated given the estimated depositions of particle-bound dioxins and the transfer of vapor
phase dioxins from the air profile. Below ground vegetative impacts will be based on soil-to-
plant transfer algorithms assuming the tilled concentration supplied as a source term. Surface
water impacts (water and fish) are a function of direct depositions of particle bound contaminants
onto the water body and erosion from watershed soils. The soil concentration used for
calculation of water body impacts will be the untilled soil concentration. Terrestrial animal food
products are calculated as a function of above ground terrestrial vegetation (impacted only by air
to plant transfers) and the initialized untilled soil concentrations.
Table 5-2 summarizes the source terms used for Scenarios 1 and 2, which include the
deposition rates, the air concentrations, and the soil concentrations.
Scenario 3
This scenario was designed to be plausible for properties located near inactive industrial
sites with contaminated soil. The selection of 1 |ig/kg (ppb; or 1000 ppt) for the three
compounds was based on 2,3,7,8-TCDD findings associated with the Dow Chemical site in
Midland, MI (EPA, 1985; Nestrick, et al. 1986) as well as the 100 industrial sites evaluated in the
National Dioxin Study (which included the Dow Chemical site; EPA, 1987). In that study, most
of the sites studied had soil concentrations in the parts per billion range. The other key source
information is the size of the contaminated area. This scenario will assume a contaminated site
40 hectares, or 40,000 m2.
Scenarios 4 and 5
Chapter 3 described the application of the ISCST3 atmospheric dispersion model to
estimate air-borne concentrations and deposition rates of the contaminants in the vicinity of the
hypothetical incinerator, given contaminant emission rates in units of g/sec. As discussed in
Chapter 3, the emission factors (mass compound emitted per mass feed material combusted) and
resulting emission rates and concentrations (rate = mass compound emitted per time period and
concentration = mass compound emitted per unit volume of air emitted) for all the congeners was
typical of incinerators with a high level of air pollution control, e.g., scrubbers with fabric filters.
The I-TEQ emission factor for the hypothetical incinerator, 4.5 ng I-TEQ/kg material combusted,
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was within a range of 0.3 ng I-TEQ/kg municipal solid waste incinerated, to 200 ng I-TEQ/kg
hospital waste incinerated. This range was developed from representative test data for source-
specific incinerators with a similar high level of pollution control technology. Two hundred
metric tons per day of material was assumed to be incinerated at the hypothetical incinerator in
order to arrive at emissions in appropriate units of g/sec. The TEQ emission rate was 1.5*10"9
g/sec. Wet and dry particle-bound deposition rates, in units of pg/m2 -yr, were determined for all
dioxins and furans, at various distances from the stack and in the prevailing wind direction. The
exposure sites of Scenarios 4 and 5 are located downwind at 500 and 5000 meters, respectively,
from the emission source. Other deposition rates needed for the stack emission source category
were those used to estimate average watershed soil concentrations and direct deposition onto the
impacted water body. For both the central and high end scenarios, rates of deposition at 5000
meters were used for these purposes. Since the watershed is 100,000 ha, which would be
10,000,000, meters long if it was square, assuming rates of deposition at 5000 meters might
translate to an assumption that the stack was located relatively near the impacted water body.
Key source terms for Scenarios 4 and 5 are shown in Table 5-3. To facilitate comparison
with the background scenarios, #1 and #2, these terms include the depositions, air concentrations,
and soil concentrations.
Scenario 6
All key parameters used in Scenario 6 demonstrating the effluent discharge source
category were developed using data associated with the 104 pulp and paper mill study (EPA,
1990). Derivation of the physical parameters including the flow rate of the receiving water body,
flow rate of the effluent stream, suspended solids concentrations of the receiving water body and
the effluent stream, and so on, are described in Section 4.5 of Chapter 4. An exercise evaluating
the simple dilution model for predicting impacts to suspended solids in water body and
subsequently to fish tissue concentrations resulting from discharges from these mills is described
in Chapter 7. The bottom line conclusion from that exercise was that the simple dilution model
appears to work satisfactorily for a screening model: predicted whole fish tissue concentrations
for the majority of mills averaged about half as much as measured fish tissue concentrations.
This could be due to an underestimate of the uncertain bioconcentration factor, BSAF, or it could
be due to other factors. For the minority of mills, those with the highest volumes of receiving
water, the model did not work as well. Predicted fish tissue concentrations were around an order
of magnitude lower than measured concentrations. The precise reason for this discrepancy is not
known, but the most likely explanation that larger water bodies have more uses and more sources
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of dioxin-like input - assuming that the fish tissue concentrations result singly from the mill
discharge and a few proximate mills may be inappropriate.
Parameters for Scenario 6 were derived from the mills for which the model best
performed. The average discharge rate from these mills was 0.197 mg 2,3,7,8-TCDD/hr.
However, this data was valid for the time of sampling, which was 1988. Since then, pulp and
paper mills have reduced the discharge of dioxin-like compounds in their effluents by altering the
pulp bleaching processes. Gillespie (1992) reports that data on effluent quality from all 104 mills
demonstrate reductions in discharges of 2,3,7,8-TCDD of 84% overall. On this basis, the
discharge rate assumed for 2,3,7,8-TCDD was 0.0315 mg/hr (16% of 0.197 mg/hr). This same
rate was assumed for the other two example compounds, although the claim is not being made
that they are emitted by pulp and paper mills.
It is important to note that these discharge assignments are not intended to reflect current
discharges of dioxin-like compounds from pulp and paper mills, even for 2,3,7,8-TCDD. Data
from the 104-mill study did allow for development of a "composite" effluent discharger in
certainly a plausible setting (receiving water body and discharge flow rates, suspended solids,
etc.) for pulp and paper mills. Assigning what might be evaluated as a reasonable discharge rate
of 2,3,7,8-TCDD from pulp and paper mills for current conditions allows for the example
scenario to placed in some context, which was a primary objective of crafting all example
scenarios. Individual sources must be evaluated on an individual basis.
In summary, the key source term for the demonstration of the effluent source category
include a discharge rate of 0.0315 mg/hr for all three compounds demonstrated, and the discharge
of this rate into a water body of size 4.65* 109 L/yr.
5.6. RESULTS
The results of this exercise include the exposure media concentrations for all exposure
pathways and scenarios, and the LADD exposure estimates. These two categories of results are
summarized in Tables 5-4 through 5-10. Following now are several observations from this
exercise. As a reminder for the background conditions scenarios, #1 and #2, and stack emission
demonstration scenarios, #4 and #5, individual dioxin and furan congeners with non-zero toxic
equivalency factors (TEFs) were modeled with unique fate and transport parameters until
estimates of exposure media concentration were made. At that point, the WHO98-TEQDF
exposure media concentrations were estimated. For the sake of brevity, the WHO98-TEQDF
results are emphasized in this chapter.
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It is important to understand that all observations made below are not general
comments. Different results would arise from different source strength characteristics,
proximity considerations, model parameter values, different models altogether, and so on.
Chapters 6, 7, and 8 on User Considerations, Model Validation and Model Comparisons, and
Uncertainty describes many areas of this assessment which should be considered when
evaluating the methodology or viewing the results.
5.6.1. Observations Concerning Exposure Media Concentrations
Exposure media results are given in Tables 5-4 through 5-6.
*\]Soil Concentrations:
1. The lowest exposure site soil concentrations resulted from deposition of particles 5000 m
away from the example stack emission source. This was the location of the exposure site in the
central stack emission demonstration scenario, Scenario 5. The highest exposure site soil
concentrations were predicted for the demonstration of the soil contamination scenario. About 6
orders of magnitude separate the exposure site soil concentrations of 2,3,7,8-TCDD predicted for
the central stack emission demonstration scenario, Scenario 4, and the soil contamination
scenario, Scenario 5.
2. Concentrations for the stack emission central and high end scenario were about 3 and 2orders
of magnitude lower than the central and high end scenarios demonstrating background
conditions, respectively. This suggests that the example stack emission source, which was a
single emission source with a high level of pollution control, would contribute little to overall
background levels in soil.
3. The order of magnitude difference in distance from the stack between the central (5000
meters away) and high end (500 meters) scenarios is matched by the same order of magnitude
difference in soil concentrations.
4. For both the background scenarios, 1 and 2, and the stack emission scenarios, 4 and 5,
WHO98-TEQDF soil concentrations were over an order of magnitude higher than 2,3,7,8-TCDD
concentrations. The difference in 2,3,7,8-TCDD and WHO98-TEQDF impacts to all media mirrors
the difference in stack emissions of 2,3,7,8-TCDD and stack emissions of WHO98-TEQDF. This
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trend in differences between 2,3,7,8-TCDD and TEQ impacts occurs in all exposure media
estimations for both the background scenarios and the stack emission scenarios.
5. For the demonstration of the soil contamination source, exposure site soil concentrations
resulting from erosion were the same for all three compounds. This is because the same initial
soil concentration was assumed at the site of contamination, and the erosion algorithm contains
only one chemical specific parameter. This is the rate of dissipation for eroding contaminants. It
was assigned a value of 0.0277 yr"1 (25-year half life) for all three example compounds. The
stack emission source also has only one contaminant-specific parameter in the algorithm, the soil
dissipation rate, and it was also assigned a value of 0.0277 yr"1 for all congeners.
• D Vapor and Particle-Phase Air Concentrations:
1. The partitioning of air-borne dioxins is modeled differently for the stack emission and the
soil contamination sources. For the stack emission source, dioxins are assumed to be in
equilibrium between the particle and the vapor phase from stack to receptor. The equilibrium
partitioning model is explained in detail in Chapter 3. The application of this model in the
demonstration scenario resulted in the 2,3,7,8-TCDD to be approximately 51% in the vapor
phase and 49% in the particle phase. For the WHO98-TEQDF air concentration, the partitioning,
as seen in Table 5-4, is about 88% in the particle phase and 12% in the vapor phase for the
background scenario, and about 71% particle/29% vapor for the stack emission source category.
However, the modeling of dioxins above a site of soil contamination does not result in
partitioning that approaches these equilibrium calculations. The volatilization, wind erosion, and
dispersion algorithms are described in Chapter 4. As seen in Table 5-5, the vapor phase
dominates the total air concentration and is about 95% of the total concentration. Residues
which volatilize from the soil are assumed to remain in the vapor phase. However, it is possible
that dioxin-like compounds released into the air this way would not remain in vapor phase, but
would partly sorb to air-borne particles. An alternate approach to the one take for this
assessment would be to sum the total concentrations of dioxins modeled to be emitted from soil,
and to repartition them according to the equilibrium calculations. This is not done in this
assessment.
2. The background WHO98-TEQDF air concentration was 0.021 pg/m3. In contrast, the WHO98-
TEQDF air concentration for the stack emission source was 2 orders of magnitude lower at 500
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meters from the stack, at 0.00024 pg/m3, and was over 2 orders of magnitude lower at 5000
meters from the stack, at 0.000085 pg/m
3
3. The air concentration of 2,3,7,8-TCDD is highest in the soil contamination source category at
0.0042 pg/m3. The background air concentration of this congener, used in Scenarios 1 and 2, is
actually not that much lower at 0.0014 pg/m3. There is the same 2 and 3 order of magnitude
difference in the stack emission air concentrations of this congener compared to background that
is seen in the comparison of other media concentrations - at 5000 m, the 2,3,7,8-TCDD
concentration is 4.8* 10'6 pg/m3, and at 500 m, it is 1.4*10'5 pg/m3.
4. The vapor phase air concentration over a site of soil contamination is a function of
contaminant-specific parameters including the partition coefficient, Koc, and the Henry's
Constant, H. As seen in Table 5-5, the vapor phase concentrations of the three demonstration
congeners are different: 0.004 pg/m3 for 2,3,7,8-TCDD, 0.007 pg/m3 for 2,3,4,7,8-PCDF, and
0.002 pg/m3 for 2,3,3',4,4',5,5'-HPCB. The particle phase concentrations were not different,
however, since the wind erosion algorithm was not a function of contaminant specific properties.
•D Water Impacts Including Water, Sediment, and Fish:
1. There was a 2 order of magnitude difference in all water impacts between the background
scenario and the stack emission scenario. This is easily seen in Table 5-4.
2. For the stack emission source category, surface water impacts were not a function of the
location of the exposure site, unlike other media concentrations associated with the exposure site
including air, soil, and home grown foods. Therefore, the media concentrations will be the same
for the central and high end scenarios.
3. The surface water impacts are comparable for the contaminated soil demonstration, Scenario
3, the effluent discharge scenario, Scenario 6, and the background scenarios (#1 and #2).
Examining the 2,3,7,8-TCDD fish lipid concentrations, they are: 6.4 ppt for the effluent
discharge scenario, 4.3 ppt for the soil contamination scenario, and 3.0 ppt for the background
scenarios. The surface water impacts are much lower for the stack emission scenarios, #4 and #5
- the fish lipid concentration is 0.0003 ppt for the stack emission scenarios. This observation is
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particularly noteworthy in that the assumed effluent discharge rate is 84% lower than originally
measured in the 104-mill study in 1989.
4. The PCB concentrations were between 1 and 2 orders of magnitude higher than the dioxin
and furan because the key bioaccumulation variables estimating fish tissue concentrations, the
Biota Sediment Accumulation Factor, BSAF, and the Biota Suspended Solids Accumulation
Factor, BSSAF (used only for the effluent discharge source category), is 2.0 for the example PCB
while it is 0.09 for the example dioxin and and 0.14 for the example furan.
5. Concentrations of WHO98-TEQDF in water in the background and stack emission scenarios
were all less than 0.01 pg/L (ppq), and for the individual congeners in the soil contamination and
effluent discharge scenarios was less than 0.1 pg/L. These very low concentrations are the result
of high lipophilicity of the dioxins, furans, and PCBs. The water ingestion pathway had the
lowest exposure estimates of all pathways.
•D Terrestrial Vegetation Concentrations.
1. At first glance, there appears to be roughly a 2-3 order of magnitude difference in above
ground vegetables/fruits and above ground leafy vegetation. In fact, this is due to two modeling
differences: 1) the fruit/vegetable concentrations are presented in fresh weight. The dry weight to
fresh weight conversion factor is 0.15, or equivalently, a dry weight concentration is about 6.7
times higher than fresh weight concentration, and 2) fruit/vegetables are bulky above ground
vegetation. Literature data and experimental studies supported the hypothesis that dioxins
impacted mainly the outer portions of bulky above ground vegetation and did not translocate to
inner plant parts. The vapor phase air-to-plant algorithm, meanwhile, was calibrated to predict
leafy vegetation, whole plant, concentrations. Therefore, to reduce predicted leafy whole plant
concentrations to more appropriate dilute whole plant concentrations for bulky vegetation, an
empirical parameter, VGag, was introduced. It was assigned a value of 0.01 for bulky above
ground fruits/vegetables and 1.00 for leafy vegetation. With these two modeling differences,
leafy vegetation dry weight concentrations are 666 times greater than bulky vegetable/fruit fresh
weight concentrations. Other differences in concentrations are explained by differences in the
particle phase impact algorithms of the two types of vegetation.
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2. A more significant difference is found in the algorithms predicting below and above ground
vegetation concentrations for the different source categories. Below ground vegetables are
higher in concentration as compared to above ground vegetables, as seen in Tables 5-4, for the
background and stack emission demonstrations, and in Table 5-5, for the soil contamination
source category. However, the degree of difference is significantly more for the soil
contamination source category as compared to the stack emission category or background
demonstration scenarios. For these latter two cases, below ground vegetables are only between 1
and 2 times higher than above ground vegetables, but for the soil contamination source category
demonstration, below ground vegetables are over 3 orders of magnitude higher than above
ground vegetables.
The explanation for this trend is found in the air-to-soil model validation exercise which
is described in Chapter 7. In that exercise, the background air profile used in the demonstrations
in this chapter was modeled to deposit onto soil and mix in a 7.5-cm reservoir. The predicted
soil concentrations were shown to match the measured soil concentrations, also used in the
demonstrations in this chapter, reasonably well. Therefore, it would appear that the overall
model seems to mimic air to soil relationships when air is the principal source of the dioxins in
soil. Except for cases of specific soil contamination, this will often be the case, and certainly is
expected to be case for background settings where there are no major sources for soil
contamination. However, when the soil concentrations were assumed to be the source for air
concentrations, and the soil contamination algorithms were used to predict air concentrations
above the background soil, it was found that the predicted air concentrations were much lower
than the measured air concentrations. Two possible explanations were offered for this trend: 1)
the models predicting volatilization and dispersion were underpredicting air concentrations,
and/or 2) measured air concentrations in the specific background setting used in the
demonstration, and for background settings in general, are not only due to soil emissions, but
also from the long range transport of residues from distant sources. In fact, it may be the case
that distant sources of dioxin emissions to the air, such as stack emissions, followed by long
range transport, explain significantly more of the background air concentrations found than local
soil emissions from soils with background concentrations. If so, than a model prediction of
background air concentration based on background soil emission will be significantly lower than
background air concentrations.
For the purpose of this explanation, one can develop a ratio of air to soil concentration to
more fully understand this difference. For the background scenario, and taking air and untilled
soil concentrations from Table 5-4, an air to soil WHO98-TEQDF concentration ratio is, 0.14 for
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the background scenario (total air concentration divided by untilled soil concentration, Table 5-
4), 0.008 for the high end stack emission scenario, and 0.02 for the central stack emission
scenario. The same ratio for the soil contamination scenario is on the order of 1 * 10"5.
Therefore, the relative strength of air dioxins to soil dioxins is about three to four orders of
magnitude higher when air is the source, as in the background scenarios, than when soil is the
source, as in the soil contamination scenario.
Since above ground vegetables are a function of air concentrations, it then stands to
reason that the discrepancy between below and above ground vegetables will be much higher
when soil is the source of contamination as compared to when air is the source of contamination.
3. For the soil contamination demonstration, the tilled and untilled soil concentrations were the
same for the three contaminants demonstrated. As noted in the observations for soil
concentrations, this is because the parameters predicting exposure site soil concentrations from a
distant site of soil contamination are the same for the three contaminants. However, there are
differences in the predicted above and below ground vegetation for the three contaminants.
Transfers from soil to plant are driven by chemical parameters, particularly the octanol water
partition coefficient, Kow. 2,3,3',4,4',5,5'-HPCB and 2,3,7,8-TCDD had similar Kow, with
2,3,4,7,8-PCDF at a lower Kow. Higher Kow translates to tighter sorption to soil, and less
transfer to plant, either through root uptake or air-to-leaf transfer. This trend translated to the
lower fruit/vegetable concentrations for 2,3,3',4,4',5,5'-HPCB and 2,3,7,8-TCDD as compared to
2,3,4,7,8-PCDF.
•D Terrestrial Animal Product Lipid Concentrations.
1. Within each demonstration scenario, there appears to be a reasonably narrow range of
predicted lipid concentrations among beef, milk, chicken, and egg fat. The difference is about a
factor of 3 to 4. The lowest concentrations are noted for the stack emission demonstration
scenarios, in the 10"3 to 10"2 pg WHO98-TEQDF/g (ppt) range. The background concentrations
were next highest, about two orders of magnitude higher in the 10"1 to 10° ppt range, and the soil
contamination demonstration was the highest at about two orders of magnitude higher still, at 102
to 103 pg 2,3,7,8-TCDD/g lipid.
2. The differences within a scenario can be explained by a combination of three factors: the
apportioning of dry matter intake by the animal between soil and terrestrial vegetation, the
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differences in the bioconcentration factors between beef/milk, chicken, and eggs, and the
relationships between soil and vegetation as described above. For example, milk fat
concentrations were lower than beef fat concentrations in all cases, but within about a factor of
two. This was due to assumptions concerning apportioning of total dry matter intake between
contaminated soil, contaminated pasture grass, and home-grown contaminated feeds. Beef cattle
were assumed to take in twice as much soil as lactating cattle, 4% of their dry matter intake
versus 2%, and much more leafy vegetation than lactating cattle, 48% pasture grass versus 8%
pasture grass. Another interesting trend is that the chicken and egg fat concentrations are much
higher than the beef/milk fat concentrations for the contaminated soil demonstration scenario, but
the chicken and egg fat concentrations are lower or comparable for the background and stack
emission scenarios. This is due to two factors: the free range chickens had 10% of their diet in
soil as compared to 4 and 2% for beef and dairy cattle - this obviously will be important in a
contaminated soil scenario, and the above ground vegetation were substantially less impacted,
relatively speaking, in the soil contamination scenario as compared to the background or stack
emission scenarios, as explained above in the vegetation observations. This would tend to
minimize the importance of the vegetation in the diet of beef or dairy cattle in the soil
contamination scenario.
3. In the observations concerning surface water impacts, it was noted that the fish lipid
concentration of the PCB congener was much higher than the dioxin or furan congener. This was
because the BSAF/BSSAF of the PCB congener was much higher at 2.10 as compared to the
BSAF/BSSAF of the dioxin and furan congeners, 0.09 and 0.14, respectively. However, the
literature suggests that the terrestrial animal bioconcentration factors are more similar for the
three congeners. Hence, and as seen in Table 5-5, the beef, milk, chicken, and egg fat
concentrations are comparable among the three congeners.
4. Table 5-6 shows the individual congener concentrations in beef for the high end background
and stack emission scenarios. Recall that the TEQ beef concentration was about two orders of
magnitude higher for the background as compared to the stack emission scenario. For all
congeners except the tetra congeners, the difference is this same two order of magnitude
difference, and up to 3 orders of magnitude difference for the higher chlorinated congeners. For
the tetra congeners, 2,3,7,8-TCDD and 2,3,7,8-TCDF, the difference is an order of magnitude
and less. This suggests that the congener profile in the hypothetical incinerator is distinctly
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different than the background profile: the incinerator emissions would appear to have a greater
proportion of emissions in the tetra congeners as compared to background air or soil.
5.6.2. Observations Concerning LADD Exposure Estimates
Much of the differences between exposure pathways and scenarios is due to differences in
exposure media estimation. Therefore, much of the above discussion is also appropriate for
analysis of Lifetime Average Daily Dose, LADD, estimates. What will be noted below are
unique observations. LADD results are given in Tables 5-7 through 5-11.
1. Like in exposure media estimation, LADDs for the stack emission scenarios, 4 and 5, were
the lowest at 10"11 to 10"7 ng WHO98-TEQDF/kg-day, followed by the background scenarios, 1 and
2, at 10"8 to 10"5 ng/kg-day, the effluent discharge scenario which had a fish ingestion LADD in
the 10"5 range, and finally the soil contamination scenario with the highest LADDs ranging from
10"8 to 10"3 ng/kg-day for all three compounds demonstrated - the dioxin, furan and PCB.
2. Tables 5-7 and 5-8 also show the percent of total scenario exposure which is accounted for by
each pathway. The total scenario LADD was calculated simply as the sum of the pathway
LADDs in the scenario, without accounting for any differences in body absorption. It should be
remembered, however, that the amount of dioxin absorbed by the soil dermal contact pathway is
estimated at 3%. This was accounted for in the calculation of LADD, so that the LADD for the
soil dermal contact pathway was "absorbed" dose, while for all other pathways, the LADD was
the "administered" or "potential" dose. As discussed in Chapter 2, the absorption for these other
pathways was in the neighborhood of 80%, except for soil ingestion, where the absorption is on
the order of 30%. Because of this 3% absorption accounted for in the dermal contact pathway,
the LADD for this pathway is almost always the lowest of all pathways. From Tables 5-7 and 5-
8, it is seen from the individual percentages that the food pathways dominate the scenarios, with
fish ingestion dominating the central scenarios and beef ingestion dominating the high end
scenarios. Furthermore, the beef ingestion pathway LADD was over an order of magnitude
higher than the fish ingestion pathway LADD. This was more due to differences in the exposure
parameters including the ingestion and contact rates, and the differences in the lipid content of
the full product, rather than lipid concentrations themselves since the fish lipid concentrations
tended to be higher than the beef lipid concentrations for a given source. For example, in the
background scenario, the fish lipid concentration was modeled as 6.33 ppt WHO98-TEQDF, while
the beef lipid concentration was about one-fourth of that at 1.58 ppt WHO98-TEQDF.
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3. Differences between analogous "central" and "high end" exposure pathway estimates for the
background demonstration scenarios, 1 and 2, were near or less than an order of magnitude
(inhalation exposure for the central background scenario and the inhalation exposure for high end
on-site scenario are analogous exposures). This is because the exposure parameters used to
distinguish typical and high end exposures, the contact rates, contact fractions, and exposure
durations, themselves did not differ significantly, and these were the only distinguishing features
for analogous pathways in the background demonstrations. For the total exposure, however,
there was a difference of a factor of 20 between high end and central exposure in the background
demonstration scenarios. This is because the high end scenario included consumption of beef,
which was the highest exposure pathway and exceeded the fish pathway of the central scenario
by over an order of magnitude.
4. In the stack emission scenarios, placing exposed individuals either 500 or 5000 meters away
from the incinerator did significantly impact the results. The order of magnitude difference in
distance added about an order of magnitude difference in exposure media concentrations and
hence LADD estimates. Therefore, the full difference in analogous pathways between the central
and high end was closer to 2 orders of magnitude for the stack emission demonstration scenarios.
5. Tables 5-9 and 5-10 show results for the food ingestion pathways that were not included in the
scenarios. One observation here is that the terrestrial animal product pathways, including milk,
chicken, and egg ingestion pathways, are all less than the beef ingestion pathway, by up to an
order of magnitude, despite the fact that the terrestrial food product lipid concentrations were
fairly near each other. For example, the chicken fat concentration in the background scenario
was 0.61 ppt WHO98-TEQDF, compared to the beef fat concentration for that scenario of 1.58 ppt.
The chicken ingestion pathway LADD was over an order of magnitude less than the beef
ingestion pathway, however. This was due to the differences in the four other exposure related
parameters which differ for chicken and beef: 1) beef was assumed to be 19% fat while chicken
was assumed to be 13% fat, 2) the whole product ingestion rate of beef was 2.45 g/kg-day while
the whole product ingestion rate of chicken was 0.97 g/kg-day, 3) according to the analysis of the
National Food Consumption Survey described in Chapter 2, the beef ingestion pathway had a
higher contact rate of 0.478 compared to the chicken contact rate of 0.151, and 4) the chicken and
beef pathways had an additional food preparation factor which considers discarded portions
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(bones, etc.) and cooking loss. This factor did not differ greatly for the two food products, 0.55
for beef and 0.49 for chicken.
6. Table 5-11 relates all the pathways considered in this demonstration for the background,
the stack emission, and the soil contamination demonstrations. This table includes the food
ingestion pathways that were not in the demonstration scenarios. It was constructed by assigning
a value of 1.00 to the beef ingestion pathway, and then determining the ratio of the other
pathways to the beef pathway. This table again shows the domination of beef and milk
exposures, at least given the exposure parameters, lipid contents, and so on, assigned to the
demonstration scenarios. The fish pathway was very important in the background scenario as
compared to the other two scenarios. The main reason for this was how the models predicted
bottom sediments. For the background scenario, the predicted sediment concentration was nearly
three times higher at 3.4 ppt WHO98-TEQDF than the soil to which the cattle were exposed, 1.3
ppt. In contrast, the sediment concentration was nearly an order of magnitude lower at 0.0024
ppt WHO98-TEQDF than the soil concentration to which cattle were exposed in the high end stack
emission scenario, at 0.035 ppt WHO98-TEQDF. Even more dramatic, there was a two order of
magnitude difference in the sediment and soil concentration for the soil contamination site
scenario- 1.4 vs. 357 ppt 2,3,7,8-TCDD. Table 5-11 also shows that a childhood pattern of soil
ingestion can be an important pathway, ranking along with chicken and egg exposures in the
background and stack emission demonstrations. The chicken and egg pathways were
considerably more important in the soil contamination scenario as compared to the other two
pathways. This is due to the trend of predicting much higher chicken and egg concentrations in
the soil contamination scenario as compared to the background and stack emission scenarios; this
was discussed earlier in the observations for the exposure media concentrations. Vegetable
ingestion was also more important in the soil contamination scenario, which was driven by high
below ground vegetable concentration. Vegetable ingestion and inhalation were comparable to
the chicken, egg, and soil ingestion pathways in the background and stack emission scenarios.
Fruit ingestion, dermal exposure, and water ingestion are all relatively minor compared to the
animal ingestion pathways and were less than 1% of the exposures estimated for beef ingestion.
7. Fish was the principal impacted media for the effluent discharge source category, with fish
ingestion 19 times higher than water ingestion, the only two pathways considered for the effluent
discharge category. Fish was an important route of exposure in the central scenarios for the
5-29 December 2003
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background and stack emission scenarios, 1 and 3, explaining over half of all exposures
estimated for those scenarios.
5.7. HEALTH RISK DEMONSTRATIONS
Chapter 2 described the procedures to generate estimates of excess cancer risks and the
ratio JOB to evaluate potential non-cancer impacts. This section will demonstrate these health
risk assessment procedures, using the background and the high end stack emission scenarios.
Recall that excess cancer risk is estimated as the product of the LADD and cancer
potency factor, q^. For 2,3,7,8-TCDD, the qt* is 1.0 kg-day/ng, and this value is also used for
TEQ LADDs. Table 5-12 shows the cancer risk estimates for the background and the stack
emission high end scenarios, where LADDs are for WHO98-TEQDF exposures.
As seen in Table 5-12, there is a 2 order of magnitude difference between the total cancer
risk of both scenarios, the same 2 order of magnitude difference in the total LADDs as noted
earlier. The cancer risk associated with the high end scenario for the incinerator was 9*10"7,
while the background high end cancer risk was 9*10"5.
The cancer risk for the background scenario corresponds to a lifetime average daily dose,
LADD, of 6 pg WHO98-TEQDF/day. This is about a factor of seven lower than the background
dose of 43 pg WHO98-TEQDF/day generated in Volume n, Chapter 4 of the Exposure
Reassessment Document. The reasons for this difference are: 1) the Volume II background
exposure estimate was an average daily dose, ADD, not an LADD calculated in the
demonstration scenarios here. The LADD estimated in this chapter assumes 30 years of
exposure. The ADD during the exposure period would be just over twice, or 70/30, the LADD;
2) the Volume n background exposure considered additional pathways including fish, dairy
ingestion (milk and otherwise), eggs, pork, and poultry. If one adds the additional pathways for
the background high scenario - milk, chicken, egg, and fish shown in Table 5-9 - the LADD (and
ADD) roughly doubles; 3) the exposure factors are different, with the most important difference
being that in the exposure scenarios considered in this chapter, contact fractions of less than 1.0
were assumed - less than 0.5 for the terrestrial animal pathways, in fact.
Some of these differences also are relevant for the procedures demonstrated here to
characterize non-cancer risk. Specifically, the procedures described in Chapter 2 require the
assignment of a "background body burden" in the calculation of an Increment Over Background,
IOB, ratio. The IOB is defined as the ratio of the incremental of body burden due to the source
being evaluated (IBB) and the background body burden (BBbk) times 100%. As described in
Chapter 2, the BBbk can be a generic U.S. background body burden, or a site-specific background.
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The generic adult background body burden is currently evaluated as 5 ng WHO98-TEQDF/kg.
However, this quantity represents the full range of the current adult population; an assessor could
consider the background body burden for younger adults rather than the full range of adult ages
(younger adults would have a lower background body burden), whether to consider specific
populations such as women of child-bearing age (which again might imply a lower concentration
as compared to a full population average), and so on. As well, one could develop a body burden
that is specific to the site being evaluate. This could be a non-trivial exercise and could involve
estimating quantities that have not been considered when evaluating only an increment of
exposure due to a specific source. Chapter 2 went into some of the issues to consider when
developing a site-specific estimate of background exposure dose/intake or background body
burden.
If an assessor could determine an appropriate background exposure dose for a specific
site being evaluated, he could then use the simple first order pharmacokinetic model to convert
this site-specific dose to a site-specific BBbk. To do so, an assessor needs to estimate the total
exposure of an individual (or individuals) to dioxins as though the nearby source being evaluated
was not in existence. In a farm family scenario, the family would still be consuming home
produced foods, but these foods would only be impacted by background dioxins in the
environment, and no longer by the source being evaluated. But they would also be consuming
store-bought or restaurant-bought foods. The "total" exposure would include all pathways
considered in the scenarios of this chapter, but other pathways as well.
For the purposes of this demonstration, it will be assumed that the farming family in the
background scenario consumes foods at similar rates whether or not they are consuming home-
produced or store-bought food products, and that their exposure is characterized by all the
pathways in the formal scenarios of Table 5-7, as well as the additional scenarios shown in Table
5-9. To estimate their average background daily dose over a lifetime, the exposure duration will
increase from 30 to 70 years, and the contact fractions will all rise to 1.00. The resulting daily
exposure is 1.16 pg WHO98-TEQDF/kg-day. This 1.16 pg WHO98-TEQDF/kg-day will be used
here as the "site-specific background dose" against which one can develop lOBs for the
incinerator source.
For generation of the increment of body burden due to the incremental exposure, one
needs to estimate the ADD during the period of exposure. The total LADD for the stack
emission high end scenario, as displayed in Table 5-7, is 1.01 * 10"3 pg WHO98-TEQDF /kg-day.
The ADD can be simply calculated as this LADD times 70/30, or 2.36 * 10'3 pg WHO98-
TEQDF/kg-day. It is now possible to calculate the site-specific background body burden, BBbk,
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and the increment of body burden due to the site-specific source, IBB, using this generalized
equation:
BB. -
kCF,
(5-2)
where:
BB;
DD
AF
k
CF
t
body burden of interest, either BBbk or IBB, pg/g (ppt) whole weight basis
dose quantity for calculating the BB, either the site-specific lifetime
average daily dose, LADD, or the daily dose during the period of exposure
to the specific source, ADD, pg/kg-day (whole body weight basis)
absorption fraction
first-order rate of decline of dioxin residues from the body, day"1,
calculated as (ln2/t1/2), where t1/2 is the half-life, days
conversion factor, lOOOg/kg
time of exposure, either lifetime for BBbk or the exposure duration for
ADD, days
Using assumptions used in the Risk Characterization as first approximations for pharmacokinetic
modeling of TEQs, AF will be assumed to be 0.8, t1/2 will be 7.1 years so that k is calculated as
0.000267 day"1. The appropriate values for t include the exposure duration corresponding to the
ADD described above for the high end scenario, which is 30 years (10950 days), and the 70 year
(25550 days) lifetime assumed for calculation of BBbk. Finally, the DDs equal the 1.16 and
0.00236 pg WHO98-TEQDF/kg-day, as discussed above.
Substituting these values yields body burdens of 0.007 pg WHO98-TEQDF/g for IBB and
3.5 pg WHO98-TEQDF/g for BBbk. The IOB is then easily solved for as, 0.2 % ([0.007/3.5]*100).
This increment of body burden increase is very low and can probably be characterized as
insignificant. It is also interesting to note that the BBbk at 3.5 ppt TEQ is lower than the general
US population background body burden of 5.0 ppt TEQ. As discussed in Volume II of these
Exposure Documents, and as alluded to in discussions above, the general US population
background body burden is influenced by higher concentrations in older individuals who
experienced higher doses in the past. The "steady state" body burden at the current general US
background exposure dose of 1.0 pg WHO98-TEQDFP/kg-day is also lower than 5.0 ppt, at 3.0 ppt.
The assessor can choose either the general US background exposure, this steady state exposure at
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December 2003
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current US background doses, a site-specific background exposure, or even another background
quantity in developing the IBB term for non-cancer risk assessing at specific sites.
It is once again emphasized that the scenarios and all exposure parameters, and the fate
modeling with their parameters, used in this demonstration chapter, are not being offered as
default values or recommendations for all uses. Chapter 6 contains additional information
pertaining to these models, including sensitivity analysis exercises and discussions of model
parameters. Chapter 7 provides a critical evaluation of the fate models selected in this
methodology, including model comparisons and model validation exercise. Chapter 8 on
Uncertainty critically evaluates the fate and exposure modeling approaches and parameters used
in this assessment. Information in these three Chapters should be reviewed when evaluating the
validity of the approaches demonstrated in this Chapter.
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REFERENCES FOR CHAPTER 5
Brzuzy, L.P.; Kites, R.A. (1995) Estimating the atmospheric deposition of polychlorinated
dibenzo-p-dioxins and dibenzofurans from soils. Environmental Science and Technology
29:2090-2098.
Gillespie, WJ. (1992) Summary of data reflective of pulp and paper industry progress in
reducing the TCDD/TCDF content of effluents, pulps and waterwater treatment sludges.
Unpublished report available from the National Council of the Paper Industry for Air and
Waste Stream Improvement, Inc., 260 Madison Ave, New York, NY 10016. August 17,
1992.
Koester, C.J.; Kites, R.A. (1992) Wet and dry deposition of chlorinated dioxins and furans.
Environmental Science and Technology 26:1375-1382.
Lorber, M; Pinsky, P; Gehring, P; Braverman, C; Winters, D; Sovocool, W. (1998)
Relationships between dioxins in soil, air, ash, and emissions from a municipal solid
waste incinerator emitting large amounts of dioxins. Chemosphere 37:2173-2197.
Nestrick, T.J.; Lamparski, L.L.; Frawley, N.N.; Hummel, R.A.; Kocher, C.W.; Mahle, N.H.;
McCoy, J.W.; Miller, D.L.; Peters, T.L.; Pillepic, J.L.; Smit, W.E.; Tobey, S.W. (1986)
Perspectives of a Large Scale Environmental Survey for Chlorinated Dioxins: Overview
and Soil Data. Chemosphere 15: 1453-1460.
OEPA. (1994) Franklin County Ohio Ambient Air Monitoring Study for Dioxins and
Dibenzofurans. Division of Air Pollution Control. Ohio Environmental Protection
Agency. July 27, 1994.
OEPA (1995) Dioxin Monitoring Study 1995. Franklin County, Ohio. Division of Air Pollution
Control, Ohio Environmental Protection Agency, September, 1995.
Travis, C.C., and H.A. Hattemer-Frey. (1991) Human exposure to dioxin. The Science of the
Total Environment 104: 97-127.
U.S. Environmental Protection Agency. (1985) Soil Screening Survey at Four Midwestern
Sites. Region V. Environmental Services Division, Eastern District Office, Westlake,
Ohio, EPA-905/4-805-005, June 1985.
U.S. Environmental Protection Agency. (1987) National Dioxin Study. Office of Solid Waste
and Emergency Response. EPA/530-SW-87-025. August 1987.
U.S. Environmental Protection Agency. (1990) USEPA/Paper Industry Cooperative Dioxin
Study "The 104Mill Study" Summary Report and USEPA/Paper Industry Cooperative
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Dioxin Study "The 104 Mill Study" Statistical Findings and Analyses. Office of Water
Regulations and Standards, July 13, 1990.
U.S. Environmental Protection Agency. (1996) Columbus Waste-to-Energy Municipal
Incinerator Dioxin Soil Sampling Project. United States Environmental Protection
Agency, Region 5, Chicago, HI. April, 1995.
U.S. Environmental Protection Agency (2002a). Exposure and Human Health Reassessment of
2,3,7,8-Tetrachlorodibenzo-p-Dioxin (TCDD) and Related Compounds. Part 1:
Estimating Exposure to Dioxin-Like Compounds. National Center for Environmental
Assessment, Office of Research and Development. EPA/600/P-00/001B(b-d).
Van den Berg, M, L. Birnbaum, A.T.C. Bosveld, B. Brunstrom, P. Cook, M. Feeley, J.P. Giesy,
A. Hanberg, R. Hasegawa, S.W. Kennedy, T. Kubiak, J.C. Larsen, F.X.R. van Leeuwen,
A.K.D. Liem, C. Nolt, R.E. Peterson, L. Poellinger, S. Safe, D. Schrenk, D. Tillitt, M.
Tysklind, M. Younes, F. Warn, T. Zacharewski 1998. Toxic Equivalency Factors (TEFs)
for PCBs, PCDDs, PCDFs for Humans and Wildlife. Environmental Health Perspectives
106: 775-792.
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Table 5-1. Fate and transport parameters for the dioxin-like congeners demonstrated in this chapter (see bottom of table for column
definitions).
Congeners
2378-TCDD
12378-PCDD
123478-HxCDD
123678-HxCDD
123789-HxCDD
1234678-HpCDD
OCDD
2378-TCDF
12378-PCDF
23478-PCDF
123478-HxCDF
123678-HxCDF
123789-HxCDF
234678-HxCDF
1234678-HpCDF
1234789-HpCDF
OCDF
233'44'55'-HPCB
TEF
1.0
1.0
0.1
0.1
0.1
0.01
0.0001
0.1
0.05
0.5
0.1
0.1
0.1
0.1
0.01
0.01
0.0001
0.0001
H
3.29*10'5
2.60*10-6
1.07*10-5
i.io*io-5
i.io*io-5
1.26*10-5
6.75*10-6
1.44*10-5
5.00*10-6
4.98*10-6
1.43*10-5
7.31*10-6
i.io*io-5
i.io*io-5
1.41*10-5
1.40*10-5
1.88*10-6
6.60*10-5
P°L
6.27*10-10
9.20*10-"
2.01*10-"
2.01*10-"
2.01*10-"
5.05*10-12
1.32*10-12
6.80*10-10
1.96*10-10
1.15*10-10
4.21*10-"
4.21*10-"
2.56*10'"
2.56*10'"
1.13*10-"
6.51*10-12
1.24*10-12
1.46*10-"
V/P
51/49
13/87
3/97
3/97
3/97
1/99
0.2/99.8
47/53
25/75
16/84
7/93
7/93
4/96
4/96
2/98
1/99
0.2/99.8
42/58
Log Kow
6.80
6.64
7.80
7.30
7.30
8.00
8.20
6.10
6.79
6.50
7.00
7.00
7.00
7.00
7.40
8.00
8.80
7.71
Koc
3.98*106
2.69*106
3.89*107
1.23*107
1.23*107
6.17*107
9.77*107
7.76*105
3.80*106
1.95*106
6.17*106
6.17*106
6.17*106
6.17*106
1.55*107
6.17*107
3.89*108
3.16*107
Bvpa
6.55*104
2.39*105
5.20*105
5.20*105
5.20*105
9.10*105
2.36*106
4.57*104
9.75*104
9.75*104
1.62*105
1.62*105
1.62*105
1.62*105
8.30*105
8.30*105
2.28*106
1.49*105
BSAF/
RSSAF
0.090
0.083
0.028
0.011
0.013
0.003
0.001
0.072
0.020
0.144
0.007
0.017
0.060
0.057
0.001
0.035
0.001
2.10
RCF
5200
3916
30600
12600
12600
43700
62200
1500
5110
3050
7410
7410
7410
7410
15100
43700
180000
26100
BCF
5.76
5.55
2.69
2.32
2.99
0.48
0.69
1.25
0.97
4.13
3.12
2.67
2.67
2.37
0.55
1.32
0.27
2.30
CCF
8.8
6.8
3.6
5.6
2.4
1.4
0.3
3.1
18.0
7.4
4.8
5.3
4.1
2.1
1.0
0.9
0.3
6.5
ECF
7.8
6.0
5.4
10.2
4.5
4.8
4.3
2.7
20.5
7.8
7.4
8.2
6.2
3.0
3.1
2.2
1.4
7.4
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December 2003
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Table 5-1. (con't.)
Definitions for Table 5-1:
TEF:
H:
PV
V/P:
RCF:
Toxicity Equivalency Factor
Henry's Constant, atm-m3/mole
liquid sub-cooled vapor pressure, 20°C, atm
Vapor phase/particle phase percentages
Root concentration factor, unitless
Log Kow: log octanol water partition coefficient
Koc: Organic carbon partition coefficient, L/kg
B^: Air-to-leaf biotransfer factor, (pg PCDD/g leaf dry )/(pg PCDD/g air)
BSAF/BSSAF: Biota-to-(suspended) sediment accumulation factor, unitless
BCF/CCF/ECF: Beef/milk, chicken, egg fat bioconcentration factor, unitless
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December 2003
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Table 5-2. Summary of key source terms for the background scenarios, 1 and 2.
Congeners
2378-TCDD
12378-PCDD
123478-HxCDD
123678-HxCDD
123789-HxCDD
1234678-HpCDD
OCDD
2378-TCDF
12378-PCDF
23478-PCDF
123478-HxCDF
123678-HxCDF
123789-HxCDF
234678-HxCDF
1234678-HpCDF
1234789-HpCDF
OCDF
WH098-TEQDF
TEF
1.0
1.0
0.1
0.1
0.1
0.01
0.0001
0.1
0.05
0.5
0.1
0.1
0.1
0.1
0.01
0.01
0.0001
Dry Dep,
p^/m2-vr
43
286
482
570
826
14,170
56,900
82
308
394
780
909
168
555
4277
893
4198
1180
Wet Dep,
p^/m2-vr
43
286
482
570
826
14,170
56,900
82
308
394
780
909
168
555
4277
893
4198
1180
Cair, pg/m3
0.0014
0.0052
0.0079
0.0093
0.0135
0.227
0.904
0.0028
0.0065
0.0074
0.0133
0.0155
0.0028
0.0092
0.0692
0.0143
0.0667
0.021
Csoib Pg/g
0.37
0.14
0.35
0.82
1.23
17.73
160.89
0.64
0.17
0.21
0.15
0.11
0.15
0.64
4.06
0.27
10.72
1.29
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Table 5-3. Summary of key source terms for Scenarios 4 and 5, the stack emission demonstration scenarios.
Congeners
2378-TCDD
12378-PCDD
123478-HxCDD
123678-HxCDD
123789-HxCDD
1234678-HpCDD
OCDD
2378-TCDF
12378-PCDF
23478-PCDF
123478-HxCDF
123678-HxCDF
123789-HxCDF
234678-HxCDF
1234678-HpCDF
1234789-HpCDF
OCDF
WH098-TEQDF
TEF
1.0
1.0
0.1
0.1
0.1
0.01
0.0001
0.1
0.05
0.5
0.1
0.1
0.1
0.1
0.01
0.01
0.0001
Scenario 4 - Central; 5000 meters downwind
Wet Dep
pa/m2-vr
0.05
0.17
0.25
0.33
0.36
3.30
6.85
2.89
0.30
0.54
0.87
0.83
0.56
0.33
1.15
0.51
2.27
1.12
Dry Dep
p(j/m2-vr
0.10
0.36
0.52
0.69
0.75
6.92
14.4
6.07
0.62
1.14
1.83
1.73
1.18
0.69
2.42
1.06
4.77
2.35
Can, Pg/m3
4.84*10-6
i.oi*io-5
1.30*10-5
1.72*10-5
1.89*10-5
1.70*10^
3.50*104
3.17*W'4
2.02*10-5
3.31*10-5
4.80*10'5
4.54*10-5
2.94*10'5
1.74*10-5
6.01*10'5
2.61*10-5
1.16*10^
8. 12 *105
CSOii, Pg/g
1.72*10^
6.40*10^
9.22*104
1.22*10-3
1.34*10-3
1.23*10-3
2.56*10'2
1.08*10-2
i.io*io-2
2.03*10-3
3.25*10'3
3.07*10-3
2.10*10-3
1.22*10-3
4.30*10'3
1.88*10-3
8.47*1 0'3
4.17*10°
Scenario 5 - High End; 500 meters downwind
Wet Dep
p
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Table 5-4. WHO98-TEQDF environmental and exposure media concentrations for the background
conditions scenarios, #1 and #2, and the stack emissions demonstration scenarios, #4 and #5.
Description
Air, vapor phase, pg/m3
Air, particle phase, pg/m3
Soil, untilled, pg/g
Soil, tilled, pg/g
Soil, watershed, pg/g
Surface water, pg/L
Sediment, pg/g
fish lipid, pg/g*
leafy vegetation, pg/g dry
above ground fruit/veg, pg/g
fresh
below ground vegetables,
pg/g fresh
beef fat, pg/g*
milk fat, pg/g*
chicken fat, pg/g*
egg fat, pg/g*
Background,
Scenarios 1 and 2
2.59*1Q-3
1.87*1Q-2
1.29
0.65
1.29
2.63 *10'3
3.37
6.33
0.45
5.74*1Q-3
1.94*1Q-2
1.58
1.10
0.61
0.71
Emission, Central
Scenario 4
2.45*1Q-5
6.04* ID'5
4.46* ID'3
4.46* ID'4
8.91*1Q-4
3.80*1Q-5
2.39*1Q-3
5.64*10-3
1.86*10-3
1.20*10-5
1.63*10-5
4.35*10-3
3.05*10-3
2.02* 10-3
2.25*1Q-3
Emission, High
End Scenario 5
6.94* 1Q-5
1.74*10-4
3.51*10-2
3.51*10-3
8.91*10-4
3.80*10-5
2.39*10-3
5.64*10-3
6.39*10-3
6.37*10-5
1.29*10-4
1.65*10-2
1.11*1Q-3
1.38*1Q-2
1.55*10-2
* These food concentrations were not uniformly required for all scenarios. For example, the
central scenarios did include a fish ingestion pathway, but the high scenarios did not. Similarly,
chicken, milk, and egg pathways are demonstrated outside the context of a scenario. These
concentrations are presented here for completeness.
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Table 5-5. Environmental and exposure media concentrations for 2,3,7,8-TCDD ("dioxin"), 2,3,4,7,8-PCDF ("furan") and
2,3,3',4,4',5,5'-HPCB (PCB) for the soil contamination demonstration, scenario #3, and the effluent discharge demonstration, scenario
#6 (NA = not applicable).
Description
Air, vapor phase, pg/m3
Air, particle phase, pg/m3
Soil, untilled, pg/g
Soil, tilled, pg/g
Sediment, pg/g*
Surface water, pg/L
fish lipid, pg/g**
leafy vegetation, pg/g dry
above ground fruit/veg, pg/g fresh
below ground vegetables, pg/g fresh
beef fat, pg/g**
milk fat, pg/g**
chicken fat, pg/g**
egg fat, pg/g**
Scenario 3 - Soil Contamination
rhnvin
0.004
0.0002
357
61
1.44
0.012
4.3
0.23
0.0006
2.0
54.4
27.5
204.1
180.9
fiirnn
0.007
0.0002
357
61
0.53
0.091
2.6
0.60
0.0011
23.4
40.1
20.4
171.8
181.1
PPR
0.002
0.0002
357
61
1.56
0.0016
108.9
0.26
0.0006
1.30
21.8
11.0
171.7
.150.8
Scenario 6 - Effluent Discharge
Hiovin
NA
NA
NA
NA
4.91
0.018
6.4
NA
NA
NA
NA
NA
NA
NA
fiirnn
NA
NA
NA
NA
3.84
0.029
8.0
NA
NA
NA
NA
NA
NA
NA
PPR
NA
NA
NA
NA
6.40
0.0029
195.7
NA
NA
NA
NA
NA
NA
NA
* The sediment concentration given for Scenario 3 is the bottom sediment, while the concentration for Scenario 6 is the suspended sediment. These are the concentrations used in
the prediction offish tissue concentrations.
** These food concentrations were not uniformly required for all scenarios. For example, the central scenarios did include a fish ingestion pathway, but the high scenarios did
not. Similarly, chicken, milk, and egg pathways are demonstrated outside the context of a scenario. These concentrations are presented here for completeness
5-42
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Table 5-6. Individual congener and Toxic Equivalent (WHO98-TEQDF) concentrations for
predicted beef concentration for the background high scenario, scenario # 2, and the stack
emission high scenario, scenario 5.
Congeners
2378-TCDD
12378-PCDD
123478-HxCDD
123678-HxCDD
123789-HxCDD
1234678-HpCDD
OCDD
2378-TCDF
12378-PCDF
23478-PCDF
123478-HxCDF
123678-HxCDF
123789-HxCDF
234678-HxCDF
1234678-HpCDF
1234789-HpCDF
OCDF
WH098-TEQDF
TEF
1.0
1.0
0.1
0.1
0.1
0.01
0.0001
0.1
0.05
0.5
0.1
0.1
0.1
0.1
0.01
0.01
0.0001
Scenario 2:
Background
high, pg/g lipid
0.25
0.74
0.37
0.40
0.75
1.59
9.08
0.08
0.13
0.50
0.57
0.56
0.09
0.27
0.71
0.24
0.25
1.58
Scenario 5:
Stack emission
high, pg/g lipid
0.021
0.005
0.002
0.003
0.004
0.004
0.011
0.023
0.001
0.008
0.008
0.007
0.004
0.002
0.002
0.002
0.001
0.017
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Table 5-7. Lifetime average daily doses, LADD, of Toxic Equivalents (TEQs), for the
background scenarios, #1 and #2, and for the stack emission scenarios, #4 and #5.
Scenario/Pathway
Scenario 1 - Background Central
Soil Ingestion
Soil Dermal Contact
Inhalation
Water Ingestion
Fish Ingestion
Vegetable Ingestion
Fruit Ingestion
Total
Scenario 2 - Background High
Soil Ingestion
Soil Dermal Contact
Inhalation
Water Ingestion
Beef Ingestion
Vegetable Ingestion
Fruit Ingestion
Total
LADD, ng/kg-day
5.42*1Q-7
3.23*10'9
4.57*1Q-7
8.70*10-8
6.51*1Q-6
6.95*1Q-7
1.09*10-7
8.40* ID'6
3.25*1Q-6
4.40* ID'8
2.34*1Q-6
2.90*10-8
8.30*10-5
2.32*1Q-6
3.65*1Q-7
9.16*1Q-5
Percent of total
scenario exposure
6
<1
5
1
78
8
1
100
4
<1
O
<1
91
2
<1
100
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Table 5-7. (Cont'd)
Scenario/Pathway
Scenario 4 - Stack Emission Central
Soil Ingestion
Soil Dermal Contact
Inhalation
Water Ingestion
Fish Ingestion
Vegetable Ingestion
Fruit Ingestion
Total
Scenario 5 - Stack Emission High
Soil Ingestion
Soil dermal contact
Inhalation
Water ingestion
Beef ingestion
Vegetable ingestion
Fruit ingestion
Total
LADD, ng/kg-day
1.87*1Q-9
3.22*1042
1.43*1Q-9
6.85*1041
5.81*1Q-9
8.28*1040
2.29* 1040
1.02*10-8
8.86*1Q-8
2.55*1040
2.68*1Q-8
4 19*10-io
8.65*1Q-7
1.83*1Q-8
4.05*1Q-9
1.00*1Q-6
Percent of total
scenario exposure
18
<1
14
1
57
8
2
100
9
<1
2
<1
86
O
<1
100
5-45
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Table 5-8. Lifetime average daily doses, LADD, for 2,3,7,8-TCDD ("dioxin"), 2,3,4,7,8-PCDF
("furan") and 2,3,3',4,4',5,5'-HPCB (PCB) for the soil contamination demonstration, scenario #3,
and the effluent discharge demonstration, scenario #6.
Scenario/Pathway
Scenario 3 - Soil Contamination
Soil Ingestion
Soil dermal contact
Inhalation
Water ingestion
Beef ingestion
Vegetable ingestion
Fruit ingestion
Total
Scenario 6 - Effluent Discharge
Water ingestion
Fish ingestion
Total
Dioxin,
ng/kg-day
8.99*10'4
4.20* ID'6
4.75*1Q-7
1.33*1Q-7
2.85*1Q-3
1.71*1Q-4
3.75*1Q-8
4.06* ID'3
3.22*1Q-8
6.60* ID'6
6.63*1Q-6
Furan,
ng/kg-day
8.99*1Q-4
4.20* ID'6
8.12*1Q-7
i.oo*io-6
2.10*1Q-3
2.05*1Q-3
7.25*1Q-8
5.19*1Q-3
5.15*1Q-8
8.26* ID'6
8.31*1Q-6
PCB,
ng/kg-day
8.99*1Q-4
4.20* ID'6
2.40* ID'7
1.81*1Q-8
1.14*1Q-3
1.08*1Q-4
4.04* 1Q-8
2.29* 1C'3
5.29*1Q-9
2.01*10-4
5.01*1Q-5
Percent of
total
scenario
exposure*
23
<1
<1
<1
73
4
<1
100
<1
100
100
* Results in this column are for dioxin
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Table 5-9. Lifetime Average Daily Doses, LADD, of Toxic Equivalents (WHO98-TEQDF) for
exposure pathways evaluated outside of the scenarios for background conditions and stack
emissions.
Setting/Exposure Pathway
WHO98-TEQDFLADD, ng/kg-day
Background Conditions, high end setting
Milk ingestion
Chicken ingestion
Egg ingestion
Fish ingestion, high ingestion rate
4.09*10-5
2.64* ID'6
3.79*1Q-6
6.78*1Q-5
Stack emissions, high end setting
Milk ingestion
Chicken ingestion
Egg ingestion
Fish ingestion, high ingestion rate
4.12*1Q-7
5.92*1Q-8
8.31*1Q-8
6.05*10-8
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Table 5-10. Lifetime Average Daily Doses, LADD, of 2,3,7,8-TCDD ("dioxin"), 2,3,4,7,8-
PCDF ("furan") and 2,3,3',4,4',5,5'-HPCB ("PCB") for exposure pathways evaluated outside of
the scenarios for the soil contamination and the effluent discharge settings.
Setting/Pathway
Soil Contamination
Milk ingestion
Chicken ingestion
Egg ingestion
Fish ingestion, high ingestion rate
Scenario 6 - Effluent Discharge
Fish ingestion, high ingestion rate
Dioxin,
ng/kg-day
1.19*1Q-3
8.79*1Q-4
1.09*10-3
4.62*10-5
2.06*10-5
Furan,
ng/kg-day
8.89*10-4
7.48*10-4
1.09*10-3
2.74* It)'5
3 44*! 0-5
PCB,
ng/kg-day
4.77* It)'4
7.39*1Q-4
9.09* It)'4
1.17*1Q-3
6.28*1Q-4
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Table 5-11. Relative magnitude of all exposure pathways evaluated for the background setting
and the stack emission, high exposure scenario setting (see table bottom for notes).
Exposure Pathway
Beef Ingestion
Milk Ingestion
Fish Ingestion
Egg Ingestion
Soil Ingestion
Chicken Ingestion
Inhalation
Vegetable Ingestion
Soil Dermal - high end
Fruit Ingestion
Water ingestion
Background conditions
1.00
0.49
0.82
0.05
0.04
0.03
0.02
0.02
<0.01
<0.01
<0.01
Stack emissions
1.00
0.48
0.07
0.10
0.10
0.07
0.03
0.02
<0.01
<0.01
<0.01
Soil
Contamination
1.00
0.42
0.02
0.38
0.31
0.31
<0.01
0.06
<0.01
<0.01
<0.01
Notes:
1. 1.00 is the highest pathway, and the values less than 1.00 describe the relation of that pathway
to the highest pathway.
2. This table is for the high exposure farm setting only. For the stack emission scenario, the
farm was located 500 meters from the stack. Also, the fish ingestion pathway was for the high
ingestion rate, 25 g/day, and the soil pathways - dermal and soil ingestion - were for the high
contact assumptions only.
3. For the background and stack emission scenarios, results are for TEQs; for the soil
contamination scenario, results are for 2,3,7,8-TCDD.
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Table 5-12. Cancer risk estimates for the background and stack emission high end scenarios.
Setting/Exposure Pathway
Cancer Risk
Background Conditions, high end setting
Soil Ingestion
Soil Dermal Contact
Inhalation
Water Ingestion
Beef Ingestion
Vegetable Ingestion
Fruit Ingestion
Total
1.22*1Q-6
5.27*1Q-8
2.34*1Q-6
2.90* ID'7
8.30*10-5
2.36*1Q-6
3.65*1Q-7
8.96*1Q-5
Stack emissions, high end setting
Soil Ingestion
Soil Dermal Contact
Inhalation
Water Ingestion
Beef Ingestion
Vegetable Ingestion
Fruit Ingestion
Total
3 32*10-8
3 19*10-io
2.68*1Q-8
4 19*10-io
8.65*1Q-7
1.83*10-8
4.05*10-9
9.48*1Q-7
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6. USER CONSIDERATIONS
6.1. INTRODUCTION
The methodology in this document has been earlier described as screening level in terms
of theoretical sophistication, but site specific in its application. Chapter 2 described concepts of
exposure and assigned values to exposure parameters which define, for purposes of
demonstration, a central and a high end exposure pattern. Chapters 3 and 4 described algorithms
for the fate, transport, and transfer of dioxin-like compounds, and also assigned parameter values
for purposes of demonstration. The methodology was demonstrated in Chapter 5, using exposure
and fate and transport parameters which had been laid out in earlier chapters. Those who wish to
use the methodology for further analysis of incremental exposures to sources of dioxin-like
compounds are now in a position to use the same algorithms, perhaps many of the same
parameter values. The purpose of this chapter is to provide guidance on some key issues for
potential users.
Section 6.2 discusses the use of the parameter values selected for the demonstration
scenarios in Chapter 5 for other applications. Section 6.3 is a sensitivity analysis exercise on the
parameters required for algorithms estimating exposure media concentrations. Section 6.4
addresses the issue of mass balance with regard to the source strength terms of the four source
categories.
6.2. CATEGORIZATION OF METHODOLOGY PARAMETERS
Table 6-1 lists all the parameters, including names, definitions, and units, that are
required for the methodologies of this assessment except the exposure parameters. Exposure
parameters are given in Table 2-1 of Chapter 2. Table 6-1 also gives four additional pieces of
information for each parameter listed. Three are numerical values which were used in the
sensitivity analysis exercises that are described in Section 6.3. below. The parameter values
labeled "selected" were the ones used in the demonstration of the methodologies in Chapter 5.
Section 6.3. below justifies the high and low values of parameters selected for sensitivity
analysis. Other users of this methodology may wish to view these high and low values as
reasonable high and low possible values for their applications; note however that the chemical
specific parameters are those only for 2,3,7,8-TCDD. The fourth piece of information is a
qualitative judgement on the part of the authors of this document as to the appropriateness of
using the "selected" parameter values for other assessments. This judgement is categorized in
three ways:
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1) First Order Defaults, or FOD: As defaults, these parameters are independent of site
specific characteristics and can be used for any assessment. Also, as first order defaults, it is felt
that the values selected for the demonstration scenarios carry a sufficient weight of evidence
from current literature such that these values are recommended for other assessments. Several of
the chemical specific parameters, such as the Henry's Constant, H, and the organic carbon
partition coefficient, Koc, fall into this category. The qualifier above, "current literature",
indicates that new information could lead to changes in these values.
2) Second Order Defaults, or SOD: Like the above category, these parameters are judged to
be independent of site specific characteristics. However, unlike the above category, the current
scientific weight of evidence is judged insufficient to describe values selected for demonstration
purposes as first order defaults. SOD parameters of principal note are the bioconcentration
parameters specific to the chemicals, such the Biota Sediment Accumulation Factor, or BSAF.
This parameter translates the ratio of a bottom sediment concentration to a fish tissue
concentration. The science is evolving for this parameter, including thought on the extent to
which BSAFs generated for one species at one site can be generalized to other sites and/or
species, the differences in BSAF between column and bottom feeders, the differences between
past and ongoing contamination, and so on. Users should carefully review the justification for
the SOD values selected for the demonstration scenarios before using the same values.
3) Site Specific, or SS: These parameters should or can be assigned values based on site-
specific information. The information provided on their assignment for the demonstration
scenarios, and for selection of high and low values for sensitivity analysis testing, is useful for
determining alternate values for a specific site. A key class of SS parameters included in Table
6-1 above are the source strength terms - the soil concentrations, effluent discharge rates, and
stack emission rates. There are likely to be site-specific applications of this methodology for
which detailed information is unavailable. Often the midrange values selected for the
demonstration scenarios are suitable for site specific applications when data is unavailable. An
example of this category of parameters would be the soil characteristics, such as the porosity,
bulk density, and so on.
The exposure parameters have not been categorized as have the contaminant fate and
transport/transfer parameters. Assignment of these values are critical as LADD estimates are
linearly related to parameter assignments - doubling exposure duration assumptions double
LADDs, and so on. All exposure parameters were developed based on information and
recommendations in EPA's Exposure Factors Handbook (EPA, 1989; 1997) and Dermal Exposure
Assessment: Principals and Applications (EPA, 1992). Some of the exposure parameters of
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Table 2-1, Chapter 2, are appropriately described as FOD. These include: lifetime, body
weights, water ingestion rates, inhalation rates, and an exposure duration for a childhood pattern
of soil ingestion. All of the other exposure parameters are better described as either SOD or SS.
Attaining site-specific information is recommended for them. However, this is often difficult for
site specific assessments and impractical if the procedures in this assessment are used in general
assessments. In the absence of site specific information, the following parameters can be
considered SOD: adult exposure durations of 9 years for central scenarios (whether they be
modeled after "residential" settings or not) and 30 years for high end scenarios (whether
"farming" be the model for high end exposures or not), childhood soil ingestion rates, the
fruit/vegetable food ingestion rates, the fraction of fruit/vegetable consumption that comes from a
home garden, the food preparation factors that were developed for home produced meats, and
the fractions of time spent at home (which are applied to inhalation and water ingestion
pathways). The remaining exposure parameters pertain to the exposure pathways evaluated as
most critical to dioxin exposures. For this reason, users should either pursue site specific
information or carefully justify parameter selections in the absence of site specific information.
These include the rate of beef, milk, and fish ingestion and the fraction of these food products
which are produced at home and hence impacted by the source. Fish ingestion rates for the
demonstration of methodologies in this assessment were 8 g/day as the central assumption and 25
g/day for the high end assumption. These were the mean values for the central and upper end
ingestion rates from several fish consumption studies characterized as "recreational" fishing
studies in EPA (1997). These rates are both more than a national average estimate offish
consumption that was published in an water quality criteria document for 2,3,7,8-TCDD, 6.5
g/day (EPA, 1984). The setting for the demonstration scenarios was a rural setting which
contained farm and non-farm residences, and which contained a major water body for
recreational fishing purposes. The other parameters are the ingestion rates and contact fractions
for beef, milk, chicken, and egg ingestion. The ingestion rates for these food products were
developed in EPA (1997) from the 1987-88 National Food Consumption Survey (NFCS)
conducted by USD A, and specifically, they were from the "household" portion of the NFCS.
This portion of the survey included questions on consumption of home produced foods, which
was why it was felt to be appropriate for the demonstration scenarios of this assessment. The
contact fractions assigned for the high end scenarios were also developed from information in the
household survey of the NFCS.
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In addition to the above qualifications, the parameters of this methodology have been
categorized in terms of their role in the methodology. The following is a brief description of
three principal categories.
Category 1. Human behavior exposure parameters
These are the contact rates, contact fractions, exposure durations, lifetime and body
weights used in the following equation for lifetime average daily dose:
Lifetime Average Daily Dose (LADD) = (exposure media concentration x
contact rate x contact fraction x exposure duration ) /
(body weight x lifetime) (6-1)
Category 2. Fate, transport, and transfer parameters
These parameters are all the parameters required to estimate exposure media
concentrations, except those specifically associated with a contaminant - chemical-specific
parameters are included in Category 3 below. All fate, transport, and transfer parameters are
listed, defined, and further subcategorized in Table 6-1. Not included in the discussions in
Section 6.3 are perhaps the most important terms in this category, and these are critical source
strength terms: the concentrations of dioxin-like compounds for the soil contamination scenario,
and the release quantities of dioxin-like compounds into the air for the stack emission source
category and into the surface water for the effluent discharge source category. A general
comment that can be made for fate and transport parameters is that values for the demonstration
scenarios were selected to be midrange and plausible, and that this document provides
information on selecting alternate values for site-specific applications. Most of the parameters in
this category fall under the SS qualification. Subcategories within the fate and transport category
include:
- Contaminated and exposure site characteristics: These are areas, soil properties, and
depths of tillage (which are depths to which residues transported by erosion or deposition are
mixed in conditions of tillage such as agriculture or gardening, and no tillage). Like the soil
concentration term, the area of contamination is a site-specific parameter. Soil properties were
assigned to be midrange and typical of agricultural soils. Depths of mixing for tilled and untilled
circumstances are not known with certainty, and these two parameters were characterized as
SOD.
- Soil and sediment delivery parameters: These include parameters associated the
erosion of contaminated soil from a site of contamination to a nearby site of exposure and/or to a
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nearby surface water body. All but one of the parameters in this subcategory are physical, site-
specific parameters which should be evaluated for site specific applications. The one parameter
not of this description is the enrichment ratio, which describes the enrichment of eroded soil with
dioxin-like compounds, and was assigned a rating of SOD. Geometric parameters include
watershed drainage area, water body volumes, and distances. Physical parameters include soil
loss estimates, organic carbon contents, water body suspended solids, and background watershed
contaminant concentrations.
- Volatilization and dust suspension parameters: These parameters are associated with
suspension, dispersion, and transport of contaminants from contaminated soils. One parameter
included in this category is the exposure duration, which appears to be misplaced. In fact, the
exposure duration is used to determine the average vapor phase air concentration - this is further
discussed in Section 6.3 below. Parameters in this category are site-specific and should be
evaluated for specific methodology applications.
- Bioconcentration and biotransfer parameters: These include parameters describing the
biota and the media surrounding the biota which influence the transfer of dioxin-like compounds
from the media to the biota. Some of these parameters are site-specific, although obtaining
values may be difficult. Included here are annual rainfall, fish lipid contents, a fresh to dry
vegetable weight conversion factor, and yields and intercept fractions for vegetation categories.
Others are theoretical; values for these were determined from the literature and can be used for
other assessments if better information is unavailable. Included here are atmospheric deposition
velocities of particles, washout of wind-suspended particles from the atmospheric, the retention
of wet particle depositions on vegetation, empirical correction factors for vapor-phase air-to-
plant transfers and soil-to- plant transfers, and the bioavailability of soil as compared to
vegetation as a vehicle of transfer of dioxin-like compounds to terrestrial animals. These were
given a rating of SOD. A third group describes exposure of the terrestrial animals to dioxin-like
compounds through their diet. These include fractions of animal diet which are soil, pasture
grass, and feed, and the extent to which these three are impacted by the source of contaminant.
Sensitivity analysis below was conducted on beef parameters only, not on the dairy cow, chicken,
or egg parameters. It is expected that, in general, the trends should be the same for all the
terrestrial animals. The analysis below shows how beef concentrations are impacted by changes
in assumptions of how cattle are exposed to dioxin-like compounds through their diet. Since
terrestrial animal exposures are most critical for human exposure, the animal exposure
assumptions made for demonstrating the methodologies of this assessment should be carefully
considered before using them for other assessments.
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- Effluent discharge source category: These are three physical parameters that can be
determined on a site-specific basis, and include flow rates of the effluent and receiving water
body, organic carbon contents of suspended solids in the effluent and the receiving water body,
and suspended solids content of the effluent and the receiving water body.
- Stack emission source category: In fact, most of the parameters required to evaluate the
impact of stack emissions to a nearby site of exposure have been included in other categories.
Sensitivity analysis only focuses on parameters and issues unique to this category. One set of
input values are contaminant wet and dry deposition rates. Three depositions are required: one
for the site of exposure, one to represent depositions on watershed soils which drain into the
water body, and one to represent direct deposition onto the water body. These were all generated
using the ISCST3 model, as described in Chapter 3. Two other key inputs generated by the
ISCST3 model are the ambient air vapor phase and particle phase concentrations of contaminant
at the site of exposure. All such quantities are a function of that model's algorithms and
parameter input requirements, particularly the release rate from the stack. Information on the
ISCST3 model and its application is given in Chapter 3 and not discussed further in this chapter.
Users can determine air concentrations and contaminant deposition rates in other ways, and use
those in the methodologies to determine impacts and exposures. The no-till depth of mixing at
the site of exposure, dnot, is required for the contaminated soil source algorithm as well. It's
selected value for the stack emission source category was 2 cm, similar to the 2 cm used in the
soil contamination source demonstration. The only other unique parameters not included in other
subcategories are the average watershed mixing depth (used for determining watershed soil
concentrations, which are then used to determine impacts to water bodies) and the fraction of
particles depositing on water bodies which remain in suspension. These are both theoretical
values and can be used in other assessments lacking better information.
Category 3. Chemical properties of dioxin-like compounds
The thirteen chemical-specific parameters required for the algorithms of this assessment
fall under two categories, FOD and SOD. As such, they are all independent of the specifics of
the site. The parameters deemed FOD are chemical fate and transport parameters, some of which
are common and often determined in laboratory conditions. These include the Henry's Constant,
the organic carbon partition coefficient, and the molecular diffusivity in air. The selected values
for these parameters are, in the authors' opinion, the best values derivable from current data. A
second set of chemical specific parameters are associated with bioconcentration/biotransfer
algorithms. Some of them are determined from field data (data on dioxin-like compounds or
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other compounds), and others are determined by experimentation and with that experimentation,
development of empirical relationships between a critical transfer factor and the chemical's
octanol water partition coefficient. The authors cannot be definitive in a judgement that values
given to these parameters be considered default, hence the SOD rating. For these compounds,
field/experimental data is conflicting or there simply is a lack of appropriate data. Parameters
included in this category are a soil to below ground vegetation transfer factor, two air-to-plant
factors: the air-to-leaf vapor phase transfer coefficient and the plant washoff rate constant, two
water body to fish parameters: the biota to sediment accumulation factor and the related biota to
suspended solids accumulation factor, and the bioconcentration factors for beef, milk, chicken,
and eggs. The sorbed fraction was given a "SOD/SS" rating because its assignment is a function
of chemical properties as well as an assumption regarding particle density in the airshed. This
assessment assumed, "background plus local sources" as the appropriate descriptor for particle
density in the airshed. This particle density selection is the site-specific aspect to this parameter
assignment. Therefore, for other applications where this airshed particle density is appropriate,
users may consider the values for the particle density to be, SOD.
6.3. SENSITIVITY ANALYSIS
Sensitivity analysis was undertaken in order to evaluate the impact of model results with
changes in model parameters. The following sections describe the limitations, methodology and
parameter selections, and results.
6.3.1. Limitations of the Sensitivity Analysis Exercises
The exercises were not comprehensive and/or definitive. Following are some key
limiters:
• The ISCST3 model was not evaluated in this section. Chapter 3 describes the
ISCST3 model. No sensitivity analysis runs were performed on ISCST3 model output for this
chapter. This section does evaluate the impact of different deposition rates and modeled ambient
air concentrations on exposure sites soils, surface water, and biota. Chapter 7 describes a model
validation exercise on the ISCST3 model, and this exercise includes an evaluation of the impacts
of selecting different meteorological data and different source strength terms (i.e., different stack
emission rates of dioxins).
• Sensitivity to changes in exposure parameters was not evaluated. The basic
equation for evaluating lifetime average daily dose was given above as Equation (6-1).
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Chapter 2 described all terms in this equation except the exposure media concentration, which
was the focus of Chapter 4. Because LADD estimates are a linear function of all exposure
parameters, sensitivity analysis was not performed on LADD exposure estimates. The focus of
this section instead is on the fate, transport, and bioconcentration/biotransfer algorithms used to
estimate the exposure media concentration term in Equation (6-1).
• The analysis was not exhaustive in its coverage. Principal algorithms in the fate,
transport, and transfer of dioxin-like compounds were evaluated, and all parameters required for
algorithms were tested at least once. However, not all possible tests were conducted. Before
noting those, following is a list of algorithms which were tested:
- Volatilization/suspension and transport/dispersion of vapor/particle phase airborne
residues from a site of contamination to a site of exposure (using algorithms for the soil
contamination source category);
- Transport via erosion of contaminants at a site of soil contamination to a nearby site of
exposure to impact exposure site soils (soil contamination source category);
- Transport via erosion of contaminants at a site of soil contamination to a nearby surface
water body, to impact bottom sediments, water, and fish (soil contamination source category);
- Transfers of contaminants from soils to below ground vegetables and from air to above
ground vegetation (soil contamination source category);
- Transfers of contaminants from soils to vegetation to beef ( soil contamination source
category) and from air to vegetation to beef (stack emission source category);
- Direct discharges of dioxin-like compounds into surface water bodies, and the effect of
surface water and effluent parameters on fish and water concentration estimation (effluent
discharge source category); and
- Particle depositions and ambient air concentrations, which result from stack emissions,
onto exposure site soils, watershed soils, surface water bodies, and biota (stack emission source
category);
The exercise was purposefully limited since several possible exercises would have been
duplicative. For example, impacts to terrestrial animal products was limited to an evaluation of
the algorithm estimating beef concentrations. Similar trends are expected for the milk, chicken,
and egg bioconcentration algorithms. For all the animal pathways, including fish ingestion, the
impact to changes in the lipid, or fat, contents was not evaluated in this exercise. All the
bioconcentration algorithms estimate the concentration of the dioxin-like compound in fat tissue.
The fraction of fat parameters are all required simply to translate a fat concentration to a whole
product concentration for purposes of exposure estimation. Therefore, changes to the assumption
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of fat contents will translate to a linear change in the estimation of the whole animal product
concentration.
A related limitation has to do with the cascading effect of certain parameters. For
example, a key contaminant parameter is the organic partition coefficient, Koc, which impacts
(among other concentrations) vapor phase air concentrations. Air concentrations are used to
estimate above ground vegetation concentrations, including those of grass and cattle feed. Beef
concentrations are a function of concentrations in grass and cattle feed. The impact of Koc is
evaluated in the context of the soil contamination source category. What is not done in the
sensitivity analysis below for this important model parameter (and others as well) is to evaluate
the impact of changes in Koc to beef concentrations. What is done, however, is as follows. The
sensitivity of air concentration predictions to changes in the partition coefficient are evaluated.
Then, the sensitivity to grass and cattle feed concentrations to plus and minus one order of
magnitude differences in estimated vapor phase air concentrations are evaluated. In this way, any
possible parameter change(s) which influences air concentrations within a plus/minus order of
magnitude range is evaluated for grass and feed concentrations. Finally, beef concentration
estimations are evaluated within a similar plus/minus order of magnitude change for grass and
feed concentrations. With some examination, therefore, the effect of cascading impacts can be
determined.
The impact of changing soil concentrations (in the soil contamination source category) to
estimates of exposure media concentrations (air, water, biota) is linear and direct in all cases - i.e,
increasing soil concentrations by a factor of five increases all impacted exposure media by the
same factor of five. For this reason, soil concentrations are not displayed in the sensitivity graphs
displayed in the next section, with one exception. This was in the estimation of beef
concentrations from soil contamination. Beef concentrations are a function of concentrations in
the dry matter diet of the cattle, including soil, grass, and cattle feed. Therefore, if soil
concentrations were to change and concentrations on the other intakes were to not change, than
beef concentrations would not be a linear and direct function of soil concentrations. However,
and in the context of this sensitivity analysis, when changing only soil concentrations, vegetative
concentrations are linearly and directly impacted by the same order of magnitude change.
Therefore, beef and milk concentrations turn out to be linearly related to soil concentrations.
A final limitation to note is that this exercise does not evaluate the multiple effects of
changing more than one independent parameter simultaneously. Other numerical methods,
particularly Monte Carlo, can be used to evaluate the impact of simultaneous changes to model
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parameters. Applications of this technique to dioxin exposure assessments are discussed in
Chapter 8 of this volume.
There are instances where parameters were evaluated as dependent and changes were
made simultaneously. One example is in three parameters which are related to the size of a
watershed (also termed the "effective drainage area" since such an area might be smaller than a
surrounding river system watershed), and which are important in determining the impact of a
bounded area of soil contamination to a nearby surface water body. These three include the
watershed size, the watershed sediment delivery ratio (which decreases as watershed size
increases), and the surface water body volume (which increases as watershed size increases,
assuming sources of water - surface runoff, interflow, and groundwater recharge - remain the
same on a per unit area basis). To test the impact of watershed size to surface water and
sediment concentrations, all three parameters were changed simultaneously in modeling a small
and a large watershed. One set of parameters which might not be independent, but which were
treated as such in the sensitivity testing, are the chemical specific parameters. For example, a
higher organic carbon partition coefficient might be associated with a lower Henry's Constant -
tighter binding to soils means less of a tendency to volatilize. Empirical relationships between
such chemical specific parameters have not been established, and since there is uncertainty in
precise values selected for the dioxin-like compounds, chemical specific parameters were treated
as independent parameters.
• Only a high and a low value for model parameters were tested; no discussions of
likelihood for parameter values or distributions of parameter values are included.
Certainly the identification of all model parameters and the justification for assignment of high
and low values will be helpful to others using the methodology. Assignment of parameter values
for purposes of demonstrating the methodologies in Chapter 5 should be carefully considered
when users apply this methodology for specific purposes or specific sites.
6.3.2. Methodology Description and Parameter Assignments
Only two of the six example scenarios of Chapter 5 served as "baselines" in the
sensitivity analysis exercises. The single scenario for the soil contamination source category,
Scenario 3 in Chapter 5, served as the basis for testing on these algorithms: 1) transport of vapor
and paniculate phase airborne contaminants from a site of contamination to a nearby site of
exposure, 2) transport of soils via erosion to nearby sites of exposure and to surface water bodies
to impact bottom sediments, fish, and water, 3) impacts of soil concentrations and other
parameters to below ground vegetation, and air concentrations and other parameters to above
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ground vegetation, and 4) impacts of soil, grass, and feed concentrations, and other parameters,
to beef concentrations. The source strength for this scenario, in summary, was a 40,000 m2 (4 ha,
10 ac) area of soil concentrations of 1 pg/kg (ppb) within a watershed of size 4,000 ha
(40,000,000 m2; 10,000 ac; 15.5 mi2) with soils otherwise at 0.0 ppb. Most of the sensitivity
analyses focused on predictions that were to occur at the exposure site, which was a farm located
150 meters away. The high end example scenario for the stack emission source category,
example scenario #5, served as the basis for the testing the impact of particle depositions and
ambient air concentrations on soils and biota. The ambient air concentrations and deposition
rates at the site of exposure 500 meters from the stack served as the baseline source strength
terms. The single scenario for the effluent discharge source category, example scenario #6, was
used to evaluate the impact of parameters required for that source category on fish and water
concentrations. The source strength in that case was a discharge of 0.0315 mg/hr into a surface
water body with a harmonic mean flow rate of 4.7x108 L/hr. Assignment of that baseline
discharge was based on data from the 104 pulp and paper mill study, and then considering
reductions is discharges which have occurred in these pulp and paper mills since the 104 mill
study in 1988.
The baseline chemical for all these sensitivity runs was 2,3,7,8-TCDD; i.e., all the
chemical specific parameters were those assigned to this example compound. The high and low
values for parameter testings were determined starting with the 2,3,7,8-TCDD assignments. Care
was not taken to encompass a range of possible values for all dioxin-like compounds. However,
the ranges that were tested are mostly inclusive of the dioxin-like compounds. What will be
noted and discussed below is that mostly the model response to chemical-specific parameters is
linear or nearly linear, so that model responses to values outside the ranges tested can be
evaluated easily.
All the initial parameter values required for all four source categories, and the values
selected for high and low sensitivity analysis were listed above in Table 6-1. Following are brief
discussions on the selection of these high and low values. Longer discussions on all parameter
values can be found in Chapter 4, which included justifications for all parameter values selected
for the demonstration of the methodologies in Chapter 5. Often, ranges of possible values were
discussed in Chapter 4; those ranges were the basis of high and low parameter values selected
below. Discussions in Chapter 4 are not repeated here, but are referenced below. The summaries
below are organized in the same order as the parameter listings in Table 6-1.
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• Contaminated and exposure site characteristics: These are the area and distance
parameters, and the soil characteristic parameters of the site of contamination and the site of
exposure. The "site of contamination" refers to the bounded area of high soil concentration for
the soil contamination source category. The "site of exposure" for these sensitivity runs is the
small farm which was the basis for the definition of the "high end" example scenarios
demonstrated in Chapter 5. The area of the site of exposure, AES, and site of contamination,
ASC, are both 40,000 m2 in the demonstration scenarios, which is equal to 4 ha or 10 ac. Low
and high values tested were 4,000 m2 (0.4 ha, 1 ac) and 400,000 m2 (40 ha, 100 ac). The soil
description parameters include soil porosity, ESLP, particle bulk density, Psoil, soil bulk density,
Bsoil, and the organic carbon fraction, OCsl. The assignment of high/low values to these
parameters were developed from Brady (1984) and cover a reasonable range of agricultural field
soils. The no-till and tillage depths, dnot and dt, refer to the depth to which eroded soil or
depositing particulates mix at the site of exposure. The no-till depth was set at 2 cm and was
varied between 1 and 10 cm, and the tilled depth was varied between 10 and 30 cm. The no-till
concentrations were used to estimate soil concentrations for soil related exposures: soil ingestion
and soil dermal contact, and also for the beef and milk bioconcentration algorithm. The tilled
concentrations were used only to estimate the concentration in below ground vegetation.
• Soil and Sediment Delivery Parameters: Contaminated soil erodes from a site of
contamination, a 4 ha site in the demonstration scenarios, to a nearby site of exposure and also to
a nearby river. The distance to the site of exposure from a site of contamination, DLe, was set at
150 meters for the example scenarios, and varied between 50 and 1000 meters in this exercise.
The same initial distance of 150 meters was the distance to the nearby river, DLW, and it was also
varied between 50 and 1000 meters. The unit amount of soil eroding off the site of
contamination, SLS, was initialized at 21520 kg/ha-yr, equal to 9.6 Eng. ton/ac-yr (abbreviated
t/ac-yr hereafter). Assumptions inherent in this estimate include: midcontinent range of annual
rainfall erosivity (which is also the middle of the range of rainfall intensities of the US),
midrange agricultural soil erosivity, a gentle 2% slope, no man-made erosion protection (ditches,
etc.), and bare soil conditions. A doubling of this amount to 42,000 kg/ha-yr (19 t/ac-yr) was
used as a high erosion estimate off the site of contamination. This could reflect any number of
different assumptions, such as more erosive soil, more erosive rainfall, steeper slopes, and so on.
A low estimate of one-tenth the default value, at 2100 kg/ha-yr (1 t/ac-yr), could reflect all the
same assumptions except a dense cover of grass or weeds, which changes the bare soil
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assumption leading to a "C" (cropping management factor) of 1.0 to a C of 0.1. The erosion
amount of 2152 kg/ha-yr was the initial amount assumed for a second unit erosion term needed
in this assessment, a unit erosion typical of land area between the contaminated and the exposure
site, SLec. The critical assumption in this initialization was that all conditions for this land area
were similar to the contaminated site, except that the ground was densely covered with grass or
weeds. The value of SLec was reduced to 0 kg/ha-yr for the low value, which is unrealistically
low but might give a sense of how the algorithm would perform if mixing with soil between the
contaminated and exposure site were not considered. The high value was 21,000 kg/ha-yr, which
is similar to the initial assumption for the contaminated site, could reflect similar erosion
conditions between the contaminated site and the exposure site. The third unit soil loss
parameter required is one which reflects average erosion conditions within the watershed
draining into the water body, SLW. This was initialized at 6455 kg/ha-yr (2.88 t/ac-yr) which
reflects similar erosion conditions as the contaminated site (soil erosivity, rainfall intensity,
average slopes, lack of support practices) except some erosion protection due to vegetation - C
equal to 0.3 instead of 1.0. It was reduced to 2100 kg/ha-yr, which might translate to C equal to
0.1, and increased to 21,000, which was equal to the initial higher erosion from the contaminated
site. The range of the enrichment ratio, ER, was noted at between 1 and 5 for its application in
agricultural runoff field data and model simulations, and was given an initial value of 3 in this
application. High and low values tested were 5 and 1. An average watershed concentration of
contaminant was set at 0 for the soil contamination demonstration scenarios, where the soil
concentration of 2,3,7,8-TCDD (and the other example compounds) was set at 1 ppb. This was
selected so that the impact to surface water bodies could be demonstrated as an incremental
impact. A concentration of 2,3,7,8-TCDD of 1 ppt was chosen to represent "background"
conditions. This value is near the 0.3 ppt that was measured in the background setting near
Columbus, Ohio, and used in the demonstration of the background scenarios. The value was
used to evaluate the impact of a bounded site at 1 ppb when a background concentration of 1 ppt
is also assumed to exist. Four parameters reflect watershed size. These include the effective
drainage area, Aw, the watershed sediment delivery ratio, SDW, the volume of the receiving water
body, VOLW, and the surface area of the water body, AREAW These are related and should
therefore be changed in tandem. The initial watershed size of 100,000 ha (385 mi2) was reduced
to 10,000 ha (39 m2) and increased to 1,000,000 ha (3850 mi2). Since the water body volume
was estimated using a in/yr runoff times an area, it was concurrently reduced 1 order of
magnitude for the small watershed test and increased one order of magnitude for the large
watershed. The surface area of the water body was also increased or reduced by an order of
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magnitude with the concurrent change in water body volume. The values of SDW were estimated
using Figure 4-4 (Chapter 4), which shows watershed delivery ratios as a function of watershed
area. The remaining three parameters further described the water body, and were the total
suspended solids, TSS, and the organic carbon contents of suspended and bottom sediments,
OCssed and OCsed. The initial value of TSS of 10 mg/L is typical of a moving water body (stream,
river) supportive of fish and other aquatic life. It was reduced to 2 mg/L, which is typical of a
stationary water body (pond, lake, reservoir) and increased to 50 mg/L, which begins to be high
for a water body expected to be supportive offish. The organic carbon contents were initialized
at 0.05 for OCssed and 0.03 for OCsed. The premise was that they were related - that sediments in
suspension were lighter and likely to be higher in organic carbon content than bottom sediments.
They were also changed in tandem to 0.02 (OCssed) and 0.01 (OCsed) for a low organic carbon
sensitivity test and 0.10 and 0.05 for a high organic carbon test.
• Volatilization and Dust Suspension Parameters: Distances and areas are pertinent to
estimating vapor-phase and particulate-phase air concentrations, and these have been discussed
above in the first two categories. One parameter included for sensitivity testing in this category
is the exposure duration, ED. It is included in these exercises because the estimation of average
volatilization flux over a period of time is a function of that period of time. The derivation of the
flux model assumed contamination originates at the soil surface at time zero, and over time,
originates from deeper within the soil profile. Therefore, the flux decreases over time (because
residues have to migrate from deeper in the profile), and the average flux over a period of time
will decrease as that period of time increases. This is further discussed in Chapter 4, Section
4.3.3., and in the original citation for the volatilization flux algorithm, Hwang, et al. (1986). The
exposure duration assumed in the high end scenarios was 30 years, this was changed to 1 and 70
years in sensitivity tests. A range of average windspeeds, Um, around the U.S. was noted at 2.8
and 6.3 m/sec, and these two values were used around the selected value of 4.0 m/sec. The
frequency with which wind blows from a site of contamination to a site of exposure, FREQ, was
set at 0.15, which is appropriate if one assumes that wind blows in all directions roughly equally.
It was changed to 0.05 and 0.50, which might translate to an assumption of a prevailing wind
direction, either away from or towards a site of exposure. The remaining parameters, fraction of
vegetative cover, V, threshold wind speed, Ut, and model specific function, F(x), all refer to the
wind erosion algorithm which suspends contaminated particulates into the air. Sensitivity tests
were applied to this trio for the on-site and the off-site source categories. V for the off-site
scenario was initialized at zero, implying bare ground cover; it was increased to 0.9 reflecting
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dense ground cover in the single sensitivity test here. It was set at 0.5 for the on-site small farm
demonstration scenario, reflecting some bare ground conditions (in the agricultural fields, e.g.) as
well as some dense vegetation (in other grassed areas of the farm property). It was decreased to 0
and increased to 0.9. The parameters Ut and F(x) reflect intrinsic erodibility of the soil and were
varied together. Values were selected to reflect a high and low wind erodibility soil, following
guidance in EPA (1985), the primary reference for the wind erosion algorithm.
• Bioconcentration and Biotransfer Parameters: Several parameters are required for
the vegetation concentration algorithm, most of which were associated with the algorithm for dry
plus wet deposition of particulates in the soil contamination source category. One parameter not
associated with fate and transport was the dry to fresh weight conversion factor, FDW. The
algorithm calculates vegetative matter concentrations on a dry weight basis, which is appropriate
for the role of vegetation in the beef/milk bioconcentration algorithm. However, ingestion rates
of fruits and vegetables are on a fresh weight basis, so dry weight concentrations have to be
converted to a fresh weight basis. The initial value of 0.15 assumes that fruits and vegetables are
85% liquid. The high and low values tested for this parameter were 0.30 (70% liquid) and 0.05
(95% liquid). Four parameters are described as empirical correction factors for the air-to-leaf
algorithm adopted for vapor phase transfers to vegetation (three of the parameters), and for the
soil-water-to-root algorithm adopted for below ground vegetation. There is one each for the four
principal vegetation considered: below ground vegetables/fruits - VGbg, above ground
vegetables/fruits - VGvg, grass - VGgr, and feed - VGctfd/chfd (cattle and chicken feed, respectively).
The concept for assignment of values to these parameters was the same, and briefly is as follows.
The principal biotransfer factors, vapor phase air-to-leaf and soil-water-to-root, were developed
for relatively thin vegetation, grass leaves for air-to-leaf transfers and barley roots for soil-water-
to-root transfers. Concurrently, there is evidence that the strongly hydrophobic/lipophilic dioxin-
like compounds are found only in outer portions of vegetation and not inner portions of bulky
vegetation; there is very little translocation of dioxin-like compounds into and within vegetation.
Therefore, the full vegetation concentrations of bulky vegetation are expected to be much lower
than the concentrations that would be found in their thin outer layers. For above ground bulky
fruits/vegetables, two considerations were included in the final assignment of 0.01 to VGvg: 1) a
surface area to volume ratio based on this tendency not to translocate into innner portions of the
vegetation, and 2) additional reductions in whole fruit/vegetable concentrations that would occur
due to washing or peeling. For bulky below ground vegetation, a final value of 0.25 was selected
based on: 1) again, this tendency not to translocate into inner portions of below ground
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vegetation, and 2) experimental evidence on carrots and potatoes that did indicate more within
plant translocations than have been measured for above ground vegetation. The above ground
VGag was reduced to 0.001 and increased to 0.10 in sensitivity testing, and the below ground
VGbg was reduced to 0.01 and increased to 1.00. The VGgr was set at 1.00 since the air-to-leaf
vapor transfer factors were developed from data on grass, so no correction is warranted.
Although there is no justification to change VG^, a lower value of 0.50 was chosen simply for
illustration. The VGctfd was set at 0.50, recognizing that some cattle feed is unprotected and thin
vegetation such as the leaves in silage, while others are protected grains such as corn grain. That
value was changed to 0.25 and 0.75 in sensitivity testing. There is one required parameter for the
dry deposition algorithm, and this is the particle deposition velocity by gravity settling, Vp, in
m/yr. The initial value of 3.2xl05 m/yr, from a velocity assumption of 1 cm/sec, was given by
Seinfeld (1986) as the gravitational settling velocity for 10 pm particles. This is the appropriate
size to consider since the wind erosion algorithm was developed only for inhalable size
particulates, those less than 10 [am (EPA, 1985). This was reduced to 0.5 cm/sec and 2 cm/sec
(transformed to m/yr) for sensitivity testing. Three of the vegetation bioconcentration parameters
are associated with the particulate wet deposition algorithm. These are the atmospheric washout
ratio, Wp, the retention of particles on vegetation, Rw, and the annual rainfall amount, R. The
definition, derivation, and ranges for these values are described in Chapter 4, Section 4.3.4.2, and
are not repeated here (the ranges are given in Table 6.1). The remaining bioconcentration
parameters are the yield and crop intercept values for the three above ground vegetation:
vegetables/fruits (Yveg, INTveg), grass (Ygr, INTgr), and cattle feed (Yctfd, INTctfd). Again,
discussions of chosen, and high and low, values for these quantities are given in Chapter 4,
Section 4, and displayed in Table 6.1. It is noted that these two terms are correlated - high yields
are correlated with high interception amounts. In sensitivity testing, therefore, these parameters
were changed in tandem.
The remaining bioconcentration/biotransfer parameters are for the terrestrial animal
bioconcentration algorithms, for beef, milk, chicken, and eggs. For the sake of brevity, only the
beef bioconcentration algorithm will be evaluated. The trends found in the testing of this
algorithm are expected to be duplicated for the other terrestrial animal products. One of the
parameters relates the bioavailability of soil relative to the bioavailability of vegetation, where
bioavailability refers to the efficiency of transfer of a contaminant attached to a vehicle. Fries
and Paustenbach (1990) developed the bioconcentration factor, BCF, from studies where cattle
were given contaminated feed. The studies of McLachlan, et al. (1990), from which BCFs for
dioxin congeners were derived and used for this assessment, also used standard cattle feeds. This
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feed is assumed to be analogous to the vegetation in cattle diet; therefore, the experimental BCFs
can be directly applied to vegetation in cattle diets. However, Fries and Paustenbach also
hypothesized that soil is less bioavailable than feed, based on some rat feeding studies, and
therefore the BCF developed from feed cannot directly be used on a soil concentration - it should
be reduced. Information in Fries and Paustenbach led to an assignment of 0.65 for the soil
bioavailability factor, Bs. This was reduced to 0.30 and increased to 0.90 in sensitivity testing.
Three parameters describe the proportion of the dry matter in the diet of beef cattle that is soil,
BCSDF, grass, BCGDF, and feed, BCFDF. The sum of these three terms, by definition, equals
1.00. Beef cattle are principally pastured (where incidental soil ingestion occurs), with
supplemental feeds including hay, silages, and grain, particularly in cooler climates where they
are housed during the winter. Values of 0.04 for BCSDF, 0.48 for BCGDF, and 0.48 for BCFDF
were used in the demonstration scenarios. A final set of two parameters describes the proportion
of these dietary intakes that are contaminated. One is defined as the fraction of grazing land that
is contaminated, BCGRA for beef cattle. The initial assumption of 1.00 for this parameter meant
that all the grass as well as all the soil in which the cattle grazed was contaminated (since soil
was assumed to be ingested during grazing). The last one similarly is defined as the proportion
of feed that is contaminated, BCFOD for beef cattle. It were also set at 1.00, perhaps indicating
that feed was grown on-site. Rather than change these diet fraction assumptions and extent of
contamination assumptions individually or in tandem (if necessary), what is done instead is to
model four different scenarios relating to cattle exposures. These four scenarios and the
parameter changes made are:
n High and low soil insestion Low: BCSDF = 0.01
BCGDF = 0.50
No changes to BCGRA or BCFOD; BCFDF = 0.49
diet assumptions changed to
reflect high and low soil High: BCSDF = 0.15
ingestion patterns BCGDF = 0.43
BCFDF = 0.42
2) Low exposure conditions BCSDF = 0.01
Grazing is under lush conditions, so BCGDF = 0.50
soil ingestion and diet pattern is BCFDF = 0.49
modeled as "low" soil ingestion above; BCFOD = 0.25
also, most feed is purchased externally
and uncontaminated; BCFOD reduced
from 1.00 to 0.25
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3) Low extent of contamination BCGRA = 0.25
Diet assumptions are unchanged from BCFOD = 0.25
initial assumptions; only it is assumed
that 25% instead of 100% of dry matter in
cattle diet is contaminated
4) High/low lifetime pasturing Low: BCSDF = 0.02
Tests for beef cattle only assuming BCGDF = 0.08
heavy lifetime pasturing, 90% grass, and BCFDF = 0.90
light lifetime pasturing, 08% grass High: BCSDF = 0.08
BCGDF = 0.90
BCFDF = 0.02
• Effluent Discharge Source Category: Section 4.6, Chapter 4, discusses briefly how
data from the 104-mill pulp and paper mill study (EPA, 1990) were used to develop initial
parameters required for this source category in its demonstration in Chapter 5. The use of the
104-mill data in a model evaluation exercise is expanded upon in Chapter 7, Section 7.3.6. The
data is also used here to assign high and low values for four of the seven required parameters for
this source category. Two have to do with flow rates: Qe which is the effluent flow rate, and Qu
which is the receiving water flow rate. The range of Qe is from 10s to 107 L/hr, which are the low
and high surrounding the 4. IxlO6 rate used in the demonstration scenario in Chapter 5. The
range of Qu is 107 to 109 L/hr (excluding the top ten receiving water bodies, which were in the
1010 L/hr range and for which model did not appear to perform adequately), and these were the
low and high around the 4.7xl09 L/hr rate used in Chapter 5. Two parameters describe the
suspended solids content of the effluent, TSSe, and the suspended solids content of the receiving
water body, TSSU. TSSe ranged from 10 to 250 mg/L in the 104-mill study, so this was the range
around the 70 mg/L used as the initial value. Data from STORET used to develop TSSU led to an
average of 9.5 mg/L and a range of less than 1 to 50 mg/L; a range of 2 (a reasonable value for a
stationary water body such as a pond or lake) to 50 mg/L was tested. One required parameter
was, of course, the rate of contaminant discharge, LD, in units of mg/hr. The assumed value was
0.0315 mg/hr, and this decreased and increased an order of magnitude for low and high testing.
The remaining two parameters are the organic carbon contents of effluent solids, OCe, and
upstream river suspended solids, OCU. A range based on data was not available for these
parameters. OCe was assigned a value of 0.36 based on the fact that solids in effluent discharges
are primarily biosolids, and this value was one cited for surface water algae; values of 0.15 and
0.50 were tested. The value of 0.05 for OCU was the value assumed for demonstration of other
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source categories, where the parameter was called OCssed. The same range of 0.02 to 0.10 for
OCssed was used for OCU.
• Stack Emission Source Category: The parameters in this category listed in Table 6-1
are the only ones which are unique to this source category. As seen, there are only a very few
unique parameters. Most of these are associated with surface water impact, and one series of
tests evaluated the impact of parameter changes to surface water concentrations and fish
concentrations. These include the contaminant deposition rates, KDEPwat and RDEPSW, which
are depositions onto the watershed draining into the surface water body and the surface water
body itself (units are |ag/m2-yr). The initial values for these were those modeled to occur 5000
meters from the stack. This assignment for the stack emission demonstration scenarios, #4 and
#5 in Chapter 5, assumes that the stack is located reasonably distant from the impacted water
body. These depositions rates are specific to 2,3,7,8-TCDD. Rates of 2,3,7,8-TCDD deposition
at 200 meters and at 10,000 meters were used as high and low values, respectively. It should be
noted that depositions are higher at 200 meters and lower at 10,000 meters as compared to 5,000
meters, but air concentrations are lower at 200 meters as compared to 5,000 meters. This trend
occurs because wet deposition is highest nearest the stack. Total depositions are driven by these
high wet deposition totals; hence total depositions at 200 meters exceed those at 5,000 meters.
However, dispersion modeling shows that ambient air concentrations of contaminants in the
vapor phase (given the wind data and all other parameters and assumptions in using the ISCST3
model for the demonstration scenarios) are highest 500-1000 meters from the stack. For
sensitivity testing, differences in model performance as a function of distance from the stack will
be evaluated. RDEPp is the deposition of particles themselves and was supplied in order to
maintain a mass balance of solid materials entering the water body. The default value of 0.03
g/m2-yr was taken from Goeden and Smith (1989) for a study on the impacts of a resource
recovery facility on a lake. They estimated a total deposition of particles to the lake from all
sources was 74.4 g/m2-yr. Assuming the stack is unlikely to contribute all sources of particles to
a water body, a high value was chosen as 3 g/m2-yr, and a low value was given as 0.003. The
fraction of depositing particles remaining in suspension, fsd, was initialized as 1.00 (meaning that
all directly depositing particles remain in suspension) based on an argument that the small
particles emitted from the stack and transported directly to the surface water body would settle to
surface water bottoms much more slowly than other solids entering water bodies. A low value of
0.00 was tested (meaning that all solids directly depositing within a year settle quickly to become
bottom sediments). The average watershed mixing zone depth, dwmx, was initialized at 0.10 m
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(10 cm) which is midway between the 2 cm assumed for non-tilled conditions and 20 cm
assumed for tilled conditions. This assumption might translate to a rural watershed comprised
equally of farmed and unfarmed land. It was reduced to 1 cm and increased to 20 cm in
sensitivity testing. A second series of tests evaluated biota impacts at the site of exposure,
vegetables/fruits and beef/milk. Parameter inputs for these tests include the ambient air
concentration and depositions at the site of exposure, Cva and RDEPe, and the no-till depth of
mixing, dnot. The no-till depth of mixing was increased from 2 to 5 cm. Concentrations and
depositions of 2,3,7,8-TCDD at 200 and 10,000 meters were tested. The baseline quantities at
5,000 meters were varied to reflect different vapor/particle partitioning assumptions. Currently,
the assumption is that 2,3,7,8-TCDD emissions are 51% in the vapor phase and 49% in the
particle phase. Linear adjustments to the emissions in vapor and in particle form can be made to
stack emissions. Concentrations and depositions at specific locations are then adjusted in the
same linear manner to reflect different vapor/particle partitioning assumptions. Two assumptions
tested include 10% vapor/90% particle and 90% vapor/10% particle.
• Contaminant Physical and Chemical Properties: The initial values for testing of this
category of parameters were the ones used for 2,3,7,8-TCDD. Generally, the high and low values
tested are those which may represent a range for this contaminant only, not all dioxin-like
compounds. However, several of the ranges also encompass values that could be pertinent to
other compounds. It should be remembered that this is simply a model performance exercise and
nothing else. Also, it could be argued that some of the parameters should be changed in tandem -
that there may be a relationship between soil/water adsorption, as modeled by Koc, and
bioconcentration. Such relationships were not explored in these exercises. Notes on the
parameters are as follows:
1. Henry's Constant, H - The value of 3.29xlO"5 atm-nrVmole was used for 2,3,7,8-
TCDD. Except for a heptachloro-PCB, Henry's Constants for the dioxin-like compounds ranged
from 10"6 to 10"4. Because of this, the initial value was reduced and then increased an order of
magnitude for this test.
2. Molecular Diffusivity in Air, Da - This parameter is needed for the volatilization flux
algorithm. Because no values were available for the dioxin-like compounds, values were
estimated based on the ratios of molecular between a dioxin-like compound of interest and a
compound for which a Da was available - in this case, diphenyl. The range of values tested are
0.005 cm2/s as a low and 0.10 cm2/s around the initial value of 0.047 cm2/sec.
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3. Organic Carbon Partition Coefficient, Koc: The Koc is perhaps the single most
influential parameter governing the fate and transport of the dioxins from contaminated soils in
this assessment, impacting surface water concentrations, vapor phase air concentrations, and
directly or indirectly, all biomass concentrations (fish, vegetation, beef/milk). The literature for
2,3,7,8-TCDD shows a range of Koc under 106 (from Schroy, et al., 1985) to over 2xl07 L/kg
(Jackson, et al., 1986). The value selected for 2,3,7,8-TCDD was 3.98*106, based on an
examination of available literature on the subject. The values tested were one order of magnitude
less (4*105) and one order of magnitude more (4*107) than the value initially assumed for
2,3,7,8-TCDD.
4 Air-to-Leaf Vapor Phase Transfer Factor, Bvpa: The initial value for 2,3,7,8-TCDD
was developed in a calibration exercise using field data which included air concentrations and
grass concentrations which corresponded to these air concentrations. Details of this calibration
are provided in Chapter 4, and will not be repeated here. Plus or minus an order of magnitude
will be tested as a high and low value for B^.
5. Particle-Phase Fraction, $: This fraction was used in the stack emission source
category for determining the portion of emitted contaminant that was and remained in the particle
phase from stack to exposure site. Details on the measured and theoretical partitioning is given
in Chapter 3 of this Volume. As discussed there, measured partitioning of 2,3,7,8-TCDD in
ambient air showed a very small amount in the particle phase, 13%. However, speculation was
that the monitoring method itself could lead to an underestimate in the particle phase, and for that
reason, a theoretical approach was used to partition the dioxin. This led to a cj) of 0.49 for
2,3,7,8-TCDD. The stack emission demonstration will be used to evaluate the impact of
assuming 0.20 or 0.80 for 2,3,7,8-TCDD cj).
6. Root Bioconcentration Factor, RCF: The initial value for 2,3,7,8-TCDD was
estimated as a function of octanol water partition coefficient, Kow. Assuming a log Kow of 6.8,
RCF was solved as 5,200. Different assumptions for log Kow were used to estimate high and
low values of RCF for this exercise. Examining literature Kow for the dioxin-like compounds,
no log Kow are less than 6.0 (the lowest at 6.2) and only one value estimated to exceed log Kow
equal 8.5. A high and low RCF were estimated, therefore, using log Kow of 6 and 8.5. This led
to tested values of RCF of 1,260 and 106,000.
7. Beef/milk Bioconcentration Factor, BCF: Unlike the RCF (but like the BSAF and
BSSAF as noted blow), there are no empirical formulas developed for BCF as a function of more
common parameters such as Kow. The literature summary and interpretation of 2,3,7,8-TCDD
cattle feeding studies by Fries and Paustenbach (1990) led them to assign a value of 5.0 for
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2,3,7,8-TCDD. The study by McLachlan, et al. (1990) allowed for generation of BCF values for
16 of the 17 dioxin and furan congeners of dioxin toxicity equivalency, and the results from that
study are used for this assessment. Fries, et al. (1999) presented another set of field-derived
BCFs for 14 of the 17 dioxin-like dioxins and furans, and the BCF he calculated for 2,3,7,8-
TCDD was 7.1. The 2,3,7,8-TCDD BCF used in this assessment was 5.76, which is close to the
value of 5.0 promoted by Fries and Paustenbach (1990). Their summary, included in Table 4-5
in Chapter 4, showed BCF less than 1.0 for higher chlorinated dioxin-like compounds. For
sensitivity testing, values of 1.0 and 10.0 were used as low and high values for BCF.
8. Biota Sediment and Biota Suspended Solids Accumulation Factors, BSAF and
BSSAF: EPA (1993; 1995) summarizes several water column based and sediment (both
suspended and bottom) based empirical parameters used to estimate fish concentrations given a
water or sediment concentration. Two of these are the BSAF and BSSAF, which are used in this
assessment. Although no data exists to determine values of the suspended solids factor, BSSAF,
EPA (1993) suggests that BSAF values could be used. The range of BSAF values for 2,3,7,8-
TCDD discussed in EPA (1993; 1995) is 0.03 to 0.30, and this was the low and high values
selected for both BSAF and BSSAF. The literature summary on BSAF included in Chapter 4 of
this assessment does include studies which imply higher BSAF for 2,3,7,8-TCDD. One study,
which focused on bottom feeders (carp, catfish, etc.), found a BSAF for 2,3,7,8-TCDD (CDEP,
1992) of 0.76, whereas the range of 0.03 to 0.30 focused on column feeders. A high value of
2.94 (Kjeller, et al., 1990) was found in a lake in Sweden speculated to be impacted by an active
pulp and paper mill. This high value appears to be an outlier not found in other field data sets.
9. First-order Plant Weathering Factor, kw: This is used to simulate the weathering
of contaminated particulates which have settled on plant matter via dry and wet deposition.
Several modeling efforts have used the same kw as used in this effort; that kw is 18.01 yr"1,
which corresponds to a half-life of 14 days. Values of 51 (half-life of 5 days) and 8.4 (half-life of
60 days) yr"1 were used to test the impact of this parameter.
10 Dissipation Rate Constant for Eroding or Depositing Contaminants, k:
Evidence for soil degradation of the dioxin-like compounds indicates that residues even
millimeters below the soil surface degrade at a very slow rate, if at all (see Chapter 2, Volume 2
of this assessment). This was the basis for not considering degradation of soil sources of dioxin-
like compounds in this assessment. However, when residues migrate to impact only a thin layer
of soil at a distant site, the processes of volatilization or photolysis (the one degradation process
which appears to transform dioxin-like compounds in the environment) are likely to impact
delivered residues. A rate constant of 0.0277 yr"1, which corresponds to a 25-year half-life, was
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used in two instances for this methodology - for erosion of off-site soils onto exposure site soils,
and for deposition of stack emissions onto exposure site soils. This value was changed to 0.277
yr"1 (half-life of 2.5 years) and 0.00277 yr"1 (half-life of 250 years) in sensitivity testing.
6.3.3. Results
The results of the sensitivity analysis are principally described in a series of high/low bar
graphs. The Y-axis is on a log scale and shows changes in media concentration estimation when
the high and low parameter substitutions are made. The Y=l line is the value of the media
concentration with all baseline parameter selections; the precise value of that media
concentration is noted on each graph. Other y-axis values are arrived at as the ratio of the
pertinent media concentration estimated with the altered parameter over the baseline
concentration; a y-axis value of 0.1, for example, means that the concentration with the
parameter substitution was one-tenth the concentration under baseline conditions. Also noted on
each graph is the pertinent source strength term - for air concentration sensitivities, soil
concentrations are noted, and so on. The parameters tested are named on the x-axis, and these
names correspond to the names in Table 6.1. The definition and baseline value of these key
parameters are noted below each graph. The high and low values tested are appropriately placed
either above (when the concentration increases with the parameter change) or below the bar
graphs. These parameters are the only ones which impact the tested media concentration. Of
course, the soil concentration also impacts the media concentration, but as noted in the previous
section, soil concentrations have a direct and linear impact in all cases, and so are not displayed
on the figures. Observations from each figure now follow.
6.3.3.1. Estimation of Vapor-Phase and Particle-phase Air Concentrations Distant from a
Site of Soil Contamination
Results for this test are shown in Figures 6-1 and 6-2. For the test of the vapor-phase
algorithm, Figure 6-1, no single change resulted in estimations over an order of magnitude
different from that made with baseline parameters. The model is insensitive to porosity and
particle bulk density parameters, Eslp and Psoil. The results are also reasonably insensitive to
ranges for organic carbon content of soil, OCsl, and windspeed, Um. For all other parameters,
there appears to be roughly an order of magnitude spread over the range of parameters tested.
Increasing the exposure duration to 70 years would decrease air concentration predictions by
about 35% and decreasing the duration to 1 year would roughly double concentrations. As
discussed earlier in Section 6.2, the volatilization algorithm assumes that contamination begins at
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the soil surface at time zero, and residues available for volatilization originate from deeper in the
profile over time. The result of this assumption is that the flux decreases as time increases. This
is the only algorithm of this assessment where an assumption of a decreasing source strength
over time is made.
Results from the test of the particle phase flux and dispersion algorithm are shown in
Figure 6-2. The y-axis in this test spans two orders of magnitude since changes in the parameters
describing the inherent wind credibility of the soil, Ut and F(x), results in over an order of
magnitude higher and lower than concentration estimations as compared to estimations using the
selected values of Ut and F(x). The assumption of bare soil conditions at the site of
contamination led to a value of 0.0 for V, the vegetative cover parameter. If the contaminated
site had a reasonably dense vegetative cover leading to a V of 0.9, air concentrations at the
nearby site of exposure would be about an order of magnitude less. The impact of area (ASC),
distance (DLe), and frequency (FREQ) on exposure site concentrations mirror those for vapor-
phase air concentrations. That is because these three are used in the same far-field dispersion
algorithm. Another parameter used for the far-field dispersion algorithm is windspeed, Um.
However, interestingly, the impact of that parameter is reversed between the vapor and
particulate phase algorithms. For the paniculate phase, the windspeed has more of an impact in
increasing wind erosion and hence the reservoir of airborne contaminant - increasing windspeed
increases air concentrations. For the vapor phase, windspeed does not play a role in estimating
volatilization flux, but only a role in the far-field dispersion model. In that role, increasing wind
speed increases dispersion and decreases concentrations.
Noteworthy for the particle phase algorithm is that estimated concentrations are
independent of any chemical-specific parameters; wind erosion suspending the particles is only a
function of climate, ground cover, and soil erodibility. Also noteworthy is that the baseline air
concentration of contaminants on particles is over an order of magnitude lower than the baseline
air concentration of contaminants in the vapor phase. Besides having implications for particle
phase and vapor phase inhalation exposures, this difference also has implications for impacts to
vegetation concentrations and subsequently to beef and milk concentrations.
The results shown in Figure 6-1 and 6-2 are specific to algorithms estimating emissions
from soil, volatilization and wind erosion, and dispersion of those emissions to calculate an air
concentration at a distant site. Chapter 4 also described an algorithm to estimate "near-field"
dispersions, which can be used to estimate air concentrations above a site of soil contamination.
Brief tests were conducted to evaluate the difference in impacts when the near field dispersion
model is used instead of the far field dispersion model. One observation was that all parameters
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associated with flux calculations had identical impacts to near-field air concentrations as
compared to far-field concentrations. Included in this group were: for the volatilization
algorithm - the exposure duration, ED, the organic carbon content, OCsl, soil porosity, Eslp,
particle bulk density, Psoil, and the three chemical-specific parameters, Henry's Constant, H,
organic carbon partition coefficient, Koc, and molecular diffusivity, Da, and for the wind erosion
algorithm - fraction of vegetative cover, V, average windspeeds, Um, and the parameters
associated with the erodibility of the soil, Ut and F(x). The impact of area is different for
concentrations calculated with the near-field algorithm compared to the far-field algorithm. This
is because the area term, ASC, has a different role for the near field as compared to the far field
algorithms. For the far field algorithm, ASC in effect impacts the source strength, with an order
of magnitude increase in ASC increasing exposure site air concentrations by a little over 2 times
(>200%). For the near field dispersion, ASC impacts the dispersion algorithm, and the same
order of magnitude increase in area only increases concentrations by around 30%.
6.3.3.2. Estimation of Soil Erosion Impacts to Nearby Sites of Exposure
Results from this test are shown in Figure 6-3. This model shows little sensitivity to two
parameters, the bulk density of soil at the site of exposure, Bsoil, and the amount of "clean" soil
(that which is between the contaminated and exposure site) which erodes onto the exposure site,
SLec, along with the contaminated soil. These will not be discussed further. In contrast to SLec,
the model has a direct linear impact with the amount of soil eroding from the contaminated site,
SLS. Decreasing that amount by a factor of 10 decreases exposure site soil concentrations by the
same amount, and doubling contaminated site erosion also doubles exposure site soil
concentrations.
The model appears to show insensitivity to the distance between the exposure and
contaminated site, DLe. However, this result should be viewed cautiously. The sediment
delivery ratio equation was developed to estimate sediment loads from construction sites to
nearby surface water bodies, and from distances up to 250 m. Its application to distances beyond
that are questionable, and applications from one land area to another land area rather than from
one land area to surface water, should also be questioned. At the model baseline distance of 150
m, the SDS (sediment delivery ratio) is 0.26. At 1000 m, it is 0.17, which is a marginal dropoff
for what appears to be a significant increase in distance. The distance becomes increasingly
important when there are obstructions between the contaminated and the exposure site such as
ditches, roads, and so on. When using this methodology, one should consider not relying on the
sediment delivery ratio equation for: 1) transport of soils beyond 250 meters, 2) when the
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exposure site is upgradient from the site of contamination (in its development for construction
sites, the assumption that a water body is downgradient soil concentration at the exposure site
only doubled; it did not increase by an order of magnitude. It is unreasonable to assume that all
the eroded soil would crowd into the smaller exposure site. When the contaminated site
decreased an order of magnitude to 4,000 m2, the exposure site soil concentration likewise
decreased by an order of magnitude. In this case, like the case when the contaminated and
exposure site were of the same size, all the contaminated soil eroding in the direction of the
exposure site mixes into exposure site soil, so the resulting average soil concentration at the
exposure site is linearly related to the concentration at the contaminated site. A similar trend is
noted with changes in the exposure site area term..
The impact to changes in depth of tillage is nearly, but not quite, linear. Decreasing the
no till depth of mixing, dnot, from 0.02 m to 0.01 m increased soil concentrations by a factor of
1.4 roughly, while increasing dnot to 0.10 decreased concentrations by 70%. A similar, nearly
linear, impact is noted with the changes tested for tillage depth, dt. For figure clarity, these
results were left off Figure 6-3, but decreasing the depth from an initial 0.20 m to 0.10 m
increased concentrations by just under a factor 2, and decreasing it to 0.30 m decreased
concentrations by just under 33%.
The impact of changing the dissipation rate is not linear. Decreasing the rate by an order
of magnitude, which is equal to increasing the half-life from 25 to 250 years, only about doubles
the predicted soil concentrations, while increasing the dissipation rate by an order of magnitude,
or reducing the half-life to 2.5 years, reduces the soil concentration by a factor of 5.
It is interesting that some of the tested model parameter changes, including manipulation
of the areas of contamination and exposure sites, and the dissipation rate, result in increasing the
exposure site soil concentration to nearly the same concentration as the contaminated site. This
is a somewhat counterintuitive result; it seems unlikely that an off-site location would have
concentrations close to the contaminated site. If, in fact, the model has a tendency to overpredict
exposure site concentrations, there are several reasons why this might occur: 1) overestimation of
the sediment delivery ratio, which was discussed above, 2) a shallow mixing depth for untilled
situations, 3) a low dissipation rate (which translates to a long half-life), 4) high estimates of
erosion from the contaminated site, and/or 5) use of a steady state solution.
Regarding this latter point, a steady state solution means that if erosion continues
indefinitely and the contaminated site soil concentrations do not lessen during this time, than the
exposure site soil concentrations reaches the level predicted by the model. This could lead to a
significant overestimation if the soil contamination was relatively recent. For example, if the
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erosion from a contaminated site to an exposure site has been occurring for 25 years, it can easily
be shown that the full solution to the soil concentration, Equation (4-16) in Chapter 4, will result
in a soil concentration that is 72% of the concentration estimated using the steady state
simplification, Equation (4-17) in Chapter 4. At 50 years, the modeled soil concentration will be
92% of the steady state solution. These calculations assumed the half-life of 25 years.
Therefore, if the soil contamination occurred within 20 years or so, assessors may wish to model
a lower and perhaps more accurate soil concentration by using the full solution rather than the
steady state solution to estimate exposure site soil concentrations and impacts.
6.3.3.3. Estimation of Soil Erosion Impacts to Nearby Surface Water Bodies
Results from this test are shown in Figure 6-4. One immediate point to make about this
bar graph is that the results displayed are essentially identical for water, bottom sediment, and
fish concentrations, with the exception of two trends which will be described below. Also, since
bottom and suspended sediment concentrations are assumed to be linearly related by the
equation: Cssed = (OCssed/OCsed) * Csed, where Cssed and Csed are suspended and bottom sediment
concentrations, respectively, and OCssed and OCsed are suspended and bottom organic carbon
fractions, than the relationships in Figure 6-4 apply to suspended sediment concentrations as
well.
One of the exceptions has to with trends regarding partitioning of dioxins between
sediments and water. The parameters involved in this algorithm are the organic carbon content
parameters, OCssed and OCsed, and the organic carbon partition coefficient, Koc. First, the
direction of the change is not the same. The sorption, and hence concentration, of dioxin-like
compounds onto sediments can be increased by increasing the organic carbon content of the
sediments or increasing the Koc. However, doing either decreases the concentration in water.
For the "low organics" test, water concentration increases by a factor of 2.7 rather than slightly
decreases as in Figure 6-4, which for this case, displays only the impact to bottom sediments.
For the "high organics" test, water concentrations decrease to 0.60 of what they were in baseline
conditions. The high Koc decreases water concentrations to 0.1, and the low Koc increases water
concentrations 7 times. Both these trends are distinctly different than the sediment trends; they
were left out of the graph in order not to crowd the graph (or require another one be drafted), and
also because water concentrations in the sub-ppq range are of minimal concern for exposure.
The other exception has to do with the impact of changes to the organic carbon content of bottom
and suspended sediments in the calculation offish concentrations. Fish tissue concentrations for
three of the four source categories of this assessment are a direct function of bottom sediment
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concentrations; the one source category where this is not true is the effluent discharge source
category, where fish tissue concentrations are a function of suspended sediment concentrations.
As laid out in Chapter 4, whole fish tissue concentrations are estimated as: (Csed/OCsed) * BSAF
* f(lipid), when fish tissue concentrations are a function of bottom sediment concentrations, and
for the effluent source category, fish tissue concentrations are estimated as: (Cssed/OCssed) *
BSSAF * f(lipid). It is seen from Figure 6-4 that the concentration on bottom sediments, Csed, is
impacted by the value assigned to OCsed. However, the impact to Csed with changes to OCsed is
marginal and in the same direction. For example, reducing OCsed from its baseline of 0.03 to
0.01, reduces Csed by a small amount. The impact to fish tissue from changes in OCsed is more
pronounced and essentially in an inverse linear manner, as shown by the formulation above.
Other than these two exceptions, a principal message from Figure 6-4 is that all surface
water impacts are identically impacted by surface soil and erosion parameters. The comments in
the above section concerning the distance between the contaminated soil and the target site,
which in the above section was the exposure site, but for here it is the surface water body, also
pertains to this algorithm. Specifically, it was noted that the sediment delivery ratio equation
seemed relatively insensitive to changes in distance. In Figure 6-4, this also seems to be the case,
as little change in predicted water impacts occurs between the tested values of 50 and 1000
meters. However, if clear impediments such as roads or ditches are between the site and the
target area as the distance increases, than it is quite possible that the sediment delivery ratio
equation is not appropriate to use when predicting the delivery of contaminated soil to the water
body. On the other hand, if a site is near a surface water body, it seems that the origins of the
sediment delivery ratio equation - developed from data on construction sites near surface water
bodies - are more appropriate.
The key source strength terms tested, the area of contamination, ASC, and the soil loss
rate from the site of contamination, SLS, both have a direct linear impact on the both sediment
and surface water concentrations. The other soil loss term, the erosion rate for the watershed,
SLW, also has a direct linear impact. Somewhat less critical in this algorithm is the size of the
watershed. The reduction of watershed size by about an order of magnitude increased water
concentrations by about a factor of 2, and an increase in size by an order of magnitude reduced
impacts by about 70%.
The algorithm seemed fairly insensitive to the remaining five parameters tested. The
average watershed concentration, initialized at 0.0 in order to just show the incremental impact
from the contaminated site, was increased to 1 ppt. This approximates a background
concentration of 2,3,7,8-TCDD and was an order of magnitude lower than the contaminated site
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concentration of 1 ppb. It is seen in Figure 6-4 that background soils have a marginal impact on
a water body which is impacted from a site of elevated soil concentrations located near the water
body. The impact of the organic carbon partition coefficient, Koc, on bottom sediments appears
small despite the fact that the Koc range spans two orders of magnitude. This is an indication
that it is so high for the dioxin-like compounds, that (at least in the algorithm of this assessment),
its assignment is not critical for sediment concentration estimations. The same lack of impact
appears to be the case for the organic carbon content of water body sediments, and the level of
suspended solids in the water column. The range of enrichment ratios tested, 1 to 5, represents
the appropriate high and low value this parameter would take, based on literature studies of this
phenemona. As will be discussed in Chapter 7, a site in Connecticut had sediment
concentrations in background settings about 2.8 times higher than surface soil concentrations. In
this tested range, only a small impact to surface water is noted.
6.3.3.4. Vapor-Phase Transfers and Particle-Phase Depositions to Above Ground Vegetation
Concentrations in above ground vegetation are a function of vapor-phase transfers and
particle phase depositions. Vapor and particle reservoirs originate from contaminated soils as
volatilization and wind erosion, respectively. Atmospheric dispersion and deposition modeling
delivers concentrations and depositions, respectively, from a stack to a site of exposure. The
principal difference in the soil and stack emission source categories is in the relative proportions
of the contaminant which are in the vapor and particle phases. As discussed below, more
contaminant is delivered via particle depositions for the stack emission source category as
compared to the soil contamination source category.
Vapor transfers and particle depositions for the soil contamination source category are
evaluated in Figures 6-5 and 6-6. The same general trends shown in these figures also occurs in
the stack emission source category. Three types of vegetation are modeled for this assessment,
including vegetables/fruit, grass, and animal feeds. The latter two are for the terrestrial animal
bioconcentration algorithms, the first for human exposure via consumption of unprotected fruits
or vegetables.
For vapor-phase impacts shown in Figure 6-5, it would appear that changes to total
vegetation concentrations are critically a function of parameters specific to the vapor transfer
algorithm. There is between one and two orders of magnitude range of plant concentrations
predicted over the range of the vapor phase transfer coefficient, Bvpa, tested. This parameter is
uncertain as well as very influential in this methodology. There is about a one order of
magnitude range for the vegetable/fruit category and a two order of magnitude range for the grass
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and feed categories. The reason there is a difference in the influence of Bvpa in the vegetative
categories has to do with the use of a second and also influential and uncertain parameter, the VG
parameter. This parameter was introduced to model the difference between the leaves of the
experiment for which Bvpa was developed an the bulky vegetation to which the Bvpa is applied, the
VG parameters (VGveg, VG^, and VGfod). The need for such a correction factor is justified given
the evidence that dioxin-like compounds to do not translocate into vegetation. The grass leaf
concentrations in the experiments for which Bvpa was derived are likely to be analogous only to
the outer layer concentrations in bulky vegetation, not the whole plant (or whole fruit/vegetable)
concentrations. This empirical parameter was set to 0.01 for bulky fruits/vegetables, but was set
at 1.00 for grass, under the assumption that grass is similar to leaves, and 0.50 for cattle fodder,
which is assumed to contain some bulky (grains) and leafy (hay) vegetation. Relatively speaking,
therefore, the impact of grass concentrations to vapor phase concentrations are 100 times higher
than the impact of vegetable/fruit concentrations to vapor phase concentrations because of this
VG parameter. There is a linear impact for grass and feed to changes in VG - halving VG for
grass halves the grass concentration, for example. The impact of changes to VG is less for
vegetables/fruits, again because its influence is minimized due to its low initial value of 0.01.
A dry weight to fresh weight conversion factor, FDW, is required for estimating above
ground concentrations of vegetable/fruits. This is because the algorithms estimate above ground
vegetative concentrations on a dry weight basis, and the concentrations need to be diluted since
fruit and vegetable consumption are given in this assessment on a fresh weight basis. The impact
to concentrations is direct and linear, and since the range of likely FDW is small, the impact is
small as well. This parameter is also required for the particle deposition algorithm, but is. left
out of Figure 6-6 for clarity. In fact, FDW is applied once vapor phase and particulate phase
contributions to vegetable/fruit concentrations are already summed; in other words, it is not tied
to either the vapor or particle phase algorithms.
The impact of all the particle phase parameters to overall plant concentrations is less than
that of vapor transfers, as seen in a comparison between Figures 6-5 and 6-6. For the parameters
including rainfall amount (R), washout factor (Wp), denseness of vegetation (as modeled by
yield, Y, and intercept fraction, INT), velocity of particle deposition (Vp), and plant weather
dissipation rate, kw, results in Figure 6-6 are for vegetable/fruits and not grass or fodder.
Vegetables/fruits are more impacted by particle depositions than grass/fodder, and as seen, there
is less than half an order of magnitude impact from the range of values for these parameters
tested. As noted above, the impact of depositions on vegetable/fruit concentrations occurs
because the correction factor for vegetables, VGveg, is equal to 0.01, which minimizes the vapor-
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phase contributions to vegetable concentrations in comparison to the contributions of the vapor
phase concentrations for grass and fodder concentrations, which have correction factors of 1.00
(for grass) and 0.50 (for fodder).
This trend is quantified in Table 6-2. Model results on the proportion of above ground
plant concentrations that are due to air-to-leaf transfer and particulate deposition were examined
for the soil contamination and stack emission source categories for 2,3,7,8-TCDD, and results are
summarized in Table 6-2. Results show that vapor phase transfers tend to dominate vegetative
concentrations, although particle phase concentrations are important for bulky fruits and
vegetables. Results also show that the relative impact of vapors and particles is a function of
distance for the stack emission source category. For the central stack emission Scenario, #4,
where the site of exposure is 5000 meters from the stack, vapor transfers generally have more of
an impact to vegetation as compared to the high end Scenario, #5, where the site of exposure is
500 meters away.
It is possible that the impact of particle depositions is being underestimated, for at least
four reasons:
• The wind erosion algorithm estimating air-borne contaminant concentrations for the soil
contamination source category only estimates concentrations of PM-10, or inhalable size
particulates, those 1 0 pm size diameter and less, while the ISCST3 model considers all
size particulates emitted from stacks. Larger size air-borne particulates, while not
inhalable, would deposit onto vegetation.
• For the demonstration of the soil contamination source category which involves soil
contamination distant from the site of exposure, only the off-site locations provide the
source of air-borne particulates. Meanwhile, algorithms are in place estimating exposure
site contamination, albeit to thin surface levels. Certainly, the reservoir of air-borne
particulates depositing onto vegetation would also include contributions from where the
vegetation is located and the surrounding land, not only from the area of soil
contamination.
For the stack emission source category, resuspension of deposited particles and
deposition onto plants is not considered. This omission is similar to the omission noted
in the bullet above.
• The modeling does not consider the splash effect of rainfall, which would deposit soil
onto the lower parts of plants. This would make the most impact for grass and for
vegetables near the ground surface such as lettuce.
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For the soil contamination source category, an additional factor is on the way the model
does not reapportion volatilized residues onto airborne particles. As was discussed in
Chapter 5, the 2,3,7,8-TCDD vapor phase dominates the total air concentration and is
about 95% of the total concentration. Residues which volatilize from the soil are
assumed to remain in the vapor phase. In contrast, the 2,3,7,8-TCDD vapor phase is 51%
of total air concentrations in the stack emission source category, because this is the
fraction developed from the equilibrium vapor/particle partitioning algorithm. If dioxins
were reapportioned after volatilizing, the vapor/particle partitioning for the soil
contamination source category would shift over to the particle phase and the particle
phase impacts to vegetation would be increased.
The precise impact of these factors might be investigated more fully in a later assessment
with additional models. Tests were run for this sensitivity analysis by increasing the amount of
paniculate phase contaminants depositing onto vegetation by an order of magnitude to the soil
contamination demonstration scenario, without changing the vapor phase contributions. The
vapor phase/particulate phase contributions to above ground fruits and vegetables, originally
56%/44% (from Table 6-2), changed to 11%/89% with an order of magnitude increase in
particulate phase contributions. Vegetable concentrations increased by a factor of 6. The impact
was less for grass and fodder, with concentrations increasing by a factor of 1.7.
The impact of partitioning of airborne dioxins between a vapor and a particle phase was
more fully investigated using results from the high end demonstration of the stack emission
source, Scenario 5. The results from this analysis are shown in Figure 6-7. For this test, the
partitioning of 2,3,7,8-TCDD was altered from the originally assigned values of 51% vapor/ 49%
particle. The vegetation examined include above and below ground vegetable concentrations,
and above ground grass and cattle feed.
The obvious trend to note from this figure is that the impact of this repartitioning is the
opposite for vegetables/fruits, both above and below ground, as compared to grass and cattle
feed. For above ground vegetation, this is the result of the use of the empirical correction factor,
VG. As discussed above, assignment of the value of 0.01 for vegetable/fruit VG minimizes the
impact of vapors on above ground vegetables/fruits. Therefore, when particle phase depositions
are increased, as in the right-hand side of Figure 6-7, vegetable/fruit concentrations increase, and
likewise, decreases in particle phase depositions decrease the concentrations. The reason that the
trend is the same for below ground vegetables is that changes in particle depositions lead to
concurrent changes in soil concentrations. Since the leafy vegetation of grass and cattle feed are
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dominated by vapor phase transfers, reductions and increases in the vapor phase concentration
lead to reductions and increases in the concentrations of these vegetation. Even at the lower
vapor fraction, 20% in the right-hand side of Figure 6-7, vapor impacts still dominate the
predicted concentrations for these vegetation.
6.3.3.5. Estimation of Below Ground Vegetation Concentrations
One important factor to note up front about below ground vegetable concentrations as
compared to above ground vegetable concentrations (no underground fruits are assumed in this
assessment) is that below ground vegetable concentrations of 2,3,7,8-TCDD are about four
orders of magnitude higher than above ground vegetable concentrations for the soil
contamination demonstration scenario. For the stack emission scenario, above and below ground
concentrations of 2,3,7,8-TCDD are comparable. Also, the below ground vegetable ingestion
rate of 1.16 g/kg-day is quite comparable to the above ground vegetable ingestion rate of 1.52
g/kg-day. Given the difference in concentration estimations in the soil contamination
demonstration, below ground vegetables explain over 99% of the total exposure via ingestion of
impacted vegetables in that demonstration. Again, they are about half the total vegetable
exposure for the stack emission source category. Sensitivity of underground vegetable
concentrations to parameter changes for the soil contamination source category becomes
important from this perspective.
The reason for this dichotomy in performance between the soil contamination and stack
emission source categories has been examined in other parts of this document, including the
examination of vegetable results in Chapter 5, and the soil-to-air model testing in Chapter 7. To
review these discussions, it was found that when soil is assumed to be source for air
concentrations, as in the soil contamination source category, then a ratio of air to soil
concentrations will be very low compared to an air to soil concentration ratio when the air is
assumed to be source for soil concentrations, as in the stack emission source category. This air to
soil ratio does not have any important meaning except in the context of this discussion. Two
possible explanations were offered for this trend: 1) the models predicting volatilization and
dispersion were underpredicting air concentrations, and/or 2) the soil to air models are not
unpredicting air concentrations - the tendency for dioxins to escape soil (i.e., the fugacity of
dioxins in soil to air transfers) is very low compared to the tendency for dioxins to move towards
soil.
This question now is whether this dichotomy in performance is reasonable. A model
exercise in Chapter 7 evaluated the air-to-soil algorithm and the soil-to-air algorithm using air
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and soil data from the same rural site. It was shown that the air to soil model very reasonably
was able to duplicate the soil concentrations, but that the predictions of air concentrations above
the soil were much lower than observed, by about an order of magnitude. Again, this could be
due to the fact that the models may be underpredicting air concentrations above them. Also, it
could be due to the fact that air concentrations measured in the rural setting are due to long range
transport from distant sources and not the soil concentrations. Given that the model appeared to
perform well in air-to-soil modeling, it is expected that the observation that above, and below
ground vegetable concentrations in the stack emission are comparable, is supportable. The same
confidence cannot immediately be placed on the difference in modeling between above and
below ground vegetables for the soil contamination source category.
In any case, the impacts of parameter changes for the algorithm predicting concentrations
in underground vegetables are shown in Figure 6-8. These results were generated by the soil
contamination source category, although analogous results would result for the stack emission,
source category. All results are essentially linear, which is not surprising since below ground
vegetable concentrations are a linear function of all the parameters tested: Cveg =(Csoil * RCF *
VGbg) /(Koc * OCsl). The two orders of magnitude range for the root concentration factor, RCF,
translates to a two order of magnitude range of concentration estimation. The same is true for the
empirical correction factor applied to below ground vegetables, VGbg, and the organic carbon
partition coefficient, Koc. A smaller impact is noted for the organic carbon fraction of soil, OCsl.
Koc and OCsl are required for this algorithm because vegetable concentrations are a function of
soluble phase concentrations, not soil concentrations. Increasing Koc and/or increasing OCsl
results in decreasing the water concentrations, explaining why the high values for these
parameters reduce vegetable concentrations. The smallest range in Figure 6-8 is the depth of
tillage parameter, which directly influences the soil concentration, Csoil. Reducing the depth of
tillage by 2 increases the soil concentration by about this same amount, as seen in Figure 6-8.
One final note is that the dry to fresh weight ratio, FDW, is not on this figure, while it
does appear on Figure 6-7. This is because the RCF was developed on a fresh weight basis
already, so no conversion to a fresh weight is required.
6.3.3.6. Beef Fat Concentration Estimation in the Soil Contamination and Stack Emission
Source Categories
The impacts of parameter changes to beef fat concentration estimation for the soil
contamination and stack emission source categories are shown in Figures 6-9 and 6-1 0. These
sensitivity runs were both run on 2,3,7,8-TCDD, the only dioxin congener demonstrated for the
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soil source category in Chapter 5, but 1 of the 17 dioxin-like congeners evaluated in the
demonstration of the stack emission source in Chapter 5.
The overriding difference in the way the two source categories predict beef fat
concentrations is that, in the soil contamination source category, the soil-to-cattle pathway
dominates the prediction of beef fat concentration, whereas in the stack emission source
category, the air-to-plant-to-cattle dominates. In Figure 6-9 showing results for the soil
contamination source, there is essentially a linear relationship between changes in soil
concentration and the beef impact, whereas in Figure 6-10 showing the stack emission
relationships, a nearly linear impact is noted instead for changes in the grass and fodder
concentrations. This overall trend is principally due to trends that have previously been
discussed - that is, when soil is the source of contamination, the impact to air and above ground
vegetation is proportionally smaller as compared to when air is the source of contamination.
This trend is further elucidated in the impact of two other soil related tests - one on the
soil bioavailability factor, Bs, and one on the proportion of soil ingested in the cattle diet. In both
cases, changes to these parameters have a significant impact in the soil source category but a
minor impact in the stack emission category. Actually, the importance of soil exposure in the
soil source category and the importance of vegetation in the stack ,emission category influences
most of the other results on Figures 6-9 and 6-10. In the "low" exposure conditions, the
reduction in soil intake from 4% of the diet to 1% of the diet influenced the results for the soil
source category, but the influence was minor for the stack emission category because intake of
contaminated grass was essentially unchanged and the "lower" exposure was mainly reflected in
a reduction of intake of contaminated cattle feed. In the "extent of pasturing" test, the increase in
soil intake from 4% to 8% resulted in a significant impact for the "high" condition in the soil
source category, but less of an impact in the stack emission category, because again the
vegetative intakes were not significantly changed for this test.
In addition to the sensitivity analysis shown in Figures 6-10 and 6-1 1, this trend can be
described simply by displaying the following results from the demonstration of the soil source
and stack emission source categories from Chapter 5:
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Percent impact due to ingestion of:
Description Soil Grass Feed
Soil contamination, beef 98 < 1 > 1
Soil contamination, milk 97 < 1 > 2
Stack emission, beef 11 61 28
Stack emission, milk 7 14 79
As seen here, soil only accounts for 11 and 7% of beef and milk concentration impacts from
stack emissions in the example scenario, but overwhelms the soil source category.
The only parameter which was equally influential for both categories, was the
bioconcentration factor, BCF. To estimate beef fat concentrations, the concentrations in the soil
and vegetation of the cattle diet are multiplied by this factor, so logically, its influence is separate
from the modeling of concentrations in soil or vegetation. Also, the response to changes in this
parameter are linear.
As seen in the above results, the relative impacts of soil and vegetation to milk fat is
similar to that of beef fat in the two source categories. The baseline scenario for a dairy cow's
exposure has a significantly greater amount in cattle feed, 90%, as compared to the beef cattle,
45%. That is why the percent of impact for milk is more driven by feed than grass as compared
to beef in the stack omission source.
The free range chicken scenario is driven by soil in both the stack emission and the soil
contamination scenario. The following results display this trend:
Percent impact due to ingestion of:
Description Soil Grass Feed
Soil contamination, chicken > 99.9 < 0. 1 0
and eggs
Stack emission, chicken 79 21 0
and eggs
The reason for this trend is that the free range chicken exposure scenario has the chicken diet
comprised of 10% soil, 5% incidental leafy vegetation while scavenging, and 85% residue free
chicken feed. The justification for assuming that 85% of the chicken diet was residue free came
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from Stephens, et al. (1995), who did not detect dioxins in the analysis of a standard chicken
feed. They also developed the chicken/egg bioconcentration factors used in this assessment and
assumed a 10% soil percent in the diet of the free range chicken. Further detail on the use of the
Stephens, et al. (1995) data can be found in Chapter 4. In any case, it is expected that the chicken
and egg models will respond to parameter changes in the same manner as the beef fat
sensitivities as displayed in Figure 6-9, showing sensitivities in the soil contamination source
category, rather than Figure 6-10, which shows the stack emission source sensitivities.
6.3.3.7. Impact of Distance from the Stack Emission Source on Concentrations in Soil,
Vegetables, and Beef Fat
For this test, the high end scenario for the stack emission demonstration, Scenario 5, was
used as the baseline. The exposure site was located 500 meters from the stack in the downwind
direction. Two other locations, 200 and 5000 meters, were, evaluated, and predictions for soil,
below and above ground vegetables, and beef fat were examined. The results of this examination
are shown in Figure 6-11.
In the high end stack emission demonstration scenario, the farm was assumed to be
500 meters from the stack. Nearer to the stack at 200 meters, ambient air concentrations and dry
deposition amounts were lower, but wet deposition was at its maximum. One effect of this was
that vegetable concentrations increased. Below ground vegetables increased by about a factor of
about 4, due to the same increase in soil concentration as a result of much higher wet deposition.
Above ground vegetation increased by about 50%. As was described earlier, particle depositions
dominated above ground vegetable/fruit concentrations. Therefore, an increase in overall particle
depositions due to an increase in wet depositions led to increased above ground vegetable/fruit
concentrations. However, the trend was not the same for beef fat. As discussed in earlier
sections, vapor contributions dominated grass and feed concentrations for the stack emission
source category. Therefore, a drop in ambient air vapor phase concentrations at 200 meters as
compared to 500 meters dominated the result, and the net impact was to reduce beef fat
concentrations. From Figure 6-11, it is seen that beef fat concentrations were reduced by about
30% when using ISCST3 output from 200 m instead of 500 m. Further from the stack at 5000
meters, all biota concentrations were lower. Vapor phase air concentrations were roughly halved
as compared to what they were at 500 m, and dry and wet deposition were lower by 60 and 80%
respectively compared to levels at 500 m. This led to substantial reductions in vegetable
concentrations. Beef fat concentrations were also lower at 5000 meters as compared to 200 m
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and the baseline distance of 500 m. Overall, beef fat concentrations were about 70% less at 5000
meters as compared to 500 m.
Trends for milk fat concentration are not exactly the same, but very similar to those of
beef fat. Trends for chicken and egg are similar to that of soil and below ground vegetables,
because these tissues are modeled primarily as a function of the free-range chicken exposure to
soil - this was discussed in the previous section to this one.
6.3.3.8. Water and Fish Concentrations Resulting from Effluent Discharges
The impacts of parameter changes for algorithms estimating water and fish concentrations
are shown in Figure 6-12. First, it should be noted that fish and water impacts are included in the
same graph because the impacts to both concentrations are exactly the same with the noted
changes in parameters, with one exception. This exception is the partition coefficient, Koc.
Increases in Koc result in higher suspended sediment concentrations, which lead to higher fish
tissue concentration estimations, but lower water concentration estimations. Increasing Koc by
an order of magnitude actually decreases water concentrations to 14% of its baseline value, or
0.14 on the y-axis of Figure 6-12. Decreasing Koc by an order of magnitude increases water
concentrations by a factor of 2.4. Roughly, the location of the high and low Koc points on Figure
6-12 should be reversed for water concentration impacts. Also, the biota to suspended solids
accumulation factor, BSSAF, and the fish lipid content, fUpid, are specific to fish tissue
estimations.
Clearly, both concentrations are mostly impacted by the loading rate - the impact is linear
and direct. Of all the parameters describing the effluent stream and the receiving water body,
only the two order of magnitude change in receiving water body flow rate seems to have about an
order of magnitude range of predictions. The effluent flow rate is ultimately low in comparison
to the receiving water body, so its impact is limited. The range of organic carbon contents of the
effluent and of the suspended solids in the receiving water are reasonably assigned and appear to
have a small impact.
Higher suspended solids content in the receiving water body can result in lower fish and
water concentrations. This might be termed a "solids dilution effect". Few studies are available
in the literature which support this result, but two studies were found which are consistent with a
solids dilution effect. One "simulated field experiment" conducted by Isensee and Jones (1975)
maintained a constant water concentration of 239 ppb, but reported a decrease in 2,3,7,8-TCDD
concentrations in both mosquite fish (2200 ppb to 90 ppb) and catfish (720 to 90 ppb) as the
amount of sediment increased from 20 to 440 g. Sherman (1992), in a review of this and other
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simulated field experiments and laboratory flow through experiments, points out that a
bioconcentration factor for these simulated field experiments would decrease as the sediment
increases. He speculates that, in comparing water flow through experiments with field simulated
data, the bioconcentration factors tend to be less in field simulated experiments because 2,3,7,8-
TCDD may sorb to sediments and be less bioavailable. A second study supporting a solids
dilution effect was conducted by Larsson, et al. (1992). They studied uptake of PCBs and p,p'-
DDE in 341 northern pike in 61 lakes in southern Scandinavia. They found that the levels of
these persistent pollutants in the fish decreased as productivity increased. Productivity was
measured by total phosphorus, chlorophyll a, and lake water transparency, which was mainly
influenced by phytoplankton biomass. Their hypothesis was that the levels decreased because
humus adsorbs persistent pollutants, rendering them less available for uptake in fish.
The two order of magnitude range in Koc translates to about a one order of magnitude
range in estimated fish and water concentration estimations. Fish tissue concentrations are
linearly and directly related to the BSSAF and flipid. About an order of magnitude of
concentration estimation is noted with about the same order of magnitude in likely values.
6.3.3.9. Water and Fish Concentrations Resulting from Stack Emissions
Results of sensitivity analysis of algorithms estimating surface water and fish
concentrations resulting from stack emissions are shown in Figure 6-13. First, it is noted that the
impact to both these media is the same with impacts to all parameters. The impact with changes
in the deposition of particles onto the water body, RDEPp, and with the fraction of deposited
particles remaining in suspension, fsd, is negligible. The assigned values to these parameters for
the demonstration are, therefore, sufficient for any purpose. It is importantly noted the ISCST3
model or other atmospheric transport models do not need to estimate the concentration of
contaminants on emitted particles - all that is required are mass emissions of contaminants (in
g/sec units) and the delineation of size fractions of particles emitted. The ISCST3 model does
not require a particle emission rate. An assumption of a greater deposition of particles directly
into surface waters might translate back to an assumption of particle emissions. The RDEPp is
only required to maintain a mass balance of solids entering the surface water body, and as it turns
out, particles entering surface waters by this route are only a miniscule part of the total solids
entering the body. There are no impacts to water or fish concentration estimations with
reasonable values for this parameter. The same appears true for fsd, which determines the extent
to which directly depositing contaminants remain in suspension. The assigned value of 1.00
(meaning that all directly depositing contaminants remain in suspension), based on the argument
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that particles emitted from stack are likely to be lighter than eroding soil particles, appears
sufficient for general purposes.
Water body impacts are essentially linearly related to the average watershed depth of
mixing. The value assigned for the demonstration scenario was 0.1 m, which is midway between
the value assumed for non-tilled conditions, 0.02 m, and tilled conditions, 0.20. The value of
0.10 m suggests that half the watershed is tilled. The linear relationship underscores the
importance of this uncertain parameter, and also suggests that erosion drives water body impacts
rather than direct deposition. This trend is also apparent for the tests on depositions to the
watershed, the RDEPwat, input, versus depositions directly onto the surface of the water body,
the RDEPSW input. The impact to changes in RDEPwat are roughly linear - an increase in
watershed depositions (but not depositions directly onto the water body) by about a factor of 13
leads to an increase in the water body impacts by a factor of 11 . There was less but still a
noteworthy change when depositions directly onto the water body increased or decreased - a 13-f
old increase led to a 2.5 fold increase in water body impacts.
Changing the size of the watershed did not have much of an impact to water body
impacts. In changing the size, the four parameters as noted on Figure 6-13 were changed
simultaneously. Intuitively, increasing the size of watershed should increase water body impacts,
since more land impacted by depositions would be draining into the water body. However, two
factors counter this intuition: 1) the water body volume also changes concurrently, tending to
dilute any additional soil inputs, and 2) soil inputs are not proportionally increased because the
sediment delivery ratio decreases as the size of the watershed increases - from 0.06 in the
baseline setting to 0.01 in the test for the large watershed. In fact, water body impacts dropped
slightly for the large watershed and increased only very slightly for the smaller watershed. This
increase was due also to the change in the sediment delivery ratio, which increased from 0.06 to
0.25.
6.3.4. Key Trends from the Sensitivity Analysis Testing
These are as follows:
1) Source terms are the most critical for exposure media impacts. Source terms include soil
concentrations, stack emissions, and effluent discharges. In nearly all cases, the impact to
exposure media is linear with changes to source terms. Proximity to the source term can be
important as well, as demonstrated with differences in distance from the stack emission source.
2) Chemical-specific parameters, particularly the bioconcentration/biotransfer
parameters, are the second most critical model inputs. Some of these have lesser impacts,
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such as the organic carbon partition coefficient, Koc, for surface water impacts. Generally, at
least an order of magnitude in range in possible media concentrations is noted with the range of
chemical-specific parameter ranges tested. The impact of changes to
bioconcentration/biotransfer parameters is mostly linear. This is because these transfer factors
estimate media concentrations as a linear transfer from one media to another - fish lipid
concentrations are a linear function of the concentration of contaminants in sediments. These
transfer parameters are also identified as uncertain parameters. Tested ranges sometimes
spanned over an order of magnitude for 2,3,7,8-TCDD.
3) All other parameters had less of an impact as compared to source strength and chemical
specific parameters; nearly all impacts were within an order of magnitude for the range of
tested values. Part of the reason for this trend is that there is a reasonably narrow range for
many of the parameters in this range - soil properties, wind speeds, vegetation yields, and others.
It is important, nonetheless, to carefully consider all the model parameters. While impacts were
generally within an order of magnitude of the values selected for the demonstration scenarios,
there was often an order of magnitude or more difference between plausible high and low values
for individual parameters.
4) A principle trend of note concerns the air to soil algorithm for the stack emission source
category compared to the soil to air algorithm of the soil source category. Several tests in
this chapter demonstrated the difference in model performance when soil is the source of
contamination compared to when air is the source of contamination. The relationship between
air and soil concentrations is distinctly different for the soil contamination source category as
compared to the stack emission source category. It is found that the "air/soil" concentration ratio
is much smaller in the soil source category as compared to that same ratio in the stack emission
source category. This air to soil ratio does not have any important meaning except in the context
of this discussion. An air-to-soil model validation exercise in Chapter 7 showed that the
deposition modeling appeared to very reasonably predict soil concentrations. This exercise relied
on concurrent measurements of air concentrations and soil concentrations in an actual rural
background setting. The air concentrations became the independent variable used to model soil
concentrations. The reasonable validation was the result of predicted soil concentrations
matching observed soil concentrations. However, when the same exercise was turned around -
i.e., the observed soil concentration was used to predict air concentration, it was found that the
predicted air concentration was much lower than the observed air concentration. Two possible
explanations were offered for this trend: 1) the models predicting volatilization and dispersion
were underpredicting air concentrations, and/or 2) the soil to air models are not underpredicting
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air concentrations - air concentrations over a soil are perhaps more a function of atmospheric
transport from distant sources than volatilization or wind erosion from soils. In any case, this
dichotomy in air/soil model performance had several cascading impacts as were found in this
chapter as well as Chapter 5 demonstrating the methodologies. Results include, for example: 1)
soil concentrations drive terrestrial animal food concentrations for the soil source category, since
vegetation concentrations are so low as not to be a large influence in comparison to soil. On the
other hand, the vegetation concentrations drive the terrestrial animal food concentrations in the
stack emission source category - the air-to-plant pathway is most important here, and 2) below
ground vegetable concentrations are four orders of magnitude higher than above ground
vegetables for the soil contamination source category while they are roughly comparable for the
stack emission source category.
6.4. MASS BALANCE CONSIDERATIONS
As has been discussed in this document more than once is the characterization of
this methodology as a screening level methodology. Steady state, equilibrium partitioning, and
assumptions of nondegradation of source strengths are key assumptions which lead to this
qualification. Stacks are assumed to emit a constant amount of contaminant over a duration of
exposure for the stack emission source category. Effluent discharges are assumed to continue
unabated over a duration of exposure. These are both reasonable assumptions for evaluating the
long-term impacts of these sources where no change in practices occur. Any violation of mass
balance principals will, therefore, not be examined for these sources. The same assumption of
unabated and c6nstant releases might be questioned, however, for the soil contamination source
category. Soil concentrations are assumed to remain constant, despite mechanisms which would
dissipate concentrations over time. Volatilization and transport off-site, and wind erosion and
transport off-site, are two mechanisms which dissipate residues into the air and deplete the
source strength. Soil erosion off the site to a nearby exposure site and to nearby water bodies
also is a mechanism of release. A key dissipation mechanism is soil degradation. There is
evidence that photolysis is a mechanism of degradation of dioxin-like compounds, as discussed
in Chapter 2 of Volume 11 of this assessment. However, this would only apply to those residues
directly on the soil surface and, as such, it may be reasonable to make an assumption of
nondegradation if a concurrent assumption is that residues exist below the soil surface. In any
case, releases for a bounded area of soil contamination including volatilization, wind erosion, and
soil erosion, which are estimated for purposes of estimating off-site impacts, are not also used to
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estimate dissipation of the reservoir of contaminant in the soil. Said another way, the amount
lost via these pathways is not a function of a soil reservoir which decreases over time.
The purpose of this section is to examine this assumption for the case of a bounded area
of hig.h soil concentration. The demonstration of the soil contamination source category will be
the focus of discussions below. First, an estimate of the "reservoir" of 2,3,7,8-TCDD that is
implied with the default parameters will be made. Then, an estimate of the rate at which this
reservoir dissipates using the solution algorithms for dissipation: volatilization and wind erosion
flux from soils, and soil erosion, will be made. Other routes of dissipation that will be examined
are the soil ingestion by cattle and children, the loss via dermal contact, and the removal via
harvest of below ground vegetation. These will be shown to be minuscule in comparison to air
and soil erosion. The loss of soluble residues via surface runoff or leaching will be evaluated.
Surface water bodies and above ground vegetation are sinks for dioxin-like compounds and
therefore are not mechanisms of soil dissipation. If it can be shown, for example, that it takes
several hundred years to dissipate a given reservoir, then it may be fair to conclude that
exposures assuming non-dissipation over a 20 or even a 70 year exposure period are not
significant overestimates. On the other hand, complete dissipation within a time period less than
or even near to the period of exposure would mean that exposures and risks are being
overestimated.
As will be shown, the rates of reservoir dissipation are very important considerations for
soil contamination. Users of this-methodology should consider dissipation of available residues
and the discussions below when determining the duration of exposure for site-specific
assessments. A recommended rule of thumb for users of this methodology is to evaluate the time
to dissipation using the methodology below, and if it is less than or even near the assumed period
of exposure (2 years to dissipate versus 20 years of assumed exposure, e.g.), then it may be
appropriate to assign a duration of exposure equal or less than the calculated time to residue
dissipation.
One of the key parameters in determining how rapidly residues will dissipate is one which
is not required for this methodology. This is the depth of contamination. This depth, plus the
initial concentration and the areal extent of contamination, describe the full extent of the source
strength. The exercises below have assumed a shallow depth of 0.15 meters, or 6 inches, in soil.
The impact of this assumption is demonstrated below. Also, the exercises below are specific to
2,3,7,8-TCDD.
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The demonstration of the off-site soil contamination source category, were as follows:
40,000 m2 soil contaminated with an initial concentration of 1 ug/kg (ppb). It is assumed that
the contamination extends to 0. 1 5 meters (6 inches).
Step 1. Estimate the amount bound to soil:
(total volume of contaminated site = 40,000 m2 * 10,000 cm2/m2 * 15 cm =
6x 109cm3
(soil bulk density) x 1.5 gm/cm3
(unitless 2,3,7,8-TCDD soil con., g/g) x (1/109)
= grams 2,3,7,8-TCDD in soil 9 gms
Note: at a soil concentration of 1 pg/kg, there will also be some 2,3,7,8-TCDD in soil pore
water. This amount is insignificant in comparison to the amount bound to soil, and will be
neglected.
Step 2. Now estimate the amounts lost by various routes of dissipation
- Volatilization -Volatilization flux is a function of exposure duration, with less average
flux calculated over longer durations - this is, in fact, the only model algorithm which accounts
for reservoir depletion over time. The durations of exposure for the high end scenarios was 20
years. The release rate via volatilization is given as the term FLUX and is shown in Equation (4-
22) in Chapter 4. Plugging in baseline parameter values for 2,3,7,8-TCDD and a duration of 20
years results in a calculated flux of 1.06xlO"18 g/cm2-sec. Over a year and over the 40,000 m2
contaminated area, this translates to an annual dissipation rate of 0.013 g/yr of 2,3,7,8-TCDD.
- Wind erosion: Unlike the volatilization algorithm, the flux due to wind erosion is not
dependent on the duration of exposure. The wind erosion algorithm is described in Section 4.3.3
in Chapter 4. Plugging in baseline parameter values results in a flux of 2,3,7,8-TCDD of 5.74x10"
20 g/cm2-sec, or an annual flux over the 40,000 m2 contaminated area of 0.0007 g/yr.
- Soil erosion: The annual erosion rate off the contaminated site was 21515 kg/hayr.
This rate was assumed to erode towards the exposure site as well as towards the impacted surface
water body. However, it would not be appropriate to double that quantity since it is used in two
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DRAFT-DO NOT QUOTE OR CITE
different algorithms - the exposure site could be in the direction of the water body, for example.
Or, if applied to a specific site, one could ascertain that the exposure site is upgradient from the
contaminated site, and so on. In any case, 21515 kg/ha-yr can be translated to a cm/yr of soil
erosion as follows:
volume per 1-cm hectare slice = 10,000 m2/ha * 10,000 cm2/m2 * 1 cm =
IxlO8 cm3
X 0.015 kg/cm3 (soil bulk density)
kilograms per 1-cm hectare slice = 150,000 kg/cm-hectare
Therefore, 21515 kg/ha-yr translates to a loss of soil equal to 0.14 cm/yr. Given that 9 g 2,3,7,8-
TCDD are estimated to occur in 15 cm, the annual loss of 2,3,7,8-TCDD is 0.084 g/yr.
- Runoff and Leaching: Transport via water is not considered in this methodology since
the dioxin-like compounds are so tightly sorbed that these are expected io be negligible. An
estimate of loss via water will nonetheless be made for this exercise. Surface water body volume
was estimated assuming a runoff rate of 15 in/yr, which was defined as all surface water
contributions (surface runoff, interflow, and ground water recharge). This is a reasonable
estimate for water-borne losses for this exercise. The annual amount of 2,3,7,8-TCDD lost in
this water can be estimated using the soil partition coefficient, Kds, relationship, which is CS/CW.
Kds is equal to 39,800 for 2,3,7,8-TCDD (organic carbon partition coefficient, Koc of 3.98xl06
* soil organic carbon, OCsl of 0.01), so the concentration in water, Cw, given a soil concentration,
Cs, of 1 |_ig/kg, is 2.5xlO'5 |_ig/L, or 2.5X10'11 g/L. Translated to a 40,000 m2 area, 15 in/yr equals
1.524xl07 L, so the total annual loss in water equals 0.00038 g/yr 2,3,7,8-TCDD.
Except for soil degradation, these are the dissipation routes that would be considered for a site of
soil contamination that is not used for any purpose - residence, agriculture, and so on. For the
sake of completeness, other routes that will be looked at now include soil ingestion, soil dermal
contact, and harvesting of underground vegetation.
- Soil Ingestion: Soil ingestion by children in the high end scenario is 600 mg/day, or
0.22 kg/yr. Soil ingestion by cattle will also be considered. First, an assumption of how
many cattle would be feeding on a 40,000 m2 area should be made. A daily cattle dry matter
ingestion rate is 19 kg/day. For beef cattle that are assumed to principally graze, for 90% of their
dry matter intake, the daily ingestion of grass would be 17.1 kg/day, and their daily intake of soil
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while grazing, 8% of total dry matter intake, is 1.52 kg/day. With this daily ingestion of grass,
their annual need for grass would be 6200+ kg/yr. The yield of grass assumed for other purposes
in this assessment was 0.15 kg/m2-yr dry weight, or 6000 kg/40,000 m2-yr. Therefore, it appears
that one grazing cow requires the 40,000 m2 to himself (as a rough approximation). The annual
intake of soil by this cow equals 555 kg/yr, which as expected, is much higher than child soil
ingestion. The annual removal of 2,3,7,8-TCDD by cattle soil ingestion is 555 |_ig/yr, or 0.0006
g/yr.
- Dermal Contact: The dissipation of 2,3,7,8-TCDD residues via dermal contact is
estimated as, EV*CA*CR*CS, where EV = event frequency in terms of number of dermal contact
events per year, which equals 365 in the high end scenario, CA = contact area, which ranges from
1000-10,000 cm2, CR = contact rate, which ranges from 0.005-0.1 mg/cm2-event, and Cs =
2,3,7,8-TCDD concentration, which is 1 pg/kg, or in more convenient units, 10"12 g/mg. Using
the higher values, the annual loss via dermal contact is negligible at 3.7xlO"7 g/yr.
- Underground Vegetation Harvests: The yield of vegetables required for other
algorithms of this assessment, is 7.8 kg/m2 fresh weight. The vegetable concentration/soil
concentration ratio for tilled soils is about 0.033 (from results of the demonstration scenario).
Therefore, for a 1 pg/kg soil concentration, the fresh weight vegetable concentration would be
0.033 |-ig/kg fresh weight. Therefore, the removal per m2 is 0.26 pg/m2, and the removal over
40,000 m2 in g/yr is 0.01 g/yr if all the 40,000 m2 were devoted to underground vegetables.
This exercise has shown that the principal mechanism of removal is soil erosion at
0.084 g/yr 2,3,7,8-TCDD, with volatilization explaining 0.013 g/yr removal. The sum of these
two routes is 0.097 g/yr, and the sum of all the other routes examined briefly above is 0.01 g/yr,
leading to a round total estimate of 0.11 g/yr. With an initial reservoir of 9 gr, it would take 82
years to dissipate the available reservoir, not including degradation and assuming that surface
concentrations remain constant. The limited field data that is available on the loss of dioxins
from surface soils suggested anywhere from 10 to 100 years for surface and subsurface residues.
These losses would include degradation and transport. This assessment assumes a 25-year half-
life for residues arriving at exposure sites from distant sources of contamination such as soils or
stack emissions. As a rough estimate, half of the 9 gram reservoir would be dissipated at 25
years; 4.5 grams loss per 25 years translates to 0.18 g/yr lost. The algorithms of this assessment
already lead to 0.11 g/yr lost, and therefore the observation that it would take about 80 years to
6-46 December 2003
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DRAFT-DO NOT QUOTE OR CITE
dissipate the reservoir appears sound. In other words, this simple exercise suggests that transport
from a site, rather than degradation, could dominate the dissipation rates of 10 to 100 years that
have been observed in the field.
This was not a definitive exercise, by any means, but it does lend some confidence that a
principal of mass balance may not have been violated for the soil source categories, and for the
assumption of 30 years exposure duration. As this section began, the algorithms of this
assessment are characterized as screening level methodologies. Users of this methodology
should be cognizant, nonetheless, of the possibility of depleting a reservoir of soil contamination
prior to an assumed duration of exposure.
6-47 December 2003
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REFERENCES FOR CHAPTER 6
Brady, N.C. (1984) The Nature and Properties of Soils. Ninth Edition. New York, NY:
Macmillan.
CDEP. 1992. Data on the Connecticut Department of Environmental Protection (CDEP)
program to monitor soil, sediment, and fish in the vicinity of Resource Recovery
Facilities. Data supplied by C. Fredette, CDEP, 165 Capitol Ave, Hartford, CT, 06106.
Fries, G.F.; Paustenbach, DJ. (1990) Evaluation of Potential Transmission of 2,3,7,8-
Tetrachlorodibenzo-p-dioxin-Contaminated Incinerator Emissions to Humans Via Foods.
Journal of Toxicology and Environmental Health 29: 1-43.
Fries, G.F.; Paustenbach, D.J.; Mather, D.B.; Luksemburg, WJ. (1999) A congener specific
evaluation of transfer of chlorinated dibenzo-p-dioxins and dibenzofurans to milk of cows
following ingestion of pentachlorophenol-treated wood. Environmental Science and
Technology 33:1165-1170.
Goeden, H.M.; Smith, A.H. (1989) Estimation of human exposure from fish contaminated with
dioxins and furans emitted by a resource-recovery facility. Risk Analysis 9:377-383.
Hwang, S.T.; Falco, J.W.; Nauman, C.H. (1986) Development of Advisory Levels for
Polychlorinated Biphenyls (PCBs) Cleanup. U.S. Environmental Protection Agency,
Exposure Assessment Group, Office of Research and Development. EPA/600/6-86/002.
Isensee, A.R.; Jones, G.E. (1975) Distribution of 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD)
in aquatic model ecosystem. Environmental Fate 9(7):668-672.
Jackson, D.R., M.H. Roulier, H.M. Grotta, S.W. Rust, and J.S. Warner (1986) Solubility of
2,3,7,8-TCDD in Contaminated Soils, pp. 185-200 in Rappe, C., G. Choudhary, and L.H.
Keith (eds.), Chlorinated Dioxins and Dibenzofurans in Perspective. Lewis Publishers,
Inc.
Kjeller, L.O., S.E. Kulp, S. Bergek, M. Bostrom, P.A. Bergquist, C.Rappe, B.Jonsson, D.de Wit,
B. Jansson, M. Olson (1990) Levels and Possible Sources of PCDD/PCDF in Sediment
and Pike Samples from Swedish Lakes and Rivers (Part One). Chemosphere 20: 1489-
1496.
Larsson, P.; Collvin, L; Okla, L.; Meyer, G. (1992) Lake productivity and water chemistry as
governors of the uptake of persistent pollutants in fish. Environmental Science and
Technology 16:346-352.
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McLachlan, M.S.; Thoma, H.; Reissinger, M.; Hutzinger, O. (1990) PCDD/F in an agricultural
food chain. Part 1: PCDD/F mass balance of a lactating cow. Chemosphere 20:1013-
1020.
Schroy, J.M.; Hileman, F.D.; Cheng, S.C. (1985) Physical/chemical properties of 2,3,7,8-
TCDD. Chemosphere 14: 877-880.
Seinfeld, J.H. (1986) Atmospheric Chemistry and Physics of Air Pollution. John Wiley and
Sons, New York.
Sherman, W.R.; Keenan, R.E.; Gunster, D.G. (1992) Reevaluation of Dioxin Bioconcentration
and Bioaccumulation Factors for Regulatory Purposes. Journal of Toxicology and
Environmental Health, Vol 37:211-229.
Stephens, R.D.; Petreas, M.X.; Haward, D.G. (1995) Biotransfer and bioaccumulation of
dioxins and furans from soil: chickens as a model for grazing animals. Science of the
Total Environment 175:253-273.
U.S. Environmental Protection Agency. (1984) Ambient water quality criteria document for
2,3,7,8-tetrachlorodibenzo-p-dioxin. Office of Water Regulations and Standards,
Washington, D.C. EPA-440/5-84-007.
U.S. Environmental Protection Agency. (1985) Rapid Assessment of Exposure to Particulate
Emissions from Surface Contamination Sites. Environmental Protection Agency, Office
of Health and Environmental Assessment, Office of Research and Development.
EPA/600/8-85/002, February, 1985.
U.S. Environmental Protection Agency. (1989) Exposure Factors Handbook. Exposure
Assessment Group, Office of Health and Environmental Assessment, Office of Research
and Development, U.S. Environmental Protection Agency. EPA/600/8-89/043. July,
1989.
U.S. Environmental Protection Agency. (1990) USEP A/Paper Industry Cooperative Dioxin
Study "The 104 Mill Study" Summary Report and USEPA/Paper Industry Cooperative
Dioxin Study "The 104 Mill Study" Statistical Findings and Analyses Office of Water
Regulations and Standards, July 13, 1990.
U.S. Environmental Protection Agency. (1992) Dermal Exposure Assessment: Principals and
Applications. Exposure Assessment Group, Office of Health and Environmental
Assessment, Office of Research and Development, U.S. Environmental Protection
Agency. EPA/600/8-91/01 IB.
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U.S. Environmental Protection Agency. (1993) Interim Report on Data and Methods for
Assessment of 2,3,7,8-Tetrachlorodibenzo-p-dioxin Risks to Aquatic Life and
Associated Wildlife. Office of Research and Development, Environmental Research
Laboratory at Duluth, MN. EPA/600/R-93/055. March, 1993.
U.S. Environmental Protection Agency. (1995b) Great Lakes Water Quality Initiative Technical
Support Document for the Procedure to Determine Bioaccumulation Factors. Office of
Water. EPA-820-B-95-005. March, 1995.
U.S. Environmental Protection Agency. (1997) Exposure Factors Handbook. Office of Research
and Development. External Review Draft. EPA/600/P-95/002A.
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Table 6-1. Parameters used to estimate exposure media concentrations for this assessment.
Parameter Name and Definition
Low1
Selected1
High1
Rating2
1. Contaminated and Exposure Site Characteristics
A. Site of Exposure
AES Area of exposure site, m2
Eslp Soil porosity, unitless
Psoil Particle bulk density, g/cm3
Bsoil Soil bulk density, g/cm3
OCsl Soil organic carbon fraction
dt Depth of tillage, m
dnot No-till depth, m
B. Contaminated Site for Soil Contamination Source Demonstration
Cs Soil concentration of 2,3,7,8-TCDD, |ig/kg (ppb)
ASC Area of contamination, ha
Eslp Soil porosity, unitless
Psoil Particle bulk density, g/cm3
OCsl Soil organic carbon fraction
4,000
0.35
2.55
1.20
0.005
0.10
0.01
0.01
4,000
0.35
2.55
0.001
40,000
0.50
2.65
1.50
0.01
0.20
0.02
1.00
40,000
0.50
2.65
0.01
400,000
0.60
2.75
2.00
0.05
0.30
0.10
100.0
400,000
0.60
2.75
0.05
SS
SS
FOD
SS
SS
SOD
SOD
SS
SS
SS
FOD
SS
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Table 6-1. (cont'd)
(continued on next page)
Parameter Name and Definition
Low
Selected
High
Rating
2. Soil and Sediment Delivery Parameters
SLS Contaminated site soil loss, kg/ha-yr
SLec Soil loss between cont. and exposure site, kg/ha-yr
SLW Watershed soil loss, kg/ha-yr
ER Enrichment ratio
Cw Watershed 2,3,7,8-TCDD concentration for
contaminated soil source demonstration, pg/g (ppt)
OCssed Suspended sediment organic carbon fraction
OCsed Bottom sediment organic carbon fraction
Aw Watershed drainage area, ha
SDW Watershed sediment delivery ratio, unitless
TSS Total suspended sediment, mg/L
DLe Distance from contaminated to exposure site, m
DLW Distance from contaminated site to water body, m
Vwat Volume of water body, L/yr
2,100
0
2,100
1
0
0.02
0.01
10,000
0.25
2
50
50
5*1010
21,520
2,150
6,455
3.0
0
0.05
0.03
100,000
0.06
10
150
150
4.8*10n
42,000
21,000
21,500
5.0
1.00
0.10
0.05
1,000,000
0.01
70
1,000
1,000
5*1012
SS
SS
SS
SOD
SS
SS
SS
SS
SS
SS
SS
SS
SS
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AREAW Surface area of water body, m2
4*105
4*106
4*107
SS
Table 6-1. (cont'd)
(continued on next page)
Parameter Name and Definition
Low
Selected
High
Rating
3. Volatilization and Dust Suspension Parameters
ED Exposure duration, yrs
V Fraction of vegetative cover at contaminated site
Um Average windspeed, m/sec
Ut Threshold wind speed, m/sec
F(x) Model-specific parameter, unitless
FREQ Frequency wind blows to site, unitless
1
0
2.8
2.5
0.87
0.05
30
0
4.0
8.25
0.50
0.15
70
0.9
6.3
11.3
0.05
0.50
SS
SS
SS
SS
SS
SS
4. Bioconcentration and Biotransfer Parameters
Fish: fUpid Fish lipid fraction
Vegetation, FDW Dry to fresh weight conversion
Particle Vp Particle deposition velocity, m/yr
Impacts: R Annual rainfall, m/yr
Wp Washout factor, unitless
—
0.05
1.5*105
0.30
2,000
0.07
0.15
3.2*105
1.00
50,000
—
0.30
7.0*105
2.00
1,000,000
SS
SS
SOD
SS
SOD
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(continued on next page)
Table 6-1 (cont'd)
Parameter Name and Definition
Rw Fraction of wet deposition retained
Ygj. Grass yield, kg/m2 dry
INTgj. Grass interception fraction
Yctfd Cattle feed yield, kg/m2 dry
INTctfd Cattle field interception fraction
Ychfd Chicken feed yield, kg/m2 dry
INTchfd Chicken feed interception fraction
Yveg Above ground veg. yield, kg/m2 fresh
INTveg Vegetable interception fraction
Yfrt Fruit yield, kg/m2 fresh
INTfrt Fruit interception fraction
Vegetation, VGvg Vegetable/fruit correction factor
Vapor VGg,. Grass correction factor
Impacts: VGctfd Cattle feed correction factor
VGchfd Chicken feed correction factor
Low
0
0.15
0.13
0.25
0.20
—
—
2.7
0.18
—
—
0.001
0.50
0.25
—
Selected
0.30
0.15
0.35
0.63
0.62
0.63
0.62
7.8
0.48
7.8
0.48
0.01
1.00
0.50
0.00
High
1.00
0.35
0.64
1.30
0.93
—
—
8.6
0.72
—
—
0.10
1.00
0.75
—
Rating
SOD
ss
ss
ss
ss
ss
ss
ss
ss
ss
ss
SOD
SOD
SOD
SOD
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(continued on next page)
Table 6-1. (cont'd)
Parameter Name and Definition
Below Gr. Veg: VGbg Below ground veg. correction factor
Beef: BCSDF Beef cattle soil diet fraction
B C GDF B eef cattl e gras s di et fracti on
BCFDF Beef cattle feed diet fraction
BCGRA Beef cattle fraction of cont. grazing land
BCFOD Beef cattle fraction of cont. feed
bfat Beef fat fraction
Dairy: DCSDF Dairy cow soil diet fraction
DCGDF Dairy cow grass diet fraction
DCFDF Dairy cow feed diet fraction
DCGRA Dairy cow fraction of cont. grazing land
DCFOD Dairy cow fraction of cont. feed
dfat Dairy fat fraction
Chicken/Egg: CSDF Chicken soil diet fraction
CGDF Chicken grass diet fraction
Low
0.01
0.01
0.02
0.02
0.25
0.25
—
—
—
—
—
—
—
—
—
Selected
0.25
0.04
0.48
0.48
1.00
1.00
0.19
0.02
0.08
0.90
1.00
1.00
0.035
0.10
0.00
High
1.00
0.15
0.90
0.90
1.00
1.00
—
—
—
—
—
—
—
—
—
Rating
SOD
SOD
SOD
SOD
SOD
SOD
ss
SOD
SOD
SOD
SOD
SOD
SS
SOD
SOD
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Table 6-1. (cont'd)
(continued on next page)
Parameter Name and Definition
CFDF Chicken feed diet fraction
CGRA Chicken fraction of cont. grazing land
CFOD Chicken fraction of cont. feed
All animals: Bs Bioavailibity of dioxin in soil relative to
vegetative feeds
Low
—
—
—
0.30
Selected
0.90
1.00
1.00
0.65
High
—
—
—
0.90
Rating
SS
SS
SS
SOD
5. Effluent Discharge Source Category
LD Loading to surface water body, mg/hr
Qe Effluent flow rate, L/hr
Qu Upstream receiving water flow, L/hr
OCe Effluent organic carbon content, fraction
OCU Upstream organic carbon content, fraction
TSSe Effluent total suspended solids, mg/L
TSSu Upstream total suspended solids, mg/L
0.00315
1*105
1*107
0.15
0.02
10
2.0
0.0315
4.1*106
4.7*108
0.36
0.05
70
9.5
0.315
1*107
1*109
0.50
0.10
250
50
SS
SS
SS
SS
SS
SS
SS
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Table 6-1. (cont'd)
(continued on next page)
Parameter Name and Definition
Low
Selected
High
Rating
6. Stack Emission Source Category
RDEPe Wet+dry deposition at high end exposure site, |ig/m2-yr
RDEPwat Wet+dry deposition onto watershed, |ig/m2-yr
RDEPSW Wet+dry deposition onto surface water, |ig/m2-yr
Cva Vapor phase concentration at high end exp. site, |ig/m3
RDEPp Deposition of particles onto water body, g/m2-yr
dwmx Average mixing depth of deposition over watershed, m
fsd Fraction of particles depositing onto water body from the
atmosphere which remain in suspension
1.82*1Q-6
1.82*1Q-6
1.82*1Q-6
434*10-i3
0.003
0.01
0
1.13*1Q-6
1.43*1Q-7
1.43*1Q-7
6.99* 1042
0.03
0.10
1.00
1.43*1Q-7
5.20*1Q-8
5.20*1Q-8
2.49* 1042
3.00
0.20
1.00
SS
SS
SS
SS
FOD
SOD
FOD
7. 2,3,7,8-TCDD Physical, Chemical, and Bioconcentration/Biotransfer Parameters
H Henry's Constant, atm-m3/mole
Da Molecular diffusivity in air, cm2/s
Koc Organic carbon partition coefficient, L/kg
c|) Fraction of airborne reservoir sorbed
3.29*1Q-6
0.005
4*105
0.80
329*io-5
0.047
3.98*106
0.49
3.29*10'4
0.10
4*107
0.20
FOD
FOD
FOD
SOD/SS
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(continued on next page)
Table 6-1. (cont'd)
Parameter Name and Definition
Bvpa Air-to-leaf vapor phase transfer factor, unitless
BCF Beef/milk fat bioconcentration factor, unitless
CCF Chicken fat bioconcentration factor, unitless
ECF Egg fat bioconcentration factor, unitless
BSAF Biota sediment accumulation factor, unitless
BSSAF Biota suspended sediment accumulation factor, unitless
k Soil dissipation rate for eroding/depositing dioxins, yr"1
kw Plant weathering rate constant, yr"1
RCF Root bioconcentration factor, unitless
Low
6.55*103
1.00
—
—
0.03
0.03
0.0028
51.0
1,600
Selected
6.55*104
5.76
8.80
7.80
0.09
0.09
0.0277
18.1
5,200
High
6.55*105
10.00
—
—
0.30
0.30
0.28
8.4
100,000
Rating
SOD
SOD
SOD
SOD
SOD/SS
SOD/SS
SOD
SOD
SOD
Notes:
1 "Selected" is the value chosen for the demonstration scenarios. The "Low" and "High" values were selected for the sensitivity
analysis.
2 "Ratings" are qualitative judgements pertaining to the use of the selected values for use in other assessments - see text for more
detail.
3 "—" means that low and high values were not selected because these parameters were not tested in the sensitivity analysis exercises.
Trends with these parameters were demonstrated with related parameters.
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Table 6-2. Contribution of above ground vegetation concentrations of 2,3,7,8-TCDD from air-
to-leaf transfers and particulate depositions.1
Description2
Air-to-leaf vapor transfers
Particle depositions
Scenario 3: Soil contamination
vegetables/fruit
grass
feed
56
96
97
44
4
Scenario 4: Stack emissions, central
vegetables/fruit
grass
feed
35
91
92
65
9
Scenario 5: Stack emissions, high
vegetables/fruit
grass
feed
20
82
87
80
18
13
Results are in percent of total contribution.
2 Scenario 3 demonstrated the soil contamination source category, where soil at a contaminated
site ISOmeters away was initialized at 1 pg/kg (ppb) 2,3,7,8-TCDD; Scenarios 4 and 5
demonstrated the stack emission source category - in Scenario 4, the exposure site was 5000
meters from the emitting stack, and in Scenario 5, the exposure site was 500 meters from the
stack.
6-59
December 2003
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DRAFT-DO NOT QUOTE OR CITE
U) 'U
o ~
s
w
.Q
(O
L_
-HJ
E
(0
1" 1
.2 ~
^
O)
!c
^
.^
(0
Q.
r\ -i
1 Note:
Cair = O.Oi
Csoil = 1 pg/g (ppb)
4s+5
3.3e-4
D4pg/m3 0.50 __ 400,000
0.005 I . .
I °-60|2.75 1
1
0.35
70 y
0.05
2.55
u
6.3
ou
0.05
0.1
4000 3-3e'6
0
46+7 0.005
1000
ED Ocsl Eslp Psoil Um FREQ DLe ASC H Koc Da
Parameter Name
Figure 6-1. Results of sensitivity analysis of algorithms estimating exposure site
vapor phase air concentrations resulting from a distant contaminated soil site.
Parameter Name
(-'soil
air
ED
ocsl
Eslp
p
rsoil
um
FREQ
DLe
ASC
H
Koc
Definition
soil concentration at contaminated area, ng/g (ppb)
air concentration at exposure site, pg/m3
exposure duration, yrs
soil organic carbon fraction
soil porosity, unitless
particle bulk den, g/cm3
average windspeed, m/sec
frequency wind blows to site, unitless
distance to exposure site, m
area of off-site contamination, m2
Henry's Constant, atm-m3/mole
organic carbon partition coefficient, L/kg
molecular diffusivity in air, cmVs
Selected
1.00
0.004
30
0.01
0.50
2.65
4.0
0.15
150
40,000
3.29*10'5
3.98*106
0.047
6-60
December 2003
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DRAFT-DO NOT QUOTE OR CITE
i uu -
(O
O
;g
1 10
W
3
E
2
o
O)
.c
=| 0.1
-5
CD
Q.
E
n n 1 -
note:
Csoil = 1 ppb
6.3
0
1 I
2.8
*
0.9
high erodibility
400,000 5Q °'5°
>
4,000 °'05
1,000
low erodibility
Cair =
0.0002
pg/m3
/
V Urn Ut,F(x) ASC DLe FREQ
Parameter Name
Figure 6-2. Results of sensitivity analysis of algorithms estimating exposure site
particle phase air concentrations resulting from a distant contaminated soil site.
Parameter Name
V
um
ut
F(x)
ASC
DLe
FREQ
Definition Selected
contaminated site soil concentration, ng/g (ppb) 1.00
exposure site air concentration, pg/m3 0.0002
fraction of vegetative cover, unitless 0.0
average windspeed, m/sec 4.0
threshold wind speed, m/sec 8.25
model-specific parameter 0.50
area of soil contamination, nf 40,000
distance to exposure site, m 150
frequency wind blows to exposure site, unitless 0.15
6-61
December 2003
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DRAFT-DO NOT QUOTE OR CITE
parameter substitutions
* c
Impact with high/low |
3
^ _
Csoil = 1.00 ppb
4'000 400,000 r
400,C
0.0
1.20
1
1
2.00
0.
)00
1 50
0 1.000
4,000
42,OC
,0
21,000
2,100 0.
277
277
Ces =
0.357 ppb
AES Bsoil dnot DLe ASC SLs SLe c k
Parameter Name
Figure 6-3. Results of sensitivity analysis of algorithms estimating exposure
site soil concentrations resulting from erosion from a site of soil contamination.
Parameter Name
Definition
Selected
(-'soil
ces
AES
DLe
ASC
SLS
SLec
k
contaminated site soil concentration, ng/g (ppb)
exposure site soil concentration, ng/g (ppb)
area of exposure site, nf
soil bulk density, g/cm3
no-till mixing depth, m
distance to exposure site, m
area of off-site contamination, m2
contaminated site soil loss, kg/ha-yr
soil loss between exp. and cont. site, kg/ha-yr
dissipation rate for eroding/depositing cont., yr"1
1.00
0.357
40,000
1.50
0.02
150
40,000
21520
2152
0.0277
6-62
December 2003
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DRAFT-DO NOT QUOTE OR CITE
M IU
.0 :
3
'w
-Q
ID
-S3
"S3
co
ro ^
Q_ 1
"- I
—
^
D)
^
1
co
Q.
E
n -i
50
1000
400,000
small Cfish =
watershed le-6 Csed =
2,100 • Cwat =
42,000
large
watershed
4,000
I 5 /
/
high
organics
•
o 1
low
organics
21,500 !
4e7 2 /
||
OU
4e5
1
2,100
DLw ASC AW SLs SLw Cs OCsed ER Koc TSS
SDw Ocssed
Vwat „ , kl
Parameter Name
Figure 6-4. Results of sensitivity analysis of algorithms estimating surface
water impacts, including sediment, water, and fish concentrations, resulting
from a site of soil contamination.
Parameter Name
Definition
Selected
(-'fish
r
^wat
DLW
ASC
Aw
SDW
Vwat
SLS
SLW
Cw
ocsed
ocssed
ER
Koc
TSS
concentration in contaminated soil area, ng/g (ppb) 1.00
concentration in bottom sediment, pg/g (ppt) 1.44
concentration in fish lipid, pg/g 4.3
concentration in water, pg/L (ppq) 0.012
distance to water body, m 150
area of off-site contamination, m2 40,000
watershed drainage area, ha 100,000
watershed sediment delivery ratio, unitless 0.06
volume of water body, L/yr 4.8x10"
contaminated site soil loss, kg/ha-yr 21,520
watershed soil loss, kg/ha-yr 6,455
watershed contaminant cone, mg/kg 0
bottom sediment organic carbon fraction 0.03
suspended sediment organic carbon fraction 0.05
enrichment ratio, unitless 3
organic carbon partition coefficient, L/kg 3.98xl06
total suspended sediment, mg/L 10
6-63
December 2003
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DRAFT-DO NOT QUOTE OR CITE
to
o
to
.a
10
10x 6e5
10x 6e5
0)
&
£ 1
01
Q.
0.1
10x 6e5 0.1
0.'
x 6e
53 O.C
0.30
)01
0.05
Vegetables/Fruit
0.
1x 6f
1.0
I
0.50
Grass
;3
0.
x Rp
Cveg =
Cgr =
Cfeed
0.75
i j
0.25
Feed
Cair Bvpa VG FDW Cair Bvpa VG Cair Bvpa VG
Parameter Name
Figure 6-5. Results of sensitivity analysis of algorithms estimating above ground
vegetation concentrations due to vapor phase transfers.
Parameter Name
Cveg
Cgr
Cfeed
Cair
Bvpa
VG
FDW
FREQ
Definition
vegetable concentration, pg/g (ppt) fresh
grass concentration, pg/g (ppt) dry
cattle feed concentration, pg/g (ppt) dry
vapor-phase air concentration, pg/m3
air-to-leaf transfer factor, unitless
Vegetable correction factor, unitless
vegetables/fruit
grass
feed
fresh to dry weight ratio
frequency wind blows to site, unitless
Selected
6e(-4)
0.2
0.1
0.004
6.55e(-4)
0.01
1.00
0.50
0.15
0.15
6-64
December 2003
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DRAFT-DO NOT QUOTE OR CITE
10
2
—
0.3
1J6 7e5 8.4
1-° dense n n
n Q
n i
o.ix 0.1 sParse 1 5e5 U
KK
\J\J
Vegetables/
Fruits
10x
Cveg =
Cgr =
Cfeed
i
10x /
1 I
u v
0.1x
0.1x
Grass/
Feed
Veg/
Fruits
to
o
1
CO
fe
(U
2
0)
"5
0.1
Wp Rw Y.INT Vp
Parameter Name
Kw Cpart Cpart
Figure 6-6. Results of sensitivity of algorithms estimating above ground vegetation
concentrations from deposition of particle-bound dioxins.
Parameter Name
Cveg
Cgr
Cfeed
Cair
Wp
Rw
Y
INT
Vp
kw
Definition
vegetable concentration, pg/g (ppt) fresh
grass concentration, pg/g (ppt) dry
cattle feed concentration, pg/g (ppt) dry
vapor-phase air concentration, pg/m3
washout factor, unitless
rainfall retention factor, unitless
vegetable yield, kg/m2 fresh
vegetable interception fraction, unitless
particle deposition velocity, m/yr
plant wash-off rate constant, yf'
Selected
6e(-4)
0.2
0.1
0.0002
1.0
0.3
7.8
0.48
3.2e(-5)
18.01
6-65
December 2003
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DRAFT-DO NOT QUOTE OR CITE
to
o
1
in
a>
£
2.
O)
•5
10
0.1
Vapor = 0.80
Particle = 0.20
j i
Vapor = 0.20
Particle = 0.80
Vapor = 0.51
Particle = 0.49
ABF/V BGV Feed Grass AGFA/ BGV Feed Grass
Parameter Name
Figure 6-7. Impact of vapor/particle partitioning on vegetation concentrations in the
stack emission source category.
Parameter Name
Definition
Selected
AG FA/ above ground fruit/vegetable cone., pg/g (ppt) fresh 3e(-6)
BG V below ground vegetable cone, pg/g (ppt) fresh 4e(-6)
Feed cattle feed concentration, pg/g (ppt) dry 2e(-4)
Grass grass concentration, pg/g (ppt) dry 5e(-4)
Vapor vapor fraction, unitless 0.51
Particle particle fraction, unitless 0.49
6-66
December 2003
-------
DRAFT-DO NOT QUOTE OR CITE
to IUU i
c
o
|5
1
3 ID-
'S
2
2. 1
.0)
t-
| 0.1-
ro :
1" !
r\ rd
100,000
0.10 4e5 Cve9 =
1,600
i
0.005 10 /
i — i /
n n /
4.
y
u 30
0.03
0.01 4e7
Note: Csoil = 73 ppt
fresh weight
RCF VGbg Koc OCsl dt
Parameter Name
Figure 6-8. Results of sensitivity analysis of algorithms estimating below ground
vegetable concentrations in the soil contamination source category.
Parameter Name
Cveg
Csoil
VGbg
OCsl
RCF
Koc
dt
Definition Selected
vegetable concentration, pg/g (ppt) fresh 2.4
tilled soil concentration, pg/g (ppt) 61.0
below ground vegetation correction factor, unitless 0.01
soil organic carbon fraction 0.01
root bioconcentration factor, unitless 5,200
organic carbon partition coefficient, L/kg 3.98e6
depth of tillage, cm 20
6-67
December 2003
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DRAFT-DO NOT QUOTE OR CITE
in IU
o
*=
i
.a
in
55
"S3
2
1 1~
| \
IE
.c
•g
(0
Q.
ft 1
mv u- u Extent of
10x h|9h Pasturing
10
r> n
10x
0.1x
7
0.3
high Cbfat =54 ppt
/
Exposure
Conditions
/
/
/
/
low
'°w low low
Q 1x Soil Extent of
1 Ingestion Contamination
Cs Cgr BCF Bs
Cfeed
Parameter Name
Exposure Scenario
Figure 6-9. Results of sensitivity analysis of algorithms estimating beef fat
concentrations in the soil contamination source category.
Parameter Name
Cs
Cgr
Cfeed
Cbfat
BCF
Bs
Definition
2,3,7,8-TCDD soil concentration, pg/g
2,3,7,8-TCDD grass concentration, pg/g dry wt.
2,3,7,8-TCDD feed concentration, pg/g dry wt.
2,3,7,8-TCDD beef fat concentration, pg/g
beef/milk bioconcentration factor, unitless
bioavailability of contaminant on soil relative
to vegetation
Exposure Scenario Parameters:
BCSDF beef cattle soil diet fraction
BCFDF beef cattle feed diet fraction
BCGDF beef cattle grass diet fraction 0.48
BCGRA beef cattle fraction of contaminated grazing land
BCFOD beef cattle fraction of contaminated feed
Selected
1000
0.2
0.1
54
5.76
0.65
0.04
0.48
1.00
1.00
6-68
December 2003
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DRAFT-DO NOT QUOTE OR CITE
10
to
o
'w
.a
(0
e- 1
•5
CO
Q.
E
0.1
10x
0.1x
10x
Cbf
10 Exposure Extent of
Conditions Pasturing /
0.1x
00 high high /
• /
n 0 II I
u
0.3 low u
„ ., low
Soil
Ingestion
II
U
low
low
Extent of
1 Contamination
Cbfat = 0.002 ppt
Cs Cgr BCF Bs
Cfeed
Parameter Name
Exposure Scenario
Figure 6-10. Results of sensitivity analysis of algorithms estimating beef fat
concentrations in the stack emission source category.
Parameter Name
Cs
Cgr
Cfeed
Cbfat
BCF
Bs
Definition
2,3,7,8-TCDD soil concentration, pg/g
2,3,7,8-TCDD grass concentration, pg/g dry wt.
2,3,7,8-TCDD feed concentration, pg/g dry wt.
2,3,7,8-TCDD beef fat concentration, pg/g
beef/milk bioconcentration factor, unitless
bioavailability of contaminant on soil relative
to vegetation
Exposure Scenario Parameters:
BCSDF
BCFDF
BCGDF
BCGRA
BCFOD
beef cattle soil diet fraction
beef cattle feed diet fraction
beef cattle grass diet fraction
beef cattle fraction of contaminated grazing land
beef cattle fraction of contaminated feed
0.48
Selected
0.001
0.0004
0.0002
0.002
5.76
0.65
0.04
0.48
1.00
1.00
6-69
December 2003
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DRAFT-DO NOT QUOTE OR CITE
10
2
to
Q.
D)
a
0.1
200 20° Csoil = 1e(-3
5000
)PPt
Cbgv = 4e(-6) ppt fresh
Cagv = 3e(-6) ppt fresh
200 Cbf = 2<*3
i
200
I
5000
5000 5000
)ppt
Csoil Cbgv Cagv
Exposure Media
Cbf
Figure 6-11. Impact of distance from the stack emission source to soil,
vegetable, and beef fat concentrations.
Parameter Name
Csoil
Cbgv
Cagv
Cbf
Definition
2,3,7,8-TCDD soil concentration, ng/g
2,3,7,8-TCDD below grd. veg. cone.,
fresh wt, pg/g (ppt)
2,3,7,8-TCDD above grd. veg. cone.,
fresh wt, pg/g (ppt)
2,3,7,8-TCDD beef fat concentration, pg/g (ppt)
Selected
l*l(r3
4*l(r6
3*i(r6
2*l(r3
6-70
December 2003
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DRAFT-DO NOT QUOTE OR CITE
i u -
CO
c
o
1
z:
CO
1
2
1 1
O>
._
1
CO
o
E
n 1 -
10X LD = 0.03 mg/hr
1e7
1e5
1e7
°'30 0.20
| 1 I
0.50
0.10 250
1e9
! 2.7e7
50
0.03
0.03
2.7e5
0.1X
Cwat =
0.02 ppq
Cfish =
0.4 ppt
/
LD Qe Qu OCe OCu TSSe TSSu Koc BSSAF f(lipid)
Parameter Name
Figure 6-12. Results of sensitivity analysis of algorithms estimating surface water
and fish concentrations resulting from effluent discharges.
Parameter Name
Cwat
Cfish
LD
Qe
Qu
OCe
OCu
TSSe
TSSu
Koc
BSSAF
flipid
Definition
water concentration, pg/L (ppq)
whole fish concentration, pg/g (ppt)
loading to surface water body, mg/hr
effluent flow rate, L/hr
upstream receiving water flow, L/hr
effluent organic carbon content, unitless
upstream organic carbon content, unitless
effluent total suspended solids, mg/L
upstream total suspended solids, mg/L
organic carbon partition coefficient, L/kg
biota suspended solids ace. factor, unitless
fish lipid fraction
Selected
0.02
0.4
0.0315
4.1xl06
4.7xl08
0.36
0.05
70
9.5
2.69xl06
0.09
0.07
6-71
December 2003
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DRAFT-DO NOT QUOTE OR CITE
100
D>
c
(0
•g
0>
2
(0
Q.
TO
£
5
•3
a
1-
1.8e(-6)
1.8e(-6)
3.0,1.0
5.2e(-8)
0.003, 0.0
Cfish = 2e(-5) ppt
Cwat = 8e(-7) ppq
small
watershed
5.2
2Q
large
watershed
0.1
RDEPwat RDEPsw RDEPp dwmx Aw, Awat,
fsd Vwat, SDw
Parameter Name
Figure 6-13. Results of sensitivity analysis of algorithms estimating surface
water and fish concentrations resulting from stack emissions.
Parameter Name
Cfish
Cwat
RDEPwat
RDEPsw
RDEPp
dwmx
fsd
Aw
Awat
Vwat
SDw
Definition
2,3,7,8-TCDD whole fish concentration, pg/g (ppt)
2,3,7,8-TCDD surface water concentration, pg/L (ppq)
2,3,7,8-TCDD dep. rate on watershed, ng/m2-yr
2,3,7,8-TCDD dep. rate on surface water, ng/nf-yr
particle dep. onto surface water, g/nf-yr
watershed soil mixing depth, m
fraction of deposited particles remaining
in suspension
area of watershed, ha
surface area of water body, m2
water body annual volume, L/yr
watershed sediment delivery ratio
Selected
2*l(r5
8*l(r7
1.4*10-7
i.4*i(r7
0.03
0.10
1.00
100,000
4*106
4.8*10"
0.06
6-72
December 2003
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DRAFT-DO NOT QUOTE OR CITE
7. MODEL COMPARISONS AND MODEL VALIDATIONS
7.1. INTRODUCTION
This chapter is comprised of two principle sets of exercises aimed at lending credibility to
the models selected for use in this site-specific methodology. One set compares alternate
approaches to modeling the fate and transport of dioxins, which users can consider in place of the
models selected. The comparisons involve generating results with both sets of models, and then
seeing how the results compare. Assuming the models selected in this document and the
alternates have some inherent credibility, the model results should compare favorably. In most
cases, they do compare well, but there are a few where model comparisons are not satisfactory.
If the two sets of models compare favorably, one would expect that this lends credibility to both
sets of models, and hopefully some confidence in the use of the models selected for this
document.
The fate, transport, and transfer models presented in this document can also attain a
measure of credibility if it can be shown that estimations of environmental and exposure media
concentrations are consistent with those found in the literature. Some of those comparisons can
use the exposure media concentrations generated in the demonstration scenarios because the
source strength terms of the demonstrations were crafted to be meaningful. Specifically, the
background scenario was demonstrated with both air concentrations and soil concentrations that
are from an actual rural background setting that is justified as being generally typical for rural
settings. The soil contamination source category was demonstrated with a bounded area of high
soil concentrations of 1 ppb. This was also supported by literature showing this that sites of high
soil contamination contained dioxin-like compounds in the ppb range. Other comparisons are
more site-specific: for a specific site, the source strength is known and input into the model, and
the model predictions of environmental media concentrations are compared with site-specific
measurements of these concentrations.
Tests of this latter case can be termed "validation" exercises. In this document, the word
"validation" refers to an exercise in which the following holds true:
1. An impacted media concentration is known. A concentration of a contaminant in a
media that can be predicted by the fate models, such as a concentration in soil, vegetation,
water, sediment, biota, or ambient air, is known through a site-specific monitoring
program. Model predictions of these concentrations have often been termed the
"dependent" model results in a validation exercise.
7-1 December 2003
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DRAFT-DO NOT QUOTE OR CITE
2. The source causing this impact is known. The source term information has often been
called the "independent" model input in a validation exercise. For example, if the
concentration of a contaminant in ambient air represents the known dependent quantity,
than the independent, or source, term could be the emissions from one or more tall stacks
nearby. Specifically, source term information would translate to the level of emissions
(in units of mass/time such as g/sec) of the particular contaminant from the identified
stacks. The model validation exercise would take these known emissions, put them into
an air dispersion model along with other parameters (i.e., stack heights, exit velocities,
site-specific meteorological data, etc.), and predict ambient air concentrations. In other
model validation exercises, however, the source is likely not going to be the incinerator
emissions. For example, if underground vegetables were the impacted media, than the
independent source term could be the concentration in the soil in which the vegetables
were growing. One could also start with ambient air concentrations in a model validation
exercise involving underground vegetables. In this case, the air concentrations would be
used to predict the soil concentrations, and these soil concentrations would be used to
predict the vegetable concentrations. In any case, the source term needs to be known and
the model user needs to be reasonably certain, or at the very least, the model user needs to
assume, that the source directly impacts the effected media.
3. All other model parameters are assigned values using the best available information.
Site-specific information is the most appropriate to use, if it is available. Chemical-
specific parameters, such as the bioconcentration/biotransfer parameters, can either rely
on site-specific information or are inherent properties of the chemical. An example of the
former are the soil (or sediment)/water partition coefficients: Kd, Kdsed, and Kdssed.
These parameters are calculated as the product of the organic carbon partition, Koc,
which is an inherent chemical property, and the organic carbon content of the sorbing
media (soil, sediment, or suspended sediment), which is site-specific. Examples of
inherent properties are the bioconcentration parameters which are used to predict, for
example, biota concentrations (vegetation, animal fat) as a function of the concentration
in the contacting media (soil, vegetation).
4. Model predictions of the impacted media are compared with observations. Once all
the parameters are assigned values, the model is run and model predictions of the
dependent media concentration are compared to the real world observations of this
concentration. This is the final step in a model validation exercise.
7-2 December 2003
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DRAFT-DO NOT QUOTE OR CITE
Model validation needs to be distinguished from model "calibration". Calibration
exercises require real world measurements of independent source terms and dependent media
impacts, as do validation exercises. What distinguishes the two exercises is that a calibration
exercise is conducted in order to determine values for one or more key model input parameters
by "forcing" the model to duplicate the observed data by adjusting the value of this key input
parameter until this duplication is reached. As described in step 3 above, all model parameters
are assigned values in a validation exercise; none are adjusted in order for model predictions to
fit the observed data. An example of a model calibration exercise is the calibration of the air-to-
leaf transfer factor for the vapor phase, Bvpa. The exercise is described in detail in Chapter 4.
Briefly, air concentrations of these compounds in experimental conditions were known, as were
the final concentrations in grass harvested after three weeks of growth. All other model
parameters necessary for the prediction of the concentration of the dioxins and furans in the
grass, including the physical parameters (mass of grass harvested, fresh to dry weight conversion,
etc.), vapor/particle partitioning in the air, and particle deposition algorithm parameters, were
assigned values based on site-specific measurements or the literature. The Bvpa was then adjusted
until the model predictions of grass concentrations matched the observed grass concentrations.
Finally, it should be understood that model testing is an ongoing process. The model
comparisons and validations described in this chapter are, by no means, expected to establish
model validity beyond any doubt. Users of this methodology are encouraged to subject the
models to any number of tests, validation or otherwise, as they use the models described in this
document to conduct site-specific assessments for dioxin-like compounds
7.2. MODEL COMPARISON EXERCISES
7.2.1. Evaluation of Alternative Air-to-Leaf Modeling Approaches
The first section below describes the field data that was used in this model testing
exercise. The second section describes the two alternative empirical models of air-to-plant
impacts of the dioxin-like compounds. One was developed only for the impact of vapor phase
dioxins, and does not have an explicit particle phase impact model. It will be compared to the
vapor transfer model of this methodology. The other model assumes that plants "scavange" a
fixed volume of air of dioxins; grass concentrations are very simply modeled as the total
(vapor+particle) air concentration times a "scavenging" coefficient. The third section presents
the results of this exercise. These first three section are paraphrased from Lorber and Pinsky
(1999), where further details on this exercise can be found. Finally, the fourth section reviews a
7-3 December 2003
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DRAFT-DO NOT QUOTE OR CITE
very similar model comparison exercise published in the literature in which the model of this
methodology, along with these same two alternative models, are applied to different field data.
7.2.1.1. The Field Data
Jones and Duarte-Davidson (1997) present the results of an extensive monitoring study of
dioxin concentrations in air and grass, as well as deposition fluxes, from three sites over three
time periods between 1992 and 1993 in the United Kingdom. The three sites include a rural
background site, an urban site, and an industrial site. The study was originally funded as a
monitoring program to evaluate the environmental levels of dioxin-like compounds in a
contaminated industrial area in Bolsover, UK (Sandalls, et al, 1996). By also including an urban
site and a rural background site in their study, the authors were able to use the data to better
understand the processes of air-to-plant transfer of the dioxins. The regional background site was
located about 6 km upwind of the industrial complex in Bolsover, the urban site was about 2 km
in the town of Bolsover, and the industrial sampling location was located just outside the
industrial complex, about 100 m away.
Jones and Duarte-Davidson (1997) present two sets of data which are appropriate for air-
to-plant model validation and model comparison purposes. Specifically, they presented
concurrently measured concentrations of dioxin and furan congeners, and homologue groups, for
air and grass sampled for two of the sites, the industrial and rural background site, for one of the
sampling periods, Sep. 14 - Oct. 30, 1993. They also presented the results of their deposition
collection for those two sites/sampling periods. Although other data sets of this kind were
collected during the program, only these two sets were presented. Specifically, the following
observed data were available for one sampling period each at the rural background and industrial
sites: 1) average total air concentrations of 17 dioxin congeners and 8 homologue groups for a
6+-week period (9/14/93-10/30/93), 2) average concentrations of these compounds in grass
grown during this period, 3) average deposition rates, in units of pg/m2-day for these compounds,
and 4) grass yields, in g/m2. The air, grass, and deposition data are provided in Table 7-1.
Further details on the sampling design, monitoring program, analytical methods, and other
aspects of the study can be found in Jones and Duarte-Davidson (1997).
7.2.1.2. Model Descriptions and Application to the Field Data
The vapor transfer approach was parameterized and applied to 2,3,7,8-TCDD by Smith,
et al. (1995a) and Trapp and Matthies (1995). The steady state solution for their vapor transfer
velocity approach is given as:
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F
where:
Cvpa = plant concentration due to vapor-phase transfer, pg/g dry weight
Fv = deposition of vapor-phase congener, pg/m2-day
kv = first-order dissipation constant, day"1
Yj = yield of crop j, g/m2
The non-steady state solution has an additional term in the numerator, l-e("kvt), where t is the time
to harvest. This non-steady state term was used for the particle phase solution in the model
validation exercise. However, given that the kv was assigned a relatively large value by both
researchers for 2,3,7,8-TCDD vapors depositing onto plants (a large kv corresponds to a short
half-life), and the growing period for grasses is 45+ days in the field data set applied, this
additional term approaches 1.0, and it can therefore be neglected in the vapor phase solution.
The two articles evaluated diverge at this point. The Trapp and Matthies (1995) approach
is actually a comprehensive approach involving root uptake impacts. They summarize the
literature to conclude that vapor phase impacts for lipophilic compound such as dioxin dominate
the plant contamination and hence, they do not model particle phase impacts. They also present
their solution in a more generalized fashion by having a volume term in the denominator of
Equation (7-1) above instead of a plant yield term; the volume term is easily converted to a mass
(or yield) term with a plant density factor. Their solution for Fv is:
Fv = AgCva 86400 (7-2)
where:
Fv = deposition of vapor-phase congener, pg/m2-day
A = leaf area index, m2 leaf area/m2 ground area
g = conductance, m/sec
Cva = vapor phase air concentration, pg/m3
86400 = converts sec to days
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Trapp and Matthies (1995) state that g has a range of 0.0001 m/sec to 0.005 m/sec, where
the upper boundary conductance is appropriate for a plant species with relatively permeable
cuticles and the compounds are very lipophilic, and the lower boundary conductance is where
uptake is mainly via stomata and the compounds are less lipophilic. For 2,3,7,8-TCDD, Trapp
and Matthies (1995) assume a g of 0.001 m/sec. For the leaf area term, they have assumed a
value of 5 for the condition they describe as "meadow". Without further information on
refinement of these terms for the experimental conditions of the Jones and Duarte-Davidson
(1997) settings, a g of 0.001 m/sec and an A of 5 will be assumed for this exercise.
Smith, et al. (1995a) estimate the Fv as a multiplication of the vapor phase air
concentration, Cva (pg/m3), the transfer velocity, Vt, (cm/sec), and the plant interception (fraction,
unitless). They state that the transfer velocity is represented as the inverse of the sum of the
resistances to transfer to the plant surface as:
V' ~~ R, + jlt + Rc <7-3>
where:
V, = transfer velocity, cm/sec
Ra = atmospheric resistance, sec/cm, a function of vertical turbulent transport
Rb = surface boundary layer resistance, sec/cm, a function of molecular
diffusivity
Rc = plant canopy/leaf resistance, sec/cm, a function of vegetative density,
stomatal uptake, surface effects, humidity, and so on
They have assigned values of Ra = 0.4 sec/cm, Rb = 0.38 sec/cm, and Rc = 0.5 sec/cm, leading to
an overall V, of 0.78 cm/sec. They assumed a crop interception fraction of 1.00.
Both research efforts have, therefore, arrived at fairly similar quantities in front of the air
concentration term (Cva), despite having slightly different theoretical frameworks. Trapp and
Matthies (1995) arrive at the quantity of 0.005 m/sec (A g = 5 * 0.001 m/sec), and Smith, et al.
(1995a) arrive at 0.0078 m/sec ( INT * V, = 1 * 0.0078 m/sec).
Both also have used the same experimental work of McCrady and Maggard (1993) as part
of their derivation of the kv. Trapp and Matthies (1995) assumed that the overall k term was a
function of losses by photodegradation, volatilization, and dilution from plant growth. They used
the photodegradation k term of 0.3744 day"1 taken from McCrady and Maggard (1993), but then
multiplied it by 0.30 assuming that the plant was in full sunlight only 30% of the time. They
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calculated a volatilization rate term of 0.012 day"1, and assumed a dilution term of 0.035 day"1,
leading to an overall kv term of 0.159 day"1. Smith, et al. (1995a) took the full photodegradation
plus volatilization rates determined by McCrady and Maggard (1993), without any correction for
time in sunlight, to arrive at a k of 0.495 day"1. They did not consider dilution by plant growth.
McLachlan (1995) developed a simple "scavenging" approach to predict grass
concentrations of dioxins from air concentrations of dioxins. He suggests that grass scavenges
the equivalent of 9 m3 or air per gram of grass, and that corn scavenges 4.5 m3 of air. The
important assumption in this approach is that plants can scavenge vapors and particles
equivalently; therefore, vapor/particle partitioning is unnecessary, and grass concentrations are
very simply modeled as:
Cgi - SCCai (7-4)
where:
Cgj = grass concentration of congener i, pg/g dry weight basis
SC = scavenging coefficient, m3/g
Cai = total (vapor + particle) phase air concentration of congener i, pg/m3
With this as background, it is now possible to describe how the three models will be
applied to the field data. For simplicity, the three models will be referred to as the EPA model,
the scavenging model, and the vapor deposition model. In applying the EPA model, total dioxin
concentrations were first partitioned into a vapor and a particle phase following the procedures
outlined in Chapter 3. In that chapter, particle fractions, cj) (the vapor fractions are solved as, 1 -
cj)), are developed for an ambient air temperature of 20 °C for airsheds described as, "clean
continental", "background", "background plus local conditions", and "urban". For this
application, these cj) were rederived for 10 °C, the air temperature more typical of September and
October in the UK, and the "average background" and "urban" cj) were used for the rural and
industrial sites, respectively. According to the modeling procedures of this assessment, described
in detail in Chapter 4, vapor dioxins "transfer" to the grass; grass concentrations due to air-borne
vapors are modeled simply as the air concentration times an air-to-leaf transfer factor, Bvpa.
Chapter 4 describes the derivation of this empirical transfer factor. Particle-phase dioxins
deposit onto grass, under both wet and dry conditions. Jones and Duarte-Davidson (1997) do not
provide the rainfall data for the sampling period necessary to estimate wet deposition. For
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simplicity, wet deposition will not be modeled. Dry deposition, as outlined in Chapter 4, Section
4.3.4, will be modeled as a function of the particle phase concentration times a deposition
velocity, which will be assumed to be 0.20 cm/sec. This deposition mixes in a reservoir of grass,
described by the grass yields, which were provided in Jones and Duarte-Davidson (1997) as 89
and 42 g dry weight/m2 for the rural and industrial sites, respectively. As outlined in Chapter 4,
Section 4.3.4, interception fractions for grasses corresponding to these yields are solved for as
0.23 (for 89 g/m2) and 0.11 (42 g/m2). The first-order weathering constant will be assigned a
value of 0.0495 day"1, corresponding to a half-life of 14 days. This value has also been used in
the literature and otherwise in this document.
For testing of the scavenging model, the measured air concentrations in the rural and
industrial site will be multiplied by the scavenging coefficient of 9.0. For testing of the vapor
deposition model, the total air concentration of 2,3,7,8-TCDD will be partitioned into the particle
and vapor phase for these field data, again assuming "average background" for the rural site and
"urban" for the industrial site. The vapor portion of the air concentration will be used in the
vapor deposition model. The model predictions of the vapor component of the grass
concentration will be compared against the vapor component of grass concentration as predicted
by the models of this assessment and the measured grass concentration.
There were 25 modeled/measured concentration pairs in the rural site data set, including
the 17 individual congeners and the 8 homologue groups. In order to obtain independent data
points for model testing, the measured air and grass concentrations of the individual congeners
were subtracted from the homologue group concentrations; individual congener concentrations
are, by definition, contained within the homologue group concentrations. Doing this subtraction
should have resulted in 25 independent measured/modeled pairs for model testing. However, it
was found that, for HpCDD, HxCDF, and HpCDF, subtraction of the congener air concentrations
from these homologue group air concentrations resulted in negative concentrations. There was
obviously some measurement error in this data set. The airgrass pairs for these three homologue
groups were, therefore, not considered for further model testing. Without these three readings,
the air and grass data at the rural site were quite correlated; the correlation coefficient of the
remaining 22 data pairs was 0.92.
Similar measurement error resulted in the deletion of 4 airgrass pairs in the industrial
data set. The air concentrations of the homologue groups HpCDD and HpCDF also were lower
than the sum of the individual congener concentrations. The grass concentrations of 1,2,3,7,8-
PCDD and 1,2,3,7,8-PCDF were given as non-detected, but the air concentrations of these
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congeners in the urban settings were similar to the air concentrations in the rural settings, and
significant grass concentrations were noted in the rural setting.
Even with these four pairs deleted, the industrial site data was not as consistent as the
rural data. The correlation coefficient for the remaining 21 data pairs was 0.66. An examination
of the data highlights some of the differences between the two data sets. In some instances, air
concentrations that were similar in the rural and industrial sites led to grass concentrations that
were more impacted in the industrial site. For example, the air concentration of 2,3,7,8-TCDD
in the rural setting was 0.01 pg/m3, while it was 70% higher than that in the industrial setting at
0.017 pg/m3. However, the grass concentration in the industrial site was about 4 times as high as
in the rural setting, 2.8 pg/g in the industrial site versus 0.72 pg/g in the rural site. Similar
observations can be made for 4 other congeners. On the other hand, there were several instances
where similar rural and industrial air concentrations resulted in lower grass concentrations in the
industrial as compared to the rural setting. Similar OCDD concentrations of 2.5 pg/m3 in the two
settings resulted in a grass concentration of 94 pg/g in the rural setting but only 43 pg/g in the
industrial site setting. This trend can be found in 13 other instances. Also, the industrial data
contained three data points that appeared to be flawed.
In summary, then, 22 airgrass pairs for the rural data set and 21 airgrass pairs for the
industrial site were retained for model testing
Model goodness-of-fit tests were run for the rural site only, not on the industrial site,
simply because the rural site data was better correlated. The absolute and signed difference
between the natural logs of the measured and modeled grass concentrations provided the
goodness-of-fit measure. The signed error, or bias, measures the systematic tendency of the
model to under or overpredict; a bias near 0 suggests that the model underpredicts and
overpredicts by about the same amount. The absolute error calculation describes model variation;
how close the model predictions come to the observations, regardless of whether the model over
or underpredicted. A value close to 0 suggests a very good match between predictions and
observations. Log concentrations were used because there were a wide range in grass
concentrations, from sub-ppt concentrations for the lower chlorinated dioxins to concentrations
near 100 ppt for the homologue groups.
7.2.1.3. Results and Discussion of the Air-to-Leaf Model Comparison Exercise
Results of this model comparison are shown in Figures 7-1 through 7-4, and Table 7-2.
Figures 7-1 and 7-2 compare the measured grass concentrations with the EPA and the
Scavenging Model for the rural and urban site, respectively. The natural log of the observed
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concentrations are shown on the x-axis and the predicted concentrations on the y-axis. The
dashed line shows where predicted equals observed; points above the line show overpredictions
by the models while points below the line show model underpredictions. As seen in Figure 7-1,
model predictions of rural grass concentrations using the EPA Model matched the observed
concentrations better than the Scavenging model, but both models underpredicted concentrations.
The predicted total toxic equivalent (I-TEQ) concentration of the EPA model was 3.7 pg/g,
compared to the observed I-TEQ concentration of 6.0 pg/g. The bias was -0.66, giving a bias
factor of exp [-0.66] = 0.51 and suggesting that the EPA model underpredicted by about a factor
of 2.0. The absolute error was close to the bias at 0.68, again indicating that the EPA model
mostly underpredicted plant concentrations; of 22 observation:prediction pairs, the EPA model
underpredicted concentrations 21 times.
Figure 7-1 shows that the Scavenging Model underpredicted grass concentration at the
rural site more than the EPA model. The modeled I-TEQ concentration was 1.85 pg/g, less than
one-third the measured I-TEQ concentration of 6.0 pg/g. The absolute error was 1.325, and the
bias was its negative counterpart, -1.325. This means that the model underpredicted
concentrations in all instances, and that this underprediction was by an average factor of 3.8
(e°25) •
Figure 7-2 shows the EPA and the Scavenging Model predicting essentially the same
concentration for nearly every data point, and both underpredicted grass concentrations
significantly. The TEQ concentrations predicted by the EPA and the Scavenging Models were
3.26 and 2.94 pg/g, respectively, while the observed TEQ concentration was 7.35 pg/g. The bias
and absolute differences for both models on this data set also mirrored each other. For the EPA
Model, the bias and absolute differences were -1.01 and 1.09, and for the Scavenging Model, the
bias and absolute difference were nearly the same at -1.07 and 1.15. These results indicate that
both models underpredicted by about the same factor of 3.
One reason for this similarity in performance is that the EPA model reduced to principally
a particle-phase deposition model; nearly all the dioxin was in the particle phase for the "urban"
setting at 10 °C. Like the Scavenging Model, therefore, plant concentrations were mostly a linear
function of total air concentrations for the application of the EPA Model at the industrial site.
As described earlier, the data at the industrial site was not nearly as well correlated as the
data at the rural site - the correlation between air and grass data at the industrial site was 0.66
compared to 0.92 at the rural site. One factor likely to have influenced this is the fact that the
soil at the industrial site was much higher for some of the dioxins as compared to the rural site.
While the soil concentrations were not reported by Jones and Duarte-Davidson (1997), Sandalls
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(1996) reported exceedingly high concentrations of TCDD (up to 9400 ppt, and several hundred
ppt even 4-5 km from the major air source identified in the Bolsover area), and elevations in
2,3,7,8-TCDD, PCDD and TCDF in soils near the industrial site. Not ironically, these same four
compounds, along with 1,2,3,6,7,8-HxCDD, were identified earlier as the compounds which had
high grass concentrations at the industrial site despite air concentrations that were comparable to
air concentrations of the same compounds at the rural site. If these five airgrass pairs are
subtracted from the industrial site data set, the correlation between air and grass data now is
much improved: it is at 0.80, up from 0.66. Also, the absolute error for both the Scavenging and
the EPA Model for this smaller observed:predicted test improve to 0.61, indicating that modeled
grass concentrations are now within exp [0.61] = 1.85 of measurements concentrations, rather
than within a factor of 3.0. Obviously, the grass concentrations of these five compounds appear
to have been influenced by high soil concentrations, and the ability of the models to reproduce
these concentrations is limited because they are air-to-grass models and not air/soil-to-grass
models. While it has been demonstrated that there is essentially no translocation from soil to
above-ground plants (although pumpkins, cucumbers, squash, and other members of the
cucumber family have been shown to translocate dioxins for an unknown reason (Hulster, et al.,
1994)), there may have been some rainsplash impact or soil-to-air-to-plant impacts such as from
wind erosion or soil volatilization for these compounds at the industrial site.
On the other hand, it was also true that air concentrations reasonably similar at the
industrial and rural site led to lower grass concentrations at the industrial site for all the other
dioxin and furan compounds. This is a trend that can possibly be explained and modeled by the
EPA Model of this evaluation. For an urban setting, more of the dioxins are modeled to partition
into the particle phase. As will be described below when discussing the calibration of the
Scavenging Model, vapor phase dioxins have been shown to have a greater impact to plants
compared to particle phase dioxins. Therefore, equivalent total air concentrations in a rural and
an urban setting would lead to higher vegetation concentrations in the rural setting, because
dioxins partition more into the vapor phase in such a setting, both in reality and as modeled by
the EPA Model.
Table 7-2 shows the comparison of the measured grass concentration of 2,3,7,8-TCDD
with the modeled vapor transfer concentration using the EPA vapor transfer model and the two
Vapor Deposition Models. It is clear from this table that the vapor deposition algorithm, as
parameterized by Smith et al. (1995) and Trapp and Mattheis (1995), predicts concentrations that
are 2 to 4 times lower than predictions made by the vapor transfer algorithms of the EPA Model,
and even lower still than observed grass concentrations. As will be described below, vapor phase
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deposition velocity and the decay rate of the dioxins on the plant are two parameters likely to
have been assigned inappropriate values for this exercise, and also likely to be difficult to assign
in any application of the vapor deposition approach.
Whereas the scavenging ratio of 9.0 may have been appropriate for the field data used by
McLachlan (1995) in the development of this approach, it is by far too low for this particular data
set. A calibration exercise was performed on the 22 rural air/grass data points. In this exercise,
the least squares fit of the difference between predicted and measured log grass concentrations
was sought. The best fit was found at the constant scavenging coefficient of 36.4. With this
value, the goodness-of-fit measures improve substantially: the bias goes to 0 (by definition of the
least squares fit) from -1.325, and an absolute error goes to 0.417 (predictions are within a factor
of 1.5, sometimes higher, sometimes lower) from 1.325 (predictions are always lower by about a
factor of 4.0).
A critical assumption of the scavenging approach is that vapor and particle-phase dioxins
are scavenged equivalently from the air. Therefore, a constant scavenging coefficient can be
applied to total air concentration to predict total grass concentration. The error terms for the
best-fit scavenging ratio suggest this might be reasonable. However, this assumption is not
supported by the data in this field site. Figure 7-3 shows the scavenging ratios calculated for the
22 airgrass data points of the rural field site graphed as a function of the degree of chlorination.
For example, there are four data points plotted for 4 on the x-axis: 2,3,7,8-TCDD, 2,3,7,8-TCDF,
the TCDD homologue group, and the TCDF homologue group. The scavenging ratios are simply
calculated as the grass concentration (in pg/g dry weight) divided by the air concentration (in
pg/m3) at the rural field site (with subtractions of congener concentrations from homologue group
concentrations). As seen in the figure, there is a clear trend in that the scavenging ratio appears
to generally decrease from the tetra to the hepta degrees of chlorination, with perhaps an increase
at the octa degree of chlorination. It also suggests more of a trend for the dioxins as compared to
the furans: there may be a higher scavenging ratio, in general, for the dioxins.
The experiments on Welsh Ray Grass (Welsch-Pausch, et al., 1995) used to calibrate the
EPA's air-to-leaf transfer factor (Lorber, 1995) provided a reason for this trend: when blocking
out the particle deposition impacts to potted grass, the authors found that the grass concentrations
of the tetra through hexa chlorinated dioxins and furans were similar to concentrations in potted
grass where particle depositions were not blocked out. The authors concluded that the plant
concentrations for these dioxin/furan homologue groups were dominated by vapor-phase dioxins,
even though the total air concentration itself was not necessarily dominated by the vapor phase.
Therefore, given these experimental results, it follows that the lower chlorinated congeners
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would have a larger overall scavenging coefficient. The field data certainly shows that trend;
Figure 7-3 shows larger scavenging coefficients for the lower chlorinated dioxins and furans.
Figure 7-4 compares measured and modeled deposition amounts for the rural and
industrial site combined. It is clear that the modeled rates of deposition were consistently higher
than the measured rates. There was a high degree of correlation between measured and modeled
rates, however, with a correlation coefficient of 0.99. The absolute error and bias were 1.16 and
1.11, respectively, suggesting that the model predictions were about 3.2 times higher than
observed. This would indicate a systematic bias, either that the model tended to overpredict
depositions or that the measurements tended to under-represent depositions.
If the model tended to overpredict deposition, this may have been due to inappropriate
parameter assignment: too rapid a velocity of deposition, or too much dioxins assumed to be in
particle phase. Measured deposition velocities can be calculated from the data of Jones and
Duarte-Davidson (1997) in Table 7-1 simply as the deposition flux divided by the air
concentration (with proper conversions). Average velocities calculated this way were 0.06 cm/s
for the rural site and 0.08 cm/s for the industrial site and only one calculated deposition velocity
was greater than the 0.20 cm/s velocity assigned for this modeling exercise. The predicted
depositions would be lower still if measured particle phase fractions were used instead of
modeled fractions since, measured particle-particle fractions of dioxins tend to be lower than
modeled using the Junge model for vapor/particle partitioning. For this particular field site,
measured vapor/particle fractions were not available to evaluate this possibility.
On the other hand, it could be the case that the deposition collectors are underestimating
depositions. Jones and Duarte-Davidson (1997) suggest two possible causes for the upturned
frisbees to be underestimating deposition: 1) they are smooth and therefore less efficient at
capturing particles as compared to leafy vegetation or ground surfaces, and 2) dioxins in wet
deposition can be adsorbed onto the sampler surface and presumably, not be available to be
measured.
In any case, it can be concluded that the deposition model, which was the simple product
of the particle-phase reservoir times a deposition velocity of 0.20 cm/s, resulted in the deposition
amounts that about 3 times higher than were measured in the rural and industrial sites of this data
set.
In the introduction to Section 7.2.1, this exercise was described as both a model
validation and model comparison exercise. It can be concluded that the EPA model was
reasonably successful in this validation with the rural site field data, but less successful on the
industrial site data. On the other hand, both the vapor transfer and scavenging models could not
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be successfully validated on this field data. In the case of the vapor transfer model, the likely
shortcoming was in the assignment of the parameters, vapor deposition velocity and degradation
of vapor phase 2,3,7,8-TCDD deposited on the grass. In the case of the scavenging coefficient,
the value of 9.0 suggested in its development (McLachlan, 1995) was too low. One can calibrate
both models such that the fit between observed and predicted grass concentrations is favorable.
These calibrated models can then be applied to other field data sets in order to see if the
calibrated values are "valid". The deposition portion of the EPA model was found to
consistently overpredict deposition, which could be due to either inappropriate parameter
assignments, or the tendency of the upturned frisbee to underestimate deposition amounts.
7.2.1.4. Literature Comparisons of Air-to-Plant Modeling Approaches
Douben, et al. (1997) presents an exercise comparing the three air-to-plant approaches
that were compared in the previous section: the approach in this document, the scavenging
approach, and the vapor deposition approach. Their exercise used, as observed air data,
concentrations from a semi-rural site in the United Kingdom (UK). This was not the same as the
rural site in the Jones and Duarte-Davidson (1977) study. Since this data set had several non-
detects, the authors supplemented it with data from a rural site showing similar detected
concentrations in Germany; the final observed air concentrations were crafted from these two
data sets. The observed grass data came from a different site in the UK. Comparing model
performance on a set of data from a single site, as was done in the previous section, is preferable
to "crafting" observed air and grass data. Still, if the data can be considered representative, than
using data from different sites may not be invalid; developing representative air and beef profiles
was done for the air-to-plant-to-beef model validation described in Section 7.3.11 below. The
application of the approach of this document used an earlier vapor transfer factor, from Lorber
(1995), which also had an earlier vapor/particle partitioning scheme. They used the vapor
deposition approach described and parameterized by Smith, et al. (1995) for all dioxin congeners,
not just 2,3,7,8-TCDD - the vapor deposition velocity (0.78 cm/sec) and plant degradation (0.495
day"1, corresponding to a half-life of 1.4 days) parameters were assigned the same the same for all
congeners. For the vapor deposition algorithm, they used measured vapor fractions (from the
site in Germany) as compared to use of the modeled vapor fractions in the exercise above.
Finally, they used the scavenging approach promoted by McLachlan (1995) including the
assignment of a scavenging coefficient of 9 for all congeners.
In general, they showed some of the similar trends that were described above. The
models of this assessment had the highest predicted grass concentration; this was also the trend
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in the exercise described above. However, these predictions were higher than the grass
observations of the crafted air/grass data set in Douben, et al. (1997), sometimes by upwards of a
factor of 10. The vapor transfer approach was the lowest predictor, but in their case, predictions
were usually within an order of magnitude, sometimes within a factor of 2. The scavenging
approach performed the best on their data set. Similar to the conclusion in Jones and Duarte-
Davidson (1997), Douben, et al. (1997) concludes the following regarding vapor and particle
atmospheric scavenging (p. 342 in Douben, et al. (1997)): "However, an implication of the good
predictions obtained across the range of PCDD/Fs with the scavenging model is that airborne
PCDD/Fs which may be present either in the vapour- or particulate-phase appear to transfer with
similar efficiencies to pasture grass and may remain associated with the grass after deposition."
However, even in the crafted air and grass profile, there is similar evidence as described above in
that the scavenging coefficient increases as the degree of chlorination decreases, at least clearly
so for the tetra congeners. From their data, calculated scavenging coefficients are: 57 for
TCDD/Fs (n=2), 9 for PCDD/Fs (n=3), 5 for HxCDD/Fs (n=7), 11 for HpCDD (n=3), and 11 for
OCDD/Fs (n=2).
Currado and Harrad (1998) evaluated two of the three modeling approaches described in
the previous section to a data set including air concentrations, deposition amounts, and grass
concentrations of 20 individual PCB congeners from an urban site in Birmingham, UK. The two
models they tested were the EPA air-to-leaf model (which they further developed for CDD/Fs to
include a soil-to-plant algorithm as described in Harrad and Smith (1997), although they only
used the air-to-plant algorithm for this application to PCBs), and the Scavenging Model. For the
EPA model, they partitioned the PCBs into particle and vapor phases using the Junge-Pankow
model advocated in this assessment, corrected for air temperature, and then transferred the vapor-
phase and deposited the particle-phase PCBs onto the grass. The vapor phase transfer factors
were determined as a function of the PCB log Kow and H, as described in Lorber, et al. (1994),
including an empirical reduction factor of 40. For the test of the Scavenging Model, they simply
plotted total (vapor + particle) air concentrations against measured air concentrations, and
investigated the correlation between the two. According to the Scavenging Model approach, the
total air concentration times a constant (equal to the volume of air being "scavenged" of PCB to
produce the grass concentration), which is the same constant for all PCBs, should equal the grass
concentration.
Using the EPA framework, they found very good agreement between predicted and
measured grass concentration for the PCBs: observed/predicted ratios ranged from 0.34 to 1.97,
with a mean of 0.93 and a correlation coefficient of 0.46. They also found good agreement
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observed and predicted vapor/particle partitioning (observed/predicted particle fraction ratios
ranged from 0.17 to 2.15, mean of 0.89 and correlation coefficient of 0.59) and observed and
predicted particle deposition fluxes (observed/predicted ratios from 0.33 to 4.19, mean of 1.33,
and correlation coefficient of 1.09).
On the other hand, they did not get as good results with the Scavenging Coefficient
model. They did find that this would work for the penta- and hexachlorobiphenyls - their grass
scavenged the PeCBs and HxCBs present in 22 m3 of air, but there was not a similar relationship
found for tri- and tetrachlorobiphenyls. Although the authors didn't investigate further, there
may be a relationship between portion of the PCBs in the particle-phase, there would be more in
the particle phase as the degree of chlorination increases (as it is with CDD/Fs), and the
capabilities of the Scavenging Approach.
7.2.2. An Alternate Modeling Approach for Estimating Water Concentrations Given a
Steady Input Load from Overland Sources
A study to evaluate the bioaccumulation of 2,3,7,8-TCDD in fish in Lake Ontario
included an extensive modeling exercise (EPA, 1990a). The model used was WASP4 (Ambrose,
et al., 1988). This is a substantially more complicated model than used in this assessment. The
underlying principal for the WASP4 model is a conservation of mass. Contaminant source
terms, described in mass/time units, enter what are termed control volumes, or segments. The
contaminant partitions between sorbed, bound, and dissolved phases; it is not required to specify
whether the contaminant enters via soil erosion, water runoff, surface deposition, or otherwise.
Contaminants are, however, assumed to enter via the surface or as part of inflows to the water
body, in contrast to ground water recharge. The mass transported into a segment is either
transported out of the segment, accumulates in the segment, or is transformed by chemical or
biological reactions.
As noted, 2,3,7,8-TCDD input into the Lake Ontario application partitions within the
water column into a sorbed compartment, a dissolved compartment, and a bound compartment.
This bound compartment is further described as non-settling organic matter. Three analogous
compartments receive 2,3,7,8-TCDD in the bottom sediment layer. Several exchanges between
the six compartments and contaminant losses within each compartment are modeled. For
example, losses from water column compartments include downstream transport, volatilization
and photolysis; the loss mechanism from the bottom sediment layer is sedimentation. Exchanges
between compartments consider partitioning, diffusion, and sediment settling and resuspension.
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This model requires substantial parameterization. Once values were selected for the Lake
Ontario application, an evaluation was made on the impact of different levels of 2,3,7,8-TCDD
input. Dynamic and steady state results were discussed. Principally examined for the steady
state results were the concentrations of bottom sediment sorbed 2,3,7,8-TCDD and water column
dissolved (soluble) phase 2,3,7,8-TCDD. A given level of steady 2,3,7,8-TCDD input, in kg/yr,
resulted in a steady state concentration sorbed to bottom sediment and dissolved in the water
column.
The premise in both the Lake Ontario steady state application of WASP4 and the water
concentration algorithms in this assessment is that contaminants continue to enter water bodies
over time unabated. Ground water entry of contaminants is not considered in either approach.
Although a direct modeling comparison cannot be done, it is possible to slightly adjust the
algorithms of this assessment to evaluate how results from a simple partitioning approach would
compare with results from the complex fate and transport approach of the WASP4 steady state
application.
Assume a surface water body is initially free of contaminant and at time t equals 1 day, a
strongly hydrophobic contaminant, such as the dioxin-like compounds of this assessment, begins
to enter a lake. Assuming the contaminant enters via soil particles, as in the approach of this
assessment, it will then partition between those soil particles and surrounding water. The soil
particles will slowly move toward the bottom of the lake at a rate described by a particle settling
velocity. A settling velocity of 1 m/day is assumed in the Lake Ontario simulations. The amount
of time it takes to settle to the bottom once entering from the surface equals the lake depth
divided by this settling time. The Lake Ontario depth was 86 m. Therefore, it might take 86
days to settle. This, of course, neglects resuspension of settled particulates. With this simplistic
framework, a steady state amount coming into the lake after 86 days is matched by an amount
depositing onto the lake bottom; the amount of contaminant within the water column has reached
steady state. Water concentrations can then be estimated assuming equilibrium partitioning.
Results of sediment and water column steady state concentrations are described for any
loading of 2,3,7,8-TCDD in the WASP4 steady state application; those loadings are described in
kg/yr. Loadings in kg/yr are easily correlated to a steady state water column amount, given the
above analysis. For example, a loading of 1.0 kg/yr could translate to a within water column
steady state amount of 0.24 kg (1.0 kg/yr * (86 d)/(365 d/yr)).
This steady water column amount partitions between suspended sediment and
surrounding water. First, the total concentration (sorbed + soluble) simply equals:
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Ctn. = 1000 - (7-
tot VQL (.'
where:
Ctot = total concentration, mg/L
LD = water column steady state amount of contaminant, kg
VOL = lake volume, m3
1000 = converts kg to mg and m3 to L
The dissolved phase portion of total is given by:
where:
Cwat = soluble phase water concentration, mg/L
Ctot = total concentration, mg/L
Kdssed = partition coefficient between suspended sediment and surrounding water,
L/kg
Koc*OCssed
Koc = organic carbon partition coefficient, L/kg
OCssed = fraction organic carbon of suspended sediments
TSS = total suspended sediments, mg/L
10"6 = converts mg/kg to mg/mg
Parameters in this equation for the Lake Ontario WASP4 application include VOL, Koc,
OCssed, and TSS. Lake Ontario volume was given as 1.68 x 1012 m3, Koc was estimated for the
WASP4 application as 3,162,000, OCssed was estimated at 0.03, and TSS was estimated 1.2
mg/L. For a steady load of 1 kg/yr and a resulting LD of 0.24 kg, the steady state water column
2,3,7,8-TCDD concentration, using the simplistic approach described above, is estimated as 0.13
pg/L (ppq). The steady state water column concentration estimated by WASP4 given the same
parameters and a load of 1 kg/yr is roughly 0.20 pg/L. An uncertainty analysis done with these
WASP4 results concluded that 95% confidence limits around this prediction are 0.03 and 0.40
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This would seem to imply that the simple partitioning approach used in this assessment
compares favorably with the more complex fate and transport modeling assessment using
WASP4, for Lake Ontario.
7.2.3. Estimating Fish Tissue Concentrations Based on Water Column Concentrations
Rather than Bottom Sediment Concentrations
EPA prepared a document titled, "Interim Report on Data and Methods for Assessment of
2,3,7,8-Tetrachlorodibenzo-p-Dioxin Risks to Aquatic Life and Associated Wildlife" (EPA,
1993). That document provides details on the two key bioaccumulation parameters used for the
methodologies of this document, the Biota Sediment Accumulation Factor, BSAF, used for the
soil and stack emission source categories, and the Biota Suspended Solids Accumulation Factor,
BSSAF, used for the effluent discharge source category. That document also discussed several
water column based bioaccumulation factors, which are the focus of this section. A later
publication prepared by the same group at EPA titled, "Great Lakes Water Quality Initiative
Technical Support Document for the Procedure to Determine Bioaccumulation Factors" (EPA,
1995a), also describes water and sediment based bioaccumulation factors.
Before discussing these factors, it is noted that food chain modeling is a well developed
alternate approach for estimating fish tissue concentrations of bioaccumulating contaminants
(Thomann, 1989), which has also been applied to 2,3,7,8-TCDD (Parkerton, 1991). This
approach is significantly more complex than the bioaccumulation/biotransfer approach of this
methodology. It involves detailed site-specific characterizations, specifically the identification
and transfer modeling between trophic levels of a food chain in a water body. Food chain
modeling is a mechanistic approach, while the transfer approaches of this methodology are
empirical. No judgement is rendered as to the relative merit of food chain models versus use of
bioaccumulation coefficients. If detailed site-specific data is available, and given time and
resources, assessors should consider food chain modeling for estimating fish tissue
concentrations.
One water column measure which has been classically used is termed the
Bioconcentration Factor, or BCF. Bioconcentration refers to the net accumulation of a chemical
from exposure via water only, and BCFs are most often obtained in laboratory conditions. BCFs
are defined as the ratio of the chemical concentration in organism (mass of chemical divided by
wet weight of organism tissue) to that in water.
Another water column measure of the potential for a contaminant to accumulate in fish
tissue is termed the Bioaccumulation Factor, or BAF. Bioaccumulation refers to the net
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accumulation of a chemical from exposure via food and sediments as well as water. Similar to
the BCF, BAFs are defined as the ratio of the chemical concentration in the organism to that in
the water.
For chemicals that are not strongly hydrophobic (unlike the dioxin-like compounds), the
distinction between bioconcentration and bioaccumulation is small. Whereas food intake is
generally a few percent of body weight per day, water passing over gills will equal hundreds to
thousands times the organism weight per day, depending on species, activity, temperature, and
other factors. Given this, the concentration of chemical in food must be 3 or more orders of
magnitude greater than that in water before food can substantially contribute to uptake. EPA
(1993) estimates that food intake becomes a critical contributor to the accumulation of
contaminants in fish tissue for contaminants with log Kow of 5 and greater.
Since the dioxin-like compounds fall into this category, the remainder of this section will
focus on the Bioaccumulation Factor. EPA (1993) defines steady-state lipid-based BAFs for
total chemical in water and freely dissolved chemical in water (i.e., chemical which is truly in a
dissolved phase and not bound to dissolved or suspended particulate organic materials) as:
(7.7a)
SSBAF? - iO. (7.7b)
where:
ssBAF/ = steady-state lipid-based BAF for total chemical in water, unitless
Clipid = the mass of contaminant in fish lipid tissue divided by the mass of fish
lipid tissue, mg/kg
Cwl = the mass of total contaminant in water divided by the mass of water in the
water body, mg/kg (note: 1 L water nearly equals 1 kg, therefore, 1 mg/L
can be assumed to equal 1 mg/kg)
= steady-state lipid-based BAF for freely dissolved chemical in water,
unitless
the mass of freely dissolved contaminant in water divided by the mass of
water in the water body, mg/kg
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EPA (1993) then develops relationships between BsEAE^ and ssBAF/, based on dissolved
and paniculate organic carbon reservoirs in the water column, and partition coefficients for these
reservoirs. This is meaningful in complex modeling where these two reservoirs of organic
carbon can be accounted for, such as in the WASP4 model. Alternately, EPA (1993) defines the
TBFOC, a total binding factor to organic carbon, which empirically considers the reservoir of
dissolved organic material (i.e., increases total binding and reduces truly dissolved phase
concentrations) when such a reservoir is not explicitly modeled. The modeling frameworks in
this assessment have only one compartment of suspended material to which contaminants sorb,
with one associated organic carbon content. A second reservoir to which contaminants bind, the
reservoir of dissolved organic material, is not modeled.
EPA (1993) developed a ssBAF/ and a BsEAE^ for lake trout, 2,3,7,8-TCDD, and for
Lake Ontario 1987 contamination conditions. The WASP4 model was used to model three
hypothetical loading conditions that might have resulted in fish tissue concentrations observed in
1987: steady state loading, a steady state loading followed by a 90% reduction in annual loads for
20 years (i.e., 1968-1987), a steady state loading followed by a 100% reduction (i.e., no loading)
for 20 years. The BSAF for lake trout estimated for 1987 data is given in EPA (1990a) as 0.07.
The BSAF is determined from measured bottom sediment concentrations and fish tissue
concentrations; an assumption of historical loading is not necessary for BSAF development.
Details of the Lake Ontario study, including initial modeling efforts with the WASP4 model can
be found in EPA (1990a). Slight refinements to the WASP4 runs were later made (cited in EPA,
1993 as an unpublished report: Endicott, D.D., W.L. Richardson, T.F. Parkerton, and D.M.
DiToro. 1990. A steady-state mass balance and bioaccumulation model for toxic chemicals in
Lake Ontario: Report to the Lake Ontario Fate of Toxics Committee. U.S. EPA, Environmental
Research Laboratory, Duluth, MN: 121 pp). The BAFs determined in these later runs will be
tested using the models of this assessment.
In order to do this exercise, all critical model parameters used to develop the BAFs for
this WASP4 modeling exercise will be used in the model framework of this assessment. The
most critical parameter is the organic carbon partition coefficients, Koc, assumed for 2,3,7,8-
TCDD. BAFs were determined assuming Koc of 107 and 108. Since the models of this
assessment assume steady loading into water bodies, only the BAFs developed under "steady
state" loading conditions will be used. As noted, the WASP4 model considers binding to more
than one suspended compartment. The increased binding can be modeled using a TBFOC, which
was assumed to be 1.5 for Lake Ontario by Cook. For the models of this assessment, this factor
will be applied to Koc - it effectively increases Koc by 50%. The concentration of suspended
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solids in Lake Ontario and used in the WASP4 modeling exercise was 1.2 mg/L. The other
critical parameters are the fraction organic carbon contents of the suspended solids and the
bottom sediments, OCssed and OCsed, respectively. Assigned values to these parameters, based on
Lake Ontario data, in the WASP4 exercise and in this exercise were 0.15 (15%) and 0.03 (3%),
respectively.
Since the purpose of this exercise is to evaluate how the modeling approaches of this
document perform using the BSAF or the alternate BAF approach, duplicating the source
strength terms used in the WASP4 modeling exercise is not necessary. The pertinent question is,
with a given source strength, how would both approaches predict fish tissue concentrations. For
simplicity, the background demonstration scenario described in Chapter 5 will be adopted for use
here. In this scenario, the soil within the watershed is assumed to at a uniform concentration.
For the exercise here, a uniform concentration of 1.0 ppt for 2,3,7,8-TCDD will be assumed.
In summary, the parameters for this exercise including the steady state BAFs are:
Testl: Koc= 1.5*107;ssBAF!d= 1.9xl06; ssBAF/= 5.16xl05; BSAF = 0.07
Test2: Koc= 1.5*108; ssBAF^ 1.9xl07; ssBAF/= 6.78xl05; BSAF = 0.07
The 1.5 in the Kocs was the TBFOC noted above. The BAFs specific to each Koc were the ones
developed also specific to those Koc in the WASP4 modeling exercises. For both tests: soil
concentration of 2,3,7,8-TCDD = 1.0 ng/kg (ppt), total suspended solids (TSS) = 1.2 mg/L, the
organic carbon content of suspended sediments (OCssed) = 0.15, and the organic carbon content of
bottom sediments (OCsed) = 0.03. Whole fish tissue concentrations are estimated as Clipid * flipid,
where flipid is 0.07.
The whole fish tissue concentration for the BSAF approach in Test 1 was estimated to be
0.61 ppt. Using the ssBAF/ and BsEAE^, the whole fish tissue concentrations were estimated
very nearly to be the same at 0.867 ppt for ssBAF/ and 0.863 ppt for BsEAE^. The test results
did not change substantially for Test 2. The BSAF approach led to a fish tissue concentration of
0.62 ppt, and the concentration was identical for BAFs at 0.869 ppt.
While it appears that the water column based approaches estimate fish tissue
concentrations identical to each other and very close to estimates made based on bottom
sediment concentrations, in fact the performance of the models differ when parameters are
changed in these tests. More incoming 2,3,7,8-TCDD can be modeled to remain in the water
column with an increase in the reservoir of total suspended solids, the TSS parameter initialized
in above tests at 1.2 mg/L. Continuing with Test 1 parameters above, increasing TSS from 1.2
mg/L to 10 mg/L has the following changes to fish tissue concentrations: 0.54 ppt for the BSAF
test, 4.85 ppt for the ssBAFlt test and 0.76 ppt for the BsEAE^ test. Decreasing the organic
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carbon content of the suspended solids will have the effect of reducing the amount of incoming
2,3,7,8-TCDD simulated to remain in the water column, while increasing the amount modeled to
reside in bottom sediments (because a mass balance of 2,3,7,8-TCDD is maintained), and also
increases the dissolved phase concentration. Changing the TSS back to 1.2 mg/L and reducing
the organic carbon content of suspended solids from 0.15 to 0.05 results in the following changes
to fish concentrations: 0.62 ppt for the BSAF test, 0.45 ppt for the ssBAF/ test and 0.88 ppt for
the ssBAF^ test. These two tests have demonstrated the variability in fish tissue concentrations
when key water column parameters are altered. Fish concentrations would also differ if the key
bottom sediment parameter, the organic carbon content of bottom sediments, was different.
Returning to original Test 1 parameters and reducing the organic carbon content of bottom
sediments from 0.03 to 0.01 results in the following changes to fish concentrations: 1.73 ppt for
the BSAF test, 2.45 ppt for the ssBAF/ test and 2.44 ppt for the BsEAE^ test.
The predictions for all tests might be considered reasonably close, given the uncertainties
in the bioaccumulation and water modeling parameters. The one test described above where the
BSAF and BAF approaches led to the most differences was the one which increased suspended
material contents from 1.2 mg/L to 10 mg/L. In that case, nearly a ten-fold difference was noted
in fish concentrations with the ssBAF/ as compared to the BSAF or the BsEAE^.
An important consideration in using the water column based approaches is that the BAFs
developed by Cook (or that could be developed otherwise) are based on modeled rather than
measured water column concentrations, and measured lake trout tissue concentrations. In that
sense, the BAFs were calibrated for Lake Ontario conditions and specific to the WASP4
modeling exercise. Therefore, using these BAFs in the modeling framework of this assessment
is, strictly speaking, invalid. Further, the values of the BAFs varied depending on the
assumptions on historical loadings into Lake Ontario. As noted above, three loading conditions
were tested. The steady state BAFs were given above. For the 20 year - 90% reduction tests, the
following BAFs were determined: BAF^ was 3.03xl06 for Koc = 107 and 2.86xl07 for Koc =
108, and BAF/ was 8.26xl05 for Koc = 107 and 1.02xl06 for Koc = 108. For the 20 year - 100%
reduction tests, the following BAFs were determined: BAF^ was 3.86xl06 for Koc = 107 and
3.40xl07forKoc= 108, and BAF/was l.OSxlO6 for Koc = 107 and 1.21xl06 for Koc = 108. The
BSAF developed for lake trout for Lake Ontario was developed using measurements of both fish
tissue and bottom sediment concentrations.
Both the BSAF and BAF are most appropriately developed using site specific data
(coupled with a modeling exercise for BAF). Inasmuch as that can be impractical or difficult for
many sites, efforts are underway to determine the general applicability of BSAFs and BAFs
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determined for one site to other sites. EPA (1993) proposes that BAT^s for different congeners
can be roughly estimated as the BAT^ for 2,3,7,8-TCDD multiplied by the ratio of the BSAF for
the congener and the BSAF for 2,3,7,8-TCDD. Such an estimate will incorporate differences in
uptake, metabolism and chemical partitioning but not differences caused by chemical loss
processes such as volatilization and photolysis. This approach for estimating BAT^s for other
congeners does allow for some generality since sediment and fish tissue data for other congeners
and water bodies is available.
Another bioaccumulation term discussed in one literature article for dioxin is termed the
Regulatory Bioaccumulation Multiplier, or RBM (Sherman, et al., 1992). Multiplication of this
term and a "nominal water concentration" estimates a 3% lipid fish concentration. A nominal
water concentration equals an amount of a contaminant, 2,3,7,8-TCDD in this application, added
or entering a water body over time, divided by a flow volume over that same time. Assuming a
fish lipid content of 3%, an RBM of 5000 was recommended based on examination of laboratory
flow through data, simulated field data, and actual field data (EPA's Lake Ontario study and data
downstream of pulp and paper mills). Dividing the 5000 by 0.03 gives 1.67*105, and this
number is now analogous to the BsEAE/ developed by EPA (1993) described above, and in the
same range as the 5.2-6.8*105 range for ssBAF/.
7.2.4. Other Modeling Approaches and Considerations for Air Concentrations Resulting
from Soil Volatilization
Volatilization was modeled for the soil contamination source category, using an approach
given in Hwang, et al. (1986), developed for PCB flux from soils. Another model often used to
estimate volatilization from soil was presented by Jury and coworkers in a series of papers in the
early 1980s (Jury, et al., 1983, 1984a,b). The full solution to Jury's model is complex and not
amenable to spreadsheet programming structure. It was available for use in this exercise in an
EPA model known as EMSOFT (EPA, 1997a). There is a steady state, simplified solution to the
Jury model which is used in EPA's Superfund Soil Screening Methodology (EPA, 1996). Both
of these Jury approaches account for movement of the organic contaminant in the vapor phase
via diffusion and the dissolved phase via solute movement, and they also account for changes in
volatilization rate over time: volatilization decreases with time after an initially assumed soil
concentration in the surface soil depletes and deeper residues volatilize to a lesser extent (the
Hwang model also accounts for a decrease in the volatilization rate over time).
If one assumes that the contaminant moves through the soil column in only the vapor
phase, a simplification of the fundamental equations used by Jury offers another option for
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modeling soil volatilization. The steady-state vapor diffusion equation was used by Farmer, et al.
(1980a) in modeling hexachlorobenzene vapor diffusion in a laboratory soil column, and also by
Johnson and Lindberg (1995) in modeling mercury volatilization from soil. It will be applied
hereto2,3,7,8-TCDD.
The two Jury models and the vapor phase diffusion model are compared with the Hwang
approach, as applied in this document for 2,3,7,8-TCDD. Following now are more complete
descriptions of the Hwang model, the Jury models and the vapor diffusion model. The
parameters used for all models are shown in Table 1.
I. Hwang Model: Farmer, et al. (1980b) applied a basic diffusion equation to the
problem of soil volatilization of pesticides. This diffusion equation does not consider movement
of the contaminant with soil water or degradation in the soil column:
1 dCT
= 0 (7-8)
dz2 D dt
where:
CT = mass of chemical per unit soil volume, g/cm3
z = soil depth, cm
D = apparent diffusion coefficient for contaminant in soil, cm2/sec
t = time, sec
Hwang, et al. (1986) redefined this basic equation to consider instead the gas phase, CG, which
they assumed was in local equilibrium with soil concentration, Cs. Their steady state equation
was:
BD Kd 1 dCr
XV *g] J: G = Q ( .
®K* Dei 8t ^ }
where:
CG = concentration of chemical in the gas phase, g/cm3
BD = soil bulk density, g/cm3
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Kd = soil/water partition coefficient, cm3/g
= Koc * foe
Koc = organic carbon partition coefficient, cm3/g
foe = fraction organic carbon in soil, unitless
cj) = soil porosity, cm3/cm3, or unitless
KH = dimensionless Henry's Constant
H/RT (= 41 H, substituting R and T below)
H = Henry's Constant, atm-m3/mol
R = universal gas constant, 8.21* 10"5atm-m3/mol-°K
T = standard temperature, 20 °K
Dei = effective diffusivity of contaminant in soil, cm2/sec
z = distance from surface soil, cm
t = time, sec
Hwang, et al.(1986) assumed that PCBs in soil were in equilibrium between the sorbed and the
gaseous phase as follows:
(7-10)
where:
CG = concentration of chemical in the gas phase, g/cm3
KH = dimensionless Henry's Constant (= 41H, as defined above)
Cs = soil concentration, g/g (unitless form here for units consistency)
Kd = soil/water partition coefficient, cm3/g (= foc*Koc, as defined above)
They also assumed that the effective diffusion was related to the diffusion coefficient of the
contaminant in air as:
Dei = Dg $1/3 (7-11)
where:
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Dei = effective diffusion coefficient, cm2/sec
Dgair = diffusion coefficient for contaminant in air, cm2/sec
cj) = soil porosity, cm3/cm3, or unitless
Hwang, et al. (1986) solved this equation for two cases: when the contaminated soil was bare to
the atmosphere and where the contaminated soil was covered with a layer of clean soil.
Obviously, the option chosen for this methodology, and for this examination of alternate
volatilization methods, was the bare soil contamination. Their initial and boundary conditions
were: 1) the concentration of the contaminant in the air at the soil surface is 0 continually, and 2)
the concentration in the soil air just below the surface and at an infinite depth remains constant
and is a function of the soil concentration and contaminant properties - this function is given as
Equation (7-10) above.
The flux rate that occurs after a time period t is given as:
J ft) (De) (Cs) (H) (41) (10-6)
[ 00 (/) (0 ]°-5
where:
Js = average volatilization flux rate of contaminant from soil, g/cm2-s
4> = soil porosity, cm3/cm3, unitless
Dei = effective diffusivity of contaminant in air, cm2/s
Cs = contaminant concentration in soil, ppm or mg/kg
H = Henry's Constant of contaminant, atm-m3/mol
Kds = soil/water partition coefficient, cm3/g
t = time, sec
I = interim undefined term for calculation, cm2/s
[ Dd 4> ] / [ 4> + Psoil (I-*) [Kds/(41 H)] ]
PSOii = particle bulk density of soil, g/cm3
The average flux rate during the exposure period is given as 2 * JS(ED), where ED is the
exposure duration in seconds.
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It is noted in Hwang, et al. (1986) that this procedure would tend to overestimate
emissions and resulting exposures in situations involving small spills which would not involve
deep contamination. It is also noted that the average flux rate is inversely proportional to the
square root of the duration of exposure - the longer the duration of exposure, the lower will be
the average flux rate. Whereas this solution assumes an unlimited reservoir of contaminant, it is
an unsteady state solution (unlike most other solution strategies) and is essentially an average
flux rate over an amount of time defined by the exposure duration. Inherent in the solution was
the consideration that residues dissipate by volatilization at the surface layers, resulting in
contaminants diffusing upwards from deeper soil layers over time. With this longer path of
diffusion, volatilized amounts decrease, and hence the average flux over time also decreases.
The parameters required for the Hwang model are provided in Table 7-3, and these
include the initial soil concentration of 2,3,7,8-TCDD, C0, the soil parameters Psoil, foe, cj), and
BD, the exposure duration (or total time during which volatilization occurs starting from time t =
0 at the initial soil concentration, C0), and the chemical-specific parameters Dgair, Koc, and H.
II. Jury Model: This model assumes: 1) uniform soil properties throughout the soil
column to an infinite depth, 2) there is a stagnant air boundary layer at the soil surface across
which diffusion occurs and the chemical concentration at the top of this boundary layer is zero,
3) linear equilibrium liquid-solid and liquid-air partitioning is valid, and 4) degradation follows
first-order kinetics. With these conditions, the mass conservation equation is:
8CT 8J
—T- + -1 + vCT - 0 (7-13)
at oz
where:
CT = mass of chemical per unit soil volume, g/cm3
Js = chemical mass flux per unit soil are per unit time, g/cm2-sec
\i = net degradation rate, I/sec
t = time, sec
z = soil depth, cm
The volumetric total soil concentration, CT, is given as:
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CT = BD Cs + Q CL + a CG (7.14)
where:
CT = mass of chemical per unit soil volume, g/cm3
Cs = concentration of chemical on soil, g/g (expressed in unitless form here for
units consistency)
BD = soil bulk density, g/cm3
CL = concentration of chemical in the liquid phase, g/cm3
6 = volumetric soil water content, cm3/cm3, or unitless
CG = concentration of chemical in the gas phase, g/cm3
a = volumetric soil air content, cm3/cm3, or unitless
The terms BD, 0, and a are in front of the concentration terms to convert them to
volumetric units, to be consistent with the volumetric definition of CT. It should be noted that for
2,3,7,8-TCDD, which is very tightly sorbed to soil, CG and CL become very small and CT can be
calculated as Cs BD. The mass flux, J can be written as:
8CC 3CT
J = -Dr —° - Dj —± + JC, (7-15)
G L w L ^ }
where:
Js = chemical mass flux per unit soil are per unit time, g/cm2-sec
DG = soil-gas diffusion coefficient, cm2/sec
CG = concentration of chemical in the gas phase, g/cm3
DL = soil-liquid diffusion coefficient, cm2/sec
CL = concentration of chemical in the liquid phase, g/cm3
Jw = water flux, cm/sec
z = soil depth, cm
DG and DL are given by appropriate forms of the Millington-Quirk equations:
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n 10/3
u r\air
T~\ ••* T~\ u"
DG = -^ DG (7-16a)
alO/3
j-. " r\ water
DL = ~^DL (7-16b)
where:
DG = soil-gas diffusion coefficient, cm2/sec
a = volumetric soil air content, cm3/cm3, or unitless
cj) = soil porosity, cm3/cm3, or unitless
Dgair = chemical gaseous diffusion coefficient in air, cm2/sec
DL = soil-liquid diffusion coefficient, cm2/sec
DLwater = chemical liquid diffusion coefficient in water, cm2/sec
Q = volumetric soil water content, cm3/cm3, or unitless
Soil porosity is defined as the amount of void space in the soil, and can be calculated as, 1 - (soil
bulk density)/(soil particle density). A common value used for soil bulk density, in the absence
of site-specific data, is 1.5 g/cm3. Soil particle density is not site-specific, rather it is an inherent
soil property. It has been estimated to be between 2.6 and 2.7 g/cm3, and is often assigned a
value of 2.65 g/cm3. Therefore, soil porosity is calculated at 0.434 (ie., 1 - 1.5/2.65). This
porosity is comprised of air and water; that is, soil porosity, cj) = volumetric soil water content, Q
+ volumetric soil air content, a, using the terms defined above. For a soil that is sometimes wet
and sometimes dry, an average soil water content would range between field capacity, around
0.30 for typical soils, and wilting point, around 0.15. This exercise will assume a water content
of 0.23 and a porosity of 0.43, leaving air content to be calculated at 0.20.
This solution requires the partitioning of the chemical into sorbed (Cs), liquid (Cl) , and
gas (Cg) phases. The soil concentration is initially given, and liquid and gas phases are then
solved as:
~ Cs
c' " J^ <7-17)
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where:
CL = liquid phase concentration, g/cm3 soil water
Cs = soil concentration, g/g (unitless form here for units consistency)
Kds = soil/water partition coefficient, cm3/g (= Koc*foc, as above)
CG = concentration of chemical in the gas phase, g/cm3
KH = dimensionless Henry's Constant (= 41 H, as above)
Like the Hwang model, initial and boundary conditions are required for the solution to
this model. The key initial condition is that the total concentration is constant to some depth, and
that below this depth, the concentration is zero. The upper boundary condition is represented by
a stagnant boundary layer condition, and the lower boundary conditions is that the total
concentration is 0 at infinite depth. With these initial and boundary conditions, Equation (7-13)
is solved using the Laplace transform method. This is not amenable to spreadsheet calculations.
The EPA model, EMSOFT, was coded in fortran using original code supplied by Jury for his
equations, and it performs this transformation. EMSOFT calculates water flux under user-
specified water flux conditions, entering a positive flux amount (meaning a net leaching rate at
that amount), 0 (no net leaching or evapotranspiration), or a negative flux amount (meaning a net
evapotransiration rate) in units of cm/day. For this exercise, the solution at water flux = 0 will be
assumed.
The parameters required for EMSOFT are provided in Table 7-3, and these include the
initial soil concentration of 2,3,7,8-TCDD, C0, the soil parameters foe, 6, cj), a, and BD, the water
flux assumption, the depth of constant soil concentration, the exposure duration (or total time
during which volatilization occurs starting from time t = 0 at the initial soil concentration, C0),
the boundary layer thickness, and the chemical-specific parameters Dgair, D!Water, Koc, H, and the
2,3,7,8-TCDD soil half-life. Of various output options in EMSOFT, average volatilization rate
was selected.
CG - KHCL (7-18)
IJJ. Steady-State Solution to the Jury Model: This steady-state, simplified solution to
the Jury model was used in Superfund's soil screening model. The simplifying assumptions (in
addition to other assumptions of the Jury approach) which allowed for this solution were: 1)
there is no stagnant boundary layer, 2) there is no water evapotranspiration or leaching
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(equivalent to the selection of 0 water flux in the full Jury model described above), 3) the
chemical is at a uniform concentration from the soil surface until depth dz, and 4) there is no
degradation of the chemical over time. The volatilization flux rate, Js, at any given time after t=0
when the uniform soil concentration is CTO, is given by:
D -d1
Js (0 = CTO ( -* )1/2 ( 1 - exp (—^ ) ) (7-19)
ra
where:
Js = volatilization flux, g/cm2-sec
CTO = initial total concentration on a volumetric basis, g/cm3
DE = effective soil diffusion coefficient, cm2/sec
dz = depth of uniform soil concentration at t=0
t = time, sec
Equation (7-19) was derived specifically for the case where there is a finite reservoir of
contaminant to volatilize; i.e., where the depth of contamination, dz, is meaningful. At infinite
depth, this term drops out. With a combination of large enough dz and small enough DE, the
exponential term quickly approaches 0, so the parenthetical approaches 1 and can be neglected in
the solution. Mayer, et al. (1974) suggests that the infinite reservoir solution is violated for a
finite reservoir when t (time) exceeds (dz2)/(14.4*DE). With dioxin parameters, DE is calculated
to be 2*10"10. Therefore, time t calculates out to over 1000 years with dz equal to 10 cm before
the infinite solution becomes violated. Needless to say, the infinite reservoir steady state solution
can be used for dioxin, and the exponential term can be neglected. The total soil concentration, or
concentration expressed as mass divided by a volume of (soil+soil pore space), is required for
this solution, rather than just the soil concentration expressed as mass of contaminant divided by
mass of soil to which it is adsorbed. As discussed above, total soil concentration is the sum of
the concentrations in soil, soil air, and soil water, and for 2,3,7,8-TCDD and similarly very
tightly sorbed contaminants, the total volumetric soil concentration can be estimated as BD * Cs.
The effective soil diffusion is given by:
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K
DE = — - - - - (7-20)
E
ED Kds + Q + aKH
where:
DE = effective soil diffusion coefficient, cm2/sec
Dgair = chemical air-gas diffusion coefficient, cm2/sec
j-^water _ chemical liquid diffusion coefficient in water, cm2/sec
6 = volumetric water content, cm3/cm3, or unitless
a = soil air content, cm3/cm3, or unitless
cj) = soil porosity, cm3/cm3, or unitless
BD = soil bulk density, g/cm3
Kd = soil/water partition coefficient, cm3/g (= Koc * foe, as above)
KH = dimensionless form of the Henry's Constant (= 41H, as above)
As can be seen in this formulation, Equation (7-19), the maximum volatilization flux decreases
as time increases. Again, the Jury algorithm considers the depletion of the surface residues as a
function of time. The Superfund Soil Screening Guidance document (EPA, 1996) suggests that
this formulation, Equation (7-19), be run several times over the exposure duration in order to
determine the average flux. However, assuming the infinite reservoir solution which neglects the
exponential term in Equation (7-19) (and like the Hwang model), the average flux can more
easily be calculated as, 2 * Js (t), where t is the full duration of volatilization flux. That is what is
assumed for this exercise.
The parameters required for this simplified solution to the Jury model are provided in
Table 7-3, and these include the initial soil concentration of 2,3,7,8-TCDD, C0, the soil
parameters foe, 6, ct>, a, and BD, the exposure duration, and the chemical-specific parameters
Dgair, D!water, Koc, and H.
IV. State-State. Infinite Reservoir. Vapor-Phase Diffusion Only: If the liquid phase in
the mass flux Equation (7-15) above is neglected, than the flux through soil can be very simply
calculated as:
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*,
where:
Js = chemical mass flux per unit soil are per unit time, g/cm2-sec
DG = effective soil diffusion coefficient, cm2/sec
(a10/3/ci>2) Dg"; a, cj>, and Dgair defined as above
CG1 = concentration of the contaminant in soil air-filled pore space, g/cm3
CQQ = concentration of the contaminant in air at the soil-air interface, g/cm3
dz = depth over which a constant concentration is assumed, cm
Maximum vapor phase diffusion occurs when C^, the concentration of the contaminant in air at
the soil-air interface (essentially just above the soil surface), is set to 0.0, which is done for this
solution. This assumes, essentially, that wind is sufficiently high to cause a 0 concentration
directly above the soil. The concentration in air-filled pore space, CG1, is simply solved as, (KH
Cs)/Kds, as described above. The depth of the soil column will be assumed to be 10 cm in this
example.
The parameters required for this simplified vapor diffusion solution are provided in Table
1, and these include the initial soil concentration of 2,3,7,8-TCDD, C0, the soil parameters foe, cj),
a, and BD, and the chemical-specific parameters Dgair, Koc, and H.
V. Results of Alternate Soil Volatilization Model Testing: The results of this simple test
are shown in Table 7-4. This model comparison test showed that the Hwang model predicted an
average flux over 30 years roughly four times higher than the average flux predicted by the full
Jury model, and about three times higher than the simplified Jury model used in the Superfund
Soil Screening methodology. The close match between the full Jury model coded in Fortran in
EMSOFT and the simplified Jury solution coded into the spreadsheet was evidence that both
models were correctly coded and used (or that they were both incorrect in the same way, which is
unlikely). The exact reason for this three- to four-fold difference in the Jury versus the Hwang
models was not investigated, and could lie in differences in assumed boundary conditions
(Hwang, et al. (1986) discusses differences in boundary conditions between his and Jury's
models). In any case, it is judged that both models predict comparable volatilization fluxes. On
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the other hand, the vapor diffusion model predicted volatilization rates that were 100 times less
than the Jury models and about 250 times lower than the Hwang models. The reason for this
discrepancy could not be ascertained.
The Jury model was run altering the boundary layer assumption and the half-life
assumption. The simplified Jury solution assumes no boundary layer to offer resistence to
volatilization. That was one key reason that the simplified model predicted higher concentrations
than the full Jury model. When the boundary layer assumption was reduced from 0.5 cm to 0.01
cm, the volatilization rate predicted by EMSOFT increased slightly from 2.8*1049 to 3.0*1049
g/cm2-sec. Another simplification of the Soil Screening Methodology solution was that no soil
degradation was assumed. The full Jury model in EMSOFT does allow for consideration of soil
degradation. When the half-life was assigned a value of 25 years, which was what has been
assumed for dioxins which had deposited in soils from distant sources in this document
(depositing by air from incinerators or overland by erosion from contaminated soil sites), the
average volatilization dropped slightly from 2.8*10"19 to 2.2*10"19 g/cm2-sec.
These differences between the Hwang and Jury models are insignificant considering that
the compounds modeled by this methodology - the dioxins, furans, and PCBs, are all relatively
tightly bound and resist degradation in soil, so that losses in the 10"19 g/cm2-sec range represent a
minisicule part of the entire soil reservoir.
VI. Alternate Approaches for Dispersion of Soil-Emitted Dioxins: Near-field and far-
field dispersion models are used to estimate air concentrations resulting from soil emissions,
including volatilization and wind erosion, for the soil contamination source category. The near-
field model can be used to estimate concentrations which occur on-site whereas the far-field
model can be used to estimate off-site air concentrations. An alternate approach to estimating
on-site dispersion given a volatilization flux is the "box -model" approach. This simple approach
can be visualized as follows: air above soil is contained within a structure which has two walls,
say a north and south wall, and a ceiling - wind blows through the building in an east-west
direction mixing the volatilized flux. This is expressed mathematically as:
FLUX AREA 106
<7-22)
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where:
Cair = total concentration of contaminant in air, pg/m3
FLUX = average volatilization + wind erosion flux rate of contaminant from soil,
g/cm2-sec
AREA = area over which flux occurs, cm2
b = side length perpendicular to wind direction, m
\]mix = mean annual wind speed corresponding to mixing zone height, m/sec;
estimated as /^*Um, where Um is average wind speed
z = mixing zone height, m
106 = converts g to |_ig
Before testing the box-model equation, results for the approach used in this assessment
are summarized. The key factors impacting air concentration calculations for the soil
contamination source category include characteristics of the contaminant (Henry's Constant,
etc.), the duration of exposure, the area over which contamination occurs, and whether the near
field or far field dispersion algorithms are used. For the demonstration of the soil contamination
scenario, Scenario 3, the contaminated soil area was 40,000 m2 (10 acres). The exposure
duration was 30 years. The flux of 2,3,7,8-TCDD was 1.1 IxlO"18 g/cm2-sec. The air
concentration estimated for Scenario 3 where the exposure site was 150 meters from the site of
soil contamination was 0.0043 pg/m3. This calculation used the far field dispersion algorithm.
When the flux rate is input into the near field algorithms, the air concentrations is a little over ten
times higher at 0.045 pg/m3.
The values used to evaluate the box model approach were the fluxes, as given above, the
mixing zone wind speed, 2 m/sec, which is half the average wind speed assumed in this
assessment, the areas noted above, the side length, estimated as the square root of the area, and a
mixing zone height estimated initially at 2 m. The box-model air concentration is estimated at
0.55 pg/m3. This is 10 times higher than the near-field dispersion modeling and 100 times higher
than the far-field solution.
An uncertain parameter for both modeling approaches is the area of soil contamination.
The mixing zone height for the box model is also a parameter of uncertainty. Users of the box
model approach have often assumed a conservative 2 m height approximating the height of
exposed individuals. However, others have claimed this is far too low a mixing height,
suggesting 10 meters or even an atmospheric height closer to 100 meters. Higher mixing zone
heights would have brought the box model estimations more in line with estimations made in this
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assessment. The closest analogous parameter in the dispersion model to the mixing zone height
is the height of exposed individual, which is more unambiguously the breathing zone height of 2
m.
One key assumption concerning the exposure site air concentrations resulting from a
distant area of soil contamination should be questioned. The current approach assumes that air-
borne contaminates originate at the site of contamination and are transported to the site of
exposure. On the other hand, this assessment also assumes that exposure site soil becomes
contaminated over time due to erosion. It is at least plausible that volatilization and wind erosion
from soils other than the area of elevated contamination would contribute to air-borne
contamination, and concentrations to which individuals are exposed to at sites of exposure near
sites of contamination.
This was tested by using the near field algorithms and assuming soil concentrations
predicted to occur at the exposure site. In more detail, the soil contamination demonstration
scenario included a 10 ha field at 1 ppb 150 m from the exposure site, also at 10 ha. The soil
concentrations estimated to occur at the exposure site were 0.39 ppb for a 2-cm no-till depth and
0.06 ppb for a 20 cm tilled depth. The near field algorithms for volatilization, wind erosion, and
dispersion were run starting with these concentrations, and resulting concentrations were
compared with those estimated to occur only from volatilization and wind erosion from the
contaminated site followed by transport to the exposure site. The air concentration estimated to
occur from untilled soil is about 4.3 times higher than that estimated to occur from the off-site
area and transported; the air concentration estimated to occur from tilled soil is 25% less than
estimated to occur from volatilization and transport.
This might imply that exposure site air concentrations are being underestimated if air
concentrations at the site of exposure are assumed to only originate at the site of contamination,
and not also at the site of exposure, or even from other areas. This exercise implies that the
underestimation might be less than a factor of 5.0. Of course, this conclusion is contingent on
the off-site impact algorithms which have estimated that a 0.39 or a 0.07 ppb soil concentration
will result 150 meters from an area whose concentration is 1.00 ppb.
7.2.5. Alternate Models for Estimating Plant Concentrations from Soil Concentrations
The models of this assessment separate above and below ground vegetation for estimating
concentrations. Root concentrations (roots are below ground vegetation) are a function of soil
water concentrations and a Root Concentration Factor, RCF. Above ground vegetation, which in
this assessment include above ground fruits and vegetables, pasture grass, and cattle feed, are
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modeled as a function of vapor phase transfers and wet plus dry particle depositions. This
section examines one alternate approach for above ground vegetation; alternate approaches for
below ground vegetation could not be found.
One approach to modeling plant concentrations would be with passive uptake via
evapotranspiration. The assumption here is that soluble phase contaminants move passively with
transpiring water. This approach has been applied for contaminants which are soluble in water.
However, nearly all the evidence suggests that this would not be appropriate for the dioxin-like
compounds. Specifically, the evidence suggests that residues do not translocate to within
portions of either above or below ground vegetation. Such would be case for soluble
contaminants moving passively with transpiring water. This conventional wisdom was, however,
challenged with recent experiments by Hulster, et al. (1994) on vegetation of the cucumber
family. Their results were most striking for zucchini, which showed uniform plant
concentrations from inner to outer portions of the zucchini fruit, and the highest whole fruit
concentrations they had ever measured, despite careful experimental conditions which physically
isolated the fruit from the soil. Pumpkins also showed high plant contamination, with more
expected plant concentrations measured for the cucumber. Assuming the vegetation of this
assessment - fruit/vegetables for human consumption and vegetation of the beef/dairy food chain
- do not behave as in the Hulster, et al. (1994) experiments, than translocation to inner plant parts
is not expected.
The specific issue of uptake and translocation via transpiration was investigated using
soybean and corn plants grown hydroponically in carefully constructed growth chambers
(McCrady, et al., 1990). Roots and the hydroponic growth solution were separated from the
shoots and leaves of these plants using two separate chambers, one inverted over the other.
Separate air-flow systems for each chamber included traps for volatile organics. Mass balance on
the tritiated TCDD experiments was able to recover 98% in the soybean experiment and 86% for
the corn experiment. Most of the recovered material was found in the roots; 75% for soybeans
and 67% for corn, with the second highest recovery was on the inside surface of the root
chamber, around 15% for both experiments. Recovered TCDD was also found, in order of
decreasing percentage, in the growth solution, root chamber air, shoot chamber air, and shoots.
The recovery from the shoots was negligible at 0.004% and 0.001% of the total TCDD for the
soybean and corn, respectively. McCrady, et al. (1990) concluded that transpiration stream
transport of 2,3,7,8-TCDD to plant shoots is an insignificant mechanism of plant contamination,
and that volatilization of TCDD is an important transport mechanism that can result in significant
quantities of airborne TCDD being absorbed by plant shoots.
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Briggs, et al. (1982) provide another way to evaluate the translocation of contaminants
from roots to above ground vegetation. Experiments with barley roots in growth solution led to
the development of an empirical parameter describing the efficiency of transport of organic
chemicals to plant shoots from root uptake. This parameter is called the Transpiration Stream
Concentration Factor (TSCF) and is defined as (concentration in transpiration
stream)/(concentration in external solution). The empirical formula presented for this factor is:
TSCF = 0.784 e"[ log Kow " L78 f'2M (7-23)
Given a log Kow for 2,3,7,8-TCDD of 6.8, TSCF is solved for as roughly 2 * 10'5. Assuming
that the concentration of external solution concentration for the experimental conditions of
Briggs' experiments is equivalent to the concentration in soil water in a field situation, then the
TSCF for 2,3,7,8-TCDD implies that the transpiration stream water of a plant is over 5 orders of
magnitude lower than the soil water concentration. Like McCrady's experiments, this also shows
the insignificance of translocation of residues from roots to shoots.
The one approach that was found that might have been used in the place of the algorithms
for above ground vegetation, is simpler and more general in nature. It was developed from field
data on above ground vegetation concentrations correlated to soil concentrations of contaminants
and the octanol water partition coefficient (Travis and Arms, 1988). This correlation led to an
empirical bioconcentration factor for vegetation, Bv, regressed against the contaminant log Kow,
and defined by the authors as the concentration in above ground plant parts divided by the
concentration in soil:
log Bv = 1.588 - 0.578 log Kow n = 29, r = 0.73 (7-24)
With 2,3,7,8-TCDD log Kow equal to 6.8, the Bv translates to a value of 0.0041. Note
that this Bv is defined identically to the plant:soil contaminant concentration ratios that are
discussed in Section 7.3.10 below which compares the model's estimations of these ratios with
those found under experimental conditions. As discussed in that section, plant:soil ratios
calculated using the soil contamination algorithms were in the range of 10"5 for bulky vegetables
and 10"3 for leafy vegetation. It is not clear how to compare the Bv of 0.0041 to these ratios
without retrieving the studies which Travis and Arms (1988) used, although this value is clearly
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higher than the fruit/vegetable ratio and consistent with the grass/feed ratios. The studies used by
Travis and Arms were not retrieved. An examination of the chemicals used by Travis and Arms
show that 25 of 29 used are pesticides, which suggests that plant concentrations may be those of
agricultural crops, which might make it a closer kin to bulky vegetables rather than leafy
vegetation. If so, a comparison of the above-ground vegetable 10"5 ratio with this 0.0056 ratio
would be appropriate. An examination of the chemicals also reveals that 10 of the 29 are
moderately to very soluble (log Kow less than 4.00), while others are similarly insoluble as the
dioxin-like compounds (including DDT, TCDD, Aroclor 1254, and others; 15 with log Kow
greater than 5.0). Developing such an empirical relationship which mixes chemicals whose
mode of action is passively with water (which would be the case with aldicarb and simazine,
among others on the list) with those whose mode is through vapor transfers or particle
depositions (TCDD, and so on) does not appear to be technically valid. Nonetheless, the fact that
the Travis and Arms Bv is much higher than the plant:soil ratio for vegetables generated for the
soil contamination source category demonstration is noted. Also, there is no provision in the
Travis and Arms approach to distinguish between bulky and leafy vegetation, and this appears to
be an important consideration for the dioxin-like compounds.
7.2.6. Alternate Modeling Approaches for Estimating Beef and Milk Concentrations
Webster and Connett (1990) compared five models which estimated the 2,3,7,8-TCDD
content of cow's milk from 2,3,7,8-TCDD air contamination. The five models were described in
Michaels (1989), Connett and Webster (1987), Stevens and Gerbec (1988), Travis and Hattemer-
Frey (1987), and McKone and Ryan (1989). Ironically, a sixth model by Fries and Paustenbach
(1990), noted by Webster and Connett as available but received too late for inclusion in their
article, formed the basis for the approach taken in this assessment.
All five models compared by Webster and Connett have the same basic framework.
Particulate-bound 2,3,7,8-TCDD deposits onto the ground and vegetation (cattle feed and pasture
grass). Algorithms to estimate resulting vegetation and soil concentrations in these models are
the same ones used in this approach, although parameter assignments are different. A daily
dosage of 2,3,7,8-TCDD to the cattle is calculated and converted to a concentration in whole
milk using a "biotransfer factor". This same structure was used to estimate concentrations in
beef, using a beef biotransfer factor different than the milk biotransfer factor. Mathematically,
this is expressed as:
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-m,b
= Fm,b D°Se
(7-25)
where:
-'m.b
m,b
concentration in whole milk/beef, mg/kg
milk/beef biotransfer factor, day/kg
BCF
mfbf
im,b
Q
Dose =
experimentally-derived unitless bioconcentration factor defined as the
concentration in milk fat/beef fat divided by the concentration in the
experimental vehicle (cattle feed, e.g.); similar to BCF of this assessment
fat content of milk/beef, unitless
daily mass intake of cattle in experiment, kg
total daily dose of 2,3,7,8-TCDD, mg/day
sum
* *
aj = relative bioavailability on intake vehicle] (soil, air, vegetation, etc)
Cj = concentration of 2,3,7,8-TCDD in vehicle], mg/kg (or equivalent units)
Qj = mass of vehicle] intake, kg (or equivalent units)
Further details on the models can be found in their primary references and in Webster and
Connett's comparison. Some highlights, including comparisons of the five approaches to the
approach taken in this assessment, are:
1) Two of the approaches, that of Stevens and Gerbec (1988), and McKone and Ryan
(1989), consider inhalation of contaminated air by cattle to contribute to their daily dose of
2,3,7,8-TCDD. One of the approaches, that of Travis and Hattemer-Frey (1987), considers
ingestion of contaminated water by cattle. A later assessment by Travis and Hattemer-Frey
(1991) has all the components of their earlier assessment, and adds cattle inhalation exposures.
This assessment does not consider cattle inhalation of contaminated air nor ingestion of
contaminated water in estimating beef and milk concentrations. However, these intakes were
shown to be insignificant when estimated by these researchers. Stevens and Gerbec estimate
inhalation contributions to be less than 0.05% (0.0005 in fractional terms) of total daily dose, or
an essentially insignificant amount. Travis and Hattemer-Frey (1991) estimate inhalation to
contribute between 0.3 and 1.0% to milk and beef concentrations, respectively. McKone and
Ryan (1989) did not provide sufficient information to easily determine the relative contribution
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of inhalation on estimation of cattle beef and milk concentrations by their estimations. Travis
and Hattemer-Frey (1987, 1991) estimate water contributions to be less than 0.01% (0.0001) of
total daily cattle dose of 2,3,7,8-TCDD.
2) None of the approaches considered vapor phase transfers from air to plant, although
Webster and Connett recommended its inclusion in their article. The later assessment by Travis
and Hattemer-Frey (1991) on 2,3,7,8-TCDD did include vapor phase transfers into vegetation
consumed by cattle. According to results of the example scenarios in this assessment, these
transfers appear to be particularly critical, and this was also the conclusion of Travis and
Hattemer-Frey based on their modeling results.
3) Two of the assessments, that of Stevens and Gerbec (1988) and Fries and Paustenbach
(1990) considered a period of residue-free grain only diet for a period of time before slaughter for
purposes of fattening the cattle. Stevens and Gerbec (1988) assumed that the residues in cattle
would depurate during the last 130 days of their lives on this regime. Assuming a half-life of
2,3,7,8-TCDD in cattle of 115 days, they showed a 54% reduction in beef concentrations due to
this practice. Fries and Paustenbach (1990) note that cattle can gain as much as 60-70% in body
weight, so dilution can also result in lower beef concentrations at slaughter. Based on these
findings, a "feedlot fattening" factor of 0.50 was used in the air-to-beef model validation
exercise that is described in Section 7.3.12. The procedures to estimate a reduction in
concentration used by these researchers is straightforward. Assuming first order kinetics
sufficiently describes reduction in concentrations during a period prior to slaughter, the fractional
reduction during such a period is given as, 1 - exp(-kdt), where kd is the depuration rate constant,
in days"1, and t is the depuration period, in days. The rate constant can be estimated from the
depuration half-life, HL, as 0.693/HL. The 115 day half-life assumed by Stevens and Gerbec
(1988) corresponds to a rate constant of 0.006 day"1, and assuming a 130 day depuration period,
the fractional reduction is easily calculated as 0.54 (i.e., 1 - exp(-kdt)). The amount remaining
after 130 days is estimated as the initial amount multiplied by 0.46 (i.e., exp(-kdt)).
4) Two of the assessments did not assume any cattle ingestion of contaminated soil, and
two of the assessments estimated the contribution to milk concentrations due to ingestion of
contaminated soil was minor at 1 and 2%. Only one of the assessments, Travis and Hattemer-
Frey (1987), estimated any significant impact due to soil ingestion, attributing 19% of the
concentration due to ingestion of contaminated soil. Their later assessment (Travis and
Hattemer-Frey (1991)) estimated soil to contribute 29 and 20% of beef and milk concentration
estimations, respectively. They estimated this high a contribution by contaminated soil even
though they assumed that contaminated soil comprised 1% of the total dry matter intake by cattle.
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Fries and Paustenbach (1990) recognized the importance of cattle soil ingestion, evaluating
scenarios where cattle soil ingestion ranged from 1 to 8% of total cattle dry matter intake.
The example scenarios in Chapter 5 assumed that beef cattle ingestion of contaminated
soil was 4% of their total dry matter intake, and 2% of a dairy cattle's intake was contaminated
soil. The percentage of beef and milk concentrations of 2,3,7,8-TCDD attributed to soil, feed,
and pasture grass, when soil contamination is the source and when stack emissions are the
source, was examined in Section 6.3.3.6 in Chapter 6. It is noted there that soil ingestion appears
significantly more critical for soil contamination as compared to stack emissions. Soil ingestion
by beef and dairy cattle explain around 90% of final beef and milk concentration for soil sources.
On the other hand, soil ingestion explained only around 5% of final beef and milk concentration
for the stack emission source.
The earlier literature noting only 1-2% impact by soil ingestion were more analogous to
the stack emission source category than the soil source category, in that impacts were estimated
starting from air-borne contaminants depositing onto soils and vegetation. One difference in the
assessments estimating the 1-2% impact with this assessment indicating about 5% impact was
that the other assessments assumed less soil ingestion, 0.5% in Stevens and Gerbec (1988) and 1-
3% in Travis and Hattemer-Frey (1987) and McKone and Ryan (1989).
The critical focus of the Webster and Connett (1990) comparison, is the milk fat
bioconcentration factor, BCFmf. As shown in Equation (7-25), the biotransfer factor, Fm, is
estimated using experimental data which yields a milk fat bioconcentration factor, BCF^.
Experiments most relied upon by these modelers are those described in Jensen, et al. (1981), and
Jensen and Hummel (1982). A key difference in the early modeling approaches is the
interpretation of these two and other studies and the resulting assignment of BCFmf, with values
ranging from 5 to 25. Webster and Connett (1990) discuss issues of experimental interpretation.
Parameter assignments and assumptions (cattle soil ingestion versus no ingestion, etc.)
obviously all impact estimations and can be a critical source of variation and uncertainty in
estimates of beef and milk concentrations. The uncertainty associated with the modeling
framework described above was explored by McKone and Ryan (1989) using Monte Carlo
techniques. They found that the 90% confidence range for human exposure to 2,3,7,8-TCDD,
where the source was air contamination and the human exposure route was through milk,
spanned two to three orders of magnitude.
The approach taken by all five researchers centers on the milk biotransfer factor,
abbreviated Fm in Webster and Connett (1990) and in units of day/kg. Beef bioaccumulation was
modeled in the same way using a beef biotransfer factor, Fb. Travis and Arms (1988) developed
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this concept to the fullest, taking several data sets from the literature on a variety of contaminants
and animals, to derive empirical formulas for Fb and Fm, which they termed Bb and Bm, as a
function of contaminant octanol water partition coefficient, Kow:
log Bb = log Kow - 7.6 (7-26a)
log Bm = log Kow - 8.1 (7-26b)
Given a log Kow of 6.8 for 2,3,7,8-TCDD (assumed in this assessment), Bb is solved for as 0.16
and Bm is solved for as 0.05. Travis and Hattemer-Frey (1991) used 0.80 and 0.03 for 2,3,7,8-
TCDD Bb and Bm.
Simple transformations can show how the earlier approaches, summarized above in
Equation (7-25), and the approach of Fries and Paustenbach (1990) (which is the approach used
in this assessment), are the same. First, the concentration of dioxin-like compounds in the fat of
beef and milk is given in this assessment by (also see Chapter 4):
Cfat = BCF DFSBSACS + BCF DFg ACg + BCF DFfACf (7.27)
where:
Cfat = concentration in beef fat or milk fat, mg/kg
BCF = bioconcentration ratio of contaminant as determined from cattle vegetative
intake (pasture grass or feed), unitless
DFS = fraction of cattle diet that is soil, unitless
Bs = bioavailability of contaminant on the soil vehicle relative to the vegetative
vehicle, unitless
ACS = average contaminant soil concentration, mg/kg
DFg = fraction of cattle diet that is pasture grass, unitless
ACg = average concentration of contaminant on pasture grass, mg/kg
DFf = fraction of cattle diet that is feed, unitless
ACf = average concentration of contaminant in feed, mg/kg.
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Transformation steps are: 1) factor out the BCF from Equation (7-27) , 2) multiply Equation (7-
27) by unity expressed as Q/Q, where Q equals total dry matter intake by cattle; 3) the
multiplication of Q by the diet fraction terms, DFS, DFg, and DFf, gives the values for soil dry
matter intake, Qs, grass - Qg, and feed - Qf, 4) with BCF factored out, and Q*DFs replaced by Qs,
etc., the parenthetical now reads, (Qs*Bs*ACs + Qg*ACg + Qf*ACf) - this is the "Dose" term
defined earlier in Equation (7-25), 5) finally, multiply the right hand side of Equation (7-27) by
fat content, say fm for milk, which would transform the right and hence left hand side of that
equation to whole product concentration. Transformed Equation (7-27) is analogous to Equation
(7-25):
BCFf
C= —^[QSBSACS + QgACg + QfACf ] (7-28)
While this analysis has shown how the biotransfer approach can be transformed into the
bioconcentration approach of this methodology, one has to be careful with the assignment of the
biotransfer and bioconcentration parameters. The following analysis shows why the Travis and
Arms (1988) empirical algorithms shown above in Equations (7-26a) and (7-26b) are not
appropriate for dioxin-like compounds.
McLachlan, et al. (1990) kept an inventory of the dioxins ingested by a lactating cow as
well as the dioxins being emitted through the milk. This was the data used to develop the
bioconcentration factors for the dioxins used in this assessment. The volume of milk generated
by the cow was also given, allowing for the calculation of the biotransfer factor. The
experimental biotransfer factor for milk derived from McLachlan's data is compared against the
factor which can be estimated using the log Kow of the individual dioxin congeners (the
congener log Kow are listed below in the issue regarding TEQ parameters) combined with the
Travis and Arms (1988) biotransfer equation:
Congener Travis and Arms Bm McLachlan Bm
2378-TCDD 0.03 0.01
12378-PCDD 0.03 0.01
123478-HxCDD 0.49 0.006
123678-HxCDD 0.16 0.005
123789-HxCDD 0.16 0.005
1234678-HpCDD 1.26 0.001
OCDD 0.31 0.001
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2378-TCDF 0.03 0.003
23478-PCDF 0.07 0.009
12378-PCDF 0.05 0.002
123478-HxCDF 0.16 0.007
123678-HxCDF 0.16 0.006
123789-HxCDF 0.16 0.006
234678-HxCDF 0.16 0.005
1234678-HpCDF 0.63 0.001
1234678-HpCDF 0.63 0.003
OCDF 5.00 0.001
It is clear that the Travis and Arms' biotransfer relationship will greatly overestimate the
transfer of dioxins into milk, given the data of McLachlan. It would appear from McLachlan's
data that as the log Kow increases, the biotransfer decreases, which is the opposite of the trend
implied from the Travis and Arms' relationship.
The Travis and Arms trend is explainable, however, given the data from which Travis and
Arms developed their relationship. In their literature article, they supplied the log Kow and the
experimentally derived biotransfer factor for all the data points they used to derive their empirical
relationship. For determining a milk biotransfer factor, they had 28 data points, and only 6 of
them were for chemicals with log Kow greater than 6.00. The range of log Kows for data they
had was 2.8 to 6.5. The dioxin-like compounds, on the other hand, have log Kow that range
from 6.5 to 8.0. In this 2.8-6.5 log Kow range, it would appear that as log Kow increases, the
tendency to bioaccumulate in milk increases. Interestingly, of the 6 data points Travis and Arms
had for chemicals with log Kow over 6.00, their actual data point leads to a higher biotransfer
point than is calculated with their derived empirical relationships:
Chemical log Kow1 Bm from the data2 Bm calculated3
Aroclorl254 6.47 0.01 0.02
Chlordane 6.00 0.0004 0.008
ODD 6.02 0.003 0.008
fenvalerate 6.20 0.0008 0.01
mirex 6.89 0.009 0.06
TCDD 6.15 0.01 0.01
1 as used by Travis and Arms
2 the actual data point claimed by Travis and Arms (1988)
3 as calculated with Equation (7-26b)
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As discussed above, it would appear that the trend of higher bioaccumulation with higher
log Kow is true for the range of log Kow used by Travis and Arms - 2.8 to 6.5. However, for the
dioxin congeners where log Kow is higher at 6.5 to 8.0, the trend is the opposite - the biotransfer
decreases as the log Kow increases. This could be due to greater rates of metabolism for organic
compounds of higher log Kow, or just for the dioxins, leading to lower concentrations in the
animal food products. In any case, it is clear that the Travis and Arms' biotransfer factor
equations for beef and milk are not appropriate for the dioxin-like compounds, and perhaps as a
general rule, for other organic compounds with log Kow 6.5 or higher.
Douben, et al. (1997) compares three approaches for air-to-plant-to-milk modeling. One
of the approaches is the one of this document. The second employs the "carryover" factor, which
is very similar to the biotransfer factor defined above. Whereas the biotransfer factor is defined
as the concentration in milk (mg/L, e.g.) divided by the mass of dioxin ingested (mg/day), the
carryover factor is defined as the mass of compound excreted in milk (mg/day) divided by the
mass of dioxin ingested (mg/day). Douben, et al. (1997) assigns values to the carryover factor
based on the data of McLachlan, et al. (1990). The third approach is the "scavenging" approach
developed by McLachlan (1995) and described above for the transfer of dioxins from air to grass.
McLachlan (1995) developed the concept further to show that the concentration in milk can be
estimated as a function of the total mass of dioxins ingested by the dairy cow (equal to the dry
weight of vegetation consumed times the scavenging coefficient times the air concentration )
times the absorption fraction (the amount absorbed by the dairy cow) divided by the mass of milk
excreted each day:
CA.(SCM+SCM) ABS.
C = J g g s s J (
milk LAC (
where:
C^k = milk concentration, ng/kg (ppt; 1 liter = 1kg)
CAj = concentration of congener j in air, ng/m3
SCg = scavenging coefficient for grass, m3/g
Mg = mass of grass ingested, g/day
SCCS = scavenging coefficient for corn silage, m3/g
Mcs = mass of corn silage ingested, g/day
ABSj = absorption fraction for congener]
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LAC = lactation rate, kg/day
Assuming 9 kg dry weight of grass and 4 kg dry weight of corn silage ingested per day, and
scavenging coefficients for grass and silage to be 9 and 4.5 m3/g, respectively, Douben, et al
(1997) reduced the parenthetical above to a constant of 100,000 m3/day (actually calculates to
99,000). They were able to test the model assuming a lactation rate of 0.6 kg/day and using
absorption efficiencies provided in McLachlan (1995). While differences in specific
congener/model predictions were noted, Douben, et al. (1997) generally found comparable
predictions for milk TEQ concentrations - all were within a factor of 5 of observed
concentrations.
One critical theoretical assumption not explored in these modeling comparison exercises
is whether 2,3,7,8-TCDD and other congeners bioaccumulate equally in beef fat and milk fat -
are the BCF^ and BCFbf equal? Fries and Paustenbach (1990) emphasize that differences in
observed concentrations in beef and milk are critically a function of the differences in the diets of
cattle raised for beef versus those raised for milk. They assumed that the beef and milk
bioconcentration factor was equal for their example calculations. The key difference Fries and
Paustenbach cite is the tendency for beef cattle to graze while lactating cattle are more often barn
fed. Grazing cattle intake more contaminated soil than barn fed cattle. Fries and Paustenbach
derived F for higher chlorinated dioxin-like compounds from experimental data, noting that the F
value is less with higher chlorination. Webster and Connett (1990) made the analogous
observation, saying that 2,3,7,8-TCDD equivalents transferred from air to milk less efficiently
than 2,3,7,8-TCDD. This is also consistent with the data of McLachlan, et al (1990), which is
used in this assessment for assignment of BCFs to dioxin-like compounds.
Besides different diets between beef cattle and lactating cows is that lactation provides a
mechanism for dioxin excretion from the body, which theoretically would lead to body fat
concentrations in lactating cows being less than body fat concentrations in non-lactating cattle
which don't have this excretion mechanism. However, researchers have observed that the dioxin
concentrations in the fat of milk and of body fat in non-lactating cattle are similar. McLachlan
(1994) attributes this to the fact that cattle are most often slaughtered while they are still growing
and increasing their body fat reservoir. Therefore, dioxins appear to reach steady state in the fat
of non-lactating cattle at concentrations similar to those found in cow's milk.
Some conclusions from this analysis of these efforts for estimating bioconcentration in
beef and milk are:
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• Although the biotransfer and carryover frameworks look different than the
framework used in this assessment, they are actually the same with a simple
mathematical transformation;
• All of these approaches are empirical; that is, values for the critical dioxin-
specific parameters (scavenging coefficients, carryover factors, bioconcentration
factors, biotransfer factors) are developed from field data. When they are
developed by different researchers on different field data, they will (of course) not
predict equally. However, and as evidenced by the results of Douben, et al.
(1997), they all appear to predict milk TEQ concentrations about equally.
• A caution is noted, nonetheless, with the use of the Travis and Arms (1988)
empirical algorithm for the assignment of biotransfer factors for the dioxin
compounds: as described above, this empirical relationship does not hold for the
dioxin-like compounds, which have higher log Kow than the log Kow of the
compounds used by Travis and Arms (1988) to develop their relationship.
Provided above are biotransfer factors for dioxins developed from the same data
used to develop the bioconcentration factors of this methodology.
• The possible dosage to cattle of 2,3,7,8-TCDD via contaminated air or water was
considered in earlier assessments, but was not found to be a significant pathway,
and was not considered in this assessment;
• Earlier assessments (before 1990) did not consider vapor phase transfers to
vegetation consumed by cattle; key studies in the literature as well as the results of
the demonstration scenarios suggest that this transfer is particularly critical;
• Even though the structure of the analysis has been consistent from the earlier to
the current approaches, different assumptions on parameter values greatly impacts
modeling results. The critical bioconcentration factor, earlier termed BCFm (for
milk) and termed simply BCF in this assessment, has been estimated to be
between 5 and 25 for 2,3,7,8-TCDD in different assessments. This assessment
uses a BCF value of 5.76 for 2,3,7,8-TCDD. Using Monte Carlo techniques on
this model structure for estimating human exposure to milk resulting from air
contamination of 2,3,7,8-TCDD, McKone and Ryan (1989) showed a 90%
confidence interval spanning 2 to 3 orders of magnitude.
7.2.7. An Alternate Approach to Vapor/Particle Partitioning in the Air
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Chapter 3 described the application of the Junge (1977) and Pankow (1987) model for
partitioning air-borne dioxin-like compounds into vapor and particle phases. The governing
equation for this model is:
^
* =
where:
cj) = fraction of the compound adsorbed to aerosol particles
p°L = saturation liquid phase vapor pressure of the pure compound at ambient
temperature, Pa
6 = the particle surface area per unit volume of air, cm2 aerosol/cm3 air
c = a constant which is related to the difference between the heat of desorption
from the particle surface, Qd, and the heat of vaporization of the
compound, Qv. The value of c is often estimated at 17.2 Pa-cm
A disadvantage to using this model is that the parameters 6 and c in this equation must be
estimated as they cannot be measured directly. There is also uncertainty is the saturation liquid
vapor pressure, p°L, which must be estimated from the solid-phase vapor pressure, p°s.
Assignment of these parameters and the development of the vapor/particle partitioning algorithm
is provided in Chapter 3.
A second model was developed in the latter part of the 1990s and is now widely used to
characterize the partitioning of semivolatile organic compounds to aerosol particles (Finizio et al.
1997, Harner and Bidelman, 1998; Pankow, 1998). The derivation of this model begins with this
alternate equation for the particle fraction, cjx
C ( TSP )
-
Cg + Cp(TSP)
where:
cj) = fraction of the compound adsorbed to aerosol particles
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Cp = the concentration of semivolatile compounds associated with aerosols,
ng/|_ig particles
Cg = the gas-phase concentration, ng/m3
TSP = the total suspended particle concentration, pg/m
3
Defining the ratio of the particle phase to the gas phase (Cp/Cg) as Kp, a particle-gas partition
coefficient, and dividing all terms on the right side of the equation by Cg, Equation (7-31) can be
rewritten as:
K (TSP)
(b = £ (7-32}
T 1 + Kp ( TSP ) { }
Once deriving Kp, cj) can be solved for in this equation. Kp can be measured in the field, but
alternately it has been related to the octanol air partition coefficient, Koa (Finizio et al., 1997):
(7-33)
where:
Kp = particle-gas partition coefficient, m3/ng
Koa = octanol air partition coefficient, dimensionless
fom = fraction of organic matter in the aerosol involved in partitioning
YoCt, om = activity coefficients in octanol and aerosol organic matter
Moct om = molecular weights of octanol (130 g/mol) and the organic matter
poct = density of octanol, 820 kg/m3
Simplifying assumptions that have been made for this formulation include that Yoc/Yom and
Moct/Mom are equal to 1 (Bidleman and Harner, 2000), leading to:
LogKp = Log Koa + Log fom - 11.91 (7-34)
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Bidleman and Harner (2000) make the further simplification that urban aerosols contain 15%
carbon, present as compounds with the average molecular formula of octanol (74% carbon), thus
leading to an assignment of 0.20 for fom, and this equation:
Log Kp = Log Koa - 12.61 (7-35)
The octanol air partition coefficient is directly measurable at ambient temperatures and has been
reported for several classes of semivolatile organic compounds, including PCBs (Harner and
Bidleman, 1996; Komp and McLachlan, 1997), PAHs (Harner and Bidleman, 1998),
polychloronaphthalenes (Harner and Bidleman, 1998), and other compounds (Harner and
MacKay, 1995). However, measurements of Koa for dioxin-like dioxin, furan, and PCB
congeners could not be found in the literature. Alternately, Koa can be estimated from the ratio of
the octanol water partition coefficient and the Henry's Constant, as:
K R T
Koa = — (7-36)
where:
Koa = octanol air partition coefficient, dimensionless
Kow = octanol water partition coefficient, dimensionless
R = universal gas constant, 8.2* 10"5 atm-m3/mole-K
T = ambient temperature, K
H = Henry's Constant, atm-m3/mole
Lee and Jones (1999) discuss the difference between the Koa calculated in this manner and
measured values. For PCBs, they cite literature showing that measurements of Koa were 1.4 to
4.7 times higher than modeled Koa. Using a relationship derived for PCBs, they attempted a
correction for the dioxin-like compounds. For this section, Equation (7-36) will be used to
model the Koa for dioxin-like compounds.
Another approach taken by some to develop a more site-specific relationship between Koa
and Kp is to measure the gas and particle fractions for a given site and compound, and then
directly assign values for Kp (as Cp/Cg). Then, these site-specific Kp can be correlated to reported
or estimated Koa using the general formula:
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Log K = m Log Koa + b (7.37)
Kaupp and McLachlan (1999) took measurements of polychlorinated dibenzo-p-dioxins
(PCDDs), dibenzofurans (PCDFs), polychlorinated biphenyls (PCBs), napthalenes (PCNs), and
polycyclic aromatic hydrocarbons (PAHs) over the course of a year in southern Bavaria. They
investigated the relationship between Kp and the saturation liquid vapor pressure, p°L, as well as
the relationship between Kp and Koa, using the generalized logarithmic equation shown above.
They found a good correlation between Kp and p°L when they grouped PCDD/Fs and PCBs (r2 =
0.93) and separately when they grouped PAHs and PCNs (r2 = 0.98), but not when they grouped
all four sets of compounds together. On the other hand, they found a reasonable correlation
existed for the whole group of four compound classes when they correlated Kp and Koa (r2 =
0.76). The linear relationship they found was:
LogKp - 0.6368 Log Koa - 8.9111 (7-38)
Given values of Koa, Kp can be solved for using Equations (7-38) or (7-35). In order to
compare this approach for assigning vapor/particle partitioning with the one chosen for this
document (the Junge-Pankow model based on the saturation liquid vapor pressure), these two
versions of the particle-gas partition coefficient, Kp, will be generated from Koa. In both cases,
the octanol air partition coefficient, Koa, will be assigned values based on Equation (7-36) since
measured values the dioxin-like compounds could not be found in the literature. Then, the two
sets of Kps will be used to predict values of the particle phase fraction, cj), as given in Equation
(7-32). As described in Chapter 3, four values for the atmospheric particle concentration, the
parameter TSP of Equation (7-32), describe four conditions termed, "clean continental",
"background", "background plus local sources", and "urban". For this exercise, the value
selected for urban conditions, 98 pg/m3, will be used. Finally, the two sets of cj) for the 17 dioxin
and furan congeners generated here using Koa will be compared with the cj) generated in this
methodology document using the Junge-Pankow model. This comparison is shown in Table 7-5.
As shown there, while the trend is generally similar between the two models (higher
particle fraction as the degree of chlorination increases), there is a large discrepancy between the
predictive methods. The calculation of the particulate fraction, cj), using the Junge-Pankow
model, results in much larger values as compared to the procedure described in this section using
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the octanol air partition coefficient. A similar discrepancy is found when calculating $ for other
atmospheric conditions and lower values of the particle density term, TSP - background plus
local conditions, background, and clean continental. There is not much difference in using a
theoretical relationship between Koa and Kp given above in Equation (7-35) or the empirical
relationship shown as Equation (7-38).
The same discrepancy between the Junge-Pankow model and the Koa model was found in
two studies which both measured the concentration and partitioning of dioxin-like compounds in
air and then tried to predict the particulate fraction using the same two models described in this
section (Lee and Jones, 1999; Oh et al., 2001). Not only did both studies find that the octanol air
partition coefficient method predicted a much smaller fraction to occur in the particulate phase
for all congeners and homologue groups modeled, but they also both concluded that the octanol
air partition coefficient method fit the measured data better. Chapter 3 discussed the general
trend that the Junge-Pankow model based on p°L would lead to greater particulate fractions than
measured, but it also discussed issues identified for the measurement of dioxin-like compounds -
that "blow-off and other causes could cause the filter-PUF derived measurements to be
overestimates of the vapor phase fraction (equivalently, they would underestimate the particle
phase).
In any case, this methodology document recommends use of the Junge-Pankow model for
vapor/particle partitioning and, in particular, derives a set of particle-phase fractions for four
atmospheric conditions for the dioxin-like compounds. The results of this analysis are shown in
Table 3-7 of Chapter 3. Further, these results are used in several model validation exercises in
this chapter, including air-to-leaf modeling, air-to-soil modeling, and air-to-leaf-to-beef
modeling. Users may, of course, use other modeling approaches for the dioxin-like compounds,
including the alternate vapor/particle model based on the octanol air partition coefficient
described in this section.
7.3. MODEL VALIDATION EXERCISES
7.3.1. The Impact of Dioxin Soil Contamination to Nearby Soils
Contaminated soils from a bounded area of soil contamination are assumed to migrate via
erosion and impact the soils of a nearby exposure site. This section examines the model
algorithms for estimating impacts to nearby soils from a contaminated soil source.
Contaminated soils erode onto a nearby site of exposure and mix into a depth of either 2
cm for untilled conditions or 20 cm for tilled conditions. The 2-cm depth was chosen as the
mixing depth for both depositing residues in the stack emission source category and for residues
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migrating from a contaminated site to a nearby impacted site in the soil contamination source
category. This 2-cm depth replaced the 1-cm depth which was the value selected for earlier
versions of this dioxin exposure reassessment (EPA, 1992a; EPA, 1994). Principal justification
for this assumption came from a report showing soil concentration profiles taken at 2 cm
increments to be generally uniform for background undisturbed soils to a depth of 5 cm, with
dropoffs in concentrations below this (Brzuzy and Kites, 1995). For two sandy profiles, the
peaks were found at greater than 30 cm. The authors speculated that their findings are the result
of depositions corresponding to the rise of dioxins in the environment starting 50 or so years
ago. A mixing depth of 5 cm may be more appropriate for the algorithms of this assessment if a
source being evaluated has been emitting for that length of time. However, exposure durations
and source emissions for the categories of this assessment are likely to be emitting for
substantially less than the past 50 years, justifying the selection of 2 cm for the untilled mixing
depth.
A contaminant concentration ratio is defined for purposes of this discussion as the ratio
of soil concentration at the site of exposure to the soil concentration at the site of contamination.
For example Scenario 3, soil eroded from a 40,000 m2 (10-acre) contaminated site was assumed
to partially deposit onto a 40,000 m2 exposure site. The contaminant concentration ratio was
0.39 for the 2-cm depth of mixing at the site of exposure and 0.06 for the 20-cm mixing depth.
Data to rigorously validate the approach taken in this assessment to model the impacts of
soil erosion from a site of contamination to a nearby site is unavailable. However, there have
been documented evidence of migration of 2,3,7,8-TCDD away from industrial sites with soil
contamination of 2,3,7,8-TCDD, resulting in off-site soil contamination. Off-site soil
concentrations of concern were identified in 7 of 100 Tier 1 and Tier 2 sites of the National
Dioxin Study (EPA, 1987). The study noted that in most cases, 2,3,7,8-TCDD had not migrated
off-site. Most, but not all, Tier 1 and 2 sites did have some off-site soil sampling without
detection. It should be noted, however, that soil detection limits for most of these 100 Tier 1 and
2 sites were at 1 ppb; this would have precluded finding concentrations less than 1 ppb in some
of the off-site soil sampling, particularly important for many of the sites where on-site detections
were in the low ppb range. Summary data from the 7 sites noted above is provided in Table 7-6.
Contaminant concentration ratios cannot be evaluated by this summary because of lack of detail
provided in the National Dioxin Study.
Further detail on the 1984 sampling at the Dow Chemical site in Midland is provided in
Nestrick, et al. (1986). An evaluation of the information in that reference is more informative
than the Dow Chemical summary in Table 7-6. The entire site is 607 hectares. On-site sampling
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included areas identified as chlorophenolic production areas, a waste incinerator area, and
"background" areas. Background areas were within the 607 ha site but away from production
areas. Two of the on-site areas were further identified as areas with Localized Elevated Levels
(LELs). These two areas comprise less than 0.5% of the total site area, but had the three highest
occurrences of 2,3,7,8-TCDD at 25, 34, and 52 ppb. Including these three high occurrences in
the total of 33 samples taken on-site at sites of concern (i.e., not including the background sites)
leads to an average concentration of 4.3 ppb; excluding them leads to an average of 1.0 ppb. The
average of 11 background samples (including two ND assumed to be 0.0) was 0.15 ppb. A
contaminant concentration ratio of 0.035 is calculated assuming an average concentration for
contaminated soil of 4.3 ppb (0.15/4.3 = 0.035), and a ratio of 0.15 is calculated if the average
soil contamination concentration is more like 1.0 ppb rather than 4.3 ppb.
This ratio of 0.035 is about one-tenth as much as the 0.39 ratio estimated assuming the
shallow 2-cm depth of contamination, although the ratio of 0.15 is similar to the 0.39 ratio of
Scenario 3. The depth of 20 cm led to a modeled ratio of 0.06, which is more in line with the
Dow contaminant ratio of 0.035. The 2-cm depth ratios are probably more pertinent for
comparison, however, since it is unlikely that there were tillage operations (or other soil practices
which would distribute residues) in background areas of the 607 ha Dow site.
It appears reasonable that the no-till contaminant ratio of 0.39 is higher than the Dow
ratios for several reasons. First, the contaminated areas sampled were those likely to be of
concern and comprising only a small percentage of the total 607 hectare site. That might
question the representativeness of 4.3 ppb as average soil contamination in impacted areas; the
three highest concentrations came from specifically identified LELs comprising only 0.5% of the
607 ha site area. Second, a map provided in Nestrick, et al. (1986) including a distance scale
clearly shows that all of the background samples were much further than 150 meters from the
contaminated sample points, with several sample points hundreds to over a thousand meters from
the contaminated sample points. The contaminant concentration ratio of example Scenario 3,
0.39, was estimated with a distance of 150 meters. Third, the example scenarios had specific
assumptions about erosion which may or may not have been appropriate for application to the
Dow site.
Ideally validation of the soil erosion model would involve direct application at the Dow
site and comparison of predicted values to measured values. This was not feasible due to lack of
information regarding the Dow site. Instead, this analysis has shown that the model predictions
of contaminant concentration ratios differ logically from observed ratios at the Dow site.
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7.3.2. Soil Concentrations and Concurrent Concentrations in Bottom Sediments and Fish
The Connecticut Department of Environmental Protection (CDEP, 1992; MRI, 1992)
established a program in 1986 for monitoring TCDD, TCDF, and other dioxin-like isomers of
comparable toxicity in several environmental matrices near resource recovery facilities (RRFs).
Matrices monitored include ambient air, residues and leachate from the ash disposal sites,
surficial soils, surface water surficial bottom sediments, and whole fish. The purpose of the
program is to evaluate the impact of RRFs. Eight locations were evaluated through 1990, with
one location serving as a baseline or reference site. Of the seven remaining locations, RRFs
began operation in 1983 (1 RRF), 1987 (3), 1988 (1), and 1990 (2). This section will examine
the soil, sediment, and fish data from that program.
The soil concentrations throughout all eight sites might be characterized as typical of
background concentrations mainly because the concentrations of 2,3,7,8-TCDD measured
through 1990 averaged 0.56 ppt (n = 77; assuming non-detects were !/2 detection limit), with
roughly a 50% non-detect rate (at a detection limit which has varied by data set, but has been
around 0.1 ppt). In studies measuring soil concentrations of 2,3,7,8-TCDD in background or
rural settings, either none was found, or concentrations were found in the low ppt range - this
seems to also characterize the Connecticut data. For example, the soil concentration of 2,3,7,8-
TCDD of 0.37 used in the background demonstration scenarios in Chapter 5 was measured at a
rural background site located 28 miles from Columbus, Ohio. In a statistical analysis of the
Connecticut data collected through 1988, the average soil concentration for 2,3,7,8-TCDD as
0.44 ppt (n = 42; CDEP, 1992; same procedures for estimating average concentrations), which is
lower than the 0.56 ppt concentration for all samples through 1990. Concentrations of 2,3,7,8-
TCDF taken after 1988, however, are lower than those taken in 1987 and 1988: the average
including samples through 1988 was 8.20 ppt (n = 41; CDEP, 1992); while through 1990 was
6.77 (n = 77; CDEP, 1992). (Unlike the concentrations for 2,3,7,8-TCDD, this 2,3,7,8-TCDF
concentration in the Connecticut data is higher than the background site near Columbus, Ohio:
0.64 ppt in Columbus versus 6.8-8.2 ppt in Connecticut.) This simple examination of averages
over time does not seem to indicate statistically significant change, if any. As well, in a
statistical analysis of the data (principal component analysis of the concentration levels of all
isomers in soil, fish, and sediment to attempt to identify stratification of the data by year) for four
of the RRFs through 1990, MRI (1992) concluded that the RRFs had no apparent effect on the
levels of the dioxin-like compounds in the three matrices.
The purpose of developing the argument that levels in soil are low and perhaps typical of
background conditions, although that is questionable for 2,3,7,8-TCDF, is to be able to compare
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the soil to sediment, and sediment to fish ratios that arise from this data with those that were
generated in example Scenarios 1 and 2 in Chapter 5. Those scenarios were crafted to be typical
of "background" settings. It is noted that the comparison of soil-to-sediment ratios and the
sediment-to-fish ratios in this exercise is not, strictly speaking, a validation exercise since the
Connecticut circumstances were not duplicated.
Information and results from the CDEP program for the soil, sediment, and fish matrices
are presented in Tables 7-7 through 7-9. The data and supporting documentation was supplied by
CDEP (1992). Table 7-7 provides a summary of the eight sites in the CDEP program for which
data was available. One "reference" or "control" site includes two areas, which for 1988 was at
Union, Connecticut, and for 1990 was at Stafford, Connecticut. No nearby potential sources of
dioxin release (industrial, commercial) were identified for these two reference sites. One of the
sites, the Hartford site, is near the Connecticut River. All water bodies sampled were coves with
direct links to the river. Industrial and commercial enterprises which use the river are speculated
to have resulted in the generally higher fish concentrations noted in the Hartford site, as
compared to the other sites. Twenty-one water bodies have been sampled, including harbors,
channels, impoundments, reservoirs, coves, ponds, rivers, and lakes. Six species offish have
been sampled, including carp, channel and white catfish, white sucker, brown bullhead, and
yellow perch. All but the yellow perch are bottom feeders. The yellow perch was sampled
mostly when a sufficient sample of bottom feeder could not be obtained. Samples of bottom
feeders were sought because it was felt that they would have the highest tissue concentrations
due to their association with bottom sediments, and therefore be the best markers for impact and
change over time (C. Fredette, CDEP, personal communication). The soil sampling program was
not extensive; samples were only taken near ambient air monitoring stations, and only 77
samples were taken through 1990. It certainly cannot be claimed that the samples are statistically
representative of soils which drain into the water bodies. However, given the consistency in
concentrations noted and their low values, the supposition is made that concentrations are
adequately representative of soils which impact the water bodies. Maps of the sites were
obtained from CDEP to evaluate the distance from the soil sampling sites to the nearest water
bodies. Nearly all soil sampling sites were within 3 miles of the nearest water body, and most
were near to and less than one mile away.
Table 7-8 lists the frequency of non-detects for all data through 1990, and incomplete
information on detection limits. For determining average concentrations in sampled matrices,
non-detects were assumed to equal 1A the detection limit. The detection limits for these matrices
varied over time with different data sets. The detection limits noted were those cited for 1987
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and 1988 data (from a draft Monitoring Progress Report supplied by CDEP). That report did not
list detection limits for three matrices noted. In parenthesis is noted the lowest concentration in
the data sets, which would correspond to /^ the detection limit at the time the non-detect was
measured. The purpose of presenting this data is simply to argue that assuming 1A the detection
limit for computing averages will not greatly impact the averages. This can be demonstrated for
the one matrix where this is most likely to be a concern - soil concentrations of 2,3,7,8-TCDD
where a 50% non-detect rate was noted. If half the samples were assigned a value of 0.0 instead
of perhaps 0.07 ppt (half the noted detection limit of 0.13 ppt), than the overall average would
drop from 0.56 ppt to 0.52 ppt.
Table 7-9 summarizes the key results from the CDEP data. The Csed:Csoil ratio is the ratio
of sediment concentration to soil concentration for the eight sites for 2,3,7,8-TCDD - these are
not organic carbon normalized concentration ratios. The second ratio noted is called the BSAF,
because it is defined in the same way that the Biota Sediment Accumulation Factor is defined:
the ratio of the lipid-normalized whole fish tissue concentration over the organic carbon
normalized bottom sediment concentration. The BSAF is used to estimate fish tissue
concentrations from bottom sediment concentrations in this assessment. The fish lipid contents
and organic carbon contents for each site were supplied by CDEP (1992). The BSAFs for the
entire data set and the four concentrations are based on averages offish lipid and organic carbon
contents from the entire data set.
Key observations from the demonstration scenarios and the results of the CDEP program
are:
1) Demonstration scenarios 1 and 2 in Chapter 5 estimated the impact from basin-wide
soil concentrations of the 17 dioxin congeners that have been found in a typical background
setting. The difference in the scenarios was in exposure patterns and exposure site characteristics
- the impacts to surface water sediments and fish were the same in both scenarios. The estimated
concentrations of 2,3,7,8-TCDD, 2,3,7,8-TCDF, and 2,3,4,7,8-PCDF in bottom sediments were
0.99, 1.22, and 0.51, respectively, and the sediment to soil ratios for these three compounds were
2.55, 1.82, and 2.43. The differences in the sedimentsoil ratios of these congeners is due to
slightly different organic carbon partition coefficients assumed for the three congeners. These
ratios compare to the overall 3.86, 2.59, and 1.58 estimated in the Connecticut data set for
2,3,7,8-TCDD, 2,3,7,8-TCDF, and 2,3,4,7,8-PCDF, respectively. The ratio for total toxic
equivalents was 2.69, compared to the modeled 2.64. These ratios tend to support the model's
approach.
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One of the key model parameters in the soil to sediment algorithm which is uncertain is
the soil enrichment ratio. It was assigned a value of 3.0, which means that concentrations in soil
eroding from the field are three times higher than concentrations on the field. If the soil
enrichment ratio is set to 1.0, it would lead to sedimentsoil ratios less than 1.00. The close
match of sediment:soil ratios with an enrichment ratio of 3.0 does not necessarily validate the
model's approach to evaluating surface water impacts from low basin-wide soil concentrations,
however. The model assumes that all surface water impacts are from erosion of basin soils.
However, sediment concentrations in water bodies are also a function of direct atmospheric
depositions onto water bodies and other direct, industrial related, discharges into water bodies.
Such depositions may originate from sources other than soil contamination, such as air emissions
from cars or industry. The impact of industrial sources to the sediments in the CDEP data is
unclear. As noted, evidence collected so far does not indicate an impact from incinerator
emissions. The Hartford site has been cited as being impacted with industrial use of the nearby
Connecticut River. However, the sediment concentrations of the water bodies at this site are not
higher than other sites - in fact, the sediment concentrations from the Bridgeport, Bristol,
Preston, and even the background Union/Stafford sites are comparable or higher. In any case,
deposition of air-borne contaminants are likely to impact bottom sediments to some degree, and
the soil contamination models of this assessment do not include such an impact (the stack
emission source category does include this impact for emissions reaching water bodies).
In summary, the CDEP data appears to indicate that bottom sediment concentrations
exceed surface soil concentrations by more than a factor of 2.0 in environmental settings that
mostly do not appear to be impacted by industrial activities. The models also predict a similar
enrichment of sediment concentrations, mainly due to the use of soil enrichment ratio of 3.0
2) The overall BSAF ratios for the three dioxin compounds and the I-TEQ ratio, ranging
from 0.24-0.86, are higher than the BSAF used in the demonstration scenario of 0.09 for 2,3,7,8-
TCDD and 0.144 for 2,3,4,7,8-PCDF. Higher BSAF in the CDEP data are expected because the
fish species sampled were bottom feeders, except for the yellow perch. The selected BSAF of
0.09 is mainly supported by Lake Ontario data (EPA, 1990a), which was on brown and lake
trout, smallmouth bass, and white and yellow perch, all column feeders. Bottom feeders are
expected to have more exposure to the contaminants because of their direct contact with
sediments. This implies that use of the BSAF for site-specific assessments should consider the
dietary pattern of exposed individuals. If a significant portion of local fish consumption includes
bottom feeders (such as catfish), then perhaps a BSAF greater the 0.09 used for the
demonstration scenarios is warranted.
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3) Of the six sites for which BSAFs were individually determined for 2,3,7,8-TCDD, the
highest BSAF was from the Hartford site at 0.97. The claim is not made that it is substantially or
significantly different from BSAFs at the other sites - it is simply a point of interest for comment.
The Hartford site has been previously identified as likely to have been impacted by activity on
Connecticut River - all the fish are taken from coves directly connected to the river. Although
the bottom sediment concentrations at this site are not different from other sites, one hypothesis
is that the water column is more impacted for this site as compared to other sites.
In Chapter 4, Section 4.3.4, a key issue identified for the validity of the BSAF approach is
the issue of past versus ongoing contamination. Generally, the hypothesis offered was that fish
are likely to be more exposed with ongoing impacts to the water body as compared to a situation
where impacts were principally historical. The effluent discharge source category is a case of
ongoing impacts. The argument presented in Section 4.6. of Chapter 4 was that the BSSAF
(biota suspended sediment accumulation factor) should be greater in numerical value than a
BSAF whose value is derived from data on a water body whose impacts have been primarily
historical. This was the case for the assignment of a 0.09 for the BSAF, which was based on data
on column feeders in Lake Ontario, a lake whose impact has been speculated as primarily
historical. Although the numerical difference between the Hartford BSAF, at 0.97, and the next
largest BSAF at Bristol, at 0.78, is not that large, perhaps that difference is due to the fact that the
fish are more exposed at Hartford due to ongoing impacts from the Connecticut River.
In summary, this section has evaluated data supplied by the Connecticut Department of
Environmental Protection on fish, sediment, and soil data. It is the only data set that could be
found where soil and sediment data were concurrently taken in areas evaluated as (mostly) not
impacted by industrial activity. An examination of the sediment to surface soil concentration
ratios, showing them generally to be in the range of 1.6 to 3.9, supports the soil contamination
model of this assessment for estimating sediment impacts from uniform basin-wide soil
concentrations, which showed sediment to surface soil concentration ratios ranging from 1.8 to
2.6. The BSAFs determined from the CDEP data are higher than the BSAFs used in the
demonstration scenarios of this assessment. This was likely due to use of bottom feeders for fish
concentration of the CDEP - bottom feeders are likely to have more exposure to dioxin-like
compounds in water bodies than column feeders due to their association with contaminated
bottom sediments.
7.3.3. Other Bottom Sediment Concentration Data
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Assuming elevated sediment concentrations are a function of elevated surface soil
concentrations is reasonable when the only source of water body contamination is soil
contamination. However, comparing soil and sediment concentrations would not be appropriate
if sediments and water were impacted by industrial discharges, which has often been cited as the
cause for sediment and water impacts (see Bopp, et al., 1991; Norwood, et al., 1989; e.g.).
Sediment concentrations of note have also been found in surface water bodies near urban
settings, with car and industrial stack emissions cited as likely causes (Gotz and Schumacher,
1990; Rappe and Kjeller, 1987). Rappe, et al. (1989) collected samples from the Baltic Sea,
which were described as background samples. They note that the pattern of tetra-CDF congener
concentrations found in the Baltic Sea were typical of what they termed the "incineration
patterns" - air and air particulate concentrations that were attributed to sources such as
incineration, car exhausts, steel mills, etc. On the other hand, sediment samples collected
between 4 and 30 km downstream from a pulp mill revealed a congener pattern typical of
bleaching mills. The stack emission and effluent discharge source categories provide separate
models for water body impacts. The capability of the effluent discharge model is estimating fish
tissue concentrations is examined in Section 7.3.6 below. The remainder of this section
examines some of the data available which is not attributed to industrial or urban sources.
Smith, et al. (1995b) evaluated sediment core data from the Hudson River National
Estuary Research Reserve system located on the lower (southern) Hudson River. They also took
soil cores near the estuaries studied. Using principal component analysis, as well as a mass
balance approach, they concluded that the CDD/CDF concentrations in the river sediments were
dominated by soil erosion (76% of total influx) and sewage-containing effluents (19%). In doing
their mass balance exercise, they used an "organic enrichment factor" of 1.6. Their data was on
homologue groups, not on individual congeners, so their data is not directly amenable to
comparison to model simulations or other data discussed in this section.
Except for the CDEP data described in Section 7.3.2 above, and possibly this data on
Hudson River sediments (Smith, et al., 1995b), data was not found linking sediment
concentrations to soil concentrations, in an urban or more pertinent to this assessment, a rural
setting. Some sampling has occurred in areas described as rural or background. Sediment
sampling in Lake Orono in Central Minnesota in such a setting found no tetra- and penta-CDDs,
although occurrences of total hexa-CDDs were found in the low ng/kg (ppt) level, occurrences of
hepta-CDDs to a high of 110 ppt, and total OCDD concentrations ranged from 490-600 ppt for
three samples (Reed, et al., 1990). A report on sampling of several estuaries in Eastern United
States included a "reference" or relatively clean site, central Long Island Sound. There were no
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occurrences of 2,3,7,8-TCDD, although 2,3,7,8-TCDF was found at 15 ppt in this clean site.
Other sites had identified industrial source inputs and higher noted concentrations (Norwood, et
al., 1989). 2,3,7,8-TCDD was extensively found in sediments of Lake Ontario (EPA, 1990a).
The average of samples from all depths of sediment collection from 49 stations including 55
samples was 68 ppt. The average of 30 surficial sediment samples was 110 ppt. A modeling
exercise implied that an annual load of 2.1 kg/year into Lake Ontario corresponds to a
concentration of 110 ppt. One identified source was the Hyde Park Landfill, located about 2000
feet from the Niagara River, which drains into Lake Ontario. Between 1954 and 1975, an
estimated 0.7 to 1.6 tons of 2,3,7,8-TCDD were deposited in the landfill. A principal conclusion
from the modeling exercise, however, was that a characterization of historical loadings of
2,3,7,8-TCDD into the lake was not available and would be necessary to evaluate the
contributions by the Hyde Park Landfill.
7.3.4. Data on Water Concentrations of Dioxin-Like Compounds
Chapter 3 of Volume n summarizes available data on surface water concentrations of the
PCDDs and PCDFs. Results summarized there are not directly amenable to comparison because
the sources of contamination were unspecified except to note that, in some studies, a portion of
the sampling occurred for water bodies known to be impacted by industrial discharges. The 104-
mill pulp and paper mill study, which measured discharges of 2,3,7,8-TCDD and 2,3,7,8-TCDF
into surface water bodies, was the only such study currently available which measured impacts to
surface water bodies. Section 7.3.6 below discusses the use of this data to evaluate the effluent
discharge source category. However, this study did not measure water concentrations, and no
other studies could be found which measured both source strength and resulting surface water
concentrations.
Nonetheless, the data on water concentrations of dioxin-like compounds does indicate
that occurrences of PCDDs and PCDFs are generally not-detected or in the low pg/L (ppq) range;
detection limits were generally at or near 1 pg/L. The one exception to this is occurrences in tens
to hundreds of pg/L range for PCDFs in one of twenty community water systems sampled in New
York (Meyer, et al., 1989). Concentrations exceeding 200 pg/L were found in the hepta- and
octa-CDFs; concentrations between 2 and 85 pg/L were found in the tetra to hexa-CDFs for this
impacted water system.
The highest water concentrations estimated in the demonstration scenarios in this
assessment were the concentrations associated with the soil contamination demonstration. There,
soil concentrations in a bounded area of soil contamination were 1 ppb for the three example
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compounds. Also, watershed soils were assumed to be at 0.00 in order to demonstrate the
incremental impact from the bounded area only. Water concentrations for 2,3,7,8-TCDD,
2,3,4,7,8-PCDF, and 2,3,3',4,4',5,5'-HPCB were 0.012, 0.091 and 0.0016 pg/L, respectively.
Water concentrations for the effluent discharge scenario, #6, were comparable to these at 0.018,
0.029, and 0.0029 pg/L for the three example compounds, respectively. For Scenarios 1 and 2,
where watershed soil concentrations of 2,3,7,8-TCDD and 2,3,4,7,8-PCDF were set at 0.39 and
0.67 ng/kg (ppt), respectively, surface water concentrations were lower 8*10"3 pg/L (ppq) for
2,3,7,8-TCDD and 9*10 '3 pg/L for 2,3,4,7,8-PCDF. For Scenarios 4 and 5 demonstrating stack
emission depositions and where watershed soil concentrations of toxic equivalents, WHO98-
TEQDF, were estimated to be in thelO"3 ppt range, surface water concentrations were in the 10"5
ppq range.
7.3.5. Data on Fish Concentrations in the Literature
This assessment estimated fish lipid concentrations of 2,3,7,8-TCDD for the various
source categories to be: 1) background conditions - 3.0 ppt, 2) soil contamination - 4.3 ppt, 3)
stack emission source category - 0.0003 ppt, and 4) effluent discharge source category - 6.4 ppt.
The fish lipid content assumed was 0.07. Therefore, whole fish tissue concentrations are
estimated at about one order of magnitude lower than these lipid concentrations. Data was not
found to appropriately compare the stack emission source category results, and data on the
effluent discharge source category is examined in the next section below. This section will
examine some available data on fish concentrations in order to compare results from the first two
categories with measured results.
The most appropriate study with which to make comparisons is the National Study of
Chemical Residues in Fish (EPA, 1992b; hereafter abbreviated NSCRF). Fish tissue data on a
variety of species and contaminants of concern in aquatic environments and fish from around the
country were developed. Most important for current purposes, the sites were carefully
characterized in terms of potential sources offish contamination. There were 353 sites from
which fish tissue data were available, of which 347 had data on 2,3,7,8-TCDD. Results from
four site categories might be appropriate for comparison with concentrations estimated to occur
from low, possibly background, soil concentrations of 2,3,7,8-TCDD. The four categories and
number of sites per category were: the USGS water quality network NASQAN - 40 sites;
Background (B) - 34 sites, Agricultural (A) - 17 sites, and Publicly Owned Treatment Works
(POTW) - 8 sites. The average 2,3,7,8-TCDD whole fish tissue concentrations (lipid contents
not provided) measured for these four categories were: NASQAN - 1.02 ppt; B - 0.56 ppt; A -
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0.75 ppt, and POTW - 0.90 ppt. Background conditions were demonstrated in Chapter 5 using a
soil concentration of 2,3,7,8-TCDD that was found in an actual background conditions - 0.37
ng/kg (ppt). The resulting fish tissue concentrations estimated for this soil concentration was 0.2
ppt (on a whole tissue basis assuming a fish lipid fraction of 0.07). Four of the site categories of
the NSCRF might be considered representative of sources characterized as land areas of high soil
concentrations of 2,3,7,8-TCDD. These were: Industrial/Urban site (IND/URB) - 105 sites,
Refinery/Other Industry (R/I) - 20 sites, Wood Preservers (WP) - 11 sites, and Superfund Sites
(NPL) - 7 sites. Average fish tissue concentrations measured for these site categories were:
IND/URB - 4.04 ppt, R/I - 4.38 ppt, WP - 1.40 ppt, and NPL - 30.02 ppt. The source category of
this assessment most similarly characterized to these would the category of soil contamination,
where a bounded area of contaminated soil had 2,3,7,8-TCDD concentrations at 1.00 ppb. The
resulting fish tissue concentration predicted was 0.3 ppt (assuming a fish lipid fraction of 0.07).
The two remaining site categories of the NSCRF were Paper Mills Using Chlorine (PPC), and
Other Paper Mills (PPNC). These data served as the basis for the comparison discussed below in
the effluent discharge source category.
In general, the range offish tissue concentrations measured for (perhaps) background
conditions, 0.56 - 1.02 ppt, were comparable to the 0.21 ppt fish tissue concentration estimated
assuming the background soil concentration of 0.37 ppt. The same may not be true, however, in
the comparison offish tissue concentrations ranging from 1 to 30 ppt associated with
urban/industrial contamination. The fish tissue concentration modeled in the demonstration of
the soil contamination source category was much lower at 0.3 ppt. However, it may not be
entirely appropriate to compare the demonstration of the soil contamination source category with
the urban/industrial sites of the NSCRF. For the demonstration scenario, the contaminated site
was 4 ha within a 100,000 ha watershed which had concentrations of 2,3,7,8-TCDD set to 0.0.
The fact that the background demonstration predicted a fish concentration 0.2 ppt, which was
within a factor of 3 to 5 of observations from the NSCRF (or closer to values in the NSCRF if
the fish lipid content was higher than 0.07), might be considered a limited validation of the
models. This is not a validation exercise, strictly speaking, since specific field data were not
input and compared.
One data point from that study of interest is the 30.02 ppt concentration found for the
NPL site. This is two orders of magnitude higher than the 0.3 ppt estimated for the soil
contamination source category. No insights can be gained from this difference because
information was unavailable on the seven sites which were characterized as Superfund sites and
which were expected to have been the cause of the 30.02 ppt fish concentration. It would be
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interesting to know the surface soil concentrations of the 7 NPL sites, the size of these sites
including the receiving water body, and their proximity to the receiving water body - that
information may be sufficient to conduct a partial model validation exercise.
Another comprehensive data base offish concentrations of 2,3,7,8-TCDD is from EPA's
National Dioxin Study (EPA, 1987; abbreviated NDS), which actually provided the motivation
for the NSCRF when significant residues of 2,3,7,8-TCDD were found in fish in the NDS. Fish
concentrations from the NDS are also listed and discussed in Kuehl, et al. (1989). Travis and
Hattemer-Frey (1991) summarized the fish data from the NDS. Their summary is as follows.
Data collected from 304 urban sites in the vicinity of population centers or areas with known
commercial fishing activity, including the Great Lakes Region, showed concentrations to range
from non-detected to 85 ng/kg (ppt). The geometric mean concentration was 0.3 ppt, and only
29% had detectable levels of 2,3,7,8-TCDD. The Great Lakes data had more contamination,
with 80% detection rate and a geometric mean concentration of 3.8 ppt.
The NSCRF also collected data on 2,3,4,7,8-PCDF, the second example compound
demonstrated. Briefly, the range of average fish tissue concentrations noted for the site
categories evaluated as background above is 0.42-0.78 ppt, very similar to the 2,3,7,8-TCDD
range of 0.56-1.02 ppt. The modeled fish tissue concentration of 2,3,4,7,8-PCDF for background
conditions was about the same as that for 2,3,7,8-TCDD at 0.17 ppt. The range of 2,3,4,7,8-
PCDF average fish concentrations for the sites of elevated soil concentration was 1.86-5.44 ppt,
which, like the comparison above for 2,3,7,8-TCDD, is higher than the modeled 2,3,4,7,8-PCDF
concentrations of 0.18 for the soil contamination source category with initial soil concentrations
of l.Oppb.
The NSCRF also collected data on PCB concentrations in fish, although the results were
expressed in terms of total tetra-, hepta-, and so on. The data indicates concentrations well into
the part per billion range for this breakout, and even higher considering total PCBs. The average
concentration of total heptachlorobiphenyls over all study sites was 96.7 pg/kg (ppb). The
average concentration of total PCBs over all sites was estimated as 1897.88 ppb, and the average
concentration of total PCBs for background sites was 46.9 ppb. The modeled concentration of
the example heptachlorobiphenyl, 2,3,3',4,4',5,5'-HPCB, for the soil contamination source
scenario, where the soil concentration was 1 ppb, was 7.6 ppt.
Data from the Great Lakes region indicate that PCB concentrations are significantly
higher than CDD/F concentrations in this area. PCB concentrations from fish in Lake Ontario
are in the tens to hundreds of ppb level (Niimi and Oliver, 1989), while 2,3,7,8-TCDD
contamination in Lake Ontario was in the tens of ppt level (EPA, 1990a) - a three order of
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magnitude difference. Other data in Table B. 10, Appendix B, Volume n, where concentrations
were similarly in the tens to hundreds of ppb level were from Lake Michigan (Smith, et al. 1990)
and Waukegan Harbor in Illinois (Huckins, et al., 1988). The single data point from that table for
2,3,3',4,4',5,5'-HPCB, the example PCB congener in Chapter 5, was for carp in Lake Michigan,
and was 29 ppb (29,000 ppt).
While the modeled CDD/F fish concentrations for background settings seem reasonably
in line with measured concentrations from similar settings, this assessment may have
underestimated concentrations of 2,3,3',4,4',5,5'-HPCB. As noted, concentrations for fish in the
Great Lakes Region were in the tens to hundreds of ppb range, while this assessment derived
estimates in the low ppt range. It is inappropriate to make direct comparisons without also
comparing source strengths. Concentrations of PCBs in bottom sediments ranged from the low
ppb for the tri-PCBs, to the tens of ppb for the tetra through hexa-PCBs, back to the low ppb for
the hepta and octa-PCBs, in Lake Ontario (Oliver and Niimi, 1988). Another literature source
showing fish concentrations in Waukegan Harbor, IL, in the hundreds of ppb range, had sediment
concentrations of specific congeners as low as 5 ppb to as high as 131 ppm. The concentration of
2,3,3',4,4',5,5'-HPCB in bottom sediments was estimated to be 1.6 ppt in the soil contamination
scenario. Therefore, one reason PCB concentrations in fish estimated in this assessment are as
much as four orders of magnitude lower than noted in the literature is because sediment
concentrations estimated for the source categories in this assessment are also about four orders of
magnitude lower. The BSAF for PCBs also was noted to be variable, with values below 1.0 to
values over 20.0 (see Chapter 4, Section 4.3.4). The BSAF for the example PCB congener in this
assessment was 2.0. Higher BSAFs would also increase PCB concentrations estimated for fish.
The fish concentration of 2,3,7,8-TCDD estimated for the stack emission source category
was lowest at 0.00002 ppt. Data was unavailable to place this in any comparative framework.
This is because the incinerator modeled was a well-controlled incinerator and the impacts
modeled were incremental - they did not include a background load into the water body which
would undoubtably drive fish concentrations in an area where there is a well-controlled
incinerator.
7.3.6. Impact of Pulp and Paper Mill Effluent Discharges on Fish Tissue Concentrations
a. Description of Exercise and Model Parameters
This section describes a validation exercise of the effluent discharge algorithm. The
description of this exercise as a "validation" exercise tentative, since much of the data used is of
uncertain quality. Discharge rates of 2,3,7,8-TCDD (mass/time units) into surface water bodies
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from a subset of 104 pulp and paper mills, which were sampled on a one-time basis in 1988 for
such discharges and other parameters (EPA, 1990b; hereafter referred to as the 104-mill study),
represent the key observed source term for this exercise. Fish concentrations of 2,3,7,8-TCDD
for fish sampled downstream of these sources as part of the National Study of Chemical Residues
in Fish (EPA, 1992b; abbreviated NSCRF hereafter) represent the key predicted model result for
this exercise.
The National Council of the Paper Industry for Air and Stream Improvement (abbreviated
NCASI hereafter) has already performed this exercise, and a brief description of their efforts and
results can be found in Sherman, et al. (1992). NCASI carefully matched NSCRF data to
appropriate mills of the 104-mill study. In many cases, they found more than one fish sample to
correspond to a given discharge. Also, they considered circumstances where more than one mill
effluent discharge can be considered to have impacted the environment where fish were sampled.
In these cases, discharge rates from the contributing mills were fed into the model as source
terms.
In NCASI's careful examination of the available data, they only considered 47 of the 104
mills as appropriate for this type of model testing. From these 47 mills, 95 fish samples with
detectable residues of 2,3,7,8-TCDD were identified. Some mills had only one fish sample
corresponding to it while others had up to four fish samples. The following explains why 57 of
the remaining mills were not considered for this exercise:
1. Downstream of 10 pulp and paper mills was an estuary. NCASI considered the model
appropriate for riverine situations only and did not calculate fish concentrations for estuarine
settings.
2. The measurement for 2,3,7,8-TCDD in the effluent was listed as non-detect, and no
further data examination and modeling occurred. There were 13 mills in this category.
3. NCASI could not identify appropriate fish measurements in the NCSRF downstream
of the mill, and did not model further. Seven mills were in this category.
4. Some of the mills in NCASI's exercise were only considered "proximate" mills adding
to the source term associated with another mill and one or more fish concentrations. Five mills
were described in this manner.
5. For the remaining 22 mills, no explanation was provided for their lack of inclusion in
the validation exercise.
Details of the NCASI modeling assumptions were supplied to EPA by NCASI (personal
communication, Steven Hinton, PhD., P.E., NCASI, Inc.; Department of Civil Engineering, Tufts
University, Medford, MA, 02155) and adopted for this exercise. Several other source materials
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were used to develop the parameters for this exercise. First, Figure 7-5 shows the effluent
discharge model and all the numerical quantities required, including the source term and the
observed fish concentration, and model parameters associated with the mill discharge and the
aquatic environment. Further description of the effluent discharge model can be found in
Chapter 4. The model parameters and their source materials are now listed.
1) Mill parameters including the 2,3,7,8-TCDD discharge rate, the effluent flow rate,
the suspended solids content of the effluent flow, and the organic carbon content of the
suspended solids in the effluent flow: The 104-mill pulp and paper mill study (EPA, 1990b),
a cooperative study between EPA and the paper industry, measured mass releases of 2,3,7,8-
TCDD (actually effluent flow and concentrations, from which mass releases can be estimated),
effluent flow, and total suspended solids content of the effluent flow (and other information such
as releases of 2,3,7,8-TCDF, which were not needed for this exercise). For purposes of this
validation exercise, actually only the total suspended solids content of effluent discharges was
used from the primary reference of this study (EPA, 1990b). Data from the 104-mill study was
also used in a modeling study, described more fully below, and in that reference, it was more
conveniently organized and compiled. As such, effluent flow and 2,3,7,8-TCDD discharge rates
came from a secondary reference. The organic carbon content of the solids in the effluent was
assumed to be 0.36. This was the value used in the example scenario of Chapter 5, and was
based on the fact that effluent solids are principally biosolids).
2) Receiving water body parameters including flow rate, suspended solids content,
and organic carbon content of suspended solids. A modeling study conducted by EPA (EPA,
1990c) used a simple dilution and the EXAMS model to evaluate the impact from discharges of
2,3,7,8-TCDD and 2,3,7,8-TCDF from chlorine bleaching mills. Mills from the 104-mill study
were the ones evaluated in this report. This study developed key receiving water parameters for
these mills which are pertinent to the dilution model of this assessment, including harmonic
mean flows at the point of effluent discharges, which were based on the nearest STORET
sampling point, and suspended solids concentration of the receiving water body at this point.
Details on how these key quantities were developed are included in that report and will not be
discussed here. The organic carbon content of the suspended solids was assumed to be 0.05,
which was also the content assumed for the example scenarios in Chapter 5.
3) Parameters associated with 2,3,7,8-TCDD, including the organic carbon partition
coefficient, Koc, and the biota suspended sediment accumulation factor (abbreviated
BSSAF). The Koc for 2,3,7,8-TCDD was the same 3.98xl06 otherwise assumed in this
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assessment, and the BSSAF value was assumed to be 0.09, which is the same value as the BSAF,
Biota (bottom) Sediment Accumulation Factor. Sections in Chapter 4 further discuss the Koc,
BSAF, and BSSAF.
4) Fish data including the fraction lipid and the observed fish concentrations: The
core reference for this information is the National Study of Chemical Residues in Fish (EPA,
1992b), as noted above. NCASI compiled the fish concentrations and associated lipid content of
the samples as part of their modeling exercise, and these were used here as well.
Table 7-10 lists the parameters used for each identified mill and receiving water body, as
well as the modeled and observed fish concentrations. Not included in this table are the
parameters assumed for all model runs, including the organic carbon contents of the suspended
solids terms, and the 2,3,7,-TCDD Koc and BSSAF.
b. Results and Discussion
One important point to discuss up front is that 38 of the 47 eligible mills discharged into
surface water bodies that were characterized as "low", while the remaining 9 mills discharged
into "high" receiving water bodies. This characterization refers to the flow rates of the receiving
water bodies. The average harmonic mean flow rate of the 38 low water bodies was 5.3 * 108
L/hr, with a range of 107 to 109 L/hr, while for the other nine, the average flow was 2.6 * 1010
L/hr, with a narrow range of 1 to 4 * 1010 L/hr.
This distinction appears to be non-trivial for a few reasons. One, model predictions
appear to more closely match observations for the smaller water bodies. The average of 38 mills
and 74 fish for modeled and observed fish concentrations is 7 ppt and 15 ppt, respectively. The
average of 9 mills and 21 fish associated with large receiving water bodies for modeled and
observed fish concentrations is 0.1 and 5.3 ppt, respectively. However, some paired data
(predicted versus observed fish concentration) showed over 3 orders of magnitude difference -
0.001 pg/g TCDD predicted versus 1.4 pg/g TCDD observed, for example. As evaluated by
NCASI, another important feature of the larger receiving water bodies that they were the ones
principally considered to have multiple discharges.
A final observation concerning the large receiving water bodies is that the suspended
solids data is also significantly different than the low receiving water bodies. For the 38 water
bodies associated with the small water bodies, the receiving water body solids content averaged 9
mg/L, while for the nine high receiving water bodies, the suspended solids content averaged 73
mg/L. This importance of the suspended solids content is principally seen for mills 39-42. The
solids content of these water bodies ranged from 107 to 221 mg/L. The average modeled fish
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concentration for these mills was 0.005 ppt, while the average observed fish concentrations was
3.0 ppt. The impact is one of "dilution": discharged 2,3,7,8-TCDD mixes into a larger reservoir
of suspended particles, leading to a low 2,3,7,8-TCDD concentration on suspended solids
concentration and lower predicted fish tissue concentrations. This dilution effect may also be
real, as the average observed fish concentrations for these circumstances of 3.0 ppt may indicate
a significant difference with the average 15.0 ppt observed for the smaller receiving water bodies.
Nonetheless, these high suspended solids data must be considered suspect; if the suspended
solids concentration were, in fact, lower on average, than model predictions would have closer
than 3 orders of magnitude away from measurements.
Considering all 47 mills and 95 fish observations, it was found that 73 and 87% of
predictions within a factor of 10 and 20 of observed concentrations, respectively. The predicted
and observed results of this exercise for these 47 mills is shown graphically in Figure 7-6, which
also shows the bounds of + or - a factor of 10 difference in predictions and observations Of note
and perhaps not ironically, the highest observed fish concentration of 143.3 ppt is matched by the
highest predicted fish concentration of 89.2 ppt. .
While Figure 7-6 appears to show a poor match in predictions and observations, the data
available must be carefully considered. Only one discharge measurement is made, and a limited
number offish at various points downstream were available for this exercise. It would certainly
be more meaningful if several discharge measurements per mill were made and several fish
measurements were made downstream of the discharge. To be more rigorous, several
measurements of discharge would have to be made over time to best reflect an average discharge.
Likewise, other mill-specific parameters are uncertain, such as receiving water body flow,
suspended solids in the water body, and so on. Finally, and perhaps most importantly, the
assumption of this exercise is that the mill discharges of 2,3,7,8-TCDD represent the only
sources impacting the fish. This is most unlikely to be the case for the large receiving water
bodies, which may be receiving other industrial point discharges or non-point sources (runoff,
atmospheric deposition). Given the factor of 2 difference in average predicted and observed fish
concentrations for the low receiving water bodies, one might cautiously conclude that the effluent
discharge model of this assessment is generally valid for, at least, receiving rivers with flows in
the range of 108L/hr.
Given this last cautious statement, one can continue this exercise by attempting a
calibration on an appropriate parameter(s) so that predictions better match observations. The
appropriate parameter for calibration is the BSSAF. The choice of 0.09 for the 2,3,7,8-TCDD
BSSAF was based on data from Lake Ontario (EPA, 1990a). Specifically, 0.09 was the BSAF -
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lake bottom sediment to fish lipid accumulation factor - for measured fish and bottom sediments
of Lake Ontario. As this is a lake and not a riverine situation, and inasmuch as 2,3,7,8-TCDD
contamination of Lake Ontario sediments have been attributed to historical impacts and not
ongoing causes, the 0.09 may be inappropriate. As well, a range of BSAFs for 2,3,7,8-TCDD
were noted in the literature in Chapter 4 of this assessment, ranging from less than 0.05 to greater
than 1.00. EPA (1993) suggests that data collection methods limit the usefulness of some of the
available literature, particularly those showing very high BSAF, and in a similar examination of
BSAF data, suggests a range of 2,3,7,8-TCDD BSAF from 0.03 to 0.3. In any case, this suggests
that the BSSAF is a reasonable candidate for calibration in this exercise.
A different selection for BSSAF significantly improves model performance. If the
BSSAF is increased to 0.20 (up from 0.09), the average predicted fish tissue concentration for the
38 mills discharging into the smaller water bodies increases as expected from 7.0 to 15.6 ppt,
comparing better now to the average observed concentration of 15.0 ppt.
Conclusions from this exercise include:
1. For at least smaller receiving water bodies, those with harmonic mean flows on the
order of 107 to 109 L/hr, the effluent discharge model is appropriate for assessing effluent
discharge impacts to fish for 2,3,7,8-TCDD and perhaps other dioxin-like compounds.
2. There appears to be a distinction in model performance for the large and small
receiving water bodies. The high suspended solids concentrations generated in an earlier
modeling exercise for the larger water bodies is one cause for model underprediction; these
solids concentrations should be further reviewed. Also, these water bodies were evaluated by
NCASI as ones with multiple sources. Other sources not identified by NCASI could also have
been the cause for higher measured fish concentrations as compared to model predictions. The
NSCRF report (EPA, 1992b) contains an appendix giving a matrix indicating point source
categories of discharges which may have affected fish concentration results. Pulp and paper
mills with and without chlorine were listed as point sources for 125 episodes (an episode is a fish
sampling site). In 37 of these episodes, other point sources were identified, including one or
more of the following: refinery (refinery using the catalytic reforming process), NPL site (a
Superfund site), or other industry (an industrial discharge other than a paper mill or refinery).
Given other sources, it is in fact a benefit to the exercise that predictions were lower than
observations.
3. The model more closely predicts fish concentrations for the smaller receiving water
bodies when the BSSAF is calibrated from 0.09 to 0.20. Considering that 0.09 was a value for
2,3,7,8-TCDD developed with data from Lake Ontario, a standing water body with principally
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historic and not ongoing 2,3,7,8-TCDD impacts, this setting is probably an inappropriate
surrogate for ongoing discharges to a riverine situation. This would argue that a calibration is
warranted.
7.3.7. Air Dispersion and Soil Concentration Modeling Around an Incinerator Known to
be Emitting Large Amounts of Dioxins
The Columbus Municipal Solid Waste-to-Energy (CMSWTE) Incinerator in Columbus,
OH, operated between June 1983 and December 1994, and processed an average of 1600 metric
tons of solid waste per day during its operation. A stack test taken in 1992 (EERC, 1992)
indicated that the annual emission rate of dioxin I-TEQs was 984 g. Measures were taken to
reduce dioxin emissions by the operators of CMSWTE. A second stack test was taken in 1994
(EMC, 1994) to evaluate the effectiveness of these dioxin reduction measures. The rate of
emission from this test was calculated at 267 g TEQ/yr, indicating about a 75% reduction in
dioxin emissions. These rates of emission can be compared against United States estimates of
total annual emissions from all known sources of dioxin release of 12 kg TEQ in 1987 and 3 kg
in 1995; see Volume I of these Dioxin Exposure Reassessment documents.
An ambient air monitoring study undertaken by the Ohio Environmental Protection
Agency (OEPA) included two rounds of sampling in 1994, one during a concurrent stack test,
and one round in 1995 after the incinerator had shut down (OEPA, 1994; 1995). For both
sampling events in 1994, the concentration was highest in the air monitor (1 of 6 total monitors;
5 operational for each sampling event) located in the downwind direction (southeast) from the
CMSWTE. Also, the profile of dioxins in the air matched the stack emission profile much more
closely than the other air samples, which had lower and more typical urban air concentrations. A
soil monitoring study conducted during 1995 and 1996 included 34 soil samples taken on-site
and up to 8 km in all directions from the plant (Lorber, et al., 1998). An evaluation of these soil
data clearly showed an imprint from the CMSWTE, with concentrations decreasing as a function
of distance from the stack, approaching a local background after about 3 km.
Complete descriptions of the stack, air, and soil measurements conducted around the
CMSWTE are available in previous papers (EERC, 1992; EMC, 1994; Ohio EPA, 1994, 1995;
Lorber, et al., 1998) and only summarized here.
The ISCST3 was run twice, once to obtain predicted concentrations over the 48-hour
period corresponding to the two ambient air monitoring events in 1994, and once to obtain
annual average wet and dry deposition of sorbed dioxins to input into a simple soil reservoir
mixing model to predict dioxin concentrations. The objective of this exercise was to use current
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EPA guidance on the use of ISCST3 for air dispersion/deposition modeling of dioxins (EPA,
1994;1995b), coupled with a soil concentration model, and after doing so, determine how well
the model was able to reproduce observed air and soil concentrations. This might be described as
a "model validation" exercise because it has these characteristics: 1) all available site-specific
information - stack emission rates, meteorological data, stack parameters, and others - are input
into the model, 2) all other parameters for which no site-specific data is available - soil half-lives,
atmospheric particle densities and mass fractions, and others - are input into the model using best
available information with no attempts at "calibration" in order to make the model results best
match observations, and 3) model results including predicted air and soil concentrations are
compared against corresponding monitored concentrations.
On the other hand, it is recognized that this exercise falls short of a rigorous model
validation exercise for ISCST3. The observed ambient air data set includes only two monitoring
dates, with five ambient measurements for one date and four for the other date. Actual stack
measurements of emissions are available for one of those dates, so a comparison of measured and
predicted air concentrations for the second date does not qualify as a "validation" exercise. It
must be assumed that emissions were the same for this second air sampling date. This is
admittedly a small data set and a resulting rigorous test for ISCST3 dispersion model testing.
Also, ISCST3 (and similar gaussian dispersion models) is expected to perform better for longer
averaging periods (e.g., annual) than for short term events. Expectations for the "success" or
characterization of the "failure" of the ISCST3 dispersion algorithms have to be tempered by
these considerations.
The input of the average predicted depositions of dioxins into a simple soil mixing model
to predict soil concentrations, and then comparing those to observed soil concentrations, is an
exercise that may come closer to being a "model validation" exercise. In that case, the ISCST3 is
applied in an hourly short-term mode over one year's worth of meteorological data to predict
average long-term depositions to soil. These depositions are input into a soil mixing model to
predict soil concentrations, and then the model results are compared with observations of soil
concentrations. Unlike 48-hour air measurements, soil impacts are "long-term", particularly
since dioxins are known to accumulate in soils over time and not undergo very meaningful
dissipation. Also, there are 34 soil measurements around the CMSWTE available, and this
number allowed for a reasonable characterization of the elevation of dioxin concentrations near
the facility and the decline of concentration with distance.
In any case, discrepancies between predictions and observations in both the air and soil
model comparison exercises were examined in order to gain insight on the capability of the
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ISCST3 model to predict ambient air and soil impacts of emitted dioxins, and to gain insight on
potential issues for further study of atmospheric and soil fate for dioxins.
7.3.7.1. Modeling Procedures
ISCST3 Modeling ISCST3 is a Gaussian plume model, which accepts a variety of
source geometries and emissions schedules in order to compute ambient air concentrations and
surface deposition fluxes at specified receptor points. Two applications of ISCST3 were
conducted for this effort. In one, the air dispersion algorithms alone were run, and
meteorological data requirements included hourly wind speed, wind direction and stability for
describing dispersion. These runs were conducted for the purpose of predicting 48-hr air
concentrations, to compare with the 48-hr ambient air measurements. For the other, the particle-
phase deposition algorithms were employed and dioxins were depleted from the plume by an
amount equal to that depositing as the plume moved outward from the CMSWTE. The key
output from these runs were long-term average dry and wet deposition of particle-bound dioxins,
which were used for predicting soil concentrations of dioxins, to compare with the soil
measurements.
For the dispersion model test, there were, in fact, two separate tests of the air dispersion
algorithm - each test had a different meteorological data set: an "airport" set and an "on-site"
meteorological data set. The airport set includes surface data (wind speed, wind direction and
atmospheric stability) from the Columbus, OH, airport and upper air data for the mixing height
from Dayton, OH. airport (BEEline Software, Inc. ISCST3 driver diskette 1998). The airport
data was applied only for the first of two air monitoring events. On-site data for both of the 1994
measurement periods include wind speed and wind direction (Ohio Environmental Protection
Agency, private communication 1998). The Columbus stability and Dayton mixing height were
used in the on-site set. The purpose of obtaining and testing two meteorological data sets was to
be able to evaluate the importance and uncertainty associated with key input stream. Besides
meteorological data, other required inputs for modeling dispersion alone included: (1) building
configuration data, (2) emissions data, and (3) receptor data. These dispersion model runs
omitted particle-phase deposition, plume depletion, and chemical decay in the air. The dioxins
were modeled as if the entire emission were in the form of a conservative pollutant, with no
differentiation in fate of the individual compounds as a function of vapor/particle partitioning
behavior, or atmospheric degradation or transformations.
For the deposition application of ISCST3, wet and dry deposition of particle-bound
dioxins were modeled and then input to a separate soil mixing model to predict soil
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concentrations. Therefore, additional meteorological data required were precipitation data. The
prediction of depositions of particle-bound CDD/Fs with ISCST3 relies on particle-specific
(particle diameter, e.g.) and dioxin-specific (vapor/particle partitioning, e.g.) parameters which
are not required for dispersion modeling. The ISCST3 model estimates deposition flux values by
multiplying the pollutant concentration in airborne particles by a deposition velocity. The
deposition velocity is calculated considering gravitational settling velocities and atmospheric
resistance. Annual average depositions were predicted using a single year of meteorological data
from 1989; modeling from 1983 to 1994 would obviously have been preferable, but only one
year of data was available. Meteorological data was provided by the National Climatic Data
Center and from EPA's Support Center for Regulatory Air Modeling internet page
(www.epa. gov/ttn/scram). The surface and precipitation data was collected at the Columbus,
Ohio Weather Service Office. Atmospheric mixing heights were determined using upper air data
collected at the Dayton, Ohio Weather Service Station. An examination of the meteorological
data from 1989 compared with historical averages showed that the wind speed and direction were
very similar to historical means, and the precipitation was slightly above normal for 1989 (111
cm for 1989 compared to an historical average for Columbus, OH, of 96 cm/yr). The ISCST3
was run in plume depletion mode, meaning that dioxins were depleted from the plume moving
away from the incinerator by an amount equal to the dioxins depositing by dry and wet particle-
phase deposition.
Like the air dispersion tests of ISCST3, two sets of outputs were generated for soil
concentration modeling. There were two stack tests available, and it was unclear as to which
would better characterize long term emissions of dioxins from CMSWTE. Both were used to
predict soil concentrations. This is described in more detail in the next section below on Source
Characterization.
The ISCST3 model was run on a "unitized" basis for both dispersion and deposition
simulations, meaning that ambient air concentrations and deposition results were generated for an
emission rate of 1 g/sec. For the dispersion-only runs, the individual total emission rates of all 25
CDD/Fs (17 congeners on non-zero toxicity and 8 homologue groups) were multiplied by the
predicted unit concentration to give the predicted ambient concentrations at the receptor points.
Deposition predictions for the CDD/Fs were generated using this two-step procedure: 1) the total
amount of the CDD/F emitted was assumed to partition into vapor and particle fractions
according to ambient conditions at 20 °C (in contrast to partitioning assuming conditions at the
stack exit); this step allowed for an estimation of dioxin-specific particle-bound emission rates in
g/sec, 2) then, these particle-bound mass emission rates were multiplied by the unitized dry and
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wet deposition rates predicted to occur at the receptor point to provide the compound-specific
deposition rates.
All model parameters for both runs, with the exception of the details on receptor locations
(air and soil monitoring locations around the CMSWTE) are provided in Table 7-11. Further
detail on modeling algorithms for the ISCST3 can be found in EPA (1995b).
Source Term Characterization. Two stack tests were available to supply the critical
source term for this exercise (EERC, 1992; EMC, 1994). The first was conducted in 1992 by the
Ohio Environmental Protection Agency (OEPA) for purposes of permit renewal. High dioxin
emissions at 6799 ng total/dscm concentration (total = sum of the homologue group
concentrations; dscm = dry standard cubic meter) and 976 g TEQ/yr (when extrapolating the
results from 1 stack to the 3 stacks at CMSWTE and assuming historical average operation times
for the CMSWTE) mass emissions were found, leading to regulatory actions by the state and
federal environmental agencies. Process modifications were undertaken for purposes of reducing
dioxin emissions, and the CMSWTE was retested in March of 1994. Total concentrations were
reduced to 3685 ng/dscm and the mass TEQ emissions were reduced by about 75% to 267 g
TEQ/yr (estimated using the same historical CMSWTE operation practices).
This second stack test occurred during March 16-18 of 1994. This corresponds closely to
the time that the OEPA was sampling the air for dioxins - on the 15-17th of March. Therefore,
the air dispersion model tests for March used the March stack test results. Unfortunately, the
CMSWTE was not stack-tested during the April air sampling events. It was necessary to use the
March stack test results for the April dispersion model tests, and then, of course, to assume that
the April emissions were similar to the March stack test emissions.
For deposition modeling, a decision also needed to be made regarding characterization of
long-term emission rates. Rather than select either the 1992 or the 1994 stack emission test for
this evaluation, or an average of the two, to represent long-term dioxin emission rates, results
were generated for both emission tests to demonstrate the importance of this critical and
uncertain term in the modeling procedure.
Soil Concentration Modeling: Wet and dry depositions are summed and become the
source term for a simple reservoir mixing model for predicting soil concentration Cs, as:
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where:
Cs = the soil concentration, pg/g
F = the annual total (wet + dry) deposition of dioxins as predicted by ISCST3,
pg/m2-yr
k = the first order annual soil dissipation rate, yr"1
t = the time during which deposition occurs, yr
M = the soil mixing mass, g/m2
The dissipation rate assumed here for all dioxin compounds was 0.02772 yr"1 (half-life of
25 years), a mid-range value selected to be between a value of 0.0693 (half-life of 10 years) often
assumed for surficial dioxin residues (EPA, 1994) and 0.00693 (half-life of 100 years) speculated
to be an upper range for subsurface dioxin residues (Paustenbach, et al., 1992). The best
validation of this choice of half-lives for all dioxin congeners comes from McLachlan, et al.
(1996), who reported on an analysis of soil taken from experimental plots which had been
amended with sewage sludge in 1968 and sampled in 1972, 76, 81, 85, and 90. These archived
samples were analyzed for all 17 dioxin-like CDD/Fs, and based on an analysis of results,
McLachlan and coworkers concluded that half-lives were on the order of 20 years, with dioxin
removal from the plots being mainly physical removal processes (overland runoff, wind erosion).
Furthermore, their results suggested that all congeners had been removed at roughly the same
rate, which is why they concluded that removal processes were mainly physical and very little in-
situ degradation appeared to be occurring. A time of operation, t, of 11.5 years was used,
corresponding to the time of operation of the CMSWTE. The soil mixing mass, M, equaled
112,500 g/m2, which assumes a mid-range soil bulk density of 1.5 g/cm3 and the soil sampling
depth of 7.5 cm.
Description of the Measured Air and Soil Concentrations: Ambient air monitoring was
conducted by the Ohio Environmental Protection Agency (OEPA) in 1994 to evaluate ambient
air concentrations after process modifications reduced dioxin emissions from the CMSWTE.
General Metal Works model PS-1 high volume samplers were used to collect 48-hr samples.
Concentrations were, therefore, the sum of vapor + particle phase concentrations. Six monitors
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were in the city of Columbus between 1.8 and 3.0 km from the site, mostly in the historical
downwind direction, northeast, but one in the upwind southwest direction. Two of the samplers
were co-located (for purposes of quality control), so results from these two samplers were
averaged to represent one sampling point A seventh sampler was located 45 km southwest of the
facility in a rural "background" setting; results from this sampler were not used in this modeling
study. Five samples (4 sampling locations; the co-located samples were averaged) were taken in
March and 6 samples (5 locations) were taken in April, 1994. The March set, taken on the 15-
17th of the month, occurred at nearly the precise time that the March 1994 stack testing occurred,
on the 16-18th. The April sampling event occurred during April 19-21. Exact starting and
stopping times of the air monitors were not available for this test. For purposes of air dispersion
modeling, the starting and stopping times were assumed to be the mid-day of the beginning and
ending days of each sampling periods. In all, there were 9 urban air sampling events taken
during 1994 that comprise the "observed" air concentration data set used in this modeling study.
Wind rose data for the March and April sampling periods were also available, and the provided
insights into the expected impact patterns. A final round of air samples from the seven air
monitors was taken in 1995 after the CMSWTE had shut down. The purpose of this data set was
to evaluate the air quality now that the CMSWTE was no longer operating. Full details on the air
monitoring studies, including analytical methodologies, quality control, and final results, are
described in OEPA (1994, 1995).
A first phase of soil sampling was conducted by the United States Environmental
Protection Agency (EPA) in December of 1995. Sampling in this round included 4 samples on
the site of the incinerator, 18 samples within about 3 km of the incinerator in the city of
Columbus, and 3 samples at a background site 45 km from the CMSWTE. This background site
was the same as the air monitoring background site. The study design for this phase employed a
stratified random selection process, involving sites in the four major quadrants around the
incinerator (northeast, southwest, etc.) with an emphasis of sampling in the quadrant which was
historically downwind from the incinerator, the northeast quadrant. The following conditions
were sought during site selection: 1) level, undisturbed soils, 2) away from trees, 3) not adjacent
to roads, 4) not near pressure treated wood, and 5) not known or suspected to have high dioxin
concentrations for any other reason. All samples were collected using pre-cleaned equipment
dedicated to each sampling location. Each sample site consisted of an area of 1.5 m x 1.5 m. A
grid of 25 sections was established at each site and used for random selection of aliquot sample
sites. Four random aliquots were collected for each sample. A "sample" for this study was,
therefore, a composite of four aliquots. Aliquots were collected using a stainless steel tulip bulb
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planting device. This device removed a plug approximately 7.5 cm in diameter to a depth of
about 7.5 cm.
A second phase of soil sampling was undertaken in August of 1996. Thirteen samples
were taken from about 2 km away from the incinerator to about 8 km distant. The purpose of
this second phase was to ascertain whether a background concentration for the city of Columbus
could be determined. A similar selection criteria for sample sites was employed in this second
phase.
Altogether, there were 4 soil samples on the incinerator property, 31 samples in the city
of Columbus taken from right outside the incinerator to upwards of 8 km away, and 3
background samples taken 45 km away, for a total of 38 soil samples. This modeling used 34 of
the samples - it did not have use for the 3 background samples, and 1 of the remaining samples
was found to be contaminated by a local source not associated with the CMSWTE. Full details
of the soil monitoring study can be found in Lorber, et al. (1998).
Figure 7-7 shows the location of the CMSWTE in relation to the 32 soil samples in
Columbus and the 5 urban air sampling locations. Not shown in this figure are 3 of the 4 soil
samples taken on the site of the incinerator, and the background site in which 3 soil samples were
taken and the 1 background air sampler was located. This figure identifies the groupings of the
soil samples, as described in the results section below.
Subtracting Local Background Concentrations From Measured Concentrations:
The ISCST3 will predict only the increments of dioxin concentration in the air and soil that are
due to emissions from the CMSWTE. Therefore, a procedure had to be developed to subtract a
local "background" of dioxins from both the air and soil observed data.
The average of 6 air measurements taken in 1995 after the CMSWTE shut down was
assumed to represent the background dioxin air concentrations for this site. The average total
concentration from 1995 was 2870 fg/m3, with a range of 2030 to 4760 fg/m3. The 1995 average
concentrations of each dioxin-like congener as well as those of the homologue groups were
subtracted from each of the March and April 1994 corresponding measurements. When such a
subtraction resulted in a concentration less than 0, the concentration was assumed to be 0 for
purposes of this exercise.
An analysis of the observed soil data in Lorber, et al. (1998) showed that concentrations
decrease to the local soil background at about 3 km from the CMSWTE, at a TEQ soil
concentrations of 4.0 pg/g (ppt). The soil profile of CDD/Fs for this background provided in
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Lorber, et al. (1998) was subtracted from each of the 34 observed soil measurements; when this
subtraction resulted in a concentration less than 0, the concentration was set to 0.
Procedures for Evaluating the Performance of the Models: The paucity of the
observed data, particularly the air measurements, makes a rigorous "goodness-of-fit" statistical
comparison of predicted versus observed inappropriate. Rather, tabular summaries of predicted
and observed concentrations are utilized, and simple qualitative discussions address the
goodness-of-fit. For the air dispersion comparisons, predicted air concentration quantities
associated with a 48-hour air monitoring event are compared with the appropriate observed
quantities. For the deposition comparisons, soil samples are "clustered" and simple mean
concentrations are generated for both modeled and observed concentrations. Four clusters which
are displayed include: 1) "on-site" - 3 soil samples taken on the site on the CMSWTE, 2) "off-
site" - 5 samples just off-site and in the historical downwind direction, northeast, within 500
meters of the incinerator, 3) "urban" - 14 samples taken from about 500 meters to about 3
kilometers, and 4) "urban background" - 12 samples taken from about 3 to about 8 kilometers.
As discussed in Lorber, et al (1998), the high soil concentrations found in the on-site cluster were
speculated to have resulted in ash drift from piles or trucks transporting the ash to nearby
landfills rather than deposition. Therefore, a comparison of predicted and observed
concentrations for this on-site cluster are displayed for information purposes only, not to be
considered in the context of model testing. Otherwise, all observed soil samples, and clusters,
can be considered to represent long-term deposition trends as the monitoring study protocols
insured that they were in relatively flat, undisturbed locations away from any nearby potential
dioxin sources (roadways, PCP treated wood, etc.). The predicted and observed concentration
quantities which are displayed include: 1) homologue group concentrations, 2) total
concentrations, which are sum of the 10 homologue group concentrations, and 3) TEQ
concentrations. These terms were defined above.
In addition to tabular summaries, isoline figures were generated. These are lines of equal
concentration around the CMSWTE, either air or soil concentrations, that were generated using
Arc View® - a desktop GIS package. First the point data, measured or modeled concentrations,
are brought into Arc View® as point coverages. Then, using the Arc View® kriging routing,
surfaces of the concentrations are generated using the exponential function to estimate the
semivariogram. For the air and soil concentration isoline generation, modeled concentrations
were generated for 250 meter intervals to about 3 km in all directions, and these were input as
point coverages into Arc View®. There were too few observed air concentration measurements,
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so isolines could not be generated for these. Instead, measured air concentrations were overlain
on the predicted isolines. For soil concentration, there was judged to be sufficient coverage with
34 soil samples to generate "observed" isolines to compare with predicted isolines.
Finally, it is reiterated that all "observed" concentrations, both soil and air, were
generated by subtracting out background concentrations in the procedure described above.
Therefore, all tabular or figure notations of a "0" observed concentration means that, if
subtracting out the background concentrations from the measured concentrations resulted in a
negative concentration, the measured concentration was set to zero for purposes here.
7.3.7.2. Results and Discussions
Air Dispersion Modeling: Even before air dispersion modeling was undertaken,
examination of the data revealed clear trends. Analyses of on-site wind roses for the March and
April 1994 sampling dates reveal that there is one dioxin monitoring station likely to have been
influenced by the CMSWTE. This station, termed SE-3 by the Ohio Environmental Protection
Agency (OEPA) was about 2 km east of the source and was downwind from the source
approximately 53% of the time during the sampling period in March 1994 and 78% of the time in
April 1994 (OEPA, 1994). In contrast, none of the other 5 stations was downwind for time
fractions approaching those of SE-3. The measurements confirmed that SE-3 was the most
impacted of the samplers, with TEQ measurements of 168 fg TEQ/m3 in March and 353 fg
TEQ/m3 in April. The average of the measurements from the other 5 samplers over the two dates
(a total of 8 samples; one sampler was not operational for both events) was 52 fg TEQ/m3, with a
range of 10 to 98 fg TEQ/m3 (note: background not subtracted out for these observations).
Lorber, et al (1998) examined this trend further, showing also that the profile of CDD/Fs found
in March and April in SE-3 matched the stack emission profile of CDD/Fs more closely than the
other ambient air samples, which displayed profiles more typical of background air.
Table 7-12 compares the observed total concentrations at each reporting monitoring
station with the model predictions for both meteorological data sets, the "on-site" and "airport"
sets. As noted earlier, two important considerations for evaluating the comparison of predicted
and measured air concentrations are: 1) having 4 and 5 air measurements for sample dates in
March and April, respectively, is a small sample size, and 2) one can expect the ISCST3 to
perform better for longer averaging times as compared to shorter averaging times. It would be
fair to conclude that the paired comparisons of predicted and observed 48-hr air concentrations
are severe tests of model performance, and Table 7-12 shows the large scatter expected from this
test.
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Still, some meaningful observations might be possible from Table 7-12. First, it does not
appear that either of the meteorological data sets provides a superior fit to the data for the March
sampling event. The on-site runs appeared to better identify SE-3 as the monitor of most impact,
and also to identify the sampler SN-2 as having some impact, but not as much impact as SE-3.
The airport meteorological set appeared to show a significant impact to SN-2, but not as much of
an impact for SE-3. On the other hand, the simulations using on-site meteorological data
identified SNW-1 as having the highest concentrations, while the simulations using the airport
data correctly modeled this site perhaps more correctly as having little impact. Both
meteorological data sets correctly identified SSW-4 as the monitor which showed no impact.
For the April sampling date, the on-site meteorological data correctly identified SE-3 and
SSW-4 as monitors having some impact, with little or no impact for the other three monitors.
However, SE-3 was not simulated to have the most impact, as was found.
The difference between using the on-site and air meteorological data was further
examined using isoline figures. Six such isoline drawings, with observed concentrations
overlain, are shown in Figure 7-8. These include TCDD, OCDD, and TEQ predicted/observed
results from March for the on-site data and the airport data set. Observations from this figure
include:
1) ISCST3 modeling runs using both meteorological data sets appear to have correctly
identified the western quadrants (northwest and southwest) as being areas of little impact. The
observed "0" concentration in the southwest quadrant (sampler SSW-4) is consistent with this
trend, as are wind rose that are displayed and discussed in OEPA (1994). Both figures appear to
have identified the northeast and southeast as areas of principal impact, with little impact due
east. The two observed air measurements in the northeast quadrant (samplers SN-2 and SNW-1)
do, in fact, suggest low impact due north with increasing impact as one moves in the northeast
direction. The observed air measurement in the southeast quadrant (sampler SE-3) may, in fact,
have missed areas of higher impact during the two days, which the model runs suggest are either
further north or further south.
2) The discussion above comparing the measured point estimates with the modeled point
estimates suggests that there may be significant differences in the way the two meteorological
data sets simulated impacts. More specifically, the "airport" data simulation showed three times
as much impact for sampler SN-2 compared to the "on-site" simulation: 20,833 fg/m 3 total
(airport) vs. 6,606 fg/m3 (on-site). On the other hand, the "on-site" simulation showed 7 times
more impact for sampler SNW-1: 8943 fg/m 3 (on-site) vs. 1270 fg/m 3 (airport). Looking at
Figure 7-8, however, the differences do not appear that meaningful. The on-site simulations
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seemed to push the plume a little more northeast and southeast compared to the airport runs,
which showed more impact due north and south. Sampler SN-2 was simulated to be in a zone of
important impact according to the airport meteorological data set, while sampler SNW-1 was
simulated to approach this zone more so with the on-site meteorological data set. These types of
trends emphasize the potential problems and misinterpretations that can occur when one attempts
a validation exercise with ISCST3 with short-term data and a limited number of air
measurements.
3) The biggest discrepancy for the three dioxin quantities compared in Figure 2 is for
OCDD. It appears as though much higher concentrations, ranging from 1 to almost 5 pg/m3 in
the northeast and southeast were modeled, while measurements of 0.5 pg/m3 and less were
observed. These high modeled OCDD concentrations were the main reason that the ISCST3
modeled much higher observed total concentrations than were measured (see Table 7-12). For
the other quantities, TCDD and TEQ, while the location of high impacts may not have been
perfectly identified, at least the magnitude of the high measurements were in the range of the
high modeled concentrations. This important trend is discussed in more detail below.
As mentioned above, station SE-3 stands out in both the March and April sampling as
having the highest impact of all stations. Thus, the data from this station have the best chance of
avoiding the uncertainties introduced by background fluctuations. Predicted and observed
homologue group concentrations for SE-3 for the both sampling dates are compared in Table 7-
13. These results were generated using on-site meteorological data. Table 7-13 also shows the
CMSWTE stack emission rate of these homologue groups.
Being only an exercise in air dispersion modeling (no wet/dry deposition; no stack
speciation; no atmospheric chemical reactions), there is a perfect correlation between the
homologue profile of the emissions and air concentration predictions for both March and April.
The observed air concentrations clearly do not have this stack emission profile, however.
Specifically, the speciation pattern from source to receptor has shifted in these ways: 1) the lower
chlorinated tetra and penta CDD/Fs have greatly magnified in importance in the ambient air
profile as compared to the stack profile, and 2) conversely, the hexa through octa homologues,
with the exception of OCDF, have been reduced in importance in the ambient air profile as
compared to the stack profile. Said another way, the model predicted lower concentrations for
the lower chlorinated CDD/Fs than were measured, and higher concentrations for the higher
chlorinated CDD/Fs. The total concentration predictions were, however, within about a factor of
two of observations. Not that it has meaning with regard to fate and transport considerations, but
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the TEQ concentrations were comparable: 125 and 309 fg TEQ/m3 measured during March and
April compared with 142 and 156 fg TEQ/m3 modeled for SE-3.
Three possible explanations are offered to explain why the model did not predict the
measured shift in homologue profile between the stack and field:
1) It is known that CDD/Fs with fewer chlorines have higher vapor to particle (V/P)
ratios; indeed, high temperatures in the stack could generate even higher V/P ratios
(Eschenroeder, 1994). If stack sampling methods underestimate the amount of vapor pollutant
being emitted, than the lower chlorinated dioxin emission rates are being underestimated - an
error that would be exacerbated by the even higher V/P ratios in the high temperature stack gas.
The PS-1 samplers capturing both vapor and particle-phase CDD/Fs in ambient air are well
tested and not expected to have caused error in the characterization of total ambient air
concentrations of dioxins. There has been some speculation that PS-1 samplers may
overestimate the vapor fraction of dioxins, but this would not affect their characterization total
concentrations (sum of vapor and particle phase concentrations; see Chapter 3 for a complete
discussion of vapor/particle partitioning).
2) Running the air dispersion algorithms of ISCST3 alone did not account for particle
deposition, yet some of the higher chlorinated CDD/Fs, expected to be sorbed to ambient air
particles or fly ash, may have deposited by dry deposition prior to the air sampling locations.
The results for the deposition modeling described below support this hypothesis, at least for the
dioxins. It compares model predictions of soil concentration with measured soil concentrations.
One clear trend was that the model consistently underpredicted the soil concentration of the hepta
and octa dioxin homologue groups. This result, combined with the observation that the higher
chlorinated dioxins were the most overpredicted in air concentrations in this paper, suggests that
the plume is being depleted of higher chlorinated dioxins by deposition. However, this trend was
not duplicated by the higher chlorinated furans. There, modeled soil concentrations were more
nearly consistent with measured soil concentrations, with a small degree of overprediction.
3) Another possible physical explanation is that dechlorination may occur between the
emission point and the ambient measuring station a kilometer or two downwind. Workers at
Monsanto Laboratories (Orth, et. al., 1987) and at the Agro-Environmental Science Institute in
Japan (Koshioka, et al., 1989) have observed photolysis of TCDD. Generally, polychlorinated
organic compounds easily experience photochemical loss of chlorine atoms. If the higher
chlorinated CDD/Fs dechlorinated to form lower chlorinated CDD/Fs in the atmosphere, than
more lower chlorinated CDD/Fs would have arrived at the ambient air monitoring stations to
cause the distinct ambient air profile.
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Deposition and Soil Concentration Modeling: Table 7-14 provides results from this
exercise, which are observed and predicted homologue and TEQ concentrations for 4 clusters of
soil samples. These clusters were developed for purposes of displaying results from the soil
monitoring study conducted around the CMSWTE (Lorber, et al., 1998), and generally
correspond to increasing distance in all directions from the incinerator. As discussed above, the
observed and predicted soil concentrations for soil samples taken on-site, the first cluster of
Table 7-14, are shown for informational purposes only; it is not expected that the on-site soil
samples represented long-term deposition trends. Some trends that may be observed from the
results in Table 7-14 include:
1) Since emission rates between the 1994 and 1992 stack tests differed by about a factor
of 4, subsequent predictions of soil concentration made with each stack emission rate also
differed by this factor of 4. Generally, the 1994 stack test predictions appear to better match the
observed soil concentrations compared to the 1992 stack test with all homologue groups except
TCDD; the TCDD predictions using the 1992 stack test are a better match. Most of the time,
however, both sets of predicted homologue group soil concentrations were higher than observed
soil concentrations, sometimes by more than a factor of 10 when using the 1992 stack test.
The question that this study is unable to answer is which stack test is more likely to have
been representative of long term emission trends from the CMSWTE. The 1994 test occurred
specifically after measures had been taken to reduce dioxin emissions. Because of process
changes made to the CMSWTE, it would be reasonable to assume that the 1994 test is not
representative of long term emissions. On the other hand, the 1992 test was occurring during
heavy rainfall, which soaked the refuse to be burned. Data on the refuse moisture content
showed that the average moisture content of the refuse burned in 1992 was about 10% higher
than in 1994 - it was about 38% during the 1992 test compared to 28% in 1994. Some have
suggested that wetter refuse may result in higher dioxin emissions (personal Communication, K.
Jones, Zephyr Consulting, Seattle, WA.), although this hypothesis is unproven and the moisture
content of feed materials is not considered to be a principal factor in predicting dioxin emissions
- factors such as feedstock content, combustion efficiency, pollution control device, and pollution
control inlet gas temperature are more often cited as the critical factors.
2) Noteworthy for results with both stack tests is that much more OCDD is found in the
soil than predicted, and the same is true but to a lesser, although still noticeable, extent with
HpCDD; in other words, the model under-predicted the soil concentrations of these homologue
groups. As noted above in the description of air dispersion results, the ISCST3 was found to
greatly over-predicted OCDD and HpCDD ambient air concentrations. Taken together, these
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trends suggest that OCDD and HpCDD deposited near the incinerator to a much greater extent
than was modeled. Since both dioxin homologue groups exist in the atmosphere principally
sorbed to particles, this may reflect inappropriate parameter assignments relating to particle
phase deposition algorithms, or possibly inappropriate deposition algorithms in general.
However, the model appears to overpredict OCDF and HpCDF, and like OCDD/HpCDD,
OCDF and HpCDF are also tightly sorbed to airborne particles, so perhaps the model's treatment
of particle fate may not be the cause of significant underprediction of OCDD.
3) With both stack test results, the model would appear to proportionally overpredict most
congeners (not OCDD/HpCDD) to a greater degree the further downwind one gets. This
suggests that more dioxin mass is being removed from the plume as it disperses downwind than
ISCST3 is able to simulate. Removal mechanisms include particle and vapor phase deposition,
plant capture, and atmospheric degradation (photolysis and photooxidation).
Figure 7-9 shows a series of 9 isoline maps crafted to additionally display the trends of
the measured versus the modeled soil concentration. Each group of three isoline maps pertains to
one CDD/F compound; there are three isoline figures each for TCDD, OCDD, and TEQ
concentrations. The first in the sequence of three are isoline maps drawn from the measured
data, and the next two are maps drawn from using the 1992 and then the 1994 stack test.
Observations from Figure 7-9 include:
1) The shape of the isoline figures developed using the 1992 stack emission test will be
the same as those developed using the 1994 stack emission test, because they were all developed
from the same unitized simulation - the only difference will be the mass of particle-bound
emissions as a function of the compound and stack test.
2) The observed maximum soil concentration appears to occur in the northeast quadrant
about a kilometer away. The predicted maximum soil concentrations are also found in the
northeast quadrant, but they are a bit closer, at about 1A kilometer away. Also, the isolines drawn
from model simulations seem to suggest that the maximum will occur more due north of the
CMSWTE as compared to isolines drawn from measured data.
3) As was noted above, these isolines suggest much higher OCDD concentrations, in the
thousands of parts per trillion (or equivalently, parts per billion), are found near the CMSWTE,
as compared to modeled OCDD concentrations, which are in the hundreds of parts per trillion. It
is noted that smooth isolines could not be drawn from the observed OCDD data because of the
inhomogeniety of the results. Specifically, of the 8 highest soil samples nearest the CMSWTE, 5
had observed concentrations of OCDD above 1900 pg/g, ranging from 1930 to 6651 pg/g, but the
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other 3 measurements were less than 1000 pg/g, ranging from 309 to 731 pg/g. The observation
that OCDD was elevated in soils well above 1000 pg/g is supported by the data, despite oddly
shaped isolines. The observed TCDD concentrations in the vicinity of the CMSWTE appear in
range from 40 to a peak of 160 ppt, which is close to the range of 50 - 200 ppt modeled when
using the 1992 stack emission test. However, when using the 1994 stack emission, the elevation
in TCDD is only suggested to be in the 5-20 ppt range. Although not meaningful with regard to
fate and transport, per se, the 1994 stack test appears to better duplicate the observed range of
elevated TEQ concentrations - between 20 and 50 ppt, while the 1992 stack test simulations
suggest elevations as high as 200 ppt TEQ.
7.3.7.3. Discussion and Concluding Remarks
Caution was expressed in the opening paragraphs that this exercise should not be
characterized as a "model validation" exercise, mainly because of the weaknesses and
uncertainties in the data and model parameters. To reiterate, some of those
weaknesses/uncertainties include: a) a very small number of observed air monitoring data points
(and the lack of precise information on when the air monitors were turned on and off, which can
be important for short term air dispersion testing), and a relatively small number of soil
measurements, b) the lack of consideration of all possible plume depletion mechanisms in the
dispersion and deposition modeling. For the deposition modeling, the plume depletion by
particle-phase deposition was considered, but other plume depletion mechanisms include
atmospheric degradation of either vapor or particle phase dioxins, vapor phase deposition, and
vapor- and particle-phase vegetative capture, c) a reasonable but still possibly flawed means to
subtract "background" from measured air and soil concentrations. It is possible, for example,
that air monitoring locations have their own, very localized, "background". Therefore, averaging
all 5 air concentration measurements from 1995 to represent "background" to subtract equally
from all measurements in 1994 may not be appropriate, d) uncertainties in dioxin-specific fate
parameters including vapor/particle partitioning of the CDD/Fs and soil half-lives, and e)
uncertainties and/or lack of representativeness in the important source term, the rate of dioxin
emissions from the stack, and the equally important meteorological data used to drive the model
simulations.
These latter uncertainties in source term and meteorological data were evaluated by using
different data sets. Specifically, two meteorological data sets were used in the dispersion
modeling exercise - an "on-site" meteorological data set supplied by Ohio EPA (who took the air
samples), and a publicly available data set from a nearby airport. Two possible stack tests were
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used to characterize long term emission rates for deposition and soil concentration modeling. As
discussed above, there were no clear "superior" choices in either meterological data set or stack
emission test. While it was clear that air concentration and soil concentration results differed
when by using both data sets, in fact it was also clear that both data sets seemed to predict some
quantities better than the counterpart data set.
With these cautions, it may be fair nonetheless, to make these statements regarding the
ability of the ISCST3 to model the impact of dioxin emissions from the CMWSTE:
1) Elevations of dioxins in air and soil were clearly identified in the sampling programs,
and they were also clearly modeled by ISCST3. Predicted and measured dioxin elevations in air
and soil appear to generally be within a factor of 10 of each other, with both under and over
predictions identified above. These elevations appear to be restricted to only within a few
kilometers, 2-3 kilometers, and this was also found in the dispersion and deposition modeling.
From the soil modeling exercise, it appears as though the model overpredicted soil
concentrations to a greater degree the further downwind one went. This suggests that the plume
was being depleted by dioxins in a manner that was not duplicated by the ISCST3 modeling.
2) It is clear from the analysis in this paper that the stack emission profile of CDD/Fs is
very different from the profiles measured in the soil and in the air. This could be explained by
"changes" in the profile at some point between the stack and both air and soil measurement sites,
or it could be that there were problems in the measurement of CDD/Fs in either the stack or the
environmental media. Assuming no major problems with measurement, it can be said that these
trends cannot be duplicated in ISCST3 without the input of congener-specific atmospheric
degradation rates, and/or congener-specific soil dissipation rates. Also, one hypothesis offered to
explain the change in the dioxin profile from stack emission to air measurement was that some
dechlorination might be occurring - the higher chlorinated CDD/Fs may be dechlorinating to
form lower chlorinated CDD/Fs. If so, and if attempting to duplicate this trend, the ISCST3
model would need additional algorithms to model these transformations.
While admittedly a limited field test of deposition and soil concentration models, the data
used here had these important features, which are not readily (if at all) available for similar
model testing of ISCST3 with CDD/Fs: multiple stack tests offering a full suite of dioxin
homologue and congener data; a historically high emission rate and over 11 years of emissions
such that a signal is left behind in the soil and an imprint in the ambient air while monitoring was
occurring, and a reasonable approach to determining the local background of dioxin soil and air
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concentrations that could be subtracted from the total measured soil and air concentrations to
characterize a "signal" of higher dioxin concentrations found near the incinerator.
7.3.8. Air-to-Soil and Soil-to-Air Modeling
The observed air and soil data used in the background demonstration scenarios in Chapter
5 were from the rural background site of the Columbus site described above. These rural data
also allow for an opportunity to test the air-to-soil modeling and the soil-to-air modeling
algorithms. To summarize those scenarios, actual air concentrations and soil concentrations of
the 17 dioxin-like congeners from the rural background site in Ohio were used as source terms.
The I-TEQ air and soil concentrations from this site were 0.019 pg/m3 and 1.37 pg/g,
respectively. The individual congener concentrations in air and soil were used to predict
concentrations in terrestrial foods (vegetables/fruits, animal food products) and the aquatic
environment (water, fish, and sediments). Soil concentrations were not predicted from air
concentrations and likewise, air concentrations were not modeled from soil emissions. For this
model validation exercise, however, the opportunity presents itself to model air-to-soil impacts
and soil-to-air impacts, and then to compare model predictions with observations.
Chapter 4 describes the models for estimating soil concentrations based on dry and wet
deposition of particle-bound dioxins. The models were used in the stack emission source
category, and in that context, dry and wet deposition amounts are modeled using the ISC3 air
dispersion and deposition model. The full amounts of dry and wet deposition (i.e., no subtraction
for plant interception) are mixed into a reservoir of soil and dissipated according to a defined
dissipation rate. The reservoir of soil for this exercise will be that defined by a 7.5-cm depth,
which was the depth of the soil sampling at the rural site in Ohio, and the assumed soil bulk
density will be 1.50 g/cm3. The dissipation rate is 0.0277 yr"1, which corresponds to a half-life of
25 years. Since deposition was not monitored at the rural site, it will be estimated as the particle
bound air-borne reservoir times a velocity of deposition. Koester and Kites (1992) measured a
dry deposition rate of 0.002 m/sec for dioxins in Indiana. They also measured wet deposition and
found it be roughly comparable for two sites. On this basis, dry deposition in this exercise will
be modeled using the 0.002 m/sec velocity of deposition and wet deposition will be assumed to
be equal to dry deposition.
Chapter 4 also described models for estimating air concentrations given soil
concentrations. These algorithms were for the soil source category. The algorithms for
volatilization were developed for PCBs and made simplifying assumptions such as no
degradation, an infinite source of contaminant, and so on. An "unlimited reservoir" approach
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was used to estimate the flux of particle bound dioxins from soils due to wind erosion. A near-
field dispersion algorithm estimated air borne concentrations given flux estimates of vapor and
particle-bound dioxins. Despite using an "unlimited reservoir" approach, it was observed in
Chapter 5 that the particle-bound air-borne concentrations were about an order of magnitude less
than the vapor-phase concentrations for the demonstration of the soil contamination source
category.
The results of this validation exercise are shown in Table 7-1. There is a clear
dichotomy in the results. The air-to-soil model appears to model soil impacts reasonably well,
with modeled soil concentrations somewhat lower but within the realm of field observations. On
the other hand, the soil-to-air modeling did not show a good match. All predicted congener
concentrations were 2-3 orders of magnitude lower than observed congener concentrations, and
there was a 500-fold difference in observed and predicted TEQ air concentrations.
Regarding the air-to-soil modeling, 11 of the 17 observed soil concentrations were higher
than model predictions. All but three congeners were modeled to within a factor of five of
observations, and the three other congeners were about a factor of 10 from observations (i.e.,
either observations were 5 to 10 times higher than predictions, or predictions were 5 to 10 times
higher than observations) . The four highest observations are matched with the four highest
predictions as follows: 17.7 ppt observed 1,2,3,4,6,7,8-HpCDD versus 9.1 ppt modeled, 161.0
versus 36.2 ppt for OCDD, 4.06 versus 2.74 ppt 1,2,3,4,6,7,8-HpCDF, and 10.7 versus 2.70 ppt
OCDF. The observed I-TEQ soil concentration of 1.37 ppt matches well with the predicted TEQ
concentration of 0.70 ppt.
While the soil model appears to work reasonably well based only on impact of particle
depositions, vapor impacts are not considered, and consideration of such would increase
predictions of soil concentrations. As noted above, it does appear that soil concentrations may
generally be underpredicted, although for six congeners, the model predictions were higher than
observations. Direct vapor deposition could impact soils, but for the soil observations in the
rural site in Ohio, all samples were taken in grassy areas, and with a vegetative cover, it is
speculated that there would little direct vapor deposition. Therefore, detritus production would
be a mechanism for vapor impacts to soils. Barbour, et al. (1980) list a detritus production rate
for a setting described as "tallgrass prairie" as 520 g/m2-yr. Given the concentrations predicted to
occur in grass due to vapor transfers, one can estimate the loadings of dioxin corresponding to a
detritus production of this magnitude. The predicted TEQ concentration in leafy vegetation due
to vapor transfers was 0.27 pg/g dry weight given the rural air concentrations at this site in Ohio.
This concentration times the detritus rate leads to a loading of 112 pg TEQ/yr. In contrast, the
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dry and wet deposition loading estimated by dry and wet deposition of particle-bound dioxins (as
described above) is 2130 pg TEQ/yr. Therefore, on a TEQ basis, vapor impacts via detritus
production would only be about 5% of loadings by dry and wet deposition of particle bound
dioxins. There was also a reasonably narrow range for individual congeners, where detritus
loadings were about 5-15% of atmospheric deposition of particle-bound dioxins. Particle-bound
dioxins also impact vegetation, and detritus production might be considered as an additional
loading due to particle bound dioxins. However, since 100% of atmospheric depositions are
loaded into the soil model, detritus production is inherently handled, and considering detritus
production would double-count the impact of particle-bound dioxins.
Another factor to consider is the representativeness of the air concentrations. The air
concentration profile was generated as the average of 3 samples, one taken in March, 1994, one
in April, 1994, and one in June, 1995. It has been observed that air concentrations are higher
during winter months (Reed, et al., 1990), and perhaps the inclusion of additional winter samples
from the site in Ohio would lead to a higher air concentration profile and higher predicted soil
impacts.
Regarding soil-to-air modeling, it is not that clear that emissions and resulting air
concentrations above soils at background levels should be lower by up to 2 orders of magnitude
than what would occur in background setting. The argument has been made in Volume U,
Chapter 2 of this assessment that emissions from tall industrial stacks, followed by long range
transport, are principal sources of these compounds in rural environments where the food supply
is produced. The question remains as to how much of the contaminant in rural air is due to urban
emissions followed by long range transport versus emissions from the soil reservoir source. If
the modeling of this assessment is correct, than soils contribute very little to rural air
concentrations. However, other evidence developed in this assessment suggests that the soil
release and dispersion algorithms of this assessment may be underestimating air concentrations.
One piece of that evidence is discussed in the next section below. Plant: soil ratios, defined as the
ratio of 2,3,7,8-TCDD concentration in plants divided by that in the soil, were found to be lower
in model predictions as compared to literature values. Two possible hypotheses were offered
below: 1) the model is underpredicting air concentrations resulting from soil releases, and/or 2)
plant:soil ratios derived in experiments are not only the result of soil related impacts, but also
from distant sources of air-borne release and long range transport - i.e., the air reservoir is not
solely explained by soil releases. This is the same issue that is discussed for the soil-to-air model
exercise here. One other possibility for the difference in the inability of the model to duplicate
plant: soil ratios would be that the algorithms estimating air to plant transfers are modeling too
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low a transfer rate. However, the air to plant transfer algorithms were examined in Section 7.2. 1
above and in Section 7.3.12 below, which describes a broader air-to-beef food chain validation
exercise. In both sections, concentrations of the dioxin congeners in leafy vegetation were
compared with model predictions for leafy vegetation, and predicted concentrations were found
to be in line with observations. Also, the vapor-phase air-to-leaf transfer coefficient was
calibrated with field data, and since it is shown that vapor phase transfers tend to dominate plant
concentrations, air-to-plant transfers should, by definition, be modeled adequately.
In summary, this section has shown that the air-to-soil model based on deposition of
particle-bound dioxins appears to work well. Vapor impacts would occur primarily by die-back
of vegetative materials, but the additional increment to soil concentrations are estimated to be
less than 10% by this route. Inclusion of winter-time air concentrations in the average air profile
used to predict soil concentrations could lead to higher predictions of soil concentrations and a
superior match of predicted and observed soil concentrations in this exercise. The soil-to-air
models may be underpredicting air concentrations. The observed air concentrations are 2-3
orders of magnitude higher than predicted to occur by soil emissions alone. This is due, in part,
by the fact that some fraction of the observed air concentration is due to long range transport
from distant sources. It is not known what fraction this is, but other evidence has suggested that
dioxins in rural settings distant from known sources of dioxin release originate, in fact, from
those distant sources. Other evidence suggests that the soil-to-air models may still be
underestimating the impacts to air from contaminated soils. The only way to truly test the soil-
to-air models would be to have air concentrations measured above soils, where it is known that
there are no other sources to measured air concentrations. Unfortunately, this data would be hard
to develop for the ubiquitous dioxins and none was available for testing.
7.3.9. Transfer of Dioxins From Soils to Below Ground Vegetables
This section describes a validation of the transfer algorithm from soils to below ground
vegetables. The equation for calculating the concentration of dioxins in below ground vegetables
is:
C RCF VG,
= S 8 (7-40)
where:
Cbgv = fresh weight concentration of below ground vegetables, pg/g
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Cs = contaminant concentration in soil, ppt or pg/g
Kds = soil-water partition coefficient, L/kg
Koc*OCsl
Koc = contaminant organic partition coefficient, L/kg
OCsl = fraction organic carbon in soil, unitless.
RCF = root concentration factor equaling the ratio of the contaminant
concentration in roots (fresh weight basis) and the concentration in soil
water, unitless
VGbg = empirical correction factor for below ground vegetation which accounts
for the differences in the barley roots for which the RCF was derived and
bulky below ground vegetables, unitless
The key contaminant-specific parameter, RCF, was developed from data developed by
Briggs, et al. (1982) on the transfer of organic contaminants from solution into barley roots. The
soil concentration, Cs, is divided by the soil partition coefficient, Kds, so as to convert it to a soil
water concentration. The VGbg is an empirical factor introduced to describe the difference
between the thin barley roots of the Briggs experiments and the bulky below ground vegetation to
which it is applied for the dioxin documents. Evidence shows that dioxins translocate only to a
small degree into bulky below ground vegetables (see next section). The assignment of values to
VGbg in this assessment also considers other factors pertinent for estimating concentrations for
human exposures, including factors which would further reduce whole vegetable concentrations
including washing or peeling. Further detail on the algorithm can be found in Chapter 4.
Data from Muller, et al. (1994) was used to validate this model. Specifically, carrots
were grown in pots with soil of two concentrations, a control soil and a contaminated soil.
Muller, et al. (1994) provided graphs showing the congener group concentrations for soil, and for
three parts of the carrot: peel, cortex, and stele. The precise concentrations from these graphs
was unavailable. However, the graphs were digitized by Cambridge, Environmental (58 Charles
Street, Cambridge, MA; concentration values contained in discussions in a public comment
provided to EPA), and their concentrations were used in this exercise. Data in this article also
included the soil organic carbon content, 8.1% (OCsl = 0.081).
Part of this data set was used in conjunction with the model displayed above. The VGbg
was taken out of the equation and the soil concentration data was input into the model to predict
the peel concentrations. The cortex and stele concentrations were not used in the validation
exercise. These predicted peel concentrations were compared with the observed peel
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concentrations. Since the data was available in congener groups and not individual congeners,
the values for the parameters RCF and Koc were estimated as the congener-specific value if only
one dioxin-like congener was in the congener group (2,3,7,8-TCDD, e.g.), or as the average of
the congener-specific values of the multiple dioxin-like congeners in the congener group (the
three HxCDDs, e.g.). Finally, the data in Muller, et al (1994) was only given in dry weight terms
without discussion of the dry weight fraction, and the model predicts a fresh weight. For this
exercise, it was assumed that carrots are 15% dry weight; the fresh weight was divided by 0.15 to
estimate the dry weight concentration.
Table 7-16 shows the data that went into this validation exercise and the results. It
would appear that the model works reasonably well. The difference in peel concentrations due to
soil concentrations is apparent in both the data and the model predictions, and the magnitude of
the difference appears to be captured. With one exception, the predictions and observations are
within a factor of 4 of each other, with the exception being a factor of 5. Thirteen of 20
predictions/observations are within a factor of 2 of each other. The biggest discrepancies are the
HpCDF and OCDF congener groups, with predictions exceeding observations by a factor of
about 4 for both the control and contaminated soils. This data is further examined in Chapter 4
to determine the VGbg parameter. Generally, it is concluded from this validation exercise that
this data supports the use of the Briggs, et al. (1982) RCF empirical formulation to predict the
peel concentration in underground bulky vegetation.
7.3.10. Impacts of Contaminated Soils to Vegetation
There have been several studies in addition to the carrot study described above which
have measured plant concentrations of 2,3,7,8-TCDD for plants grown in soils with known
concentrations of 2,3,7,8-TCDD or dioxin Toxic Equivalents (TEQs) or dioxin congener groups.
One quantity that can be estimated from these studies is aplant:soil contaminant concentration
ratio. The plant:soil ratio equals the concentration in the plant divided by the concentration in
soil in which the plant is growing. Concentration ratios predicted to have occurred in the
demonstration scenarios can be compared against those that have been measured in the various
studies.
These ratio comparisons cannot strictly be considered model validations. Only the
exercise described in the previous section, where experimental data were duplicated with
modeling, can be considered a model validation. Still, trends ascertained from the literature will
be compared with concentration ratios from the demonstration scenarios. The literature articles
measuring soil and resulting plant concentrations of dioxin-like compounds are summarized in
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Table 7-17. This table also includes concentration ratios, and separates sections for above and
below ground vegetation.
In measuring both the soil and the plant concentration, several of the early literature
articles, particularly those from Seveso (Wipf, et al., 1982; and Coccusi, et al., 1979) presumed
that the soil in which the plant was growing was the ultimate source for the 2,3,7,8-TCDD
contamination of above ground plant parts, if not from direct uptake than from deposition of
suspended particles. However, recent research has concluded that the contamination of above
ground plant parts is due principally to air-to-plant transfers (Hulster and Marschner, 1993;
Muller, et al, 1993; Muller, et al., 1994; Welsch-Pausch, et al, 1995; and others). These cited
research efforts have concluded that there is no consistent relationship between soil
concentrations of dioxin-like compounds and above ground vegetative concentrations of these
compounds, which has led the researchers to conclude that air-to-plant transfers explain plant
concentrations (one study did strongly imply a direct soil/plant for dioxin-like compounds for at
least one family of above ground vegetables, the cucumber family (Hulster, et al., 1994); this will
be discussed below). This fact, coupled with the fact that sources of airborne contamination by
dioxins include both distant sources and soil releases, make it difficult to compare literature
reports of plant: soil contamination concentrations with those predicted by the soil contamination
modeling of this assessment.
Recall that for the soil contamination source category, the presumption is that air
concentrations and depositions to which the plant are exposed originate only from the
contaminated soil. One would expect that the modeled plant:soil ratio for above ground plant
parts would be lower than plant: soil ratios measured in actual field settings, since the field
measured ratios are influenced by more than just the soil releases into the air.
What is more pertinent in the demonstration scenarios for comparing plant:soil ratios for
above ground vegetation are the results for the "background" scenarios. Here, air concentrations
from an actual setting provide the source of dioxins for above ground plants. Simultaneously,
soil concentrations which correspond to the actual setting where air concentrations were
measured are input into the scenario. Therefore, plant: soil ratios from this demonstration should
be analogous, at least, to plant: soil ratios derived from other actual field settings.
Unlike above ground vegetation, the literature is consistent in concluding that soil
provides the source for underground soil to root transfers. For this reason, Table 7-17 and the
following discussions distinguish between above and below-ground vegetation.
The following plant: soil contaminant concentration ratios for TEQs were estimated for
the two scenarios demonstrating background conditions in Chapter 5, Scenarios 1 and 2: below
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ground vegetables - 0.19 (dry weight basis, assuming vegetables are 15% dry matter, and using
tilled soil concentrations which are 50% of untilled soil concentrations), above ground
vegetables/fruit - 0.10 (dry weight basis, 15% dry matter, tilled soil concentrations), grass - 0.33
(dry weight, untilled soil concentrations), and feed 0.16 (dry weight, untilled soil concentrations).
Some observations from experimental results found in the literature, and comparison with the
results of the model, are:
1) The largest body of consistently developed experimental data on soil-plant
relationships of dioxin-like compounds comes from a research group in Germany who have
published numerous articles for different vegetation and experimental conditions in the 1990s
(Hulster and Marschner, 1991; Hulster and Marschner, 1993; Hulster, et al., 1994; Muller, et al.,
1993; Muller, et al, 1994). Some of the earlier literature showed much higher impacts to
vegetation than measured by these German researchers (Coccusi, et al., 1979; Facchetti, et al.,
1986; Young, 1983), which, in the judgement of the authors of this assessment, renders them
suspect. One early report, that of Wipf, et al. (1982), does show results consistent with the
German research. The observations following will focus mainly on this research from Germany.
2) Experimental results for both above and below ground vegetation suggest that
plant:soil ratios decrease as soil concentration increases. For below ground vegetation, this
suggests that the movement into plants is not a passive and unimpeded process occurring with
transpiration water, for if it were, plant: soil ratios would be constant as concentration increases.
For above ground vegetation, the observations given above that air-to-plant transfers and not
soil-to-plant transfers better explain plant concentrations, and that air concentrations include soil
releases as well as long term transport, leads one to conclude that a consistent relationship
between soil concentrations and plant concentrations is not to be expected. An explanation for
this trend for below ground vegetative trends could not be found.
The models of this assessment - soil to below ground vegetation, soil to air to above
ground vegetation, and air to above ground vegetation - cannot duplicate these observed trends,
that is, the models will not show a decrease in plant:soil ratios as soil concentration increases.
When soil is the only source of contamination, as in the soil contamination source category,
above and below ground vegetation concentrations are a linear function of a biotransfer factor
and an appropriate media concentration - air, soil, water. For particle depositions, no transfer
parameters are used, but plant concentrations are a linear function of model inputs, including
deposition rates, plant interceptions and yield, and a plant washoff factor. Therefore, plant
concentrations will be a linear function of soil concentrations for the soil contamination source
category.
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3) Plant:soil ratios for below ground vegetables for soil concentrations in the low ppt
range would appear to be in the 10"1 to 10"2 range (Muller, et al, 1993; Hulster and Marschner,
1991), which may be a little lower than the 0.19 predicted by the model. Much higher ratios
were found in the earlier studies (Coccusi, et al., 1979; Facchetti, et al., 1986; Young, 1983),
which earlier had been speculated as being questionable. One earlier study, that of Wipf, et al.
(1982), does report ratios similar to these later studies, as noted above. At higher soil
concentrations in the sub to low ppb range, plant soil ratios are more in the 10"4 to 10"3 range
(Hulster and Marschner, 1993; Hulster and Marschner, 1991).
4) The results for above ground bulky vegetation, fruits and vegetables, indicate
plant: soil ratios that are lower than plant: soil ratios for bulky below ground vegetation, for
comparable soil concentrations. The data of Muller, et al. (1994) can be used to demonstrate this
point. This data was used over others because the soil concentrations, at 5 and 56 ppt TEQ, are
nearer to the background soil concentration of 1.37 ppt TEQ used in the background
demonstration scenarios than any of the other studies found. First, their results include total crop
dry weight concentrations of TEQs for carrots, lettuce, and peas (including pods) in soils with
TEQ concentrations of 5 and 56 ppt. At these two soil concentrations, the carrot:soil ratios were
0.07 (at 5 ppt TEQ soil concentration, dry weight of the carrot) and 0.017 (at 56 ppt). Their
lettuce and pea pod:soil ratios for the control and contaminated plots ranged from 0.0016 to
0.016 (total crop, dry weight basis). The modeled plant:soil ratio of 0.10 for vegetables/fruits in
the background scenarios is higher than the 0.0016 to 0.016 for lettuce and peas in the Muller, et
al. (1994) experiments. This could suggest an overestimation of the modeling of air-to-plant
impacts.
5) Several of the articles, both from the German work and the earlier work, noted that
most of the concentration was in the outer portions of the below and above-ground vegetation,
and not the inner portions. Despite significant increases in soil concentration from the ppt to the
ppb range, inner potato tuber concentrations remained constant (Hulster and Marschner, 1991,
1993). This evidence was the principal justification for the use of the empirical adjustment
factors termed VG for soil to below ground transfers, VGbg, and vapor-phase air transfers to
bulky above ground vegetation, VGag. The chemical-specific empirical transfers factors for both
of these transfers were developed in laboratory experiments with several chemicals using thin
vegetation - solution phase transfers to barley roots for below ground vegetation concentrations,
and vapor phase transfers to azalea leaves and grass leaves for vapor phase transfers. For the
dioxin-like compounds, direct use of these transfer factors would be most appropriate for the
outer few millimeters, perhaps, of below and above ground bulky vegetation. The assignment of
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a VG of 0.01 for bulky above and below ground vegetation was based on an outer surface
volume to whole plant volume ratio for a common vegetation such as a carrot or an apple. A VG
of 1.00 was used for grass, since that is a thin vegetation.
Further evidence for the above ground VG came from a study by McCrady (1994), who
measured the uptake rate constants of vapor-phase 2,3,7,8-TCDD to several vegetation including
grass and azalea leaves, kale, pepper, spruce needles, apple, and tomato. The uptake rate for the
apple divided by the uptake rate for the grass leaf was 0.02 (where uptake rates were from air to
whole vegetation on a dry weight basis). For the tomato and pepper, the same ratios were 0.03
and 0.08. The VGag was 0.01 for fruits and vegetables in this assessment. McCrady (1994) then
went on to normalize his uptake rates on a surface area basis instead of a mass basis; i.e., air to
vegetative surface area uptake rate instead of an air to vegetative mass uptake rate. Then, the
uptake rates were substantially more similar, with the ratio of the apple uptake rate to the grass
being 1.6 instead of 0.02; i.e., the apple uptake rate was 1.6 times higher than that of grass,
instead of 1/50 as much when estimated on an air to dry weight mass basis. The ratios for tomato
and pepper were 1.2 and 2.2, respectively. Therefore, since the Bvpa in this assessment is an air to
plant mass transfer, the McCrady experiments would appear to justify the use of an above-ground
VG of a magnitude less than 0.10.
6) An experiment by the Hulster, et al. (1994) on vegetation of the cucumber family
contradicted the conventional wisdom that direct soil to root to above ground plant impact would
not occur for the dioxin-like compounds. Their results were most striking for zucchini, which
showed uniform plant concentrations from inner to outer portions of the zucchini fruit, and the
highest whole fruit concentrations and plant: soil ratios they had ever measured, despite careful
experimental conditions which physically isolated the fruit from the soil. Pumpkins also showed
high plant contamination and plant: soil ratios, with more expected plant concentrations measured
for the cucumber. No explanation was offered for these results. It was assumed for this exposure
assessment that the fruits and vegetables for human consumption, and the grasses, hay, and other
vegetation animals consume, would not follow this pattern.
A principal observation that can be drawn from this examination is that the plant:soil
contaminant concentration ratios from the background scenario may be higher than observed in
experiments, although this conclusion must be tempered by the fact that the soil concentrations in
the experiments were always higher than the 1.29 ppt WHO98-TEQDF soil concentrations used in
the background scenario. One other important observation made above was that, as the soil
concentration increased, the plant: soil concentrations decreased. For the experiment where soil
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concentrations were closest to background, the 5 and 56 ppt TEQ concentrations in the
experiments of Muller, et al. (1994), the overprediction by the model was on the order of 10.
The same story is not necessarily told, however, when comparing results from the
demonstration of the soil contamination demonstration with these literature ratios. The plant: soil
ratios for the background scenarios ranged from 0.10 to 0.33. The following plantsoil
contaminant concentration ratios were estimated for the soil contamination scenario. Again,
tilled soil concentrations and dry weight vegetative concentrations were used: below ground
vegetables - 0.22, above ground vegetables/fruit - 0.00005, grass - 0.003, and feed 0.002. These
ratios were calculated using the soil concentrations predicted to occur at the site of exposure,
which is valid for the below ground vegetables, but not quite for the above ground vegetation.
Ratios calculated by using the near field dispersion models and assuming plants are grown on the
impacted soils would result in slightly higher ratios, by about a factor of three. Below ground
vegetables still show a clear relationship with soil concentrations, as in the background scenarios.
However, now it appears that plant: soil ratios for above ground vegetation are much lower for
this demonstration scenario as compared to the background scenarios.
In fact, in a slight variation to this exercise, one could use near background soil
concentrations for the soil contamination algorithms and show similarly low plant:soil ratios. In
this instance, one can observe that at low background soil concentrations, the soil-to-plant
algorithms of the soil contamination source category underestimate above ground plant
concentrations.
This observation that plantsoil ratios for above ground vegetation are higher in the
literature as compared to modeled ratios for the soil contamination algorithm has to be carefully
considered. Two explanations are offered. For experiments conducted outdoors, the source of
air reservoirs of dioxin-like compounds are the soil in which the plant is growing as well as from
distant sources and long-term transport. Also, it is possible that the model is underpredicting air
concentrations and hence underpredicting soil-to-air-to-plant transfers. The same issue arose in
Section 7.3.8 earlier, where it was noted that the soil contamination model predicted very low air
concentrations given background soil concentrations, much lower than observed background air
concentrations. In that section, like in this section, it was unclear whether long range transport
explained most of background air concentrations and/or the model was underpredicting air
concentrations.
7.3.11. Comparison of Measured and Modeled Vapor/Particle Distributions for
Semivolatile Compounds Other Than Dioxin
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In Chapter 3, the Junge-Pankow model was described and applied to the dioxin-like
compounds in order to partition air-borne dioxins into a vapor and a particle phase. These
modelled particle percentages were then compared to measured particle percentages, and a
consistent pattern among several field measurements versus model predictions emerged: the
model tended to predict that more of the dioxin would be in the particle phase compared to what
was measured. Whether the model is "correct" or the measurements are "correct" is a matter of
ongoing debate, as described in Chapter 3. This section will compare measured and modelled
particle percentages for semivolatile organic compounds (SOCs) other than the dioxins. These
include PAHs, PCBs, and organochlorine pesticides.
Table 7-18 presents a summary of the analysis of monitoring data on SOCs from several
cities and rural locations. The parameters in this table come from the following empirical
correlation, which is described in more detail in Chapter 3:
Log Kp = m Log p°L + b (7.4 1)
where:
Kp = particle/gas partition coefficient, m3/|_ig, defined as: Cp (ng/|ig)/Cg (ng/m3),
where Cp is the concentration associated with aerosols, and Cg is the gas-
phase concentration
p°L = liquid sub-cooled vapor pressure, Pa
m, b = empirically derived slope and intercept from the data sets
The sub-cooled liquid vapor pressures for the SOCs were taken from reports in the literature
including values for PAHs (Yamasaki et al., 1984), PCBs (Falconer and Bidleman, 1994), and
OC pesticides (Hinckley et al., 1990). The particle-bound fraction, cj), is related to Kp, as follows:
K [TSP]
(7-42)
where:
cj) = particle bound fraction, unitless (not the same as the particle bound
concentration, Cp)
Kp = particle/gas partition coefficient, m3/|_ig
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TSP = total suspended particulates, i-ig/m3
If TSP was not reported in the original paper (in most cases), it was assumed that TSP = 98
l_ig/m3 for urban air and 10-42 |_ig/m3for non-urban air of varying cleanliness.
The two equations above, in combination with the data reported in the literature article,
were used to develop the dotted line "observed" particle percentages shown in Figures 7-10 and
7-11. The modeled particle fractions were determined for these sites using the Junge-Pankow
model detailed in Chapter 3. According to that model, the particulate fraction is estimated as:
A C &
* = ~
where:
cj) = fraction of the compound adsorbed to aerosol particles
p°L = saturation liquid phase vapor pressure of the pure compound at ambient
temperature, Pa
6 = the particle surface area per unit volume of air, cm2 aerosol/cm3 air
c = a constant which is related to the difference between the heat of desorption
from the particle surface, Qd, and the heat of vaporization of the
compound, Qv, estimated at 17.2 Pa-cm
In applying this model, values of 6 assumed include 4.2*10"7 for "clean continental", 3.5 *10"6
for "background plus local sources" and 1.1*10"5 for "urban" conditions. Further detail on this
model can be found in Chapter 3.
The agreement between the measured and predicted aerosol -bound fractions of PAHs is
remarkably good, as seen in Figure 7-10. Two model curves are shown for rural air in Figure 7-
lOb, representing "clean continental background" and "background plus local sources" regimes.
Experimental PAH distributions fall reasonably close to the range of parti culate values predicted
by the model, although the fraction of aerosol -bound PAHs at rural sites (e.g., Lake Superior) is
greater than expected for the more volatile compounds. This may be due to a portion of these
PAHs being "non-exchangeable" (Pankow, 1988).
Fewer data are available for non-dioxin organochlorine compounds. Figure 7-1 1 shows
the measured parti culate percentages of PCB congeners and chlorinated pesticides in comparison
to predictions. Again, the two depictions of model predictions for rural conditions, Figure 7-1 Ib,
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show a range from clean continental to background plus local sources. The monitored data fall
below the model values, but the discrepancy is generally not as great as for the PCDD/Fs.
Overall, it can be concluded the Junge-Pankow model appears to predict vapor/particle
partitioning very similar to measurements for PAHs, but not as similar for PCBs and chlorinated
pesticides, although the match there is still superior to that of PCDD/Fs. This exercise does lend
additional credibility, in general, to the Junge-Pankow Model. However, the model and the
measurements for PCDD/Fs do diverge, as described at length in Chapter 3, and the debate
remains as to which is "correct" for the PCDD/Fs - the model or the measurements.
7.3.12. An Update of the Air-to-Beef Model Validation Exercise
In the previous version of this dioxin exposure document (EPA, 1994) as well as in a
journal article (Lorber, et al., 1994), a validation of the air-to-beef food chain model used in this
assessment was presented. As a result of public comments received on the review of the dioxin
exposure document, that exercise has been refined and updated. This section will review the
principal comments made and these updates. Except for a brief overview, this section will not
describe the previous air-to-beef model validation exercise.
Figure 7-12 presents an overview of the air-to-beef food chain model. The premise of this
modeling exercise to test the beef food chain model for dioxin-like compounds is that air-borne
reservoirs of these compounds in rural environments are the "source term" explaining
concentrations found in beef. The principal assumption in the validation exercise is that one can
define an "average" rural air profile of dioxins, route this profile through the food chain model,
and predict an "average" beef concentration. This exercise probably would not qualify as a
validation exercise in the traditional sense. Most environmental model validation exercises rely
on data obtained from a single site. For a traditional model validation of the air-to-beef model,
one would need the following: a representative air concentration profile, including all the dioxin-
like congeners, during the lifetime of the cattle, information on the cattle diet during his lifetime,
a set of vegetation congener-specific concentrations typical of the cattle diet (and the cattle
should be fed from vegetation grown in the area corresponding to the air concentration), and a set
of congener-specific concentrations from beef from the slaughtered cattle. As this information
was unavailable, the model validation exercise proceeded by attempting to define these "average"
air profiles, vegetative profiles, and beef concentration profiles.
The key components of the previous model validation exercise were:
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1. The representative air profile was crafted based on a representative profile of urban air
concentrations generated as the average of 85 data points of air concentrations from urban
settings, coupled with information suggesting that rural air profiles were lower than urban air
profiles, by about a factor of 5. The rural air profile was crafted, therefore, by dividing each
congener concentration in the urban air profile by 5.
2. Each congener was separated into a vapor fraction and a particle fraction. The vapor/particle
partitioning model is described in Chapter 3.
3. The vapor fraction "transfers" into cattle vegetation, including categories described in Chapter
4 as "grass" and "hay/silage". The particle bound fraction is assumed to deposit onto these
vegetation. Dry deposition was modeled as a product of the concentration times a deposition
velocity of 0.002 m/sec, based on the findings of Koester and Kites (1992). Also, wet deposition
was assumed to equal dry deposition, based on measurements of Koester and Kites (1992)
showing these two components to be roughly comparable for settings in Illinois and Indiana.
4. Forty-eight percent of the cattle diet was assumed to be in grass, 48% was assumed to be in
hay/silage, and 4% was assumed to be in soil. The concentration in the fat of beef was modeled
as a function of the concentrations in these media times a bioconcentration factor.
5. The cattle were assumed to undergo a period of feedlot fattening prior to slaughter. This is a
predominant practice in the United States, and it was felt that the observed beef concentrations
were from cattle which went through feedlot fattening. Based on previous modeling efforts, the
impact of this fattening regime was to reduce concentrations in the body fat by about a factor of 2
compared to body fat concentrations prior to entry into the feedlot. Therefore, concentrations
predicted to occur without this feedlot consideration were reduced by 50% to model the impact
of feedlot fattening.
6. The "observed" beef concentration profile was crafted as the average of 14 samples from 3
"grab bag" studies measuring the concentration in beef and veal from grocery stores.
Four principal comments received on the air-to-beef model validation exercise following the
release of the 1994 dioxin exposure document were:
1. Although the final predicted TEQ beef concentration was reasonably close to the observed
beef TEQ concentration, there was not a good match in the concentrations of the individual
congeners not a good match of total concentrations (i.e., the sum of the concentrations of all
congeners).
2. The air-to-leaf transfer factor was overestimating the impact of vapor-phase dioxins to
vegetation.
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3. A simple division of the crafted urban profile by 5 to arrive a crafted rural profile would not
appropriately consider changes in the profile from urban to rural centers that have been studied
and are believed to occur.
4. The results of the USD A/EPA beef study that is described in Volume I of this Exposure
Reassessment and in Winters, et al. (1996) is preferable as an observed data set to the grab
sample of 14 data points.
The first comment was addressed by revising the air-to-beef model validation exercise
based on the next three comments. The discussion of the revised model exercise will now
proceed by first reviewing the model changes made, then reviewing the revised air and the new
beef concentration profiles used, and how the revised model validation exercise compares with
the original validation exercise.
a. Revisions to the air-to-beef model parameters: Table 7-19 compares the four key
changes made to the model validation exercise. Following are brief notes on each:
1. B^: Chapter 4 describes how this version of the air-to-leaf transfer factor was derived from
experiments conducted by Welsch-Pausch, et al. (1995). As seen in Table 7-19, this procedure
resulted in a lowering of the transfer factor for all congeners by an order of magnitude and less,
except the octa congeners which were lowered by 2-3 orders of magnitude and the hepta furan
congeners, which were slightly higher in their current form. It is noted that these Bvpa are slightly
different than a set of Bvpa published as proceedings of the 15th International Symposium on
Chlorinated Dioxins and Related Compounds (Lorber, 1995). The procedure to derive the B^., in
this assessment is the same as in that publication. However, this assessment uses a different set
of vapor/particle fractions based on a reassignment of dioxin fate parameters. With different v/p
fractions, the calibrated Bvpa was slightly different.
2. As just noted, the vapor/particle partitioning changed slightly in the current version, due to the
reassignment of critical parameters required for the calculation of the vapor/particle percentages.
These parameters include the Henry's Constants and the liquid sub-cooled vapor pressures. In
their current form, there is generally less concentration predicted to occur in the vapor form,
particularly for the penta dioxin, the tetra furan, and the two penta furans. It is noted that the
1994 vapor percentages for the octa congeners were assigned a value of 0.00. In fact, if the
percentages were calculated to two decimal places with the 1994 parameters, they would equal
0.02% (or, on a fraction basis, 0.0002) instead of 0.00 as noted. Although a small percentage, it
is seen that the air-to-leaf transfer factors for the octa congeners were on the order 108 to 109. As
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discussed in the prior air-to-beef model validation (EPA, 1994; Lorber, et al., 1994), this became
critical as assignment of the very small vapor fraction did make a significant difference to octa
vegetation predictions, and hence octa beef concentration predictions. For the current exercise,
there was no rounding to 0.00; the values calculated and used are 0.2% for both octa congeners
with the revised fate parameters.
3. The bioconcentration factors for each congener were uniformly increased by about 30% as
compared to the 1994 version. This was due to a recalculation using the same data as was used
for the 1994 version. Based on a personal communication with the study author, it was
determined that the total dry matter intake by the lactating cow was miscalculated for the 1994
version. Instead of 15 kg/day, the correct total dry matter intake was 21 kg/day. Therefore, intake
concentrations decreased by about one-third, and so calculated bioconcentration factors increased
by about one-third.
4. The soil concentrations used in the current version were the actual measured soil
concentrations corresponding to the site where the air concentrations were taken. As will be
described below, the air concentration profile was taken from an actual rural site near Columbus,
Ohio. These were the same air profile and soil profile used in the demonstration background
scenario in Chapter 5. The soil concentrations used in the 1994 model validation exercise were
not model inputs, but were rather predicted by the deposition of particle-bound dioxins. There
does not appear to be substantial differences in the two profiles, and as soil is a small part of the
cattle diet, these changes were not meaningful to final predictions of beef concentrations.
b. New air profile: While a straight division of an urban profile by a factor of 5 may
recognize overall reductions in air concentrations when going from an urban to a rural location,
and concurrent reductions in TEQ air concentrations, they may not recognize a key trend
observed by researchers concurrently studying urban and rural air profiles. The trend was best
stated by Eitzer and Kites (1989), who studied such profiles including a statistical analysis of
profiles. As they stated, "The geographic variability suggests the following atmospheric
transport scenario. Urban air is contaminated with PCDD/F by proximity to the combustion
sources of these compounds. As the air mass moves away from the urban area, it is diluted with
cleaner air, lowering the PCDD/F concentrations. As the air is transported, transformations
occur changing the profile. One transformation is photodegradation of vapor-phase PCDD/F.
The less chlorinated PCDD/F have greater proportions of their total concentration in the vapor
phase. Thus, vapor-phase photodegradation during the transport process would have a greater
effect on the less chlorinated PCDD/F. Like the washout process, these degradation processes
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would favor an ultimate PCDD/F profile with enhanced concentrations of the more chlorinated
compounds." Simply dividing an urban profile by a factor of 5 did not account for these changes.
It should be noted that photodegradation has not been definitely proven to occur and explain the
trends noted by Eitzer and Kites (1989). Most importantly, they identified a trend that appears to
be true, not only for their profiles, but for air concentration profiles from the Columbus area, as
will be described shortly.
Ideally, one would want several rural profiles representing beef production areas in the
United States. Lacking that information, what was available was a profile derived from a study
of ambient air in the city of Columbus undertaken by the Ohio EPA (OEPA, 1994; 1995), and
described above in Section 7.3.7 on a model validation exercise of the ISCST3 model. That
study included two sampling events of urban air in 1994 and one sampling in 1995. Concurrent
with the urban samples were two background rural samples in 1994 and a third in 1995. The
rural sampling site was located 28 miles in the upwind direction from Columbus (i.e., it was least
likely to be impacted by urban sources in the nearest largest city, Columbus). The sampling
program was undertaken to evaluate air quality in the vicinity of a municipal solid waste
incinerator. There were six samplers in the city of Columbus, 5 operating for each of the 1994
sampling events (not the same 5), and 6 operating in 1995. Therefore, there were a total of 16
urban air samples. This incinerator was operating in 1994, but was shut down in 1995.
Therefore, the 1995 sampling was undertaken to evaluate the air quality in the absence of the
incinerator. During the sampling in 1994, OEPA (1994) identified a clear trend in the data: that
the measurements were highest in the air samplers which were located in the predominant wind
direction, from the incinerator to the air sampler, during the sampling. The 1995 did show a
reduction in the measured air concentrations (OEPA, 1995). There was one rural air sampler, so
the sampling program included three rural air samples. The three rural air samples did not show
any trend related to the incinerator, and as will be seen, were lower in magnitude than the urban
samples. Chapter 5 describes how the rural air profile for the background scenario was crafted
from these three rural samples.
This revised air-to-beef model validation exercise will use the rural air profile that was
crafted for the background scenario. Table 7-20 compares the air concentration profiles
discussed above, including the crafted 1994 urban and rural air profiles compared against the
Columbus urban air profile and the Columbus rural air profile. The Columbus urban air profile
was developed as the average of the 16 data points from the three air sampling, including the
assumption that the air concentration was !/2 detection limit for all non-detects. The final
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column shows the ratio of the Columbus urban air concentration with the Columbus rural air
concentration.
The Columbus urban air concentration looks substantially like the crafted urban air
profile with notable differences only for 1234678-HpCDD, OCDD, and 2378-TCDF. In each of
these cases, the Columbus air profile is smaller than the crafted urban profile. The Columbus
2378-TCDF concentration is more than a factor of 5 lower than the crafted urban concentration
of 2378-TCDF. OCDD in Columbus is almost a factor of 3 lower than the crafted urban profile,
and the absolute difference in the OCDD concentrations of nearly 2.0 pg/m3 explains most of the
difference of about 2.2 pg/m3 of total dioxin in the profiles (with "total" being defined as the sum
of the 17 dioxin-like congeners). The I-TEQ concentrations in the urban profiles are similar - the
Columbus urban air I-TEQ concentration of 0.070 pg/m3 is about 75% of the crafted urban
profiles of 0.095 pg/m3.
The two rural air profiles have the same I-TEQ concentration of 0.019 pg/m3, but the
Columbus rural total concentration is about 50% higher than the crafted rural profile. This is due
principally to the higher OCDD and 1234678-HpCDD concentrations in the Columbus profile.
Most importantly, it would appear that the Columbus urban and rural profiles conform to the
expectations as laid out earlier in the quote from Eitzer and Kites (1989). Specifically, it does
appear that the lower chlorinated congeners undergo more of a loss, proportionally speaking, as
compared to the higher chlorinated congeners. The ratio of the urban hepta and octa dioxin and
furan congeners to their rural counterparts ranges from 1.2 to 3.8; while the range for the tetra
through hexa dioxin and furan congeners is 1.0-6.3. The 1.0 ratio is for a congener which has a
very low concentration to start with, 0.003 pg/m3, and whose rural concentration is driven by
detection limits. Without this congener, the second lowest ratio is 2.4. In other words, it would
appear that a more appropriate range for the tetra through hexa congeners is 2.4-6.3. Of note are
the very low changes in the hepta dioxin congener and octa dioxin congener, whose ratios are
1.2. Also of note is the very lower tetra furan congener, which dropped by a factor of 6.3 and is
also 6 times lower than the concentration for this congener in the crafted rural profile of the 1994
exercise. These trends for the OCDD and tetra furan congener will later be important in
explaining improvements in beef concentration predictions for the current model validation
exercise.
c. New beef profile:
The EPA/USDA study on dioxins in beef is described in Chapter 3, Volume n of this
assessment and also in Winters, et al. (1996). The congener profile for this study is compared
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with the congener profile for the 14 grab samples from three studies in the next section on
results. It is noted that the EPA/USDA study shows lower congener concentrations for all but
one congener, and total and TEQ concentrations that are 2-3 times lower that the 14 grab
samples. The precise reason for this difference in profiles is not known, but some possible
reasons could be:
1) The three studies from which the 14 samples originated were not statistically designed studies,
and may have simply taken samples with higher concentrations.
2) There may, in fact, be a trend of reduced concentrations in the beef because of the time periods
in which the data was obtained. Two of the three studies took samples prior to 1990, the other
one taking samples in 1992. The EPA/USDA study took samples in 1994. Furst and Wilmers
(1995) did note nearly a 25% reduction in cow milk concentrations of I-TEQs taken in 1994 as
compared to 1990. This is a much smaller reduction than the 60-70% reduction noted in the two
groups of beef samples.
3) There was no detailed examination of the quality assurance programs for the three studies
taking grab samples from the grocery stores. There may have been laboratory problems.
4) There has been some data suggesting the leaching of dioxins into milk from milk cartons. The
packaging and handling of beef may also introduce dioxins into beef. Since the EPA/USDA beef
study obtained samples directly from the slaughterhouse, such introductions by packaging and
handling would not occur. However, this explanation was contradicted by three additional
grocery store grab samples of ground beef taken in Mississippi in 1995 (Cooper, et al., 1995),
which showed concentrations quite comparable to the EPA/USDA beef sampling program. If
packaging and handling did effect the 14 grab bag samples, they did not affect the Mississippi
sampling.
5) The EPA/USDA samples were from back fat. The back fat samples were from a reservoir in
the cow, which at a point nearby, is the fat which is the outer part of meat cuts from the cattle rib
area. Back fat itself is not used or consumed. There is very little data on the differences in
concentrations of dioxin-like compounds in the different edible and non-edible fat reservoirs of
cattle. A recent study showed that there are not significant differences in dioxin and dioxin-like
coplanar PCBs among fat reservoirs except a much higher concentration in the lipid of liver
(Lorber, et al., 1997). Nonetheless, the grocery store samples were from different reservoirs of
fat than the back fat of the EPA/USDA study.
If it is true that the fat concentrations of dioxins are similar across different body
reservoirs of fat (with the exception of the lipid in liver), than it is certainly appropriate to
conclude that the results of the EPA/USDA statistical monitoring study are a more appropriate
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representation of edible fat in beef as compared to the compilation of 14 grab samples used in the
prior air-to-beef model validation exercise.
d. Results of the revised validation exercise: Table 7-21 shows the 1994 and 1996 model
predictions for leafy vegetation, and compares then against five available data sets for leafy
vegetation. The data on hay in the US (Reed, et al., 1990) seems to conflict the other available
data on the table - it has significantly higher concentrations of the congeners it does have
available, particularly the value for 285 ppt concentration of OCDD. This data could have been
influenced by a local source, or because of the particular type of hay it was, it may have had
much higher particulate dioxin contributions as compared to other grass data sets. One set from
a rural setting in the UK (Jones and Duarte-Davidson, 1997), however, also has somewhat high
OCDD concentrations at 94 ppt. All other sets had OCDD concentrations less than 25 ppt
OCDD. This site in the UK also had relatively high air concentrations at 0.21 pg I-TEQ/m3
compared to the rural air profile of 0.019 pg I-TEQ/m3 from the Columbus site used in the
exercise here. As described in Section 7.2.1. above, the modeling framework of this assessment
used this data set to model a grass concentration of 3.7 pg I-TEQ/g, which is reasonably close to
the measured 6.0 pg I-TEQ/g. The other two data sets from the UK are reasonably similar, with
the more recent data set (Kjeller, et al., 1996) showing lower concentrations, and in fact, this data
was used by the authors (among other data) to suggest that emissions of dioxins are being
reduced in the UK. The data set on alfalfa is consistent with this data on grass in the UK.
It is easily seen that the current set of model predictions of dioxins in grass is much more
in line with these two UK observations and the US alfalfa observation as compared to the
predictions in the 1994 data set. There was a general reduction in the concentrations predicted
between 1994 and the current predictions, due primarily to the reductions in the air-to-leaf vapor
transfer factor, B^. There were noticeable improvements in some of the congeners, particularly
the predictions for 2378-TCDF and 12378-PCDF, and to a lesser extent but still noticeable,
improvements in 12378-PCDD, 123478-HxCDD, 23478-PCDF, 123478-HxCDF, 123678-
HxCDF, 234678-HxCDF, and OCDF. There was also an improvement to OCDD. It was noted
above that the vapor fraction assumed for OCDD was 0.00 in the 1994 exercise, but in a detailed
examination in Lorber, et al. (1994) and EPA (1994), use of a vapor fraction of 0.0002 for
OCDD, which is what the V/P model calculated for the vapor fraction for OCDD, actually
resulted in grass concentration approaching the hay concentration found in Reed, et al. (1990).
Given the other leafy concentrations, it would appear that an OCDD grass concentration in the
10-30 ppt range is more appropriate than one greater than 100 ppt.
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As a final and simple test of the current model predictions for leafy vegetation, the two
data sets from the UK were averaged with the one set from alfalfa in the United States and
correlated against the two sets of model predictions. The best fit regression line for the 1994
model predictions and this average of three observations had a slope of 0.26, an intercept of 0.84,
and importantly a rather poor r2 of 0.14. In contrast, the best fit regression line for the 1996
model predictions had a slope of 1.13, an intercept of 0.007, and a much improved r2 of 0.98.
Table 7-22 compares the final predicted and observed beef data sets from the 1994 and
the current validation exercise. There are reductions in both the observed and predicted beef
concentrations from the 1994 results to the current results. However, given the conclusion
stated above regarding the superiority of the EPA/USDA beef data set for this validation
exercise, it is most appropriate to compare the predictions made in 1994 and those made in the
current exercise with this observed data set. Just by visual inspection, it is seen that notable
improvements in model predictions are noted for the following congeners: 12378-PCDD,
OCDD, and 2378-TCDF. There are some congener predictions for which the 1994 validation
exercise appears to result in a superior match, including 123478-HxCDD, 12378-PCDF, 123789-
HxCDF, and 234678-HxCDF. Overall, the TEQ concentration in the current validation is closer
to the observed TEQ concentration, although the total concentration appears more favorable for
the 1994 validation exercise.
A simple correlation test suggests that the current model validation is superior to the 1994
model predictions. The following shows the results of linear regressions of the 1994 and 1996
model predictions with the EPA/USDA monitoring results assuming ND = 1A detection limit and
ND = 0.0:
1994 model/ND = V2 detection
1994model/ND = 0
1996 model/ND = V2 detection
1996 model/ND = 0
Slope
0.70
0.74
1.02
0.76
Intercept
0.58
0.13
0.55
0.25
r
0.11
0.16
0.57
0.41
As seen, there is a better regression with the current modeling exercise as compared to the 1994
model exercise, with current r2 of 0.57 and 0.41 compared against r2 of 0.11 and 0.16.
It is also easy to see why individuals commenting on the air-to-beef model validation
exercise of 1994 would note a poor correlation between observed and predicted concentrations.
Very poor matches between predicted and observed are seen for 123678-HxCDD, 1234678-
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HpCDD, OCDD, 2378-TCDF, 123478-HxCDF, 1234678-HpCDF, 1234789-HpCDF, and
OCDF. Overall, the r2 for the 1994 predictions against the observations from the 14 grocery
store samples was 0.16. Also, the slope for this test was the highest of all the tests so far at 2.4,
indicating that, for the best fit regression line, observations were higher than predictions by a
greater margin than when testing against the EPA/USDA data set. For these tests, the slopes
ranged from 0.74 to 1.02.
e. Remaining uncertainties in the validation exercise and in air-to-beef modeling in
general: Following is a summary of the key uncertainties of this exercise and of food chain
modeling in general:
1. A characteristic rural air environment: A profile of air concentrations of dioxin-like
congeners in a rural environment would be better achieved as the average of several profiles in
appropriate environments instead of just one.
2. A better understanding of the differences in the EPA/USDA sampling program and the
grocery store samples taken earlier: There were 5 possible explanations listed as to why the
earlier beef samplings appeared to have uniformly higher concentration profile as compared to
the USD A/EPA beef profile. Before fully relying on the EPA/USDA sampling of cattle back fat,
it would be appropriate to understand how the concentrations may differ, if any, with fat in beef
purchased at grocery stores.
3. Vapor/particle partitioning: A theoretical modeling approach was used to partition
the total reservoir of congeners into particle and vapor phases. Monitoring data suggests a
different partitioning regime. This dichotomy was discussed in Chapter 3 and further in this
Chapter in Section 7.3.11. A carefully designed monitoring experiment could shed some light on
vapor/particle partitioning for dioxin-like compounds. This is obviously critical given the major
conclusion of the dominance of vapor phase concentrations in explaining beef concentrations.
4. Vapor transfers to vegetation: Like the partitioning issue, the quantification of
transfers onto vegetation is critical. There is some suggestion that the Welsch-Pausch
experiments for which the air-to-leaf vapor transfer factor was developed may be unrealistic for
field situations. Factors which make it unrealistic includes pots raised off the ground, and the
grass being a dense monoculture. More typical field situations - at ground level with varied
vegetation of lesser density may lead to lower transfers of vapors from the atmosphere to the
canopy (M. McLachlan, Bayreuth University, FRG, personal communication). A data set
including both field level pasture measurements coupled with a corresponding air profile would
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be preferable to the data set of Welsch-Pausch for calibrating a vapor transfer factor, but none
could be found for this assessment.
5. Particle depositions onto vegetation: The impact of wet deposition needs to be further
investigated. A literature article suggesting that about 30% of particles depositing in rain are
retained on the canopy after the rainfall justified the assignment of 0.30 to the parameter, Rw
(fraction retained on vegetation from wet deposition). The weathering half-life of 14 days, while
often used for dioxins, is also identified as uncertain. This half-life was based on studies on
particle depositions on plants. It is possible that the dioxins would preferentially sorb onto the
plants. Therefore, while particles themselves may have a 14-day half-life on the plants, the
dioxins on the particles may remain behind on the plants and have a much longer half-life.
Umlauf and McLachlan (1994) modeled the deposition of particle bound semi-volatile organic
compounds (SOCs) to spruce leaves and assumed that the SOCs were fully retained on the
leaves. In their publication, they did discuss the transfer of the SOCs from the particles to the
leaves. Finally, the deposition velocity of 0.2 cm/sec should be considered further.
6. The bioconcentration factor: Only one study was found from which congener-specific
bioconcentration factors for the suite of congeners could be developed, and this was for one cow,
for one lactating period, and was for milk and not beef. The differences in bioconcentration
between beef and milk need to be further investigated and quantified.
7. Cattle diet and the impact of feedlot fattening: A cattle diet was simplistically
assumed to consist of 4% soil and equal parts of grass and non-grass feeds. Perhaps a more
representative diet could be crafted, which would lead to a different exposure pattern by the beef
cow prior to feedlot fattening. Equally if not more important is the impact of this feedlot
fattening. It is clear that commercial beef cattle in the United States undergo a period of feedlot
fattening. However, before and after monitoring quantifying the impact of this practice could not
be found. Two modeling studies, which assumed that dilution and depuration were occurring
during feedlot fattening, estimated that concentrations were halved due to this process. This was
the assumption also made in this paper, and it needs to be further evaluated.
7.3.13. Expansion of the Terrestrial Food Chain Model for Dioxins and Applications to other
Foodstuffs in the United Kingdom
Harrad and Smith (1997) adopted the food chain model developed initially in the first
draft of the Dioxin Reassessment (EPA, 1994), expanded it to include soil-to-above ground plant
transfers, and applied it to UK data in very much the same manner as in the air-to-plant-to-beef
exercise described just above in Section 7.3.12. Soil-to-above ground impacts are assumed not
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to occur in this assessment, and in Harrad and Smith (1997), they found that soil-to-above ground
translocations explained very little of the predicted grass concentrations. They also expanded the
initial development of this approach in EPA (1994) to include poultry and eggs, both of which
are now modeled in this current assessment.
Although they didn't exactly state where the air data come from in their exercise, it is
surmised that they selected a rural air concentration profile of the 17 dioxin-like CDD/Fs they
deemed to be typical of UK conditions. Then they routed this concentration through the food
chain models to predict the dioxin concentrations in various kinds of animal feeds, and then the
concentration in animal meats. They compared predictions of dioxins in grass (one of the animal
feeds) to those reported for a bulk herbage representing the years 1979-1988 reported in Kjeller,
et al. (1991). They found the ability of the air-to-grass model to predict grass concentration to be
"reasonably satisfactory" with predictions falling within an order of magnitude of observations,
and I-TEQ predicted and observed concentrations to be close at 1.3 pg/g (predicted) and 0.86
pg/g (observed). They did note more variability in congener-by-congener predictions for the
other food products but found that I-TEQ concentrations were reasonably well predicted,
including: 1) retail meat products (assumed to be beef, but not identified as such), pg/g fresh
weight: predicted - 0.252, observed - 0.254; 2) milk, pg/g fresh weight: predicted - 0.090,
observed - 0.057; 3) poultry, pg/g fresh weight: predicted - 0.630, observed - 0.399; 4) eggs, pg/g
fresh: predicted - 0.262, observed - 0.194; and 5) potatoes, pg/g fresh: predicted - 0.0072,
observed - 0.037.
7.3.14. Beef and Milk Fat Concentrations when Soil is the Source of Contamination
Sampling of beef and milk near areas of elevated soil concentrations, or where cattle
were raised on soils with known high concentrations of 2,3,7,8-TCDD, were not found in the
literature. Therefore, the beef fat concentration of 60 ppt estimated to occur near an area where
the soil concentrations of 2,3,7,8-TCDD was 1 ppb cannot easily be evaluated. There are some
studies on other animals indicating high tissue concentrations in areas of high soil contamination
of 2,3,7,8-TCDD. Lower, et al. (1989) studied animal tissues for wild animals in the abandoned
town of Times Beach, Missouri, and compared their results for similar wild animals tissue
concentrations found in Eglin Air Force Base in Florida; Seveso, Italy; and Volgermeerpolder,
Holland. With 2,3,7,8-TCDD soil levels in these areas in the hundreds to thousands of ppt,
tissue levels for earthworm, mouse, prairie vole, rabbit, snake, and liver samples from some of
these animals, were in the tens to thousands of ppt.
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There is an episode of beef and dairy cows being raised on lots where the soil was heavily
contaminated with polybrominated biphenyls (PBB; details can be found in Fries and Jacobs,
1986; and Fries, 1985). Soil concentrations to which dairy and beef cows were exposed were
830 and 350 |_ig/kg (ppb), respectively. Body fat of the dairy cows had PBB concentrations of
305, 222, and 79 ppt (dairy heifers, primiparous dairy, and multiparous dairy, respectively).
Body fat for the beef cows exposed to 350 ppb soil levels were 95 (cows) and 137 ppt (calves).
Milk fat concentrations from the primiparous dairy and multiparous dairy cows exposed to 830
ppb soil levels were 48 and 18 ppt.
Fries (1985) estimated a quantity which is useful for purposes of comparison - this
quantity is the ratio of concentration in animal fat to concentration in soil to which the animal is
exposed. His justification for deriving this ratio is that soil was speculated as the principal
source of body burdens of PBB in the data listed above. This is also the case for the soil
contamination source category of this assessment. Ratios he derived for body fat of dairy heifers
ranged from 0.10 to 0.37, while it was 0.02 and 0.06 for milk fat. For body fat of beef cows,
these ratios were 0.27 and 0.39. Fries also measured a ratio of 1.86 for sows and gilts. He
attributes much higher sow ratios to their tendencies to ingest more soil.
Analogous ratios can be derived for the demonstration of the soil contamination source
category for beef and milk fat. For the demonstration in Chapter 5, the soil concentration
predicted to occur at the farm was 0.36 ppb, and the beef fat and milk fat concentrations were
predicted to be 0.06 and 0.03 ppb, respectively. These correspond to ratios of 0.17 for beef fat
and 0.08 for milk fat. The milk fat ratio compares favorably with PBB ratios derived by Fries
(1985), but the beef fat ratio appears generally lower. The beef fat concentrations in the
demonstration scenarios were driven by the fraction of soil in the diet of the beef cattle, as were
the concentrations in the milk fat. It may be possible that the cattle slaughtered for beef raised in
the lots with high PBB concentrations were exposed to more soil than was assumed in the
demonstration scenarios. There, soil was 4% of the diet. If this value were increased to 8%, than
predicted beef fat concentrations would also double, and the beef fat: soil ratio would be 0.34
instead of 0.17.
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Paustenbach, D.J.; Price, P.; Ollison, W.; Blank, C.; Jernigan, J.; Bass, R.; Peterson, H. (1992)
Reevaluation of benzene exposure for the pliofilm (rubberworker) cohort. Journal of
Toxicology and Environmental Health, 36:177-231.
Rappe, C.; Bergqvist, P. A.; Kjeller, L.O. (1989) Levels, trends and patterns of PCDDs and
PCDFs in Schandinavian environmental samples. Chemosphere 18: 651-658.
Rappe, C.; Kjeller, L.O. (1987) PCDDs and PCDFs in environmental samples, air, particulates,
sediments, and soil. Chemosphere 16: 1775-1780.
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assessment of PCDDs/PCDFs in the vicinity of the Elk River, Minnesota generating
station. Chemosphere 21: 159-171.
Sacchi, G.A.; Vigano, P.; Fortunati, G.; Coccusi, S.M. (1986) Accumulation of 2,3,7,8-TCDD
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Sandalls, J.F.; Berryman, B; Bennett, L; Newstead, S; Jones, P. (1996) Dioxins on land around
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25-30.
Sherman, W.R; Keenan, R.E.; Gunster, D.G. (1992) Reevaluation of dioxin bioconcentration
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7-124 December 2003
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Smith, R.M.; O'Keefe, P.W.; Hilder, D.;Connor, S.; Posner, E. (1995b) Direct and indirect
contributions of atmospheric PCDDs and PCDFs to Hudson River national estuarine
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polychlorinated biphenyls, 2,3,7,8-tetrachloro-p-dioxin and 2,3,7,8-tetrachlodibenzofuran
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Stevens, J.B.; Gerbec, E.N. (1988) Dioxin in the agricultural food chain. Risk analysis 8(3):
329-335.
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2331-2342.
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7-125 December 2003
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U.S. Environmental Protection Agency (1990c) Risk Assessment for 2378-TCDD and 2378-
TCDF Contaminated Receiving Waters from U.S. Chlorine-Bleaching Pulp and Paper
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September, 1992.
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TCDD content of cow's milk. Chemosphere 20:779-786.
7-126 December 2003
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Welsch-Pausch, K; McLachlan, M.; Umlauf, G. (1995) Determination of the princpal pathways
of polychlorinated dibenzo-p-dioxins and dibenzofurans to Lolium multiflrom (Welsh
Ray Grass). Environmental Science and Technology 29:1090-1098.
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statistical survey of dioxin-like compounds in United States beef: A progress report.
Chemosphere 32:469-478.
Wipf, H.K.; Homberger, E.; Neuner, N.; Ranalder, U.B.; Vetter, W.; Vuilleumier, J.P. (1982)
TCDD levels in soil and plant samples from the seveso area. In: Chlorinated Dioxins
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Pergamon, NY.
Yamasaki, H.; Kuwata, K.; Kuge, Y. (1984). Determination of vapor pressures of polycyclic
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atmospheric polycyclic aromatic hydrocarbons. Environmental Science and Technology
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Compounds, Eds. Tucker, R.E., A.L. Young, A.P. Gray, Plenum Press, New York, NY.
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Table 7-1. Observed data for the air-to-plant model comparison exercise.*
Compounds
2378-TCDD
12378-PCDD
123478-HxCDD
123678-HxCDD
123789-HxCDD
1234678-HpCDD
OCDD
2378-TCDF
12378-PCDF
23478-PCDF
123478-HxCDF
123678-HxCDF
123789-HxCDF
234678-HxCDF
1234678-HpCDF
1234789-HpCDF
OCDF
TCDD
PCDD
HxCDD
HpCDD
TCDF
PCDF
HxCDF
HpCDF
Rural Background Site
Air
0.01
0.03
0.04
0.08
0.1
0.82
2.5
0.33
0.06
0.1
0.3
0.1
0.02
0.14
0.53
0.11
0.42
0.72
0.56
0.65
0.71
1.6
0.84
0.45
0.22
Deposition
O.46
2.3
2.3
4.8
3.8
41
166
12
2.5
4.1
11
4.5
1.8
4.8
19
2.9
28
73
54
38
41
24
27
18
<1.8
Grass
0.72
1.3
0.93
2.3
1.8
22
94
14
1.8
2.2
5.6
2.2
0.61
2.6
12
1.1
13
66
33
26
22
93
31
13
0.22
Industrial Site
Air
0.017
0.04
0.04
0.09
0.13
0.84
2.5
0.57
0.1
0.19
0.45
0.16
0.07
0.19
0.71
0.14
0.48
3.1
1.2
0.78
0.73
3.3
2.3
1.4
0.43
Deposition
1.6
3.8
3.2
10.8
8.9
51
153
18
2.2
6
12
5.7
1.9
6
25
3.2
35
509
477
73
48
131
64
26
<1.6
Grass
2.8
0.08
0.73
6
4.2
13
43
16
0.09
1.2
4.6
1.8
0.54
2.4
11
0.89
8
750
410
38
11
290
21
19
0.31
* Units: air - pg/m3; deposition - pg/m2-day; grass - pg/g dry weight of dioxin in the grass. Grass
yield for rural = 89 g/m2 dry weight, for industrial = 42 g/m2 dry weight.
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Table 7-2. Model results comparing the EPA vapor transfer model and the Vapor Deposition
Model with the field data for 2,3,7,8-TCDD (concentrations in pg/g dry weight).
Description
Observed data
EPA vapor transfer model
Smith et al. (1995) model
Trapp & Mattheis (1995) model
2,3,7,8-TCDD grass concentrations, pg/g dry
Rural
0.72
0.25
0.06
0.13
Industrial
2.8
0.21
0.05
0.1
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Table 7-3. Model parameters used in the Hwang and the alternate volatilization models tested in
this comparison exercise.
Description
Jury model
Simplified
Jury model
Hwang
model
Vapor
diffusion
I. Soil Parameters
C0, initial soil concentration, mg/kg
6, volumetric water content, cm3/cm3, or
unitless
a, soil air content, cm3/cm3, or unitless
cj), soil porosity, cm3/cm3, or unitless
BD, soil bulk density, g/cm3
Psoil, soil particle bulk density, g/cm3
Jw, water flux, cm/sec
foe, fraction organic carbon
dz, soil depth of constant concentration,
cm
0.001
0.23
NR
0.43
1.5
NR
0
0.01
10
0.001
0.23
0.2
0.43
1.5
NR
NR
0.01
NR
0.001
NR
NR
0.43
NR
2.65
NR
0.01
NR
0.001
NR
0.2
0.43
NR
NR
NR
0.01
10
n. Chemical Properties
Dgair, chemical gaseous diffusion
coefficient in air, cm2/sec
D!Water, chemical liquid diffusion
coefficient in water, cm2/sec
Koc, organic carbon partition
coefficient, cm3/g
H, Henry's Constant, atm-m3/mole
p, soil degradation rate, I/sec
4.7*10-2
5.6*1Q-6
3.98*106
32*10-5
0
47*10-2
5.6*1Q-6
3.98*106
32*10-5
NR
47*10-2
NR
3.98*106
32*10-5
NR
47*10-2
NR
3.98*106
32*10-5
NR
IE. Model Solution Parameters
BL, boundary layer thickness, cm
Time of Volatilization, days
0.5
10950
NR
10950
NR
10950
NR
NR
NR = Not required for solution
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Table 7-4. Results of model volatilization comparison exercise.
Description
Hwang model
Full Jury model as coded in EMSOFT
Simplified Jury Model as used in Superfund Soil Screening
Vapor diffusion solution only
Full Jury model, boundary layer = 0.01 cm
Full Jury model, half-life = 25 years
Volatilization, g/cm2-sec
1.03 * 1048
2.81 * 1049
3.89 * 1049
4.03 * ID'21
3.02 * 1049
2.16* 1049
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Table 7-5. Comparison of the derivation of the fraction of sorbed dioxin congener based on the
octanol air partition coefficient, Koa, or based on the sub-cooled liquid vapor pressure, as done for
this document as described in Chapter 3.
Congener
2378-TCDD
12378-PCDD
123478-HxCDD
123678-HxCDD
123789-HxCDD
1234678-HpCDD
OCDD
2378-TCDF
12378-PCDF
23478-PCDF
123478-HxCDF
123678-HxCDF
123789-HxCDF
234678-HxCDF
1234678-HpCDF
1234789-HpCDF
OCDF
Solving for Koa l
log Kow
6.81
6.64
7.80
7.30
7.30
8.00
8.20
6.10
6.79
6.50
7.00
7.00
7.00
7.00
7.40
8.00
8.80
H
3.95*1Q-5
2.60* ID'6
1.05*1Q-5
1.10*1Q-5
1.10*1Q-5
1.26*1Q-5
6.75*1Q-6
1.44*1Q-5
5.00*1Q-6
4.98*1Q-6
1.43*1Q-5
7.31*1Q-6
1.10*1Q-5
1.10*1Q-5
1.41*1Q-5
1.40*1Q-5
1.88*1Q-5
logKoa
9.63
10.58
11.13
10.61
10.61
11.25
11.72
9.29
10.44
10.15
10.19
10.49
10.31
10.31
10.60
11.20
11.88
cj) for urban conditions2
theoretical
0.09
0.48
0.76
0.49
0.49
0.81
0.92
0.05
0.40
0.26
0.27
0.42
0.33
0.33
0.49
0.79
0.95
empirical
0.14
0.40
0.60
0.41
0.41
0.64
0.78
0.09
0.35
0.26
0.27
0.36
0.31
0.31
0.40
0.62
0.82
cj) for urban
conditions as
in Chapter 3
0.75
0.95
0.99
0.99
0.99
0.997
0.999
0.73
0.91
0.94
0.98
0.98
0.99
0.99
0.99
0.997
0.999
1 Koa, the octanol air partition coefficient, is solved as, [Kow*R*T]/[H], where Kow is the octanol water partition
coefficient, dimensionless; R is the universal gas constant, 8.2*10"5 atm-m3/mole-K;T is ambient temperature, 273
°K, H is the Henry's Constant, atm-mVmole.
2 c[>, the fraction of sorbed particle, is solved as [Kp (TSP)]/[1 + Kp (TSP)]. Kp is solved for either "theoretically",
where log Kp = log Koa -12.61, or "empirically" based on field data, as done by Kaupp and McLachlan (1999) as, log
K = 0.6368 log Koa -8.9111. See Section 7.2.7 for more detail on these algorithms.
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Table 7-6. Summary of off-site soil contamination from Tier 1 and 2 sites of the National
Dioxin Study.
Site name
On-site
# samples/range
(ppb)
Off-site
# samples/range
(ppb)
Comments
Diamond Alkali
Newark, NJ
9/60-51,000
537/ND-725
Facility involved in the manufacture of 2,4,5-T; off-site
sampling covered a 4000-ft radius including public areas
such as a public housing unit, park, streets, and river. Two
of 11 samples from a park were positive at 1-3.1 ppb;
detection limit was 1 ppb. Other off-site positives were from
streets and river sediments.
Brady Metals
Newark, NJ
10/1.9-3500
30/1.7-1156
Site directly associated with the Diamond Alkali site
summarized above; text did not provide any further detail on
off-site soil sampling.
Love Canal
Niagara, NY
NA/NA-6.7
20/3-263
Love Canal contamination well documented elsewhere; few
details provided in reference for soil sampling programs; it
was noted that 3,000 cubic yards of fly ash and BHC cake
were taken from Love Canal in 1954 and used as fill at the
nearby 93rd Street School, a subsurface sample 3+ ft deep
showed a concentration of 6.7 ppb. The off-site summary
provided here was from an area identified as Hyde Park.
Vertac
Jacksonville, AR
45/
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Table 7-7. Description of soil, sediment, and fish sampling program of dioxin-like compounds
undertaken by the Connecticut Department of Environmental Protection.
Site/Sampling Media
Description
Data Available
1. Bridgeport
Year RRF began operation
1987
Soil
Years of collection
Number of sampling sites
Total number of samples
1987, 1988, 1990
7
21
Sediment
Years of collection
Number of sampling sites
Water body descriptions
Total number of sediment samples
Range, samples per water body
1987, 1988, 1990
6
harbor (2), channel (1; off harbor),
river, pond (2)
66
4-22
Fish
No fish sampling at this site
2. Bristol
Year RRF began operation
1987
Soil
Years of collection
Number of sampling sites
Total number of samples
1987, 1988, 1990
4
12
Sediment
Years of collection
Number of water bodies sampled
Water body descriptions
Total number of sediment samples
Range, samples per water body
1987, 1988, 1990
2
pond (2)
60
29 and 30
Fish
Years of collection
Number of water bodies sampled
Water body descriptions
Total number offish samples
Range, samples per water body
Fish species
1987, 1988, 1989, 1990
2
pond (2)
140
68 and 72
brown bullhead, white sucker,
yellow perch
(continued on following page)
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Table 7-7. (cont'd).
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Site/Sampling Media
3. Hartford
Soil
Sediment
Fish
4. Preston
Soil
Sediment
Fish
Description
Year RRF began operation
Years of collection
Number of sampling sites
Total number of samples
Years of collection
Number of sampling sites
Water body descriptions
Total number of sediment samples
Range, samples per water body
Years of collection
Number of water bodies sampled
Water body descriptions
Total number offish samples
Range, samples per water body
Fish species
Year RRF began operation
Years of collection
Number of sampling sites
Total number of samples
Years of collection
Number of water bodies sampled
Water body descriptions
Total number of sediment samples
Range, samples per water body
no fish sampling at this site
Data Available
1987
1987, 1988, 1990
4
12
1987, 1988, 1990
3
impoundment (1), cove (2)
90
30 from each water body
1987, 1988, 1989, 1990
2
cove (2)
159
81 and 78
carp, channel catfish, white catfish,
white sucker
1990
1990
4
4
1990
3
2 ponds, 1 reservoir
30
10 from each water body
perch
(continued on following page)
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Table 7-7. (cont'd).
Site/Sampling Media
Description
Data Available
5. Sterling
Year RRF began operation
1990
Soil
Years of collection
Number of sampling sites
Total number of samples
1990
4
4
Sediment
Years of collection
Number of sampling sites
Water body descriptions
Total number of sediment samples
Range, samples per water body
1990
2
pond (2)
20
10 from each pond
Fish
Years of collection
Number of water bodies sampled
Water body descriptions
Total number offish samples
Range, samples per water body
Fish species
1990
2
pond (2)
40
20 from each pond
white sucker, yellow perch
6. Union/Stafford
No associate RRF, used as "control" or "reference" site
Soil
Years of collection
Number of sampling sites
Total number of samples
1988 (Union), 1990 (Stafford)
4 (Un), 4 (St)
4 (Un), 4 (St)
Sediment
Years of collection
Number of water bodies sampled
Water body descriptions
Total number of sediment samples
Range, samples per water body
1988, 1990
2
pond (Un), reservoir (St)
20
10 from each water body
Fish
Years of collection
Number of water bodies sampled
Water body descriptions
Total number offish samples
Range, samples per water body
Fish species
1988, 1990
2
pond and reservoir
47
27 (reservoir), 20 (pond)
brown bullhead, white sucker,
yellow perch
(continued on following page)
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Table 7-7. (cont'd).
Site/Sampling Media
Description
Data Available
7. Windham
Year RRF began operation
1983
Soil
Years of collection
Number of sampling sites
Total number of samples
1988, 1990
4
Sediment
Years of collection
Number of sampling sites
Water body descriptions
Total number of sediment samples
Range, samples per water body
1988, 1990
1
reservoir
20
20 from each water body
Fish
Years of collection
Number of water bodies sampled
Water body descriptions
Total number offish samples
Range, samples per water body
Fish species
1988, 1989, 1990
1
reservoir
59
59
white sucker, brown bullhead,
yellow perch
8. Preston
Year RRF began operation
1988
Soil
Years of collection
Number of sampling sites
Total number of samples
1988, 1990
4
Sediment
Years of collection
Number of water bodies sampled
Water body descriptions
Total number of sediment samples
Range, samples per water body
1988, 1990
2
impoundments
40
20 from each water body
Fish
Years of collection
Number of water bodies sampled
Water body descriptions
Total number offish samples
Range, samples per water body
Fish species
1988, 1989, 1990
2
impoundments
75
59
brown bullhead, carp, white sucker
Source: CDEP, 1992
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Table 7-8. Frequency of non-detects and detection limits for soil, sediment, and fish, for three
congeners in the Connecticut Department of Environmental Protection data set.
Congener/Description
2,3,7,8-TCDD
Percent of non-detects
Detection limit, ppt
2,3,7,8-TCDF
Percent of non-detects
Detection limit, ppt
2,3,4,7,8-PCDF
Percent of non-detects
Detection limit, ppt
Soil
50
0.13
O
NDA (0.09)
1
NDA (0.09)
Sediment
27
0.25
2
0.17
5
0.26
Fish
3
0.05
0.2
NDA (0.09)
7
0.04
Source: for percent non-detects: MRI, 1992; for detection limits, draft Monitoring Progress
Report for 1988, supplied by CDEP (1992) specific to MRI laboratories; NDA = no data
available; number of parenthesis is 1A detection limit for time when non-detect was noted, see
text for further information and interpretation
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Table 7-9. Results for Connecticut Department of Environmental Protection sampling, including
soil, sediment and fish concentrations, and the key concentration ratios of sediment to soil and
the Biota Sediment Accumulation Factor (BSAF) ratio.
Site/Description
Soil
Sediment
Fish
A. 2,3,7,8-TCDD RESULTS
1. Bridgeport
Number of samples
Range of concentration, ppt
Mean concentration, ppt
CSed:Csoil Ratio: 7.7
2. Bristol
Number of samples
Range of concentration, ppt
Mean concentration, ppt
fish lipid: 0.038
sediment organic carbon: 0.190
CSed:Csoil Ratio: 9.8
BSAF: 0.78
3. Hartford
Number of samples
Range of concentration, ppt
Mean concentration, ppt
fish lipid: 0.072
sediment organic carbon: 0.056
CSed:Csoil Ratio: 12.3
BSAF: 0.97
4. Bridgeport
Number of samples
Range of concentration, ppt
Mean concentration, ppt
Csed:Csoil Ratio: 7.1
21
0.07-4.62
0.59
12
0.01-0.61
0.17
12
0.07-0.32
0.16
4
0.14-0.80
0.39
66
0.20-51.50
4.53
59
0.16-6.50
1.67
90
0.04-23.10
1.96
30
0.08-17.9
2.75
no data
140
0.03-0.83
0.26
159
0.03-10.90
2.41
no data
7-139
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Table 7-9. (Cont'd)
Site/Description
5. Sterling
Number of samples
Range of concentration, ppt
Mean concentration, ppt
fish lipid: 0.053
sediment organic carbon: 0.067
Csed:Csoil Ratio: 0.4
BSAF: 0.15
6. Union/Stafford
Number of samples
Range of concentration, ppt
Mean concentration, ppt
fish lipid: 0.041
sediment organic carbon: 0.178
Csed:Csoil Ratio: 5.6
BSAF: 0.71
7. Windham
Number of samples
Range of concentration, ppt
Mean concentration, ppt
fish lipid: 0.044
sediment organic carbon: 0.129
Csed:Csoil Ratio: 3.9
BSAF: 0.76
8. Wallingford
Number of samples
Range of concentration, ppt
Mean concentration, ppt
fish lipid: 0.071
sediment organic carbon: 0.019
Csed:Csoil Ratio: 0.3
BSAF: 0.68
Soil
4
0.01-7.96
2.12
8
0.02-1.56
0.28
8
0.15-0.54
0.25
8
0.07-6.00
1.61
Sediment
20
0.07-3.08
0.90
20
0.23-3.69
1.58
20
0.18-1.97
0.97
40
0.03-3.10
0.54
Fish
40
0.03-0.37
0.11
48
0.07-0.85
0.26
59
0.07-0.60
0.25
75
0.03-8.92
1.37
7-140
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Table 7-9. (Cont'd)
Site/Description
Soil
Sediment
Fish
B. TOTALS BY CONGENER (fish lipid = 0.0557, organic carbon fraction = 0.0982 for results
below; these are means for the full data set)
1. 2,3,7,8-TCDD
Number of samples
Mean concentration, ppt
Csed:Csoil Ratio: 3.86
BSAF: 0.86
2. 2,3,7,8-TCDF
Number of samples
Mean concentration, ppt
Csed:Csoil Ratio: 2.59
BSAF: 0.25
3. 2,3,4,7,8-PCDF
Number of samples
Mean concentration, ppt
Csed:Csoil Ratio: 1.58
BSAF: 0.47
4. 1-TEQ
Number of samples
Mean concentration, ppt
CSed:Csoil Ratio: 2.69
BSAF: 0.24
770.56
776.77
773.56
778.42
3462.16
34617.52
3465.62
34622.69
5211.06
5212.53
5211.49
5213.1
Source: CDEP, 1992
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Table 7-10. Model parameters and results for effluent discharge model validation testing.
Number
Company/City
Plant flow
L/hr*106
TCDD
mg/hr
TSSe
mg/L
River flow
L/hr*108
TSSu
mg/L
Lipid
%
Fish concentrations
pg/g (ppt ) whole wt
Predicted
Observed
Multiple Discharges
Mill Numbers
I. Mills with receiving water bodies of lower magnitude; suitable for model testing according to NCASI (see text for more explanation)
1
2
o
5
4
5
6
7
8
9
James R. Corp, Old Town
International Paper Co, Jay
Second fish listing
Third fish listing
Fourth fish listing
Fifth fish listing
James R. Corp, Berlin
Westvaco Corp, Luke
Second fish listing
Penntech Pap, Johnsonburg
Second fish listing
Chesap. Corp, West Point
Second fish listing
Third fish listing
Fourth fish listing
Westvaco Corp, Covington
Second fish listing
Union Camp Corp, Franklin
Champion Int, Courtland
2.52
6.31
2.74
3.12
0.87
2.35
4.18
19.7
9.3
0.098
0.56
0.104
0.05
0.01
0.038
0.227
1.343
0.716
127
89
47
57
44
94
46
60
23
8.57
3.21
5.13
0.3
0.39
0.41
0.31
0.35
43.3
2
2
4
13
17
13
13
0.3
10
10.9
6.2
0.6
0.9
6.3
2.1
3.7
4.9
4.7
1.6
2.5
2.1
2.1
6.2
4.1
1.2
9.7
1.9
11.1
1.9
8.8
0.9
1.3
8.9
3.0
2
2.8
2.7
0.2
0.3
0.6
0.6
1.7
1.1
2.4
19.2
5.1
1.9
8
41.0
3.6
2.9
16.1
23.1
7.8
58.2
35.5
3.6
5.8
0.8
1.1
2.5
1.9
5.9
54.1
1.8
3.4
49
26
(continued on next page)
7-142
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Table 7-10. (cont'd)
Number
10
11
12
13
14
15
16
17
18
19
20
21
Company/City
Cont Corp Amer, Brewton
Boise Case Corp, Jackson
Kimb-Clark Corp, C. Pines
Second fish listing
Alab River Pulp, Claiborne
Second fish listing
Buckeye Cellulose, Perry
Geo-Pac Corp, Palatka
Fed Pap Bd Co, Augusta
ITT-Rayonier, Inc, Jesup
Second fish listing
Int. Paper Co, Moss Point
Second fish listing
L.R. For Prod, New Aug
Second fish listing
Champion Int, Canton
Second fish listing
Wayerhauser Co, Plymouth
Second fish listing
Plant flow
L/hr*106
5.63
3.08
6.91
3.53
8.71
5.84
4.73
9.42
2.71
2.76
6.94
6.15
TCDD
mg/hr
0.037
0.332
0.242
0.148
0.235
0.093
0.076
0.226
0.434
0.552
0.104
1.968
TSSe
mg/L
13
19
19
87
39
8
101
26
57
46
22
15
River flow
L/hr*108
1.01
8.25
6.41
15.2
0.003
0.04
6.56
7.12
0.25
1.62
0.3
0.56
TSSu
mg/L
6
10
18
12
2
2
8
8
12
12
3
8
Lipid
%
2.2
5.3
1.4
5.8
3.8
15.5
8.4
20.3
4.1
2.0
5.9
0.7
7.7
0.9
8.8
3.4
6.9
0.9
3.9
Fish concentrations
pg/g (ppt ) whole wt
Predicted
0.8
2.1
0.4
1.4
0.3
1.3
14.1
81.8
0.4
0.6
1.8
3.4
37.6
1.6
15.2
4.3
8.8
20.6
89.6
Observed
0.6
8.8
8.8
30.0
16.8
28.7
13.2
1.4
4.5
0.9
4.6
7.8
34.4
3.8
98.9
12.0
75.7
18.2
143.3
Multiple Discharges
Mill Numbers
5657581213
1357
1257
(continued on next page)
7-143
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Table 7-10. (cont'd)
Number
22
23
24
25
26
27
28
29
30
31
Company/City
Wayerhauser, New Bern
Second fish listing
Fed Pap Bd, Riegelwood
Second fish listing
Bowater Corp, Catawba
Second fish listing
Union Camp Corp, Eastover
Second fish listing
Mead Corp, Kingsport
Second fish listing
Champion Int, Quinnesec
Second fish listing
Third fish listing
Badger P M, Inc, Pestigo
Second fish listing
James R. Corp, Green Bay
Nekoosa Papers, Inc., Nek
Second fish listing
Wayerhauser Co, Rothchild
Second fish listing
Plant flow
L/hr*106
3.77
4.42
5.3
1.4
1.53
2.02
0.24
1.57
4.78
0.99
TCDD
mg/hr
0.166
0.124
0.127
0.028
0.01
0.018
0
0.017
0.191
0.012
TSSe
mg/L
14
241
13
2
88
32
124
177
36
27
River flow
L/hr*108
1.22
2.32
2.89
3.95
1.53
1.92
0.64
3.02
3.18
2.54
TSSu
mg/L
4
7
5
15
6
o
3
4
14
6
5
Lipid
%
0.8
8.2
0.9
8.2
1.4
6.1
1.5
8.5
6.4
10.7
1.4
1.6
16.8
24.4
1.9
8
1.7
21.5
1.3
16.3
Fish concentrations
pg/g (ppt ) whole wt
Predicted
1.3
13.2
0.2
1.7
0.8
3.4
0.1
0.5
0.4
0.6
0.2
0.2
2.2
0.5
0.04
0.3
1.0
13.1
0.1
1.0
Observed
5.5
49.2
0.9
22.3
3.2
15.3
1.2
9.1
1.0
6.6
1.4
1.4
21.0
8.5
0.3
5.6
7.1
67.2
0.2
4.6
Multiple Discharges
Mill Numbers
(continued on next page)
7-144
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Table 7-10. (cont'd)
Number
32
33
34
35
36
37
38
Company/City
Int. Paper - Bastrop
Second fish listing
Third fish listing
Fourth fish listing
Int Paper Co, Pine Bluff
Second fish listing
Nek Pap, Inc, Ashdown
Second fish listing
Boise Case Corp, Derrider
Second fish listing
Temple-East Inc., Evadale
Second fish listing
Potlatch Corp, Lewiston
Second fish listing
Pope and Talbot, Inc, Halsey
Second fish listing
SIMPLE MEANS
Plant flow
L/hr*106
4.44
4.34
6.07
3.66
8.67
5.43
1.83
4.7
TCDD
mg/hr
1.47
0.478
0.249
0.034
0.763
0.407
0.055
0.31
TSSe
mg/L
82
71
21
59
26
126
14
58
River flow
L/hr*108
10.7
9.97
4.02
0.12
1.5
36.4
7.75
5.3
TSSu
mg/L
13
7
42
10
7
19
7
9
Lipid
%
1.0
12.3
3.0
6.2
5.2
10.4
3.5
1.8
8.2
1.4
1.0
8.0
4.4
6.4
8.8
9.6
5.8
Fish concentrations
pg/g (ppt ) whole wt
Predicted
1.1
13.3
16.4
33.9
2.8
5.5
0.8
0.4
2.8
0.5
3.5
28.0
0.3
0.5
0.8
0.8
7
Observed
1.0
3.6
5.5
5.2
8.9
33.9
4.2
1.7
13.7
1.4
0.7
0.4
0.7
0.5
4.6
0.8
15
Multiple Discharges
Mill Numbers
3 8 mills 774 fish
I. Mills with receiving water bodies of higher magnitude; suitable for model testing according to NCASI (see text for more explanation)
39
Westvaco Corp, Wickliffe
Second fish listing
3.53
0.124
34
"511 1
321.3
129
1.9
7.4
0.001
0.004
1.4
4.8
967
(continued on next page)
7-145
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Table 7-10. (cont'd)
Number
40
41
42
43
44
45
46
47
Company/City
Int Paper Co, Natchez
Potlatch Corp, Mcghee
Second fish listing
James Riv C, St. Francis
Second fish listing
Third fish listing
Georgia Pac, Zachery
Second fish listing
Boise Cac C., St. Helens
Second fish listing
Third fish listing
Wayerh Co, Longview
Second fish listing
Boise Case, Wallula
Second fish listing
Third fish listing
Fourth fish listing
James River, Clat
Second fish listing
SIMPLE MEANS
Plant flow
L/hr*106
5.99
1.92
4.46
4.1
5.54
8.36
3.15
6.43
4.83
TCDD
mg/hr
0.228
0.077
0.366
0.718
0.122
0.071
1.1
0.097
0.32
TSSe
mg/L
115
21
36
130
59
46
157
40
71
River flow
L/hr*108
407.2
375.2
355.3
355.3
183.5
191.6
145.8
191.6
280.8
TSSu
mg/L
221
130
107
13
22
22
14
46
78
Lipid
%
22.6
3.5
5.8
2.3
2.3
10.8
2.0
8.7
2.0
9.6
3.2
3.0
11.4
3.9
10.9
25.1
0.7
7.0
2.9
7
Fish concentrations
pg/g (ppt ) whole wt
Predicted
0.01
0.001
0.002
0.004
0.004
0.017
0.035
0.15
0.008
0.04
0.01
0.007
0.026
0.2
0.7
1.6
0.04
0.01
0.005
0.14
Observed
3.1
1.4
4.7
1.8
0.8
6.0
1.4
1.8
1.3
2.6
1.1
1.5
5.2
5.2
7.9
56.0
0.4
2.8
1.73
5.7
Multiple Discharges
Mill Numbers
92633394167
92633394067
9.31333439404e+18
9.26333439404e+18
373846
37384446
37
3738444546
9 mills 721 fish
(continued on next page)
7-146
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Table 7-10. (cont'd)
Number
Company/City
Plant flow
L/hr*106
TCDD
mg/hr
TSSe
mg/L
River flow
L/hr*108
TSSu
mg/L
Lipid
%
Fish concentrations
pg/g (ppt) whole wt
Predicted
Observed
Multiple Discharges
Mill Numbers
III. No fish in NCSRF according to NCASI
48 Georgia-Pac., Woodland
49 Lincoln Pulp and Pap, Lincoln
50 Scott Paper, Westbrook
51 Scott Paper, Hinckley
52 Int. Paper Co., Mobile
53 Scott Paper
54 Potlatch Corp., Cloquet
IV. Mill already considered as multiple source
55 Boise Cas. Corp, Rumford
56 Gulf St Pap Co, Demopolis
57 International Paper Co, Selma
58 James River Corp, Butler
59 Georgia-Pac Corp, Crosset
V. Effluent discharge at ND
60 Finch & P. & Co, GlenF.
61 Appleton Pap, Roaring Sp.
62 P.H. Glat. Co, Spring Gr.
63 Proc & Gam, Mehoopany
64 Champion Int., Cantonment
65 Oilman Paper Co, St. Marys
66 Buckeye Cell, Oglethorpe
67 Wilamette Ind, Hawesville
68 Bowater Corp., Calhoun
69 Mead Corp, Escanaba
70 Pentair, Inc., Park Falls
71 Wausau P Mills Co, Brokaw
72 Longv. Fiber C, Longview
VI. Mill discharges into estuary, not suitable for model testing
73 ITT-Ray, Inc., Fernandina B.
74 Stone Cont Corp, Pan C.
75 Bruns P. & Paper, Brans
76 Int. Paper Co, Georgetown
77 Alaska Pulp Corp, Sitka
78 Ketch Pulp & Pap 1, Ketch
79 ITT-Ray, Inc., Port Angeles
80 WayerhCo., Cosmopolis
81 Wayerh Co., Everett
82 ITT-Rayonier, Inc., Hoquiam
Table Headings:
Number:
Company, city:
Plant flow:
TCDD:
Lipid:
mill number, established for this table only
abbreviated name and city location
effluent flow rate
2,3,7,8-TCDD discharge rate
measured fish lipid, percent
River flow:
TSSe,u:
Fish cone:
Multiple discharges:
receiving water body flow rate
effluent, upstream suspended solids
concentrations as measured in NCSRF
other mills assumed to influence
fish concentrations
7-147
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Table 7-11. ISCST3 and soil model input assumptions and parameters.
Description
Parameter Value and Comments
I. ISCST3 Model Inputs
Source Characterization
Dispersion Coefficients
Terrain
Regulatory Default Option
- Stack tip downwash
- Final Plume Rise
- Buoyancy induced dispersion
- Wind profile exponents
- Calm winds processing
- Vertical potential temp, gradient
- Decay coefficient
- Building wake effects
Wind Speed/Stability Category
Wet/dry particle-phase deposition
Wet/dry vapor-phase deposition
Plume depletion by deposition
Building height/stack height
Stack temperature
Exit velocity
For Deposition Modeling Only
Particle Category 1
Category 2
Category 3
1992 stack test: 6799 na/m3 total; 136 na/m3TE(
concentration from tested stack;
extrapolated to: 3.12* 10~5 g TEQ/sec emission i
considering three stacks, equal to 985 g TEQ/yr
1994 stack test: 3685 na/m3 total; 64 na/m3 TEO
J emission
ate
emission
concentration from tested stacks;
extrapolated to: 8.47 * 10~6 g TEQ/sec emission rate, equal to
267 g TEQ/yr
rural
flat
yes
yes
yes
yes
regulatory defaults
calm hours not included in cone, calculations
regulatory defaults
0 (no decay of contaminant in plume)
Building dimensions were input to the model
regulatory defaults
yes
no
yes
36 m, 83 m
434 °K
5.5 m/sec
Diam. Mass Density Scav. Coef.
[im fraction g/cm3 (liq)
l/(s-mm/hr)
1.00 0.88 1.4 0.00043
6.78 0.09 1.4 0.0046
20.0 0.03 1.4 0.0066
II. Soil Modeling Inputs
Soil half-live, yrs
Particle Fraction (vapor fraction = 1 -
particle fraction)
Scav. Coef
(ice)
l/(s-mm/hr)
0.00014
0.0016
0.0022
All homologue groups assume 25 year half-life
TCDD: 0.49; PCDD: 0.87; HxCDD: 0.97; HpCDD: 0.99;
OCDD: 0.998
TCDF: 0.53; PCDF: 0.80; HxCDF: 0.945; HpCDF: 0.985;
OCDF: 0.998
7-148
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Table 7-12. Comparison of observed and modeled total CDD/F concentration increments at the
urban monitoring stations (total = sum of homologue group concentrations; on-site, airport =
model results generated using on-site and airport meteorological data; NA = not available).
Station
SN-2
SE-3
SNW-1
SSW-4
HSCNE
March 94 Sampling, fg/m3
Observed
1321
6368
0
0
NA
On-site
6606
8181
8943
0
NA
Airport
20833
2388
1270
0
NA
April 94 Sampling, fg/m3
Observed
0
16105
557
3682
1493
On-site
0
8994
0
8638
8028
7-149
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Table 7-13. Comparison of observed and modeled homologue and TEQ concentrations at
station SE-3 using on-site meteorological data for model input.
Homologue
Group
TCDD
PCDD
HxCDD
HpCDD
OCDD
TCDF
PCDF
HxCDF
HpCDF
OCDF
Total
TEQ
Stack
Emission Rate,
ng/dscm
32
97
300
508
578
293
439
648
616
170
3681
64
SE-3, March 94,
fg/m3
Observed
490
594
543
424
384
904
1226
951
718
134
6368
125
Modeled
71
215
666
1126
1281
651
977
1439
1366
391
8181
144
SE-3, April 94,
fg/m3
Observed
851
1144
1402
1378
1575
1976
2982
2518
1846
433
16105
309
Modeled
78
236
732
1237
1408
716
1074
1582
1502
429
8994
156
7-150
December 2003
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Table 7-14. Results of ISCST3 deposition and soil prediction modeling, comparing measured
concentrations for clusters of soil samples with modeled concentrations assuming either the 1992
or the 1994 stack tests.
Cluster->
Description
ofCluster->
Homologue
TCDD
PCDD
HxCDD
HpCDD
OCDD
TCDF
PCDF
HxCDF
HpCDF
OCDF
TOTAL
TEQ
On-site
n = 3; on incinerator
property
Obs
1118
1820
1885
1666
1431
2147
255
1195
1183
222
1292
466
'92
265
815
1202
781
445
1304
2335
2769
1079
274
11269
236
'94
19
102
351
606
696
187
425
740
732
212
4070
69
Off-site
n = 5; just outside
property,
downwind within
500m
Obs
98
64
150
654
2901
153
194
116
193
88
4611
45
'92
93
286
421
273
156
457
818
970
378
96
394
83
'94
7
35
123
212
243
66
149
259
256
74
142
24
Urban
n = 14; all
directions within
about 3 km.
Obs
19
13
43
154
613
35
33
22
37
15
984
9
'92
38
117
173
112
64
188
336
399
155
40
162
34
'94
3
15
51
87
100
27
61
107
105
31
587
10
Urban
n = 12; all
directions from 3
to 8 km.
Obs
<1
2
4
20
150
2
5
3
5
O
194
<1
'92
9
29
43
28
16
47
83
99
39
10
403
8
'94
<1
4
13
21
25
7
15
26
26
8
146
2
notes: soil concentrations in pg/g, obs = observed; '92, '94 = ISCST3 results using 1992 and
1994 stack test data; "on-site" observed data not expected to represent deposition trends - see text
for more details.
7-151
December 2003
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DRAFT-DO NOT QUOTE OR CITE
Table 7-15. Results of the air-to-soil and soil-to-air model testing
Congener
2378-TCDD
12378-PCDD
123478-HxCDD
123678-HxCDD
123789-HxCDD
1234678-HpCDD
OCDD
2378-TCDF
12378-PCDF
23478-PCDF
123478-HxCDF
123678-HxCDF
123789-HxCDF
234678-HxCDF
1234678-HpCDF
1234789-HpCDF
OCDF
TEQ
Observed Soil
Pg/g
0.39
0.14
0.35
0.82
1.23
17.7
161.0
0.64
0.17
0.21
0.16
0.11
0.15
0.67
4.06
0.27
10.7
1.37
Predicted Soil
Pg/g
0.03
0.19
0.31
0.36
0.54
9.1
36.2
0.06
0.20
0.26
0.50
0.59
0.11
0.36
2.74
0.58
2.70
0.70
Observed Air
pg/m3
1.4*1Q-3
5.2*1Q-3
79*10-3
93*10-3
1.4*1Q-2
23*10-i
90*10-!
2.8*1Q-3
6.5*1Q-3
7.4* ID'3
1.3*1Q-3
1.6*1Q-2
2.8*1Q-3
9.2* ID'3
6.9*10-2
1.4*1Q-3
6.7*1Q-3
1.9H02
Predicted Air
pg/m3
1.8*1Q-5
2.5*1Q-6
4.0*1Q-6
1.4*1Q-5
2.1*1Q-5
1.9*1Q-4
1.3*1Q-3
4.2*1Q-5
3.5*1Q-6
5.1*1Q-6
3.8*1Q-6
2.1*1Q-6
3.4*1Q-6
1.5*1Q-5
70*10-5
3.0*1Q-6
8.0*1Q-5
3.6H05
7-152
December 2003
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DRAFT-DO NOT QUOTE OR CITE
Table 7-16. Data and results of the soil to below ground vegetable validation exercise.
Congener
group
TCDD
PCDD
HxCDD
HpCDD
OCDD
TCDF
PCDF
HxCDF
HpCDF
OCDF
Koc, L/kg
3.98*106
2.69*106
2.12*107
6.17*107
9.77*107
7.76*105
2.88*106
6.17*106
3.86*107
3.89*108
RCF
5200
3900
18600
43700
62200
1500
4080
7410
29400
180000
Control Soil
Soil, ppt
11.0
6.8
23.5
45.6
85.4
21.8
34.8
38.5
49.0
46.6
Observed
Peel, ppt dry
1.7
1.2
1.6
1.6
3.0
11.9
5.9
3.2
1.2
0.4
Predicted
Peel, ppt dry
1.2
0.8
1.7
2.7
4.5
3.5
4.1
3.8
3.1
1.8
Contaminated Soil
Soil, ppt
24.3
80.5
176.7
238.6
297.4
270.7
361.9
418.4
667.2
687.3
Observed Peel,
ppt dry
2.9
5.6
7.3
5.4
6.3
36.7
24.8
22.0
12.2
5.0
Predicted Peel,
ppt dry
2.6
9.6
12.7
13.9
15.6
43.0
42.1
41.0
41.8
26.2
7-153
December 2003
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DRAFT-DO NOT QUOTE OR CITE
Table 7-17. Summary of plant concentration versus soil concentration data for 2,3,7,8-TCDD.
Plant: soil
Concentrations
Contaminant
Ratio
Reference and Comments
I. Below-Ground Vegetation
54-167 ppt/
1-5 ppb
0.8-9.2 ppb/
2.7-8.3 ppb
156-1807 ppt/
160-752 ppt
735 ppt/
411 ppt
0.5-40.2 ppt/
2-6000
0.1-15 ppt/
6-690 ppt
0.01-0.17
0.24-1.73
1.00-2.40
1.8
0.001-0.3
0.001-0.5
Wipf, et al., 1982; results are for 2,3,7,8-TCDD and greenhouse carrots grown in Seveso contaminated soil; the 54 ppt
concentration listed was for carrot peels and inner portions; the 167 ppt listed includes the 54 ppt plus additional
residues found in wash water and can be described as "unwashed" concentration; 96% of 167 ppt unwashed
concentration includes that found in wash water (67%) and peels (29%).
Coccusi, et al., 1979; results are for 2,3,7,8-TCDD and carrots, potatoes, narcissus, and onions grown on contaminated
soil the spring following the Seveso contamination; aerial plant part ratios were 0.25-0.40 - underground part ratios
were 0.23-1.73; residues in contaminated plants were found to dissipate when contaminated plants transplanted to
unpolluted soils; results show higher ratios than the Wipf, et al. (1982) noted above; results were expressed in fresh
plant weight and fresh soil basis; very high ratios and plant impacts render these data suspect.
Facchetti, et al., 1986; results are for 2,3,7,8-TCDD and bean and maize roots grown in indoor greenhouse pots and
outdoor pots; unclear whether plant concentrations are fresh or dry weights. Data considered highly suspect due to very
high ratios found and also reporting 16 and 37 ppt in roots when "blank" soil had 1.5 ppt (ratios of 10.7 and 24.7).
Young, 1983; results are for 2,3,7,8-TCDD and roots of grass and broadleaf plants at Eglin Air Force Base; unclear
whether root concentrations are fresh or dry weight.
Hulster and Marschner, 1991; results at right are for unpeeled potato tubers, in TEQ and dry weight basis. Plant: soil
ratio decreased as soil concentrations increased; highest ratios were at the 2.4 ppt low soil concentration. Peeled tuber
concentration stayed below 0.5 ppt over all soil concentrations, indicating insignificant within plant translocation. Plant
concentrations given in dry weight basis.
Muller, et al. 1994; results at right describe the range of concentrations and ratios for data on ten congener groups, in
two soils (a control and a contaminated soil), and for carrots. For the control soil, which had a TEQ concentration of 5
ppt, typical of background soils, the average plant: soil ratio was 0. 10; for the contaminated soil with a TEQ
concentration of 56 ppt, the plant:soil ratio was 0.02.
7-154
December 2003
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DRAFT-DO NOT QUOTE OR CITE
Table 7-17. (cont'd).
Plant: soil
Concentrations
Contaminant
Ratio
Reference and Comments
I. Below-Ground Vegetation (cont'd)
0.2-6.0 ppt/
328-12,800 ppt
0.00001-0.009
Hulster and Marschner, 1993. Results are for potato tubers, peeled and unpeeled, and for potato shoots, results for TEQ
and in dry matter terms. Concentrations for peeled potato tubers stayed consistently less than 0.5 ppt, despite soil
concentrations, while shoots and unpeeled tubers increased as concentration increased. Plant: soil ratios remained
relatively constant for tubers and shoots with soil concentration increases, leading authors to conclude that a soil/plant
relationship exists for plants growing in the soil. Less transfer was noted for higher chlorination.
II. Above-Ground Vegetations
9-42 ppt/
lOppb
8-9 ppt/
lOppb
1-63 ppt/
12-3300 ppt
0.0009-0.0042
0.0008
0.003-0.35
Wipf, et al., 1982; analysis of apples, pears, plums, figs, peaches, and apricots grown in Seveso, Italy year following
contamination; apples, pears, and peaches showed >95% of whole fruit concentrations listed here was in the peels;
analysis of vegetative samples in less contaminated areas showed non-detections at 1 ppt detection limit; reference was
unclear as to whether reported concentrations in fruit was based on fresh or dry weight.
Wipf, et al., 1982; concentrations listed were those found in sheaths of corn grown year following Seveso
contamination; none found in cobs and kernels at 1 ppt detection limit.
Sacchi, et al., 1986; data was for: "aerial parts" of bean and maize plants, tritiated TCDD amended soil with
concentrations ranging as noted, taken at different intervals including 7, 34 and 57 days (one test), 17, 34, and 57 days
(another test), 8 and 77 days, and 8 and 49 days, and in tests where soil was and was not amended with peat. Results
showed increasing plant concentrations with increasing soil concentrations, but the ratio of plant to soil concentrations
was inversely related to increasing soil concentrations (lowest ratios at highest soil concentrations). Soils without peat
had higher ratios than soils with peat. Plant concentrations were fresh weight basis; high plant impact and trend for
increasing impact over time renders these results suspect.
7-155
December 2003
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DRAFT-DO NOT QUOTE OR CITE
Table 7-17. (cont'd).
Plant: soil
Concentrations
Contaminant
Ratio
Reference and Comments
I. Above-Ground Vegetation (cont'd)
ND(DL=1
ppb)/60 ppt
10-270 ppt/
411 ppt
0.3, 0.1 ppt/
8750,5215 ppt
0.1-0.6 ppt/
326-5752 ppt
4-38 ppt/
326-12,800 ppt
O.017
0.02-0.66
0.00003,
0.00002
0.00002-
0.0008
0.001-0.01
Isensee and Jones, 1971; results are for mature oat and soybean tops, and oat grain and the bean of soybean, in soil
treated with [14C]TCDD to achieve a concentration of 60 ppb - no residues of TCDD were found; ratios of 0.14 and
0.28 were found for 2,4,-dichlorophenol (DCP) in oat and soybean tops, and 0.20 for 2,7-dichlorodibenzo-p-dioxin
(DCDD) in oat tops; trace amounts of DCP and DCDD were found in the bean of soybean.
Young, 1983; data was for 2,3,7,8-TCDD and above ground plant parts of perennial grasses and broadleaf plants grown
on 2,4,5,-T treated soils. Unclear whether plant concentrations are fresh or dry weight basis. Soil concentration was
average over 3 depth increments to 15 cm. Crown near soil surface at 270 ppt and 0.66 ratio was highest; plant tops had
ratios of 0.02-0. 17.
Muller, etal, 1993. Result at right are for whole pear (0.3) and whole apple (0.1) dry weight concentrations (article
presented TEQs for two pears from one tree which were averaged, and one apple, and for fresh weight; dry weight was
estimated assuming 12% dry matter in pears/apples) and the average concentration over 70 cm (article supplied
concentrations for the 0-30 and 30-70 cm depths). Article also provided peel and pulp results and results for congener
groups. Article concluded: soil levels were not correlated to fruit concentrations and therefore fruits were impacted by
airborne contamination, and that concentrations were higher in peel than in pulp.
Hulster and Marschner, 1993. Results are for inner and outer leaves of lettuce, expressed as dry matter, and in TEQs.
Results indicate a drop in ratio as soil concentration increases, and unexpected small differences between inner and
outer leaves.
Hulster and Marschner, 1993. Results are for hay, dry matter, and TEQs. Results indicate a drop in ratio as soil
concentrations increase.
7-156
December 2003
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DRAFT-DO NOT QUOTE OR CITE
Table 7-17. (cont'd).
Plant: soil
Concentrations
Contaminant
Ratio
Reference and Comments
I. Above-Ground Vegetation (cont'd)
-------
DRAFT-DO NOT QUOTE OR CITE
Table 7-18. Parameters for the empirical relationship relating the sub-cooled liquid vapor
pressure, p°L, to the particle/gas partition coefficient, Kp, of semivolatile organic compounds
(SOC).
Setting/SOC
Location
m
b
Reference
I. Urban
PAHs
PAHs
PAHs
PCBs
PAHs
PCBs
PAHs
PAHs
PAHs
OC pesticides
Portland, Oregon
Portland, Oregon
Denver, Colorado
Denver, Colorado
Chicago, Illinois
Chicago, Illinois
London, U.K.
Osaka, Japan
Brazzaville, Congo
Brazzaville, Congo
-0.882
-0.890
-0.760
-0.946
-0.694
-0.726
-0.631
-1.04
-0.810
-0.740
-5.38
-4.75
-5.10
-5.86
-4.61
-5.18
-4.61
-5.95
-5.31
-5.76
1
2
O
3
4
4
5
6
7
7
II. Rural
PAHs
PAHs
PAHs
PAHs
PCBs, OC
pesticides
Coastal Oregon
Lake Superior
Lake Superior
Green Bay
Bayreuth, Germany
-0.724
-0.586
-0.614
-1.00
-0.610
-4.94
-3.83
-4.25
-5.47
-4.74
1
8
9
4
10
References:
1. Ligocki and Pankow (1989);
4. Gotham and Bidleman (1995);
7. Ngabe and Bidleman (1992);
10. Kaupp and Umlauf (1992)
2. Hart (1989);
5. Baek, etal. (1991);
8. McVeety and Kites (1988);
3. Foreman and Bidleman (1990);
6. Yamasaki, et al. (1982);
9. Baker and Eisenreich (1990);
7-158
December 2003
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DRAFT-DO NOT QUOTE OR CITE
Table 7-19. Summary of modeling changes from the 1994 air-to-beef model validation exercise to the present update.
Congener
2378-TCDD
12378-PCDD
123478-HxCDD
123678-HxCDD
123789-HxCDD
1234678-HpCDD
OCDD
2378-TCDF
12378-PCDF
23478-PCDF
123478-HxCDF
123678-HxCDF
123789-HxCDF
234678-HxCDF
1234678-HpCDF
1234789-HpCDF
OCDF
Byp,,, unitless
1994 1996
1.0*105
6.3*105
2.3*106
6.9*105
6.9*105
1.0*107
2.4*109
1.5*105
3.8*105
5.3*105
5.9*105
1.4*106
8.3*105
8.3*105
6.8*105
6.8*105
1.7*108
6.55*104
2.39*105
5.20*105
5.20*105
5.20*105
9.10*105
2.36*105
4.57*104
9.75*104
9.75*104
1.62*105
1.62*105
1.62*105
1.62*105
8.30*105
8.30*105
2.28*106
Vapor/Particle Partitioning
1994 1996
55/45
26/74
7/93
4/96
2/98
2/98
0/100
71/29
42/58
30/70
6/94
6/94
11/89
7/93
4/96
3/97
0/100
51/49
13/87
3/97
3/97
3/97
1/99
0.2/99.8
47/53
25/75
16/84
7/93
7/93
4/96
4/96
2/98
1/99
0.2/99.8
BCF, unitless
1994 1996
4.32
4.16
2.02
1.74
2.24
0.36
0.52
0.94
0.73
3.10
2.34
2.00
2.00
1.78
0.41
0.99
0.20
5.76
5.55
2.69
2.32
2.99
0.48
0.69
1.25
0.97
4.13
3.12
2.67
2.67
2.37
0.55
1.32
0.27
Cs, pg/g
1994 1996
0.1
0.6
0.6
0.9
1.2
13.9
69.3
0.8
0.7
0.5
1.4
1.3
0.3
1.0
4.9
0.7
4.1
0.4
0.1
0.4
0.8
1.2
17.7
160.9
0.6
0.2
0.2
0.2
0.1
0.2
0.6
4.1
0.3
10.7
B^: air-to-leaf vapor transfer factor, unitless
BCF: beef bioconcentration factor, unitless
Vapor/Particle: vapor phase/particle phase percentages
Cs: soil concentration, pg/g
7-159
December 2003
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DRAFT-DO NOT QUOTE OR CITE
Table 7-20. Comparison of air concentration profiles used in the 1994 air-to-beef model
validation compared against the current air profiles.
Congener
2378-TCDD
12378-PCDD
123478-HxCDD
123678-HxCDD
123789-HxCDD
1234678-HpCDD
OCDD
2378-TCDF
12378-PCDF
23478-PCDF
123478-HxCDF
123678-HxCDF
123789-HxCDF
234678-HxCDF
1234678-HpCDF
1234789-HpCDF
OCDF
TOTAL
I-TEQ
1994 urban air
profile, pg/m3
0.010
0.030
0.025
0.035
0.050
0.580
2.930
0.115
0.050
0.030
0.060
0.060
0.015
0.045
0.210
0-.030
0.173
4.448
0.095
Columbus urban
air profile, pg/m3
0.0065
0.017
0.022
0.035
0.033
0.280
1.053
0.019
0.036
0.030
0.068
0.087
0.003
0.050
0.262
0.044
0.173
2.220
0.070
1994 rural air
profile, pg/m3
0.002
0.006
0.005
0.007
0.010
0.116
0.586
0.023
0.010
0.006
0.012
0.012
0.003
0.009
0.042
0.006
0.034
0.890
0.019
Columbus rural
air profile, pg/m3
0.0014
0.005
0.008
0.009
0.014
0.227
0.904
0.003
0.007
0.007
0.013
0.016
0.003
0.009
0.069
0.014
0.067
1.380
0.019
Ratio, Col
urban/Col rural
4.6
3.4
2.8
3.9
2.4
1.2
1.2
6.3
5.1
4.3
5.2
5.4
1.0
5.6
3.8
3.1
2.6
...
...
7-160
December 2003
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DRAFT-DO NOT QUOTE OR CITE
Table 7-21. Comparison of predicted leafy vegetation samples of the current, revised validation
exercise with the previous predictions of leafy vegetations and several observations in the
literature (units are pg/g dry weight).
Congener
2378-TCDD
12378-PCDD
123478-HxCDD
123678-HxCDD
123789-HxCDD
1234678-HpCDD
OCDD
2378-TCDF
12378-PCDF
23478-PCDF
123478-HxCDF
123678-HxCDF
123789-HxCDF
234678-HxCDF
1234678-HpCDF
1234789-HpCDF
OCDF
TOTAL
I-TEQ
Predicted
1994 1996
0.1
0.9
0.7
0.2
0.2
21.0
6.0
7.2
1.4
0.8
0.5
0.9
0.3
0.5
1.4
0.1
0.4
42.6
3.2
0.05
0.18
0.18
0.22
0.32
4.12
13.20
0.07
0.19
0.16
0.26
0.31
0.04
0.14
1.68
0.25
0.96
22.3
0.46
US alfalfa1,
1994
0.11
0.16
0.29
0.25
0.23
0.85
6.21
0.06
0.21
0.08
0.19
0.30
0.24
0.20
0.31
0.45
0.96
11.1
0.44
UK grass2,
1979-1988
0.03
0.14
0.14
3.00
1.40
7.10
24.0
0.46
0.18
0.20
0.32
0.16
0.02
0.15
1.90
0.14
2.00
41.34
0.89
UK grass3,
1996
0.12
0.07
0.10
0.17
0.08
2.80
15.60
1.28
0.29
0.28
0.21
0.09
0.02
0.09
1.02
0.13
0.98
32.2
0.57
UK grass4,
1997
0.72
1.3
0.93
2.3
1.8
22
94
14
1.8
2 2
5.6
2.2
0.61
2.6
12
1.1
13
178.2
6.0
US hay5,
1989
ND
ND
ND
1.2
ND
30.0
285.0
ND
ND
ND
ND
ND
ND
ND
5.4
ND
7.5
—
—
1 From an unpublished data set for alfalfa supplied by V. Fiel, United States Department of Agriculture, for a beef
feeding study which is currently underway. For these results, all but the hepta dioxin and the two octa congeners
were not detected - results above are 1A detection for the two alfalfa samples taken.
2 Kjeller, et al, (1991);3 Kjeller (1996); 4Jones and Duarte-Davidson (1997);s Reed, et al. (1990) - detection limits
not supplied for non-detects, but described as between 0.31 and 6.5 ppt.
7-161
December 2003
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DRAFT-DO NOT QUOTE OR CITE
Table 7-22. Results of the 1994 air-to-beef model validation exercise compared against results
from the current air-to-beef model validation exercises (all beef concentrations in terms of pg/g
lipid; values in parentheses are observations calculated assuming non-detects equal 0.0; values
not in parenthesis assume non-detects equal 1A detection limit).
Congener
2378-TCDD
12378-PCDD
123478-HxCDD
123678-HxCDD
123789-HxCDD
1234678-HpCDD
OCDD
2378-TCDF
12378-PCDF
23478-PCDF
123478-HxCDF
123678-HxCDF
123789-HxCDF
234678-HxCDF
1234678-HpCDF
1234789-HpCDF
OCDF
TOTAL
I-TEQ
1994 Validation Results
Observed beef Predicted beef
0.13 (0.06)
1.17(0.13)
1.38(0.74)
4.40 (4.40)
1.08(0.34
10.13(9.99)
15.32(14.84)
0.30 (0.25)
0.23 (0.005)
1.11 (0.90)
2.68 (2.44)
0.33 (0.10)
0.30 (0)
0.38(0.11)
2.08(1.74)
0.68 (0.07)
1.18(0.55)
42.9 (36.7)
2.51 (1.55)
0.16
1.42
0.53
0.16
0.21
1.53
1.53
2.42
0.37
0.89
0.42
0.68
0.21
0.37
0.21
0.05
0.05
11.21
1.85
Current Validation Results
Observed beef Predicted beef
0.05 (0.03)
0.35 (0.04)
0.64(0.18)
1.42(1.21)
0.53 (0.26)
4.48 (4.39)
4.78(3.21)
0.03 (0)
0.31 (0)
0.36 (0.06)
0.55 (0.27)
0.40(0.12)
0.31 (0)
0.39(0.10)
1.00(0.75)
0.31 (0)
1.88(0)
17.8 (10.7)
0.93 (0.35)
0.13
0.37
0.19
0.20
0.38
0.79
4.54
0.04
0.07
0.25
0.29
0.28
0.05
0.14
0.35
0.11
0.13
8.29
0.61
7-162
December 2003
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DRAFT-DO NOT QUOTE OR CITE
4 -,
•o
-Sf 2
"oil
G.
•O*
o
•3
1
0
-1
X
x
* EPA Model
-Scavenging Model
-101234
Ln Observed, pg/g dry
Figure 7-1. Comparison of observed and predicted grass concentrations of dioxin and furan
congeners for the EPA and the scavenging models at the rural site. The perfect match of
observed and predicted is shown in the dashed observed = predicted line.
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4 -,
'—
•o
-Sf
"oil
G.
2 0
-o
£ -1
-3
/
* EPA Model
- Scavenging Model
-101234567
Ln Observed, pg/g dry
Figure 7-2. Comparison of observed and predicted grass concentrations of dioxin and furan
congeners for the EPA and the scavenging models at the industrial site. The perfect match of
observed and predicted is shown in the dashed observed = predicted line.
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Scavenging Coefficient
100 -
80 -
60 -
40 -
20 -
* * Dioxins
— Furans
~ *
i i i i
4567
!
i
8
Degree of Chlorination
Figure 7-3. The observed scavenging coefficient (grass concentration over air concentration)
calculated from the rural site data.
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^s
03
2
E
^JD
^
13
-^
o
1
PH
S3
i
7 -, X
6 -
5
4 -
3 -
2 -
0
_1 _
X
_ -*x~
4- *~ "^
*" ^
*"*^~ ~* s
— ^% ^^
^ ^
"*X * Rural site
• ^^ - Industrial site
X
X
-2-101234567
Ln Observed, pg/m2-day
Figure 7-4. Comparison of observed and predicted deposition at the rural and industrial sites.
The perfect match of observed and predicted is shown in the dashed observed = predicted line.
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Effluent Data:
1. Flow
2. Suspended Solids
3. Organic carbon content
Receiving Water Body Data:
1. Flow
2. Suspended Solids
3. Organic Carbon Content
EFFLUENT
DISCHARGE
Observed Source
Term:
2,3,7,8-TCDD
Discharge Rate
Total Water
Concentration
Predicted Result:
Whole fish tissue
concentrations
2,3,7,8-TCDD Parameters:
1. Organic Carbon Partition
Coefficient
2. Biota suspended solids
accumulation factor
Fish Data:
1. lipid
content
Figure 7-5. Schematic of effluent discharge model showing all parameter inputs and
observed fish concentrations.
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160 -,
0
20 40 60
Predicted concentrations, ppt
80
100
Figure 7-6. Comparison of predicted and observed fish tissue concentrations for validation of
the effluent discharge model.
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A Air Sur^l-.ag ICC&t Von*
So,' l Sanpi • Locat >
3 W
a a«
1003 nezer rftditc circle
^nd XZB
an-site
Iccaiions «= -;
'i-utal back^rwund- sit«s 45
£aciii-y site 'K-
r.ot chotm on
Figure 7-7. Site map showing locations of soil and air samples in the vicinity of the Columbus
Municipal Solid Waste-To-Energy (CMWSTE, abbreviated WTE above) Facility.
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0 1 K
0 1 Km
(a) TCDD, March, On-site
(b) OCDD, March, On-site
(c) TEQ, March, On-site
Legend
n
+
"" = Columbus MSWTE
(0.5 ''t = Observed, pg/m3
05, = Predicted, pg/m3
0.16
0 1 Km
0 1 Km
0.15
0.30
(d) TCDD, March, Airport
(e) OCDD, March, Airport
(f) TEQ, March, Airport
Figure 7-8. Isoline figures of predicted air concentrations overlain by measured air concentrations of TCDD, OCDD, and TEQ
(pg/m3) when using the "on-site" meteorological data set (sub-figures a, b, and c) and when using the "airport" meteorological data set
(sub-figures d, e, and f).
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400
0 05 Km
(a) TCDD, Observed
(b) TCDD,'92 Stack Test (c) TCDD,'94 Stack Test
0 0.5 Km
200
0 0.5 Km
0 0.5 Km
(d) OCDD, Observed
(e) OCDD, '92 Stack Test (f) OCDD, '94 Stack Test
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0 as Km
(h) TEQ, Observed
(i) TEQ, '92 Stack Test
(j) TEQ, '94 Stack Test
0.5
= Columbus MSWTE
= Predicted and
Observed, pg/m3
Figure 7-9. Isoline figures of predicted soil concentrations of TCDD, OCDD, and TEQ (sub-figures a, d, g) compared against isoline
figures of measured soil concentrations using the 1992 stack emission test (sub-figures b, e, and h) and the 1994 stack emission test
(sub-figures c, f, and i).
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100
(D 80
4->
JC
•^ 60
03
Q_
>S 40
20
0
(a)
PAHs, Urban
(b)
1>^A\^POR2
OSK
J-P
PAHs, Rural
100
80 -
60 -
-7 -6 -5 -4 -3 -2
-7 -6 -5 -4 -3
Key: J-P: Junge - Pankow model; the two solid lines in the rural setting represent clean
continental background and background + local sources regimes.
DEN = study in Denver (Foreman and Bidleman, 1990)
POR1 and POR2 = two studies in Portland (Ligocki and Pankow, 1989; Hart, 1989)
CHI = study in Chicago (Cotham and Bidleman, 1995)
SUP1 and SUP2 = two studies in Lake Superior (McVeety & Kites, 1988;Baker &
Eisenreich, 1990)
BRZ = study in Brazzaville, Congo (Ngabe and Bidleman, 1992)
LON = study in London (Baek, et al., 1991)
OSK = study in Osaka, Japan (Yamasaki, et al., 1982)
GB = study in Green Bay (Cotham and Bidleman, 1995)
Figure 7-10. Comparison of measured and predicted particulate percentages of PAHs in urban
and rural air.
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PCBs and OC Pesticides
(a) Urban Rural (b)
100
0)
80
60
o
40
20
0
J-P
100
-7 -6 -5
-3 -2
40 -
20 -
-7 -6 -5 -4 -3
LogPL°(Pa)
Key: J-P: Junge - Pankow model; the two solid lines in the rural setting represent clean
continental background and background + local sources regimes.
DEN = study in Denver (Foreman and Bidleman, 1990)
CHI = study in Chicago (Cotham and Bidleman, 1995)
BRZ = study in Brazzaville, Congo (Ngabe and Bidleman, 1992)
BAY = study in Bayreuth, Germany (Kaupp and Umlauf, 1992)
Figure 7-11. Comparison of measured and predicted paniculate percentages of PCBs and
organochlorine pesticides in urban and rural air.
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TOTAL CONCENTRATION
VAPOR
PHASE
VAPOR
TRANSFERS
PARTICLE
PHASE
J
PARTICLE
DEPOSITIONS
PASTURE
GRASS
BIOCONCENTRATION
CATTLE
DIET
HAY, SILAGE,
GRAIN
X
SOIL
BEEF CONCENTRATION
Figure 7-12. Overview of model to predict beef concentrations from air concentrations.
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8. UNCERTAINTY
8.1. INTRODUCTION
This chapter addresses uncertainty in dioxin exposure assessment performed with the
methodologies presented in this document. Some discussion of the issues commonly lumped
into the term "uncertainty" is needed at the outset. The following questions capture the range of
issues typically involved in uncertainty evaluations:
(1) How certain are site specific exposure predictions that can be made with the methods?
(2) How variable are the levels of exposure among different members of an exposed local
population?
(3) How variable are exposures associated with different sources of contamination?
The emphasis in this document is in providing the technical tools needed to perform site-
specific exposure assessments. For the assessor focusing on a particular site, question (1) will be
of preeminent importance. Therefore, the emphasis of this Chapter is to elucidate those
uncertainties inherent to the exposure assessment tools presented in this document. This chapter
examines the uncertainties associated with estimating exposure media concentrations of the
dioxin-like compounds using the fate, transport, and transfer algorithms, and also identifies and
discusses uncertain parameters associated with human exposure patterns (contact rates and
fractions, exposure durations, etc.).
Section 8.2 focuses on uncertainty issues associated with the use of the ISCST3 model for
air transport modeling for the stack emission source category. The ISCST3 model and its
application in this assessment are presented in detail in Chapter 3. Section 8.3 discusses the
variability and uncertainty with chemical-specific parameters which are required for all source
categories of this assessment methodology. Section 8.4 provides a general overview of all key
uncertainties with each pathway.
A site specific assessment will also need to address the variability of risks among
different members of the exposed population, the second key question above. The level of detail
with which this can be done depends on the assessors knowledge about the actual or likely
activities of these residents. In this document, one approach to evaluating this variability is
demonstrated. Separate "central" and "high end" scenario calculations are presented to reflect
different patterns of human activities within an exposed population. "Central" scenarios are
constructed to represent typical behavior patterns for residential exposures in a hypothetical rural
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setting. "High end" calculations focus on a farming scenario where individuals raise food for
their own consumption, in the same rural area. It should be emphasized that high end
calculations could also have been developed for residential exposures by making, for example,
higher range assumptions about the duration of residence or contact rates with the contaminated
media. Indeed, this would be recommended for an assessment where considerable emphasis was
placed on residential exposures. The key issue with regard to intra-population variability is that
it is best (if not only) addressed within the context of a specifically identified population. If such
information is available, a powerful tool that can be used to evaluate the variability within a
population is Monte Carlo Analysis. Section 8.5. reviews recent Monte Carlo studies which have
been done for exposure to 2,3,7,8-TCDD. Assumptions on distributions of exposure patterns and
fate and transport parameter distributions are described, as are the results of their analyses. Aside
from this review, this chapter does not address question (2) in any further manner.
With regard to question (3), this document does not present a detailed evaluation of how
exposure levels will vary between different sources of release of dioxin-like compounds into the
environment. Volume I of this assessment examines sources of release of dioxin-like compounds
into the environment. This document, Volume HI, presents methodologies for three types of
sources - soil, stack emissions, and effluent discharges into surface water bodies. While this
document demonstrates the methodologies developed for these sources with source strengths and
environments crafted to be plausible and meaningful, there is still a great deal of variability on
both the source strengths and on the environments into which the releases occur. For example,
the frequency with which farms and rural residences are near stack emissions of dioxin-like
compounds is not addressed. The scenario calculations in Chapter 5 are intended to be
illustrative; the exposure levels that are obtained there are not intended to be typical of actual
exposures for the sources and pathways assessed.
Nonetheless, some readers might ideally wish information on both the magnitude of
actual exposures and the variability of these exposures associated with different sources of
dioxin-like compound releases into the environment. However, the analysis presented in this
chapter cannot support so broad a goal. Representative data to address the variation of dioxin
exposures are becoming available for sources as well as exposure media. Volume I discusses
and quantifies releases from known sources in the US, and the compilation of environmental and
exposure media concentrations presented in Chapter 3 of Volume n of this assessment displays
the range of measured concentrations in the environment. The careful selection of certain
literature reports on concentrations of dioxin-like compounds to represent background
conditions, described in Chapter 4 of Volume II, is one way such environmental measurements
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can be used. References to EPA and other assessments on dioxin-like compounds have been
made throughout this document, such as those related to soil exposures (Paustenbach, et al.,
1992a), exposures to contaminated fish (EPA, 1991a), exposures resulting from land disposal of
sludges from pulp and paper mills (EPA, 1990), just to name a few. Still, studies comparing and
ranking different sources and exposure patterns, and elaborations on ranges of source strengths
and exposures, are generally not available. Information in Volumes I and n of this assessment,
and procedures for source specific evaluations in Volume in, can provide others with
information and tools to begin such analysis.
8.2. A DISCUSSION OF UNCERTAINTY ISSUES ASSOCIATED WITH THE USE
OF ISCST3 FOR TRANSPORT AND DISPERSION OF STACK EMITTED
CONTAMINANTS
Air dispersion and deposition analysis was performed using the ISCST3 Model. The
model is intended to provide long term average air concentrations and wet and dry deposition
flux. This section discusses some of the uncertainties and critical parameters associated with the
general modeling approach used in ISCST3, and reviews some of the literature on model testing
and validation.
Atmospheric dispersion in ISCST3 is modeled using the common Gaussian plume model.
Downwind concentrations of the dioxin-like chemicals are calculated as a function of stack
height, the mass emission rate, the wind speed, and general atmospheric conditions. The
Gaussian model assumes that the emission concentrations predicted by the model will fit a
normal distribution. The principal assumptions in the Gaussian model are (Kapahi, 1991):
• The air concentration of the chemical at a fixed distance from the source is directly
proportional to the emission rate from the source;
• The air concentration of a given chemical is inversely proportional to the wind speed
corresponding to the effective height of release of the chemical into the air;
• The predicted ground-level concentration of the chemical approaches zero at large
distances from the initial point of release.
• The model is steady-state.
• The model assumes constant wind speed, wind direction, and atmospheric stability over
time and space for a given time period.
In general, the Gaussian plume model has been shown to predict annual average ambient
air concentrations of a chemical emission from an industrial source to within a factor of one-
order of magnitude of measured values, and in some cases, within a factor of 3 to 4-fold of field
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measurements (Cohrssen and Covello, 1989). This modeling error spans both sides of the
predicted concentration, that is, the actual concentration may be plus or minus this amount of the
predicted value. Even more assertive, an early position paper on the application of gaussian
short-term dispersion models claimed an approximate factor-of-two accuracy in the absence of
complicating factors (complex terrain, building wake effects) (AMS, 1978).
The most sensitive aspects to variability in modeled predictions of ambient air impacts,
if emissions are held constant, are stack height (height of the release), and terrain (flat verses
complex topography). To investigate modeling variability, EPA placed a prototype hypothetical
hazardous waste incinerator in flat terrain and elevated terrain in geographical areas around the
U.S. (EPA, 1991b; analysis conducted with the Industrial Source Complex, or ISC, model).
Then the stack height was varied at these particular locations. Numerous runs were made at
twelve specific sites to compare and contrast the influence of stack height and terrain on
predicted ambient air concentrations of various mass emission rates of specific inorganic
pollutants. A series of tables were developed from this sensitivity analysis from which the
numerical estimation of the variability as a function of stack height and terrain can be inferred.
When the hypothetical hazardous waste incinerator was modeled in flat terrain, e.g., topography
within a distance of 5 km is not above the height of the stack, and the stack height was varied
from 4 meters to 120 meters, the variability in the predicted ambient air concentration spanned
two orders of magnitude (100). The lower stack height resulted in a predicted ambient air
concentration that was 100 times greater than the concentration predicted using the tallest stack
height. When the hypothetical hazardous waste incinerator was located in complex terrain over
the same range of physical stack heights, the variability in estimated groundlevel concentration of
the subject pollutant spanned two orders of magnitude (100-fold). In the latter case the stack
height was computed as the terrain-adjusted stack height by subtracting from the physical stack
height the influence of terrain on plume rise. From the limited sensitivity analysis of hazardous
waste incinerators, it can be assumed that the predictions of spacial ground-level ambient air
concentrations of dioxin-like compounds could differ from values in Tables 3-17 and 3-18 by
two-orders of magnitude in consideration of changes in stack height or changes in terrain. For
example, Tables 3-17 and 3-18 show that the maximum annual average ambient air concentration
of 2,3,7,8-TCDD predicted near the hypothetical incinerator is approximately 10"11 |ag/m3 for the
stack height of 30.5 meters, and assuming flat terrain. If only the stack height is varied from 20
meters to 120 meters, and all other modeling parameters are held constant, then the predicted
ambient air concentration would be approximately 10 times greater and 10 times less than the
estimated concentration, respectively. The uncertainty is broader when considering the influence
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of topography on predictability of the ground-level concentrations from the model. If only terrain
elevation is varied at a distance of 5 km from the hypothetical incinerator from zero elevation to
30.5 meters, e.g., the height of the stack, then the predicted ambient air concentration of 2,3,7,8-
TCDD would be approximately ten times greater. The tables derived in the hazardous waste
incineration analysis have a limitation of elevation of terrain to the height of the stack.
The most uncertain aspect to the modeling is the estimation of dry and wet deposition
flux of dioxin-like compounds on the vicinity of a hypothetical incinerator. Contributing most to
this uncertainty seems to be the settling velocities and scavenging coefficients estimated for
specific particle size diameters (Cohrssen and Covello, 1989; Doran and Horst, 1985). Seinfeld
(1986) found that particles over 20 microns in diameter settle primarily by gravity, whereas
smaller particles deposit primarily by atmospheric turbulence and molecular diffusion.
Considerable, but non-quantifiable, uncertainty exists with respect to deposition velocities of
particles 0.1 to 1.0 microns in diameter (Seinfeld, 1986). The uncertainty is difficult to define.
The wide variation of predicted deposition velocities as a function of particle size, atmospheric
turbulence and terrain adds to this uncertainty (Sehmel, 1980). However, Gaussian plume
dispersion models have been field validated for their ability to spatially predict dry deposition
flux over some specified distance (Doran and Horst, 1985). In a series of field experiments
conducted by Pacific Northwest Laboratory (Doran and Horst, 1985), zinc sulfide was used as a
depositing tracer gas, and sulfur hexafluoride was used as a non-depositing tracer gas to compare
and contrast modeling results with field measurements of dry deposition and atmospheric
diffusion of the gases. The tracer was released from a height of 2 meters, and all releases were
made under relatively stable atmospheric conditions. Five sampling stations were located
downwind of the release from 100 to 3200 meters. The results of these experiments showed
good agreement with the predicted verses the measured deposition of the tracer ZnS. The overall
correlation coefficient between predicted and measured deposition concentration was found to be
0.82 (Doran and Horst, 1985), but the models marginally over-predicted deposition flux near the
source of release, and under-predicted deposition flux at 3200 meters.
Travis and Yambert (1991) have evaluated the uncertainty in modeling the dry deposition
flux of particulates using four standard Gaussian plume dispersion models. Since deposition flux
is dependent on deposition velocity for a given particle mass and diameter, comparisons were
made between model-generated deposition velocities and measured values found in the open
literature for particles ranging from 0.01 to 30 microns in diameter. It was found that measured
deposition velocities for a given particle size in the scientific literature exhibit variability
spanning roughly two orders of magnitude. The analysis of the mean predicted deposition
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velocities to mean measured values showed that most measured data exceeded the predicted data
for all four models. Moreover, the models underestimated the mean deposition velocities for
particles in the range of diameters from 0.05 to 1.0 microns.
Similar uncertainty probably exists with regard to scavenging of various diameter
particles by various intensity of rainfall. Seinfeld (1986) has calculated scavenging coefficients
in terms of the removal efficiency of particles of a given size by rain droplets having a given
momentum. Seinfeld (1986) found that the scavenging coefficient of a given particle diameter
corresponding to a given rainfall intensity can be calculated based on physical laws, but there is a
complete absence of research data to verify these calculations. Hence it is not possible to address
the accuracy nor uncertainty of the wet deposition flux estimated in Table 3-19.
There have been some limited validation work done with ISCST3 and its ISC
predecessors. Chapter 7 described a model validation exercise for air dispersion and
deposition/soil concentration modeling done for dioxins in the vicinity of a municipal solid waste
incinerator known to be emitting large amounts of dioxins. The predicted concentrations were
mostly within a factor of 10 of observations, higher or lower, for both air and soil. There was
evidence that the profile of dioxins in both the air and the soil were distinct from the profile of
dioxins being emitted from the incinerator. This observation suggests transformations in the
dioxin profile in either, or both, the air and soil environments. In clearly impacted ambient air
samples that were downwind of the incinerator during sampling events, for example, the
measured profile suggested a more predominance of lower chlorinated dioxins than was seen in
the stack emission. Two explanations were offered to explain this observation: the higher
chlorinated dioxins deposited much more so than the lower chlorinated dioxins, which lessened
their predominance in the profile and/or higher chlorinated dioxins dechlorinated to form lower
chlorinated dioxins. When testing air dispersion alone (no deposition, no atmospheric decay or
transformation of emitted dioxins), the air concentration profile perfectly matched the stack
emission profile, as it should, so neither of these possibilities could be tested. However, when
testing the deposition/soil concentration capabilities of ISCST3, evidence did strongly suggest
that the model was underpredicting the deposition rate of OCDD, at least. Even with this
possible finding, the disparity between the soil concentration profile and the stack emission
profile continued to suggest that transformations may be taking place in soils and/or the air which
were not captured in the model testing at this site. In general, the model was able to duplicate the
trend of elevations in both air and soil near the facility, to within a factor of 10 of these
elevations.
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Early ISC (the predecessor to ISCST3) model validation work was conducted by Bowers,
et al. (1981). They tested the gravitational dry particle deposition algorithms, new at that time,
and showed that the model predicted deposition rates generally within a factor of two of
measured depositions of glass microspheres of 50 to 200 pm measured in an experimental
setting. They also tested the capabilities of building wake effects using data from diffusion
experiments conducted at a Nuclear Power Station in which the tracer SF6 was released from the
reactor building main vent and the tracer Freon 12B2 was simultaneously released from three
vents on the adjacent turbine building. They then predicted concentrations of these tracers with
and without building wake effects, and found that the inclusion of building wake effects
improved the average correspondence between modeled and observed concentrations by almost a
factor of 2.
8.3. UNCERTAINTIES AND VARIABILITIES WITH CHEMICAL-SPECIFIC
MODEL PARAMETERS AND ASSUMPTIONS
This assessment assumed that levels of dioxin-like compounds in soil and sediment were
constant over the period of exposure, with two exceptions. One circumstance was when
contaminated soil eroded from one site and deposited on a site of exposure nearby - the soil
contamination source category. The other was when stack emitted particulates deposited onto a
site of exposure - the stack emission source category. In both these instances, it is assumed that
only a relatively thin layer of surface soil at the site of exposure would be impacted, and that this
thin layer is subject to dissipation processes - erosion, volatilization, possibly degradation. Data
in Young (1983) implied a soil half-life of 10 years for surficial 2,3,7,8-TCDD residues, although
the circumstances of the soil contamination were not analogous. Specifically, a 37 ha test area at
the site had received an estimated 2.6 kg of 2,3,7,8-TCDD over a two year period. Soil sampling
which occurred over 9 years from the last application suggested that less than 1 percent remained
at the test area. Although Young hypothesized that photodegradation at the time of application
was principally responsible for the dissipation of residues, other mechanisms of dissipation
including volatilization, erosion, and biological removal may also have contributed to the loss of
residues. Soil sampling over time after application implied a dissipation half-life of 10 years for
soil residues of 2,3,7,8-TCDD. Mclachlan, et al. (1996) reported on an analysis of soil taken
from experimental plots which had been amended with sewage sludge in 1968 and sampled in
1972, 76, 81, 85, and 90. These archived samples were analyzed for all 17 dioxin-like CDD/Fs,
and based on an analysis of results, McLachlan and coworkers concluded that half-lives were on
the order of 20 years, with dioxin removal from the plots being mainly physical removal
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processes (overland runoff, wind erosion). Furthermore, their results suggested that all
congeners had been removed at roughly the same rate, which is why they concluded that removal
processes were mainly physical and very little in-situ degradation appeared to be occurring.
Paustenbach, et al. (1992a) reviewed several reports of the soil dissipation of 2,3,7,8-TCDD,
including Young (1983), and concluded that the half-life of 2,3,7,8-TCDD residues below the
surface varied from 25-100 years. A half-life of 25 years (k = 0.0277 yr"1 ) was assumed to apply
to all dioxin-like compounds in this assessment.
Section 2.6.1, Chapter 2 in Volume II of this assessment, reviewed the literature on
degradation of dioxin-like compounds. As discussed, biological transformations as well as
chemical processes (oxidation, hydrolysis, and reduction) do not appear to result in substantial
degradation of these compounds. There is evidence of photolysis, particularly when dissolved in
solution and when organic solvents are present. Most of these data are specific to 2,3,7,8-TCDD.
Uncertainty is introduced into parameter assignment when information specific to one congener
is assumed to apply to all dioxin-like congeners. However, it is judged that there is no good data
available to assign different soil dissipation rates to different dioxin congeners in this assessment,
and McLachlan's (1996) data is judged to be reasonably strong to support an assumption that all
dioxin congeners dissipate with roughly the same half-life.
Dissipation of surficial residues could translate to lower soil-related exposures including
particulate inhalations, soil ingestion, and soil dermal contact. However, it is not clear that
reductions in exposure would, in fact, occur, particularly if the soil is contaminated below the
surface. Processes such as wind erosion, soil erosion, or volatilization originating from deeper in
the soil profile, could serve, in a sense, to replenish reservoirs at the soil surface. Depositions
back onto soils from other soils, or depositions from distant sources, also replenish soils. Given
very low rates of degradation (for all degradation processes except photolysis), the assumption of
no degradation for the soil contamination source category is reasonable with moderate, but
unquantifiable uncertainty.
In evaluating an assumption of no degradation, another issue to consider is the depletion
of the original source of contamination. For the stack emission and effluent discharge source
categories, the assumption is made that steady releases occur while the source is active.
Therefore, depletion of the original source is not an issue. For the soil contamination source
category, it is assumed that the reservoir of contaminant is constant throughout the duration of
exposure. If such a duration is assumed to be very long, then degradation or dissipation of soil
residues would be more critical than if the duration were relatively short. Uncertainties
associated with the duration of exposure are discussed in Section 8.4 below. Also, Section 6.4 in
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Chapter 6 evaluated the assumption of a constant soil concentration by estimating the time it
would take for a 15-cm reservoir of soil contamination to be depleted, using the dissipation
algorithms of this assessment. These algorithms include volatilization, soil erosion, and wind
erosion, with lesser releases due to biological uptake, and leaching and runoff. It was found that
it would take over 90 years to deplete a 15-cm reservoir, lending some credibility to a non-
degradation assumption if the exposure duration were in the range assumed for the demonstration
scenarios of this assessment, 30 years.
A critical contaminant parameter required for the procedures in this assessment is the
octanol water partition coefficient, Kow, although none of the fate and transport algorithms
directly require a Kow. One of the empirical biota transfer parameters is, however, a function of
Kow. This is the RCF, or Root Concentration Factor, which estimates the transfer of
contaminant from soil water to root. Log Kow estimates for dioxin-like compounds range from
6.00 to 8.5, with higher log Kow associated with higher chlorination. However, this is not a
certain parameter. Estimates in literature for 2,3,7,8-TCDD, for example, range from 6.15 to 8.5.
The uncertainty of the RCF is addressed in Chapter 7, Section 7.3.9, where experimental data on
the transfer of dioxins from soil to carrots was used in a validation exercise. It was found that the
RCF allowed for the reasonably accurate simulation of the transfer of dioxins to the carrot peel,
with the model able to predict peel concentrations within a factor of 2 for 15 of 20 observations,
and for the other five observations, predictions and observations differed by a factor of 5 or less.
Two biota transfer coefficients are used to estimate fish tissue concentrations based on
water body sediment concentrations: the Biota Sediment Accumulation Factor, BSAF, and the
Biota Suspended Solids Accumulation Factor, BSSAF. There are no empirical relationships
which estimate these as a function of the more common Kow for dioxin-like compounds.
Rather, values were assigned based only on experimental and field data. Needless to say, most of
the data available was for 2,3,7,8-TCDD, leaving large gaps for other compounds. Also, there is
no data available for estimating the BSSAF, a parameter proposed in EPA (1993) which was
used in the effluent discharge source category. The BSSAF was set equal to the BSAF for this
assessment. Field data including bottom sediment concentrations and concurrent fish
concentrations were used to determine values for BSAF. The limited field data available for
BSAF suggests values in the range of 0.03 to 0.30 for 2,3,7,8-TCDD, with higher values
approaching 1.00 indicated for bottom feeders (catfish, carp, etc.), and decreasing values as the
degree of chlorination increases - limited information suggests values in the 10"3 to 10"2 range for
hexa- through octa- CDDs and CDFs. EPA (1995) used available data to develop the
"bioequivalency factors", BEFs, or multipliers to the BSAF or BSSAF to assign values for
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congeners other than 2,3,7,8-TCDD, when data on only 2,3,7,8-TCDD is available. The BEF
concept and the BEFs are described further in Chapter 4. They were used to assign values for the
BSAF/BSSAF for other dioxin-like congeners assuming a BSAF/BSSAF of 0.09 for 2,3,7,8-
TCDD. Data on PCBs suggest that BSAFs are higher than those of CDDs and CDFs by an order
of magnitude and more, and that the trend with increasing degrees of chlorination is not the
same. The data indicates that BSAFs for PCBs increase from dichloro- through hexa- or perhaps
hepta-chloro PCBs, and decrease thereafter.
A bioconcentration factor, BCF, translates the average contaminant in the diet of the
cattle into a beef or milk fat concentration. Experimental rather than field data was available for
estimates of BCF for dioxin-like compounds. Farm animals were fed known quantities of these
compounds and their body tissues and milk were monitored over time to arrive at BCFs. Data
showed that the BCF decreased to below 1.0 as the degree of chlorination increased. An
experimental data set, including analysis of 16 of the 17 dioxin-like congeners, described in
McLachlan, et al (1990), was used to assign BCF values for this assessment. A more recent
study, by Fries, et al. (1999), developed BCFs for 14 of 17 congeners in a feeding experiment
where four cows were fed PCP-contaminated wood. Results showed a good agreement between
these BCFs and those developed from the data of McLachlan, et al. (1990), although the BCF for
2,3,7,8-TCDD was highest in this experiment at 7.1 as compared to the BCF of 5.76 developed
from McLachlan's data and used in this assessment. Limited data showed PCB BCFs to be the
same order of magnitude, although trend data for increasing degrees of chlorination was not
available.
Similar bioconcentration factors, also termed BCF in Chapter 4, were described for
chicken fat. Like the beef/milk fat BCF, they were developed from experimental data on
chickens and eggs (Stephens, et al., 1995). The transfer of vapor-phase dioxins from air to plant
is also modeled with a simple biotransfer factor, termed Bvpa, is also developed from field data.
Obviously, a degree of uncertainty is introduced when relying on these empirical
bioconcentration or biotransfer coefficients to estimate concentrations in fish, beef, milk,
chicken, eggs, and terrestrial vegetation. The variability in the data suggests up to an order of
magnitude range of variation may result from use of these parameters. All but one of these
factors (the RCF) were developed from field or experimental data on dioxin-like congeners or
homolog groups. This, by definition, will lend a degree of credibility to their assignment. Also,
a validation exercise described in Chapter 7 testing the air-to-beef algorithm is a test of two of
these biotransfer/bioconcentration factors, the Bvpa and the BCF, and both appear to be supported
by this exercise. It appears likely, therefore, that the actual variation in these
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biotransfer/bioconcentration factors, is less than an order of magnitude, perhaps less than a factor
of five.
Another important chemical-specific parameter that can be estimated from Kow or
estimated experimentally is the organic carbon partition coefficient, Koc. Koc describes the
steady state partitioning between soil or sediment organic carbon and water; it impacts the
volatilization flux from soils, and the partitioning between suspended sediment and water in the
water column. Koc is used to estimate in-situ partitioning using a fraction organic carbon in the
soil or sediment, OCsl, OCsed, and OCssed, as Koc*OCsl, etc. The resulting chemical-specific
parameter is termed the soil (or sediment) partition coefficient, Kds (or Kdsed, Kdssed). The
empirical equation used to estimate Koc from Kow in this assessment was derived by Karickhoff
(1979). This equation was chosen over others available (Lyman, 1982) because it was derived
from laboratory testing of 10 hydrophobic contaminants. Others available would have led to
lower estimates of Koc. The Koc for 2,3,7,8-TCDD estimated for this assessment using
Karickhoff s relationship was 3,980,000. Some data implies that this estimate itself may be low
for 2,3,7,8-TCDD. Studies reviewed in Section 2.4.5., Chapter 2 of Volume II of this
assessment, particularly those Jackson, et al. (1986) and Lodge (1989), indicate 2,3,7,8-TCDD
Koc estimates in the range of 20,000,000 to greater than 30,000,000.
Another contaminant parameter is the Henry's Constant. Volume U, Chapter 2, provides
the values of the Henry's Constants, H, for dioxin-like compounds, some of which were
estimated given vapor pressure and water solubility data. The CDD/F Henry's Constants were in
the 10"6 to 10"5 atm-m3/mol range, while coplanar PCBs were in the 10"5 to 10"4 range, with one
high value at 3xlO"3 atm-m3/mol.
Finally, the contaminant molecular diffusivity in air is required for estimates of
volatilization flux from soils. The molecular diffusivity in air is set at 0.05 cm2/sec for all
dioxin-like compounds. Molecular diffusivity is a property of both the chemical and the
medium. It represents the propensity of a chemical to move through a medium. It is recognized
to be largely a function of molecular weight. The values selected are evaluated as reasonable for
all dioxin-like compounds, since the molecular weight for these compounds are similar.
8.4. UNCERTAINTIES ASSOCIATED WITH EXPOSURE PATHWAYS
The purpose of this section is to qualitatively describe the uncertainties associated with
exposure estimates for the exposure pathways that are included in this methodology. The
principal focus is on the exposure parameters - the contact rates and fractions, exposure
durations, and so on. A brief summary is also presented on some of the findings pertaining to the
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fate, transport, and transfer algorithms used to estimate the exposure media concentrations. This
summary will highlight findings that have been included in other sections of this chapter, Chapter
7 on model comparisons and model validations, as well as a section in Chapter 6 on User
Considerations. Each section below includes a table summarizing key points of uncertainty.
Section 8.4.1 looks at three key exposure parameters which are common among all pathways -
lifetime, body weights, and exposure durations. Sections 8.4.2. to 8.4.11 are pathway-by-
pathway discussions.
8.4.1. Lifetime, Body Weights, and Exposure Durations
Values for lifetime of 70 years and adult body weight of 70 kg are traditionally used for
risk assessment purposes, although data in the Exposure Factors Handbook (EPA, 1997) suggest
that the current average body weights may be lower and the lifetime may be longer. The
deviations are small and more precise numbers would not change exposure estimates by a
meaningful amount. The uncertainty regarding body weight is reduced in the ingestion pathways
of fruit/vegetables and the terrestrial animal food products including beef, milk, chicken, and
eggs. This is because the consumption rates used in these pathways for the demonstration does
in this assessment are in units of g/kg/day and were derived from survey data which incorporated
the amount consumed with the individual body weight. Specifically, these rates originated from
the household portion of the National Food Consumption Survey conducted by USDA (USDA,
1992). Chapter 2 describes the use of this survey data in detail and Section 8.4.7 below
summarizes some of the uncertainties in using it. The assumed child body weight of 17 kg (for
ages 2-6) is well founded and not expected to introduce uncertainty into soil ingestion exposure
estimates.
Assumptions on exposure durations are the most uncertain of the three parameters
discussed here. A value of 9 years assumed for central exposure scenarios was the 50th
percentile of time living at one residence derived from census survey data (EPA, 1997). Such
mobility surveys typically ask respondents how much time they are living at one residence, so a
result such as this one will likely be an underestimate because respondents are likely to continue
to live at their residence beyond the time they answered the survey question. The estimate of 30
years for the average residence time of farming families (used to define high end exposure
scenarios) was also based on survey data which showed that the 90th percentile time spent in one
residence was 32.7 years. For the high end scenarios of this assessment, this 90th percentile is
justified based on the definition of high end. Also, however, it is supported based on a
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qualitative judgement that farming families may tend to live longer in one spot as compared to
non-farming families.
Exposure durations are also tied to assumptions about source strength over time.
Assuming 30 years of exposure to stack emissions, for example, assumes that the source of stack
emissions will be (or has been) in operation for this length of time with the same stack emission
controls in place. The same is noted for the effluent discharge source category. If the source is
contaminated soil, assumptions include whether or not the soil will be removed, the site will be
capped, and so on. Another consideration is the dissipation of soil residues. Section 8.3
discussed uncertainties with the assumption of non-degradation of dioxin-like compounds in soil
when the soil itself is contaminated. A 25-year dissipation half-life is applied to residues which
migrate to an exposure site to impact only a thin layer of surface soil. Specifically, a simple soil
mixing model incorporating the 25-year dissipation half-life is used to calculate steady state soil
concentrations of dioxin in a thin surface layer resulting from atmospheric depositing dioxins,
from the stack emission source, or from soil eroding from a nearby site of soil contamination. As
discussed above in Section 8.3., an assumption of non-degradation during periods of exposure in
the range of 30 years is reasonable, since degradation/dissipation pathways lead to very slow
decline of dioxin concentrations in soil.
Exposure estimates are linearly related to all three exposure parameters - increasing body
weight and lifetime decreases exposures in an inverse linear fashion, while increasing exposure
durations increase estimates in a direct linear fashion.
Uncertainties associated with body weight, lifetime, and exposure durations are
summarized in Table 8-1.
8.4.2. Soil Ingestion Exposure
This exposure is directly a function of the concentration of contaminants in surface soil
layers. For example Scenarios 1 and 2, demonstrating background conditions, soil
concentrations at the site of exposure were set at levels corresponding to an actual setting which
can described as, "background". For example Scenario 3, demonstrating the soil contamination
source category, erosion onto the site of exposure deposited residues into a thin, no-till, surface
layer of 2 cm, and a thicker, 20-cm, till layer of soil. Soil ingestion exposures were based on
concentrations in the 2-cm layer. In Scenarios 4 and 5 demonstrating the stack emission source
category, contaminated particles deposited onto the exposure site, also creating a till and a no-till
concentration. The no-till depth for this category was also 2 cm.
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Discussions on the methodology to estimate exposure site soil concentrations resulting
from erosion of contaminated soil from a nearby site are contained in Section 6.3.3.2, Chapter 6,
which was on sensitivity analysis and the impact of different parameter values on estimated
exposure site soil concentrations, and in Chapter 7, Section 7.3. discussing literature reports of
off-site impacts from soil contamination. While off-site impacts were noted in the literature, no
data could be found that was directly amenable to comparison with the scenarios of Chapter 5.
The closest site for which data was available was the Dow Site in Midland, Michigan. The ratio
of soil concentrations of 2,3,7,8-TCDDin areas described as "background" in the 600 ha site to
soil concentrations in the contaminated areas was 1/8 to 1A as much (depending on how the
contaminated area soil concentration was interpreted) as the ratio modeled in the off-site
demonstration scenario. This might imply that the model overpredicts off-site soil impacts,
except that the "background" areas in the Dow Site appear substantially further away than the
150 meters in the off-site demonstration scenario. Also, data was unavailable to determine the
erodibility of soil at the Dow Site. Had this and other site-specific information been available, a
more precise test of the off-site soil impact algorithms of this assessment may have been
possible. Still, a key finding in the sensitivity analysis exercises was that the erosion algorithms
may be overestimating off-site impacts. No information is available on estimating how much of
an overestimation may have resulted, and this finding is not a definite conclusion.
If, in fact, an overestimation is occurring, it could be due to a few different factors: 1) an
uncertain dissipation rate - increasing it could reduce soil concentrations, 2) assumed depth of
mixing for untilled situations - increasing it could also reduce soil concentrations, and 3) the
steady state simplification. These factors were examined in the sensitivity analyses conducted in
Chapter 6.
In contrast to the possible overprediction of soil concentrations for the soil contamination
source category, an exercise described in Chapter 7, Section 7.3.8 suggested that the stack
emission source category may be underpredicting soil concentrations. Measured air
concentration in an actual rural setting were used in a model validation exercise which attempted
to duplicate measured soil concentrations at that same setting. It was seen that modeled soil
concentrations were slightly lower than measured soil concentrations. Two possible causes for
this underprediction were offered: 1) the model does not account for deposition of vapor-phase
dioxins, either through direct deposition or by detritus production, and 2) the representative air
profile was derived from samples in March, April, and June, and the average may not have
represented typically higher wintertime air concentrations.
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In the stack emission source category and the soil contamination source category where
the site of exposure is distant from the site of contamination, the two key uncertain parameters
are the depth of mixing and the soil half-life for dioxins depositing onto the site of exposure.
The mixing depth is a theoretical parameter for which little data is available. The data of Brzuzy
and Kites (1995) on soil profiles of dioxins for undisturbed soils does show that dioxins migrate
below the surface, in some cases under sandy conditions, to depths greater than 30 cm. However,
their non-sandy soil profiles showed most of the dioxins within 5 cm of the surface, and
considering that their undisturbed soil cores reflect depositions of dioxins which were speculated
to have occurred 50 years or more, the assumption of 2 cm is felt to be reasonably justified.
Others have assumed depths of mixing of 1 cm for analogous applications. Evidence from
radioactive fallout suggests depths no deeper than 5 cm. Sensitivity analysis on the erosion
algorithms showed that assuming a depth of 1 cm instead of 2 cm would have increased soil
concentrations by a factor of 2.5, while decreasing the mixing depth to 10 cm decreases soil
concentrations by 60%. Very little data is available on dioxin soil half-lives, but the assumption
of a half-life of 25 years is within the range of 25-100 years hypothesized by Paustenbach, et al.
(1992a) for surface and buried residues based on their survey of the available literature. The
analysis by McLachlan, et al. (1996) on data on dioxin concentrations in a plot of soil amended
with sewage sludge over 20 years earlier showed half-lives consistently around 20 years for the
suite of dioxin congeners, and this is probably the best support for the use of a constant half-life
for all dioxin congeners.
Another issue is whether children should be assumed to be exposed to tilled soils - tilled
by home gardening, farming, etc. - or untilled soils. It is feasible that children would be exposed
to tilled soils in farming or home garden settings. If the soil was impacted by stack emission
depositions or erosion from a nearby site of soil contamination, then tilling would reduce soil
concentrations. However, it is more reasonable to assume that they generally play outside in
areas that are not mechanically tilled.
The estimated soil ingestion quantity is based on field measurements, using trace
elements, of soil ingested by relatively small groups of children over brief periods.
Methodological issues in these studies remain to be addressed. In particular, ingestion estimates
may have been lower if dietary intake of the trace elements was taken into account. Research is
underway to refine soil ingestion estimates obtained through trace element measurements. Given
the available data, EPA (1997) suggests that 100 mg/day is a reasonable central estimate for
children under 6 years of age, and that value is used in this assessment in the central scenarios.
Due to the behavior known as pica, some children are known to ingest high amounts of various
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non-food materials. Estimates of pica ingestion of soil by children have ranged as high as 5000
mg/day. The high end estimate of 600 mg/day is not characterized as pica. It was determined
from studies evaluated in EPA (1997) which showed upper percentile estimates ranging from 106
mg/day to 1,432 mg/day with an average of 587 mg/day for soil and dust ingestion.
Soil ingestion exposure estimates also depend on the duration of the period over which
children are assumed to ingest soil. Data on soil ingestion by age are not available, and the
estimate that significant ingestion occurs between ages 2 and 6 is broadly supportable on
behavioral grounds.
No measurement data are available on soil ingestion in infants (0-2 yrs. old) or in older
children or adults, and no ingestion is assumed for these groups. While some soil ingestion will
occur in these groups, e.g., through contact of soiled hands with food, it is plausible that such
ingestion is of a lesser degree than occurs in early childhood. If Hawley's (1985) estimate that an
adult ingests an average 60 mg/d of soil is used, after accounting for differences in exposure
duration (9-20 yrs versus 5 yr) and body weight (70 kg versus 17 kg), the adult soil ingestion
exposure is close to the estimated exposure for children (at 200 mg/d). The high end example
scenarios in Chapter 5 assumed that the exposed family was involved in farming operations. One
implication is that individuals on the farm would be working closely with the soil, which may
result in some soil or dust ingestion (dust ingestion is distinct from the particulate inhalation
exposure pathway). The other implication is that, should this be the case, they might be in
contact with tilled or otherwise well mixed soil, whose concentration could be as much as 10
times less than the no-till soil for which children are assumed to be exposed.
Considering these uncertainties, the soil ingestion exposure estimates presented for
children are plausible. Further consideration may be warranted for considering adult soil
ingestion, particularly in farming situations. Uncertainties associated with the soil ingestion
pathway are summarized in Table 8-2.
8.4.3. Soil Dermal Contact Pathway
Estimates of dermal exposure to soil rely largely on four factors unique to this pathway:
exposed skin area, soil adherence (also termed soil contact), frequency of soil contact and
fraction of contaminant absorbed. The uncertainty in these three terms are discussed below.
Before that discussion, a brief note is made on uncertainties associated with soil
concentrations. Discussions above on the soil ingestion pathway addressed uncertainties
associated with soil concentrations which result from migration of residues from a distant source
to the site of exposure. Distant sources in this assessment include off-site soil contamination and
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stack emissions. Discussions in the soil ingestion pathway section above pertain to this exposure
pathway and are not repeated here. However, there is one key difference in the soil dermal and
soil ingestion pathways. Soil ingestion exposures are assumed to occur only from surficial soil
layers and from untilled soils, which translates to the 2-cm mixing depth for both the "central"
(residential) and "high end" (farming properties) scenarios. Soil dermal contact, on the other
hand, is assumed to occur in association with both tilled and untilled soils . "Indoor" soil is
assumed to have concentrations equal to that of untilled soils, while "outdoor" dermal contact
events are assumed to occur in association with gardening or farming activities, where the
concentrations are the more dilute tilled concentrations.
The range of possible estimates of exposure via dermal contact is probably more a
function of variability in the population than uncertainty in the dermal contact methodology and
assignment of exposure parameters. Relatively accurate measurements have yielded a good data
base on total skin area. Thus the uncertainty in this factor is derived more from the assumptions
of how much of the total skin area is exposed. EPA (1992b) recommends approaching this issue
by determining the coverage of normal apparel in the exposed population and assuming exposure
is limited to the uncovered skin. As discussed in EPA (1992b), this assumption could lead to
underestimates of exposure since studies have shown that some exposure can occur under
clothing, especially in the case of vapors or fine particulates. Assignment of skin surface areas in
this assessment have assumed estimates for various combination of areas for hands, arms, and
legs. The extent to which individuals where short or long sleeve shirts and trousers is part of the
variability in skin surface area assignment.
The potential for soil contact and subsequent adherence probably varies little across the
population, but few actual measurements have been made. A wide range of from <0.002 to >20
mg/cm2-event has been identified in EPA (1997). The very high adherence rates were found for
the scenario described as, "kids-in-mud", and was from data on children playing by a lakeshore.
The lower range was found for an indoor Tae Kwon Do setting. Adherences for a day-care
setting ranged from 0.03 for arms and legs to 0.1 for hands. Outdoor adherences for gardeners
ranged from 0.005 for legs to 0.02 for arms to 0.2 for hands. The uncertainty in these estimates
reflect primarily the lack of measurement data rather than population variability. Site variability
is probably important as well since soil properties such as moisture content, clay content and
particle size distribution are likely to affect adherence.
Exposure frequency to soil reflects largely personal habits and thus the range in values for
this parameter is primarily based on population variability. Seasonal and climate conditions can
also affect this behavior introducing site variability as well. Indoor contact events were assumed
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to occur daily, gardening events were assumed to occur 100 times per year and farming events
350 times per year. These values were assigned based on judgement, and not any particular
studies.
The dermal absorption fraction of compounds varies widely across chemicals, whereas
skin properties that affect absorption, i.e. thickness and composition vary little across the
population. Thus the uncertainty in this factor is derived primarily from measurement error
rather than population variability. Soil properties, such as organic carbon content, can also affect
the extent of dermal absorption and thus create site variability as well. EPA (1992b) reports two
studies which measured dermal absorption of 2,3,7,8-TCDD from soil. Testing included human
skin in vitro, rat skin in vitro and rat skin in vivo. On the basis of these tests, a range of 0.1 -
3.0% was recommended in EPA (1992b). Dermal absorption testing, especially for soils, is a
relatively new field and many uncertainty issues are involved. These include extrapolation of
animal tests to humans, extrapolation of in vitro to in vivo conditions, and extrapolation of
experimental conditions to expected exposure conditions. Extrapolation of the tests on 2,3,7,8-
TCDD to the other dioxin like compounds (which have not been tested) introduces further
uncertainties. A dermal absorption fraction of 3.0% was adopted here for application to all the
dioxin like compounds. Based on the observed range of values for 2,3,7,8-TCDD this
assumption may lead to overestimates of a factor of 30. Considering all possible uncertainties,
under estimates are also possible, though judged less likely.
In summary, dermal exposure estimations rely on a number of parameters whose values
are not well established. The range of possible dermal contact estimations is judged to be mainly
a function of population variability, rather than parameter uncertainty. One parameter that is
uncertain is the absorption fraction. The value selected for this assessment. 0.03 (3% absorption)
is on the upper end of the range of suggested values, so its selection is likely to result in
overestimating, rather than underestimating, the exposure due to this pathway. Although it is
difficult to estimate the overall variability and uncertainty with this pathway, it is judged to be
plus or minus one to two orders of magnitude. A summary of the uncertainties associated with
the dermal absorption pathway is given in Table 8-3.
8.4.4 Water Ingestion
The strong sorptive tendencies of the dioxin-like compounds result in very low water
concentrations. Monitoring for CDD/Fs mostly have not found these compounds at a detection
limit around 1 pg/L (ppq), and when found, have generally been very near this concentration.
The one exception is an upstate New York community water system, where tetra through octa-
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CDFs were found at concentrations ranging from 2 pg/L (tetra) to over 200 pg/L (octa). The
surface water concentrations predicted by the algorithms of this assessment for all source
categories are 10"2 pg/L and lower, which is consistent with the sparse monitoring data.
Although there was no data found that could be directly applicable to the source categories, it
does not appear that the models estimating water concentrations will introduce significant
uncertainty into water ingestion exposure estimates.
The classically assumed water ingestion rate of 2.0 L/day was examined in EPA
(1997). The conclusion was that this estimate is more appropriately described as an upper
percentile consumption rate for adults, and recommended 1.4 L/day for use as an average. This
value was used for water ingestion in the central scenarios. EPA (1997) cautions that data on
consumption rate for sensitive subpopulations such as manual laborers are unavailable. As such,
the 1.4 L/day rate for individuals in farming families who work the field may be low. For this
reason, a 2.0 L/day was assumed in the high end, farming, scenarios.
The contact fraction is defined as the fraction of total contact with an exposure media that
is contact with contaminated media. For drinking water, this translates to the fraction of water
ingestion that comes from the contaminated water source. In the example scenarios, it was
assumed that the impacted water was a river which supplied water to the exposed individuals,
perhaps through a public water system. The contact fraction of 0.70 for central scenarios is based
on time use surveys which showed roughly this fraction of time spent in and around the home
environment on the average (EPA, 1997). The upper limit is, by definition, 1.00; this was felt
to be unrealistic even for high end scenarios. EPA (1997) recommends an upper end value for
time at residence at 0.90, and this value was used for the high end scenarios.
The uncertainties associated with the water ingestion pathway are summarized in Table 8-
4.
8.4.5. Fish Ingestion Exposure
Chapter 7, Sections 7.3.5 and 7.3.6 addressed the capabilities of the models of this
assessment to estimate fish tissue concentrations, by comparing measured fish concentrations
with modeled concentrations. In general, it was concluded that modeled fish tissue
concentrations in background settings are consistent with those found in the literature for similar
settings. Also, impacts of point source discharges into surface water appear to have been
appropriately modeled.
Chapter 7, Section 7.3.2. looked at a comprehensive data set developed and supplied by
the Connecticut Department of Environmental Protection which included soil concentrations,
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sediment concentrations of water bodies near where soil samples were taken, and fish
concentrations from the same water bodies. Data on 2,3,7,8-TCDD, 2,3,7,8-TCDF, 2,3,4,7,8-
PCDF, and total TEQ were examined. Soil concentrations of 2,3,7,8-TCDD were found to be in
the low ppt range, which has been described in various places in this document as a range for
"background" soil conditions. Sediment concentrations of the three congeners and total TEQ
were generally in range of 2-3 times higher than soil concentrations, which was consistent with
the demonstration of background conditions. This demonstration scenario had a basin-wide
2,3,7,8-TCDD soil concentration of 0.37 ppt, and the sediment concentration was estimated at
0.99 ppt. The Biota Sediment Accumulation Factor, BSAF, from this field data was estimated to
be 0.86 for 2,3,7,8-TCDD. This was higher than the assumed 0.09 in the demonstration
scenarios. Two explanations were offered for this difference. One was that the fish sampled
were bottom feeders, which would put them in more contact with contaminated sediments
compared to column feeders, and the 0.09 value was based on data from column feeders; higher
impact from contaminated sediments is expected from bottom feeders as compared to column
feeders. Two, the 0.86 may have been skewed from two (of seven) sites in the Connecticut data
which had high BSAFs at greater than 1 and 3. Although the soil sampling in this data set was
generally sparse, the result that bottom sediment concentrations exceeded surface soil
concentrations by 1.6-3.9 times generally supports the model's algorithms for estimating
sediment concentrations in areas with low basin-wide concentrations.
Chapter 7, Section 7.3.5 looked at fish concentrations in background areas and where
point source impacts to water bodies were identified. A principal source of information was
EPA's National Study of Chemical Residues in Fish (EPA, 1992a; abbreviated NSCRF). The
range offish tissue concentrations of 2,3,7,8-TCDD measured for (perhaps) background
conditions in this study, 0.56 - 1.02 ppt, were comparable to the fish tissue concentration
estimated assuming the low (perhaps) background soil concentration of 0.37 ppt soil
concentration, 0.2 ppt. It may not be appropriate, however, to make the same observation for the
source categories assuming higher soil concentrations as compared to measured concentrations.
In this case, the range of measured concentrations, 1.4 - 30.02 ppt, does not compare with the
modeled 0.3 ppt. It was noted that the soil contamination source category was demonstrated with
a setting that had four hectares of contaminated soil at 1 ppb surrounded by a watershed of
100,000 hectares with a 0.0 soil concentration, which may explain partly why the results did not
compare with the concentrations in the NSCRF that were taken near contaminated sites. Specific
field data were not available for more detailed analysis. In general, it would appear that the
magnitude of concentrations appears to have been captured for background situations.
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While the modeled PCDD/PCDF fish concentrations seem reasonably in line with
measured concentrations, this assessment may have underestimated concentrations of
2,3,3',4,4',5,5'-HPCB in the demonstration scenarios. Concentrations for fish in the Great Lakes
Region were in the tens to hundreds of ppb range, while this assessment derived estimates all
under 1 ppb. However, an examination of bottom sediment concentrations of PCBs in the
literature showed them to be roughly three orders of magnitude higher than estimated with the
algorithms of this assessment. This mirrors the difference in observed versus estimated fish
tissue concentrations. The Biota Sediment Accumulation Factors, BSAFs, for PCBs also was
noted to be variable, with values below 1.0 to values over 20.0 (see Chapter 4, Section 4.3.4.1).
The BSAF for the example PCB congener in this assessment was 2.0. Higher BSAFs would also
increase PCB concentrations estimated for fish.
Chapter 7, Section 7.3.6 evaluated the model for estimating fish tissue concentrations for
the effluent discharge source category, using data from the 104-mill study. Comparing model
predictions of fish tissue concentrations with observed concentrations, it was found that there
was generally an underprediction of observed fish tissue concentrations, although the average
predicted concentration 7 ppt cannot be considered significantly different then the observed
average concentration of 15 ppt. An important qualifier is that this exercise assumed that the
effluent discharges were the sole source of contaminants which may have impacted the water
bodies. Also, the maximum "observed" fish tissue concentration of 143 ppt was matched by a
predicted concentration of 89 ppt, which was also the maximum predicted concentration.
Finally, there was discussion that the BSSAF (biota suspended sediment accumulation factor)
assigned value of 0.09 for 2,3,7,8-TCDD, the same value used for the BSAF, might be low for
the effluent discharge source category. The justification for this hypothesis concerns the
differences between past and ongoing water body impacts, and the fact that the 0.09 value was
based on field data for a water body where impacts are speculated as principally occurring in the
past (see Section 7.2.3.6 for a further discussion of this issue). When the BSSAF was
"calibrated" to 0.20, the average predicted fish concentration of 15 ppt for 2,3,7,8-TCDD now
matched the observed average fish tissue concentration.
The model did not perform as well for pulp and paper mills discharging into the largest
receiving water bodies. The average fish tissue concentration observed for 21 fish was about 7
times higher than predicted concentration. No precise conclusion can be reached with this result,
although modeling lower fish concentrations in a large receiving water body than are measured
does not appear unexpected. Large water bodies are likely to be ones having multiple sources of
dioxin release in comparison with small water bodies. Therefore, the assumption that one or
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more proximate mills are solely responsible for observed fish concentrations is most likely to be
flawed for large water bodies.
In summary, the evaluations for model performance regarding fish tissue concentration
estimation seem to lend credibility to the approaches taken, despite the simplicity of the of
dilution models chosen. The sensitivity analyses exercises on the algorithms to estimate fish
tissue concentration discussed the variability and uncertainty with the parameters required for the
algorithms. Generally, the most sensitive input was the source strength characteristics - soil
concentrations, contaminant discharge rates in effluents, and so on. A single order of magnitude
or less range in predicted concentrations would result with singular changes in all other model
parameters.
An exposure parameter of paramount importance in estimating exposure to contaminated
fish is the fish ingestion rate. Available fish consumption surveys are discussed in EPA (1997).
They were divided into five subsets of surveys, one of which was titled, "freshwater recreational
anglers". Three surveys in this subset were deemed appropriate for generation of consumption
rates, and EPA (1997) recommended a mean and a 95th percentile consumption rates of
recreationally caught fish of 8 and 25 g/day, respectively. Another possible approach is
described in EPA (1989) and was used in a previous version of this dioxin reassessment
document (EPA, 1994). Briefly, this approach assumes a meal size and then determines, on a
site-specific basis, the number of meals an individual would consume from fish obtained from
the impacted water body. EPA (1994) assumed meal sizes of 150 g/meal, and 3 and 10
meals/year for the central and high end assumptions, respectively, which led to daily
consumption rates of 1.2 and 4.1 g/day. Assessors should also be cognizant of situations where
subsistence fishing can lead to much higher rates offish consumption. EPA (1997) summarizes
studies where subsistence patterns offish consumption can lead to consumption rates in the
hundreds of grams per day. Like other food consumption pathways, which have the highest
exposure estimates for dioxin-like compounds, obtaining site-specific information for fish
ingestion is critical for this pathway.
A summary of the uncertainties associated with the fish ingestion pathway is given in
Table 8-5.
8.4.6. Vapor and Particle Phase Inhalation Exposures
This section will address the uncertainty associated with vapor and particulate phase
inhalation exposures. Sources addressed in this assessment include stack emissions and
contaminated soils; this section will only address contaminated soils. The fate and transport of
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dioxin-like compounds from stack emissions to exposure sites, and the resulting air
concentrations, are discussed in Chapter 3.
The respiration rates of 13 and 20 m3/day used for inhalation exposures is based on data
described in EPA (1997). The contact fraction is 0.70 for central scenarios and 0.90 for high end
scenarios. Like the water ingestion contact fractions, these were based on time at home surveys.
The inhalation rate and contact fractions are not expected to introduce much uncertainty into
inhalation exposure estimates.
Another exposure parameter critical for the inhalation pathway is exposure durations,
which is 9 years for central and 30 years for high end exposures. The uncertainties associated
with this parameter in its use as an exposure parameter are discussed above in Section 8.4.1.
However, exposure duration is additionally critical for the inhalation pathway for the soil
contamination source category, as estimated volatilization flux is a function of the time during
which volatilization is occurring. Essentially, the model assumes that contamination is at the soil
surface at time zero, and over time, residues which volatilize originate from deeper in the profile
leading to lower volatilization fluxes after time, and also lower average volatilization flux as the
averaging time increases. The sensitivity analyses exercises in Chapter 6, Section 6.3.3.1.,
evaluated the sensitivity of air concentration predictions to changes in exposure duration. It was
shown that there is roughly a factor of four difference between concentrations predicted over one
year duration to a seventy year duration. Therefore, there is both a direct and an indirect impact
from changing the exposure duration in these procedures. The direct impact from changing
exposure duration is in the exposure equation - increasing the exposure duration increases the
exposure estimate. What is seen also with increases in exposure, however, is a decrease in the
estimated average air concentrations to which individuals are exposed. The impact in the
exposure estimates is more driven by having more years of exposure rather than being exposed to
a lower average air concentration, as expected.
Vapor-phase emissions from soils are estimated with a volatilization flux algorithm. The
procedures were developed in Hwang, et al. (1986). A near-field dispersion model estimates air
concentrations for the circumstance where the soil contamination is at the site of exposure.
Where the site of contamination is located distant from the site of exposure, the same
volatilization flux model is used, but exposure site concentrations for these sources are estimated
using a far-field dispersion model.
Sensitivity analyses in Chapter 6 showed that the air concentration varied roughly over an
order of magnitude with testing of key contaminant parameters, the organic carbon partition
coefficient, Koc, and the Henry's Constant, H. Air concentration predictions are also sensitive to
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other key parameters, including those associated with source strength (area of contamination,
concentration), geometry, (distance to receptor in off-site source category), and climate (average
windspeed). However, these might be expected to be known with a reasonable degree of
certainty for a site-specific application. If they are, it can be concluded that the most uncertainty
associated with the vapor phase algorithm is in the contaminant parameters, and it would appear
that a range of about an order of magnitude difference in predicted air concentrations might be
expected with different pairs of these parameters.
A model validation exercise described in Chapter 7, Section 7.3.8 tested the algorithms
modeling air concentrations above a soil of known concentrations. Using measured soil
concentrations at a site near Columbus, Ohio, and measured air concentrations at this same site, it
was shown that the model predictions of air concentrations were orders of magnitude lower than
measured air concentrations. While this suggests that the model is underpredicting the release of
dioxins from soil into the air and/or underpredicting the dispersion of released residues, it may be
true, on the other hand, that the measured air concentrations in the rural setting near Columbus
are the result of long range transport of air-borne dioxins from distant sources of release.
Another piece of evidence came in an examination of above ground plant: soil ratios as
generated by the models and found in experimental testing. The models underestimated these
ratios by 1 to 2 orders of magnitude as compared to the literature when vegetation in the field
studies described in the literature were grown in soils with concentrations in the ppt range, a
range typical of background settings. Two explanations were offered for this trend: the
experiments were impacted by sources of dioxins other than the soil in which the plant was
growing, and/or, the soil-to-air models may be underestimating air concentrations. Like the
model validation exercise described above, it is unclear which explanation dominates the
observed trend.
An alternate model for volatilization flux and an alternate model for air dispersion were
evaluated in Chapter 7, Section 7.2.4. It was found that the alternate volatilization model
predicted about a third as much volatilization as the Hwang model, but that the alternate
dispersion model predicted air concentrations that may be up to an order of magnitude higher
than the models predicted in this assessment.
There was no data on concentrations of air-borne contaminants in the particle phase only.
The procedures used to estimate the suspension of particles were developed from information on
highly erodible soils. As such, fluxes and hence concentrations may be higher than expected.
However, with no data to compare, this cannot be ascertained. It was seen that vapor phase
concentrations exceeded particle phase concentrations by over an order of magnitude. The
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sensitivity analysis exercises in Chapter 6 did indicate a two order of magnitude range in
estimated concentrations depending on the assumptions concerning wind erodibility of the soil.
Also, several issues of uncertainty concerning the suspension of contaminated particles and
relationship between air-borne vapor and particle phases were examined. It was noted that the
total reservoir of suspended contaminated particulates was likely to be underestimated because
the algorithm for wind erosion was developed only for inhalable size, < 10 |_im, particles, which
is appropriate for inhalation exposures but would lead to an underestimate of the depositions
onto vegetation, including fruits/vegetables for consumption and grass/feed for the beef/milk
bioconcentration algorithm. Vegetation concentrations might also be low because the impact of
rainsplash on transferring soil to the lower parts of vegetation was not considered.
A critical assumption made was that volatilized residues remained in the vapor phase and
did not sorb to airborne particles. This led to a dominance of vapor phase contaminants - 90%
and more of the total airborne reservoirs (vapor + particle phases) estimated for the on-site and
off-site soil source categories were in the vapor phase. Even though only three contaminants
were modeled for the soil source category, this trend would be repeated for essentially all the
dioxins (except not as much for the octa dioxins since the models would predict much less vapor
phase release than the other dioxins). Having much more vapor phase dioxins than particle phase
dioxins is inconsistent with the vapor/particle partitioning models used to partition ambient air
dioxins into vapor and particle phases, and also inconsistent with monitored vapor/particle
partitioning. For example, the vapor/particle partitioning model resulted in a prediction that 51%
of the total airborne 2,3,7,8-TCDD would exist in the vapor phase, not over 90%. For the other
dioxins, the particle phase is predicted to dominate the air concentrations. This suggests that the
soil models of this assessment are deficient in that they do not repartition soil emitted dioxins.
Specifically, a portion of the vapor-emitted dioxins are unlikely to remain as vapor, but are likely
to sorb to particles. Transferring portions of the vapor phase contaminants to the particulate
reservoir to get balances suggested by the vapor/particle partitioning models of this assessment
would not change total inhalation exposures, but would impact concentrations in above ground
vegetation. Currently and even with transfers such as these, vapor phase transfers dominate plant
concentrations. Because vapor phase reservoirs would be reduced after transferring a portion to
the particle phase, such transfers translate to reductions in plant concentrations, and for grass and
feed, subsequent reductions in beef and milk concentrations and exposure estimates.
Perhaps the most critical assumption which could be questioned is that airborne vapor
and particle phase contaminants at the site of exposure originate only from the site of
contamination when the site of contamination is distant from the site of exposure. Meanwhile,
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soils at the exposure site are impacted - concentrations in the air at the exposure site do not
consider possible fluxes from exposure site soils, or from soils between the contaminated and
exposure sites.
A test was conducted for this assumption using the demonstration scenario for the soil
contamination source category, which had a 4-ha site at 1 ppb 2,3,7,8-TCDD 150 meters from an
exposure site of the same size. The soil concentrations at the exposure site were 0.36 ppb for a
2-cm notill mixing depth and 0.06 ppb for a 20-cm tilled mixing depth. These concentrations
were then input as soil concentrations for the soil contamination source algorithms to determine
what air concentrations would result above the soil. For this test, the "near field" dispersion
algorithms described in Chapter 4 were used instead of the "far field" algorithms used in the
demonstration of that source category in Chapter 5. These near field exposure site air
concentrations, generated with a starting soil concentration of 0.36 ppb, were compared with
exposure site air concentrations generated when using the far field dispersion algorithms, starting
with the soil concentration of 1 ppb. It was found that on-site air concentrations with soil
concentrations at 0.36 ppb exceeded exposure site vapor and particle air concentrations estimated
for a 1 ppb contaminated site 150 meters away by a factor of about 5. When the same test was
run using a tilled concentration of 0.06 ppb, concentrations predicted using the near field
algorithms and this concentration were similar to the concentrations predicted using far field
algorithms and a starting concentration of 1 ppb.
Several uncertainties were discussed, but a lack of data and a complete understanding of
atmospheric processes for dioxin-like compounds precludes any final quantitative judgements on
uncertainties in the air concentration algorithms. Some of the uncertainties imply that procedures
and assumptions adopted overestimate pertinent environmental media, and others imply that such
media concentrations were underestimated. The assumption that air-borne reservoirs of
contaminant originate only at an off-site area of contamination and not from other soils should be
examined further.
A summary of the uncertainties associated with the vapor and particle inhalation routes is
given in Table 8-6.
8.4.7. Fruit and Vegetable Ingestion
Consumption rates of 1.49, 1.52, and 1.16 g/kg/day were derived in EPA (1997) from the
household portion of the National Food Consumption Survey (NFCS; USD A, 1992). Contact
fractions of 0.101 for fruits and 0.173 for vegetables were also obtained from an analysis of
NFCS data. Briefly, the household portion of the NFCS was a survey filled out by the head of a
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household and includes the amount of food product brought into the house for consumption. The
data includes the number, age, and weight of all household members, in addition to critical
questions concerning home production of foods. Further details on this survey and the use of the
data is described in Chapter 2.
Use of this portion of the NFCS has its benefits and drawbacks. One major benefit is that
it reduces uncertainty by the calculation of rates which include body weights. The earlier version
of the dioxin reassessment document (EPA, 1994) had the consumption of all food items in terms
of g/day. In this assessment, fish ingestion is still handled this way, but all other foods
considered (vegetables/fruits, terrestrial animal food products) more appropriately consider the
interaction between rate of consumption and body weight. Another major benefit is that it allows
one to estimate how much of a food product is consumed in a household which was produced by
the household, which is precisely what is desired for the demonstration scenarios of this
assessment. This estimation includes a reported consumption rate and also a contact fraction
ascertained from survey data. Another part of the NFCS was called the " 1-day individual
consumption survey". One cannot ascertain consumption rates for home-produced foods from
the 1-day survey. However, the individual survey does ascertain the consumption rate for foods
"as eaten" by the individual. In contrast, the household survey asked for total food product
brought into the house for consumption that week. That necessitates assumptions on the meal
size per individual in the household, and in addition to data on the weight of the household
individuals, EPA (1997) derived estimates of g/kg/day consumption rates, which were used in
this assessment. That also necessitates a consideration of the amount of the total food product
brought into the house which is not eaten by individuals in the house, since the "total food
product" is not a quantity analogous to, "as eaten". Reductions in this total would include losses
such as from cooking, discarding part of the food product, such as bones or uneaten portions, or
portions given to guests. This is one disadvantage to the household survey, in contrast to the "as
eaten" data from the 1-day consumption survey. In Chapter 2, reduction factors are described
and used in this assessment to describe cooking (weight reduction) and post cooking (bones, etc)
for beef and chicken, as well as other meats not considered in this assessment.
EPA (1997) also ascertained, from questions on specific fruits/vegetables from the
household survey, consumption rates for "exposed above ground vegetables/fruits" and "root
vegetables". Protected vegetables/fruits, as opposed to exposed, were defined as
vegetables/fruits which have outer protective coverings which are removed prior to consumption
such as peas or oranges. No root vegetables were considered to be protected although, of course,
it is common to consume some below ground vegetables such as carrots or potatoes after removal
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of the skin. Consumption rates for exposed fruits and vegetables are desired because the
evidence is fairly clear that dioxin-like compounds will not penetrate through thick skins which
are peeled prior to consumption. This assessment does consider, however, the peeling of skins
off exposed vegetables. This consideration is in the form of a "VG" parameter. The VG
parameter, which includes separate assignments for above ground vegetables/fruits, VGag, and for
below ground vegetables, VGbg, considers the following: evidence that little translocation from
the surface of bulky vegetation, below or above ground, to the inner portions of these vegetation,
and any additional consideration of the peeling of the skin (carrots, potatoes, e.g) prior to
consumption. In this assessment, values of 0.01 and 0.25 were assigned to VGag and VGbg,
respectively.
All these assumptions discussed: total consumption rates, protected or unprotected, above
or below ground, and fraction home grown, are probably reasonable for general assessment
purposes as long as exposures are to the broad categories of fruits or vegetables, and not for
individual fruits or vegetables. For a site specific assessment, there will likely be wide variability
on the types of produce grown at home, what percentage of that is unprotected, and so on.
Finally, and as is also true for beef and milk exposures, this assessment only considers the impact
of home-grown fruits and vegetables. In rural settings, it is plausible that a large percentage of an
individual's total fruit and vegetable intake comes from nearby and impacted sources, more than
the 10-20% assumed in this assessment. If all of the consumption of fruit and vegetables is from
local sources, and adjustments are made to correctly predict concentrations in local fruits and
vegetables, than contact fractions should be set at 1.0, and exposures could increase up to 10
times compared to the demonstration scenarios depending on concentration estimation.
Several issues of uncertainty pertinent to the estimation of concentrations in below and
above ground vegetation have been examined in other parts of this document and are not
repeated here. Key issues include: 1) the uncertainty associated with empirical parameters, VGag
and VGbg, 2) the assumption that residues which volatilize from contaminated soils remain in the
vapor phase and not partially partition into the vapor phase, 3) the possible underestimation of
total particle reservoirs of contaminant in the air resulting from wind erosion of contaminated
soils because the wind erosion algorithm only estimated suspension of inhalable size and not all
particulates, and also because the possible effect of rainsplash onto vegetables low to the ground
such as lettuce, was not considered, 4) for the stack emission source, uncertainties associated
with air dispersion and deposition modeling using the ISCST3 model as discussed earlier in
Section 8.2, and therefore the subsequent impacts of air-to-plant and soil-to-plant transfers, 5) for
the stack emission and off-site soil source categories, air borne concentrations in the vapor and
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particle phases at the exposure site are assumed to only originate at the source of contamination
(the off-site contaminated soil and stack emissions) and not on impacted soil at the exposure site
- considering additional fluxes from impacted soils other than exposure site soils could lead to up
to an order of magnitude higher concentrations in the vapor and particle phases, which in turn
affect above ground vegetation, and 6) also for the stack emission and off-site soil source
categories where garden soil concentrations are predicted and then used to predict concentrations
in underground vegetables, there are uncertainties for the soil concentration algorithm,
particularly in the assignment of half-life, mixing depths, and lack of consideration of detritus
production and vapor impacts to soils.
Quantitative judgements as the uncertainties associated with these issues are difficult to
make. An examination of experimental data in Chapter 7, Section 7.3.10, where most of the
vegetation were grown in well characterized conditions implied that the soil contamination
models may be underestimating concentrations in above ground vegetables. The evidence
examined was plant:soil ratios for experimental conditions versus what the models would
predict. This could be due to underestimation of air concentrations of dioxins originating from
soils, and there was some suggestion of that. However, it could also be due to the fact that the
residues affecting the plants in the experiments were not only from soil releases but from other
sources leading to air-borne residues. The models of this assessment only consider air
concentrations from the source in question. Therefore, it is hard to ascertain whether the models
underpredict, overpredict, or adequately predict above ground vegetation concentrations resulting
from soil contamination.
Chapter 7, Section 7.2.1 did look at three modeling approaches for the air-to-plant
pathway for leafy vegetation (grass, in particular), including the EPA model. That did section
did suggest that the EPA model led to reasonable matches between predictions and observations,
with just about a factor of two separating predictions and observations. Also, the air-to-beef
model exercise described in Chapter 7, Section 7.3.12 also included an examination of the
capability of the air-to-plant model. Although there wasn't a good data set for validation - i.e.,
measured air concentrations above measured grass concentrations, the examination in that
section did support the model's algorithms. In the same vein, it is noted that the vapor phase air-
to-leaf transfer algorithm was developed from actual field data. By definition, therefore, it would
appear that the air-to-plant modeling are going to predict reasonable plant concentrations. This
discussion is put forth only to suggest that the air-to-plant modeling would not be an issue for
uncertainty regarding the impact of contaminated soils on above ground plants.
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A summary of uncertainties associated with the fruit and vegetable ingestion exposure
pathway is provided in Table 8-7.
8.4.8. Ingestion of Terrestrial Animal Food Products Including Beef, Milk, Chicken, and
Eggs
The algorithms for the calculation of dioxin concentrations in all these animal food
products is the same: they are a function of the weighted average concentration in the diet of the
cattle (dairy or beef) and chicken, which is a function of the proportion of the diet in soil and
animal vegetation, and a bioconcentration factor. Therefore, previous sections on soil
contamination, soil transport algorithms, and plant concentration estimation, are relevant to
estimating terrestrial animal food concentrations.
The most critical and uncertain parameters in these algorithms are the bioconcentration
factors. The multiplication of the weighted average dietary concentrations of the chicken and
cattle by the bioconcentration factors yields a product fat concentration (beef fat, chicken meat
fat, milk fat, and egg fat). A set of bioconcentration factors were developed from laboratory
feeding experiments for chicken meat, specifically from data on chicken thighs, and a separate
set from eggs. There is uncertainty in applying these laboratory derived BCFs to field situations.
Data is being developed by these same researchers from chickens which are raised in the field.
One purpose of these additional experiments is to verify the laboratory derived BCFs (M.
Petreas, Department of Toxic Substances Control, California EPA, personal communication).
The bioconcentration factors used for calculation of beef and milk fat were less certain. They
were derived from one experiment on one cow and on milk. Besides the sparsity of data, there is
obviously uncertainty in applying bioconcentration factors developed from milk fat to beef fat.
However, researchers have noted that the dioxin concentrations in beef and milk fat tend to be
similar, and this they attribute to the fact that most cattle are slaughtered within 2 years of life
while they are still growing. Therefore, the body fat pool is expanding which provides dilution to
dioxins taken in by the beef cattle, and as a result, body fat concentrations are found to be similar
to milk fat concentrations.
What also strengthens the use of the milk fat BCF to beef fat is the air-to-beef model
validation exercise described in Chapter 7, Section 7.3.12. That section described a validation
exercise where air concentrations of dioxin-like compounds were routed through the food chain
model to estimate concentrations in beef. Generally, that section showed that an air
concentration of 0.019 pg I-TEQ/m3, speculated to be an appropriate air concentration for rural
environments where cattle are raised for beef, translates to a beef fat I-TEQ concentration of 0.61
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ppt, using the models and parameters of this assessment. The observed beef fat concentration of
0.89 ppt I-TEQ (assuming non-detects were equal to !/2 detection limit) was the average from a
national, statistically design, monitoring study of dioxins in beef back fat conducted jointly by
EPA and USDA. Besides a very reasonable match between observed and predicted I-TEQ beef
fat concentration, Section 7.3.12 also describes a reasonable match in the concentrations of the
individual congeners.
Other than the critical bioconcentration factors, there is uncertainty with the soil
bioavailability factor, Bs, and the parameters describing the chicken and cattle diet which include
dietary fractions in soil, grass, and feed (the sum of the three adding to 1.00). The Bs was
assigned a value of 0.65 for the beef and chicken algorithms, and reflects an assumption that
dioxins are less bioavailable to the animals when the vehicles are soil rather than vegetative
feeds. This is a critical assumption for chickens, particularly, since the algorithm for free range
chicken impact assumes 10% of the diet in soil, and no exposure through their other diet. This
was based on analysis of the chicken feed showing non-detects for dioxins at low detection limits
done by the researchers who developed the BCFs, who also developed the rationale for the 10%
soil assumption (Stephens, et al., 1995b). The beef cattle diet differs from the dairy cattle diet in
that the beef cattle diet is dominated by leafy vegetation (i.e., pasture grass) and partially
protected vegetation (a combination category which would include barn feeds such as hay or
silage), with 8% in soil. The dairy cattle diet is assumed to be dominated by grains, which are
assumed to be protected and residue-free. Only 10% of the dairy cattle diet is assumed to
originate from soil (4%) and leafy vegetation (6%).
Section 6.2.3., Chapter 6, described the results of sensitivity analysis of these parameters
applied to the beef and milk algorithms. It was shown that there is a small range of possible
values for Bs and a small impact on results, for beef and milk at least. The impact, as noted
above, would be greater for chickens. Data indicates that range of values for BCF for 2,3,7,8-
TCDD is 1 to 10, with a concurrent order of magnitude difference between the upper and lower
values. The parameters describing cattle exposure to soils and vegetation at the site are also
critical, with up to an order of magnitude difference in concentrations for the example exposure
situations examined in Section 6.2.3. It is expected that cattle exposure assumptions can be
reasonably described for a specific site. Therefore, the most uncertainty in the bioconcentration
algorithm itself lies with the bioconcentration factor, BCF.
Besides the air-to-beef model validation exercise, there was one other literature
comparison that was made was comparing beef fat: soil and milk fat: soil concentration ratios
derived for PBBs with those estimated for 2,3,7,8-TCDD in the soil contamination demonstration
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scenario. Such a comparison is thought to be valid since PBBs are similar in fate and
bioconcentration tendencies to the dioxin-like compounds. The field data was from an
experiment where the cattle were raised in soils very high in PBB concentration. This provided
some evaluation of the beef bioconcentration algorithm as applied to soil contamination. In this
comparison, differences in beef and milk bioconcentration tendencies appear to be captured.
Fries (1985) found body fatsoil PPB and milk fatsoil PBB concentration ratios for dairy heifers
to range from 0.10 to 0.37, and from 0.02 and 0.06, respectively. For body fat of beef cows,
these ratios were 0.27 and 0.39. Analogous ratios were derived for the contaminated soil
scenario, and for beef and milk fat. For the contaminated soil demonstration scenario, Scenarios
3, beef fatsoil and milk fatsoil ratios were 0.15 and 0.08, respectively. These appear a bit lower
than the PBB ratios derived by Fries (1985). The interpretation of this result was that, again here
was some evidence that models may be underestimating the impacts of soil contamination to air,
and hence air to plants and plants to animals.
Chapter 7, Section 7.2.6 evaluated other beef and milk bioconcentration models; none
were found for chickens. It was found that most efforts are quite similar to the model of this
assessment, with simple mathematical transformations. Other efforts had considered cattle
inhalation exposures and cattle ingestion of impacted water, and found them to be of minimal
importance in estimating beef and milk concentrations. They were not considered in this
assessment. Two efforts, that of Stevens and Gerbec (1988) and Fries and Paustenbach (1990),
evaluated the practice of placing beef cattle on a grain-only diet for fattening prior to slaughter.
Both assumed that the reduction in beef concentrations could be modeled as a first-order process
with a half-life of around 115 days. With grain only diet periods of 120-130 days, they showed
beef concentrations to be reduced by about 50%. The models of this assessment allow for the
incorporation of an empirical reduction factor to account for a fattening program prior to
slaughter. In the demonstration scenarios, it was assumed that the beef cattle slaughtered by the
farmer for his home use were not fattened, and a value of 1.00 was assumed. For the air-to-beef
model validation exercise, however, a value of 0.50 was applied, as suggested by these two
research efforts.
The air-to-soil algorithms of the stack emission source category, and the soil-to-air
algorithms of the soil contamination source categories have both been highlighted as algorithms
which may have uncertainties. These uncertainties are detailed in Section 8.4.7. Generally, it
was found that the air-to-soil algorithms may be slightly underestimating soil concentrations,
while the soil-to-air algorithms may be underestimating air concentrations by an order of
magnitude (although this speculation may not even be warranted, given that appropriate
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experiments were not available to test the soil-to-air models). As a result, an examination of
model trends show a key dichotomy in the way the stack emission source category performed as
compared to the soil contamination source categories. Specifically, soil alone accounted for
about 90% of the milk and beef impacts for the soil source category, whereas soil accounted for
only about 5% of the milk and beef impacts for the stack emission source category. Refinements
to the model algorithms or the model parameters which would increase air concentrations
resulting from soils, and increase soil concentrations resulting from depositions would narrow
this gap.
Data on rates of consumption of these food products, as well as the contact fractions used
in the demonstration scenarios, were from the household component of the National Food
Consumption Survey conducted by the USDA (USDA, 1992). A review of the uncertainties
inherent in the use of this data is included in Section 8.4.7 above on fruit and vegetable ingestion,
and will not be repeated here. One additional factor considered for meats of the terrestrial food
pathways is the pre- and post-cooking losses, including factors such as weight loss by cooking,
weight of bones, and so on. Based on data on such losses, consumption are reduced by about
one-half based on these considerations.
A summary of uncertainties associated with the terrestrial animal food pathways is given
in Table 8-8.
8.5. USE OF PROBABILISTIC TECHNIQUES FOR ASSESSING EXPOSURE TO
DIOXIN-LIKE COMPOUNDS
The purpose of this discussion is to 1) briefly discuss how probabilistic techniques, such
as Monte Carlo or Latin Hypercube simulations, work and could be applied in exposure
assessments and 2) summarize recent efforts by five investigators to apply probabilistic
procedures to assessments involving dioxin-like compounds.
Basically, Monte Carlo and Latin Hypercube assessments are generic statistical methods
which generate a distribution for an output of a mathematical model using the distributions of the
input variables. Computer simulations are used to repeatedly generate outputs based on
parameter inputs, where values for parameters are selected from their distributions. The outputs
are compiled and expressed as a frequency distribution. In the context of exposure assessment,
for example, a Monte Carlo application could involve developing distributions for each of the
parameters in the exposure equation and generating a distribution showing how the exposure
levels vary in the exposed population. The final distribution can be interpreted as the
probabilities of one individual (randomly selected from the exposed population) experiencing
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various exposures. Since exposure levels are not only a function of the exposure parameters but
also of the concentration in exposure media, another application of the Monte Carlo method
would be to estimate the distribution of exposure media concentrations using mathematical
models for fate and transport.
Probabilistic techniques can be a powerful tool for expressing variability and evaluating
scenarios in exposure assessments. However, their use requires detailed input data which are
frequently unavailable. Although the procedure may make an analysis look more elegant, it may
actually yield misleading results if based on poor data. Accordingly, exposure assessors should
be very cautious when trying to apply Monte Carlo techniques or interpreting the results.
Generally, Monte Carlo procedures should be applied only when credible distribution
data are available for most of the key variables. Distribution data refers to empirical information
on the statistical variation of the variable that is relevant to the site assessed. Usually this data
should be obtained from surveys conducted at the site of interest. However, data on human
behavioral characteristics could be obtained from survey information based on populations
distant from the site, if comparability can be established.
Paustenbach et. al. (1992b) used Monte Carlo procedures to develop soil cleanup levels
for 2,3,7,8-TCDD at residential and industrial sites. The following exposure pathways were
included: dermal contact, soil ingestion, dust inhalation and fish ingestion. For each parameter a
range of values was identified (on the basis of reported values in the literature) and a uniform
distribution assumed. These assumptions are summarized in Table 8-9. For the residential
scenario, the soil level corresponding to the 50th percentile (defined as 50% of the population
being exposed below a risk of 10"5) was 17 ppb and the 95th percentile was 7 ppb. For the
industrial scenario (outdoors), the soil level corresponding to the 50th percentile was 160 ppb
and the 95th percentile was 50 ppb.
Anderson et. al. (1992) used Monte Carlo procedures to describe the distribution of
exposures to 2,3,7,8-TCDD occurring in various U.S. population segments as a result of
ingesting fish caught near pulp and paper mills. The populations considered were all U.S.
residents, all sport fishermen, U.S. residents living near (within 50 km) mills, and sport
fishermen living near mills. The distributions for the various parameters were derived by either
fitting idealized curves to empirical data or using personal judgement. These distributions are
summarized in Table 8-10. The distribution of 2,3,7,8-TCDD concentrations in fish was derived
from data collected in EPA's National Study of Chemical as exposure parameters. Distributions
were developed for input factors and Monte Carlo Residues in Fish (EPA, 1992a). The
following 50th and 95th percentile risks were estimated (using EPA cancer potency values):
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all US residents - 1 x 10'9 & 3 x 10'7
near mill residents - 4 x 10'8 & 2 x 10'6
all sportfishermen - 2 x 10'8 & 3 x 10'6
near mill sportfishermen - 6 x 10"7 & 2 x 10"5
McKone and Ryan (1989) developed an exposure assessment procedure based on simple
steady state transfer factors called PEFs or pathway exposure factors. These factors were applied
to two paths: air/plant/food and soil/plant/food. This is an example of Monte Carlo techniques
being applied to estimate exposure media concentrations as well as describe the variability in the
distribution of exposure behaviors such as ingestion rates. The procedure was demonstrated
using 2,3,7,8-TCDD and four pathways: ingestion of fruit/vegetables, grains, meat and dairy
products. The distributions used for the various input parameters are summarized in Table 8-11.
Two recent assessments (Cullen, 1995; Keenen, et al., 1995) looked at the modeling of
the impacts of dioxin-like compounds on indirect pathways from combustor emissions. Cullen
(1995) looked the exposure to 2,3,7,8-TCDD through consumption of produce grown near a
municipal solid waste incinerator. She included several parameters used to model the
concentration of 2,3,7,8-TCDD in vegetables, and through a decomposition analysis, attempted
to evaluate which parameters are most uncertain with regard to the modeling of vegetation
concentration. Table 8-12 looks at the various parameters which Cullen (1995) evaluated to
model vegetation concentrations.
Keenen, et al. (1995) evaluated another indirect pathway for 2,3,7,8-TCDD, also from an
incinerator. The pathway they evaluated was the beef ingestion pathway, and the modeled
incinerator type was a hazardous waste incinerator. They conducted a two-dimensional Monte
Carlo exercise which separately characterized uncertainty and variation using a nested loop
approach. They characterized the modeling of beef concentrations, including the modeling of the
transfer of 2,3,7,8-TCDD from air to plant to animal, as uncertain, and characterized various
exposure related quantities as variable. The "variable" considerations included locations of
farms in relation to the incinerator (which was important because the air dispersion model
predicted different concentrations and depositions of dioxins at sites of exposure), the
individuals' body weights, their beef consumption rates, and their exposure durations. In the
nested approach, a beef concentration was estimated using the food chain model and parameter
selections from probability density functions of the uncertain parameters. Then, a second nested
procedure modeled a distribution of dose rates associated with the uncertainty calculation of the
beef concentration. They results of their analysis suggest that exposures to 2,3,7,8-TCDD via
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consumption of beef produced near a hazardous waste incinerator could have a total uncertainty
spanning three orders of magnitude, and that the uncertainty was dominated by interindividual
variability.
The five articles discussed above differ widely in how they have applied Monte Carlo
methods, particularly in the selection of input parameter distributions. In some cases, it appears
that uniform distributions were assumed due to the lack of data needed to support more complex
distributions. The central values in these ranges probably occur more often than those near the
ends, so the uniform distribution assumption probably underestimates the occurrence of central
values and overestimates the occurrence of values near the ends of the distribution. Clearly more
data are needed to better support input parameter distributions.
Also, the benefit of conducting Monte Carlo or other numerical methods to evaluate the
uncertainty of model predictions of exposure media concentrations that result from a source of
contamination is unclear. If attempting such an exercise, assessors must be aware of the
following: 1) the relationship between contaminant fate parameters which are included in the
same modeling exercise - high log Kow is associated with lower bioconcentration, lower
volatility, etc.; and 2) the certainty in the range of parameters reported upon which a distribution
is to be based - old literature, different and/or inappropriate experimental conditions, and so on.
The authors of this assessment are of the opinion that the following exercises are far preferable in
understanding and using fate and transport models for dioxins: 1) gaining confidence in a single
set of dioxin fate parameters through model validation exercises, 2) checking the "validity" of
predicted exposure media concentrations by comparing them with existing known
concentrations, such as background concentrations or concentrations found in known settings of
contamination, as a regular part of any fate modeling exercise, 3) understanding what optional
models are available and, when possible, seeing if they result in a substantially different exposure
media prediction, 4) identifying parameters of most uncertainty, and then determining how final
predicted exposure media concentrations could vary as a result of varying that single parameters
(as in the sensitivity analyses exercises in Chapter 6), and 5) compiling field data to assign and
check on the all-important biotransfer/bioaccumulation parameters for the models of this
assessment. These are exactly the exercises that have been undertaken in support of all the fate
and transport models promoted in this document.
The five articles are just a small set of the growing body of literature on the topic of
applying Monte Carlo methods to exposure and risk assessments. For example, the application
of Monte Carlo methods to problems involving contaminated groundwater and related exposure
pathways such as ingestion, indoor air inhalation and dermal contact with water has been
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examined (McKone and Bogen, 1991). Although this work does not deal specifically with
dioxin, it may be informative to readers generally interested in Monte Carlo procedures.
Similarly, Paustenbach has published additional articles dealing with the application of Monte
Carlo methods to exposure problems involving other chemicals (Paustenbach et al. 1991;
Paustenbach, et al., 1992). Burmaster has also published numerous articles on this topic which
may be of general interest to readers (ie. Burmaster and Stackelberg, 1991).
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Cohrssen, J.; Covello, V. (1989) Risk analysis: A guide to principles and methods for analyzing
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Fries, G.F. (1985) Bioavailability of soil-borne polybrominated biphenyls ingested by farm
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Fries, G.F.; Paustenbach, D.J. (1990) Evaluation of potential transmission of 2,3,7,8-
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Fries, G.F.; Paustenbach, D.J.; Mather, D.B.; Luksemburg, WJ. (1999) A congener specific
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Hawley, J.K. (1985) Assessment of health risk from exposure to contaminated soil. Risk
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Hwang, S.T.; Falco, J.W.; Nauman, C.H. (1986) Development of Advisory Levels for
Polychlorinated Biphenyls (PCBs) Cleanup. Exposure Assessment Group, Office of
Research and Development, U.S. Environmental Protection Agency. EPA/600/6-86/002.
Jackson, D.R.; Roulier, M.H.; Grotta, H.M.; Rust, S.W.; Warner, J.S. (1986) Solubility of
2,3,7,8-TCDD in Contaminated Soils, pp. 185-200 in Rappe, C., G. Choudhary, and L.H.
Keith, eds., Chlorinated Dioxins and Dibenzofurans in Perspective. Lewis Publishers,
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Kapahi, R. (1991) Modeling the dispersion of toxic air pollutants emitted from municipal solid
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Keenan, R.E.; Price, P.S.; Ebert, E.S.; Su, S.; Harrington, J.R. (1995b) Uncertainty and
variation in indirect exposure assessments: an analysis of exposure to TCDD from a beef
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Dioxins and Trelated Compounds. Held August 21-25, 1995b, in Edmonton, Canada.
Abstract in Organohalogen Compounds, Volume 26:45-50.
Lodge, K.B. (1989) Solubility studies using a generator column for 2,3,7,8-tetrachlorodibenzo-
p-dioxin. Chemosphere 18:933-940.
Lyman, W.J.; Reehl, W.F.; Rosenblatt, D.H. (1982) Handbook of Chemical Property Estimation
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McKone, T.E.; Ryan, P.B. (1989) Human exposures to chemicals through food chains: an
uncertainty analysis. Environmental Science and Technology 23: 1154-1163.
McKone,T.E; Bogen, K. (1991) Predicting the uncertainties in risk assessment. Environmental
Science and Technology 25(10):1675-1681.
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McLachlan, M.S.; Sewart, A.P.; Bacon, J.R.; Jones, K.C. (1996) Persistence of PCDD/Fs in a
sludge-amended soil. Environmental Science and Technology 30:2567-2571.
McLachlan, M.S.; Thoma, H.; Reissinger, M.; Hutzinger, O. (1990) PCDD/F in an Agricultural
Food Chain. Part 1: PCDD/F Mass Balance of a Lactating Cow. Chemosphere 20:1013-
1020.
Paustenbach, D.; Wenning, R.; Lau, V.; Harrington, N.; Rennix, D.; Parsons, A. (1992a) Recent
developments on hazards posed by 2378-TCDD in soil: Implications for setting risk-
based cleanup goals at residential and industrial sites. Journal of Toxicology and
Environmental Health, 36:103-149.
Paustenbach, D.J.; Price, P.; Ollison, W.; Blank, C.; Jernigan, J.; Bass, R.; Peterson, H. (1992b)
Reevaluation of benzene exposure for the pliofilm (rubberworker) cohort. Journal of
Toxicology and Environmental Health, 36:177-231.
Paustenbach, D.J.; Meyer, D.; Sheenan, P.; Lau, V. (1991) An assessment and quantitative
uncertainty anlaysis of the health risk to workers exposed to chromiumm contaminated
soils. Journal of Toxicology and Industrial Health, 7(3):159-196.
Sehmel, G.A. (1980) Paritcle and gas dry deposition. A review. Atmospheric Environment
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Seinfeld, J.H. (1986) Atmospheric chemistry and physics of air pollution. New York, NY., John
Wiley and Sons.
Stephens, R.D.; Petreas, M.X.; Haward, D.G. (1995) Biotransfer and bioaccumulation of
dioxins and furans from soil: chickens as a model for grazing animals. Science of the
Total Environment 175:253-273.
Stevens, J.B.; Gerbec, E.N. (1988) Dioxin in the agricultural food chain. Risk analysis 8(3):
329-335.
Travis, C.; Yambert, M. (1991) Accounting for dry depostion in incinerator risk assessments.
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U.S. Department of Agriculture (1992) Agricultural Statistics 1992. U.S. Government Printing
Office, Washington, D.C. 20402-9328. ISBN 0-16-041621-3.
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U.S. Environmental Protection Agency (1990) Assessment of Risks from Exposure of Humans,
Terrestrial and Avian Wildlife, and Aquatic Life to Dioxins and Furans from Disposal
and Use of Sludge from Bleached Kraft and Sulfite Pulp and Paper Mills. Office of
Toxic Substances and Office of Solid Waste, EPA 560/5-90-013. July, 1990.
U.S. Environmental Protection Agency (1991a) A Methodology for Estimating Population
Exposures from the Consumption of Chemically Contaminated Fish. Prepared by Tetra
Tech, Inc., Fairfax VA. for Office of Policy, Planning, and Evaluation and Office of
Research and Development, US EPA. EPA/600/9-91/017.
U.S. Environmental Protection Agency (1991b) Burning of hazardous waste in boilers and
industrial furnaces, Final Rule. Federal Register, volume 56, number 35. U.S.
Government Printing Office, Wash. DC., February 21, 1991, pp 7134 - 7240.
U.S. Environmental Protection Agency (1992a) National Study of Chemical Residues in Fish.
Volumes I and II. Office of Science and Technology EPA 823-R-92-008a & 008b.
September, 1992.
U.S. Environmental Protection Agency (1992b) Dermal Exposure Assessment: Principals and
Applications. Exposure Assessment Group, Office of Health and Environmental
Assessment, Office of Research and Development, US EPA. EPA/600/8-91/01 IB.
U.S. Environmental Protection Agency (1993) Interim Report on Data and Methods for
Assessment of 2,3,7,8-Tetrachlorodibenzo-p-dioxin Risks to Aquatic Life and
Associated Wildlife. Office of Research and Development, Environmental Research
Laboratory at Duluth, MN. EPA/600/R-93/055. March, 1993.
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Compounds. Volume I. Executive Summary. Volume II. Properties, Sources,
Occurrence and Background Exposures. Volume in. Site-Specific Assessment
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Compounds, Eds. Tucker, R.E., A.L. Young, A.P. Gray, Plenum Press, New York, NY.
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Table 8-1. Uncertainties associated with the lifetime, body weight, and exposure duration
parameters.
Assumption/
Method
Lifetime
Body weight
Exposure duration
Approach
70yrs
70kg
9 and 30 years
Rationale
Standard EPA assumption.
Standard EPA assumption. Not
needed for food pathways of
fruit/vegetables, beef, milk,
chicken and eggs because
consumption rates are units of
g/kg/day and hence incorporate
body weight
Assumptions for central and
high end exposure scenarios.
Estimates are 50th and 90th
percentile mobility survey
results; higher estimate also
justified based on the
assumption that rural farming
families live in one location
longer than non-farming families
in rural settings.
Uncertainty
Actuary data indicate that
lifetime may be increasing
Not much uncertainty.
Current data suggests
average body weights are
lower and are different for
men or women - averages
above 60 kg for both.
Can vary for site-specific
applications. Source
strength dissipation not a
consideration for effluent
discharge or stack
emission sources assuming
discharges/emissions
continue for duration of
exposure. However,
source strength dissipation
may be a consideration for
soil contamination source.
Comments
Not a major source of
uncertainty
Not a major source of
uncertainty.
The 30 year duration for
high end farming families
assumes such families are
less transient than non-
farming families.
Overall: Of these three parameters, the exposure duration is the most uncertain. The values used in this assessment were from
mobility studies and they also considered that farming families may tend to live in one location longer than non-farming families.
Evidence in the literature and a sensitivity analysis exercise in Chapter 6 suggest that soil concentrations of dioxins dissipate slowly,
such that the assumption of non-reduction of the soil concentration over the duration of exposure for the soil contamination source
category is a reasonable assumption.
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Table 8-2. Uncertainties associated with the soil ingestion pathway.
Assumption/
Method
Approach
Rationale
Uncertainty
Comments
Exposure site
soil
concentrations
estimated when
source is not at
exposure site
Air dispersion and
deposition modeled
for stack emission
source category;
erosion modeled for
soil contamination
source category; for
both, soil mixing
depths of 2 and 20
cm, and 25 year
half-lives assumed
Algorithms assume steady
state; limited research
suggests that the selected
parameters are reasonable
estimates for dioxin-like
compounds.
A model validation
exercise suggests that the
deposition model is
underestimating soil
concentrations, most likely
due to lack of
consideration of vapor
deposition and detritus
additions. On the other
hand, the soil erosion
algorithm may be
overestimating off-site
impacts.
Future refinements
should focus on
improvements to air-to-
soil modeling which add
vapor impacts and
detritus production.
Soil ingestion
exposure
assumptions
Ingestion occurs
between 2-6 years
old; central and high
end rates of 100 and
600 mg/day; adult
ingestion not
considered
Soil ingestion is most
likely to occur for this age
range; ingestion rates
selected from a review of
studies in EPA (1997).
Adult soil ingestion could
be important for farming
situations where soil
contact is frequent. Proper
soil ingestion rates is an
ongoing issue of research.
Ingestion rates do not
consider pica behavior,
which could lead to
ingestion rates
significantly higher than
selected here. Adult soil
ingestion should be
considered for site
specific application.
Overall: The modeling algorithms which are used to predict soil concentrations at a site of exposure when the source is distant
from the site of exposure can result, in one case, an overprediction of soil concentrations, and in the other case, an
underprediction of soil concentrations. The air-to-soil algorithm of the stack emission may be underestimating soil concentrations
because of a lack of consideration of vapor phase impacts and a lack of consideration of detritus production. On the other hand,
the erosion algorithm may be overestimating soil concentrations, based on a comparison of off-site impacts noted at an industrial
site in Midland, MI, with the modeled off-site impacts. Uncertain parameters identified for soil concentration modeling include
the soil dissipation rate (half-life of 25 years), the soil erosion and transport algorithm, the mixing depths, and for the stack
emission source category, the uncertainties associated with the ISCST3 model. Soil ingestion for older children and adults was
not considered. Assessors may wish to consider these pathways if soil concentrations at a site (modeled or measured) are high.
Otherwise, soil exposure parameters are expected to be reasonable for general assessment purposes.
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Table 8-3. Uncertainties associated with the dermal exposure pathway.
Assumption/
Method
Soil concentration
modeling
Use of tillled vs.
untilled
concentrations
Contact/
adherence rate
Contact frequency
Surface area
Absorption
fraction
Approach
Rationale
Uncertainty
Comments
See Section 8.4.2 and Table 8-2 for a summary of the uncertainties in soil concentration modeling.
used "tilled"
concentration for all
outdoor dermal
exposures and
"untilled" for indoor
dermal exposures
0.005 mg/cm2-event
for indoor contact;
0.03 and 0.1
mg/cm2-event for
residential gardening
and farming,
respectively.
365 events/yr for
indoor; 350 events/yr
for farming; 100
events/yr for
gardening.
1 ,000 cm2 for indoor
events; 10,000 for
residential gardening
(central scenario) and
3600 for farming
(high end scenario)
0.03 for all
compounds
Soil dermal contact
assumed to occur while
farming/gardening
outdoors; indoor dust
assumed to originate from
untilled soils.
Based on data suggesting
a much larger range from
O.002 to >20 (for "kids-
in-mud"); measurement
data available described
in EPA, 1997
based on judgement and
assumption that farmers
spend more time in soil
than non-farmers; indoor
events judged to occur
daily.
Based on total body
surface area data and
clothing assumptions:
bare hands indoors; hands
and arms for farmers;
hands, arms, and legs for
gardeners
DatainEPA(1992c)
suggested a range of
0.001 to 0.03 based on
data for 2,3,7,8-TCDD.
Tilled concentrations are
lower than untilled
concentrations; no data
available to show
relationship between
outdoor and indoor dioxin
concentrations in soil/dust.
measurement data may
have uncertainties;
variability expected due to
behaviors, soil type, and so
on.
uncertainty judged to be
relatively small given
assumptions for behaviors
Good data and small
variability on body surface
area; clothing assumptions
based on judgement and
site-specific conditiosn.
Value chosen was upper
end of range, so
refinements would appear
to lead to lower estimates
of dermal exposure.
Assessors should
consider site-specific
behaviors to determine
patterns of behavior
leading to soil dermal
contact
Should be considered an
uncertain parameter, but
little data is available to
make better parameter
assignments.
climatic conditions,
behaviors, other site-
specific factors could be
important
Studies have shown that
fine particulates can
deposit under clothing.
Different behaviors can
lead to different
assumptions regarding
exposed body areas.
Soil properties may also
affect absorption.
Overall: The uncertainties and variabilities in the soil contact/adherence and absorption fraction parameters make the overall
exposure estimates highly uncertain; judged to be plus or minus 1 to 2 orders of magnitude. Assessors should also be aware of
uncertainties associated with prediction of soil concentration if source is distant from site of exposure.
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Table 8-4. Uncertainties associated with the water ingestion pathway.
Assumption/
Method
Approach
Rationale
Uncertainty
Comments
Water
Concentrations
See modeling approaches for the soil,
stack emission, and effluent discharge
source categories in Chapter 4
literature data show few occurrences
of dioxin-like compounds at 1 pg/L
detection; models estimate 0.01 pg/L
range and lower; cannot therefore
ascertain uncertainty due to modeling,
although little uncertainty expected
due to low concentrations both found
and predicted; and suitability of model
predictions of sediment
concentrations.
No major uncertainty
expected due to
modeling of water
concentrations
Water Ingestion Rate
1.4L/day
central; 2.0
L/day high end
The classically assumed
2.0 L/day was
evaluated as upper end
rather than central
value; 1.4 L/day
recommended instead
for central value.
EPA (1997) also noted that
information on sensitive
subpopulations such as laborers was
unavailable; still, their analysis
indicated that 2 L/day corresponds to
high end value; hence it is appropriate
for high end settings
Not expected to be a
critical factor for
uncertainty.
Contact Rate
0.70 central;
0.90 high end
values correspond to
central and high end
values for time at
residence from several
time use studies
reviewed in EPA
(1997).
The major uncertainty has to do with
the extent to which exposed
individuals rely on impacted water
body for drinking water consumption.
By using contact rates based on time
at home, the assumption is that 100%
of drinking water at home comes from
impacted water body (which is
assumed to supply water to
household).
Exposure could be
less if exposed
individuals rely on
water supply for
drinking water other
than impacted water
body.
Overall: Data in the literature suggests concentrations mostly below 1 pg/L, which is consistent with modeling of concentrations
0.01 pg/L and lower in demonstration of all source categories. With this evidence, uncertainty with modeling is unknown, but the
uncertainty is expected to be low because there is evidence that sediment predictions are consistent with field information. In
general, water exposure is essentially the lowest exposure pathway.
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Table 8-5. Uncertainties associated with the fish ingestion pathway.
Assumption/
Method
Bioaccumulation
approaches for fish
tissue
concentration
estimation
Fish ingestion rate
Approach
Modeled
bottom and
suspended
concentrations
multiplied by
BSAF or
BSSAF
8 and 25 g/day
for central and
high end
Rationale
Bioaccumulation approaches
rather than bioconcentration
approaches are appropriate for
lipophilic persistent organic
compounds; water-based rather
than sediment based approaches
could be used, but sediment based
approaches offer two advantages:
1) sediment data can and has been
measured to derive field-based
BSAF/BSSAFs - water
concentrations for dioxins are too
low, and 2) models for predicting
sediment concentrations can
likewise be tested.
Based on a review of recreational
angler surveys in EPA (1997)
Uncertainty
Strictly speaking, BSAFs
developed from one set of
data are not transportable to
other water bodies, and/or
to other fish species;
uncertainty exists with
sediment concentration
modeling; range of
measured BSAF from
selected value of 0.09 to
greater than 1.00.
Assignment of this
parameter should be based
on site-specific
considerations; subsistence
behaviors leading to much
higher fish ingestion rates
should be ascertained.
Comments
Model validations
and comparisons of
predicted with
measured fish
concentrations
speak well for fate
and transport
algorithms
Example settings
were defined as
rural/agricultural,
but with a major
river used as
drinking water
supply and suitable
for fishing; hence,
freshwater
recreational fishing
data was used.
Overall: Comparison offish concentrations generated in the demonstration scenarios with literature values offish concentrations
of dioxin-like compounds shows them to be comparable. The validation using 104-mill data and testing the effluent discharge
algorithms showed that fish concentrations were low by about one-half, but two important considerations for that test include: the
discharging mill was assumed to be the only source of 2,3,7, 8-TCDD, and uncertainties with the field data and the BSSAF lead to a
conclusion that the model behaves quite adequately. Fish concentrations of PCBs may have been underestimated, but this
conclusion is tempered by the fact that the modeled PCB sediment concentrations are similarly lower than has been measured.
Alternate modeling approaches based on water column factors show comparable fish concentrations than sediment-based methods.
Assignment of the fish ingestion rates was based on data from "recreational angler surveys"; "general population" and
"subsistence" ingestion rates would be lower and higher, respectively, than selected for this assessment.
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Table 8-6. Uncertainties and sensitivities associated with estimating vapor and particle-phase air
concentrations from contaminated soils.
Assumption/
Method
Approach
Rationale
Uncertainty
Comments
Exposure
parameters
13and20m3/day
for central and
high end; 0.70 and
0.90 contact
fractions for
central and high
end.
Central and high end
estimates as described in
EPA (1997)
not much uncertainty or
variability expected for
inhalation rates and contact
fractions
uncertainty introduced
by exposure durations of
9 and 30 years because
of their role in the
volatilization algorithm;
otherwise uncertainty
more an issue for
methodologies
estimating air
concentrations.
Volatilization
followed by near
or far field
dispersion for
vapor phase
concentrations
Used model
developed by
Hwang, et al
(1986) for
volatilization of
PCBs; standard
area source
modeling for
dispersion
Like PCBs, dioxin-like
compounds are highly sorbed
and persistent. Hwang
(1986) model also has the
advantage that the solutions
were simplified using
assumptions deemed
reasonable for soils
contaminated with dioxins.
Chemical parameters H
and Koc are most
uncertain with an order of
magnitude range in
estimated concentrations;
estimations also sensitive
to area, distance, and
frequency wind blows to
receptor.
An analysis of model
performance suggests
that the soil to air
algorithms may be
underestimating air
concentrations, although
this cannot be
ascertained for certain
because measured air
concentrations are due to
distant sources as well as
soils.
Wind erosion
followed by same
dispersion
algorithms
Used model based
on highly erodible
soils for dust flux
to estimate fluxes
for particle sizes <
10 nm
Assuming highly erodible
soils may tend to
overestimate flux, but not
considering particles of size
> 10 [im would underestimate
total airborne reservoir.
Parameters associated with
the credibility of soils can
lead to a 2 order of
magnitude range for
estimated concentrations;
much less sensitivity noted
for other parameters.
No data to evaluate
model results; however,
particle concentrations
are over 1 order of
magnitude lower than
vapor concentrations.
Considering that the
model was based on
erodible soils, dust flux
concentrations may be
generally unimportant.
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Table 8-6. (cont'd).
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Assumption/
Method
Volatilization or
resuspension of
eroded
contaminants not
considered
Approach
Contaminants
eroding to
exposure site
assumed not to
volatilize or
resuspend to
contribute to
exposure site
concentrations
Rationale
Traditionally, evaluation of
the off-site impacts from a
site of soil contamination
considered only the impacts
from the contaminated site.
Uncertainty
If delivered contaminants
volatilize or resuspend at
site of exposure, exposure
site air concentrations
would increase by a factor
of 2 to over 10.
Comments
More consideration of
the fate of delivered
contaminants is
warranted.
Overall: The results of a model validation exercise showed that model predictions of air concentrations of dioxins resulting only
from soil emissions were less than observed air concentrations by 2-3 orders of magnitude. The fact that they are lower is to be
expected, since observed air concentrations over soils in an actual setting are very likely to be due mostly to long range air-borne
transport from distant sources. Still, an analysis of data of plants growing in soils of known concentrations also suggests that the
air-to-soil model may be underpredicting air concentrations. Ultimately, no data could be found on air concentrations over soils
where it is definitely known that the soil is the only source of the dioxin-like compounds, so a degree of underprediction, if that is in
fact occurring, could not be ascertained. Sensitivity analysis showed estimations of vapor phase air concentrations to be sensitive to
Koc and H, and also to key source strength and delivery terms such as areas of contamination and wind speed. Assuming these non-
chemical specific parameters can be known with reasonable certainty for site-specific applications, the most uncertainty lies with
chemical specific data. Alternate approaches for volatilization and air dispersion generally estimate comparable air concentrations
(within an order of magnitude or lower). Approaches to estimate paniculate phase concentrations are empirical and based on field
data. They are based on highly erodible soils but are specific to inhalable size particles, those less than 10 p,m. As such, they may
overestimate inhalation exposures, but may underestimate the total reservoir of particulates, which becomes critical for the particle
deposition to vegetation algorithms. Another area of uncertainty is the assumption that volatilized contaminants do not become
sorbed to airborne particles - this is also critical because vapor phase transfers dominate plant concentration estimation. A final key
area of uncertainty is that transported contaminants from a contaminated to an exposure site via erosion are assumed not to
volatilize or resuspend at the exposure site - air borne exposure site concentrations may be underestimated as a result.
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Table 8-7. Uncertainties associated with vegetable/ fruit ingestion exposure algorithms.
Assumption/
Method
Approach
Rationale
Uncertainty
Comments
Ingestion Rates
and Contact
Fractions
1.16, 1.52, and
1.47 g/kg/day
root/above veg,
and fruit; 0.101
and 0.173 for fruit
and veg contact
fractions
Derived from National Food
Consumption Survey (NFCS) of
1987-88; Household portion.
Available data allows for most
appropriate estimates of
homegrown rates and contact
fractions, and also ties body weight
to ingestion rate by expression in
terms of g/kg/day.
Household survey relies on
head of household recall for
week long food brought into
home; whole food product do
not include cooking losses,
discarded amounts, etc.
All parameters
evaluated as
reasonable for
general exposure to
categories of fruit
and vegetables; more
refinement desired
for specific
assessments.
Below ground
vegetable
concentration
Used empirical
RCF, root
concentration
factor, based on
Kow, and VGbg,
below ground
correction factor
Approach based on laboratory
experiments; validation exercise
on data for carrot peel
concentrations grown in soils of
known concentrations supports
model capabilities.
VGbg of 0.25 based on
evidence of some
translocation into carrots and
potatoes; however, it remains
most uncertain parameter;
Kow is also uncertain,
although validation exercise
supports use of RCF
Further refinements
to VGbg may be
warranted.
Above ground
vegetable, fruit
concentration
Air-to-leaf vapor
phase transfer
algorithm based
on B^ (transfer
factor) which was
developed from
field data; vapor
phase impacts
also include VGag;
particle
deposition
algorithm for
particle bound
dioxins
Field experiments and modeling
both show that vapor phase
impacts dominate total plant
concentrations; B^ calibrated
from field data; particle deposition
algorithm developed for
radionuclide impacts to
agriculture; VGag assignment of
0.01 considers both evidence of
little within plant translocation for
exposed bulky vegetations and
reduction in plant concentrations
due to peeling prior to
consumption.
Model validation/comparison
exercise showed the air-to-
leaf model to work
reasonably well in rural
setting, but to underestimate
grass concentrations when
grass was grown in
contaminated soils in an
industrial setting; VGag still
the most uncertain parameter.
Limited literature
data and model
validation exercise
suggests that above
ground vegetative
impacts from
contaminated soils
may be
underestimated;
could be due to lack
of consideration of
rainsplash.
Overall: All ingestion parameters assumed are evaluated as reasonable for general exposure to broad categories of fruits and
vegetables. However, great variability is expected if using these procedures on a specific site where home gardening practices can be
more precisely ascertained. Validation exercises support both the soil to below ground vegetable and air-to-leaf algorithms. The
most uncertain parameters for both algorithms are the "VG" parameters, VGag and VGbg, which correct for evidence that there is
little within plant translocation of dioxins in below as well as above ground bulky vegetations, and additionally considers peeling or
washing of vegetations, which would further reduce whole plant concentrations.
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Table 8-8. Uncertainties associated with the terrestrial animal food pathways.
Assumption/
Method
Approach
Rationale
Uncertainty
Comments
Ingestion rates,
contact fractions,
food preparation
consideration
Like for fruit/vegetables, ingestion rates and contact fractions developed from the household component of
the National Food Consumption Survey (USDA, 1992; as interpreted in EPA, 1997). For meats, an
additional pre- and post-cooking factor of about 0.50 further reduces consumption rates derived from this
data to better relate food "brought into the house" to food "as eaten".
Terrestrial animal
bioconcentration
models
Weighted average
concentration in diet
times BCF equals
fat concentration;
BCF for beef/milk
developed from one
experiment on one
lactating cow.
Precedence and data
support this approach.
Two field studies
collecting data to
develop BCF for cow's
milk arrive at very
similar BCFs.
Uncertainty in applying milk-
derived BCF to beef; chicken
and egg BCFs separately
derived in laboratory
experiments; uncertainty in
applying laboratory feeding
experiments to field situations
for chickens; dietary
assumptions are variable and
soil bioavailability correction
factor, Bs, is uncertain and
important for free range
chicken scenario
Air-to-beef model
validation exercise
supports the use of milk-
derived BCFs for beef,
and approach in general.
For predicting beef
concentrations, site-
specific consideration of
fattening regime is
important.
Related models
See previous sections for discussions on uncertainties in associated models including air dispersion and
deposition modeling, soil-to-air and air-to-soil modeling, air-to-plant modeling, and soil concentration
modeling.
Overall: The demonstration scenarios showed that the terrestrial animal food pathways dominate human exposure. This was
supported by similar findings in Volume II of this assessment, which estimated background exposures based on measured
concentrations coupled with consumption rates. In site-specific applications, animal diet fractions in the various categories of
animal feeds (leafy, partially protected, fully protected, soil) becomes important. The air-to-beef model validation exercise
described earlier lends confidence to the use of the milk/beef bioconcentration algorithms.
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Table 8-9. Distributions for a Monte Carlo exercise which developed soil cleanup levels at
residential and industrial sites.
Parameter
Soil Contact us/cm2/d
Dermal Bioavailability Fraction
Fraction soil from site
Fraction indoor dust contaminated
Indoor exposure duration
Outdoor exposure duration
Soil ingestion rate, ng/d
Oral Bioavailability
Air paniculate concen., us/m3
Fraction outdoor dust contaminated
Inhalation rate m3/hr
Lipid Content of Fish
Fish Bioavailability Index
Organic Carbon
content of sediment
Fish Consumption, g/d
Fraction remaining
after cooking
Range
200-1800
0.01 -0.025
0-5 yr:
6-30 yr:
0.1 -1.0
0.1 -0.5
(not considered)
0-1. Syr:
1.5-30yr:
0- I.Syr
1.5-30vr
0- I.Syr
1.5 -Syr
6- 12 yr
13-30yr
0.38,0.40,0
182-365 d/yr
200-365 d/yr
60- 120 d/yr
60-240 d/yr
100 - 10000
9000 - 50000
5000 - 50000
100 - 50000
47, 0.49
25-45
0.1 -0.5
0-1. Syr:
1.5-5yr:
6-12yr:
13-30 yr:
0.03-0.07
0.3 -0.9
0.75-1.5
0.5- 1.5
0.01 -0.05
0.01 -0.5
0.01 -0.5
0-1. Syr:
1.5-5yr:
6-12yr:
13-30 yr:
0
0.38-0.62
0.63-1.0
1.1 - 1.8
0.3-0.75
Range
fin rlii stria 11
same
same
0.1 -1.0
0.25-1.0
0 - 8 hr/d
220 - 260 d/yr
0 - 8 hr/d
220 - 260 d/yr
100 - 50000
(indoors)
100 - 10000
(outdoors)
same
same
same
9 - 14.6 m3/d
Source: Paustenbach et. al. (1992a); uniform distributions assumed over ranges shown.
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Table 8-10. Summary of Monte Carlo distributions used in a fish consumption assessment.
Exposure Parameter
Dioxin Cone, (ppt of
TEQ)
Fraction caught in
affected waters
Consumption
(g/d)
Duration (yr)
Cooking Loss
Fraction
Body Weight
(kg)
Distribution Type
truncated
lognormal
triangular
truncated
lognormal
truncated
lognormal
uniform
normal
Mean
3.3
0.09 (all US)
0.4 (near mill)
2.5 (all US)
19.1 (sport -
fishermen)
13.3
0.1
71
Standard
Deviation
8.7
0.2
0.2
7.3
27.9
12.3
0.3
18.1
Min./Max.
0.0002/16,000
0/1.0
0/1.0
0/240
0.2/403
0.1/70
0.25/0.75
29.9/143.2
Source: Anderson et. al. (1992).
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Table 8-11. Summary of Monte Carlo distributions used in food chain study.
Parameter
Milk Ingestion 0-15 yr:
kg/kg/d 15-70yr:
Meat Ingestion 0-15 yr:
kg/kg/d 15-70yr:
Fruit/Veg Ing. 0-1 Syr:
kg/kg/d 15-70yr:
Graining. 0-1 Syr:
kg/kg/d 15-70yr:
Particle to Food Deposition Factor, m/d
Plant/Soil Part. Factor
Biotransfer Fac. Cattle
Intake to Meat, d/kg
Biotransfer Fac. Cattle
Intake to Milk, d/kg
Annual Inventory Food
Crops, kg/m2
Annual Inventory Pasture Crops, kg/m2
Weathering Rate Constant, 1/d
Cattle Inhalation Rate, mVd
Beef Cattle Ingestion of Pasture Grass,
kg/d
Dairy Cattle Ingestion of Pasture Grass,
kg/d
Cattle Soil Ingestion, kg/d
Geometric Mean
0.014
0.0033
0.0044
0.0029
0.0081
0.0045
0.0074
0.0030
300
0.013
0.055
0.01
Lower Bound
1.0
0.1
0.01
63
4.0
11
0.1
Geometric Standard
Deviation
1.2
1.1
1.1
1.2
1.4
1.3
1.2
1.2
3
4.0
3.0
3.0
Upper Bound
10.0
1.0
0.1
177
20
23
0.83
Distribution
log normal
log normal
log normal
log normal
log normal
log normal
log normal
log normal
log uniform
log uniform
log uniform
uniform
uniform
uniform
uniform
Source: McKone and Ryan, 1989.
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Table 8-12. Summary of parameter distributions used for modeling terrestrial fruits and
vegetables for human consumption in a Monte Carlo exercise.
Definition
Forms and Parameters
Distribution
I. Plant Concentration Modeling
soil decay constant, I/day
soil mixing depth, m
crop interception fraction
soil bulk density, g/m3
growing season duration, days
root uptake factor for translocation of TCDD
from soil to crop, Cplmt/ Csoll
deposition velocity of particle or vapor class,
m/day
particle weather rate constant on plant, I/day
crop yield, g/m2
0.0001-0.0002
0.15-0.25
root, vine, tree: 0.05-0.25
Leafy: 0.16-0.40
median = 1.4; geo. stan. dev =1.15
tree: 120-150
leafy: 40 - 60
root crop: 0.25-1.0
Vine, leafy: 0.05-0. 15
median by diameter, variability = 0.8
log units
0.01-0.1
vine, tree: 5-15
leafy: 6 - 10
Uniform
Uniform
Uniform
Lognormal
Uniform
Uniform
Loguniform
Loguniform
Uniform
II. Air Concentration Modeling
dispersion factor calculated from air
dispersion model, defined as air concentration
in ith sector per unit mass emitted,
(mg/m3)/(mg/s)
2,3,7,8-TCDD mass emissions, mg/sec
median = -0.00017, geo. stan. dev =
1.4
1.5*10-6 -5*10-6
Lognormal
Loguniform
Source: Cullen (1995b)
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1. DISPOSITION AND PHARMACOKINETICS
The disposition and pharmacokinetics of 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD)
and related compounds have been investigated in several species and under various exposure
conditions. Several reviews on this subject focus on TCDD and related halogenated aromatic
hydrocarbons (Neal et al., 1982; Gasiewicz et al., 1983a; Olson et al., 1983; Birnbaum, 1985; van
den Berg et al., 1994). The relative biological and toxicological potency of TCDD and related
compounds depends not only on the affinity of these compounds for the aryl hydrocarbon
receptor (AhR), but on the species-, strain-, and congener-specific pharmacokinetics of these
compounds (Neal et al., 1982; Gasiewicz et al., 1983a; Olson et al., 1983; Birnbaum, 1985; van
den Berg et al., 1994, DeVito and Birnbaum, 1995).
2,3,7,8-TCDD and other similar compounds discussed here are rapidly absorbed into the
body and slowly eliminated, making body burden (bioaccumulation) a reliable indicator of time-
integrated exposure and absorbed dose. Because of the slow elimination kinetics, it will be
shown in this section that lipid or blood concentrations, which are often measured, are in
dynamic equilibrium with other tissue compartments in the body, making the overall body
burden and tissue disposition relatively easy to estimate. Finally, it will be shown that body
burdens can be correlated with adverse health effects (Hardell et al., 1995; Leonards et al., 1995),
further leading to the choice of body burden as the optimal indicator of absorbed dose and
potential effects.
1.1. ABSORPTION/BIOAVAILABILITY FOLLOWING EXPOSURE
Gastrointestinal, dermal, and transpulmonary absorptions represent potential routes for
human exposure to this class of persistent environmental contaminants. Parenteral absorption is
a route of exposure that has been used to generate disposition and pharmacokinetic data on these
compounds.
1.1.1. Oral
1.1.1.1. Gastrointestinal Absorption in Animals
Much of human exposure to TCDD and related compounds is thought to be through the
diet. Experimentally, these compounds are commonly administered in the diet or by gavage in an
oil vehicle. Gastrointestinal absorption is usually estimated as the difference between the
administered dose (100%) and the percent of the dose that was not absorbed. The unabsorbed
fraction is estimated as the recovery of parent compound in feces within 24 to 48 hours of a
single oral exposure by gavage. Table 1-1 summarizes gastrointestinal absorption data on TCDD
and related compounds.
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In Sprague-Dawley rats given a single oral dose of 1.0 \ig [14C]-2,3,7,8-TCDD/kg bw in
acetone:corn oil (1:25, v/v), the fraction absorbed ranged from 66% to 93%, with a mean of
-84% (Rose et al., 1976). With repeated oral dosing of rats at 0.1 or 1.0 |_ig/kg/day (5 days/week
for 7 weeks), gastrointestinal absorption of TCDD was observed to be approximately that
observed for a single oral exposure (Rose et al., 1976). Oral exposure of Sprague-Dawley rats to
a larger dose of TCDD in acetone:corn oil (50 pg/kg) resulted in an average absorption of 70% of
the administered dose (Piper et al., 1973). More recently, Diliberto et al. (1996a) reported 88%
absorption of TCDD in male Fischer 344 rats following oral exposure in Emulphor/95%
ethanol/water (1:1:3). (Emulphor EL-620 is a polyoxyethylated vegetable oil preparation [GAP
Corp., New York, NY]).
One study in the guinea pig reported that -50% of a single oral dose of TCDD in
acetone:corn oil was absorbed (Nolan et al., 1979). The gastrointestinal absorption of TCDD
was also examined in the hamster, the species most resistant to the acute toxicity of this
compound (Olson et al., 1980). Hamsters were given a single, sublethal, oral dose of [1,6-3H]-
2,3,7,8-TCDD in olive oil (650 |_ig/kg), and an average of 75% of the dose was absorbed. When
TCDD was administered to rats in the diet at 7 or 20 ppb (0.5 or 1.4 pg/kg/day) for 42 days, 50%
to 60% of the consumed dose was absorbed (Fries and Marrow, 1975). These findings indicate
that oral exposure to TCDD in the diet or in an oil vehicle results in the absorption of >50% of
the administered dose.
The intestinal absorption of [3H]-2,3,7,8-TCDD has also been investigated in thoracic
duct-cannulated rats (Lakshmanan et al., 1986). The investigators concluded that TCDD was
absorbed into chylomicrons and transported through the lymphatic system before entering the
systemic circulation.
The absorption of 2,3,7,8-TBDD in male Fischer 344 rats was studied after oral exposure
by gavage at 5 ng/kg in Emulphor:ethanol:water (1:1:3) (Diliberto et al., 1990). The percent of
the dose absorbed for this study was defined as 100% (% total oral dose in feces on days 1 and 2
minus % total intravenous dose in feces on days 1 and 2) using the intravenous pharmacokinetic
data of Kedderis et al. (199la).
The relative absorbed dose or bioavailability of 2,3,7,8-TBDD after oral exposure was
estimated at 78%, 82%, 60%, and 47% at dose levels of 0.001, 0.01, 0.1, and 0.5 |_imol/kg,
respectively. These results suggest nonlinear absorption at the higher doses, with maximal oral
absorption at an exposure of <0.01 |_imol/kg (5 pg/kg).
The absorption of 2,3,7,8-TCDF has been investigated after oral exposure by gavage.
Approximately 90% of the administered dose (0.1 and 1.0 |_imol/kg) of 2,3,7,8-TCDF in
Emulphor:ethanol (1:1) was absorbed in male Fischer 344 rats (Birnbaum et al., 1980).
Similarly, >90% of the administered dose (0.2 |_imol/kg, 6 pg/kg, and 1-15 pg/kg) of 2,3,7,8-
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TCDF in Emulphorethanol:water (1:1:8) was absorbed in male Hartley guinea pigs (Decad et al.,
1981a; loannou et al., 1983). Thus, 2,3,7,8-TCDF appears to be almost completely absorbed
from the gastrointestinal tract. This absorption may be related to the greater relative solubility of
2,3,7,8-TCDF compared with that of 2,3,7,8-TCDD or 2,3,7,8-TBDD.
The oral bioavailability of 2,3,4,7,8-PeCDF and 3,3',4,4'-TCB in corn oil was similar to
that of 2,3,7,8-TCDD (Brewster and Birnbaum, 1987; Wehler et al., 1989; Clarke et al., 1984).
Furthermore, 2,3,4,7,8-PeCDF absorption was independent of the dose (0.1, 0.5, or 1.0 |_imol/kg).
Incomplete and variable absorption of 1,2,3,7,8-PeCDD was reported in rats, with 19% to 71% of
the dose absorbed within the first 2 days after oral exposure (Wacker et al., 1986).
Early studies on the pharmacokinetic behavior of OCDD by Williams et al. (1972) and
Norback et al. (1975) demonstrated that OCDD was poorly absorbed after oral exposure. More
recently, Birnbaum and Couture (1988) also found that the gastrointestinal absorption of OCDD
in rats was very limited, ranging from 2% to 15% of the administered dose. Lower doses (50
l-ig/kg) in an o-dichlorobenzene:corn oil (1:1) vehicle were found to give the best oral
bioavailability for this extremely insoluble compound.
Recently Mes et al. (1995) have reported PCBs in nondosed infants of dosed rhesus
monkeys. Transfer to the infants was from both intrauterine and lactational exposure. Also, the
infants showed a larger percentage of heptachorobiphenyls than did the dosed dams. Busbee and
Zipring (1994) reported the direct absorption across the gastric mucosa of Aroclor 1232 (DCB) in
an ovine. The absorption was rapid and the circulating DCB was not found to be associated with
plasma lipid fractions. The researchers report no occurrence in the thoracic duct lymph prior to
appearance in the circulating plasma. Also, the DCB did bind with plasma lipids in vitro.
These data suggest that this DCB is transported similar to water soluble compounds.
1.1.1.2. Gastrointestinal Absorption in Humans
The above animal data indicate that gastrointestinal absorption of TCDD and related
compounds is variable, incomplete, and congener- and vehicle-specific. More soluble congeners,
such as 2,3,7,8-TCDF, are almost completely absorbed, whereas the extremely insoluble OCDD
is poorly absorbed. In some cases, absorption has been found to be dose dependent, with
increased absorption occurring at lower doses (2,3,7,8-TBDD, OCDD). The limited database in
experimental animals also suggests that there are no major interspecies differences in the
gastrointestinal absorption of these compounds.
Poiger and Schlatter (1986) investigated the absorption of TCDD in a 42-year-old man
after ingestion of 105 ng [3H]-2,3,7,8-TCDD (1.14 ng/kg bw) in 6 mL corn oil and found that
>87% of the oral dose was absorbed from the gastrointestinal tract. Following absorption, the
half-life for elimination was estimated to be 2,120 days.
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Schlummer et al. (1998) used a mass balance approach to assess the gastrointestinal
absorption of CDDs, CDFs, PCBs, and hexachlorobenzene (HCB) from food in seven
individuals, 24 to 81 years of age, with different contaminant body burdens. The difference
between the ingested (food concentration) and excreted (fecal concentration) amounts of each
compound was defined as net absorption. Three types of net absorption were observed in this
study: (1) nearly complete net absorption (e.g. 2,3,7,8-TCDF and 1,2,3,7,8-PeCDF); (2)
incomplete net absorption (e.g., TCDD and 1,2,3,7,8-PeCDD in the younger subjects); and (3)
net excretion (excretion to a greater extent than ingestion, e.g., 1,2,3,6,7,8-HxCDD and OCDD).
In terms of TEQs, the maximum net absorption of CDDs and CDFs was 63% in one individual,
while a net excretion of TEQs was found for the three oldest subjects, which also have the
highest serum concentration of these congeners. When PCB 126 (3,3'4,4',5), with a TEF of 0.1,
is included in the TEF calculation, the TEQ balance was dominated by this congener, resulting in
a maximum net TEQ absorption of 80% and a net TEQ absorption in all but the oldest subject.
Table 1-1 illustrates that compounds showing nearly complete net absorption had very low or
nondetectable levels in the serum lipids, and for other congeners, there was a trend for decreasing
net absorption/increasing net excretion with increasing congener concentration in serum lipids.
Together, the data support the passive diffusion model for gastrointestinal absorption, where the
concentration of the contaminant in the blood is the major factor determining absorption.
However, the relatively high absorption levels of many congeners could not be explained on the
basis of diffusive gradients estimated from the difference between the lipid-based food and serum
concentrations, as the lipid-based food levels were always lower, favoring net excretion. The
authors propose a fat-flush theory, which hypothesizes that the fat compartment of the absorbing
tissue (gut wall) expands because of the uptake of dietary fat, resulting in a decrease in the lipid-
based concentration of the gut wall below that of the food, thus facilitating absorption.
Therefore, as food passes through the duodenum and the jejunum, CDDs, CDFs and PCBs
experience a diffusion gradient and net absorption as a result of the fat-flush. As the gut contents
reach the colon, dietary fat has been absorbed, causing a reduction in the concentration gradient
favoring absorption of CDDs, CDFs, and PCBs. Thus, the fat-flush theory supports the
hypothesis that absorption and excretion of CDDs, CDFs, and PCBs are distinct processes
occurring at different locations in the digestive tract.
Duarte-Davidson and Jones (1994) report an average intake of a sum of several PCB
congeners in the contemporary United Kingdom population of 0.53 micrograms/person/day.
They estimate 97% of exposure can be accounted for by food consumption. This indicates that
PCBs can likewise be absorbed through the gastrointestinal system.
Because CDDs, CDFs, and PCBs are present in human milk, McLachlan (1993)
investigated the net absorption of these compounds in a nursing infant. The contaminant input,
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through the ingestion of mother's milk, and the contaminant output in the feces were measured to
estimate the digestive tract absorption of these compounds. For almost all congeners, more than
90% of the ingested compound was absorbed, indicating that the common assumption of 100%
absorption of CDDs, CFFs, and PCBs in nursing infants is reasonable. Dahl et al. (1995) provide
further evidence of this, as they report > 95% absorption in postpartum infants (1, 2, 3 months) in
Sweden. Abraham et al. (1996) assessed the oral intake and fecal excretion of CDDs and CDFs
in one formula-fed and two breast-fed infants at 1 and 5 months of age. The breast-fed infants
had significantly more exposure to CDDs and CDFs, with >90% of the 2,3,7,8-TCDD, 2,3,4,7,8-
PeCDF, 1,2,3,7,8-PeCDD, and 1,2,3,6,7,8-HxCDD (>93% of TEQs) being absorbed from
mother's milk. Less complete bioavailability of higher CDDs was observed, with 62% to 88% of
1,2,3,4,6,7,8-HepCDD and 16% to 75% of OCDD absorbed from mother's milk (Abraham et al.,
1996). Furst et al. (1994), Hong et al. (1994), Schecter et al. (1994a,b), Georgii et al. (1994), and
others provide further evidence of the presence of dioxins, PCPs, and PCBs in human milk. This
important route for excretion and exposure is discussed later in the chapter.
1.1.1.3. Bioavailability Following Oral Exposure
Oral exposure of humans to TCDD and related compounds usually occurs as a complex
mixture of these contaminants in food, soil, dust, water, or other mixtures that would be expected
to alter absorption.
The influence of dose and vehicle or adsorbent on gastrointestinal absorption has been
investigated in rats by Poiger and Schlatter (1980), using hepatic concentrations 24 hours after
dosing as an indicator of the amount absorbed (Table 1-2). Administration of TCDD in an
aqueous suspension of soil resulted in a decrease in the hepatic levels of TCDD as compared
with hepatic levels resulting from administration of TCDD in 50% ethanol. Likewise, Diliberto
et al. (1996a) report 24.4% (+ 1.4) in the liver after dosing male Fischer 344 rats with TCDD in a
solution of 1:1:3 ratio of Emulphor/95% ethanol/distilled water. The extent of the decrease was
directly proportional to the length of time the TCDD had been in contact with the soil. When
TCDD was mixed in an aqueous suspension of activated carbon, absorption was almost totally
eliminated (<0.07% of the dose in hepatic tissues).
Philippi et al. (1981) and Huetter and Philippi (1982) have shown that radiolabeled
TCDD becomes progressively more resistant with time to extraction from soil. Similarly, the
feeding of fly ash, which contains CDDs, to rats in the diet for 19 days resulted in considerably
lower hepatic levels of CDDs than did the feeding of an extract of the fly ash at comparable
dietary concentrations of CDDs (van den Berg et al., 1987a). The CDDs were tentatively
identified as 2,3,7,8-TCDD, 1,2,3,7,8-PeCDD, 1,2,3,6,7,8-HxCDD, and 1,2,3,7,8,9-HxCDD, and
the difference in hepatic levels noted between fly ash-treated and extract-treated rats was greater
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for the more highly chlorinated isomers than for 2,3,7,8-TCDD. These results indicate the
importance of the formulation or vehicle containing the toxin(s) on the relative bioavailability of
2,3,7,8-TCDD, PeCDD, and HxCDDs after oral exposure.
Because TCDD in the environment is likely to be absorbed to soil, McConnell et al.
(1984) and Lucier et al. (1986) compared the oral bioavailability of TCDD from environmentally
contaminated soil with that from TCDD administered in corn oil in guinea pigs and rats,
respectively. As indicated by biological effects and the amount of TCDD in the liver, the
intestinal absorption from soil from Times Beach and Minker Stout, Missouri, was -50% less
than that from corn oil. Shu et al. (1988a) reported an oral bioavailability of-43% in rats dosed
with three environmentally contaminated soil samples from Times Beach, Missouri. This figure
did not change significantly over a 500-fold dose range of 2-1,450 ng 2,3,7,8-TCDD/kg bw for
soil contaminated with -2, 30, or 600 ppb of TCDD TCDD. In studies of other soil types,
Umbreit et al. (1986a,b) estimated an oral bioavailability in the rat of 0.5% for soil at a New
Jersey manufacturing site and 21% for a Newark salvage yard. These results indicate that
bioavailability of TCDD from soil varies between sites and that TCDD content alone may not be
indicative of potential human hazard from contaminated environmental materials. Although
these data indicate that substantial absorption may occur from contaminated soil, soil type and
duration of contact, as suggested from the data that demonstrated decreased extraction efficiency
with increasing contact time between soil and TCDD (Philippi et al., 1981; Huetter and Philippi,
1982), may substantially affect the absorption of TCDD from soils obtained from different
contaminated sites.
1.1.2. Dermal Absorption
Brewster et al. (1989) examined the dermal absorption of TCDD and three CDFs in male
Fischer 344 rats (10 weeks old; 200-250 g). The fur was clipped from the intrascapular region of
the back of each animal. A single compound in 60 |_iL of acetone was then applied over a 1.8
cm2 area of skin, which then was covered with a perforated stainless steel cap. Table 1-3
summarizes data on the absorption of each compound at 3 days after a single dermal exposure.
At an exposure of 0.1 |_imol/kg, the absorption of 2,3,7,8-TCDF (49% of administered dose) was
greater than that of 2,3,4,7,8-PeCDF, 1,2,3,7,8-PeCDF, and 2,3,7,8-TCDD. For each compound,
the relative absorption (percentage of administered dose) decreased with increasing dose, while
the absolute absorption (pg/kg) increased nonlinearly with dose. Results also suggest that the
majority of the compound remaining at the skin exposure site was associated with the stratum
corneum and did not penetrate through to the dermis. In a subsequent study, Banks and
Birnbaum (199la) examined the rate of absorption of TCDD over 120 hours after the dermal
application of 200 pmol (1 nmol/kg) to male Fischer 344 rats. The absorption kinetics appeared
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to be first order, with an absorption rate constant of 0.005 hour"1. With a similar exposure
protocol, the dermal absorption of 2,3,7,8-TCDF was found to follow a first-order process, with a
rate constant of 0.009 hour"1 (Banks and Birnbaum, 1991b). Together, these results on dermal
absorption indicate that at lower doses (<0.1 |_imol/kg), a greater percentage of this administered
dose of TCDD and three CDFs was absorbed. Nonetheless, the rate of absorption of TCDD is
still very slow (rate constant of 0.005 hour"1), even following a low-dose dermal application of
200 pmol (1 nmol/kg). Results from Table 1-3 also suggest that the dermal absorption of 2,3,7,8-
TCDF, 2,3,4,7,8-PeCDF, and 1,2,3,7,8-PeCDF occurs at a very slow rate. Using a similar
exposure protocol, the dermal absorption of 2,3,7,8-TBDD was only 30% to 40% of that
observed for TCDD (Diliberto et al., 1993a; 1996a).
The dermal absorption of several polyhalogenated aromatic hydrocarbons in male F344
rats was also compared with estimates of their respective octanol-water partition coefficients
(Jackson et al., 1993). Inverse correlations were found between octanol-water partition
coefficient estimates and the single-dose (-1.0 nmol/kg and -0.1 |_imol/kg) dermal absorption for
most of the compounds studied. The differential dermal absorption of TCDD and 2,3,7,8-TBDD
may result, in part, from the diminished ability of the more lipophilic 2,3,7,8-TBDD to partition
out of the stratum corneum and into the underlying epidermal and dermal layers.
Rahman et al. (1992) and Gallo et al. (1992) compared the in vitro permeation of TCDD
through hairless mouse and human skin. In both species, the amount of TCDD permeated
increased with the dose, but the percentage of the dose permeated decreased with increasing
dose. The permeability coefficient of TCDD in human skin was about one order of magnitude
lower than that in mouse skin. The hairless mouse skin does not appear to be a suitable model
for the permeation of TCDD through human skin because the viable tissues were major barriers
to TCDD permeation in hairless mouse skin, while the stratum corneum layer provided the
greater resistance in human skin. A significant increase in TCDD permeation through human
skin was observed when the skin was damaged by tape-stripping. Gallo et al. (1992) suggested
that washing and tape-stripping of the exposed area might remove most of the TCDD and reduce
the potential for systemic exposure and toxicity because most of the TCDD remained within the
horny layer of human skin even at 24 hours following exposure.
Weber et al. (1991) also investigated the penetration of TCDD into human cadaver skin at
concentrations of 65-6.5 ng/cm2. This study found that the stratum corneum acted as a protective
barrier, as its removal increased the amount of TCDD absorbed into layers of the skin. With
intact skin and acetone as the vehicle, the rate of penetration of TCDD into the dermis ranged
from 6 to 170 pg/hour/cm2, while penetration into the dermis and epidermis ranged from 100 to
800 pg/hour/cm2. With mineral oil as the vehicle, there was about a five-to-tenfold reduction in
the rate of penetration of TCDD into the intact skin.
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Wester et al. (1993a) studied the dermal permeation potential of two PCBs, Aroclor 1242
and Aroclor 1254, from soil. Soil is the most common medium of contact for humans; hence any
permeation potential is of great interest for risk assessment purposes. This study consisted of
experiments conducted in rhesus monkeys (in vivo), in vitro human skin, and powdered human
corneum. The monkeys were exposed topically for a 5-week period. Percutaneous absorption
was determined by urinary and fecal [14C]-PCB excretion. The percutaneous absorption in the
monkey was 13.8% (±2.7%) for Aroclor 1242 and 14.1% (±1.0%) for Aroclor 1254. The authors
report that these absorption rates for soil are similar to those from vehicles such mineral oil,
trichlorobenzene, and acetone. These rates are somewhat higher than those for other compounds
such as the pesticide butachlor, which was about 5.0% when applied to human skin (Ademola et
al., 1993). On the other hand, Wester et al. (1993b) report a much higher absorption for
pentachlorophenol (PCP) (24.4% from soil and 29.2% from acetone) in the rhesus monkey. The
authors report that PCP is one of the more extensively absorbed compounds they have studied.
Thus, at least for these two PCBs (Aroclor 1242 and Aroclor 1254), the absorption in the rhesus
monkey appears to be intermediate between that of other compounds tested. In summary, these
studies indicate that these two PCBs show significant partitioning into the stratum corneum from
soil, a common environmental matrix.
1.1.2.1. Bioavailability Following Dermal Exposure
Dermal exposure of humans to TCDD and related compounds usually occurs as a
complex mixture of these contaminants in soil, oils, or other mixtures, which would be expected
to alter absorption. Poiger and Schlatter (1980) presented evidence that the presence of soil or
lipophilic agents dramatically reduces dermal absorption of TCDD compared with absorption of
pure compound dissolved in solvents. In a control experiment, 26 ng of TCDD in 50 |_iL
methanol was administered to the skin of rats; 24 hours later the liver contained 14.8±2.6% of the
dose. By comparing this value to the hepatic levels obtained after oral administration in 50%
ethanol (in the same study), the amount absorbed from a dermal application can be estimated at
-40% of the amount absorbed from an equivalent oral dose. This comparison assumes that
hepatic levels are valid estimates of the amount absorbed from both oral and dermal routes and
that absorption from methanol is equivalent to absorption from 50% ethanol. The dose-
dependent distribution of TCDD in the liver is another factor that may limit quantitative
conclusions regarding bioavailability that are based solely on hepatic levels following exposure
to 2,3,7,8-TCDD. As compared with dermal application in methanol, dermal application to rats
of TCDD in vaseline or polyethylene glycol reduced the percentage of the dose in hepatic tissue
to 1.4% and 9.3%, respectively, but had no observable effect on the dose of TCDD required to
induce skin lesions (~1 pg/ear) in the rabbit ear assay. Application of TCDD in a soil/water
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paste decreased hepatic TCDD to -2% of the administered dose and increased the amount
required to produce skin lesions to 2-3 |_ig in rats and rabbits, respectively. Application in an
activated carbon/water paste essentially eliminated absorption, as measured by percent of dose in
the liver, and increased the amount of TCDD required to produce skin lesions to -160 |_ig. These
results suggest that dermal absorption of TCDD depends on the formulation (vehicle or
adsorbent) containing the toxin.
Shu et al. (1988b) investigated the dermal absorption of soil-bound TCDD in rats.
Relative dermal bioavailability was estimated by comparing the level of TCDD in the liver of
rats given soil-bound TCDD dermally to that of rats given oral doses of TCDD dissolved in corn
oil. The level of TCDD in livers of rats dosed orally with TCDD in corn oil, following
correction for unabsorbed TCDD TCDD, is assumed to represent 100% bioavailability. The
dermal penetration of TCDD after 4 hours of contact with skin was -60% of that after 24 hours
of contact. After 24 hours of contact with the skin, the degree of dermal uptake from
contaminated soil was -1% of the administered dose. The authors observed that the degree of
uptake does not appear to be influenced significantly by the concentration of TCDD in soil, by
the presence of crankcase oil as a co-contaminant, or by environmentally versus laboratory-
contaminated soil.
A major limitation of these studies is the uncertainty regarding the extrapolation of
dermal absorption data on these compounds from the rat to the human. The in vitro dermal
uptake of TCDD has been investigated in hairless mouse and human skin (Gallo et al., 1992;
Rahman et al., 1992). In vitro dermal uptake of TCDD from laboratory-contaminated soil
indicated that aging of soils (up to 4 weeks) and the presence of additives (2,4,5-trichlorophenol
and motor oil) in the soil did not have any significant effect on dermal uptake (Gallo et al., 1992).
Because most of the TCDD remained in the stratum corneum layer of human skin, the
permeation of TCDD was significantly lower in human than in hairless mouse skin. There are no
published quantitative in vivo data on the dermal absorption of TCDD and related compounds in
the human, and data on the rhesus monkey are very limited. Brewster et al. (1988) found that
1,2,3,7,8-PeCDF was poorly absorbed in the monkey after dermal application, with <1% of the
administered dose being absorbed in 6 hours. This provides further evidence for the very slow
rate of dermal absorption of TCDD and related compounds.
1.1.3. Transpulmonary Absorption
The use of incineration as a means of solid and hazardous waste management results in
the emission of contaminated particles that may contain TCDD and related compounds into the
environment. Thus, significant exposure to TCDD and related compounds may result from
inhalation of contaminated fly ash, dust, and soil. In an attempt to address the bioavailability and
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potential health implications of inhaling contaminated particles, Nessel et al. (1990) examined
the potential for transpulmonary absorption of TCDD in female Sprague-Dawley rats after
intratracheal instillation of the compound in a corn oil vehicle or as a laboratory-prepared
contaminant of gallium oxide particles. Several biomarkers of systemic absorption were
measured, including the dose-dependent effects of TCDD on hepatic microsomal cytochrome
P-450 content, AHH activity, and liver histopathology. Significant dose-related effects were
observed at an exposure of >0.55 |_ig TCDD/kg. The authors found that induction was slightly
higher when animals received TCDD in corn oil than when animals received TCDD-
contaminated particles, and was comparable to induction after oral exposure. The results from
Nessel et al. (1990) indicate that systemic effects occur after pulmonary exposure to TCDD,
suggesting that transpulmonary absorption of TCDD does occur.
The pulmonary bioavailability of TCDD was also examined in female Sprague-Dawley
rats following intratracheal instillation of PCDD-contaminated soil from a former 2,4,5-
trichlorophenoxy-acetic acid manufacturing site (Nessel et al., 1992). A size-dependent
enrichment of PCDDs and PCDFs was observed, with the smaller particles being more highly
contaminated. TCDD was enriched up to 33-fold in small respirable particles as compared with
unfractionated soil. Pulmonary bioavailability of TCDD was assessed by hepatic enzyme
induction (AHH activity) and TCDD concentration. The data indicate that the relative
pulmonary bioavailability of TCDD on respirable soil particles is 100% as compared with
laboratory-recontaminated gallium oxide.
The transpulmonary absorption of TCDD was assessed in male Fischer 344 rats following
intratracheal instillation of a 1 nmol/kg dose in Emulphorethanol:water (1:1:3) (Diliberto et al.,
1996a). Transpulmonary absorption was 95%, suggesting that there was almost complete
absorption of TCDD by inhalation under these conditions. Similar results also were observed for
the transpulmonary absorption of 2,3,7,8-TBDD under similar exposure conditions (Diliberto et
al., 1993a,b). Recent studies (Diliberto et al., 1996a) further show the importance of
transpulmonary absorption for 2,3,7,8-TCDD. Tissue distributions were measured 3 days after
administration via different routes. Comparisons show that the percentage of dose distributed to
the liver after intratracheal (itr) injection is similar to that after intravenous administration (iv)
(33% for itr, 37% for iv). Also, both the iv and itr routes show a preference for greater
sequestration in the liver over fat when compared to the oral (po) route. These results suggest
that the transpulmonary absorption of 2,3,7,8-TCDD and 2,3,7,8-TBDD was similar to that
observed following oral exposure.
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1.1.4. Parenteral Absorption
In an effort to obtain more reproducible and complete absorption of TCDD and related
compounds for pharmacokinetic studies, Abraham et al. (1989a, b) used various vehicles to
investigate the absorption of TCDD after parenteral application in rats. These investigators
observed optimal results with the subcutaneous injection of TCDD with a mixture of
toluene: DM SO (1:2) as the vehicle. At 3 and 5 days after treatment, the percentages of
administered dose remaining at the injection site under the skin of the back were -10% and 2%,
respectively. The vehicle did not cause adverse effects at an applied volume of 0.2 mL/kg bw.
The absorption of a defined mixture of CDDs and CDFs in the rat was also examined after
subcutaneous injection with toluene: DM SO (1:2) as a vehicle. Of the 97 congeners analyzed, 70
were >95% absorbed 7 days after exposure; 21 were 90%-95% absorbed; and 1,2,3,9-TCDD,
1,2,3,6,7,9-71,2,3,6,8,9-HxCDD, 1,2,3,4,6,7,9-HpCDD, OCDD, 1,2,4,6,8,9-HxCDF and
1,2,3,7,8,9-HxCDF were 84%-89% absorbed. Greater than 90% absorption of CDDs and CDFs
also was observed under these conditions in the marmoset monkey, with the exception of
1,2,3,4,7,8,9-HpCDF, OCDF, and OCDD, which had ~50%-80% of the administered dose
absorbed (Neubert et al., 1990; Abraham et al., 1989a). Although the absorption of CDDs and
CDFs after subcutaneous administration in toluene:DMSO (1:2) is somewhat slow in rats and
monkeys, absorption of most congeners was >90% within 7 days. Even for highly chlorinated
insoluble congeners, such as OCDD and OCDF, subcutaneous absorption was >84% in the rat
and >50% in the monkey.
Less complete and slower absorption of CDDs and CDFs was observed after
subcutaneous injection of these compounds using an oil-containing vehicle (Brunner et al., 1989;
Abraham et al., 1989a). Using a corn oil:acetone vehicle (24:1, v/v), Lakshmanan et al. (1986)
observed that only 7% of the administered dose of TCDD was absorbed 24 hours after
subcutaneous injection and that only 35% was absorbed after intraperitoneal injection. Brunner
et al. (1989) also reported that intraperitoneal administration of CDDs and CDFs revealed a
delayed absorption from the abdominal cavity that varied for the different congeners. Therefore,
concentrations measured in abdominal adipose tissue after intraperitoneal administration may not
represent average values of adipose tissue in the whole body, particularly at early time points
following exposure. This may also be true following oral exposure because of different
perfusion rates of different fat depots (McKinley et al., 1993).
1.2. DISTRIBUTION
1.2.1. Distribution in Blood and Lymph
Once a compound is absorbed, its distribution is regulated initially by its binding to
components in blood and its ability to diffuse through blood vessels and tissue membranes.
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Lakshmanan et al. (1986) investigated the absorption and distribution of TCDD in thoracic duct-
cannulated rats. The results suggest that after gastrointestinal absorption, TCDD is absorbed
primarily by the lymphatic route and is transported predominantly by chylomicrons. Ninety
percent of the TCDD in lymph was associated with the chylomicron fraction. The plasma
disappearance of TCDD-labeled chylomicrons followed first-order decay kinetics, with 67% of
the compound leaving the blood compartment very rapidly (t,/2=0.81 minutes), whereas the
remainder of the TCDD had a t,/2 of 30 minutes. TCDD was then found to distribute primarily to
the adipose tissue and the liver.
In vitro studies have investigated the distribution of TCDD in human whole blood.
Henderson and Patterson (1988) found -80% of the compound associated with the lipoprotein
fraction, 15% associated with protein (primarily human serum albumin), and 5% associated with
cellular components. A subsequent in vivo investigation reported a similar distribution of TCDD
in the various fractions of human whole blood (Patterson et al., 1989). Theoretical and limited
experimental data also suggest that TCDD and related compounds may be associated with
plasma prealbumin (McKinney et al., 1985; Pedersen et al., 1986). The distribution of
[3H]-2,3,7,8-TCDD among lipoprotein fractions from three fasting, normolipemic donors
indicated a greater percentage associated with LDL (55.3% ± 9.03% SD) than with VLDL
(17.4% ± 9.07% SD) or HDL (27.3% ± 10.08% SD). The distribution of TCDD among the
lipoprotein fractions was similar to that reported earlier by Marinovich et al. (1983). When the
binding of TCDD was calculated per mole of lipoprotein, it was suggested that the maximal
binding capacity was exerted by VLDL, followed by LDL and HDL (Marinovich et al., 1983).
The results also suggest that variations in the amounts of each lipoprotein class may alter the
distribution of TCDD among lipoproteins in a given subject. Significant species differences also
exist; in the case of the rat, which has markedly lower plasma lipids compared to humans, TCDD
was distributed almost equally among the lipoprotein fractions (Marinovich et al., 1983).
Congener-specific differences have been observed for the in vivo binding of the 2,3,7,8-
substituted PCDDs and PCDFs to different serum fractions in human blood (Patterson et al.,
1989). Binding to the lipoproteins gradually decreased with increasing chlorine content, with
about 75% of TCDD bound to lipoproteins while approximately 45% of OCDD was bound to
this fraction. In contrast, binding to other serum proteins increased with chlorine content, from
approximately 20% for TCDD to 50% for OCDD. The results indicate that the higher
chlorinated PCDDs and PCDFs do not partition according to the lipid content of these blood
fractions. However, Busbee and Zipring (1994) report that at least one lower chlorinated PCB,
dichlorobiphenyl, absorbs and distributes with the nonlipid plasma fractions, rather than the lipid
fractions as might be expected.
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In addition, there is indirect evidence that suggests that the binding of TCDD to
lipoproteins may alter the pharmacokinetics and toxic potency of the compound. Marinovich et
al. (1983) found that experimentally induced hyperlipidemia in rats delayed the development of
overt toxicity (lethality). However, the disposition of TCDD was not investigated under these
conditions. These investigators suggest that the release of lipoprotein-bound TCDD is related to
the metabolic turnover of lipoproteins. In hyperlipidemic rats, the turnover of VLDL and LDL is
delayed significantly compared to that in normolipidemic animals, and this may contribute to the
plasma lipoprotein binding modifying the toxicity of TCDD in hyperlipidemic rats.
The time- and temperature-dependent cellular uptake of lipoprotein-associated TCDD by
cultured human fibroblasts was greatest from LDL, intermediate from HDL, and least from
serum (Shireman and Wei, 1986). Decreased cellular uptake of LDL and TCDD was observed in
mutant fibroblasts, which lack the normal cell membrane receptor for LDL. This provides some
evidence that specific binding of LDL and the LDL receptor pathway may account for some of
the rapid early uptake of TCDD with LDL entry. The results suggest that the entry of TCDD into
cells may not be solely by simple diffusion. However, nonspecific binding of the LDL and
transfer of TCDD from LDL to the cell membranes are probably also important, as significant
time- and temperature-dependent uptake of TCDD and LDL occurred in the mutant fibroblasts.
Thus, upon absorption, TCDD and probably related compounds are bound to
chylomicrons, lipoproteins, and other serum proteins that assist in distributing these uncharged,
lipophilic compounds throughout the vascular system. These compounds then partition from
blood components into cellular membranes and tissues, probably largely by passive diffusion. In
addition, cellular uptake may be facilitated partly through the cell membrane LDL receptor, the
hepatic receptor for albumin (Weisiger et al., 1981), and other systems.
1.2.2. Tissue Distribution
Once absorbed into blood, TCDD and related compounds readily distribute to all organs.
Tissue distribution within the first hour after exposure parallels blood levels and reflects
physiological parameters such as blood flow to a given tissue and relative tissue size. For
example, high initial concentrations of TCDD, 1,2,3,7,8-PeCDF, and 3,3',4,4'-TCB were
observed in highly perfused tissue such as the adrenal glands during the 24-hour period after a
single exposure (Birnbaum et al., 1980; Olson et al., 1980; Pohjanvirta et al., 1990; Brewster and
Birnbaum, 1988; Durham and Brouwer, 1990). A high percentage of the dose of 2,3,7,8-TCDF
and 1,2,3,7,8-PeCDF was also found in muscle within the first hour after intravenous exposure
because of the large volume of this tissue (Birnbaum et al., 1980; Birnbaum, 1985; Brewster and
Birnbaum, 1988). Nevertheless, within several hours the liver, adipose tissue, and skin become
the primary sites of disposition, when expressed as percent of administered dose per gram tissue
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and as percent of dose per organ. Liver, adipose tissue, skin, and thyroid were the only tissues to
show an increase in the concentration of TCDD during the initial 4 days after a single intra
peritoneal exposure of rats (Pohjanvirta et al., 1990). In this study, a similar general pattern of
disposition was observed in Han/Wistar and Long-Evans rats, which are, respectively, most
resistant and susceptible to the acute toxicity of TCDD (Pohjanvirta et al., 1990).
Table 1-4 illustrates the tissue distribution of TCDD in female Wistar rats 7 days after a
single subcutaneous exposure (Abraham et al., 1988). This general pattern of distribution is
similar to that observed in mice, rats, rhesus monkeys, hamsters, and guinea pigs, where liver and
adipose tissue consistently have the highest concentrations of TCDD (Piper et al., 1973; Fries
and Marrow, 1975; Rose et al., 1976; Allen et al., 1975; van Miller et al., 1976; Kociba et al.,
1978a,b; Gasiewicz et al., 1983b; Manara et al., 1982; Olson et al., 1980; Gasiewicz andNeal,
1979; Birnbaum, 1986; Pohjanvirta et al., 1990; Abraham et al., 1988). A similar pattern of
disposition also was observed for 2,3,7,8-TCDF in the guinea pig, rat, C57BL/6J and DBA/2J
mouse, and rhesus monkey, with 2,3,7,8-TCDF concentrations highest in liver and adipose tissue
(Decad et al., 1981b; Birnbaum et al., 1980, 1981). In summary, there do not appear to be major
species or strain differences in the tissue distribution of TCDD and 2,3,7,8-TCDF, with the liver
and adipose tissue being the primary disposition sites. For the PCBs, Ness et al. (1994) provide
evidence of accumulation of PCB congeners in rat brain. Dams were dosed with Aroclor 1242
and pups from litters were euthanized at weaning. No differences in PCB concentrations
between sexes or among brain regions were found, but the different congeners differed from each
other in degree of bioaccumulation.
The tissue distribution of the coplanar PCBs and PBBs also appears to be similar to that
of TCDD and 2,3,7,8-TCDF. Limited studies in rats and mice found that 3,3',4,4'-TCB,
3,3',4,4'-TBB, and 3,3',4,4',5,5'-HxBB distributed preferentially to adipose tissue and liver
(Clarke et al., 1983, 1984; Millis et al., 1985; Wehler et al., 1989; Clevenger et al., 1989).
Lindenau et al. (1994) studied the distribution of PCBs in the reproductive tissues of female
rabbits after long-term low-dose exposures. Chlorinated hydrocarbons were found to accumulate
especially in oviductal and uterine tissues and in follicular and uterine fluids.
Although the liver and adipose tissue contain the highest concentrations of TCDD and
2,3,7,8-TCDF, there are some congener-specific differences in the relative tissue distribution of
related compounds. 2,3,7,8-TBDD and 1,2,3,7,8-PeCDD disposition in the rat was very similar
to that of TCDD (Kedderis et al., 1991a; Wacker et al., 1986). The hepatic concentration of
OCDD and 2,3,4,7,8-PeCDF in the rat, however, was approximately 10- to 20-fold greater than
that in adipose tissue, which generally contains the second highest levels of these compounds
(Birnbaum and Couture, 1988; Norback et al., 1975; Williams et al., 1972; Brewster and
Birnbaum, 1987). The tissue distribution of a defined mixture of CDDs and CDFs (28.8 |_ig
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CDDs+CDFs/kg bw containing 120 ng TCDD/kg bw) was measured in marmoset monkeys 7
days after a single subcutaneous exposure (Abraham et al., 1990). For most of the 2,3,7,8-
substituted congeners, the highest concentrations were detected in hepatic and adipose tissue,
with correspondingly lower values detected in kidney, brain, lung, heart, thymus, or testes. The
hepatic and adipose tissue concentrations were similar for TCDD, 1,2,3,7,8-PeCDD, 2,3,7,8-
TCDF, and 1,2,3,7,8-71,2,3,4,8-PeCDF. Nonetheless, the hepatic concentrations were
approximately tenfold or more greater than those of adipose tissue for 1,2,3,4,7,8-HxCDD,
1,2,3,6,7,8-HxCDD, 1,2,3,7,8,9-HxCDD, 1,2,3,4,6,7,8-HpCDD, OCDD, 2,3,4,7,8-PeCDF,
1,2,3,4,7,8-71,2,3,4,7,9-HxCDF, 1,2,3,6,7,8-HxCDF, 1,2,3,7,8,9-HxCDF, 2,3,4,6,7,8-HxCDF,
1,2,3,4,6,7,8-HpCDF, 1,2,3,4,7,8,9-HpCDF, and OCDF. The lungs and thymus contained higher
concentrations of all of these congeners than were detected in kidney, brain, heart, and testes.
Unexpectedly, the concentrations of these HxCDDs, HpCDDs, OCDD, and HxCDFs were
similar in the adipose tissue, lungs, and thymus. In the case of HpCDFs and OCDF, the
concentrations were greater in the lungs than in the adipose tissue. The enhanced disposition of
highly chlorinated congeners to the lungs and thymus is of interest and deserves further
investigation. For example, it is possible that the high concentration in the lungs could be related
to the insolubility of these compounds. Further, Diliberto et al. (1995) report that in mice, the
ratio of liver to adipose tissue concentration of TCDD changes with dose. As the dose increases,
the adipose (and other nonhepatic tissues) tissue dose/g decreases while it increases in the liver.
Section 1.2.5 of this document discusses induction of hepatic cytochrome P-450 1A2 (CYP1A2)
as the primary factor responsible for the hepatic sequestration of TCDD and related compounds.
It is also to be expected that exposure to a mixture of these compounds can cause effects
in the uptake and distribution. Darnerud et al. (1993) report on the effects of pretreatment of
various PCBs on the hepatic uptake of 2,3,7,8-TCDF in mice. After a 4-hour pretreatment,
several mono- and di-ortho PCB congeners and Aroclor 1254 increased the hepatic uptake of
TCDF. Non-ortho congeners decreased the uptake of TCDF. At longer pretreatment times (48
hours), both non-ortho and mono-ortho PCBs increased the hepatic uptake of TCDF. These
studies illustrate the complexity of the pharmacokinetics of mixtures.
Whole-body autoradiography of mice and rats after intravenous administration of
[14C]-2,3,7,8-TCDD showed a selective localization of radioactivity in the liver and nasal
olfactory mucosa (Appelgren et al., 1983; Gillner et al., 1987). The selective localization of
TCDD in the nasal olfactory mucosa was apparently overlooked by other distribution studies that
only examined selected organs. Gillner et al. (1987) found no TCDD-derived radioactivity in the
olfactory mucosa after solvent extraction of sections, suggesting that TCDD was not covalently
bound in this tissue. In addition, Gillner et al. (1987) reported induction of mRNA coding for P-
4501A2 (CYP1A2) in the absence of P-4501A1(CYP1A1) induction in olfactory mucosa of rats.
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The selective distribution of 2,3,7,8-TCDD in the liver and olfactory mucosa correlates with the
tissue-specific localization of CYP1A2, which represents a potential sequestration (binding)
protein (see Section 1.2.5). Increases in the incidence of squamous cell carcinoma of nasal
turbinates and carcinoma of the liver were observed in rats after a 2-year exposure to TCDD in
rat chow (Kociba et al., 1978a); however, this effect was not observed in nasal tissues of mice or
rats intubated with TCDD.
Evidence has also been reported that suggests that TCDD uptake and retention by the
liver is dependent on the cell type within the liver. Hakansson et al. (1989) found that at 4 days
after exposure of rats to TCDD, 60% of the dose distributed to hepatocytes and 12% was retained
by stellate cells. Half-lives for TCDD in hepatocytes and stellate cells were calculated to be 13
and 50 days, respectively, suggesting that TCDD is more persistent in nonparenchymal cells.
The induction of CYP1A2 in hepatocytes is the primary factor responsible for the hepatic
sequestration of TCDD and related compounds (see Section 1.2.5 of this document).
1.2.2.1. Tissue Distribution in Humans
Fachetti et al. (1980) reported tissue concentrations of TCDD at levels of 1-2 ng/g in
adipose tissue and pancreas, 0.1-0.2 ng/g in the liver, and <0.1 ng/g in thyroid, brain, lung,
kidney, and blood in a woman who died 7 months after potential exposure to TCDD from the
Seveso accident. This pattern of TCDD distribution, however, may not be representative for
humans because the woman at the time of death had an adenocarcinoma (which was not
considered related to the accident) involving the pancreas, liver, and lung.
Ryan et al. (1985a) examined the distribution of TCDD in two humans at autopsy. They
determined on a weight basis that TCDD distributed in descending order to fat (~6 ppt) and liver
(~2 ppt), with levels in muscle and kidney below detection; however, TCDD levels compared on
a per lipid basis were similar between tissues. These data should be interpreted with caution
because only two subjects were examined and one of the subjects was suffering from fatty liver
syndrome; therefore, the data cannot be generalized to the entire population.
Poiger and Schlatter (1986) estimated that -90% of the body burden of TCDD was
sequestered in the fat after a volunteer ingested [3H]-2,3,7,8-TCDD in corn oil at a dose of 1.14
ng/kg. During this 135-day study, elevated radioactivity was detected in the blood only during
the first 2 days after treatment. The data would be consistent with the high lipid bioconcentration
potential of TCDD in humans, as calculated by Geyer et al. (1986) from daily intake
assumptions, levels in human adipose tissue, and pharmacokinetic models. Geyer et al. (1986)
estimated a BCF of between 104 and 206 for TCDD in human adipose tissue.
In human adipose tissue, levels of TCDD averaging 5-10 ppt have been reported for
background populations in St. Louis, Missouri, by Graham et al. (1986), in Atlanta and Utah by
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Patterson et al. (1986), and in Canada by Ryan et al. (1985b). Sielken (1987) evaluated these
data and concluded that the levels of TCDD in human adipose are log-normally distributed and
positively correlated with age. Among the observed U.S. background levels of TCDD in human
adipose tissue, more than 10% were >12 ppt.
Patterson et al. (1987) developed a high-resolution gas chromatographic/high-resolution
mass spectrometric analysis for TCDD in human serum. The arithmetic mean of the individual
human serum samples was 47.9 ppt on a whole-weight basis and 7.6 ppt on a lipid-weight basis.
Paired human serum and adipose tissue levels of TCDD have been compared by Patterson et al.
(1988), Kahn et al. (1988), and Schecter et al. (1990a). All three laboratories reported a high
correlation between adipose tissue and serum TCDD levels when the samples were adjusted for
total lipid content. This correlation indicates that serum TCDD is a valid estimate of the TCDD
concentration in adipose tissue.
Congener-specific partitioning of 2,3,7,8-substituted PCDDs and PCDFs between adipose
tissue and plasma lipids has also been reported in a study of 20 Massachusetts Vietnam veterans
(Schecter et al., 1990b). The distribution ratio between plasma lipid and adipose tissue increased
with chlorine substitution on the PCDDs and PCDFs. Whereas 2,3,7,8-substituted TCDD,
TCDF, PeCDD, PeCDF, HxCDD, and HxCDF had a plasma lipid-to-adipose tissue ratio of about
1.0, OCDD had a ratio of 2.0. On the other hand, whole blood PCDDs and PCDFs seem to be
found at the same concentrations as in adipose tissue on a lipid basis (Schecter, 1991).
The disposition of 2,3,7,8-substituted PCDDs and PCDFs in human liver and adipose
tissue was assessed in a study of 28 people from the Munich area (Thoma et al., 1989; 1990).
Table 1-5 summarizes these results, which are expressed both on a lipid and wet-weight basis.
The concentrations of PCDDs and PCDFs in adipose tissue and liver are not the same when
calculated on a lipid basis. This is in contrast to the high correlation that was reported between
adipose tissue and serum TCDD levels when expressed on a lipid-weight basis (Patterson et al.,
1988; Kahn et al., 1988; Schecter et al., 1990a). Furthermore, the liver/adipose tissue ratio
increased with the higher chlorinated PCDDs and PCDFs. The congener-specific hepatic
disposition is also similar to that observed in rats and marmoset monkeys exposed to a complex
mixture of PCDDs and PCDFs (Abraham et al., 1989b; Neighbored et al., 1990). Therefore, it is
important to consider congener- and tissue-specific differences in disposition of PCDDs and
PCDFs when blood levels are used to estimate tissue levels or body burdens.
Schecter et al. (1998a) assessed the disposition of PCDDS, CDFS, and coplanar PBS in
the blood, milk, adipose tissue, placenta, and cord blood from five American women. When
expressed on a pg/g lipid basis, the mean total TEQswere 11.6, 12.1, 10.5, 5.8, 10.0, and 10.2 in
adipose tissue, predelivery blood, placenta, cord blood, postpartum blood, and breast milk,
respectively. The results suggest that CDDs, CDFs, and PCBs, when expressed as total TEQs on
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a lipid basis, partition to a similar extent between these tissues. Even though 2,3,4,7,8-PeCDF
and PCB 126 were lower in cord blood than other tissues, the levels of TCDD were similar in
these tissues.
In a study of potentially heavily exposed Vietnam veterans, MMWR (1988) reviewed an
Air Force study of Ranch Hand veterans who were either herbicide loaders or herbicide
specialists in Vietnam. The mean serum TCDD levels of 147 Ranch Hand personnel was 49 ppt
in 1987, based on total lipid weight, while the mean serum level of the 49 controls was 5 ppt. In
addition, 79% of the Ranch Hand personnel and 2% of the controls had TCDD levels >10 ppt.
The distribution of TCDD levels in this phase of the Air Force health study indicates that only a
small number of Ranch Hand personnel had unusually heavy TCDD exposure. Similar results
were obtained by Kahn et al. (1988), who compared TCDD levels in blood and adipose tissue of
Agent Orange-exposed Vietnam veterans and matched controls (Kahn et al., 1988). This study
also examined moderately exposed Vietnam veterans who handled herbicides regularly while in
Vietnam. Although this study can distinguish moderately exposed men from others, the data do
not address the question of identifying persons whose exposures were relatively low and who
constitute the bulk of the population, both military and civilian, that may have been exposed to
greater than background levels of TCDD.
1.2.3. Time-Dependent Tissue Distribution
2,3,7,8-TCDD and related compounds exhibit congener-specific disposition, which
depends on tissue, species, and time after a given exposure. In general, these compounds are
cleared rapidly from the blood and distributed to liver, muscle, skin, adipose tissue, and other
tissues within the first hour(s) after exposure. This is followed by redistribution to the liver and
adipose tissue, which exhibit increasing tissue concentrations over several days after exposure.
Elimination from tissues then occurs at rates that are congener-, tissue-, and species-specific.
Thus, the ratio of the concentration of TCDD and related compounds in different tissues (i.e.,
liver/adipose) may not remain constant over an extended period after a single exposure.
Abraham et al. (1988) examined the concentrations of TCDD in liver and adipose tissue of
female Wistar rats over a 91-day period after a single subcutaneous exposure at a dose of 300
ng/kg bw (Figure 1-1). The maximum concentration of TCDD in the liver and adipose tissue
was reached at 3 and 7 days after exposure, respectively. The liver/adipose tissue concentration
ratio does not remain constant over time because the concentration of TCDD decreases more
rapidly in the liver than in the adipose tissue. For example, the liver/adipose tissue concentration
ratio (for TCDD) was 10.3 at 1 day after exposure and 0.5 at 91 days after exposure (Figure 1-1).
Results from other disposition studies also indicate that the ratio of the concentration of TCDD
and related compounds in liver, adipose tissue, and other tissues does not remain constant over
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an extended period after a single exposure (Pohjanvirta et al., 1990; Birnbaum, 1986; Birnbaum
et al., 1980; Decad et al., 1981a; Birnbaum and Couture, 1988; Olson et al., 1980; Kedderis et al.,
1993a; Brewster and Birnbaum, 1987, 1988; Neubert et al., 1990). This relationship is important
in attempting to correlate dose-response data with tissue concentrations of TCDD and related
compounds.
In an attempt to maintain constant TCDD levels in tissues to study long-term effects,
Krowke et al. (1989) investigated several loading-dose/maintenance-dose exposure regimens.
They found that similar liver/adipose tissue concentrations ranging from 5 to 8 could be
maintained in rats over a 22-week period with a loading dose of 25 pg/kg followed by weekly
maintenance doses of 5 pg/kg.
A large body of data on the tissue concentrations of TCDD and related compounds over
time after exposure can be evaluated by estimating congener-specific half-life values for a given
tissue and species. Table 1-6 summarizes pharmacokinetic elimination parameters for TCDD
and related compounds from major tissue depots. Data from Abraham et al. (1988) (see Figure
1-1) were used to estimate the half-life for TCDD in the liver and adipose tissue of rats
(Table 1-6). The decrease in the TCDD concentration in adipose tissue is a linear function in the
semilogarithmic plot in Figure 1-1 (log concentration versus time), which indicates apparent
first-order elimination kinetics with a half-life of 24.5 days (Table 1-6). Liver tissue exhibits a
biphasic (two-component) exponential decay pattern, with a half-life of 11.5 days for the first
component (days 10-49) and a half-life of 16.9 days for the second component (days 49-91) (see
Figure 1-1 and Table 1-6). Santostefano et al. (1997) reported a similar half-life of
approximately 10 days in female Sprague-Dawley rats administered a 10 ng/kg oral dose of
TCDD/kg. Results of Abraham et al. (1988) and Lakshmanan et al. (1986) indicate that in the
rat, TCDD is more persistent in the adipose tissue than in the liver. This is in contrast to the
mouse, where liver and adipose tissue have similar half-lives (Birnbaum, 1986). TCDD is
exceptionally persistent in the adipose tissue of the rhesus monkey, with a half-life approximately
ten- to fortyfold greater than that observed in the rat and mouse (Bowman et al., 1989). Thus, the
relative persistence of TCDD is tissue-specific and exhibits marked interspecies variability.
Most of the pharmacokinetic data on the relative persistence of other congeners in
Table 1-6 have been reported in rat studies, which limits interspecies comparisons. Results in the
rat suggest that the distribution and elimination of 2,3,7,8-TBDD from tissue are similar to those
of 2,3,7,8-TCDD. The most persistent congeners are OCDD, 2,3,4,7,8-PeCDF, and 1,2,3,6,7,8-
HxCDF, which distribute almost entirely to the liver. OCDD and 2,3,4,7,8-PeCDF also exhibit
similar elimination kinetics, with a relative half-life in the liver more than twofold greater than
that in adipose tissue. The least persistent congeners are 2,3,7,8-TCDF, 1,2,3,7,8-PeCDF, and
3,3 ',4,4'-TCB. These congeners exhibit similar elimination kinetics in the rat, with half-lives in
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the adipose tissue greater than those in liver. The relative tissue distribution of these congeners
varies, however, with 2,3,7,8-TCDF and 1,2,3,7,8-PeCDF distributing primarily to the liver
while 3,3',4,4'-TCB distributes predominantly to the adipose tissue.
Viluksela et al. (1997) conducted a sub chronic/chronic toxicity study with 1,2,3,4,6,7,8-
HpCDD (13 weeks of dosing, 13 week off-dosing) in Sprague-Dawley rats. Liver concentrations
during the study provided a hepatic half-life estimate for 1,2,3,4,6,7,8-HpCDD of 237 days in
male rats and 314 days in female rats. Under similar exposure conditions, TCDD had half-lives
of 12.4 and 15.8 days in male and female rats, respectively. Liver concentrations of CDDs were
also assessed in a sub chronic/chronic toxicity study in rats administered a mixture of 2,3,7,8-
TCDD, 1,2,3,7,8-PeCDD, 1,2,3,4,7,8-HxCDD, and 1,2,3,4,6,7,8-HpCDD orally in corn oil (13
weeks of dosing, 13 weeks of off-dosing) (Viluksela et al., 1998). Average liver half-lives were
14.5, 29.3, 45.6, and 100 days, respectively, in males and 17.8, 38.2, 63.3, and 195.8 days,
respectively, in females. When administered individually, 1,2,3,4,7,8-HxCDD had liver half-
lives of 36.2 days in males and 55.5 days in females and 1,2,3,7,8-PeCDD had a liver half-life of
36.1 days in male rats. Together, these results indicate that CDDs are more persistent in female
rats and that the hepatic half-life of these compounds is similar following exposure to individual
CDDs and mixtures of CDDs.
The experimental tissue distribution and elimination data in Table 1-6 were obtained after
exposure to a single congener, but real-world exposure to TCDD and related compounds occurs
as a complex mixture of congeners. Neubert et al. (1990) examined the persistence of various
CDDs and CDFs in hepatic and adipose tissue of male and female marmoset monkeys. Animals
received a single subcutaneous exposure to a defined CDD/CDF mixture (total dose of 27.8
|-ig/kg bw), which contained 0.12 |_ig 2,3,7,8-TCDD/kg bw. Using the international toxicity
equivalency (I-TE) factors (NATO, 1988; U.S. EPA, 1989), the total administered dose
corresponded to 0.464 |_ig I-TE/kg bw. The concentrations of specific congeners in liver and
adipose tissue were measured at 1, 6, 16, or 28 weeks after exposure, and elimination constants
and half-lives were estimated assuming first-order kinetics (Table 1-7). Data in Table 1-7 were
determined from pregnant and nonpregnant female and male marmosets (total of 12 animals),
because no obvious differences in tissue concentrations were observed among these groups. All
2,3,7,8-substituted CDDs and CDFs were consistently more persistent in the adipose tissue than
in the liver of marmoset monkeys. In general, the persistence in adipose tissue was from ~ 1.3- to
2.0-fold greater than that in liver, with the exception of 1,2,3,4,7,8-71,2,3,4,7,9-HxCDF,
HpCDFs, and OCDF, which were even more persistent in adipose tissue. For the latter
congeners and OCDD, there was marked variance in half-life values, which may be due to
delayed and incomplete absorption of the exceptionally persistent congeners and the relatively
short (28 weeks) period of investigation. A significant species difference exists for OCDD and
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2,3,4,7,8-PeCDF, which, in contrast to the results in the marmoset monkey, was found to be
more persistent in the liver of the rat, with half-lives more than twofold greater than that in
adipose tissue (Birnbaum and Couture, 1988; Brewster and Birnbaum, 1987) (see Table 1-6).
Further comparison of tissue elimination data in the rat (Table 1-6) and monkey (Table 1-7)
indicates that 2,3,7,8-TCDD, OCDD, 2,3,7,8-TCDF, 1,2,3,6,7,8-HxCDF, and 2,3,4,7,8-PeCDF
(adipose tissue only) are more persistent in the marmoset monkey than in the rat. The exception
to this relationship is 2,3,4,7,8-PeCDF, which is more persistent in rat liver compared to the
monkey.
The exposure of marmoset monkeys to a complex mixture of CDDs and CDFs included
exposure to both 2,3,7,8- and non-2,3,7,8-substituted congeners (Neubert et al., 1990). One
week after exposure to this complex mixture, the non-2,3,7,8-substituted CDDs and CDFs were
present in liver and adipose tissue in relatively minor quantities when compared with 2,3,7,8-
substituted congeners; however, non-2,3,7,8-substituted compounds represented a considerable
percent of the exposure mixture. In this study, none of the non-2,3,7,8-substituted TCDDs,
PeCDDs, TCDFs, or PeCDFs could be detected in the liver. Some of the hexa- and hepta-
congeners were detected in adipose tissue and liver, but after 1 week, the total amount in the liver
was >5% of the administered dose only in the case of 1,2,4,6,8,9-HxCDF. Similar results were
obtained in rats after exposure to a defined, complex mixture of CDDs and CDFs (Abraham et
al., 1989c). Additional short-term studies in rats provide evidence that the low tissue
concentrations of non-2,3,7,8-substituted congeners, measured 1 week after exposure, were the
result of rapid elimination, as these congeners were detected at higher levels in the liver 13 to 14
hours after exposure (Abraham et al., 1989d). These results in monkeys and rats are compatible
with data from analysis of human tissue samples and milk, in which the non-2,3,7,8-substituted
congeners have also not been shown to be present in significant concentrations when compared
with the 2,3,7,8-substituted congeners (Schecter et al., 1985, 1986; Ryan, 1986; Rappe et al.,
1986; Beck et al., 1987, 1989; Thoma et al., 1989).
Several human studies also show the importance of time dependence and age on tissue
distribution within the body. Duarte-Davidson and Jones (1994) reported that in a population in
the United Kingdom adipose tissue concentrations of PCBs increased at a slower rate as age
increased. The researchers attributed this to the dilution effect caused by the increase in body fat
with age. Wolfe et al. (1994) reported similar findings with TCDD. They found significant
increases in half-life with increases in percent body fat and with age. As will be discussed in a
later section, body burden levels could also be decreasing with time because of reduced exposure
levels (Furst et al., 1994).
Other recent studies also show that age is an important factor affecting distribution of
these compounds within the body. Pegram et al. (1995) show that the hepatic concentration of
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TCDD was about 25% greater in young mice than in old mice. No age-related differences in
nuclear TCDD concentration was found in the liver fat tissue concentration ratios, adipose tissue,
or blood concentrations. In old mice kidney, skin, and muscle concentrations were about twofold
higher than in younger mice at a dose of 15 pg/kg. The authors suggest that these age-related
differences represent differences in Ah receptor-mediated enzyme induction.
A potential problem of tissue distribution and elimination studies after exposure to a
complex mixture of CDDs and CDFs is the possible interaction of the mixture during the uptake
and elimination of specific congeners from tissues. A similar hepatic distribution (-25% of
dose) and liver/adipose tissue concentrations ratio (~2) for TCDD were observed in rats 7 days
after exposure to TCDD (100 ng/kg bw) when the compound was administered alone or in
combination with a large amount of other CDDs/CDFs (total 23,222 ng/kg bw) (Abraham et al.,
1988, 1989d). This suggests that under these experimental conditions, the tissue distribution of
TCDD was not altered when the exposure included a complex mixture of CDDs/CDFs. van den
Berg et al. (1989) studied the hepatic disposition and elimination of CDFs administered
individually (see Table 1-6) and as mixtures. Co-administration of 1,2,3,7,8- and 2,3,4,7,8-
PeCDF resulted in 46% of the dose of 1,2,3,7,8-PeCDF distributing to the liver, while 70% was
distributed to the liver after administration of the single-compound (see Table 1-6).
Nevertheless, this combined exposure did not alter the rate of elimination of 1,2,3,7,8-PeCDF
from the liver. Co-administration of 2,3,4,7,8-PeCDF and 1,2,3,6,7,8-HxCDF did not alter the
hepatic uptake of either congener or the hepatic elimination of 2,3,4,7,8-PeCDF but increased the
hepatic half-life of 1,2,3,6,7,8-HxCDF to 156 days from the single-compound exposure half-life
of 73 days (see Table 1-6). However, these values must be considered rough estimates because
the experimental period of 42 days was too short to calculate half-lives accurately. Although
there are few investigations of potential interactions of mixtures of CDDs and CDFs on the
uptake and elimination of individual congeners, the limited available data suggest that exposure
to complex mixtures (see Table 1-7) may alter the tissue disposition of individual congeners.
Several studies have investigated potential pharmacokinetic interactions following
exposure to two or more PCDDs, PCDFs, or PCBs (DeJongh et al., 1992, 1993a, 1993b, van
Birgelen et al., 1996a). Interactive effects on the hepatic disposition of these compounds may in
some cases explain, in part, the potentiation and antagonistic effects on CYP1A1/1A2 activities
observed with combined exposure to some of these compounds. In the case of 1,2,3,7,8-PeCDD
and 2,4,5,2',4',5'-HxCB, no pharmacokinetic bases were found to explain the antagonistic
effects of the combined exposure on CYP1A1/1A2 activities in mice (DeJongh et al., 1992). In a
subsequent study the hepatic disposition of 1,2,3,7,8-PeCDD in mice was increased with
combined exposures to 1,2,3,6,7,8-HxCDD, 2,3,4,7,8-PeCDF, and/or 2,4,5,2',4',5' HxCB (De
Jonghetal., 1993b).
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De Jongh et al. (1995) report that TCDD co-administration with 2.2',4,4'5,5'-
hexachlorobiphenyl (HxCB) in mice results in an increased disposition of TCDD in the liver. At
the same time, the results do not show any effect of the TCDD on the HxCB disposition. The
distribution of TCDD (0, 0.1, 1.0, or 10 |_ig/kg) and/or 2.2',4,4',5,5'-hexachlorobiphenyl (PCB
153; 0, 3.58, 35.8, or 358 mg/kg) were investigated alone and in all possible combinations of
these doses following oral administration in corn oil to female B6C3F1 mice (van Birgelen et al.,
1996b). Coadministration of the low doses of TCDD and PCB 153 had little or no effect on the
distribution of either compound. Interactive effects on the pharmacokinetic behavior occurred
only at high doses. For example, the hepatic disposition of TCDD increased when it was
administered in combination with the highest dose of PCB 153. Although there is evidence that
PCDDs, PCDFs, and PCBs may influence each other's pharmacokinetics when administered in
mixtures, this area needs investigation.
1.2.4. Dose-Dependent Tissue Distribution
Recent evidence suggests that the tissue distribution of TCDD and related compounds is
dose-dependent. Abraham et al. (1988) investigated the distribution of TCDD in liver and
adipose tissue of rats 7 days after a single subcutaneous exposure to TCDD at doses of 1-3,000
ng/kg bw. More than 97% of the administered TCDD was absorbed at all doses, with the
exception of the 3,000 ng/kg group, where 84% of the dose was absorbed. Figure 1-2 illustrates
the dose-dependent disposition of TCDD in liver and adipose tissue (% dose/g) 7 days after
exposure. A sharp increase in TCDD concentration in liver was observed at exposure levels >10
ng/kg bw. Disposition in the liver increased from ~ 11% of the administered dose at an exposure
level of 1-10 ng/kg bw to -37% of the dose at an exposure level of 300 ng/kg bw. The increase
in distribution to the liver was accompanied by a dose-related decrease in the concentration of
TCDD in the adipose tissue. As a result, the liver/adipose tissue concentration ratio for TCDD at
7 days after exposure increased with increasing doses, starting at an exposure level of 30 ng/kg
bw (Table 1-8). Thus, the tissue-specific disposition of TCDD is regulated by a complex
relationship, which includes species, time after a given exposure, and dose (see Figures 1-1 and
1-2; Tables 1-6 and 1-7).
Other studies on the tissue disposition of TCDD and related compounds report similar
dose-dependent behavior with disproportionately greater concentrations in the liver at high doses
compared with low doses. Poiger et al. (1989) observed a dose-related increase in distribution to
the liver (% of dose/liver) and an increase in the liver/adipose tissue concentration ratio for
2,3,7,8-TCDD, 2,3,4,7,8-PeCDF, 1,2,3,7,8-PeCDF, and 1,2,3,6,7,8-HxCDF in the rat. Kedderis
et al. (1991a) also observed a dose-related increase in hepatic disposition (1.27% versus 10.05%
of dose/liver) and an increase in the liver/adipose tissue concentration ratio (0.16 versus 2.59) for
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2,3,7,8-TBDD at 56 days after exposure at doses of 0.001 and 0.1 |_imol/kg bw, respectively. In a
related study, pretreatment of mice with TCDD (5 or 15 |ig/kg) produced a dose-related,
enhanced hepatic accumulation of a subsequent oral dose of TCDD (Curtis et al., 1990).
Diliberto et al. (1993b) also observed a dose-dependent tissue distribution of TCDD in female
B6C3F1 mice. In all tissues except the liver, the relative percent of the total dose of TCDD
decreased while it increased in the liver with higher doses. Similarly, a dose-related increase in
hepatic uptake of [125I]-2-iodo-3,7,8-trichlorodibenzo-p-dioxin was observed after pretreatment
of mice with TCDD (Poland et al., 1989a; Leung et al., 1990a). Shen and Olson (1987) also
observed an increase in the uptake of TCDD by isolated hepatocytes from TCDD-pretreated
mice.
Chronic studies also support dose-dependent alterations in the tissue distribution of these
compounds. Kociba et al. (1978a,b) found that female rats maintained on a daily dietary TCDD
intake of 100 ng for 2 years had an average TCDD content of 8,100 ppt in fat and 24,000 ppt in
the liver. Rats given 10 ng/kg/day had an average of 1,700 ppt TCDD in the fat and 5,100 ppt in
the liver. For both of these exposures the liver/adipose tissue concentration ratio of TCDD was
~3. At the lowest dose level of 1 ng/kg/day, both fat and liver contained an average of 540 ppt
TCDD. Kociba et al. (1976) presented evidence that steady state had been reached by <13 weeks
of feeding of TCDD.
Other studies do not support the dose-dependent tissue distribution of TCDD and related
compounds described above. Rose et al. (1976) reported a lack of a dose-dependent
accumulation of [14C]-TCDD in male and female rat liver and adipose tissue following 7, 21, and
49 days of exposure at 0.01, 0.1, or 1.0 |ag/kg/day, Monday through Friday. The rates of
accumulation of TCDD-derived radioactivity were similar in fat, liver and whole body; however,
the concentration in the liver was about fivefold greater than in fat. Brewster and Birnbaum
(1987) also observed similar concentrations (% dose/g) of 2,3,4,7,8-PeCDF in liver, adipose
tissue, and other tissues 3 days after oral exposure at doses of 0.1, 0.5, or 1.0 |_imol/kg bw. These
results conflict with the above studies, which support the dose-dependent tissue distribution of
these compounds. An explanation for the lack of a dose-dependent accumulation is that all of the
doses administered in the studies of Rose et al. (1976) and Brewster and Birnbaum (1987) were
inducing hepatic CYP1A2, which is now known to be primarily responsible for the hepatic
sequestration of these compounds (see Section 1.2.5).
The dose-dependent tissue distribution of TCDD and related compounds is a critical
factor that must be considered in estimating the concentration of these compounds in human
tissues after chronic low-level exposure. This is particularly important because the general
human population is exposed to much smaller daily doses (possibly 0.3 pg TCDD/kg/day) than
those used in experimental disposition studies. Owing at least partly to the long half-life of
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TCDD in humans, however, this exposure results in concentrations of 3-18 pg/g in human
adipose tissue (Leung et al., 1990b). Similar levels of TCDD in adipose tissue (14 pg/g) were
observed in rats 7 days after subcutaneous exposure to 3 ng/kg bw (see Table 1-8) (Abraham et
al., 1988). Under these experimental conditions, the liver/adipose tissue TCDD concentration
was 0.74. Nonetheless, steady state was definitely not reached under these conditions, and with
increasing time after exposure, this ratio may decrease, based on the observation that TCDD was
more persistent in adipose tissue than in liver in rats exposed to 300 ng/kg bw (see Figure 1-1
and Table 1-6) (Abraham et al., 1988). Human data on the liver/adipose tissue concentration
ratio of TCDD and related compounds are limited, but suggest that the ratio may vary by at least
an order of magnitude between individuals. Leung et al. (1990b) observed a geometric mean
adipose tissue TCDD concentration of 7.78 ppt in 26 individuals and a concentration in liver at
about one-tenth of that in adipose tissue on a whole-weight basis. When measured on a total
lipid basis, the concentrations of TCDD in both tissues were approximately the same. Thoma et
al. (1990) reported a liver/adipose tissue TCDD concentration on a wet weight basis of 0.14,
whereas on a lipid basis the ratio was 2.05 (Table 1-5). Considerable variability in CDD and
CDF concentrations in liver and adipose tissues was also observed between individual marmoset
monkeys (Neubert et al., 1990), suggesting that individual variability may also contribute to the
difficulty in assigning a constant liver/adipose tissue ratio for CDDs and CDFs in humans and
nonhuman primates.
1.2.5. Potential Mechanisms for Dose-Dependent Tissue Distribution
The observation that exposure to higher doses of TCDD and related compounds results in
a disproportionately greater hepatic concentration of these compounds may be explained by a
hepatic binding species that is induced by TCDD and other agonists for the Ah receptor. The
studies of Voorman and Aust (1987, 1989) and Poland et al. (1989a, b) provide evidence that this
binding species is CYP1A2.
Poland et al. (1989a, b) reported that TCDD and other Ah agonists (2,3,7,8-TCDF, 0-
naphthoflavone, 3,3',4,4',5,5'-hexabromobiphenyl) act through the Ah receptor to increase a
liver binding species that increases the hepatic uptake of [125I]-2-iodo-3,7,8-trichlorodibenzo:p-
dioxin (a radiolabeled isosteric analogue of TCDD) in vivo and binding of this radioligand to
liver homogenate in vitro. Twenty-four hours after the administration of a non-AHH-inducing
dose (1 x 1CT10 mol/kg) of [125I]-2-iodo-3,7,8-trichlorodibenzo-/?-dioxin to C576BL/6J mice, the
hepatic concentration of radioactivity was l%-2% of the administered dose, whereas in mice
pretreated 48 hours earlier with an AHH-inducing dose of TCDD (1 x icr7 mol/kg), the hepatic
accumulation of radiolabel was 25%-30% of that administered. A similar, though less dramatic,
effect was observed in vitro, with liver homogenate from TCDD-treated mice binding about four
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times more [125I]-2-iodo-3,7,8-trichlorodibenzo-/>-dioxin than homogenate from control mice.
The administration of TCDD to C57BL/6J mice produced a dose-related stimulation of in vivo
hepatic uptake of [125I]-2-iodo-3,7,8-trichlorodibenzo-p-dioxin, increased binding of radioligand
to liver homogenate, and induction of hepatic activity, with an ED50 ranging from 1.5 to 4.0 x
10"9 mol/kg. In congenic C57BL/6J (Ahd/Ahd) mice, which express the lower affinity Ah
receptor, the ED50 values for all three responses were shifted to doses that were about tenfold
higher. The observed effects on hepatic disposition were tissue-specific, with no remarkable
dispositional changes being observed in kidney, lung, spleen, small intestine, or muscle. This is
significant in that TCDD and other agonists for the Ah receptor induce CYP1 Al in liver and
other tissues, whereas CYP1A2 is apparently inducible only in liver (Tuteja et al., 1985; Gillner
et al. 1987). Furthermore, the changes in hepatic disposition were not species-specific; similar
responses were observed in guinea pigs, rats, mice, and hamsters (Poland et al., 1989a).
The following evidence reported by Poland et al. (1989b) supports the hypothesis that the
TCDD-inducible hepatic binding protein is CYP1A2: the TCDD-induced hepatic binding
species was found predominantly in the microsomal fraction and was inactivated by heating at
60°C, trypsin, and mercurials; the TCDD-induced hepatic binding species was specific for the
liver, with a large pool size (Bmax of 22 ± 5 nmol/g liver); the major microsomal binding species
covalently labeled with the photoaffinity ligand [125I]-2-iodo-3-azido-7,8-dibromodibenzo:p-
dioxin migrates with that immunochemically stained with polyclonal antiserum, which binds to
CYP1A2.
One observation of Poland et al. (1989a,b) does not support the hypothesis that the
TCDD-inducible hepatic protein is CYP1A2. These investigators found that dietary
administration of isosafrole did not stimulate hepatic uptake of [125I]-2-iodo-3,7,8-
trichlorodibenzo-p-dioxin or the in vitro binding of this ligand to liver homogenate. Isosafrole is
not an agonist for the Ah receptor, but it selectively induces CYP1A2 (Ryan et al., 1980). Poland
et al. (1989a,b) suggest that this may be attributable to the high affinity binding of an isosafrole
metabolite to the protein, which might inhibit the binding of [125I]-iodo-3,7,8-trichlorodibenzo-p-
dioxin to CYP1A2 at or near the active site of the enzyme. This does not explain why TCDD,
which also has high affinity for CYP1A2, cannot displace some of the isosafrole metabolite from
the protein, which should produce enhanced hepatic disposition of TCDD.
A more recent study used pretreatment with isosafrole to investigate the role of CYP1A2
in the hepatic disposition of TCDD (Kedderis et al., 1993a). Although isosafrole is an inducer of
CYP1A2, it also has high affinity for the protein and thus is a selective inhibitor of CYP1A2. A
greater than threefold decrease in the hepatic disposition of TCDD in isosafrole-pretreated rats
supports the conclusion that TCDD is bound to CYP1A2 in the liver.
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Olson et al. (1994) have reported the results of in-vitro studies with isolated hepatocytes
and liver slices of rats pretreated with TCDD. Pretreatment increased the uptake of TCDD but
not TCDF. There was an increase in both CYP1 Al and CYP1A2. The authors interpret these
results as consistent with CYP1A2 being a hepatic binding protein for TCDD but not TCDF.
Voorman and Aust (1987, 1989) support further the hypothesis that CYP1A2 is the
TCDD-inducible hepatic binding species. These investigators found that 3,3',4,4',5,5'-HxBB, an
agonist for the Ah receptor, was associated only with CYP1A2 through the immunoprecipitation
of CYP1A1 and 1A2, which were induced in 3,3',4,4',5,5'-HxBB-treated rats. In addition, they
found that 3,3',4,4',5,5'-HxBB inhibited estradiol 2-hydroxylase activity of purified CYP1A2. A
similar association of PAHs with immunoprecipitated CYP1A2 was observed for other agonists
for the Ah receptor, including TCDD, 3,3',4,4'-TCB, 3,3',4,4',5-PeCB, and 3,3',4,4',5,5'-HxCB.
The association of TCDD with CYP1A2 occurred within 2 minutes, with maximum inhibition of
estradiol 2-hydroxylase occurring at a concentration comparable to the concentration of the
enzyme (50 nm). CYP1A2 was inhibited (complexed) by TCDD with nearly 1:1 stoichiometry,
and the K; for TCDD was calculated to be 8 nM. Therefore, TCDD can be considered a higher
binding inhibitor of CYP1A2.
Santostefano et al. (1996) assessed the subcellular and tissue-specific disposition of
TCDD in rats and mice. TCDD was equally distributed between the hepatic P9 (mitochondrial,
lysosomal, and nuclear) and S9 (cytosol and microsomal) fractions, with the microsomal fraction
retaining the TCDD present in the S9 fraction. In contrast, TCDD was retained in the P9
fractions of lung and liver at all doses tested. The lack of pulmonary or renal sequestration
coupled with the lack of localization of TCDD to pulmonary and renal microsomes supports the
role of CYP1A2 as a hepatic microsomal binding protein involved in the hepatic sequestration of
TCDD. Recently, Santostefano et al. (1999) assessed the intralobular hepatic distribution of
TCDD in rats and observed that centrilobular hepatocytes had a 2.7- to 4.5-fold higher
concentration of TCDD than did periportal hepatocytes. The enhanced centrilobular distribution
of TCDD was associated with elevated CYPlA2-mediated MROD activity and CYP1A2 mRNA
in centrilobular hepatocytes.
The TCDD-induced binding species was found to have an apparent equilibrium
dissociation constant, KD, for [125I]-2-iodo-3,7,8-trichlorodibenzo:p-dioxin of 56 ± 16 nM, and a
pool size, Bmax, of 22±5 nmol/g of liver in C57BL/6J mice (Poland et al., 1989b). The induced
microsomal binding species has an affinity about 104 times less than the Ah receptor but a pool
size that is ~2 x 103 greater. Thus, agonists for the Ah receptor may significantly affect their
disposition through a dose-related enhancement of hepatic uptake, which should correlate with
induction of CYP1A2.
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More recently, Diliberto et al. (1997) used transgenic mice lacking the CYP1A2 gene to
study the influence of CYP1A2 in the hepatic sequestration and distribution of TCDD, 2,3,4,7,8-
PeCDF, and PCB 153 (2,2',4,4',5,5'-HexCB), a non-dioxin-like PCB. The liver/fat concentration
ratios of these compounds in the parental lineage (C57BL6N and 129/Sv) were approximately
3.6, 18, and 0.07, respectively, indicating a high degree of hepatic sequestration for TCDD and
2,3,4,7,8-PeCDF. Under identical exposure conditions, the 1A2 knockout mice had liver/fat
concentration ratios of 0.17, 0.34, and 0.10, respectively. Thus, in the absence of the CYP1A2
gene, mice exhibited no hepatic sequestration of these compounds. This study and a related
study in CYP1A2 knockout mice by Diliberto et al. (1999) provide direct confirmation of the
hypothesis that CYP1A2 is the dioxin-inducible hepatic binding protein responsible for the
hepatic sequestration of TCDD and related compounds, such as 2,3,4,7,8-PeCDF.
The disposition and pharmacokinetics of 2,2',4,4',5,5'-HxCB and -HxBB have been
investigated in several species (Tuey and Matthews, 1980; Lutz et al., 1984). These lipophilic
compounds are similar to TCDD in that they are slowly metabolized and that metabolism is
required for urinary and biliary elimination; however, they do not exhibit dioxin-like activity.
2,2',4,4',5,5'-HxCB and -HxBB distribute primarily to adipose tissue, with partition coefficients
(tissue/blood ratio) ranging from 300 to 500 in the mouse, rat, monkey, dog, and human. The
liver is not a major site for the disposition of 2,2',4,4',5,5'-HxCB and -HxBB, in contrast to
TCDD and related compounds. Partition coefficients in the liver range from 10 to 30 in these
species. 2,2',4,4',5,5'-HxCB and -HxBB do not induce hepatic CYP1A1 or 1A2 and do not
exhibit dioxin-like activity. The lack of induction of hepatic CYP1A2 may explain the lack of a
dose-dependent hepatic disposition of these compounds.
Kedderis et al. (1991b) assessed the dose-response relationship for the induction of
hepatic CYP1 Al and 1A2 in male Fischer 344 rats exposed to 2,3,7,8-TBDD at doses as low as
0.1 nmol/kg. They reported that induction of P-4501A2 by 2,3,7,8-TBDD appeared to be a more
sensitive response than P-4501 Al induction over the dose range studied. In addition,
comparison of hepatic P-4501A2 levels and liveradipose tissue concentration ratios suggested
that induction of P-4501 A2 alone would not directly account for the preferential hepatic
accumulation of 2,3,7,8-TBDD and that additional factors must be involved. One explanation
may be that at low 2,3,7,8-TBDD concentrations, endogenous substrates bind to CYP1A2, not
allowing 2,3,7,8-TBDD to be sequestered by the protein (Kedderis et al., 1993b). At higher
2,3,7,8-TBDD concentrations, new protein is formed and 2,3,7,8-TBDD can compete for binding
to CYP1A2, resulting in the increased hepatic disposition observed at higher exposures of
2,3,7,8-TBDD (Kedderis et al., 1991b).
The structure-activity relationship for the disposition and hepatic sequestration of CDDs,
CDFs, and PCBs was investigated by DeVito et al. (1998). Female B6C3F1 mice were treated
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for 13 weeks with different oral doses of 2,3,7,8-TCDD, 1,2,3,7,8-PeCDD, 2,3,7,8-TBDD,
2,3,7,8-TCDF, 1,2,3,7,8-PeCDF, 2,3,4,7,8-PeCDF, OCDF, PCB126 (3,3',4,4',5-PeCB), PCB169
(3,3'4,4',5,5'HxCB), PCB105 (2,3,3',4,4' PeCB), PCB118 (2,3',4,4',5-PeCB), andPCB156
(2,3,3',4,4',5-HxCB). All of these compounds exhibited dose-dependent increases in the liver/fat
concentration except PCBs 105, 118, and 156. 4-PeCDF, PeCDD, OCDF, TCDF, and PCB126
were sequestered in hepatic tissue to a greater extent than was TCDD. Together, the results
support the presence of an inducible protein (CYP1A2) and the congener-specific binding of
some dioxin-like compounds to this hepatic sequestration protein.
Other factors may also regulate the intracellular distribution of TCDD and related
compounds. The possible role of hepatic lipoproteins as intracellular carriers in the transport of
TCDD has been assessed by in vitro and in vivo studies (Soues et al., 1989a,b). TCDD and
2,3,7,8-TCDF were bound to lipoproteins in mouse and rat liver, which subsequently underwent
rapid and pronounced degradative processing, possibly catalyzed by lipoprotein lipase, to heavier
entities. The in vitro incubation of TCDD-lipoprotein complex with separated Ah receptor
demonstrated that a passive transfer occurred. The authors suggest a carrier role for lipoproteins
in the intracellular transport of TCDD and related compounds.
1.3. METABOLISM AND EXCRETION
Although early in vivo and in vitro investigations were unable to detect the metabolism of
TCDD (Vinopal and Casida, 1973; van Miller et al., 1976), there is evidence that a wide range of
mammalian and aquatic species are capable of biotransforming TCDD to polar metabolites
(Ramsey et al., 1979, 1982; Poiger and Schlatter, 1979; Olson et al., 1980; Olson, 1986;
Gasiewicz et al., 1983a; Poiger et al., 1982; Sijm et al., 1990; Kleeman et al., 1986a,b, 1988).
Although metabolites of TCDD have not been directly identified in humans, recent data
regarding feces samples from humans in a self-dosing experiment suggest that humans can
metabolize TCDD (Wendling et al., 1990).
Table 1-9 summarizes data on the metabolism and excretion of TCDD and related
compounds after exposure to a single radiolabeled congener. Investigations of TCDD in rats,
mice, guinea pigs, and hamsters found that >90% of the radiolabeled material excreted in urine
and bile represented polar metabolites. Similar results were also observed for other congeners
(see Table 1-9), with the exception of OCDD; however, studies were often limited to the rat.
OCDD is apparently not metabolized by the rat, or metabolized to a very minimal extent
(Birnbaum and Couture, 1988). For all of the congeners in Table 1-9, essentially all of the CDD,
BDD, CDF, or PCB-derived radioactivity in liver, adipose tissue, and other tissues represented
parent compound, suggesting that metabolites of these compounds were readily excreted. Thus,
with the exception of OCDD, the metabolism of TCDD and related compounds is required for
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urinary and biliary elimination and therefore plays a major role in regulating the rate of excretion
of these compounds. In addition, direct intestinal excretion of parent compound is another route
for excretion of TCDD and related compounds that is not regulated by metabolism.
1.3.1. Structure of Metabolites
Several metabolites of TCDD have recently been identified. Sawahata et al. (1982)
investigated the in vitro metabolism of TCDD in isolated rat hepatocytes. The major product
was deconjugated with p-glucuronidase, derivatized with diazomethane, and separated into two
compounds by high-performance liquid chromatography. These metabolites were subsequently
identified as l-hydroxy-2,3,7,8-TCDD and 8-hydroxy-2,3,7-trichlorodibenzo--/>-dioxin. Poiger
et al. (1982) identified six metabolites in the bile of dogs that were given a lethal dose of
[3H]-2,3,7,8-TCDD. The major metabolite was l,3,7,8-tetrachloro-2-hydroxydibenzo-p-dioxin;
however, 3,7,8-trichloro-3-hydroxydibenzo-p-dioxin and l,2-dichloro-4,5-hydroxybenzene were
identified as minor metabolites. The structures of the three remaining metabolites were not
determined; however, two appeared to be trichlorohydroxydibenzo-p-dioxins and the third was
apparently a chlorinated 2-hydroxydiphenyl ether. Poiger and Buser (1984) reported differences
in the relative amounts of various TCDD metabolites in dog and rat bile. Trichlorodihydroxy-
dibenzo-/>-dioxin and tetrachlorodihydroxydiphenyl ether appear to be major metabolites in rat
bile. Furthermore, conjugates, presumably glucuronides, were formed in the rat but not in the
dog. The investigators also observed a generally higher metabolism rate of TCDD in the dog.
This finding is in good agreement with the unique ability of the dog to readily metabolize
persistent PCBs such as 2,4,5,2',4',5'-HxCB (Sipes et al., 1982).
Biliary metabolites of 2,3,7,8-TCDF have been investigated by Poiger et al. (1984);
however, unequivocal structure assignment of the metabolites could not be made using gas
chromatography/mass spectroscopy. With the use of synthetic standards and GC/MS, Burka et
al. (1990) identified 4-hydroxy-2,3,7,8-TCDF and 3-hydroxy-2,7,8-TCDF as major biliary
metabolites of 2,3,7,8-TCDF in rats. Small amounts of 3-hydroxy-2,4,7,8-TCDF and
2,2'-dihydroxy-4,4',5,5'-TCB were also detected. 4-Hydroxy-2,3,7,8-TCDF was also the major
TCDF metabolite formed by hepatic microsomes from TCDD-pretreated rats (Tai et al., 1993).
This suggests that the preferred site of metabolism of 2,3,7,8-TCDF is near the furan oxygen,
with oxygenation at C4 predominating over C3. The authors speculate that epoxidation of the
C4-C4a bond or the C3-C4 bond could lead to formation of the above metabolites. The results of
Burka et al. (1990) and Sawahata et al. (1982) suggest that oxygenation of the unsubstituted
carbon nearest the bridging oxygen in both 2,3,7,8-TCDF and TCDD is the major route of
metabolism of these compounds in the rat. Furthermore, data on the rate of elimination of these
compounds summarized in Tables 1-6 and 1-8 indicate that this reaction occurs at a faster rate for
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the furan, since the rate of urinary and biliary elimination and resulting persistence of these
compounds depend on metabolism.
Data summarized in Tables 1-6 and 1-9 indicate that 1,2,3,7,8-PeCDF is metabolized and
eliminated at a greater rate than 2,3,4,7,8-PeCDF. The preference for oxygenation at C4 in
2,3,7,8-TCDF offers an explanation for the observation that 2,3,4,7,8-PeCDF is metabolized at a
much slower rate than 1,2,3,7,8-PeCDF because one of the preferred sites for metabolism is
blocked in the 2,3,4,7,8-substituted compound. The rate of metabolism of these compounds and
their resulting relative persistence in rodents correlate with analysis of human tissues from the
Yusho cohort, where the relative concentrations were 2,3,4,7,8-PeCDF > 1,2,3,7,8-
PeCDF > 2,3,7,8-TCDF (Masuda et al., 1985).
Pluess et al. (1987) investigated the structure of 1,2,3,7,8-PeCDF metabolites in rat bile.
A dihydroxy-tetra-CDF was identified as the major metabolite. The authors propose that this
compound could be formed either via further oxidation of the hydroxy-tetra-CDF or possibly via
hydrolytic dechlorination of a hydroxy-penta-CDF. Minor metabolites include dihydroxy-tri-
CDF, hydroxy-tetra-CDF, and hydroxy-penta-CPF.
Pluess et al. (1987) also investigated the metabolites of 2,3,4,7,8-PeCDF in rat bile. A
total of 10 metabolites were detected, with dihydroxy-penta-CB and hydroxy-penta-CDF
representing the major metabolites. The biphenyl metabolite indicates that cleavage of the ether
bridge of the furan is an important pathway for metabolism of this congener. Other less abundant
metabolites of 2,3,4,7,8-PeCDF include hydroxy-tetra-CDF, dihydroxy-tri-CDF, dihydroxy-tetra-
CDF, and thio-tetra-CDF. Sulfur-containing metabolites were also identified as minor
metabolites of 2,3,7,8-TCDF and 1,2,3,7,8-PeCDF in rats (Kuroki et al., 1990). These sulfur-
containing metabolites probably arise from CDF-glutathione conjugates.
In another study, a dihydroxy-PeCDF was identified as the only detectable biliary
metabolite of 1,2,3,6,7,8-HxCDF, while no metabolites of 1,2,3,4,6,7,8-HpCDF were detected in
the bile of rats treated with this congener (Poiger et al., 1989).
Several in vivo and in vitro studies have investigated the metabolism of 3,3',4,4'-TCB.
Rat feces were found to contain 5-hydroxy-3,3',4,4'-TCB and 4-hydroxy-3,3',4',5-TCB as major
metabolites (Yoshimura et al., 1987) and 2,5-dihydroxy-3,3',4,4'-TCB, 4,4'-dihydroxy-3,3',5,5'-
TCB, 5,6-dihydroxy-3,3',4,4'-TCB, 4-hydroxy-3,3',4-TCB, and 4-hydroxy-4',5'-epoxy-3,3',4',5-
TCB as minor metabolites (Koga et al., 1989).
Mouse feces were found to contain 5-hydroxy- and 6-hydroxy-3,3',4,4'-TCB and
4-hydroxy-3,3',4',5-TCB, whereas urine contained 2-hydroxy-3,3',4,4'-TCB in addition to these
metabolites (Wehler et al., 1989). 3,3',4,4'-TCB was the major compound present in mouse liver
and a minor portion was due to 4-hydroxy-3,3',4,4'-TCB (Wehler et al., 1989). Darnerud et al.
(1986) found 2-hydroxy-3,3',4,4'-TCB and methylsulphonyl-TCB as major metabolites in the
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mouse fetus. Sulfur-containing metabolites of non-co-planar, nondioxin-like PCBs have also
been reported to accumulate in the bronchial mucosa and uterine luminal fluid of mice (Bergman
et al., 1979; Brandt et al., 1982) and in human lung, liver, and adipose tissue (Haraguchi et al.,
1986, 1989). PCB methyl sulfones are stable lipophilic metabolites formed by the mercapturic
acid pathway. The toxicological significance of these metabolites of nondioxin-like PCBs
remains generally unknown.
1.3.2. Toxicity of Metabolites
The discussion above indicates that metabolism of TCDD and related compounds is
required for urinary and biliary elimination and thus plays a major role in regulating the rate of
excretion of these compounds. At present, metabolism is also generally considered a
detoxification process.
Data on the metabolism of TCDD suggest that reactive epoxide intermediates may be
formed. Poland and Glover (1970) investigated the in vivo binding of [l,6-3H]-2,3,7,8-TCDD-
derived radioactivity to rat hepatic macromolecules and found maximum levels equivalent to 60
pmol of TCDD/mol of nucleotide in RNA and 6 pmol of TCDD/mol of nucleotide in DNA. This
corresponds to one TCDD-DNA adduct per 35 cells. These investigators suggest that it is
unlikely that TCDD-induced oncogenesis is through a mechanism of covalent binding to DNA
and somatic mutation. Further studies of TCDD and related compounds are needed to confirm
these results and assess the relationship between covalent binding and the short- and long-term
toxicity of these compounds.
Weber et al. (1982a) investigated the toxicity of TCDD metabolites by administering
extracts of bile from TCDD-treated dogs to male guinea pigs in single oral doses equivalent to
0.6, 6.0, and 60 |_ig/kg of parent compound. Other groups of guinea pigs were given bile extract
from untreated dogs or TCDD itself. A comparison of the mortality data at 5 weeks after dosing
indicated that the acute toxicity of TCDD to guinea pigs was at least 100 times higher than the
acute toxicity of its metabolites.
Mason and Safe (1986) synthesized 2-hydroxy-3,7,8-TCDD and 2-hydroxy-l,3,7,8-
TCDD, which are metabolites of TCDD, and assessed the toxicity of these compounds in male
Wistar rats. The compounds produced no significant effect on body weight gain and thymus,
liver, or spleen weights after exposure to a dose of <5,000 pg/kg bw. 2-Hydroxy-3,7,8-TCDD
induced hepatic microsomal AHH, EROD, and 4-chlorobiphenylhydroxylase activity at
exposures of 1,000 and 5,000 |ig/kg bw, whereas 2-hydroxy-l,3,7,8-TCDD was inactive as an
inducer. Thus, while 2-hydroxy-3,7,8-TCDD has dioxin-like activity as an inducer of the hepatic
monooxygenase system, the potency of the metabolite is more than three orders of magnitude
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less than that of TCDD. Furthermore, results are consistent with the expected rapid conjugation
and excretion of these TCDD metabolites (Weber et al., 1982b).
Metabolism of co-planar PCBs and PBBs also appears to be a detoxification process.
5-Hydroxy-3,3',4,4'-TCB and 4-hydroxy-3,3',4',5-TCB did not produce liver hypertrophy,
induction of hepatic AHH or DT-diaphorase activities, or thymus atrophy (Yoshimura et al.,
1987). Thus, monohydroxy metabolites of 3,3 ',4,4'-TCB are much less toxic than the parent
compound. Further evidence for metabolism as a detoxification process comes from comparison
of the metabolism and toxicity of two co-planar PBBs. Millis et al. (1985) found that
3,3',4,4',5,5'-HxBB exhibited greater toxic potency in rats than 3,3',4,4'-TBB, even though
3,3 ',4,4'-TBB had about a 10-fold greater affinity for the Ah receptor. Although receptor
binding affinities imply that 3,3'4,4'-TBB should be more toxic than 3,3',4,4'5,5'-HxBB, it was
less toxic than the HxBB because 3,3',4,4'-TBB was metabolized at a much greater rate than
3,3',4,4',5,5'-HxBB. In addition to supporting metabolism as a detoxification process, the
results of Millis et al. (1985) also suggest that receptor binding and in vitro AHH induction do
not accurately reflect toxicity for PAHs, which are more readily metabolized, presumably
because continued occupation of the receptor is required for toxicity.
Structure-activity studies of TCDD and related compounds support the widely accepted
principle that this parent compound is the active species. The relative lack of activity of readily
excreted monohydroxylated metabolites of TCDD and 3,3'4,4'-TCB suggests that metabolism is
a detoxification process necessary for the biliary and urinary excretion of these compounds. This
concept has also been generally applied to TCDD-related compounds, although data are lacking
on the structure and toxicity of metabolites of other CDDs, BDDs, CDFs, BDFs, PCBs, and
PBBs.
It is possible that low levels of unextractable and unidentified metabolites may contribute
to one or more of the toxic responses of TCDD and related compounds. Further studies on the
nature of the biotransformation products of these compounds will help address this uncertainty.
1.3.3. Autoinduction of Metabolism
Accurate rate constants for metabolism are important in developing pharmacokinetic
models that describe the disposition of TCDD and related compounds. Metabolism plays a
major role in regulating the excretion and relative persistence of these compounds because
metabolism is required for urinary and biliary excretion. Although the relative rate of
metabolism of TCDD and related compounds can be estimated from tissue and excretion half-life
data (see Tables 1-6 and 1-9), other factors such as relative body composition, hepatic and
extrahepatic binding proteins, and direct intestinal elimination of the parent compound can also
regulate the excretion of TCDD and related compounds. Therefore, in vivo disposition data (see
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Tables 1-6 and 1-9) provide only a limited approximation of the relative rate of metabolism of a
specific congener in a given species. In vivo disposition data were also obtained at exposures
that were associated with induction of CYP1 Al and 1A2 and other potentially adverse responses
that could alter metabolism and disposition. Therefore, it may not be appropriate to directly
extrapolate these data to predict the pharmacokinetics at low levels of exposure. Low-dose
extrapolations can be assisted by assessments of the potential for autoinduction of metabolism
that may occur at exposures associated with enzyme induction. Characterization of the dose-
dependent disposition of TCDD and related compounds is particularly important in the
extrapolation of high-exposure animal data to low-exposure human data.
The excretion of metabolites of TCDD and related compounds into bile represents a
direct means for estimating the rate of metabolism because biliary elimination depends on
metabolism and is the major route for excretion of these compounds. The rate of metabolism of
CDFs was estimated from the relative abundance of metabolites in rat bile (Poiger et al., 1989).
The rates of biotransformation of 2,3,7,8-TCDF, 1,2,3,7,8-PeCDF, 2,3,4,7,8-PeCDF, and
1,2,3,6,7,8-HxCDF were characterized as fairly high, moderate, low, and very low, respectively.
Kedderis et al. (1991b, 1993a) observed 10% of the dose of [3H]-2,3,7,8-TBDD excreted in bile
5 hours after intravenous administration of 1 nmol/kg to male Fischer 344 rats. All biliary
radioactivity was attributable to metabolites. This rate of elimination is similar to the fecal
excretion (-8% of the dose) 24 hours after intravenous administration of 1 nmol/kg [3H]-2,3,7,8-
TBDD (Kedderis et al., 1991a) and reflects the effect of intravenous bolus versus oral
administration on distribution and elimination. The large percent dose excreted within the first
few days may also be due to a rapidly excreted impurity in the radiolabeled 2,3,7,8-TBDD
(Kedderis et al., 1993a). To assess the ability of TCDD and 2,3,7,8-TBDD to induce their own
metabolism (biliary elimination), rats were pretreated with 100 nmol/kg, per of, of each
compound 3 days prior to intravenous injection of 1 nmol/kg of the respective [3H]-congeners.
Biliary excretion of the radiolabeled dose was quantitatively and qualitatively unaffected by
pretreatment, despite a twofold increase in hepatic levels of [3H] in the pretreated animals and
significant induction of CYP1 Al and 1A2 (Kedderis et al., 1991b). Therefore, under these
conditions, autoinduction of TCDD and 2,3,7,8-TBDD metabolism did not occur in the rat in
vivo at doses that elicited enhanced hepatic uptake. Similarly, Curtis et al. (1990) observed no
change, or even an apparent decrease, in gastrointestinal contents and fecal elimination of TCDD
equivalents in pretreated versus naive mice 24 hours after oral administration of [14C]-2,3,7,8-
TCDD, despite significantly enhanced levels of TCDD in the livers of pretreated mice.
Although the above studies suggest that autoinduction of TCDD metabolism does not
occur, other results indicate that metabolism may be induced under certain conditions. Poiger
and Buser (1984) observed a small yet significant increase in biliary excretion over a 72-hour
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period, with pretreated rats (10 |ig/kg, intraperitoneal) excreting 9.7±1.9% of the radiolabeled
dose of TCDD (200-300 pg/kg, per of) compared with 7.0±0.9% excreted by naive animals. In
addition to being small changes, these results were obtained using a dose of TCDD in excess of
the LD50 in the rat. Poiger and Schlatter (1985) examined the influence of pretreatment with
phenobarbital and TCDD on the biliary excretion of [3H]-2,3,7,8-TCDD metabolites in a dog
given a single oral dose of the [3H]-congener (31 or 33.8 ng/kg). Without pretreatment, 24.5% of
the absorbed dose was excreted in the bile within 110 hours. Phenobarbital did not alter this rate,
whereas pretreatment with TCDD (10 pg/kg) 9 days earlier resulted in a doubling of the amount
of metabolites excreted in bile (47.4%). Although this observation is limited to one dog and
requires further investigation, the results suggest that significant autoinduction of TCDD
metabolism and biliary excretion may occur in the dog. Nonetheless, the small increase in
metabolism and biliary excretion of TCDD in the rat observed by Poiger and Buser (1984) and
the negative results of Kedderis et al. (1991b; 1993a) and Curtis et al. (1990) suggest that
autoinduction of TCDD metabolism and biliary excretion in the rat may not occur or occurs to an
extent that is not biologically relevant.
More recently, Jackson et al. (1998) investigated the effects of age, sex, and pretreatments
with phenobarbital, dexamethasone, and 1-aminobezotriazole (a CYP450 inhibitor) to modulate
the rate of metabolism-dependent biliary elimination of TCDD in F344 rats. Biliary excretion of
TCDD-derived radioactivity was measured over a 6-hour period following iv administration.
Male adult and juvenile rats and female juvenile rats excreted from 0.63% to 0.68% of the
administered dose, while adult females, male senescent, and male adults pretreated with the
above drugs excreted from 0.28% to 0.45% of the dose of TCDD in bile. The results suggest
some variability in the metabolism-dependent biliary excretion of TCDD-derived radioactivity;
however, the differences between groups were not great.
Limited data suggest that autoinduction of metabolism and biliary excretion does occur
for CDFs. Pretreatment of rats with 2,3,7,8-TCDF (1.0 |_imol/kg, 3 days earlier) significantly
increased the biliary excretion of a subsequent dose of [14C]-2,3,7,8-TCDF (McKinley et al.,
1993). The naive rats excreted 5.69±2.35% of the dose over the initial 8 hours, while the
pretreated rats excreted 13.18±3.15% of the [14C]-2,3,7,8-TCDF. Similarly, pretreatment of rats
with 2,3,4,7,8-PeCDF (500 pg/kg, per of, 3 days earlier) resulted in a twofold increase in the
biliary elimination of a subsequent dose of [14C]-2,3,4,7,8-PeCDF (Brewster and Birnbaum,
1987). These results suggest that pretreatment with 2,3,7,8-TCDF and 2,3,4,7,8-PeCDF induces
the metabolism of these congeners.
3,3',4,4'-TCB and 3,3',4,4'-TBB appear to be metabolized by a 3-methylcholanthrene-
inducible form of hepatic CYP(1A1 or 1A2), which is also induced by 3,3',4,4'-TCB (Shimada
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and Sawabe, 1983; Mills et al., 1985; McKinley et al., 1993). This suggests that these
compounds can induce their own rate of metabolism and subsequent excretion.
Isolated hepatocytes in suspension culture have been demonstrated to provide a useful in
vitro system for studying the hepatic metabolism of TCDD under the same conditions in species
that have a wide range of sensitivity to the compound (Olson et al., 1981). The in vitro rate of
metabolism of TCDD in guinea pig, rat, C57BL/6J mouse, DBA/2J mouse, and hamster
hepatocytes was estimated to be 0.2, 1.2, 1.1, 0.9, and 1.2 pmol/mg protein/hour, respectively
(Wroblewski and Olson, 1985, 1988; Shen and Olson, 1987). These results indicate that TCDD
is metabolized by the guinea pig liver at a rate about fivefold less than that observed for the rat,
mouse, and hamster. The limited ability of the guinea pig to metabolize TCDD can explain the
limited excretion of TCDD metabolites in feces, which represents the major route for TCDD
excretion (Olson, 1986). In addition, the limited metabolism in the guinea pig may partly explain
the relatively long excretion half-life for TCDD in the guinea pig and may contribute to the
remarkable sensitivity of the guinea pig to the acute toxicity of this agent (Olson, 1986).
Isolated hepatocytes in suspension culture have been used as an in vitro system for
studying the autoinduction of metabolism of TCDD and related compounds. Wroblewski and
Olson (1988) investigated the metabolism of [14C]-2,3,7,8-TCDD (2.2 |_iM) in hepatocytes
isolated from untreated TCDD-, 3-MC-, isosafrole-, and phenobarbital-pretreated rats and
hamsters. In both species, TCDD and 3-MC pretreatments elevated the rate of TCDD
metabolism by five- to sixfold, while phenobarbital pretreatment had no effect. Isosafrole
produced a 1.8- to 2.5-fold increase in metabolism. These in vitro results at a high substrate
concentration (2.2 |_iM) indicate that TCDD can induce its own rate of metabolism in the rat and
hamster. In contrast, TCDD was not able to induce its own rate of metabolism in guinea pig and
mouse hepatocytes (Wroblewski and Olson, 1985; Shen and Olson, 1987). Together, these
results indicate that TCDD is metabolized in the liver by a TCDD-inducible enzyme, which is
expressed in the rat and hamster but not in the guinea pig and mouse. More recently, the kinetics
of TCDD metabolism were investigated in isolated rat hepatocytes incubated with [3H]-2,3,7,8-
TCDD at concentrations of 0.01, 0.1, and 1.0 |_iM (Olson et al., 1994). Lower TCDD
concentrations in the media result in concentrations in hepatocytes that are more similar to the
levels in the liver after in vivo exposure. For example, the concentration of TCDD in
hepatocytes incubated at 0.01 pM is similar to hepatic levels after in vivo exposure of rats at a
dose of-10 |_ig/kg. At 0.01 and 0.1 |_iM, the rate of metabolism of [3H]-2,3,7,8-TCDD was
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similar in hepatocytes isolated from control and TCDD-pretreated rats, whereas at 1.0 |_iM, [3H]-
2,3,7,8-TCDD metabolism was greater in hepatocytes isolated from TCDD-pretreated rats. The
results indicate that TCDD can induce its own rate of metabolism in the rat, but only at high
hepatic concentrations, which are generally not attained after in vivo exposure. Therefore, in
vitro studies of the hepatic metabolism of TCDD (at 0.01 and 0.1 |_iM) are consistent with the
lack of autoinduction of TCDD metabolism and biliary excretion observed in vivo in the rat
(Kedderis et al., 1991b; Curtis et al., 1990).
The metabolism of [3H]-2,3,7,8-TCDF was also investigated in isolated rat hepatocytes
incubated at concentrations of 0.01, 0.1, and 1.0 \iM (Olson et al., 1994). At all concentrations,
hepatocytes from TCDD-pretreated rats metabolized 2,3,7,8-TCDF at a rate from 4- to 25-fold
greater than that observed in hepatocytes from control rats. The results indicate that 2,3,7,8-
TCDF is metabolized in rat liver by a TCDD-inducible enzyme, possibly CYP1 Al or 1A2.
These in vitro results support the in vivo autoinduction of 2,3,7,8-TCDF metabolism and biliary
elimination observed in the rat (McKinley et al., 1993).
2,3,7,8-TCDF metabolism was also investigated in rat liver, kidney, and lung microsomes
in the presence and absence of selective chemical inhibitors and antibodies to CYP1A1 and
CYP1A2 (Tai et al., 1993). Together, the results of this investigation indicate that CYP1A1 is
the primary enzyme responsible for the metabolism of 2,3,7,8-TCDF. 2,3,7,8-TCDF was also
metabolized by recombinant yeast microsomes expressing human CYP1 Al and reductase.
However, based on EROD activity, a marker of CYP1A1, the relative rate of 2,3,7,8-TCDF
metabolism was about 100-fold greater in TCDD-induced rat liver microsomes than in yeast
microsomes expressing human CYP1A1 and reductase (Tai et al., 1993). Although 2,3,7,8-
TCDF was metabolized by rat and human CYP1 Al, the results indicated that there are marked
quantitative differences in metabolism that suggest that 2,3,7,8-TCDF will be more persistent in
humans.
In summary, there are in vivo and in vitro data suggesting that autoinduction of TCDD
and 2,3,7,8-TBDD metabolism does not occur in the rat after exposure to sublethal doses of these
agents. This is in contrast to 2,3,7,8-TCDF and 2,3,4,7,8-PeCDF, where in vivo and in vitro
results support the autoinduction of metabolism and biliary elimination of these compounds in
the rat.
1.3.4. Excretion in Animals
Data regarding the excretion of 2,3,4,7-TCDD and related compounds after exposure to a
single radiolabeled congener (see Table 1-9) support the assumption of a first-order elimination
process consisting of one or more components. These studies show that TCDD was excreted
slowly from all species tested, with half-lives ranging from 11 days in the hamster to 2,120 days
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in humans. TCDD is exceptionally persistent in humans relative to other animal models.
Elimination data in tissues (see Tables 1-6 and 1-7) also indicate that TCDD and related
compounds are exceptionally persistent in nonhuman primates (Bowman et al., 1989; Neubert et
al., 1990). These differences may also be in part related to the dose dependency of the excretion
of these compounds. In general, the congener- and species-specific rates of elimination of TCDD
and related compounds from major tissue depots (see Table 1-6) are similar to the excretion data
summarized in Table 1-9.
In the Syrian Golden hamster, the mammalian species least sensitive to the acute toxicity
of TCDD, excretion occurred readily through both the urine (35% of administered dose, 41% of
total excreted radioactivity) and feces (50% of the administered dose, 59% of total excreted
radioactivity) (Olson et al., 1980). A similar excretion pattern, with significant urinary
elimination, was observed in mice, although there was significant strain variability (Gasiewicz et
al., 1983b; Birnbaum, 1986). In all the other species, excretion occurred mainly through the
feces, with relatively minor amounts of TCDD metabolites found in the urine (Piper et al., 1973;
Allen et al., 1975; Olson, 1986; Rose et al., 1976; Gasiewicz andNeal, 1979; Pohjanvirta et al.,
1990). Results in Table 1-9 also indicate that fecal elimination was the primary route for the
excretion of 1,2,3,7,8-PeCDD, OCDD, 2,3,7,8-TBDD, 2,3,7,8-TCDF, 1,2,3,7,8-PeCDF,
2,3,4,7,8-PeCDF, and 3,3',4,4'-TCB. Only Piper et al. (1973) reported the excretion of
metabolites in the expired air. During 21 days following administration of a single oral dose of
[14C]-2,3,7,8-TCDD to rats, 3.2% of the administered radioactivity (4.6% of the excreted
radioactivity) was recovered in the expired air.
Studies in the rat, guinea pig, hamster, and mouse have found that essentially all of the
TCDD-derived radioactivity excreted in the urine and bile corresponds to metabolites of TCDD
(see Table 1-9). The apparent absence of TCDD metabolites in liver and fat suggests that, once
formed, the metabolites of TCDD are excreted readily. Thus, urinary and biliary elimination of
TCDD depends on metabolism of the toxin. The more limited data for other compounds also
suggest that this relationship may be true for 1,2,3,7,8-PeCDD, 2,3,7,8-TBDD, 2,3,7,8-TCDF,
1,2,3,7,8-PeCDF, 2,3,4,7,8-PeCDF, and 3,3',4,4'-TCB (see Table 1-9).
Although urine and bile appear to be free of unmetabolized TCDD, data indicate that
TCDD and its metabolites are excreted in the feces of guinea pigs, rats, mice, and hamsters
treated with [3H]- or [14C]-2,3,7,8-TCDD (see Table 1-9). Whereas 15% to 35% of the TCDD-
derived radioactivity in rat, mouse, and hamster feces represents unchanged TCDD, 81% of the
radioactivity in guinea pig feces represents unmetabolized TCDD (Olson, 1986; Neal et al.,
1982; Gasiewicz et al., 1983b; Olson et al., 1980). The daily presence of unchanged TCDD in
feces and its absence in bile suggest that direct intestinal elimination may be the source for the
fecal excretion of TCDD. Data also suggest that direct intestinal elimination of parent compound
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contributes to the fecal excretion for 2,3,7,8-TBDD (Kedderis et al., 1991a). Direct intestinal
elimination of the parent compound may occur for other congeners (see Table 1-9), but this
conclusion cannot be made at this time because of the lack of experimental data. Nonetheless,
the species-specific fecal excretion of 2,3,7,8-TCDF is very similar to that observed for TCDD,
with >90% of the 2,3,7,8-TCDF-derived radioactivity excreted in guinea pig feces representing
parent compound (Decad et al., 1981a). In addition, the excretion of unchanged CDDs and CDFs
was detected in rat feces after subcutaneous exposure to a defined mixture of congeners
(Abraham et al., 1989d). Studies in lactating rats have also found that unchanged TCDD may be
excreted in the milk of lactating animals (Moore et al., 1976; Lucier et al., 1975; Nau et al.,
1986). Lactation, direct intestinal elimination, and perhaps sebum may serve as routes for
excretion of TCDD that do not depend on metabolism of the toxin. These data suggest that the in
vivo half-life for elimination of TCDD and related compounds provides only an approximation
of the rate of metabolism of these compounds in a given animal. The results in Table 1-9 suggest
that 2,3,7,8-TCDF, 1,2,3,7,8-PeCDF, and 3,3',4,4'-TCB are metabolized and excreted more
rapidly than 2,3,7,8-TCDD, 2,3,7,8-TBDD, 1,2,3,7,8-PeCDD, 2,3,4,7,8-PeCDF, and OCDD.
The rate of excretion of TCDD and related compounds is species- and congener- specific
(see Table 1-9). TCDD is most persistent in human and nonhuman primates. In the hamster, the
least sensitive species to the acute toxicity of TCDD, the mean t,/2 was 10.8 days (Olson et al.,
1980), and in the guinea pig, the most sensitive species to the acute toxicity of TCDD, the mean
t,/2 was 94 days (Olson, 1986). 2,3,7,8-TCDF was also most persistent in the guinea pig, with a t,/2
of 20 to 40 days (Decad et al., 1981a; loannou et al., 1983). Furthermore, results indicate that the
relatively limited ability of the guinea pig to metabolize TCDD and -TCDF may contribute to the
greater persistence and greater acute toxicity of these congeners in the guinea pig.
The tissue distribution, metabolism, and excretion of TCDD were also investigated in
Han/Wistar and Long-Evans rats, which were, respectively, more resistant (LD50>3,000 pg/kg)
versus more susceptible (LD50 -10 pg/kg) to the acute toxicity of TCDD (Pohjanvirta et al.,
1990). The results suggest that the metabolism and disposition of TCDD do not have a major
role in explaining the strain differences in toxicity.
The intraspecies differences in the t,/2 of TCDD in three mouse strains may be due to the
finding that the DBA/2J strain possesses about twofold greater adipose tissue stores than the
C57BL/6J and B6D2FJ/J strains (Gasiewicz et al., 1983b). The sequestering of the lipophilic
toxin in adipose tissue stores of the DBA/2J mouse may contribute to the greater persistence of
TCDD in this strain. Birnbaum (1986) examined the effect of genetic background on the
distribution and excretion of TCDD in two sets of congenic mouse strains in which the congenic
pairs differed only at the Ah locus. The Ah locus had no effect on the tissue distribution or
excretion of TCDD. Thus, the distribution and excretion of TCDD were primarily governed by
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the total genetic background rather than the allele present at the Ah locus. These findings are
consistent with the in vitro results of Shen and Olson (1987), who found that the hepatic uptake
and metabolism of TCDD did not correlate with genetic differences at the murine Ah locus.
However, it is important to note that all of these are relatively high-dose studies, which may not
allow for detection of Ah receptor-mediated effects on disposition.
Although the dose-related tissue distribution of TCDD and related compounds has been
described recently, very limited data are available on the dose-related excretion of these
compounds. Rose et al. (1976) investigated the elimination of [14C]-2,3,7,8-TCDD in rats given
repeated oral doses of 0.01, 0.1, or 1.0 ng/kg/day Monday through Friday for 7 weeks or a single
dose of 1.0 ng/kg. In the single-dose study, no 14C was excreted in the urine or expired air; in the
repeated-dose study, however, 3% to 18% of the cumulative dose was excreted in the urine by 7
weeks. This study indicated that steady-state concentrations will be reached in the bodies of rats
in ~ 13 weeks. The rate constant defining the approach to steady-state concentrations was
independent of the dose of TCDD over the range studied. Relatively small changes in the
excretion of 2,3,7,8-TBDD were also observed after exposures at 1 and 100 nmol/kg (Kedderis et
al., 1991a). These results are consistent with the in vivo and in vitro evidence suggesting that
autoinduction of TCDD and 2,3,7,8-TBDD metabolism does not occur in the rat after exposure
to sublethal doses of these compounds (Kedderis et al., 1991b; Curtis et al., 1990; Olson et al.,
1994). In contrast to these compounds, 2,3,7,8-TCDF and 2,3,4,7,8-PeCDF can induce their own
rate of metabolism and biliary excretion (Brewster and Birnbaum, 1987; McKinley et al., 1993;
Olson et al., 1994). Autoinduction of metabolism suggests that these compounds may exhibit
dose-related excretion, with longer half-lives for elimination at lower doses, which are not
associated with enzyme induction. Further data are needed to test this hypothesis.
1.3.5. Excretion in Humans
Poiger and Schlatter (1986) investigated the excretion of TCDD in a 42-year-old man (92
kg) after ingestion of 105 ng (1.14 ng/kg) [3H]-2,3,7,8-TCDD in 6 mL corn oil (see Tables 1-9
and 1-10). The half-life for elimination was estimated to be 2,120 days based on fecal excretion
over a 125-day period following the single exposure. The concentration of 3H-TCDD-derived
radioactivity was also measured in adipose tissue in the same individual over a 6-year period
following exposure. A more accurate estimate of TCDD half-life of 9.7 years was calculated
based on adipose tissue concentrations over a 6-year period (Schlatter, 1991). Table 1-10
summarizes additional half-life estimates for TCDD and related compounds in humans, on the
basis of serum and adipose tissue concentrations at two or more time points.
The Air Force is conducting a 20-year prospective study of veterans of Operation Ranch
Hand, the unit responsible for the aerial spraying of herbicides, contaminated with TCDD, in
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Vietnam from 1962 to 1971. A subset of the Ranch Hand cohort has had a series of up to four
serum TCDD analyses conducted to investigate the elimination of TCDD in humans. Initially,
the half-life of TCDD in humans was estimated to be ~7 years on the basis of TCDD levels in
serum samples taken in 1982 and 1987 from 36 of the Ranch Hand personnel who had TCDD
levels >10 ppt in 1987 (Pirkle et al., 1989). Subsequently, Wolfe et al. (1994) investigated the
half-life of TCDD in an expanded cohort of 337 Air Force veterans of Operation Ranch Hand
that also included the 36 subjects of the earlier half-life study by Pirkle et al. (1989). Based on
paired TCDD measurements from serum collected in 1982 and in 1987, the authors reported a
mean predicted half-life of 11.6 years and a median observed half-life of 11.3 years with a
nonparametric 95% confidence interval of 10.0 to 14.1 years. The authors also investigated how
the TCDD half-life varied with percent body fat (PBF), relative changes in PBF from 1982 to
1987, and age. They found that the TCDD half-life increased significantly with a high PBF,
suggesting that persons with more body fat tend to eliminate TCDD more slowly. In contrast,
increasing age was associated with a shorter half-life. The redistribution of fat stores from
subcutaneous to abdominal areas with aging, resulting in greater mobilization of TCDD, could in
part explain the shorter half-life observed in older veterans. An increase in PBF from 1982 to
1987 was also associated with a decrease in half-life, which can be explained by a dilution of the
existing body burden of TCDD into the increasing adipose tissue mass.
More recently, Michalek et al. (1996) estimated the half-life of TCDD in 213 veterans of
Operation Ranch Hand on the basis of TCDD serum analyses conducted in 1982, 1987, and
1992. Of the 278 subjects with complete data in all 3 years, 213 were included for analysis of
half-life 3 on the basis of TCDD levels greater than 22.3 ppt in 1982, >14.9 ppt in 1987, and >10
ppt in 1992. All TCDD levels were background-corrected by subtracting 4 ppt, and the
logarithm of the background-corrected levels were modeled as a linear function of time to
estimate decay rates using first-order kinetics. Using the Toeplitz assumption, the unadjusted
estimated decay rate is 0.0797 per year (95% CI of 0.0727 to 0.0868), giving an unadjusted half-
life estimate of 8.7 years (95% CI of 8.0 to 9.5 years). The adjusted half-life was found to
increase significantly with an increase in PBF in 1982, but the half-life did not vary with age or
relative changes in PBF. Most recently, Michalek and Tripathi (1999) estimated the half-life of
TCDD in 97 veterans of Operation Ranch Hand on the basis of TCDD serum analyses conducted
in 1982, 1987, 1992, and 1997. Of the 244 subjects with complete data at all four time points,
only 97 were included for analysis of half-life based on the criteria of TCDD levels greater than
39.5 ppt in 1982, >25.0 ppt in 1987, >15.8 ppt in 1992, and >10 ppt in 1997. With increasing
time since the initial exposure to TCDD, a greater proportion of the population was excluded
from the analysis, as more subjects approached background body burdens of TCDD. Using the
methods of the previous report (Michalek et al., 1996), the unadjusted estimated elimination rate
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was 0.0915 per year (95% CI of 0.0844 to 0.0986), giving an unadjusted half-life estimate of 7.6
years (95% CI of 7.0 to 8.2 years). Because of the smaller sample size, the current elimination
rate estimate, based on four measurements per subject, has less precision than the earlier estimate
of Michalek et al. (1996), which was based on three measurements per subject. Once again, the
elimination rate decreased slightly but significantly as PBF increased, supporting the hypothesis
that individuals with more body fat tend to eliminate TCDD more slowly than those with less
body fat. Michalek and Tripathi (1999) also reported no significant change in the elimination
rate with age or with relative changes in PBF.
The half-life of TCDD has also been investigated in two additional populations. Flesch-
Janys et al. (1996) studied a group of 43 German herbicide plant workers that had initial TCDD
serum levels from 15.6 to 300 ppt. A median half-life estimate of 7.2 years was reported for this
occupational cohort, which received an initial exposure to TCDD similar to that of the Ranch
Hand veterans. A similar half-life estimate of 8.2 years was reported in 27 victims of the
accident in Seveso, Italy (Needham et al., 1994). This cohort had a greater initial exposure,
resulting in serum levels of 130 to 3,830 ppt TCDD. This study also included the early and later
portions of the TCDD decay curve, as the initial blood sampling began immediately following
exposure and continued for 15.9 years. Thus, based on results from the Ranch Hand, the
German, and the Seveso studies, the estimated half-life of TCDD in humans is from 7.2 to 8.7
years (Table 1-10).
Half-life estimates for other CDDs and CDFs have been estimated to range from 0.8 to
19.6 years (Table 1-10). Some of the half-life values in Table 1-10 are rough estimates based on
a small number of individuals and analysis at as few as two time points. Phillips (1989)
discusses this issue. Estimates also assume a simple, single-compartment, first-order elimination
process.
In the largest and most comprehensive study, Flesch-Janys et al. (1996) investigated the
elimination of 2,3,7,8-chlorine substituted CDDs and CDFs in a cohort of workers from a
herbicide-producing plant in Germany (summarized in Table 1-10). The study group consisted
of 45 males and 3 females with a mean duration of employment of 13.1 years. Mean time
between end of employment and first blood sample was 5.4 years (median 2 years) and mean
time between first and last blood sample was 5.6 yr (median 6.3 years). A total of 43 subjects
with two serum samples and 5 subjects with three serum samples were included in the study. For
each congener, only those subjects whose congener serum levels exceeded 95% of German
background concentration were included in the analysis. The mean background concentration
was also subtracted from every original measurement before analysis. For 2,3,7,8-TCDF,
1,2,3,7,8-PeCDF and OCDF, no half-life was estimated because no person in the study passed
the above inclusion criteria. 2,3,7,8-TCDF and 1,2,3,7,8-PeCDF are excreted in animals much
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more rapidly than other congeners (van den Berg et al., 1994), suggesting that these congeners
may also be excreted more rapidly in humans. Conversely, 2,3,4,7,8-PeCDF is far more
persistent in animal models than is TCDD, which supports the estimated 19.6-year half-life of
2,3,4,7,8-PeCDF and 7.2-year half-life of TCDD in humans. However, this estimate was based
on only five subjects who met the criteria for inclusion in the study. With the exception of
1,2,3,7,8-PeCDD and 1,2,3,6,7,8-HxCDD, the median half-lives of the CDDs are generally
similar. The estimates for these two congeners may be somewhat unstable because of variable
individual rate constants for elimination and the fact that about 25% of the population showed no
decrease in serum levels over the sampling period. Furthermore, the investigation found that
increasing age and PBF were associated with increasing half-life for most congeners. Finally, it
is important to note that the half-life data reflect only the elimination of CDDs and CDFs from
blood lipid and may not reflect elimination from different storage sites for all congeners. In the
case of TCDD, it can be assumed that the half-life estimate reflects elimination from the main
storage site, because about 90% of the body burden is sequestered in fat and the blood fat/adipose
tissue concentration is about 1 (Patterson et al., 1987; Kahn et al., 1988; Schecter et al., 1990a;
van den Berg et al., 1994). Data are more limited on the relative amount of other congeners
stored in adipose tissue in humans, and limited and somewhat conflicting data suggest that the
blood fat/adipose tissue concentration ratio may increase up to a factor of 2 for OCDD (Schecter
et al., 1990a; Gurn et al., 1995). Thus, some uncertainties remain regarding the extent that the
observed decrease in serum levels of higher CDDs and CDFs reflects the elimination of these
compounds from the body.
Ryan and Masuda (1991) reported on their continuing investigation into the elimination
of CDFs in humans from the Yusho and Yu-Cheng rice oil poisonings. Yu-Cheng individuals
had CDF blood levels on a lipid basis of 1-50 pg/kg, whereas Yusho patients had levels of 0.1-5
|-ig/kg. In the Yu-Cheng individuals, half-lives for three CDFs were 2 to 3 years, whereas
elimination from Yusho individuals was more variable and slower, with half-lives >5 years (see
Table 1-10) and, in several cases, no measurable elimination during the 7 years in which samples
were available. The limited results suggest that clearance of these CDFs in the human is
biphasic, with faster elimination at higher exposure. Schecter et al. (1990b) and Ryan and
Masuda (1989) also reported longer half-life values for CDFs in humans at later time points after
exposure, when concentrations are closer to the background levels of individuals with no unusual
exposure.
While results from animal studies (summarized in Table 1-9) suggest that direct fecal
excretion of unmetabolized CDDs and CDFs represents a significant mechanism for the
elimination of these lipophilic compounds, human data have been limited until recently. The
mass balance study of Schlummer et al. (1998) provided the experimental human data in support
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of the two-step model of CDD and CDF transfer in the gastrointestinal tract, where absorption
and excretion are distinct processes occurring at the small and large intestine, respectively (see
Section 1.1.1.2). Rohde et al. (1999) conducted a digestive tract mass balance study of six
German men (age 41 to 73 years) with occupational exposure to CDDs and CDFs. Blood lipid
levels of the subjects in 1996 ranged from 84 to 505 pg/g lipid for TCDD and 270 to 640 pg/g
lipid for TEQs, compared with background levels in unexposed individuals of 5.2 and 32 pg/g
lipid, respectively. The daily quantity of nonmetabolized 2,3,7,8-chlorine-substituted CDDs and
CDFs excreted in the feces exceeded the daily uptake from food, indicating significant clearance
across the gastrointestinal tract. The concentration of these compounds in feces was also found
to be highly correlated with that in blood, demonstrating that the fecal CDD and CDF content
was directly related to the body burden of these compounds. No significant clearance (excretion
via feces at least fourfold greater than uptake by food) was observed for congeners, including
2,3,7,8-TCDF, 1,2,3,7,8-PeCDF, 1,2,3,4,7,8,9-HepCDF, or OCDF, which were not markedly
elevated in the serum lipids. Together, these results support the relationship that fecal excretion
is regulated by the lipid-based blood concentration of these compounds. Because of fecal
clearance of nonmetabolized congeners, the half-lives in these subjects were estimated from the
excretion rate and current body burden and ranged from 10 years for OCDD to 22 years for
TCDD to 33 years for 2,3,4,7,8-PeCDF. Congener-specific half-lives, similar to those reported
by Flesch-Janys et al. (1996), were also calculated based on the decrease in serum lipid level of
congeners between 1990/92 and 1996. The fecal clearance of nonmetabolized CDDs and CDFs
contributed on average between 37% (2,3,7,8-TCDD) and 90% (OCDD) to the total elimination.
Thus, fecal clearance plays an important role in the overall elimination of most congeners, with
the daily fecal excretion estimated to be equivalent to the amount of TEQ present in about 1.7 g
of blood lipids (Rohde et al., 1999).
Because direct fecal excretion is a significant route for the excretion of nonmetabolized
CDDs and CDFs (Schlummer et al., 1998; Rohde et al., 1999), two recent studies investigated
whether Olestra, a nonabsorbable sucrose-polyester synthetic fat substitute, may enhance the
elimination of these compounds in humans. Moser and McLachlan (1999) compared the fecal
excretion of CDDs, CDFs, PCBs, and HCB with background exposures in three subjects who ate
an Olestra-free diet and who ate a diet supplemented with 25 g/day of Olestra. The fecal
excretion while on the Olestra diet was 1.5 to 11-fold higher, depending on the congener. If fecal
excretion is estimated to contribute 40% of the overall elimination of TCDD, and the Olestra diet
enhanced fecal excretion 5.7-fold, the overall rate of elimination of TCDD would be more than
doubled on the Olestra diet. Geusau et al. (1999) investigated the effect of an Olestra-
supplemented diet on the excretion of TCDD in two patients with chloracne and very high serum
TCDD levels of 144,000 and 26,000 pg/g lipid. A diet supplemented with fat-free potato chips
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(33 to 66 g Olestra/day) enhanced the fecal excretion of TCDD by up to eight- to ten-fold.
Results suggest that the increase in fecal excretion of TCDD was due mainly to an increase in the
amount of fat (dietary fat plus Olestra) excreted via the feces. The resulting elimination half-
lives of TCDD due to fecal excretion were estimated to be 1.4 years in the more highly exposed
patient and 1.9 years in the other individual. However, half-lives of 200 days and 230 days,
respectively, were determined through analysis of serum and adipose tissue TCDD levels over
the 8-month observation period. The observed half-lives were far shorter than could be
explained by an enhanced fecal or other elimination mechanism. In addition to the enhanced
fecal excretion with Olestra, the authors speculate that the high levels of TCDD may have also
induced the metabolism of TCDD in these subjects, resulting in the shorter observed half-lives.
In related studies, Morita et al. (1993, 1995, 1997, 1999) investigated the role of dietary
fiber or Chlorella in the fecal excretion of CDDs and CDFs in rats. Chlorella is a unicellular
green algae, sold as a health food or health supplement. Chlorella in the diet of rats also
enhanced the fecal excretion of CDDs from 0.8- to 5.6-fold and CDFs from 0.9- to 11.1-fold
above that of rats on a control diet (Morita et al., 1999). Rice bran fibers enhanced the fecal
excretion of CDDs from 0.6- to 2.3-fold and CDFs from 0.5- to 10.4-fold above that of rats on a
control diet (Morita et al., 1997). Dietary fiber, chlorophyll, and/or lipid in the Chlorella may be
factors responsible for the enhanced fecal excretion of CDDs and CDFs observed in this study.
Thus, fiber and/or Chlorella may be other dietary factors capable of increasing the fecal excretion
of CDDs and CDFs.
Because of the lipophilic nature of milk, lactation can provide a relatively efficient
mechanism for decreasing the body burden of TCDD and related compounds in females. As
discussed by Graham et al. (1986), this elimination of TCDD through mother's milk can result in
high exposure levels in the infant. The relatively high bioavailability of CDDs and CDFs from
mother's milk in nursing infants was discussed earlier in Section 1.1.1.2. Further discussion of
lactation as a route for excretion of CDDs and CDFs in women and exposure in infants is given
in Section 1.4.5.
1.4. PHARMACOKINETICS AND EXPOSURE
1.4.1. Introduction
Pharmacokinetic models, often simple models that represent the human body or a specific
tissue in the human body as a storage compartment for a chemical, can be used to relate doses to
internal tissue concentrations or to some other metric of toxic action. These models are
represented by a differential equation that accounts for the mass balance about the compartment.
In the simplest case, the change in mass of a chemical within the storage compartment is equal to
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the difference between the mass entering the compartment and the mass leaving the compartment
over a specified time period.
Physiologically based pharmacokinetic (PBPK) models are special extensions of the
simple pharmacokinetic model. These models utilize multiple compartments to represent the
different tissues or physiological regions of the body. They incorporate known or estimated
anatomical, physiological, and physicochemical parameters to describe quantitatively the
disposition of a chemical between different tissues. PBPK models are useful to extrapolate
between high- to low-dose kinetics within a species, to estimate equivalent doses by different
routes of administration, and to extrapolate doses across species (Scheuplein et al., 1990). Given
some knowledge of possible exposure patterns and scenarios, PBPK models can be used in an
inverse fashion to estimate exposures on the basis of internal body doses. Some examples of
how to use simple PK and PBPK models to estimate exposure to TCDD and related compounds
will be given here. In addition, this section will utilize model estimations to examine some
special cases of exposure, in particular, exposure to lactating infants and the elderly.
1.4.2. Estimating Daily Intake of TCDD
Because TCDD is highly lipophilic, it has been shown that a majority of the TCDD in any
body tissue is stored in the fat (van der Molen et al., 1996). As a first approximation, a one-
compartment pharmacokinetic model with first-order elimination may be used to compute the
daily intake of TCDD based on steady-state concentrations in the fat. The mass balance model
is:
"" D~keAF
where:
VF = volume of fat
CF = concentration of TCDD in fat
D = daily dose (mass/time)
AF = mass of TCDD in fat
ke = first order elimination constant (time"1)
Concentration, CF, is given by:
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AF
Then, at steady state (dCF/dt = 0), daily dose exactly balances elimination.
e F e F F
Note that ke can be expressed in terms of half-life:
(1-4)
ke= ™.
fcl/2
Substituting Equation 1-4 into Equation 1-3, one obtains the following expression for daily dose
in terms of fat concentration:
n ln2 T/^
D = — VFCF (1-5)
Ll/2
It is important to recall the two assumptions implicit in the derivation of the above
formula for daily uptake. First, steady-state conditions are assumed. Given that the half-life of
some of these compounds is long (e.g., for TCDD the half-life is 7 years), steady-state levels
would only be approached if the level of exposure were constant for 15-30 years. Pinsky and
Lorber (1998) compiled data from several studies that indicate that environmental concentrations
of TCDD and related compounds have been decreasing over the past 20-30 years. They use a
single-compartment model similar to the one presented above, but with a time-varying exposure
profile rather than the constant input. The profile was determined statistically on the basis of
previously recorded environmental trends. Using the prior exposure knowledge, Pinsky and
Lorber found that the pharmacokinetic model was better able to predict body burdens that have
been recorded over time than was the steady-state model. By manipulating the non-steady-state
model and comparing results to the steady-state approximation, it can be shown for a given body
burden measurement, the steady-state approximation would result in an overestimate of daily
intake. Using some estimates of the decreasing exposure function presented by Pinsky and
Lorber (1998), it appears that the overestimate of daily intake could be 20% or more with the
steady-state model.
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Another assumption of the simple model presented in this section is that the elimination
kinetics are assumed to be constant over the entire life of the individual. Because TCDD and
related compounds are stored primarily in fat, sudden weight loss and lactation would result in
alterations of the TCDD elimination rate. Again, it would be assumed that for calculation of
daily intake due to background exposure, the body burden data from such individuals would be
identified and calculations handled accordingly.
Figure 1-3 shows a sample calculation for TCDD using Equation 1-5. A fat volume of 14
L was chosen, representing 20% of the body weight. Also, for the purposes of this example, 1
mL of tissue was assumed to be equivalent to 1 g. Table 1-11 shows the estimated daily intake
of TCDD at several conditions. The range of daily intakes calculated is in agreement with those
reported elsewhere (Fiirst et al., 1991).
Thomaseth and Salvan (1998) developed a minimal PBPK model for TCDD in humans
and utilized this model to estimate occupational exposures to TCDD. The model was reduced to
one compartment for ease of solution and was based on the following assumptions: (1) dynamic
equilibrium of TCDD concentration between different body lipid distribution volumes, (2) first-
order elimination proportional to TCDD liver content, and (3) daily intake proportional to body
weight. The best parameter estimates based on Ranch Hand data were obtained with log-
transformed data under a mixed-effects model, with liver elimination kf = 0.022 days"1 (95% CI
of 0.02-0.024), and background input = 0.125 pg/kg/day (95% CI of 0.071-0.179). The model
accounts for changes in body mass index (BMI) over time, with higher BMI being related to a
longer half-life for TCDD. The model was then used to estimate occupational exposure of 253
U.S. chemical plant workers for whom one measure of serum TCDD was available. The
estimated exposure of the NIOSH cohort was 233 pg/kg/day (95% CI of 192-273). This model
is much more rigorous than the simple steady-state approximation, and if a PK model is to be
used to attempt to estimate intake, this model would be preferable. However, solving this model
for daily intake is more complex and requires some assumption of the pattern of exposure over
time. Alternatively, one could estimate daily intake by direct exposure calculations, that is, by
examining the interaction of humans with environmental media containing the highest
concentrations of dioxins. More information on this is provided in the chapter on exposure (Part
H, Chapter 4).
1.4.3. Daily Intake of Congeners
Steady-state average dose calculations may be performed for other congeners in a manner
similar to that presented for TCDD. Three pieces of information are necessary. First,
concentrations in the adipose tissues must be known. Second, the half-lives of the compounds
within the body must be known. Third, some understanding of the kinetics and exposure
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conditions is required to ensure that steady-state conditions were achieved at the time of
monitoring.
Concentrations of various congeners in adipose tissues can be found in several sources
(Stanley et al., 1986; Schecter, 1991). Values range from around 2 ppt for 2,3,7,8-TCDF to
several hundred ppt for 1,2,3,4,6,7,8,9-OCDD.
Half-lives could be determined from elimination data, if available. Methods have been
suggested to determine the half-life of such compounds from uptake data relative to TCDD.
Schlatter (1991) has proposed one such method. The following has been adapted from that
proposed method. Manipulation of Equation 1-5 results in:
TCDD 1/2,TCDD
V In 2
For some other congener x:
D t
1/2 , X
Thus, the ratio of concentrations of TCDD to x can be described by:
TCDD _ TCDD I/2, TCDD I V In2 |
C ~ I V In2 J ~D t.,,
x \ I \ x 1/2,xj
Which, with algebraic manipulation and simplification, becomes:
(1-8)
t
1/2, x
TCDD I/2, TCDD
C,
TCDD
C
x
D
(1-9)
Assuming intake D to be mostly from food, especially animal-fat products, it can be related to
absorption from these foods according to:
TCDD ~ (a, TCDD) (TCDD) (1-10)
where:
ka,icDD = absorption rate constant for TCDD
ATCDD = concentration of TCDD in animal fat (diet)
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and
D = ,k v ,A i
x ( a,x) ( x)
where:
kax = absorption rate constant for x
Ax = concentration of x in animal fat (diet)
As a result, the half-life for compound x can be described by:
A 1 I C \ I k
t TCDD
C
-
l/2,x l/2,rCDD r a fe-
1 '- A Ka,x
When the absorption rate constants for each are equal or when the difference between them is
small compared to differences in other parameters (concentration, half lives), Equation 1-12 can
be further simplified to:
t- - - ATCDD
Cl/2,x
TCDD
It should be noted that for some of these substances exposure is expected from other than
food sources. For such cases Equation 1-12 would be modified to include these other sources as
follows.
if a C
a, i, TCDD i,TCDD\ x , , ,, , ..
, TCDD r - : - £-T - A.,x\ (1-14)
"TCDD
where:
ka,;,TCDD= absorption rate constants for TCDD from each of the i media
AijcDD = concentration of TCDD in each of the i media
kaix = absorption rate constants for x from each of the i media
AjjX = concentration of x in each of the i media
Other symbols: as previously defined
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Again, if the differences between the absorption rate constants for TCDD and x are judged to be
small, then the following variation of Equation 1-14 can be used:
Ai,TCDD\ , - NN | (1-15)
C A
TCDD i,x
Table 1-12 shows the results of some half lives calculated in this manner.
The half lives calculated using Equation 1-15 for the first three compounds in Table 1-12
agree with those calculated by Schlatter (1991). The large difference in the two calculations for
OCDD is due to significant differences in absorption rates between TCDD and OCDD. Schlatter
notes in his paper that for some compounds, including OCDD, corrections were made of
differences in absorption. No explanation was offered on how this was done. However, Flesch-
Janys et al. (1996) report a half-life of 6.7 years for OCDD. In addition, Flesch-Janys et al.
(1996) report longer half-lives for 1,2,3,7,8-PeCDD (15.7 years) and 2,3,4,7,8-PeCDF (19.6
years). These differences could be due to a variety of factors including the dietary
concentrations, absorption rates, other background sources, etc. Table 1-13 summarizes the
results reported by Flesch-Janys.
When using this method to estimate daily intake of TCDD and congeners, it should be
noted that studies (Kim and Dubin, 1996; Shirai and Kissel, 1996) have indicated that there is
considerable uncertainty associated with half-life measurements published in the literature.
1.4.4. Induction of Liver Binding Proteins and Resultant Distribution
Andersen et al. (1997a) developed a multicompartment geometric model of the liver in
relation to regional induction of cytochrome P-450s. The model was based on five
morphological and functional zones of the liver acinus: a concentric periportal zone, a fenestrated
periportal region that interconnects among multiple functional units, and three concentric
centrilobular areas. At low doses, TCDD exhibits centrilobular expression of CYP1A1, and with
increasing dose, adjacent areas radiating out from the centrilobular region also begin to express
CYP1A1. To create realistic total induction curves that are relatively smooth, the differences in
Kd (dissociation constant) values between adjacent subcompartments must be less than fivefold.
Because of the high n (Hill constant) values, the low-dose induction characteristics predicted
with the multicompartment liver model differ significantly from those predicted with a model
that considers the liver as a single homogeneous compartment. A physiologically based
pharmacokinetic (PBPK) model for TCDD was then combined with a five-compartment
geometric model of hepatic zonation to predict both total and regional induction of CYPs within
the liver (Andersen et al., 1997b). The model predicts an 81-fold difference in the affinity of the
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AhR-TCDD complex binding to DNA response elements for CYP1 Al between the centrilobular
and the periportal regions. The PBPK analysis based on the multicompartment liver model
suggests that the low-dose behavior for hepatic CYP1 Al/1 A2 induction by TCDD is highly
nonlinear.
1.4.5. Pregnancy and Lactation (Exposure of Offspring)
The distribution and excretion of [14C]-2,3,7,8-TCDD (30 |_ig/kg) and [14C]-2,3,7,8-TCDF
(800 j-ig/kg) were studied in pregnant C57BL/6N mice after oral exposure on gestation day 11
(Weber and Birnbaum, 1985). The distribution and excretion of TCDD and 2,3,7,8-TCDF in
pregnant mice were similar to those of males of the same strain (Gasiewicz et al., 1983b; Decad
et al., 1981b) (see Tables 1-6 and 1-9), although elimination rates were higher in the pregnant
mice for both congeners. For TCDD, liver, urinary, and fecal elimination was 3.0, 3.4, and 14.4
times faster than that reported for males. For 2,3,7,8-TCDF, liver, urinary, and fecal elimination
was 1.3, 1.8, and 1.8 times faster than that observed for males. Elimination data from pregnant
mice were based on only three time points (gestation days 12, 13, and 14) and thus represent only
rough estimates. In addition, the greater fecal excretion could have been due to incomplete
absorption of TCDD after oral exposure. Although these results need further substantiation, it is
conceivable that the sex of the animal, pregnancy, and the route of exposure could have a
significant impact on the pharmacokinetics of these compounds.
In a related study, Krowke (1986) compared the TCDD concentrations in the livers of
pregnant and nonpregnant NMRI mice exposed subcutaneously to 12.5 or 25 nmol/kg/day on
gestation days 9-11. At 7 days after exposure to the lower dose, the hepatic TCDD
concentrations were 7 and 32 ng/g in pregnant and nonpregnant mice, respectively. At the higher
exposure, 5.5 times lower concentrations of TCDD were found in the livers of pregnant animals
on gestation day 18. A similar effect on hepatic TCDD levels was observed in combined
exposure, which contained 1,2,3,7,8-PeCDD, 1,2,3,4,7,8-HxCDD, or 2,3,7,8-TCDF. The
decreased hepatic levels of TCDD in pregnant mice are consistent with the Weber and Birnbaum
(1985) observation of more rapid elimination of TCDD in pregnant mice. Further investigations
are necessary to better characterize the apparently significant effects of pregnancy on the
disposition of TCDD and related compounds.
Weber and Birnbaum (1985) also investigated the distribution of [14C]-2,3,7,8-TCDD (30
l-ig/kg) and [14C]-2,3,7,8-TCDF (800 |_ig/kg) to the embryos of pregnant C57BL/6N mice after
oral exposure on gestation day 11. On gestation days 12, 13, and 14, the percent of the maternal
dose in the embryo remained constant at 0.032%-0.037%/embryo, while the concentrations in the
embryo were 0.34%, 0.17%, and 0.15% of the dose/g embryo, respectively. Embryos had
approximately 11-fold higher concentrations of TCDD than 2,3,7,8-TCDF when exposed on a
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percent of total dose/g tissue basis. This may be due to the more rapid metabolism and excretion
of 2,3,7,8-TCDF compared with TCDD. Assuming that all radioactive material found in
embryos was parent compound, at most 2.6 ng (8 pmol) of TCDD and 6.4 ng (21 pmol) of
2,3,7,8-TCDF/g tissue were detected under these conditions.
The transfer of [14C]-2,3,7,8-TCDD to the embryo during early gestation was assessed in
NMRI mice given a dose of 25 |ig/kg by intraperitoneal injection on days 7, 8, 9, 10, 11, or 13 of
gestation (Nau and Bass, 1981). The mice were sacrificed after 48 hours, and TCDD
concentrations were determined by liquid scintillation counting of solubilized tissue and by GC-
ECD and GC/MS. Similar results were given by these methods, suggesting that TCDD-derived
[14C] in maternal and embryonic tissue was the parent compound. The maternal liver contained
from 4% to 8% of the dose/g, or 40-80 ng/g. TCDD in embryonic tissue from gestation days
11-15 ranged from 0.04% to 0.1% of the dose/g, or 0.4-1.0 ng/g. In contrast, higher levels were
found earlier in gestation, with 10 ng/g embryo on gestation day 9 and 2 ng/g on day 10. The
higher levels may be related to placentation, which occurs at approximately gestation days 10-11
in this mouse strain. The affinity of fetal liver for TCDD was relatively low, as compared to
maternal liver; however, TCDD levels in fetal livers were two to four times higher than levels in
other fetal organs. Nau and Bass (1981) also attempted to correlate TCDD levels in the fetuses
with the observed incidence of cleft palate. Three groups of mice were given either a single
intraperitoneal exposure to 25 |_ig/kg TCDD on gestation day 7 or 10 or 5 pg/kg/day,
intraperitoneally, on gestation days 7-11. On gestation day 13, TCDD concentrations in maternal
tissues were very similar in the three exposure groups. At day 13, however, the embryo
contained 0.038±0.011% (0.36 ng/g), 0.096±0.027% (0.92 ng/g), and 0.12±0.05% (1.1 ng/g) of
the dose (mean±SD) in the 7-, 10-, and 7- to 11-day exposure groups, respectively. Cleft palate
incidence on gestation day 18 was 16%, 84%, and 65% for the 7-, 10-, and 7- to 11-day exposure
groups, respectively. Although further studies are needed, these results suggest that cleft palate
incidence is generally related to the TCDD concentration in the embryo. In a related study,
Couture et al. (1990) found that gestation day 12 was the peak period of sensitivity for TCDD-
induced cleft palate in C57BL/6N mice; however, tissue levels were not investigated.
In the same laboratory, Abbott et al. (1989) investigated the distribution of TCDD in the
C57BL/6N mouse fetus following maternal exposure on gestation day 11 to 30 pg/kg. 2,3,7,7-
TCDD was detected in the gestation day-11 embryo at 3 hours postexposure and was equally
distributed between the embryonic head and body. At 72 hours postexposure, 0.035% of the
total dose was in fetal tissues and 1% of the TCDD in the fetus (1.4-3.5 pg) was found in the
palatal shelf. More recently, Abbott et al. (1996) found that TCDD was detected in maternal
blood, liver, and fat and in the placenta, embryonic liver, and palate within 30 min after oral
exposure of mice on gestation day 12. The levels peaked in blood and placenta at 3 hours and in
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the other tissues at 8 hours. At 24 hours following a single oral dose of 24 |_ig TCDD /kg, the
above respective tissues contained 0.25, 98.8, 72.9, 1.22, 1.03, and 0.44 ng TCDD/g.
Krowke (1986) also measured the concentration of TCDD in the placenta, amniotic fluid,
and fetus of NMRI mice exposed to 2.5 nmol/kg by subcutaneous injection on days 9-11 of
gestation. Similar concentrations of TCDD were observed in the placenta, amniotic fluid, and
fetus (-0.5 ng/g) on day 16 of gestation. Fetal liver TCDD concentrations were at least five
times greater than in other fetal tissue. Krowke (1986) reported slightly lower TCDD levels in
the fetal head relative to other extrahepatic fetal tissue, while Weber and Birnbaum (1985) found
a slightly higher TCDD concentration in the head relative to other extrahepatic fetal tissue.
Nau et al. (1986) investigated the transfer of TCDD via the placenta and milk in NMRI
mice exposed to 25 pg/kg on day 16 of gestation. The authors confirmed the relatively low fetal
tissue levels with prenatal exposure to TCDD (Nau and Bass, 1981) and found that postnatally
TCDD was transferred efficiently to mouse neonates and offspring by lactating mothers. During
the first 2 postnatal weeks, the pups were given doses of TCDD via the milk that were, on a
body-weight basis, similar to those that had been administered prenatally to their mothers.
TCDD levels in the tissue of lactating mothers decreased within the first 3 postnatal weeks by
two to three orders of magnitude to reach levels that were only -2% of the corresponding levels
in the pups that these mothers had nursed. Thus in mice, excretion into milk represents a major
pathway for maternal elimination of TCDD and for the subsequent exposure of pups.
The disposition of TCDD in rat pups was assessed after the prenatal (via placental
transfer) and postnatal (via milk) exposure from pregnant Wistar rats given a single dose of 3, 30,
or 300 ng/kg, subcutaneously, on day 19 of gestation (Korte et al., 1990). Lactation resulted in
the rapid elimination of TCDD from maternal tissues, with the half-life of TCDD in the liver of
lactating rats estimated to be ~7 days. This compares to a half-life of 13.6 days in the liver of
nonlactating rats (Abraham et al., 1988). At postnatal day 7, exposure via the milk resulted in
pup liver TCDD concentrations that were greater than the corresponding levels in maternal liver.
In cross-fostering experiments, the concentrations of TCDD in the liver of offspring at postnatal
day 7 were 0.47, 2.59, and 4.16 ng/g in the 300 ng/kg groups exposed through the placenta only,
via the milk only, and through the placenta and via the milk, respectively. These results support
the earlier observations that the placental transfer of TCDD in rats and mice is relatively limited
compared with the efficient transfer via maternal milk.
Van den Berg et al. (1987b) investigated the transfer of CDDs and CDFs to fetal and
neonatal rats. Prenatal exposure of the fetus was assessed in pregnant Wistar rats fed a diet
containing a fly ash extract from a municipal incinerator on days 10-17 of gestation. Postnatal
exposure of 10-day-old pups was assessed through feeding lactating mothers the same
contaminated diet for the first 10 days after delivery. Although the fly ash extract contained
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almost all of the 136 tetra- to octa-CDDs and -CDFs, only 17 CDD and CDF congeners were
detected as major compounds in the tissue of fetuses, pups, and dams. All of the congeners were
2,3,7,8-substituted with the exception of 2,3,4,6,7-PeCDF. TCDD had the highest retention
(0.0092% of the dose/g) in the fetus, while 2,3,7,8-TCDF, 1,2,3,7,8-PeCDF, and hepta- and octa-
CDDs and -CDFs were not detected in the fetus. In the liver of offspring, the highest retention
was found for TCDD, 1,2,3,7,8-PeCDD, and the three 2,3,7,8-substituted HxCDDs
(0.74%-1.13% dose/g). The 2,3,7,8-substituted penta- and hexachlorinated congeners showed
the highest retention in the livers of dams (2.05%-5.17% of dose/g liver), while 2,3,7,8-TCDF,
1,2,3,7,8-PeCDF, and 2,3,4,6,7-PeCDF had the lowest retention. A linear relationship was found
between the retention of CDDs and CDFs in the livers of pregnant and lactating rats.
Furthermore, a linear relationship was found between the retention of CDDs and CDFs in the
livers of the lactating rats and livers of the offspring.
In a related study, Hagenmaier et al. (1990) investigated the transfer of CDDs and CDFs
through the placenta and via milk in a marmoset monkey. A defined mixture of CDDs and CDFs
was given as a single subcutaneous injection to a pregnant marmoset monkey at the end of the
organogenesis period (week 10 of gestation, 11 weeks prior to delivery). Transfer of CDDs and
CDFs through the placenta was investigated in a newborn 1 day after birth, and transfer through
the placenta and via milk was assessed in an infant of the same litter after a lactation period of 33
days. Tissue concentrations of the offspring were compared with those of the mother at the end
of the lactation period and with data from other adult marmosets obtained at this time of
maximum absorption (1 week after injection) and 6 weeks after injection. Deposition of CDDs
and CDFs into the newborn liver was very low, suggesting very little transplacental transport and
hepatic accumulation of these compounds. TCDD and 1,2,3,7,8-PeCDD were found at the
highest concentration in the liver of the newborn (-0.15% of dose/g). For all other congeners,
the concentrations in the liver of the newborn were <10% of the corresponding concentrations in
adults. In contrast to liver, concentrations of 2,3,7,8-substituted congeners in the adipose tissue
of the newborn were at least 33% of the levels in adults, and in the case of OCDD and OCDF,
levels were threefold higher in the newborn than in the adult. The adipose tissue/liver
concentration ratios for 2,3,7,8-substituted congeners in the newborn ranged from 2.2 for
1,2,3,4,6,7,8-HpCDF to 10.9 for 2,3,7,8-TCDF. Furthermore, the concentration of these
congeners in the newborn was highest in the adipose tissue, followed by the skin and liver. This
is in contrast to the relative distribution in the adult, where the liver generally contains the
highest levels of these congeners. The results indicate that hepatic concentrations in the fetus
may not be representative of the rate of placental transfer of CDDs and CDFs. In the marmoset
monkey, substantial placental transfer into fetal adipose tissue can be observed for most of the
2,3,7,8-substituted congeners during the fetal period. As expected from rodent studies, the
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transfer of CDDs and CDFs via mothers' milk was considerable, resulting in hepatic
concentrations of 2,3,7,8-TCDD, 1,2,3,7,8-PeCDD, and 1,2,3,6,7,8-HxCDD in the suckled infant
(postnatal day 33) higher than those in the dam. The hepatic concentration of TCDD in the 33-
day-old infant was -0.9% of the dose/g tissue. Transfer of hepta- and octa-CDDs and CDFs to
the suckled infant was rather low, only -10% of the levels in the dam. When total exposure of
the mother and offspring at the end of the 3 3-day nursing period was assessed in terms of I-TE
factors (U.S. EPA, 1989), the liver of the mother contained 2,494 pg I-TE/g, whereas the
offspring liver contained 2,022 pg I-TE/g. This approach is necessary to assess total exposure
due to the congener-specific transfer via lactation.
The pre- and postnatal transfer of TCDD to the offspring of rhesus monkeys was
investigated by Bowman et al. (1989). Animals were fed a diet containing TCDD at
concentrations of 5 or 25 ppt for -4 years and were on a TCDD-free diet for -18 months prior to
parturition. Maternal TCDD levels (mean±SE) in adipose tissue were 49±11 (n=7) and 173±81
(n=3) ppt in the 5 and 25 ppt groups, respectively. Corresponding levels in the adipose tissue of
offspring at weaning (4 months) were 187±58 and 847±298 ppt in the 5 and 25 ppt groups,
respectively. From these data, a TCDD BCF of 4.29 was estimated from mother to nursing
infant. This value is similar to that observed for TCDD in the marmoset monkey (Hagenmaier et
al., 1990). The milk of the rhesus monkeys in the 25 ppt group contained from 4 to 14 ppt of
TCDD, which corresponds to 150-500 ppt on a lipid basis. The authors calculated that the three
mothers in the 25 ppt group excreted from 17% to 44% of their TCDD body burden by lactation.
They also concluded that the results are generally consistent with overall triglyceride movement
as mediating the excretion of TCDD in milk.
In a subsequent study, Bowman et al. (1990) reported the relative persistence of TCDD in
the offspring of rhesus monkeys that were exposed earlier to 5 or 25 ppt of TCDD in the diet.
The concentration of TCDD in adipose tissue was measured in offspring at -4-5, 12, and 24
months of age. The decrease of TCDD levels in adipose tissue of seven young monkeys departed
somewhat from first-order, single-compartment kinetics, but with the limited data and an
assumption of first-order kinetics, a half-life of 121 days was estimated. When the data were
adjusted within each animal for body weight gain and for average fat content at each age, the
adjusted data apparently followed first-order, single-compartment kinetics, with a half-life of
-181 days. Thus, young monkeys apparently eliminate TCDD from adipose tissue at a faster rate
than adult rhesus monkeys, which had individual half-lives ranging from 180 to 550 days
(Bowman et al., 1989).
Several studies provide data in support of the transplacental transport of CDDs and CDFs
to the human fetus. Kreuzer et al. (1997) measured the concentration of CDDs and CDFs in the
lipids of adipose tissue and livers of 3 stillborn infants and detected 16 of a possible 17
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congeners, with the exception of 1,2,3,7,8,9-HxCDF. TEQ levels ranged from 6.2 to 10.8 pg/g
lipid, and TCDD levels ranged from 0.8 to 2.1 pg/g lipid for human infants that died at birth.
These levels are similar to those reported in the lipid fractions of maternal tissues, suggesting that
prenatal exposure to CDDs and CDFs reflects the levels present in maternal tissues. Similar
findings were reported earlier by Schecter et al. (1990c), who detected TCDD (1.3 to 4.3 pg/g
lipid) in the livers of three stillborn infants. Thus, significant prenatal exposure to CDDs and
CDFs occurs, with the concentration of these compounds in the lipids of the newborn infants
generally reflecting that in maternal lipids.
A significant source of postnatal exposure of human infants to CDDs and CDFs is
through the ingestion of human milk. Several investigators have quantified the levels of TCDD
in human milk samples. Many of the milk samples were pooled (Jensen, 1987). Rappe (1984)
reported levels of 1-3 ppt TCDD in milk fat (lipid adjusted) from five volunteers in West
Germany, and in a later report, Rappe et al. (1985) reported an average level of 0.6 ppt TCDD in
milk fat from four volunteers in northern Sweden. Furst et al. (1986) reported an average level of
9.7 ppt TCDD in milk fat from three individuals in the Netherlands and <1.0 ppt TCDD in milk
fat from two individuals in Yugoslavia. Nygren et al. (1986) reported average levels of TCDD in
human milk samples from four subjects in Sweden to be 0.6 ppt in milk fat, in five subjects from
West Germany to be 1.9 ppt in milk fat, and in four subjects from Vietnam to be <0.5 ppt in milk
fat.
High levels of TCDD have been detected in the milk of mothers exposed to high levels of
TCDD in the environment. Reggiani (1980) reported levels between 2.3 and 28.0 ppt TCDD in
whole milk from mothers in Seveso. Baughman (1975) reported levels between 40.0 and 50.0
ppt TCDD in whole milk from mothers in South Vietnam. Schecter et al. (1987) also found high
ppt levels of TCDD in human milk samples from South Vietnam. These authors found that
levels from samples taken in 1985 from South Vietnamese mothers were comparable to the level
of TCDD currently found in North American human milk samples (5 ppt).
Furst et al. (1989) examined the levels of CDDs and CDFs in human milk and the
dependence of these levels on the period of lactation. The mean concentrations of CDDs in
human milk (on a fat basis) ranged from 195 ppt for OCDD to 2.9 ppt for TCDD, with the levels
of the other congeners decreasing with decreasing chlorination. This is in contrast to the
generally lower levels of CDFs in human milk, which range from 25.1 ppt for 2,3,4,7,8-PeCDF
to 0.7 ppt for 1,2,3,7,8-PeCDF. An evaluation of the CDD and CDF levels in relation to the
number of breast-fed children found that the concentrations in milk generally decreased with the
greater number of children. The CDD and CDF levels in milk from mothers nursing their second
child are on average 20%-30% lower than those for mothers breast-feeding their first child. CDD
and CDF levels were also analyzed in one mother over a period of 1 year after delivery of her
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second baby to assess the effect of duration of lactation. After breast-feeding for 1 year, the
mother had CDD and CDF levels that were 30%-50% of the starting concentration. Levels in
milkfat (ppt) at 1, 5, and 52 weeks after delivery were 251, 132, and 119 for OCDD; 7.9, 5.9, and
1.4 for TCDD; and 33.1, 24.5, and 10 for 2,3,4,7,8-PeCDF, respectively. The results suggest a
more rapid mobilization of CDDs and CDFs and excretion into human milk during the first few
weeks postpartum. Although further studies are necessary, the limited data suggest that there are
time-dependent, isomer-specific differences in the excretion of CDDs and CDFs in human milk.
Schecter et al. (1998a) assess the decrease in the levels of CDDs, CDFs, PCBs, DDE, and
HCB in the blood and milk lipid in a mother that nursed twins over a 38-month period. During
the first 23 months of nursing, the CDD and CDF TEQ decreased 68% (15.7 to 5.0 ppt, lipid) for
blood and decreased 77% (13.6 to 3.1 ppt, lipid) for breast milk. Thus, lactation results in a
similar reduction in CDD and CDF concentrations in the lipid fractions of blood and milk.
During the first 23 months of nursing, the PCB 126 (3,3',4,4',5-PeCB) milk concentration also
decreased 71% (21.0 to 6.1 ppt, lipid). The authors estimate that approximately 115 ng TEQ
(CDDs, CDFs, coplanar PCBs) was ingested by each infant from breast feeding for this extended
period of time.
Abraham et al. (1996) investigated the intake, fecal excretion, and blood levels of CDDs,
CDFs, and PCB 126 in two breast-fed and two formula-fed infants. At 1 month, the
concentrations of CDDs and CDFs in breast milk were 19.7 and 22.2 TEQ (pg/g lipid), while the
formula diet contained only 0.38 TEQ (pg/g lipid). At the age of 11 months, the breast-fed
infants' blood CDD and CDF concentrations were 29.2 and 37.5 TEQ (pg/g lipid), whereas the
formula-fed infants' blood CDD and CDF concentrations were 2.4 and 2.6 TEQ (pg/g lipid). At
this time, the mothers that breast-fed had blood CDD and CDF concentrations of 12.3 and 10.5
TEQ, while the mothers that formula-fed had blood levels of 16.9 and 13.8 TEQ (pg/g lipid).
Because PCB 126 has a TEF of 0.1, it is important to note that at 11 months, breast-fed infants
also have much higher levels of PCB 126 (287 and 374 pg/g lipid) relative to formula-fed infants
(24 and 18 pg/g lipid). PCB 126 levels in mothers that breast-fed were 105 and 86 relative to
levels of 193 and 52 (pg/g lipid) in the mothers that formula-fed. Thus, when PCB 126 is
included in the TEQ calculation, the breast-fed infants' total TEQ blood concentration (body
burden) is more than doubled from that estimated on the basis of CDDs and CDFs alone. The
results of this study provide direct, quantitative data showing that the body burden (blood level)
of CDDs, CDFs, and PCB 126 is more than 10 times higher in 11-month-old breast-fed infants
than in 11-month-old formula-fed infants.
Although data are more limited for the co-planar PCBs, 3,3',4,4'-TCB, 3,3',4,4',5-PeCB,
and 3,3',4,4',5,5'-HxCB have been detected in human milk from Swedish mothers, at
concentrations of 16-32, 72-184, and 46-129 ppt on a fat basis, respectively (Noren et al., 1990).
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Therefore, lactation appears to be an effective means for the excretion of co-planar PCBs from
mothers and a major source of postnatal exposure of nursing infants. Because 3,3',4,4',5-PeCB
and other co-planar PCBs are present in human milk at concentrations up to 60-fold higher than
TCDD, it is important to consider the relative toxic potency of these dioxin-like compounds and
their potential health impact on nursing infants.
Kreuzer et al. (1997) measured the levels of CDDs and CDFs in the lipids of adipose
tissue and liver of 17 infants (0.43 to 44 weeks of age) who died of sudden infant death
syndrome. As expected, the concentrations of these compounds in breast-fed infants were higher
than those in non-breast-fed infants; however, the magnitude of this difference varied because of
differences in the age of the subjects and the duration of breast-feeding. The TEQ concentrations
in the livers of these subjects were slightly, but not significantly, higher than the respective levels
measured in the adipose tissue lipids. The results also suggest that the higher chlorinated
congeners preferentially accumulate in liver lipids, an observation made earlier for adults in a
study by Thoma et al. (1990) (see Table 1-5).
Kreuzer et al. (1997) used data from the above study and other published results to
validate a physiological toxicokinetic model they developed to describe the body burden of
TCDD for the entire human lifetime and the influence of breast-feeding on the body burden. The
model includes gender- and age-dependent changes in the following parameters: body weight;
volumes of liver, adipose, and muscle tissue; food consumption; and excretion of feces. The
model also assumes that TCDD exposure occurs primarily from the ingestion of contaminated
food, that TCDD is distributed freely in lipids, and that TCDD is excreted unchanged in the
lipids of the feces as well as following hepatic metabolism. More complex biochemical processes
such as protein binding, saturation of metabolism at high TCDD concentrations, or induction of
metabolism are not part of this model, which considers factors more relevant for low-level or
background human exposures. With the basis assumption of this single-compartment model and
the free distribution of TCDD in all body lipids, including the gastrointestinal tract, the half-life
of the nonmetabolic elimination (tfl/2) is proportional to the ratio of volume of body lipids (V)
to the mass of lipids in stool excreted per unit time (dFa/dt). During aging, V increases at least
40 times but dFa/dt only 1.7 times (from 3 g/day in infants to 5 g/day in adults). Consequently,
the half-life of the nonmetabolic elimination (tfl/2) is calculated to be only 0.42 years in
newborns and 9.5 years in 40-year-old adults. According to this model, most TCDD is
eliminated as unchanged compound in children, with the role of metabolism-dependent
elimination becoming more important with age. Thus, the half-life increases almost linearly
from its starting value of about 4 months in newborns and reaches a value of 5 years at the age of
40. An age dependent elimination of TCDD has also been reported experimentally in the rhesus
monkey (Bowman et al., 1989, 1990). Furthermore, the model of Kreuzer et al. (1997) predicts
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that the relatively high TCDD concentrations that might be reached after 6 months of nursing do
not lead to an elevated lifetime body burden of TCDD.
In a related study, Patandin et al. (1999) investigated dietary, including lactational,
exposure to CDDs, CDFs, and PCBs from early childhood until the early reproductive age of 25
years in order to assess exposure risk to the next generation. Based on the analysis of 83 milk
samples, previously reported analysis of food products, and food questionnaire data, the daily
TEQ intake per kg body weight is 50 times higher in breast-fed than bottle-fed infants and 3
times higher in toddlers than in adults. Although exposures are relatively high in breast-fed
infants, breast-feeding for 6 months contributes only 12% and 14% to the respective body
burdens of men and women at the age of 25 years. After babies are weaned, dairy products,
processed foods, and meat are major sources of exposure to these compounds.
1.4.6. Pharmacokinetics and Aging
The influence of aging on the intestinal absorption of TCDD was studied in 13-week-,
13-month-, and 26-month-old (senescent) male Fischer 344 rats (Hebert and Birnbaum, 1987).
Absorption was measured by an in situ intestinal recirculation perfusion procedure. When
absorption was calculated in terms of ng TCDD absorbed/g mucosal dry weight/hour, the
decrease between the senescent rats and the two younger age groups, from 544 ng/g/hour (young)
to 351 ng/g/hour (senescent), was not statistically significant (p<0.05). The results indicate that,
as with other molecules that depend on diffusion for their absorption, aging does not affect the
intestinal absorption of TCDD.
Banks et al. (1990) studied the effect of age on the dermal absorption and disposition of
TCDD and 2,3,4,7,8-PeCDF in male Fischer 344 rats. When rats were administered the same
dose per body weight, dermal absorption of TCDD at 3 days after exposure decreased from
17.7±2.7% (mean±SD) to 5.6±2.5% of the administered dose in 10- and 36-week-old rats,
respectively. Dermal absorption in the 96-week-old rats was similar to that of the 36-week-old
rats. Dermal absorption of 2,3,4,7,8-PeCDF also decreased from 22.2±0.2% to 14.7±3.8% of the
administered dose in 10- and 36-week-old rats, respectively. Dermal absorption of both
compounds was also decreased in older rats given the same total dose per surface area. Older
animals may have decreased blood flow in the upper dermis, which will decrease the clearance of
these compounds from the application site. Potential age-related changes in the intercellular
stratum corneum lipids may also play a role in the decreased dermal absorption observed in older
animals. Changes in the percentage of the administered dose detected in various depots reflected
age-related changes in dermal absorption, whereas age-related changes in the tissue distribution
of the absorbed dose reflected changes in the total mass of these tissues at various ages. Overall
elimination of the absorbed dose was not affected by age. Although this investigation was
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conducted with a lipophilic solvent system and an animal model with skin that is more permeable
than human skin, the results suggest that systemic bioavailability after dermal exposure to TCDD
or 2,3,4,7,8-PeCDF may be reduced in older age groups.
In a similar study, absorption, tissue distribution, and elimination were examined 72
hours after dermal application of a lower dose of 200 pmol (111 pmol/cm2) TCDD to weanling
(3-week-old), juvenile (5-week-old), pubescent (8-week-old), young adult (10-week-old), and
middle-aged (36-week-old) rats (Anderson et al., 1993). Dermal absorption using acetone as the
vehicle was greatest in 3-week-old rats (129 pmol; 64% of the administered dose), decreasing to
-80 pmol (40%) in 5-, 8-, and 10-week-old rats and to 45 pmol (22%) in 36-week-old rats. The
results indicate that TCDD is absorbed to a greater degree through skin of very young animals
and that a significant decrease in potential for systemic exposure may occur during maturation
and again during aging.
1.5. SELECTION OF DOSE METRIC
Risk assessment requires the scaling of exposure/dose across endpoints and across
species. Given the many responses to TCDD and its congeners, the selection of dose metrics for
use in quantitative risk assessments is a complex problem. The biochemical and toxicological
responses of TCDD and related chemicals are initiated by their interaction with the Ah receptor.
Some responses, such as enzyme induction, require short periods (minutes to hours) of Ah
receptor activation. Other responses, such as cancer, require prolonged (months to years)
activation of this pathway. Still other responses, such as the developmental toxicities, require
receptor activation during specific windows of sensitivity. Because of the different mechanisms
involved in these diverse responses, it is unlikely that a single dose metric will be adequate for all
of these endpoints. A number of studies have proposed a variety of dose metrics for a number of
different responses. These studies have taken different approaches ranging from simple curve
fitting exercises (Hurst et al., 2000; van Birgelen et al., 1996) to more complex PBPK modeling
approaches (Jusko et al., 1995; Andersen et al., 1997; Kohn et al., 1993; Portier and Kohn,
1996). Area under the curve (AUC), a dose metric traditionally used in the pharmaceutical
literature, has also been proposed as a dose metric for dioxin cancer risk estimates (Becher et al.,
1998).
The choice of dose metric not only considers mechanistic data but must also consider
pragmatic approaches as well. The use of the dose metric plays a role in its choice. Because of
differences in life-span and uncertainties in the window of sensitivities for various endpoints,
AUC may not be a useful dose metric for cross species extrapolation. However, AUC has been
used in the analysis of the human cancer data (Becher et al., 1998) and may be a useful dose
metric when applied to accidental or occupational exposures since cross species scaling is not
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required. The choice of dose metric is also dependent upon the data available. A number of dose
metrics, such as Ah receptor occupancy, induction of CYP1A2, and decreases in EOF receptor
have been proposed based on PBPK models (Jusko et al. 1995; Andersen et al. 1997; Kohn et al
1993; Portier and Kohn, 1996). While these dose metrics have been useful in hypothesis testing
in experimental systems, they are not useful in animal to human extrapolations due to the
difficulty in measuring these parameters in humans. In the following section, the strengths and
weaknesses of a variety of proposed dose metrics will be presented.
1.5.1 Administered Dose
In experimental studies, animals are administered a defined dose through a variety of
routes. A default method used by USEPA (USEPA, 1992; 1996) to estimate the human
equivalent dose when scaling across species is to use allometric scaling based on the following
equation:
,0.25
Dosehuman= Doserat(BWrat/BWhuman)'
Where BW is the body weight in kg and Dose is the daily administered dose in rats or the scaled
human daily dose expressed as ng/kg/d. This method is thought to scale administered dose in
such a way as to result in equivalent effective doses in humans and experimental animals
(USEPA, 1992). Using this equation a dose of 1 ng TCDD/kg/d in a 0.35 kg rat would result in a
scaled human dose of 0.27 ng TCDD/kg/d for a 70 kg human. If this scaling method applies to
TCDD and related chemicals, then 1 ng TCDD/kg/d in the rat should produce similar effective
dose in a human exposed to 0.27 ng TCDD/kg/d, some 3.8 times lower. Assuming similar
sensitivity between rats and humans at the tissue level, effective doses should be a function of
tissue concentration. Tissue concentrations of TCDD and related chemicals are directly related
to the concentration of TCDD in the body. The steady-state concentration of TCDD in the body,
or steady-state body burden, can be estimated in rats and humans using the following equation.
Steady-state body burden (ng/kg) = (Dose*F) * (t,/2/Ln(2))
where Dose is the daily administered dose, F is the fraction absorbed, and t,/2 is the species-
specific half-life of TCDD. In the present example, we will assume F is 50% and the species
specific half-life of TCDD is 25 days for rats and 2593 days for humans. Starting with an
administered dose of 1 ng/kg/d in rats and the scaled human dose of 0.27 ng/kg/d, the steady-
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state body burdens are presented in Table 1-14. The steady-state body burden of TCDD using
the scaled human dose is approximately 28 times that of the steady-state body burden in the rat
(Table 1-14). Using the equation above, the estimated steady-state body burden in rats exposed
to 1 ng TCDD/kg/day is 18 ng/kg. An equivalent administered dose in humans that results in a
body burden of 18 ng/kg is estimated at 0.0096 ng/kg/day. It should be noted that when body
burden is used as the dose metric, an equivalent human dose is over 100 times less than the
human equivalent dose estimated using the EPA default scaling method.
Using administered dose as a dose metric has additional uncertainties when route and
duration of exposures are varied. Tissue concentrations of a chemical are influenced by the route
and duration of exposure. For example, the time course and peak tissue concentrations for the
distribution of a dose of TCDD is different when the dose is administered orally, dermally or
intratracheally (Diliberto et al., 1994). The dosing vehicle can also influence the absorption and
distribution of a chemical (Dix et al., 1997). The amount and rate of absorption of a chemical in
an oil vehicle can be very different when the chemical is dissolved or suspended in an aqueous
vehicle or bound to soil (Olson et al., 1994). Using administered dose as the metric for
exposures occurring through the same route, rate and dosing vehicle within the same species
clearly is appropriate. However, in cases where these parameters vary across dosing scenarios,
there is increasing uncertainty in the extrapolation.
Clearly, the default scaling method results in an estimated human equivalent dose that
produces a much greater estimated steady-state body burden (505 ng/kg) than the rat's. One
reason for the discrepancy of the scaling method is that the half-life of TCDD in rodents and
humans is much larger than is typically observed for other xenobiotics (Bachmann et al., 1996;
Sarver et al., 1997). The half-life of TCDD in humans is approximately 100 fold greater than in
rats. The EPA default scaling methodology would estimate human half-lives at approximately
3.8 times greater than in rats. This exercise demonstrates that administered dose does not provide
a useful dose metric for cross species extrapolation even if the dose is scaled using the EPA
default methodology. However, administered dose can be used to compare exposures between
human populations in order to describe potential human health risks, because the species
differences in half-life would not exist in this case.
1.5.2 Area Under the Curve
Area under the curve or AUC is frequently used as a dose metric for reversible responses
of pharmaceutical agents. Typically, these agents have half-lives on the order of minutes to
hours. In the pharmaceutical literature, AUC is most frequently used for drugs with a rapid onset
of action and that are rapidly reversed following cessation of exposure (Goodman and Oilman).
In addition, the pharmacological actions of the drugs and the length of time of the response is
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clearly defined in both animals and humans. In essence, plasma concentrations are readily
determined and the time span is easily defined. In cases where repeated doses of a drug is used,
the steady-state plasma concentration is the preferred measure of dose. Also, when using drugs
with a slow onset (i.e., a week or more), such as the tricyclic antidepressants, dose is typically
expressed as steady-state blood concentrations.
Mechanistic considerations also suggest that AUC can be a useful dose metric for
chemical carcinogenesis. AUC has been applied as a dose metric in chemical carcinogenesis for
DNA reactive chemicals. In these cases, the DNA reactive chemicals, increases in the mutational
rates are expected to be related to the integrated tissue exposure to the critical DNA reactive
species (Krewski et al., 1994). What is less certain about AUC as a dose metric is whether the
risk across species for DNA reactive chemicals should be normalized based on integrated daily
tissue concentration or an integrated lifetime tissue concentration. In any event, the dose metric
would be the integrated tissue concentration of DNA reactive chemical over the appropriate
period of the animal or human life span (Krewski et al., 1994).
There are some concerns over applying AUC as a cancer dose metric for TCDD. TCDD
and related chemicals are thought to induce tumors non-genotoxic mechanisms. The evidence
supporting the use of lifetime AUC calculations for cancer risk involves genotoxic chemicals and
radiation. TCDD lacks direct acting genotoxicity and is considered a tumor promoter. This
promotional process requires sustained tissue concentrations of TCDD sufficient to maintain
increased gene expression. It is likely that AUC would be an appropriate dose metric for cancer
and may also involve the incorporation of a threshold concentration (Hays et al., 1997).
Chemicals for which AUC has been applied as a dose metric are DNA reactive chemicals. DNA
reactive chemicals produce DNA adducts and the probability of a mutational event occurring
from adduct formations increases with the number of adduct formed. Adduct formation is
associated with an integrated measure of dose. Because of the difference in mechanism of
action, there no evidence supporting or refuting the use of AUC as a cancer dose metric for
TCDD.
The need for species extrapolation in toxicology further complicates the use of AUC as a
dose metric. While blood or plasma concentrations of TCDD can be determined in both human
and animals, the determination of the time span for which the AUC is to be calculated is much
less certain. For some of the toxic responses of TCDD, the window of sensitivity is clearly
defined in rodents and humans, such as induction of cleft palate. For other responses, such as the
developmental reproductive alterations observed in male and female rats, the window of
sensitivity has been narrowed to exposures between gestational day 15 and 20 in the rats, but the
human window of sensitivity is uncertain. For carcinogenesis, the length of time required to
induce the response remains uncertain in both experimental animals and humans. In order to
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apply AUC for species comparisons of the sensitivity to TCDD, one must have a better
understanding of the species differences in the windows of sensitivity to the various biological
effects of TCDD.
Another of the uncertainties in risk assessment is time scaling across species. The
difficulty is that there does not appear to be a consistent method of time scaling between species
for different biological processes. If we were to compare the length of time of several common
biological phenomena in rats and humans, we would observe different ratios of these time
frames. For example, rats live approximately 2-2.5 years, while the average human life span is
estimated at 70 years. The gestation period is approximately 21 days in a rat and 270 days in
humans. The age at the time of puberty in rats is between postnatal days 29-45 and in humans
occurs between 8 and 14 years old. In contrast, some processes, such as diurnal cycles or cell
replication, have equivalent time spans between species. Due to uncertainties in how to properly
account for the differences in time span for different biological responses, lifetime average daily
dose is recommended as the default dose metric (Rhomberg, 1990).
In addition, differences in life-span also must be considered. Brody and Reid (1967)
proposed that the biological activity of a drug is related to its plasma concentrations. If animals
and humans had the same plasma concentrations for their entire lives, the human AUC would be
greater because humans have a longer half-life. However, because the plasma concentrations
were the same, according to Brody and Reid (1967) the responses should be similar. Hence, in
order to use AUC for chronic toxicities, such as cancer, a correction for the difference in life-
span must be applied. Typically, this involves the derivation of an average daily AUC (Alyward
et al., 1996). An estimation of the average daily AUC is directly related to steady-state body
burdens. Hence, once the AUC is corrected for life-span differences, these values are equivalent
to steady-state body burdens.
While AUC may not be an appropriate dose metric for animal to human extrapolations, it
is a use tool for comparing populations exposed to high concentrations of dioxins over a short
period of time to the background population. Becher et al (1998) successfully used this approach
to examine dose response relationships for cancer in an occupationally exposed cohort. One
difficulty in determining AUC is the accuracy of the intake measurements. Past exposures
through the diet are uncertain, although they have been estimated (Pinsky and Lorber, 1998).
Future exposures are thought to be decreasing, although the exact magnitude of this decrease are
uncertain. Hence, determination of AUC carries a number of uncertainties that must be
considered.
1.5.3 Plasma or Tissue Concentrations
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Brodie and Reid (1967) have argued that the response to a drug is determined by the
amount bound to its biological receptor and since the drug receptor complex is in dynamic
equilibrium with the free drug in the plasma, the biological response of a drug will be related to
its plasma concentrations. There is no reason to believe that this relationship will not be true for
TCDD and related chemicals. However, there are several data gaps that may prohibit the use of
plasma or blood concentrations for species extrapolation. First, few animal studies determined
blood or plasma concentrations of TCDD, particularly in the subchronic, chronic and lifetime
exposures. PBPK models can be used to estimate blood concentrations and should provide
reasonable estimates of these values. In contrast, the human exposure data is based
predominately on blood, serum or plasma dioxin concentrations. One limitation of the human
data is that it is mostly presented on a lipid adjusted basis. Hence in order to compare the human
and animal plasma or blood concentrations, one would have to first estimate the blood
concentrations in the animals using a PBPK model. Then either the animal data would have to
be expressed as a lipid basis or the human data would have to be expressed as a wet weight basis.
In either case, assumptions of the percent lipid in the blood would have to be applied as well as a
number of assumptions used in the PBPK models.
The use of tissue concentrations as a dose metric has been examined by Van Birgelen et
al., (1996) and Hurst et al., (1999). Van Birgelen and coworkers (1996) presented data
demonstrating tissue that concentrations provided an accurate prediction of enzyme induction
regardless of the exposure scenario (i.e. acute vs subchronic). The use of alternative dose metrics,
such as AUC and total administered dose, failed to demonstrate predictive relationships for
enzyme induction and immunotoxicity (van Birgelen, 1996). Similarly, Hurst et al. (1999)
presented data demonstrating that fetal tissue concentrations of TCDD on gestation day 16
predicted decreases in sperm counts, delays in puberty in males, urethra-phallus distance and the
incidence of vaginal threads in rats prenatally exposed to TCDD on either gestational day 9 or 15.
These data suggest that target tissue concentrations may be a reasonable dose metric for these
responses.
While tissue concentrations may aid in estimating risks, these data are unlikely to be
collected in humans in sufficient numbers to be useful, particularly for fetal concentrations.
Plasma concentrations are also a useful tool to compare exposures in different human
populations. Application of plasma concentrations as a dose metric for species extrapolation
require some level of assumptions as described above, but reasonable comparisons could be
made, particularly for comparing steady-state exposures in humans and animals. Comparing
plasma or blood concentrations following acute exposures in experimental animals to steady-
state human blood or plasma concentrations would not be appropriate. One limitation of the use
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of either plasma, blood, or target tissue concentrations as dose metrics is the lack of human
PBPK models to predict these values based on changes in intake patterns.
1.5.4 Steady-State Body Burdens
Body burden is defined as the concentration of TCDD and related chemicals in the body
and is typically expressed as ng/kg. Steady-state body burden is defined as the concentration of
TCDD and related chemicals in the body under steady-state exposure conditions and is also
expressed as ng/kg. Humans generally reach exposures approximating steady-state between the
ages of 20 and 30, with body burdens increasing slightly with age over the course of the next
several decades. Humans steady-state body burdens and are estimated based on an intake rate
and the half-life of TCDD in humans. Alternatively, body burdens in humans are estimated
based on lipid adjusted serum or adipose tissue TCDD or TEQ concentrations. In animals, these
values are calculated from studies at or approaching steady-state and are associate with either
biochemical or toxicological responses. In addition, these values are calculated based on either
knowledge of the species-specific half-life and the exposure regimen or they are estimated based
on the TCDD tissue concentration, the size of the tissues and the weight of the animal.
Steady-state body burdens provide a useful dose metric for several reasons. First, tissue
and blood concentrations are directly related to body burdens. Thus, body burdens are surrogates
for tissue concentrations. Second, the differences in the half-life of TCDD between species is
accounted for because these body burdens are estimated at steady-state conditions. Third,
DeVito et al. (1995) have demonstrated that for some biochemical responses, chloracne and
cancer, species have similar rates of responses when dose is expressed on a body burden basis.
Finally, body burdens provide flexibility because they can be estimated based on either intake
rates or on measured tissue concentrations.
Body burdens also have some limitations. In order to estimate body burdens from lipid
adjusted tissue concentrations, an assumption of the percent body fat must be used. In the
reassessment, a value of 25% has been used. It should be noted that there are human populations
with body fat compositions less than 10% and greater than 35%. Also when estimating the body
burden based on intake rates and half-lives, the uncertainty of these parameters should be
considered. In the reassessment, the estimated steady-state body burden of approximately 5 ng
TEQ/kg is based on measured serum concentrations from several populations in the mid 1990's.
While measured concentrations should eliminate some of the uncertainties around estimates
using intake rates and half-life assumptions, it is likely that these measured values represent a
past history of higher exposure and we must anticipate a continued downward trend to present a
"true" lifetime average concentration. Caution must be used when using body burden in
conditions other than steady-state. Under these short-term exposures conditions in experimental
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animals, the relationship between tissue concentrations and body burden may not be the same as
under steady-state conditions.
1.5.5. Mechanistic Dose Metrics
Several groups have proposed a variety of dose metrics based on mechanistic
considerations, such as concentration of occupied AhR (Jusko, 1995), induced CYP1A2
(Andersen et al., 1997; Kohn et al., 1993) and reduced epidermal growth factor receptor (EGFR)
(Portier and Kohn, 1996). While these dose metrics are intellectually appealing, it must be kept
in mind that they are still hypothesized dose metrics and require further research to demonstrate
their utility for cross-species extrapolations. In addition, these dose metrics are unlikely to be
measured in sufficient human samples to be useful.
1.5.6. Summary
A variety of dose metrics have been proposed for estimating potential human health
effects following exposure to dioxins. Many of these dose metrics have limitations that prohibit
their use, such as tissue concentrations and the mechanistic dose metrics. Other dose metrics,
such as AUC have limited utility for species extrapolations because of our limited understanding
of the concept of physiological time. Some dose metrics can be used to compare different human
exposures, such as AUC and administered dose, but are not suitable for species extrapolations.
Other dose metrics, such as steady-state body burdens or blood concentrations, are useful dose
metric because they are easily estimated, and are directly related to tissue concentrations and can
be estimated in both animals and humans. The use of any of these dose metrics requires a
number of assumptions discussed above. The choice of dose metric requires an understanding of
the data available and their application of the dose metric. Future research efforts on the issue of
dose metrics could provide better guidance in choosing the dose metrics for dioxins and related
chemicals. However, in the mean time, the use of steady-state body burdens can provide a basis
for species extrapolations.
-------
Table 1-1. Gastrointestinal absorption of TCDD and related compounds following a single oral exposure by gavage
Chemical
Species (Sex)
Dose
(|imol/kg)
(Hg/kg)
Vehicle
% Administered
Dose Absorbed3
[Mean (Range1)]
Reference
CDDs
2,3,7,8-TCDD
2,3,7,8-TCDD
2,3,7,8-TCDD
2,3,7,8-TCDD
2,3,7,8-TCDD
1,2,3,7,8-PeCDD
OCDD
Sprague-Dawley rat (M)
Sprague-Dawley rat (M/F)
Hartley guinea pig (F)
Golden Syrian hamster (M)
Human (M)
Sprague-Dawley rat (M/F)
Fischer 344 rat (M)
0.16
0.003
0.005
2.0
0.000003
0.03
0.11
1.1
1.1
11
50
1.0
1.45
650
0.001
9.2
50
500
500
5000
acetone: corn oil (1:7)
acetone: corn oil (1:25)
acetone:corn oil (1:45)
olive oil
corn oil
corn oil
o-dichlorobenzene :Emulphor
(1:1)
o-dichlorobenzene :corn oil
(1:1)
corn oil suspension
corn oil suspension
70
84 (66-93)
50
74
87
NR (19-71)
12
15
2
5
Piper etal., 1973
Roseetal., 1976
Nolan etal.,
1979
Olson etal.,
1980
Poiger and
Schlatter, 1986
Wacker et al.,
1986
Birnbaum and
Couture, 1988
o
rt>
o
rt>
CT*
rt>
to
o
o
VO
O
O
O
*
O
H
O
HH
H
W
O
&
O
c
o
H
W
-------
o
O
to
O
O
oo
Table 1-1. Gastrointestinal absorption of TCDD and related compounds following a single oral exposure
by gavage (continued)
Chemical
Species (Sex)
Dose
(fimol/kg)
(Hg/kg)
Vehicle
% Administered
Dose Absorbed8
[Mean (Ranged]
Reference
BDDs
2,3,7,8-TBDD
Fischer 344 rat (M)
0.001
0.01
0.1
0.5
0.5
5
50
500
Emulphor: ethanol : water
(1:1:3)
78
82
60
47
Diliberto et al.,
1990
CDFs
2,3,7,8-TCDF
2,3,7,8-TCDF
2,3,4,7,8-PeCDF
Fischer 344 rat (M)
Hartley guinea pig (M)
Fischer 344 rat (M)
0.1
1.0
0.02
0.1
0.5
1.0
30.6
306
6
34
170
340
Emurphorethanol (1:1)
Emulphor:ethanol:water
(1:1:8)
corn oil
90
90
90
-70
-70
-70
Birnbaum et al.,
1980
Decad et al.,
1981a
Brewster and
Birnbaum, 1987
PCBs
3,3'4,4'-T4CB
C57BL mouse (F)
34.5
10,000
corn oil
77
Wehleretal.,
1989
fe
H
O
O
O
H
O
aAbsorption is generally estimated as the difference between the administered dose (100%) and the percent of the dose that was not absorbed.
The unabsorbed fraction is estimated as the recovery of parent compound in feces within 48 hours of exposure.
NR = Not reported.
O
&
O
c
O
H
W
-------
o
O
to
O
O
Table 1-2. Percentage of TCDD in the liver of rats 24 hours after oral administration of 0.5 mL of various
formulations containing TCDD
Formulation
50% ethanol
Aqueous suspension of soil (37% , w/w) that had been in
contact with TCDD for:
10-15 hours
8 days
Aqueous suspension of activated carbon (25 % , w/w)
TCDD Dose
(ng)
14.7
12.7, 22.9
21.2, 22.7
14.7
No. of
Animals
7
17
10
6
Percentage of Dose in the
Liver
36.7 + 1.2
24.1+4.8
16.0+2.2
<0.07
fe
H
O
O
O
H
O
HH
H
W
w/w = Weight by weight.
Source: Poiger and Schlatter, 1980.
O
c
O
H
W
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Table 1-3. Dermal absorption of 2,3,7,8-TCDD and related compounds in the rat
Chemical
2,3,7,8-TCDD
2,3,7,8-TCDF
1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
Dose
(|imol/kg)
0.00015
0.001
0.01
0.1
0.5
1.0
0.1
0.5
1.0
0.1
0.5
1.0
0.1
0.5
1.0
Og/kg)
0.05
0.32
3.2
32
160
321
31
153
306
34
170
340
34
170
340
% Administered Dose
Skin Sitea
61.73+4.37
59.71 + 1.90
72.60+0.41
82.21+2.85
80.92+2.74
82.68 + 3.69
51.18 + 11.95
82.14 + 11.22
88.70 + 5.17
74.72 + 3.58
91.67+2.46
84.23 + 5.44
65.77+4.80
75.50+1.81
81.84 + 1.67
Absorbed
38.27+4.37
40.29+1.89
27.40+0.41
17.78+2.85
19.08+2.74
17.30 + 3.67
48.84 + 11.95
17.86+11.22
11.32 + 5.17
25.27 + 3.58
8.33+2.46
15.76 + 5.44
34.19+4.78
24.50+1.80
18.16+1.67
aValues are the mean+SD of three to four animals and represent the amount of administered
dose of radiolabeled congener remaining at the application site 3 days after dermal exposure.
Source: Brewster et al., 1989.
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Table 1-4. Tissue distribution of [14C]-2-3,7,8-TCDD in female Wistar rats"
Tissue
Liver
Adipose tissue
Adrenal glands
Ovaries
Thymus
Skin
Lung
Kidney
Pancreas
Spleen
Serum
Bone (with marrow)
Muscle
Brain
Range of 2,3,7,8-TCDD Concentrations
(ng/g)
29.23-30.99
3.72-4.14
0.89-1.08
0.76-0.96
0.60-1.05
0.64-0.68
0.32-0.33
0.27-0.29
0.21-0.31
0.18-0.23
0.16-0.18
0.16-0.16
0.08-0.12
0.07-0.09
aDistribution was assessed 7 days after a single subcutaneous exposure (3 i-ig/kg bw).
Source: Abraham et al., 1988.
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Table 1-5. 2,3,7,8-Substituted PCDDs and PCDFs in human liver and adipose
tissue
Tissue Concentrations on a Lipid Basis
(ppt)
TCDD
PeCDD
HxCDD
HpCDD
OCDD
TCDF
PeCDF
HxCDF
HpCDF
OCDF
Fat
8.0
16.4
94.7
106.7
373.2
2.5
35.2
41.5
14.2
4.0
Liver
16.4
20.1
166.8
1,002.4
4,416.2
5.5
173.7
389.5
218.9
29.7
Liver/Fat
2.05
1.22
1.76
9.39
11.83
2.20
4.93
9.38
15.42
7.43
Tissue Concentrations on
a Wet Weight Basis (ppt)
Livera
1.1
1.4
11.7
70.2
309.1
0.4
12.2
27.3
15.3
2.1
Liver/Fat
0.14
0.09
0.12
0.66
0.83
0.15
0.35
0.66
1.08
0.52
Values are the mean of 28 people from the Munich area.
"Estimated from the % fat in the liver (7.02±5.33%, mean±SD)
Source: Thoma et al., 1990.
December 2003
1-74
DRAFT—DO NOT CITE OR QUOTE
-------
o
O
to
O
O
Table 1-6. Elimination of 2,3,7,8-TCDD and related compounds from major tissue depots
Chemical
Species (Sex)
Dose
Tissue
Half-Life
(days)
Remarks
Reference
CDDs
2,3,7,8-TCDD
2,3,7,8-TCDD
2,3,7,8-TCDD
2,3,7,8-TCDD
2,3,7,8-TCDD
2,3,7,8-TCDD
2,3,7,8-TCDD
Wistar rat (F)
Wistar rat (M)
Sprague-Dawley rat (M)
Sprague-Dawley rat (F)
C57BL/6J mice (M)
Ahb/Ahd
C57BL/6J mice (M)
Ahd/Ahd
DBA/2Jmice(F)
Ahb/Ahd
0.3 (ig/kg, s.c.
1.0(ig//kg, i.p.
7 or 20 ppb in diet for 42
days
7 or 20 ppb in diet for 42
days
0.5 (ig/kg, i.p.
0.5 (ig/kg, i.p.
0.5 (ig/kg, i.p.
liver
liver
liver
adipose
liver
adipose
liver
liver
liver
adipose
skin
liver
adipose
skin
liver
adipose
skin
11.5
16.9
13.6
24.5
37.1
53.2
11
13
8.5
10.3
16.0
7.1
7.6
14.9
12.4
13.3
13.2
95% Confidence interval (time period
investigated):
10.7-12.3 (10-49
days)
14.0-21.4
(49-91 days)
12.8-14.4
(10-91 days)
22.4-26.8 (14-91
days)
Tissue levels were measured for 20
weeks following exposure
85% total dose
70% of total dose
Pool size (% of total dose):
36.8
23.6
7.6
Pool size (% of total dose):
20.6
31.3
10.2
Pool size (% of total dose):
29.2
30.9
21.4
Abraham et
al, 1988
Lakshmanan
etal., 1986
Fries and
Marrow, 1975
Fries and
Marrow, 1975
Birnbaum,
1986
Birnbaum,
1986
Birnbaum,
1986
O
O
2
O
H
O
HH
H
W
O
&
O
c
O
H
W
-------
o
O
to
O
O
Table 1-6. Elimination of 2,3,7,8-TCDD and related compounds from major tissue depots (continued)
Chemical
2,3,7,8-TCDD
2,3,7,8-TCDD
OCDD
Species (Sex)
DBA/2J mice (F)
Ahd/Ahd
rhesus monkey (F)
Fischer 344 rat (M)
Dose
0.5 (ig/kg, i.p.
25 ppt in diet
50 (ig/kg, i.v.
Tissue
liver
adipose
skin
adipose
liver
adipose
skin
Half-Life
(days)
11.9
11.8
12.8
391±88
84
38
3
69
Remarks
Pool size (% of total dose):
20.2
42.3
26.6
Mean±SE (n=7)
Pool size (% of total dose):
72.7
7.1
9.0, 1st
component
0.3, 2nd
component
Reference
Birnbaum,
1986
Bowman et
al., 1989
Birnbaum and
Couture, 1988
HDDs
2,3,7,8-TBDD
Fischer 344 rat (M)
0.5 (ig/kg, i.v.
(0.001 (imol/kg)
liver
adipose
skin
muscle
blood
4.5
16.5
57.8
2.5
57.8
1.6
26.7
18.2
1 st component
2nd component
1 st component
2nd component
1 st component
2nd component
Kedderis et
al., 1991a
CDFs
O
O
2
O
H
O
HH
H
W
O
-------
Table 1-6. Elimination of 2,3,7,8-TCDD and related compounds from major tissue depots (continued)
Chemical
2,3,7,8-TCDF
2,3,7,8-TCDF
2,3,7,8-TCDF
Species (Sex)
Fischer 344 rat (M)
C57BL/6Jmice(M)
DBA/2J mice (M)
Dose
30.6 (ig/kg, i.v.
(0.1 (imol/kg)
30.6 (ig/kg, i.v.
(0.1 (imol/kg)
30.6 (ig/kg, i.v.
(0.1 (imol/kg)
Tissue
liver
adipose
skin
muscle
blood
liver
adipose
skin
muscle
liver
adipose
muscle
Half-Life
(days)
0.10
1.25
3.75
0.45
11.09
0.02
0.72
0.02
1.14
1.9
1.6
0.15
4.0
0.015
1.1
1.8
7.0
0.02
4.0
Remarks
Pool size (% of total dose)
29.09 1st
component
31.392nd
component
17.85
6.84 1st
component
1.222nd
component
24.85 1st
component
6.73 2nd
component
1.31 1st
component
0.89 2nd
component
1st component
2nd component
1st component
2nd component
1st component
2nd component
Reference
Birnbaum et
al., 1980
Decadetal.,
1981b
Brewster and
Birnbaum,
1988
o
O
to
O
O
fe
H
O
O
O
H
O
O
&
O
c
O
H
W
-------
Table 1-6. Elimination of 2,3,7,8-TCDD and related compounds from major tissue depots (continued)
Chemical
1,2,3,7,8-PeCDF
1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
Species (Sex)
Fischer 344 rat (M)
Sprague-Dawley rat (F)
Fischer 344 rat (M)
Dose
34 (ig/kg, i.v.
(0.1 (imol/kg)
4.0 (ig/kg, p.o.
34 (ig/kg, i.v.
(0.1 (imol/kg)
Tissue
liver
adipose
skin
muscle
adrenal
blood
liver
liver
adipose
skin
muscle
blood
Half-Life
(days)
1.36
25.72
12.91
1.32
14.53
0.03
6.96
0.14
2.36
0.07
12.42
3.3
193
69
0.62
1.23
0.04
0.51
9.84
0.04
1.32
55
Remarks
Pool size (% of total dose):
42.59 1st
component
1.272nd
component
10.19
7.14 1st
component
1.492nd
component
34.81 1st
component
7.42 2nd
component
0.26 1st
component
0.02 2nd
component
5.33 1st
component
1.292nd
component
69.8% of total dose
Pool size (% of total dose):
67.71
10.53
3.541st
component
1.372nd
component
29.40 1st
component
2.01 2nd
component
0.78 3rd
component
3.18 1st
component
0.37 2nd
component
0.008 3rd
component
Reference
Brewster and
Birnbaum,
1988
Van den Berg
etal., 1989a,b
Brewster and
Birnbaum,
1987
-------
Table 1-6. Elimination of 2,3,7,8-TCDD and related compounds from major tissue depots (continued)
Chemical
2,3,4,7,8-PeCDF
1,2,3,6,7,8-
HxCDF
Species (Sex)
Sprague-Dawley rat (F)
Sprague-Dawley rat (F)
Dose
5.6 (ig/kg, p.o.
6.0 (ig/kg, p.o.
Tissue
liver
liver
Half-Life
(days)
108
73
Remarks
78.3% of total dose
63. 4% of total dose
Reference
Van den Berg
et al., 1989b
Van den Berg
etal., 1989b
PCBs
3,3',4,4'-TCB
3,3',4,4'-TCB
Sprague-Dawley rat (F)
ICR mice (M)
5 mg/kg/day, p.o., for
21 days
8 mg/kg, p.o., every
other day for 10 doses
liver
adipose
liver
adipose
serum
0.8
2.5
2.15
2.60
1.07
21 -day exposure produced steady state
with 300 ng/g in liver and 8 [ig/g in
adipose tissue.
Elimination was assessed over a 22-day
postexposure period.
Steady-state tissue concentrations:
1.5 (ig/g
19.2 (ig/g
0.04 (ig/mL
Clarke et al.,
1984
Clevenger et
al., 1989
i.v. = intravenous; s.c. = subcutaneous; i.p. = intraperitoneal; p.o. = per os.
-------
o
O
to
O
O
Table 1-7. Elimination constants and half-lives of various 2,3,7,8-Substituted CDDs and CDFs in hepatic
and adipose tissue of marmoset monkeys*b
oo
o
O
O
*
O
H
O
HH
H
W
O
?d
O
c
o
H
W
Congener
2,3,7,8-TCDDc
l,2,3,7,8-PeCDDc
1,2,3,4,7,8-HxCDD
1,2,3,6,7,8-HxCDD
1,2,3,7,8,9-HxCDD
1,2,3,4,6,7,8-HpCDD
OCDD
2,3,7,8-TCDF
1,2,3,7,8-71,2,3,4,8-PeCDF
2,3,4,7,8-PeCDF
1,2,3,4,7,8-71,2,3,4,7,9-HxCDF
1,2,3,6,7,8-HxCDF
1,2,3,7,8,9-HxCDF
2,3,4,6,7,8-HxCDF
Hepatic Tissue
Ke
(weeks'1)
0.0841±0.0109
0.0649±0.0101
0.0702±0.0059
0.0558±0.0046
0.0767±0.0078
0.0518±0.0081
0.0089±0.0084
0.8012±0.0549
0.7476±0.0294
0.0786±0.0048
0.0307±0.0039
0.0486±0.0037
0.0848±0.0057
0.0373±0.0057
Half-Life
(weeks)
8.3
10.7
9.9
12.4
9.0
13.4
78
<0.87e
0.93
8.8
23
14.3
8.2
18.6
95% Conf.
Interval
(weeks)
6.6-11.1
8.2-15.4
8.4-11.8
10.7-14.9
7.5-11.3
10.2-19.3
27-c°d
-<1.00
0.86-1.00
7.9-10.0
18-30
12.4-16.7
7.2-9.4
14.3-26.5
Adipose Tissue
Ke
(weeks'1)
0.0658±0.0072
0.0490±0.0057
0.041 1±0.0083
0.0373±0.0073
0.0525±0.0089
0.0372±0.0060
0.0122±0.0093
0.4986±0.0829
0.4735±0.0408
0.0563±0.0059
0.0103±0.0074
0.0290±0.0091
not analyzedf
0.0182±0.0082
Half-Life
(weeks)
10.5
14.2
16.9
18.6
13.2
18.6
101
1.39
1.46
12.3
68
24
NA
38
95% Conf.
Interval
(weeks)
8.7-13.4
11.5-18.3
12.1-27.9
13.4-30.2
9.9-19.7
14.2-27.2
20-<=od
1.05-2.06
1.25-1.76
10.2-15.5
28-»d
15-62
NA
20-327
-------
o
O
to
O
O
oo
Table 1-7. Elimination constants and half-lives of various 2,3,7,8-Substituted CDDs and CDFs in hepatic
and adipose tissue of marmoset monkeys*b (continued)
Congener
1,2,3,4,6,7,8-HpCDF
1,2,3,4,7,8,9-HpCDF
OCDF
Hepatic Tissue
Ke
(weeks'1)
0.0186±0.0072
0.0088±0.0127
0.0040±0.0096
Half-Life
(weeks)
37
79
174
95% Conf.
Interval
(weeks)
21-152
20-<»d
30--d
Adipose Tissue
Ke
(weeks'1)
-0.0140±0.0137
0.0011±0.0112
-0.0042±0.0148
Half-Life
(weeks)
ood
660
cod
95% Conf.
Interval
(weeks)
54-ood
30-<»d
28-c°d
"Source: Neubertetal., 1990.
bAnimals were treated subcutaneously with a single dose of a defined CDD/CDF mixture, and the tissues were analyzed at different times
following treatment. Half-lives were calculated from tissue concentrations of the 2,3,7,8-substituted congeners in hepatic and adipose tissue. Values are given
as elimination rate constant Ke including estimated SD and half-life including 95% confidence intervals.
"Calculated from the time period: >6 weeks after injection.
dCalculated half-life is apparently infinite. Data for OCDD and OCDF are unreliable due to delayed absorption.
eNot detected in hepatic tissue 6 weeks after treatment; limits of detection used for calculation.
TJue to interference.
NA = Not applicable.
fe
H
O
O
O
H
O
HH
H
W
O
c
O
H
W
-------
Table 1-8. TCDD concentrations in liver and adipose tissue following different
doses and calculated concentration ratios (liver/adipose tissue)'
Dose
(ng/kg)
1
3
10
30
100
300
1000
3000
Number
6
6
12
6
6
6
6
5
TCDD Concentration
Liver
(ng/g)
0.003 1±0.0009
0.0102±0.0020
0.0406±0.0121
0.162±0.032
0.699±0.130
3.38±0.22
10.7±2.2
27.9±2.4
TCDD Concentration
Adipose Tissue
(ng/g)
ND
0.0139±0.0015
0.0494±0.0084
0.139±0.021
0.335±0.065
0.819±0.075
2.02±0.17
3.66±0.31
Concentration Ratio:
Liver/ Adipose Tissue
NA
0.74±0.15
0.82±0.20
1.16±0.07
2.10±0.27
4.14±0.31
5.27±0.96
7.65±0.64
'Concentrations were measured 7 days after injection.
ND = Not detectable; NA = not applicable.
Source: Abraham et al, 1988.
December 2003
1-82
DRAFT—DO NOT CITE OR QUOTE
-------
o
O
to
O
O
Table 1-9. Metabolism and excretion of 2,3,7,8-TCDD and related compounds"
Chemical
Species
Dose
Chemical Nature of Excretion
Products
(% Metabolites)
Urine
Bile
Feces
Ratio of % of
Dose Excreted
(Feces/Urine)
Half-Life11
(days)
Comment
Reference
CDDs
2,3,7,8-TCDD
2,3,7,8-TCDD
2,3,7,8-TCDD
2,3,7,8,-TCDD
2,3,7,8-TCDD
2,3,7,8-TCDD
2,3,7,8-TCDD
2,3,7,8-TCDD
2,3,7,8-TCDD
Sprague-Dawley
rat (M)
Sprague-Dawley
rat (M)
Sprague-Dawley
rat (F)
Sprague-Dawley
rat (M, F)
Sprague-Dawley
rat (M, F)
Han/Wistar rat
(M)
Long-Evans rat
(M)
Sprague-Dawley
(M)
C57BL/6J mice
(M)
50 |-ig/kg, p.o.
7 or 72 ppb
in diet for
42 days
7 or 72 ppb
in diet for
42 days
1.0 ng/kg,
p.o
0.1 and 1.0
Hg/kg/day,
5 days/week
for 7 weeks
5 ng/kg, i.p.
5 ng/kg, i.p.
500 ng/kg, i.p.
10 |-ig/kg, i.p.
NA
NA
NA
NA
NA
>90
>90
100
100
NA
NA
NA
NA
NA
NA
NA
100
100
NA
NA
NA
NA
NA
-70-90
-20-90
NA
85
4.0
NA
NA
9.9
8.5
14.1
12.0
NA
2.7
17.4+5.6°
12
15
31+6d
23.7
21.9
20.8
NA
11.0 + 1.2d
NC
NC
NC
NC
NC
NC
NC
NC
NC
Piper et al.,
1973
Fries and
Marrow, 1975
Fries and
Marrow, 1975
Rose et al.,
1976
Rose et al.,
1976
Pohjanvirta et
al., 1990
Pohjanvirta et
al., 1990
Neal etal.,
1982
Gasiewicz et
al., 1983a
oo
O
O
2
O
H
O
HH
H
W
O
1-83
-------
Table 1-9. Metabolism and excretion of 2,3,7,8-TCDD and related compounds8 (continued)
Chemical
2,3,7,8-TCDD
2,3,7,8-TCDD
2,3,7,8-TCDD
2,3,7,8-TCDD
2,3,7,8-TCDD
2,3,7,8-TCDD
2-Iodo-3, 7, 8-TCDD
2-Iodo-3,7,8-TCDD
2,3,7,8-TCDD
2,3,7,8-TCDD
2,3,7,8-TCDD
Species
DBA/2J mice
(M)
B6D2F1J mice
(M)
C57BL/6J mice
Ahb/Ahd (M)
C57BL/6J mice
Ahd/Ahd (M)
DBA/2J Ahb/Ahd
(F)
DBA/2J Ahd/Ahd
(F)
C57BL/6J mice
(F)
C57BL/6J mice
(F)
Hartley guinea
pig (M)
Hartley guinea
pig (M)
Golden Syrian
hamster (M)
Dose
10 ng/kg, i.p.
10 ng/kg, i.p.
500 ng/kg, i.p.
500 ng/kg, i.p.
500 ng/kg, i.p.
500 ng/kg, i.p.
[125I] 0.1 nmol/kg,
i.p.
[125I] 0.1 nmol/kg,
i.p., 3 days
following
pretreatment with
2,3,7,8-TCDD (0.1
|-imol/kg, i.p.)
0.5 ng/kg, i.p.
0.56 ng/kg, i.p.
[3H] 650 ng/kg,
i.p.
Chemical Nature of Excretion
Products
(% Metabolites)
Urine
100
100
NA
NA
NA
NA
NA
NA
NA
100
NA
Bile
100
100
NA
NA
NA
NA
NA
NA
NA
100
NA
Feces
82
86
NA
NA
NA
NA
NA
NA
NA
19
NA
Ratio of % of
Dose Excreted
(Feces/Urine)
1.2
2.5
3.1
2.1
5.3
6.8
NA
NA
15.7
11.2
1.4
Half-Life1"
(days)
24.4 + 1.0d
12.6+0.8d
9.42
9.74
10.40
11.11
14.2
8.0
30.2+5. 8d
93. 7 + 15. 5d
11.95 + 1.95d
Comment
NC
NC
NC
NC
NC
NC
whole body counting was
used to estimate body
burden over 30-day period
whole body counting was
used to estimate body
burden over 30-day period
NC
NC
NC
Reference
Gasiewicz et
al., 1983a
Gasiewicz et
al., 1983a
Birnbaum, 1986
Birnbaum, 1986
Birnbaum, 1986
Birnbaum, 1986
Leung et al.,
1990b
Leung et al.,
1990b
Gasiewicz and
Neal, 1979
Olson, 1986
Olson et al.,
1980; Neal et
al., 1982
1-84
-------
Table 1-9. Metabolism and excretion of 2,3,7,8-TCDD and related compounds8 (continued)
Chemical
2,3,7,8-TCDD
2,3,7,8-TCDD
2,3,7,8-TCDD
2,3,7,8-TCDD
2,3,7,8-TCDD
1,2,3,7,8-PeCDD
OCDD
OCDD
Species
Golden Syrian
hamster (M)
Golden Syrian
hamster (M)
human (M)
rainbow trout
yellow perch
Sprague-Dawley
rat (M, F)
Fischer 344 rat
(M)
Fischer 344 rat
(M)
Dose
[14C] 650 ng/kg,
i.p.
[3H] 650 ng/kg,
p.o.
1.14 ng/kg, p.o.
494 ppt in diet for
13 weeks
494 ppt in diet for
13 weeks
8.42-10.06 ng/kg,
p.o.
50 |-ig/kg, iv
50 |-ig/kg/day, p.o.,
for 10 days
Chemical Nature of Excretion
Products
(% Metabolites)
Urine
100
NA
NA
NA
NA
NA
<33
NA
Bile
100
NA
NA
-75
-90
100
0
NA
Feces
55-75
NA
-50
NA
NA
NA
0
NA
Ratio of % of
Dose Excreted
(Feces/Urine)
NA
NA
>3.1
NA
NA
12
>65
NA
Half-Life"
(days)
10.82+2.35
14.96+2.53
2120"
105
126
29.5+2.7
-70
-173
Comment
NC
NC
NC
elimination followed for
13 weeks following
exposure
elimination followed for
13 weeks following
exposure
NC
whole body t1/2 estimated
from body burden in liver,
skin, and adipose tissue
over 56-day period
whole body t1/2 estimated
from body burden in liver,
skin, and adipose tissue
over 112-day period
Reference
Olson et al.,
1980; Neal et
al., 1982
Olson et al.,
1980; Neal et
al., 1982
Poiger and
Schlatter, 1986;
Wendling et al.,
1990
Kleeman et al.,
1986b
Kleeman et al.,
1986a
Wacker et al.,
1986
Birnbaum and
Couture, 1988
Birnbaum and
Couture, 1988
HDDs
o
c
o
H
W
1-85
-------
o
O
to
O
O
Table 1-9. Metabolism and excretion of 2,3,7,8-TCDD and related compounds8 (continued)
Chemical
2,3,7,8-TBDD
2,3,7,8-TBDD
Species
Fischer 344 rat
(M)
Fischer 344 rat
(M)
Dose
0.001 pmol/kg, iv
0.1 |-imol/kg, iv
Chemical Nature of Excretion
Products
(% Metabolites)
Urine
NA
NA
Bile
100
100
Feces
80-90
80-90
Ratio of % of
Dose Excreted
(Feces/Urine)
11.1
9.2
Half-Life1"
(days)
0.7
2.9
17.8
0.6
17.8
Comment
Pool size (% of dose):
11.63 1st component
2.78 2nd component
1.45 3rd component
Pool size (% of dose):
22.47 1st component
2.35 2nd component
Reference
Kedderis et al.,
1991a
Kedderis et al.,
1991a
oo
O
O
^
O
H
O
O
&
O
c
O
H
W
1-86
-------
Table 1-9. Metabolism and excretion of 2,3,7,8-TCDD and related compounds8 (continued)
Chemical
Species
Dose
Chemical Nature of Excretion
Products
(% Metabolites)
Urine
Bile
Feces
Ratio of % of
Dose Excreted
(Feces/Urine)
Half-Life"
(days)
Comment
Reference
CDFs
2,3,7,8-TCDF
2,3,7,8-TCDF
2,3,7,8-TCDF
2,3,7,8-TCDF
2,3,7,8-TCDF
2,3,7,8-TCDF
1,2,3,7,8-PeCDF
Fischer 344 rat
(M)
C57BL/6J mice
(M)
DBA/2J mice (M)
Hartley guinea pig
(M)
Hartley guinea pig
(M)
rhesus monkey (M)
Fischer 344 rat
(M)
0.1 |-imol/kg, iv
0.1 |-imol/kg, iv
0.1 |-imol/kg, iv
0.02 nmol/kg, iv
4 ng/kg, p.o.
0.1 |-imol/kg, iv
0.1 |-imol/kg, iv
100
100
100
>90
NA
100
-90
>96
NA
NA
NA
NA
>92
100
99
80
80
<10
NA
>92
NA
31.4
6.5
2.8
1.0
NA
5.4
12.8
1.8
0.3
2.8
1.8
2.0
4.9
5.4
4.0
20
40
6.24
10.30
~8
0.92
3.32
1.26
17.32
1.12
6.30
fecal excretion
urinary excretion
urine
feces
urine and feces
urine
feces
urine and feces
animal exhibited body
weight loss
no observable toxicity
urine
feces
urine and feces
Pool size (% of dose):
feces:
57.79 1st component
6.92 2nd component
urine:
2.68 1st component
0. 16 2nd component
feces and urine:
59.97 1st component
2.51 2nd component
Birnbaum et
al., 1980
Decad et al.,
198 Ib
Decad et al.,
198 Ib
Decad et al.,
198 la
loannou et al.,
1983
Birnbaum et
al., 1981
Brewster and
Birnbaum, 1988
o
&
o
c
o
H
W
1-87
-------
o
O
to
O
O
Table 1-9. Metabolism and excretion of 2,3,7,8-TCDD and related compounds8 (continued)
Chemical
2,3,4,7,8-PeCDF
2,3,4,7,8-PeCDF
Species
Fischer 344 rat
(M)
rhesus monkey (M)
Dose
0.1 |-imol/kg, iv
0.1 |-imol/kg, iv
Chemical Nature of Excretion
Products
(% Metabolites)
Urine
NA
NA
Bile
>90
NA
Feces
>90
63-70
Ratio of % of
Dose Excreted
(Feces/Urine)
>100
-34
Half-Life"
(days)
1.27
63.82
38-49
Comment
Pool size (% of dose):
feces
1.22 1st component
0.57 2nd component
t1/2 represents minimum
value; all animals lost
body weight and exhibited
other signs of toxicity
Reference
Brewster and
Birnbaum, 1987
Brewster et al.,
1988
CBs
3,3'4,4'-TCB
3,3'4,4'-TCB
CD rat (M, F)
rhesus monkey (F)
0.6 mg/kg, iv
0.6 mg/kg, iv
>90
97
NA
NA
>90
97
42
7.2
-1.3-1.5
-8-10
NC
NC
Abdel-Hamid et
al., 1981
Abdel-Hamid et
al., 1981
oo
oo
O
O
2
O
H
O
aAH studies measure the excretion of radiolabeled parent compound and metabolites following exposure to a single congener labeled with 3H, 14C, or 125I.
bHalf-life for excretion estimates assume first-order elimination kinetics.
c(mean+SE).
d(mean+SD).
en=l.
i.p. = intraperitoneal; i.v. = intravenous; NA = not available; NC = no comment; p.o. = per os.
O
&
O
c
O
H
W
-------
o
O
to
O
O
Table 1-10. Half-life estimates for 2,3,7,8-TCDD and related compound in humans
Chemical
CDDs
2,3,7,8-TCDD
2,3,7,8-TCDD
2,3,7,8-TCDD
2,3,7,8-TCDD
1,2,3,6,7, 8-HxCDD
1, 2,3,4,6,7, 8-HpCDD
OCDD
CDFs
2,3,4,7, 8-PeCDF
1,2,3,4,7, 8-HxCDF
1,2,3,6,7, 8-HxCDF
Exposure Incident
Male volunteer
Male volunteer
Ranch Hand
Vietnam veterans
Ranch Hand
Vietnam veterans
technical pentachlorophenol in
wood of home
technical pentachlorophenol in
wood of home
technical pentachlorophenol in
wood of home
Binghamton, New York, state
office building
Binghamton, New York, state
office building
Binghamton, New York, state
office building
Number of
Individuals
1
1
36
337
1
1
1
1
1
1
Sample
fecal excretion
adipose tissue
serum
serum
adipose tissue
adipose tissue
adipose tissue
adipose tissue
blood
combined
adipose tissue
blood
combined
adipose tissue
blood
combined
Time Period
Between First and
Last Analysis
125 days
6 years
5 years
5 years
28 months
28 months
28 months
initial 43 months
final 29 months
total 6 years
initial 43 months
final 29 months
total 6 years
initial 43 months
final 29 months
total 6 years
Number of
Time Points
28
5
2
2
2
2
2
4
4
7
4
4
7
4
4
7
Half-Life
(years)
5.8
9.7
7.1a
11.3"
3.5
3.2
5.7
4.7
7.2
4.5
2.9
4.4
4.0
3.5
4.3
4.9
Reference
Poiger and
Schlatter, 1986
Schlatter, 1991
Pirkle et al.,
1989
Wolfe et al.,
1994
Gorski et al. ,
1984
Gorski et al. ,
1984
Gorski et al. ,
1984
Schecter et al.,
1990b
Schecter et al.,
1990b
Schecter et al.,
1990b
oo
VO
O
O
2
O
H
O
HH
H
W
O
1-89
-------
o
O
to
O
O
oo
Table 1-10. Half-life estimates for 2,3,7,8-TCDD and related compound in humans (continued)
Chemical
1,2,3,4,6,7,8-HpCDF
2,3,4,7,8-PeCDF
1,2,3,4,7,8-HxCDF
1,2,3,4,6,7,8-HpCDF
2,3,4,7,8-PeCDF
1,2,3,4,7,8-HxCDF
1,2,3,4,6,7,8-HpCDF
2,3,4,7,8-PeCDF
1,2,3,4,7,8-HxCDF
1,2,3,4,6,7,8-HpCDF
OCDF
PCBs
3,3',4,4',5-PeCB
3,3',4,4',5,5'-HxCB
Exposure Incident
Binghamton, New York, state
office building
Yu-Cheng
Yu-Cheng
Yu-Cheng
Yu-Cheng
Yusho
technical pentachlorophenol in
wood of home
technical pentachlorophenol in
wood of home
Yu-Cheng
Yu-Cheng
Number of
Individuals
1
4
3
2
4
3
2
4
3
2
3
9
1
1
NA
NA
Sample
adipose tissue
blood
combined
blood
blood
blood
blood
blood
adipose tissue
adipose tissue
blood
blood
Time Period
Between First and
Last Analysis
initial 43 months
final 29 months
total 6 years
initial 2. 9 years
final 2.7 years
total 5.6 years
initial 2. 9 years
final 2.7 years
total 5.6 years
initial 2. 9 years
final 2.7 years
total 5.6 years
9 years
7 years
28 months
28 months
NA
NA
Number of
Time Points
4
4
7
2
2
3
2
2
3
2
2
3
5-6
3-5
2
2
NA
NA
Half-Life
(years)
6.5
4.1
6.8
1.3
2.9
1.7
2.1
5.1
2.4
1.6
6.1
2.4
2-3
>5
<1.7
1.8
<1
10
Reference
Schecter et al.,
1990a
Ryan and
Masuda, 1989
Ryan and
Masuda, 1989
Ryan and
Masuda, 1989
Ryan and
Masuda, 1991
Ryan and
Masuda, 1991
Gorskietal,
1984
Gorskietal.,
1984
Ryan and
Masuda, 1991
Ryan and
Masuda, 1991
VO
O
O
O
*
O
H
O
HH
H
W
O
&
O
c
O
H
W
a95% confidence interval about the median of 5.8-9.6 years.
b95% confidence interval about the median of 10.0-14.1 years.
NA = Not applicable.
1-90
-------
Table 1-11. Calculated daily intakes for TCDD
Half-life (yrs)
5.8
7.0
5.8
7.0
5.8
7.0
5.8
7.0
Fat vol. (L)
14.0
14.0
14.0
14.0
7.0
7.0
7.0
7.0
Fat cone, (ppt)
6.72
6.72
5.00
5.00
6.72
6.72
5.00
5.00
Calculated daily
intake
(PG/KG/Day)
0.44
0.37
0.33
0.27
0.22
0.18
0.16
0.14
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Table 1-12. Half-life calculations
Chemical
2378-TCDD
2378-TCDF
12378-PeCDD
23478-PeCDF
OCDD
Food-ppta
0.23
0.84
0.7
1.4
19.2
Body-ppt"
6.72
3.9
21.5
36.8
653.0
t,/2c years
7.0
1.11
7.35
7.94
8.14
t1/2d years
6.0
1.3
5.0
6.3
50.0
•Concentrations in food for TCDD, TCDF, and OCDD were obtained as discussed in Part I, Volume 3; those for PCDD and PCDF were taken
from Schlatter( 1991).
'Concentrations in body (adipose tissue) were all taken from Schecter (1991).
'Calculated using Equation 1-19, except for TCDD.
•"Calculated by Schlatter (1991), except for TCDD.
December 2003
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Table 1-13. Half-life estimates taken from Flesch-Janys et al. (1996)
Compound
2,3,7,8-TCDD
1,2,3,7,8-PeCDD
1,2,3,4,7,8-HCDD
1,2,3,6,7,8-HCDD
1,2,3,7,8,9-HCDD
1,2,3,4,6,7,8-HpCDD
OCDD
2,3,4,7,8-PeCDF
1,2,3,4,7,8-HCDF
1,2,3,6,7,8-HCDF
2,3,4,6,7,8-HCDF
1,2,3,4,6,7,8-HpCDF
1,2,3,4,7,8,9-HpCDF
N individuals
48
40
41
40
39
26
32
5
42
31
6
22
6
Median half-life estimate
(years)
7.2
15.7
8.4
13.1
4.9
3.7
6.7
19.6
6.2
6
5.8
3
3.2
December 2003
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Table 1-14. Comparison of administered dose and body burden in rats and humans.
Dose
(ng/kg/d)
Body Burden
(ng/kg)
(A)
Rat Daily
Administered
Dose/Body
Burden
1
18
(B)
Human
Scaled
Administered
Dose/Body
Burden1
0.27
505
(C)
Human
Equivalent
Administered
Dose/Body
Burden2
0.0096
18
(A/B)
Ratio of
Rat to
Human
Scaled
Dose
3.7
0.036
(A/C)
Ratio of
Rat to
Human
Equivalent
Dose
104
1
1. Assumes administered dose scales across species as a function of B W 3/4
2. Assumes administered dose scales across species as a function of equivalent body burdens
December 2003
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Figure 1-1. Time course of the connection of 14C-TCDD in rat
liver and adipose tissue after a single subcutaneous injection of
300 ng TCDD/kg bw to female rats (M±SD).
Source: Abraham et al., 1988.
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Figure 1-2. Dose dependency of the percentage of the
administered dose of 14C-TCDD/g of tissue recovered in liver
and adipose tissue after single subcutaneous doses (values from
animals treated with 3000 ng TCDD/kg bw were corrected for
84% adsorption). Concentrations were measured 7 days after
the injection.
Source: Abraham et al., 1988.
December 2003 1-96 DRAFT—DO NOT CITE OR QUOTE
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D = ln2 l4L 1000 ^1 6.72^
S.Qyears) ( L ) \ ml) { 10KG) { 365days)
D = 0.44 pg/kg/day
Figure 1-3. Sample calculation of daily intake for TCDD.
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Weber, H; Poiger, H; Schlatter, C. (1982a) Acute oral toxicity of TCDD-metabolites in male guinea pigs. Toxicol
Lett 14:117-122.
Weber, H.; Poiger, H.; Schlatter, C. (1982b) Fate of 2,3,7,8-tetrachlorodibenzo-/>-dioxin. Xenobiotica
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Weber, LWD; Zesch, A; Rozman, K. (1991) Penetration, distribution and kinetics of 2,3,7,8-TCDD in human skin in
vitro. Arch Toxicol 65:421-428.
Wehler, EK; Bergman, A; Brandt, I; et al. (1989) 3,3',4,4'-Tetrachlorobiphenyl excretion and tissue retention of
hydroxylated metabolites in the mouse. Drug Metabol Dispos 17(4):441-448.
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Weisiger, R; Gollan, J; Ockner, R. (1981) Receptor for albumin on the liver cell surface may mediate uptake of fatty
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Wendling, JM; Orth, RG; Poigner, H. (1990) Determination of [3H]-2,3,7,8-tetrachloro-dibenzo-/>-dioxin in human
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Wester, RC; Maibach, HI; Sedik, L; et al. (1993b) Percutaneous absorption of pentachlorophenol from soil. Fundam
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dioxin in the rat. Bull Environ Contain Toxicol 7(1): 57.
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toxicological assessment of the metabolites in rats. Xenobiotica 17(8):897-910.
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2. MECHANISM(S) OF ACTION*
2.1. INTRODUCTION
2,3,7,8-Tetrachlorodibenzo-p-dioxin (TCDD, dioxin) is the prototype for a class of
halogenated aromatic hydrocarbons (HAHs) that produce similar patterns of toxicity and appear
to have a common mechanism of action, although they differ in potency (Poland and Knutson,
1982; Safe, 1986). Because it is the most potent, TCDD has been studied much more
extensively than other structurally related compounds (Figure 2-1). TCDD achieved notoriety in
the 1970s, when it was discovered to be a contaminant in the herbicide Agent Orange and was
shown to produce birth defects in rodents. Subsequently, dioxin has continued to generate
concern because of its widespread distribution, its persistence as an environmental contaminant,
its accumulation within the food chain, and its toxic potency. In animals, TCDD elicits a wide
range of biological effects, including alterations in metabolic pathways, immunological changes,
reproductive and developmental abnormalities, and neoplasia (Poland and Knutson, 1982; Safe,
1986; Birnbaum, 1994b). In humans, dioxin and related compounds can produce the skin
condition known as chloracne. Increased cancer rates have also been associated with exposures
to dioxin-like chemicals (International Agency for Research on Cancer, 1997). The possibility
that dioxins also produce birth defects and developmental abnormalities is a particular public
health concern. Many individuals have been exposed to TCDD, primarily from dietary sources,
although occupational and accidental exposures have also occurred. TCDD is a poor substrate
for detoxification systems such as the microsomal cytochrome P450 enzymes, which oxygenate
other lipophilic compounds to inactive derivatives during their metabolic processing. Because of
its relative resistance to metabolism, TCDD persists in the body, with a half-life in humans on
the order of 7 to 10 years (Pirkle et al., 1989; Michalek et al., 1996; Michalek and Tripathi,
1999). Therefore, dioxin tends to accumulate in human tissues over time, raising concern that
repeated exposures, even to "low" concentrations, may evoke adverse health effects.
Epidemiological studies performed to date have not produced well-defined estimates of the
health risk that dioxin poses to humans. There has been hope that knowledge of the mechanism
of dioxin action will bring about a greater understanding of these issues (Andersen et al., 1994;
Denes etal., 1996).
*Reprinted in large part with permission from Chemical Research in Toxicology 1993, 6, 754-763. Copyright 1993
American Chemical Society. 0893-228x/93/2706-0754$04.00/0.
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Mechanistic studies can reveal the biochemical pathways and types of biological and
molecular events that contribute to dioxin's adverse effects. For example, much evidence
indicates that TCDD acts via an intracellular protein (the aryl hydrocarbon receptor; Ah
receptor), which functions as a ligand-dependent transcription factor in partnership with a second
protein (known as the Ah receptor nuclear translocator; Arnt). Therefore, from a mechanistic
standpoint, TCDD's adverse effects appear likely to reflect alterations in gene expression that
occur at an inappropriate time and/or for an inappropriately long time. Mechanistic studies also
indicate that several other proteins contribute to TCDD's gene regulatory effects and that the
response to TCDD probably involves a relatively complex interplay between multiple genetic
and environmental factors. If TCDD operates through such a mechanism, as all evidence
indicates, then there are certain constraints on the possible models that can plausibly account for
TCDD's biological effects and, therefore, on the assumptions used during the risk assessment
process (e.g. Poland, 1996; Limbird and Taylor, 1998). Mechanistic knowledge of dioxin action
may also be useful in other ways. For example, a further understanding of the ligand specificity
and structure of the Ah receptor will likely assist in the identification of other chemicals to which
humans are exposed that may either add to, synergize, or block the toxicity of TCDD.
Knowledge of genetic polymorphisms that influence TCDD responsiveness may also allow the
identification of individuals at greater risk from exposure to dioxin. In addition, knowledge of
the biochemical pathways that are altered by TCDD may help identify novel targets for the
development of drugs that can antagonize dioxin's adverse effects.
As described below, biochemical and genetic analyses of the mechanisms by which
dioxin may modulate particular genes have revealed the outline of a novel regulatory system
whereby a chemical signal can alter cellular regulatory processes. Future studies of dioxin
action have the potential to provide additional insights into mechanisms of mammalian gene
regulation that are of a broader interest. Additional perspectives on dioxin action can be found
in several recent reviews (Birnbaum, 1994a,b; Schecter, 1994; Hankinson, 1995; Schmidt and
Bradfield, 1996; Gasiewicz, 1997; Rowlands and Gustafsson, 1997; Denison et al., 1998; Hahn,
1998; Wilson and Safe, 1998; Gu et al., 2000).
2.2. THE "RECEPTOR" CONCEPT
The idea that a drug, hormone, neurotransmitter, or other chemical produces a
physiological response by interacting with a specific cellular target molecule, i.e., a "receptor,"
evolved from several observations. First, many chemicals elicit responses that are restricted to
specific tissues. This observation implies that the responsive tissue (e.g., the adrenal cortex)
contained a "receptive" component whose presence is required for the physiologic effect (e.g.,
cortisol secretion). Second, many chemicals are quite potent. For example, picomolar to
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nanomolar concentrations of numerous hormones and growth factors elicit biological effects.
This observation suggests that the target cell contains a site(s) to which the particular chemical
binds with high affinity. Third, stereoisomers of some chemicals (e.g., catecholamines, opioids)
differ by orders of magnitude in their ability to produce the same biological response. This
observation indicates that the molecular shape of the chemical strongly influences its biological
activity. This, in turn, implies that the binding site on or in the target cell also has a specific,
three-dimensional configuration. Together, these types of observations predict that the
biological responses to some chemicals involve stereospecific, high-affinity binding of the
chemicals to specific receptor sites located on or in the target cell.
The availability of compounds of high specific radioactivity has permitted quantitative
analyses of their binding to cellular components in vitro. To qualify as a potential "receptor," a
binding site for a given chemical must satisfy several criteria: (1) the binding site must be
saturable, i.e., the number of binding sites per cell should be limited; (2) the binding should be
reversible; (3) the binding affinity measured in vitro should be consistent with the potency of the
chemical observed in vivo; (4) if the biological response exhibits stereospecificity, so should the
in vitro binding; (5) for a series of structurally related chemicals, the rank order for binding
affinity should correlate with the rank order for biological potency; and (6) tissues that respond
to the chemical should contain binding sites with the appropriate properties.
The binding of a chemical ("ligand") to its cognate receptor is assumed to obey the law of
mass action; that is, it is a bimolecular, reversible interaction. The concentration of the liganded,
or occupied, receptor [RL] is a function of both the ligand concentration [L] and the receptor
concentration [R]:
[L] + [R] [RL]
k2
Inherent in this relationship is that the fractional occupancy (i.e. [RL] / [Rt]) is a function of
ligand concentration [L] and the apparent equilibrium dissociation constant KD, which is a
measure of the binding affinity of the ligand for the receptor, that is, [RL] / [Rt] = [L] / (KD +
[L]), where KD = [L] [Rt] / [LR] = k2 / kj. Therefore, the relationship between receptor occupancy
and ligand concentration is hyperbolic. At low ligand concentrations (where [L]«KD), a small
increase in [L] produces an approximately linear increase in fractional receptor occupancy. At
high ligand concentration (where [L]»KD), the fractional occupancy of the receptor is already
very close to 1, that is, almost all receptor sites are occupied. Therefore, a small increase in [L]
is likely to produce only a slight increase in receptor occupancy.
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Ligand binding constitutes only one aspect of the receptor concept. By definition, a
receptor mediates a response, and the functional consequences of the ligand-receptor binding
represent an essential aspect of the receptor concept. Receptor theory attempts to quantitatively
relate ligand binding to biological responses. The classical "occupancy" model of Clark (1933)
postulated that (1) the magnitude of the biological response is directly proportional to the
fraction of receptors occupied and (2) the response is maximal when all receptors are occupied.
However, analyses of numerous receptor-mediated effects indicate that the relationship between
receptor occupancy and biological effect is not as straightforward as Clark envisioned. In certain
cases, no response occurs even when there is some receptor occupancy. This suggests that there
may be a threshold phenomenon that reflects the biological "inertia" of the response (Aliens et
al., 1960). In other cases, a maximal response occurs well before all receptors are occupied, a
phenomenon that reflects receptor "reserve" (Stephenson, 1956). Therefore, one cannot simply
assume that the relationship between fractional receptor occupancy and biological response is
linear. Furthermore, for a ligand (such as TCDD) that elicits multiple receptor-mediated effects,
one cannot assume that the binding-response relationship for a simple effect (such as enzyme
induction) will necessarily be identical to that for a different and more complex effect (such as
cancer). The cascades of events leading to different complex responses (e.g., altered immune
response to pathogens or development of cancer) are likely to be different, and other rate-
limiting events likely influence the final biological outcome resulting in different dose-response
curves. Thus, even though ligand binding to the same receptor is the initial event leading to a
spectrum of biological responses, ligand-binding data may not always mimic the dose-effect
relationship observed for particular responses.
Another level of complexity is added when one considers different chemical ligands that
bind to the same receptor. Relative potencies are determined by two properties of the ligand:
affinity for the receptor, and capacity to confer a particular response in the receptor (e.g., a
particular conformational change), also called efficacy (Stephenson, 1956). Ligands with
different affinities and the same degree of efficacy would be expected to produce parallel dose-
response curves with the same maximal response within a particular model system. However,
ligands of the same affinity with different efficacies may result in dose-response curves that are
not parallel or that differ in maximal response.
2.3. THE Ah (DIOXIN) RECEPTOR
The unusual toxic potency of TCDD suggested the possible existence of a receptor for
dioxin. Poland and coworkers, using radiolabeled TCDD as a ligand, demonstrated that the
cytosolic fraction of C57BL/6J mouse liver contained a protein that bound the dioxin saturably
(i.e., ~105 binding sites per cell), reversibly, and with high affinity (i.e., in the nanomolar range,
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consistent with TCDD's biological potency in vivo). Competition binding studies with
congeners of TCDD revealed that ligands with the highest binding affinity were planar and
contained halogen atoms in at least three of the four lateral positions; thus, ligand binding
exhibited stereospecificity. In addition, the ligand-receptor binding relationship resembled a
rectangular hyperbola and, therefore, appeared to obey the law of mass action. Together, these
findings demonstrated that the intracellular TCDD-binding protein had the ligand binding
properties expected for a "dioxin receptor." The protein has been designated as the "Ah
receptor" because it binds and mediates the response to other aromatic hydrocarbons (such as 3-
methylcholanthrene) in addition to TCDD (Poland and Knutson, 1982).
Inbred mouse strains differ quantitatively in their responsiveness to TCDD and other
aromatic hydrocarbons. For example, TCDD elicits its effects at about 10-fold lower
concentrations in the more responsive mouse strains (typified by C57BL/6) than in the less
responsive strains (typified by DBA/2). This polymorphism in responsiveness is genetic in
origin, and, in crossbreeding studies, the more responsive phenotype segregates as an autosomal
dominant trait. Numerous responses to TCDD (e.g., enzyme induction, thymic involution, cleft
palate formation, hepatic porphyria) exhibit a segregation pattern identical to that for the binding
of TCDD to the Ah receptor. Thus, the genetic locus (designated Ah) that governs this receptor
polymorphism also governs the biological responses to TCDD. These findings implicate the Ah
receptor in the mechanism of dioxin action (Poland and Knutson, 1982; Nebert et al., 1991). The
resistance of recently developed Ah receptor null-allele ("knockout") mice to the enzyme
inductive and toxic effects of very high doses of TCDD further corroborates the role of this
protein in these responses (Fernandez-Salguero et al., 1996; Schmidt et al., 1996; Mimura et al.,
1997; Hundeiker et al., 1999; Peters et al., 1999).
Studies of structure-activity relationships (SARs) reveal that, within groups of
structurally-related compounds, a ligand's receptor-binding affinity often correlates with its
potency in eliciting a biological response(s). Such SAR analyses are useful for assessing
possible participation of the AhR in ligand-induced responses in experimental systems where
genetic polymorphisms of the receptor are not available. Such SAR studies constitute
biochemical evidence that implicates the Ah receptor in the mechanism of dioxin action. In
general, the rank order binding affinity of TCDD and related chemicals to the Ah receptor has
been demonstrated to be similar to their rank order of potency to elicit a broad spectrum of
biochemical, morphologic, immunologic, neoplastic, developmental, and reproductive effects
(Poland and Knutson, 1982; Safe, 1986, 1990). This rank order appears largely dependent on
several structural constraints. For example, relatively planar aromatic compounds with
approximate van der Waals dimensions of 14 x 12 x 5 A with few bulky substituent groups have,
in general, the highest ligand binding affinity and are the most potent for eliciting biological
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effects (Poland and Knutson, 1982; Gillner et al., 1993; Waller and McKinney, 1995).
However, it is becoming increasingly recognized that some Ah receptor ligands have both
agonist and antagonist activity (Harris et al., 1989b; Kurl et al., 1993; Lu et al., 1996; Henry et
al., 1999), suggesting that not all Ah receptor ligands have the same degree of efficacy
(Hestermann et al., 2000).
The cloning of receptor cDNA (Burbach et al., 1992; Ema et al., 1992) has added
important new insights into Ah receptor structure and function (reviewed in Hankinson, 1995;
Rowlands and Gustafsson, 1997). The deduced amino acid sequence reveals that the Ah
receptor has several features in common with a class of transcription factors known as basic
helix-loop-helix (bHLH) proteins. The Ah receptor contains a bHLH domain, located towards
the N-terminal end of the receptor. An analysis of this and other bHLH proteins indicates that
the basic region mediates DNA binding, whereas the helix-loop-helix domain is necessary for
dimerization with other proteins. Two other receptor regions function in dimerization. These
are designated as "PAS" regions due to their sequence homology with Per (a Drosophila
circadian rhythm protein), Arnt (another protein that contributes to dioxin responsiveness,
described below), and Sim (a regulatory protein that participates in Drosophila central nervous
system development) (Huang et al., 1993). In the AhR, the PAS domain consists of
approximately 300 amino acid residues containing two copies of a repeat of about 50 amino
acids, referred to as the PAS-A and PAS-B repeats. In the absence of an agonist, the PAS-B
region associates with one heat shock protein 90 (hsp90) molecule, permitting binding of a
second hsp90 to the HLH region (Whitelaw et al., 1993; Antonsson et al., 1995; Coumailleau et
al., 1995; Whitelaw et al., 1995; Fukunaga et al., 1995). TCDD has been shown to interact with
a ligand-binding pocket near the PAS-B region, the conformation of which is maintained by
hsp90. Dimerization between AhR and Arnt is mediated through their HLH regions, but is
further stabilized by PAS-PAS interactions (Reisz-Porszasz et al., 1994; Fukunaga et al., 1995).
Immunohistochemical studies, using anti-receptor antibodies, reveal that the unliganded receptor
resides in the cytoplasm; exposure of cells to (under in vitro conditions) TCDD leads to the
accumulation of the receptor within the nucleus (Pollenz et al., 1994). Association with hsp90 is
also thought to limit nuclear uptake of the receptor by blocking a N-terminal nuclear localization
sequence (Pongratz et al., 1992; Ikuta et al., 1998). The carboxyl end of the protein contains a
glutamine-rich region, which resembles certain "activation domains" present in some other
transcription factors; by analogy, this region could interact with coactivator proteins that have
yet to be characterized. Thus, like many proteins, the Ah receptor appears to be composed of
several different functional domains. It is notable that the TCDD-bound Ah receptor does not,
by itself, bind strongly to DNA; acquisition of DNA-binding capability appears to require that
the receptor interact with another factor (such as the Arnt protein). Thus, the active form of the
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receptor is heteromeric. The bHLH proteins identified to date are involved in transcriptional
regulation and have a variety of roles in tissue growth and differentiation processes (Murre et al.,
1994; Schmidt and Bradfield, 1996).
Human cells also contain an intracellular protein whose ligand-binding and
hydrodynamic properties resemble those of the Ah receptor identified in other species (see Cook
and Greenlee, 1989; Harris et al., 1989a; Roberts et al., 1990; Lorenzen and Okey, 1991; Harper
et al., 1991; Ema et al., 1994). Furthermore, the sequence of this protein shows homology with
that of other mammalian species (Dolwick et al., 1993; Ema et al., 1994; Hahn, 1998).
Compared with the rat or mouse Ah receptor, however, the human Ah receptor appears, at least
under cell-free conditions, to have a several-fold lower affinity for TCDD (Manchester et al.,
1987; Ema et al., 1994). Some data also suggest that the human Ah receptor may be many times
less sensitive in terms of eliciting a response. For example, cultured human embryonic palatal
cells were approximately 200 times less sensitive than mouse palatal cells with respect to the
inducibility ofCYPJAJ by TCDD (Abbott et al., 1999a). Although these data might imply that
human tissues would be less sensitive to the toxic effects of TCDD, the Ah receptor in human
cells has also been shown to exist in more than one form (Perdew and Hollenbeck, 1995). The
relative sensitivity and function of these different forms have not been evaluated. Furthermore,
these observed differences may be related to the greater lability of the human receptor during
tissue preparation and cell fractionation procedures (Manchester et al., 1987). Data also suggests
considerable heterogeneity of Ah receptor concentrations and characteristics in the human
population (e.g., Roberts et al., 1986, 1990, 1991). However, a limited analysis for human AhR
polymorphisms identified only one amino acid exchanging polymorphism, and this is thought to
have little or no functional significance (Wanner et al., 1999). Additional studies of the human
receptor should increase our knowledge of its functional properties and role in mediating altered
cell- and tissue-specific responses elicited by TCDD and related chemicals.
Evidence indicates that the Ah receptor evolved prior to the introduction of HAHs into
the environment (Czuczwa et al., 1984; Hahn et al., 1997). Furthermore, what is known about
the structure, regulation, and expression of the Ah receptor in different tissues indicates a
purposeful regulation for some normal function. The ontogenically related and tissue-specific
expression of the receptor (e.g., Abbott et al., 1995; Jain et al., 1998), as well as the conservation
of its presence and protein sequence in diverse groups of vertebrates, implies an essential
function (Hahn, 1998). Furthermore, the level and activity of the Ah receptor appear to be
regulated by changes in cell differentiation stages, growth factors, cell activation, diurnal cycle,
and prior exposure to receptor agonists (Vaziri et al., 1996; Crawford et al., 1997; Shimba et al.,
1998; Hayashi et al., 1995; Wanner et al., 1995; Liu et al., 1996; Pollenz, 1996; Richardson et
al., 1998). These aspects might, at least in part, contribute to the ability of TCDD to cause
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tissue- and developmental stage-specific effects. The best evidence for a normal physiological
function of the Ah receptor comes from studies with Ah receptor-deficient animals. These mice
have altered hepatic growth and development, immune system abnormalities, development of
vasculature, adverse reproductive outcomes and reproductive tissue development, and abnormal
processes in a variety of tissues (Fernandez-Salguero et al., 1995; Schmidt et al., 1996;
Fernandez-Salguero et al., 1997; Abbott et al., 1999b; Hushka et al., 1998; Thurmond et al.,
2000; Robles et al., 2000; Benedict et al., 2000; Lahvis et al., 2000).
Several reports have indicated stimulation of Ah receptor-dependent responses under
certain conditions in the absence of added ligand (Sadek and Allen-Hoffman, 1994; Ma and
Whitlock, 1996; Weiss et al., 1996; Crawford et al., 1997; Chang and Puga, 1998). However, it
is not yet clear whether this occurs via an endogenously present ligand or some other process.
Nevertheless, it has been postulated that some other compound(s) must represent the "natural"
ligand(s) for the receptor (Poellinger et al., 1992). Naturally occurring high-affinity ligands for
the receptor exist in the environment, particularly in plants (Gillner et al., 1985, 1989; Rannung
et al., 1987; Bjeldanes et al., 1991). Other candidates for endogenous ligands include tryptophan
derivatives (Helferich and Denison, 1991), carotinoids (Gradelet et al., 1996), arachidonic acid
metabolites (Schaldach et al., 1999), and tetrapyrroles or their derivatives (Sinai and Bend,
1997). Thus, it is possible that the Ah receptor might have evolved as part of a substrate-
inducible system designed to metabolize and/or activate dietary lipophilic substances, and TCDD
may mimic the binding of such substances to the receptor. TCDD has been observed to produce
changes in the proliferative/differentiated phenotype of a variety of cell types (Knutson and
Poland, 1980; Blankenship et al., 1993; Gaido and Maness, 1994; Gierthy et al., 1994; Brodie et
al., 1996; Yang et al., 1999). Thus, an additional possibility is that TCDD mimics an
endogenous Ah receptor ligand involved in the regulation of such tissue-specific phenotypes.
Several reports have also suggested a role of the Ah receptor in cell cycle control (Ma and
Whitlock, 1996; Weiss et al., 1996; Kolluri et al., 1999), possibly through regulation of tissue
growth factors such as transforming growth factor-p (Zaher et al., 1998).
2.4. THE ARNT PROTEIN
Biochemical and hydrodynamic findings suggest that more than one protein participates
in the response to TCDD. In particular, overwhelming evidence indicates that the Ah receptor
interacts with the Arnt protein to form a heteromeric, DNA-binding protein complex that can
activate gene transcription (reviewed in Hankinson, 1995; Schmidt and Bradfield, 1996;
Rowlands and Gustafsson, 1997; Denison et al., 1998; Whitlock, 1999).
The human Arnt cDNA encodes a protein of about 86 kDa, which has several features in
common with the Ah receptor. Like the Ah receptor, it contains a bHLH domain, which
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contributes both to DNA binding and to protein-protein interactions (Hoffman et al., 1991; Li et
al., 1994; Reisz-Porszasz et al., 1994). Furthermore, the Arnt protein contains PAS domains
homologous to Per and Sim, and C-terminal domains functional in transcriptional activation
(Huang et al., 1993; Reisz-Porszasz et al., 1994; Lindebro et al., 1995) . The Arnt protein does
not bind TCDD. Furthermore, it does not bind to Ah receptor recognition sites on DNA (termed
dioxin or xenobiotic response elements; DREs or XREs) on DNA in the absence of the liganded
Ah receptor protein (Whitelaw et al., 1993). Immunohistochemical studies, using anti-Arnt
antibodies, reveal that the Arnt protein resides in the nucleus in uninduced mouse hepatoma cells
and that TCDD exposure of cells produces no change in its intracellular distribution (Pollenz et
al., 1994). Thus, Arnt appears to be a nuclear protein. Furthermore, the nuclear accumulation of
the Ah receptor occurs in Arnt-defective cells. Together, these findings argue against a primary
role for Arnt in the translocation per se of the receptor from cytoplasm to nucleus. Instead, it has
been suggested that Arnt interacts with the liganded Ah receptor to form a heteromeric, DNA-
binding protein complex that can activate gene transcription. Experiments in vitro support this
idea. For example, immunoprecipitation experiments reveal that the liganded Ah receptor and
the Arnt protein can interact in solution. The liganded receptor alone does not exhibit substantial
DNA-binding activity in the absence of Arnt; rather, the presence of both proteins is required to
generate a specific DNA-binding species and to activate the expression of a dioxin-responsive
reporter gene. Furthermore, deletion of the bHLH domain of Arnt abrogates its functional
interaction with the liganded Ah receptor (Whitelaw et al., 1993). Together, these findings
imply that the transcriptionally active component of the dioxin-responsive system is a protein
heteromer consisting of (at least) the liganded Ah receptor and Arnt. Although both proteins
contain a C-terminal transactivation domain, the Ah receptor appears to provide the dominant
activation function, and the relative contribution of the Arnt domain largely depends on the
availability of other cell-specific factors (Gorton et al., 1996; Ko et al., 1996).
Multiple forms of Arnt have been detected in several species (Drutel et al., 1996; Hirose
et al., 1996; Pollenz et al., 1996). In mice and rats, Arntl and Arnt2 are 83% identical in amino
acid sequence, and have the ability to dimerize with the AhR and bind to specific DNA elements
in vitro (Hirose et al., 1996). However, the expression patterns for these two isoforms are quite
different. Arntl is widely expressed in a variety of tissues, whereas Arnt2 is detected primarily
in adult brain and kidney (Drutel et al., 1996; Hirose et al., 1996). In addition, each protein has a
distinct expression pattern in the developing mouse (Jain et al., 1998). The respective roles of
these proteins in contributing to the variety of TCDD-elicited responses are not yet clear.
Both the Ah receptor and Arnt protein belong to the bHLH class of transcription factors,
which function as heterodimers and contribute to the control of numerous genes (Kadesch,
1993). The dimerization capabilities of bHLH proteins provides a potential mechanism for
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generating regulatory diversity. For example, different heterodimers may exhibit different
stabilities, may have different DNA-binding affinities, or may recognize different DNA
sequences. The bHLH structure of the Ah receptor raises the possibility that it might form
heterodimeric complexes with proteins other than Arnt, generating regulatory molecules with
potentially novel properties. By analogy with other bHLH systems, both the absolute amount
of each partner and their relative ratios could influence the extent and type of response to TCDD.
Thus, diversity in heteromer formation might contribute to the diversity of responses that typifies
dioxin action. Arntl has been shown to be a dimerization partner for several proteins including
the Ah receptor, single-minded (Sim), hypoxia inducible factor-la, and endothelial PAS protein
1, and several other as yet uncharacterized proteins (Ema et al., 1997; Hogenesch et al., 1997;
Probst et al., 1997). In addition, Arntl has been shown to have an essential role in development
(Kozak et al., 1997; Maltepe et al., 1997). However, Arnt is the only protein that has been
demonstrated to be a functional partner for the Ah receptor in terms of conferring specificity of
DNA binding and transactivation. Some HLH proteins, which lack a basic region (typified by
Id), may act as dominant negative regulators of transcription by dimerizing with bHLH proteins
and inhibiting their DNA binding ability (Kadesch, 1993). There is evidence for an inducible
dominant negative regulator of the Ah receptor, termed the Ah receptor represser, that interacts
with Arnt (Mimura et al., 1999). It is possible that the tissue-specific presence or absence of this
factor regulates Ah receptor function and thus its response to xenobiotics by competing with the
receptor for Arnt. In any case, the bHLH structure of the Ah receptor and the Arnt protein
suggests that some of the diversity in TCDD's biological effects might reflect differential gene
regulation by a mechanism involving formation of different protein heterodimers. Future
mechanistic studies should aid in elucidating how such processes regulate dioxin action.
2.5. OTHER PROTEINS THAT PARTICIPATE IN THE RESPONSE TO DIOXIN
Attempts to purify the unliganded Ah receptor under nondenaturing conditions revealed
that it tends to associate with other proteins in vitro, in particular the 90-kDa heat shock protein
(hsp90). The hsp90 protein is an abundant factor that can interact with numerous other proteins
and that may have multiple functions (Buchner, 1999). Immunoprecipitation studies and
immunosedimentation experiments using anti-hsp90 antibodies reveal that the unliganded Ah
receptor associates with hsp90 in vitro (Denis et al., 1988; Perdew, 1988). In view of previous
findings implicating hsp90 in glucocorticoid receptor function (Picard et al., 1990), the
association between the Ah receptor and hsp90 in vitro may be more than fortuitous. Hsp90 may
be needed to maintain the unliganded receptor in a configuration that facilitates ligand binding.
It might have a role in regulating nuclear translocation and DNA binding of the Ah receptor
(Pongratz et al., 1992; Antonsson et al., 1995; Coumailleau et al., 1995; Phelan et al., 1998).
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Studies in yeast have demonstrated the requirement for hsp90 in the formation of a functional Ah
receptor (Carver et al., 1994). However, since complete hsp90 dissociation does not appear to be
essential for nuclear localization (Heid et al., 2000), the dissociation of the two hsp90 molecules
may occur during separate events in the cytosol and the nucleus, initiated by ligand binding and
Arnt association, respectively. An additional protein, ARA9/AIP/XAP2, has been shown to
enhance the transcriptional activity of the AhR-Arnt complex (Ma and Whitlock, 1997; Carver et
al., 1998; Meyer et al., 1998). Although this protein does not appear to be required for AhR-
hsp90 interaction, it apparently stabilizes this interaction and has some function in regulating the
rate of AhR turnover in the cytosol or intracellular localization (Meyer and Perdew, 1999;
LaPres et al., 2000; Petrulis et al., 2000; Bell and Poland, 2000). Recently, a 23-kDa protein has
been shown to associate with the ligand-binding form of the Ah receptor. This protein is thought
to play a role in stabilizing the complex containing receptor and hsp90 (Kazlauskas et al., 1999).
Given the important functions of these proteins in stabilizing an Ah receptor form that can bind
ligand and transduce the ligand-initiated signal to the nuclear compartment, the relative presence
of these proteins might play an important role in the tissue-specific sensitivity to Ah receptor
ligands like TCDD.
Several lines of evidence suggest that phosphorylation/dephosphorylation of the Ah
receptor and the Arnt protein may contribute to the function of the dioxin-responsive system.
Treatment of nuclear extracts with potato acid phosphatase, which dephosphorylates proteins,
inhibits the binding of the liganded receptor heteromer to its DNA recognition sequence in vitro
(Pongratz et al., 1991). Modulation of protein kinase C (PKC) activity is also associated with a
reduction in DNA-binding capability of the receptor heteromer in vitro and alteration of Ah
receptor-dependent transcriptional function in vivo (Carrier et al., 1992; Okino et al., 1992;
Berghard et al., 1993; Chen and Tukey, 1996; Long et al., 1998). Furthermore,
immunoprecipitation experiments using anti-receptor antibodies reveal that the receptor can
undergo phosphorylation in vivo (Berghard et al., 1993; Mahon and Gasiewicz, 1995).
Additional experiments in vitro suggest that phosphorylation of the Arnt protein is required for
its heterodimerization with the Ah receptor; however, phosphorylation of the receptor is not
required (Berghard et al., 1993). Together, these findings suggest that PKC and other protein
kinases might influence heterodimerization, binding of the receptor-Arnt heteromer to DNA, or
transcriptional activation of the Ah receptor. Although tyrosine phosphorylation in particular
appears to be important for regulation of DNA binding of the AhR-Arnt complex (Park et al.,
2000), the exact amino acid residue, the mechanism of regulation, and if this, or other,
phosphorylation may be partially responsible for tissue- and developmental stage-specific
regulation of AhR activity, have yet to be determined. It is known that many mammalian
transcription factors undergo cycles of phosphorylation and dephosphorylation; however, in
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most cases, the physiological significance of the modification is unknown (Hunter and Karin,
1992). Additional research is necessary to fully delineate the role of protein phosphorylation in
the biological response to TCDD, and to identify the particular protein kinases and phosphatases
that participate in the regulation of Ah receptor and Arnt protein.
The Ah receptor has been shown to physically interact with both retinoblastoma and NF-
kB proteins (Ge and Elferink, 1998; Tian et al., 1999; Puga et al., 2000a). Both proteins are
known to function in the regulation of cell cycle and cell differentiation in response to a variety
of cytokines and growth factors (e.g., Beg and Baltimore, 1996; Van Antwerp et al., 1996;
Zacksenhaus et al., 1996). It is reasonable to speculate that these interactions contribute to the
noted ability of TCDD and related xenobiotics to affect cell cycle and cellular differentiation
states, but the exact pathways that lead to these cell- and tissue-specific responses have yet to be
determined. Nevertheless, this certainly represents a promising area of future research.
It appears likely that additional proteins, which remain to be identified and characterized,
also contribute to the dioxin-response system. Both the Ah receptor and Arnt activate gene
expression, at least in part, through direct interaction with basal transcription factors (Rowlands
et al., 1996). Furthermore, there appears to be considerable cross-regulation of the Ah receptor
with other signaling proteins including hypoxia inducible factor, estrogen receptors, and the
retinoic acid and thyroid hormone receptors (e.g., Caruso et al., 1999; Chan et al., 1999; Duan et
al., 1999; Kumar et al., 1999; Nguyen et al., 1999). There is evidence to indicate that protein-
protein interactions have an important part in regulating the transcriptional activity of several
nuclear receptors (e.g., Chen and Evans, 1995). Likewise, Ah receptor-Arnt complex-mediated
transcriptional activities may be modulated by coactivator/corepressor proteins in a cell- and
gene-specific manner (Watson and Hankinson, 1988; Watson et al., 1992; Ma et al., 1995;
Kobayashi et al., 1997; Kress and Greenlee, 1997; Gradin et al., 1999; Kumar et al., 1999;
Nguyen et al., 1999; Eltom et al., 1999). The relative importance of these interactions in the
dose-related and tissue-specific responses elicited by TCDD are unknown.
2.6. ACTIVATION OF GENE TRANSCRIPTION BY DIOXIN
2.6.1. In Vitro Studies
Much of our current understanding of the mechanism of dioxin action is based on
analyses of the induction of particular enzyme activities by TCDD. Aryl hydrocarbon
hydroxylase activity reflects the action of the cytochrome P4501A1 (CYP1A1) enzyme, which
catalyzes oxygenation of poly cyclic aromatic substrates as the initial step in their metabolic
processing to water-soluble derivatives (Conney, 1982). TCDD induces CYP1A1 activity in
many tissues. In particular, the relatively strong induction ofCYPJAJ activity by TCDD in
cultured cells has facilitated the application of molecular genetic techniques to the analysis of the
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induction mechanism (Whitlock, 1999). Table 2-1 and Figure 2-2 summarize some of the
molecular events involved in this induction response.
In mouse hepatoma cells, nuclear transcription experiments reveal that TCDD induces
hydroxylase activity by stimulating transcription of the corresponding CYP1A1 gene. The
response to TCDD occurs within a few minutes and is direct in that it does not require ongoing
protein synthesis. Thus, the regulatory components required for the activation ofCYPlAl
transcription are present constitutively within the cell. TCDD fails to activate CYP1A1
transcription in Ah receptor-defective cells and in Arnt-defective cells, indicating that the
response requires both the Ah receptor and Arnt.
Observations that TCDD activates transcription and the liganded Ah receptor binds to
DNA led to the discovery of a dioxin-responsive regulatory DNA sequence upstream of the
CYP1A1 gene. Recombinant DNA methods were used to construct chimeric genes in which
potential regulatory DNA sequences from the CYP1A1 gene were ligated to a heterologous
"reporter" gene. After transfection of the recombinant genes into mouse hepatoma cells, TCDD
was observed to activate the expression of the reporter gene. Additional transfection
experiments defined the size of the dioxin-responsive domain and revealed that it had the
properties of a transcriptional enhancer (Jones et al., 1986; Neuhold et al., 1986; Fujisawa-
Sehara et al., 1987; Fisher et al., 1990). Furthermore, the recombinant gene responded poorly
when transfected into Ah receptor-defective cells or Arnt-defective cells. Thus, both the
receptor protein and the Arnt protein are required for enhancer function. Analyses of stable
transfectants revealed that the dioxin-responsive enhancer can function in a chromosomal
location distinct from that of the CYP1A1 gene (Fisher et al., 1989). Therefore, in principle, an
analogous enhancer element could mediate the transcriptional response of other genes to TCDD.
The DNA upstream region of the CYP1A1 gene contains a second control element, a
transcriptional promoter, that functions to ensure that transcription is initiated at the correct site.
The promoter binds proteins that are expressed constitutively by the cell; however, the promoter
contains no binding sites for the liganded Ah receptor heteromer. Transfection experiments
indicate that neither the enhancer nor the promoter functions in the absence of the other (Jones
and Whitlock, 1990). On might question how the enhancer and promoter, which are separated
by hundreds of nucleotides, function in concert. TCDD-induced alterations in the chromatin
structure of the CYP1A1 gene play an important part in this process, as described later.
As indicated above, enhancer function requires both Ah receptor and Arnt proteins.
Furthermore, the liganded, heteromeric form of the receptor exhibits an increased affinity for
DNA. Together these data indicate that activation of CYP1A1 transcription involves binding of
the receptor heteromer to the enhancer. Analyses of protein-DNA interactions in vitro by gel
retardation were done using enhancer DNA sequences and nuclear extracts from uninduced and
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TCDD-induced cells. These studies revealed the existence of an inducible, receptor-dependent,
and Arnt-dependent protein-DNA interaction, whose characteristics were those expected for the
binding of the receptor heteromer to DNA (Denison et al., 1989; Hapgood et al., 1989;
Saatcioglu et al., 1990a,b). The receptor heteromer recognizes the specific core nucleotide
sequence
5'-NTGCGTG-3'
3'-NACGCAC-5'
present in multiple copies within the enhancer. These are often referred to as a DRE or XRE.
Studies with a [125I]-labeled dioxin indicate that the receptor heteromer binds in a 1:1 ratio to its
DNA recognition sequence (Denison et al., 1989). Methylation protection and interference
experiments in vitro reveal that the receptor heteromer lies within the major DNA groove and
contacts the four guanines of the recognition sequence (Neuhold et al., 1989; Shen and Whitlock,
1989; Saatcioglu et al., 1990a, b). Transfection analyses of the six enhancer binding sites (in the
mouse) for the receptor heteromer, as well as several mutated sites synthesized in vitro, reveal
that nucleotides adjacent to the core recognition sequence contribute to enhancer function
because the core nucleotides alone fail to exhibit enhancer activity (Denison et al., 1988).
Studies of protein-DNA interactions reveal that there is no strict relationship between the affinity
of the receptor heteromer for DNA and the extent of enhancer activation (Shen and Whitlock,
1992; Neuhold et al., 1989). These latter observations suggest that the protein-DNA interaction
per se does not suffice to activate transcription and that an additional event (such as DNA
bending—see below) is necessary. Mutational analyses reveal that the four base-pair sequence
5'-CGTG-3'
3'-GCAC-5'
is required for the receptor heteromer to bind to DNA in vitro (Shen and Whitlock, 1992; Yao
and Denison, 1992; Lusska et al., 1993). Six distinct DRE sites have been identified within the
mouse CYP1A1 promoter (Lusska et al., 1993), whereas the rat and human CYP1A1 gene
promoters contain two and three copies of the core DRE sequence, respectively (Swanson and
Bradfield, 1993). Thus, the homologous gene from different species may have different
regulatory units. It is inappropriate to assume that the relative sensitivity of these genes to
modulation by TCDD may be increased or decreased based on the number of DREs or their
relative position in the upstream regulatory region, because this is also influenced by a variety of
other regulatory factors. It is also possible that additional transcription factors, activators or
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repressers, may have overlapping DNA binding specificities with that of the Ah receptor-Arnt
complex (e.g., Duan et al., 1999; Klinge et al., 1999).
The DNA recognition sequence for the receptor heteromer contains two CpG
dinucleotides. Studies in other systems have revealed that cytosine methylation at CpG is
associated with decreased gene expression, often in tissue-specific fashion. Cytosine
methylation of the CpG dinucleotides within the recognition sequence diminishes both the
binding of the receptor heteromer to the enhancer (as measured by gel retardation) and the
functional activity of the enhancer element (as determined by transfection experiments).
Therefore, given that the TCDD-responsive receptor/enhancer system can regulate the
transcription of genes other than CYP1A1, methylation of the enhancer may constitute one
mechanism for controlling expression of such genes in a tissue-specific fashion (Shen and
Whitlock, 1989).
An extensive analysis of the manner in which other bHLH proteins dimerize and bind to
their respective DNA recognition sequences has been performed (reviewed in Swanson et al.,
1995; Schmidt and Bradfield, 1996; Wilson and Safe, 1998). The bHLH/PAS family can be
subdivided based on the basic region amino acid sequence and the DNA recognition sequence of
its members. Arnt-like proteins recognize a 3'-half-site GTG, whereas heterodimer partners for
Arnt, such as the Ah receptor, are more selective with unique basic domains. This suggests that
although the Arnt-like component may function as a generic coregulator, specificity may be
designated by Arnt's dimerization partner. These data are consistent with the finding that Arnt
can dimerize with several other bHLH proteins; to date, the Ah receptor has been found to
dimerize only with Arnt. Thus the family of bHLH/PAS proteins may exhibit a multitude of
possible dimerizations, each complex recognizing a unique DNA sequence. The relative
presence and state of activation of these proteins represent a novel mechanism to determine
diversity of tissue-, cell-, and gene-specific regulation.
Promoter regions of TCDD-responsive genes have also been found to contain negative
regulatory elements (Hines et al., 1988; Walsh et al., 1996; Piechocki and Hines, 1998).
Superinducibility of CYP1A1 by TCDD has been observed following the treatment of cells with
cycloheximide, a protein synthesis inhibitor, suggesting that constitutively bound proteins are
involved in negative regulation ofCYPlAl (Lusska et al., 1992). The function and regulation of
these negative elements are not completely understood and are likely to be gene-, cell-, and
species-specific.
Gel retardation analyses also reveal that binding of the receptor heteromer to its
recognition sequence bends the DNA in vitro. The site of the bend is at, or very near, the site of
the protein-DNA interaction (Elferink and Whitlock, 1990). These findings suggest that binding
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of the receptor heteromer to chromatin might also alter the configuration of the enhancer DNA in
vivo.
2.6.2. In Vivo Studies
Studies that use transfection and in vitro techniques for analyzing protein-DNA
interactions provide important clues about the functional components of the TCDD-responsive
system. However, such experimental approaches necessitate removing the DNA regulatory
elements from their native context within the chromosome and, therefore, have the potential to
generate misleading results. For example, in the intact cell, nuclear DNA is complexed with
histones and other chromosomal proteins, and the structure of the nucleoprotein complex
(chromatin) makes important contributions to the control of gene transcription (Grunstein, 1990;
Felsenfeld, 1992; Kornberg and Lorch, 1992). Transfection experiments and studies of protein-
DNA interactions in vitro do not adequately control for this variable. For this reason, the
protein-DNA interaction at the dioxin-responsive enhancer was analyzed in intact cells (Wu and
Whitlock, 1993). These experiments revealed that the inactive (i.e., uninduced) enhancer binds
few, if any, proteins within the major DNA groove. This finding implies that the inactive
enhancer is relatively inaccessible to DNA-binding proteins in vivo. In addition, from a
mechanistic standpoint, the absence of protein-enhancer interactions in uninduced cells argues
against the idea that, at least for the CYP1A1 gene, a specific represser protein maintains the
enhancer in an inactive configuration. Exposure of cells to TCDD leads to rapid binding of
receptor heteromers, and a few other proteins, to the enhancer in the regulatory region of the
CYP1A1 gene. Therefore, in isolated hepatoma cells the liganded receptor heteromer appears to
activate transcription of CYP1A1 by a mechanism that does not require other enhancer-binding
proteins (Wu and Whitlock, 1993).
Proteins that bind to the CYP1A1 promoter are expressed constitutively, and TCDD has
no effect on their interactions with promoter DNA in vitro, as measured by DNase footprinting
(Jones and Whitlock, 1990). However, in intact cells, these proteins fail to bind to the inactive
(i.e., uninduced) promoter. Thus, the promoter, like the enhancer, is inaccessible in uninduced
cells. Exposure of cells to TCDD induces a rapid conformational change at the promoter region,
such that it becomes accessible to the constitutively expressed proteins. This change represents a
primary effect of TCDD that does not require transcription since it is insensitive to actinomycin
D. In addition, it is receptor dependent and Arnt dependent, because it does not occur in Ah
receptor- and Arnt-deficient cells. These observations indicate that the receptor-enhancer
interaction increases accessibility of the downstream promoter to transcription factors (Durrin
and Whitlock, 1989; Wu and Whitlock, 1992).
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Studies ofCYPlAl gene chromatin structure reveal that, in the transcriptionally inactive
state, the enhancer/promoter region assumes a nucleosomal structure that is specifically
positioned at the promoter (Morgan and Whitlock, 1992). Its organization into nucleosomes
plausibly accounts for the inaccessibility of the enhancer/promoter region to DNA-binding
proteins in uninduced cells. Exposure to TCDD produces a rapid and actinomycin D-insensitive
loss of the positioned nucleosomes at the promoter; this change in chromatin structure accounts
for the TCDD-induced increase in promoter accessibility and increased CYP1A1 transcription in
vivo (Morgan and Whitlock, 1992). However, studies indicate that Ah receptor-Arnt binding to
DNA and increased chromatin accessibility at the enhancer are not sufficient to induce CYP1A1
gene expression, but that enhancer-promoter communication is also required. This is postulated
to be mediated, at least in part, by the ability of the C-terminal region of the Ah receptor to
interact with other proteins (Okino and Whitlock, 1995; Ko et al., 1996).
The mechanism by which binding of liganded receptor heteromers to the enhancer alters
chromatin structure is unknown. One possibility is that the DNA-bound receptor complex
affects histones (e.g., by recruiting other proteins possessing and/or activating histone acetylase
activity), thereby weakening histone-DNA interactions and destabilizing nucleosomes. Studies
using trichostatin A, a histone deacetylase inhibitor, show that histone acetylation plays an
important role in Ah receptor-elicited activation of the CYP1A1 and 7^42 genes (Xu et al., 1997).
The Ah receptor-Arnt complex has been shown to interact with several coactivator proteins that
may enhance transcription under some contexts (Kobayashi et al., 1996; Kumar et al., 1999;
Nguyen et al., 1999). Furthermore, several coactivators have been identified that contain regions
homologous to the PAS domain of the Ah receptor, Arnt and other bHLH proteins (Kamei et al.,
1996; Voegel et al., 1996). Under in vitro conditions, AhR and Arnt have been found to directly
interact with several proteins that make up the basal transcriptional machinery, such as TBP
(TATA-box binding protein), TFIIF, and TFIID, which has acetyltransferase activity (Rowlands
et al., 1996; Swanson and Yang, 1998). A second possibility is that the receptor-enhancer
interaction alters the DNA structure of the enhancer/promoter region, thereby stabilizing it in a
non-nucleosomal configuration. This idea is consistent with the observation that the receptor
heteromer bends DNA in vitro (Elferink and Whitlock, 1990).
The six binding sites for the receptor heteromer on the CYP1A1 enhancer in mice are
arranged in an irregular pattern. The absence of regular spacing between sites suggests that
enhancer activation does not require protein-protein interactions between adjacent DNA-bound
receptor heteromers. Instead, irregular spacing of binding sites may reflect constraints imposed
by chromatin structure because the receptor heteromer must bind to nucleosomes. For example,
as the DNA helix wraps around the histone core of the nucleosome, the major groove (which
contains the binding sites for the receptor heteromer) is periodically accessible. Increasing the
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number of binding sites at irregular intervals increases the probability that at least one site will
be accessible even when the DNA is nucleosomal. In addition, the receptor heteromer contacts a
relatively short (six base-pair) DNA segment, increasing the probability that the entire binding
site in the nucleosome will be accessible. Thus, the multiplicity, irregular distribution, and small
size of the binding sites may have evolved as a mechanism for overcoming the steric constraint
imposed by the nucleosomal organization of the inactive enhancer in vivo.
2.7. EVIDENCE FOR DIFFERENT MECHANISMS OF TOXICITY
As indicated above, much of our understanding of the possible mechanisms by which
TCDD and related chemicals may act has been elucidated through analyses of how they induce
CYP1A1 gene expression. Based on this model, it is logical to hypothesize that the binding of
TCDD to the Ah receptor, receptor-Arnt dimerization, binding of this complex to DREs present
in 5' promoter regions of responsive genes, and inappropriate modulation of gene expression,
represent the initial steps in a series of biochemical, cellular, and tissue changes that result in the
toxicity observed. This hypothesis is further supported by numerous studies evaluating
structure-activity relationships of various Ah receptor ligands, the genetics of mutant Ah
receptor genes, receptor-deficient mice, and the molecular events contributing to and regulating
expression of the Ah receptor and its activity. For example, it is striking that dosages of TCDD,
that produce a variety of toxic effects in normal mice, produce no effects in Ah receptor
knockout mice. Although the promoter regions of many genes, including CYP1A1, contain
DREs (Lai et al., 1996), only a few of these are known to be directly regulated by the Ah
receptor-Arnt complex (reviewed in Denison et al., 1998). However, the modulated expression
of these genes does not completely explain (at least not as yet) the diversity of toxic effects
elicited by TCDD in numerous animal species. By analogy, it is predicted that other genes have
inappropriate expression (or repression) directly related to particular toxic events. The findings
that many Ah receptor-modulated genes are regulated in a species-, cell-, and developmental
stage-specific manner suggest that molecular and cellular pathways leading to any particular
toxic event are extremely complex. Recent work demonstrated the modulation of at least 310
known genes in human hepatoma cells exposed to TCDD (Puga et al., 2000b). Indeed, precise
dissection of these events represents a considerable challenge, especially in that a toxic response
may depend on timely modulation of several genes rather than of just one particular gene, and
possibly modulation of these genes in several rather than just one cell type.
As our understanding of the receptor and the molecular events that regulate its activity
has progressed, it has become apparent that biochemical and biological outcomes of TCDD
exposure can be modulated by numerous other proteins with which the Ah receptor interacts.
Thus, it is possible that dioxin could modulate gene expression by pathways that do not involve
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interaction of the receptor with either Arnt or DREs. Although conditional disruption of the Arnt
gene results in the inability of TCDD to induce several responsive genes including CYP1A1
(Tomita et al., 2000), there are, in fact, no data proving that Arnt is required for any toxic effects
elicited by dioxin. No other functional heterodimer partner for the Ah receptor has been
identified. Thus, it is conceivable that, through interaction with other proteins, the receptor
could bind to DNA elements that are uniquely different from the consensus DRE identified.
This possibility seems even more plausible given the multiple dimerization partners identified
for Arnt and other bHLH-PAS proteins.
It is also reasonable to hypothesize that the Ah receptor might modulate gene expression
by a mechanism that does not require its direct interaction with DNA. It is plausible, for
example, that the Ah receptor and the Ah receptor-Arnt complex could divert other proteins and
transcription factors from other signaling pathways. Several studies provide evidence to support
this possibility. For example, direct interaction between the Ah receptor and retinoblastoma
protein has been shown (Ge and Elferink, 1998; Puga et al., 2000a). Progression of the cell
cycle through Gl phase is regulated, in part, by the retinoblastoma protein (Weinberg, 1995).
TCDD exposure has been shown to arrest cells in Gl (Weiss et al., 1996; Kolluri et al., 1999),
and the Ah receptor appears to be necessary for progression of mouse hepatoma cells through Gl
(Ma and Whitlock, 1996). These observations are consistent with a hypothesis that the dioxin-
activated Ah receptor may disrupt normal retinoblastoma protein-mediated differentiation
processes. Notably, another protein, p27Kipl, which is important for regulating the progression
of cells from Gl to S phase, has been shown to be induced by TCDD (Kolluri et al., 1999).
Furthermore, the Ah receptor is expressed in cells in a cycle-dependent manner, with expression
peaking in the late S phase (Vaziri et al., 1996). Similarly, recently described interactions
between the Ah receptor and the transcription factor NF-kB could potentially explain several
biological effects associated with TCDD exposure (Tian et al., 1999). Data suggests that
activation of the AhR may increase the activity of specific NF-kB subunits (Kim et al., 2000;
Schlezinger et al., 2000). For example, the AhR has been shown to associate with the RelA
subunit of NF-kB to activate the C-myc gene promoter in human breast cancer cells (Kim et al.,
2000). Likewise, dioxin treatment has been shown to inhibit progesterone receptor signaling
(Kuil et al., 1998). This might be due to the sequestration of common accessory factors or
coactivator proteins used by other transcription factors. It has been demonstrated that
overexpression of one steroid hormone receptor can decrease transcriptional activity of another,
presumably through this mechanism (Meyer et al., 1989). It is also possible that because Arnt
(or other as yet unidentified partners for the Ah receptor) can dimerize with other proteins, some
of the biological effects of the dioxins could be related to recruitment of Arnt by the Ah receptor
away from these other pathways. Thus, toxicity could result from loss of Arnt function. At this
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point, there is no evidence for this mechanism. In fact, recent data suggest that the noted
functional interference between hypoxia-induced pathways and Ah receptor-mediated signaling
(Gradin et al., 1996) does not occur through competition between the Ah receptor and HIF-loc
for Arnt (Pollenz et al., 1999). Data also suggest that ligand binding to the Ah receptor initiates
a phosphorylation/dephosphorylation cascade resulting in modulated activity of other
transcription factors (Enan and Matsumura, 1996; Blankenship and Matsumura, 1997). Precise
components and events of these modulated pathways have yet to be delineated. How or whether
any of these interactions in fact lead to any or all of the toxic effects elicited by dioxin exposure
has yet to be established. Nevertheless, it is clear that inappropriate activation of the Ah receptor
could have profound effects on regulation of a variety of signal transduction pathways;
disruption of these could have serious consequences on a number of cell and tissue processes.
Despite the wealth of evidence to indicate a role of the Ah receptor in the toxicity elicited
by the dioxins, it also seems possible that these chemicals may cause toxic effects through
mechanisms involving their interaction with biological effector molecules other than the
receptor. Clearly, a causal link between Ah receptor-regulated gene expression (regardless of
the mechanism of gene modulation) and any of the demonstrated toxic effects has not been
established. Furthermore, even though data from structure activity relationship and genetic
studies are consistent with the notion that the Ah receptor mediates some toxic effects, not all
effects have been thoroughly examined by these parameters. Similarly, Ah receptor knockout
mice have not been thoroughly examined for the ability of dioxin to elicit all of its known toxic
effects. Finally, although the Ah receptor is present in human cells and tissues, and studies
using human cells are consistent with the hypothesis that the Ah receptor mediates effects of the
dioxins, it is not known whether these same relationships would exist following exposure of
intact humans. Thus, it seems possible that dioxin might cause effects in people and animals by
a mechanism involving macromolecules other than the Ah receptor. Furthermore, within the
same organism some effects may involve the receptor and others may not. To date, there is,
however, no relevant evidence to support these possibilities.
It has been demonstrated that Ah receptor-deficient mice show no signs of toxicity at
doses of TCDD (200 ng/kg) approximating the LD50 dose in mice containing the Ah receptor
(Fernandez-Salguero et al., 1996). However, a single exposure of 2000 |ig/kg to Ah receptor-
deficient animals produced several minor lesions including scattered necrosis and vasculitis in
the liver and lungs. These data suggest that a pathway leading to toxicity exists, albeit at very
high doses, that is independent of the Ah receptor. However, these data also clearly indicate that
the major in vivo effects of TCDD are mediated, at least in mice, through the Ah receptors.
Notably, the level of TCDD exposure in these Ah receptor-deficient animals is well beyond that
known to occur in any human population.
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There is much evidence to indicate that the immunotoxic effects of TCDD are mediated
by the Ah receptor (Luster et al., 1985; Silkworm and Antrim, 1985; Davis and Safe, 1988, 1989,
1990; Safe, 1990; Ackerman et al., 1989; Pavylak et al., 1989; Kerkvliet et al., 1990; Silkworm
et al., 1993). These studies primarily investigate structure-activity relationships following a
single dose, and compare mouse strains that are sensitive or less sensitive to TCDD because of
alterations in the amino acid sequence of the Ah receptor. However, the results of other
investigations using repeated dosing schedules could be interpreted to indicate that the Ah
receptor is not involved in some immunotoxic responses. When TCDD was administered to
mice daily for 2 weeks, no differences in the inhibition effects of TCDD on antibody-forming
cells in the spleen were observed between sensitive and less sensitive strains of mice (Morris et
al., 1992). A simple interpretation of these results might be that molecules other than the Ah
receptor may mediate the effects of TCDD following multiple or chronic exposures. However,
the authors did observe differences in sensitivity between different control groups, and when the
data were expressed as a percentage of the appropriate control, strain differences in response to
TCDD were observed. It also seems likely that relative sensitivity to TCDD contributes to strain
differences in the pharmacokinetics of this chemical. TCDD and related chemicals have been
shown to induce expression of cytochrome P4501A2 (CYP1A2) in the liver, and the presence or
absence of this protein has been shown to influence tissue distribution of TCDD (Poland et al.,
1989; Kedderis et al., 1993; Diliberto et al., 1997). Thus, it is likely that, because of the
induction of CYP1A2 and greater sequestration of dioxin in the livers of responsive animals, less
is available to other tissues compared with less responsive animals. Data have been presented
that indicate this (Diliberto et al., 1999).
Several other studies performed in isolated cells might also be interpreted to indicate that
some of the immunotoxic effects of the dioxins are Ah receptor independent (Tucker et al., 1986;
Davis and Safe, 1991; Morris et al., 1991; Morris and Holsapple, 1991). However, these data
appear to conflict with results obtained in vivo and have been shown to be dependent on culture
conditions (Morris and Holsapple, 1991). Furthermore, actual cellular concentrations of
chemicals used in several of these in vitro studies were much higher than those known to occur
in vivo where immunotoxic effects are observed (Neumann et al., 1992).
Several biochemical responses have been shown to occur rapidly in isolated cells
following exposure to TCDD and related dioxins. These include increases in protein kinase and
phospholipase C activities, and affects on plasma membranes (Bombick et al., 1984; Bombick et
al., 1985; Beebe et al., 1990; Ma et al., 1992; Puga et al., 1992; Ashida et al., 2000b). Some of
these responses occur in cells lacking Arnt and in cells with highly reduced levels of Ah receptor
(Puga et al., 1992), implying either a non nuclear role of the Ah receptor in mediating these
events or an Ah receptor-independent process. Blankenship and Matsumura (1997) have shown
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that TCDD, directly or indirectly, activates protein tyrosine kinase activity in murine hepatic
cytosol, also suggesting non-nuclear activity of this chemical. In another recent study, TCDD
was shown to stimulate c-fos activity in CV-1 cells in which no Ah receptor could be detected by
immunoblot analysis or by the induction ofCYPlAl activity (Hoffer et al., 1996). However, this
study failed to completely rule out the presence of very low levels of receptor. Notably, certain
bone marrow stromal cell lines lack immunodetectable Ah receptor and fail to show induction of
CYP1A1 following treatment with TCDD. However, in these same cell lines, Ah receptor
mRNA transcripts could be detected and CYP1B1 mRNA was clearly inducible by TCDD (Lavin
etal., 1998).
Thus, at present the wealth of evidence available indicates that most, if not all, of the
biological and toxic effects of dioxins are mediated by the Ah receptor. Although the receptor
may be necessary for the occurrence of these events, clearly it is not sufficient because other
proteins and conditions are known to affect activity of the receptor and its ability to alter gene
expression. There is some evidence to support mechanisms involving pathways for Ah receptor
action that do not involve Arnt, although the exact steps involved in these pathways have yet to
be fully detailed. Certain studies could be interpreted to indicate Ah receptor-independent
mechanisms, although these have not clearly ruled out involvement of the Ah receptor. The only
consistent, but limited, evidence for dioxin-elicited effects that do not involve the Ah receptor
comes from studies using Ah receptor-deficient animals. Here however, observed minor effects
occurred only following treatment with extremely high doses of TCDD. Notably, these doses
are well beyond that which any humans are known to have been exposed.
2.8. FUTURE RESEARCH
The cloning of cDNAs encoding the Ah receptor and Arnt proteins, and the development
of anti-receptor and anti-Arnt antibodies, open the way for additional mechanistic studies of
dioxin action. It is now practical to analyze the structure and function of these proteins using
mutagenesis techniques, to analyze the structure and regulation of the corresponding genes, to
determine whether different forms of the receptor and Arnt exist, and to directly analyze the role
of posttranslational modifications on receptor and Arnt function. In vitro transcription can be
used to study the functional components of the dioxin-responsive pathway.
The observation that the Ah receptor and Arnt proteins heterodimerize via HLH motifs
raises the likelihood that they may also dimerize with other partners (perhaps in a tissue-specific
fashion) to generate different protein combinations that may have novel regulatory properties.
The types of protein complexes formed may depend on the relative amount of each potential
partner protein present in different cell types and at different times of tissue
development/differentiation, as well as on the stability of each type of heterodimer. This
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combinational mechanism for regulating dioxin responses may also allow for functional
interactions with other signal transduction pathways, increasing the potential diversity of dioxin-
induced responses even further (see, e.g., Pimental et al., 1993; Hogenesch et al., 1997; Caruso
et al., 1999; Chan et al., 1999; Nebert et al., 2000). These are promising areas for future
research, and are likely to provide novel insights into the mechanism of dioxin action, in
particular, and the regulation of mammalian gene transcription (especially by bHLH proteins), in
general. If such studies reveal new proteins (and corresponding genes) that influence rate-
limiting steps in the response to dioxin, the findings might also prove useful from a risk
assessment standpoint.
The teratogenic and tumor-promoting effects of TCDD, its effects on differentiation of a
variety of cell types, and results from Ah receptor deficient animals suggest that the Ah receptor
and other components of the dioxin-responsive system contribute to important developmental
and proliferative pathways. Further study of transgenic animals, in which both alleles for the Ah
receptor or the Arnt protein have been permanently or conditionally inactivated, will provide
new insights into such pathways. In addition, the use of transgenic animals in toxicological
studies might be helpful to assess what cellular components participate in the adverse responses
to particular chemicals.
Chromatin structure and nucleosome positioning have important effects on mammalian
gene expression (Grunstein, 1990; Felsenfeld, 1992; Kornberg and Lorch, 1992). The TCDD-
responsive CYP1A1 gene is an interesting model system that can be used to analyze the
mechanism by which a protein complex, such as the liganded receptor heteromer, can trigger
chromatin structural changes that increase DNA accessibility. Such studies may also provide
novel insights into the function of the dioxin-responsive system.
Some dioxin-responsive genes (e.g., CYP1A2, CYP1B1, glutathione S-transferase Yd)
exhibit substantial constitutive ("basal") transcription, which is increased further upon TCDD
treatment. The constitutive expression of these genes implies that the promoter must be
maintained in an accessible configuration even in the absence of dioxin; therefore, TCDD may
induce the transcription of such genes by a mechanism that does not involve major changes in
chromatin structure. As such, the liganded receptor heteromer may be able to increase gene
transcription by both chromatin-dependent and chromatin-independent mechanisms. This may
be an interesting area for future research.
The overall evidence indicates that the Ah receptor participates in every biological
response to TCDD (Poland and Knutson, 1982; Safe, 1986; Birnbaum, 1994). Thus, through its
interaction with Arnt and other transcription factors, TCDD likely activates transcription of other
genes via a receptor- and enhancer-dependent mechanism analogous to that described for the
CYP1A1 gene. For example, TCDD induces the expression of genes encoding CYP1A2,
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CYP1B1, glutathione S-transferase Ya subunit, aldehyde dehydrogenase, prostaglandin
endoperoxide H synthase 2, and quinone reductase. In some cases, induction has been shown to
occur at the transcriptional level, to be Ah receptor and Arnt dependent, and to involve a DNA
recognition sequence analogous to that found upstream of the CYP1A1 gene (Dunn et al., 1988;
Quattrochi and Tukey, 1989; Favreau and Pickett, 1991; Takimoto et al., 1992; Pimental et al,
1993; Rushmore and Pickett, 1993; Sutler et al., 1994; Kraemer et al., 1996). Other observations
reveal that TCDD activates the transcription of several other genes by unknown mechanisms
(see Denison et al., 1998). For dioxin-responsive genes other than CYPJAJ, and especially for
genes that respond in a tissue-specific fashion, the presence of the receptor/enhancer system may
not be sufficient for dioxin action. Rather, other tissue-specific regulatory components may play
a dominant role in governing the response to TCDD. For example, estrogens (presumably via
estrogen receptors) influence TCDD-induced liver neoplasia in rats (Lucier et al., 1991). In
addition, in vitro experiments suggest that, in some cell types, a represser protein inhibits the
response to TCDD by competing for the receptor binding site(s) on DNA (Gradin et al., 1993;
Walsh et al., 1996; Piechocki and Hines, 1998). Future research may reveal the existence of
additional stimulatory or inhibitory gene regulatory components that can modulate the activity of
the dioxin-responsive receptor/enhancer system. Further research is also needed to determine
whether Ah receptor-dependent modulation of gene expression is exclusively dependent on Arnt.
Other heterodimeric partners for the Ah receptor may be identified and these complexes may
recognize unique DNA response elements that are involved in the control of a variety of genes.
In addition, it is also possible that the Ah receptor controls certain activities, such as
phosphorylation/dephosphorylation, through pathways that do not directly involve nuclear
localization and transcriptional activation.
The potential teratogenic, developmental, and neoplastic effects of TCDD have raised
particular concerns about the human health effects of dioxin. In particular, experimental studies
in animals suggest that developing tissues are especially sensitive. These effects have been
characterized by altered cell proliferation, metaplasia, and modified terminal differentiation, and
can occur at dosages that have no overt toxicity to the pregnant dam (see Theobald and Peterson,
1994; Gray and Ostby, 1995; Roman et al., 1995). Concentrations of TCDD as low as 0.8 ng/g
in the murine embryonic palate have been shown to result in cleft palate (Abbott et al., 1996).
These responses may reflect complicated cascades of biochemical changes that are difficult to
analyze mechanistically; therefore, a major challenge for the future will be to establish
experimental systems in which such complex phenomena are amenable to study at the molecular
level.
The actual molecular mechanisms underlying differences in sensitivity between species,
tissues, and periods of development have yet to be determined. Indeed, this is a central issue for
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relating animal studies to human exposures and sensitivity. A major obstacle has been our lack
of understanding of the direct tissue and cell targets, critical periods of increased sensitivity, and
actual dose to the target cell type. Even with the same dosage per cell, it is not difficult to
envision that given the multitude of regulatory controls for any particular gene at a particular
period of cellular development, different dose-dependent response curves for these genes would
be observed. For example, rat UDP glucuronosyltramferase 1A6 gene is about 1000 times less
sensitive than the CYP1A1 gene to induction of transcription by dioxin (Vanden Heuvel et al.,
1994). Furthermore, considering the likely multitude of additional molecular events that
contribute to the ultimate biological/toxic response following initial gene modulation, it is also
not difficult to envision that different endpoints might have very different sensitivities, even
though binding to the same receptor is the common initial trigger. Considering this complexity,
it remains a particular challenge to develop appropriate model systems that accurately mimic the
in vivo effects associated with exposure. It is also important to consider that a particular
response to TCDD may be mediated through effects on multiple cells. Evidence was recently
presented to indicate that Ah receptor activation in both hepatic parenchymal and hematopoietic
cells contributes to the hepatic lesions induced by TCDD in mice (Thurmond et al., 1999).
Clearly, most data are consistent with the belief that the Ah receptor has some normal
cellular function. We can now appreciate that the Ah receptor is a member of a family of
proteins that are conserved through evolution and involved in growth and differentiation
processes. The expression and activity of the Ah receptor appear to be well regulated in a
differentiation and cell cycle stage-specific manner. Furthermore, we now know that the genes
regulated by the receptor are involved in not only xenobiotic metabolism, but also in cell growth
and differentiation processes. Further identification of other Ah receptor-regulated genes and
their function, as well as factors that may control the ability of the receptor to regulate these in a
cell-specific manner, will undoubtedly assist in our understanding of its normal function and
how aberrant function may lead to toxicity. For example, recognition that the Ah receptor
influences cell cycle regulation, and a further dissection of the pathways involved, will likely
help us understand how dioxins affect developmental and neoplastic processes. In addition,
further research to characterize and identify possible endogenous ligands for the receptor, and its
role in regulating cellular processes, will have a great impact in these areas. It seems plausible
that the spectrum of genes regulated by the receptor may mediate both endogenous ligand
function and prevention of its inappropriate action through metabolism (Nebert et al., 2000).
2.9. MECHANISTIC INFORMATION AND RISK ASSESSMENT
A substantial body of evidence from investigations using experimental animals indicates
that the Ah receptor mediates the biological effects of TCDD. Although studies using human
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tissues are much less extensive, it appears reasonable to assume that dioxin's effects in humans
are also receptor mediated. Studies using human organs and cells in culture are consistent with
this hypothesis. A receptor-based mechanism would predict that, except in cases where the
concentration of TCDD is already high (i.e., [TCDD]~KD), incremental exposure to TCDD will
lead to some increase in the fraction of Ah receptors occupied. However, it cannot be assumed
that an increase in receptor occupancy will necessarily elicit a proportional increase in all
biological response(s), because numerous molecular events (e.g., cofactors, other transcription
factors, genes) contributing to the biological endpoint are integrated into the overall response.
That is, the final biological response should be considered as an integration of a series of dose-
response curves with each curve dependent on the molecular dosimetry for each particular step.
Dose-response relationships that will be specific for each endpoint must be considered when
using mathematical models to estimate the risk associated with exposure to TCDD. It remains a
challenge to develop models that incorporate all the complexities associated with each biological
response. Furthermore, the parameters for each mathematical model may only apply to a single
biological response within a given tissue and species.
Given TCDD's widespread distribution, its persistence, and its accumulation within the
food chain, it is likely that most humans are exposed to some level of dioxin; thus, the
population at potential risk is large and genetically heterogeneous. By analogy with the findings
in inbred mice, polymorphisms in the Ah receptor probably exist in humans. Therefore, a
concentration of TCDD that elicits a particular response in one individual may not do so in
another. For example, studies of humans exposed to dioxin following an industrial accident at
Seveso, Italy, fail to reveal a simple and direct relationship between blood TCDD levels and
development of chloracne (Mocarelli et al., 1991). These differences in responsiveness to
TCDD may reflect genetic variation either in the Ah receptor or in some other component of the
dioxin-responsive pathway. Therefore, analyses of human polymorphisms in the Ah receptor
and Arnt genes have the potential to identify genotypes associated with higher (or lower)
sensitivities to dioxin-related effects. Such molecular genetic information may be useful in the
future for accurately predicting the health risks dioxin poses to humans.
Complex responses (such as cancer) probably involve multiple events and multiple
genes. For example, a homozygous recessive mutation at the hr (hairless) locus is required for
TCDD's action as a tumor promoter in mouse skin (Poland et al., 1982). Thus, the hr locus
influences the susceptibility of a particular tissue (in this case skin) to a specific effect of dioxin
(tumor promotion). An analogous relationship may exist for the effects of TCDD in other
tissues. For example, TCDD may produce porphyria cutanea tarda only in individuals with
inherited uroporphyrinogen decarboxylase deficiency (Doss et al., 1984). Such findings suggest
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that, for some adverse effects of TCDD, the population at risk may be limited to individuals with
a particular genetic predisposition.
Other factors can influence an organism's susceptibility to TCDD. For example, female
rats are more prone to TCDD-induced liver neoplasms than are males; this phenomenon is
related to the hormonal status of the animals (Lucier et al., 1991). In addition, hydrocortisone
and TCDD synergize in producing cleft palate in mice. Retinoic acid and TCDD produce a
similar synergistic teratogenic effect (Couture et al., 1990). These findings indicate that, in some
cases, TCDD acts in combination with hormones or other chemicals to produce adverse effects.
Such phenomena might also occur in humans. If so, the difficulty in assessing risk is increased,
given the diversity among humans in hormonal status, lifestyle (e.g., smoking, diet), and
chemical exposure. For example, several compounds present in the diet have been found to
inhibit activation of the AhR induced by TCDD (Ashida et al., 2000a).
Dioxin's action as a tumor promoter and developmental toxicant presumably reflects its
ability to alter cell proliferation and differentiation processes. There are several plausible
mechanisms by which this could occur. First, TCDD might activate a gene (or genes) that is
directly involved in tissue proliferation. Second, TCDD-induced changes in hormone
metabolism may lead to tissue proliferation (or lack thereof) and altered differentiation
secondary to altered secretion of a trophic hormone. Third, TCDD-induced changes in the
expression of growth factor or hormone receptors may alter the sensitivity of a tissue to
proliferative stimuli. Fourth, TCDD-induced toxicity may lead to cell death, followed by
regenerative proliferation. These mechanisms likely differ among tissues and periods of
development, and might be modulated by different genetic and environmental factors. As such,
this complexity increases the difficulty associated with assessing the human health risks form
dioxin exposure.
Under certain circumstances, exposure to TCDD may elicit beneficial effects. For
example, TCDD protects against the carcinogenic effects of PAHs in mouse skin, possibly
reflecting induction of detoxifying enzymes (Cohen et al., 1979; DiGiovanni et al., 1980). In
other situations, TCDD-induced changes in estrogen metabolism may alter the growth of
hormone-dependent tumor cells, producing a potential anticarcinogenic effect (Spink et al.,
1990; Gierthy et al., 1993). However, several recent studies in mice indicate that the Ah receptor
has an important role in the genetic damage and carcinogenesis caused by components in
tobacco smoke such as benzo[a]pyrene through its ability to regulate CYP1A1 gene induction
(Dertinger et al., 1998, 2000; Shimizu et al., 2000). These issues complicate the risk assessment
process for dioxin.
TCDD's biological effects likely reflect a complicated interplay between genetic and
environmental factors. Thus, it may be overly simplistic to use mechanistic information derived
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largely from the relatively simple responses described in this chapter (i.e., CYP1A1 induction) as
the basis for developing a quantitative approach to dioxin risk assessment in humans. While this
approach represents a start toward incorporating mechanistic information into risk assessment,
future biologically based dose-response models will require a better understanding not only of
the TCDD-induced biochemical alterations that produce disease, but also of the relationships
between genetic and environmental factors that influence an individual's susceptibility to TCDD.
Molecular toxicology and mechanistic studies have great potential to provide new insights into
such issues in the future.
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Table 2-1. Events in the activation of CYP1A1 gene transcription by dioxin
>D Diffusion into the cell
• D Binding to the Ah receptor protein
• D Conversion of liganded receptor to the DNA-binding form
>D Dissociation from hsp90
• D Active translocation from cytoplasm to nucleus
>D Association with Arnt protein
> D Binding of liganded receptor heteromer to enhancer DNA
• D Enhancer activation
>D Altered DNA configuration
• D Histone modification
• D Recruitment of additional proteins
>D Nucleosome disruption
• D Increased accessibility of transcriptional promoter
>D Binding of transcription factors to promoter
>D Enhanced mRNA and protein synthesis
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o
O
to
O
O
to
I
OJ
o
O
O
*
O
H
O
HH
H
W
O
?d
O
c
o
H
W
Cl
2,3,7,8-Tetrachlorodibenzo-p-dioxin
Polychlorinated
dibenzopara-dioxins
75 congeners
Cl
Cl
2,3,7,8-Tetrachlorodibenzofuran
Polychlorinated
dibenzofurans
135 congeners
Cl
Cl
Cl
Cl/ \CI
S^'^'^S'-Hexachlorobiphenyl
Figure 2-1. Chemical structure of dioxin and smiliar compounds.
-------
o
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to
O
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&
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hsp90
TCDD ci
p h osjJtrec^hat i o n/
dephosphor^tetion
hsp90
Proteins
TOXIC ^(CYP1A1,
Response
and others)
Q,
mRNA
Figure 2-2. Cellular Mechanisms for Ah Receptor Action
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Waller, C.L.; McKinney, J.D. (1995) Three-dimensional quantitative structure-activity relationships of dioxins and
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Walsh, A.A.; Tullis, K.; Rice, R.H.; Denison, M.S. (1996) Identification of a novel cis-acting negative regulatory
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Wanner, R.; Brommer, S.; Czarnetzki, B.; Rosenbach, T. (1995) The differentiation-related upregulation of aryl
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Watson, A.J.; Hankinson, O. (1988) DNA transfection of a gene repressing aryl hydrocarbon hydroxylase induction.
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3. ACUTE, SUBCHRONIC, AND CHRONIC TOXICITY
3.1. SCOPE AND LIMITATIONS
The acute, subchronic, and chronic toxicology of the chlorinated dioxins, dibenzofurans,
biphenyls, and related compounds have been reviewed extensively in recent years. This chapter
summarizes knowledge on the toxicology of tetrachlorodibenzo-p-dioxin (TCDD), but also
includes references to other dioxin-like compounds when relevant data are available. Included
are selected various data that are considered to be of importance to risk assessment, particularly
experimental animal data. Immunotoxicity, reproductive/developmental toxicity,
carcinogenicity, toxicity to humans, and epidemiology are all covered in other chapters.
Ecotoxicology is not covered in this chapter, but examples from mammalian and avian
laboratory species are included.
3.2. ACUTE TOXICITY
The range of doses of TCDD that are lethal to animals varies extensively with both
species and strain, as well as with sex, age, and the route of administration within a single strain
(Table 3-1). One of the characteristics of TCDD-induced toxicity is delayed manifestation of
lethality after acute exposure, with the time to death after exposure being several weeks. Death
usually occurs as a consequence of loss of body weight (wasting syndrome) from TCDD-induced
inhibition of gluconeogenesis and appetite suppression. Deaths within the first week after
exposure—an unusually rapid course for TCDD toxicity—have been observed in guinea pigs
(Schwetz et al., 1973), rabbits (Schwetz et al., 1973), and Syrian Golden hamsters (Olson et al.,
1980). A more than 8,000-fold difference exists between the dose of TCDD reported to cause
50% lethality (LD50) in male Hartley guinea pigs, the most sensitive species tested (Schwetz et
al., 1973), and the LD50 dose in male Syrian Golden hamsters (Henck et al., 1981). Another
animal with extremely high sensitivity is the mink (Mustela vision); for the female, the
calculated 28-day LC50 value is 0.264 |ig TCDD/kg bw/day (Hochstein et al., 1998), which is an
order of magnitude less than the 28-day LD 50 of 4.2 jig TCDD/kg bw/day for male mink
(Hochstein et al., 1988).
The rat seems to be the third most sensitive species among experimental animals,
although there is a >300-fold variability in LD50 values among different strains. The Han/Wistar
(H/W) Kuopio strain of rat has been shown to be particularly resistant to TCDD exposure
(Pohjanvirta and Tuomisto, 1987). Among the five-rats-per-dose group (0, 1,500, 2,000, 2,500,
or 3,000 jig TCDD/kg bw), only one animal died within the 40-day observation period. The
DBA/2 male mouse has also been shown to have a high resistance to TCDD toxicity (Chapman
and Schiller, 1985).
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Data on gender differences in sensitivity to the lethal effects of TCDD are conflicting.
The gender differences in the acute toxicity of TCDD are likely due to differences in
toxicokinetics, i.e., higher tissue concentrations and longer half-life in females than in males (Li
et al., 1995). Acute toxicity data that address the effect of age at the time of exposure to TCDD
are scarce, and comparisons are hampered by either the absence or inadequacy of information on
the age and body weight of the tested animals. As demonstrated with other chemicals, the acute
toxicity of TCDD may vary several-fold depending on the vehicle used or the presence of other
substances that affect uptake.
Differences in sensitivity toward TCDD among various strains of mice have been shown
to depend on a genetic variability in the Ah locus (see Chapter 2). In two strains of male
C57B/6J mice that differ only at the Ah locus, Birnbaum et al. (1990) found LD50 values of 159
and 3,351 |ig/kg for wild-type mice (Ahb/b) and congenic mice (Ahd/d), respectively. The mean
time to death, 22 days, was independent of dose and genotype. Signs of toxicity were similar in
the two strains, and it was concluded that the spectrum of toxicity is independent of the allele at
the Ah locus. The relative dose needed to bring about various acute responses, however, is
-8-24 times greater in congenic mice homozygous for the "d" allele than in the wild-type mice
carrying two copies of the "b" gene.
The DBA/2 mouse strain requires 10 to 20 times higher doses of 2,3,7,8-TCDD than does
the C57BL/6 strain for lethality (Chapman and Schiller, 1985). The reason for this difference
between the two strains is the low TCDD-binding affinity to the Ah receptor in the DBA/2 strain
(Okey et al., 1994). The difference in ligand-binding affinity, associated with susceptibility to
TCDD-induced lethality that segregates with the Ah locus (Chapman and Schiller, 1985), is due
to a point mutation (translated as alanine to valine) in the ligand-binding domain of codon 375
(Poland et al., 1994; Ema et al., 1994).
Wasting, hemorrhage, and anemia are the three primary causes for dioxin-induced
lethality in rats, and a body weight loss of 25% is considered to be the minimum threshold to
assign the presence of wasting syndrome for rats (Viluksela et al., 1997a,b, 1998).
1,2,3,4,5,6,7,8-HeptaCDD (HpCDD)-induced dose-response for wasting and hemorrhage
overlap in female Sprague-Dawley rats (Rozman, 1999). Death from wasting and hemorrhage
occur within the first few weeks of exposure. Animals that did not exhibit wasting or
hemorrhage died from anemia, which did not start before day 126 postexposure (Rozman, 1999).
Furthermore, unlike rats dying of wasting syndrome, the ones dying of anemia or hemorrhage
had fat depots in the body, suggesting that increased body fat may aid them in surviving beyond
the 30-day mark (Rozman, 1999), only to succumb later to hemorrhage and anemia.
Long-Evans (L-E) Turku AB strain rats are around 1000-fold more sensitive to TCDD-
induced acute lethality (LD50 about 10 |ig/kg) than H/W Kuopio strain rats (LD50 > 9,600 |ig/kg)
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(Pohjanvirta et al., 1999). This feature of the H/W rat being highly resistant to acute TCDD
toxicity, yet sensitive to enzyme induction, may be due in part to differences in AhR types
between rat strains. The H/W strain has point mutations at exon 10 and at the first invariant
nucleotide at the 5' end of intron 10 in the AhR gene. These cause alterations in AhR protein
structure, leading to loss and alteration of multiple amino acid sequences at the carboxyl terminal
region of the transactivation domain (Pohjanvirta et al., 1998). The homozygous AhR hw/hw type
fails to mediate some endpoints of TCDD toxicity that parallel lethality. At lethal doses, H/W
rats show only slight changes in bilirubin and body weight, while L-E rats show a five-fold
increase in bilirubin and a 20% to 30% decrease in body weight as early as 6 days postexposure
(Unkila et al., 1994a). Furthermore, at lethal doses H/W rats manifest only slight or transient
inhibition of daily food intake and body weight gain, whereas in L-E rats progressive decrease in
daily feed intake and body weight gain occur within 4 to 7 days postexposure (Unkila et al.,
1994a).
Tuomisto et al. (1999) suggested that an uncharacterized gene, other than AhR,
determines resistance of H/W Kuopio rats to TCDD-induced acute toxicity.
Geyer et al. (1990) utilized both their own and other data to determine a correlation
between total body fat content and acute toxicity in various species and strains of laboratory
mammals. They found a correlation of 0.834, and suggested that the reason for this correlation
was that an increased total body fat content (TBF) may enhance the capacity to remove TCDD
from the systemic circulation. This factor may be important, but it almost certainly does not
explain all of the interspecies differences. Geyer et al. (1997) have determined that there is a
linear relationship in mammals independent of strain and species between the logarithm of the
oral 30-day LD50 in units of |ig/kg bw and the mammal's TBF in percent via the regression
equation:
log LD50 = 5.30 x log TBF - 3.22
Data from studies of H/W Kuopio rats, which are extremely resistant to TCDD-induced lethality
(Pohjanvirta and Tuomisto, 1987), were not included in this equation.
In chickens, acute toxicity is characterized by clinical signs such as dyspnea, reduced
body weight gain, stunted growth, subcutaneous edema, pallor, and sudden death (chick edema
disease). The disease first gained attention in 1957, but the causal agents were not identified as
CDDs until much later (Firestone, 1973). Chick edema occurred in birds given oral doses of 1 or
10 jig TCDD/kg/day or 10 or 100 jig hexaCDD/kg/day, but it was not observed in chicks
maintained on a diet containing 0.1% or 0.5% OCDD (Schwetz et al., 1973).
The female mink (Mustela vision) is more sensitive than the male mink to TCDD-
induced lethality. Hochstein et al. (1998) fed 2- or 3-year-old adult female mink diets
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supplemented with 0, 0.001, 0.01, 0.1, 1, 10, or 100 ppb TCDD for up to 125 days. Feed
consumption was significantly depressed in the 10 and 100 ppb groups beginning in weeks 4 and
3, respectively. When adjusted for body weight (g food intake/100 g bw/day), the feed intake in
TCDD-exposed groups was not significantly different from the control, except in the 10 ppb-
dosed group during week 5. Significant body weight loss associated with classic symptoms of
wasting syndrome resulting in mortality was observed in the 1,10, and 100 ppb-dosed groups,
respectively, from the third, second, and first week of exposure. Mortality reached 12.5%,
62.5% and 100% by day 28 in the 1, 10, and 100 ppb-dosed groups, respectively. By day 125,
mortality increased to 62.5% and 100% in the 1 and 10 ppb groups. Based on the average feed
intake of 5.5 g/100 g bw/day for the control mink, the dietary LC50 values of 4.8 and 0.85 ppb
approximate 0.264 and 0.047 jig TCDD/kg bw/day, respectively, for 28 and 125 days of
exposure.
3.2.1. Signs and Symptoms of Toxicity
TCDD affects a variety of organ systems in different species. It should be noted that
much of the comparative database is derived from high-dose effects. The liver is the organ
primarily affected in rodents and rabbits, while atrophy of the thymus and lymphatic tissues
seems to be the most sensitive marker of toxicity in guinea pigs (WHO/IPCS, 1989; U.S. EPA,
1984, 1985). It is not possible to specify a single organ whose dysfunction accounts for lethality.
Dermal effects are prominent signs of toxicity in nonhuman primates, and changes in epithelial
tissues dominate both cutaneously and internally. This is most apparent in nonhuman primates
in which the TCDD-induced cutaneous lesions closely mimic the chloracne and hyperkeratosis
observed in humans. The histopathological alterations observed in epithelial tissues include
hyperplastic and/or metaplastic alterations, as well as hypoplastic responses. The toxic
responses of various species to TCDD are summarized in Table 3-2.
Loss of body weight, or wasting syndrome, is a characteristic sign observed in most
animals exposed to TCDD. The weight loss usually manifests itself within a few days after
exposure, and results in a substantial reduction of the adipose (Peterson et al., 1984) and muscle
tissue (Max and Silbergeld, 1987) observed at autopsy. With sublethal doses of TCDD, a dose-
dependent decrease in body weight gain occurs.
The greatest species-specific differences in toxicity concern pathological alterations in
the liver. Administering lethal doses to guinea pigs does not result in liver damage comparable
to the liver lesions observed in rabbits and rats, or to the liver changes observed in mice
(McConnell et al., 1978a; Moore et al., 1979; Turner and Collins, 1983). In the hamster,
manifest liver lesions do not occur even after fatal doses of TCDD; however, the ED50 for
increased hepatic weight is only -15 |ig/kg (Gasiewicz et al., 1986). Liver-related enzyme
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activities in serum are elevated in those animal species where liver damage is a prominent sign
of TCDD toxicity. In animal species where hepatotoxicity is not as apparent, such as monkeys
and guinea pigs, these enzyme activities are nearly normal.
Thymic atrophy has also been found in all animal species given lethal doses of TCDD.
Treatment with TCDD inhibits bone marrow hematopoiesis in mice, both in vivo and in vitro, by
directly altering the colony growth efficiency of stem cells (Chastain and Pazdernik, 1985;
Luster etal., 1980, 1985).
Among other signs and symptoms that have been demonstrated in various species, the
following should be noted: hepatic porphyria, hemorrhages in various organs, testicular atrophy,
reduced prostate weight, reduced uterine weight, increased thyroid weight, lesions of the adrenal
glands, inhibited bone marrow hematopoiesis, decreased serum albumin, and increased serum
triglycerides and free fatty acids. The details of all underlying studies for these observations
have been extensively reviewed (U.S. EPA, 1984, 1985; WHO/IPCS, 1989).
Effects on heart muscle have also been observed in guinea pigs and rats (Brewster et al.,
1987; Kelling et al., 1987; Canga et al., 1988). Five days after a single lethal dose of TCDD (10
jig/kg intraperitoneally) was administered, a significantly decreased beta-adrenergic
responsiveness was observed in the right ventricular papillary muscle of the guinea pig (Canga et
al., 1988). In the TCDD-treated animals, a decrease in the positive inotropic effects of
isoproterenol at 0.03-0.3 jiM, but not at 0.1-10 nM, was also demonstrated. Additionally,
enhanced responsiveness to low-frequency stimulation and increases in extracellular calcium
were observed in these animals. Based on these findings, the authors suggest that the heart may
be a major target for TCDD lethality at acutely toxic doses.
In the monkey, several additional symptoms have been registered, such as periorbital
edema, conjunctivitis, and thickening of the meibomian glands followed by loss of the eyelashes,
facial hair, and nails (McConnell et al., 1978b). These symptoms are similar to those observed
in cases of human intoxication, such as from occupational exposure, the Seveso incident, and the
Yusho and Yu-Cheng toxic oil intoxications, the latter involving exposure to PCBs and CDFs
(see Chapter 7).
3.2.2. Studies In Vitro
Over 30 cell types, including primary cultures and cells from established and transformed
cell lines derived from various tissues of at least six animal species, have been examined for
their general cellular responses to TCDD (Beatty et al., 1975; Knutson and Poland, 1980a; Niwa
et al., 1975; Yang et al., 1983a). The effects studied were changes in viability, growth rate, and
morphology. Overall, there were few effects documented on these general cellular parameters in
early studies.
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Other in vitro studies, using more specific endpoints of toxicity, have clearly indicated
effects of TCDD at comparatively low concentrations. For example, several studies have shown
that TCDD affects cultured epidermal keratinocytes through interactions with differentiation
mechanisms, and that this effect may be regulated by the modulation of epidermal growth factor
(EOF) binding to the cells (Hudson et al., 1986). Additionally, in epithelial cells of human
origin, TCDD has been shown to alter differentiation (Hudson et al., 1985), while aryl
hydrocarbon hydroxylase (AHH) and 7-ethoxyresorufm O-deethylase (EROD) activity have
been induced in vitro (see Section 3.5.4).
TCDD was found to inhibit high-density growth arrest in human squamous carcinoma
cells in culture (Hebert et al., 1990a). Wiebel et al. (1991) identified a cell line (H4IIEC3-
derived 5L hepatoma cells) that responds with decreased proliferation at low TCDD
concentrations. In this cell line, half-maximum inhibition of proliferation occurs at a
concentration of 0.1-0.3 nM. The onset of the effect is fairly rapid, manifesting itself as early as
4-8 hours after treatment. Further studies demonstrated that insensitive variants of this cell line
were deficient in cytochrome P-4501A1 activity and lacked measurable amounts of the Ah
receptor (Gottlicher et al., 1990). In addition, 3,3',4,4'-TCB inhibited proliferation in the
sensitive cell line, although at higher concentrations.
3.2.3. Appraisal
Numerous studies of acute toxicity in various mammalian species have demonstrated
dramatic species- and strain-specific differences in sensitivity. However, most species and
strains respond at some level with a spectrum of symptoms that is generally the same, although
species differences do exist.
Lethality is typically delayed by several weeks, and there is a pronounced wasting
syndrome in almost all laboratory animals. Studies in congenic mice differing in their Ah
responsiveness indicate that the sensitivity to acute toxicity of TCDD segregates with the Ah
locus. Furthermore, studies of other CDDs, CDFs, and coplanar PCBs demonstrate that the
potency for inducing lethality correlates with their ability to bind to the Ah receptor. In contrast,
studies in various other species, including various strains of rats, have demonstrated a wide range
of sensitivities regardless of rather comparable levels of the Ah receptor. This in no way
obviates the necessary, but not sufficient, role of AhR.
3.3. SUBCHRONIC TOXICITY
Available studies on the subchronic toxicity of TCDD have been reviewed by the U.S.
EPA (1984, 1985) and WHO/IPCS (1989). Overall, the signs and symptoms observed are in
agreement with those observed after administration of single doses.
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The study of Kociba et al. (1976) is of special interest, as it has been used for
comparisons of the relative toxicities of other CDDs and CDFs (Pliiess et al., 1988a,b). Adult
male and female SD rats, in groups of 12, were given 0, 0.001, 0.01, 0.1, and 1.0 jig TCDD/kg
bw by gavage 5 days/week for 13 weeks. At the end of the treatment period, five rats of each
sex were sacrificed for histopathological examination. The remaining animals were observed for
postexposure effects. The highest dose caused five deaths among the females, three during the
treatment period and two after, while two deaths occurred in males in the posttreatment period.
The rats given 0.01 jig TCDD/kg did not differ overtly from the controls except for a slight
increase in the mean liver-to-body weight ratio.
A 13-week dietary study of SD rats given 1,2,3,4,8-PeCDF, 1,2,3,7,8-PeCDF, 2,3,4,7,8-
PeCDF, or 1,2,3,6,7,8-HxCDF demonstrated that both subchronic toxicity and the depletion of
hepatic vitamin A followed the rank order of the ability of the compounds to bind to the Ah
receptor and to cause induction of AHH (Pliiess et al., 1988a,b; Hakansson et al., 1990). Direct
comparisons of the effects are hampered, however, by differences in the toxicokinetic behavior
of the compounds. Slightly different relationships with regard to toxicity were observed in a
tumor promotion study, where an initial loading dose (subcutaneous) of 2,3,4,7,8-PeCDF was
given, followed by repeated lower doses (subcutaneous), in order to obtain a steady-state
concentration (Waern et al., 1991a). Both of these studies support the assumption that most signs
and symptoms obtained may be mediated through the Ah receptor.
In another study primarily aimed at investigating TCDD-induced porphyria (Goldstein et
al., 1982), groups of eight female SD rats were exposed to 16 weekly oral doses of 0, 0.01, 0.1,
1.0, and 10.0 jig TCDD/kg bw. The animals were killed and studied 1 week after the last
treatment. Additional groups of rats received doses of 0 or 1.0 jig/kg/week for 16 weeks and
were allowed to recover for 6 months. The high-dose level was lethal to all animals within
12 weeks, while the only other gross sign of toxicity was a decrease in body weight gain in the
group receiving 1.0 |ig/kg/week. After 16 weeks of exposure to TCDD, liver porphyrins were
elevated ~1,000-fold in 7 of 8 animals receiving 1.0 |ig/kg/week. Only 1 of 8 animals in the
0.1 |ig/kg/week group had elevated porphyrin levels. The no-effect dose for porphyria was
0.01 |ig/kg/week. After a 6-month recovery period, the porphyrin level in animals exposed to
1.0 |ig/kg/week was still 100-fold higher than the values in the control group. A similar pattern
was observed for urinary excretion of uroporphyrin. A 6-month recovery period was not
sufficient for complete reversal of TCDD-induced porphyria.
Two studies were conducted (Harris et al., 1973; Vos et al., 1973) in which four weekly
oral doses of 0.2, 1, 5, or 25 jig TCDD/kg bw were given to male C57B1/6 mice in corn oil. No
effects were noted at 1 |ig/kg/week, which corresponds to -0.1 |ig/kg bw/day. In a subchronic
exposure study, van Birgelen et al. (1996a) observed a synergistic effect of PCBs and TCDD on
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hepatic porphyrin in rats at levels comparable with those found in human milk and fat samples.
Coadministration of TCDD with 2,2',4,4',5,5'-PCB (PCB 153) resulted in elevated hepatic
porphyrin levels not observed in TCDD cotreated with 3,3',4,4',5-PCB (PCB 126) or 2,3,3',4,4',5-
PCB (PCB 156) groups. In this experiment, LOAELs for hepatic porphyrin accumulation for
TCDD, PCB 126, and PCB 156 were found to be 0.047, 3.18, and 365 (ig/kg/d, respectively, van
Birgelen et al. (1996b) have further extended the observation on hepatic porphyrin activity in
female B6C3F1 mice after subchronic exposure to individual PCDD, PCDF, and PCB
congeners. A dose-response relationship with potencies, relative to TCDD, for increased hepatic
porphyrin accumulation was observed for all of the individual congeners studied. The relative
potencies of PCDDs and PCDFs tested, based on hepatic porphyrin and enzymatic activities
associated with hepatic CYP1 Al and CYP1A2, were found to be in a comparable range.
A 90-day TCDD feeding study of male and female Hartley guinea pigs was performed by
DeCaprio et al. (1986), in which surviving animals were subjected to extensive pathologic,
hematologic, and serum chemical analyses. The diets contained 0, 2, 10, 76, or 430 ng
TCDD/kg bw. The two lowest doses, 2 and 10 ng/kg, produced no dose-related alterations.
Based on this study, a no-observed-adverse-effect level (NOAEL) of 0.6 ng TCDD/kg bw/day in
guinea pigs was estimated. At the highest dose, severe body weight losses and mortality were
observed. No dose-related mortality occurred at 76 ng/kg.
A cumulative dose of 0.2 jig TCDD/kg bw, which was divided into nine oral doses
3 times/week during days 20-40 of gestation, produced no clinical signs of toxicity in pregnant
rhesus monkeys (Macaca mulatta) (McNulty, 1984). Signs of toxicity such as body weight loss,
epidermal changes, and anemia did occur, however, in monkeys that received cumulative doses
of 1.0 and 5.0 jig TCDD/kg bw over the same time period.
3.3.1. Appraisal
Utilizing the above data, subchronic no-observable-adverse-effect levels (NOAELs) for
rats, mice, and guinea pigs are estimated to be 1 ng, 100 ng, and 0.6 ng TCDD/kg bw/day,
respectively. These studies cannot be directly compared with each other, however, and these
subchronic NOAELs cannot be used for extrapolating human risk. None of the studies utilized
initial loading doses and, due to the long half-life of TCDD, steady-states may not have been
reached in the animals except toward the end of the study periods. Distribution between tissues
in the animals depends on both time of exposure and dose level (see Chapter 1), which further
complicates any comparisons.
In spite of this, the limited data available seem to indicate that signs and symptoms of
subchronic toxicity follow the same rank order as Ah receptor-mediated effects, such as
induction of AHH.
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3.4. CHRONIC TOXICITY
The results of chronic toxicity studies performed on laboratory animals exposed to
TCDD are summarized in Table 3-3. Details have been reviewed by the U.S. EPA (1984, 1985)
and WHO/IPCS (1989).
The most important study in rats is the chronic toxicity study of Kociba et al. (1978,
1979). Groups of 50 male and 50 female SD rats were fed diets providing daily doses of 0.001,
0.01, and 0.1 jig TCDD/kg bw for 2 years. Control rats, 86 males and 86 females, received diets
containing the vehicle alone. Increased mortality was observed in females given 0.1 jig/kg/day,
while increased mortality was not observed in male rats at this dose or in animals receiving doses
of 0.01 or 0.001 ng/kg/day. From month 6 to the end of the study, the mean body weights of
males and females decreased at the highest dose and, to a lesser degree, in females given 0.01
|ig/kg/day. During the middle of the study, lower-than-normal body weights were also
occasionally recorded in the low-dose group, although during the last quarter of the study the
body weights were comparable with those of the controls.
Increased urinary coproporphyrin and uroporphyrin were noted in female rats, but not in
males, given TCDD at a dose rate of 0.01 and 0.1 jig/kg/day. Analyses of blood serum collected
at terminal necropsy revealed increased enzyme activities related to impaired liver function in
female rats given 0.1 jig TCDD/kg/day. Necropsy examination of the rats surviving TCDD
exposure until the end of the study revealed that effects in the liver constituted the most
consistent alteration in both males and females. Histopathological examination revealed
multiple degenerative, inflammatory, and necrotic changes in the liver that were more extensive
in females. Multinucleated hepatocytes and bile-duct hyperplasia were also noted. Liver
damage was dose related, and no effect was observed at the low-dose rate. The NOAEL was
estimated to be 0.001 jig/kg/day. At the end of the study, the fat and liver concentration of
TCDD at this dose was 540 ppt.
In male Swiss mice, weekly oral doses of 0, 0.007, 0.7, and 7.0 jig TCDD/kg bw for
1 year resulted in amyloidosis and dermatitis (Toth et al., 1979). The incidence of these lesions
was 0 of 38, 5 of 44, 10 of 44, and 17 of 43 in the control-, low-, medium-, and high-dose
groups, respectively. The LOAEL in this study was estimated to be 0.001 jig/kg/day
(=1 ng/kg/day).
In the National Toxicology Program (NTP, 1982) gavage study of B6C3F1 male and
female mice, no adverse effects were seen at the lowest dose tested (0.01 and 0.04 |ig/kg
bw/week for males and females, respectively; corresponding to ~ 1.4 and 6 ng/kg bw/day).
The limited studies (9-20 months) available in rhesus monkeys (Allen et al., 1977;
Barsotti et al., 1979; Schantz et al., 1979) revealed signs and symptoms similar to those recorded
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in more short-term studies. Adverse effects were noted down to the lowest dose tested (-2-3
ng/kg bw/day for 20 months) (Schantz et al., 1979).
3.4.1. Appraisal
From different long-term studies on TCDD, it can be estimated that the NOAEL for the
rat is 1 ng/kg bw/day, corresponding to a fat and liver concentration (NOAEL) of 540 ppt. For
the male Swiss mouse, dermatitis and amyloidosis in 5 of 44 animals were noted at the lowest
dose tested (the LOAEL was 1 ng/kg bw/day). NOAELs of 1.4 and 6 ng/kg/day were obtained
for male and female B6C3F1 mice, respectively. The reported studies on rhesus monkeys are
problematic for use in such a determination, because adverse effects were observed at the lowest
dose tested, -2-3 ng/kg bw/day.
3.5. SPECIFIC EFFECTS
3.5.1. Wasting Syndrome
TCDD at high doses (lethal or near lethal) causes a starvation-like effect, or wasting
syndrome, in several animal species. In young animals, or following a sublethal dose to adults,
this response is manifested as a cessation of weight gain. Animals exposed to near lethal or
higher doses characteristically lose weight rapidly. Numerous studies utilizing pair-feeding,
total parenteral nutrition, and everted intestinal sacs have been performed to elucidate the
mechanisms behind the wasting syndrome (U.S. EPA, 1984, 1985; WHO/IPCS, 1989), but no
single explanation has been obtained thus far. No generalized impairment of intestinal
absorption seems to occur.
Peterson et al. (1984) conducted behavior experiments and suggested a model for the
TCDD-induced wasting syndrome that is based on the hypothesis, advocated by Keesey and
Powley (1975, 1986), that body weight in rats is regulated to an internal standard or
hypothalamically programmed set-point. According to this hypothesis, the body weight at a
given age is constantly being compared to this set-point value and, if differences occur, feed
consumption is adjusted. When TCDD lowers this set-point, reduction in food consumption
results as the rat attempts to reduce its weight to a new lower level. This hypothesis has been
tested in several experiments under carefully controlled feeding conditions. Repeated studies
have demonstrated that reduction of feed intake due to increased food spillage is not sufficient to
account for the loss of body weight in TCDD-treated SD rats. Additionally, TCDD-treated rats
maintain and defend their reduced weight level with the same precision that ad libitum-fed
control rats defend their normal weight level (Seefeld and Peterson, 1983, 1984; Seefeld et al.,
1984a,b). The percentage of the daily feed intake that is absorbed by the gastrointestinal tract of
TCDD-treated and control rats is similar (Potter et al., 1986; Seefeld and Peterson, 1984).
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Reduced appetite as a result of inhibition of tryptophan-2,3,-dioxygenase causes gradual
development of eventual lethal hypoglycemia in TCDD-induced wasting syndrome in rats
(Weber et al., 1994). No reduced appetite associated with gradual body weight loss and no
tryptophan effects are observed in TCDD-exposed mice, although appetite and body weight loss
are observed in mice at the terminal stage of wasting syndrome. Hypophagia was the major
cause of adipose and lean tissue loss in male Fischer 344 rats, C57B1/6 mice, and albino guinea
pigs when exposed to a calculated LD50 dose of TCDD. Body weight loss followed a similar
time-course in TCDD-treated and pair-fed control animals of all three species (Kelling et al.,
1985).
Body weight loss appears to contribute to lethality in a species- and strain-dependent
fashion, but weight loss appears to play a greater role in causing death in SD rats and guinea pigs
than it does in Fischer 344 rats and C57B1/6 mice. Loss of body weight and loss of appetite are
also prominent signs of thyroid dysfunction. However, some data indicate that the effect of
TCDD on thyroid hormones cannot explain the TCDD-induced decrease in body weight gain.
Reduced gluconeogenesis due to inhibition of phosphoenol pyruvate carboxykinase
(PEPCK) by TCDD has been suggested as one of the primary contributing factors to a gradual
development of an eventual lethal hypoglycemia in wasting syndrome in rats (Stall et al., 1993)
and mice (Weber et al., 1995).
TCDD-induced wasting is associated with reduction of adipose tissue mass,
hypertriglyceridemia, redistribution of fatty acids (Gasiewicz and Neal, 1979; Chapman and
Schiller, 1985; Brewster and Matsumura, 1988), and diabetic-like symptoms (Brewster and
Matsumura, 1988). Carbohydrate and lipid metabolism are severely impaired in the liver and
adipose tissue by TCDD. Glucose transport systems play vital roles in controlling the rate of
energy utilization in adipose tissues. TCDD also affects lipoprotein lipase (LPL) activity. The
rate of fat storage is determined by LPL, which controls the serum level of triglycerides.
Brewster and Matsumura (1984) found that LPL activity was decreased in guinea pigs to 20% of
the value of ad libitum-fed controls after 1 day, and that this effect persisted throughout the study
(10 days). The authors suggest that TCDD irreversibly reduces adipose LPL activity, thus
making the animals less capable of adapting to nutritional changes and needs. In the pancreas,
LPL regulates the production and release of insulin, and in the liver it controls glucose
metabolism and fatty acid synthesis. From their observations on the significant reduction of
glucose-transporting activity in adipose tissue and pancreas in guinea pigs by TCDD at a very
low dose (single IP injection of 0.03 |ig/kg), Enan et al. (1992a,b) concluded that the reduction
in glucose transporters is one of the major causes of TCDD-induced wasting syndrome in this
species. Downregulation of the cellular glucose uptake in NIH 3T3 LI preadipocyte cell line in
culture by TCDD has also been observed (Olsen et al., 1994). Pretreatment of these cells with
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4,7,-phenanthroline, an Ah receptor blocker, prevents the effect of TCDD on glucose uptake,
suggesting that TCDD-induced downregulation of functional glucose transporter proteins is
mediated through the Ah receptor.
The insulin-recruitable form of glucose transporter Type 4 (GLUT4) provides energy to
the cell by supplying glucose to the muscle and tissue tissues. Impairment of GLUT4 in adipose
tissue, liver, and pancreas (Enan et al., 1992a,b), and reduction of PEPCK in liver (Viluksela et
al., 1995), could play important roles in the pathogenesis of TCDD-induced diabetes.
In a series of studies on Wistar rats, Lakshman et al. (1988, 1989, 1991) demonstrated
that single intraperitoneal injections of TCDD (from 1 |ig/kg) caused a dose-dependent
inhibition of fatty acid synthesis in the liver and adipose tissue. Adipose tissue was found to be
more sensitive than the liver. They also found an increased mobilization of depot fat into the
plasma compartment, accompanied by an increase in plasma free fatty acid concentrations.
In vitro studies of isolated heart mitochondria have indicated that a TCDD concentration
of 1.5 nmol/mg in mitochondrial protein affects oxygen activation associated with cell
respiration. Superoxide radicals and H2O2 were indicated to be involved in the development of
the observed effects (Nohl et al., 1989).
Loss of muscle tissue, accompanied by a decreased glucocorticoid receptor-binding
capacity and an increased glutamine synthetase activity, has been observed in male Fischer 344N
rats given a single oral TCDD dose of 100 |ig/kg (Max and Silbergeld, 1987).
Another biochemical effect associated with TCDD-induced wasting syndrome is the
decrease in hepatic vitamin A storage in TCDD-exposed animals (Thunberg et al., 1979;
Hakansson et al., 1989a, 1991). Vitamin A is necessary for growth; vitamin A deficiency will
result in depressed body weight gain and reduced food intake. However, in contrast to TCDD-
treated animals, the vitamin A-deficient animals continue to eat and grow, though body weight
gain is less than normal (Hayes, 1971).
The hypothesis that decreased feed intake could be a result of a direct TCDD effect on
the brain was initially indicated by Pohjanvirta et al. (1989), although contradictory information
has been provided by other studies (Stall and Rozman, 1990). The intraperitoneal administration
of TCDD at 50 |ig/kg to male SD rats (~LD50 level) caused a significant decrease in the serum
concentration of prolactin, detectable after 4 hours, compared with pair-fed vehicle controls and
noninjected controls (Jones et al., 1987). Further studies have demonstrated that the effect of
TCDD was reversed by pimozide, a dopamine receptor antagonist, and [that the rate constant of
dopamine depletion after oc-methyl-p-tyrosine and the turnover rate were significantly elevated.]
This suggests a hypothalamic site of TCDD action in their experiments (Russell et al., 1988), a
finding supported by additional data on changes to central thermoregulation by dioxin in golden
hamsters (Gordon et al., 1996) and rats (Gordon and Miller, 1998).
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Changes in intermediary metabolism have been demonstrated in TCDD-treated
experimental animals. Conflicting data on how TCDD affects serum glucose and hepatic
glycogen levels have been reported earlier (WHO/IPCS, 1989). Several studies have suggested
that the ultimate cause of death in some mammalian species may be a progressive hypoglycemia
(Ebner et al., 1988; Gorski and Rozman, 1987; Gorski et al., 1990). Serum glucose levels in the
guinea pig, however, were not affected by treatment of the animals with TCDD (Gasiewicz and
Neal, 1979). Slight reductions in serum glucose levels were noted in both L-E and H/W rats
(Pohjanvirta et al., 1989). Rozman et al. (1990) have suggested that the subchronic and chronic
toxicities of TCDD are related to the inhibition of key enzymes of gluconeogenesis. They
demonstrated that the induction of appetite suppression was preceded by the inhibition of
PEPCK, which caused a reduction in gluconeogenesis. This was followed by a progressive
increase in plasma tryptophan levels that was suggested to cause a serotonin-mediated reduction
of the feed intake. In SD rats, TCDD in doses of 25 and 125 jig caused a rapid decrease (50%)
in PEPCK activity 2 days after dosing, followed by a dose-dependent decrease in glucose-6-
phosphatase activity 4 to 8 days after exposure. Both appetite suppression and reduced PEPCK
activity occurred in the same dose range (Weber et al., 1991). TCDD-induced impairments of
carbohydrate synthesis have also been suggested by studies in chick embryos (Lentnek et al.,
1991).
Numerous studies have measured serum levels of free fatty acids, cholesterol, and
triglycerides in various species after TCDD treatment (WHO/IPCS, 1989), but no pronounced
qualitative differences have been observed between species or strains of mice.
The wasting syndrome seems to be a generalized effect, elicited in all species and strains,
but at various dosages (single or repeated administration). Specific studies have not been
performed to elucidate the extent to which this syndrome is elicited through the interaction of
TCDD with the Ah receptor, although the binding affinities of various CDDs and CDFs to the
Ah receptor, as well as those of related PCBs, have been shown to strongly correlate with their
potency to induce the wasting syndrome in both rats and guinea pigs (Safe, 1990).
3.5.2. Hepatotoxicity
Even at sublethal doses, TCDD induces hyperplasia and hypertrophy of parenchymal
cells and, thus, hepatomegaly in all species investigated. There is, however, considerable
variation in the extent and severity of this lesion among the species tested. Other liver lesions
are more species-specific. Lethality following the administration of TCDD cannot be explained
by these liver lesions alone, although they may be a contributing factor in the rat and rabbit. The
morphological changes in the liver are accompanied by impaired liver function characterized by
liver enzyme leakage, increased microsomal monooxygenase activities, porphyria, impaired
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plasma membrane function, hyperlipidemia, and increased regenerative DNA synthesis (U.S.
EPA, 1984, 1985; WHO/IPCS, 1989).
Hepatotoxic reaction in various strains of rats given lethal doses of TCDD is
characterized by degenerative and necrotic changes including the appearance of mononuclear
cell infiltration, multinucleated giant hepatocytes, increased numbers of mitotic figures and
pleomorphism of cord cells, an increase in the hepatic smooth endoplasmic reticulum, and
parenchymal cell necrosis. The histological findings are accompanied by hyperbilirubinemia,
hypercholesterolemia, hyperproteinemia, and increased serum glutamic oxaloacetic transaminase
(SGOT) and serum glutamic pyruvic transaminase (SGPT) activities, which further indicate
damaged liver function (WHO/IPCS, 1989). These lesions may be severe enough to be a
contributing factor in death. The lesions observed after sublethal doses are qualitatively almost
identical to those observed after lethal doses.
Early studies in mice found similar effects. More recently, Shen et al. (1991) reported a
comparative study on the hepatotoxicity of TCDD in Ah-responsive and Ah-nonresponsive mice
(C57BL/6J and DBA/2J, respectively). The C57BL/6J mice given a single dose of 3 |ig/kg
TCDD developed mild to moderate hepatic lipid accumulation but no inflammation or necrosis.
Severe fatty change, mild inflammation, and necrosis occurred at 150 |ig/kg. The DBA/2J mice
given 30 |ig/kg developed hepatocellular necrosis and inflammation but no fatty change. Lipid
accumulation was only slight after 600 |ig/kg. The authors concluded that the Ah locus may be
involved in determining the steatotic effects of TCDD. This is consistent with the findings of
Birnbaum et al. (1990) on the differential toxicity of TCDD in C57BL/6J mice congenic at the
Ah locus. In this study, wild type mice were 8- to 24-fold more sensitive than congenic mice
deficient at the Ah locus for a spectrum of effects, including increased liver weight,
hepatocellular cytomegaly, fatty change, bile duct hyperplasia, and serum liver enzyme changes.
The guinea pig shows less severe morphological alteration in the liver than other species,
although ultrastructural changes of the liver are found. Likewise, the hamster exhibits little or no
liver damage even after a fatal dose, but liver lesions have been observed after prolonged periods
following the administration of nonlethal doses.
Several parameters relating to disturbed hepatic plasma membrane function have been
studied (U.S. EPA, 1984, 1985; WHO/IPCS, 1989). Adenosine triphosphatase (ATPase)
activities were depressed and protein kinase C activity was increased in rats, but not in guinea
pigs, treated with TCDD (Bombick et al., 1985). TCDD also induced a decrease in the binding
of EOF. The relative doses of TCDD needed to suppress EOF binding to 50% of the control
level were 1, 14, and 32 |ig/kg for the guinea pig, the SD rat, and the Syrian Golden hamster,
respectively (Madhukar et al., 1984). A single intraperitoneal dose of 115 jig TCDD/kg bw
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decreased the EOF binding by 93.1%, 97.8%, and 46.0% in C57B1/6, CBA, and AKR mice,
respectively, 10 days after treatment (Madhukar et al., 1984).
Further studies on the interaction of TCDD with the EGF receptor have been performed
in congenic mice of the strain C57BL/6J (Lin et al., 1991a,b). The ED50 for the TCDD-induced
decrease in maximum binding capacity of the EGF receptor was 10 times higher in the Ah-
nonresponsive mice than the Ah-responsive animals. This study supports the hypothesis that the
effects of TCDD on EGF receptor ligand binding are mediated by the Ah receptor.
The effects of TCDD on biliary excretion of various compounds have also been studied.
Of special interest are studies on the excretion of ouabain, a model compound for neutral
nonmetabolized substrates such as estradiol, progesterone, and cortisol, which was depressed in
a dose-related manner by a single oral dose of TCDD in rats (Yang et al., 1977, 1983b). The
available data suggest that the hepatic membrane transport of ouabain may be selectively
impaired by TCDD. Peterson et al. (1979a,b) have indicated that changes in ATPase activities
are not responsible for reduced ouabain excretion.
TCDD administration stimulates the accumulation of porphyrins in the liver and an
increase in urinary porphyrin excretion (Goldstein et al., 1973, 1976, 1982). Indeed, during
manifest porphyria, accumulation of porphyrins occurs not only in the liver but also in the
kidney and spleen of rats (Goldstein et al., 1982).
Contradictory results on species variations have been published. It seems clear that
porphyria can be produced in both mice and rats, but the condition is always the result of
subchronic or chronic administration. Exposure to single doses has not been demonstrated to
produce porphyria. The mechanism underlying the induction of porphyria has not been
elucidated. Cantoni et al. (1981) exposed rats orally to 0.01, 0.1, and 1 jig TCDD/kg bw/week
for 45 weeks. Increased coproporphyrin levels were observed at all dose levels. A marked
porphyric state appeared only at the highest dose tested, after 8 months of exposure.
Induction of aminolevulinic acid (ALA)-synthetase, the initial and rate-limiting enzyme
involved in heme synthesis, does not seem to be a necessary event in TCDD-induced porphyria.
Despite porphyria being evident, mice exposed to 25 jig TCDD/kg bw/week for 11 weeks were
not found to have any increased ALA activity (Jones and Sweeney, 1980). A more likely
suggestion is that decreased hepatic porphyrinogen decarboxylase is the primary event in
porphyria induced by halogenated aromatics (Elder et al., 1976, 1978). TCDD depresses this
enzyme activity in vivo in the liver of mice (Cantoni et al., 1984a,b; Elder and Sheppard, 1982;
Jones and Sweeney, 1980), but not in vitro (Cantoni et al., 1984b). Of interest, too, are the
results reported in van Birgelen et al. (1996a), where the porphyrinogenic effects of TCDD were
correlated with CYPIA2 induction, and demonstrated a strong synergistic relationship with
coadministered PCBs.
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A comparative study of TCDD-induced porphyria has not been conducted in responsive
and nonresponsive mice. In a study on Ah-responsive (Ahb) and Ah-nonresponsive (Ahd)
C57BL/6J female mice, however, the urinary excretion of porphyrins was examined after
treatment of the animals with hexachlorobenzene for 17 weeks (Hahn et al., 1988). After 15
weeks of treatment with 200 ppm hexachlorobenzene in the diet, the excretion of porphyrins was
200 times higher in the Ahb mice than the controls. In contrast, the Ahd mice only showed a
sixfold increase. Induction of P-450c(lAl) was observed only in Ahb mice, while induction of
P-450d(lA2) was observed in both strains, but to a lesser degree in the Ahd mice.
3.5.3. Epidermal Effects
Chloracne and associated dermatological changes are common responses to high
exposures to TCDD in humans. However, this type of toxicity is expressed only in a limited
number of animal species (e.g., rabbits, monkeys, cows, and hairless mice).
In a rabbit ear bioassay, a total dose of 80 ng TCDD gave a chloracnegenic response,
while no response was obtained when the total dose applied to the ear was 8 ng (Jones and
Krizek, 1962; Schwetz et al., 1973). The application of TCDD in various vehicles has been
demonstrated to markedly decrease this response (Poiger and Schlatter, 1980). The hairless
mouse is a less sensitive model for chloracnegenic response than the rabbit ear bioassay
(Knutson and Poland, 1982; Puhvel et al., 1982). Following repeated applications of -0.1 jig
TCDD over several weeks, however, an acnegenic response was noted in the hairless mouse
strains, SkH:HRl and HRS/J. An acnegenic response was also caused by repeated applications
of 2 mg of 3,4,3',4'-TCB (Puhvel et al., 1982). Female HRS/J hairless mice have also been used
to test the dermal toxicity and skin tumor-promoting activity of 2,3,7,8-TCDD, 2,3,4,7,8-PeCDF,
and 1,2,3,4,7,8-HxCDF (Hebert et al., 1990b). All of the tested compounds induced coarse,
thickened skin with occasional desquamation. These effects were more severe after the
application of PeCDF and HxCDF.
Keratinocytes, the principal cell type in the epidermis, have been utilized as an in vitro
model for studies of TCDD-induced hyperkeratosis both in human- and animal-derived cell
cultures. The response to TCDD is analogous to the hyperkeratinization observed in vivo.
A TCDD-induced keratinization response in vitro was first demonstrated in a
keratinocyte cell line derived from a mouse teratoma (XB cells). The keratinization was
doserelated (Knutson and Poland, 1980b). Late-passage XB cells (termed XBF cells) lost their
ability to respond by keratinization after TCDD treatment. Both XB cells (keratinization assay)
and XBF cells (flat-cell assay) have proven to be useful in in vitro bioassays to determine the
dioxin-like activities of both environmental samples and pure isomers (Gierthy and Crane,
1985a,b; Gierthy et al., 1984).
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Several continuous lines of human keratinocytes, derived from neonatal foreskin or
squamous cell carcinomas, have been shown to respond to TCDD in nM concentrations, with a
variety of signs indicating alterations in the normal differentiation process (WHO/IPCS, 1989).
The responses include decreased DNA synthesis, decreased number of proliferating basal cells,
decreased binding of EOF, and an increase in the state of differentiation (Osborne and Greenlee,
1985; Hudson et al., 1986). The responses were also obtained with TCDF, but not with
2,4-diCDD (Osborne and Greenlee, 1985). TCDD has also been shown to inhibit high-density
growth arrest in human squamous carcinoma cell lines. Indeed, the minimum concentration for
increases in cell proliferation was 0.1 nM in the most sensitive cell line (SCC-15G). This effect
is not due to modulation of the transforming growth factor-p binding (Hebert et al., 1990b,c).
3.5.4. Enzyme Induction
TCDD has repeatedly been found to increase the activities of various enzymes. While
observations of enzyme inhibition have also been made, enzyme induction has been one of the
most extensively studied biochemical responses produced by TCDD. The mixed-function
oxidase (MFO) system is the most thoroughly investigated, and AHH and EROD (as markers for
CYP1A1 induction) are the most frequently assayed enzyme activities. The induction of MFO
activities might potentiate the toxicity of other foreign compounds that require metabolic
transformation by the MFO system before they can exert their toxic effects. Furthermore,
increased MFO activities might adversely affect important metabolic conversions of endogenous
compounds. TCDD also affects a variety of other enzymes (e.g., uridine diphosphate-
glucoronosyltransferase [UDPGT] and glutathione-s-transferase [GST]) that are components of
multifunctional enzyme systems involved in the conjugation, biotransformation, and
detoxification of a wide variety of endogenous and exogenous compounds.
Several investigators have studied the relative potency of various halogenated dioxins,
dibenzofurans, and biphenyls to induce AHH or EROD activities (Safe, 1990). An apparent
structure-activity relationship was found between the location of the halogen atoms on the
dibenzo-p-dioxin molecule and the ability to induce AHH activity both in vivo and in vitro.
Isomers with halogens at the four lateral ring positions produced a greater biological response
than those with halogens at three lateral ring positions. Two lateral halogen atoms seemed to be
insufficient to produce a biological response. Numerous studies have indicated that there is very
strong agreement between the Ah-binding affinity of various CDDs, CDFs, and related PCBs
and their potency to induce AHH, both in vivo and in vitro (Safe, 1990). Structure-activity
studies have also demonstrated a clear correlation between the toxicity and induction potency of
a series of CDDs, CDFs, and coplanar PCBs (Poland and Glover, 1973; Safe, 1990). This is
discussed in Chapter 2 of this report.
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On a molecular basis, TCDD is the most potent MFO-inducing compound known, and
MFO induction seems to be the most sensitive biochemical response produced. Measurements
of the induction of AHH or EROD (mediated through CYP1A1) are considered to be very
sensitive markers of TCDD-induced enzyme induction. According to Kitchin and Woods
(1979), induction in the rat takes place at doses as low as 0.002 jig TCDD/kg bw. The NOAEL
for a single administration to rats seems to be 1 ng/kg, while a single dose of 3 ng/kg causes a
detectable induction of AHH or EROD (Kitchin and Woods, 1979; Abraham et al., 1988). For
more detailed dose-response information, see Chapter 8 of this report. Enzyme induction has
also been observed in the offspring of various species after prenatal and postnatal (milk)
exposure to TCDD (Lucier et al., 1975; Korte et al., 1990; Waern et al., 1991b).
The effect of TCDD on enzyme activities has been most frequently investigated in the rat
(WHO/IPCS, 1989). TCDD has been shown to increase both the contents of cytochrome
P-4501A1 and cytochrome P-4501A2 in the liver, as well as other microsomal enzyme activities
involved in the oxidative transformation and conjugation of xenobiotics (e.g., aniline
hydroxylase, AHH, biphenyl hydroxylase, 7-ethoxycoumarin-O-deethylase [ECOD], EROD, and
UDPGT) (U.S. EPA, 1984, 1985; WHO/IPCS, 1989).
TCDD also affects some other hepatic enzymes not related to the MFO system, including
aldehyde dehydrogenase, 6-ALA synthetase DT-diaphorase, transglutaminase, ornithine
decarboxylase, transaminases (L-alanine aminotransferase [ALT] and L-aspartate
aminotransferase [AST]), plasma membrane ATPases, porphyrinogen carboxylase, prostaglandin
synthetase, enzymes involved in testosterone metabolism, and RNA polymerase (U.S. EPA,
1984, 1985; WHO/IPCS, 1989).
Studies of different species have also revealed that enzyme induction due to TCDD
exposure varies with both species and strain. Pohjanvirta et al. (1988) studied enzyme induction
in the L-E and H/W (Kuopio) rat strains (LD50 -10 and >3,000 |ig/kg, respectively). Differences
in the inducibility of EROD, ECOD, or ethylmorphine N-demethylase were not found, nor were
there any differences with regard to the amount of available Ah receptor or the amount of
cytochrome P-450 in the hepatic microsomal fractions. Similarly, differences regarding possible
induction of UDPGT were absent (Pohjanvirta et al., 1990).
Enzyme induction studies on mice have been performed mainly with strains that are
genetically different at the Ah locus, thus making them responsive or nonresponsive to the
induction of hepatic cytochrome P-4501Al-related enzyme activities. Qualitatively, and in
general, the same responses can be obtained in both strains, but there may be more than one
order of magnitude difference with regard to the doses required to elicit a response. TCDD is
thus 10-fold more potent in inducing hepatic cytochrome P-4501A1 and the related AHH activity
in C57BL/6J mice (Ah-responsive) than in DBA/2 mice (Ah-nonresponsive) (Poland and
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Knutson, 1982; Nebert, 1989) and C57BL/6L mice congenic at the Ah locus (Birnbaum et al.,
1990).
Although the guinea pig is the most sensitive species to the toxic effects of TCDD, it
does not respond to the administration of TCDD with liver toxicity or extensive enzyme
induction. Indeed, even at lethal doses, the induction of MFO, as measured by AHH activity, is
only very slight (Beatty and Neal, 1977; Hakansson et al., 1994). The data on enzyme induction
in rabbits are rather limited and somewhat conflicting with regard to increases in the amount of
cytochrome P-450 (Hook et al., 1975; Liem et al., 1980). Similarly, hepatic enzyme induction
has been only partially studied in Syrian Golden hamsters. When hamsters were given a lethal
dose of TCDD, increased hepatic GST and glutathione reductase activities were found. The
ED50 values for the induction of hepatic ECOD and reduced NADP:menadione oxidoreductase
activities and cytochrome P-450 content in male Syrian Golden hamsters were 1.0, 2.0, and 0.5
jig TCDD/kg bw, respectively (extremely low doses, compared with doses that produce tissue
damage and lethality in this species) (Gasiewicz et al., 1986).
In a comparative study of EROD induction in guinea pigs, rats, C57BL/6 and DBA/2
mice, and Syrian Golden hamsters, the animals were given single doses that were intended to be
equitoxic (i.e., 1, 40, 100, 400, and 400 jig TCDD/kg, respectively) compared with the acute
toxicity for the respective species and strain. EROD induction was noted in all species except
for the hamster. During the observation period (112 days), the EROD induction dropped to more
or less normal values in all rats and mice, while the induction (albeit low compared with the
other species) was sustained for the whole period in the guinea pig (Hakansson et al., 1994).
This might be due to higher half-life of TCDD in guinea pigs than rats or mice.
The N-demethylation of caffeine has been applied as a noninvasive method for studying
enzyme induction in vivo. Studies on the marmoset monkey (Callithrix jacchus), utilizing
14C-labeled caffeine and measuring 14CO2 exhalation by a breath test, has indicated a NOAEL of
1 ng/kg and a LOEL of 3 ng/kg (Kruger et al., 1990). Studies by Butler et al. (1989) and others
indicate that this reaction is dependent on cytochrome P-4501A2.
In the chick embryo, both AHH and 6-ALA synthetase have been reported to be
extremely sensitive to the inductive effects of TCDD and related compounds (Poland and
Glover, 1973; Brunstrom and Andersson, 1988; Brunstrom, 1990).
Although TCDD is relatively nontoxic in cell cultures, it is a very potent inducer of
AHH or EROD activities in in vitro systems, including lymphocytes and primary hepatocytes, as
well as established and transformed cell lines.
The ED50 values for AHH induction by TCDD have been determined in 11 established
cell lines and in fetal primary cultures from 5 animal species and cultured human lymphocytes.
The values ranged from 0.04 ng/mL medium in C57BL/6 mouse fetal cultures and 0.08 ng/mL in
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the rat hepatoma H-4-II-E cell line to >66 ng/mL in the HTC rat hepatoma cell line (Niwa et al.,
1975). Several cultured human cells or cell lines have been shown to be inducible for AHH
activity by TCDD including lymphocytes (Atlas et al., 1976), squamous cell carcinoma lines
(Hudson et al., 1983; Hebert et al., 1990a), breast carcinoma cell lines (Jaiswal et al., 1985), and
lymphoblastoid cells (Nagayama et al., 1985).
TCDD was demonstrated to be the most potent AHH inducer of 24 chlorinated dibenzo-
p-dioxin analogues (Bradlaw et al., 1980) in a rat hepatoma cell culture (H-4-II-E) that is
extremely sensitive to AHH induction. The EC50 values for AHH and EROD induction in the
same cell system varied over 7 orders of magnitude for 14 different CDDs, the most potent being
TCDD and the least potent being 2,3,6-triCDD (Mason et al., 1986). Additional details on these
and other enzyme induction dose-response characteristics and modeling are included in Chapter
8 of this report.
3.5.4.1. Appraisal
Based on data from Kitchin and Woods (1979), Abraham et al. (1988), Kruger et al.
(1990), and Neubert (1991), a NOAEL value of 1 ng/kg bw can be calculated for enzyme
induction for both rats and marmoset monkeys. At this dose, the tissue concentrations for both
species were found to be 4 ppt for adipose tissue and 3 ppt for the liver. It is interesting to note
that the wide range of sensitivities toward the acute toxicity of TCDD is also reflected in the
wide range of sensitivities for enzyme induction both in vivo and in vitro, although the two
groups of effects are not necessarily parallel. Finally, it is evident that the structure-activity
relationships revealed from in vitro testing correlate fairly well with in vivo studies within a
given species or strain.
3.5.5. Endocrine Effects
In many respects, TCDD toxicity mimics endocrine imbalance. Alterations in endocrine
regulation have been suggested from human exposure to TCDD that resulted in hirsutism and
chloracne. Chronic exposure to TCDD causes impaired reproduction in experimental animals,
possibly by interfering with the estrus cycle in combination with some steroid-like activities of
TCDD. This has prompted studies on the interaction of TCDD with steroid hormones and their
receptors.
Evidence has been provided suggesting that chronic or subchronic exposures to TCDD
impair thyroid functions. Dose-dependent reductions of plasma thyroid hormone levels have
been observed in TCDD- and PCB-exposed animals (van der Kolk et al., 1992; van Birgelen et
al., 1995a,b).
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In a subchronic 13-week TCDD feeding study with female Sprague-Dawley (S-D) rats, a
decrease in thyroid hormone (T4) levels occurred, associated with elevation of microsomal
UDPGT activity when thyroxine was used as substrate for thyroxine glucuronosyltransferase
(T4UGT) (van Birgelen et al., 1995b). In addition, involvement of CYP1A1 and UGT1A1 by
TCDD indicates that the TCDD-induced thyroid functional abnormalities are mediated though
the AhR (van Birgelen et al., 1995b). 2,3,3',4,4',5-hexachlorobiphenyl (PCB 156) has also been
shown to reduce plasma total T4 levels, and induces UDPGT by using thyroxine as substrate for
T4UGT (van Birgelen, 1995a). Similar results have also been observed in a 30-week chronic
study with female S-D rats suggesting that TCDD-induced thyroid hormone function is caused
by chronic perturbation of the liver-pituitary-thyroid axis (Sewell et al., 1995).
van Birgelen et al. (1995a,b) demonstrated the effects of TCDD and coplanar PCB126
(3,3,4,4',5-PCB) on thyroid hormone metabolism in female SD-rats. Oral exposure to 0.2, 0.4,
0.7, 5, and 20 |ig/kg diet of TCDD and 7 to 180 |ig/kg diet of PCB 126 significantly decreased
the plasma total thyroxine (TT4) levels. An intake of 0.047 jig/kg/day was estimated to be the
LOAEL for decrease in plasma thyroid hormone levels.
A dose-dependent decrease in serum T4 levels has also been observed in male and female
SD rats as a result of high-dose subchronic exposures to 1,2,3,7,8-pentaCDD (PeCDD) or
1,2,3,4,7,8-hexaCDD (HxCDD) and low-dose subchronic exposures to TCDD/kg. Serum T4
levels in PeCDD or HxCDD-exposed males returned to close to normal levels by the end of the
off-dose period (Viluksela et al., 1998).
van Birgelen et al. (1995b), in a 13-week TCDD feeding study using 7-week-old female
S-D rats, found that the LOAEL for decrease in plasma T4 was 47 ng/kg bw/day. Dose-response
relationships for CYP1 Al and CYP1A2 were determined by nonlinear curve fitting. The critical
values for the 95% confidence limits for CYP1 Al and CYP1A2 inductions ranged from 0.7 and
4 ngTCDD/kg bw/day.
Janz and Bellward (1997) reported that a single intraperitoneal dose of 20 |ig/kg bw of
TCDD to adult great blue heron, Ardea heidias, increased plasma T4 levels (control: 39 + 4
ng/mL; exposed: 55 + 5 ng/mL;/><0.05), but no effect occurred on plasma total T3 levels or on
the plasma T3 to T4 ratio.
Increased systemic levels of glucocorticoids may mimic some of the symptoms of TCDD
toxicity (e.g., involution of lymphoid tissues, edema, and mobilization of fatty acids from
adipose tissues). Thus, it has been suggested that TCDD increases glucocorticoid activity
through indirect effects on glucocorticoid receptors. Poland et al. (1976) demonstrated that
cortisol and synthetic glucocorticoids do not bind to the TCDD receptor.
Conflicting data have been reported on TCDD-induced levels of glucocorticoids.
However, significant changes to the liver cytosolic glucocorticoid receptor were induced by
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TCDD at doses 10,000-fold lower in adrenalectomized SD rats than in control rats (Sunahara et
al., 1989). The data further indicate that the binding capacity of hepatic glucocorticoid receptor
was altered, but not the apparent equilibrium dissociation constant (Kd). Studies in congenic
strains of Ah-responsive and Ah-nonresponsive C57BL/6J female mice (Goldstein et al., 1990;
Lin et al., 1991a,b) have also demonstrated that TCDD decreased the maximum binding capacity
of the hepatic glucocorticoid receptor in both strains of mice by -30%.
Steroids are endogenous substrates for the hepatic MFO system. TCDD influences the
activity of this enzyme system and may alter steroid metabolism in vivo and the magnitude of
steroid-mediated functions.
Early studies reported contradictory data on changes in steroid levels. Umbreit and Gallo
(1988) suggest that estrogen receptor modulation, and the animal's physiological response to this
modulation, can explain some of the toxicity observed in TCDD-treated animals. The
susceptibility of different species to TCDD correlates, to some extent, with their steroid
glucuronidation capacity. For example, hamsters have low steroid UDPGT activity while guinea
pigs have a corresponding high activity. Another example is given by comparing the SD and
Gunn rat, the latter being defective in producing some UDPGTs. The homozygous Gunn rat is
3-10 times more resistant to the effects of TCDD than is the SD rat (Thunberg, 1984; Thunberg
and Hakansson, 1983). The results of TCDD exposure in various species and strains are
complex. The ability of the strain to counteract TCDD-induced modulation of the estrogen
receptor depends on its ability to synthesize and excrete estrogens. Interactions of TCDD and
related compounds with estrogen have been reviewed by Safe et al. (1991).
The importance of estrogens as modulators of TCDD-induced toxicity has also been
demonstrated by Lucier et al. (1991), who found that the tumor-promoting effects of TCDD
could be effectively prevented by removing the ovaries from female rats before exposure to
TCDD. This finding agrees with the results obtained from long-term bioassays that
demonstrated liver tumors only in female rats (Kociba et al., 1978; NTP, 1982).
Studies on congenic strains of Ah-responsive and Ah-nonresponsive C57BL/6J female
mice found a statistically significant difference in the responsiveness of the hepatic estrogen
receptor. This indicates that the Ah receptor regulates the effects of TCDD on the hepatic
estrogen receptor (Goldstein et al., 1990; Lin et al., 1991a, b).
TCDD-induced changes in levels or activities of testosterone or its metabolites have been
reported from several studies (Keys et al., 1985; Mittler et al., 1984; Moore and Peterson, 1985;
Neal et al., 1979). A single oral dose of 50 jig TCDD/kg bw increased the plasma corticosterone
level in SD rats 7 and 14 days postexposure (Neal et al., 1979). It has also been shown,
however, that a single oral dose of 25 jig TCDD/kg bw decreases the plasma corticosterone in
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SD rats 14 and 21 days postexposure. It is important to note that Neal et al. (1979) also observed
a slight decrease in serum corticosterone during days 1-4 posttreatment.
Mittler et al. (1984) demonstrated a decreased activity of testicular 16-oc-testosterone
hydroxylase, 6-B-hydroxytestosterone, and 7-oc-hydroxytestosterone in young SD rats 90 hours
postexposure to single intraperitoneal doses of 0.2, 1, or 5 jig TCDD/kg bw.
A single dose of 0.06 jimol TCDD/kg bw decreased levels of 3oc-, 6oc-, and 16B-
hydroxytestosterone and an increase of 7oc-hydroxytestosterone has also been observed in young
male Wistar rats (Keys et al., 1985). Moore et al. (1985) noted decreases in serum testosterone
and dihydrotestosterone levels in 15 ng TCDD/kg bw-dosed male SD rats. The data do not,
however, allow for any conclusions with regard to the possible relationship to receptor-mediated
toxicity. TCDD induces several enzymes related to testosterone metabolism, which suggests that
the changes observed may be secondary to the induction of various enzymes. Serum testosterone
and dihydrotestosterone were found to be dose-dependently depressed by TCDD treatment in
male SD rats, when compared with pair-fed and ad libitum-fed controls. The ED50 for this effect
was -15 |ig/kg (Moore et al., 1985). It was further shown that testosterone synthesis was
decreased in the animals due to depressed production of pregnenolone by the testis (Kleeman et
al., 1990). In the same strain of rats, a single 100 |ig/kg oral dose of TCDD was found to cause a
55 percent decrease in testicular cytochrome P-450SCC activity and to inhibit the mobilization of
cholesterol to cytochrome P-450SCC. The authors concluded that the latter effect probably was
responsible for the inhibition of testicular steroidogenesis (Moore et al., 1991). Maternal
exposure to TCDD has been shown to affect the male reproductive system at low doses; the
lowest dose tested was 64 ng/kg (Mably et al., 1991, 1992a,b,c). This is discussed in Chapter 5.
In ovo exposure of white Leghorn chickens to TCDD, in the dose range of 1-10,000
pmol/egg, increased the cardiac release of prostaglandins (Quilley and Rifkind, 1986). Studies
on chick embryos have indicated that the induction of cytochrome P-450 by TCDD results in a
major increase in the NADPH-dependent metabolism of arachidonic acid (Rifkind et al., 1990).
These effects are clearly related to receptor-mediated enzyme induction.
3.5.6. Vitamin A Storage
Decreased hepatic vitamin A storage has been reported in animals exposed to various
chlorinated aromatic compounds. Because only minute quantities are needed to produce ill
effects, and because of its persistence in nature, TCDD is unique in its capacity to reduce the
vitamin A content of the liver. A single oral dose of 10 jig TCDD/kg bw decreased both the total
amount and the concentration of vitamin A in the liver of adult male SD rats (Thunberg et al.,
1979). The decrease was evident 4 days after dosing and progressed with time. After 8 weeks,
the treated animals had a total liver vitamin A content corresponding to 33% of that of controls.
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Decreased dietary intake of vitamin A could not account for this difference. A significant
increase in the UDPGT activity was observed, suggesting an increased excretion of glucuronide-
conjugated vitamin A. No correlation between the UDPGT activity and the hepatic vitamin A
reduction was seen, however, when homozygous Gunn rats lacking inducible UDPGT (Aitio et
al., 1979) and heterozygous Gunn rats with inducible UDPGT were treated with a single oral
dose of 10 jig TCDD/kg bw (Thunberg and Hakansson, 1983).
A study combining pair-feed restriction and a single TCDD treatment found that
decreases in liver reserves of vitamin A were not related to a decreased intake of vitamin A via
the diet (Hakansson et al., 1989b).
Puhvel et al. (1991) reported a comparative study in which congenic haired (+/+) and
hairless (hr/hr) HRS/J mice were fed a vitamin A-deficient diet and treated topically with TCDD.
The sensitivity to TCDD-induced cutaneous changes was essentially 100 times higher in hairless
mice than in haired mice (0.01 and 1.0 ng 3 times/week for 3 and 2 weeks, respectively). In the
haired phenotype, the effects of vitamin A depletion by itself were not seen by cutaneous
histology, nor were any changes observed in cutaneous morphology attributable to TCDD. In
the hairless mice, however, vitamin A deficiency increased the keratinization of dermal epithelial
cysts and increased the sensitivity of these cysts to TCDD-induced keratinization. Analysis of
vitamin A demonstrated that TCDD exposure did not affect cutaneous levels of the vitamin but
did significantly lower levels of vitamin A in the liver. TCDD-induced body weight loss and
atrophy of the thymus glands were not affected by the vitamin A status in either strain.
In a study on tumor promotion by TCDD, in which enzyme-altered hepatic foci were
induced in the livers of female SD rats, Flodstrom et al. (1991) found that vitamin A deficiency
by itself enhanced foci development. The effects of TCDD treatment were also markedly
enhanced, including TCDD-induced thymus atrophy.
Several studies have been performed to elucidate the mechanism of TCDD-vitamin A
interaction. Hakansson et al. (1989c) and Hakansson and Hanberg (1989) have demonstrated
that TCDD specifically inhibits the storage of vitamin A in liver stellate cells. Brouwer et al.
(1989) demonstrated that a single dose of TCDD (10 |ig/kg) to female SD rats reduced vitamin A
in the liver, lungs, intestines, and adrenal glands, while increasing its concentration in serum,
kidneys, and urine. They also found a 150% increase in the free fraction of serum retinol
binding protein. Taken together, all of these data in the rat indicate that TCDD induces an
increased mobilization of vitamin A from hepatic and extrahepatic storage sites into the serum,
accompanied by an enhanced elimination of the vitamin via the kidney into the urine.
In a comparative study of TCDD toxicity in male SD rats and Hartley guinea pigs
(Hakansson et al., 1989a), the animals were given single intraperitoneal doses of 40 and
0.5 |ig/kg bw, respectively (i.e., comparable fractions of their respective LD50). Similar
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reductions in hepatic vitamin A were observed for both species, while serum and renal vitamin A
concentrations were increased in the rat but unaffected in the guinea pig. Hepatic EROD activity
was markedly increased in the rat but unchanged in the guinea pig. Furthermore, rats seemed to
recover from the wasting, thymic atrophy, and liver enlargement and resumed their ability to
store vitamin A in the liver at 4-8 weeks after exposure. No such trends for wasting and vitamin
A storage were observed in guinea pigs, even 16 weeks after exposure. A complementary study
also included C57BL/6 mice, DBA/2 mice, and Syrian Golden hamsters (Hakansson et al.,
1991). The effects on TCDD-induced decrease of vitamin A in the liver and lung correlated
reasonably well with other toxic symptoms observed in the animals. On the other hand, studies
of two strains of rats, L-E and H/W (the H/W being >300 times more resistant to TCDD
toxicity), could not demonstrate significant differences in the TCDD-induced changes in vitamin
A in the liver, kidney, testicles, or serum after a sublethal dose of 4 ng/kg (Pohjanvirta et al.,
1990). These findings show that the correlation between TCDD-induced lethality and changes in
vitamin A status found among other species also apply to these strains of rats.
The interaction of 3,4,3',4'-TCB with vitamin A has been studied by Brouwer and van
den Berg (1983, 1984, 1986), Brouwer et al. (1985, 1986), and Brouwer (1987). The effects of
TCB on vitamin A differ in many respects from those of TCDD. TCB is rapidly converted in
vivo into a polar 5-OH-TCB metabolite, which binds with a relatively high affinity to
transthyretin (TTR). As a consequence of this interaction, the physiological functions of TTR in
retinoid and thyroid hormone transport are severely affected in TCB-exposed animals. The
model proposed by Brouwer (1987) may explain some of the characteristic toxicological lesions
related to exposure to this PCB. This mechanism of action seems to be clearly separated from
the Ah receptor-mediated toxicity of CDDs and CDFs. Hydroxylated metabolites of TCDD have
also been demonstrated to bind in a similar manner to TTR (Lans et al., 1993). Due to the very
slow metabolism of TCDD (or other 2,3,7,8-substituted CDDs/CDFs), however, this mechanism
probably plays a very minor role in toxicity.
Taken together, these data indicate that TCDD interferes with the metabolism and storage
mechanisms for vitamin A (Kelley et al., 1998). Because supplementation of dietary vitamin A
seems unable to counteract all of the observed toxic effects, this would imply either that the
effect on vitamin A storage is secondary to TCDD toxicity or that the cellular utilization of
vitamin A is affected by TCDD.
3.5.7. Lipid Peroxidation
Lipid peroxidation and oxidative stress have been indicated as factors that affect the
acute toxicity of TCDD (WHO/IPCS, 1989; Wahba et al., 1989a,b, 1990a,b; Pohjanvirta et al.,
1989; Alsharif et al., 1990; Stohs et al., 1990). Among the effects noted have been membrane
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lipid peroxidation, decreased membrane fluidity, and increased incidence of single-strand breaks
in DNA. No studies relating these observations to the Ah receptor have been performed. When
considering the available data on TCDD and lipid peroxidation, it is not possible to define a
relationship between lipid peroxidation and TCDD-induced lethality. However, oxidative stress
is observed only at high doses of TCDD following acute exposure. Acute TCDD exposure at
high doses has been shown to produce reactive oxygen species (Alsharif et al., 1994 a,b), lipid
peroxidation (Alsharif et al., 1994b), and decreased membrane fluidity (Alsharif et al., 1990) in
the mouse and rat.
Oxidative stress has been proposed as one of the reasons for increased susceptibility of
female mice to TCDD-induced toxicity. In female C57BL/6J mice, intraperitoneal exposure to 5
jig/kg of TCDD for 3 consecutive days results in a long-term increase in hepatic oxidized
glutathione and 8-hydroxydeoxyguanosine levels. Levels of 8-hydroxydeoxyguanosine, a
product of DNA base oxidation and subsequent excision repair, remain elevated about 20-fold 8
weeks after treatment. This suggests a sustained TCDD-induced oxidative stress resulting in
potentially promutagenic DNA base damage (Shertzer et al., 1998). Induction of CYP1A1 by
TCDD has also been suggested to cause an increased excretion rate of 8-oxoguanine, a
biomarker of oxidative DNA damage (Park et al., 1996).
Oxidative brain tissue damage may play a role in TCDD-induced central nervous system
abnormalities. Hassoun et al. (1998) reported that subchronic oral exposure of B6C3F1 mice to
TCDD for 13 weeks can result in a dose-dependent increase in superoxide anions (indicated by
reduction in cytochrome c), lipid production, and DNA single-strand breaks in brain tissues. The
authors posited involvement of the cytochrome P-450 system in TCDD-induced oxidative stress.
Slezak et al. (1999), using CYP1A2 knockout (CYP1A2'7-) mice, demonstrated that TCDD-
induced oxidative stress (indicated by production of thiobarbituric acid-reactive substances as a
measure of lipid peroxidation, production of reactive oxygen species via in vitro reduction of
CYC, and changes in glutathione) is not mediated through the cytochrome P-450 type 1A2
isozyme (CYP1A2). Hassoun et al. (1997) also posited that TCDD-induced fetal death and fetal
and placental weight reductions in C57BL/6J mice may be caused by oxidative damage induced
by TCDD. Ellagic acid at 6 mg/kg/day on days 10, 11, and 12 of gestation and 3 mg/kg on day
13 protected against TCDD administration on day 12 at 30 |ig/kg bw. Vitamin E succinate
administered at 100 mg/kg/day through gestation days 10, 11, and 12 and at 40 mg/kg on day 13,
instead of ellagic acid, was a less effective protective agent.
Iron administered before TCDD administration (75 |ig/kg bw) to AhR-responsive
AhRb-1 C57BL/6J mice potentiated hepatic porphyria, hepatocellular damage, and plasma
hepatic enzyme markers (Smith et al., 1998). The mechanism was oxidative because
hydroxylated and peroxylated derivatives of the uroporphyrins formed from uroporphyrinogen,
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and |i-glutathione transferase were also induced. Iron overcame the weak porphyria and toxicity
responses of TCDD in AhRb-2 BALB/c and AhRd SWR mice, but not in DBA/2 mice, which
remained TCDD resistant. Thus, metabolic factors may play a part in the responses of some
mice strains to TCDD through an oxidative process that disturbs iron regulatory protein capacity.
Increased accumulation of lipofuscin pigments, which are by-products of lipid
peroxidation, in heart muscles of TCDD-exposed rats (Albro et al., 1978) and iron deficiency in
animals resulting in in vitro inhibition of lipid peroxidation and reduced TCDD-induced
hepatotoxicity (Sweeney et al., 1979) suggested that oxidative stress may play a role in TCDD-
induced acute toxicity. Subsequently, Stohs et al. (1983) demonstrated that lipid peroxidation is
increased in isolated liver microsomes from TCDD-exposed rats. Further observations suggest a
possible role of reactive oxygen species in TCDD acute toxicity. Alsharif et al. (1994a,b)
observed that a maximum increase in superoxide anion production occurs on day 1 of
posttreatment in female SD rats treated with 50 and 125 |ig/kg bw of TCDD, and that TCDD-
induced oxidative stress is mediated through the Ah receptor in mice. TCDD-induced
superoxide anion production by peritoneal lavage primary macrophages and its mediation
through the Ah receptor suggests involvement of reactive oxygen species in a broad spectrum of
TCDD-induced toxicity. Bagchi et al. (1993) found that products from altered lipid peroxidation
and increased oxidative stress result in elevated serum and urinary levels of certain lipid
metabolic products, such as malondialdehyde, formaldehyde, acetaldehyde, and acetone,
following a single oral exposure to 50 |ig/kg bw of TCDD in female SD rats. Vos et al. (1978)
suggested that endotoxin shock may be the cause of TCDD-induced lethality, and that the tumor
necrosis factor-cc (TNF-oc) may be a contributing factor, even though it has not been detected in
the serum of TCDD-treated mice without exposure to endotoxin. Alsharif et al. (1994c)
demonstrated that anti-TNF-oc antibody can decrease phagocytic cell activity following TCDD
treatment. This suggests that TNF-oc release, a possible activator of TCDD-induced oxidative
stress, may have some role in TCDD-induced activation of phagocytic cells.
3.5.8. Neurotoxicity
Exposure to dioxin-like coplanar PCBs may result in neurotoxicity. Eriksson et al.
(1991) suggested that dioxin-like coplanar PCBs have neurologic activities that affect the
cholinergic receptors in the hippocampus. Seegal (1996) provided evidence that perinatal
exposure to coplanar 3,3',4,4'-PCB (PCB 77) results in significant elevation of dopamine in the
frontal cortex. Dopamine is an important neurotransmitter, dependent on tyrosine, that is
associated with initiation and control of motor behavior, learning, and memory functions. The
neurons and astroglia of rat hippocampal neural cells are responsive to relatively low levels of
TCDD through mechanisms that are probably not associated with altered gene transcription and
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that may involve other cellular targets (Hanneman et al., 1996). TCDD induces phosphorylation
and other responses within minutes of treatment, probably through a nonnuclear role of the Ah
receptor.
Eriksson and Fredriksson (1998) demonstrated that a single oral exposure of NMRI mice
to either 0.51 or 51 mg/kg bw of 3,3',4,4',5,5'-hexachlorobiphenyl (PCB 169) on postnatal day 10
can result in derangement of spontaneous motor behavior. In addition, permanent impairment of
learning and working memory was revealed when these animals reached adulthood. This study
suggests that exposure to PCB 169 during the neonatal period, at a time when there is incomplete
development of the infant's blood-brain barrier and during rapid brain development, can result in
vulnerability of the brain to neurologic effects, which in many cases can only be manifested
during adulthood. In utero and lactational exposure to TCDD (100 ng/kg/d) or coplanar PCBs
resulted in reduction of errors on a radial arm maze working memory task in grown up S-D rats
exposed through their mothers (Seo et al., 1999). The effect was more pronounced in males than
females. There was no difference in performance of the Morris water maze task or the spatial
discrimination-reversal learning task for exposed males and females or unexposed rats. Both
adult male and female S-D rats exposed maternally to TCDD showed a deficit in learning a
visual discrimination-reversal learning task, a finding also observed in monkeys.
Postnatal oral exposure of primates to PCBs can result in long-term behavioral
dysfunctions. In monkeys, oral exposure from birth to 20 weeks of age to 7.5 |ig/kg/day of a
PCB mixture, representative of the PCB residues generally found in human breast milk samples,
also results in significant impairment in discrimination-reversal learning activities (Rice, 1997).
3.6. MECHANISMS OF TOXICITY
The most reliable and consistent symptom of TCDD toxicity among all experimental
animals is weight loss. The cause of the body weight loss seems to be reduced food intake,
apparently occurring secondarily to a physiological adjustment that reduces the body weight to a
maintenance level that is lower than normal. The physiological trigger for this body weight set-
point might be a target for TCDD.
Delayed expression of TCDD-induced toxic responses, including lethality, suggests that
these toxic responses may not be the result of a direct insult by the parent compound (Mukerjee,
1998; Rozman, 1999). Progressive hypoglycemia from feed refusal and reduced
gluconeogenesis seems to be the ultimate cause of TCDD-induced lethality (Gorski et al., 1990).
In the L-E strain rat, reduced gluconeogenesis, indicated by decreased PEPCK activity, has been
suggested to contribute to the acute toxicity of TCDD (Fan and Rozman, 1994; Viluksela et al.,
1999). One of the major causes of TCDD-induced lethality also is dose-dependent reduction of
tryptophan 2,3-dioxygenase (TdO) activity (Fan and Rozman, 1994; Stall et al., 1993), indicating
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that subtle differences in the regulation of intermediary metabolism may be responsible for strain
differences in the susceptibility of rats to TCDD (Fan and Rozman, 1994).
There have been significant advances in understanding the cause of TCDD-induced
voluntary feed refusal. The neurotransmitter 5-hydroxytryptamine (5-HT), or serotonin,
controlled by the availability of the amino acid tryptophan (Carlsson and Lindquist, 1978),
suppresses feed intake behavior (Leibowitz, 1993). TCDD increases the plasma level of free
tryptophan in L-E rats but not H/W rats (Unkila et al., 1994a). Increase in brain tryptophan
levels (Rozman et al., 1991; Unkila et al., 1994b) and 5-HT turnover are closely connected with
changes in plasma tryptophan (Unkila et al., 1994b). In L-E and H/W rats, the potencies of
dioxin congeners highly correlate with their ability to disrupt tryptophan homeostasis. The order
of potency is: TCDD > 1,2,3,7,8-PeCDD > 1,2,3,4,7,8-HxCDD > 1,2,3,4,6,7,8-HpCD (Unkila et
al., 1998). TCDD lethal dose exposure results in increased brain 5-HT synthesis in L-E rats
(Unkila et al., 1993), whereas in resistant H/W rats no such increase of 5-HT occurs. The dose-
related changes in plasma free tryptophan are closely associated with the severity of the wasting
syndrome observed in L-E rats (Unkila et al., 1994b). Increased circulating tryptophan and rapid
turnovers of tryptophan and 5-HT in the brain are associated with TCDD-induced reduced feed
intake, wasting, and lethality (Rozman et al., 1991; Unkila et al., 1994b). However, tryptophan
metabolism or carbohydrate homeostasis does not explain the wide interspecies differences in
susceptibility to acute lethality encountered between guinea pigs (the most acutely susceptible
species) and hamsters (the most resistant species) (Unkila et al., 1995).
Despite extensive research to elucidate the ultimate events underlying the toxic action of
TCDD, definitive answers are not yet available. The toxicity of TCDD apparently depends on
the fact that the four lateral positions of the molecule are occupied by chlorine, resulting in high-
affinity binding to the AhR. Mechanisms of toxicity are discussed in detail in Chapter 2. TCDD
toxicity involves many different types of symptoms, which vary from species to species and
from tissue to tissue, both quantitatively and qualitatively. Age- and sex-related differences in
sensitivity have also been reported. Another characteristic of TCDD toxicity is the delay before
all the endpoints of toxicity are manifested (from 2 weeks to 2 months), which is seen in all
species.
Polymorphism in the Ah locus, which has been shown to be the structural gene for the
cytosolic receptor, seems to determine the sensitivity of genetically different strains of mice to
TCDD and congeners. Ah-responsive strains of mice (e.g., C57BL/6) are characterized by high
hepatic levels of a high-affinity TCDD-receptor protein, highly elevated levels of hepatic
cytochrome P-4501 Al and associated enzyme activities in response to treatment with 3-MC (3-
methylcholanthrene), and sensitivity to the ulcerative action of DMBA (7,12-
December2003 3-29 DRAFT—DO NOT CITE OR QUOTE
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dimethylbenz[a]anthracene) on the skin. Ah-nonresponsive mice (e.g., DBA/2) lack these
characteristics.
Based on these findings, several genetic studies have been performed to elucidate the role
of the receptor in TCDD toxicity. In contrast to 3-MC, TCDD induces AHH activity and several
toxic effects both in Ah-responsive and Ah-nonresponsive strains of mice. The dose required to
produce the effect in an Ah-nonresponsive strain, however, is approximately 10-fold greater than
that needed in an Ah-responsive strain. This indicates that the Ah-nonresponsive strain also
contains the TCDD receptor, but the receptor is defective (Okey and Vella, 1982). Data from
studies of DBA/2 mice given either single or multiple doses of TCDD (Jones and Sweeney,
1980; Smith et al., 1981) suggest that the LD50 in this strain of mice is at least fivefold greater
than the values recorded for the C57BL/6 and C57BL/10 strains (Jones and Greig, 1975; Smith
et al., 1981; Vos et al., 1974). TCDD-induced hepatic porphyria has also been shown to
segregate with the Ah locus in mice (Jones and Sweeney, 1980). The correlative differences
between the C57B1/6 and DBA/2 strains of mice, in terms of altered specific binding of TCDD
and sensitivity to this compound, may not be applicable to other species (Gasiewicz and Rucci,
1984).
In a genetic-crossing experiment between L-E and H/W rats (Pohjanvirta, 1990), it was
demonstrated that the Fx offspring were as resistant to TCDD toxicity as the H/W rats (LD50,
>3,000 jig/kg). Further studies on the F2 generation indicated that the distribution of resistant
and susceptible phenotypes was consistent with inheritance regulated by 2 (possibly 3)
autosomal genes displaying complete dominance, independent segregation, and an additive
coeffect.
Despite enormous variability in the recorded LD50 values for the guinea pig, rat, mouse,
rabbit, and hamster, the amount and physical properties of the hepatic and extrahepatic receptors
are comparable in these species (Gasiewicz and Rucci, 1984; Poland and Knutson, 1982).
Furthermore, although the recorded LD50 values for TCDD vary >100 times among the chick
embryo, the C3H/HeN mouse, and the SD rat, the ED50 doses for AHH induction in these species
are comparable (Poland and Glover, 1974). Even between strains of rats with a difference of
>300 times in LD50, no differences in enzyme induction could be demonstrated (Pohjanvirta et
al., 1988). In the guinea pig, the most TCDD-susceptible species, AHH induction is not a
prominent symptom, even at lethal doses of TCDD. A number of cell types, including primary
cultures and established and transformed cell lines from several species and tissues, are inducible
for AHH activity, indicating the presence of the receptor. Yet toxicity is not expressed in these
systems (Knutson and Poland, 1980a). The available data thus suggest that the receptor for
TCDD may be a prerequisite but is not sufficient in itself for the mediation of toxicity.
December 2003 3-30 DRAFT—DO NOT CITE OR QUOTE
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Recent observations suggest that some of the TCDD-induced toxicity in mice require
other modes of action, beyond AhR-mediated DNA transcription. For example, wasting
syndrome, thymus involution, and loss of adipose tissue in c-src+/+ mice are correlated to c-src
kinase activation, which is physically linked to AhR. These TCDD-induced toxic effects are not
induced in src"7" mice and are marginal in c-src"/+ mice (Matsumura et al., 1997a,b). These toxic
effects can also be prevented in c-src+/+ mice pretreated with geldanamycin, a c-src kinase
inhibitor (Enan et al., 1998a; Dunlap et al., 1999). Based on c-src deficiency not affecting
TCDD induction of the cytochrome P-450 type 1A1 isozyme (CYP1A1), the gene activation
pathway of TCDD's action through the AhR nuclear translocator (ARNT) gene appears to be
independent of the phosphorylation pathway of TCDD toxic activities modulated through the c-
src gene. Involvement of c-src kinase activation in TCDD-induced toxicity has also been
observed in the guinea pig. Enan et al. (1998b) showed that male guinea pigs pretreated with the
src-kinase inhibitor geldanamycin did not suffer TCDD wasting. These investigators obtained
similar results with src-deficient mice. Treatment with estradiol also protected male guinea pigs
from TCDD-induced wasting. Furthermore, a nuclear AhR complex is not required for one of
the signal transduction pathways associated with TCDD-induced early response of the c-fos and
junB genes (Puga et al., 1992; Hoffer et al., 1996).
A strong correlation between lack of AhR affinity and lack of acute TCDD toxicity has
been demonstrated in the knockout AhR"'"mouse. No significant difference in short-term
toxicity was observed between the vehicle control group and knockout homozygous AhR"7" mice
receiving TCDD at 2,000 |ig/kg bw. Postexposure effects at day 28 were limited to vascularities
of the lung and scattered necrosis of hepatocytes in AhR"7" resistant mice. In contrast, lipid
accumulation and inflammatory cell infiltration of the liver were seen in heterozygous AhR+7"-
susceptible mice at the much lower dose of 200 jig/kg TCDD (Fernandez-Salguero et al., 1996).
Although some of the TCDD-induced toxicity of the liver and thymus are mediated by the AhR,
the mechanism for vascularities of the lung and the scattered necrosis of the lung and liver in
AhR knockout mice may involve alternative pathways. As proposed by Matsumura et al.
(1997a, b), these toxicity pathways still require the AhR and associated cytosolic proteins (Enan
and Matsumura, 1996), but not nuclear AhR and DNA transcription.
Mutation of the p53 tumor suppresser gene associated with certain cancers confers
resistance to TCDD-induced acute toxicity. The DBA/2 mouse has a complex mutation in the
promoter region of the Tryp53 locus (the p53 region of the mouse). Both homozygous and
heterozygous Tryp53 knockout mouse types have a high spontaneous incidence of cancer
(Harvey et al., 1993). Inhibition of hepatocellular proliferation due to acute TCDD exposure
also increases expression of the hepatic tumor suppressor p53 gene associated with the cell cycle
inhibitory protein in the Balb/c mouse (Rininger et al., 1997). Results from these studies support
December 2003 3-31 DRAFT—DO NOT CITE OR QUOTE
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the hypothesis, proposed by Blagosklonny (1997), that high levels of the tumor suppressor p53
protein that confer protection against cancer may also increase sensitivity to the acute toxicity of
TCDD.
3.7. SUMMARY
Most of the toxicity data available for TCDD are from oral experiments in animals. Very
few percutaneous and no inhalation exposure toxicity data are available in the literature. Animal
data following oral exposure indicate that TCDD is one of the most toxic compounds known and
that it produces a wide spectrum of toxic effects.
There is a wide range of differences in sensitivity to TCDD lethality. The male guinea
pig is the most sensitive, with an oral LD50 value of 0.6 |ig/kg (Schwetz et al., 1973), and the
male hamster the least sensitive, with an LD50 value of 5,051 jig/kg (Henck et al., 1981). This
difference in sensitivity is more than 8,000-fold. The mink seems to be the second most
sensitive to lethality, with an oral LD50 dose of 4.2 |ig/kg for the male (Hochstein et al., 1988)
and an LC50 value of 0.26 |ig/kg for the female (Hochstein et al., 1998). The oral LD50 value for
the male rat is 22 |ig/kg (Schwetz et al., 1973). The rabbit LD50 value is 115 |ig/kg (Schwetz et
al., 1973). Unlike most toxic chemicals, the lethality of TCDD is delayed, with time to death
being species and strain specific. Single lethal dose exposure results in death within 7-50 days
and is generally associated with a wasting syndrome involving progressive loss of up to 50%
body weight and eventual death without any clear or identifiable lethal pathological lesions. The
characteristic signs and symptoms of lethal toxicity by TCDD are severe weight loss and thymic
atrophy.
At least for acute exposure, the TCDD-induced toxicity appears to depend on the total
dose administered over a given time, either through a single treatment or a limited number of
multiple treatments. One of the consistent signs of TCDD toxicity in most species is thymic
atrophy. Other toxic effects include hyperplasia or atrophy of the spleen, testes, or ovaries, bone
marrow depletion, and systemic hemorrhage. Severe chloracne is one of the signs of TCDD
exposure in people (Crow, 1978). Similar lesions or precursor lesions can be induced by TCDD
in cattle (McConnell et al., 1980), rhesus monkeys (Norback and Allen, 1973; McConnell et al.,
1978b), rabbits (Schwetz et al., 1973), and hairless mice (Knutson and Poland, 1982; Puhvel et
al., 1982). The liver is extremely sensitive to TCDD toxicity in all animals, regardless of
duration of exposure. The degree of severity of liver toxicity seems to be species-specific.
Morphological alterations in liver toxicity are less severe in the guinea pig, the most sensitive
species to lethality, than other species. The hamster, the most resistant species for lethality,
shows liver lesions after a prolonged period of chronic exposure to nonlethal doses. Toxic
effects include liver weight changes, fatty liver, impaired liver function characterized by
increased microsomal monooxygenase, SGOT and SGPT activities, porphyrin accumulation,
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impaired membrane function, hyperbilirubinemia, hypercholesterolemia, and hyperproteinemia.
Severe episodes of toxic hepatitis have been observed in rats and mice. Various physiological
equilibrium processes, such as vitamin A storage, plasma membrane functions, and the formation
of keratin and cell differentiation, are affected by TCDD exposure. Pericardial and peritoneal
edema resulting in death occur in chickens (Firestone, 1973). Systemic edema has also been
observed in monkeys (Norback and Allen, 1973) and mice (Vos et al., 1974).
Sensitivity to TCDD toxicity segregates with the Ah locus. The potency of other
congeners to induce lethality correlates with their ability to bind to the Ah receptor. Other
congeners are less toxic than 2,3,7,8-TCDD. The lateral 2,3,7, and 8 position of the dioxin
molecule must be chlorinated (or halogenated) to induce the greatest toxicity. The addition of
chlorine atoms reduces toxicity. Increased microsomal AHH and EROD activities are markers
for CYP1 Al (discussed in Chapter 2) and are associated — not necessarily causally — with the
systemic toxicity of PCDDs, PCDFs, and coplaner PCBs.
Except for 2,3,7,8-TCDD and a mixture of 1,2,3,6,7,8- and 1,2,3,7,8,9-HxCDD, there are
no long-term chronic bioassay data for PCDD/PCDF congeners that can be used for assessing
the chronic risk. In the absence of any long term chronic toxicity bioassay data, enzymatic
activity and other short-term in vivo and in vitro data are used in developing a toxicity ranking
scheme (see Chapter 9).
One of the possible mechanisms by which TCDD and related compounds interfere with
normal endocrine function is the ability to disrupt natural hormones. Hirsutism and diminished
libido caused by TCDD seems to be due to its endocrine disruptive activities. A single oral dose
of 20 |ig/kg to rats can reduce serum testosterone levels (Nienstedt et al., 1979). Catabolism of
exogenous estrone in TCDD-pretreated, ovariectomized rats is decreased (Shiverick and Muther,
1983). Alterations in hormonal levels by TCDD and its antiestrogenic action are discussed in
Chapter 5. TCDD tumor promotion activity in liver carcinogenesis can be prevented in
ovariectomized rats, indicating that estrogen status influences TCDD toxicity (Lucier et al.,
1991). Animals exposed to TCDD and related compounds in utero or as infants exhibit varying
degrees of behavioral disorders. These disorders resemble those seen in infants exposed to
agents resulting in thyroid hormone deficiencies in utero or in infancy. Thyroidectomy of
animals resulted in partial protection from TCDD-induced wasting syndrome and
immunotoxicity, suggesting possible involvement of thyroid function in the manifestation of
pathological conditions (Bastomsky, 1977; Rozman et al., 1984; Pazdernik and Rozman, 1985).
Monkeys exposed for 4 years to TCDD at low ppt levels and studied for an additional 7-10 years
have been reported to develop endometriosis (Rier et al., 1993). Estrogen, glucocorticoid,
prolactin, insulin, gastrin, melatonin, and other hormones are affected by TCDD either by its
December 2003 3-33 DRAFT—DO NOT CITE OR QUOTE
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activity on the hormone or receptor. Further studies are needed to elucidate the mechanism of
TCDD-induced endocrine disruptive activities.
The subchronic NOAEL for porphyria in female SD rats is estimated to be 0.01
|ig/kg/week (= 0.001 |ig/kg/day = 1 ng/kg/day) (Goldstein et al., 1982). A chronic NOAEL of
1 ng/kg/day for hepatotoxicity is estimated for SD rats from a 2-year chronic study (Kociba et
al., 1978). In addition to liver toxicity, chronic exposure has been found to be associated with
amyloidosis and dermatitis in Swiss mice (Toth et al., 1979). From this study, a LOAEL of
1 ng/kg/day for amyloidosis and dermatitis has been estimated for mice. Chronic exposure to 1.5
ng/kg/day in diet results in hair loss, edema, and pancytopenia in monkeys (Allen et al., 1977;
Schantz et al., 1979). The lowest doses that have been demonstrated to elicit various biological
responses in certain animals are compiled in Table 3-4.
Single-dose acute exposures to CDDs yield a large area under the curve (AUC) because
of their long half-life and thus may be viewed as subchronic exposures. Sub chronic/chronic
exposures yield similar toxicity profiles to an acute exposure when similar total cumulative
doses are administered. When corrected for excretion, depending on half-life, the corrected
cumulative sub chronic/chronic exposure doses seem to be analogous to the acute exposure
doses. Similar data are available for 2,3,7,8-TCDF (loannou et al., 1983).
It is evident, from the complex picture evolving from the data outlined above, that TCDD
elicits a variety of toxic responses following both short-term and long-term exposure. It is also
clearly evident that there are very large differences in the sensitivity to specific TCDD-induced
toxicities among various species and strains. This conclusion is valid for the severity of effects
of almost all the responses studied. Qualitatively, however, there seems to be fairly good
agreement among the types of responses that can be observed. For example, almost all responses
can be produced in every species and strain if the right dose is chosen. Tissues or cell lines from
humans and animals seem to respond to dioxin at similar exposure levels and in identical ways in
regard to CYP1A1 activity, cytotoxicity, and inhibition of cell proliferation (DeVito et al., 1995).
In highly sensitive species (e.g., the guinea pig), lethality may prevent some responses from
occurring. Our present knowledge rules out enzyme induction as such, as the proximate cause of
toxicity and death. Although the toxicokinetics of TCDD vary between species, these
differences are not sufficient to explain the variabilities in sensitivity to TCDD lethality. The
available data indicate an involvement of TCDD in processes regulating cellular differentiation
and proliferation, as well as those controlling endocrine homeostasis. Alterations in the
regulation of such processes, which are not equally active in all cells throughout the organism,
would be expected to result in effects that vary among tissues and species. The overwhelming
number of toxic responses to TCDD, including lethality, typically show a delay in their
appearance. This supports the assumption that these responses are not the result of a direct insult
December 2003 3-34 DRAFT—DO NOT CITE OR QUOTE
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from the compound. A lethal dose of TCDD in rats increases the neurotransmitter 5HT,
controlled by increased levels of tryptophan in plasma and brain, suppresses voluntary feed
consumption resulting in wasting syndrome, and leads eventually to mortality a few weeks
postexposure. The induction of hepatic cytochrome P-450-dependent monooxygenases (such as
CYP1A1) is one of the hallmarks of TCDD exposure. This effect has been demonstrated to be
mediated through interaction with a specific protein called the Ah receptor. This process
involves the binding of TCDD to the receptor, followed by the binding of the receptor-ligand
complex to DNA recognition sites. This leads to the expression of specific genes and translation
of their protein products, which then mediate their biological effects. As discussed in detail in
Chapter 2, the mechanisms of action for enzyme induction are understood mainly at the
molecular level, where the Ah receptor and its genetic regulation is clearly an important
mechanistic step. Very little, however, is known about the mechanisms of the middle and high
dose responses at the molecular level.
Studies in congenic mice that are relatively Ah-responsive or Ah-nonresponsive have
demonstrated that the majority of TCDD-induced toxic responses segregate with the Ah locus.
However, the number and affinity of Ah receptor expressed in most laboratory species and
strains are rather comparable. The Ah receptor is thus unlikely to be the only determinant of
TCDD-induced toxicity. Rather, it has to be assumed that species and strain differences are
confined to the latter parts of the receptor-mediated chain of events (e.g., binding of the receptor-
ligand complex to DNA and the subsequent expression of specific genes). In some cases, the
binding affinity of the Ah receptor is different or defective. Some of the responses may be
secondary in the sense that they are caused by the altered homeostasis of endogenous
compounds, caused by TCDD-induced increased activities of various enzymes. An additional
AhR-related pathway involving c-src kinase in the cytoplasm has been implicated in wasting
syndrome, thymus atrophy, and loss of adipose tissue in mice.
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Table 3-1. Acute lethality of TCDD to various species and substrains
Species/strain (sex)
Guinea pig/Hartley (male)
Mink/NR (male)
Chicken/NR (NR)
Monkey/rhesus (female)
Rat/L-E (male)
Rat/Sherman, Spartan
(male)
(female)
Rat/Sprague-Dawley
(male)
(female)
(weanling male)
Rat/Fischer Harlan (male)
Rat/H/W (male)
Mouse/136 (male)
D2A/2J (male)
B6D2F1 (male)
Mouse/B6
D2
B6D2F1
Rabbit/New Zealand White (male and
female)
Route
per os
per os
per os
per os
intraperitoneal
per os
intraperitoneal
per os
intraperitoneal
per os
intraperitoneal
per os
dermal
LD50
(US/kg)
0.6-2.1
4.2
<25
-70
-10
22
13-43
60
25
25
340
>3,000
182
2,570
296
132
620
300
115
275
Time of Death
(days
postexposure)
5-34
7-17
12-21
14-34
15-23
9-27
NR
28b
23-34
24"
21"
25b
NR
6-39
12-22
Follow-
up
(days)
NR
30
28
NR
42-47
48-49
NR
20
30
39-48
30
NR
NR
22
Body
weight
loss3
(%)
50
31
NR
13-38
39
NR
NR
43
40-53
25
33
34
NR
NR
NR
References
McConnell et al., 1978a; Schwetz et
al., 1973
Hochsteinetal., 1988
Greig et al., 1973
McConnell et al., 1978b
Tuomisto and Pohjanvirta, 1987
Schwetz et al., 1973
Beatty et al., 1978
Walden and Schiller, 1985
Pohjanvirta and Tuomisto, 1987;
Pohjanvirta et al., 1988
Chapman and Schiller, 1985
Neal et al., 1982
Schwetz et al., 1973
-------
o
O
to
O
O
Table 3-1. Acute lethality of TCDD to various species and substrains (continued)
Species/strain (sex)
Rabbit/New Zealand White (male and
female)
Hamster/Golden Syrian (male and female)
Hamster/Golden Syrian (male and female)
Route
intraperitoneal
per os
intraperitoneal
LD50
3,000
Time of death
(days post-exposure)
7-10
2-47
14-32
Follow-up
(days)
10-20
50
55
Body weight
loss"
(%)
11
NR
r
References
Brewsteretal., 1988
Hencketal., 1981
Olson et al., 1980
O
O
*
O
H
O
HH
H
W
O
&
O
c
O
H
W
"Of succumbed animals.
bMean time to death.
°Data from five animals.
NR = Not reported.
-------
o
O
to
O
O
Table 3-2. Toxic response following exposure to 2,3,7,8-TCDD: species differences"
oo
O
O
*
O
H
O
HH
H
W
O
?d
O
c
O
H
W
Response
Monkey
Guinea pig
Cow"
Rat
Mouse
Rabbit"
Chicken"
Hamster
Hyperplasia or metaplasia
Gastric mucosa
Intestinal mucosa
Urinary tract
Bile duct or
gall bladder
Lung: focal
alveolar
Skin
++
+
++
++
++
0
++
0
0
+
++
+
+c
0
0
++
0
0
0
++
0
++
0
++
0
0
Hypoplasia, atrophy, or necrosis
Thymus
Bone marrow
Testicle
+
+
+
+
+
+
+
+
+
+
±
+
+
+
+
Other responses
Liver lesions
Porphyria
Edema
+
0
+
±
0
0
++
+
+
0
++
++
+
+
+
+
++
±
0
+
"Sources: Allen and Lalich, 1962; Allen et al., 1977; Henck et al., 1981; Kimmig and Schultz, 1957; Kociba et al., 1978, 1979; McConnell, 1980; McConnell et al.,
1978a,b; Moore et al., 1979; Norback and Allen, 1973; Olson et al., 1980; Schwetz et al., 1973; Turner and Collins, 1983; Vos et al., 1973; Vos and Beems, 1971; Vos
andKoeman, 1970.
bResponses followed exposure to 2,3,7,8-TCDD or stucturally related chlorinated hydrocarbons.
°Skin lesions in cattle have been observed, but they differ from the skin lesions observed in other species.
0 = lesion not observed, + = lesion observed (number of "+" denotes severity), ± = lesion observed to a very limited extent, blank = no evidence reported in literature.
-------
o
O
to
O
O
Table 3-3. Studies on chronic exposure (except for studies on cancer) to TCDD in laboratory animals
Species/strain
Rats/Sprague-
Dawley
Rats/Sprague-
Dawley
Mice/Swiss
Mice/B6C3Fl
Monkey/Macaca
mulatta
Sex and no.
per group
M/10
M, F/10
M/38-44
M/50, F/50
M/75, F/75
F/8
Doses tested
0, 1, 5, 50, 500, 1,000, 5,000,
50,000, 500,000, 1,000,000 ng/kg
0.001, 0.01, 0.1 ng/kg/day
0, 0.007, 0.7, 7.0 ng/kg/week
0.01, 0.05, 0.5 ng/kg/week
(males)
0.04, 0.2, 2.0 ng/kg/week
(females)
0.0
500 ng/kg
Treatment
schedule
Continuous in diet
for 65 weeks
Continuous in diet
for 2 years
Gavage weekly
for 1 year
Gavage biweekly
for 2 years
Continuous in the
diet for 9 months
Parameters monitored
Survival
Extensive histopathology,
hematology, and clinical
chemistry
Histopathology
Extensive histopathology
Extensive histopathology,
hematology, and clinical
chemistry
References
van Miller et
al., 1977
Kociba et al.,
1978, 1979
Toth et al.,
1979
NTP, 1982
Allen et al.,
1977
OJ
VO
fe
H
O
O
O
H
O
HH
H
W
O
c
O
H
W
-------
o
O
to
O
O
oo
Table 3-4. Lowest effect levels for biological responses of 2,3,7,8-TCDD in experimental animals
Species
Guinea pigs
Rhesus monkey
Sprague-Dawley
rat
Marmoset
monkey
Guinea pig
Swiss mouse
Rhesus monkey
Rhesus monkey
Sprague-Dawley
rat
Dose or concentration and duration
0.6 ng/kg, single oral dose
1.0 fig/kg, single oral dose
2.0 ng/kg, single oral dosea
3.0 ng/kg, single oral dose
1 ng/kg-day for 8 weeks
1 ng/kg-day for 1 year
500 ppt in diet for 9 months
(12 ng/kg-day); 2 ppb in diet for 61 days
(50 ng/kg-day)
50 ppt in diet for 20 months
(1.5 ng/kg-day)
10 ng/kg-day for 2 years in feed
Effect
Lethality (single dose LD,0)
Acute (systemic) toxicity
Induction of AHH (CYP1A1)
Induction of N-demethylation (CYP1A2)
Immunosuppression (decreased response
to tetanus toxin)
Amyloidosis and dermatitis
Chronic lethality
Chronic toxicity (hair loss)
Porphyrin metabolism
Reference
Schwetzetal., 1973
McNulty, 1977
Kitchin and Woods, 1979
Krugeretal., 1990
Zinkletal., 1973
Tothetal., 1979
Allen et al., 1977; McNulty,
1977
Schantz et al., 1979
Kocibaetal., 1978
OJ
o
fe
H
O
O
o
H
O
a0.6 ng/kg = no effect level.
O
&
O
c
O
H
W
-------
REFERENCES FOR CHAPTER 3
Abraham, K; Krowke, R; Heubert, D. (1988) Pharmacokinetics and biological activity of 2,3,7,8-tetrachlorodibenzo-
p-dioxin. 1. Dose-dependent tissue distribution and induction of hepatic ethoxyresorufin-O-deethylase in rats
following a single injection. Arch Toxicol 62:359-368.
Aitio, A; Parkki, MG; Marniemi, J. (1979) Different effect of 2,3,7,8-tetrachlorodibenzo-/>-dioxin on glucuronide
conjugation of various aglycones. Studies in Wistar and Gunn rats. Toxicol Appl Pharmacol 47:55-60.
Albro, PW; Corbett, JT; Harris, M; et al. (1978) Effects of 2,3,7,8-tetrachlorodibenzo-/>-dioxin and lipid profiles in
tissues of the Fischer rat. Chem Biol Interact 23:315-330.
Allen, JR; Lalich, JJ. (1962) Response of chickens to prolonged feeding of crude "toxic fat." Proc Soc Exp Biol Med
109:48-51.
Allen, JR; Barsotti, DA; van Miller, JP; et al. (1977) Morphological changes in monkeys consuming a diet
containing low levels of 2,3,7,8-tetrachlorodibenzo-p-dioxin. Food Cosmet Toxicol 15(5):401-410.
Alsharif, NZ; Grandjean, CJ; Murray, WJ. (1990) 2,3,7,8-Tetrachlorodibenzo-/>-dioxin (TCDD)-induced decrease in
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4. IMMUNOTOXICITY
4.1. INTRODUCTION
Concern over the potential toxic effects of chemicals on the immune system arises from
the critical role of the immune system in maintaining health. It is well recognized that
suppressed immunological function can result in increased incidence and severity of infectious
diseases as well as some types of cancer. Conversely, inappropriate enhancement of immune
function or the generation of misdirected immune responses can precipitate or exacerbate
development of allergic and autoimmune diseases. Thus, both suppression and enhancement of
immune function are considered to represent potential immunotoxic effects of chemicals.
The immune system consists of a complex network of cells and soluble mediators that
interact in a highly regulated manner to generate immune responses of appropriate magnitude and
duration. Consequently, comprehensive evaluation of immunotoxicity must include specific
assessments of multiple functional parameters on a kinetic basis. In addition, because an
immune response develops in a time-dependent manner relative to antigen exposure, the
immunotoxicity of a chemical can be profoundly influenced by the timing of chemical exposure
relative to antigen challenge. Consideration of these levels of complexity involved in
immunotoxicology assessment is critical for interpreting the effects of chemical exposure on
immune function (Kerkvliet, 1994).
Extensive evidence has accumulated to demonstrate that the immune system is a target
for toxicity of 2,3,7,8-tetrachlorodibenzo:p-dioxin (TCDD) and structurally related
polyhalogenated aromatic hydrocarbons (PHAHs), including the polychlorinated dibenzodioxins
(PCDDs), polychlorinated dibenzofurans (PCDFs), polychlorinated biphenyls (PCBs), and
polybrominated biphenyls (PBBs). This evidence was derived primarily from numerous studies
in various animal species, mainly rodents, but also guinea pigs, rabbits, monkeys, marmosets,
and cattle. Epidemiological studies also provide some evidence that PHAHs alter immune
parameters in humans. In animals, relatively high doses of PHAHs produce lymphoid tissue
depletion, except in the thymus, where lower doses cause cellular depletion. Alterations in
specific immune effector functions and increased susceptibility to infectious disease have been
identified at doses of TCDD well below those that cause significant lymphoid tissue depletion.
Both cell-mediated and humoral immune responses are suppressed following TCDD exposure,
which suggests that multiple cellular targets within the immune system are altered by TCDD.
Evidence also suggests that the immune system is indirectly targeted by TCDD-induced changes
in nonlymphoid tissues. In addition, in parallel with increased understanding of the cellular and
molecular mechanisms involved in immunity, studies on TCDD are beginning to establish
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biochemical and molecular mechanisms of TCDD immunotoxicity. These advances are
highlighted in this document.
There is an enormous literature based on descriptive studies of the immunotoxic effects
of TCDD and related PHAHs in laboratory animals. Unfortunately, widely differing
experimental designs, exposure protocols, and immunologic assays used, have made it difficult
to define a "TCDD-induced immunotoxic syndrome" in a single species, let alone across species.
Before 1994, only one report directly compared the effects of TCDD on the immune system of
rats, mice, and guinea pigs, and even then, different immunologic parameters were assessed, and
different antigens were used in the different species (Vos et al., 1973). In that study, the
delayed-type hypersensitivity (DTK) response to tuberculin was evaluated in guinea pigs and rats
for assessment of cell-mediated immunity, and the graft versus host (GVH) response was
measured in mice. A decreased DTH response to tuberculin was observed in guinea pigs
following 8 weekly doses of 40 ng/kg TCDD (total dose, 320 ng/kg), whereas in rats the DTH
response to tuberculin was unaffected by 6 weekly doses of 5 |ig/kg TCDD (total dose, 30,000
ng/kg). The GVH response in mice was suppressed by 4 weekly doses of 5 |_ig/kg TCDD (total
dose, 20,000 ng/kg). The greater sensitivity of guinea pigs compared with rats and mice to the
immunosuppressive effects of TCDD is consistent with their greater sensitivity to other toxic
effects of TCDD (McConnell et al., 1978; Poland and Knutson, 1982).
Although the results of Vos et al. (1973) appear to suggest that cell-mediated immunity in
mice is more sensitive to TCDD than in rats, no studies have directly compared cell-mediated
immunity in rats and mice using the same antigens and endpoints. However, a 1994 study made
a direct comparison of the effects of TCDD on humoral immunity in rats and mice (Smialowicz
et al., 1994). In this study, the primary plaque-forming cell (PFC) response to sheep red blood
cells (SRBCs) was suppressed in E6C3Fl mice (ED50 of 0.68 |_ig/kg TCDD), but the anti-SRBC
response was either unaffected or enhanced in Long Evans and Fischer 344 rats, respectively, at a
dose as high as 30 pg/kg TCDD. This response of rats was corroborated by Fan et al. (1996),
who observed that TCDD at 20 and 40 pg/kg did not alter serum IgM levels to SRBC, whereas
IgG levels were enhanced. In contrast, the primary PFC and serum antibody response to the T
cell-independent (TI) antigen trinitrophenyl lipopolysaccharide (TNP-LPS) was reported to be
suppressed in both mice and rats following exposure to TCDD at 10 and 30 pg/kg, respectively
(Smialowicz et al., 1996).
In a study in mice (Clark et al., 1981), the DTH response to oxazolone was suppressed by
4 weekly doses of 4 pg/kg TCDD (total dose, 16,000 ng/kg), whereas the DTH response to
SRBC was unaffected by a 10-fold higher dose of TCDD, which illustrates that DTH responses
to different antigens are not equally sensitive to TCDD-induced suppression, even in the same
species. When PCB and PBB studies are considered, variable effects on DTH and other immune
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reactions are also apparent (Vos and van Driel-Grootenhuis, 1972; Thomas and Hinsdill, 1978;
Fraker, 1980; Luster et al., 1980a). Because the exact basis for the interstudy variability is not
known, it would serve no useful purpose in terms of risk assessment to catalog all of the reported
effects of TCDD and other PHAHs on the immune system. Several comprehensive reviews have
been published on the immunotoxic effects of PHAHs in general (Kerkvliet, 1984; Vos and
Luster, 1989; Kerkvliet and Burleson, 1994; Holsapple, 1995) and TCDD in particular
(Holsapple et al., 1991a, b). The reader is also referred to the previous EPA TCDD risk
assessment document (Sonawane et al., 1988) for another perspective on TCDD immunotoxicity.
The present document does not address this extensive literature, but rather emphasizes more
recent developments in the field of PHAH immunotoxicity that may assist in the risk assessment
process. Gaps in our knowledge that require further research are also identified.
4.2. ROLE OF THE AH LOCUS IN PHAH IMMUNOTOXICITY
One of the most important advances in the study of PHAH toxicity in recent years has
been the elucidation of a genetic basis for sensitivity to the toxicity of these chemicals, which
may ultimately provide a logical explanation for many of the controversial data in the literature
on PHAH toxicity in different species and in different tissues of the same species. In this regard,
many biochemical and toxic effects of PHAHs appear to be mediated via binding to an
intracellular protein known as the aryl hydrocarbon (Ah) or TCDD receptor in a process similar
to steroid hormone receptor-mediated responses (Poland and Knutson, 1982; Cuthill et al., 1988).
Ah receptor (AhR) activation follows stereospecific ligand binding; interaction of the
receptor-ligand complex with dioxin-response elements (DREs) in the genome induces
transcription of the structural genes encoding mRNA for CYP1A1 enzyme activity (i.e.,
cytochrome P4501A1) as well as expression of additional unidentified genes, the products of
which are hypothesized to mediate PHAH toxicity (Whitlock, 1990). Differences in toxic
potency between various PHAH congeners generally correlate with differences in AhR-binding
affinities. The most toxic PHAH congeners are approximate stereoisomers of 2,3,7,8-TCDD and
are halogen substituted in at least three of the four lateral positions in the aromatic ring system.
In mice, allelic variation at the Ah locus has been described (Poland et al., 1987; Poland
and Glover, 1990). The different alleles code for AhRs that differ in their ability to bind TCDD
and thus help to explain the different sensitivities of various inbred mouse strains to TCDD
toxicity. Ahbb-C57Bl/6 (B6) mice represent the prototype "responsive" strain and are the most
sensitive to TCDD toxicity, whereas Ahdd-DBA/2 (D2) mice represent the prototypic
"nonresponsive" strain and require higher doses of TCDD to produce the same toxic effect.
Congenic Ahdd mice on a B6 background have been derived that differ from conventional B6
mice primarily at the Ah locus. The spectrum of biochemical and toxic responses to TCDD
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exposure was similar in both strains, but the doses needed to bring about the responses were
significantly higher in congenic mice homozygous for the Ahd allele compared with mice
carrying two Ahb alleles (Birnbaum et al., 1990; Kerkvliet et al., 1990a).
Two lines of evidence have been used to investigate AhR dependence of acute
immunotoxicity of TCDD and related PHAHs: (1) comparative studies using PCDD, PCDF, and
PCB congeners that differ in their binding affinity for the AhR and (2) studies using mice of
different genetic background known to differ at the Ah locus.
A comparison of the relative potency of PHAHs that differ in their binding affinity for the
AhR is presented in Tables 4-1 and 4-2. These data are based on single-dose exposure (oral or
intraperitoneal) of B6 mice to the various PHAHs for suppression of the anti-SRBC response and
the cytotoxic T lymphocyte (CTL) response, respectively. As shown in Table 4-1, the potency of
TCDD to suppress the primary antibody response to SRBCs has been reported by several
laboratories, with remarkable agreement in the ID50 value of 0.7 pg/kg in B6 mice. The ID50 of
B6C3FJ mice has been reported to be lower (<0.1 pg/kg) (Narasimhan et al., 1994), similar (<1
|-ig/kg) House et al., 1990; Smialowicz et al., 1994), or slightly higher (1.2 pg/kg) (Holsapple et
al., 1986a) in comparison with B6 mice. It should be noted that 4 weekly i.p. doses of 10 but not
1 or 0.1 i-ig/kg TCDD significantly suppressed the anti-SRBC response in B6 mice (Clark et al.,
1981). This sub-chronic dosing protocol (Clark et al., 1981) does not readily explain this
decreased potency because Vecchi et al. (1983) reported that 5 weekly doses of 2 pg/kg or 8
weekly doses of 0.5 pg/kg TCDD significantly suppressed the anti-SRBC response. The basis
for the discrepancies between the data of Clark et al. (1981) and other laboratories regarding the
potency of TCDD to suppress the anti-SRBC response is unknown.
In contrast to the reproducible data on TCDD, relative potency for other PHAH congeners
shown in Table 4-1 is difficult to evaluate because few congeners have been examined in more
than one study. In the few cases where the same congener has been evaluated independently,
discrepancies in the data exist. For example, both Davis and Safe (1990) and Silkworm et al.
(1984) evaluated the potency of the 2,3,4,5,3',4'-HxCB congener (PCB 156) in the anti-SRBC
response. The rD50s from these two data sets differ by almost two orders of magnitude (0.7
mg/kg vs. 31 mg/kg, respectively). When the same congener was compared with TCDD for
suppression of the CTL response, the ID50 was 70 mg/kg (Table 4-2). Although the basis for
these discrepancies between laboratories and immune function endpoints is unknown, it is
apparent that the immunotoxicity database should be expanded so that these differences can be
resolved.
Vecchi et al. (1983) were the first to report that antibody response to SRBCs was
differentially suppressed by TCDD in B6 mice compared with D2 mice, such that D2 mice
required a dose approximately 10 times higher to produce the same degree of suppression.
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Immunosuppression in Fl and backcross mice supported the role of the Ah locus in expression of
TCDD immunotoxicity. 2,3,7,8-TCDF was significantly less potent than TCDD and showed a
similar differential immunosuppressive effect in B6 and D2 mice. At the same time, Silkworm
and Grabstein (1982) reported a B6 versus D2 strain-dependent difference in sensitivity to
suppression of the anti-SRBC response by 3,4,3',4'-tetrachlorobiphenyl (PCB 77), a ligand for
the AhR. In comparison, the 2,5,2',5'-tetrachlorobiphenyl (PCB 52) isomer, which lacks affinity
for the AhR, was not immunosuppressive in either B6 or D2 mice. Structure-activity
relationships were extended by Kerkvliet et al. (1985) in studies that compared the
immunosuppressive potency of chlorinated dioxin and furan isomers that contaminate technical-
grade pentachlorophenol. The 1,2,3,6,7,8-hexachlorinated dibenzo-^-dioxin (HxCDD),
1,2,3,4,6,7,8-heptachlorinated dibenzo-p-dioxin (HpCDD), and 1,2,3,4,6,7,8-heptachlorinated
dibenzofuran (HpCDF) isomers, which bind the receptor, were all significantly
immunosuppressive. The dose of each isomer that produced 50% suppression of the anti-SRBC
response (ID50) was 7.1, 85, and 208 |_ig/kg for HxCDD, HpCDD, and HpCDF, respectively
(Figure 4-1). The ID50 for TCDD was 0.65 |_ig/kg based on the data of Vecchi et al. (1980).
More extensive structure-dependent immunosuppressive activities of technical grade PCB
mixtures (Davis and Safe, 1990), PCB congeners (Davis and Safe, 1989), and PCDF congeners
(Davis and Safe, 1988) have also been reported. Results of these studies using different PHAH
congeners are summarized in Table 4-1.
The role of the AhR in suppression of the anti-SRBC response was verified in studies
using B6 mice congenic at the Ah locus (Kerkvliet et al., 1990a). As expected, congenic Ahdd-B6
mice were significantly less sensitive to TCDD-induced immune suppression as compared with
wild-type Ahbb-B6 mice. Unexpectedly, however, the dose response in congenic Ahdd-B6 mice
appeared to be bimodal, with a portion of the response sensitive to suppression by low doses of
TCDD. Because of the bimodal response, the data did not permit extrapolation of an ID50 dose in
the congenic mice. The results were interpreted to suggest potential non-AhR-mediated
immunosuppressive effects. It should be noted, however, that studies by Silkworm et al. (1993)
using rederived congenic Ahdd-B6 mice did not corroborate a bimodal dose response. Their data,
which indicated an ID50 of 7.5 |_ig/kg in congenic Ahdd-B6 mice compared with an ID50 of 0.54
|-ig/kg in Ahbb-B6 mice, are consistent with an AhR-dependent mechanism of immune
suppression.
AhR dependency of PHAH immunotoxicity has also been demonstrated in mice using
other immunologic responses. For example, Kerkvliet et al. (1990a) reported that the ID50 for
suppression of the antibody response to TNP-LPS in Ahbb-B6 mice was 7.0 |_ig/kg compared with
a significantly higher ID50 of 30 |_ig/kg in congenic Ahdd-B6 mice. Because the antibody response
to TNP-LPS shows little requirement for macrophages or T helper cells (Jelinek and Lipsky,
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1987), these results suggest an AhR-dependent B cell response. In terms of cytotoxic T cells,
Clark et al. (1983) were first to report data suggesting that TCDD and PCB isomers suppressed in
vitro CTL responses of B6 and D2 mice through an AhR-dependent mechanism. Subsequently,
Kerkvliet et al. (1990b) reported that B6 mice congenic at the Ah locus showed Ah-dependent
sensitivity to suppression of the CTL response following exposure to either TCDD or
3,4,5,3',4',5'-hexachlorobiphenyl (HxCB) (PCB 169). Furthermore, the potency of TCDD and
of three HxCB congeners to suppress the CTL response of mice directly correlated with their
relative binding affinities for the AhR (Table 4-2, from Kerkvliet et al., 1990b). The ID50 of
TCDD for suppression of the CTL response in B6 mice was 7.0 pg/kg.
It should be noted that the dose of TCDD required to suppress the CTL response reported
by Kerkvliet et al. (1990b) is significantly greater than that reported by Clark et al. (1981), who
reported CTL suppression following 4 weekly doses of 0.1 |_ig/kg TCDD. Clark et al. (1983) also
reported that doses of TCDD as low as 4 ng/kg to B6 mice suppressed in vitro generation of CTL
and that the suppression was Ah dependent. The potency of TCDD described in Clark's studies
has not been corroborated by other laboratories (Holsapple et al., 1991b; Hanson and
Smialowicz, 1994). For example, the in vivo and in vitro CTL response in B6 mice was not
affected at doses ranging from 0.01 to 3.0 pg/kg given at weekly intervals for 4 weeks (Hanson
and Smialowicz, 1994).
If immunotoxicity of TCDD and structurally related PHAHs depends on AhR-mediated
mechanisms, then co-exposure to subsaturating levels of more than one Ah agonist should
produce additive effects. An additive interaction has been demonstrated in mice coexposed to
1,2,3,6,7,8-HxCDD and 1,2,3,4,6,7,8-HpCDD, two relatively strong AhR ligands (Kerkvliet et
al., 1985). On the other hand, coexposure of mice to an immunotoxic dose of TCDD and a
subimmunotoxic dose of different commercial Aroclors or certain PCB congeners resulted in
partial antagonism of TCDD suppression of the anti-SRBC response (Bannister et al., 1987;
Davis and Safe 1988, 1989). An apparently similar antagonism was observed following
coexposure to 2,3,7,8-TCDF (10 |_ig/kg) and TCDD (1.2 |_ig/kg) (Rizzardini et al., 1983). The
mechanism for this antagonism has not been fully elucidated, but the effects are consistent with
competition for binding at the AhR because the weaker agonist was administered in excess
compared with TCDD. In other studies, Silkworm et al. (1988, 1993) have shown that
immunotoxicity of TCDD can be modified by coexposure to other PHAHs present as
cocontaminants of actual environmental samples from Love Canal, New York. Smialowicz et al.
(1997) examined the anti-SRBC response in mice cotreated with TCDD and 2, 2', 4, 4', 5, 5'-
hexachlorobiphenyl (PCB 153). PCB 153 alone enhanced this response such that when given with
an immunosuppressive dose of TCDD (1 pg/kg) the anti-SRBC response was not suppressed.
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These results indicate that PCB153 acts as a functional rather than an AhR or dispositional
antagonist of TCDD-induced immunosuppression.
Recent work by Sulentic et al. (1998) provides evidence for the critical role of the AhR in
TCDD-induced suppression of IgM secretion. B cell lines, which differ in their expression of the
AhR (i.e., CH12.LX cells express AhR and BCL-1 cells do not), were used in this study. TCDD
treatment resulted in marked induction of AhR expression as well as suppression of LPS-induced
IgM secretion in CH12.LX but not BCL-1 cells. Furthermore, TCDD induced CYP1AI
induction in CH12.LX but not BCL-1 cells. These results implicate the AhR as a critical factor
in TCDD-induced inhibition of IgM secretion.
The data indicate that suppression of the antibody response to T cell-dependent and
-independent antigens and the CTL response by PHAHs are primarily AhR-dependent.
However, the underlying mechanism(s) for these effects remain to be elucidated. It should also
be emphasized that the data supporting an AhR dependency have been obtained from studies in
inbred mice using an acute or subacute exposure regimen. Except for thymic atrophy,
structure-immunotoxicity relationships in other species, including rats, have not been established,
and inbred strains of other species with defined Ah genotype are not currently available.
Nevertheless, it is important to note that thymic cortical atrophy does not occur in AhR deficient
(AhR"7") mice despite having received a 10-fold higher TCDD dose than mice expressing a
functional AhR (AhR+/"), which experience significant reduction in the size and cellularity of
thymic cortical areas (Fernandez-Salguero et al., 1996).
Results from other studies suggest that non-Ah-dependent effects may also occur. For
example, mice exposed for 14 days to 2,7-dichlorodibenzo:p-dioxin (2,7-DCDD), a dioxin
congener with very weak affinity for the AhR, had suppressed antibody responses to SRBC
(Holsapple et al., 1986b). This suppression was observed in the absence of any change in thymus
weight or in AHH activity. More recently, Morris et al. (1992) reported that sensitivity of D2
mice to TCDD-induced suppression of the anti-SRBC response increased significantly when
TCDD was administered daily over 2 weeks rather than as an acute single dose. Unfortunately,
in these studies, the lowest dose of TCDD produced near-maximum suppression of the anti-
SRBC response of B6C3Fj mice in the acute exposure model, precluding detection of any similar
increase in sensitivity of the B6C3FJ mice to chronic dosing. In contrast to these findings,
Vecchi et al. (1983) reported that multiple exposures to TCDD (2 pg/kg for 5 weeks or 0.5 |_ig/kg
for 8 weeks) did not increase the sensitivity of D2 mice to suppression of the anti-SRBC
response. Thus, the basis for any change in potency resulting from multiple treatment or chronic
exposure to TCDD and the role of AhR-mediated events in the phenomenon remain to be
elucidated.
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Some in vitro studies also suggest that suppression of the in vitro antibody response may
occur independent of the AhR. Tucker et al. (1986) and Holsapple et al. (1986a) reported that
direct addition of TCDD in vitro suppressed the antibody response to SRBCs. However, based
on the response of cells from congenic mice as well as a limited structure-activity study, the data
of Tucker et al. (1986) supported an AhR-dependent suppression, whereas the data of Holsapple
et al. (1986a) did not. In the latter study, the magnitude of suppression was comparable using
cells from responsive B6C3FJ or congenic heterozygous (Ahbd-B6) mice compared with
nonresponsive D2 or homozygous Ahdd-B6 mice. In addition, Holsapple et al. (1986a) reported
that 2,7-DCDD, which lacks affinity for the AhR, was equipotent with TCDD in suppressing the
in vitro response.
Davis and Safe (1991) directly compared the in vitro structure-immunotoxicity
relationships for a series of PHAH congeners that show >14,900-fold difference in in vivo
immunotoxic potency. Results of these studies indicated that all of the congeners were
equipotent in vitro and produced a similar concentration-dependent suppression of the in vitro
anti-SRBC response using cells from either B6 or D2 mice. Coexposure to the AhR antagonist
oc-napthoflavone antagonized the immunosuppression induced by either TCDD or 1,3,6,8-TCDF
(a weak AhR agonist). Collectively, these results suggested a mechanism of suppression in vitro
that was independent of the AhR.
Morris et al. (1991) demonstrated that suppression of the in vitro antibody response to
SRBC by TCDD was critically dependent on the type and concentration of the serum used in the
in vitro culture. When splenocytes were cultured in the presence of normal mouse serum (NMS),
the profile of activity was dependent on the genotype (i.e., Ahbb or Ahdd) of the lymphocytes
rather than the source of NMS (Morris et al., 1994). These results are important, because they
may help to explain the variable effects observed by different laboratories performing in vitro
TCDD-antibody response assays.
The majority of evidence indicates that immunotoxicity of PHAHs is AhR mediated.
This evidence comes primarily from the in vivo mouse data described above. Recent studies also
indicate that murine T lymphocytes and leukocytes express the Ah receptor (Lawrence et al.,
1996; Williams et al., 1996). A complete understanding of the role of the AhR in murine
leukocytes and PHAH-mediated immunotoxicity, however, requires further study. Although
other data suggest that non-AhR mechanisms may have some role in TCDD-induced
immunotoxicity, further studies are required to provide definitive evidence for this mechanism.
Studies employing novel approaches -such as the development and use of variant lymphoma cells
that express variable levels of the AhR (e.g., hepatoma cells [Miller et al., 1983; Karenlampi et
al., 1988]), the use of AhR knockout mice (Fernandez-Salguero et al., 1995; Gonzalez et al.,
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1995), or the use of pure binding antagonists (Lu et al., 1995, 1996), would help to provide such
evidence.
4.3. SENSITIVE TARGETS FOR PHAH IMMUNOTOXICITY
Despite considerable investigation, the cells that are most sensitive to alteration by PHAH
exposure leading to suppressed immune function have not been unequivocally identified. The in
vivo immunotoxicity of TCDD, expressed in terms of suppression of the anti-SRBC response of
B6 or B6C3FJ mice, is highly reproducible between laboratories. Because the magnitude of the
anti-SRBC response depends on the concerted interactions of antigen-presenting cells (APCs),
regulatory T cells (helper and suppressor), and B cells, this response has been used most widely
to evaluate target cell sensitivity to PHAHs. In addition, the CTL response has served as a model
for evaluating PHAH-induced suppression of T cell function. These immune responses can be
modulated by nonimmunological factors, including hormonal and nutritional variables, and
PHAHs are known to affect endocrine and metabolic functions. These indirect effects will be
apparent only in in vivo studies, whereas direct effects on APC and lymphocyte functions would
be evident following in vitro exposure to PHAHs.
One potentially important indirect mechanism is through effects on the endocrine system,
because the activity of several endocrine hormones (e.g., glucocorticoids, sex steroids, thyroxine,
growth hormone, and prolactin) that regulate immune responses have been shown to be altered
by TCDD and other PHAHs (see Chapter 3, Acute, Subchronic and Chronic Toxicity and
Chapter 5, Developmental and Reproductive Toxicity). Consequently, studies have been
performed to examine the possible role of PHAH-induced indirect effects on the immune systems
of rodents.
Kerkvliet et al. (1990b) reported that exposure of mice to 3,4,5,3',4',5'-HxCB
(PCB169)followed by injection of P815 allogeneic tumor cells induced a dose-dependent
elevation of serum corticosterone concentrations that correlated with the dose-dependent
suppression of the anti-P815 CTL response. However, because adrenalectomy or treatment with
the glucocorticoid receptor antagonist RU38486 failed to protect mice from the
immunosuppressive effect of PCB169 (DeKrey et al., 1993), a role for the elevated CS in the
suppression of the CTL response seems unlikely. Adrenalectomy and hypophysectomy also
failed to prevent TCDD-induced thymic atrophy in rats (van Logten et al., 1980).
In other studies using the P815 allogeneic tumor model, Kerkvliet and Baecher-Steppan
(1988a) reported that male mice were more sensitive than female mice to suppression of the CTL
response by PCB169. Castration of male mice partially ameliorated the immunosuppressive
effects of HxCB (DeKrey et al., 1993), suggesting a role for testosterone in suppression of this
response. Male mice were found to be more sensitive than female mice to PCB169-induced
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reductions in serum prolactin levels (DeKrey et al., 1994). However, it was concluded that
PCB169-induced hypoprolactinemia was not responsible for CTL suppression in mice, because
bromocryptine, which severely suppressed serum prolactin levels, failed to alter CTL activity.
Pazdernik and Rozman (1985) suggested that thyroid hormones may play a role in TCDD
immunotoxicity based on the finding that radiothyroidectomy prevented the suppression of the
anti-SRBC response in rats treated with TCDD. However, because thyroidectomy alone
suppressed immune function, the significance of the findings requires further study. Taken
together, the current data do not provide convincing evidence supporting a role for hormones in
PHAH-induced indirect mechanisms of immunosuppression.
Kerkvliet and Brauner (1987) compared the sensitivity of antibody responses to antigens
that differ in their requirements for APC and T cells as an in vivo approach to evaluate the
cellular targets of 1,2,3,4,6,7,8-HpCDD humoral immunotoxicity. The TI antigens, DNP-Ficoll
and TNP-LPS, were used in these studies. These TI antigens differ from each other in their
requirement for APC (higher for DNP-Ficoll) and their sensitivity to regulatory (amplifier and
suppressor) T cell influence (DNP-Ficoll is sensitive, TNP-LPS is not) (Braley-Mullen, 1982).
Obviously, all antibody responses require B cell differentiation into antibody-secreting plasma
cells. Although HpCDD produced dose-dependent suppression of the antibody response to all
three antigens, sensitivity to suppression directly correlated with the sensitivity of the response to
T cell regulation. The ID50s were 53, 127, and 516 jig/kg for SRBC, DNP-Ficoll, and TNP-LPS,
respectively. These results were interpreted as follows: If one assumes that B cell function is
targeted in the TNP-LPS response, then regulatory T cells and/or APC may represent the more
sensitive target in the SRBC and DNP-Ficoll responses. The difference in sensitivity between
the SRBC and DNP-Ficoll responses suggests that the T helper cell may be a particularly
sensitive target. The differential sensitivity of the antibody responses to TNP-LPS versus SRBC
has been corroborated in TCDD-treated mice (House et al., 1990; Kerkvliet et al., 1990a). Thus,
the in vivo sensitivity of the antibody response to SRBC, described in these studies, appears to
depend on the T cell and/or APC components of the response rather than the B cell, unless the B
cells that respond to SRBC are different from the B cells that respond to TNP-LPS. Currently,
evidence for such a difference is lacking.
These in vivo results differ from the ex vivo data of Dooley and Holsapple (1988). Using
in vitro immunization with SRBC, DNP-Ficoll or LPS of separated and reconstituted splenic T
cells, B cells, and adherent cells from vehicle- and TCDD-treated mice, they reported that B cells
from TCDD-treated mice were functionally compromised in in vitro antibody responses but T
cells and macrophages were not. These ex vivo results corroborated an earlier study in which
TCDD suppressed the in vitro antibody response to these same antigens in a dose-related manner
and at comparable concentrations (Holsapple et al., 1986a). Taken together, these ex vivo data
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suggested that the B cell is the primary target for the direct effects of TCDD, because the
antigens employed differ in their dependence on T cells and accessory cells (Holsapple, 1995).
The basis for the different responses to TI antigens in these studies has not been
established. However, it has been suggested for the ex vivo work that the effects of TCDD on T
cells may be indirectly induced following antigen exposure such that removal of the cells from
the TCDD environment of the host prior to antigen challenge would preclude detection of T cell
dysfunction (Kerkvliet and Burleson, 1994). This interpretation is supported by the findings of
Tomar and Kerkvliet (1991) that spleen cells taken from TCDD-treated mice were not
compromised in their ability to reconstitute the antibody response of lethally irradiated mice, and
the reported lack of direct effects of TCDD and other PHAHs on T cells in vitro (Clark et al.,
1981; Kerkvliet and Baecher-Steppan, 1988a, b).
Although the direct effects of TCDD on T cells in vitro have not been demonstrated, it is
clear that functional T cell responses generated in vivo are compromised following in vivo
exposure. Nude mice that are congenitally T cell deficient were significantly less sensitive to
HpCDD-induced immunotoxicity when compared with their T cell-competent littermates
(Kerkvliet and Brauner, 1987). Likewise, exposure to TCDD or PCB169 suppressed the
development of CTL activity following alloantigen challenge (Kerkvliet et al., 1990b). The
influence of TCDD exposure on regulatory T cell functions has been addressed in several studies.
Clark et al. (1981) first proposed that T suppressor cells were induced by TCDD in the thymus
that were responsible for the suppressed CTL response. However, increased suppressor cell
activity in peripheral lymphoid tissue was not observed in mice exposed to TCDD (Dooley et al.,
1990) or PCB169 (Kerkvliet and Baecher-Steppan, 1988b). In terms of T helper cell activity,
Tomar and Kerkvliet (1991) reported that a dose of 5 pg/kg TCDD suppressed the in vivo
generation of carrier-specific T helper cells. Lundberg et al. (1990) reported that thymocytes
from B6 mice treated with TCDD (50 pg/kg) were less capable of providing help for an in vitro
anti-SRBC response. However, Clark et al. (1983) reported in ex vivo studies that T cells from
TCDD-treated mice produced normal levels of interleukin-2 (IL-2). A study using the P815
tumor allograft model suggests that TCDD alters early CD4+ T cell activation events, which lead
to premature termination of cytokine production by CD8+ T cells, suppression of CTL activity,
and suppression of alloantibyody production by B cells (Kerkvliet et al., 1996). Tumor necrosis
factor (TNF), IL-2, and interferon y (TFNy) were suppressed in P815-injected mice exposed to
TCDD. In contrast, TNF and IL-2 were not affected and LFNy was reduced in anti-CD3 injected
mice exposed to TCDD (Prell et al., 1995). These results suggest that the effects of TCDD on
cytokine production may be determined by the inducing stimulus.
The influence of TCDD exposure on B cell function has been addressed primarily in in
vitro studies, but direct effects of PHAHs on macrophages and T cells in vitro have not been
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described. The issue of TCDD effects on B cells is difficult to address in vivo given that most B
cell responses (except perhaps anti-LPS responses) depend on interactions with T cells and
macrophages. In vitro studies have described the direct effects of TCDD on activation and
differentiation of purified B cells (Holsapple et al., 1986a; Luster et al., 1988; Morris et al., 1991;
Karras and Holsapple, 1994a, b; Karras et al., 1995). These studies suggest that TCDD inhibits
terminal differentiation of B cells via alteration of an early activation event (Luster et al., 1988;
Karras and Holsapple, 1994b). Increased phosphorylation and tyrosine kinase activity in
TCDD-treated B cells may underlie this B cell dysfunction (Kramer et al., 1987; Clark et al.,
1991a). Also, Karras and Holsapple (1994a) suggest that the antiproliferative effect of TCDD on
B cells is due to inhibition of calcium-dependent activation; this inhibition results in suppression
of B cell surface Ig-induced antibody production (Karras et al., 1996).
Macrophage functions have also been examined following TCDD exposure and generally
found to be resistant to suppression by TCDD when assessed ex vivo. Macrophage-mediated
phagocytosis, macrophage-mediated tumor cell cytolysis or cytostasis, oxidative reactions of
neutrophils and macrophages, and spontaneous natural killer (NK) cell activity were not
suppressed following TCDD exposure, with doses as high as 30 pg/kg failing to suppress NK
and macrophage functions (Vos et al., 1978; Mantovani et al., 1980). More recent studies,
however, indicate that virus-augmented pulmonary NK activity, but not spontaneous pulmonary
NK activity, is suppressed by TCDD (Yang et al., 1994). Phorbol ester-activated antitumor
cytolytic and cytostatic activity of neutrophils is also selectively inhibited by TCDD (Ackermann
etal., 1989).
On the other hand, it is interesting to note that the pathology associated with TCDD
toxicity often includes neutrophilia and an inflammatory response in liver and skin characterized
by activated macrophage and neutrophil accumulation (Vos et al., 1973; Weissberg and Zinkl,
1973; Vos et al., 1974; Puhvel and Sakamoto, 1988; Herbert et al., 1990). Although these
observations may reflect a normal inflammatory response to tissue injury, some experimental
evidence suggests that inflammatory cells may be activated by TCDD exposure. For example,
Alsharif et al. (1994) reported that TCDD increased superoxide anion production in rat peritoneal
macrophages. In addition, it has been shown that TCDD exposure results in an enhanced
inflammatory response following SRBC challenge (Kerkvliet and Oughton, 1993). This effect of
TCDD was characterized by a twofold to fourfold increase in the number of neutrophils and
macrophages locally infiltrating the intraperitoneal site of SRBC injection. However, the
kinetics of the cellular influx were not altered by TCDD. Likewise, the expression of
macrophage activation markers (I-A and F4/80) and the antigen-presenting function of the
peritoneal exudate cells were unaltered by TCDD. Using specific inhibitors of the
proinflammatory cytokines TNF and IL-1 (Moos et al., 1994) found that the TCDD-induced
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hyperinflammatory response was mediated by TNF . However, although exogenous TNF
suppressed the antibody response to SRBC, the increased inflammatory response and suppression
of the response to SRBC were not apparently linked, because coincident treatment with the
soluble TNF receptor rhuTNFR:Fc, which blocks TNF activity, further suppressed rather than
normalized the immunosuppression by TCDD alone (Moos and Kerkvliet, 1995). Thus, the
relationship, if any, between the inflammatory and immune effects of TCDD remains to be
elucidated.
Evidence also suggests that the long-recognized hypersusceptibility of TCDD- and
PCB-treated animals to endotoxin (LPS) (Thomas and Hinsdill, 1978, 1979; Vos et al., 1978;
Loose et al., 1979) may be related to an increased production of proinflammatory factors. TNF
production may be responsible for endotoxin hypersensitivity in TCDD-treated mice and that the
Ah locus mediates this response (Clark et al., 1991b; Taylor et al., 1992). The ability of
methylprednisolone to reverse the mortality associated with TCDD/LPS treatment is also
consistent with an inflammatory response (Rosenthal et al., 1989). Similarly, increased
inflammatory mediator production may underlie the enhanced rat paw edema response to
carrageenan and dextran in TCDD-treated rats (Theobald et al., 1983; Katz et al., 1984).
Whereas serum complement activity has been reported to be suppressed in dioxin-treated mice
(White et al., 1986), enhanced activity was reported at the lowest exposure level when
1,2,3,6,7,8-HxCDD was tested. Work by Sutler et al. (1991) indicates that IL-lp gene
expression, as well as plasminogen activator inhibitor-2, in keratinocytes is elevated by TCDD.
On the other hand, House et al. (1990) reported that inflammatory macrophages obtained from
TCDD-treated mice produced control levels of IL-1 when examined ex vivo. Thus, the effect of
TCDD on inflammatory mediator production may be a "priming effect" and require coexposure
to antigen or LPS. The influence of TCDD on inflammatory mediator production and action is
an important area for further study.
Since the rapid influx of phagocytic cells to the site of pathogen invasion is an important
factor in host resistance to infection, the ability of TCDD to augment the production of
inflammatory chemoattractive mediators would imply that TCDD exposure could result in
enhanced host resistance. However, because TCDD exposure is, at the same time,
immunosuppressive, which results in decreased specific immune responses generated by T and B
lymphocytes, the overall impact of TCDD exposure on disease susceptibility will likely vary
depending on the nature of the pathogen and the major mode of host response to the specific
infectious agent. Such effects may in fact help explain the disparate effects of TCDD in different
host resistance models that are described below.
Taken together, it is obvious that multiple targets exist for PHAH-induced
immunosuppression, including both T and B lymphocytes. Lymphocyte precursor cells as well
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as lymphoid-associated tissue such as thymus epithelium and bone marrow stromal elements are
also affected by dioxins (discussed later in the chapter). The class or subclass of T or B
lymphocytes that is the proximate target for PHAH-induced immunosuppression remains to be
determined, as do the mechanisms by which immunosuppression by these chemicals is achieved.
4.4. INFLUENCE OF TCDD ON HOST RESISTANCE TO DISEASE
The ability of an animal to resist and/or control viral, bacterial, parasitic, and neoplastic
diseases is determined by both nonspecific and specific immunological functions. Decreased
functional activity in any immunological compartment may result in increased susceptibility to
infectious and neoplastic diseases. Animal host resistance models that mimic human disease are
available and have been used to assess the effect of TCDD on altered host resistance.
TCDD exposure increases susceptibility to challenge with the gram-negative bacterium
Salmonella. TCDD was given per os at 0.5 to 20 |_ig/kg once a week for 4 weeks to male 4-
week-old C57Bl/6Jfh (J67) mice and challenged 2 days after the fourth dose (when mice were 8
weeks old) with either Salmonella hern or Herpesvirus suis (also known as pseudorabies virus).
Results with S. hern indicated increased mortality at 1 |_ig TCDD/kg (total dose of 4 pg/kg) and
reduced time to death after bacterial challenge with 5 |_ig TCDD/kg (total dose of 20 |ig/kg). In
contrast, the same doses of TCDD did not alter the time to death or the incidence of mortality
following Herpesvirus suis infection (Thigpen et al., 1975). A TCDD feeding study by Hinsdill
et al. (1980) also demonstrated increased susceptibility of 7-week-old Swiss Webster outbred
female mice to S. typhimurium var. Copenhagen. Mice were fed control feed or feed containing
10, 50, or 100 ppb TCDD for 8 weeks, after which they were injected intravenously with 103'5 S.
typhimurium var. Copenhagen. Results indicated that 50 and 100 ppb TCDD increased mortality
from Salmonella and shortened the time to death, whereas 10 ppb caused an increased
bacteremia.
Vos et al. (1978) reported that TCDD resulted in increased sensitivity to endotoxin (E.
coli O 127:B 8 lipopolysaccharide) and suggested that the increased susceptibility to Salmonella
caused by TCDD might be caused by the endotoxin of this gram-negative bacterium. Vos et al.
demonstrated reduced resistance to endotoxin with a single oral dose of 100 |_ig TCDD/kg using
3- to 4-week-old outbred female mice and challenged with endotoxin 5 days later. Vos et al. also
reported enhanced mortality from intravenous injection of endotoxin 2 days after the final oral
dose of TCDD (1.5, 5, 15, or 50 pg/kg, once a week for 4 weeks) in 3- to 4-week-old male
outbred Swiss mice. These studies indicate a reduced resistance to endotoxin after single or
multiple doses of TCDD. Thomas and Hinsdill (1979), using S. typhimurium lipopolysaccharide,
demonstrated a reduced resistance to endotoxin in the offspring of female Swiss Webster mice
fed TCDD prior to mating, during gestation, and between parturition and weaning. Rosenthal et
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al. (1989) used female B6C3FJ, DBA/2, as well as congenic mice to demonstrate that acute doses
of 50, 100, or 200 |_ig/kg TCDD per os increased endotoxin-induced mortality in B6C3FJ mice,
which was associated with hepatotoxicity and decreased clearance of the endotoxin. D2 and Ahdd
congenic mice were relatively resistant to this effect, implicating AhR-dependent mechanisms in
endotoxin hypersensitivity.
White et al. (1986) reported that Streptococcuspneumoniae., a gram-positive bacterium
that does not contain endotoxin, caused increased mortality in 5- to 6-week-old female B6C3FJ
mice after subchronic oral administration of TCDD (1 |_ig/kg for 14 days) and challenged with S.
pneumoniae intraperitoneally 1 day after the last treatment. The 1,2,3,6,7,8-hexachlorodibenzo-
/>-dioxin (HCDD) isomer also resulted in a dose-dependent increase in susceptibility to S.
pneumoniae.
Enhanced susceptibility to viral disease has also been reported after TCDD
administration. Clark et al. (1983) injected TCDD intraperitoneally once a week for 4 weeks and
challenged mice 7 to 22 days later with Herpes simplex type n strain 33 virus. Mice receiving
TCDD at 0.04, 0.4, or 4.0 |_ig/kg weekly (total dose of 160, 1,600, and 16,000 ng/kg) all had
significantly enhanced mortality to Herpesvirus type II infection. House et al. (1990) also
reported an enhanced susceptibility to viral infection following low-level singledose TCDD
administration intraperitoneally. B6C3FJ female mice, 6 to 8 weeks of age, were challenged with
influenza A/Taiwan/1/64 (H2N2) virus 7 to 10 days following TCDD. TCDD administration at
10, 1.0, or 0.1 |-ig/kg decreased resistance to virus. The lowest observable effect level was 0.1
|-ig/kg, making this one of the most sensitive endpoints for TCDD immunotoxicity. However, the
only immune parameter suppressed by TCDD, which is related to host resistance to this virus,
was the serum hemagglutination antibody liters to influenza. Unfortunately, only mice dosed at
10 |-ig/kg TCDD were evaluated for these virus-specific antibodies. No alterations in
macrophage function, NK cell activity, or interferon levels were observed in TCDD-exposed
mice (House et al., 1990).
Recently, the sensitivity of TCDD-exposed mice to influenza virus challenge was
corroborated by Burleson et al. (1996), who observed enhanced mortality to influenza A/Hong
Kong/8/68 (H3N2) virus following a single exposure to 0.1, 0.05, or 0.01 |_ig/kg TCDD.
Increased mortality at 0.01 pg/kg TCDD was the lowest observable effect level and as such
represents the most sensitive adverse effect level yet reported for TCDD (Burleson et al., 1996).
However, the role of TCDD in altering immune-mediated mechanisms important in resistance to
this virus remains to be elucidated. Furthermore, because increased mortality was not correlated
with increased virus liters in the lungs of mice, and TCDD did not alter the expected virus-
enhanced increase in lung:body weight ratio nor the virus-induced decrease in thymus weight,
other nonimmune virus-mediated physiological changes may be involved in this increased
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mortality. Further studies are warranted to confirm this very low level, TCDD-induced effect
and to delineate the underlying mechanisms responsible for this altered host resistance.
Rats exposed to TCDD daily for 14 days at a total dose of 10 pg/kg had significantly
augmented rat-adapted influenza virus replication in the lungs (Yang et al., 1994). The increased
virus replication was correlated with a significant suppression of virus-augmented but not
spontaneous pulmonary NK cell activity, suggesting that reduced NK activity may be at least in
part related to enhanced susceptibility of rats to influenza virus (Yang et al., 1994).
TCDD exposure has also been shown to result in more severe infections from parasites.
Tucker et al. (1986) studied the effects of TCDD administration on Plasmodium yoelii 17 XNL, a
nonlethal strain of malaria, in 6- to 8-week-old B6C3FJ female mice. A single dose of TCDD at
5 |-ig/kg or 10 |-ig/kg per os resulted in increased susceptibility to P. yoelii. The peak parasitemia
was greater and of longer duration in TCDD-treated animals than in controls, the difference
being significant at 5 |ig/kg on day 10 and at 10 |ig/kg on days 12 and 14. A single dose of
TCDD at 10 or 30 |_ig/kg intraperitoneally 7 days prior to infection of B6C3FJ mice with
Trichinella spiralis resulted in delayed onset of adult parasite elimination, and at 1.0 |_ig/kg
TCDD suppressed the proliferative response of splenocyte and mesenteric lymph node cells
stimulated with T. spiralis antigen (Luebke et al., 1994). Tissue levels of TCDD were also
higher in infected versus noninfected mice in this study. In a separate study, TCDD at 1, 10, or
30 |-ig/kg administered intraperitoneally 7 days prior to infection of Fischer 344 rats with T.
spiralis did not affect adult parasite elimination or the numbers of encysted larvae in muscle.
Furthermore, proliferative responses of lymphocytes from rats dosed at 30 |ig/kg and stimulated
with parasite antigen were enhanced in contrast to the suppression observed in B6C3Fj mice
(Luebke et al., 1995). The different responses observed in mice versus rats for this host
resistance model are similar to those reported by Smialowicz et al. (1994) for the antibody
response to SRBCs, in that the latter response was suppressed in mice and enhanced in rats. In a
recent study, aged (76-week-old) rats exposed to TCDD and infected with T. spiralis, were less
able to limit the burden of encysted larvae, compared to young (10-week-old) rats (Luebke et al.
1999). These results suggest that TCDD may exacerbate the age-related decreased resistance to
this parasite.
Luster et al. (1980a) demonstrated enhanced growth of transplanted tumors in mice
treated with TCDD at doses of 1.0 or 5.0 pg/kg in B6C3FJ mice. Dams were given TCDD by
gavage at day 14 of gestation and again on days 1, 7, and 14 following birth; host resistance
studies were performed 6 to 8 weeks after weaning. This exposure protocol resulted in an
increased incidence of PYB6 tumors in pups from dams receiving repeated doses of 1.0, but not
5.0, |agTCDD/kg.
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Although it is clear that TCDD adversely affects numerous host resistance models
detailed above, the effects of TCDD on susceptibility to Listeria monocytogenes infections are
ambiguous. The disparate results may reflect different study designs, including dose, route,
single versus multiple administrations, mouse strain, age, or sex. However, it is clear that
TCDD, under certain conditions, results in increased susceptibility to Listeria. Hinsdill et al.
(1980) reported the increased susceptibility of 7-week-old Swiss Webster outbred female mice to
Listeria. Mice were fed control feed or feed containing 10 or 50 ppb TCDD for 8 weeks, after
which they were injected intravenously with 10s Listeria. Results indicated that the 50 ppb diet
increased bacteremia and mortality. Luster et al. (1980b) used doses of 1.0 or 5.0 |_ig TCDD/kg
in B6C3FJ mice. Dams were given TCDD by gavage at day 14 of gestation and again on days 1,
7, and 14 following birth, and host resistance studies were performed 6 to 8 weeks after weaning.
This exposure protocol resulted in an increased susceptibility to Listeria in pups from dams
receiving repeated doses of 5.0 pg TCDD/kg. However, Vos et al. (1978) reported that oral
administration of 50 |_ig TCDD/kg once a week for 4 weeks to 3- to 4-week-old male Swiss mice
followed by intravenous challenge 4 days after the last dose with Listeria had no effect on
nonspecific phagocytosis and killing of Listeria. House et al. (1990) used B6C3FJ female mice,
6 to 8 weeks of age and challenged intravenously with Listeria 7 to 10 days following a single
dose of TCDD at 10, 1.0, and 0.1 pg/kg. TCDD did not enhance mortality from Listeria.
In summary, results from host resistance studies provide evidence that under certain
conditions exposure to TCDD results in increased morbidity and mortality for bacterial, viral,
parasitic, and neoplastic disease. These effects are observed at relatively low doses and likely
result from TCDD-induced suppression of immunological function. However, the specific
immunological functions targeted by TCDD in each of the host resistance models remain to be
fully defined. Possible nonimmunological mechanisms, which may also contribute to altered
host resistance by PHAHs, should also be investigated. Future work should also address the role
of the AhR in host resistance.
4.5. ROLE OF THE THYMUS IN PHAH IMMUNOTOXICITY
Thymic involution is one of the hallmarks of exposure to TCDD and related PHAHs in
all species examined. In mice, thymic involution occurs by an AhR-dependent mechanism.
Poland and Knutson (1982) demonstrated that C57B1/6 mice were 10-fold more sensitive to
TCDD-induced thymic atrophy than DBA/2 mice. Because the thymus has a critical role in the
ontogeny of T lymphocytes, thymic involution is often referred to as an immunotoxic effect.
However, although an intact thymus is crucial to the developing immune system during the
prenatal and early postnatal period of rodents as well as during the prenatal period of humans, the
physiological role played by the thymus in adult life has not been established. In animal models,
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adult thymectomy has little effect on the quantity or quality of T lymphocytes, which have
already matured and populated the secondary lymphoid organs (Benjamini and Leskowitz, 1991).
Likewise, in humans, childhood and adult thymectomy produces no clearly identifiable adverse
consequences in terms of altered immune function, although some might argue that such studies
have not been done. On the basis of this knowledge, it is not surprising that a direct relationship
between the effects of TCDD on the thymus and immune suppression has not been established in
studies using adult animals. In fact, adult thymectomy prior to PHAH exposure did not modify
TCDD- or HpCDD-induced suppression of the anti-SRBC response (Tucker et al., 1986;
Kerkvliet and Brauner, 1987). Furthermore, suppression of immune responses occurs at dose
levels of PHAH significantly lower than those required to induce thymic atrophy (Vos et al.,
1978; Silkworm and Antrim, 1985; Holsapple et al., 1986b; Tucker et al., 1986; Kerkvliet and
Brauner, 1990). Thus, thymic involution does not represent a surrogate marker for TCDD
immunotoxicity in adult animals. On the other hand, it is possible that chronic exposure to
TCDD resulting in chronic thymic atrophy may produce more delayed, subtle effects on immune
function not yet identified (Clarke and MacLennan, 1986).
In contrast to adult animals, congenital thymic aplasia or neonatal thymectomy results in
severe reduction in the number and function of T lymphocytes and produces a potentially lethal
wasting disease (Benjamini and Leskowitz, 1991). Similarly, there is evidence from studies that
rodents exposed to TCDD or PCBs during the prenatal or neonatal period are more sensitive to
immune suppression compared with rodents exposed as adults and that the prenatal effects are
more selective for cell-mediated immunity (Vos and Moore, 1974; Faith and Moore, 1977;
Luster et al., 1980b). Perinatal exposure of mice and rats to TCDD alters thymocyte
differentiation and maturation in vivo (Holladay et al., 1991; Blaylock et al., 1992; Gehrs and
Smialowicz, 1997, 1999; Gehrs et al., 1997).
TCDD has also been shown to alter thymocyte maturation in vitro by inducing terminal
differentiation of thymic epithelial cells (Greenlee et al., 1985; Cook et al., 1987). Addition of
TCDD to fetal thymic organ cultures (FTOCs) also alters thymocyte maturation (Dencker et al.,
1985; d'Argy et al., 1989). 3,3',4,4'-Tetrachlorobiphenyl (PCB77) has also been found to alter the
normal developmental pathways of fetal thymocytes in vitro (Esser and Welzel, 1993; Kremer et
al., 1994). In mouse FTOCs the addition of TCDD or PCB77 resulted in an overall reduction in
thymocytes but a significant increase in mature CD4"CD8+ thymocytes which were
CD3>PTCR+,CD69+,HSA-,IL-2R- (Esser and Welzel, 1993; Kremer et al., 1995). These mature
CD8+ thymocytes were functionally competent, in that they responded to stimulation by Con A or
anti-CD3 and possessed cytotoxic activity when cultured with H-2 allogeneic spleen cells (Lai, et
al., 1995). Data from experiments using FTOC recultivation techniques, in which intact thymic
lobes and thymocyte-depleted (i.e., stroma) lobes were employed, suggested that TCDD drives
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thymocytes into differentiation faster than the precursor pool can be replenished by self-renewal
(Kremer et al., 1994). Because TCDD affected thymic stroma but did not directly affect
thymocytes, the findings of Kremer et al. (1994) suggest that the nonlymphoid compartment (i.e.
stroma) rather than the thymocytes themselves may be the target of these PHAHs.
Work by Silverstone et al. (1994a) indicates that TCDD-induced thymic atrophy can be
mediated, at least in part, by damage to prethymic T cell precursor stem cells in both bone
marrow and liver. Perinatal exposure of mice to TCDD resulted in alteration in the lymphocyte
stem cell population, as indicated by a significant reduction in the lymphocyte stem cell-specific
enzyme terminal deoxynucleotidyl transferase (TdT) in fetal liver and neonatal bone marrow
lymphoid cells (Fine et al., 1989). In contrast, thymic TdT synthesis was relatively unaffected on
a per cell basis by perinatal TCDD exposure. TCDD also inhibited the ability of pre-T stem cells
(prothymocytes), from both fetal livers and neonatal bone marrow, to repopulate the thymus of
irradiated syngeneic mice (Fine et al., 1990). TCDD-induced thymic atrophy did not involve
apoptotic mechanisms in thymocytes affected by the bcl-2 proto-oncogene, and this atrophy
appears to be mediated through the AhR and not through effects on the estrogen receptor
(Silverstone et al., 1994a, b; Frazier et al., 1994).
Thymic alterations in adult mice exposed to TCDD appear to depend on AhR activation
in hemopoietic cells. Using chimeric mice with TCDD-responsive (AhR[+/+J) stromal
components and TCDD-unresponsive (AhR[-/-J) hematopoietic components, or the reverse,
Staples et al. (1998a) demonstrated that the hemopoietic compartment is the target of TCDD-
induced thymic atrophy and thymic phenotype alterations. The mechanisms underlying these
alterations are not clear, although TCDD-induced apoptosis of thymocytes, via Fas-Fas ligand
interactions, has been reported (Kamath et al., 1997, 1999). However, other evidence indicates
that apoptosis is not a key mechanism of TCDD-induced thymic atrophy (Staples et al. 1998b)
Taken together, the data indicate that PHAHs induce alterations of T cell maturation in
the thymus. These are mediated in part by effects on accessory cells and on extra-thymic
precursor cells. In addition, TCDD also influences B cell development in the bursa of chick
embryos (Nikolaidis et al., 1990), as well as lymphocyte stem cells in the fetal liver and bone
marrow of mice (Fine et al., 1989, 1990). Such alterations may have an important role in the
observed suppression of immune function by PHAHs in perinatally exposed animals.
4.6. IMMUNOTOXICITY FOLLOWING PRENATAL/NEONATAL EXPOSURE TO
PHAHS
The reported increase in susceptibility of very young animals to PHAH immunotoxicity
necessitates close examination of the available literature on prenatal or neonatal immunotoxic
effects. Several studies have examined immune function in mice, rats, and guinea pigs following
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exposure to TCDD or PCB during fetal development (Vos et al., 1973; Vos and Moore, 1974;
Thomas and Hinsdill, 1979; Luster et al., 1980b).
Results of work in which exposure of the progeny occurred via placental transfer and
lactation are summarized in Table 4-3. Study 1 presents results of two studies, one using B6
(Vos and Moore, 1974) and the other B6C3FJ mice (Luster et al., 1980a); study 2 presents
results of outbred Swiss mice (Thomas and Hinsdill, 1979); and study 3 presents results from two
studies of rats (Vos and Moore, 1974; Faith and Moore, 1977). The most sensitive indicator of
TCDD immunotoxicity in these studies was an increase in the growth of transplanted tumor cells
in the offspring of B6C3FJ mice (Ah responsive strain) treated with 1 |ig/kg TCDD at 4 weekly
intervals. (Total TCDD dose to dam was 4 pg/kg; dose to offspring was not determined.) The
offspring of Swiss mice fed a diet containing 1 ppb TCDD for 7 weeks showed enhanced
mortality following endotoxin challenge, while the plaque-forming cell response to SRBCs and
delayed hypersensitivity response were suppressed in offspring of mice fed 5.0-ppb TCDD diets.
(Estimated daily dose to 20 g dam consuming 5 g of 5-ppb TCDD diet is equivalent to 1.25
l-ig/kg TCDD/day.) Rats appeared to be relatively more resistant to the immunotoxic effects of
prenatal or neonatal exposure to TCDD than mice based on the finding that 5, but not 1, pg/kg
TCDD given four times at weekly intervals produced immunotoxicity in the offspring.
Immunotoxic end points that were unaffected by the highest exposure levels in these studies
included blastogenesis induced by LPS and serum antibody liters to bovine gamma globulin
(BGG). However, a significant finding of this study was that suppression of the DTH response to
oxazalone was relatively long lived in that suppression of this response was evident in 145-day-
old rats. Taken together, these studies suggest that cell-mediated rather than humoral immunity
is more sensitive to perinatal TCDD exposure.
Two later studies examined immune function in offspring of female mice, in the first
study exposed to TCDD (Holladay et al., 1991) and in the second study to PCBs (Kanechlor 500)
(Takagi et al., 1987), with the offspring cross-fostered to unexposed lactating mice at birth.
Thus, exposure was limited to placental exposure. B6 mice exposed to 3.0 pg/kg TCDD on
gestational days 6 to 14 gave birth to offspring that had significant thymic atrophy and hypoplasia
measured on gestational day 18 or on day 6 postnatally. The thymic effects were no longer
apparent by day 14. At 7 to 8 weeks postnatally, mitogen responses and antibody plaque-forming
cell response to SRBCs were unaltered, but the CTL response was significantly suppressed
compared with controls (Holladay et al., 1991). These results suggest a selectivity of prenatal
TCDD on the CTL and not the T helper cells involved in the antibody response to SRBCs. In
contrast to these results, Takagi et al. (1987) exposed female C3H mice per os to 50 mg/kg
Kanechlor 500 twice per week for 4 weeks, at which time steady-state tissue levels were noted.
The offspring derived from mating to unexposed males had an unaltered antibody response to the
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T-independent antigen DNP-dextran. On the other hand, carrier-primed T helper cell activity
assessed by adoptive transfer was significantly suppressed by PCB exposure when assessed 4 and
7 weeks after birth but fully recovered by 11 weeks. Together, these studies confirm prior
studies to indicate that T cell function is selectively altered by PHAH when exposure is prenatal.
Although both T helper cells and CTL show altered function, T helper cell activity may recover
faster than CTL function.
Fine et al. (1990) reported on TCDD levels in mouse offspring following maternal
treatment with TCDD (10 pg/kg) on gestational day 14. The fetal liver had the highest
concentration on gestational day 18 (235 fg/mg), which declined slightly by postnatal day 6 to
around 100 fg/mg. Concentration of TCDD in the thymus on gestational day 18 was 140 fg/mg,
which declined to 20 fg/mg on day 6 after birth. (These thymic TCDD concentrations are
equivalent to 60 to 425 pM, assuming 1 kg of tissue is equivalent to 1 L of water.) TCDD
concentrations in the spleen remained constant at about 40 fg/mg during the same timeframe,
whereas bone marrow concentrations were very low (about 3 fg/mg). These concentrations of
TCDD were associated with thymic atrophy (Fine et al., 1989) and significant reduction in the
ability of prothymocytes in liver and bone marrow to repopulate an irradiated thymus (Fine et al.,
1990).
Recent studies in rats indicate that perinatal exposure to TCDD results in relatively long-
lived and persistent suppression of immune responses. Lactating female Leeds rats were exposed
to TCDD in their feed, starting on postnatal day 1 through 18, at 0.1, 0.5, or 2.5 |_ig TCDD/kg,
with an estimated total TCDD consumption of 0.2, 1.0, or 5.0 |_ig/kg/body weight, respectively
(Badesha et al., 1995). Both T cell-dependent (i.e., SRBC) and T cell-independent (i.e., DNP-
Ficoll, TNP-LPS, and LPS) antibody responses were suppressed in a dose-related manner in 130-
day-old offspring.
In another group of studies, a single oral exposure of pregnant Fischer 344 dams on GD-
14 to 0.1, 0.3, 1.0, or 3.0 |_ig TCDD/kg resulted in suppression of the DTK response to BSA
(Gehrs et al., 1997; Gehrs and Smialowicz, 1999). Depressed DTH responses persisted for up to
19 months in the male offspring of dams exposed to 3.0 |_ig TCDD/kg. While both male and
female offspring exposed to TCDD on GD-14 displayed reduced DTH responses, the DTH
response of males appears to be more affected than that of females. A cross-fostering study
indicated that both gestational and lactation exposure of the offspring are required for
suppression of the DTH response following gestational day 14 exposure of the dams (Gehrs et
al., 1997). A single low dose (0.1 pg/kg) of TCDD given to dams on gestational day 14 resulted
in suppression of the DTH response that persists in the offspring for up to age 14 months (Gehrs
and Smialowicz, 1999). These results suggest that perinatal exposure to TCDD may lead to a
permanent defect in the immune defenses associated with the DTH response, which are critical
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for protection against certain intracellular bacterial and parasitic infections as well as certain
viruses. If so, then the fetus and neonate may be more at risk for TCDD-induced immune
perturbation, which may manifest, early or later in life, as susceptibility to certain infectious
diseases.
4.7. IMMUNOTOXICITY OF PHAHS IN NONHUMAN PRIMATES
Studies using nonhuman primates as surrogate models for humans have been conducted
to assess PHAH immunotoxicity. Immunological effects were described in rhesus monkeys and
their offspring chronically exposed to TCDD at levels of 5 or 25 ppt for 4 years (Hong et al.,
1989). In the mothers, the total number of T cells increased in monkeys fed 25 ppt TCDD, with
a selective increase in CD8+ cells and a decrease in CD4+ cells. However, no significant effect
on T cell function was established when assessed as proliferation response to mitogens,
alloantigens, or xenoantigens. NK cell activity and production of antibodies to tetanus
immunization were normal. In the offspring of TCDD-exposed dams examined 4 years after
exposure, a significantly increased antibody response to tetanus toxoid (TT) immunization was
observed that correlated with TCDD tissue levels. Body burden of TCDD in the offspring ranged
from a low of 290 ppt to a high of 1,400 ppt. Interestingly, there was no strict correlation
between exposure levels and resulting body burden. Immunoenhancement of the antibody
response to TT in the offspring of the monkeys exposed to TCDD is in contrast to
immunosuppression observed in rodents exposed perinatally. However, other studies in
nonhuman primates cited below indicate that humoral immunity is suppressed in nonhuman
primates by other PHAHs.
In other TCDD studies, a single injection of TCDD in marmosets (Callithrix jacchus)
resulted in a delayed decrease in the percentage of CD4+ T cells and CD20+ B cells in the blood
and an increase in the percentage of CD8+ cells (Neubert et al., 1990). The total number of T
cells was not significantly altered by TCDD exposure. The CD4+ subset most affected was the
CDw29+ "helper-inducer" or "memory" subset, with significant effects observed after a TCDD
dose of 10 ng/kg. The no-observed-effect level for this effect was 3 ng/kg TCDD. Concomitant
with suppression of the CDw29 subset in TCDD treated animals, the percentage of
CD4+CD45RA+ cells increased. This subset has been classified as "suppressor-inducer" or
"naive" cells. The changes in the T cell subsets were intensified following in vitro culture of the
cells with mitogen (Neubert et al., 1991).
Interestingly, however, another study from the same laboratory reported that chronic
exposure of young marmosets to very low levels of TCDD (0.3 ng/kg/week for 24 weeks)
produced the opposite effect on the CD4+CDw29+ subset, resulting in a significant increase in
this population (Neubert et al., 1992). Concomitantly, the CD4+CD45RA+ subset decreased.
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Upon transfer of the animals to a higher dose of TCDD (1.5 ng/kg/week) for 3 weeks, the
enhancement effect was reversed and suppression of the CD4+CDw29+ subset was observed,
with maximum suppression after 6 weeks of exposure to the higher dose. In addition, the
CD8+CD56+ T cytotoxic T cell subset was transiently increased but normalized even though
TCDD dosing continued. After discontinuation of dosing, the reduction in the percentage and
absolute number of CD4+CDw29+ cells persisted for 5 weeks, reaching normal range 7 weeks
later. These results led the authors to conclude that extrapolation of the results obtained at higher
doses to very low exposures is not justified with respect to the effects induced by TCDD on the
immune system of marmosets (Neubert et al., 1992).
Neubert et al. (1995b) performed a study to determine if a functional deficiency could be
detected in marmosets that displayed altered peripheral blood T and B subsets following
exposure to TCDD (Neubert et al., 1990). No reduction was observed in the in vitro
lymphoproliferative response to TT by peripheral blood lymphocytes from marmosets vaccinated
with tetanus and exposed to a single dose of 100 ng/kg TCDD at the time of the second booster
vaccination (Neubert et al., 1995b). These results are interesting because no association was
established between this functional endpoint and the earlier observed TCDD-induced peripheral
blood lymphocyte subset changes in mature marmosets given a single injection of TCDD
(Neubert et al., 1990).
Immunomodulatory effects of chronic low-level PCB exposure in monkeys have also
been investigated. In early studies, Thomas and Hinsdill (1978) reported that rhesus monkeys
fed diets containing 2.5 or 5 mg/kg of Aroclor 1248 had significantly suppressed antibody
responses to SRBCs but not to TT. These monkeys also had chloracne, alopecia, and facial
edema. Similarly, exposure of cynomolgus monkeys to Aroclor 1254 (100 or 400 pg/kg/day) for
3 months suppressed antibody responses to SRBCs but not TT (Truelove et al., 1982).
Suppressive effects on anti-SRBC responses were more severe in cynomolgus monkeys when the
PCB mixture contained PCDFs (Hori et al., 1982). Tryphonas et al. (1989; 1991a, b) reported
results of studies in rhesus monkeys exposed chronically to Aroclor 1254 (5 to 80 pg/kg/day) for
23 or 55 months. These exposures resulted in steady-state blood PCB levels that ranged from a
mean low of 0.01 ± 0.001 ppm in the 5 |_ig/kg group to a mean high of 0.11 ± 0.01 ppm in the 80
l-ig/kg group. The only consistently altered immune parameter was the primary and anamnestic
antibody responses to SRBCs, which were suppressed in a dose-dependent manner. In contrast,
the antibody response to pneumococcus vaccine antigen measured at 55 months of exposure was
not significantly altered. At 23 months, the percentage of T helper cells in the blood was
significantly decreased in the 80 pg/kg group, and the percentage and absolute number of T
suppressor cells were increased; however, these effects were not apparent at 55 months of
exposure (Tryphonas et al., 1991b). Lymphoproliferative responses to PHA and Con A were not
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significantly altered at 23 months but were dose- dependency suppressed at 55 months.
Proliferation to alloantigens was not significantly altered. Likewise, serum immunoglobulin and
hydrocortisone levels did not differ between treatment groups. After 55 months,
chemiluminescent response (time to peak) of monocytes from PCB-exposed monkeys was slower
than that from controls. Also noted at 55 months were a significant elevation in serum hemolytic
complement levels, a dose-related increase in NK cell activity, and a dose-related increase in
thymosin alpha-1 levels but not thymosin beta-4 levels (Tryphonas et al., 1991a). Effects on
interferon levels were inconsistent, and TNF production was not altered.
The studies in nonhuman primates are important from the standpoint that the antibody
response to SRBCs emerges as the only immunological parameter consistently suppressed by
PHAH in several different animal species. Notable exceptions are the reports that TCDD does
not suppress antibody response to SRBCs in rats (Smialowicz et al., 1994; Fan et al., 1996). At
the present time, it is not clear why the antibody response to SRBCs is most consistently altered
by PHAH exposure in different species. Sensitivity of the anti-SRBC response does not appear
to be caused solely by T cell dependency of the response because antibody responses to other
T-dependent antigens (e.g., TT, BGG) are not suppressed and may be enhanced following PHAH
exposure. It is possible that the particulate nature of the SRBC antigens is an important factor
even though a mechanistic basis for this is not readily apparent. The sensitivity of the technique
used to quantify the antibody response may also contribute to apparent increased sensitivity of
the SRBC model, which is most often measured as the PFC response rather than serum antibody
liters which are usually more variable. Nonetheless, the finding that the SRBC response is also
suppressed in nonhuman primates exposed to PCBs lends support to the use of the anti-SRBC
database for risk assessment of PHAHs.
4.8. IMMUNOTOXICITY OF PHAHS IN HUMANS: IN VIVO EXPOSURE
Immunotoxicity of TCDD and related PHAHs in humans has been the subject of several
studies derived from accidental, occupational, or environmental exposure to PCDDs, PCDFs,
and/or PCBs(see Chapter 7, Human Effects). Perhaps the most important human case studies
involve two incidents in which large numbers of individuals were exposed to PCB-contaminated
rice oil containing PCDFs and other PHAHs. The first occurred in Japan in 1968 and the second
in Taiwan in 1979, and the resulting symptoms associated with exposure were called "Yusho"
and " Yu-Cheng" which mean "oil disease" in Japanese and Chinese, respectively (Tsukamoto,
1969; Chang et al., 1980a). The most common clinical symptoms included acne-form eruptions
and follicular accentuation, pigmentation of the skin and nails, swelling of the eyelids and
increased discharge, nausea, headaches, and numbness of the limbs (Chang et al., 1980b).
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Clinical studies revealed decreased serum concentration of y-globulin and decreased DTK
responses in Yu-Cheng patients (Chang et al., 1980b).
Patients also presented with increased frequency of various kinds of infection, especially
of the respiratory tract and skin (Shigematsu et al., 1978; Nakanishi et al., 1985; Lu and Wu,
1985). Immunologic effects, in studies that compared a group of 30 Yu-Cheng patients and 23
normal controls, included decreased serum IgA and IgM, but not IgG, and decreased percentage
of total T cells (E rosettes), active T cells (active E rosettes), and T helper (T|_i) cells in peripheral
blood (Chang et al., 1981). Monocytes and polymorphonuclear cells from these exposed patients
also had reduced numbers of Fc receptors compared with controls (Chang et al., 1982a). In a
subsequent study, the DTK response to streptokinase and streptodornase in 30 exposed patients
was significantly reduced compared with 50 controls (Chang et al., 1982b). The percentage of
anergic patients increased, and the degree of induration decreased with increased PCB
concentration in the blood. In contrast, lymphoproliferative responses of peripheral blood
lymphocytes (PBLs) to PHA, pokeweed mitogen (PWM), and tuberculin PPD, but not Con A,
were significantly augmented in PCB-exposed patients (Lu and Wu, 1985). PCB concentrations
in the blood ranged from 3 to 1,156 ppb, with a mean of 89 ± 6.9 ppb. The oil was contaminated
at PCB concentrations of 4.8 to 204.9 ppm, with a mean of 52 ± 39 ppm (Ko et al., 1981a, b).
Followup studies 3 years later indicated that the DTK to PPD was reduced, that the percentage of
total T cells had recovered but the percentage of helper T cells was reduced and of suppressor T
cells increased, and that lymphoproliferative responses were enhanced in exposed patients
compared with controls (Wu et al., 1984; Lu and Wu, 1985).
The patterns of immune function alterations in the Yu-Cheng patients described above
were relatively consistent between the original and 4-year followup studies. These observations
would suggest a rather robust and persistent alteration in the immune parameters examined.
Unfortunately, it is not clear how many, if any, of the same patients were tested in each of these
different studies to allow a direct assessment of the persistence of these effects.
Mothers of Yu-Cheng children reported increased incidence of pneumonia and bronchitis
in their children during the first 6 months of life (Rogan et al. 1988). In a followup study,
school-age children prenatally exposed to PCBs and PCDFs (n=103), were compared with
nonexposed control children (n=96) for middle-ear disease (Chao et al., 1997). The exposed
children had a higher prevalence of otitis media compared with their matched controls.
Furthermore, exposed children with ear disease had higher serum levels of 2,3,4,7,8-pentachloro-
and 1,2,3,4,7,8-hexachloro-dibenzofurans than did children with no middle-ear disease. In the
summer of 1995, 105 Yu-Cheng children and 101 control children were given physical exams
(Yu et al., 1998). The frequency of influenza, but not otitis media, asthma, or enteronitis attacks,
during the 6 months prior to examination, as reported by the parents of the Yu-Cheng children,
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was higher than for the controls. Blood samples were obtained from 29 Yu-Cheng and 22
control children for evaluation of total serum IgM, IgG, and IgA levels, as well as percentages of
circulating T cells, B cells, and NK cells. There were no differences between Yu-Cheng and
control children for any of these immune parameters (Yu et al., 1998).
Tests of altered immune function were also described in Michigan dairy farmers exposed
to PBBs through contaminated dairy products and meat in 1973 (Bekesi et al., 1979). As in the
PCB-exposed patients, the percentage and absolute numbers of T cells in peripheral blood of
PBB-exposed farmers were significantly reduced compared with a control group. However, in
contrast to PCB-exposed individuals (Lu and Wu, 1985), lymphoproliferation responses to PHA,
PWM, and allogeneic leukocytes were significantly decreased in PBB-exposed persons. Also in
contrast to PCB exposure, skin testing using standard recall antigens indicated that PBB-exposed
farmers had significantly increased responses, particularly to Candida and Varidase. Tissue levels
of PBBs in the subjects were not determined in this study.
In contrast, another study of Michigan residents exposed to PBBs revealed no significant
differences in lymphocyte number or function compared with nonexposed individuals (Landrigan
et al., 1979; Silva et al., 1979). The exposed cohort in this study was drawn from individuals
who lived on PBB-quarantined farms, who received food directly from such farms, or who
worked or were related to workers engaged in PBB manufacture. No differences in the total
leukocyte counts, the absolute numbers of T or B peripheral blood lymphocytes, or the in vitro
responses to PHA, Con A, and PWM were observed between this high-exposure group (mean
serum PBB level of 787 ppb [range of 188 to 2560], n=32) and a low- exposure group (mean
serum PBB level of 2.8 ppb [range of <1 to 11], n=51).
Several studies have also examined the effects of occupational or environmental exposure
to TCDD in human populations. Webb et al. (1989) reported the findings from immunologic
assessment of 41 persons from Missouri with documented adipose tissue levels of TCDD
resulting from occupational, recreational, or residential exposure. Of the participants, 16 had
tissue TCDD levels less than 20 ppt, 13 had levels between 20 and 60 ppt, and 12 had levels
greater than 60 ppt. The highest level was 750 ppt. Data were analyzed by multiple regression
based on adipose tissue level and the clinical dependent variable. Increased TCDD levels were
correlated with an increased percentage and total number of OKT8+ (CD8+) cells and increased
percentages of OKT11+ (i.e., CD2+) and OKT3+ (i.e., CD3+) T lymphocytes. However, the
percentage and total number of OKT4+ cells were not altered. Lymphoproliferative responses to
Con A, PHA, PWM, or TT were unaltered, as was the cytotoxic T cell response. Serum IgA, but
not IgG, was increased. No adverse clinical disease was associated with TCDD levels in these
subjects. Only 2 of the 41 subjects reported a history of chloracne.
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The above findings differ from those reported for the Quail Run Mobile Home Park
resident cohort study using 152 exposed and 151 unexposed individuals (tissue levels unknown)
in which decreased T cell percentages and suppressed cell-mediated immunity were reported
(Hoffman et al., 1986). The exposed group was reported to have decreased percentages of PEL
OKT3+, OKT4+, and OKT11+ T cells. Using seven standardized recall antigens (i.e., tetanus,
diphtheria, Streptococcus., tuberculin, Candida, Proteus, and Trichophyton\ the researchers
observed a significant decrease in the DTH response of TCDD-exposed individuals compared
with unexposed controls. The exposed group had an increased frequency of anergy (11.8% vs.
1.1%) and relative anergy (35.3% vs. 11.8%) compared with unexposed controls. However, it is
important to note that there were significant technical problems with the interpretation of the skin
test responses in this study. Nearly 50% of the skin test data were not used because two of the
four skin test readers were inexperienced. Subsequent retesting of these anergic subjects,
however, failed to confirm the DTH anergy (Evans et al., 1988). The only T cell measures
outside the normal range, which were determined from the initial Quail Run study of Hoffman et
al. (1986), were the percentage of OKT4+ cells and the OKT4+/OKT8+ ratios, which were lower
compared with unexposed controls (Evans et al., 1988). On the other hand, when serum from
some of these individuals was tested for levels of the thymic peptide, thymosin alpha-1 (Thya4),
the entire frequency distribution for the TCDD-exposed group shifted toward lower Thya4 levels
(Stehr-Green et al., 1989). The significant difference between the TCDD-exposed persons and
controls remained after controlling for age, sex, and socioeconomic status, with a trend of
decreasing Thya4 levels with increasing number of years of residence in the TCDD-contaminated
residential area. Thy.^ levels were not correlated with changes in other immune system
parameters or with any increased incidence of clinically diagnosed immune suppression. The
decrease in Thya4 levels in humans contrasts with the increase in Thya4 seen in PCB-treated
monkeys (Tryphonas et al., 1991b). Thya.l3 a product of thymic epithelial cells, has a role in
modulating the differentiation of prothymocytes to mature thymocytes (Low and Goldstein,
1984).
In July 1976 an explosion in a chemical plant producing trichlorophenol herbicides near
Seveso, Italy resulted in the release of TCDD estimated at 1.7 kg (Pocchiari et al., 1979). Several
epidemiological evaluations were performed on exposed individuals after the accident. Pocchiari
et al. (1979) summarized results of periodic evaluations of immune status of a group of 45
children (21 of whom had chloracne) age 3 to 7 years exposed to TCDD in Zone A, which was
the most heavily contaminated area. No abnormalities were reported for the following
parameters: serum immunoglobulin concentrations, levels of circulating complement,
lymphoproliferative responses to T and B cell mitogens or alloantigens in the MLR, or PEL T
and B cell populations. Interestingly, in the summary of a study conducted 6 years after the
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explosion, a different cohort of TCDD-exposed children was reported to have exhibited a
significant increase in complement protein levels, which reportedly correlated with incidence of
chloracne as well as increased numbers of peripheral blood lymphocytes and increased
lymphoproliferative responses (Tognoni and Bonaccorsi, 1982; Mocarelli et al., 1986, 1991).
However, no specific health problems were correlated with dioxin exposure in these children.
Unfortunately, none the studies cited above present any quantitative data. Specific information
about the methods and results are not presented. A cursory description of the tests employed and
the results obtained are the only information provided for the immune function evaluations. This
is extremely unfortunate, because a critical evaluation of these studies and their conclusions
cannot be made.
A more thorough study examined possible associations between occupational exposure to
the herbicide Agent Orange and its dioxin contaminate and adverse health experienced by Air
Force personnel who served in Operation Ranch Hand units in Vietnam from 1962 to 1971
(Roegner et al., 1991) and is summarized by Wolfe et al. (1992). Immunological tests were
carried out on a random sample of approximately 40% of the 1,670 participants for whom dioxin
measurements in serum were made. Statistical models were used to evaluate associations
between test parameters and serum dioxin levels using estimated initial and current serum dioxin
levels. The statistical models were implemented using minimal assumptions (only Ranch Hands
with current dioxin levels above 10 ppt) and maximal assumptions (all Ranch Hands with current
dioxin levels above 5 ppt). The immunological tests included the following: DTH response to
the recall antigens Candida, mumps, Trichophyton, and Staphage-lysate; PEL subsets CD2 (total
T cells), CD20 (B cells), CD4 (helper/inducer T cells), CDS (suppressor/cytotoxic T cells), CD14
(APC monocytes), CD25 (IL-2 receptor positive activated T cells), HLR-DR (B cells, activated T
cells and monocytes that present antigen to CD4+ T cells), CD4/CD8 ratio, and TLC (total
lymphocytes in circulation); total serum IgM, IgG, and IgA levels; lymphoproliferative response
to PHA; NKCI and NKCA activity, which measure NK cell lytic activity with and without IL-2
treatment, respectively; and mixed leukocyte culture (MLC) response (Roegner et al., 1991).
This immunological assessment did not find any clinically significant alterations associated with
current or initial levels of serum dioxin. A significant association between initial dioxin level
and increased IgA levels was found; however, IgM and IgG levels did not indicate the presence
of any dioxin-related effects. There was no association between the DTH response and serum
dioxin levels. Despite several significant findings for some of the other immunological
parameters examined, these data were deemed to be either internally inconsistent or not in a
direction expected in an impaired immune system (Roegner et al., 1991; Wolfe et al., 1992).
Results of a followup examination of Air Force personnel involved in Operation Ranch
Hand are presented in a report by Grubbs et al. (1995). Many of the immunologic parameters in
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the earlier study were examined in this study, with the following exceptions. The PEL subset
CDS (pan T cells) replaced CD2, and CDS (T cells and B cells) and CD16+CD56 (NK cells)
were added to the test screen. The PEL PHA response, NKCI and NKCA activity assays, and
MLC response were not performed. However, an autoantibody lupus panel, including tests for
antinuclear antibodies; thyroid microsomal antibody; mouse stomach and kidney (MSK) smooth
muscle, mitochondrial, and parietal antibodies; and rheumatoid factor, was included in this study.
A marginal positive association was found between IgA levels and initial serum dioxin (Grubbs
et al., 1995), as reported in the earlier study (Roegner et al., 1991). Also, an inverse relationship
was found with dioxin exposure and the presence of autoantibodies to MSK smooth muscle,
rheumatoid factor, and the lupus panel summary index. Although Grubbs et al. (1995)
recommended that these findings be investigated and clarified in further followups, they
concluded that no clinically significant indicators that reflected a consistent relationship between
serum dioxin and immune function deficiencies were found in this study.
The most recent followup examination of Air Force personnel involved in Operation
Ranch Hand occurred in 1992, and the results and analyses of immune parameters and dioxin
body burden were reported by Michalek et al. (1999). A total of 2,233 veterans (Ranch Hand,
n=952; Comparison, n=l,281) participated in this physical exam. The immunologic parameters
examined in this study were the same as those in the previous followup study (Grubbs et al.,
1995). Analysis of the results of the 1992 followup exam revealed only three statistically
significant differences. First, an increase was observed in absolute CD20+ (B cells) count in the
Background group, whereas the mean absolute count of CD16+CD56+CD3+ (T) cells was
decreased in the High category. Second, three dioxin-exposed veteran categories (Background,
Low, and High) had increased positive thyroid microsomal autoantibody tests; however, only the
Low category was significant. Third, there was no significant association between dioxin-
exposed veterans and total serum immunoglobulin levels, or in the presence of other
autoantibodies. As with the earlier Ranch Hand studies, the authors concluded that there was no
consistent relationship between dioxin exposure category and immune system alteration in Ranch
Hand veterans (Michalek et al., 1999).
Other human studies involving smaller cohorts, in which exposure to PHAHs was
documented, report alterations in certain immunological parameters. Eighteen workers (8 of
whom had chloracne) were evaluated for immunological abnormalities 17 years after exposure to
TCDD (no tissue levels were reported) in an industrial accident in a plant manufacturing the
herbicide 2,4,5 trichlorophenoxyacetic acid (Jennings et al., 1988). Immunological parameters
evaluated were the following: serum IgM, IgG, and IgA levels; antinuclear antibodies; immune
complexes; T lymphocyte subsets and NK cells; and lymphoproliferative response to PHA.
Antinuclear antibodies and immune complexes were detected more frequently in the peripheral
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blood of workers exposed to dioxin. No differences were observed in the total number of T or B
lymphocytes or in the T4/T8 ratio; however, NK cells identified by the surface marker Leu-7
were higher in the dioxin-exposed workers (Jennings et al, 1988). The significance of these
findings is unknown, because only a small cohort was studied and the autoantibody results were
opposite to those reported by Grubbs et al. (1995). Also, there are no animal data that indicate
increased NK cells associated with TCDD exposure. The NK results also differ from results of a
more recent human study by Svensson et al. (1994), who reported that men with high
consumption of PHAH-contaminated Baltic Sea fatty fish (n=23) had lower proportions and
numbers of PEL NK cells (i.e., CD56+ cells) compared with men with virtually no fish
consumption (n=20). NK cells were negatively associated with blood levels of several persistent
organochlorine compounds and with estimated intake offish. Fish consumption, however, was
not associated with any alterations in other cell subsets, plasma immunoglobulin levels, or liver
enzyme activities. It is important to point out that no attempt was made to determine if the
decreased numbers of NK cells were correlated with reduced NK function in this study or with
increased NK activity in the study described by Jennings et al. (1988).
A retrospective cohort morbidity study of 158 men accidentally exposed in 1953 to
TCDD during the production of trichlorophenol was reported by Zober et al. (1994), in which 73
men had back calculated TCDD values of >1,000 ppt and 85 had values of <1,000 ppt. Increased
frequency of upper respiratory tract infections was observed in individuals who had severe
chloracne. However, it was indicated that differences in the utilization rates of medical care by
the exposed and referent groups could have biased the findings. A clinical study of the 138 men
exposed to TCDD in the accident described above attempted to determine if any TCDD dose
relationships existed within the exposed population for a number of clinical and immunological
parameters (Ott et al., 1994). Increases in serum IgA and IgG and increases in complement C3
and C4 were seen with high current and back-calculated TCDD concentrations and with high
current TCDD concentrations, respectively. Marginal decreases in the percentage of
lymphocytes, NK cells, T cells, T helper cells, and T suppressor cells were also observed (Ott et
al., 1994). However, mean values for all of these parameters were comparable to those of
internal referent groups of 42 to 196 individuals (number of referents differed among the
parameters examined) who were not part of this clinical study but who participated in routine
occupational medical examinations during the same period (Ott et al., 1994).
In another retrospective study, T helper cell function was evaluated in 11 workers, 45 to
63 years of age, who were exposed to high levels of TCDD and other PCDDs between 1966 and
1976 in production and maintenance operations at a German chemical factory producing 2,4,5-
trichlorophenol (Tonn et al., 1996). TCDD body burdens were still high (43 to 874 pg/g blood or
a mean of 330, which is 10 times higher than in the average German population) 20 years after
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exposure. No differences were detected between exposed and control groups for surface marker
distribution (e.g., CDS, CD4, CDS, CD19, CD45RO, CD45RA, CD56 or CD57 ) or mitogen-
induced lymphoproliferation responses (e.g., PHA or PWM). However, the exposed group
showed reduced lymphoproliferation in response to human lymphocyte antigen-allogeneic
lymphocytes (e.g., MLR) and IL-2 stimulation. It was concluded that TCDD has a long-term
immunosuppressive effect on T helper cells manifested by reduced function of these cells rather
than by reduction in the cell numbers in peripheral blood (Tonn et al., 1996).
Neubert et al. (1993) attempted to determine if workers with moderately increased body
burdens of TCDD and related PHAHs (25 to 140 ppt TCDD or 104 to 522 ppt Internal! onal-
Toxicity Equivalencies [I-TE] in blood lipids) displayed altered patterns of PEL subsets similar
to those observed in TCDD-exposed marmosets (Neubert et al., 1990, 1991, 1992). A slight
trend in increased percentage of CD4+CD45RO+ helper-inducer T cells was observed. However,
Neubert et al. (1993) concluded that these alterations were of no medical relevance.
Furthermore, the data did not support an assumption that moderately increased body burdens of
PCDDs/PCDFs in adults result in decreased cellular components of the human immune system.
Because adult humans, as well as adult marmosets, are less susceptible to PCDD/PCDF -
associated alterations in PEL subsets than are adolescent marmosets, Neubert et al. (1993)
suggested that exposure to PHAHs during early development may be the important factor
influencing altered lymphocyte subsets.
In a more recent study, Neubert et al. (1995a) evaluated the in vitro lymphoproliferative
responses to Con A, PHA, PWM, anti-CD3, and TT of PEL from the same workers described
above. No decrease in the lymphoproliferative capacity of lymphocytes from these workers with
moderately increased PCDD/PCDF body burdens was observed compared with lymphocyte
responses of individuals with PCDD/PCDF body burdens within the reference range (1 to 3 ppt
TCDD or 9 to 29 ppt I-TE in blood lipids) for any of these lymphocyte stimulators.
A retrospective examination of possible associations between altered immune parameters
and occupational exposure to TCDD of chemical plant workers involved in the manufacture of
2,4,5-trichlorophenate and its derivatives between 1951 and 1972 was reported by Halperin et al.
(1998). A total of 259 workers and 243 unexposed referents were included in this study. The
workers had had substantial exposure to TCDD as indicated by a lipid adjusted mean serum
TCDD concentration of 229 ppt, whereas the controls had a mean TCDD concentration of 6 ppt.
Peripheral blood leukocytes and lymphocytes were enumerated, and lymphocytes were evaluated
by flow cytometry for a variety of populations including T cells, B cells, and NK cells. In vitro
assays for peripheral blood NK cell activity and lymphoproliferative activity of cultured
lymphocytes in the presence of the mitogens PHA, ConA, or PWM, or the antigens mumps,
Candida, or TT were performed. Total serum concentrations of IgM, IgG, and IgA and
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complement factor C3 were also determined. Of the various TCDD categories (based on
workers' serum TCDD concentration) except the lowest, there were increased odds of having
lower CD26 (activated T cells) counts. In addition, there was a decreased spontaneous
proliferation of cultured lymphocytes (i.e., no mitogen or antigen present in the culture);
however, lymphocytes from workers in the high TCDD category had increased proliferation in
the presence of ConA and PWM. No other immune parameters were affected. It was concluded
that the results were unlikely to be of clinical significance (Halperin et al., 1998).
The immunologic effects of pre- and postnatal background exposure to PCBs/dioxins of
Dutch infants from birth to 18 months of age were presented in a study by Weisglas-Kuperus et
al. (1995). Prenatal PCB exposure was estimated from the sum of PCB congeners 118, 138,
153, and 180 in maternal blood (i.e., 2.25 ± 0.98 |_ig/L, n=206) and the TEQ level in milk based
on 17 PCDDs/PCDFs and 8 dioxin-like PCB congeners (i.e., 66.6 ± 24.4 pg/g fat, n=80).
Postnatal exposure was calculated as a product of the total TEQ level in milk multiplied by the
weeks of breast-feeding. No relationship was found between pre- and postnatal PCB/dioxin
exposure and respiratory tract symptoms (i.e., number of periods with rhinitis, bronchitis,
tonsillitis, and otitis) or humoral antibody production at 18 months to vaccination against
mumps, measles and rubella at 14 months. Higher prenatal exposure was associated with
alterations in T cell subsets, in which increased number of TCRy§+ T cells correlated with a total
higher TEQ level at birth. Further, higher prenatal exposure was also associated with increased
total numbers of T cells, CD8+ cells and TCRy§ T cells at 18 months of age; correlating with
higher TEQ levels. A higher TEQ level was also associated with a decreased number of
monocytes and granulocytes at 3 months. Despite these statistical associations of cell types with
TEQ levels, all values were found to be within normal range. It was indicated that the subtle
changes in the number of blood leukocytes do not necessarily mirror alterations in the cell
composition of lymphoid and nonlymphoid organs nor do they reflect functional defects
(Weisglas-Kuperus et al., 1995).
In a followup study, Weisglas-Kuperus et al. (2000) examined whether changes
associated with prenatal exposure to PCBs persist into later childhood. At 42 months of age,
antibody levels to measles and mumps were negatively correlated with cord and maternal PCB
levels, respectively. A higher prevalence of recurrent middle ear infections and chicken pox, as
well as a lower incidence of allergic reactions, were associated with current PCB body burdens.
Also, there were significant positive correlations between prenatal PCB exposure and the
numbers of peripheral blood lymphocytes and CD3+CD8+(cytotoxic), CD4+CD45RO+ (memory),
TcR«p+, and CD3+HLA-DR+ (activated) T cells. These values, however, were within the normal
range for children at this age. The authors concluded that perinatal background exposure to
PCBs and dioxins might be associated with increased susceptibility to infectious diseases.
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In summary, no clear pattern of altered immune parameters following exposure to TCDD
or related PHAHs has emerged from studies in humans. The basis for the lack of consistent,
exposure-related effects is unknown and may depend on several factors. These include the
generic difficulties in assessing subclinical immunomodulation using assays with very broad
ranges of normal responses, which reduce the sensitivity to detect small changes in a
heterogeneous human population. Furthermore, the choice of immune parameters used to
evaluate humans exposed to TCDD and related PHAHs has been based to a greater extent on
what is clinically feasible than on what has been shown to be sensitive in animal studies. Thus,
lack of consistent or significant immunotoxic effects in humans resulting from TCDD exposure
may be as much a function of the assays used as of the immune status of the cohort.
An interesting observation, relative to the human data, is that T cells and T cell functions
appear to be more frequently affected than other immune cells or functions when PHAH
exposure related effects have been reported. This may simply reflect the fact that T cell assays
predominate over other assays employed in these studies. Nevertheless, it is interesting that
several studies report slight reductions in CD4+ T helper cells (Chang et al., 1981; Wu and Lu,
1984; Lu and Wu, 1985; Hoffman et al., 1986; Evans et al., 1988). Reductions in CD4+ cells and
CD4+ subsets have also been reported in non-human primates exposed to PHAHs (Hong et al.,
1989; Neubert et al., 1990, 1991, 1992; Tryphonas et al., 1991b). These reductions may not
translate into significant immune effects. However, the fact that these cells play a pivotal role in
regulating immune responses and that their reduction may presage immunosuppression suggests
that these observations are worth further investigation.
4.9. IMMUNOTOXICITY OF PHAHS IN HUMANS: IN VITRO EXPOSURE
Several laboratories have examined the direct effects of TCDD on human lymphocytes in
vitro. Neubert et al. (1991) reported decreased PEL subpopulations from humans and nonhuman
primates cultured in the presence of TCDD. CD4+CD29+ helper-inducer/memory T cells and
CD20+ B cells were dose-dependently decreased in PWM-stimulated cultures of human PBLs at
concentrations as low as 10"12 to 10"14 M TCDD. However, an attempt to corroborate these
findings failed to detect any suppression in human PEL subpopulations, including CD4+CD29+
helper-inducer/memory T cells and CD19+ B cells, at TCDD concentrations ranging from 10"7 to
10"14 M (Lang et al., 1994). Furthermore, PWM- or anti-CD3-stimulated lymphocyte
proliferation was not altered by TCDD at concentrations of 10"7 to 10"11 M (Lang et al., 1994).
Similar negative results were obtained by Wood et al. (1992), who reported that exposure
of human tonsillar lymphocytes (HTLs) to concentrations of 3 x 10'8 to 3 x 1CT10 M TCDD did
not affect either PWM-induced proliferation or IgM antibody production. In contrast, 3 x 10"8 to
3 x 10"10 M TCDD suppressed toxic shock syndrome toxin (TSST-l)-induced IgM secretion of
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HTL B cells (Wood and Holsapple, 1993). The sensitivity of the HTL B cells to TCDD
suppression of TSST-1-induced IgM secretion, however, was found to be highly variable among
the different donors. In a separate study, concentrations of 3 x 10"8 to 3 x 10"10 M TCDD
suppressed the background proliferation and IgM secretion of low-density HTL B cells
(predominately activated cells), but not high-density HTL B cells (predominately resting cells)
(Wood et al., 1993). TCDD also suppressed LPS plus T cell replacement factor-stimulated
proliferation and IgG secretion of low-density, but not high-density, HTL B cells. These results
suggest that TCDD may have a direct effect on HTL low-density B cells and that this lymphocyte
subpopulation may be a sensitive target for TCDD.
More recently, Masten and Shiverick (1995) investigated the effect of TCDD on
expression of the CD 19 gene in the EVI-9 human B lymphocyte cell line in an attempt to
determine a possible mechanism for TCDD-induced inhibition of human and murine B
lymphocyte Ig production. CD 19 is a B cell surface signal transducing protein that is expressed
from early stages of B cell development, but is lost when B cells differentiate into antibody-
producing plasma cells (Tedder et al., 1994). TCDD treatment of EVI-9 cells decreased the steady
state levels of CD 19 mRNA by 67%. A DNA-binding complex in EVI-9 nuclear extracts was
identified that, based on several criteria, appeared to be the AhR. Furthermore, the AhR complex
recognized a DNA binding site for B cell lineage-specific activator protein (BSAP) in the
promotor region of the CD19 gene. This BSAP binding site is similar to the consensus AhR
DNA binding site. Based on these results, Matsen and Shiverick (1995) suggest that the decrease
in CD 19 gene expression following TCDD exposure may result from the AhR interfering with
B SAP-stimulated CD19 transcription. Further work, however, is required to support this
hypothesis. Nevertheless, it is interesting that this work supports the animal in vitro work of
Holsapple and coworkers, which indicates that the B cell is a primary cellular target for the direct
effects of TCDD (Holsapple, 1995). It also goes beyond the in vitro animal work in that it
provides further evidence that the AhR is involved in PHAH-induced immunotoxicity.
4.10. SUMMARY
Cumulative evidence from a number of studies indicates that the immune system of
various animal species is a target for toxicity of TCDD and structurally related PHAHs, including
other PCDDs, and PCDFs and PCBs. Both cell-mediated and humoral immune responses are
suppressed following TCDD exposure, suggesting that multiple cellular targets within the
immune system are altered by TCDD. Evidence also suggests that the immune system is
indirectly targeted by TCDD-induced changes in nonlymphoid tissues. TCDD exposure of
experimental animals results in decreased host resistance following challenge with certain
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infectious agents, which likely result from TCDD-induced suppression of immunological
functions.
The primary antibody response to the T cell-dependent antigen SRBCs is the most
sensitive immunological response that is consistently suppressed in mice exposed to TCDD and
related PHAHs. The degree of immunosuppression is related to the potency of the dioxin-like
PHAH congeners. There is remarkable agreement among several different laboratories for the
potency of a single acute dose of TCDD (suppression at a dose as low as 0.1 |_ig TCDD/kg with
an average ID50 value of approximately 0.7 |_ig TCDD/kg) to suppress this response in Ah-
responsive mice. Results of studies that have compared the effects of acute exposure to
individual PCDD, PCDF, and PCB congeners, which differ in their binding affinity for the AhR,
on this response have provided critical evidence that certain dioxin-like congeners are also
immunosuppressive. The degree of immunosuppression has been found to be related to potency
of the dioxin-like congeners. Antibody responses to T cell-independent antigens, such as TNP-
LPS, and the CTL response are also suppressed by a single acute exposure to TCDD, albeit at
higher doses than those that suppress the SRBC response. A limited number of studies reveal
that dioxin-like congeners also suppress these responses, with the degree of suppression related
to the congeners' AhR binding affinity. Although a thorough and systematic evaluation of the
immunotoxicity of TCDD-like congeners in different species and for different immunological
endpoints has not been performed, it can be inferred from the available data that dioxin-like
congeners are immunosuppressive.
In addition to the TCDD-like congener results, studies using strains of mice that differ in
the expression of the AhR have provided critical evidence to support a role for Ah-mediated
immune suppression following PHAH exposure. Recent in vitro work also supports a role for
Ah-mediated immune suppression. Other in vivo and in vitro data, however, suggest that non-
Ah-mediated mechanisms may also function in PHAH-induced immunotoxicity. However, more
definitive evidence remains to be developed to support this latter view.
Although in mice the immunosuppressive potency of individual PHAHs is related to their
structural similarity to TCDD, this pattern of suppression is observed only after exposure to an
individual PHAH. The immunotoxicity of TCDD and related congeners can be modified by
co-exposure to PHAHs in simple binary or more complex mixtures resulting in additive or
antagonistic interactions. Dose-response data are needed on acute, subchronic, and chronic
exposure to the individual PHAHs in a mixture and on the mixture itself in order to fully evaluate
potential synergistic, additive, or antagonistic effects of environmentally relevant PHAH
mixtures.
Perinatal exposure of experimental animals to TCDD results in suppression of primarily
T cell immune functions, with evidence of suppression persisting into adulthood. The effects on
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T cell functions appear to be related to the fact that perinatal TCDD exposure of mice alters
thymic precursor stem cells in the fetal liver and bone marrow, and thymocyte differentiation in
the thymus. These studies suggest that perinatal development is a critical and sensitive period for
TCDD-induced immunotoxicity. Efforts should be made to determine the consequences of
perinatal exposure to TCDD congeners and PHAH mixtures on immune system integrity.
Animal host resistance models that mimic human disease have been used to assess the
effects of TCDD on altered host susceptibility. TCDD exposure increases susceptibility to
challenge with bacteria, viruses, parasites, and tumors. Mortality is increased in TCDD-exposed
mice challenged with certain bacteria. Increased parasitemia occurs in TCDD-exposed mice and
rats challenged with parasitic infections. Low doses of TCDD also alter resistance to virus
infections in rodents. Increased susceptibility to infectious agents is an important benchmark of
immunosuppression; however, the role of TCDD in altering immune-mediated mechanisms
important in murine resistance to infectious agents remains to be elucidated. Also, since little is
known about the effects of dioxin-like congeners on host resistance and nothing is known about
the relation between the AhR and host resistance, more research is recommended in this area.
Studies in nonhuman primates exposed acutely, subchronically, or chronically to PHAHs
have revealed variable alterations in lymphocyte subpopulations, primarily T lymphocyte subsets.
On the other hand, in three separate studies in which monkeys were exposed subchronically or
chronically to PCBs, the antibody response to SRBC was consistently found to be suppressed.
These results in nonhuman primates are important because they corroborate the extensive
database of PHAH-induced suppression of the antibody response to SRBC in mice and thereby
provide credible evidence for immunosuppression by PHAHs across species. In addition, these
data indicate that the primary antibody response to this T cell-dependent antigen is the most
consistent and sensitive indicator of PHAH-induced immunosuppression.
The available database derived from well-controlled animal studies on PHAH
immunotoxicity can be used for the establishment of no-adverse-effect levels. Because the
antibody response to SRBCs has been shown to be dose-dependently suppressed by TCDD and
related PHAHs, this database is best suited for the development of dose-response modeling.
Accidental or occupational exposure of humans to TCDD and related PHAHs variably
affects a number of immunological parameters. Unfortunately, evaluation of immune system
integrity in humans exposed to PHAHs has provided data that are inconsistent across studies.
The broad range of "normal" responses in humans due to the large variability inherent in a
heterogenous population, the limited number and sensitivity of tests performed, and the poor
exposure characterization of the cohorts in these studies compromise any conclusions about the
ability of a given study to detect immune alterations. Consequently, there are insufficient clinical
data from these studies to fully assess human sensitivity to PHAH exposure. Nevertheless, the
December 2003 4-36 DRAFT-DO NOT CITE OR QUOTE
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database of the results of the extensive animal work is sufficient to indicate that immune effects
could occur in the human population from exposure to PHAHs at some dose level.
It is interesting that a common thread in several human studies is the observed reduction
in CD4+ T helper cells, albeit generally within the "normal" range, in cohorts exposed to PHAHs.
These reductions may not translate into clinical effects, but it is important to note that such cells
have an important role in regulating immune responses and that their reduction in clinical
diseases is associated with immunosuppression. Another important consideration is that a
primary antibody response following immunization was not evaluated in any of the human
studies. In that this immune parameter has been revealed to be the most sensitive in animal
studies, this parameter should be included in future studies of human populations exposed to
PHAHs. It is also recommended that research continue on delineating the mechanism(s)
underlying PHAH-induced immunotoxicity.
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Table 4-1. Acute single dose ID50s for polychlorinated dioxins, furans, and
biphenyls based on suppression of the PFC response to SRBCs in Ah-responsive B6
mice
Cnnpener
2,3,7,8-TCDD
1!
1!
1!
1,2,3,6,7,8-HxCDD
1,2,3,4,6,7,8-HpCDD
OCDD
2,3,4,7,8-PCDF
2,3,7,8-TCDF
1,2,3,4,6,7,8-HpCDF
1,2,3,7,9-PenCDF
1,3,6,8-TCDF
3,4,3',4'-TCB
2,3,4,5,3',4'-HxCB
2,3453'4'-HxCB
2,4,3',4',5',6'-HxCB
2,3,4,3',5'-PenCB
2,3,4,5,3 ',5 '-HxCB
2,4,2',4'-TCB
2,4,5,2',4',6'-HxCB
2,4,6,2 ',4 ',6 '-HxCB
2,4,5,2',4',5'-HxCB
Aroclor 1260
Aroclor 1254
Aroclor 1254
Aroclor 1248
Aroclor 1242
Aroclor 1016
Aroclor 1232
m,n
0.74 ng/kg
0.65 ng/kg
0.77 ng/kg
0.60 ng/kg
7.1 ng/kg
85.0 ng/kg
>500 ng/kg
l.Ong/kg
4.3 ng/kg
208 ng/kg
239 ng/kg
1 1 mg/kg
28 mg/kga
0.7 mg/kg
31 mg/kga
43 mg/kg
65 mg/kg
72 mg/kg
> 100 mg/kg
>360 mg/kg
>360 mg/kg
>360 mg/kg
104 mg/kg
118 mg/kg
207 mg/kg
190 mg/kg
391 mg/kg
408 mg/kg
464 mg/kg
Reference
Kerkvliet and Brauner, 1990
Vecchietal, 1980
Davis and Safe, 1988
Kerkvliet et al, 1990a
Kerkvliet etal, 1985
Kerkvliet et al., 1985
Kerkvliet et al., 1985
Davis and Safe, 1988
Davis and Safe, 1988
Kerkvliet et al., 1985
Davis and Safe, 1988
Davis and Safe, 1988
Silkworth and Grabstein, 1982
Davis and Safe, 1990
Silkworth et al., 1984
Davis and Safe, 1990
Ibid.
Ibid.
Silkworth et al., 1984
Davis and Safe, 1990
Ibid.
Biegeletal, 1989
Davis and Safe, 1989
Ibid.
Lubetetal, 1986
Davis and Safe, 1989
Ibid.
Ibid.
Ibid.
alnterpolated from two data points.
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Table 4-2. ID50s for sppression of alloantigen (P815)-specific CTL response in
C57B1/6 mice
Conpener
TCDD
3,4,5,3',4',5'-HxCB
2,3,4,5,3 ',4'-HxCB
2,4,5,2',4',5'-HxCB
m.n
7 jig/kg
7mg/kg
70 mg/kga
>300 mg/kg
Reference
Kerkvliet et al.,
Ibid.
Ibid.
Ibid.
1990b
Interpolated from two data points.
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Table 4-3. Immunotoxic effects of TCDD in the offspring following
prenatal/neonatal exposure to TCDD
Protocol"
FnHnoints
F.ffept
T,nAF.T,b
Study 1
Pregnant B6 or
B6C3F! mice given 1,2, 5,
or 15 (ig/kg TCDD orally
on day-7, 0, +7, +14
relative to parturition
on day 0
Study 2
Pregnant Swiss mice
fed diets containing
1.0, 2.5, or 5.0 ppb
TCDD for 7 weeks prenatally
and postnatally
Study 3
Pregnant Fischer 344
rats given 1 or 5 (ig/kg
TCDD orally on day -3, 0,
+7, and +14 relative to
parturition on day 0
PYB6 tumor incidence Increased 1 (ig/kg x 4
Allograft rejection time Increased 2 (ig/kg x 4
Body, thymus, spleen wt Decreased 5 (ig/kg x 4
Bone marrow cellularity Decreased 5 (ig/kg x 4
T cell blastogenesis Decreased 5 (ig/kg x 4
Listeria monocytogenes-induced Decreased 5 (ig/kg x 4
mortality
Bone marrow colony formation Decreased 5 (ig/kg x 4
(CFU-S) — >15ng/kgx4
LPS blastogenesis — >15(ig/kgx4
Anti-SRBC serum liters
Endotoxin mortality Increased 1.0 ppb diet
Thymus weight Decreased 2.5 ppb diet
PFC response to SRBC Decreased 5.0 ppb diet
DTK response Decreased 5.0 ppb diet
Anti-SRBC serum liters — >5.0 ppb diet
T and B cell blastogenesis — >5.0 ppb diet
Listeria-induced mortality — >5.0 ppb diet
Allograft rejection time Increased 5 (ig/kg x 4
T cell blastogenesis Decreased 5 (ig/kg x 4
DTK response Decreased 5 (ig/kg x 4
Listeria-induced mortality Decreased 5 (ig/kg x 4
Body and thymus weight Decreased 5 (ig/kg x 4
Anti-BGG serum titers — >5 (ig/kg x 4
"Study 1: Vos and Moore, (1974), Luster et al. (1980a); Study 2: Thomas and Hinsdill (1979); Study 3: Vos and
Moore, (1974) , Faith and Moore (1977).
bAbbreviations used: LOAEL - lowest observable adverse effect level; BGG - bovine gamma globulin; LPS -
lipopolysaccharide; PHA - phytohemagglutinin; Con A - Concanavalin A; SRBC - sheep red blood cell; DTH -
delayed-type hypersensitivity; PFC - plaque-forming cell; CFU-S - colony-forming units-spleen.
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o
o
o>
w
c
o
Q.
W
S!
O
CQ
100 --
80
60 --
40
+= 20
£
<
0
iD50 frig/kg)
0,1
• TCDD
O HxCDD
A HpCDD
A HpCDF
• OCDD
0.65
7.1
85
208
>500
1 10
Dose (M9/kg)
100
Figure 4-1. Structure-dependent immunotoxicity of some polychlorinated dioxin and furan
isomers. Immunotoxicity assessed by suppression of the splenic antibody response to SRBCs
(modified from Kerkvliet et al., 1985).
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studies of PCB (Aroclor 1254) in the adult rhesus (Macaco mulatto) monkey— preliminary report. Int J
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Tryphonas, H; Luster, MI; Schiffman, G; Dawson, LL; Hodgen, M; Germolec, D; Hayward, S; Bryce, F; Loo, JCK;
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5. DEVELOPMENTAL AND REPRODUCTIVE TOXICITY*
5.1. INTRODUCTION
The potential for dioxins and related compounds to cause reproductive and
developmental toxicity has been recognized for many years. Recent laboratory studies have
broadened our knowledge in this area and demonstrate that altered development is among the
most sensitive endpoints of 2,3,7,8-tetrachlorodibenzo-/>-dioxin (TCDD). This chapter reviews
much of the literature on dioxin's developmental and reproductive toxicity but is not intended to
be exhaustive. Special emphasis is placed on that part of the database that has accumulated since
the last major EPA review of this topic (Kimmel, 1988; Peterson et al., 1993). In addition, the
database is viewed in light of the Ah receptor model of TCDD action that is being examined for
its applicability in the current EPA risk assessment.
To focus the analysis of the database, the chapter is divided into developmental toxicity
and male and female reproductive toxicity. The authors recognize the interrelatedness of
developmental and reproductive events at all levels of biological complexity. Therefore, the
reader should not view the chapter subheadings within each of these divisions as defining
discrete endpoints that are exclusive of other endpoints. For example, the effects of TCDD on
circulating levels of sex hormones or on responsiveness to sex hormones may be translated into
reproductive dysfunction if exposure occurs in adulthood or abnormal development of sexual
behavior if exposure occurs perinatally. Likewise, even though organ structure and growth are
considered separate manifestations in developmental toxicity that are associated with perinatal
exposure to TCDD, the normal development of an organ is dependent on normal growth
processes, and inhibiting perinatal growth can significantly disrupt the structural integrity of an
organ system.
2,3,7,8-TCDD is one of 75 possible CDD congeners and 135 possible CDF congeners. It
is one of the most potent of the CDDs, BDDs, CDFs, BDFs, PCBs, and PBBs, and as such serves
as the prototype congener for investigating the toxicity elicited by these classes of chemicals.
Developmental and reproductive toxicity is generally believed to be caused by the parent
compound; there is no evidence that TCDD metabolites are involved. The toxic potency of
TCDD is due to the number and position of chlorine substitutions on the dibenzo-/?-dioxin
molecule. CDD congeners with decreased lateral (2, 3, 7, and 8) or increased nonlateral chlorine
and bromine substituents are less potent than TCDD (Safe, 1990); however, most of these
*The information contained in portions of this chapter has been published previously as follows: Peterson, RE;
Theobald, HM; Kimmel, GL. (1993) Critical Reviews in Toxicology 23(3):283-335. The article underwent Agency
and peer review and was approved for publication.
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congeners will produce toxicity, and the pattern of responses within animals of the same species,
strain, sex, and age will generally be similar to that of TCDD (McConnell and Moore, 1979;
Poland and Knutson, 1982). PCB congeners with zero or one ortho chlorines, two para
chlorines, and at least two meta chlorines can assume a coplanar conformation sterically similar
to TCDD and also produce a pattern of toxic responses similar to that of TCDD. In contrast,
PCB congeners with two or more ortho chlorines cannot assume a coplanar conformation and do
not resemble TCDD in toxicity (Poland and Knutson, 1982; Safe, 1990).
CDD and CDF congeners chlorinated in the lateral positions, as compared with those
lacking chlorines in the 2, 3, 7, and 8 positions, are preferentially bioaccumulated by fish,
reptiles, birds, and mammals (Stalling et al., 1983; Cook et al., 1991; U.S. EPA, 1991).
Furthermore, coplanar PCBs and/or monoortho-chlorine-substituted analogs of the coplanar
PCBs bioaccumulate in fish, wildlife, and humans (Tanabe, 1988; Kannan et al., 1988; Mac et
al., 1988; Kubiak et al., 1989; Smith et al., 1990). This is of concern because combined effects
of the lateral-substituted CDD, BDD, CDF, BDF, PCB, and PBB congeners acting through an Ah
receptor mechanism have the potential of decreasing feral fish and wildlife populations
secondary to developmental and reproductive toxicity (Gilbertson, 1989; Walker and Peterson,
1991; Walker et al., 1991; Cook et al., 1991). Humans are not exempt from the developmental
and reproductive effects of complex halogenated aromatic hydrocarbon mixtures. Such mixtures
that contain both TCDD-like congeners and non-TCDD-like congeners have been implicated in
causing developmental and reproductive toxicity in the Yusho and Yu-Cheng poisoning incidents
in Japan and Taiwan (Kuratsune, 1989; Hsu et al., 1985; Rogan, 1989). Thus, exposure to
TCDD-like congeners is a health concern for humans as well as for domestic animals, fish, and
wildlife, although the relative contributions of TCDD-like and non-TCDD-like congeners are not
known in some exposure situations.
A mechanism of action that CDD, BDD, CDF, BDF, PCB, and PBB congeners
substituted in the lateral positions have in common is that they bind to the Ah receptor, which
dimerizes with the Ah receptor nuclear translocator protein (ARNT) in the nucleus. These
liganded heterodimeric complexes bind to specific sequences of DNA referred to as dioxin-
responsive enhancers (DREs), resulting in alterations in gene transcription. There is evidence
that this Ah receptor mechanism may be involved in the antiestrogenic action of TCDD and in its
ability to produce the structural malformations of cleft palate and hydronephrosis in mice.
Recent interest has also focused on the potential nonnuclear interaction of the Ah receptor within
the cell (see Chapter 2).
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5.2. DEVELOPMENTAL TOXICITY
The manifestations of developmental toxicity from exposure to TCDD have been divided
into three categories for convenience in assessing the database with respect to an Ah receptor-
mediated response. These categories include death/growth/clinical signs, structural
malformations, and postnatal functional alterations. Exposure-related effects on
death/growth/clinical signs are described for fish, birds, laboratory mammals, and humans along
with structure-activity results that are consistent with, but do not prove, an Ah receptor-mediated
mechanism. Structural malformations, particularly cleft palate formation and hydronephrosis,
occur in mice. In other mammalian species, however, postnatal functional alterations, some of
which may be irreversible, are the most sensitive adverse developmental effects of TCDD-like
congeners. These include effects on the male and female reproductive systems in rats and
hamsters, and object learning behavior in monkeys.
5.2.1. Death/Growth/Clinical Signs
5.2.1.1. Fish
Early life stages offish appear to be more sensitive to TCDD-induced mortality than
adults. This is suggested by the LD50 of TCDD in rainbow trout sac fry (0.4 ng/kg egg weight)
being 25 times less than that in juvenile rainbow trout (10 ng/kg body weight) (Walker and
Peterson, 1991; Kleeman et al., 1988). The significance of this finding is that early life stage
mortality caused by high concentrations of TCDD-like congeners in fish eggs may pose the
greatest risk to feral fish populations (Walker and Peterson, 1991; Cook et al., 1991). Cooper
(1989) reviewed the developmental toxicity of CDDs and CDFs in fish, and Cook et al. (1991)
discussed components of an aquatic ecological risk assessment for TCDD in fish. The reader is
referred to this literature for more indepth coverage than is presented here.
TCDD is directly toxic to early life stages offish. This has been demonstrated for
Japanese medaka, pike, rainbow trout, and lake trout exposed as fertilized eggs to graded
concentrations of waterborne TCDD. In these species, TCDD causes an overt toxicity syndrome
characterized by edema, hemorrhages, and arrested growth and development culminating in death
(Helder, 1980, 1981; Wisk and Cooper, 1990a; Spitsbergen et al., 1991; Walker et al., 1991;
Walker and Peterson, 1991). Histopathologic evaluation of lake trout embryos and sac fry has
shown this syndrome to be essentially identical to that of blue sac disease (Helder, 1981;
Spitsbergen et al., 1991). Following egg exposure to TCDD, signs of toxicity are not detected in
medaka until after the liver rudiment forms (Wisk and Cooper, 1990a), and in lake trout toxicity
is first detected ~ 1 week prior to hatching but becomes fully manifest during the sac fry stage
(Spitsbergen et al., 1991; Walker et al., 1991). Among all fish species investigated thus far, lake
trout are the most sensitive to TCDD developmental toxicity. Following exposure of fertilized
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lake trout eggs to graded waterborne concentrations of TCDD, the no observable adverse effect
level (NOAEL) for sac fry mortality is 34 pg TCDD/g egg, the lowest observed adverse effect
level (LOAEL) is 55 pg TCDD/g egg, and the egg TCDD concentration that causes 50%
mortality above control at swim up (LD50) is 65 pg TCDD/g egg (Walker et al., 1991). Thus,
TCDD is a potent developmental toxicant in fish, and the effect is not secondary to maternal
toxicity.
The Ah receptor has not been identified in early life stages offish; however, it is assumed
to be present because PCBs induce hepatic cytochrome P-4501 Al in lake trout and brook trout
embryos and fry (Binder and Stegeman, 1983; Binder and Lech, 1984). The Ah receptor has
been identified in adult rainbow trout liver (Heilmann et al., 1988) and in a rainbow trout
hepatoma cell line (Lorenzen and Okey, 1990). CDD and CDF congeners that are approximate
isostereomers of TCDD produce essentially the same pattern of toxic responses as TCDD in early
life stages of medaka and rainbow trout, suggesting that they may act through a common
mechanism (Wisk and Cooper, 1990b; Walker and Peterson, 1991). Also, in rainbow trout their
potencies relative to TCDD (i.e., toxic equivalency factors, TEFs) for causing early life stage
mortality (TCDD LD50/congener LD50) are in the same range as those proposed for human health
risk assessment based on a diverse spectrum of acute and subchronic toxicity tests in mammalian
species (Safe, 1990; Walker and Peterson, 1991). However, for the coplanar PCBs and
monoortho-chlorinated analogs of the coplanar PCBs, TEFs based on early life stage mortality in
rainbow trout are 1/14 to 1/80 less (Walker and Peterson, 1991) than the TEFs proposed for risk
assessment (Safe, 1990).
5.2.1.2. Birds
Bird embryos are also more sensitive to TCDD toxicity than adults. The LD50 of TCDD
in the chicken embryo (0.25 ng/kg egg weight) is 100 to 200 times less than the TCDD dose that
causes mortality in adult chickens (25-50 ng/kg body weight) (Greig et al., 1973; Allred and
Strange, 1977). The LD50 of TCDD injected into fertilized ring-necked pheasant eggs (1.1-1.8
ng/kg egg weight) is 14 to 23 times less than the TCDD dose that causes 75% mortality in ring-
necked hen pheasants (25 ng/kg body weight) (Nosek et al., 1993).
Among bird species, most developmental toxicity research has been done on chickens.
Injection of TCDD or its approximate isostereomers into fertilized chicken eggs causes a toxicity
syndrome in the embryo characterized by pericardial and subcutaneous edema, liver lesions,
inhibition of lymphoid development in the thymus and bursa of Fabricius, microphthalmia, beak
deformities, cardiovascular malformations, and mortality (Cheung et al., 1981a,b; Brunstrom and
Darnerud, 1983; Rifkind et al., 1985; Brunstrom and Lund, 1988; Brunstrom and Andersson,
1988; Nikolaidis et al., 1988a,b). On the other hand, injection of a coplanar PCB into fertilized
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turkey eggs at a dose high enough to cause microphthalmia, beak deformities, and embryo
mortality did not produce liver lesions, edema, or thymic hypoplasia, all hallmark signs of TCDD
toxicity in the chicken embryo (Brunstrom and Lund, 1988). This disparity in signs of TCDD
embryotoxicity among bird species is not unique to the turkey and chicken. In fertilized eggs of
ring-necked pheasants and eastern bluebirds, injection of TCDD produces embryo mortality, but
all of the other signs of toxicity seen in the chicken embryo are absent, including cardiovascular
malformations (Thiel et al., 1988; Martin et al., 1989; Nosek et al., 1993). Thus, in bird embryos
the signs of toxicity elicited by TCDD and its approximate isostereomers are highly species
dependent; the only toxic effect common to all bird species is embryo mortality.
There is evidence in chicken embryos that the Ah receptor may be involved in producing
developmental toxicity. The Ah receptor has been detected in chicken embryos (Denison et al.,
1986; Brunstrom and Lund, 1988) and the rank order potency of PCB congeners for producing
chicken embryo mortality (3,3',4,4',5-PCB > 3,3',4,4'-TCB > 3,3',4,4',5,5'-HCB > 2,3,3',4,4'-PCB
> 2,3,4,4',5-PCB, with 2,2',4,5'-TCB, 2,2',4,4',5,5'-HCB, and 2,2',3,3',6,6'-HCB being inactive) is
similar to that for a classic Ah receptor-mediated response in the chicken embryo, cytochrome
P-4501A1 induction (Rifkind et al., 1985; Brunstrom and Andersson, 1988; Brunstrom, 1989).
However, although induction of cytochrome P-4501 Al and toxicity may both be part of a
pleiotropic response linked to the Ah receptor, they are not otherwise causally related. This is
demonstrated by the nonsteroidal anti-inflammatory drug benoxoprofen that suppresses 3,3',4,4'-
TCB-induced toxicity in the chicken embryo without altering its ability to induce microsomal
enzyme activity (Rifkind and Muschick, 1983). Also, for 3,3',4,4'-TCB, 3,3',4,4',5,5'-HCB, and
TCDD there is a marked dissociation of the dose-response relationship for lethality and enzyme
induction in the chicken embryo (Rifkind et al., 1985).
A decreased activity of uroporphyrinogen decarboxylase (URO-D) and an increased
accumulation of uroporphyrins are effects that are readily produced by exposure of cultured
chicken embryo liver cells to TCDD, 3,3',4,4'-TCB, and other PCBs (Sinclair et al., 1984; Marks,
1985; Lambrecht et al., 1988). Coplanar PCB congeners are more potent inhibitors of URO-D
activity in cultured chicken embryo liver cells than are noncoplanar PCB congeners (Sassa et al.,
1986), suggesting an Ah receptor-mediated mechanism. Unlike the results in cultured cells,
however, a lethal dose of TCDD (6 nmol/egg) does not affect URO-D activity or cause an
increased accumulation of uroporphyrins in chicken embryos (Rifkind et al., 1985). Thus,
TCDD-induced lethality in chicken embryos is not associated with the effects of TCDD on URO-
D activity, even though a decrease in URO-D activity might be expected to occur if a sufficient
dose of TCDD could be reached without being lethal.
The chicken embryo heart is a target organ for TCDD and other halogenated aromatic
hydrocarbons that act by an Ah receptor mechanism. Expression of the Ah receptor occurs
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ubiquitously in cardiac myocytes, while ARNT expression is restricted to myocytes that overlay
the atrioventricular canal, outflow tract, and atrial and ventricular septa (Walker et al., 1997).
Both Ah receptor and ARNT appear to be absent from the endocardium and endocardial- derived
mesenchyme. In addition, cardiac expression of cytochrome P-4501A1 is restricted to
myocardium that expresses Ah receptor and ARNT. The classic sign of chick embryo toxicity
involving the heart is pericardial edema. However, TCDD has other effects on the chick embryo
heart that are less well known. These include its ability to produce cardiovascular malformations
and to increase cardiac release of arachidonic acid metabolites. When fertilized chicken eggs are
injected with graded doses of TCDD, cardiovascular malformations are produced including
ventricular septal defects, aortic arch anomalies, and conotruncal malformations. Approximately
1.6 pmol TCDD/egg (9 ng/kg egg, assuming a 55 g egg weight) causes cardiovascular
malformations in 46% of treated embryos versus 29% of control embryos (Cheung et al.,
1981a,b). The cardiovascular malformation response may be unique to the chicken embryo
because in fertilized ring-necked pheasant and eastern bluebird eggs injected with TCDD the
incidence of such malformations is not increased (Thiel et al., 1988; Martin et al., 1989; Nosek et
al., 1993).
In the chicken embryo heart, arachidonic acid metabolism is stimulated by TCDD,
resulting in increased formation of prostaglandins (Quilley and Rifkind, 1986). Dose-response
relationships for the release of immunoreactive PGE2, PGF2a, and TxB2 from chick embryonic
heart are biphasic, with an apparent maximally effective dose of 100 pmol TCDD/egg. When the
egg tetrachloroabenzene (TCDD) dose is further increased, release of these prostaglandins tends
to decline towards levels in control hearts. Biphasic dose-response curves for cardiac PGE2
release also were obtained with 3,3',4,4'-TCB and 3,3',4,4',5,5'-HCB (Quilley and Rifkind, 1986).
The thymus and bursa of Fabricius are other TCDD target organs in the chicken embryo. TCDD,
3,3',4,4'-TCB, and 3,3',4,4'-TCAOB cause dose-related decreases in the lymphoid development
of both of these immune system organs (Nikolaidis et al., 1988a,b, 1990). Cultured thymus
anlage from chick embryos are 100 times more sensitive to TCDD's inhibitory effect on
lymphoid development than cultured thymus anlage from turkey and duck embryos (Nikolaidis et
al., 1988a). This suggests that the reason thymic atrophy was not seen in turkey embryos at egg
doses of 3,3',4,4'-TCB that were overtly toxic (Brunstrom and Lund, 1988) was not because the
turkey embryo thymus was incapable of responding to 3,3',4,4'-TCB. Rather, turkey embryos
appear to be more sensitive to the lethal than to the immunotoxic effect of this coplanar PCB.
Within the same bird species, the signs of developmental toxicity elicited by TCDD and
its approximate isostereomers are similar. In the chicken embryo, TCDD, 3,3',4,4',5-PCB,
3,3',4,4'-TCB, and 3,3',4,4',5,5'-HCB all cause pericardial and subcutaneous edema, liver lesions,
microphthalmia, beak deformities, and mortality, and TCDD, 3,3',4,4'-TCB, and 3,3',4,4'-
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TCAOB inhibit lymphoid development (Cheung et al., 1981a; Brunstrom and Andersson, 1988;
Nikolaidis et al., 1988a,b). In pheasant embryos, an altogether different pattern of responses is
seen. Nevertheless, the TCDD-like congeners injected into fertilized pheasant eggs, TCDD and
3,3',4,4'-TCB, produce the same pheasant embryo-specific pattern. This pattern consists of
embryo mortality in the absence of edema, liver lesions, thymic hypoplasia, and structural
malformations (Brunstrom and Reutergardh, 1986; Nosek et al., 1993).
The lethal potency of TCDD and its approximate isostereomers in embryos of different
bird species varies widely. The chicken embryo is an outlier in that it is by far the most sensitive
of all bird species to TCDD. Turkey, ring-necked pheasant, mallard duck, domestic duck,
domestic goose, golden-eye, herring gull, black-headed gull, and eastern bluebird embryos are
considerably less sensitive to the embryo-lethal effect of TCDD and TCDD-like congeners
(Brunstrom and Reutergardh, 1986; Brunstrom and Lund, 1988; Thiel et al., 1988; Martin et al.,
1989; Elliott et al., 1989; Nosek et al., 1993). TCDD is 4 to 7 times more potent in causing
embryo mortality in chicken than pheasant embryos, and 3,3',4,4'-TCB is 20 to 100 times more
potent in chicken than turkey embryos (Allred and Strange, 1977; Brunstrom and Lund, 1988;
Nosek et al., 1989). In chicken embryos, an egg dose of 3,3',4,4'-TCB of 4 ng/kg increased
embryo mortality, whereas an egg dose of 100 ng/kg of the same coplanar PCB had no
embryotoxic effect in pheasants and mallard ducks and a dose of 1,000 ng/kg egg had no effect
on embryo mortality in domestic ducks, domestic geese, golden eyes, herring gulls, and black-
headed gulls (Brunstrom, 1988; Brunstrom and Reutergardh, 1986). In contrast to the above
species differences, the potency of 3,3',4,4'-TCB in causing embryo mortality among different
strains of chickens is quite similar, with the LD50 in six different strains varying less than
fourfold (Brunstrom, 1988).
Graded doses of TCDD have been administered to fertilized eastern bluebird and ring-
necked pheasant eggs for the purpose of determining a LOAEL and NOAEL for embryotoxicity.
Mortality was the most sensitive embryotoxic effect in both species. For eastern bluebirds, the
LOAEL was 10,000 pg TCDD/g egg and the NOAEL was 1,000 pg TCDD/g egg (Martin et al.,
1989). For ring-necked pheasants, the LOAEL was 1,000 pg TCDD/g egg and the NOAEL was
100 pg TCDD/g egg. The LD50 for embryo mortality in the ring-necked pheasant is 1,354 pg
TCDD/g egg when the dose is injected into the egg albumin and 2,182 pg TCDD/g egg when the
dose is injected into the egg yolk (Nosek et al., 1993). In contrast, for chickens the LD50 for
embryo mortality is 240 pg TCDD/g egg (Allred and Strange, 1977).
5.2.1.3. Laboratory Mammals
5.2.1.3.1. Developmental expression of Ah receptor andARNT. The Ah receptor and its
dimerization partner ARNT are expressed in a specific spatial and temporal pattern in the
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developing mammalian embryo and fetus, suggesting that they play a fundamental role in
development. Preimplantation mouse embryos express Ah receptor mRNA and protein (Peters et
al., 1995), and spatial and temporal patterns of ARNT1, ARNT2, and Ah receptor mRNA
expression occur in specific developing tissues and organs of the mouse embryo from gestational
day 9.5 to 16 (Abbott et al., 1995; Abbott et al., 1995; Jain et al., 1998). On gestational day 9.5,
ARNT1 mRNA is highly expressed in the neuroepithelium of the brain and spinal cord,
trigeminal ganglion, branchial arches 1 and 2, heart, hepatic primordia, and primitive gut.
ARNT2 message is also expressed in the neuroepithelium and in the remainder of the embryo but
at comparatively lower levels. Ah receptor mRNA, in contrast to ARNT1 and ARNT2, is not
expressed significantly at gestational day 9.5, but by gestational day 10 Ah receptor mRNA was
expressed in the neuroepithelium of the developing brain, in the visceral arches, and in the heart.
By day 13.5 or 14 of gestation, Ah receptor mRNA was abundantly expressed in the primitive
pituitary, palatal shelf, nasal septal cartilage, dorsal surface of the tongue, developing thymus,
lung parenchyma, liver, developing gut mucosa, kidney, urogenital sinus, and tip of the genital
tubercle. ARNT1 mRNA was expressed to a high extent in various cell types of endodermal and
mesodermal origin such as the lung and tongue muscle and was barely above background in the
developing nervous system. The tissue distribution of ARNT2 mRNA was the inverse of
ARNT1, being highest in the mantle layer of the spinal cord and brain and lowest in the
endodermal and mesodermally derived tissues. The expression patterns observed at gestational
day 13.5 or 14 continued to be found at gestational day 15.5 or 16 with the additional finding that
ARNT2 was clearly expressed in neural crest derivatives like the dorsal root ganglia, adrenal
medulla, and in developing tubules in the renal cortex. Thus, the expression of Ah receptor,
ARNT1, and ARNT2 mRNAs was specific for cell type, organ/tissue, and developmental stage.
Furthermore, immunohistochemical localization of Ah receptor and ARNT1 protein correlated,
in general, with in situ localization of Ah receptor and ARNT1 mRNA expression at each
gestational age (Abbott et al., 1995; Abbott and Probst, 1995).
5.2.1.3.2. Transgenic Ah receptor null mice andARNT null mice. Ah receptor null (knockout)
mice have been developed to determine which adverse effects of TCDD exposure are Ah
receptor mediated, and to determine the effects of absence of the Ah receptor on organ system
development and function. Three lines of Ah receptor null mice have been generated using
different targeting methods and they are on the following genetic backgrounds: C57BL/6N x
Sv/129 (Fernandez-Salguero et al., 1995), substrain of C57BL/6 x Sv/129 (Schmidt et al., 1996)
and C57BL/6J x Sv/129 (Mimura et al., 1997). Ah receptor null mice in all the lines are viable,
and offspring of both sexes are fertile and capable of reproduction. However, Abbott et al.
(1999) reported adverse reproductive outcomes in homozygous AhR null female mice in the line
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of Fernandez-Salguero et al. (1995). The range of adverse reproductive effects included deaths
of the females during pregnancy and lactation, small litter size at birth, poor survival of pups
during the first 2 weeks after birth, and death of Ah receptor null pups after weaning. Because
low survival of the weaned homozygous Ah receptor null pups was independent of genotype of
the dam, it was probably not caused by maternal factors like lactational insufficiency or aberrant
maternal behaviors. However, the increased mortality of fetuses and pups prior to weaning could
be due in part to impaired ability of the homozygous Ah receptor null female to support
development of the fetuses, to survive pregnancy and lactation herself, and to rear pups until
weaning (Abbott et al., 1999c).
The profile of effects observed in the homozygous Ah receptor null offspring were
dependent on the Ah receptor null line investigated and consisted of lesions in the immune
system, skin, liver, heart, stomach, spleen, and uterus (Fernandez-Salguero et al., 1995, 1996,
1997; Schmidt et al., 1996). These findings suggest that the AhR signaling pathway plays an
important physiological role in development and in maintaining homeostasis as offspring age
(Fernandez-Salguero et al., 1996, 1997; Schmidt et al., 1996;). Transgenic mice with a null
mutation in the ARNT1 gene have also been evaluated, and unlike their AhR counterparts
homozygous ARNT1 null embryos are not viable (Kozak et al., 1997; Maltepe et al., 1997).
They are affected by neural tube closure defects, forebrain hypoplasia, delayed rotation of the
embryo, placental hemorrhaging, visceral arch abnormalities, and death between gestational days
9.5-10.5 (Kozak et al., 1997). The primary cause of embryo mortality may be failure of the
embryonic component of the placenta to vascularize and form the labyrinthine
spongiotrophoblast, which is consistent with ARNTl's role in hypoxic induction of angiogenesis
(Kozak et al., 1997). Thus, the ARNT1 protein appears to play an indispensable role during
development that is essential for embryo survival.
5.2.1.3.3. Prenatal mortality. When exposed to TCDD during adulthood, laboratory mammals
display wide differences in the LD50 of TCDD. It is interesting to note, however, that when
exposure occurs during prenatal development, the potency of TCDD tends to be more similar
across species. The LD50 of TCDD in adult hamsters, 1,157 to 5,051 ng/kg, makes adult
hamsters three orders of magnitude more resistant to TCDD-induced lethality than are adult
guinea pigs (Olson et al., 1980; Henck et al., 1981). Yet, a maternal dose of 18 ng TCDD/kg can
increase the incidence of prenatal mortality in the hamster embryo/fetus. Because this dose is
only twelvefold higher than the dose (1.5 \ig TCDD/kg) that increases the incidence of prenatal
mortality in the guinea pig, the hamster embryo/fetus approaches other rodent species in its
sensitivity to TCDD-induced lethality (Olson and McGarrigle, 1990, 1991). Thus, the magnitude
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of the species differences in lethal potency of TCDD is affected by the timing of TCDD exposure
during the life history of the animal.
Exposure to TCDD during pregnancy causes prenatal mortality in the monkey, guinea
pig, rabbit, rat, hamster, and mouse (Table 5-1). The rank order of susceptibility from the most
sensitive to least sensitive species would appear to be monkey = guinea pig > rabbit = rat =
hamster > mouse. However, an important caveat must be applied to the information presented in
Table 5-1; i.e., that the time period during which exposure of the embryo/fetus to TCDD occurs
is just as important a determinant of prenatal mortality as is the dose of TCDD administered.
This point will be illustrated in the text that follows when prenatal mortality is described for
different strains of mice.
It is important to note that the concept of a critical time period for exposure makes the
analysis of lethality data in the embryo/fetus qualitatively different from that which might be
applied to similar data in adult animals. For example, a common dosing regimen used in mice,
rats, and rabbits (Table 5-1) is to administer 10 daily doses of TCDD to the pregnant dam on
days ~6 to 15 of gestation. This dosing regimen is expected to cover the critical period of early
development that results in the greatest incidence of prenatal toxicity. In nearly all species of
adult laboratory mammals, however, a single lethal dose of TCDD would be expected to produce
a similar delayed-onset death regardless of the age of the adult animal. Susceptibility to TCDD-
induced prenatal mortality, in contrast, may be greatly dependent on the age of the embryo/fetus.
In this case, multiple doses of TCDD that cover this critical period might result in prenatal
mortality, whereas a single dose might miss the critical time and not result in prenatal mortality.
The following paragraphs illustrate a type of analysis using an index of cumulative
maternal dose similar to the type of analysis that might be applied to lethality data resulting from
multiple dosing of adult animals. After presenting the results of applying this type of analysis to
prenatal mortality data from different species, the caveat of critical time dependence will be
applied to the data obtained by using different strains of mice. This will illustrate the importance
of considering dosage regimen when evaluating prenatal mortality data that are available in the
literature. In this case, a difference of 1 gestational day might be critically important. It turns out
that the form of analysis using cumulative maternal dose may give the greatest possible degree of
species variation. As such, different species may actually be more similar with respect to
susceptibility to prenatal mortality than would be apparent from results of this type of analysis.
Using the cumulative dose data that are given in Table 5-1, there appears to be a tenfold
to twentyfold difference in the fetolethal potency of TCDD when the monkey/guinea pig is
compared with the rabbit/rat/hamster. In the CD-I mouse treated with TCDD on gestational days
7 to 16, it appears that a daily dose of 200 \ig TCDD/kg is required to significantly increase
prenatal mortality. Given a ~5.5-day half-life of TCDD in the pregnant dam (Weber and
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Birnbaum, 1985), the pregnant CD-I mouse would be exposed to a maximal accumulated dose of
-1,200 [ig TCDD/kg by the lowest dosage regimen that significantly increased prenatal mortality.
Therefore, by using the index of cumulative dose, the CD-I mouse would appear to be ~ 1,200-
fold less sensitive than the monkey/guinea pig for TCDD-induced prenatal mortality. However,
in NMRI mice administered TCDD only on day 6 of gestation, prenatal mortality begins to
increase after a single dose of 45 ng TCDD/kg (Neubert and Dillman, 1972). The NMRI
embryo/fetus is less susceptible to TCDD-induced prenatal mortality when the TCDD is
administered on later gestational days up to day 15. Thus, there appears to be only an
approximate 45-fold difference between the monkey/guinea pig and the NMRI mouse when the
NMRI embryo/fetus is exposed specifically on day 6. In C57BL/6 mice, prenatal mortality is
significantly increased after a single maternal dose of 24 \ig TCDD/kg given on gestational day 6
(Couture et al., 1990a). This mouse strain, therefore, is about twentyfold to thirtyfold less
sensitive to TCDD-induced prenatal mortality than is the monkey/guinea pig when exposed
specifically on day 6. As with the NMRI mouse, there was little or no increase in prenatal
mortality for the C57BL/6 strain when TCDD was administered to the pregnant dam on
gestational days 8, 10, 12, or 14.
Peters et al. (1999) administered a single maternal dose of 25 ng/kg of TCDD on
gestational day 10 to Ah receptor wild-type or null female mice (Fernandez-Salguero et al.,
1995). In the homozygous Ah receptor wild-type dams and their homozygous Ah receptor wild-
type fetuses there was no increase in prenatal mortality. However, in the homozygous Ah
receptor null dams and their homozygous Ah receptor null fetuses TCDD increased prenatal
mortality, as evidenced by an increase in percentage of resorptions. Mimura et al. (1997) also
found that TCDD increased resorptions to a greater extent in Ah receptor null dams compared
with Ah receptor wild-type dams. These findings suggest that mechanisms that do not require
the Ah receptor may mediate, in part, the increase in prenatal mortality caused by TCDD (Peters
etal., 1999).
An important finding about predicting TCDD-induced prenatal mortality is that strain
differences in lethal potency of TCDD, when animals are exposed in adulthood, does not predict
strain differences in lethal potency of TCDD for causing embryo/fetal mortality. Certain rat
strains display wide differences in sensitivity to lethality when TCDD is given in adulthood. The
Long Evans rat has a wild-type Ah receptor while the Han/Wistar rat contains a point mutation in
its Ah receptor gene that results in a splice variant Ah receptor protein that binds TCDD
(Pohjanvirta et al., 1998). While Long Evans and Han/Wistar rats are equally sensitive to
TCDD-induced hepatic cytochrome P-4501 Al induction, the Han/Wistar strain is far less
sensitive to TCDD-induced lethality than the Long Evans strain when both strains are treated
with TCDD in adulthood (Pohjanvirta et al., 1993; Unkila et al., 1994). However, when these
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same strains are exposed to TCDD during pregnancy, the maternal doses of TCDD administered
on gestational days 8 and 12 that cause fetotoxicity and fetal lethality are similar (Huuskonen et
al., 1994).
Mammalian pregnancies (including human) are characterized by critical periods or
"windows" during which the embryo/fetus exhibits different susceptibilities and responses to
chemical exposures. The susceptibility of any particular endpoint depends on the developmental
state of that endpoint at the time of exposure. The embryo/fetus is constantly changing at all
biological levels (e.g., cellular, tissue, organism), and the mechanisms of action, response, and
repair of a particular endpoint at the time of exposure are the determinants of whether a response
to a given exposure will result in a developmental alteration or not.
The concept of a critical window for TCDD-induced lethality in the embryo/fetus
suggests an explanation for the apparent insensitivity of the CD-I mouse embryo/fetus exposed
to cumulative doses of TCDD. It could very well be that the critical window for prenatal
mortality in the mouse occurs on or before gestational day 6. If the embryo/fetus is not exposed
to TCDD by gestational day 6, much larger doses of TCDD are required to produce prenatal
mortality. Given that exposure of the pregnant CD-I dams did not begin until gestational day 7,
this interpretation is consistent with the ability of a single 24 ng TCDD/kg dose to increase the
incidence of prenatal mortality when administered to pregnant C57BL/6 mice on gestational day
6, but not when administered on gestational days 8, 10, 12, or 14 (Couture et al., 1990a).
Similarly, Neubert and Dillman (1972) found that the largest increase in prenatal mortality
occurred when a single dose of TCDD was given on gestational day 6 compared with prenatal
mortality when the TCDD dose was administered on one of gestational days 7 to 15. In addition,
this would suggest that the CD-I embryo/fetus does not have quite the relative insensitivity to the
lethal effects of TCDD compared with the embryo/fetus of other species indicated by using
cumulative maternal dose as the index of exposure.
It should be noted that the concept of a critical window for prenatal mortality could
potentially alter all of the species comparisons made previously that were based on the
cumulative maternal doses shown in Table 5-1. If this turned out to be the case, then the true
differences between species with respect to their susceptibility to TCDD-induced prenatal
mortality could be substantially less than those indicated by using the cumulative maternal dose.
This, of course, would involve a comparison between species using only single doses of TCDD
given during the critical time period for each species. At the present time, it is not possible to
make such a comparison from the information available in the literature.
Similar to fish and birds, the mammalian embryo/fetus is more sensitive to the lethal
action of TCDD than the adult. The maternal dose of TCDD that causes 58% fetal mortality in
hamsters is 64 to 280 times less than the LD50 of TCDD in adult hamsters (Olson et al., 1980;
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Henck et al., 1981; Olson et al., 1990). In Sprague-Dawley rats, the cumulative maternal dose of
TCDD that causes 41% prenatal mortality is 5 to 10 times less than the approximate LD50 of
TCDD in adult rats of the same strain (Sparschu et al., 1971; Seefeld et al., 1984). In rhesus
monkeys, the cumulative maternal TCDD dose that causes 81% prenatal mortality is 6 and 25
times less, respectively, than the lowest TCDD dose reported to cause mortality in 1-year-old and
adult rhesus monkeys (McNulty, 1977, 1985; Seefeld et al., 1979).
Table 5-1 suggests that in many animal species (guinea pig, rabbit, rat, and mouse),
TCDD-induced prenatal mortality is most commonly associated with maternal toxicity that is not
severe enough to result in maternal lethality. In each of these species, the dose-response
relationship for maternal toxicity, indicated by decreased maternal weight gain and/or marked
subcutaneous edema of the dam, is essentially the same as that for increased prenatal mortality.
Even in the hamster, where maternal toxicity is far less severe, fetuses exhibit increases in
neutrophilic metamyelocytes and bands, and increases in leukocyte number and bands are also
found in maternal blood (Olson and McGarrigle, 1991). In mice, it has been shown that TCDD
exposure causes rupture of the embryo-maternal vascular barrier, which results in hemorrhage of
fetal blood into the maternal circulation (Khera, 1992). Also, pregnant CF1 mice treated with
30 [ig TCDD/kg on gestational day 12 exhibited 1.9- and 1.5-fold increases in lipid peroxidation
in placental and fetal tissues, respectively, on gestational day 14. This was associated with 1.4-
to 2.5-fold increases in lipid metabolite levels of malondialdehyde, formaldehyde, acetaldehyde,
and acetone in amniotic fluid (Hassoun et al., 1995).
In spite of this general association between maternal and fetal toxicity, prenatal and
postnatal lethality can occur in the absence of overt maternal toxicity. Olson and McGarrigle
(1991) reported prenatal death but no maternal toxicity in the hamster at 18 ng/kg TCDD, the
highest dose in their study. Likewise, studies in the rat demonstrate that both prenatal death
(Bjerke and Peterson, 1994) and postnatal death (Gray et al., 1995) can occur in response to
exposure during gestation that does not result in overt maternal toxicity (see Section 5.2.3).
In rhesus monkeys, fewer data are available to make the association between prenatal
mortality and maternal toxicity. Nevertheless, the results following dietary exposure to 25 ppt
TCDD (Bowman et al., 1989a; Schantz and Bowman, 1989) and 50 ppt TCDD (Allen et al.,
1977, 1979; Barsotti et al., 1979; Schantz et al., 1979) before and during pregnancy suggest that
TCDD-induced prenatal mortality can occur in monkeys in the absence of overt toxic effects on
the mother (see Section 5.3.1). In other studies, developmental toxicity in monkeys exposed to a
total cumulative maternal dose of 1 ng TCDD/kg administered during the first trimester indicated
a high incidence of prenatal mortality (McNulty, 1984, 1985). However, maternal toxicity
occurred in some but not all of the mothers exposed. In these monkeys, 13 of 16 pregnancies
resulted in prenatal mortality. Within 20 to 147 days after aborting, 8 of the 13 females that had
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aborted showed signs of maternal toxicity and 3 of these monkeys died. Thus, the remaining 5 of
13 instances of prenatal mortality apparently occurred in the absence of overt maternal toxicity.
The results of these studies indicate that some levels of TCDD exposure can result in prenatal
mortality in monkeys even though overt toxicity seems absent in the mother. As will be
described (Section 5.3.1.1), however, only limited attention has been given to female
reproductive toxicity in general and to the effects of maternal toxicity during pregnancy on fetal
development in particular. Therefore, the relationship between maternal toxicity and prenatal
mortality in the monkey is not well established. The integrity of the embryo-vascular barrier, for
example, has not been evaluated after TCDD exposure.
Gestational exposure to TCDD produces a characteristic pattern of fetotoxic responses in
most laboratory mammals consisting of thymic hypoplasia, subcutaneous edema, decreased fetal
growth, and prenatal mortality. Added to these common effects on development are other effects
of TCDD that are highly species-specific. Examples of the latter are cleft palate formation in the
mouse and intestinal hemorrhage in the rat. Table 5-2 shows those maternal and developmental
responses that are produced by gestational exposure to TCDD in various species of laboratory
mammals. In the mouse, hydronephrosis is the most sensitive effect of prenatal toxicity,
followed by cleft palate formation and atrophy of the thymus at higher doses, and by
subcutaneous edema and mortality at maternally toxic doses (Couture et al., 1990b; Courtney,
1976; Courtney and Moore, 1971; Neubert and Dillman, 1972). In the rat, TCDD prenatal
toxicity is manifested by intestinal hemorrhage, subcutaneous edema, decreased fetal growth, and
mortality (Sparschu et al., 1971; Khera and Ruddick, 1973). Structural abnormalities do occur in
the rat, but only at relatively large doses (Couture et al., 1990b). In the hamster fetus,
hydronephrosis and renal congestion are the most sensitive effects, followed by subcutaneous
edema and prenatal mortality (Olson and McGarrigle, 1991). In the rabbit, an increased
incidence of extra ribs and prenatal mortality is found (Giavini et al., 1982a), and in the guinea
pig and rhesus monkey, prenatal mortality is seen (Olson and McGarrigle, 1991; McNulty, 1984).
5.2.1.4. Structure-Activity Relationships in Laboratory Mammals
The structure-activity relationship for developmental toxicity in laboratory mammals is
generally similar to that for Ah receptor binding. Gestational treatment of rats with CDD
congeners that do not bind the Ah receptor (2-MCDD, 2,7-DCDD, 2,3-DCDD, or 1,2,3,4-TCDD)
do not cause TCDD-like effects on development (Khera and Ruddick, 1973). On the other hand,
hexachlorodibenzo-^-dioxin, which has intrinsic Ah receptor activity, produces fetotoxic
responses in rats that are essentially identical to those of TCDD (Schwetz et al., 1973).
Similarly, when administered to pregnant rhesus monkeys or CD-I mice, PCB congeners that act
by an Ah receptor-mediated mechanism (3,3',4,4'-TCB and 3,3',4,4',5,5'-HCB) cause the same
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type of developmental effects as TCDD. In contrast, 4,4'-DCB, 3,3',5,5'-TCB, 2,2',4,4',5,5'-HCB,
2,2',4,4',6,6'-HCB, and 2,2',3,3',5,5'-HCB, which have essentially no or a very weak affinity for
the Ah receptor, do not produce a TCDD-like pattern of prenatal toxicity in mice (Marks and
Staples, 1980; Marks et al., 1981, 1989; McNulty, 1985). Thus, most structure-activity results
for overt developmental effects of the halogenated aromatic hydrocarbons are consistent with an
Ah receptor-mediated mechanism. Nevertheless, one finding that stands out as being
inconsistent is that 2,2',3,3',4,4'-HCB, which has a very weak, if any, affinity for binding to the
Ah receptor, causes the same pattern of developmental effects in mice as TCDD (Marks and
Staples, 1980).
5.2.1.5. Humans
In the Yusho and Yu-Cheng poisoning episodes, developmental toxicity was reported in
babies born to affected mothers who consumed rice oil contaminated with PCBs, CDFs, and
PCQs (Hsu et al., 1985; Yamashita and Hayashi, 1985; Kuratsune, 1989; Rogan, 1989; Masada,
1994; Hsu et al., 1994). In these incidents, it is essentially impossible to determine the
contribution of TCDD-like versus non-TCDD-like congeners to the fetal/neonatal toxicity.
Nevertheless, high perinatal mortality was observed among hyperpigmented infants born to
affected Yu-Cheng women who themselves did not experience increased mortality (Hsu et al.,
1985). Thus, in humans the developing embryo/fetus may be more sensitive than the intoxicated
mother to mortality caused by halogenated aromatic hydrocarbons.
In most cases, women who had affected children in the Yusho and Yu-Cheng episodes
had chloracne themselves (Rogan, 1982). Based on this evidence, Rogan (1982) suggested that
"exposure to amounts insufficient to produce some effect on the mother probably lessens the
chance of fetopathy considerably." In support of this interpretation, overt signs of halogenated
aromatic hydrocarbon toxicity were not observed in infants born to apparently unaffected
mothers in the Seveso, Italy, and Times Beach, Missouri, TCDD incidents (Reggiani, 1989;
Hoffman and Stehr-Green, 1989).
Effects of chemical exposure on normal development of the human fetus can have four
outcomes depending on the dose and time during gestation when exposure occurs: fetal death,
growth retardation, structural malformations, and organ system dysfunction. In the Yusho and/or
Yu-Cheng incidents, all of these outcomes were found (Yamashita and Hayashi, 1985;
Kuratsune, 1989; Rogan, 1989; Masada; 1994; Hsu et al., 1994). Increased prenatal mortality
and low birthweight suggesting fetal growth retardation were observed in affected Yusho and
Yu-Cheng women (Wong and Hwang, 1981; Law et al., 1981; Yamashita and Hayashi, 1985;
Hsu et al., 1985; Miller, 1985; Lan et al., 1989; Rogan et al., 1988). In a follow-up of the Yu-
Cheng children at elementary school age, Guo et al. (1994) reported decreased height and muscle
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development in children who were the first born to women who were exposed. A structural
malformation, rocker bottom heel, was observed in Yusho infants (Yamashita and Hayashi,
1985). Organ dysfunction involving the central nervous system (CNS) that was characterized by
delays in attaining developmental milestones and by neurobehavioral abnormalities was reported
in Yu-Cheng children exposed transplacentally (Rogan et al., 1988).
Organs and tissues that originate from embryonic ectoderm are well-known targets for
toxicity following exposure to TCDD-like halogenated aromatic hydrocarbons. For example,
treatment of adult monkeys with TCDD results in effects involving the skin, meibomian glands,
and nails (Allen et al., 1977). Similarly, a hallmark sign of fetal/neonatal toxicity in the Yusho
and Yu-Cheng episodes is an ectodermal dysplasia syndrome. It is characterized by
hyperpigmentation of the skin and mucous membranes, hyperpigmentation and deformation of
fingernails and toenails, hypersecretion of the meibomian glands, conjunctivitis, gingival
hyperplasia, presence of erupted teeth in newborn infants, altered eruption of permanent teeth,
missing permanent teeth, and abnormally shaped tooth roots (Taki et al., 1969; Yamaguchi et al.,
1971; Funatsu et al., 1971; Wong and Hwang, 1981; Hsu et al., 1985; Yamashita and Hayashi,
1985; Rogan et al., 1988; Kuratsune, 1989; Rogan, 1989; Lan et al., 1989). Accelerated tooth
eruption has been observed in newborn mice exposed to TCDD by lactation (Madhukar et al.,
1984), as well as in the human infants mentioned above. In addition, other effects have been
reported in Yusho and Yu-Cheng exposed infants that resemble those observed following TCDD
exposure in adult monkeys. These include subcutaneous edema of the face and eyelids (Allen et
al., 1977; Moore et al., 1979; Law et al., 1981; Yamashita and Hayashi, 1985; Rogan et al.,
1988). Also, larger and wider fontanels and abnormal lung auscultation were found in the human
infants (Law et al., 1981; Yamashita and Hayashi, 1985; Rogan et al., 1988). The similarities
between certain effects reported in human infants exposed during the Yusho and Yu-Cheng
incidents, as well as in adult monkeys and neonatal mice exposed to TCDD, enhance the
probability that certain effects reported in human infants were caused by the TCDD-like PCB and
CDF congeners in the contaminated rice oil ingested by the mothers of these infants.
Although chloracne is the most often cited effect of TCDD exposure involving the skin in
adult humans, has an animal correlate in the hairless mouse, and can be studied by using a mouse
teratoma cell line in tissue culture (Poland and Knutson, 1982), it has rarely been recognized in
the TCDD literature that the nervous system, like the skin, is derived from embryonic ectoderm
(Balinsky, 1970). As will be described in Section 5.2.3.2, neurobehavioral effects occur
following transplacental and neonatal exposure to TCDD-like congeners in mice, as well as
transplacental exposure to TCDD itself in monkeys. In addition, in some of the Yu-Cheng
children who were exposed transplacentally to PCBs, PCDFs, and PCQs, there was a clinical
impression of developmental delay or psychomotor delay including impairment of intellectual
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development (Rogan et al., 1988). As there is a clustering of effects due to TCDD-induced
toxicity in organs derived from ectoderm, it is possible to speculate that direct effects of TCDD-
like congeners on the CNS are responsible for some of the neurobehavioral effects observed in
these children. Effects of TCDD on EGF receptors are associated with certain aspects of the
ectodermal dysplasia syndrome such as hyperkeratinization of the skin (Osborne and Greenlee,
1985) and accelerated tooth eruption (Madhukar et al., 1984). Decreased autophosphorylation of
the EGF receptor in human placentas is associated with decreased birthweight in infants born to
exposed mothers 4 years after the initial Yu-Cheng exposure incident (Sunahara et al., 1987).
This last result supports the earlier conclusion that careful study is needed to define the
relationship between maternal toxicity, placental toxicity, and developmental toxicity in humans.
In addition, further research is needed to characterize and elucidate the mechanisms by which
TCDD affects the nervous system.
5.2.2. Structural Malformations
Developmental effects consisting of cleft palate, hydronephrosis, and thymic hypoplasia
are produced in mice following in utero exposure to halogenated dibenzo-p-dioxin, dibenzofuran,
biphenyl, and naphthalene congeners, which bind stereospecifically to the Ah receptor (Weber et
al., 1985; Miller and Birnbaum, 1986; Birnbaum et al., 1987a,b, 1991). Of these effects in the
mouse, cleft palate is less responsive than hydronephrosis, as the latter is induced in the absence
of cleft palate (Couture et al., 1990b). Both responses can be induced at TCDD doses that are not
otherwise overtly toxic (Couture et al., 1990a). The oral surface of the palate in the mouse is
characterized by 8 or 9 pairs of transverse ridges, rugae. TCDD and 3,3',4,4',5-PCB (PCB 126)
produce palatal ruga anomalies in mice (Yasuda et al., 1999). The potency of TCDD for
producing teratogenesis in the mouse is clearly evident when one considers that only 0.0003% of
a maternally administered dose can be isolated from the fetal palatal shelves or kidneys. More
specifically, a maternal TCDD dose of 30 ng/kg administered on gestational day 11 results in a
tissue concentration of 0.65 pg TCDD/mg in the palatal shelves 3 days after dosing, and the same
tissue concentration of TCDD is present in the kidneys at that time (Abbott et al., 1989).
Susceptibility to the developmental actions of TCDD in mice depends on two factors:
genotype of the fetus and stage of development at the time of exposure. One genetically encoded
parameter that determines the responsiveness of different mouse strains is the Ah receptor
protein. The Ah receptor is thought to mediate the structural malformations caused by TCDD in
the mouse, namely cleft palate and hydronephrosis (Poland and Knutson, 1982). After
gestational day 12, the Ah receptor and its dimerization partner ARNT are expressed in the
embryonic palate and developing urinary tract of the C57BL/6 mouse fetus. Expression of Ah
receptor and ARNT mRNA increases significantly during palatal shelf outgrowth from
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gestational day 12 to 14. While the increase in Ah receptor expression was not affected by a
maternal dose of 24 ng/kg of TCDD administered on gestational day 12, there was a decrease in
the expression of ARNT (Abbott et al., 1999b). Similarly, Ah receptor protein levels in the
mouse urinary tract increase from gestational days 12 to 14 regardless of exposure to 12 ng/kg of
TCDD on gestational day 10, whereas the expression of ARNT protein on gestational day 14 is
reduced by TCDD (Bryant et al., 1997). Thus, Ah receptor and ARNT are expressed in the
developing palate and urinary tract, and the opportunity exists for the Ah receptor-ARNT
complex to regulate gene expression in these developing tissues. It may be important for normal
development that an appropriate relative expression of these genes is maintained, and decreasing
the availability of ARNT could be a significant factor in the response of the embryonic palate and
urinary tract to TCDD.
Mouse strains that produce Ah receptors with relatively high affinity for TCDD respond
to lower doses of TCDD than mouse strains that produce relatively low-affinity Ah receptors
(Poland and Glover, 1980; Hassoun et al., 1984a). The differences that exist between mouse
strains with respect to developmental responsiveness to these chemicals are not absolute, as all
strains, including those with Ah receptors of relatively low affinity, respond when exposed to
sufficiently large doses during the critical period of organogenesis (Birnbaum, 1991). In the
mouse, the peak times of fetal sensitivity vary slightly depending on which developmental effect
is used as the endpoint. However, exposure between days 6 and 15 of gestation will produce
teratogenesis (Couture et al., 1990a,b).
In inbred strains of mice, the developmental response, characterized by altered cellular
proliferation, metaplasia, and modified terminal differentiation of epithelial tissues (Poland and
Knutson, 1982), is extremely organ-specific, occurring only in the palate, kidney, and thymus
(Birnbaum, 1991). Pharmacokinetic differences are not responsible for this high degree of tissue
specificity, and Ah receptors are not found exclusively in the affected organs (Carlstedt-Duke,
1979; Gasiewicz et al., 1983). Therefore, other factors intrinsic to the palate, kidney, and thymus
appear to play a role along with the Ah receptors in these tissues in producing the structural
malformations. For certain developmental effects, the time at which exposure occurs is
important, as there may be a critical period during which the toxicant must be present in order to
produce the effect. This critical period can be different for different organs and tissues.
Differences exist between mammalian species with respect to susceptibility to the
developmental effects of TCDD. Although genetic differences between species or strains might
affect absorption, biotransformation, and/or elimination of TCDD by the maternal system and its
absorption across the placenta, such species differences do not account for the lack of cleft palate
formation in species other than mice (Birnbaum, 1991). Rather, the species differences in
susceptibility to cleft palate formation appear due to differences in the interaction between
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TCDD and the developing palatal shelves themselves. This is demonstrated by the occurrence of
similar responses when palatal shelves from different species are exposed to TCDD in organ
culture (Abbott et al., 1989; Abbott and Birnbaum, 1990a, 1991). The key difference is that in
other species much higher concentrations of TCDD are required to elicit essentially the same
palatal response that is seen in the mouse (Table 5-3). Thus, since palatal shelves of the mouse
are 200 times more sensitive to TCDD than those of the human, it is considered unlikely that
human embryos would be exposed to high enough concentrations of TCDD to cause changes in
palatal differentiation sufficient to produce cleft palate (Abbott et al., 1999a).
With respect to the occurrence of similar developmental effects in mammalian species
other than the mouse, no other species develops cleft palate except at maternal doses that are
fetotoxic and maternally toxic (Couture et al., 1990a; Birnbaum, 1991). In mice and hamsters,
hydronephrosis can be elicited at TCDD doses that are neither fetotoxic nor maternally toxic
(Olson and McGarrigle, 1991), whereas thymic hypoplasia is a fetal response to TCDD observed
in virtually all laboratory mammalian species that have been tested (Vos and Moore, 1974).
Studies in humans have not clearly identified an association between TCDD exposure and
structural malformations (Fara and Del Corno, 1985; Mastroiacovo et al., 1988; Stockbauer et al.,
1988; Reggiani, 1989).
5.2.2.1. Cleft Palate
5.2.2.1.1. Characterization of TCDD effect. Palatal shelves in the mouse originate as
outgrowths of the maxillary process. Eventually, they come to lie vertically within the oral cavity
on both sides of the tongue. In order to form the barrier between the oral and nasal cavities, the
shelves in the mouse must reorient themselves from a vertical direction to a horizontal direction.
Once they come together horizontally, their medial aspects bring apposing epithelia into close
contact (Coleman, 1965; Greene and Pratt, 1976). At this stage, the apposing medial edge
epithelia of the separate palatal shelves each consist of an outer layer of periderm that overlays a
strata of cuboidal-shaped basal cells. These basal cells, in turn, rest on top of a continuous basal
lamina. There is a sloughing of the outer periderm cells followed by the formation of junctions
between the newly apposing basal epithelial cells. The midline seam so formed consists of the
two layers of basal cells, all of which appear healthy, even though the outer periderm cells are
shed before adhesion occurs. As fusion proceeds, the bilayer seam breaks up into small islands
of cells. Eventually, the basal lamina disappears and the elongating former basal cells within the
small islands extend filopodia into the adjacent connective tissue. During this process, the
former basal cells lose epithelial characteristics and gain fibroblast-like features. Essentially, the
medial edge epithelium is an ectoderm that retains the ability to transform into mesenchymal
cells. Upon completion of this epithelial to mesenchyme transformation, the once separate and
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apposing palatal shelves are fused so that a single continuous tissue is formed (Fitchett and Hay,
1989;Shuleretal., 1992).
Cleft palate can result from a failure of the shelves to grow and come together or a failure
of the shelves to fuse once they are in close apposition (Pratt et al., 1985). TCDD and other Ah
receptor agonists are unusual inducers of cleft palate because the shelves grow and make contact,
but the subsequent processes involving loss of periderm, shelf adhesion, and the epithelial to
mesenchyme transformation does not occur. Therefore, a cleft is formed as the palatal shelves
continue to grow without fusing. When TCDD is administered to pregnant mice on gestational
days 6 to 12, the incidence of cleft palate formation increases with time. However, day 12 is a
critical window, after which the incidence of cleft palate formation decreases. No cleft palates
are formed when TCDD is administered on day 14, since fusion has already occurred (Couture et
al., 1990b).
Palatal shelves of the mouse, rat, and human can be removed from the fetus and placed
into organ culture. Under these conditions, when the separate shelves are placed in an apposing
condition in vitro, sloughing periderm cells are trapped within the seam (Fitchett and Hay, 1989).
Thus, due to the presence of these trapped dead cells, the fusion process was previously believed
to require programmed cell death to remove epithelial cells at the fusion seam (Coleman, 1965;
Greene and Pratt, 1976; Pratt et al., 1984). However, the newer model, which involves
transformation of the basal epithelial cells into mesenchyme rather than their death, is believed to
be valid under explant conditions in vitro, as well as in vivo (Fitchett and Hay, 1989). When
exposed to TCDD as explants in vitro, the palatal shelves of the mouse, rat, and human all
respond to TCDD in a similar way by retaining medial epithelial cells that proliferate and
differentiate into a stratified epithelium (Abbott et al., 1989; Abbott and Birnbaum, 1989, 1990a,
1991). The epithelial to mesenchyme transformation of the basal epithelial cells does not occur,
and instead there is a differentiation into a stratified squamous epithelium such that these cells
resemble the squamous keratinizing oral cells within the tissue.
Table 5-3 shows the lowest TCDD concentration that prevents the epithelial to
mesenchyme transformation process in isolated palatal shelves (lowest observed effect level,
LOEL), TCDD concentration that produces a 100% maximal response (EC100), and lowest
concentration of TCDD that produces cytotoxicity. Palatal shelves of rats and humans respond to
TCDD in a manner identical to the mouse; however, higher concentrations of TCDD are required
to induce the epithelial responses. The relative insensitivity of rat palatal shelves may explain
the lack of cleft palate when fetal rats are exposed to nonmaternally toxic doses of TCDD.
Sensitivity of human palatal shelves to TCDD in vitro is similar to the rat. This suggests that
exposure to maternally toxic and fetotoxic doses of TCDD may be required to cause cleft palate
formation in humans.
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A disruption in the normal spatial and temporal expression of EOF, thyroid growth factor
(TGF)-a, TGF-pl,and TGF-p2 correlates with altered proliferation and differentiation in the
medial region of the developing palate, resulting in a palatal cleft. Thus, the abnormal
proliferation and differentiation of TCDD-exposed medial cells may be related to reduced
expression of EOF and TGF-a. Also, decreased levels of immunohistochemically detectable
TGF-pl could contribute to the continued proliferation and altered differentiation of medial cells
(Abbott and Birnbaum, 1990b). It is important to note that EGF and TGF-a both exert their
actions by binding to EGF receptors.
Based on these results, biochemical and genetic differences between mouse and human
palates have been described that may explain the different sensitivities to cleft palate formation
in the mouse and human. Ah receptor concentrations in the mouse palate are 346 times greater
than those in the human, and ARNT levels are also greater in the mouse (Abbott et al., 1999a).
In addition, gene expression studies have demonstrated that human and mouse palates cultured in
vitro are dissimilar with respect to their particular spatial and temporal patterns of EGF, EGF
receptor, TGF-a, and TGF-p3 mRNA expression. Because the proteins that are the translation
products of these mRNAs are important for palatal development, it has been suggested that
species differences in the expression patterns of these genes could contribute to the lower
sensitivity of human palates to TCDD when compared with the mouse (Abbott et al., 1998).
The differentiation of basal cells to a stratified squamous epithelium, which resembles the
keratinizing oral epithelium within the developing palate that is mentioned above, is similar to
certain effects of TCDD that can be studied in cultured human keratinocytes. These effects in
cultured human keratinocytes involve altered EGF binding to those cells. In addition, the Ah
receptor is implicated in producing this response in cultured cells (Osborne and Greenlee, 1985).
Thus, the mechanisms by which TCDD produces a palatal cleft in the mouse may have
similarities to the mechanisms by which TCDD produces other effects that are part of the
ectodermal dysplasia syndrome. This is consistent with the description given by Fitchett and Hay
(1989) that the medial edge epithelium within the developing palate is essentially an ectoderm
that retains the ability to transform into mesenchymal cells.
5.2.2.1.2. Evidence for an Ah receptor mechanism.
5.2.2.1.2.1. Genetic. When wild-type C57BL/6 (AhbAhb) mice are crossed with DBA/2
(AhdAhd) mice that contain a mutation at the Ah locus, all of the heterozygous B6D2F1 progeny
(AhbAhd) resemble the wild-type parent in that AHH activity is inducible by TCDD and other
halogenated aromatic hydrocarbons (Nebert and Gielen, 1972). Test crosses between the
B6D2F1 progeny and each original parent strain, and other B6D2F1 progeny mice, demonstrate
that in the C57BL/6 and DBA/2 strains, susceptibility to AHH induction segregates as a simple
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dominant trait in the backcross and F2 progeny. Thus, the trait of AHH induction is expressed in
progeny that contain the AhbAhb and AhbAhd genotypes, but is not expressed in the AhdAhd
progeny from these crosses. Certain other effects of TCDD, such as its binding affinity for the
hepatic Ah receptor (Okey et al., 1979), thymic atrophy (Poland and Glover, 1980), hepatic
porphyria (Jones and Sweeney, 1980), and immunosuppressive effects (Vecchi et al., 1983;
Nagarkatti et al., 1984) have been shown in similar genetic crosses and test crosses to segregate
with the Ah locus that permits AHH induction. Thus, for these effects of TCDD, genetic
evidence demonstrates an involvement of the Ah locus (Poland and Knutson, 1982).
Nebert's group was the first to relate developmental toxicity to the Ah locus in mice
(Lambert and Nebert, 1977; Shum et al., 1979). Subsequently, Poland and Glover (1980)
administered a single 30 \ig TCDD/kg dose to pregnant mice on gestational day 10. A 54%
incidence of cleft palate was found in homozygous C57BL/6 (AhbAhb) fetuses, a 13% incidence
in heterozygous B6D2F1 (C57BL/6 and DBA/2 hybrid, AhbAhd) fetuses, and only a 2%
incidence in homozygous DBA/2 (AhdAhd) fetuses. This pattern of inheritance, in which the
incidence of developmental toxicity in the heterozygous Fl generation is intermediate between
that of the homozygous parental strains, is consistent with the autosomal dominant pattern of
inheritance described for AHH inducibility and the Ah locus (Nebert and Gielen, 1972), even if
dominance is incomplete in the case of developmental toxicity. However, the pattern of
inheritance for developmental toxicity described when Poland and Glover (1980) crossed
C57BL/6 and DBA/2 mice is not sufficient proof that the Ah locus is the genetic locus that
controls susceptibility to TCDD-induced developmental toxicity in these mouse strains.
To provide such proof, it is necessary to show genetic linkage between the susceptibility
for developmental toxicity and the Ah locus. The standard of proof would be that developmental
toxicity and a particular allele at the Ah locus must always segregate together in genetic crosses
because if the loci are the same there can be no recombination between the loci. This is generally
accomplished by demonstrating cosegregation between the two loci, not only in crosses between
the two homozygous parental strains, which in and of itself is insufficient proof of genetic
linkage, but also in test crosses or backcrosses between the heterozygous Fl hybrids with each
homozygous parental strain.
It was stated previously that certain effects of TCDD are well known to segregate with the
Ah locus due to the results of appropriate crosses and backcrosses between responsive and
nonresponsive mouse strains and their hybrid Fl progeny. With this standard of proof in mind,
the evidence that specifically links certain endpoints of developmental toxicity with the Ah locus
can be described. It is intended that this information be provided with a considerable degree of
detail, so the reader can independently determine whether the standard of proof has been satisfied
by the evidence available.
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To strengthen their conclusion based on the results of simple crosses between C57BL/6
and DBA/2 mice, Poland and Glover (1980) planned to perform a backcross between the hybrid
B6D2F1 and DBA/2. However, the low incidence of cleft palate in B6D2F1 mice would have
required characterizing and phenotyping a prohibitively large number of fetuses. Alternatively,
the backcross between B6D2F1 and C57BL/6 was considered, in which AhbAhb and AhbAhd
progeny would have been distinguished by the amount of high-affinity specific binding for
TCDD in fetal liver. In this case, however, overlap between individual mice would have made
the results uncertain in some of the progeny. Therefore, it was not possible to obtain satisfactory
results from either backcross.
Instead, Poland and Glover (1980) examined the incidence of cleft palate in 10 inbred
strains of mice: 5 strains with high-affinity Ah receptors and 5 strains with low-affinity
receptors. In the five latter strains, there was only a 0% to 3% incidence of cleft palate
formation, whereas four of the five strains with high-affinity Ah receptors developed a -50%
incidence. The one strain with high-affinity Ah receptors that did not follow the pattern, CBA
strain, is also resistant to cleft palate formation induced by glucocorticoids. Overall, these results
indicate that cleft palate formation probably segregates with the Ah locus.
The incidence of cleft palate formation was studied in fetuses from a cross between
C57BL/6 and AKR/NBom mice administered 3,3',4,4'-TCAOB on gestational day 12 (Hassoun
et al., 1984b). Although C57BL/6 mice are responsive for AHH induction and cleft palate
formation, AKR mice are less responsive, requiring higher doses for both effects. In a manner
unlike the result of a cross between C57BL/6 and DBA/2, the incidence of cleft palate formation
in the B6AKF1 progeny was <2%, showing that nonresponsiveness segregates as the dominant
trait when C57BL/6 mice are crossed with AKR mice. Similarly, cleft palate formation was
virtually absent in the progeny of a backcross between AKR/NBom and B6AKF1, demonstrating
dominance of the noninducible trait. Although Ah phenotyping of the backcross progeny was not
performed in this particular study, Robinson et al. (1974) had previously evaluated segregation of
the Ah locus in backcrosses between C57BL/6 and AKR/N mice. They found in these two
strains that noninducibility for AHH activity segregates as the dominant trait. Thus, inducibility
for cleft palate formation and AHH activity both segregate as dominant traits when C57BL/6
mice are crossed with DBA/2, but noninducibility is dominant for both traits when C57BL/6
mice are crossed with AKR/N. These results are consistent with the interpretation that cleft
palate induction probably segregates with the Ah locus.
Like Poland and Glover (1980), Hassoun et al. (1984a) were unable to determine whether
cleft palate formation segregates with the Ah locus in C57BL/6 and DBA/2 mice by performing
simple backcrosses. Instead, they evaluated cosegregation of the Ah locus and 2,3,7,8-TCDF-
induced cleft palate formation using a series of recombinant strains called BXD mice. These
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strains are fixed recombinants produced from an original cross between the two parental strains
C57BL/6J and DBA/2J. Hybrid B6D2F1 mice were crossed to produce F2 progeny and these
were strictly inbred by sister and brother matings into several parallel strains. The mice used in
this study were from the F42 and F58 generations of inbreeding. It was found that the incidence of
TCDF-induced cleft palate formation after matings within eight different BXD strains with high-
affinity Ah receptors is >85%. After similar matings with eight different BXD strains with low-
affinity Ah receptors, the incidence of TCDF-induced cleft palate formation is <2%. These
results of Hassoun et al. (1984a) corroborate those of Poland and Glover (1980) and provide
further evidence that cleft palate formation segregates with the Ah locus. Thus, the Ah locus and
the Ah receptor are involved in the formation of palatal clefts that are induced by TCDD-like
congeners.
Consistent with this interpretation, the Ah receptor null mouse line of Mimura et al.
(1997) is completely resistant to TCDD-induced cleft palate formation and the Ah receptor null
line of Fernandez-Salguero et al. (1995) is almost entirely resistant to this teratogenic effect of
TCDD (Peters et al., 1999). Taken together, these findings support the conclusion that the Ah
receptor plays a key role in TCDD-induced cleft palate formation. However, because 9% of the
homozygous Ah receptor null fetuses of the Fernandez-Salguero et al. (1995) transgenic line
developed cleft palate when exposed to TCDD (compared with 0% of vehicle-exposed wild-type
and 0% of vehicle-exposed Ah receptor null fetuses), a TCDD-induced alteration in processes
that do not require the Ah receptor might also be involved. Further research is needed to explore
this possibility.
As additional evidence for an Ah receptor-mediated mechanism for cleft palate formation
by TCDD and related compounds, stereospecific, high-affinity Ah receptors can be isolated from
cytosol fractions prepared from embryonic palatal shelves. These receptors are present in palatal
shelves of AhbAhb, C57BL/6 fetuses but are not detectable in similar tissue from AhdAhd, AKR/J
fetuses (Dencker and Pratt, 1981). However, the significance of this finding may be mitigated to
some extent by the following observation. In cytosols prepared from homogenates of whole
embryo/fetal tissue (minus head, limbs, tail, and viscera), the concentration of specific binding
TCDD receptors is 256 fmol/mg protein in C57BL/6 mice, compared with a concentration of 21
fmol/mg protein in the less responsive DBA/2 strain, 15 fmol/mg protein in the less responsive
AKR/J strain, and 19 fmol/mg protein in the less responsive SWR/J strain. However, when
embryonic tissue is cultured, the differences between the strains in receptor number are less
pronounced, and in the receptors isolated from cultured embryonic cells of different strains, there
is only about a twofold difference in the relative binding affinity for 3H-TCDD. The mechanistic
reasons for the diminished degree of difference between responsive and less responsive mouse
strains during embryonic cell culture are not known (Harper et al., 1991).
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The possible influence of maternal toxicity on cleft palate formation was evaluated by
performing reciprocal blastocyst transfer experiments using the high-affinity-Ah receptor NMRI
and lower affinity-Ah receptor DBA strains of mice (D'Argy et al., 1984). After administration
of 30 ng TCDD/kg or 8 mg TCAOB/kg to pregnant dams on gestational day 12, 75% to 100% of
all NMRI fetuses developed cleft palates. This is true whether the fetuses remained within the
uterus of their natural mother or were transferred into the uterus of a DBA mouse. Under the
same conditions, none of the 24 DBA fetuses transferred into an NMRI mother developed a cleft
palate, even though 89% of their NMRI litter mates were affected. Thus, these results, along
with the presence of Ah receptors in palatal shelves and responsiveness of palatal shelves in
organ culture to TCDD, indicate that cleft palate formation in mice is due to a direct effect of
TCDD on the palatal shelf itself and is not secondary to maternal toxicity.
5.2.2.1.2.2. Structure activity. As genetic evidence in mice indicates that the Ah receptor
mediates TCDD-induced cleft palate formation and hydronephrosis (see Section 5.3.2.2.2.1),
structure-activity requirements based on Ah receptor-binding characteristics should predict the
relative potencies of different agonists for producing cleft palate and hydronephrosis. Of the
halogenated aromatic hydrocarbons, TCDD has the greatest affinity for binding to the Ah
receptor and it is the most potent teratogen in inbred mouse strains. Table 5-4 shows the relative
potencies for cleft palate induction and hydronephrosis in C57BL/6 mice for a number of TCDD-
like congeners. As TCDD is the most potent, it is assigned a value of 1.000. When examined by
probit analysis, the dose-response curve of each congener, compared with all of the others, did
not deviate from parallelism. Therefore, the relative potencies of the congeners are valid for any
given incidence of cleft palate formation or hydronephrosis. The main finding, however, is that
the rank order potency of the various congeners for producing these two developmental effects is
generally similar to that for binding to the Ah receptor (see Table 5-4), with the notable
exception that the apparent binding affinities for the brominated dibenzofurans have not yet been
reported. There are additional ligands for the Ah receptor that cause cleft palate formation in
C57BL/6 mice at nonmaternally toxic doses, but they are not listed in the table. These include
3,3',4,4'-TCAOB (Hassoun et al., 1984a), 3,3',4,4'-tetrachlorobiphenyl (Marks et al., 1989),
3,3',4,4',5,5'-hexachlorobiphenyl (Marks et al., 1981), and a mixture that contained 1,2,3,4,6,7-
and 2,3,4,5,6,7-hexabromonaphthalenes (Miller and Birnbaum, 1986).
Also consistent with the structure-activity relationships for binding to the Ah receptor is
the finding that a number of hexachlorobiphenyls do not induce cleft palate formation. These
congeners either lack sufficient lateral substitution or are substituted in such a manner that they
cannot achieve a planar conformation. Included in this category are the diortho and tetraortho
chlorine-substituted 2,2',3,3',5,5'-; 2,2',3,3',6,6'-; 2,2',4,4'5,5'-; and 2,2',4,4',6,6'-hexachloro-
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biphenyls (Marks and Staples, 1980). In addition, it is consistent with the structure-activity
relationships that the monoortho chlorine-substituted 2,3,4,5,3',4'-HCB is a weak teratogen. Its
potency relative to that of TCDD varies from 3 x 10"5 to 9 x 10"5 for cleft palate formation, AHH
induction, and hydronephrosis (see Table 5-4) (Kannan et al., 1988).
A result that would not be expected according to the structure-activity relationships for
binding to the Ah receptor is that the diortho chlorine-substituted 2,2',3,3',4,4'-hexachloro-
biphenyl causes cleft palate formation and hydronephrosis in mice (Marks and Staples, 1980).
However, another diortho chlorine-substituted PCB congener, 2,2',4,4',5,5'-hexachlorobiphenyl,
also can cause hydronephrosis and is a very weak inducer of 7-ethoxyresorufm-O-deethylase
(EROD) activity (Biegel et al., 1989; Morrissey et al., 1992). It is consistent with the
interpretation that 2,2',4,4',5,5'-hexachlorobiphenyl is a partial Ah receptor agonist, that it can
competitively displace TCDD from the murine hepatic cytosolic receptor, and that at large
enough doses it can inhibit TCDD-induced cleft palate formation and immunotoxicity in
C57BL/6 mice (Biegel et al., 1989; Morrissey et al., 1992). These results suggest that PCB
congeners do not have to be in a strictly planar configuration to cause teratogenesis.
5.2.2.1.3. Species differences. Cleft palate is induced in rats only at maternally toxic TCDD
doses that are associated with a high incidence of fetal lethality. Schwetz et al. (1973) reported
an increased incidence of cleft palate after maternal administration of 100 ng hexachloro-
dibenzo-/7-dioxin/kg/day to Sprague-Dawley rats on days 6 to 15 of gestation. Couture et al.
(1989) also observed an increased incidence of cleft palate formation after a single dose of 300
ng/kg of 2,3,4,7,8-pentachlorodibenzofuran given to Fischer 344 rats. In Long Evans rats
administered 5 ng/kg of TCDD on gestational day 8 there was a 71.4% incidence of cleft palate
(Huuskonen et al., 1994). However, in Han/Wistar rats that have a mutated form of the Ah
receptor, exposure to 10 ng/kg of TCDD on gestational day 8 failed to cause cleft palate
formation (Huuskonen et al., 1994). Thus, there are rat strain differences in susceptibility to cleft
palate formation as has been shown for mice. Cleft palate also can be produced in fetal hamsters
following maternally toxic and fetotoxic doses of TCDD (Olson et al., 1990).
In monkeys, bifid uvula (Zingeser, 1979) and bony defects in the hard palate (McNulty,
1985) were reported, but there were no corresponding soft tissue defects or clefts of the
secondary palate. Cleft palates have not been reported in human fetuses of mothers accidentally
exposed to TCDD or mixtures of PCBs and CDFs (Fara and Del Corno, 1985; Mastroiacovo et
al., 1988; Stockbauer et al., 1988; Rogan, 1989). Thus, sensitivity of the palate in mice to TCDD
is unique. In other species, including humans, other forms of fetal toxicity occur at doses lower
than those required for cleft palate formation.
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5.2.2.2. Hydronephrosis
5.2.2.2.1. Characterization ofTCDD effect. Hydronephrosis is the most sensitive
developmental response elicited by TCDD in mice. It is produced by maternal doses ofTCDD
too low to cause palatal clefting and is characterized as a progressive hydronephrosis
preferentially occurring in the right kidney, which can be accompanied by hydroureter and/or
abnormal nephron development (Courtney and Moore, 1971; Moore et al., 1973; Birnbaum et al.,
1985; Weber et al., 1985; Abbott et al., 1987a,b). Hyperplasia of the ureteric lumenal epithelium
results in ureteric obstruction. Therefore, the TCDD-induced kidney malformation in the mouse
is a true hydronephrosis in that blockage of urine flow results in back pressure damaging or
destroying the renal papilla (Abbott et al., 1987a). In addition, mRNA and protein for both the
Ah receptor and ARNT are expressed in the fetal ureters and metanephric tubules of the mouse
(Bryant et al., 1997), so it is possible that hydronephrosis is caused by a direct action ofTCDD
on the developing kidney.
When dissected on gestational day 12 from control embryos, isolated ureters exposed to 1
x 10"10 M TCDD in vitro display evidence of epithelial cell hyperplasia (Abbott and Birnbaum,
1990c). This is significant in that it shows that the hydronephrosis response is due to a direct
effect of TCDD on the ureteric epithelium. Embryonic cell proliferation within the ureter may be
regulated by the actions of growth factors, including EGF (Abbott and Birnbaum, 1990c). In
control ureteric epithelia, the expression of EGF receptors decreases with advancing
development, whereas after TCDD exposure the rate of 3H-thymidine incorporation and
expression of EGF receptor does not decline. Therefore, in TCDD-treated mice there is a
correlation between excessive proliferation of ureteric epithelial cells and inappropriate
expression of EGF receptors.
Other effects of TCDD on the developing kidney involve changes in the extracellular
matrix components and basal lamina (Abbott et al., 1987b). In TCDD-exposed fetal kidneys,
extracellular matrix fibers are of a diameter consistent with Type HI collagen similar to such
fibers in unexposed fetal kidneys. However, the abundance of these Type in collagen fibers is
reduced by TCDD treatment. In the developing kidney, these collagen fibers are associated with
undifferentiated mesenchymal cells. Similarly, the expression of fibronectin, which is also
associated with undifferentiated mesenchymal cells, is decreased by TCDD exposure. In the
glomerular basement membrane, the distribution of laminin and Type IV collagen is altered by
TCDD exposure. These changes in the glomerular basement membrane may affect the functional
integrity of the filtration barrier and could exacerbate the hydronephrosis and hydroureter. The
proteins within the extracellular matrix and basal lamina that are altered by TCDD exposure
(laminin, fibronectin, and collagen) are considered markers of a commitment to differentiate into
epithelial structures. In the mouse embryo/fetus, TCDD exposure also blocks differentiation
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within the epithelium of the developing palate. Although there are effects of TCDD exposure on
EOF in the developing ureter as well as the developing palate, the urinary system, unlike parts of
the soft palate, is derived from mesoderm. Thus, it is important to note that the ectodermal
dysplasia syndrome is intended to denote a clustering of effects that appears to involve ectoderm-
derived organs. It is not intended to imply that all TCDD-induced developmental toxicity
involves organs derived from ectoderm.
5.2.2.2.2. Evidence for an Ah receptor mechanism.
5.2.2.2.2.1. Genetic. With respect to involvement of the Ah locus in TCDD-induced
hydronephrosis, very few genetic studies have been done. Prior to the discovery of the Ah locus,
however, Courtney and Moore (1971) reported a 62% incidence of hydronephrosis in C57BL/6
mice exposed to a maternal TCDD dose of 3 ng/kg/day on days 6 to 15 of gestation, whereas the
incidence in similarly exposed DBA/2 mice was only 26%. More recently, Silkworm et al.
(1989) reported that when TCDD is administered on gestational days 6 to 15, the incidence of
hydronephrosis is dose related. As the maternal dose of TCDD is increased from 0.5 to
4ng/kg/day, the incidence of hydronephrosis in C57BL/6 mice increases from 31% to 92%,
whereas in DBA/2 mice the incidence varies from 5% to 37% over the same dose range. In
DBA/2 mice the incidence of hydronephrosis increases to 60% when the largest dose of TCDD
administered is doubled to 8 ng/kg/day (but does not reach the 92% level seen in C57BL/6 mice
at 4[ig TCDD/kg). Thus, the incidence of hydronephrosis is higher in the mouse strain that
produces high-affinity Ah receptors (C57BL/6) compared with that strain (DBA/2) that produces
Ah receptors having lower ligand-binding affinity (Okey et al., 1989). The largest dose of TCDD
used in these experiments resulted in hydronephrosis of the fetus without affecting the mean
body weight or body weight gain of the dam. In the BXD strains (Hassoun et al., 1984a), the
incidence of 2,3,7,8-TCDF-induced hydronephrosis is 34% to 48% in eight strains with high-
affinity Ah receptors and 3% to 4% in eight strains with low-affinity Ah receptors. These results
obtained in the BXD strains of mice provide the best evidence currently available of an
association between the ability of TCDD-like congeners to induce hydronephrosis and the wild-
type Ahb allele. Thus, the Ah locus and the Ah receptor are involved in the hydronephrosis that
is induced by TCDD-like congeners.
More recently, transgenic Ah receptor null mutant mice have been used to study the
effects of Ah receptor deletion on the ability of TCDD exposure to cause hydronephrosis.
Female mice heterozygous for Ah receptor expression were mated to males of the same genotype
and exposed during pregnancy to 40 \ig TCDD/kg on gestational day 12.5 (Mimura et al., 1997).
Nearly all TCDD-exposed wild-type and heterozygous progeny developed hydronephrosis. In
sharp contrast, there was no hydronephrosis in offspring from the same litters that were
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homozygous for the Ah receptor null mutation. Similarly, Ah receptor null mice generated by a
different targeting method (Fernandez-Salguero et al., 1995) were also completely resistant to
TCDD-induced hydronephrosis (Peters et al., 1999). These results, coupled with the difference
in incidence of TCDD- or TCDF-induced hydronephrosis in C57BL/6 and DBA/2 mouse strains,
demonstrate that this teratogenic response to TCDD is Ah receptor mediated. Since haplo-
insufficiency was observed for the cleft palate response, but not for hydronephrosis, Mirnura and
coworkers suggest that the mechanisms by which the Ah receptor mediates these two teratogenic
effects of TCDD may be different (Mimura et al., 1997).
5.2.2.2.2.2. Structure activity. The rank order of potencies for various halogenated aromatic
hydrocarbon congeners to cause hydronephrosis in mice is consistent with the structure-activity
requirements for binding to the Ah receptor (see Table 5-4). This provides further evidence that
the Ah receptor mediates the effects of these TCDD-like congeners on the developing mouse
kidney.
5.2.2.2.2.3. Species differences. Hydronephrosis has been reported after administration of low
maternal doses of TCDD to rats and hamsters. Possibly due to the small numbers of fetuses
examined, the observed incidences of hydronephrosis in rats after exposure to cumulative
maternal doses <2 \ig TCDD/kg have not been statistically significant (Courtney and Moore,
1971; Giavini et al., 1983). There are also interstrain differences in rats in susceptibility to
hydronephrosis. This is illustrated in the TCDD-resistant Han/Wistar and TCDD-sensitive Long
Evans rat strains by 1 and 10 ng/kg of TCDD administered on gestational day 8 causing 3% and
11.9% hydronephrosis in the Han/Wistar strain while a 5 ng/kg dose of TCDD administered on
the same day of gestation failing to cause hydronephrosis in the Long Evans strain (Huuskonen et
al., 1994). Following a 1.5 ng TCDD/kg dose administered on gestational days 7 and 9, the
incidence of hydronephrosis in hamster fetuses was 11% and 4.2%, respectively. This is in
contrast to an incidence of <1% in control hamster fetuses. Accordingly, hydronephrosis is one
of the most sensitive indicators of prenatal toxicity in hamsters (Olson and McGarrigle, 1991).
5.2.2.3. Tooth Development
The interpretation that lactational exposure to CDDs and CDFs may lead to
mineralization defects in the first molars of human infants (Alaluusua et al., 1996, 1999) was
further investigated in experiments where primordial mandibular molar teeth from mouse
embryos were cultured in the presence of 1 \iM TCDD (Partanen et al., 1998). In these cultured
primordial teeth, TCDD caused toxicity to odonotoblasts and ameloblasts. This led to a failure
of dentin to undergo mineralization and a lack of enamel deposition. Cuspal morphology also
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was disrupted by TCDD exposure in the cultured teeth. While the concentration of TCDD
required to produce these effects was high, 1 p,M, the authors suggest that barriers to diffusion
inherent in tooth structure may result in TCDD concentrations at the cellular site of action in the
primordial teeth being much lower than that in the culture medium (Partanen et al., 1998).
Exposure to EOF (10 ng/L) similarly retarded molar tooth development in cultured explants from
wild-type embryos, because layers of mineralized dentin and the enamel matrix were thinner than
those in vehicle-exposed explants. In cultured primordial molar teeth from EGF receptor null
embryos, the effects of EGF were completely ameliorated, and TCDD had only a mild effect.
When cultured explants from wild-type mouse embryos were simultaneously exposed to EGF
and TCDD, the adverse effects of TCDD on mineralization and enamel deposition were largely,
but not completely prevented (Partanen et al., 1998). In utero and lactational exposure of male
Holtzman rats to 0.064, 0.16, 0.40, or 1.0 ng/kg of TCDD on gestational day 15 failed to
accelerate the age at which incisor eruption occurred. At the highest TCDD dose used there was
a tendency for incisor eruption to be accelerated by about 1 day (9.9 days in the control versus
8.9 days in the TCDD group), but the effect was not statistically significant (Mably et al., 1992a).
Taken together, these results are consistent with the interpretation that TCDD alters tooth
development in organ culture by interfering with EGF receptor signaling. However, TCDD also
may affect tooth development by perturbing other signaling pathways, which either act in concert
with or interfere with EGF receptor signaling, and probably involve additional mechanisms of
cell and/or tissue interactions. Finally, the involvement of EGF receptor signaling in this effect
of TCDD is consistent with aberrant tooth development being a part of the TCDD ectodermal
dysplasia syndrome.
5.2.3. Postnatal Effects
5.2.3.1. Eye Opening
In utero and lactational exposure to TCDD caused external developmental effects in male
rodent offspring that are not androgen dependent. The most prominent of these is accelerated eye
opening. Mably et al. (1992a) reported that the age of eye opening was accelerated by 1.0 ng/kg
of TCDD administered on gestational day 15. Lower doses of TCDD that affected growth and
development of several male reproductive tract organs, however, had no effect on eye opening,
demonstrating that this endpoint could be clearly dissociated from the more sensitive male
reproductive endpoints by the dose of TCDD needed to cause them. Gray et al. (1997) found
accelerated eye opening to be one of the most sensitive endpoints in the Long Evans rat,
occurring at 0.05 ng/kg of TCDD administered on gestational day 15. This was the lowest dose
used in their study, and it also significantly decreased ejaculated sperm numbers, by 25%. In the
ICR mouse exposure to 15, 30, or 60 ng/kg of TCDD on gestational day 14 accelerated eye
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opening in male pups at all dosage levels (Theobald and Peterson, 1997). However, there was no
effect on age to eye opening in female pups from the same TCDD-exposed mouse litters.
5.2.3.2. Male Reproductive System
TCDD has been shown to decrease plasma androgen concentrations in the adult male rat
(see Section 5.3.2.2). Because TCDD is known to be transferred from mother to young in utero
and during lactation (Moore et al., 1976; van den Berg et al., 1987), it can be expected to have an
impact on the male reproductive system during early development (Mably et al., 1991).
Testosterone and/or its active metabolite 5a-dihydrotestosterone (DHT) are essential prenatally
and/or during the early postnatal stage for imprinting and development of accessory sex organs
(Chung and Raymond, 1976; Rajfer and Coffey, 1979; Coffey, 1988) and for initiation of
spermatogenesis (Steinberger and Steinberger, 1989). For example, exposure of the male rat
fetus on gestational days 14 to 16 to a 5«-reductase inhibitor, which inhibits conversion of
testosterone to DHT, impairs development of urogenital sinus-derived accessory sex organs such
as the prostate (Clark et al., 1993). If perinatal imprinting fails to occur in the Wolffian duct or
urogenital sinus-derived accessory sex organs of a neonatal male rat, the result could be that
these male sex organs do not develop a normal trophic response to androgenic stimulation and do
not grow and develop normally as the animal matures. In addition, aromatization of testosterone
to 1713-estradiol within the CNS is required perinatally for the imprinting of typical adult male
patterns of reproductive behavior (Gorski, 1974) and luteinizing hormone (LH) secretion
(Barraclough, 1980). Thus, normal development of male reproductive organs and imprinting of
typical adult sexual behavior patterns require sufficient testosterone to be secreted by the fetal
and neonatal testis at critical times in early development before and shortly after birth (MacLusky
andNaftolin, 1981; Wilson et al., 1981).
To determine how the male reproductive system is affected by in utero and lactational
TCDD exposure, Mably et al. (1991, 1992a,b,c) treated pregnant rats with a single oral dose of
TCDD (0.064, 0.16, 0.4, or 1.0 ng/kg) or vehicle on day 15 of gestation (day 0 = sperm positive).
Day 15 was chosen because most organogenesis in the fetus is complete by this time and the
hypothalamic/pituitary/testis axis is just beginning to function (Warren et al., 1975, 1984; Aubert
et al., 1985). The pups were weaned 21 days after birth. The consequences of this single,
maternal TCDD exposure for the male offspring were characterized at various stages of postnatal
sexual development. These original studies of male sexual development following in utero and
lactational TCDD exposure have been expanded and further defined in subsequent studies using
Holtzman, Long Evans, Sprague-Dawley, and Wistar rats, Syrian hamsters, and mice. These
studies have been conducted in five different laboratories and in the vast majority of the studies,
TCDD was used as the prototype Ah receptor agonist. However, some studies used 3,3'4,4',5-
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PCB (PCB 126), 3,3',4,4',5,5'-HCB (PCB 169), and 2,3,4,7,8-PCDF. In general, the collective
findings have produced qualitatively similar results that define a significant effect of TCDD and
related Ah receptor agonists on the developing male reproductive system. The effects do not
appear to result from reduced plasma androgen concentrations during the perinatal period as
originally hypothesized by Mably et al. (1992a) and do not overlap completely with
developmental effects of known antiandrogens (Roman et al., 1998b; Gray et al., 1999).
5.2.3.2.1. Overt toxicity assessment. Mably et al. (1992a) found that TCDD treatment had no
effect on daily feed intake during pregnancy and the first 10 days after delivery, nor did it have an
effect on the body weight of dams on day 20 of gestation or on days 1, 7, 14, or 21 postpartum.
Treating dams with graded doses of TCDD on day 15 of gestation had no effect on gestation
index, length of gestation, or litter size. Except for an 8% decrease at the highest maternal dose,
TCDD had no effect on live birth index. Neither the 4-day nor 21-day survival index was
significantly affected by TCDD. In all dosage groups, the number of dead offspring was equally
distributed between males and females, and of the females that failed to deliver litters, none were
pregnant. Signs of overt toxicity among the offspring were limited to the above-mentioned 8%
decrease in live birth index (highest dose only), initial 10% to 15% decreases in body weight
(two highest doses), and initial 10% to 20% decreases in feed intake (measured for males only,
two highest doses). The latter two effects disappeared by early adulthood, after which the body
weights of the maternally exposed and nonexposed rats were similar.
These findings have essentially been confirmed by both Gray's and Peterson's
laboratories (Bjerke et al., 1994a; Gray et al., 1995a, 1997; Roman et al., 1995). A single oral
exposure of 1 ng/kg TCDD on day 15 of gestation does not result in maternal toxicity, but
compromises perinatal viability and growth of the offspring. A difference in the findings was
that the reduced viability occurred prenatally in the studies using the Holtzman rat (Bjerke et al.,
1994a; Roman et al., 1995) and postnatally in the Long Evans rat (Gray et al., 1995a).
5.2.3.2.2. Prenatal plasma androgen levels and testicular androgen production. Exposure of
Holtzman rats to 1.0 ng/kg of TCDD on gestational day 15 was originally reported to decrease
plasma testosterone concentrations in male fetuses from gestational days 17 to 21 and in neonates
2 hours after birth (Mably et al., 1992a). However, a subsequent study from the same laboratory
was not able to reproduce these findings (Chen et al., 1993). They found, contrary to their
original study (Mably et al., 1992a), that exposure of Holtzman rats to 1.0 ng/kg of TCDD on
gestational day 15 did not reduce plasma testosterone concentrations in male fetuses on
gestational days 18 or 20 or in male neonates 2 hours after birth. Furthermore, perinatal TCDD
exposure did not decrease intratesticular testosterone content or interfere with the ability of the
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LH analog, human chorionic gonadotropin (hCG), to stimulate testosterone production from
bisected testis preparations at these times (Chen et al., 1993). No other studies have examined
the effects of in utero and lactational TCDD exposure on plasma testosterone levels or testicular
testosterone production in rat fetuses or neonates at these specific times perinatally when plasma
testosterone concentrations are elevated. In control rats the neonatal testosterone peak that
occurs 2 hours after birth is followed from 6 hours to 5 days after birth by plasma testosterone
concentrations that are 70% to 80% lower than the neonatal peak concentration. When evaluated
at these times there was no effect of 1.0 ng/kg of TCDD administered on gestational day 15 on
plasma testosterone concentrations in male offspring of either the Holtzman or Long Evans
strains (Mably et al., 1992a; Gray et al., 1995a). Also, when evaluated 6 hours after birth there
was no effect on LH-stimulated testosterone production in neonatal rats of the Long Evans strain
exposed perinatally to TCDD (Gray et al., 1995a). Taken together, these results suggest that
effects of in utero and lactational TCDD exposure on the male rat reproductive system cannot be
explained by decreased testicular testosterone production or plasma testosterone concentrations
during perinatal development (Roman and Peterson, 1998b).
5.2.3.2.3. Postnatal plasma androgen levels and testicular androgen production. Mably et al.
(1992a) failed to find a significant decrease in plasma testosterone or 5«-DHT concentrations in
male Holtzman rat offspring that were 32, 49, 63, or 120 days of age and had been exposed on
gestational day 15 to either 0.064, 0.16, 0.40, or 1.0 ng/kg of TCDD. Consistent with these
negative results, Roman et al. (1995) and Loeffler and Peterson (1999) were also unable to
observe any consistent pattern of reduction of plasma testosterone or 5a-androstane-3a,17p-diol
concentration in male Holtzman rats that were 21, 32, 49, and 63 days of age and had been
exposed to 0.25 or 1.0 ng/kg of TCDD on gestational day 15. Similarly, at the three dosage
regimens used for in utero and lactational exposure to TCDD, there was no effect on plasma
testosterone concentration in male Wistar rat offspring at postnatal day 70 and only at the highest
TCDD dosage regimen was there a reduction in plasma testosterone at postnatal day 170 (Faqi et
al., 1998). Gray et al. (1995a) found no effect on serum testosterone concentrations or on basal
or LH-stimulated testicular testosterone production from in utero and lactational exposure to 1.0
ng/kg of TCDD administered on gestational day 8 or 15 in male Long Evans rats at 49 or 270
days of age. These investigators also reported that there was no effect of in utero and lactational
exposure to 2.0 ng/kg of TCDD administered on gestational day 11 on serum testosterone
concentrations of male Syrian hamsters at 140 days of age (Gray et al., 1995a). Theobald and
Peterson (1997) failed to find a significant decrease in plasma testosterone concentrations in male
ICR mouse offspring that were 44, 65, or 114 days of age and had been exposed on gestational
day 14 to either 15, 30, or 60 ng/kg of TCDD. In utero and lactational exposure of rats to other
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Ah receptor agonists have produced results similar to those caused by TCDD. 3,3'4,4'5-PCB
(PCB 126), 2,3',4,4',5-PCB (PCB 118), or 2,3,4,7,8-PCDF administered on gestational day 1 had
no effect on plasma testosterone concentrations in male Wistar rat offspring at 112 days of age
(Bouwman et al., 1996). Overall, these findings suggest that the spectrum of effects caused by in
utero and lactational exposure to TCDD on the male reproductive system cannot be explained by
decreased postnatal testicular androgen production or plasma androgen concentrations (Gray et
al., 1995a; Roman and Peterson, 1998b).
5.2.3.2.4. External indicators ofandrogenic status. The androgenic status of the male
offspring can be determined from the structure and function of androgen-dependent systems and
from the levels of circulating androgens. Anogenital distance, which is dependent on both
circulating androgen concentrations and androgenic responsiveness (Neumann et al., 1970), was
reduced in 1- and 4-day-old Holtzman male pups by a single maternal TCDD dose as low as 0.16
ng/kg, even when slight decreases in body length were considered (Mably et al., 1992a).
However, this effect was not observed in subsequent studies in Holtzman and Long Evans rats
exposed perinatally to TCDD when anogenital distance was determined relative to body weight
(Gray et al., 1993; 1995a) or crown-rump length (Bjerke et al., 1994a,b; Roman et al, 1995).
Also, when Wistar rats were exposed in utero and via lactation to TCDD or 3,3',4,4',5,5'-HCB
(PCB 169), anogenital distance was not affected (Faqi et al., 1998a; Smits-van Prooije et al.,
1994). This lack of effect on relative anogenital distance was also found when Long Evans rats
were exposed to 3,3'4,4'5,5'-HCB (PCB 169) on gestational day 15 (Gray et al., 1999). Thus,
these findings suggest that androgenic status of the male rat neonate is not affected by perinatal
exposure to TCDD and PCB 169.
Two other external indicators ofandrogenic status are time to testis descent and time to
preputial separation (Rajfer and Walsh, 1977; Korenbrot et al., 1977). These occur in control
rats between postnatal days 20-23 and 42-45, respectively. Exposure to 0.16, 0.40, or 1.0 ng/kg
of TCDD on gestational day 15 delayed testis descent in the Holtzman rat strain by 1.0 to 1.6
days (Mably et al., 1992a). However, this effect was significant in only two of four rat studies
(Mably et al., 1992a; Bjerke et al., 1994a,b; Faqi et al., 1998a) and in the ICR mouse TCDD had
no effect on the age at testis descent (Theobald and Peterson, 1997). Puberty, assessed by age at
preputial separation, was more reproducibly affected by TCDD across rat strains and species. It
was delayed by as much as 3.6 days in Long Evans rats exposed to 1.0 ng/kg TCDD on
gestational day 15 (Gray et al., 1995, 1997). The effect was dose-related and significant at a
maternal TCDD dose as low as 0.20 ng/kg (Gray et al., 1997). Delays in age at preputial
separation were also reported in Holtzman and Wistar rats and in the Syrian hamster following in
utero and lactational exposure to TCDD (Bjerke et al., 1994a,b; Roman et al., 1995; Gray et al.,
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1995a; Faqi et al., 1998a). The only species studied where TCDD failed to delay the age at
preputial separation was the ICR mouse (Theobald and Peterson, 1997). The ability of TCDD to
delay puberty in the Long Evans rat was also observed following in utero and lactational
exposure to 1.8 mg/kg of 3,3'4,4'5,5'-HCB (PCB 169) administered on gestational day 8 (Gray et
al., 1999).
The spectrum of external effects caused by in utero and lactational exposure to TCDD
and 3,3',4,4',5,5'-HCB (PCB 169) have been interpreted not to resemble those caused by known
antiandrogens such as flutamide (Gray et al., 1999). This is evident in Holtzman and Long Evans
rats by perinatal exposure to TCDD or 3,3',4,4',5,5'-HCB (PCB 169) failing to affect external
androgen-dependent tissues either by reducing relative anogenital distance or by inducing
areolas, retained nipples, or hypospadias (Roman and Peterson, 1998b; Loeffler and Peterson,
1999; Gray et al., 1999). Flank gland development, an androgen-dependent process that occurs
in young adulthood in male hamsters, also was not affected by in utero and lactational exposure
to TCDD (Gray et al., 1995a). However, other effects of in utero and lactational exposure to
TCDD on the androgen-dependent endpoints of preputial separation (weight of the ventral
prostate, seminal vesicle, glans penis, testis, and epididymis; daily sperm production; cauda
epididymal sperm number; epididymal malformation; demasculinized and feminized sexual
behavior; and feminized regulation of LH secretion) resemble effects caused by antiandrogens
(Roman and Peterson, 1998b).
5.2.3.2.5. Prostate. One of the most sensitive effects of in utero and lactational exposure to
TCDD in the male Holtzman rat is a dose-related reduction in ventral prostate weight. The
lowest dose of TCDD that caused this effect was 0.064 ng/kg administered on gestational day 15.
It reduced ventral prostate weight in male offspring at 32 days of age (Mably et al., 1992a).
However, when expressed on a relative body weight basis, 0.16 ng/kg of TCDD was the lowest
dose that caused a significant reduction in ventral prostate weight. At a maternal TCDD dose of
1.0 ng/kg, significant decreases in ventral prostate weight have been detected in Holtzman rats as
early as postnatal day 14 and as late as postnatal day 120 (Roman and Peterson, 1998; Mably et
al., 1992a). In addition to weight of the ventral prostate being reduced by in utero and lactational
TCDD exposure in Holtzman rats (Mably et al., 1992a; Bjerke et al., 1994a,b; Roman et al.,
1995; Roman and Peterson, 1998), this effect of perinatal TCDD exposure has also been
observed in Long Evans and Sprague-Dawley rats (Gray et al., 1997; Wilker et al., 1996) and in
ICR and C57BL/6 mice (Theobald and Peterson, 1997; Sommer and Peterson, 1997; Lin et al.,
2000). Although a decrease in ventral prostate weight following in utero and lactational
exposure to TCDD was not observed in Wistar rats, this may have been caused by the low level
of TCDD exposure used in this study (Faqi et al., 1998a). When the same investigators
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administered 10 ng/kg of 3,3'4,4'5-PCB (PCB 126) on gestational day 15 to Wistar rats, ventral
prostate weights in 70- and 170-day-old offspring were reduced (Faqi et al., 1998b).
Administration of 1.8 mg/kg of 3,3',4,4',5',5-HCB (PCB 169) on gestational day 8 reduced
ventral prostate weight in Long Evans rats at 65, 260, and 600 days of age (Gray et al., 1999).
However, exposure to 100 ng/kg of 3,3'4,4'-TCB (PCB 77) on gestational day 15 had no effect on
ventral prostate weight of Wistar rats at 70 and 170 days of age (Faqi et al., 1998b). Taken
together, these results demonstrate that in utero and lactational exposure to certain Ah receptor
agonists, namely, TCDD, PCB 126, and PCB 169, are capable of reducing ventral prostate
weight in various strains of rats and mice. PCB 77 does not appear to share this effect with the
other Ah receptor agonists, and this may be due to the more rapid rate of metabolism and
elimination of PCB 77 in the rat than TCDD and the other coplanar PCB congeners tested.
The ability of in utero and lactational exposure to TCDD to decrease ventral prostate
weight in the rat is greatest from the earliest age at which the organ can be accurately weighed
until just after puberty (50 days of age). Thereafter, the magnitude of the weight reduction is
progressively attenuated with advancing age, either completely or partially, depending on the
dose of TCDD administered during pregnancy (Mably et al., 1992a). At minimally effective
doses the reduction in ventral prostate weight is transient and not seen in adulthood. However, at
maximally effective doses ventral prostate weight of adult males is reduced significantly. This
has been demonstrated for TCDD in Holtzman rats, PCB 169 in Long Evans rats, PCB 126 in
Wistar rats, and TCDD in ICR mice (Mably et al., 1992a; Gray et al., 1999; Faqi et al., 1998b;
Theobald and Peterson, 1997). In utero and lactational exposure to TCDD also decreases weight
of the dorsolateral prostate and anterior prostate (coagulating gland) in the Holtzman rat, ICR
mouse, and C57BL/6 mouse (Roman et al., 1995; Theobald and Peterson, 1997; Sommer and
Peterson, 1997; Roman and Peterson, 1998; Loeffler and Peterson, 1999; Lin et al., 2000). Thus,
TCDD exposure is capable of interfering with ventral, dorsolateral, and/or anterior prostate
growth and morphogenensis early in development. Depending on the dose administered during
pregnancy, timing of the exposure, species or strain of animal, and lobe of the prostate, TCDD is
capable of causing a prostate lesion that cannot be compensated for later in life. Besides size of
the ventral prostate being smaller in adulthood, its responsiveness to testosterone stimulation in
adulthoood is also impaired by perinatal exposure to TCDD (Bjerke et al., 1994b).
The mechanism by which in utero and lactational exposure to TCDD impairs prostate
growth and development is unknown. It cannot be explained in Holtzman rats by TCDD
decreasing plasma androgen concentrations (Chen et al., 1993; Roman et al., 1995; Gray et al.,
1995a) or inhibiting the conversion of circulating androgens to DHT in the prostate (Roman et
al., 1995; Theobald et al., 2000). TCDD probably acts directly on the urogenital sinus from
which the prostate develops and on the developing lobes of the prostate as they undergo
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differentiation. The Ah receptor and ARNT are expressed in both the rat urogenital sinus and the
developing ventral and dorsolateral prostate (Roman et al., 1998a; Sommer et al., 1998) and the
infantile rat ventral prostate is responsive to in utero and lactational TCDD exposure in terms of
CYP1A induction (Roman and Peterson, 1998). Also, various androgen-regulated mRNAs that
code for secretory proteins that are produced by prostate luminal epithelial cells and are markers
for functional differentiation of these cells show transient decreases in response to perinatal
TCDD exposure in the Holtzman rat (Roman and Peterson, 1998a).
In utero and lactational exposure to TCDD begins to impair rat prostate development
during fetal life (Roman et al., 1998a). Therefore, it is important to determine the concentration
of TCDD that is present in the fetal urogenital tract after gestational day 15 because this is when
fetal prostate development is initiated in the rat. Administration of 1.15 ng/kg of TCDD on
gestational day 8 to Long Evans rats results in concentrations of TCDD in the urogenital tract of
the fetus of 0.04 and 1.1 pg/g on gestational days 16 and 21, respectively (Hurst et al., 1998).
This is significant because a slightly lower dose of TCDD, 1.0 ng/kg, administered to Long
Evans rats on either gestational day 8 or 15, causes a decrease in ventral prostate weight of the
offspring peripubertally that disappears in adulthood (Gray et al., 1993, 1995a, 1997, 1999).
Furthermore, 1.0 ng/kg of TCDD administered on gestational day 15 to Holtzman rats impairs
prostate growth and development postnatally (Roman and Peterson, 1998b) and reduces the
number of prostatic buds that emerge from the fetal urogenital sinus on gestational day 20 to
form the various lobes of the prostate (Roman et al., 1998a). In addition, this same dose of
TCDD decreased cell proliferation in the ventral prostate of Holtzman rat neonates that were 1
day of age (Roman et al., 1998a). Subsequent analysis of the effects of TCDD on early postnatal
development of the ventral prostate revealed that differentiation of both smooth muscle cells and
luminal epithelial cells was delayed and striking alterations in histology of the ventral prostate
were apparent in male offspring at 32 days of age (Roman et al., 1998a). These alterations
consisted of epithelial hyperplasia, decreased abundance of fully differentiated luminal epithelial
cells, increased density of basal epithelial cells, altered spatial distribution of the androgen
receptor, and increased thickness of the periductal smooth muscle sheath. Thus, the effects of in
utero and lactational TCDD exposure on prostate growth and development are contributed by
impaired growth of the developing organ prenatally and neonatally and by delayed and/or
impaired differentiation postnatally that, if the dose of TCDD is high enough, could be
permanent.
Essentially nothing is known about the long-term consequences of in utero and lactational
exposure to Ah receptor agonists on the prostate of laboratory rodent species during old age. The
only study available found that the incidence of acute prostatitis in the dorsolateral prostate of
600-day-old Long Evans rats was increased significantly by exposure to a single dose of 1.8
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mg/kg of 3,3',4,4',5,5'-HCB on gestational day 8 (Gray et al., 1999). Also, 1 of 9 males displayed
diffuse epithelial hypertrophy of the ventral prostate compared with 0 of 15 control males (Gray
etal., 1999).
To understand the mechanism by which TCDD impairs the initial step in rat prostate
formation, it is important to note that both the Ah receptor and ARNT proteins are expressed in
high concentrations in the fetal Holtzman rat urogenital sinus (Sommer et al., 1999). The fetal
rat prostate develops from the urogenital sinus. Solid cords of basal epithelial cells (prostatic
buds) emerge from the urogenital sinus and invade the surrounding mesenchyme on gestational
day 18.5. By gestational day 20.5 this budding process, which TCDD partially blocks (Roman et
al., 1998a), is complete. Ah receptor and ARNT proteins were expressed at high levels in the rat
urogenital sinus on gestational days 16, 18, and 20, with mean concentrations of 600 fmoles Ah
receptor and 140 fmoles ARNT per mg total tissue lysate (Sommer et al., 1999). From a
mechanism point of view, it is significant that Ah receptor protein levels were approximately
four times greater than ARNT. Since ARNT dimerizes with several members of the bHLH PAS
family of transcription factors, it raises the possibility that TCDD activation of the Ah receptor in
the urogenital sinus might sequester ARNT from participating in endogenous protein-protein
interactions that may be essential for prostate development. Whatever the mechanism, it is clear
that the Ah receptor plays a role. The recent finding that impairment of ventral prostate growth
and development in Ah receptor wild-type mice by in utero and lactational exposure to 5 ng/kg of
TCDD on gestational day 14 is blocked in Ah receptor knockout mice administered the same
dose of TCDD supports this view (Lin et al., 2000).
Ah receptor and ARNT proteins are also expressed in human fetal, benign hyperplastic,
and malignant prostate (Kashani et al., 1998). Also, TCDD exposure in a human prostate cancer
cell line, LNCaP, dose-dependently inhibits androgen-dependent transcriptional activity and
prostate-specific antigen expression (Jana et al., 1999). Thus, the human prostate, like the rat and
mouse prostate, is capable of responding to TCDD.
5.2.3.2.6. Seminal vesicle. Weight of the seminal vesicle is decreased in Holtzman, Sprague-
Dawley, and Long Evans rats and Syrian hamsters by in utero and lactational exposure to TCDD
(Mably et al., 1992a; Bjerke and Peterson, 1994; Gray et al., 1995a; Wilker et al., 1996; Gray et
al., 1997). The same effect has been observed in Long Evans rats with 3,3',4,4',5,5'-HCB (PCB
169) and in Wistar rats with 3,3'4,4'-TCB (TCB-77) (Gray and Kelce, 1996; Gray et al., 1999;
Faqi et al., 1998b). The lowest maternal dose of TCDD to decrease seminal vesicle weight was
0.16 ng/kg administered on gestational day 15. It significantly decreased seminal vesicle weight
in Holtzman rat offspring at 32 and 63 days of age (Mably et al., 1992a).
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In Wistar rats a multiple dosing regimen with TCDD that caused a significant decrease in
cauda epididymal sperm numbers, daily sperm production, and sperm morphology failed to
decrease seminal vesicle weight (Faqi et al., 1998a). This implies that the TCDD-induced
decrease in seminal vesicle weight is not the most sensitive effect of TCDD on the developing
male reproductive system. 3,3',4,4',5-PCB (PCB 126) administered as a single dose of 10 ng/kg
on gestational day 15 also had no effect on seminal vesicle weight in the Wistar rat even though
it did significantly decrease ventral prostate weight (Faqi et al., 1998b). Thus, the TCDD-
induced reduction in seminal vesicle weight is not as sensitive as some of the other
developmental reproductive system endpoints in the Wistar rat (Faqi et al., 1998a). Similarly, in
Holtzman rats and ICR mice sensitivity of the seminal vesicle to TCDD is not as great as that of
the ventral prostate (Mably et al., 1992a; Roman et al., 1995; Theobald and Peterson, 1997). In
Long Evans rats the two accessory sex organs seem equivalent in their sensitivity to TCDD
administered on day 15 of gestation (Gray et al., 1997).
The time course of the response of the prostate and seminal vesicle to in utero and
lactational TCDD exposure in the Holtzman rat is very different (Roman et al., 1995). The
ventral and dorsolateral prostate respond with the greatest relative weight reduction early in
development and the magnitude of the response lessens with increasing age. In the case of the
seminal vesicle, the opposite is true. Significant TCDD-induced decreases in weight are
generally not detected until the peripubertal stage when androgen concentrations are rapidly
increasing (Roman et al., 1995). Also, the magnitude of the decrease in seminal vesicle relative
weight is not as great as it is for the prostate. The difference in time course of weight reduction
between the seminal vesicle and prostate suggest that small TCDD-induced reductions in plasma
androgen concentrations peripubertally might account for the small decreases in weight of the
seminal vesicles in Holtzman rats at this age (Roman et al., 1995; Roman and Peterson, 1998a).
A deficiency in number of androgen receptors in the seminal vesicle is not involved, because the
decrease in seminal vesicle weight in 330-day-old Long Evans rat exposed to 1.0 ng/kg of TCDD
on gestational day 15 was not associated with a decrease in the concentration of androgen
receptors in the seminal vesicle (Gray et al., 1995a).
Another difference between the two accessory sex organs is the effect of in utero and
lactational exposure to TCDD on their responsiveness to androgenic stimulation in adulthood.
The adult ventral prostate is clearly affected by such exposure and is relatively refractory in its
responsiveness to androgens. On the other hand, responsiveness of the adult seminal vesicle to
testosterone stimulation is not affected by in utero and lactational exposure to TCDD (Bjerke et
al., 1994b).
Consistent with the findings in Holtzman rats (Roman et al., 1995), Long Evans rats
exposed on gestational day 15 to 1.0 ng/kg of TCDD and assessed on postnatal days 15, 25, 32,
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49, 63, and 120 did not exhibit a decrease in weight of the paired seminal vesicles and attached
coagulating glands until postnatal day 32 (Hamm et al., 2000). Furthermore, in utero and
lactational exposure to TCDD reduced the amount of secretory fluid contained in the seminal
vesicles at this age, which contributed to their decreased weight (Hamm et al., 2000). As with
the TCDD-exposed ventral prostate at 32 days of age (Roman et al., 1998a), there were striking
alterations in histology of the seminal vesicle at 32 days of age (Hamm et al., 2000). Compared
with control animals where seminal vesicle epithelium displayed extensive branching, TCDD-
exposed rats had seminal vesicles with fewer and shorter epithelial branches. The epithelium of
control rats was characterized by tall columnar cells, whereas that of TCDD-exposed rats
contained smaller cells with a lower cytoplasmic-volume to nuclear-volume ratio (Hamm et al.,
1999, 2000). Immunostaining for proliferating cell nuclear antigen (PCNA) in control seminal
vesicles at 32 days of age was localized to undifferentiated basal cells and no immunoreactivity
was observed in terminally differentiated luminal epithelial cells. In contrast, the
undifferentiated seminal vesicles of TCDD-exposed rats at the same age exhibited PCNA
immunoreactivity at both the basal and luminal surfaces of poorly branched glands (Hamm et al.,
2000). Thus, the collective database demonstrates that in utero and lactational exposure to
TCDD interferes with epithelial proliferation and differentiation in the seminal vesicle as has
been reported for the rat prostate (Hamm et al., 2000; Roman et al., 1998a).
5.2.3.2.7. Glanspenis. Like the prostate and seminal vesicle, the glans penis is an androgen-
dependent tissue. In utero and lactational exposure to 1.0 ng/kg of TCDD on gestational day 15
decreased glans penis diameter and absolute weight in Holtzman rats at 63 days of age, but had
no effect on glans penis length or relative weight (Bjerke and Peterson, 1994). Weight of the
glans penis was reduced in 450-day-old Long Evans rats exposed on gestational day 15 to either
0.20 or 0.80 ^g/kg of TCDD (Gray et al., 1997).
5.2.3.2.8. Testis weight. In utero and lactational exposure to a single dose of TCDD
administered on gestational day 15 decreases testis weight in Holtzman, Long Evans, and
Sprague-Dawley rats (Mably et al., 1992c; Gray et al., 1995a; Wilker et al., 1996). The effect is
transient, being manifested to the greatest extent peripubertally and then decreasing with age. It
is not as sensitive an endpoint as the decrease in ventral prostate weight or reduction in cauda
epididymal sperm numbers in Holtzman rats or the decrease in ejaculated sperm numbers in
Long Evans rats (Mably et al., 1992a,c; Gray et al., 1995a, 1997). Also, in Wistar rats exposed in
utero and via lactation to TCDD in a multiple dosing regimen, testis weight is not affected at
levels of TCDD exposure that decrease daily sperm production (Faqi et al., 1998a). In utero and
lactational exposure to TCDD had no effect on testis weight in the ICR mouse, but did decrease
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it in the Syrian hamster (Theobald and Peterson, 1997; Gray et al., 1995a). Thus, among
laboratory rodent species there are both strain and species differences in susceptibility to TCDD-
induced decreases in testis weight.
There is also variability in the extent to which in utero and lactational exposure to
individual non-ortho-substituted PCB congeners are capable of producing this effect. Exposure
to 3,3',4,4',5,5'-HCB (PCB 169) on gestational day 8, or to 3,3',4,4'-TCB on gestational day 15,
reduced testis weight in Wistar rat offspring (Gray and Kelce, 1996; Gray et al., 1999; Faqi et al.,
1998b). However, exposure to 3,3',4,4',5-PCB (PCB 126) on gestational day 15 had no effect on
testis weight in the Wistar rat strain (Faqi et al., 1998b).
5.2.3.2.9. Epididymis weight and malformation. In utero and lactational exposure to TCDD
has been shown to reduce epididymal weight in Holtzman, Long Evans, and Sprague-Dawley rat
strains (Mably et al., 1992c; Bjerke and Peterson, 1994; Gray et al., 1995a, 1997, 1999; Wilker et
al., 1996). In these studies TCDD was administered on gestational day 15, except for the studies
by Gray et al. (1995a), where it was also administered on gestational day 8. In the Wistar rat
strain a multiple dosing regimen was used for in utero and lactational TCDD exposure and it was
not associated with a reduction in the epididymal weight of the progeny (Faqi et al., 1998a).
Weight of the epididymis was also not reduced by in utero and lactational TCDD exposure in the
ICR mouse (Theobald and Peterson, 1997), but cauda epididymal weight was reduced by TCDD
exposure on gestational day 11 in the Sryrian hamster (Gray et al., 1995a). In rat strains that
responded to perinatal TCDD exposure by reducing epididymal weight in progeny, certain non-
ortho-substituted PCB congeners had the same effect. Administration of 3,3'4,4'5,5'-HCB (PCB
169) on gestational day 8 decreased whole epididymal weight in Long Evans rats, whereas
weight of the epididymides in Wistar rats was either not affected or slightly reduced by 3,3',4,4'-
TCB (PCB 77) or 3,3',4,4',5-PCB (PCB 126) administered on gestational day 15 (Gray and
Kelce, 1996; Gray et al., 1999; Faqi et al., 1998b) .
Compared with decreased testis weight, decreased epididymis and cauda epididymis
weights in the Holtzman rat were more sensitive and persistent effects of in utero and lactational
TCDD exposure. This is demonstrated by dose-related decreases in weight of the cauda
epididymis occurring in Holtzman rats at 120 days of age and cauda epididymal weight being
reduced significantly at this age by the lowest dose of TCDD used in the study, 0.064 ng/kg
(Mably et al., 1992c). The lowest dose of TCDD reported to decrease epididymal weight in other
studies was 0.20 ng/kg in the Long Evans rat (Gray et al., 1997) and the lowest dose tested in the
Sprague-Dawley rat, 0.5 ng/kg (Wilker et al., 1996).
The reduction in epididymal weight following in utero and lactational exposure to 1.0
ng/kg of TCDD on gestational day 15 could be permanent in certain rat strains. Significant
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decreases in whole epididymal or cauda epididymal weights have been observed in 120-day-old
Holtzman rats and 240- to 330-day-old Long Evans rats (Mably et al., 1992c; Gray et al., 1995a).
Since epididymal growth is androgen dependent, a TCDD-induced androgenic deficiency and/or
decrease in androgen responsiveness of the epididymis could account for decreased size of the
organ (Setty and Jehan, 1977; Dhar and Setty, 1990). However, if an antiandrogenic mechanism
is involved, it does not appear to be associated with decreases in plasma androgen levels or
epididymal androgen receptor levels. This is because in utero and lactational exposure to TCDD
has been shown not to reduce circulating androgen concentrations in Holtzman or Long Evans
rats at various stages of postnatal development (Roman et al., 1995; Gray et al., 1995a). Also
TCDD does not reduce androgen receptor concentrations in either the caput or corpus epididymis
when measured in 240- to 330-day-old Long Evans rat progeny exposed perinatally to TCDD
(Grayetal., 1995a).
The highest dose of TCDD to be investigated on epididymal development was 2.0 ng/kg
administered on gestational day 15 to Sprague-Dawley rats (Wilker et al., 1996). The effects of
this high dose on morphological development of the rat epididymis are useful in providing
insight into possible mechanisms of action of TCDD on the epididymis. More specifically, this
dose of TCDD induced a high incidence of malformations in the epididymis (27%) that were
characterized by the segmental absence of regions of the epididymis (Wilker et al., 1996). This
high incidence is similar to that reported for rats and mice exposed in utero to the antiandrogen
flutamide (van der School, 1992; Cain et al., 1994a,b) and suggests that TCDD may alter
testosterone-dependent differentiation of the Wolffian duct into the epididymis (Wilker et al.,
1996).
5.2.3.2.10. Testicular and epididymal sperm numbers. Among the most robust, sensitive,
persistent, and reproducible effects of in utero and lactational TCDD exposure in rats, hamsters,
and mice are reductions in sperm numbers (Roman and Peterson, 1998b). Generally when
TCDD is administered as a single dose during pregnancy, daily sperm production is affected the
least. Caput epididymal sperm numbers are reduced to a greater extent than daily sperm
production (Gray et al., 1997). Cauda epididymal sperm numbers are reduced more than caput
epididymal sperm numbers by in utero and lactational exposure to TCDD. In fact, statistically
significant, dose-related reductions in cauda epididymal sperm numbers have been reported for
four strains of rats, mice, and hamsters following in utero and lactational exposure to TCDD
(Mably et al., 1992c; Gray et al., 1995a, 1997; Wilker et al., 1996; Theobald and Peterson, 1997;
Faqi et al., 1998a). The greatest magnitude of reduction in sperm numbers at any given dose of
TCDD, however, has been reported for ejaculated sperm numbers in two strains of rats and the
hamster (Gray et al., 1995a, 1997; Sommer et al., 1996). Thus, the overall effect of exposure to a
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single dose of TCDD administered on gestational day 15 in rats and hamsters is that it causes a
progressively greater percentage decrease in sperm numbers in going from the testis (daily sperm
production), to caput epididymal sperm numbers, to cauda epididymal sperm numbers, to
ejaculated sperm numbers. Taken together, these results imply that in utero and lactational
TCDD exposure alters epididymal function such that epididymal sperm storage is permanently
reduced (Gray et al., 1995a).
An entirely different profile of inhibitory effects of in utero and lactational exposure to
TCDD on sperm numbers, however, was observed in Wistar rats that were exposed to multiple
doses of TCDD during mating, pregnancy, and lactation (Faqi et al., 1998b). With this multiple-
dosage TCDD exposure paradigm in Wistar rats, the percentage decrease in daily sperm
production was greater than the percentage decrease in cauda epididymal sperm numbers (Faqi et
al., 1998a)—just the opposite of what was observed in Long Evans and Holtzman rats where
TCDD was administered as a single dose (Gray et al., 1995a, 1997; Sommer et al., 1996). It is
possible that the difference in rat strain or the TCDD exposure paradigm between these studies
accounts for the testis being more sensitive than the cauda epididymis to TCDD-induced
decreases in sperm numbers in the study by Faqi and coworkers (1998a).
The lowest single dose of TCDD to decrease cauda epididymal sperm numbers in 120-
day-old Holtzman rats was 0.064 ng/kg administered on gestational day 15 (Mably et al., 1992c).
The lowest single dose to decrease ejaculated sperm numbers in 450 day old Long Evans rats was
0.050 ng/kg of TCDD administered on gestational day 15 (Gray et al., 1997). Faqi and
coworkers (1998a), using a multiple dosing regimen for in utero and lactational exposure of
Wistar rat progeny to TCDD, found that the lowest dosing regimen to significantly decrease daily
sperm production and cauda epididymal sperm numbers in 170-day-old Wistar rats was a 0.025
ng TCDD/kg loading dose followed by a 0.005 \ig TCDD/kg weekly maintenance dose (Faqi et
al., 1998a). When the male offspring exposed in utero and via lactation to TCDD in this manner
were weaned at 22 days of age, the mean concentration of TCDD in the testis and liver was 0.25
ng/g and 0.24 ng/g, respectively (Faqi et al., 1998a).
5.2.3.2.10.1. Daily sperm production. In utero and lactational exposure to TCDD in Holtzman
rats caused a dose-related decrease in daily sperm production in progeny at 49, 63, and 120 days
of age (Mably et al., 1992c). The lowest dose of TCDD administered on gestational day 15 to
reduce daily sperm production in 120-day-old Holtzman rats was 0.064 ng/kg. Other studies
have also reported that in utero and lactational exposure to TCDD is capable of significantly
decreasing daily sperm production in Holtzman, Long Evans, and Wistar rats (Bjerke and
Peterson, 1994; Sommer et al., 1996; Gray et al., 1997; Faqi et al., 1998b), but this effect was not
observed in Sprague-Dawley rats (Wilker et al., 1996), ICR mice (Theobald and Peterson, 1997),
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or Syrian hamsters (Gray et al., 1995a). In the three rat strains where decreased daily sperm
production is observed, the response lessens in severity as the progeny age and in some cases
returns to control levels (Mably et al., 1992c; Gray et al., 1995a). The Long Evans rat is an
example of a rat strain where the decrease in daily sperm production caused by perinatal TCDD
exposure is transient (Gray et al., 1995a, 1997). Among the most persistent effects of TCDD on
daily sperm production was found in 170-day-old Wistar rats exposed to TCDD dosing during
mating, pregnancy, and lactation (Faqi et al., 1998b). It has also been observed in Long Evans
rats that administration of TCDD on gestational day 15 is more effective than administering it on
gestational day 8 (Gray et al., 1995a; Gray and Kelce, 1996).
Effects on daily sperm production in Long Evans and Wistar rat strains by certain non-
ortho-sub stituted PCBs was variable. In utero and lactational exposure to 3,3',4,4',5',5-HCB
(PCB 169) on gestational day 8 reduced daily sperm production in Long Evans rats (Gray et al.,
1995b, 1999; Gray and Kelce, 1996). However, exposure of Wistar rats on gestational day 15 to
3,3',4,4',5-PCB (PCB 126) had no effect (Faqi et al., 1998b). Furthermore, in utero and
lactational exposure to 3,3',4,4'-TCB (PCB 77) increased both testis size and daily sperm
production in 65- and 140-day-old Wistar rats. It was hypothesized that this latter paradoxical
effect for an Ah receptor agonist of increasing daily sperm production may be secondary to a
possible non-Ah receptor-mediated effect of PCB 77, such as neonatal hypothyroidism (Faqi et
al., 1998b).
While severe undernutrition of rat pups and weanlings can adversely affect the male
reproductive system and decrease spermatogenesis (Ghafoorunissa, 1980; Jean-Faucher et al.,
1982a,b; Glass et al., 1986), it is unlikely that this was involved in reducing daily sperm
production caused by in utero and lactational exposure of Holtzman rats to TCDD (Mably et al.,
1992c). At the two highest maternal TCDD doses, 0.40 and 1.0 ng/kg, feed consumption and
body weight of male offspring were decreased up to 21%, but there was essentially no effect on
feed intake or body weight at the two lowest doses, 0.160 and 0.064 ng/kg. However, reduction
in daily sperm production, cauda epididymal sperm numbers, and certain sex organ weights,
occurred at the two lowest doses. Thus, undernutrition cannot account for these effects (Mably et
al., 1992a,c).
Because follicle-stimulating hormone (FSH) and testosterone are essential for
quantitatively normal spermatogenesis (Steinberger and Steinberger, 1989), an alternative
explanation for the decreases in daily sperm production is a decrease in FSH and/or testosterone
levels. In rats, the duration of spermatogenesis is 58 days (Blazak et al., 1985; Amann, 1986;
Working and Hurtt, 1987), so the decreases in plasma FSH concentrations in 32-day-old male
offspring could contribute to the reductions in daily sperm production when the progeny were 49
and 63 days of age (Mably et al., 1992c). However, the modest depressant effect of perinatal
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TCDD exposure on plasma FSH concentrations was transitory, with no effect on plasma FSH
levels being found when the offspring were 49, 63, and 120 days old (Mably et al., 1992c).
Therefore, it was concluded that reduced daily sperm production in 120-day-old rats perinatally
exposed to TCDD is not due to decreases in plasma FSH levels when the animals were 49 to 120
days of age (Mably et al., 1992c). Also, administration of 1.0 ng/kg of TCDD on gestational day
15 to Holtzman rats did not affect testicular testosterone production of their progeny at either
prenatal or postnatal stages of development (Chen et al., 1993; Roman et al., 1995). Therefore, a
reduction in intratesticular testosterone levels following such TCDD exposure would not be
sufficient to reduce spermatogenesis (Zirkin et al., 1989; Mably et al., 1992c; Gray et al., 1993).
In normal rats, daily sperm production does not reach a maximum until 100 to 125 days
of age (Robb et al., 1978), but in rats perinatally exposed to TCDD it takes longer for sperm
production to reach the adult level. Furthermore, the length of the delay for daily sperm
production to attain control levels appears to be directly related to TCDD dose (Mably et al.,
1992c). If the dose is high enough, the reduction in daily sperm production may be permanent.
This is suggested by in utero and lactational exposure to TCDD decreasing daily sperm
production in 170-day-old Wistar rats (Faqi et al., 1998b) and 300-day-old Holtzman rats (Moore
et al., 1992). However, in Long Evans rat the TCDD-induced reduction in daily sperm
production is transient and does not last beyond the peripubertal stage of development (Gray et
al., 1997).
5.2.3.2.10.2. Testis histology. A key observation for postulating mechanisms by which perinatal
TCDD exposure reduces spermatogenesis in the Holtzman rat strain in adulthood is the finding
that the ratio of leptotene spermatocytes per Sertoli cell in the testes of 49-, 63-, and 120-day-old
Holtzman rats is not affected by in utero and lactational TCDD exposure even though daily
sperm production is reduced (Mably et al., 1992c). Because Sertoli cells provide spermatogenic
cells with functional and structural support (Bardin et al., 1988) and the upper limit of daily
sperm production in adult rats is directly dependent on the number of Sertoli cells per testis
(Russell and Peterson, 1984), three possible mechanisms for the decrease in daily sperm
production in Holtzman rats may be involved. TCDD could increase the degeneration of cells
intermediate in development between leptotene spermatocytes and terminal-stage spermatids (the
cell type used to calculate daily sperm production); decrease postleptotene spermatocyte cell
division (meiosis); and/or decrease the number of Sertoli cells per testis (Orth et al., 1988). In a
histological study of the testis in Holtzman rats exposed to TCDD in utero and via lactation, it
was found that spermatogenesis was qualitatively normal; there was no indication of a gross
histological lesion nor any evidence of germ cell degeneration (Shinomiya et al., 1994).
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In Long Evans rats exposed in utero and via lactation to TCDD, testicular histopathology
was not typically affected (Gray et al., 1995a). That is, there was generally no histological
evidence for degeneration of the seminiferous tubules, Sertoli cell abnormalities, or retained
spermatids in progeny exposed in utero and via lactation to TCDD (Gray et al., 1997). On
occasion, however, both Long Evans rat progeny and Syrian hamster progeny of dams exposed to
TCDD exhibited severe atrophy of the seminiferous tubules that was associated with a marked
loss of spermatogenic activity (Gray et al., 1997).
In Wistar rats that were exposed to TCDD using a multiple dosing regimen during
mating, pregnancy, and lactation, the decrease in daily sperm production observed at the two
lowest TCDD exposure levels was not associated with any testicular pathology (Faqi et al.,
1998a). However, at the highest level of in utero and lactational TCDD exposure some
seminiferous tubuli showed pyknotic nuclei and cell debris in the lumen (Faqi et al., 1998a). In
utero and lactational exposure to 1.0 ng/kg of TCDD on gestational day 15 did not cause
abnormal testicular histology in 62-day-old Sprague-Dawley rats (Wilker et al., 1996). The
number of Sertoli cells per testis and number of Sertoli cells per gram testis was not affected by
TCDD. However, the ratio of the number of elongated spermatids in testicular homogenates to
the number of Sertoli cells per testis was significantly lower in TCDD-exposed progeny (Wilker
etal., 1996).
5.2.3.2.10.3. Epididymal sperm numbers. The epididymis has two functions: in the caput and
corpus epididymis, proximal regions of the organ, spermatozoa mature gaining the capacity for
motility and fertility, whereas in the cauda epididymis, the distal region, mature sperm are stored
before ejaculation (Robaire and Hermo, 1989). Mably et al. (1991, 1992c) found that motility
and morphology of sperm taken from the cauda epididymis on postnatal days 63 and 120 were
unaffected by perinatal TCDD exposure. Exposure of Wistar rats to TCDD in a multiple-dosing
regimen during mating, pregnancy, and lactation caused small but significant increases in the
percentage of abnormal sperm in 170-day-old male offspring (Faqi et al., 1998a). However, in
Wistar rats exposed in utero and via lactation to a single dose of 3,3',4,4'-TCB (PCB 77) or
3,3',4,4',5-PCB (PCB 126) on gestational day 15, no effect on the percentage of abnormal sperm
was found in 65- or 140-day-old male progeny (Faqi et al., 1998b).
It bears repeating at this time that the most sensitive, robust, persistent, and reproducible
effect of in utero and lactational exposure to TCDD on the male reproductive system of
laboratory rodents is a decrease in cauda epididymal sperm numbers. This effect has been
demonstrated for Holtzman, Long Evans, Sprague-Dawley, and Wistar rats as well as ICR mice
and Syrian hamsters (Mably et al., 1992c; Gray et al., 1995a, 1997; Wilker et al., 1996; Theobald
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and Peterson, 1997; Faqi et al., 1998a). The lowest dose of TCDD to produce this effect is 0.064
ng/kg administered on gestational day 15 to Holtzman rats (Mably et al., 1992c).
Following in utero and lactational exposure to a single dose of TCDD there is a graded
decline in sperm numbers as they travel from the testis through the caput, corpus, and cauda
epididymis and are ejaculated (Gray et al., 1995a, 1997; Sommer et al., 1996). While these
results suggest that sperm transit rate through the epididymis should be increased by in utero and
lactational TCDD exposure, three studies that have determined epididymal sperm transit rates
have reached different conclusions. The most rigorous examination of this endpoint was the
study by Sommer et al. (1996). They found that in Holtzman rats administered TCDD on
gestational day 15 there was no effect on epididymal sperm transit rate. This rules out the
possibilities of sperm loss via spontaneous ejaculation or abnormal introduction of sperm into
urine (Sommer et al., 1996). In contrast, in utero and lactational exposure of Sprague-Dawley
rats to TCDD on gestational day 15 was reported to increase in epididymal sperm transit rate
(Wilker et al., 1996), and using a multiple-dosing regimen for in utero and lactational TCDD
exposure in Wistar rats, a decrease in epididymal sperm transit rate was found (Faqi et al.,
1998a).
In association with the reduction in cauda epididymal sperm numbers, there is a distinct
tendency for an increased incidence of a chronic inflammatory reaction in the epididymis of Long
Evans rats exposed in utero and via lactation to 3,3',4,4',5,5'-HCB (PCB 169) (Gray et al., 1999)
and in Holtzman rats exposed to TCDD (Sommer and Peterson, unpublished results).
Furthermore, the decrease in cauda epididymal sperm numbers in adult hamsters following in
utero and lactational exposure to TCDD is associated with an increased incidence of sperm
granulomas in epididymides and/or testes. This lesion was characterized by a nodular
accumulation of fibrous connective tissue and mixed inflammatory cells surrounding
degenerating sperm in the interstitium of the epididymides and testes. Taken together, these
findings suggest that the reduction in cauda epididymal sperm numbers caused by in utero and
lactational exposure to TCDD in the hamster are due in part to sperm resorption from the
epididymis. Furthermore, since resorption of sperm in the epididymis is often associated with
the accumulation of inflammatory cells in the organ, sperm resorption from the epididymis might
also be occurring in postpubertal Holtzman rats exposed to TCDD in utero and via lactation
(Sommer and Peterson, unpublished results).
5.2.3.2.10.4. Ejaculated sperm numbers. Although it has been assessed in only two rat strains,
Long Evans and Holtzman, and in the Syrian hamster, the effect on the male reproductive system
that is detected at the lowest dose of TCDD administered during pregnancy is that which causes a
decrease in ejaculated sperm numbers. The lowest single dose of TCDD administered during
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pregnancy that causes this effect is 0.050 ng/kg administered on gestational day 15 in the Long
Evans rat with ejaculated sperm numbers assessed in adulthood (Gray et al., 1997). In addition
to the reduction in total number of sperm ejaculated during the mating period there was also a
reduction in the number of sperm in copulatory plugs. The small reduction in sperm produced by
the testis of Long Evans rats, exposed perinatally to TCDD, was not sufficient to account for the
larger reductions in cauda epididymal sperm numbers and ejaculated sperm numbers. Finally,
there was no reduction in the number of copulatory plugs produced by TCDD-exposed males,
indicating no interference with copulation (Gray et al., 1995a).
5.2.3.2.11. Reproductive capability. To assess reproductive capability, male Holtzman rats born
to dams given TCDD (0.064, 0.16, 0.40, or 1.0 ng/kg) or vehicle on day 15 of gestation were
mated with control virgin females when the males were 70 and 120 days of age (Mably et al.,
1991, 1992c). The fertility index of the males is defined as number of males impregnating
females divided by number of males mated. The two highest maternal TCDD doses decreased
the fertility index of the male offspring by 11% and 22%, respectively. However, these decreases
were not statistically significant, and at lower doses the fertility index was not reduced. The
gestation index, defined as the percentage of control dams mated with TCDD-exposed males that
delivered at least one live offspring, was also not affected by in utero and lactational TCDD
exposure.
With respect to progeny of these matings, the results of the above study (Mably et al.,
1992c) and more recent studies are somewhat inconsistent possibly due to differences in the rat
strain used. Gray et al. (1995a) reported in the Long Evans that there were fewer implants in
females mated to gestational day 15 TCDD-treated male offspring. On the other hand, all male
Wistar rat offspring exposed during mating, pregnancy, and lactation to TCDD were able to
impregnate unexposed female rats and to produce viable fetuses (Faqi et al., 1998a). For these
TCDD-exposed male rat progeny, their mating, pregnancy, and fertility indices were similar to
control. Also, the number of implantations, resorption rate, number of viable and dead fetuses,
and sex ratio of the progeny were similar among control and TCDD treatment groups (Faqi et al.,
1998a). Similar to the results in Wistar rats, Mably et al. (1992c) reported that there was no
effect on litter size, live birth index, or 21-day survival index for male Holtzman rat offspring
that were mated to unexposed females.
Effects of in utero and lactational exposure to non-ortho-substituted PCB congeners has
also been investigated on the reproductive capability of male rat progeny when they reach sexual
maturity. Exposure to 1.8 mg/kg of 3,3',4,4',5,5'-HCB (PCB 169) on gestational day 1 reduced
the fertility of the PCB 169-exposed male Wistar rat progeny when they were mated with
unexposed control females (Smits-van Prooije et al., 1993). In contrast, treatment with either
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3,3',4,4'-TCB (PCB 77) or 3,3',4,4',5-PCB (PCB 126) on gestational day 15 had no effect on the
outcomes of matings between PCB 77- or PCB 126-exposed male Wistar rat progeny mated with
unexposed females for the following endpoints: implantations per litter, viable fetuses per litter,
or percentage resorptions (Faqi et al., 1998b).
Since rats produce and ejaculate 10 times more sperm than is necessary for normal
fertility and litter size (Aafjes et al., 1980; Amann, 1982), the absence of a reduction in fertility
of male rats exposed perinatally to TCDD is not inconsistent with the substantial reductions in
testicular spermatogenesis and epididymal sperm reserves. In contrast, reproductive efficiency in
human males is very low, the number of sperm per ejaculate being close to that required for
fertility (Working, 1988). Thus, measures of fertility using rats are not appropriate for low-dose
extrapolation in humans (Meistrich, 1992). A percentage reduction in daily sperm production in
humans, similar in magnitude to that observed in rats (Mably et al., 1991, 1992c), could be
associated with reduced fertility in men.
5.2.3.2.12. Possible mechanisms for effects on male reproductive tract growth and
development. The mechanisms by which TCDD and related compounds impair male
reproductive tract development are not known. Several possibilities exist and since these have
been reviewed recently (Roman and Peterson, 1998b), only an overview will be presented here.
It is generally assumed that most effects of in utero and lactational exposure to TCDD on
development of the male reproductive system are Ah receptor mediated. While this is probable,
it has not yet been proven conclusively for these endpoints. A comparison of male reproductive
endpoints in wild-type and Ah receptor knockout mice, following in utero and lactational
exposure to TCDD, is needed to provide this insight. Such studies in Ah receptor wild-type and
Ah receptor knockout mice have shown that the decrease in prostate and seminal vesicle weight
caused by perinatal exposure to TCDD is dependent on the Ah receptor (Lin et al., 2000).
However, this kind of information is not available for any of the other male reproductive
endpoints. Three possible Ah receptor-dependent mechanisms by which in utero and lactational
exposure to TCDD could impair male reproductive development are as follows. First, the
liganded Ah receptor could dimerize with ARNT and this complex could then bind to dioxin-
responsive elements in the 5' regulatory regions of genes and alter their transcription in male
reproductive tissues during the endocrine phase of fetal and neonatal sexual differentiation.
Second, the liganded Ah receptor could compete with other PAS proteins for dimerization with
ARNT at critical periods of male repoductive tract development and downregulate genes
dependent on ARNT and an alternative dimerization partner for transcription. Third, the ligand-
activated Ah receptor could be rapidly depleted from cells, thereby decreasing the pool of Ah
receptor available for binding of an endogenous ligand or activating the transcription of genes
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important in normal male reproductive development at the cellular level. Treatment of adult rats
with TCDD is known to downregulate Ah receptor expression in several male rat reproductive
tract tissues (Roman et al., 1998c). However, it was recently found that this does not occur in the
developing rat prostate when 1.0 ng/kg of TCDD is administered on gestational day 15, making
this latter Ah receptor mechanism seem less likely (Sommer et al., 1999).
In utero and lactational exposure to TCDD produces a novel constellation of growth and
developmental alterations in the male rat reproductive system. These consist of decreases in
accessory sex organ weights, delays in preputial separation, decreases in daily sperm production
by the testis and sperm storage in the cauda epididymis, decreases in ejaculated sperm numbers,
and partial demasculization of sexual behavior and partial feminization of sexual behavior and
the regulation of LH secretion. Taken together, these effects are consistent with decreased
testicular androgen production and/or circulating androgen concentrations. But these parameters
have not been shown to be affected perinatally or at later times by perinatal exposure to TCDD
(Mably et al., 1992a; Chen et al., 1993; Roman et al., 1995; Gray et al., 1995a). Nevertheless,
the possibility remains that the androgenic deficiency-like syndrome caused by developmental
exposure to TCDD could be the result of interference with androgen action at the level of the
androgen receptor. While no effect of in utero and lactational exposure to TCDD on androgen
receptor concentrations in the caput epididymis, cauda epididymis, ventral prostate, or seminal
vesicle was found in 336- to 339-day-old Long Evans rats (Gray et al., 1995a), alterations in the
spatial distribution of the androgen receptor were found in the ventral prostate of infantile and
weanling Holtzman rats exposed perinatally to TCDD (Roman et al., 1998a). This latter effect of
TCDD may explain why the ventral prostate exhibits decreased growth and abnormal
differentiation in the presence of normal circulating levels of androgens (Roman et al., 1998a).
Thus, it is possible that TCDD acts at or beyond the androgen receptor to interfere with prostate
development.
Just because development of androgen-dependent tissues such as the prostate, seminal
vesicle, epididymis, and testis are affected by in utero and lactational TCDD exposure does not
necessarily mean that an antiandrogenic action of TCDD is the only mechanism by which TCDD
could disrupt their development (Roman and Peterson, 1998b; Gray et al., 1999). Impaired
growth and development of these organs could arise by TCDD acting on multiple components of
the endocrine axis to alter concentrations of other hormones, growth factors and/or their
receptors. Epidermal growth factor, prolactin, thyroid hormones, and growth hormones can
influence development of certain of these organs, and their signaling pathways may be modulated
by perinatal exposure to TCDD (Gray et al., 1999). Also, an important finding is that the
spectrum of TCDD's effects on male reproductive system development and function does not
quite match that which is produced by perinatal exposure to known antiandrogens, 5«-reductase
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inhibitors, or antiestrogens (Roman and Peterson, 1998b; Gray et al., 1999). Therefore, it is
possible that TCDD modulates cell proliferation and differentiation in these tissues by interfering
with nonhormonal aspects of these processes. For example, prostatic budding and ductal
morphogenesis are of course androgen-dependent but they also involve important mesenchymal-
epithelial interactions occurring downstream of androgen receptor action that might be
modulated by TCDD (Roman and Peterson, 1998b).
5.2.3.2.13. Sexual differentiation of the CNS. Sexual differentiation of the CNS is dependent
on the presence of androgens during early development. In rats, the critical period of sexual
differentiation extends from late fetal life through the first week of postnatal life (MacLusky and
Naftolin, 1981). In the absence of adequate circulating levels of testicular androgen during this
time, adult rats display high levels of feminine sexual behavior (e.g., lordosis), low levels of
masculine sexual behavior, and a cyclic (i.e., feminine) pattern of LH secretion (Gorski, 1974;
Barraclough, 1980). In contrast, perinatal androgen exposure of rats will result in the
masculinization of sexually dimorphic neural parameters, including reproductive behaviors,
regulation of LH secretion, and several morphological indices (Raisman and Field, 1973; Gorski
et al., 1978). The mechanism by which androgens cause sexual differentiation of the CNS is not
completely understood. In the rat, it appears that 1713-estradiol, formed by the aromatization of
testosterone within the CNS, is one of the principal active steroids responsible for mediating
sexual differentiation (McEwen, 1978); however, androgens also are involved.
5.2.3.2.13.1. Demasculinization of sexual behavior. Mably et al. (1991, 1992b) assessed
sexually dimorphic functions in male rats born to dams given graded doses of TCDD or vehicle
on day 15 of gestation. Masculine sexual behavior was assessed in male offspring at 60, 75, and
115 days of age by placing a male rat in a cage with a receptive control female and observing the
first ejaculatory series and subsequent postejaculatory interval. The number of mounts and
intromissions (mounts with vaginal penetration) before ejaculation was increased by a maternal
TCDD dose of 1.0 ng/kg. The same males exhibited twelvefold and elevenfold increases in
mount and intromission latencies, respectively, and a twofold increase in ejaculation latency. All
latency effects were dose related and significant at a maternal TCDD dose as low as 0.064 ng/kg
(intromission latency) and 0.16 ng/kg (mount and ejaculation latencies). Copulatory rates
(number of mounts + intromissions/time from first mount to ejaculation) were decreased to less
than 43% of the control rate (Mably et al., 1992b). This effect on copulatory rates was dose
related, and a statistically significant effect was observed at maternal TCDD doses as low as 0.16
ng/kg. Postejaculatory intervals were increased 35% above the control interval, and a statistically
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significant effect was observed at maternal doses of TCDD as low as 0.40 ng/kg. Collectively,
these results demonstrate that perinatal TCDD exposure demasculinizes sexual behavior.
Because perinatal exposure to a maternal TCDD dose of 1.0 ng/kg has no effect on the
open field locomotor activity of adult male rats (Schantz et al., 1991), the increased mount,
intromission, and ejaculation latencies in Holtzman rats (Mably et al., 1992b) appear to be
specific for these masculine sexual behaviors, not secondary to a depressant effect of TCDD on
motor activity. The reported postpubertal plasma testosterone and DHT concentrations in litter
mates of the rats evaluated for masculine sexual behavior were as low as 56% and 62%,
respectively, of controls (Mably et al., 1991, 1992a). However, plasma testosterone
concentrations that were only 33% of controls are still sufficient to masculinize sexual behavior
of adult male rats (Demassa et al., 1977). Therefore, the modest reductions in adult plasma
androgen concentrations following perinatal TCDD exposure were not of sufficient magnitude to
demasculinize sexual behavior.
Reductions in perinatal androgenic stimulation can inhibit penile development and
subsequent sensitivity to sexual stimulation in adulthood (Nadler, 1969; Sodersten and Hansen,
1978). Therefore, the demasculinization of sexual behavior could, to some extent, be secondary
to decreased androgen-dependent penile development. However, perinatal TCDD exposure had
no effect on gross appearance of the rat penis. In addition, TCDD-exposed males exhibited
deficits in such masculine sexual behaviors as mount latency and postejaculatory interval, which
do not depend on stimulation of the penis for expression (Sachs and Barfeld, 1976). Thus,
although some effects of TCDD, such as decreased copulatory rate and prolonged latency until
ejaculation, could be due to reduced sensitivity of the penis to sexual stimulation, the twelvefold
increase in mount latency and increase in postejaculatory interval cannot be explained by this
mechanism.
The effect of in utero and lactational exposure to 0.7 ng/kg of TCDD on gestational day
15 on the expression of masculine sexual behavior was assessed in male Holtzman rats at 61-65
and 75-79 days of age (Bjerke et al., 1994). A partial demasculinization of sexual behavior was
evidenced by increased intromission latencies and a greater number of intromissions prior to
ejaculation. Overall, the effects were more similar to those observed in Holtzman rats exposed
on gestational day 15 to 0.4 |ig/kg of TCDD (Mably et al., 1992b).
The effect of in utero and lactational exposure to 1.0 ng/kg of TCDD administered on
gestational day 8 or 15 on masculine sexual behavior was assessed in Long Evans rats (Gray et
al., 1998a). The expression of masculine sexual behaviors was altered to a greater extent in rats
exposed to TCDD on gestational day 15 than gestational day 8. In males exposed to TCDD on
gestational day 15, partial demasculinization of sexual behavior was evidenced by increases in
total number of mounts prior to ejaculation, number of mounts with intromissions prior to
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ejaculation, number of mounts without intromissions prior to ejaculation, and latency prior to
ejaculation (Gray et al., 1995a). While the same profile of results was obtained in males exposed
to TCDD on gestational day 8, the effects were not as great and were not statistically significant
(Grayetal., 1995a).
Masculine sexual behavior was also assessed in male Wistar rats exposed in utero and via
lactation to TCDD administered to dams during mating, pregnancy, and lactation (Faqi et al.,
1998a). Mount latency and intromission latency were increased at two of three TCDD exposure
levels. However, ejaculation latency, number of mounts with intromissions prior to ejaculation,
and intromission frequency were not affected. Of the five endpoints of masculine sexual
behavior assessed in male Wistar rat progeny that were exposed to 10 ng/kg of 3,3',4,4',5-PCB
(PCB 126) on gestational day 15, only one endpoint was affected, the number of mounts with
intromissions prior to ejaculation, and it was increased (Faqi et al., 1998b).
Taken together, these results demonstrate in three different rat strains, Holtzman, Long
Evans, and Wistar, that in utero and lactational exposure to TCDD affects some, but not all,
endpoints of masculine sexual behavior. Therefore, TCDD only partially demasculinizes sexual
behavior. The response is not as robust as other endpoints, and the degree to which TCDD
affects the expression of masculine sexual behavior depends on the rat strain, Ah receptor
agonist, and dose administered. It is notable that male hamster progeny do not exhibit
demasculinized sexual behavior following perinatal exposure to TCDD, making it difficult to
extrapolate this response with certainty to other species.
5.2.3.2.13.2. Feminization of sexual behavior. Mably et al. (1991, 1992b) determined if the
potential of adult male rats to display feminine sexual behavior was altered by perinatal TCDD
exposure. Male offspring of dams treated on day 15 of gestation with various doses of TCDD up
to 1 ng/kg or vehicle were castrated at ~ 120 days of age, and beginning at ~ 160 days of age were
injected weekly for 3 weeks with 1713-estradiol benzoate, followed 42 hours later by
progesterone. Four to six hours after the progesterone injection at weeks 2 and 3, the male was
placed in a cage with a sexually excited control stud male. The frequency of lordosis in response
to being mounted by the stud male was increased from 18% (control) to 54% by the highest
maternal TCDD dose, 1.0 ng/kg. Lordosis intensity, scored after Hardy and DeBold (1972) as 1
for light lordosis, 2 for moderate lordosis, and 3 for a full spinal dorsoflexion, was increased in
male rats by perinatal TCDD exposure. Both effects on lordosis behavior in males were dose
related and significant at maternal TCDD doses as low as 0.16 ng/kg (increased lordotic
frequency) and 0.40 ng/kg (increased lordotic intensity). Together, they indicate a feminization
of sexual behavior in these animals. Although severe undernutrition from 5 to 45 days after birth
potentiates the display of lordosis behavior in adult male rats (Forsberg et al., 1985), the
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increased frequency of lordotic behavior was seen at a maternal TCDD dose of 0.16 ng/kg, which
had no effect on feed intake or body weight. It was concluded that perinatal TCDD exposure
feminizes sexual behavior in adult male rats independent of undernutrition.
Defeminization of sexual behavior in male rats occurs during the first week or so after
birth (Goy and McEwen, 1980; Perakis and Stylianopoulou, 1985). Therefore it was
hypothesized that if TCDD interferes with defeminization of sexual behavior that lactational
exposure to TCDD would be more important than in utero exposure. A subsequent study by
Bjerke and Peterson (1994) is consistent with this hypothesis. In their cross-fostering study on
the effects of in utero versus lactational TCDD exposure in the Holtzman rat, it was found, as
predicted, that feminization of male sexual behavior required lactational exposure. When
exposure was restricted to the in utero period, the male offspring did not display a significant
increase in lordotic behavior, whereas such behavior was increased following exposure during
the lactational period, either alone or in combination with in utero exposure. Also, in a separate
study it was shown following in utero and lactational exposure to 1.0 ng/kg of TCDD
administered on gestational day 15 that feminine sexual behavior of male rats was partially
feminized as indicated by an increase in lordosis quotient (Bjerke et al., 1994b).
In contrast to the above three studies that show in utero and lactational TCDD exposure
increases the expression of feminine sexual behavior in male Holtzman rats (Mably et al., 1992b;
Bjerke and Peterson, 1994; Bjerke et al., 1994b), this effect was not observed in male Long
Evans rats (Gray et al., 1995a). They showed no significant increase in lordotic behavior as
adults following in utero and lactational exposure to 1.0 ng/kg of TCDD on gestational day 8 or
15 (Gray et al., 1995a). This may be due to a rat strain difference in susceptibility to this
endpoint.
5.2.3.2.13.3. Feminization of LH secretion regulation. The effect of perinatal TCDD exposure
on regulation of LH secretion by ovarian steroids was determined in male offspring at -270 days
of age. There is normally a distinct sexual dimorphism to this response. In rats castrated as
adults, estrogen-primed females greatly increase their plasma LH concentrations when injected
with progesterone, whereas similarly treated males fail to respond (Taleisnik et al., 1969).
Progesterone had little effect on plasma LH concentrations in estrogen-primed control males, but
significant increases were seen in males exposed to maternal TCDD doses as low as 0.40 ng/kg.
Thus, perinatal TCDD exposure increases pituitary and/or hypothalamic responsiveness of male
rats to ovarian steroids in adulthood, indicating that regulation of LH secretion is permanently
feminized.
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5.2.3.2.13.4. Estrogen receptor concentrations in the brain and volumes of sexually dimorphic
brain nuclei. In the Holtzman rat, in utero and lactational exposure to TCDD partially
demasculinizes and feminizes sexual behavior in adult male rats, possibly by causing incomplete
sexual differentiation of the CNS. To determine if TCDD exposure affects other aspects of
sexual differentiation of the CNS in this rat strain, the effects of perinatal exposure to 0.7 ng/kg
of TCDD administered on gestational day 15 on estrogen receptor binding in specific brain nuclei
was examined along with effects on the volumes of brain nuclei that are dependent on hormone
stimulation during the period of CNS sexual differentiation (Bjerke et al., 1994b). It was found
that estrogen receptor concentrations in three brain nuclei—the medial preoptic nucleus (MPO),
the ventrolateral aspect of the ventromedial nucleus, and the peri ventricular preoptic area—were
higher in control females than males, but in utero and lactational exposure to TCDD had no
effect on estrogen receptor concentrations in these sexually dimorphic brain nuclei (Bjerke and
Peterson, 1994). It also had no effect on estrogen receptor concentrations in other brain nuclei
where there was not a sex difference in estrogen receptor concentrations.
The volumes of sexually dimorphic brain nuclei were also not affected by in utero and
lactational exposure to TCDD in Holtzman rats. In control rats the volume of the sexually
dimorphic nucleus of the preoptic area (SDN-POA) is greater in males, whereas the MPO is
greater in females. Perinatal TCDD exposure had no effect on the volume of either nucleus in
male and female Holtzman rat offspring in adulthood (Bjerke and Peterson, 1994). Thus, in
utero and lactational TCDD exposure is capable of partially demasculinizing and partially
feminizing sexual behavior of Holtzman rat progeny (Mably et al., 1992b; Bjerke and Peterson,
1994; Bjerke et al., 1994b), but it is not associated with an effect on sexual differentiation of the
estrogen receptor system in the brain or the volume of sexually dimorphic brain nuclei (Bjerke et
al., 1994b; MacLusky et al., 1998).
5.2.3.2.13.5. Comparison to other Ah receptor-mediated responses. The induction of hepatic
cytochrome P-4501 Al and its associated EROD activity are extremely sensitive Ah receptor-
mediated responses to TCDD exposure. Yet in 120-day-old male Holtzman rats that had been
exposed to TCDD perinatally, alterations in sexual behavior, LH secretion, sex organ weights,
and sperm numbers were observed when induction of hepatic EROD activity could no longer be
detected (Mably et al., 1991, 1992a,b,c). These results suggest that TCDD affects sexual
behavior, gonadotrophic function, and sperm counts when virtually no TCDD remains in the
body. Therefore, the partial demasculinization and feminization of sexual behavior, partial
feminization of LH secretion, and reduced cauda epididymal sperm numbers caused by in utero
and lactational exposure to TCDD have the potential to be irreversible effects of transient
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exposure to TCDD during the endocrine phase of fetal and neonatal sexual differentiation (Mably
etal., 1992b,c).
5.2.3.2.13.6. Possible mechanisms for effects on sexual behavior. The most plausible
explanation for the demasculinization of sexual behavior and feminization of sexual behavior
and LH secretion is that perinatal exposure to TCDD impairs sexual differentiation of the CNS.
Neither undernutrition, altered locomotor activity, reduced sensitivity of the penis to sexual
stimulation, nor modest reductions in adult plasma androgen concentrations of the male offspring
can account for this effect (Mably et al., 1992b). On the other hand, exposure of the developing
brain to testosterone, conversion of testosterone into 1713-estradiol within the brain, and events
initiated by the binding of 1713-estradiol to its receptor are all critical for sexual differentiation of
the CNS and have the potential to be modulated by TCDD. If TCDD interferes with any of these
processes during late gestation and/or early neonatal life, it could irreversibly demasculinize and
feminize sexual behavior (Hart, 1972; McEwen et al., 1977; Whalen and Olson, 1981) and
feminize the regulation of LH secretion (Gogan et al., 1980, 1981) in male rats in adulthood.
However, results that argue against this hypothesis are that in utero and lactational exposure to
TCDD does not alter either estrogen receptor concentrations in various brain nuclei or volumes
of sexually differentiated brain nuclei of male and female Holtzman rat progeny at doses that
affect the expression of masculine and feminine sexual behavior (Bjerke et al., 1994). Also,
while in utero and/or lactational exposure to TCDD may cause similar effects on sexual behavior
in other animal species, including nonhuman primates (Pomerantz et al., 1986; Thornton and
Goy, 1986; Goy et al., 1988), in which sexual differentiation is under androgenic control; this
was not able to be demonstrated for male hamster progeny exposed in utero and via lactation to
TCDD (Gray et al., 1995a). In humans, there is evidence that social factors account for much of
the variation in sexually dimorphic behavior; there is also evidence that prenatal androgenization
influences both the sexual differentiation of such behavior and brain hypothalamic structure
(Erhardt and Meyer-Bahlburg, 1981; Hines, 1982; LeVay, 1991).
5.2.3.2.14. Cross-species comparisons. Gray et al. (1995a) demonstrated that many of the
results observed on male rat reproductive system development following in utero and lactational
TCDD exposure are also observed in male hamster offspring exposed perinatally to TCDD.
Pregnant hamsters were exposed to 2 ng/kg of TCDD on gestational day 11. This exposure level
caused no maternal toxicity or decrease in viability of the offspring. The number of litters in the
study was small (three dams/treatment group). Nevertheless, growth retardation, reduced adrenal
and brain weight at postnatal day 136 to 140, delayed eye opening, and reduced sperm in the
epididymis and ejaculate were observed in the male hamster offspring exposed to TCDD.
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Anogenital distance and testicular sperm number were not affected. As regards androgen-
dependent organ development, age at preputial separation was delayed and seminal vesicle
weight was reduced, but flank gland development was not affected. In contrast to the rat, none of
the masculine sexual behavior parameters measured in male hamster offspring appeared to be
altered. At sacrifice (postnatal day 136 to 140), there was an increase in sperm granulomas and
in the severity of kidney lesions in the TCDD-exposed male hamster progeny. While the
findings of this study show some species specificity in the hamster's response to in utero and
lactational TCDD exposure, they generally support the above findings in male rat offspring.
Male ICR mice, CD-I derived, exposed in utero and via lactation to TCDD are also
affected by developmental toxicity to the reproductive system (Theobald et al., 1997). The doses
of TCDD used in this study did not alter maternal or offspring body weights and were not
associated with prenatal or postnatal mortality. However, ventral prostate weight assessed on
postnatal days 44, 65, and 114 was significantly decreased in male offspring after exposure to 15
[ig TCDD/kg administered on gestational day 14. Coagulating gland weight was also reduced on
the same postnatal days, but this lobe of the prostate was not as sensitive as the ventral prostate
because larger doses of TCDD were required to produce the effect. The dorsal prostate in the
ICR mouse was not affected even at the largest maternal TCDD dose (60 ng/kg). There was no
statistically significant decrease in daily sperm production after TCDD exposure, but whole
epididymal sperm numbers were decreased at maternal doses of 30 and 60 \ig TCDD/kg. Thus,
ventral prostate weight in this strain of mouse was a more sensitive endpoint of TCDD exposure
than the reduction in epididymal sperm number. The C57BL/6 mouse is more sensitive to the
developmental effects of TCDD on accessory sex organ growth (Lin et al., 2000). Ventral
prostate, dorsolateral prostate, and coagulating gland weight in mouse progeny exposed to 5
ng/kg of TCDD on gestational day 13 were all significantly reduced when assessed on postnatal
days 21 to 90. Histological examination revealed an impairment of ductal development in the
ventral prostate on postnatal day 21, and only 50% of the luminal epithelial cells in ventral
prostate from TCDD-exposed mice expressed androgen receptors, compared with 100% of the
epithelial cells in tissue from vehicle-exposed mice. Urogenital sinus epithelial complexes from
male offspring exposed to a single maternal dose of 5 ng/kg of TCDD administered on
gestational day 13 were examined by scanning electron microscopy, and complete agenesis of
ventral prostate buds on gestation day 18 was found. In Ah receptor knockout mice obtained
from Bradfield (Schmidt et al., 1996) and backcrossed into C57BL/6, there was no reduction in
seminal vesicle or prostate weight due to in utero and lactational TCDD exposure, and prostatic
bud formation occurred normally by gestational day 18. Therefore, these effects on the prostate
and seminal vesicle of perinatal TCDD exposure in the mouse appear to be Ah receptor
mediated.
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Taken together, these results indicate that there is species specificity in sensitivity to
certain effects of in utero and lactational TCDD exposure on male reproductive system
development. In general, however, the results reported in the hamster and mouse are consistent
with those reported in male rat offspring. There is evidence that at least some of these effects are
Ah receptor mediated, namely those that occur in the prostate and seminal vesicle. Because the
fetal human prostate expresses the Ah receptor (Kashani et al., 1998), it is plausible that the
human prostate could be affected by sufficient exposure to TCDD and TCDD-like Ah receptor
agonists during development.
5.2.3.3. Female Reproductive System
Effects of in utero and lactational exposure to TCDD on female reproductive system
development has not been investigated for as long as the male reproductive system. However,
the results from these studies clearly show that the effects of gestational exposure to TCDD is not
limited to the male offspring.
5.2.3.3.1. Vaginal thread malformation. One of the most sensitive effects of in utero and
lactational exposure to TCDD on the female reproductive system is the occurrence of a vaginal
thread malformation. It has been detected in two strains of rats, Long Evans and Holtzman, but
not in ICR mice or Syrian hamsters (Gray et al., 1995; Flaws et al., 1997; Theobald and Peterson,
1997; Gray et al., 1997b; Wolf et al., 1999; Dienhart et al. 2000). The lowest dose of TCDD to
significantly increase the incidence of vaginal threads in female progeny is 0.20 ng/kg
administered on gestational day 15 to Long Evans rat dams (Gray et al., 1997b). This dose was
effective in increasing the incidence of the malformation when expressed either as percentage of
females with a temporary or permanent vaginal thread or as percentage of females with a
permanent vaginal thread (Gray et al., 1997b). In utero and lactational exposure to other Ah
receptor agonists are also capable of producing this same type of malformation in Long Evans
rats. 3,3',4,4',5,5'-HCB (PCB 169) administered on gestational day 8 at a dose of 1.8 mg/kg
caused a significant increase in the percentage of female progeny with vaginal threads (Gray et
al., 1999).
The original study to report an increase in vaginal thread malformation following in utero
and lactational exposure to TCDD was that of Gray and Ostby (1995). They reported on the
effects in Long Evans rats of a single maternal exposure to 1 ng/kg of TCDD on either
gestational day 8 or 15 on postnatal vaginal development in female offspring. Both exposures
were associated with incomplete (vaginal thread) or absent vaginal opening, and a smaller
vaginal orifice. Similar malformations involving the vaginal canal have been reported for
Holtzman rats (Gray and Ostby, 1995; Flaws et al., 1997). The incidence of vaginal threads was
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greater in the Long Evans rat when TCDD was administered on gestational day 15 compared
with gestational day 8 (Gray and Ostby, 1995). On the other hand, in the Holtzman rat the
incidence of vaginal threads was essentially the same when TCDD was administered on
gestational days 11, 15, or 18 (Flaws et al., 1997).
The vaginal thread is manifested in pubertal rats as a persistent thread of mesenchymal
tissue surrounded by keratinized epithelium that partially occludes the vaginal opening (Flaws et
al., 1997). However, it was not known how early in development this abnormality could be
detected. Vaginal threads in TCDD-exposed Holtzman rat offspring were identified in
histological sections of the developing vagina in 2-day-old pups, demonstrating that this
malformation was actually present at birth (Flaws et al., 1997). This suggested that prenatal
exposure to TCDD should be sufficient to cause the vaginal thread malformation. This was
confirmed in a cross-fostering study in Long Evans rats (Gray et al., 1997b) where female
progeny that received prenatal TCDD exposure developed vaginal threads but those that received
only postnatal exposure to TCDD did not. The earliest time during fetal development that
morphologic signs of this malformation were present was gestational day 19 in Holtzman rats
(Dienhart et al., 2000) and gestational day 18 in Long Evans rats (Hurst et al., 1999). At this
time there was an increased thickness of mesenchymal tissue between the caudal Mullerian ducts.
The presence of this mesenchymal tissue caused the Mullerian ducts to fail to fuse, a process that
is normally completed prior to birth. TCDD was also found to block regression of the Wolffian
ducts, which contributed to the changes in morphology of the vagina (Dienhart et al., 2000).
Thus, prenatal TCDD exposure leads to altered development of the rat vagina as early as
gestational day 18 or 19 depending on the strain, 3 or 4 days after treatment of the dams. This
effect is produced by TCDD interfering with two critical morphogenetic events involved in the
formation of the female reproductive tract, namely, regression of the Wolffian ducts and fusion
of the Mullerian ducts (Hurst et al., 1999; Dienhart et al., 2000).
The mechanisms by which TCDD produces these effects at the molecular level are
unknown. TCDD modulates cellular responses to both hormones and growth factors including
androgens, estrogens, EGF, and TGF (Abbott, 1997; Birnbaum, 1998; Roman and Peterson,
1998b). Developmental processes such as the timing of morphogenetic signals and events like
cell proliferation, cell movement, receptor expression, apoptosis, and terminal differentiation are
tightly regulated by these and other hormones and growth factors. Thus, TCDD modulation or
interference with the activity of hormones and/or growth factors in the female rat reproductive
tract may play a role in causing the vaginal thread malformation (Dienhart et al., 2000).
5.2.3.3.2. Cleft phallus and mild hypospadias. Other morphological effects of in utero and
lactational exposure to TCDD on the female reproductive tract are cleft phallus and mild
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hypospadias. The hypospadias are considered mild because the urethral opening was always
separate from the vaginal opening. These two types of malformations have been observed in rats
and hamsters, but not in mice (Gray and Ostby, 1995; Flaws et al., 1997; Theobald and Peterson,
1997; Gray et al., 1997b; Wolf et al., 1999; Dienhart et al., 2000). The lowest dose of TCDD to
significantly increase the incidence of cleft phallus and mild hypospadias is 0.80 and 0.20 ng/kg
of TCDD, respectively, administered on gestational day 15 in Long Evans rats (Gray et al.,
1997b). The morphometric indices of mild hypospadias that were significantly affected by
exposure to 0.2 ng/kg of TCDD on gestational day 15 were length of the urethral slit (increased
by TCDD), distance from the tip of the phallus to the urethral opening (increased by TCDD), and
distance from the urethral to vaginal opening (decreased by TCDD) (Gray et al., 1997b). Other
Ah receptor agonists are also capable of producing cleft phallus and mild hypospadias in female
Long Evans rats. 3,3',4,4',5,5'-HCB (PCB 169) administered on gestational day 8 at a dose of 1.8
mg/kg caused a significant increase in the percentage of female progeny with cleft phallus (Gray
et al., 1999). It also caused the female offspring to have a significantly longer urethral slit and a
shorter distance between the urethral and vaginal openings (Gray et al., 1999). The incidence of
cleft phallus was greater in female Long Evans rat progeny administered TCDD on gestational
day 15 compared to gestational day 8 (Gray and Ostby, 1995). In Holtzman rats the incidence
was greater when TCDD was administered on gestational day 11 compared with gestational day
15 or 18 (Flaws etal., 1997).
These TCDD-induced malformations of the external genitalia in female rats and hamsters
(cleft phallus and mild hypospadias) closely resemble the mild form of hypospadias caused by in
utero exposure to diethylstilbestrol (DES) and other potent estrogens. In hamsters estradiol
causes cleft phallus (Whitsett et al., 1978) and in rats DES and the synthetic estrogen RU2858
are capable of producing a mild form of hypospadias (Voherr et al., 1979; Vannier et al., 1980).
This raises the possibility that TCDD, which is often characterized as being an antiestrogen,
might cause these effects through an estrogen-like developmental action (Gray et al., 1997b). In
this context, it is important to stress that the other type of malformation produced by in utero and
lactational exposure to TCDD in the female rodent, vaginal thread formation, is unique to TCDD
and TCDD-like Ah receptor agonists. Vaginal thread formation, which can be detected as early
as gestational day 19 in the rat, is not known to be produced by any other class of chemical,
including potent estrogens like DES.
Gray and Ostby reported that in utero and lactational exposure to TCDD caused a
significant reduction in ovarian and brain weights when necropsied as adults (Gray and Ostby,
1995). Hamster offspring, like rats, display clefting of the phallus, mild hypospadias, and
reduced ovarian weight, but not formation of the vaginal thread (Gray and Ostby, 1995; Gray et
al., 1997b; Wolf et al., 1999). Female ICR mouse offspring were not susceptible to either cleft
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phallus or vaginal thread malformations nor were their ovarian or brain weights reduced by
perinatal TCDD exposure (Theobald and Peterson, 1997).
5.2.3.3.3. Ovary. In utero and lactational exposure to TCDD decreased ovarian weight in the rat
and hamster but not in the mouse (Gray and Ostby, 1995; Theobald and Peterson, 1997; Wolf et
al., 1999). Shiverick and Muther (1983) reported that there was no change in circulating levels
of estradiol in the rat after exposure to 1 ng/kg/day on gestational days 4 to 15. Similarly, Gray et
al. have found no effect on serum estradiol levels after perinatal exposure to a single maternal
dose of 1 ng TCDD/kg administered on gestational day 15 in the Long Evans rat, evaluated on
postnatal days 21 and 28 (Gray et al., 1997b). In addition, these authors found no effect on
ovarian estradiol production when ovaries from vehicle- and TCDD-exposed rats removed on
postnatal days 21 and 28 were placed in organ culture for 3 hours. However, Chaffin et al. found
that serum estradiol and ovarian secretion of estradiol in vitro were decreased by a similar
exposure regimen in the Holtzman rat (Chaffin et al., 1996, 1997). Histologic examination of
ovaries from 21- to 22-day-old rats that had been exposed to a single maternal dose of 1 ng
TCDD/kg in utero and via lactation revealed decreases, compared with vehicle-exposed rats, in
the number of ovarian follicles without alterations in ovarian size, or apoptosis in the affected
follicular regions (Heimler et al., 1998). Similarly, the administration of a single maternal dose
of 0.6 mg/kg of 3,3',4,4',5,5'-HCB (PCB 169) on gestational day 1, combined with daily doses of
1 mg/kg of 3,3',4,4'-TCB (PCB 77) on gestational days 2-18 resulted in a statistically significant
increase in the incidence of cystic dilated ovarian follicles (Smits-van Prooije et al., 1994).
5.2.3.3.4. Estrous cyclicity and reproductive performance.
5.2.3.3.4.1. Rats. With regard to effects on estrous cyclicity and reproductive performance,
Long Evans rats exposed on gestational day 8 to 1.0 ng/kg of TCDD had a significantly increased
number of the female offspring displaying constant estrus by 1 year of age (Gray and Ostby,
1995). This was accompanied by a significant reduction in fertility during a continuous breeding
trial. The gestational day 15 exposure did not have the same effect on cyclicity, and the
occurrence of constant estrus was not significantly different from control rats. There was also no
effect on female sexual behavior. Nevertheless, the number of mounts of control males and the
latency to ejaculation were increased in matings with females exposed to TCDD on gestational
day 15. This was possibly due to the vaginal abnormalities interfering with normal copulation.
Gray and Ostby (1995) also compared the gestational day 15 exposure to 1.0 ng/kg of
TCDD in the Long Evans female offspring with that in the Holtzman. There was a greater
reduction in neonatal viability in Holtzman than Long Evans female offspring (50% vs. 11%,
respectively) following TCDD exposure. In the surviving Holtzman offspring, the
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morphological effects were similar to those in the Long Evans offspring, including genital
clefting and vaginal threads. Reproductive behavior was not assessed in the Holtzman strain.
As Gray and Ostby (1995) have noted, their observations are consistent with previous
reports of infertility in female offspring after in utero exposure to TCDD (Khera and Ruddick,
1973) and are likely due to the alterations in estrous cyclicity and ovarian function. In utero and
lactational exposure of Wistar rats to 0.5 ng/kg/day of TCDD administered on gestational days 6
to 15 caused infertility in both sexes (Khera and Ruddick, 1973). Also, in utero and lactational
exposure to 1.8 mg/kg of 3,3',4,4',5,5'-HCB (PCB 169) on gestational day 1 decreased mating
success and female fecundity in female Wistar rat progeny (Smits-van Prooije et al., 1994).
Taken together, in utero and lactational exposure to TCDD and TCDD-like Ah receptor agonists
in the rat causes morphological and functional reproductive alterations in female offspring at
relatively low doses that do not induce overt maternal toxicity.
5.2.3.3.4.2. Hamsters. TCDD produced adverse effects in female hamsters that persisted
through two generations (Fl and F2). This occurred despite the Fl generation being the only
generation that was exposed in utero and via lactation to 2.0 ng/kg of TCDD administered on
gestational day 11.5 (Wolf et al., 1999). In the female progeny of the Fl generation, vaginal
opening was delayed and vaginal estrous cycles were altered. However, most TCDD-exposed
females had regular 4-day behavioral estrous cycles. This suggests in utero and lactational
TCDD exposure did not cause a marked disruption in the hypothalamic-pituitary-gonadal
hormonal cyclicity. While the Fl TCDD-exposed females mated successfully with a control
male, 20% of them did not become pregnant and 38% of those that did become pregnant died
near term. Both the number of implants in pregnant TCDD-exposed hamsters and the number of
pups they produced that were born alive were reduced significantly.
An important finding was that survival of F2 generation offspring through weaning was
virtually eliminated by treating with TCDD the dams that produced the Fl generation (Wolf et
al., 1999). The cause of death of the F2 generation offspring has not been reported.
5.2.3.3.5. Histopathology of the aging female reproductive tract. In utero and lactational
exposure to TCDD affects the histopathology of the female rat reproductive tract (Gray et al.,
1997b). In the ovary of TCDD-exposed female offspring, cystic follicles with luteinization and
sertoliform hyperplasia were observed. Diffuse squamous hyperplasia of the cervix and
hyperkeratosis of the vagina were seen in the TCDD-exposed progeny, but not in the controls.
5.2.3.3.6. Ovarian and mammary gland tumors. Ovarian tumors were found in female rats
following in utero and lactational exposure to TCDD, but not in control rats (Gray et al., 1997b).
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In addition, in utero and lactational exposure of rats to 1.0 ng/kg of TCDD on gestational day 15
rendered mammary glands of the female offspring more susceptible to tumor formation induced
by 7,12-dimethylbenz[a]anthracene (DMBA) (Brown et al., 1998).
5.2.3.4. Neurobehavior
Because differentiated tissues derived from ectoderm, namely, skin, conjunctiva, nails,
and teeth, are sites of action of halogenated aromatic hydrocarbons in transplacentally exposed
human infants, another highly differentiated tissue derived from ectoderm, the CNS, should be
considered a potential site of TCDD action. In support of this possibility, sexual differentiation
of the CNS in adult male rats is irreversibly altered in a dose-related fashion by perinatal
exposure to TCDD (Mably et al., 1991, 1992b). As will be shown below, the central nervous
systems of mice transplacentally exposed to 3,3',4,4'-TCB, monkeys perinatally exposed to
TCDD, and children transplacentally exposed to a mixture of PCBs, CDFs, and PCQs in the Yu-
Cheng incident are also affected. Thus, functional CNS alterations, which may or may not be
irreversible, are observed following perinatal exposure to halogenated aromatic hydrocarbons.
5.2.3.4.1. Ah receptor andARNTin the central nervous system. Ah receptors have been
identified in rat brain (Carlstedt-Duke, 1979). However, while an early study suggested the Ah
receptor may be associated with glial cells rather than neurons (Silbergeld, 1992), a more recent
study of the adult male rat brain that used in situ hybridization to localize mRNAs for the Ah
receptor and ARNT proteins found mRNAs for both proteins in the same neuronal populations in
the olfactory bulb, hippocampus, cerebral, and cerebellar cortices (Kainu et al., 1995).
Unexpectedly, detectable levels of Ah receptor mRNA were not detected in the hypothalamus.
The significance of these findings is that they suggest that TCDD and related Ah receptor
agonists may act in discrete neuronal populations in the brain.
Following administration of 14C-TCDD in the rat, the highest concentrations of TCDD-
derived 14C are found in the hypothalamus and pituitary. Much lower concentrations are found in
the cerebral cortex and cerebellum (Pohjanvirta et al., 1990). In another study, the Ah receptor
was not detected in whole rat or mouse brain but was detected in the cerebrum of the hamster and
cerebrum and cerebellum of the guinea pig (Gasiewicz, 1983).
5.2.3.4.2. Neurobehavior in mice. CD-I mice exposed transplacentally to 3,3',4,4'-TCB at a
maternal oral dose of 32 mg/kg administered on days 10 to 16 of gestation exhibited
neurobehavioral, neuropathological, and neurochemical alterations in adulthood (Tilson et al.,
1979; Chou et al., 1979; Agrawal et al., 1981). The neurobehavioral effects consisted of circling,
head bobbing, hyperactivity, impaired forelimb grip strength, impaired ability to traverse a wire
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rod, impaired visual placement responding, and impaired learning of a one-way avoidance task
(Tilson et al., 1979). The brain pathology in adult mice exhibiting this syndrome consisted, in
part, of alterations in synapses of the nucleus accumbens (Chou et al., 1979). This suggested that
in utero exposure to 3,3',4,4'-TCB may interfere with synaptogenesis of dopaminergic systems.
In support of this possibility, Agrawal et al. (1981) found that adult mice transplacentally
exposed to 3,3',4,4'-TCB had decreased dopamine levels and decreased dopamine receptor
binding in the corpus striatum, both of which were associated with elevated levels of motor
activity. It was concluded that transplacental exposure to 3,3',4,4'-TCB in mice may permanently
alter development of striatal synapses in the brain.
Eriksson (1988) examined the neurobehavioral effects of 3,3',4,4'-TCB in NMRI mice
exposed to a single oral dose of 0.41 or 41 mg/kg on postnatal day 10. Following sacrifice of the
mice on day 17, muscarinic receptor concentrations in the brain were significantly decreased at
both dose levels. This effect was shown to occur in the hippocampus but not in the cortex. More
recently (Eriksson et al., 1991), NMRI mice were exposed to the same two doses of 3,3',4,4'-TCB
similarly administered on postnatal day 10. At 4 months of age, the effects of the PCB on
locomotor activity were assessed. At both dose levels, abnormal activity patterns were exhibited
in that the treated mice were significantly less active than controls at the onset of testing, but
were more active than controls at the end of the test period. This pattern of effects can be
interpreted as a failure to habituate to the test apparatus. In contrast to the previous results with
CD-I mice, circling or head bobbing activities were not observed in these animals. Upon
sacrifice after the activity testing was complete, a small but statistically significant increase (as
opposed to the decrease found after sacrifice on postnatal day 17) in the muscarinic receptor
concentration of the hippocampus was found in animals from the high-dose group. These results
suggest that the neurochemical effects of 3,3',4,4'-TCB are complex. Cholinergeric as well as
dopaminergic systems in the brain are involved.
Of all the developmental and reproductive endpoints reported in this chapter for
laboratory animals, the only ones that have not yet been demonstrated to occur following
perinatal exposure to TCDD are the above neurotoxic effects in mice. These have only been
studied following perinatal exposure to 3,3'4,4'-TCB. In addition, there is as yet no evidence to
show (1) that among inbred mouse strains having low- and high-affinity Ah receptors,
susceptibility to 3,3',4,4'-TCB-induced neurotoxicity segregates with the Ah locus or (2) that the
rank order binding affinity of congeners for the Ah receptor correlates with their rank order
potency for causing these neurotoxic effects in mice. The rapid metabolism of 3,3',4,4'-TCB
compared with the relatively slow metabolism of TCDD in mice causes some uncertainty about
the potential involvement of the Ah receptor in 3,3',4,4'-TCB-induced neurotoxicity.
Contributing to this uncertainty is the hypothesis that 3,3',4,4'-TCB might produce CNS effects
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by being converted to a hydroxylated metabolite that is neurotoxic. Although there is no
evidence for or against this hypothesis, there is also no evidence for or against the Ah receptor
mechanism hypothesis of 3,3',4,4'-TCB neurotoxicity. Further research is needed to test these
hypotheses. In so doing, it should become apparent whether 3,3',4,4'-TCB-induced neurotoxicity
effects are relevant to TCDD-induced developmental toxicity.
5.2.3.4.3. Neurobehavior in rats. A considerable number of neurobehavioral endpoints have
been evaluated following perinatal exposure to either TCDD and coplanar PCBs that are Ah
receptor agonists, or to ortho-substituted PCBs that do not interact with the Ah receptor. Interest
in the latter, for the purposes of this section, arises from the fact that mixtures to which children
have been exposed in utero and via lactation typically contain Ah receptor agonists and non-Ah
receptor agonists. Therefore, it is important from a mechanistic point of view to determine
whether the effects of Ah receptor agonists can be distinguished from the effects of structurally
similar non-Ah receptor agonists. It is plausible that some effects of TCDD on neurobehavior in
rodents could be Ah receptor mediated because this protein has been detected in the developing
neuroepithelium of the mouse fetus (Abbott et al., 1995) and in neuronal tissue of the adult rat
brain (Kainu et al., 1995).
Two hypotheses have been advanced: (1) TCDD and TCDD-like PCB congeners do not
produce behavioral impairment at biologically relevant doses (Rice et al., 1998; Rice, 1999) and
(2) the effects of TCDD and TCDD-like PCB congeners on learning and memory might be
distinguishable from effects of ortho-substituted PCB congeners that are not Ah receptor agonists
(Schantz et al., 1996; Rice et al., 1999). These hypotheses have not been fully resolved. To the
extent that effects of individual ortho-substituted PCB congeners on neurobehavior depend on
reductions in thyroid hormone concentrations during the perinatal period (Collins et al., 1980;
Ness et al., 1993), it is important to note that TCDD and coplanar PCBs do not reduce thyroid
hormone concentrations to the same extent as the ortho-substituted PCBs (Seo et al., 1995).
Difficulty in resolving these hypotheses also occurs because different laboratories, using the
same testing methods, have not always obtained similar results. In addition, different testing
paradigms that appear, at least superficially, to test similar phenomena can arrive at discordant
conclusions. Where the results of testing by different methods are not in agreement, the
differences are not easy to resolve, in part because the relative sensitivities of the different
measures are not always clear.
Despite these difficulties, in utero and lactational exposure to TCDD-like Ah receptor
agonists have affected endpoints that measure learning and memory, discrimination reversal
learning, transitional behavior, avoidance behavior, neurotransmitter function, and locomotor
activity. Perinatal exposure to Ah receptor agonists have inhibited long-term potentiation (LTP)
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in the visual cortex, but not in the hippocampus evaluated in vitro (Altmann et al., 1995, 1998).
In some cases, effects observed with Ah receptor agonists are similar to those of ortho-
substituted PCB congeners (Schantz et al., 1995, 1996, 1997; Seo et al., 1999). Therefore, the
available data obtained following in utero and lactational exposure of rats to these compounds
tend to support the notion that TCDD and coplanar PCBs can affect neurobehavioral endpoints
by a variety of mechanisms, only one of which is Ah receptor mediated. The results of
neurobehavioral tests following perinatal exposure to TCDD and various PCBs are summarized
below.
5.2.3.4.3.1. Spatial learnins. Female rats were administered 3,3',4,4'-TCB (PCB 77, 2 and 8
mg/kg/day), 3,3',4,4',5-PCB (PCB 126, 0.25 and 1 ^g/kg/day), and TCDD (0.25 and 1 ^g/kg/day)
by gavage on gestational days 10-16 (Schantz et al., 1996). Beginning on postnatal day 80,
spatial learning was evaluated in male and female offspring by using the radial arm maze. While
no effects on overt toxicity were found, it was observed that the exposed rats made fewer errors
than controls. This result was different from that of a previous study in which exposure to the
ortho-substituted PCBs 2,4,4'-TCB (PCB 28), 2,3,4,4',5-PCB (PCB 118), and 2,2',4,4',5,5'-HCB
(PCB 153) had no effect on the number of errors (Schantz et al., 1995).
The ability of in utero and lactational TCDD exposure to reduce the number of errors
made by male rat offspring in the radial arm maze test were confirmed, even at a reduced
exposure level (0.1 ng/kg/day) on gestational days 10-16 (Seo et al., 1999). In addition, no
significant decreases in the error rate were found in female rats. However, further statistical
analysis of the data suggested that the affected male rats were using a response strategy whereby
they tended to enter adjacent arms of the maze. Because of this strategy, there might not be an
effect of TCDD on working memory. The lack of an effect of TCDD on the Morris Water Maze
test supports this interpretation (Seo et al., 1999). However, the test for adjacent arm selection
behavior, which detects the use of a response strategy, had not been significant in the original
study at the higher level of TCDD exposure (Schantz et al., 1996).
The low-dose TCDD exposure decreased latency in male rats in the radial arm maze test
(Seo et al., 1999). This suggests an apparent feminization of this parameter similar to that caused
by PCB 153 (Schantz et al., 1995). In addition, there may be other similarities between TCDD
and at least some of the ortho-substituted PCBs, because a similar evaluation of 2,2',3,5',6-PCB
(PCB 95) demonstrated an exposure-associated decrease in the error rate of male rat offspring
(Schantz et al., 1997). Similar to the original result with the low-exposure dose of TCDD (Seo et
al., 1999), the test for use of a response strategy was negative with PCB 95, but unlike the results
with TCDD and PCB 153, there was no gender-related decrease in latency. In contrast to the
decrease in the error rate that was caused by the perinatal exposure of Sprague-Dawley rats to
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PCB 77, PCB 95, PCB 126, and TCDD (Schantz et al., 1996), there was no effect of perinatal
exposure to PCB 77 or the diortho- substituted 2,2',4,4'-TCB (PCB 47) on the error rate of male
Wistar rats that were tested on a similar radial arm maze (Weinand-Harer et al., 1997).
The male and female rat offspring that were exposed to the ortho-substituted PCB 28,
PCB 118, and PCB 153 and tested on the radial arm maze were subsequently evaluated for
delayed spatial alternation on the T-maze beginning after postnatal day 135 (Schantz et al.,
1995). Exposed females learned this task more slowly than exposed males, and all compounds
tested caused a decrease in the number of correct responses. There was no effect on the number
of correct responses in exposed male offspring, but their latency to enter the maze was decreased
compared with that of control male offspring. This effect on latency again suggests a more
female-like pattern of response in male offspring exposed to the ortho-substituted PCBs, even
though distinct gender differences remained for the delayed spatial alternation response. In
contrast, in utero and lactational exposure to the coplanar PCBs produced no effect on the errors
made by male or female offspring, or in their latency in the T-maze test (Schantz et al., 1996; Seo
et al., 1999). In addition, another group also found that delayed spatial alternation was
unaffected in both male and female Long Evans rat offspring exposed to PCB 126 and tested in
an operant chamber setting (Rice, 1999). In this case, even prolonged dietary exposure of female
rats to PCB 126, which began 7 weeks before mating to an unexposed male and continued until
the offspring were weaned, caused no treatment-related differences in performance (Rice, 1999).
However, the impression that delayed spatial alternation is selectively affected by the ortho-
substitited PCBs is again offset by the results with PCB 95. Unlike the other ortho-sub stituted
PCBs, PCB 95 did not affect the response (Schantz et al., 1997). Differences between the
activities of PCB 95 and PCB 126 have also been found on parameters relevant to neurological
function in vitro (Wong et al., 1997). Because effects of the triortho-substituted PCB 95 on
delayed spatial alternation are similar to those of PCB 126, a coplanar PCB, but different from
those of other ortho-sub stituted PCBs it remains possible that this effect of PCB 126 is non-Ah
receptor mediated. However, not all ortho-chlorinated PCBs are equivalent (Schantz et al., 1995;
Schantz et al., 1997), suggesting the existence of structural selectivity in the mechanism.
5.2.3.4.3.2. Visual discrimination reversal learning. No effect was observed when the T-maze
was used to assess spatial discrimination reversal learning following in utero and lactational
exposure of male and female rat offspring to maternal doses of 0.1 ng TCDD/kg/day on
gestational days 10-16 (Seo et al., 1999). Visual discrimination reversal learning was tested by
placing electric light stimuli onto the cross-arms of the same T-maze used to evaluate spatial
discrimination reversal learning. Male and female rat offspring were exposed to maternal doses
of 0.1 ng TCDD/kg/day administered on gestation days 10-16 (Seo et al., 1999). When evaluated
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beginning at approximately 100 days of age, the exposed offspring performed similar to controls
during the original learning phase of the trial. However, TCDD-exposed offspring were slower
to reach the testing criterion of 10 correct trials in a 12-trial session. This effect occurred equally
in males and females and was most evident during the first and second reversal period. In the
following reversal periods, no further differences were evident between the TCDD and vehicle
exposure groups. Similarly, PCB 118 and PCB 126 were reported to impair visual
discrimination learning in male rat offspring evaluated in an operant chamber (Holene et al.,
1995). However, the authors of this study used more than one male offspring per litter, and they
appear to have evaluated all rats from the same litter as if they were independent observations.
Therefore, the results of this study have been considered to be uninterpretable on statistical
grounds (Rice and Hayward, 1998; Rice, 1999), based on the criteria established by Holson and
Pearce(1992).
One study using mixtures evaluated visual discrimination learning in the offspring of
female rats exposed to Clophen A30 (32 mg total PCBs/kg diet) or a normal diet for 60 days
prior to mating, and through pregnancy (Lilienthal et al., 1991). After birth some offspring
exposed to each diet were cross-fostered to dams exposed to the other diet. When male and
female offspring were evaluated at 120 to 180 days of age, there was no effect of PCB exposure
during the acquisition phase of the paradigm (visual discrimination learning tested on a jumping
stand). However, performance in all PCB-exposed groups was inferior during the retention
phase, relative to their performance at the end of the acquisition phase. Because the effect was
more pronounced in the prenatal-only and prenatal + lactational exposure groups, compared with
the lactational-only exposure group, the results indicate that prenatal-only exposure to PCBs is
all that is required to alter visual discrimination learning.
5.2.3.4.3.3. Transitional behavior. Female Long Evans rats were exposed to PCB 126 (0.25
and 1 ng/kg/day) via dietary supplementation that began 35 days prior to mating, and continued
through pregnancy and lactation (Rice and Hayward, 1999). After postnatal day 400, transitional
behavior was tested in male and female offspring by using a concurrent random interval-random
interval reinforcement schedule in an operant chamber. In this test offspring of both sexes
apportioned their responses less accurately than control offspring with respect to the pattern of
scheduled reinforcements on the two levers. However, the treated rats perceived the reward
offered by the reinforcements similarly to the control rats, because testing by a progressive ratio
reinforcement schedule resulted in no treatment-related differences in the relative strength of the
reinforcing event (Rice and Hayward, 1999). No treatment-related differences had previously
been found in the same rats tested on postnatal day 220 by using a multiple fixed interval-fixed
ratio reinforcement schedule (Rice and Hayward, 1998). Therefore, the results obtained on the
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concurrent random interval-random interval schedule of reinforcement may indicate a selective
effect of PCB exposure on adaptive ability in the offspring (Rice and Hayward, 1999).
5.2.3.4.3.4. Behavioral responses to CNS drugs. Haloperidol-induced catalepsy was evaluated
in male Wistar and Long Evans rat offspring exposed to the diortho-substituted PCB 47 (1
mg/kg/day) or the coplanar PCB 77 (1 mg/kg/day) from days 7 to 18 of gestation (Weinand-
Harer et al., 1997; Hany et al., 1999). At 100 and 180 days of age, catalepsy was induced in the
male offspring by the dopaminergic antagonist haloperidol. Developmental exposure to PCB 77,
but not to PCB 47, caused an increase in the time required for the affected rat to move its paw
after the paw had been placed into certain positions by the experimenter. The effects of similar
exposure to PCB 77 on dopaminergic function have also been tested in Long Evans rats by
evaluating their ability to discriminate between the dopaminergic agonist apomorphine and saline
(Lilienthal et al., 1997). As a positive control, the antithyroid drug propylthiouracil (PTU) given
to adult control animals just prior to testing blocked their ability to discriminate between
apomorphine and saline, whereas no effect was found on this discrimination in rats exposed to
PCB 77 in utero and via lactation. However, the administration of buspirone to the adult animals
just prior to testing blocked the ability of vehicle-exposed rats to recognize apomorphine much
more than it blocked this ability in the PCB 77-exposed offspring, or PTU-dosed groups. As
buspirone is a mixed serotonin receptor agonist and partial dopamine receptor antagonist, the
authors suggested that perinatal exposure to PCB 77 may produce long-lasting effects on the
interaction between dopaminergic and serotonergic processes in the CNS (Lilienthal et al., 1997).
Since PCB 77 was effective in prolonging haloperidol-induced catalepsy, whereas PCB
47 was not, it is interesting that perinatal exposure to coplanar PCBs and ortho-substituted PCBs
also produce opposite effects on dopamine synthesis in the brain (Seegal et al., 1990, 1997).
Perinatal exposure to ortho-substituted PCB congeners decreases dopamine synthesis in
adulthood, whereas perinatal exposure to coplanar PCBs causes persistent elevations in brain
dopamine and metabolite concentrations (Seegal et al., 1997). An increase in endogenous brain
dopamine concentrations could be related to the ability of PCB 126 to alter the recognition of
exogenous apomorphine. Dopaminergic function is one area where the effects of coplanar and
ortho-substituted PCB congeners may be distinguishable. However, only a few PCB congeners
have been evaluated.
Female Long Evans rats were exposed to PCB 126 (0.25 and 1.0 ng/kg/day) by dietary
supplementation that began 35 days prior to mating and continued through pregnancy and
nursing (Bushnell et al., 1999). After postnatal day 112, a chlordiazepoxide (CDP, 0, 3, 5, and 8
mg/kg) challenge test was used to evaluate neurobehavior. In control offspring all doses of CDP
reduced performance. This result suggested that the control offspring were affected by an
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increase in the visual threshold. Rats exposed in utero and via lactation to the low dose of PCB
126 were unaffected by CDP, whereas those exposed to the high dose exhibited less of a
decrement in their performance than did the control offspring. Since additional test results
demonstrated that PCB 126 exposure did not cause deficits in attention, the altered performance
of these rats after the administration of CDP suggests that perinatal exposure to PCB 126 may
affect Y-aminobutyric acid (GABA)-mediated pathways in the CNS during development
(Bushnell and Rice, 1999).
5.2.3.4.3.5. Passive avoidance behavior. Male Wistar rats were exposed to the coplanar PCB
77 or the diortho-substituted PCB 47 on gestational days 7 to 18. Passive avoidance behavior
was tested on a step-down platform when the rats were 220 days old (Weinand-Harer et al.,
1997). The latency of male rat offspring to step onto a grid that had previously given them an
electric shock was used to evaluate the effects of perinatal PCB exposure. Latency was
decreased by both PCBs, compared with control rats, up to 24 hours after the initial shock.
However, only the effect of PCB 77 was significant when evaluated at a single time (5 min, 4
hours, and 24 hours). A similar paradigm was used to evaluate the effects of perinatal exposure
to PCB 77, PCB 47, and a combination of both PCBs in Long Evans rats on postnatal day 85
(Hany et al., 1999). Under these conditions the most significant effect observed was a decreased
latency in the PCB 77 and combined exposure groups at the 5-minute time. No significant effect
was found when rats were exposed to PCB 47 only. These results suggest that differences in
passive avoidance behavior may exist following perinatal exposure to non-ortho- and ortho-
substituted PCBs, but only one congener of each type has been tested.
5.2.3.4.3.6. Open field locomotor activity. When open field activity was tested in male
offspring on postnatal day 25, rats exposed to PCB 57 in utero and via lactation had a
significantly higher activity level than rats similarly exposed to PCB 77. However, there were no
significant differences between the PCB-exposed groups and the unexposed control group
(Weinand-Harer et al., 1997). When tested on postnatal day 340, offspring exposed to PCB 47,
PCB 77, and a combination of both PCBs were hyperactive when compared with controls (Hany
etal., 1999).
Locomotor activity has also been tested in an operant chamber setting in rats exposed to
PCBs only during lactation. Female DA/OLA/HSD female rats were mated to Lewis male rats,
and the pregnant dams were administered vehicle, 2,2',4,4',5,5'-HCB (PCB 153, 5 mg/kg), or
PCB 126 (2 ng/kg) (Holene et al., 1998). Dosing of the female rats was accomplished on every
second day from postnatal days 3 to 13. PCB-exposed, 112-day-old male rats were found to be
hyperactive during both the fixed interval and extinction components of the reinforcement
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schedule. In addition, the PCB 153-exposed offspring displayed a behavior pattern similar to
that of spontaneous hypertensive (SHR) rats, which are used as an animal model of attention-
deficit hyperactivity disorder (ADHD) in children. With the results of only one congener of each
type being tested, the SHR-like pattern of activity appeared to be selective for the ortho-
substituted PCB. However, with both congeners, the activity level was increased by exposure
solely during the postnatal period. In contrast to all results that show that in utero and/or
lactational PCB exposure causes hyperactivity, perinatal exposure to PCB 95 caused hypoactivity
in offspring (Schantz et al., 1997).
5.2.3.4.4. Neurobehavior in monkeys. Schantz and Bowman (1989) and Bowman et al. (1989b)
have conducted a series of studies on the long-term behavioral effects of perinatal TCDD
exposure in monkeys. Because these were the first studies to evaluate the behavioral teratology
of TCDD, monkeys exposed to TCDD via the mother during gestation and lactation were
screened on a broad selection of behavioral tests at various stages of development (Bowman et
al., 1989b). At the doses studied (5 or 25 ppt in the maternal diet), TCDD did not affect reflex
development, visual exploration, locomotor activity, or fine motor control in any consistent
manner (Bowman et al., 1989a). However, the perinatal TCDD exposure did produce a specific,
replicable deficit in cognitive function (Schantz and Bowman, 1989). TCDD-exposed offspring
were impaired on object learning, but were unimpaired on spatial learning. TCDD exposure also
produced changes in the social interactions of mother-infant dyads (Schantz et al., 1986).
TCDD-exposed infants spent more time in close physical contact with their mothers. The pattern
of effects was similar to that seen in lead-exposed infants and suggested that mothers were
providing increased care to the TCDD-exposed infants (Schantz et al., 1986).
5.2.3.4.5. Neurobehavior in humans. The intellectual and behavioral development of
Yu-Cheng children transplacentally exposed to PCBs, CDFs, and PCQs was studied through
1985 by Rogan et al. (1988). In Yu-Cheng children matched to unexposed children of similar
age, area of residence, and socioeconomic status, there was a clinical impression of
developmental or psychomotor delay in 12 (10%) Yu-Cheng children compared with 3 (3%)
control children and of a speech problem in 8 (7%) Yu-Cheng children versus 3 (3%) control
children. Also, except for verbal IQ on the Wechsler Intelligence Scale for Children, Yu-Cheng
children scored lower than control children on three developmental and cognitive tests (Rogan et
al., 1988). Neurobehavioral data on Yu-Cheng children obtained after 1985 shows that the
intellectual development of these children continues to lag somewhat behind that of matched
control children. In addition, Yu-Cheng children are rated by their parents and teachers as having
a higher activity level; more health, habit, and behavioral problems; and a temperamental
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clustering closer to that of a "difficult child." It is concluded that in humans, transplacental
exposure to halogenated aromatic hydrocarbons can affect CNS function postnatally. However,
which congeners, TCDD-like versus non-TCDD-like, are responsible for the neurotoxicity is
unknown.
Further research on the mechanism of these postnatal neurobehavioral effects, dose-
response relationships, and reversibility of the alterations is needed before the role of TCDD-like
congeners versus non-TCDD-like congeners in causing this toxicity can be understood.
Mechanisms that respond uniquely to TCDD-like congeners may not necessarily be involved, as
three lightly chlorinated, ortho-substituted PCB congeners, 2,4,4'-TCB, 2,2',4,4'-TCB, and
2,2',5,5'-TCB, have been detected in monkey brain following dietary exposure to Aroclor 1016
and appear to be responsible for decreasing dopamine concentrations in the caudate, putamen,
substantia nigra, and hypothalamus of these animals (Seegal et al., 1990). These nonplanar PCB
congeners are believed to cause these effects by acting through a mechanism that does not
involve the Ah receptor. On the other hand, the results presented for mice and monkeys suggest
that TCDD-like congeners could be involved in producing the observed postnatal
neurobehavioral effects in humans.
5.2.3.5. Thermoregulation
In adult rats TCDD-induced reductions in body temperature are associated with reduced
serum thyroxin levels and a decrease in basal metabolism (Potter et al., 1983, 1986). More
recently the offspring of rats exposed in utero and via lactation to a maternal diet that contained
Aroclor 1254 were affected by decreased body core temperature, reduced metabolic rate, and
marked reductions in serum thyroxin up to an age of 14 days (Seo et al., 1995). As part of a
larger study, male rats that had been exposed to vehicle or a single maternal dose of 1 ng/kg of
TCDD on gestational day 15 were castrated (for reasons unrelated to the study of
thermoregulation) at approximately 8 months of age (Gordon et al., 1995). When evaluated at
15.4 to 17.7 months of age, TCDD-exposed animals exhibited significantly lower core body
temperatures than controls when the ambient temperature was varied between 10° and 28°C.
However, the metabolic rate was not affected by TCDD, which indicates that the effector
regulating body core temperature during cold exposure was unaffected. In addition, in utero and
lactational TCDD exposure had no effect on evaporative heat loss or on skin blood flow when
the rats were anesthetized so that this parameter could be measured. These results suggest that
perinatal exposure to TCDD can decrease core temperature set point and cause a reduction in the
regulated body temperature.
In a subsequent study pregnant Long Evans rats were administered a single maternal dose
of vehicle or 1 ng TCDD/kg on gestational day 15, and their male offspring were implanted with
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transmitters to monitor core temperature and motor activity (Gordon et al., 1998). At various
ages these TCDD-exposed male rats were affected by a nocturnal hypothermia that was
accompanied by decreased motor activity. These effects were especially pronounced at 7 and 11
months of age, did not occur at 3 months of age, and were reduced at 16 months of age. In
addition, TCDD-exposed animals exhibited a greater febrile response compared with vehicle-
exposed control rats when challenged with lipopolysaccharide (LPS) to induce fever. However,
when 8-month-old rats were placed in a temperature gradient and allowed to select their own
most favored ambient temperature, vehicle- and TCDD-exposed offspring selected the same
ambient temperatures. This suggests that hypothalamic thermoregulatory centers were not
permanently altered and that there was not a change in body temperature set point.
Similar alterations in thermoregulation have been produced in hamsters exposed to
TCDD in utero and via lactation. When monitored by radiotelemetry, like the rats cited above,
these offspring exhibited a persistent hypothermia in spite of normal metabolic responses to cold
exposure (Gordon et al., 1996). In addition, there was no effect of TCDD exposure on the
selection of an ambient temperature when hamsters were placed in a temperature gradient for 22
hours. These results are important because the adult hamster has an unusually high resistance to
the lethal and thyrotoxic effects of TCDD. However, the rat and hamster have approximately the
same sensitivity to perinatal TCDD-induced reproductive dysfunction and thermoregulatory
dysfunction. The mechanisms for these responses have not yet been determined.
5.2.3.6. Auditory Function and Thyroid Hormones
Long Evans rats were exposed to daily maternal doses of 0, 1, 4, and 8 mg Aroclor
1254/kg/day administered from gestational day 6 to weaning. Low-frequency auditory thresholds
evaluated in these male and female offspring beginning on postnatal day 85 were increased at the
two largest levels of PCB exposure (Goldey et al., 1995a). This effect was statistically
significant at 1 kHz, but not at 4 kHz or higher test frequencies. Since similar low-frequency
hearing loss can be produced by perinatal exposure to the antithyroid drug propylthiouracil
(PTU), the effect of Aroclor 1254 is believed to be associated with neonatal hypothyroidism
(Goldey et al., 1995b). Indeed, there were dramatic decreases in total and free plasma thyroxin
(T4) concentrations at all doses of Aroclor 1254 and at all times evaluated between postnatal
days 7-42 (Goldey et al., 1995a, 1998). Plasma total and free triiodothyronine (T3)
concentrations were decreased only by the largest two exposure levels of Aroclor 1254, and
statistically significant effects were observed only on postnatal days 21 and 28, with no decrease
on postnatal day 42.
Perinatal exposure to a daily maternal dose of 1 ng/kg/day of 3,3'4,4',5-PCB (PCB 126)
administered to the dam for 7 weeks prior to breeding and throughout breeding, gestation, and
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lactation to weaning decreased the auditory threshold in Long-Evans rat offspring to 0.5 and 1
kHz (Crofton et al., 1999). However, the 0.25 |ig PCB 126/kg/day maternal dose did not affect
the auditory threshold in exposed offspring. Thus, it is plausible that AhR agonists within the
Aroclor 1254 PCB mixture caused the hearing loss.
While the daily maternal dose of 1 mg Aroclor 1254/kg/day did not significantly increase
the auditory threshold to 1 kHz, it did cause alterations in brain stem auditory-evoked responses
in exposed male and female offspring that were evaluated at 1 year of age (Herr et al., 1996).
This result is consistent with the hypothesis that developmental exposure to Aroclor 1254 can
damage the peripheral auditory system. It is believed by the authors that this apparently
irreversible damage might occur at the level of the cochlea and/or auditory nerve. This result is
important because offspring exposed to a maternally administered level of 1 mg Aroclor
1254/kg/day were affected by less substantial reductions in plasma thyroxin concentration than
were those exposed to 4 mg/kg/day or 8 mg/kg/day doses of Aroclor 1254, which increased the
auditory threshold (Goldey et al., 1995a; Goldey and Crofton, 1998). Thus, it is possible that less
of a thyroid deficit during development could result in hearing loss. In support of the
relationship between the chemical-induced auditory deficit and thyroid hormone status, the
auditory deficit was partially alleviated by daily doses of thyroxin given to the Aroclor 1254-
exposed pups from postnatal day 4 to postnatal day 21 (Goldey and Crofton, 1998).
Even as early as gestational day 20 brain thyroxin levels in the forebrain and cerebellum
of fetal rats can be depressed after maternal exposure to Aroclor 1254 (Morse et al., 1996).
However, in late-gestation fetuses, induction of the brain type n thyroxin 5'-deiodinase results in
compensation for the decrease in thyroxin levels so that brain triiodothyronine levels are
maintained. Similar alterations occur after exposure to the non-ortho-substituted PCB congeners
3,3'4,4'-TCB (PCB 77) and 3,3',4,4',5,5'-HCB (PCB 169) that are Ah receptor agonists (Morse et
al., 1993). The authors suggest that increases in deiodinase activity could be indicative of a local
hypothyroidism occurring in the brains of fetal and neonatal rats exposed to these PCBs.
In utero and lactational exposure to a daily maternal dose of 0.1 \ig TCDD/kg/day
administered on gestational days 10-16 can produce a statistically significant decrease in plasma
thyroxin concentration in female offspring evaluated on postnatal day 21 (Seo et al., 1995).
However, this reduction in plasma thyroxin concentration in Sprague-Dawley rat offspring was
less than those associated with no effect on auditory threshold in Long Evans rat offspring
(Goldey et al., 1995a). Therefore, it has not been demonstrated that perinatal exposure to TCDD
will decrease plasma thyroxin concentrations enough to evoke an increase in the auditory
threshold. It may turn out that TCDD doses larger than those already tested may be required to
cause these effects. While it is possible that prenatal TCDD exposure might produce a functional
hypothyroidism prior to birth or that mechanisms other than perinatal hypothyroxinemia may
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play a role in producing the hearing loss, it is also possible that PCBs and/or their metabolites
could affect auditory functional development by decreasing plasma thyroxin concentrations via
non-Ah receptor-related mechanisms (Brouwer et al., 1995). Paradoxically, Aroclor 1254 and a
single maternal dose of 50 ng TCDD/kg administered to Long-Evans rats on day 15 of gestation
can accelerate eye opening (Goldey et al., 1995a; Gray et al., 1997a). This effect of Arochlor
1254 is exacerbated by thyroxin replacement in the pups (Goldey and Crofton, 1998), whereas
hypothyroidism is typically associated with a delay in this developmental landmark (Comer et al.,
1982; Goldey et al., 1995b). This suggests that some developmental effects of Aroclor 1254 can
resemble those of hyperthyroidism, rather than hypothyroidism. Additional mechanistic work on
the ability of Ah receptor agonists to induce hypothyroidism early in development, and their
ability to decrease auditory function and cause postnatal hearing loss, appears to be required.
5.2.3.7. Night Vision
Pregnant Long Evans rats were exposed to the ortho-chlorinated 2,2',4,4'-tetrachloro-
biphenyl (PCB 47) and/or the coplanar 3,3',4,4'-tetrachlorobiphenyl (PCB 77) on days 7-18 of
gestation. Daily doses 1.5 mg PCB 47/kg/day, 1.5 mg PCB 77/kg/day, a combination of 1.0 mg
PCB 47/kg/day + 0.5 mg PCB 77/kg/day, or an equivalent volume of vehicle were administered
subcutaneously to each dam (Kremer et al., 1999). The effects of PCB exposure on visual
processes were then assessed in male and female offspring at 200 days of age. The scotopic b-
wave, maximum potential, and oscillatory potentials were recorded on the electroretinogram after
the rats were adapted to the dark. Perinatal exposure to PCB 77 reduced the amplitudes of these
potentials in female offspring in adulthood, but not their male littermates. Exposure to PCB 47
alone was without effect; however, many of the decreases that resulted from PCB 77 appeared to
be alleviated after simultaneous exposure to PCB 47. While this suggests that functional
antagonism between these ortho-substituted and coplanar PCBs can occur in the endpoints
measured, it is also possible that this apparent antagonism resulted from the lower level of PCB
77 administered in the combination. These results indicate that in utero and lactational exposure
to PCB 77, but not PCB 47 exposure, can produce long-lasting effects on night vision in female
rat offspring (Kremer et al., 1999). Interestingly, the susceptibility to this effect was congener-
specific, suggesting that the effect may be Ah receptor mediated. In addition, it was gender
dependent.
5.2.4. Cross-Species Comparison of Effect Levels
TCDD exposure levels that cause a variety of developmental effects in different species
are summarized for fish in Table 5-5, birds in Table 5-6, and mammals in Table 5-7. Fertilized
lake trout eggs and Japanese medaka eggs were exposed to different waterborne concentrations
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of 3H-TCDD. Estimates of the amount of TCDD in these eggs were then made from
measurement of the TCDD-derived radioactivity within them. Fertilized rainbow trout, chicken,
ring-necked pheasant, and eastern bluebird eggs were injected directly with the indicated doses of
TCDD. Thus, the doses of TCDD given in Tables 5-5 and 5-6 for all fish and bird species
represent TCDD egg burdens where a significant portion of the dose may be present within the
yolk of the egg rather than the developing embryo.
Mammalian embryo/fetuses, on the other hand, were exposed via administration of
TCDD to the pregnant female. Therefore, the doses given in Table 5-7 are maternal TCDD
doses, where a significant portion of the dose may be retained by the mother and never actually
reach the embryo/fetus. In some studies, pregnant rats and rhesus monkeys were exposed to
TCDD on a chronic or subchronic basis, respectively. The doses given in Table 5-7 for these
particular studies represent the calculated maternal body burdens at the time of conception. In
rats, the duration of chronic exposure was much longer than the whole body elimination half-life
for TCDD in rats. Therefore, the body burden of TCDD given for the rat is 92.8% of the
calculated steady-state body burden. In rhesus monkeys, the half-life for whole body elimination
of TCDD is longer than the duration of exposure prior to conception. Therefore, the steady-state
body burdens that would be expected for rhesus monkeys exposed to the different levels of
dietary TCDD intake are approximately three times greater than the maternal body burdens
estimated at the time of conception (Table 5-7).
In both rats and rhesus monkeys, the maternal body burdens are calculated using a one-
compartment open model, assuming 86.1% bioavailability for TCDD. The bioavailability used
for TCDD was determined in rats (Rose et al., 1976). As no estimate for TCDD bioavailability
has been reported in rhesus monkeys, the same 86.1% value was used. The whole body
elimination half-life used for TCDD in the rat is 23.7 days (Rose et al., 1976).
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McNulty et al. (1982) estimated a half-life of approximately 1 year for TCDD elimination from
adipose tissue in the rhesus monkey, and for calculation of the body burdens estimated in Table
5-7, this half-life was rounded to 400 days for whole body elimination. The maternal body
burden given for chronic exposure in the rat was calculated from the data of Murray et al. (1979).
The maternal body burden given for subchronic exposure in the rhesus monkey was calculated
from data obtained from Dr. R. E. Bowman (personal communication), which included the daily
dietary TCDD exposure level for each pregnant female used in the studies reported by Bowman
et al. (1989a,b) and Schantz and Bowman (1989). Dr. Bowman's results indicate that the range
of TCDD half-lives in these monkeys was 200 to 600 days, which is consistent with the results of
McNulty et al. (1982). The body burdens estimated for rhesus monkeys used in these studies are
averages based on the average daily TCDD consumption of all pregnant females used at a
particular level of maternal TCDD exposure.
As summarized in Table 5-5, lake trout and rainbow trout sac fry and Japanese medaka
embryos are similarly affected by a spectrum of lesions that includes hemorrhage, edema,
collapse of the yolk sac, cessation of blood flow, and embryo mortality. Estimates of the
NOAEL and LOAEL are given in Table 5-5 for the appearance of these lesions in Japanese
medaka embryos and for embryo mortality in the two trout species. Although fertilized lake trout
eggs and Japanese medaka eggs were exposed to various TCDD concentrations dissolved in
static water, and fertilized rainbow trout eggs were injected directly with TCDD, the egg doses
given in Table 5-5 represent the concentration of TCDD within the eggs themselves. Therefore,
the different NOAELs and LOAELs for developmental toxicity in different fish species probably
represent species differences in susceptibility to TCDD-induced developmental toxicity rather
than differences in method of TCDD exposure. Of the three fish species, lake trout sac fry are
the most sensitive to TCDD-induced mortality. However, based on the LOAELs shown in Table
5-5, the difference in susceptibility between fish species may be less than tenfold.
Based on the LOAELs shown in Table 5-6, the sensitivity of different bird species to
TCDD-induced embryo mortality varies by more than fortyfold. The chicken embryo is more
susceptible to TCDD-induced mortality than are embryos of the ring-necked pheasant and eastern
bluebird. In addition, chicken embryos are highly sensitive to the formation of TCDD-induced
structural defects in the heart and aortic arch. The incidence of cardiac malformations in the
chicken embryo is increased at an egg exposure level as low as 9 ng TCDD/kg egg. However,
such cardiac malformations have not been found in any other bird species that has been
examined.
Table 5-7 summarizes the levels of TCDD exposure that cause certain structural
malformations, functional alterations, and prenatal mortality in the embryo/fetus of different
mammalian species. Based on the LOAELs given for rats and monkeys in Table 5-7, functional
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alterations in learning behavior and the male reproductive system occur at lower TCDD doses
than those required to produce structural malformations. Maternal doses of TCDD between 19
and 160 ng/kg decreased object learning in monkeys, accelerated eye opening, produced adverse
effects on the male reproductive system, and altered sexual behavior in rats. Developmental
toxicity to the female reproductive tract in rats occurred at TCDD doses between 200 and 800
ng/kg. Although TCDD-induced developmental toxicity has been extensively studied in mice and
rats, the LOAELs in Table 5-7 indicate that the embryo/fetus of rodent species is generally not as
sensitive to TCDD-induced prenatal mortality as is the embryo/fetus of the rhesus monkey. The
sensitivity of the embryo/fetus to TCDD-induced prenatal mortality in different mammalian
species varies approximately 240-fold. This is in contrast to the 1,000- to 5,000-fold variation in
the LD50 of TCDD when adult animals of these same species are exposed. The agreement
between studies with respect to the LOAEL in Table 5-7 for prenatal mortality in rats and
monkeys is particularly striking. The 500 ng/kg dose of TCDD on gestational days 6 to 15 that
caused prenatal mortality in rats (Sparschu et al., 1971) agrees with the maternal TCDD body
burden of 270 ng/kg calculated from the chronic exposure of rats by Murray et al. (1979) to
within a factor of 2. Similarly, the TCDD dose of 111 ng/kg that was given to rhesus monkeys
nine times during the first trimester of pregnancy (McNulty, 1984) agrees with the maternal body
burden of 97 ng/kg that increased prenatal mortality in rhesus monkeys following subchronic
dietary exposure (Schantz and Bowman, 1989).
5.3. REPRODUCTIVE TOXICITY
5.3.1. Female
5.3.1.1. Reproductive Function/Fertility
TCDD and its approximate isostereomers have been shown to affect female reproductive
endpoints in a variety of animal studies. Among the effects reported are reduced fertility,
reduced litter size, and effects on the female gonads and menstrual/estrous cycle. These studies
are reviewed below. Other TCDD effects on pregnancy maintenance, embryo/fetotoxicity, and
postnatal development are covered in Section 5.2 of this chapter.
5.3.1.1.1. Rats. The study by Murray et al. (1979) employed a multigenerational approach,
examining the reproductive effects of exposure of male and female rats over three generations to
relatively low levels of TCDD (0, 0.001, 0.01, and 0.1 ng/kg/day). There was variation in the
fertility index in both the control and the exposed groups, and a lower than desirable number of
impregnated animals in the exposed groups. Nevertheless, the results showed exposure-related
effects on fertility, an increased time between first cohabitation and delivery, and a decrease in
litter size. The effects on fertility and litter size were observed at 0.1 ng/kg/day in the F0
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generation and at 0.01 ng/kg/day in the Ft and F2 generations. Additionally, in a 13-week
exposure to 1 to 2 ng/kg/day of TCDD in nonpregnant female rats, Kociba et al. (1976) reported
anovulation and signs of ovarian dysfunction, as well as suppression of the estrous cycle.
However, at exposures of 0.001 to 0.01 ng/kg/day in a 2-year study, Kociba et al. (1978) reported
no effects on the female reproductive system.
5.3.1.1.2. Monkeys. Allen and colleagues reported on the effects of TCDD on reproduction in
the monkey (Allen et al., 1977, 1979; Barsotti et al., 1979; Schantz et al., 1979). In a series of
studies, female rhesus monkeys were fed 50 or 500 ppt TCDD for ~9 months. Females exposed
to 500 ppt showed obvious clinical signs of TCDD toxicity and lost weight throughout the study.
Five of the eight monkeys died within 1 year after exposure was initiated. Following 7 months of
exposure to 500 ppt TCDD, seven of the eight females were bred to unexposed males. The
remaining monkey showed such severe signs of TCDD toxicity that she was not bred due to her
debilitated state. Of the seven females that were evaluated for their reproductive capabilities,
only three were able to conceive and, of these, only one was able to carry her infant to term
(Barsotti et al., 1979). When females exposed to 50 ppt TCDD in the diet were bred at 7 months,
two of eight females did not conceive and four of six that did conceive could not carry their
pregnancies to term. As one monkey delivered a stillborn infant, only one conception resulted in
a live birth (Schantz et al., 1979). As described in an abstracted summary, these results at 50 and
500 ppt TCDD are compared with a group of monkeys given a dietary exposure to PBB (0.3
ppm, Firemaster FF-1) in which seven of seven exposed females were able to conceive, five gave
birth to live, normal infants, and one gave birth to a stillborn infant (Allen et al., 1979).
Although the effects at 500 ppt TCDD may be associated with significant maternal toxicity, this
would not appear to be the case at the lower dose. After administration of 50 ppt TCDD, no
overt effects on maternal health were observed, but the ability to conceive and maintain
pregnancy was reduced (Allen et al., 1979).
In a similar series of experiments, female rhesus monkeys were fed diets that contained 0,
5, and 25 ppt TCDD (Bowman et al., 1989a; Schantz and Bowman, 1989). Reproductive
function was not altered in the 5 ppt group, as seven of eight females mated to unexposed males
after 7 months of dietary exposure to TCDD were able to conceive. Six of these females gave
birth to viable infants at term and one gave birth to a stillborn infant. This was not significantly
different from the results of the control group, which was fed a normal diet that contained no
TCDD. All seven of the monkeys in this control group were able to conceive and give birth to
viable infants. The 25 ppt dietary exposure level, however, did affect reproductive function.
Only one of the eight females in this group that was mated gave birth to a viable infant. As in the
50 ppt group from earlier studies, there were no serious health problems exhibited by any females
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exposed to 0, 5, or 25 ppt TCDD. Therefore, the results in the 25 and 50 ppt groups suggest that
maternal exposure to TCDD before and during pregnancy can result in fetomortality without
producing overt toxic effects in the mother.
McNulty (1984) examined the effect of a TCDD exposure during the first trimester of
pregnancy (gestational age 25 to 40 days) in the rhesus monkey. At a total dose of 1 ng/kg given
in nine divided doses, three of four pregnancies ended in abortion and two of these abortions
occurred in animals that displayed no maternal toxicity. At a total dose of 0.2 ng/kg, one of four
pregnancies ended in abortion. This did not appear to be different from the control population,
but the low number of animals per group did not permit statistical analysis. McNulty (1984) also
administered single 1 ng/kg doses of TCDD on gestational days 25, 30, 35, or 40. The number of
animals per group was limited to three, but it appeared that the most sensitive periods were the
earlier periods, days 25 and 30, and that both maternal toxicity and fetotoxicity were reduced
when TCDD was given on later gestational days. For all days at which a single 1 ng TCDD/kg
dose was given (gestational day 25, 30, 35, or 40), 10 of 12 pregnancies terminated in abortion.
Thus, of 16 monkeys given 1 \ig TCDD/kg in single or divided doses between days 25 and 40 of
pregnancy, only three normal births occurred (McNulty, 1984, 1985).
5.3.1.2. Ovarian Function
Signs of ovarian dysfunction in rats and monkeys such as anovulation and suppression of
the estrous cycle had been reported previously (Kociba et al., 1976; Barsotti et al., 1979; Allen et
al., 1979), and Li et al. (1995a,b) recently extended these studies. In their initial study (Li et al.,
1995a), adult female rats were given a single oral dose of 10 ng TCDD/kg BW and observed for
changes in estrous cyclicity and ovulation. The number of ova ovulated per female and the
number of females ovulating were decreased by 75%, and the estrous cycle was also altered, with
a significant increase in the time spent in diestrus and a decrease in proestrus and estrus.
However, these findings were clouded by the fact that the exposure caused a body weight loss in
the females. A subsequent dose-response study in immature hypophysectomized/eCG-primed
females provided support that the effects on ovulation were dose-dependent (Li et al., 1995b).
However, the effects were only statistically significant at exposure levels that also caused a
significant loss in body weight over the experimental period.
5.3.1.3. Reproductive Capability of Ah Receptor Knockout Mice
Reproductive success is adversely affected in some Ah receptor null mouse lines (in the
absence of TCDD exposure). Ah receptor null female mice (Fernandez-Salguero et al., 1995)
become pregnant at similar rates and implant similar numbers of embryos as control females.
However, these Ah receptor null dams experience increased prenatal loss of conceptuses and
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difficulty in surviving the stress of lactation, and their pups show poor survival during lactation
and shortly after weaning (Abbott et al., 1999c). In contrast, offspring from a different Ah
receptor null mouse line (Schmidt et al., 1996) exhibit low neonatal mortality. Possible reasons
for this and other phenotypic differences between offspring of these two Ah receptor null mouse
lines is unclear (Schmidt et al., 1996).
5.3.1.4. Endometriosis
5.3.1.4.1. Humans
Endometriosis is characterized by endometrial cell growth outside the uterus and can be
associated with infertility and pain. Of increasing interest is the initial report that women with
endometriosis in Germany are more likely to have elevated concentrations of PCBs in their blood
(Gerhard and Runnebaum, 1992). While this report did not provide sufficient methodological
detail (reviewed in Ahlborg et al., 1995), Koninckx and coworkers (1994) reported that Belgium
also has a high incidence of endometriosis and that TCDD concentrations in breast milk in
Belgian women are among the highest in the world. Similarly, a larger number of women in
Israel with endometriosis were found to have measurable blood levels of TCDD when compared
to age-matched control women that had tubal infertility but no endometriosis (Mayani et al.,
1997). More recently in Belgian women, high serum TCDD-like toxic equivalent concentrations
(TEQs) were associated with a greater risk for endometriosis (Pauwels et al., 1999), but no
association was found between endometriosis and total serum PCB concentrations in this study.
This suggests that only TCDD-like PCBs may be capable of producing the response in women.
A recent study has demonstrated the occurrence of certain TCDD-induced biochemical
changes that facilitate the ectopic growth of human endometrial tissue (Bruner-Tran et al., 1999).
When the human tissue is exposed to TCDD in vitro and implanted into immunologically
impaired nude mice, TCDD exposure inhibits the ability of progesterone to decrease the
expression of matrix metalloproteinase enzymes. This effect, which is associated with a TCDD-
induced decrease in the ability of human endometrial organ cultures to produce TGF-P2,
enhances ectopic growth of the endometrial lesions. These results begin to provide a
biochemical basis for the ability of TCDD exposure to facilitate the expression of endometriosis
in women, and they strengthen the association between elevated exposure to TCDD-like AhR
agonists and the increased incidence and severity of this disease.
5.3.1.4.2. Monkeys
An association between TCDD exposure and endometriosis has found some experimental
support in studies using the rhesus monkey. However, the association between PCB exposure
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and endometriosis in monkeys is less clear. Rier and coworkers chronically exposed rhesus
monkeys to TCDD in their diet for 4 years and then maintained the monkeys for an additional 10
years. These monkeys were then compared to similar unexposed animals in the same colony
(Rier et al., 1993; Rier et al., 1995). In monkeys exposed to dietary levels of 5 ppt and 25 ppt
TCDD, the incidence of endometriosis was 43% and 71%, respectively, whereas the incidence in
control monkeys was 33%. Moreover, the severity of endometriosis was TCDD dose-
dependent. Monkeys in the studies by Rier and coworkers appeared to be quite sensitive to
TCDD-induced increases in the incidence and severity of endometriosis. It has been calculated
that the female monkeys exposed to 5 ppt TCDD in the diet for 4 years had accumulated a TCDD
body burden of 69 ng/kg (DeVito et al., 1995). However, another study found no association
between the incidence and severity of endometriosis and exposure to Aroclor 1254 when rhesus
monkeys were exposed for up to 6 years. Unlike the Rier studies, these monkeys were not held
for evaluation a long time after exposure (Arnold et al., 1996). Interestingly, both the Rier and
the Arnold studies reported a similar high background incidence (33% -37%) of endometriosis in
unexposed monkeys. When taken together the results of these studies indicate that it may take
some time for a TCDD-induced increase in endometriosis to become manifest above the
background level, that sensitivity to halogen aromatic hydrocarbon-induced increases in
endometriosis may be more readily detected when TCDD equivalent concentrations (TEQs)
rather than total PCB concentrations are considered, and that the effect, if produced by PCBs at
all in monkeys, could be PCB congener-specific. In this last sense, the results in monkeys
correspond to those recently obtained by Pauwels et al. (1999) in women, which also suggest that
the effect on endometriosis is congener specific for those halogenated aromatic congeners with
AhR agonist activity.
5.3.1.4.3. Rats and mice. An animal model has been developed in the rat and mouse to evaluate
the effects of TCDD exposure on the development of endometriosis (Cummings et al., 1995,
1996). While rodents do not spontaneously develop endometriosis, the surgical implantation of
uterine tissue at ectopic sites in the abdominal cavity is a way of mimicking aspects of the
disease. The formation of clear vesicles, fibrosis, inflammation, and adhesions are common to
the disease in primates and to the rodent model of endometriosis (Cummings et al., 1996).
Female rats and mice were administered 0, 3, or 10 ng TCDD/kg 3 weeks before, at the time of,
and at 3, 6, and 9 weeks after surgery to induce endometriosis (Cummings et al., 1996). At 3, 6,
9, and 12 weeks following surgery, there were dose-dependent increases in lesion diameter in
both species if all time points were pooled. In addition, rats showed a decrease in body weight
and ovarian weight at 9 and 12 weeks, accompanied by an increase in the time spent in vaginal
estrus, and histology of the ovary at 12 weeks indicated ovulatory arrest. These effects on body
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weight and the ovary were not observed in the mouse, but the mouse seemed more susceptible to
the TCDD-increase in lesion diameter than the rat at 9 and 12 weeks postsurgery.
Additional studies done to assess the effects of TCDD in the mouse model of
endometriosis used slightly different methodology and resulted in different results. In these
studies mice were first subjected to the surgery to induce endometriosis and then were exposed
chronically to daily doses of 0, 10, 50, or 100 ng TCDD/kg for 28 days. When the effects of
TCDD exposure on the endometriosis lesions were assessed 2 days after the last dose, it was
found that there was a dose-dependent decrease in lesion diameter (Yang et al., 1997). In
addition, uterine tissue implant survival and growth was decreased in ovariectomized mice and
restored by estrogen replacement (Foster et al., 1997). Exposure to TCDD inhibited the ability of
estrogen replacement to promote implant survival and growth, suggesting that TCDD acts as an
antiestrogenic compound in this form of the model. The authors of this study noted the
difference between their results and those of Cummings et al., and they suggested that when
TCDD is administered prior to the induction of endometriosis in ovary-intact mice, immune
suppression facilitates the growth of the endometriosis implants, or that factors of ovarian origin
other than steroids may play a role in the establishment, maintenance, and growth of
endometriosis. In contrast, when TCDD is administered after surgically induced endometriosis is
established, the antiestrogenic effects of TCDD inhibit lesion growth. In spite of these
suggestions, it seems possible that insufficient time is allowed when this model is used for the
severity of surgically-induced endometriosis to be increased by TCDD exposure subsequent to
the initial inhibition.
Unlike TCDD, the Ah receptor agonists 3,3',4,4',5-PCB (PCB 126), 1,3,6,8-
tetrachlorodibenzo-p-dioxin, and 2,3,4,7,8-pentachlorodibenzofuran were not able to alter lesion
diameter or lesion weight in the mouse model (Johnson et al., 1997). This is reminiscent of the
ability of TCDD, but not Aroclor 1254, to increase the incidence of endometriosis in rhesus
monkeys. Interestingly, the dose-response relationship for TCDD in the mouse model was U-
shaped, with low doses promoting endometriosis and larger doses resulting in a decreased
response. Therefore, the effects of Ah receptor agonists on endometriosis may depend on a
complex interplay between immune suppression and antiestrogenicity.
5.3.1.5. Mammary Gland
5.3.1.5.1. Postnatal mammary gland development. The mammary gland of weanling rats and
mice is a system of branching ducts that terminate in actively growing terminal end buds (TEBs).
Elongation of the mammary ducts and penetration of the epithelium into the surrounding adipose
stroma results from the rapid cellular proliferation of the TEBs (Williams et al., 1983). The
density of TEBs (number of TEB/mm2) increases steadily after birth until it reaches a maximum
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value in the rat on postnatal day 21 (Russo et al., 1978). This is accompanied by a concomitant
increase in the total area of the mammary gland. After postnatal day 21 numerous lateral buds
develop along the growing ducts as further growth of the gland occurs. During this time
septation and cleavage in the TEBs and lateral ducts result in the formation of 3-5 smaller buds
per structure, the alveolar buds (ABs) (Russo and Russo, 1978). With the initiation of estrus
cycling (postnatal days 35-42), alveoli form from the ABs, and until cessation of mammary
growth occurs, these branched structures increase progressively in number with each successive
estrus cycle resulting in the formation of lobules. TEBs that do not further differentiate in this
manner regress into finger-shaped structures called terminal ducts (TDs).
5.3.1.5.2. Effects of postnatal TCDD exposure in vivo. Female rats that were orally
administered daily doses of 2.5 ng TCDD/kg or vehicle on postnatal days 24, 26, 28, and 30 were
affected by decreased cellular proliferation within their mammary glands and by decreased
mammary gland development (Brown et al., 1995). When evaluated 18 hours after the last
TCDD dose, body weight in the treated rats was slightly but not significantly reduced. However,
the combined uterine-ovarian weights were less than half, and mammary gland size was only
61% that of vehicle-treated control rats. TCDD treatment caused a statistically significant 59%
reduction in the number of TEBs without significantly affecting the numbers of ABs, lobules,
and TDs. Therefore, the postnatal TCDD exposure-induced inhibition of mammary growth is
accompanied by a selective size reduction within the most rapidly dividing portion of the
mammary ducts, the TEBs. While TCDD exposure did not decrease the percentage of TEB cells
that are proliferating (PCNA labeling index) or percentage of TEB cells in S-phase, it decreased
these parameters in TDs and lobules. Consistent with the decrease in the number of TEBs in
TCDD- treated rats, the total numbers of PCNA-labeled and S-phase cells were decreased (even
though percentages were not) compared with the values obtained in vehicle-exposed control rats.
In addition, TCDD exposure decreased the total numbers of PCNA-labeled and S-phase cells in
TDs and lobules. These results are consistent with the finding that early postnatal TCDD
exposure in the rat causes an inhibition of mammary epithelial cell proliferation, but the
mechanism for this effect remains to be determined. It may be a consequence of the
antiestrogenic properties of TCDD (Harris et al., 1990; Safe et al., 1991).
5.3.1.5.3. Expression of Ah receptor and effects of the Ah receptor null mutation. Mammary
glands from estrous cycling C57BL/6J mice express high levels of Ah receptor mRNA and
protein (Hushka et al., 1998). Lower or undetectable levels of the Ah receptor mRNA and
protein were found during late pregnancy and during mammary gland involution immediately
after the cessation of nursing. Transgenic female mice heterozygous for the Ah receptor null
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mutation were mated with transgenic heterozygous males. Comparative analysis of mammary
gland development in 6- to 8-week-old female Ah receptor wild-type and Ah receptor null
littermates demonstrated that there was a 50% reduction in TEBs and an increase in TDs in the
Ah receptor null females. In most Ah receptor null females the ductal architecture, branching
patterns, and overall organization of specific cell types in the mammary epithelium did not
appear to be altered. However, a small percentage of mammary glands from Ah receptor null
females exhibited little or no branching. These findings support the conclusion that Ah receptor-
dependent processes may play a role in TEB development even in the absence of endogenous
ligand. However, as indicated below, the effects of the Ah receptor null mutation and Ah
receptor activation by exogenous ligand in the mouse turn out to be similar rather than opposite.
5.3.1.5.4. Response of organ-cultured mammary gland to exogenous ligand. Nulliparous
C57BL/6J mice were primed with 15 daily injections of estradiol and progesterone (Hushka et
al., 1998). Mammary glands were removed 24 hours after the last priming and cultured in the
presence of 0.1% DMSO or 1 to 100 nM 2,3,7,8-TCDF for 5 days and prepared for histology and
immunohistochemistry. Lobule size after culturing was suppressed by TCDF in a dose-related
manner, such that lobules in mammary glands exposed to the largest dose of TCDF were less
than one-half the size of vehicle-exposed lobules. The 3H-thymidine labeling index was also
reduced in the TCDF-exposed lobules. Therefore, the growth and development of TEBs into
lobules appeared to be suppressed by TCDF, but the effects of TCDF on TEB number after organ
culture were not reported, and therefore it is not known whether TCDD exposure caused the
expected increase in the number of TEBs. Overall, these results are consistent with the effects of
TCDD on mammary growth and development that were previously mentioned for the rat. In
addition, they suggest that toxicity results from direct effects of TCDF on the mammary gland.
5.3.1.5.5. Effects of prenatal TCDD exposure. Pregnant rats were orally administered 1 ng
TCDD/kg or vehicle on gestational day 15 (Brown et al., 1998). Mammary development was
evaluated in female offspring at 21 and 50 days of age. While body weight of the TCDD-
exposed female offspring, compared with that of vehicle-exposed female offspring, was reduced
at both time points, liver weight was reduced only on postnatal day 50. TCDD exposure delayed
the time of vaginal opening and caused a disruption in the estrus cycle. However, uterus weight
and mammary gland size were unaffected by in utero and lactational TCDD exposure.
Nevertheless, the number of TEBs was increased in the mammary glands of TCDD-exposed
female offspring, and there was a corresponding decrease in the number of lobules. Cellular
proliferation (BrdU labeling index) was not affected by TCDD exposure in TDs at either time
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point, but the results indicate that the differentiation pathway from TEBs to lobules was
inhibited.
When the carcinogen DMBA is administered to 50- to 60-day-old female rats, the
differentiation pathway of the mammary gland epithelium is disrupted (Russo and Russo, 1978).
Between 14 and 21 days post-DMBA inoculation, TEBs increase markedly in size and do not
differentiate into lobules. Instead, these TEB-derived larger structures, termed intraductal
proliferations (IDPs), may progress along an alternate pathway to form microtumors that have the
characteristics of rat mammary adenocarcinomas. These structures are derived exclusively from
TEBs. In utero and lactational exposure to a single maternal dose of 1 ng TCDD/kg administered
on gestational day 15 increased the number of TEBs in 50-day-old female offspring (Brown et
al., 1998). In addition, these glands were rendered more susceptible to the formation of DMBA-
induced mammary tumors. While neither epidemiological data nor occupational studies provide
clear support for an association between TCDD and the occurrence of breast cancer in women, it
is interesting to note that prenatal and postnatal exposure to TCDD can have opposite effects in
laboratory animals. Postnatal exposure to TCDD decreased the incidence of DMBA-induced
mammary tumors in female rats (Holcomb et al., 1994). This latter effect was believed to be due
to the antiestrogenic properties of TCDD. The mechanism whereby in utero and lactational
TCDD exposure alters TEB differentiation and promotes DMBA-induced mammary
tumorigenesis is not yet known.
5.3.1.5. Summary. The primary effects of TCDD on female reproduction appear to be
decreased fertility, inability to maintain pregnancy for the full gestational period, and in the rat,
decreased litter size. It is likely that ovarian dysfunction and alterations in normal estrous
cyclicity also result from TCDD exposure. The growing body of data also indicate that TCDD
exposure may lead to or favor the appearance of endometriosis. Koninckx et al. (1994) have
noted that Belgium has a high incidence of endometriosis and that TCDD concentrations in
breast milk in Belgium women are among the highest in the world, and a similar relationship
between measurable blood levels of TCDD and endometriosis has been reported in women in
Israel (Mayani et al., 1997). Mammary gland development in rats and mice can be adversely
affected by in utero and lactational TCDD exposure potentially leading to the formation of
tumors. Effects of TCDD exposure on the formation of mammary gland tumors can depend on
the life-stage at which exposure occurs. Continued attention to these and other effects on the
female reproductive system, especially in the nonpregnant state, will be important to determining
the potential female reproductive toxicity of TCDD.
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5.3.1.6. Alterations in Hormone Levels
The potential for TCDD to alter circulating female hormone levels has been examined,
but only to a very limited extent. In monkeys fed a diet that contained 500 ppt TCDD for ~9
months, the length of the menstrual cycle, as well as the intensity and duration of menstruation,
were not appreciably affected by TCDD exposure (Barsotti et al., 1979). However, there was a
decrease in serum estradiol and progesterone concentration in five of the eight exposed monkeys,
and in two of these animals the reduced steroid concentrations were consistent with anovulatory
menstrual cycles. In summary form, Allen et al. (1979) described the effects of dietary exposure
of female monkeys to 50 ppt TCDD. After 6 months of exposure to this lower dietary level of
TCDD, there was no effect on serum estradiol and progesterone concentrations in these monkeys.
Thus, the presence of these hormonal alterations is dependent on the level of dietary TCDD
exposure.
5.3.1.7. Antiestrogenic Action
5.3.1.7.1. In vivo. Estrogens are necessary for normal uterine development and for maintenance
of the adult uterus. The cyclic production of estrogens partially regulates the cyclic production of
FSH and LH that results in the estrous cycling of female mammals. In addition, estrogens are
necessary for the maintenance of pregnancy. Any effect that causes a decrease in circulating or
target cell estrogen levels can alter normal hormonal balance and action.
Early experimental results in rats and monkeys indicated that TCDD may have an
antiestrogenic action. Following administration of 1 \ig TCDD/kg/day to rats for 13 weeks,
Kociba et al. (1976) reported morphologic changes in the ovaries and uterus that were interpreted
as being due to a suppression or inhibition of the estrous cycle. Rhesus monkeys exposed to 500
ppt of TCDD in the diet for 6 months developed hormonal irregularities in their estrous cycles
that were associated with reduced conception rates as well as a high incidence of early
spontaneous abortions (Allen et al., 1977; Barsotti et al., 1979).
In rhesus monkeys, the severity of the TCDD-associated reproductive alterations was
correlated with decreased plasma levels of estrogen and progesterone (Barsotti et al., 1979).
Thus, one possible mechanism for these effects would be increased metabolism of estrogen and
progesterone due to induction by TCDD of hepatic microsomal enzymes and/or a decrease in the
rate at which these steroids are synthesized. On the other hand, serum concentrations of 17p-
estradiol are not significantly affected when TCDD is administered to pregnant rats (Shiverick
and Muther, 1983). Thus, an alternative mechanism for TCDD-associated reproductive
dysfunction could involve effects of TCDD on gonadal tissue itself, such as a decrease in its
responsiveness to estrogen. In support of this latter mechanism, the administration of TCDD to
CD-I mice results in a decreased number of cytosolic and nuclear estrogen receptors in
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hepatocytes and uterine cells. Although TCDD treatment induces hepatic cytochrome P-450
levels in these animals, it has no effect on serum concentrations of 17p-estradiol (DeVito et al.,
1992). This indicates that the antiestrogenic effect of TCDD in CD-I mice is not caused by a
decrease in circulating levels of estrogen.
Effects of estrogen on the uterus include a cyclic increase in uterine weight, increased
activity of the enzyme peroxidase, and an increase in the tissue concentration of progesterone
receptors. Antiestrogenic effects of TCDD administration to female rats include a decrease in
uterine weight, decrease in uterine peroxidase activity, and a decrease in the concentration of
progesterone receptors in the uterus (Safe et al., 1991). In addition, when TCDD and 17p-
estradiol are coadministered to the same female rat, the antiestrogenic action of TCDD
diminishes or prevents 17p-estradiol-induced increases in uterine weight, peroxidase activity,
progesterone receptor concentration, and expression of EGF receptor mRNA (Astroff et al.,
1990; Safe et al., 1991). Similarly, in mice TCDD administration decreases uterine weight and
antagonizes the ability of 17p-estradiol to increase uterine weight (Gallo et al., 1986).
The ability of TCDD to antagonize the effects of exogenously administered estrogen in
the rat is dependent on the age of the animal. In 21-day-old rats, TCDD does not affect 17p-
estradiol-induced increases in uterine weight or progesterone receptor concentration. On the
other hand, in 28-day-old intact rats and 70-day-old ovariectomized rats, both of these 17p-
estradiol-mediated responses are attenuated by TCDD (Safe et al., 1991). Previously, it had been
reported that TCDD administration does not alter the dose-dependent increase in uterine weight
due to exogenously administered estrone in sexually immature rats (Shiverick and Muther,
1982). The later work by Safe et al. (1991) suggests that this apparent lack of an antiestrogenic
effect of TCDD may have been due to the young age of the rats used.
5.3.1.7.2. In vitro. Both TCDD and progesterone can affect a decrease in the nuclear estrogen
receptor concentration in rat uterine strips (Romkes and Safe, 1988). However, the effect of
progesterone is inhibited by actinomycin D, cycloheximide, and puromycin, whereas the effect of
TCDD is inhibited only by actinomycin D. The reasons that the TCDD-induced decrease in
nuclear estrogen receptors is blocked by a transcription inhibitor, but not by protein synthesis
inhibitors, are not understood. However, these results indicate that TCDD and progesterone
decrease the nuclear estrogen receptor concentration by different mechanisms. In addition, the
antiestrogenic actions of TCDD can be demonstrated in cell culture, and two prominent
mechanisms could potentially be involved. They are (1) increased metabolism of estrogen due to
Ah receptor-mediated enzyme induction and (2) a downregulation of estrogen receptors within
the target cell.
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In MCF-7 cells, which are estrogen-responsive cells derived from a human breast
adenocarcinoma, antiestrogenic effects caused by the addition of TCDD to the culture medium
include a reduction of the 17p-estradiol-induced secretion of a 160 kDa protein, 52 kDa protein,
and 34 kDa protein (Biegel and Safe, 1990). These last two proteins are believed to be
procathepsin D and cathepsin D, respectively. In addition, treatment of MCF-7 cells with TCDD
suppresses the 17p-estradiol-enhanced secretion of IP A and inhibits estrogen-dependent
postconfluent cell proliferation (Gierthy et al., 1987; Gierthy and Lincoln, 1988). Thus, cultured
MCF-7 cells have several estrogen-dependent responses that are inhibited by TCDD; this
characteristic makes them a useful model system for studying the antiestrogenic actions of
dioxin.
In cultured MCF-7 cells, TCDD treatment induces aryl hydrocarbon hydroxylase (AHH)
activity, the hallmark response of Ah receptor binding, and increases hydroxylation of 17p-
estradiol at the C-2, C-4, C-6«, and C-15«, positions (Spink et al., 1990). It turns out that the
particular cytochrome P-450 that catalyzes the C-2, C-15«, and C-6« hydroxylations of 17p-
estradiol is cytochrome P-4501 Al, which is identical to AHH (Spink et al., 1992). TCDD
treatment also results in reduced levels of occupied nuclear estrogen receptors (Harris et al.,
1990). These results indicate, in MCF-7 cells, that the antiestrogenic effect of TCDD could
result from (1) an increased metabolism of estrogens due to Ah receptor-mediated enzyme
induction and/or (2) a decreased number of estrogen receptors in the nucleus. Safe and his
colleagues have published TCDD-concentration-response information for both the TCDD-
induced decrease in occupied nuclear estrogen receptors (Harris et al., 1989) and the induction of
AHH and EROD activities in MCF-7 cells (Harris et al., 1990). In addition, they have reported
that TCDD causes a decreased number of cytosolic and nuclear estrogen receptors in Hepa Iclc7
cells, which are a mouse hepatoma cell line (Zacharewski et al., 1991).
Independent analysis of the data suggests that the EC50 values for these effects are not
dissimilar enough to distinguish between the proposed mechanisms. Instead, it appears as though
TCDD induces the enzymes AHH and EROD over the same concentration range that it causes a
decreased concentration of occupied nuclear estrogen receptors in MCF-7 cells. In Hepa Iclc7
cells, the lowest concentration used was 10 pM. Although exposure to 10 pM TCDD resulted in
a statistically significant downregulation of estrogen receptors, Israel and Whitlock (1983)
reported that this concentration is the approximate EC50 for the induction of cytochrome
P-4501 Al mRNA and enzyme activity in these cells. Therefore, in Hepa Iclc7 cells as well as
in MCF-7 cells, it would appear that the TCDD concentrations required to produce enzyme
induction and reduction in occupied nuclear estrogen receptor levels are not dissimilar enough to
distinguish between the two proposed mechanisms.
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More recently, Safe and his colleagues have used an analog of TCDD, MCDF, which
inhibits the 17p-estradiol-induced secretion of the 34, 52, and 160 kDa proteins and
downregulates estrogen receptors in MCF-7 cells. These effects occurred at concentrations of
MCDF at which there is no detectable induction of EROD activity (Zacharewski et al., 1992). In
addition, it has been stated that the downregulation of estrogen receptors in Hepa Iclc? cells can
be detected as early as 1 hour after exposure of the cell cultures to 10 nM TCDD (Zacharewski et
al., 1991). This time is slightly less than the 2 hours that was required for Israel and Whitlock
(1983) to detect an increase in cytochrome P-4501 Al mRNA levels after exposure of Hepa
Iclc? cells to 10 pM TCDD. After exposure of Hepa Iclc7 cells to a maximally inducing
concentration of 1 nM TCDD, however, there are significant increases in the cellular
concentration of cytochrome P-4501 Al mRNA after 1 hour, whereas the induction of AHH
activity takes slightly longer (Israel and Whitlock, 1983).
Gierthy et al. (1987) reported that exposure of MCF-7 cells to 1 nM TCDD caused
suppression of the 17p-estradiol-induced secretion of tPA. This effect of TCDD, however,
occurred in the absence of any measurable decrease in the whole cell concentration of estrogen
receptors, even though the cultures were pretreated with serum-free medium to reduce cell
proliferation and maximize the cellular content of estrogen receptors. Gierthy's group pretreated
their cultures with serum-free medium, which was done to reduce cell proliferation and
maximize the cellular content of estrogen receptors. The disparity between this result of Gierthy
et al. (1987), which suggests no effect of TCDD on the estrogen receptor content of MCF-7 cells,
and the results of Safe and his colleagues to the contrary in this same cell line remains largely
unexplained. Overall, it appears as though no obvious distinction between the two proposed
mechanisms can be made at the present time. Therefore, it seems that the antiestrogenic effect of
TCDD results from both an increased metabolism of estrogen and a decreased number of
estrogen receptors. It is important to note that TCDD does not compete with radiolabeled
estrogens or progesterone for binding to estrogen or progesterone receptors and that these
steroids do not bind to the Ah receptor or compete with radiolabeled TCDD for binding (Romkes
et al., 1987; Romkes and Safe, 1988).
5.3.1.7.3. Evidence for an Ah receptor mechanism
5.3.1.7.3.1. Ah receptor mutants. Although the precise cellular mechanism by which TCDD
produces its antiestrogenic effect is subject to a discordance between two primary schools of
thought, there is agreement that the response is mediated by the Ah receptor. Thus, the
antiestrogenic effects of TCDD in cultured cells appear to involve an Ah receptor-mediated
alteration in the transcription of genes. This is indicated by studies using wild-type Hepa Iclc7
cells and mutant Hepa Iclc7 cells in culture (Zacharewski et al., 1991). In wild-type cells,
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TCDD reduces the number of nuclear estrogen receptors, and this response can be inhibited by
cycloheximide and actinomycin D. However, in class 1 mutants, which have relatively low Ah
receptor levels, TCDD has only a small effect. Similarly, in class 2 mutants, which have a defect
in the accumulation of transcriptionally active nuclear Ah receptors, there was no effect of
TCDD on the number of nuclear estrogen receptors. Taken together, these results indicate that
the downregulation of estrogen receptors in Hepa Iclc? cells involves an Ah receptor-mediated
effect on gene transcription. As previously noted, TCDD induces cytochrome P-4501 Al mRNA
transcription and enzyme activity in Hepa Iclc? cells (Israel and Whitlock, 1983). This effect is
also Ah receptor mediated (Nebert and Gielen, 1972).
5.3.1.7.3.2. Structure-activity relationships in vivo. The relative potencies of halogenated
aromatic hydrocarbon congeners as inhibitors of uterine peroxidase activity in the rat are similar
to their relative Ah receptor-binding affinities (Astroff and Safe, 1990). Only limited relative
potency information is available for the reduction of hepatic and uterine estrogen receptor
concentrations per se by these substances in rats. TCDD and 1,2,3,7,8-PeCDD both exhibit high
affinity for the Ah receptor. At an 80 ng/kg dose of either of these two substances, hepatic
estrogen receptor concentrations are reduced 42% and 41%, whereas uterine estrogen receptor
concentrations are reduced 53% and 49% by TCDD and 1,2,3,7,8-PeCDD, respectively. On the
other hand, 1,3,7,8-TCDD and 1,2,4,7,8-PeCDD bind less avidly to the Ah receptor. At a 400
ng/kg dose of either of these two substances, hepatic estrogen receptor concentrations are reduced
36% and 40%, whereas uterine estrogen receptor concentrations are reduced 21% and 24% by
1,3,7,8-TCDD and 1,2,4,7,8-PeCDD, respectively (Romkes et al., 1987). As the potency of these
congeners for reducing estrogen receptor concentrations correlates with their Ah receptor-binding
affinities, these in vivo results provide evidence that the antiestrogenic effect of TCDD is
mediated by the Ah receptor.
5.3.1.7.3.3. Genetic evidence. Consistent with the interpretation based on structure-activity
relationships, there is a greater reduction in the number of hepatic estrogen receptors when
AhbAhb C57BL/6 mice are exposed to TCDD than when AhdAhd DBA/2 mice are similarly
exposed (Lin et al., 1991). To date, however, the antiestrogenic effects have not been studied in
the progeny of test crosses between AhbAhb and AhdAhd mouse strains that respectively produce
Ah receptors with high- or low-binding affinity for TCDD. Therefore, the potential segregation
of the antiestrogenic effects of TCDD with the Ah locus has not been verified by the results of
genetic crosses.
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5.3.1.7.3.4. Structure-activity relationships in vitro. The Ah receptor is detectable in MCF-7
cells, and AHH as well as EROD activities are both inducible in these cells (Harris et al., 1989).
The relative abilities of TCDD and other CDD, CDF, and PCB congeners to suppress 17p-
estradiol-induced secretion of tPA by MCF-7 cells are consistent with the structure-activity
relationships for other Ah receptor-mediated responses (Gierthy et al., 1987). In addition, the
rank order of potency for several Ah receptor agonists in reducing nuclear estrogen receptors in
MCF-7 cells is TCDD > 2,3,4,7,8-PeCDD > 2,3,7,8-TCDF > 1,2,3,7,9-PeCDD > 1,3,6,8-TCDF
(Harris et al., 1990). The rank order of potency for these substances is consistent with their
relative activities as Ah receptor agonists. These results in vitro support a role for the Ah
receptor in the antiestrogenic actions of TCDD.
5.3.2. Male
5.3.2.1. Reproductive Function/Fertility
TCDD and related compounds decrease testis and accessory sex organ weights, cause
abnormal testicular morphology, decrease spermatogenesis, and reduce fertility when given to
adult animals in doses sufficient to reduce feed intake and/or body weight. Certain of these
effects have been reported in chickens, rhesus monkeys, rats, guinea pigs, and mice treated with
overtly toxic doses of TCDD, TCDD-like congeners, or toxic fat that was discovered later to
contain TCDD (Allen and Lalich, 1962; Allen and Carstens, 1967; Khera and Ruddick, 1973;
Kociba et al., 1976; van Miller et al., 1977; McConnell et al., 1978; Moore et al., 1985; Chahoud
et al., 1989; Morrisey and Schwetz, 1989). In testis of these different species, TCDD effects on
spermatogenesis are characterized by loss of germ cells, the appearance of degenerating
spermatocytes and mature spermatozoa within the lumens of seminiferous tubules, and a
reduction in the number of tubules containing mature spermatozoa (Allen and Lalich, 1962;
Allen and Carstens, 1967; McConnell et al., 1978; Chahoud et al., 1989). The lowest cumulative
dose of TCDD to decrease spermatogenesis in the rat was 1 ng/kg/day administered 5 days a
week for 13 weeks (Kociba et al., 1976). With this dosage regimen, which resulted in a TCDD
body burden of approximately 20 ng/kg at the end of the dosing period (Rose et al., 1976), body
weights and feed consumption of the rats also were significantly depressed. A similar 13-week
dosing study using adult male mice found that 3 and 30 mg 3,3',4,4'-TCAOB/kg/day caused
reductions in epididymal sperm number (van Birgelen et al., 1999). In adult male Sprague-
Dawley rats a single dose of 25 ng TCDD/kg decreased epididymal sperm numbers, whereas
testicular Leydig cell volume was decreased at 12.5 \ig TCDD/kg (Johnson et al., 1992). By
comparison, daily sperm production was not affected, even by 50 \ig TCDD/kg. Thus, the
suppression of spermatogenesis and reduction in epididymal sperm number are not highly
sensitive effects when Ah receptor agonists are administered to adult animals.
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In contrast, daily sperm production assessed on postnatal day 90 was significantly
decreased in weanling Sprague-Dawley rats administered 1 ng TCDD/kg on postnatal day 21 (el-
Sabeawy et al., 1998). In addition, testis histology revealed that a dose of 10 \ig TCDD/kg
caused a decrease in seminiferous tubule diameter compared with that in vehicle-dosed control
rats. The spermatogonial population normally located in the basal area of the tubules was absent
in the TCDD-treated rats. However, these effects on testis histology were not found at TCDD
doses less than 10 ng TCDD/kg. Motility studies were performed on epididymal sperm, and
dose-related decreases in sperm curvilinear velocity and beat cross frequency were found over
the dose range from 0.1-5.0 ng TCDD/kg. Average path and straight line velocity were
significantly decreased at 5 ng TCDD/kg (el-Sabeawy et al., 1998).
Effects of TCDD administration to 21-day-old rats on epidermal growth factor receptor-,
protein tyrosine kinase-, protein kinase A-, protein kinase C-, mitogen-activated protein 2 kinase,
and c-Src tyrosine kinase-mediated pathways in the testis were also examined (el-Sabeawy et al.,
1998). Dose-related increases in the activity of c-Src kinase were found on postnatal days 34 and
90 over the dose range from 0.1-5.0 \ig TCDD/kg. In addition, the administration of multiple
doses of the c-Src kinase inhibitor geldanamycin over the time period from postnatal days 21-90
blocked the effects of TCDD on testis weight and daily sperm production. The authors conclude
that these results provide evidence for the involvement of epidermal growth factor and Src
kinasesignaling pathways in the mechanism by which TCDD disrupts testicular development and
subsequently affects testis function.
5.3.2.2. Alterations in Hormone Levels
The effects of TCDD on the male reproductive system are believed to be due in part to an
androgenic deficiency. This deficiency is characterized in adult rats by decreased plasma
testosterone and DHT concentrations, unaltered plasma LH concentrations, and unchanged
plasma clearance of androgens and LH (Moore et al., 1985, 1989; Mebus et al., 1987; Moore and
Peterson, 1988; Bookstaff et al., 1990a). The ED50 of TCDD for producing this effect in adult
male rats on day 7 after dosing is 15 ng/kg (Moore et al., 1985), and it can be detected within 1
day of treatment. As described in the following sections, the cause of the androgenic deficiency
is decreased testicular responsiveness to LH and increased pituitary responsiveness to feedback
inhibition by androgens and estrogens (Moore et al., 1989, 1991; Bookstaff et al., 1990a,b;
Kleeman et al., 1990).
5.3.2.3. Target Organ Responsiveness
5.3.2.3.1. Inhibition of testicular steroidogenesis. Testicular steroidogenesis occurs within
Leydig cells and is regulated primarily by plasma LH concentrations (Payne et al., 1985; Hall,
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1988). Binding of LH to the LH receptor causes cAMP and possibly other second messengers to
be formed (Cooke et al., 1989). In response, cholesterol is rapidly transported to the initial
enzyme in the testosterone biosynthetic pathway, a cholesterol side-chain cleavage enzyme,
which is a cytochrome P-450 (cytochrome P-450SCC) located on the inner side of the inner
mitochondrial membrane that converts cholesterol to pregnenolone. The mobilization of free
cholesterol rather than its conversion to pregnenolone and other metabolites is generally
considered to be the rate-limiting step in testicular steroidogenesis. TCDD inhibits testosterone
biosynthesis, predominantly if not exclusively by inhibiting the mobilization of free cholesterol
that acts as a substrate for cytochrome P-450SCC (Moore et al., 1991). Thus, in the testes of
TCDD-treated rats, cholesterol is provided to the cytochrome P-450SCC enzyme at too slow a rate
to maintain androgenic homeostasis, even when the plasma LH concentration characteristic of
"normal" androgen levels is present.
Leydig cell volume was significantly reduced 4 weeks after a single intraperitoneal
injection of TCDD (Johnson et al., 1994). The effect was dose related and observed at the lowest
dose tested, 12.5 \ig TCDD/kg BW. This reduction in total cell volume resulted from both a
reduced number of cells and a reduced size of individual cells. Wilker et al. (1995) were able to
establish that this effect of TCDD can be prevented by hCG.
5.3.2.3.2. Altered regulation of pituitary LH secretion. In TCDD-treated male rats, the
expected increase in plasma LH concentration that would facilitate testicular compensation for
the decreased plasma androgens does not occur (Moore et al., 1989; Ruangwises et al., 1991).
The failure of the plasma LH concentration to rise appropriately is not caused by an increase in
the plasma clearance of LH or by a decrease in the maximal rate of pituitary LH synthesis or
secretion (Bookstaff et al., 1990a,b). Rather, TCDD alters the feedback regulation of LH
secretion in male rats by increasing the potency of testosterone and its metabolites (DHT and
17p-estradiol) as inhibitors of LH secretion. The ED50 of TCDD for enhancing the testosterone-
mediated inhibition of LH secretion 7 days after treatment is the same as its ED50 for causing the
androgenic deficiency (15 ng/kg). Also, both responses are detected within 1 day of TCDD
dosing and are fully developed after 7 days when the ED50s were determined.
Decreased plasma androgen concentrations normally result in compensatory increases in
both the number of pituitary gonadotropin-releasing hormone (GnRH) receptors and the
responsiveness of the pituitary to GnRH. TCDD treatment prevents the increases in GnRH
receptor number and responsiveness that would be expected in the light of the decreased plasma
androgen concentrations (Bookstaff et al., 1990b). The pituitary is thus a target organ for TCDD
because its responsiveness to hormones secreted by the testis (testosterone) and hypothalamus
(GnRH) is altered by TCDD.
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If the plasma LH concentrations in TCDD-treated rats did increase appropriately in
response to decreased plasma androgens, it is expected that plasma androgens would return to
normal levels (Kleeman et al., 1990). This is because the testes of TCDD-treated rats are capable
of synthesizing more testosterone than is needed to maintain androgen concentrations in the
physiological range, although this would require significantly elevated levels of LH in TCDD-
treated rats. The fact that there is a testicular reserve capacity to provide for sufficient amounts
of androgen synthesis, even when compromised, underscores the importance of the effects of
TCDD on pituitary LH secretion in producing the effects of TCDD on plasma androgen
concentrations.
5.3.2.3.3. Differential responsiveness of androgen target organs. The dose-related reductions
in plasma testosterone and DHT concentrations in intact adult rats are accompanied by similar
dose-related reductions (ED5015 ng TCDD/kg) in seminal vesicle and ventral prostate weights
measured 7 days after dosing (Moore et al., 1985). In contrast, TCDD has no effect on accessory
sex organ weights (or plasma androgen concentrations) in castrated adult rats implanted with
either testosterone- or DHT-containing capsules (Moore and Peterson, 1988; Bookstaff et al.,
1990a,b). As trophic responsiveness of the seminal vesicles and ventral prostate to testosterone
and DHT are unaffected by postpubertal TCDD treatment, it follows that TCDD can increase
responsiveness of the pituitary to androgens without affecting responsiveness of the accessory
sex organs to androgens.
5.3.2.3.4. Relative sensitivity. The male reproductive system in rats is -100 times more
susceptible to TCDD toxicity when exposure occurs perinatally (ED50 for the most sensitive
effects, 0.16 ng/kg) rather than in adulthood (ED50 for the most sensitive effects, 15 ng/kg). To
illustrate this sensitivity, a single maternal TCDD dose as low as 0.064 ng/kg given on day 15 of
gestation significantly decreases epididymis and cauda epididymis weights, cauda epididymal
sperm numbers, and daily sperm production in male offspring at various stages of sexual
development. Decreases in ventral prostate weights in 32-day-old male offspring and in older
males, increases in the number of mounts preceding ejaculation, and increases in intromission
latency also are produced by maternal TCDD doses as low as 0.064 ng/kg. The 0.064 \ig
TCDD/kg dose is not maternally toxic and produces no signs of overt toxicity in male or female
offspring. Other effects of perinatal exposure on the male reproductive system were detected at a
maternal TCDD dose of 0.16 ng/kg or higher (Mably et al., 1991, 1992a,b,c). On the other hand,
when exposure occurs in adulthood, relatively large doses in the overtly toxic range are required
to cause decreases in spermatogenesis and in ventral prostate and caput epididymis weight
(Kociba et al., 1976; Moore et al., 1985). Kociba et al. (1976) reported that accessory sex organ
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weights and spermatogenesis are decreased in rats following exposure to 1 ng TCDD/kg/day, 5
days per week for 13 weeks. Using the parameters for TCDD half-life and bioavailability in the
rat determined by Rose et al. (1976), this dosage regimen results in a TCDD body burden of
approximately 20 ng/kg at the end of the dosing period.
In adult rats, the most sensitive toxic responses to TCDD have been observed following
long-term, low-level exposure. In a three-generation reproduction study, Murray et al. (1979)
reported that dietary administration of TCDD at doses as low as 0.01 ng/kg/day significantly
affected reproductive capacity in female rats, with no effects seen at 0.001 ng/kg/day (NOAEL).
The same NOAEL was found in a 2-year chronic toxicity and oncogenicity study in which an
increased incidence of certain types of neoplasms was altered among rats given TCDD doses of
0.01 or 0.1 ng/kg/day (Kociba et al., 1978). Based on the pharmacokinetics of TCDD in the rat
(Rose et al., 1976), the steady-state body burden of TCDD in these rats that were chronically
dosed (>90 days) with either 0.01 or 0.001 ng TCDD/kg/day is approximately 0.29 ng/kg
(LOAEL) and 0.029 ^g/kg (NOAEL), respectively. Yet, Mably et al. (1991, 1992a,b,c) found
that a single TCDD dose of 0.064 ng/kg given on day 15 of gestation produces a number of
statistically significant effects on the reproductive system of male rat offspring. Because 0.064
[ig TCDD/kg was the lowest dose tested, a NOAEL for developmental male reproductive
toxicity, which is defined as the lowest dose used that has no statistically significant effect, could
not be determined by Mably et al. (1991, 1992a,b,c). It is concluded that developmental effects
on spermatogenesis occur at a maternal TCDD dose that is lower than any previously shown to
produce toxicity in rats.
5.4. SUMMARY
This chapter has focused on the variety of effects that dioxin and dioxin-like agents can
have on human reproductive health and development. The review is not exhaustive, and
emphasis has been placed on the more recent reports. These have been put into context with
previous reviews of the literature applicable in risk assessment (Kimmel, 1987) to develop a
profile of the potential for dioxin and dioxin-like agents to cause reproductive or developmental
toxicity based on the available literature. A portion of this report has been previously published
(Peterson et al., 1993).
5.4.1. Human
The literature base with regard to potential human effects is detailed in Chapter 7. In
general, there is no epidemiological evidence that makes a direct association between exposure to
TCDD or TCDD-related agents and effects on human reproduction or development. However,
the evidence that has been accumulated is suggestive of such an effect, at least with respect to
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developmental toxicity. All four manifestations of developmental toxicity (reduced viability,
structural alterations, growth retardation, and functional alterations) have been observed to some
degree following presumed exposure to TCDD-related agents. The incidents at Yusho and Yu-
Cheng resulted in increased perinatal mortality and low birthweight in infants born to women
who had been exposed. Rocker bottom heal was observed in Yusho infants, and functional
abnormalities have been reported in Yu-Cheng children.
Of particular interest is the ectodermal origin of many of the organs and tissues that are
affected in the human. An ectodermal dysplasia syndrome has been clearly associated with the
Yusho and Yu-Cheng episodes, involving hyperpigmentation, deformation of the fingernails and
toenails, conjunctivitis, gingival hyperplasia, and abnormalities of the teeth. The developmental
effects that can be associated with the nervous system are also consistent with this pattern
because the nervous system is of ectodermal origin.
5.4.2. Experimental Animal
In developing a toxicological profile, it is rare to have sufficient data from human studies
for quantitative analysis. Consequently, the risk assessment most often relies on data from
experimental animal studies. For dioxin and the dioxin-related agents, the experimental animal
database is fairly extensive with respect to reproductive and developmental toxicity. Dioxin
exposure has been observed to result in both male and female reproductive effects, as well as
effects on development. These latter effects are among the most responsive health endpoints to
dioxin exposure. In general, the prenatal and developing postnatal animal is more sensitive to
the effects of dioxin than the adult.
5.4.2.1. Developmental Toxicity
Dioxin exposure results in a wide variety of developmental effects and these are observed
in three different vertebrate classes and in several species within each class. All four of the
manifestations of developmental toxicity have been observed following exposure to dioxin,
including reduced viability, structural alterations, growth retardation, and functional alterations.
As summarized previously (Peterson et al., 1993), increased prenatal mortality (rat and monkey),
functional alterations in learning and sexual behavior (rat and monkey), and changes in the
development of the reproductive system (rat) occur at the lowest exposure levels.
Dioxin exposure results in reduced prenatal or postnatal viability in virtually every
species in which it has been tested. Previously, increased prenatal mortality appeared to be
observed only at exposures that also resulted in maternal toxicity. However, the studies of Olson
and McGarrigle (1991) in the hamster and Schantz et al. (1989) in the monkey were suggestive
that this was not the case in all species. Although the data from these two studies were limited,
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prenatal death was observed in cases where no maternal toxicity was evident. In the rat,
Peterson's laboratory (Bjerke et al.,1994a,b; Roman et al., 1995) reported increased prenatal
death following a single exposure to TCDD during gestation that did not cause maternal toxicity,
and Gray et al. (1995a) observed a decrease in postnatal survival under a similar exposure
regimen. While identifying the presence or absence of maternal toxicity may be instructive as to
the specific origin of the reduced prenatal viability, it does not alter the fact that pre- and
postnatal death were observed. In either case, the Agency considers these effects as being
indicators of developmental toxicity in response to the exposure (U.S. EPA, 1991).
Some of the most striking findings regarding dioxin exposure relate to the effects on the
developing reproductive system. The findings are even more impressive with the understanding
that only a single, low-level exposure during gestation is required to initiate these developmental
alterations. Mably et al. (1992a,b,c) originally reported that a single exposure of the Holtzman
maternal animal to as low as 0.064 ng/kg could alter normal sexual development in the male
offspring. More recently, these findings have been further defined (Bjerke et al., 1994; Gray et
al., 1995; Roman et al., 1995), as well as extended to females and another strain and species
(Gray et al., 1995). In general, the findings of these later studies have produced qualitatively
similar results that define a significant effect of dioxin on the developing reproductive system.
In the developing male, dioxin exposure during the prenatal and lactational periods
results in the delay of the onset of puberty as measured by age at preputial separation. There is a
reduction in testis weight, sperm parameters, and sex accessory gland weights. In the mature
male exposed during the prenatal and lactational periods, there is an alteration of normal sexual
behavior and reproductive function. Males exposed to TCDD during gestation are
demasculinized. Feminization and a reduction in the number of implants in females mated with
exposed males have also been reported, although these effects have not been consistently found.
These effects do not appear to be related to reductions in circulating androgens, which were
shown in the most recent studies to be normal. Most of these effects occur in a dose-related
fashion, some occurring at 0.05 ng/kg and 0.064 ng/kg, the lowest TCDD doses tested (Mably et
al., 1992c; Grayetal., 1997a).
In the developing female, Gray and Ostby (1995) have demonstrated altered sexual
differentiation in both the Long Evans and Holtzman rat. The effects observed depended on the
timing of exposure. Exposure during early organogenesis altered cyclicity, reduced ovarian
weight, and shortened reproductive life span. Exposure later in organogenesis resulted in slightly
lowered ovarian weight, structural alterations of the genitalia, and a slight delay in puberty.
However, cyclicity and fertility were not affected with the later exposure. The most sensitive
dose-dependent effects of TCDD in the female rat were structural alterations of the genitalia that
occurred at 0.20 ng/kg (Grayetal., 1997b).
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Structural malformations, particularly cleft palate and hydronephrosis, occur in mice.
While these are not the most sensitive developmental endpoints, the findings indicate that
exposure during the critical period of organogenesis can affect the processes involved in normal
tissue formation. The TCDD-sensitive events appear to require the Ah receptor. Mouse strains
that produce Ah receptors with relatively high affinity for TCDD respond to lower doses than
strains with relatively low-affinity receptors. Moreover, congeners with a greater affinity for the
Ah receptor are more developmentally toxic than those with a lower affinity.
5.4.2.2. Adult Female Reproductive Toxicity
The primary effects of TCDD on female reproduction appear to be decreased fertility,
inability to maintain pregnancy for the full gestational period, and in the rat, decreased litter size.
In some studies, signs of ovarian dysfunction such as anovulation and suppression of the estrous
cycle have been reported (Kociba et al., 1976; Barsotti et al., 1979; Allen et al., 1979; Li et al.,
1995a,b). Rier et al. (1993) reported TCDD-associated endometriosis in the monkey, and similar
effects have now been reproduced in rats and mice (Cummings et al., 1996). Unfortunately, the
amount of attention given to the female reproductive system, especially in the nonpregnant state,
has been limited. Additional studies on the female reproductive system will be important to
determining the potential female reproductive toxicity of TCDD.
5.4.2.3. Adult Male Reproductive Toxicity
TCDD and related compounds decrease testis and accessory sex organ weights, cause
abnormal testicular morphology, decrease spermatogenesis, and reduce fertility when given to
adult animals in doses sufficient to reduce feed intake and/or body weight. In the testis of these
different species, TCDD effects on spermatogenesis are characterized by loss of germ cells, the
appearance of degenerating spermatocytes and mature spermatozoa within the lumens of
seminiferous tubules, and a reduction in the number of tubules containing mature spermatozoa
(Allen and Lalich, 1962; Allen and Carstens, 1967; McConnell et al., 1978; Chahoud et al.,
1989). This suppression of spermatogenesis is not a highly sensitive effect when TCDD is
administered to postweanling animals, as an exposure of 1 ng/kg/day over a period of weeks
appears to be required to result in these effects.
The effects of TCDD on the male reproductive system when exposure occurs in
adulthood are believed to be due in part to an androgenic deficiency. This deficiency is
characterized in adult rats by decreased plasma testosterone and DHT concentrations, unaltered
plasma LH concentrations, and unchanged plasma clearance of androgens and LH (Moore et al.,
1985, 1989; Mebus et al., 1987; Moore and Peterson, 1988; Bookstaff et al., 1990a). The cause
of the androgenic deficiency is decreased testicular responsiveness to LH and increased pituitary
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responsiveness to feedback inhibition by androgens and estrogens (Moore et al., 1989, 1991;
Bookstaff et al., 1990a,b; Kleeman et al., 1990).
5.4.3. Conclusion
TCDD and related compounds have reproductive and developmental toxicity potential. It
is assumed that the responses observed in animal studies are indicative of the potential for
reproductive and developmental toxicity in humans. This is an established assumption in the risk
assessment process for developmental toxicity (U.S. EPA, 1991). It is supported by the number
of animal species and strains in which effects have been observed. The limited human data are
consistent with an effect following exposure to TCDD or TCDD-like agents.
Many of the effects have been shown to be dose-response related. The effects on
perinatal viability and male reproductive development are among the most sensitive effects
reported, occurring at a single prenatal exposure range of as little as 0.05-0.075 ng/kg. In these
studies, this was the lowest exposure level tested; thus a NOAEL has not been established for
these endpoints.
In general, the structure-activity results are consistent with an Ah receptor-mediated
mechanism for many of the developmental effects that are observed. The structure-activity
relationship in laboratory mammals appears to be similar to that for Ah receptor binding. This is
especially the case with cleft palate in the mouse. However, a direct relationship with Ah
receptor binding is less clear for other effects, including those involving the nervous system.
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Table 5-1. Relationship between maternal toxicity and prenatal mortality in
laboratory mammals exposed to TCDD during gestation
Species/strain
Monkey/rhesus
Guinea pig/Hartley
Rabbit/New Zealand
Rat/Wistar
Rat/Sprague-Dawley
Hamster/Golden Syrian
Mouse/CD-I
Daily TCDD
dose
(lig/kg/day)
Of
0.1
0.25
0.5
1
Of
0.125
0.25
0.5
1
1
2
4
Of
0.03
0.125
0.5
2
8
0s
25
50
100
200
400
Cumulative
TCDD dose
(us/kg)
Oc
0.2
1
5
Oe
0.15
1.5
0
1
2.5
5
10
0
1.25
2.5
5
10
10
20
40
0
0.3
1.25
5
20
80
Oh
1.5
3
6
18
0
250
500
1,000
2,000
4,000
Overt maternal
toxicity"
_
-
+d
+d
-
-
+
.
-
+
+
+
_
-
-
-
±
+
+
+
_
-
-
+
+
+
_
-
-
-
-
_
-
-
-
+
+
Percent
prenatal
mortality1"
25
25
81
100
-
-
+
7
12
42
22
100
3
1
2
9
8
368
538
1008
25
21
15
418
958
1008
_
-
-
-
58
7
6
13
14
87
97
Reference
McNulty, 1984
Olson and
McGarrigle, 1991
Giavini et al.,
1982b
Khera and
Ruddick, 1973
Sparschu et al.,
1971
Olson and
McGarrigle, 1991
Courtney, 1976
^Decreased body weight gain or marked edema compared with vehicle dosed controls. A (+) or (-) indicates the presence or absence of an effect.
bPercentage of absorptions plus late gestational deaths relative to all implantations. A (+) or (-) indicates the presence or absence of an effect.
TCDD administered in single or divided doses between gestational days 20 and 40.
dEffects include thickening and reddening of the eyelids, weight loss, dryness and granularity of the skin, loss of hair, and in some cases anemia, purpura, and
bleeding from the nose and mouth.
^Single dose of TCDD administered on gestational day 14.
TCDD administered daily on days 6 to 15 of gestation.
Significant at^<0.05.
hSingle dose of TCDD administered on gestational days 7 or 9.
TCDD administered daily on days 7 to 16 of gestation.
Source: Couture et al., 1990a.
December 2003
5-101
DRAFT—DO NOT CITE OR QUOTE
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Table 5-2. Developmental toxicity following gestational exposure to 2,3,7,8-TCDD
Species/Strain
Mice/C57BL/6N
Mice/C57BL/6N
Mice/C57BL/6N
Mice/C57BL/6N
Mice/C57BL/6N
Mice/C57BL/6J
Mice/C57BL/6J
Mice/C57BL/6J
Mice/NMR
Mice/CF-1
Mice/DBA
Mice/DBA
Daily dose3
0, 1, or 3 ng/kg
0, 12, 17, or 22 ng/kg
0, 3, or 12 Lig/kg
0 or 3 ng/kg
0, 6, 9, 12, 15, or 18 ^g/kg
0 or 3 ng/kg
(subcutaneous)
20 ^g/kg
0,0.5, 1,2, or4ng/kg
0 3 3 4 5 or 9 ng/kg
0,0.001, 0.01, 0.1, or 1.3
Hg/kg
0 or 3 ^g/kg
(subcutaneous)
0, 0.5, 2, 4, or 8 ng/kg
Treatment
days"
10, 10-13
10
11, 10-13
10-13
10, 12
6-15
10
6-15
6-15
6-15
6-15
6-15
Sacrifice
day"
18
18
18
18
18
18
17
18
18
NR
18
18
Maternal effects0
NR
Increase in liver-to-
body weight ratio
Increase in liver-to-
body weight ratio
Increase in liver-to-
body weight ratio
Increase in liver-to-
body weight ratio and
weight gain
Increase in liver-to-
body weight ratio
Increase in liver-to-
body weight ratio
Increase in liver-to-
body weight ratio
NR
None
Increase in liver-to-
body weight ratio
Increase in liver-to-
body weight ratio;
decrease in thymus-
to-body weight ratio
Embryo/fetal effects0
Increase in cleft palate
and hydronephrosis
Increase in cleft palate
and hydronephrosis
Increase in cleft palate
and hydronephrosis
Increase in
hydronephrosis
Increase in cleft palate
and hydronephrosis
Increase in cleft palate
and kidney anomaly
Increase in cleft palate,
hydronephrosis, and fetal
body weight
Increase in cleft palate,
hydronephrosis, and fetal
body weight
Increase in cleft palate
and fetal mortality;
decrease in fetal body
weight
Increase in cleft palate
and hydronephrosis
Increase in cleft palate
and kidney anomaly
Increase in cleft palate
and hydronephrosis
Reference
Moore etal.,
1973
Weber etal.,
1985
Birnbaumetal.,
1985
Birnbaumetal.,
1986
Birnbaumetal.,
1989
Courtney and
Moore, 1971
Haake et al.,
1987
Silkworthetal.,
1989
Neubert and
Dillman, 1972
Smith et al.,
1976
Courtney and
Moore, 1971
Silkworthetal.,
1989
-------
Table 5-2. Developmental toxicity following gestational exposure to 2,3,7,8-TCDD (continued)
Species/Strain
Mice/CD-I
Mice/CD-I
Rats/CD
Rats/Sprague-
Dawley
Rats/Sprague-
Dawley
Rats/Wistar
Guinea pigs/Hartley
Hamsters/Golden
Syrian
Rabbits/New
Zealand
Daily dose3
0, 1, or 3 ng/kg
(subcutaneous)
0, 25, 50, 100, 200, or 400
Hg/kg
Oor0.5|ig/kg
2|ig/kg
(subcutaneous)
0,0.125, 0.5, or 2 ng/kg
0.03,0.125, 0.5, 2, or 8
Hg/kg
0,0.125,0.25,0.5, 1,2,4,
8, or 16 |-ig/kg
0,0.15, or 1.5 ng/kg
0, 1.5, 3, 6, or 18 fig/kg
0,0.1,0.25, 0.5, or 1
Hg/kg
Treatment
days"
6-15
6-15
6-15
9-10 or 13-
14
0-2
6-15
5-14
14
7,9
6-15
Sacrifice
day"
17
17
20
20
21
21
58
15
28
Maternal effects0
None
Increase in liver-to-
body weight ratio
None at 0.5 ng/kg
NR at 2 ng/kg
Decrease in weight
gain
Decrease in weight
gain; toxicity
Toxicity
Increase in mortality;
toxicity
Increase in liver-to-
body weight ratio
Decrease in weight
gain; toxicity
Embryo/fetal effects0
Increase in cleft palate
and kidney anomaly
Increase in cleft palate,
hydronephrosis, and fetal
mortality
Increase in kidney
anomaly
Decrease in fetal body
weight
Increase in fetal
mortality, resorptions,
edema, and
gastrointestinal
hemorrhage
Increase in fetal
mortality, edema and
gastrointestinal
hemorrhage; decrease in
fetal weight
Increase in fetal mortality
Increase in fetal
mortality,
hydronephrosis, and renal
congestion; decrease in
thymus size
Increase in fetal mortality
and resorptions; extra
ribs
Reference
Courtney and
Moore, 1971
Courtney, 1976
Courtney and
Moore, 1971
Giavini et al.,
1982a
Sparschu et al.,
1971
Khera and
Ruddick, 1973
Olson and
McGarrigle,
1990
Olson and
McGarrigle,
1990
Giavini et al.
1982b
-------
o
O
to
O
O
oo
Table 5-2. Developmental toxicity following gestational exposure to 2,3,7,8-TCDD (continued)
Species/Strain
Monkeys/rhesus
Monkeys/rhesus
Daily dose3
0, 5, 25, 50, or 500 ppt
7 months before and
during pregnancy
0, 0.2d, ld, le, or 5d ng/kg
Treatment
days"
Chronic
20-40
Sacrifice
day"
~
-
Maternal effects0
Increase in mortality;
toxicity
Increase in mortality;
toxicity
Embryo/fetal effects0
Increase in fetal mortality
Increase in fetal mortality
Reference
Allen et al.,
1979; Bowman
etal., 1989
McNulty, 1985
fj\
O
aOral exposure unless otherwise noted.
bAll days adjusted to reflect plug day—gestation day 0.
cEffects reported are only those that were statistically significant.
dCumulative dose divided into nine oral doses administered between days 20 and 40 of gestation; two to four monkeys/dose.
eThree animals given single oral dose, on either gestation days 25, 30, 35, or 40; 12 monkeys total.
NR = Not reported.
Source: Couture et al., 1990a.
O
O
2
O
H
O
HH
H
W
O
V
O
c
O
H
W
-------
Table 5-3. TCDD responsiveness of palatal shelves from the mouse, rat, and
human in organ culture
Species
Mouse
Rata
Humanb
Molar concentration of TCDD
Induction of epithelial proliferation and
prevention of epithelial-to-mesenchyme
transformation
LOEL
IxlO'13
lxlQ-10
SxlQ-11
EC100
SxlQ-11
IxlO-8
IxlO-8
Cytotoxicity
IxlO-10
IxlO-7
IxlO-7
aAt the highest concentration tested, 60% of the palatal shelves failed to undergo
programmed cell death.
bOne of four shelves responded by failing to undergo programmed cell death at 5* 10"11 M.
Source: Birnbaum, 1991.
December 2003
5-105
DRAFT—DO NOT CITE OR QUOTE
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Table 5-4. Relative teratogenic potency of halogenated aromatic hydrocarbon
congeners in C57BL/6 mice
Congener
2,3,7,8-TCDD
2,3,7,8-TBDD
2,3,7,8-TBDF
2,3,4,7,8-PeCDF
2,3,7,8-TCDF
1,2,3,7,8-PeCDF
1,2,3,4,7,8-HxCDF
2,3,4,7,8-PeBDF
1,2,3,7,8-PeBDF
2,3,4,5,3 ',4'-HxCB
Relative potency
(ED50 TCDD/ED50 congener)
Cleft palate
1.000
0.235
0.100
0.095
0.049
0.026
0.010
0.005
0.004
0.0000287
Hydronephrosis
1.000
0.444
0.333
0.057
0.021
0.074
0.049
0.009
0.018
0.0000894
Source: Weber etal, 1985; Birnbaum et al., 1987a,b, 1991.
December 2003
5-106
DRAFT—DO NOT CITE OR QUOTE
-------
o
O
to
O
O
Table 5-5. Cross-species comparison of NOAELs and LOAELs for TCDD
developmental toxicity in fish
Species
Lake trout
Japanese medaka
Rainbow trout
Lake trout
Rainbow trout
Japanese medaka
Effect
Sac fry mortality
Lesions*
Sac fry mortality
Sac fry mortality
Sac fry mortality
Sac fry mortality
Lesions3
Exposure
Static waterborne
Static waterborne
Single injection
Static waterborne
Static waterborne
Single injection
Static waterborne
Egg dose
34 ng/kg
<100 ng/kg
194 ng/kg
40 ng/kg
55 ng/kg
291 ng/kg
300 ng/kg
Effect
level
NOAEL
NOAEL
NOAEL
LOAEL
LOAEL
LOAEL
LOAEL
Reference
Walker etal., 1991
Wisk and Cooper, 1990a
Walker etal., 1992
Spitsbergen etal., 1991
Walker etal., 1991
Walker etal., 1992
Wisk and Cooper, 1990a
O
O
2
O
H
O
HH
H
W
"Consist of a spectrum of effects including hemorrhage in various areas, pericardial edema, collapse of the yolk sphere, cessation of blood flow throughout the animal,
and embryo mortality.
O
c
O
H
W
-------
o
O
to
O
O
oo
Table 5-6. Cross-species comparison of NOAELs and LOAELs for TCDD
developmental toxicity in birds
Species
Ring-necked pheasant
Eastern bluebird
Chicken
Chicken
Ring-necked pheasant
Ring-necked pheasant
Ring-necked pheasant
Eastern bluebird
Effect
Embryo mortality
Embryo mortality
Cardiac malformations
Embryo mortality
Embryo mortality
Embryo mortality
Embryo mortality
Embryo mortality
Exposure
Single injection
Single injection
Single injection
Single injection
Single injection
Single injection
Single injection
Single injection
Egg dose
100 ng/kg
1,000 ng/kg
9 ng/kg2
240 ng/kg
1,000 ng/kg
1,354 ng/kgb
2,182ng/kgc
10,000 ng/kg
Effect
level
NOAEL
NOAEL
LOAEL
LD50
LOAEL
LD50
LD50
LOAEL
Reference
Noseketal., 1993
Thieletal., 1988
Martin etal., 1989
Cheung etal., 1981a,b
Allred and Strange, 1977
Noseketal., 1993
Noseketal., 1993
Noseketal., 1993
Thieletal., 1988
O
oo
aChi-square analysis of the data in Table 1 of Cheung et al. (1981b) demonstrated that the incidence of cardiac malformations in all embryos examined at dose levels
of 1.6 pmol/egg or greater are significantly (j><0.05) increased compared to the incidence in the control group designated "all examined." Assuming a 55 g egg
weight, 1.6 pmol/egg corresponds to a TCDD egg burden of 9 ng/kg.
blnjected into the egg albumin.
clnjected into the egg yolk.
O
O
2
O
H
O
HH
H
W
O
V
O
c
O
H
W
-------
Table 5-7. Cross-species comparison of NOAELs and LOAELs for TCDD developmental
toxicity in mammals
Species
Monkey
Rat
Rat
Mouse
Mouse
Monkey
Rat
Rat
Rat
Rat
Rat
Rat
Rat
Rat
Rat
Monkey
Rabbit
Effect
Prenatal mortality
Prenatal mortality
Prenatal mortality
Hy dronephro sis
Cleft palate
Object learning
Accelerated eye
opening
Reduced ejaculated
sperm numbers
Reduced ventral
prostate weight
Reduced cauda
epididymal sperm
numbers
Partial feminization of
sexual behavior
(male)
Vaginal thread
malformation
Hypospadias
(female)
Cleft phallus
(female)
Partial demascu-
linization of sexual
behavior
(male)
Prenatal mortality
Extra ribs
Exposure
Multiple dose
1 ng/kg/day
Multiple dose
Multiple dose
Multiple dose
0.1 26 ng/kg/day
Single
Single
Single
Single
Single
Single
Single
Single
Single
0.642 ng/kg/day
Multiple dose
Multiple dose
Maternal dose
22 ng/kg, 9x,gd 20-40
27 ng/kga, chronic
30 ng/kg/day, gd 6- 15
1 00 ng/kg/day, gd 6- 15
300 ng/kg/day, gd 6- 15
1 9 ng/kga, subchronic
50 ng/kg, gd 15
50 ng/kg, gd 15
64 ng/kg, gd 1 5
64 ng/kg, gd 1 5
160 ng/kg, gd 15
200 ng/kg, gd 15
200 ng/kg, gd 15
800 ng/kg, gd 15
64 ng/kg, gd 1 5
97 ng/kga, subchronic
111 ng/kg, 9x, gd 20-40
1 00 ng/kg/day, gd 6- 15
Effect
level
NOAEL
NOAEL
NOAEL
NOAEL
NOAEL
LOAEL
LOAEL
LOAEL
LOAEL
LOAEL
LOAEL
LOAEL
LOAEL
LOAEL
LOAEL
LOAEL
LOAEL
LOAEL
Reference
McNulty, 1984
Murray etal, 1979
Sparschu et al., 1971
Smith etal., 1976
Neubert and Dillman, 1 972
Schantz and Bowman, 1989
Gray etal., 1997a
Gray etal., 1997a
Mably etal., 1992a
Mablyetal., 1992c
Mably etal., 1992b
Gray etal., 1997b
Gray etal., 1997b
Gray etal., 1997b
Mablyetal., 1992b
Schantz and Bowman, 1989
McNulty, 1984
Giavini et al., 1982b
-------
o
O
Table 5-7. Cross-species comparison of NOAELs and LOAELs for TCDD developmental
toxicity in mammals (continued)
to
O
O
oo
Species
Rat
Rabbit
Rat
Mouse
Guinea pig
Hamster
Mouse
Hamster
Mouse
Effect
Fetal growth
Prenatal mortality
Prenatal mortality
Hy dronephro sis
Prenatal mortality
Thymic hypoplasia
Cleft palate
Prenatal mortality
Prenatal mortality
Exposure
Multiple dose
Multiple dose
10 ng/kg/day
Multiple dose
Multiple dose
Single dose
Single dose
Multiple dose
Single dose
Single dose
Maternal dose
125 ng/kg/day, gd 6-15
250 ng/kg/day, gd 6-15
270 ng/kga, chronic
500 ng/kg/day, gd 6-15
500 ng/kg/day, gd 6- 15
l,500ng/kg, gd 14
l,500ng/kg, gd7or9
3, 000 ng/kg/day, gd 6- 15
18,000 ng/kg/day, gd 7 or 9
24,000 ng/kg/day, gd 6
Effect
level
LOAEL
LOAEL
LOAEL
LOAEL
LOAEL
LOAEL
LOAEL
LOAEL
LOAEL
LOAEL
Reference
Sparschuetal, 1971
Giavinietal, 1982b
Murray etal., 1979
Sparschuetal., 1971
Silkworth et al, 1989
Olson and McGarrigle, 1992
Olson and McGarrigle, 1992
Courtney and Moore, 1971
Olson etal., 1990
Couture etal., 1990b
"Maternal body burdens of TCDD at the time of conception were calculated by assuming a one-compartment open model and half-life for whole body TCDD
elimination of 400 days in the monkey (McNulty et al., 1982) and 23.7 days in the rat (Rose et al., 1976). A bioavailabilty of 86.1 percent was used in the monkey and rat (Rose et
al., 1976). The daily dietary exposure levels in rhesus monkeys were approxiately 5 and 25 ppt at the NOAEL and LOAEL doses, respectively. Rhesus monkeys were exposed to
these levels of TCDD for 7 months prior to conception. At this time (0.525 half-lives) the cumulative amount of TCDD in rhesus monkeys was 30.5 percent of the calculated steady-
state level. Rats were exposed to the indicated daily doses of TCDD for a period of 90 days (3.8 half-lives) prior to conception. At this time the cumulative amount of TCDD in rats
was 92.8 percent of the calculated steady-state level.
gd=gestational day.
O
O
2
O
H
O
HH
H
W
O
V
O
c
O
H
W
-------
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6. CARCINOGENICITY OF TCDD IN ANIMALS
6.1. INTRODUCTION
Additional scientific information on the use of animal cancer data for estimating human
risks from 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) has become available since the 1988
health risk assessment for dioxin. Much of the data on tumor incidence in experimental animals
available in 1988 demonstrated that TCDD is a carcinogen at multiple sites in both sexes of rats
and mice. Some of the cancers occurred following particularly low doses. Since 1988, TCDD
has been shown to be a carcinogen in hamsters, and some of the tumor incidence data in rat liver
have been reevaluated.
There is considerable evidence that TCDD does not damage DNA directly through the
formation of DNA adducts. Mechanisms have been proposed that support the possibility that
TCDD might be indirectly genotoxic, either through the induction of oxidative stress or by
altering the DNA-damaging potential of some endogenous compounds, including estrogens. In
addition, there have been numerous reports on TCDD-induced modifications of growth factor
signaling pathways and cytokines in experimental animals and cell systems. Some of the altered
systems include those for epidermal growth factor, transforming growth factor alpha, estrogen,
glucocorticoids, tumor necrosis factor-alpha, interleukin 1-beta, plasminogen inactivating
factor-2, and gastrin. Many of these pathways are involved in cell proliferation and
differentiation and provide plausible avenues for researching the mechanisms responsible for the
carcinogenic actions of TCDD. These effects are consistent with the generally accepted
conclusion that TCDD acts as a "tumor promoter" in multistage models for chemical
carcinogenesis and is virtually devoid of initiating activity in these models. It is important to
note that "tumor promotion" is an operational and not a mechanistic term and that multiple
mechanisms of tumor promotion are likely. Each of these mechanisms may be fundamentally
different from the others.
There is a scientific consensus that most, if not all, of the biochemical and toxic effects of
TCDD require an initial interaction with its cognate receptor, the aryl hydrocarbon (Ah). The
properties of the Ah receptor (AhR) and the mechanisms whereby this receptor regulates gene
expression are described in more detail in other chapters. However, formation of the
AhR-TCDD complex is only the first of many steps involved in the production of a biochemical
and toxic effect. Although there is considerable knowledge of details regarding activation of
expression of the TCDD-inducible cytochrome P450 1 Al by the AhR, we still know very little
about many components of AhR-mediated responses and their relationship to the development of
adverse responses such as cancer. It is clear, however, that tissue- and cell-specific factors other
than the AhR must be involved in determining tissue responses once TCDD binds the AhR.
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Evaluation of dose-response is one of the more important issues that affect dioxin risk
assessments. The focus of this controversy centers on the shape of the dose-response curve,
particularly at low doses, and whether the effects of dioxin may exhibit operational thresholds.
It now appears that for some responses there is a proportional relationship between receptor
occupancy and response, which is evidenced by a linear relationship between target dose and
effect over a wide dose range. However, different dose-response relationships are seen for
different responses, so it is probably inappropriate to use a single surrogate marker to estimate
dioxin's risks. Furthermore, these data reveal there is no unifying dose-response relationship for
all AhR-mediated events. A more detailed evaluation of dose-response relationships for
TCDD-modulated responses is described in Chapter 8.
Another controversial area in risk assessment is whether experimental animal models are
appropriate for estimating human risks. There has been increasing evidence that biochemical
and toxic responses resulting from human exposure to TCDD and its structural analogues appear
to be similar to responses in experimental animals. However, it may be possible that humans are
sensitive or resistant to some responses. There also is increasing awareness that interindividual
variations in human responses to dioxin are a complicating factor in risk assessment, as it
appears that there are individuals who are responsive and nonresponsive to numerous
environmental chemicals, including TCDD.
Much of the controversy surrounding dioxin risk assessment reflects the selection of
mathematical models: threshold, linear multistage, or others. We now know considerably more
about the mode of action of dioxin, and this knowledge has allowed the construction of
biologically based models that may reduce some of the uncertainty in current risk estimates.
These approaches and advances in our understanding of the mechanisms of tumor
promotion and dose-response relationships will be discussed in more detail in Chapter 8,
Dose-Response Modeling for 2,3,7,8 TCDD.
6.2. ANIMAL BIOASSAYS FOR CANCER
Long-term studies for carcinogenicity of TCDD have been conducted in several species
(van Miller et al., 1977; Kociba et al., 1978; NTP, 1982a; Rao et al., 1988; Johnson et al., 1992).
All studies have produced positive results. It is clear that TCDD is a multi-site carcinogen in
both sexes of rats and mice (U.S. EPA, 1985; Huff et al., 1991; Zeise et al., 1990, IARC 1997).
It is a carcinogen in the hamster (Rao et al., 1988), which is considered the most resistant species
to the acute toxic effects of TCDD, and a preliminary report indicates that TCDD is also
carcinogenic in fish (Johnson et al., 1992). The important studies are summarized in Table 6-1,
including information on species, sex, and tumor site.
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The 2-year rodent bioassays conducted by Dow Chemical (Kociba et al., 1978) and the
National Toxicology Program (NTP, 1982a) studies are the most comprehensive to date and
most relevant to risk characterization, and are described in the following paragraphs.
6.2.1. Kociba Study
The most cited cancer bioassay for TCDD was published by Kociba et al. (1978). It was
a lifetime feeding study of male and female Sprague-Dawley rats using doses of 0, 1, 10, and 100
ng/kg/day. There were 50 males and 50 females in each group. Data derived from these studies
have been used as the basis for many risk assessments for TCDD.
The most significant finding was an increase in hepatocellular hyperplastic nodules and
hepatocellular carcinomas in female rats. The incidence of hepatocellular carcinomas was
significantly elevated above the control incidence at the 100 ng/kg/day dose, whereas increased
incidence of hyperplastic nodules was evident in the 10 ng/kg/day dose group.
There have been two reevaluations of slides of liver sections from the Kociba study
(Squire, 1980; Sauer, 1990; Goodman and Sauer, 1992). The Squire review was requested by
EPA as an independent review of the slides. The Sauer review was carried out using refined
criteria for the diagnosis of proliferative hepatocellular lesions (Maronpot et al., 1986, 1989).
Liver tumor incidences for the three evaluations are compared in Table 6-2. Although there are
some quantitative differences between the evaluations, the lowest detectable effect for liver
tumor incidence is consistently observed at 10 ng/kg/day.
In the 10 ng/kg/day dose group, significant increases in the incidence of hyperplastic
nodules of the liver were observed in female rats (18/50 in the Kociba evaluation, 27/50 in the
Squire evaluation). Two females (2/50) had hepatocellular carcinomas. In the 1990 reevaluation
(Sauer, 1990; Goodman and Sauer, 1992), nine females (9/50) were identified with
hepatocellular adenomas and none with carcinomas; thus only one-third of the previously
observed "tumors" were identified when using the refined diagnostic criteria.
In addition to nodules in the liver, increased incidence of stratified squamous cell
carcinoma of the tongue and nasal turbinates/hard palate, and keratinizing squamous cell
carcinoma of the lung were also observed in female rats in the 100 ng/kg/day dose group. One
possible cause for the induction of lung tumors in the Kociba feeding study may have been the
inhalation of dosed feed into the lungs. However the promotion of lung tumors has been
observed in mice treated systemically by i.p. injections of TCDD (Beebe et al 1995). In addition
the induction of hyperplastic and metaplastic lesions in rats has been observed following chronic
oral gavage treatment with TCDD (Tritscher et al 2000). More recently chronic oral exposure
to HCDD resulted in the induction of lung tumors in treated female rats (Rozman 2000). These
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data indicate that the induction of lung tumors in the Kociba was most likely primarily the result
of systemic chronic dietary exposure to TCDD rather than due to a localized inhaled exposure.
There was no detectable increase in liver tumor incidences in male rats in any of the dose
groups (Table 6-1). The mechanism responsible for dioxin-mediated sex specificity for
hepatocarcinogenesis in rats is not clear, but may involve ovarian hormones (Lucier et al., 1991).
This is discussed in Section 6.3 on tumor promotion.
Although there was no increase in liver tumors in male rats in this study, in the 100
ng/kg/day group there was an increased incidence of stratified squamous cell carcinoma of the
hard palate/nasal turbinate, stratified squamous cell carcinoma of the tongue, and adenoma of the
adrenal cortex.
Kociba et al. (1978) had reported that chemically related increases in preneoplastic or
neoplastic lesions were not found in the 1 ng/kg/day dose group. However, Squire identified two
male rats in the 1 ng/kg/day dose group with squamous cell carcinoma of the nasal
turbinates/hard palate, and one of these male rats had a squamous cell carcinoma of the tongue.
These are both rare tumors in Sprague-Dawley rats, and these sites are targets for TCDD,
implying that 1 ng/kg/day may not represent a no-observed-effect level (NOEL). However, no
dose-response relationships were evident for tumors at these sites (Huff et al., 1991)
One of the more interesting findings in the Kociba bioassay was a TCDD-induced
reduction in the incidence of spontaneous tumors including pituitary adenoma, benign tumor of
the uterus, benign mammary neoplasm and mammary carcinoma in female rats, and acinar
adenoma of the pancreas and adrenal pheochromocytoma in male rats. For example, carcinomas
of the mammary gland occurred in 8 of 86 control female rats, whereas the incidence was 0/49 in
the 1 ng/kg/day dose group. However, the incidence of mammary gland carcinomas in the
medium- and high-dose groups was similar to that of control rats, suggesting that protection
against breast cancer might be a low-dose effect. A relationship between body-weight reduction
and spontaneous cancer incidence in rodents has been observed across numerous studies (Rao et
al., 1987). This suggest that the reduction in the incidence of the spontaneous tumors by TCDD
is likely related to the TCDD-induced reduction in body-weight gain. These findings, coupled
with the sex specificity of TCDD-induced liver tumors in rats, highlight that the carcinogenic
actions of TCDD may involve a complex interaction of hormonal factors. Moreover, it appears
likely that tissue- and cell-specific factors modulate TCDD/hormone actions relevant to cancer.
There is considerable controversy concerning the possibility that TCDD-induced liver
tumors are a consequence of cytotoxicity. Goodman and Sauer (1992) have extended the
reevaluation of the Kociba slides to include liver toxicity data and have reported a correlation
between the presence of overt hepatotoxicity and the development of hepatocellular neoplasms
in female rats. With the exception of two tumors in controls and one each in the low- and
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mid-dose groups, all liver tumors occurred in livers showing clear signs of toxicity. However,
male rat livers exhibit cytotoxicity in response to high TCDD doses, yet they do not develop
liver tumors. Moreover, both intact and ovariectomized female rats exhibit liver toxicity in
response to TCDD, yet TCDD is a more potent promoter in intact but not ovariectomized rats
(Lucier et al., 1991). Therefore, if cytotoxicity is playing a role in liver tumorigenesis, other
factors must also be involved. Also, there is little information on the role of cytotoxicity in
TCDD-mediated cancer at other sites such as the lung and thyroid.
6.2.2. NTP Study
The NTP study was conducted using Osborne-Mendel rats and B6C3F1 mice (NTP,
1982a). Groups of 50 male rats, 50 female rats, and 50 male mice received TCDD as a
suspension in corn oil:acteone (9:1) by gavage twice each week (Tuesday and Friday) to achieve
doses of 0, 10, 50, or 500 ng TCDD/kg/week for 2 years; groups of 50 female mice were treated
similarly to achieve doses of 0, 40, 200, or 2,000 ng/kg/week. These exposures correspond to
daily averaged doses of 1.4, 7.1, or 71 ng/kg/day for rats and male mice and to doses of 5.7, 28.6,
or 286 ng/kg/day for female mice, so the doses were comparable to those used in the Kociba
feeding study. There were no statistically significant dose-related decreases in survival in any
sex-species group.
Tumor data in the NTP bioassay are summarized in Tables 6-3 and 6-4. TCDD-induced
malignant liver tumors occurred in the high-dose female rats and in male and female mice.
These can be considered to result from TCDD exposure because they are relatively uncommon
lesions in control Osborne-Mendel rats (male, 1/208; female, 3/208), are seen in female rats and
mice of both sexes, and their increasing incidence with increasing dose is statistically significant
(Cochran-Armitage trend test, />=0.004). Because liver tumors were increased in both sexes of
mice, this effect is not female-specific as was observed in rats. Interestingly, liver tumor
incidences were decreased in female rats in both the NTP and Kociba low doses (not statistically
significant compared with controls). For example, the combined control incidence data were
11/161 (7%) compared with 4/99 (4%) in the low-dose group.
The incidences of thyroid gland (follicular cell) tumors were increased in all three dose
groups in male rats. Because the responses in the two highest dose groups are highly significant,
the statistically significant elevation of incidence in the lowest dose group (Fisher exact
/?-value=0.042) is considered to be caused by exposure to TCDD. Thus, for this study the
lowest-observed-effect level (LOEL) is 1.4 ng/kg/day and a NOEL was not achieved within the
specified dose range, suggesting that thyroid tumor incidence may be the most sensitive site for
TCDD-mediated carcinogenesis. Because 71 ng/kg/day is above the maximum tolerated dose
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(MTD) (Huff et al., 1991), thyroid tumors occur at doses more than 50 times lower than the
MTD.
TCDD-induced neoplasms of the adrenal gland were observed in the 7.1 ng/kg/day/dose
group in male rats and in high-dose female rats. Fibrosarcomas of the subcutaneous tissue were
significantly elevated in high-dose female mice and female rats. One additional tumor type,
lymphoma, was seen in high-dose female mice. Lung tumors were elevated in high-dose female
mice; the increase was not statistically significant when compared with concurrent controls, but
the increase was dose related (Cochran-Armitage trend test, p=0.004).
Therefore, TCDD is a multisite complete carcinogen (Huff, 1992) and induced
neoplasms in rats and mice of both sexes. As was observed in the Kociba study (Kociba et al.,
1978), liver tumors were observed with greater frequency in treated female rats, but in male rats
the thyroid appears to be the most sensitive (increased tumor incidence at doses as low as 1.4
ng/kg/day).
6.2.3. Syrian Golden Hamster
Groups of 10 to 24 male Syrian Golden hamsters were given two to six intraperitoneal or
subcutaneous injections of TCDD over a 4-week period at doses of 0, 50, or 100 jig TCDD/kg in
dioxane (Rao et al., 1988). The experiments were terminated after 12 to 13 months. The 100
jig/kg groups (total dose of 600 |ig/kg) from both injection routes developed squamous cell
carcinomas of the skin in the facial region: 4/18 (22%) from the intraperitoneal injection and
3/14 (21%) from the subcutaneous injection. The lesions were large (1.5 to 3 cm) with extensive
necrosis, and some metastasized to the lung. The earliest neoplasms were detectable 8 months
after the initial injection. Similar lesions were not seen in hamsters receiving two intraperitoneal
injections of 100 |ig/kg TCDD or six subcutaneous injections of dioxane vehicle, and none have
been reported over the past 10 years in this laboratory. An extensive study by Pour et al. (1976)
identified only 1 skin papilloma in 533 control Syrian hamsters. This report demonstrates that
the hamster, a nonresponsive species for acute toxic effects, is susceptible to the carcinogenic
actions of TCDD at doses well below the MTD.
6.2.4. B6C3 and B6C Mice
In a study by Delia Porta et al. (1987), TCDD was administered intraperitoneally in corn
oil at doses of 0, 1, 30, and 60 |ig/kg to groups of 89 to 186 B6C3 and B6C mice of both sexes
once weekly for 5 weeks starting at day 10 of life, and the animals were observed until 78 weeks
of age. Histopathological observations were limited to the liver, kidney, and organs with
apparent or suspected pathological changes. Thymic lymphomas were induced at the 60 |ig/kg
level in both sexes of both hybrids and at 30 |ig/kg in all but female B6C3 mice. Neoplasms of
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the liver occurred in male B6C3 mice at 30 |ig/kg and female B6C3 mice at 60 |ig/kg. In a
separate experiment, groups of 42 to 50 B6C3 mice were exposed to 0, 2.5, and 5.0 |ig/kg TCDD
in corn oil by gavage once weekly for 52 weeks starting at 6 weeks of age. The study was
stopped at 110 weeks. Increased incidences of liver tumors were related to TCDD exposure at
both dose levels.
6.2.5. Fish
A preliminary study, reported in abstract form only, examined the carcinogenicity of
TCDD in medaka (Oryzias latipes) immersed in 2,3,7,8 TCDD-treated water for 28 days,
followed by immersion in clean water for up to 8 months (Johnson et al., 1992). Exposure to
33.9 ppq TCDD led to an increase in tumors at multiple sites including gills, thyroid, and swim
bladder. Total body burden of TCDD in these fish was 2 ppb (Johnson et al., 1992).
6.2.6. Carcinogenicity of Related Compounds
A mixture of two isomers of hexachlorodibenzo-p-dioxin (HCDD) (1,2,3,6,7,8 and
1,2,3,7,8,9) was given by gavage twice weekly for 2 years to Osborne-Mendel rats and B6C3F1
mice (NTP, 1980). The doses of HCDD were 0, 1.25, 2.5, or 5 |ig/kg/week in rats and male
mice. Doses for female mice were 0, 2.5, 5, and 10 jig/kg/week. There was no effect of
administration of HCDD on survival of either sex of rats or mice (NTP, 1980). Results revealed
that HCDD increased liver tumors in both sexes of rats and mice, although female rats seemed to
be more sensitive than male rats (significant increases detected in female rats in the 1.25
|ig/kg/week dose group, equivalent to 180 ng/kg/day). Therefore, HCDD is approximately 1/20
as potent a liver carcinogen as TCDD.
Dermal applications of the HCDD mixture described above (NTP, 1982b) were given to
Swiss Webster mice for 104 weeks (three times per week). For the first 16 weeks, doses of 5
ng/application were used. Thereafter, doses of 10 ng/application were used. No HCDD-
exposure-related carcinogenic responses were noted.
Dibenzo-p-dioxin given in the diet for 2 years at concentrations of 0, 5,000, and 10,000
ppm did not increase carcinogenic responses in Osborne-Mendel rats or B6C3F1 mice (NCI,
1979a). 2,7-Dichlorodibenzo-^-dioxin (DCDD) in the diet of Osborne-Mendel rats for 110
weeks or B6C3F1 mice for 90 weeks at levels of 0, 5,000, or 10,000 ppm did not increase
neoplasms in male or female rats or in female mice. In male mice, increased incidences of
lymphoma or hemangiosarcoma were observed in the low-dose group and neoplasms of the liver
were observed in both dose groups (NCI, 1979b). The more highly chlorinated
dibenzo-p-dioxins (CDDs) and dibenzofurans (CDFs) have not been studied in long-term animal
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cancer bioassays. Many of the CDDs and CDFs bioaccumulate and exhibit toxicities similar to
those of TCDD and are considered to be carcinogens (EPA Science Advisory Board, 1989).
There are no carcinogenicity data on individual congeners of coplanar (dioxin-like)
polychlorinated biphenyls. However, laboratory studies found statistically significant increased
incidences of liver tumors in rats ingesting Aroclor 1260 or Clophen A60. Significant increases
in gastric cancer, leukemia, and lymphoma were found in rats ingesting Aroclor 1254. Partial
lifetime studies found precancerous liver lesions in rats and mice ingesting PCB mixtures of high
or low chlorine content. More recent studies have compared the carcinogenicity of several
Aroclor mixtures (Mayes et al., 1998). The Aroclor 1254 mixture contains the highest level of
dioxin-like coplanar PCBs of these mixtures. All Aroclors tested, 1016, 1242, 1254, and 1260,
resulted in an increased incidence of liver neoplasms in female rats. However, only Aroclor
1260 at high doses was carcinogenic in males. In addition, Aroclor, 1242, 1254, and 1260
induced thyroid tumors in male rats. Analysis of liver levels of specific PCB congeners suggests
that in males the induction of tumors is dependent on total PCB content, whereas the liver tumor
incidence in females is dependent upon the total TCDD toxic equivalents level (TEQ) as a result
of accumulation of dioxin-like PCBs from the Aroclor mixture (Silkworm et al., 1997).
With regard to studies of the carcinogenicity of dioxin-like compounds, including PCBs,
the National Toxicology Program is currently conducting 2-year carcinogenicity bioassays of
multiple dioxin-like compounds and mixtures in female Sprague-Dawley rats (van Birgelen et
al., 1997). Compounds under study include TCDD 2,3,4,7,8-pentachlorodibenzofuran (PeCDF),
3,3',4,4',5-pentachlorobiphenyl (PCB 126, a coplanar dioxin-like PCB), 2,3',4,4'5
-pentachlorobiphenyl (a mono-ortho PCB, PCB 118), 2,2',4,4',5,5'-hexachlorobiphenyl (a non-
dioxin-like PCB, PCB 153), a binary mixture of a non-dioxin-like (PCB 153) and a dioxin-like
PCB (PCB 126), and a mixture of dioxin-like compounds (TCDD, PeCDF, and PCB 126).
6.3. INITIATION/PROMOTION STUDIES
The multistage nature of chemical carcinogenesis is being defined by an increasing
understanding of the discrete steps required to produce a genetically altered cell that is clonally
expanded and ultimately progresses to a tumor (IARC, 1992; Barrett and Wiseman, 1987;
Swenberg et al., 1987; Barrett, 1992) (Figure 6-1). Briefly, the process involves damage to a
specific site on DNA, a round of cell replication to fix that damage into the genome, clonal
expansion of the genetically altered cells (tumor promotion), and additional genetic damage and
rounds of cell replication (tumor progression). Figure 6-1 schematizes the multistage nature of
cancer. The birth and death rates of genetically altered cells compared with normal cells are the
centerpiece of risk assessment models that recognize the multistage nature of chemical
carcinogenesis (Moolgavkar and Knudson, 1981; Portier, 1987).
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The roles of proto-oncogene activation and tumor suppression gene inactivation have
provided clues in attempts to discern discrete steps in carcinogenesis. It is also clear that cell
proliferation is an essential component of chemical carcinogenesis, for without it, DNA damage
would not be fixed into the genome and clonal expansion of genetically altered cells would not
occur.
Concurrent with our increased understanding of the mechanistic underpinnings of
chemical carcinogenesis, multistage models have been developed to identify the particular stage
or stages in which carcinogens act to increase tumor incidence.
There is a wealth of information on liver initiation/promotion protocols in the scientific
literature (Pitot and Sirica, 1980; Farber, 1984; Pitot and Campbell, 1987). These protocols
frequently employ a single initiating dose of a chemical that damages DNA, followed by
enhancement of cell replication (partial hepatectomy or cytotoxicity) to fix that damage into the
genome (initiation), and then chronic exposure to a chemical that produces clonal expansion of
the genetically altered cells (promotion). Increased tumor incidence is produced by chemicals
that act at either stage. It is important to note that "initiation" and "promotion" are operational
and not mechanistic terms because both stages are likely to be composed of multiple steps, and
the mechanisms are not mutually exclusive. Nevertheless, the protocols have provided valuable
information in our attempts to understand chemical carcinogenesis. Detailed descriptions of
initiation/promotion protocols in liver and skin are provided elsewhere (Pitot and Campbell,
1987; Dragan et al., 1991; Pitot et al., 1987; Farber, 1984; Slaga et al., 1982; Peraino et al., 1981;
Itoetal., 1980).
6.3.1. TCDD Is Not a Direct Genotoxic Agent
There is substantial evidence that TCDD is not a direct genotoxic agent. Because
"genotoxic" and "nongenotoxic" are controversial and often misused terms, it is prudent to
describe accurately the scientific criteria used to call a chemical "genotoxic" or "nongenotoxic"
(IARC, 1992). Some of the criteria for designating TCDD a nongenotoxic agent are that it does
not bind covalently to DNA (does not form DNA adducts). Although one study detected
radioactivity associated with crude DNA preparations after in vivo exposure, no study that has
rigorously looked for TCDD-DNA adducts has been positive. TCDD is negative in short-term
tests for genotoxicity and is a potent promoter and weak initiator in multistage models for
chemical carcinogenesis. In a another study (Turteltaub et al., 1990) using accelerator mass
spectrometry, DNA adducts were not detected in rodent tissue following exposure to TCDD.
This method is extraordinarily sensitive, being capable of detecting one adduct in 1012 normal
nucleotides. Randerath et al. (1988) were unable to detect TCDD-related DNA adducts by the
sensitive 32P postlabeling method (limit of detection of one adduct in 109 normal nucleotides).
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For comparison, approximately one adduct in 106 normal nucleotides is found in rodent tissues
following carcinogenic doses of benzo(a)pyrene (7,8-diol-9,10 epoxide deoxyguanosine DNA
adduct) or methylnitrosourea (O6 methylguanine).
Another criterion for designating TCDD a "nongenotoxic carcinogen" is that numerous
studies have demonstrated that TCDD is negative in the Salmonella!'Ames test in the presence or
absence of a mixed-function oxidase (MFO) activating system. These negative studies have
encompassed 13 different bacterial strains with tests performed in 9 laboratories (Wassom et al.,
1977; Kociba, 1984; IARC, 1982; Giri, 1987; Shu et al., 1987). Using its battery of tests for
genetic toxicity, the NTP (1984) concluded that TCDD was nonmutagenic. Additionally, several
scientific panels have stated that false negatives for TCDD genetic toxicity are highly unlikely
(EPA Science Advisory Board, 1984). TCDD has been found to promote the transformation of
C3H/10T1/2 cells; it was concluded that this response did not reflect TCDD's ability to directly
damage DNA (Abernethy et al., 1985). In human populations accidentally or occupationally
exposed to TCDD, there is no consistent evidence for increased frequencies of chromosomal
aberrations in workers exposed to TCDD (Shu et al., 1987).
However, Yang et al. (1992) demonstrated that immortalized human keratinocytes
cultured with TCDD were neoplastically transformed, as evidenced by tumorigenic activity of
those cells in nude mice. This response is characteristic of genotoxic carcinogens and occurred
at a low TCDD concentration (0.1 nM). For comparison, induction of CYP1A2 in these same
cells was not detected until a dose of 3 nM was used (Yang et al., 1992).
6.3.2. Two-Stage Models of Liver Tumor Promotion by TCDD
TCDD is designated as a nongenotoxic carcinogen because it is negative in most assays
for DNA damaging potential, a potent tumor promoter, and a weak initiator or noninitiator in
two-stage models for liver (Pitot et al., 1980; Graham et al., 1988; Lucier et al., 1991; Clark et
al., 199la; Flodstrom and Ahlborg, 1991) and skin (Poland et al., 1982).
Pitot et al. (1980) were the first to report that TCDD was a potent liver tumor promoter in
female rats. Animals were initiated with a single dose of diethylnitrosamine (DEN)(10 mg
DEN/kg) 24 hours following a 2/3 hepatectomy, followed by chronic TCDD exposure (0.14 and
1.4 |ig/kg subcutaneously once every 2 weeks for 7 months). When expressed as a daily
averaged dose, these doses are equivalent to 10 and 100 ng TCDD/kg/day (the medium and high
dose in the Kociba bioassay). Histological evaluation revealed that five of seven animals that
had received DEN and 100 ng TCDD/kg/day had hepatocellular carcinomas. No liver tumors
were evident in rats receiving DEN only, DEN/low-dose TCDD, or TCDD only (high or low
dose). Altered hepatocellular foci (AHF) exhibiting altered expression of the marker enzymes
glucose-6-phosphatase, canalicular ATPase, and gamma glutamyl transpeptidase were also
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evaluated in this study. AHF are considered to represent preneoplastic lesions because increases
in AHF are associated with liver cancer in rodents (Maronpot et al., 1989; Popp and
Goldsworthy, 1989; Pitot et al., 1989; Williams, 1989). The AHF data were consistent with the
tumor data in that a large proportion of the liver was occupied by AHF (43%) in animals
initiated with DEN and the high dose of TCDD. A much smaller proportion of the liver was
occupied by AHF in the other groups. This work provides strong evidence that TCDD is a
potent tumor promoter in liver.
A second set of studies (Graham et al., 1988; Lucier et al., 1991; Clark et al., 1991a;
Dragan et al., 1992) confirmed and extended Pilot's findings, including data suggesting a
mechanistic basis for TCDD's tumor-promoting effects in rat liver. These DEN studies, using a
necrogenic dose of DEN (200 mg/kg) as the initiator, have demonstrated that the effect of
TCDDs on the promotion of AHF are reduced following ovariectomy. This finding is consistent
with 2-year bioassays showing that TCDD is a hepatocarcinogen in female rats but not in male
rats. In the tumor-promoting studies (Graham et al., 1988; Lucier et al., 1991), DEN was used as
the initiating agent and TCDD (biweekly doses of 1.4 jig TCDD/kg, equivalent to 100 ng/kg/day
for 30 weeks) was used as the promoter. There were four groups of intact female rats (controls,
TCDD only, DEN only, and DEN/TCDD). The same four groups were used following
ovariectomy. Data revealed that TCDD was a weaker liver tumor promoter in ovariectomized
rats (Table 6-5). For example, there were 387 gamma glutamyl transpeptidase (GGT) positive
AHF/cm3 in intact rats compared with 80 in ovariectomized rats in the DEN/TCDD groups.
Corresponding differences were evident in the proportion of liver occupied by GGT positive
AHF: 0.37% in DEN/TCDD intact rats compared with 0.08% in DEN/TCDD ovariectomized
rats. Few or no AHF were found in the control or TCDD-only groups. Placental glutathione
S-transferase (PGST) is being used increasingly as a phenotypic marker of AHF (Ito et al.,
1989), and results with this marker of preneoplasia were similar to those for GGT in that
ovariectomy reduced the liver tumor-promoting actions of TCDD. The influence of ovariectomy
on liver tumor incidence was evaluated in a parallel experiment using the same treatment groups
in which TCDD was administered for 60 weeks. In the intact DEN/TCDD rats, liver tumor
incidence was 13/37, with a total of 32 tumors compared with 7/39 (11 total tumors) in
DEN/TCDD ovariectomized rats. Both hepatocellular adenomas and carcinomas were evident,
along with a smaller incidence of hepatocholangiomas and hepatocholangiocarcinomas.
The mechanisms responsible for the protective effect of ovariectomy are not clear, but
ovarian influences on liver TCDD retention do not seem to be involved; liver TCDD
concentrations were -20 ppb in both intact and ovariectomized rats (Lucier et al., 1991), which
is similar to liver concentrations reported by Kociba et al. (1978) using the same dose of TCDD
(100 ng/kg/day) but for 2 years rather than 60 weeks. One plausible mechanism may be related
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to cell proliferation. Another possible mechanism for the influence of the ovaries is that TCDD
induces cytochrome P4501A2, which could lead to DNA-reactive metabolites of 17-
beta-estradiol, the naturally occurring estrogen. P4501A2 catalyzes the formation of catechol
estrogens that are carcinogens in hamsters and are considered by some to be DNA-reactive
precursors (Metzler, 1984; Li and Li, 1990, Yager and Liehr, 1996).
In addition to these initial studies, a large number of studies have addressed the effect of
dioxins on the development of preneoplastic AHF in the rat liver. These studies are summarized
in Tables 6-5, 6-6, and 6-7. These studies, while using different rat strains, different initiation
protocols, and different dosing regimens, are consistent in showing that the induction of AHF by
TCDD in the female Sprague-Dawley rat liver is dose-dependent (Maronpot et al., 1993;
Teeguarden et al., 1999) (Table 6-8), exposure duration-dependent (Dragan et al., 1992; Walker
et al., 2000; Teeguarden et al., 1999), and reversible after cessation of treatment (Dragan et al.,
1992; Tritscher et al., 1995; Walker et al., 2000).
Other studies indicate that the capacity to induce the development of AHF in the liver by
compounds structurally related to TCDD, such as the polychlorinated dibenzo-dioxins and
polychlorinated dibenzo-furans, exhibits a rank-order potency similar to that for the induction of
CYP1A1 activity (Flodstrom and Ahlborg, 1992; Waern et al., 1991; Schrenk et al., 1994).
Although these data suggest that the potency of different dioxin-like compounds cannot be
predicted solely on the basis of their potency for induction of CYP1A1, they provide evidence
that liver tumor promotion likely requires an initial interaction with the AhR. Studies also
demonstrate that the non-ortho-substituted (dioxin-like) polychlorinated biphenyls (PCBs) that
induce the development of AHF exhibit a similar potency to that required to induce CYP1A1
activity (Hemming et al., 1995; van der Plas, 1999). Furthermore, when PCBs are administered
in combination with TCDD, the effects on AHF development are additive, suggesting that tumor
promotion by dioxins and dioxin-like PCBs likely acts through similar mechanisms.
6.3.3. Lung
Because the lung and respiratory tract seem to be target sites for TCDD carcinogenesis in
humans (Fingerhut et al., 1991), it is of interest to evaluate whether TCDD is a tumor promoter
in rodent lung. There are few published reports on the promotion of lung tumors in rats. Clark
et al. used DEN as the initiating agent and TCDD (100 ng/kg/day for 60 weeks) as the promoting
agent (Clark et al., 1991a) in both intact and ovariectomized rats. In contrast to liver tumor
promotion, lung tumors were seen only in DEN/TCDD ovariectomized rats (4/37). No lung
tumors were present in DEN/TCDD intact rats, in DEN only/TCDD only, or in control rats with
or without ovariectomy. The background incidence of lung tumors in rats is very low, so the
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lack of tumors in controls was not unexpected (Haseman et al., 1984). The four tumors in
DEN/TCDD intact rats were two squamous cell carcinomas and two adenocarcinomas.
More recently, the induction of lung lesions was examined in DEN-initiated female rats
exposed biweekly to 1,750 ng TCDD/kg for up to 61 weeks. Although there was no significant
effect on the development of lung tumors, TCDD exposure was associated with an increase in
alveo-bronchiolar metaplasia and bronchiolar hyperplasia (Tritscher et al., 1999).
There is only a single report of the effect of TCDD on promotion of lung tumors in mice
(Beebe et al., 1995). Three weeks following a single initiating dose of 25 mg NDMA/kg, male
Swiss mice were administered a single dose ranging from 0.05 jig up to 48 jig TCDD/kg, or
were treated with 50 ng TCDD/kg per week for 20 weeks. The incidence of lung tumors
(alveolar adenomas and carcinomas) in the initiated animals that received vehicle alone was
100%, but treatment with TCDD, either as a single dose of 1.6 |ig/kg or as 50 ng/kg/week,
resulted in a significant increase in tumor multiplicity. Single doses of TCDD greater than 1.6
jig/kg had no effect on tumor multiplicity, although the authors note that this may have been due
to observed pulmonary toxicity.
The rodent tumorigenicity data provide clues to the complex hormonal interactions that
produce site-specific carcinogenic actions of TCDD. Liver tumors are ovarian dependent,
whereas the ovaries appear to protect against TCDD-mediated tumor promotion in lung.
Therefore, the rat tumor data are of interest because recent epidemiologic studies (Chapter 7)
have shown that TCDD exposure is associated with an increase in respiratory tract tumors.
6.3.4. Mouse Skin
Initiation/promotion studies on skin have demonstrated that TCDD is a potent tumor
promoter in mouse skin as well as rat liver. Poland et al. (1982) administered a single dermal
initiating dose of 7V-methyl-7V-nitrosoguanidine (MNNG) to HRS/J hairless mice followed by
twice-weekly doses of TCDD (3.75, 7.5, 15, or 30 ng) or TPA (1 or 3 |ig) for 20 weeks. TCDD
promoted the development of papillomas at all doses, and the response was dose dependent
(100% of the animals in the high-dose TCDD group had tumors). Control animals or animals
receiving only MNNG or TCDD exhibited a low incidence of tumors. These studies
demonstrate that TCDD is at least two orders of magnitude more potent an agent than
tetradecanoyl phorbol acetate (TPA) in mouse skin (Poland et al., 1982). On the basis of
structure activity and genetic studies, it appears that the skin tumor-promoting actions of TCDD
are AhR dependent. Moreover, tumorigenic responses segregate with the hr locus, and
biochemical responses such as CYP1 Al induction can occur without carcinogenesis (Poland and
Knutson, 1982; Poland et al., 1982).
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Other studies have tested TCDD as an initiator and TPA as a promoter in CD-I mice
(DiGiovanni et al., 1977). Results revealed that TCDD had weak or no initiating activity in this
system. To better understand the possible influence of TCDD-mediated induction of cytochrome
P450 on the carcinogenicity of PAHs, TCDD was coadministered with benzo(a)pyrene or
dimethylbenzanthracene to mice, followed by promotion with TPA (Cohen et al., 1979). Results
revealed that TCDD decreased tumor incidence of both PAHs compared with controls.
However, coadministration of TCDD with 3-methylcholanthrene to mice produced tumor
incidences similar to those produced by 3-methylcholanthrene alone (Kouri et al., 1978). These
results are consistent with the findings that TCDD induction of drug-metabolizing enzymes is
associated with both metabolic activation deactivation of PAHs (Lucier et al., 1979).
The relative toxicity and tumor-promoting capacity of two CDFs (2,3,4,7,8-CDF and
1,2,3,4,7,8-CDF) have been investigated in hairless mice (Hebert et al., 1990). These studies
used a treatment protocol similar to that of Poland et al. (1982), including the use of MNNG as
the initiating agent and varying doses of TCDD, 2,3,4,7,8-CDF, or 1,2,3,4,7,8-CDF for 20
weeks. Proliferative lesions (squamous cell papilloma, squamous cell carcinoma, or
hyperproliferative nodules) were quantified. Results demonstrated that 2,3,4,7,8-CDF was 0.2 to
0.4 times as potent as TCDD and that 1,2,3,4,7,8-CDF was 0.08 to 0.16 times as potent as
TCDD. These data suggest that the tumor-promoting potencies of structural analogues of
TCDD, like the promotion of liver tumors, reflect relative binding properties to the AhR as well
as pharmacokinetic parameters.
Taken together, results on initiation/promotion protocols indicate that TCDD is an
extraordinarily potent promoter of liver and skin tumors (Pitot et al., 1987), and the results
provide strong evidence that the carcinogenic actions are AhR mediated. A summary of studies
on tumor promotion by TCDD or the polychlorinated dibenzofurans is given in Table 6-6.
Plausible mechanisms responsible for the tumor-promoting actions of TCDD and the impact of
these mechanisms on dose-response relationships are presented in Section 6.4.
6.3.5. Transgenic Models
Studies on the effect of TCDD on tumor promotion in rat liver and mouse skin require
the use of an exogenous initiating agent such as diethylnitrosamine or
7V-methyl-7V-nitrosoguanidine. Recently transgenic models for classifying the mechanism of
action of carcinogens have been used to examine the mechanism of carcinogenicity of TCDD in
mice (Eastin et al., 1998). These are the Tg.AC transgenic mouse, which harbors an activated
mouse v-Ha-ras oncogene, and the p53 +/- transgenic mouse models, which are heterozygous for
the wild-type tumor suppressor p53. Dermal application of tumor promoters such as phorbol
esters results in the development of epidermal papillomas in the Tg.AC. Topical application of
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166 ng TCDD/kg in acetone three times per week for 24 weeks led to a significant increase in
the incidence of squamous cell papillomas in both male Tg.AC mice (8/15 TCDD-treated vs.
1/15 controls) and female Tg.AC mice (10/15 TCDD-treated vs. 1/15 controls) (Eastin et al.,
1998). Treatment of p53 +/- mice by gavage with 250 ng/kg (males) or 1,000 ng/kg (females)
twice a week for 24 weeks did not result in any neoplastic lesions.
Subsequent studies showed that the induction of papillomas by dermal application of
TCDD to hemizygous Tg.AC mice is dose dependent over a dose range of 0-760 ng TCDD/kg 3
times per week for 26 weeks, with the lowest observed effect occurring in the 17 ng/kg dose
group (7.3 ng/kg/day) (van Birgelen et al., 1999, Dunson et al., 2000). In addition, the induction
of skin papillomas in this model occurs when administration is at a site distant to the site of
administration. Treatment of Tg.AC mice for 26 weeks by oral gavage with 0, 105, 450, or
1,250 ng TCDD/kg led to an increase in skin papillomas in the 1,250 ng/kg dose group only
(5/20 TCDD-treated vs. 0/18 in controls) (van Birgelen et al., 1999). These data provide further
support for the potent tumor-promoting action of TCDD.
6.4. MECHANISMS OF TCDD CARCINOGENICITY
6.4.1. Indirect DNA Damage
Although TCDD is negative in genetic toxicity tests, high doses of TCDD (50 to 100
jig/kg) induce single-strand breaks in Sprague-Dawley rats, presumably as a consequence of
increased lipid peroxidation (Wahba et al., 1988, 1989). In addition, though TCDD may not be
directly genotoxic, it has been suggested that it may be indirectly genotoxic through the
formation of potentially DNA reactive oxygen species. This may result from cytochrome P450
induction by TCDD (Park et al., 1996), through the induction of oxidative stress (Slezak et al.,
1999), or through the formation of catechol estrogens (Graham et al., 1988; Spink et al., 1992;
Yager et al., 1996). Indeed, higher levels of oxidative DNA damage (8-OH-dG adducts) have
been observed in chronically exposed female rats (Tritscher et al., 1996) and these TCDD-
induced increases were not observed in ovariectomized rats. Other evidence to support this
hypothesis includes the observation that mathematical modeling of the development of altered
hepatocellular foci indicates that TCDD may have an effect on the initiation rate within the
framework of a one-cell two-stage initiation-promotion model (Portier et al., 1996; Moolgavkar
et al., 1996)(see Chapter 8). However, alternate two-cell models for tumor promotion do not
suggest an effect on the initiation rate (Conolly et al., 1997).
6.4.2. Endocrine Disruption/Growth Dysregulation/Altered Signal Transduction
One of the characteristics of TCDD is that it is a potent growth dysregulator and alters
the signaling of numerous hormonal systems. TCDD induces the expression of a large number
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of genes involved in growth regulation, hormonal signaling and signal transduction, and
hormone metabolism. In addition to these effects, which are presumably mediated through the
AhR-ARNT heterodimer, there are also AhR-dependent effects on signaling pathways
independent of activation of gene expression by the AhR-ARNT heterodimer that may be related
to the mechanism of toxicity of TCDD. These effects are described in more detail in Chapter 2.
Although many of the effects of TCDD have not been directly assessed for their role in the
carcinogenicity of TCDD, it is likely that sex, species, and tissue specificity of dioxin
carcinogenicity is due to a combination of these effects. Consequently, it is unlikely that a single
mechanism is responsible for all the carcinogenic effects of TCDD in all tissues and species.
However, it is now accepted by the scientific community that most, if not all, of TCDD's toxic
and biochemical effects, including tumor promotion, are AhR dependent and that TCDD
provides an example for evaluating the issues relevant to risk assessment for receptor-mediated
carcinogens.
The list of biochemical effects produced by TCDD in humans, experimental animals, and
cell systems is expanding. These effects include those that may alter normal cell regulatory
processes, such as cell proliferation and differentiation, metabolic capacity, and hormonal
pathways. Potentially the effects of TCDD on the endocrine system and tissue differentiation
may play a role in susceptibility to carcinogenesis induced by other compounds, that is distinct
from effects on metabolism of procarcinogens. Brown and co-workers showed that prenatal
exposure of female rats to TCDD resulted in an increased susceptibility to DMBA-induced
mammary adenocarcinomas. This was likely due to an increase in mammary gland terminal end
buds as a result of prenatal exposure (Brown et al., 1998).
6.4.3. Cell Replication/Apoptosis and Tumor Promotion
One mechanism that has been proposed for the reduced tumor promotion capacity of
TCDD in ovariectomized rats is the effect of TCDD on cell proliferation. TCDD did not
stimulate cell proliferation rates in ovariectomized rats, whereas a mean increase of tenfold was
apparent in intact rats receiving 100 ng TCDD/kg for 30 weeks (Table 6-5) (Lucier et al., 1991).
There was considerable interindividual variation in both cell proliferation rates and
enzyme-altered foci in the DEN/TCDD groups. Comparisons of the two data sets revealed a
strong positive correlation between enzyme-altered foci and cell proliferation, although the
importance of this finding is diminished by the fact that cell proliferation was quantified in
nonlesioned hepatocytes. The mechanism whereby ovarian hormones and TCDD interact to
produce cell proliferation in hepatocytes may involve growth factor pathways. Consistent with
this idea, TCDD induced a loss of plasma membrane epidermal growth factor receptor (EGFR)
in intact rats but not in ovariectomized rats (Sewall et al., 1993) EGF is thought to provide a
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mitogenic stimulus in hepatocytes and to play a key role in hepatocarcinogenesis (Vickers and
Lucier, 1991; Velu, 1990; Shi and Yager, 1989; Eckl et al., 1988). A schematic representation of
a plausible mechanism for the role of estrogen in TCDD-mediated liver cancer in rats is given in
Figure 6-2.
These observations of the ovarian hormone-dependent increase in hepatocyte replication
following chronic exposure to TCDD (Lucier et al., 1991) parallel the observed sex-dependent
induction of liver tumors in rats. This observation has led to the hypothesis that the induction of
cell replication by TCDD may be a critical event in the mechanism of hepatocarcinogenesis.
This hypothesis was supported by the observation that hepatocyte replication was
dose-dependently increased after chronic exposure to TCDD (Maronpot et al., 1993)(Table 6-8).
Other studies, however, have failed to observe any effect of TCDD on nonfocal
hepatocyte replication (Buchmann et al., 1994; Stinchcombe et al., 1995). More recently, it was
shown that induction of hepatocyte replication is exposure-duration dependent and is only
observed following 30 weeks of exposure to TCDD (Walker et al., 1998). Indeed, after 14
weeks of exposure, hepatocyte replication is lower in TCDD-treated animals than in controls.
These data indicate that the induction of hepatocyte replication is not an early event in tumor
promotion by TCDD and likely represents a secondary response to the induction of putatively
preneoplastic AHF. However, data are insufficient to conclude that induction of hepatocyte
replication is not involved in development of liver tumors.
Although cell replication is not seen after subchronic exposure to TCDD, it was observed
that there was a suppression of hepatocyte apoptosis following TCDD treatment (Stinchcombe
et al., 1995). The suppression of UV-inducible apoptosis by TCDD has also been observed in
vitro (Worner et al., 1996), suggesting that this suppression may be an early event in tumor
promotion. The suppression of apoptosis by TCDD in AHF may provide a growth advantage to
these preneoplastic lesions, and therefore may be involved in the mechanism of
hepatocarcinogenesi s.
6.4.4. Thyroid Cancer-Proposed Mechanism of Action
TCDD causes a dose-related increase in thyroid follicular cell adenomas and carcinomas
in rats and mice. One hypothesis for the induction of thyroid tumors involves the disruption of
thyroid hormone homeostasis via induction of phase II enzymes UDP-glucuronosyltransferases
(UGTs) (Hurley, 1998; Hill et al., 1998). Dioxin-like compounds induce the synthesis of UDP-
glucuronosyltransferase-1 (UGT1) mRNAby an AhR-dependent transcriptional mechanism
(Bock et al., 1998; Nebert et al., 1990). It is proposed that dioxin-like compounds increase the
incidence of thyroid tumors through an extrathyroidal mechanism. Dioxin-like compounds
induce hepatic UGT resulting in increased conjugation and elimination of thyroxine (T4),
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leading to reduced serum T4 concentrations. T4 production is controlled by the thyroid
stimulating hormone (TSH) which is under negative and positive regulation from the
hypothalamus, pituitary, and thyroid by the thyrotrophin-releasing hormone (TRH), TSH, T4,
and triiodothyronine (T3). Consequently, the reduced serum T4 concentrations would lead to a
decrease in the negative feedback inhibition on the pituitary gland. This would then lead to a
rise in the secreted TSH and stimulation of the thyroid. The persistent induction of UGT by
dioxins and subsequent prolonged stimulation of the thyroid would result in thyroid follicular
cell hyperplasia and hypertrophy of the thyroid, thereby increasing the risk of progression to
neoplasia. In support of this hypothesis, Kohn et al. modeled the effect of TCDD on UGTS and
thyroid hormones in female rats within the framework of a pharmacologically based-
pharmacokinetic (PBPK) model (Kohn et al., 1996). This mathematical model described release
and uptake of thyroid hormones, metabolism, TCDD induction of UGT 1, regulation of TSH
release from the pituitary by T4 and feedback on TRH and somatostatin which inhibits TSH
release. The model successfully reproduced the observed effects of TCDD on serum T3, T4, and
TSH, and UGT1 mRNA and enzyme activity, suggesting that this is a plausible mechanism for
an indirect role of TCDD on the thyroid. This model is supported by the more recent
experimental work of Schuur et al. which demonstrated the extrathyroidal effects of TCDD on
thyroid hormone turnover (Schuur et al., 1997).
6.4.5. Mechanisms of Reduced Spontaneous Tumor Incidence
In the Kociba study, there was a significant reduction in the incidence of spontaneous
benign tumors of the uterus, benign neoplasms of the mammary gland, mammary carcinomas,
and pituitary adenomas in female rats, and pheochromocytoma of the adrenal gland and
pancreatic adenomas in male rats. It is likely that the reduction in body weight gain as a result of
TCDD exposure is responsible for the reduced incidence in these spontaneous lesions. Studies
by the National Toxicology Program (Haseman and Johnson, 1996; Seilkop, 1995) indicate that
chemical exposures resulting in a reduction of body weight by 10%-20% is correlated with a
decrease in incidence of both mammary tumors and anterior pituitary tumors. This phenomenon
may be related to a homeostatic growth suppression in reproductive organs during periods of
reduced nutritional status. In addition, reduction in pancreatic adenomas (Kociba et al., 1978)
has been observed in diet-restricted animals, and consequently, this reduction in incidence may
be due to TCDD induced body weight reduction. The reduction in pheochromocytoma in male
rats does not appear to be a consequence of changes in body weight, yet it exhibited a dose-
dependent reduction in TCDD exposed males. The mechanism for the reduction in
pheochromocytoma is unknown. The decrease in hormone-dependent cancers also may be
related to the ability of TCDD to alter estrogen metabolism and/or its ability to act in some
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tissues/cell systems as an antiestrogen. Increased estrogen metabolism may result from
increased expression of CYP1 cytochromes P450, UGT, and GST. Decreased estrogen action
could result from effects on the estrogen receptor (ER) levels or ER transcriptional function.
TCDD may also induce the inactivation of estrogen in target cells by metabolism by TCDD
inducible cytochromes CYP1A2, CYP1B1, or conjugating enzymes such as UGT1/GST without
altering circulating estradiol concentrations.
6.5. BIOCHEMICAL RESPONSES
This section will summarize some of the changes produced by TCDD, including
discussion of (1) possible relevance of the response to TCDD-mediated cancer, (2) whether the
response is AhR mediated, (3) whether information is available on the role of transcriptional
activation, (4) dose-response relationships, and (5) whether animal models are consistent with
human responses. This chapter will not attempt to evaluate all of the biochemical and molecular
responses to TCDD, but will focus on the ones that are either the most relevant to carcinogenic
responses or have received the most study. The responses include induction of P4501 Al
(CYP1A1), cytochrome P4501A2 (CYP1A2), EGFR, estrogen receptor (ER), and
UDP-glucuronosyltransferase (UDPGT). Table 6-9 lists many of the biochemical changes
affected by TCDD in in vivo and/or in vitro systems and some information on mechanisms of
action.
6.5.1. Cytochrome P450
The most studied response to TCDD has been induction of cytochrome P450 isozymes
(Whitlock, 1990; Silbergeld and Gasiewicz, 1989; Poland and Knutson, 1982). The first reports
of P450 induction in vivo and in vitro appeared in 1973 (Lucier et al., 1973; Greig and
DeMatteis, 1973; Poland and Glover, 1973), and hundreds of papers have been published on the
subject since that time. These papers have dealt with various aspects of TCDD-mediated
induction of P450, such as isozyme specificity, time course, structure-activity relationships,
molecular mechanisms of transcriptional activation of the CYP1A1 gene, identification of
transcriptional activating factors, tissue and cell specificity, and dose-response relationships.
The molecular mechanisms responsible for enzyme induction are described elsewhere in this
volume.
The mechanistic relationship of CYP1A1 and 1A2 induction to cancer or any other toxic
endpoint following dioxin exposure has not yet been demonstrated, yet considerable controversy
exists on this subject (Roberts, 1991). Because CYP1 Al catalyzes the metabolic activation of
many chemicals, such as the PAHs, to DNA-reactive metabolites, it has been postulated that
induction of CYP1A1 might enhance the carcinogenic actions from a given exposure level to
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many PAHs. Recently it has been shown that benzo(a)pyrene is not carcinogenic in transgenic
mice that are AhR deficient (Shimizu et al., 2000). The lack of carcinogenicity is presumably
due to the lack of induction of CYP1A1 by B(a)P in these animals, supporting a proposed role
for CYP1A1 in the carcinogenicity of B(a)P. Usually, however, preinduction of CYP1A1
diminishes the carcinogenic potency of PAHs such as 3-methylcholanthrene, benzo(a)pyrene,
and 7,2-dimethylbenzanthracene if exposure to an inducing agent (such as TCDD) is short term
(Parkinson and Hurwitz, 1991; Wattenberg, 1985; Cohen et al., 1979; Wattenberg, 1978; Miller
et al., 1958). Induction also protects against the carcinogenic actions of aflatoxin,
diethylnitrosamine, arylamines, and urethane. Protection occurs at numerous cancer sites,
including liver and lung. Several lines of evidence support the idea that enzyme induction is the
mechanism responsible for the protective effect. First, treatment of mice deficient in AhR with
inducers does not protect against PAH-mediated cancer (Kouri et al., 1978). Second, the ability
of inducing agents to protect against cancer is positively correlated with their potency as
inducing agents (Wattenberg and Leong, 1970; Arcos et al., 1961). Third, the inducing agent
must be administered at least 1 day prior to treatment, which allows sufficient time for the
inducer to produce elevated levels of CYP1A1 (Parkinson et al., 1983; Wheatley, 1968).
The most probable mechanism for the protective effect of enzyme induction is that it
leads to decreased concentrations of promutagenic DNA adducts in target tissues. These
findings appear to contradict the knowledge that CYP1 Al is required for the metabolism of
PAHs, aflatoxin, and several other carcinogens to DNA-reactive arene oxides (Guengerich,
1988; Levin et al., 1982; Conney, 1982). For example, the promutagenic DNA adduct of
benzo(a)pyrene appears to be a 7,8-diol-9,10 epoxide metabolite adducted to deoxyguanosine,
and formation of this metabolite requires two separate actions of CYP1A1. The contradiction
can be resolved by analysis of all the metabolic pathways for chemical carcinogens whose
potencies are decreased by pretreatment with inducing agents. In addition to CYP1 Al-mediated
increases in metabolic activation, CYP1A1 also converts PAHs to inactive metabolites (Thakker
et al., 1985; Pelkonnen andNebert, 1982). Moreover, induction of uridine
diphosphoglucuronyltransferase also occurs concurrently with CYP1A1 induction (Lucier et al.,
1986). This enzyme also detoxifies metabolites of PAHs and other carcinogens and facilitates
their excretion from the body (Thakker et al., 1985; Nemoto and Gelboin, 1976). Therefore, it
appears that TCDD-mediated enzyme induction increases the rate of detoxification of some
carcinogens to a greater extent than it increases the rate of formation of DNA-damaging
metabolites.
Increased frequency of sister chromatid exchanges was observed in lymphocytes of
people exposed to pentachlorinated dibenzo-p-dioxins (PCDFs) in Taiwan when those
lymphocytes were challenged with beta-naphthoflavone (Lundgren et al., 1986, 1988). This may
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be because the PCDFs cause increased rates of metabolic activation of beta-naphthoflavone to
DNA-reactive metabolites (Lundgren et al., 1987). These findings are consistent with the idea
that TCDD's ability to induce drug-metabolizing enzymes (CYP1A1 and 1A2) may lead to an
increased rate of formation of DNA-reactive metabolites of some carcinogens, most notably the
PAHs and aromatic amines. However, there is evidence that the opposite effect occurs in some
cases, because in vivo exposure to CYP1 Al inducers actually leads to a decrease in DNA
adducts in target tissue following in vivo exposure to PAHs such as benzo(a)pyrene (Cohen et
al., 1979; Parkinson and Hurwitz, 1991). It can reasonably be concluded that TCDD exposure
may increase the rate of DNA adduct formation for some carcinogens but decreases the rate for
others, and that predictions should not be made without experimental data on DNA adduct
concentrations in control and TCDD-treated animals.
Although there is no clear mechanistic link between CYP1 Al induction and cancer, it is
important to note that many CYP1 Al inducers are themselves carcinogens when encountered in
chronic dosing regimens; therefore, the protective effect of inducing agents appears to be limited
to short-term exposure. For example, benzo(a)pyrene, 3-methylcholanthrene, and TCDD are
CYP1A1 inducers and multisite carcinogens (Vanden Heuvel and Lucier, 1993; Levin et al.,
1982; Slaga et al., 1979; Sims and Glover, 1974).
The relationship of CYP1A2 induction to the carcinogenic actions of other compounds is
less clear than it is for CYP1A1. For example, CYP1A2 catalyzes the formation of catechol
estrogens from 17-beta-estradiol (Graham et al., 1988). The catechol estrogens are considered to
be possible toxic metabolites because they could lead to increased free radical damage to cellular
macromolecules such as DNA (Li and Li, 1990; Metzler, 1984; Yager and Liehr, 1996). This
mechanism could be responsible, in part, for the findings that TCDD is a hepatocarcinogen in
female rats but not male rats, and that ovariectomy protects against the hepatocarcinogenic
actions of TCDD. Also consistent with the hepatocarcinogenicity data is the observation that
CYP1A2 is induced in liver but not in extrahepatic organs, with the possible exception of the
nasal mucosa (Goldstein and Linko, 1984). In contrast, CYP1 Al induction occurs in virtually
every tissue of the body, which is consistent with the observation that the AhR is found in a wide
variety of cell types.
In addition to the well-characterized induction of CYP1A1 and CYP1A2, TCDD also
induces another cytochrome P450, CYP1B1, that has been identified in humans and rodents
(Bhattacharyya et al., 1995; Savas et al., 1994; Sutler et al., 1994; Walker et al., 1995). CYP1B1
is expressed in a variety of human tissues and is inducible by TCDD in numerous human cell and
rodent tissues including liver, lung, and kidney (Hayes et al., 1996, Sutler et al., 1994; Walker et
al., 1995). CYP1B1 is active in the metabolism of numerous polycyclic aromatic hydrocarbons
and arylamines (Otto et al., 1992; Shimada et al., 1996; Crofts et al., 1998) and can catalyze the
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4-hydroxylation of 17-beta-estradiol in humans cells (Hayes et al., 1996). The potent
carcinogenicity of 4-hydroxyestradiol in Syrian Golden hamsters (Liehr et al., 1986) and the
observed elevation of 4-hydroxylase activity in human tumors (Liehr et al., 1996) suggest that
the estradiol hydroxylase activity of CYP1B1 may play a critical role in tumorigenesis. This
implication has been further extended to suggest that the induction of CYP1B1 in rat liver may
play a role in the ovarian hormone-dependent hepatocarcinogenicity of TCDD (Yager et al.,
1996). However, there are no reports in the literature that CYP1B1 in rodents has any significant
estradiol hydroxylase activity, and therefore it is not clear if CYP1B1 is involved in the
mechanism of hepatocarcinogenesis in rats.
CYP1B1 in both humans and rodents is active in the metabolism of PAHs and
arylamines, and therefore, like CYP1A1, CYP1B1 may play a role in modulating the
carcinogenicity of procarcinogens in both humans and experimental models. A recent report
indicates that CYPIB 1-dependent DMBA metabolism is required for the induction of DMBA-
induced lymphomas in mice (Buters et al., 1999).
There are a number of studies on dose-response relationships for TCDD's effects on
CYP1A1 and 1A2 (DeVito et al., 1991; Lin et al., 1991a; Kedderis et al., 1991; Harris et al.,
1990a; Goldstein and Safe, 1989; Abraham et al., 1988; Lucier et al., 1986; Vecchi et al., 1983;
Kitchin and Woods, 1979; Poland and Glover, 1973). These studies (Tritscher et al., 1992;
Graham et al., 1988; Sloop and Lucier, 1987) include single and chronic dosing, time-course
evaluations, and species comparisons. Dose-response relationships have been evaluated by
quantitation of CYP 1-dependent enzyme activities, quantitation of mRNA levels by Northern
blot analysis, and quantitation of CYP 1 protein by radioimmunoassay and immunolocalization in
tissue sections. Dose-response modeling of these studies is described in detail in Chapter 8 of
this document. Evaluations of various data sets for TCDD-mediated dose-response relationships
have revealed some interesting information. One way of analyzing data for linearity or
nonlinearity of dose-response for receptor-mediated events is the Hill equation (Hayashi and
Sakamoto, 1986). A Hill coefficient of 1 suggests a linear relationship between exposure and
dose throughout the experimental dose range, and would predict a proportional relationship
between target tissue concentration of TCDD and biological response at all dose levels. This
would imply that the response had no practical threshold or "no effect level." Hill coefficients
greater than 1 would indicate sublinearity in dose-response, whereas a Hill coefficient of less
than 1 would indicate supralinearity for response in the low-dose region. Analyses of single-
exposure and chronic exposure data for CYP1 Al and CYP1A2 induction in rat or mouse liver
indicate a Hill coefficient of slightly greater than 1 for CYP1 Al and slightly less than 1 for
CYP1A2 (Portier et al., 1992; Kohn et al., 1993). Although these analyses involve an
extrapolation beyond the range of experimental data, they are consistent with the hypothesis that
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there is no threshold for TCDD-mediated induction of CYP1A1 and 1A2. Time-course and
dose-response analyses indicate that CYP1B1 is expressed at significantly lower levels than
either CYP1A isozyme and is induced only at higher doses than those required for CYP1 Al or
CYP1A2 (Santostefano et al., 1997; Walker et al., 1999). Furthermore, the Hill coefficient for
CYP1B1 induction is greater than that for CYP1A1 (Walker et al., 1999). A more detailed
analysis of dose-response relationships for cytochrome P450 induction and other
dioxin-inducible responses can be found in Chapter 8 of this volume.
Immunological detection of induced CYP1A1 and 1A2 in liver sections obtained from
rats exposed chronically to TCDD indicates hepatocyte heterogeneity in response to TCDD
(Tritscher et al., 1992; Bars and Elcombe, 1991). For example, relatively low doses of TCDD
(1 ng/kg/day) appear to maximally induce some cells around the centrilobular region. Increasing
doses of TCDD increase the number of cells responding, rather than the amount of induction in
responding cells. Like CYP1A1 and CYP1A2, CYP1B1 is also induced by TCDD in the rat
liver in a centrilobular pattern of expression (Walker et al., 1997). It has been suggested that the
heterogeneous pattern of expression may be due to differences in expression of the AhR across
the acinus (Lindros et al., 1997) or to differences in binding affinity (Andersen et al., 1997).
Alternatively, the observation that CYP1A2 is responsible for hepatic sequestration of TCDD
(Diliberto et al., 1999) suggests that the heterogeneity in expression of the CYPs may be in part
due to a heterogeneity in distribution of TCDD across the liver acinus. In support of this theory,
the concentration of TCDD in periportal hepatocytes is higher than that seen in centrilobular
hepatocytes (Santostefano et al., 1999). These data, which document cell differences in
sensitivity to induction, complicate evaluation of dose-response relationships. For example,
some hepatocytes appear to be maximally induced by low doses of TCDD, whereas other
hepatocytes exhibit no detectable P450 induction response at the same doses. As discussed
earlier, a mechanistic link between P450 induction and cancer has not been established.
Evaluations of P450 induction and TCDD-mediated cell proliferation by immunocytochemical
methods in rat liver reveal that cells expressing CYP1 Al and 1A2 are different from those
exhibiting TCDD-mediated increases in DNA replication (Lucier et al., 1992).
Placentas from Taiwanese women exposed to rice oil contaminated with polychlorinated
dibenzofurans have markedly elevated levels of CYP1A1 (Lucier et al., 1987; Wong et al.,
1986). Comparison of these data with induction data in rat liver suggests that humans are at least
as sensitive as rats to the enzyme-inductive actions of TCDD and its structural analogues
(Lucier, 1991). Consistent with this contention, the in vitro EC50 for TCDD-mediated induction
of CYP1A1-dependent enzyme activities is approximately 1.5 nM when using either rodent or
human lymphocytes (Clark et al., 1992). Also, binding of TCDD to the AhR occurs with a
higher affinity in rat cellular preparations compared with humans (Lorenzen and Okey, 1991;
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Okey et al., 1989). This difference may be related to the greater lability of the human receptor
during tissue preparation and cell fractionation procedures, or to an inherent property of the
human AhR (Manchester et al., 1987). In any event, it does appear that humans contain a fully
functional AhR (Cook and Greenlee, 1989), as evidenced by significant CYP1 Al induction in
tissues from exposed humans, and this response occurs with sensitivity similar to that observed
in experimental animals.
6.5.2. Epidermal Growth Factor Receptor
EOF is a potent mitogen and stimulates the generation of mitotic signals in both normal
and neoplastic cells (Stoscheck and King, 1986; Carpenter and Cohen, 1979). Several lines of
evidence suggest that the EGF receptor and its ligands, including transforming growth
factor-alpha, possess diverse functions relevant to cell transformation and tumorigenesis (Velu,
1990; Marti et al., 1989; Mukku and Stancel, 1985). In fact, the mechanism of action for several
tumor promoters, such as phenobarbital and the phorbol esters, is thought to involve the EGF
receptor pathway (Stoscheck and King, 1986). A schematic representation of the proposed
mechanism for EGF-stimulated mitogenesis is given in Figure 6-3.
Several studies have shown that TCDD decreases the binding capacity of the plasma
membrane EGF receptor for its ligand without a change in Kd (Clark et al., 1991a; Lin et al.,
1991a; Abbott and Birnbaum, 1990; Astroff et al., 1990; Sunahara et al., 1989; Stoscheck and
King, 1986; Hudson et al., 1985; Madhukar et al., 1984). One study used a range of TCDD
doses (3.5 to 125 ng/kg/day) for 30 weeks to evaluate the effects of TCDD exposure on EGF
receptor in rat liver plasma membranes. There was a clear dose-response relationship for
TCDD's effects on the total binding capacity of the EGF receptor, although TCDD did not
produce a change in binding affinity of the receptor. The maximal effect was a threefold
decrease in the concentration of plasma membrane EGF receptor; the ED50 was -10 ng/kg/day
based on administered dose and ~2 ppb TCDD based on liver TCDD concentration. These
values are similar to the ED50 for induction of CYP1A1 and CYP1A2 for 30-week exposures.
The dose-response data, like the data for CYP1 Al and CYP1A2 induction, were subjected to
curve-fitting analyses using the Hill equation (Portier et al., 1992). This analysis indicated that a
Hill coefficient of 1 provided the best fit, suggesting that there is a linear relationship between
target tissue dose and response for effects on the EGF receptor. Although Hill analyses of
dose-response data for TCDD's effects on the EGF receptor, CYP1A1 induction, and CYP1A2
induction are inconsistent with the idea of a threshold, the lowest dose used in these experiments
was 100 pg/kg/day, so the possibility exists that dose-response relationships are different in the
very low-dose region (1 to 10 pg/kg/day) encountered as background human exposures.
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Dose-response data on EGFR were compared with dose-response relationships for
TCDD-mediated increases in cell proliferation and growth of preneoplastic lesions within the
framework of a two-stage model for hepatocarcinogenesis in rats (Lucier et al., 1992, Sewall et
al., 1993, 1995a). Results indicate that cell proliferation and the growth of preneoplastic lesions
are less sensitive responses to TCDD than is loss of plasma membrane EOF receptor. Therefore,
the EGF receptor may be involved in the hepatocarcinogenic actions of TCDD, but
dose-response relationships for this effect may be different from dose-response relationships for
liver cancer in rats. These data reflect the knowledge that several steps and/or several genes are
involved in the modulation of coordinated biological responses.
The mechanism by which TCDD alters EGF receptor-binding capacity is not fully
understood, although TCDD does not appear to decrease EGF receptor mRNA (Lin et al., 199la;
Osborne et al., 1988). By using congenic mice deficient in the high-affinity AhR, TCDD's
effects on the EGF receptor were shown to require the AhR (Lin et al., 199la). In control
animals, the EGF receptor is distributed on the surface of the plasma membrane and is composed
of an external ligand-binding domain, a transmembrane domain, and an intercellular domain
(Velu, 1990; Carpenter, 1987). Ligands for the EGF receptor (EGF or TGF-alpha) in the
intracellular space bind the EGF receptor, producing a conformational change that stimulates the
intercellular region to catalyze phosphorylation of the receptor itself as well as other proteins
involved in cell regulation. The process results in internalization of the receptor, characterized
by an increase in cytosolic EGFR coupled with a decrease in membrane-bound receptor. The
effects of TCDD and CDFs on the number of binding sites for the plasma membrane EGF
receptor are correlated with a concomitant decrease in EGF-stimulated autophosphorylation of
the EGF receptor, indicating that TCDD produces a true functional change in the EGF receptor
(Clark et al., 1991a; Sunahara et al., 1989; Nelson et al., 1988; Sunahara et al., 1988).
Importantly, the addition of EGF to hepatocytes or several cell lines in culture produces a loss of
plasma membrane EGF receptor coupled with a loss of EGF-stimulated autophosphorylation
(Velu, 1990; Carpenter, 1987). Therefore, TCDD produces an EGF receptor-like response
consistent with the idea that TCDD enhances the generation of cellular mitotic signals.
Although TCDD exposure mimics EGF actions in hepatocytes, TCDD itself does not
appear to bind to the EGF receptor. The most plausible mechanism for effects on the EGF
receptor involves the finding that TCDD induces production of TGF-alpha in hepatocytes as well
as human keratinocytes (Choi et al., 1991). This response could alter control of normal growth
patterns because TGF-alpha binds the EGF receptor with high affinity, leading to enhanced
production of mitogenic signals. Alternatively, TCDD may affect EGF receptor transcription.
In fact, TCDD has been shown to decrease uterine EGF receptor mRNA levels (Astroff et al.,
1990). Receptor concentrations may also be altered by other events including posttranslational
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glycosylation, increased lysosomal degradation, or alterations in signal transduction pathways
such as protein kinases (Madhukar et al., 1988). It is also possible that TCDD alters
phosphorylation of the EGF receptor by activation of protein kinase C, resulting in decreased
binding capacity of the plasma membrane EGF receptor. This effect occurs following exposure
to the tumor promoter TPA and is associated with decreased autophosphorylation rates and EGF
receptor internalization (Beguinot et al., 1985; Cochet et al., 1984). In any event,
TCDD-mediated alterations in EGF receptor pathways may, in part, be responsible for the
tumor-promoting actions of TCDD by enhancement of mitotic signals.
The effects on the EGF receptor system may be mediated by estrogen action, and it has
been postulated that the estrogen and EGF receptor pathways are integrated by "cross talk"
mechanisms (Ignar-Trowbridge et al., 1992; Astroff et al., 1990). In vivo and in vitro studies
have demonstrated that TCDD alters the ER (DeVito et al., 1992; Lin et al., 1991a; Clark et al.,
1991a; Umbreit and Gallo, 1988; Romkes et al., 1987) and estrogens can, in turn, alter EGF
receptor binding and cellular distribution (Vickers and Lucier, 1991; Vickers et al., 1989; Mukku
and Stancel, 1985). Moreover, studies conducted within the framework of a two-stage model for
hepatocarcinogenesis have demonstrated that TCDD-mediated decreases in plasma membrane
EGF receptor are ovarian hormone dependent (Sewall et al., 1993). These studies concluded that
ovarian hormones are essential to the tumor-promoting actions of TCDD because TCDD does
not induce hepatocyte proliferation or stimulate the growth of preneoplastic lesions in
ovariectomized rats (Section 6.3, Initiation/Promotion Studies).
Evidence indicates that TCDD and its structural analogues produce the same effects on
the EGF receptor in human cells and tissues as observed in experimental animals. First,
incubation of human keratinocytes with TCDD decreases plasma membrane EGF receptor, and
this effect is associated with increased synthesis of TGF-alpha (Choi et al., 1991; Hudson et al.,
1985). Second, placentas from humans exposed to rice oil contaminated with poly chlorinated
dibenzofurans exhibit markedly reduced EGF-stimulated autophosphorylation of the EGF
receptor, and this effect occurred with similar sensitivity to that observed in rats (Lucier, 1991;
Sunahara et al., 1989). The magnitude of the effect on autophosphorylation was positively
correlated with decreased birth weight of the offspring.
6.5.3. UDP-Glucuronosyltransferases
Several studies have shown that TCDD induces synthesis of at least one isozyme of
UDPGT (Lucier et al., 1973, 1974, 1986) by a mechanism that requires the AhR (Bock, 1991).
The gene UGT-1 regulates synthesis of the UDPGT isozyme, which conjugates numerous
substrates including 1-naphthol, p-nitrophenol, and thyroxine (Burchell et al., 1991). This gene
contains a TCDD-responsive element that permits transcriptional activation following binding of
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the TCDD-AhR complex. Other chemicals that bind the AhR, such as 3-methylcholanthrene and
benzo(a)pyrene, also induce UGT-1 (Bock, 1991). UDPGTs are considered a deactivation
pathway for numerous environmental chemicals and endogenous compounds, such as steroid
hormones, by rendering them water soluble and excretable as a consequence of the catalytic
addition of a glucuronide moiety (Tephly and Burchell, 1990). Therefore, induction of UDPGT
may be responsible, in part, for the finding that pretreatment with TCDD leads to diminished
DNA adducts for PAHs and decreased concentrations of some steroid hormones.
Conjugation of thyroxine by UGT-1 leads to deactivation and elimination of this thyroid
hormone (Henry and Gasiewicz, 1987; Bastomsky, 1977). The decreased levels of thyroxine
associated with UDPGT induction produce decreased feedback inhibition of the pituitary gland,
which responds by secreting increased amounts of TSH (Sanders et al., 1988; Barter and
Klaassen, 1992). Several studies have provided evidence that prolonged stimulation by TSH
produces an oncogenic effect on the thyroid (Hill et al., 1989). Interestingly, rat liver EGF
receptor may, in part, be regulated by thyroid hormones (Mukku, 1984). Increased incidence of
thyroid tumors is the most sensitive endpoint in cancer bioassays, as evidenced by a statistically
significant increase at a dose of 1.4 ng/kg/day. Consistent with this hypothesis, rodent studies
have shown that TCDD and other inducers of hepatic UDPGT decrease thyroxine concentration
in blood, which is associated with increased levels of thyroid-stimulating hormone (Barter and
Klaassen, 1992; Henry and Gasiewicz, 1987).
Dose-response studies for TCDD's inductive effects on hepatic UDPGT in rats have
demonstrated that the single dose ED50 is approximately 0.7 |ig/kg, which is similar to the ED50
for CYP1A1 induction (Lucier et al., 1986). Furthermore, the shape of the dose-response curve
for both responses is similar. Analysis of the expression of UGT1 Al in rodent liver showed that
induction of UGT1A1 RNA was dose dependency increased following a single dose of TCDD
(Vanden Heuvel et al., 1994). Further studies showed that chronic exposure of female rats to
0-125 ng TCDD/kg/day for 30 weeks led to a significant increase in UGT and subsequent
alterations in thyroid function (Sewall et al., 1995b). A mathematical pharmacokinetic-
pharmacodynamic model for TCDD (Kohn et al., 1993) was modified to include effects of
TCDD on UGT and thyroid hormone levels (Kohn et al., 1996) (see Chapter 8). Model
outcomes accurately predicted changes in thyroid hormone levels in TCDD-treated female rats
and lend support to the hypothesis that induction of UGT, and subsequent persistent stimulation
of the thyroid by TSH, may be involved in the promotion of thyroid tumorigenesis. It is
noteworthy, however, that these data were obtained from female Sprague-Dawley rats and that
the thyroid carcinogenicity of TCDD was observed in male but not female Osborne-Mendel rats.
Furthermore, in the Kociba study chronic exposure to TCDD did not induce thyroid tumors in
female Sprague-Dawley rats. Although gender-specific difference in carcinogenicity may be due
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to higher circulating levels of TSH in male rats, the model predictions increase confidence in the
hypothesis that the induction of UDPGT by TCDD is directly involved in the mechanism.
Because humans have the dioxin-responsive UDPGT (UGT-1) (Burchell et al., 1991) and
TCDD induces UDPGT in human hepatocyte cell cultures, it is reasonable to assume that TCDD
and its structural analogues would induce UDPGT in humans, although laboratory data are
needed to validate this assumption.
6.5.4. Estrogen Receptor
Several lines of evidence have demonstrated that interactions of TCDD and estrogens are
critical to some of the carcinogenic responses to TCDD. Although the precise mechanisms of
those interactions have not been established, recent data indicate that TCDD effects on the ER
and on estrogen metabolism are involved. The mechanisms for TCDD/estrogen interactions
appear to be tissue specific. Of particular interest is the finding that TCDD increases liver tumor
incidence in rats, and at the same time decreases tumor incidence in organs such as the mammary
gland, uterus, and pituitary (Kociba et al., 1978). Therefore, TCDD/estrogen interactions will be
examined separately for liver and other endocrine organs.
The liver contains a fully functional ER that possesses characteristics similar to those
identified for ER in the mammary gland and uterus (Mastri and Lucier, 1983; Powell-Jones et
al., 1981; Eisenfeld et al., 1976). For example, the liver exhibits high-affinity binding for 17-
beta-estradiol and other potent estrogens, liver ER binding is specific for estrogens, the ligand
receptor complex interacts reversibly with DNA, and this interaction leads to transcriptional
activation of estrogen-responsive genes. Synthesis of hepatic ER, unlike ER in other target
tissues, is under pituitary control (Lucier et al., 1981). Treatment of rats with a single dose of
TCDD decreases binding capacity of the hepatic ER, and this effect is correlated with a decrease
in ER protein (Zacharewski et al., 1991, 1992; Harris et al., 1990b; Romkes and Safe, 1988;
Romkes et al., 1987). TCDD also decreases rat hepatic ER in chronic exposure experiments,
with a threefold decrease evident following a dose of 100 ng/kg/day for 30 weeks (Clark et al.,
1991b). TCDD also decreases hepatic ER binding in C57B16 mice, but a much higher dose is
needed to produce this effect in congenic mice deficient in the high-affinity AhR, indicating that
TCDD-mediated decreases in ER are dependent on the AhR (Lin et al., 1991b). Dose-response
studies in mice demonstrate that the single-dose ED50 is -0.7 jig TCDD/kg, similar to the ED50
for other biochemical end points such as CYP1 Al induction, loss of plasma membrane EGF
receptor, and induction of UDPGT. The observation that TCDD decreases hepatic ER is in
apparent contradiction to the finding that TCDD increases hepatocyte proliferation, because the
ER is thought to produce mitogenic signals. However, quantitation of ER in control and
TCDD-treated rats was done using preparations from liver homogenates. Immunolocalization
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studies are needed so that the relationship of ER concentrations to cell proliferation in normal
and preneoplastic cells can be more carefully evaluated.
In addition to effects on hepatic ER, TCDD may influence estrogen action in another
way. CYP1A2 efficiently catalyzes the conversion of estrogens to catechol estrogens in liver
(Graham et al., 1988; Dannan et al., 1986). CYP1A2 is not found in extrahepatic tissues, with
the possible exception of the nasal cavity, so catechol estrogen formation would be expected to
occur only in liver. Catechol estrogens have been postulated to possess macromolecule-
damaging properties as a consequence of free radical generation (Li and Li, 1990; Metzler,
1984). Therefore, TCDD may increase the DNA-damaging capacity of estrogens in liver as a
function of CYP1A2 induction. This effect may, in part, explain the carcinogenic actions of
TCDD in female rat liver, and is consistent with the knowledge that ovariectomy protects against
the hepatocarcinogenic actions of TCDD and that male rats do not appear to be susceptible to
TCDD-induced liver tumors (Lucier et al., 1991; Kociba et al., 1978). It is important to note that
cancer is more than a two-stage process, and the stage-specific actions of TCDD in multistage
cancer models are not known, although TCDD-mediated cell proliferation and possible indirect
genotoxic effects may be critical at more than one stage. A hypothetical mechanistic scheme for
TCDD-mediated liver cancer is shown in Figure 6-2.
The finding that chronic TCDD exposure decreases tumor incidences in the pituitary,
mammary gland, and uterus may also reflect TCDD's effects on ER and estrogen metabolism.
As discussed above, TCDD decreases uterine ER concentrations in cytosolic and nuclear
fractions of rats and mice, and these changes are associated with diminished estrogen action in
both in vivo and in vitro studies. TCDD also increases estrogen metabolism, presumably as a
consequence of CYP1A2 in liver and UDPGT induction in liver and extrahepatic tissues
(Shiverick and Muther, 1982). Likewise, the addition of TCDD to a breast cancer cell line
(MCF-7) results in increased estrogen degradation (Gierthy et al., 1988). However, there are
only small effects on serum 17-beta estradiol levels following administration of TCDD to either
rats or mice (Shiverick and Muther, 1983). Therefore, the effect on serum estradiol is
considerably less sensitive than the effects on the uterine receptor. This comparison has led
investigators to conclude that the antiestrogenic actions of dioxins are primarily caused by
effects on ER levels in reproductive tract tissues. Consistent with this hypothesis, Fernandez and
Safe (1992) have shown that TCDD is antimitogenic in human breast cancer cells. Final
evaluation of the role of estrogen metabolism awaits data on concentrations of estrogens in
responsive cells of control and TCDD-treated rats, which may be different from serum estradiol
levels. In any event, it appears clear that TCDD does possess antiestrogenic properties that are
likely to be important to decreased tumor incidences in some reproductive tract and endocrine
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organs. Numerous studies have documented that the ER is found in virtually every tissue of the
body, although the effects of TCDD on human ER in vivo have not been studied.
6.5.5. Other Biochemical Endpoints
TCDD alters a number of other pathways involved in the regulation of cell differentiation
and proliferation (see Chapter 3). The specific relationships of these effects to multistage
carcinogenesis are not known, but the broad array of effects on hormone systems, growth factor
pathways, cytokines, and signal transduction components is consistent with the notion that
TCDD is a powerful growth dysregulator (Table 6-9). It is also consistent with the findings that
TCDD alters cancer risks at a large number of sites, possibly reflecting multiple mechanisms of
carcinogenicity. Biochemical/molecular/endocrine changes produced by TCDD include the
glucocorticoid receptor (Sunahara et al., 1989), tyrosine kinase (Madhukar et al., 1988), gastrin
(Mably et al., 1990), interleukin-lbeta (Sutler et al., 1991), plasminogen activator inhibitor
(Sutler et al., 1991), tumor necrosis factor-alpha (Clark et al., 1991b), gonadotropin-releasing
hormone (Moore et al., 1989), testosterone (Moore et al., 1985), and luteinizing hormone (Mably
et al., 1992). The importance of these responses to the carcinogenic process should not be
diminished by the lack of detail presented here. In every case studied, these responses have been
shown to be dependent on the AhR.
6.6. SUMMARY AND WEIGHT OF EVIDENCE FROM ANIMAL STUDIES
There have been several long-term studies designed to determine if TCDD is a
carcinogen in experimental animals. All of these studies have been positive and demonstrate
that TCDD is a multisite carcinogen, is a carcinogen in both sexes and in several species
including the Syrian hamster, is a carcinogen in sites remote from the site of treatment, and
increases cancer incidence at doses well below the MTD. In two-stage models for liver and skin
cancer, it is clear that TCDD is a potent promoting agent with weak or no initiating activity. Of
those compounds that make up the bulk of the human body burdens of TEQ, only three of them,
namely TCDD and a mixture of two congeners of hexachlorodibenzo-p-dioxin (HCDD)
(1,2,3,6,7,8 and 1,2,3,7,8,9), have been tested in chronic rodent bioassays. In both cases, there
was clear evidence of carcinogenicity. Commercial mixtures of PCBs that have a high TEQ also
have been shown to be carcinogenic in rat liver. While many of the other dioxin-like compounds
have not been tested in chronic carcinogenicity studies, 1,2,3,7,8-PCDD; 1,2,3,4,6,7,8-HpCDD;
2,3,4,7,8-PCDF; 1,2,3,4,7,8-HCDF; PCB126; and PCB105 all promote the development of
putatively preneoplastic altered hepatocellular foci (AHF) within rodent liver suggesting that
they also act as tumor promoters like TCDD (see Table 6-5). Together, dioxins and furans
comprising 94% of the total dioxin/furan TEQ, and PCBs comprising 85% of the total coplanar
December 2003 6-30 DRAFT—DO NOT CITE OR QUOTE
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PCB TEQ, have all shown to be positive in either rodent bioassays, rodent liver tumor promotion
studies, or mouse skin tumor promotion studies. In addition, complex mixtures of dioxins and
furans and commercial PCB mixtures act as promoters of liver AHF. These data suggest that,
while the majority of dioxin-like congeners have not been tested for carcinogenicity in chronic
rodent bioassays, it is likely that those individual congeners and mixtures of dioxin-like
compounds, which comprise the majority of the dioxin-like activity in human tissues, are
carcinogenic to rodents. Furthermore, when one considers the impact of current TEF values on
compounds that make up the majority of the current TEQ, it is clear that more than 90% of the
current TEQ for either dioxins/furans or PCBs is made up of compounds for which the current
TEF is supported by data on relative potencies based on tumor promotion or carcinogenic
endpoints. More information on this point is provided in Table 6-10.
The finding of weak or no initiating activity is not surprising because TCDD does not
form DNA adducts and is negative in short-term tests for genetic toxicity. The general
consensus is that TCDD is an example of receptor-mediated carcinogenesis in that (1) interaction
with the AhR appears to be a necessary early step, (2) TCDD modifies a number of receptor and
hormone systems involved in cell growth and differentiation, such as the epidermal growth
factor receptor and the ER, and (3) sex hormones exert a profound influence on the carcinogenic
actions of TCDD. Although tumor promotion data for the polychlorinated dibenzofurans and
coplanar polychlorinated biphenyls are limited, it appears that these compounds are liver tumor
promoters with potencies dependent on their binding affinity to the AhR.
Some of the central issues in the risk assessment of TCDD and its structural analogues
are (1) characterization of the shape of the dose-response curve for receptor-mediated events, (2)
evaluation of the relevance of animal data in estimating human risks, and (3) the health
consequences of background exposures (1 to 10 pg TEQ/kg/day) of dioxin and its structural
analogues. With regard to the shape of the dose-response curve, it is clear from animal studies
that there are different dose-response curves for different TCDD effects, which is consistent with
the generally accepted dogma for steroid receptor-mediated responses. In general, the
biochemical/molecular responses such as cytochrome P450 induction do not show evidence for a
threshold, although unequivocal conclusions cannot be made about the mechanistic link, if any,
between biochemical responses and toxic effects. In fact, coordinated biological responses such
as TCDD-mediated cell proliferation and growth of preneoplastic lesions (foci of cellular
alterations in liver) appear to be less sensitive endpoints, although evaluation of these responses
is complicated by a high degree of interindividual variation: some animals do not exhibit any
increase in cell proliferation in response to TCDD exposure.
The mechanistic basis for interindividual variation is unclear, and this lack of knowledge
complicates approaches to estimate human risks from experimental animal data. However,
December 2003 6-31 DRAFT—DO NOT CITE OR QUOTE
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several studies indicate that, for the most part, humans appear to respond like experimental
animals for biochemical and carcinogenic effects. However, data from epidemiology studies are
difficult to evaluate because the carcinogenic effects, if any, resulting from background TCDD
exposures are not known, although biochemical effects such as cytochrome P450 induction may
be produced by background exposures.
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Table 6-1. Sites for increased cancer in animal bioassays
Species/Strain
Rats/Sprague-Dawley
Rats/O sb orne-Mendel
Mice/B6C3Fl
Mice/B6C3 and B6C
Hamsters/Syrian Golden
Sex
Male
Female
Male
Female
Male
Female
Male
Female
Male
Site
Tongue
Nasal
turbinates/hard
palate
Lung
Nasal
turbinates/hard
palate
Liver
Thyroid
Adrenal cortex
Liver
Adrenal cortex
Subcutaneous
fibrosarcoma
Liver
Liver
Thyroid
Subcutaneous
fibrosarcoma
Thy mi c
lymphomas
Liver
Facial skin
carcinoma
Reference
Kocibaetal., 1978
NTP, 1982a
NTP, 1982a
Delia Portaetal., 1987
Raoetal., 1988
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Table 6-2. Different evaluations of Kociba study liver tumor data in female rats
Evaluation
Kociba et al.,
1978
Squire, 1980
Goodman and
Sauer, 1992
Tumor classification
Hyperplastic nodule
Hepatocellular carcinoma
Hyperplastic nodule;
hepatocellular carcinoma0
Neoplastic noduled:
hepatocellular carcinoma
Hepatocellular adenoma
Hepatocellular carcinoma
Hepatocellular adenoma;
hepatocellular carcinoma
Control
8/86
/KO.OOOI*
1/86
/KO.OOOI
9/86
/K0.001
16/86
/KO.OOOI
2/86
/KO.OOOI
0/86
/K0.01
2/86
/KO.OOOI
TCDD(ng/kg/day)
1
3/50
0/50
3/50
8/50
1/50
0/50
1/50
10
18/50
/X0.001b
2/50
18/50
/K0.001
27/50
/K0.001
9/50
/K0.01
0/50
9/50
/K0.01
100
23/49
/K0.001
11/49
/K0.001
34/48
/K0.001
33/47
/K0.001
14/45
/K0.001
4/45
/K0.05
18/45
/K0.001
"p-values for Mantel-Haenszel trend tests are given below the control group incidences (Huff et al., 1991).
b/>-values for Fisher exact tests are given below the incidence data for TCDD-treated animals.
"Combined incidence data for hyperplastic nodule and hepatocellular carcinoma in the Kociba study is as described
byHuffetal., 1991.
dHyperplastic nodule, neoplastic nodule, and hepatocellular adenoma are interchangeable lesions.
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Table 6-3. Tumor incidences" in Osborne-Mendel rats
Sex
Male
Female
Target organ
Thyroid: follicular cell
adenoma
Liver: neoplastic
nodule
Adrenal cortex:
adenoma
Liver: neoplastic
nodule
Adrenal cortex:
adenoma or carcinoma
Subcutaneous
fibrosarcoma
Control
1/69
p=0.006b
0/74
p=0.005
6/72
p=0.26
5/75
/K0.001
11/73
/7=0.014
0/75
—
TCDD (ng/kg/day)
1.4
5/48
p=0.042c
0/50
—
9/50
p=0.09
1/49
—
9/49
p=OA
2/50
p=0.l6
7.1
6/50
p=0.02l
0/50
—
12/49
p=0.0l5
3/50
—
5/49
—
3/50
p=0.06
71
10/50
/7=0.001
3/50
p=0.6
9/49
/?=0.09
12/49
^=0.006
14/46
p=0.039
4/49
p=0.023
aNTP, 1982a;Huffetal., 1991.
b/> value obtained from Cochran-Armitage test for dose-related trend.
cp value obtained from Fisher exact test compared with control group.
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Table 6-4. Tumor incidences" in B6C3Ft mice
Sex
Male
Target organ
Liver:
carcinoma
Liver:
adenoma
Lung: adenoma
or carcinoma
TCDD (ng/kg/day)
Control
8/73
p=0.002b
7/73
p=0.024
10/71
p=0.004
1.4
9/49 p=0.\9c
3/49 —
2/48 —
7.1
8/49 ^=0.28
5/49 p=0.6
4/48 —
71
17/50
p=0.002
10/50
p=0.09
13/50
^=0.08
Sex
Female
Target organ
Subcutaneous
fibrosarcoma
Liver:
carcinoma
Liver:
adenoma
Thyroid:
adenoma
Hematopoietic:
all lymphomas
TCDD (ng/kg/day)
Control
1/74
p=0.007
1/73
/7=0.008
2/73
p=Q.\\
0/69
p=0.0l6
18/74
p=0.011
5.7
1/50 p=0.6
2/50 p=QA
4/50 p=0.2
3/50 p=0.07
11/50 —
28.6
1/48
p=0.6
2/48
p=OA
4/48
p=0.2
1/47
p=OA
13/48
p=OA
286
5/47
p=0.032
6/47
/?=0.014
5/47
/?=0.8
5/46
/7=0.009
20/47
p=0.029
aNTP, 1982a;Huffetal., 1991.
b/> value obtained from Cochran-Armitage test for dose-related trend.
cp value obtained from Fisher exact test compared with control group.
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Table 6-5. Summary of positive tumor promotion studies for PCDDs and PCDFs in
rats
Strain/sex
SD/F
F344/F
F344/F
SD/F
SD/F
SD/F
SD/F
SD/F (ovx)
SD/F
F344/F
SD/F
SD/F
SD/F
CR/F
SD/F
SD/F
Wistar/F
Wistar/F
SD/F
SD/F
Wistar/F
SD/F
SD/F
SD/F
SD/F
SD/F
SD/F
SD/F
SD/F
Initiator
PH/DEN
PH/DEN
PH/DEN
DEN
DEN
DEN
DEN
DEN
PH/DEN
PH/DEN
PH/DEN
DEN
DEN
PH/DEN
DEN
DEN
DEN
NNM
DEN
DEN
DEN
DEN
DEN
DEN
DEN
DEN
PH/DEN
PH/DEN
DEN
Promoter
TCDD
TCDD
TCDD
TCDD
TCDD
TCDD
TCDD
TCDD
TCDD
TCDD
TCDD
TCDD
PCDFs
TCDD
TCDD
PCB 126, PCB 105
TCDD, HCDD
TCDD, HCDD, PCDD
TCDD
TCDD, PCB 126
TCDD
TCDD
PCB 118
TCDD
TCDD
TCDD
TCDD
PCDD, PCDF, PCB
TCDD
Site
Liver
Liver
Liver
Liver
Liver
Liver
Liver
Lung
Liver
Liver
Liver
Liver
Liver
Liver
Liver
Liver
Liver
Liver
Liver
Liver
Liver
Liver
Liver
Liver
Liver
Liver
Liver
Liver
Liver
Reference
Pilot etal., 1980
Pilot etal., 1987
Hendrichetal., 1986
Graham etal., 1988
Flodstrom and Ahlborg, 1991
Lucieretal., 1991
Clark, 1991
Clark, 1991
Flodstrom et al., 1991
Draganetal., 1991
Waernetal., 1991
Flodstrom and Ahlborg, 1992
Flodstrom and Ahlborg, 1992
Draganetal., 1992
Maronpot et al., 1993
Hemming et al., 1993
Buchmann et al., 1994
Schrenk et al., 1994
Sills etal., 1994
Hemming et al., 1995
Stinchcombe et al., 1995
Tritscher et al., 1995
Haag-Gronlund et al., 1997
Walker etal., 1997
Mann, 1997
Wydeetal., 1999
Teeguarden et al., 1999
vanderPlas, 1999
Walker etal., 2000
Abbreviations: DEN, diethylnitrosamine; PH, 2/3 hepatectomy;
N-nitrosomorpholine; ovx, ovariectomized.
SD, Sprague-Dawley; F, female; M, male; NNM,
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Table 6-6. Summary of positive tumor promotion studies for PCDDs and PCDFs in
mice
Strain/sex
HRS/J hairless
HRS/J hairless
C57/BL6 (M)
DBA/2 (M)
B6D2F1 (M)
Swiss
Tg.AC transgenic
Tg.AC transgenic
Initiator
MNNG
MNNG
DEN
DEN
DEN
NDMA
v-Ha-ras
transgene
v-Ha-ras
transgene
Promoter
TCDD
TCDD PCDF HCDF
TCDD, Aroclor 1254
TCDD, Aroclor 1254
TCDD, Aroclor 1254
TCDD
TCDD
TCDD
Site
Skin
Skin
Liver
Liver
Liver
Lung
Skin
Skin
Reference
Poland et al., 1982
Hebertetal., 1990
Beebeetal., 1995
van Birgelen et al.,
1999;Dunsonetal.,
2000
Eastinetal., 1998
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Table 6-7. Putatively preneoplastic GGT-positive altered hepatocellular foci (AHF)
after 30 weeks of treatment with TCDD as promoter"
Endpoint
AHF/cm3
Volume fraction
BrdU LP
Ovarian status
Intact
Ovariectomized
Intact
Ovariectomized
Intact
Ovariectomized
Saline
Control
6
0
0.01
0
0.3
1.1
TCDDC
5
0
0.01
0
6.0
1.0
DEN-initiatedb
Control
44
30
0.03
0.03
0.8
1.1
TCDD
387d
80
0.37d
0.08
7.3d
0.7
aLucier et al., 1991.
bAnimals were initiated with 175 mg diethylnitrosamine/kg.
'Biweekly treatment with 1,400 ng TCDD/kg.
dSignificantly different from Ovariectomized animals.
Significantly different from Ovariectomized animals.
eBromodeqxyuridine labeling indices; percentage of non-AHF hepatocyte nuclei undergoing replicative DNA
synthesis in a 7-day period.
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Table 6-8. Preneoplastic altered hepatocellular foci and bromodeoxyuridine
labeling indices after 30 weeks of promotion with TCDD
TCDDng/kg/daya
Od
3.5
10.7
35.7
125
AHFb/cm3
442.2
759.2
791.7
530.4
751.7
Volume
fraction
0.57
0.85
1.00
0.93
2.23 e
Mean AHF
volume
13
15
11
18
30e
BrdULP
5.3
3.3e
3.3
6.4
14.4e
a Daily averaged dose of a biweekly treatment of TCDD in corn oil.
bPlacental glutathione-s-transferase positive altered hepatocellular foci (AHF)
"Labeling indices (LI) are the percentage of hepatocytes undergoing replicative DNA synthesis in 7 days following
30 weeks of exposure to TCDD.
dAll animals were initiated with 175 mg diethylnitrosamine/kg, 2 weeks prior to start of treatment with TCDD.
e Significantly different from control values.
Source: Maronpot et al, 1993.
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Table 6-9. Some biochemical responses to TCDD
CYP1A1
CYP1A2
CYP1B1
GSTYa
GSTYb
GSTYc
UDP glucuronyl transferase
QR quinone reductase/ Nmo
Aldehyde dehydrogenase
Ornithine decarboxylase
Malic enzyme
Phospholipase A2
60kDa microsomal esterase
Aminolevulinic acid synthetase
Choline kinase
EctoATPase
Prostaglandin synthetase -2 (COX-2)
Plasminogen activator inhibitor-2
Urokinase plasminogen activator
Nedd-4-like ubiquitin protein ligase
PEPC kinase
Terminal transferase
Testosterone 7alpha hydroxylase
Human chorionic gonadotrophin
Interleukin-lbeta
Gastrin
TNF alpha
TGF-beta
EGF
Fibrinogen
Plastin
EGFR
c-erbA related hormone receptor
Estrogen receptor
25Dx-putative progesterone receptor
MDR-1 multidrug resistance
Aryl hydrocarbon binding protein
c-fos
c-jun
Cystatin-like protein
MHC-Q1
Protein kinase C
pp60 c-src protein kinase
p21 ras
p27/Kipl
bcl-2
Note: This list is not a comprehensive list of all responses known to be affected by TCDD.
Source: Sutler etal., 1992; Lai el al., 1996.
December 2003
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o
O
to
O
O
Table 6-10. Relative potency factors based on tumor promotion/cancer endpoints for "high TEQ contribution"
dioxin-like compounds
Compound
?378-TCDD
12378-PeCDD
1234678-HpCDD
1 23678/1 23789-HCDD (binary mixture)13
?3478-PeCDF
53'44'5PCB(PCB126)
?33'44' PCB (PCB105)
?33'44'5 HCB (PCB156)
?3'44'5HCB(PCB118)
REPa
0.8-1.04
0.02
0.05
0.1-0.21
0.071-0.11
<0.0002
0.001-0.0001
<0.0002
TEF -WHO™
1.0
1.0
0.01
0.1/0.1
0.5
0.1
0.0001
0.0005
0.0001
%TEO
17
32
3.3
21 + 3.9
15
Total0 =92.2 %
64
17
11
4.3
Totald=96.3 %
Reference
Waernetal., 1991
Schrenketal., 1994
NTP, 1980
Waernetal., 1991
Hemming et al., 1993,
Hemming et al., 1993
Haag-Gronlund et al.,
Haag-Gronlund et al.,
1995
1997
1997
to
O
o
*
o
H
W
O
aRange of individual relative potency factors based on cancer/tumor promotion (WHO-TEF database).
bRelative potency from this document (Chapter 6) based on relative dose levels.
c% contribution to total TEQDF-WH098 in adipose tissue of humans with ambient exposures (Table 4-46 in Part I. Volume 3 Chapter 4).
d% contribution to total TEQP-WH098 in adipose tissue of humans with ambient exposures (Table 4-47 in Part I. Volume 3 Chapter 4).
O
V
O
c
o
-------
Figure 6-1. Schematic representation of multistep carcinogenesis including the roles of
genetic damage and cell proliferation. It is important to note that several DNA-damaging
steps and several cell proliferation steps are likely to be involved during the complete
process of chemical carcinogenesis.
Source: Swenberg et al., 1987.
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Figure 6-2. Operational model of TCDD/estrogen interactions relative to tumor promotion
in a two-stage model of hepatocarcinogenesis. Clonal expansion of initiated cells may
reflect stimulation of mitogenesis through receptor-mediated events involving epidermal
growth factor receptor, estrogen receptor, and the AhR.
Source: Vickers and Lucier, 1991.
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Figure 6-3. Plausible mechanism for the role of EGF-mediated stimulation of mitotic
activity.
Source: Stoscheck and King, 1986.
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CHAPTER 7. EPIDEMIOLOGY/HUMAN DATA
PART A: CANCER EFFECTS
7.1. INTRODUCTION
Animal bioassay data provide substantial presumptive evidence of the human
carcinogenicity of 2,3,7,8-tetrachlorodibenzo:p-dioxin (TCDD) (see Chapter 6), but confirmation
must come from well-designed human studies. TCDD is a multiorgan carcinogen in animals.
Target organs include the liver, thyroid, lung, skin, and soft tissues. There is no assumption of
target tissue concordance from animals to humans, although site concordance would add support
to a causal interpretation. This chapter reports on the cancer epidemiology evidence of TCDD
and its congeners.
This review and analysis of the epidemiologic literature on dioxins and cancer begins by
defining the scope of chemical exposures, cancers, and research reports to be considered. Then,
following a brief summary of previous EPA assessments of epidemiologic literature, a
description is given of the methods used in the present review. The original research reports are
then discussed in four groups: (1) follow-up studies of chemical manufacturing and processing
workers, (2) case-control studies in general populations, (3) studies of pulp and paper mill
workers, and (4) other studies (including studies of pesticide applicators; Vietnam veterans with
potential exposure to Agent Orange; residents of Seveso, Italy, exposed to TCDD during an
accidental explosion of a phenoxy herbicide factory; and victims of contaminated rice oil
poisonings). Because the discussions of the first two groups of studies are relatively lengthy,
brief summaries are given at the end of each of those sections. Conclusions are drawn following
an overall discussion of all the studies.
7.2. SCOPE
Epidemiologic studies of cancer among persons exposed to TCDD and other
polychlorinated dibenzodioxins (PCDDs) and dibenzofurans (PCDFs) are included in this
review. Primary emphasis is placed on studies with exposures to TCDD itself, occurring
primarily in the manufacture and use of 2,4,5-trichlorophenol, hexachlorophene, and the
herbicide 2,4,5-trichlorophenoxyacetic acid (2,4,5-T). Because exposures to 2,4,5-T and 2,4-
dichloroacetic acid (2,4-D) often occur among the same groups who manufacture and use these
herbicides, some studies of groups exposed only to 2,4-D are also included. Exposure to lower-
chlorinated PCDDs (dichlorinated and trichlorinated isomers) may occur in the manufacture and
use of 2,4-D. Also included are studies of groups exposed to higher-chlorinated PCDDs (i.e., the
hexachlorinated, heptachlorinated, and octachlorinated isomers), occurring primarily in the
manufacture and use of pentachlorophenol (PCP) and in the paper and pulp industries.
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A major weakness in nearly all of these studies is the lack of good exposure information.
Most studies rely solely on interviews and questionnaires of work history to ascertain exposure
surrogates. Until recently, there was little, if any, verification of actual internal dose of these
compounds. Some studies use chloracne as a surrogate for exposure to TCDD. This presence of
chloracne usually indicates a high dose effect. The absence of chloracne does not indicate lack of
exposure. Some of the recent cohort studies of chemical production workers (Fingerhut et al.,
1991; Becher et al., 1996; Flesch-Janys, et al., 1995, 1996, 1999; Ott et al., 1996; Manz et al.,
1991; Zober et al., 1990) do provide estimates of TCDD exposure in cohort samples via serum
blood levels taken decades after cessation of exposure. However, these estimates have been
chiefly used to provide support for preselected qualitative categories of estimated exposure. But
these can also be used to determine possible dose-response trends and estimate the risk of cancer
to populations with low-level exposure to TCDD (see Chapter 8). Measures of exposure by
individual study have been discussed.
At the time of EPA's last review in 1988, evidence of human carcinogen!city of TCDD
and the phenoxy herbicides focused on soft tissue sarcomas (STSs) and malignant lymphomas.
Consequently, this report will update the strengths and weaknesses of the evidence pertaining to
these cancers. But emerging as a more important topic are evaluations of the evidence of cancer
at other sites. The case-control studies reviewed for EPA's last analysis generally considered
herbicide applicators with potential exposures to both 2,4-D and 2,4,5-T. Recent case-control
studies of U.S. farmer groups in which exposure to 2,4-D and 2,4,5-T can be separated (Hoar et
al., 1986; Zahm et al., 1990; Cantor et al., 1992) provide a validation mechanism to separate
potential effects of these herbicides and, possibly, their different PCDD contaminants. Thus,
these and other recent studies (Hardell and Eriksson, 1988; Eriksson et al., 1990; Woods et al.,
1987) will be reviewed and compared with those discussed in EPA's earlier reports.
Five recent cohort mortality studies (Steenland et al., 1999; Fingerhut et al., 1991; Becher
et al., 1996; Flesch-Janys et al., 1995, 1998, 1999; Manz et al., 1991; Ott et al., 1996; Zober et
al., 1990; Kogevinas et al., 1997; Saracci et al., 1991; Hooiveld et al., 1996, 1998) totaling more
than 23,000 workers potentially exposed to TCDD and/or phenoxy herbicides/chlorophenols
provide a more important database for analyzing cancer effects and are discussed in Section 7.5.
The first three of these, and especially the large U.S. study of Fingerhut et al. (1991) and its
update by Steenland et al. (1999), as well as the Dutch cohort study by Hooiveld et al. (1996,
1998), are considered to be the most important new studies in the field of TCDD cancer
epidemiology because of their attention to cohort selection, to TCDD exposures or exposure
surrogates (chloracne), and to the fact that exposure to dioxin is associated with an increasing
risk of cancer at multiple sites. The fifth study (Saracci et al., 1991; Kogevinas et al., 1997)
encompasses all the occupational cohorts that are referenced here. Although it has the largest
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cohort, it has less reliable information on the TCDD-exposed subcohort and only a little
information that would allow a quantitative estimate of exposure. Two other studies of phenoxy
acid manufacturers are included (Lynge, 1985, 1993, 1998; Coggon et al., 1986), but their
usefulness is limited because of the low unsubstantiated exposure to TCDD. Also included is a
study of occupationally exposed women (Kogevinas et al., 1993) who had probable exposure to
TCDD.
Four recent cohort studies of workers in the pulp and paper mill industry are also included
(Robinson et al., 1986; Jappinen et al., 1987; Henneberger et al., 1989; Hertzman et al., 1997)
because of potential for worker exposure to higher-chlorinated PCDDs but not
hexa-, hepta-, or octaphenoxy herbicides, and one with exposure to hexa-, hepta-, and
octochlorinated dioxin isomers. However, none of these studies provide any additional
information about which PCDD exposures were likely, and these studies are not given much
weight.
Studies of Vietnam veterans potentially exposed to TCDD in Agent Orange are reviewed
briefly, with only two (Ketchum et al., 1996, 1999; Michalek et al., 1990, 1998) judged to have
sufficient information on potential TCDD exposure to be useful for analysis of cancer. Also, the
recent studies of cancer in Seveso, Italy, residents are discussed (Bertazzi et al., 1998, 1997,
1992, 1993, 1989a,b; Landi et al., 1998; Consonni et al., 1999; Mocarelli et al., 1991; Pesatori et
al., 1999, 1992); these studies provide some exposure data, but the cancer response analysis is
limited because of inadequate follow-up time (maximum of 20 years).
Finally, the studies of the rice oil poisonings of residents in Taiwan and Japan with
polychlorinated biphenyl (PCB) and PCDF contaminants are reviewed (Kuratsune et al., 1988,
1975; Chen et al., 1980; Koda and Masuda, 1975; Rogan et al., 1988). Even though these
poisoned oils did not contain TCDD, they did contain many TCDD-like congeners currently
considered by EPA to have carcinogenic potential that can be compared to TCDD. Also, certain
dioxin-like PCBs are suspected human carcinogens on the basis of their receptor-binding
characteristics and animal studies. Non-dioxin-like PCBs are known animal carcinogens.
Only follow-up and case-control studies are considered in this review. Case reports, other
clinical observations, and prevalence surveys are excluded. The review is restricted to studies
that have been published or are about to be published and that are available in the open scientific
literature. Prepublication reports and studies published only in abbreviated form (as well as
abstracts or letters to editors) are included only where they supplement the published articles.
These restrictions limit the review to studies that have received at least a minimum of peer
review and that have been described fully enough to permit a thorough assessment of materials,
methods, and results.
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7.3. PREVIOUS EPA REVIEWS
In the Health Assessment Document for Polychlorinated Dibenzo-p-Dioxins, dated
September 1985 (U.S. EPA, 1985), the majority of the epidemiology studies pertained to groups
of herbicide applicators with potential exposure to phenoxy acids and/or chlorophenols. In that
report, the analysis emphasized case-control studies of STSs and non-Hodgkin's lymphoma
(NHL). That report concluded that the epidemiologic research available at that time provided
"limited evidence for the carcinogenicity of phenoxy acids and/or chlorophenols in humans.
However, with respect to the dioxin impurities contained therein, the evidence for the human
carcinogenicity for TCDD based on the epidemiologic studies was only suggestive because of the
difficulty of evaluating the risk of TCDD exposure in the presence of the confounding effects of
phenoxy acids and/or chlorophenols." In its next report, the review draft dated June 1988 of A
Cancer Risk-Specific Dose Estimate for TCDD (U.S. EPA, 1988), the focus was essentially the
same, and EPA concluded that "the human evidence supporting an association between exposure
to TCDD and cancer is considered inadequate."
7.4. REVIEW METHODS
This review will follow the spirit of the EPA Risk Assessment Guidelines of 1986 (U.S.
EPA, 1987) by considering alternative explanations for results observed in epidemiologic studies.
These explanations fall into the general categories of causality, chance, bias, and confounding.
The basic approach is akin to a process of elimination, by which one attempts to determine the
direction and to quantify the magnitude of the influence that chance, bias, and confounding may
have had on the results of each study. Wherever possible, the results of all studies will be
reported in units of relative risk estimates and 95% confidence limits.
Most biases in epidemiologic studies can be placed into one of two categories: biases of
classification and biases of selection. Classification biases can result from inaccurate
ascertainment of exposure, disease, or confounders. Selection biases can result from
nonrepresentative sampling of populations, as in the selection of controls in case-control studies,
or from incomplete participation by study subjects. Any bias gains tenability as an explanation
for an observed result if empirical evidence can be adduced to buttress the mere suggestion that
the bias might have occurred. Only those biases considered to be potentially important will be
addressed explicitly in this review.
When imprecise exposure estimates are available, such as with much of the
epidemiologic data on dioxin, estimates of risk can be potentially biased toward the null.
Misclassification, if random, could potentially lead to a masking of a true effect.
Classification biases are of two kinds: differential and nondifferential. Differential
misclassification will lead to either an exaggeration or an underestimation of an effect.
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Nondifferential misclassification occurs when the exposure or disease classification is incorrect
in a portion of the subjects (cases or controls). This type of bias is generally toward the null, and
the risk estimate may reflect this. Such misclassification can happen when some subjects are
classified as having exposure to dioxin when they really were not exposed. Similarly, some
actually may have had exposure but were classified as not having had it. In studies where few or
no effects were seen, researchers must seriously consider the problem of nondifferential
misclassification. This can be the reason that nonpositive risk estimates or even disparate risk
estimates are seen from different studies of the same effect. On the other hand, in studies with
significant results, nondifferential misclassification is not likely to be a cause of a significant
finding.
Recall bias may produce the opposite effect. Persons with a disease may tend to
remember exposure to a substance better when they know that such exposure might be associated
with the disease. This could potentially lead to inflated risk estimates. In fact, it has been
suggested that such biases are present in many of the case-control studies on dioxin. The
Swedish studies by Hardell and colleagues have been particularly singled out for criticism in this
respect. That some recall bias may be present is confirmed in a later case-control study (Hardell
and Eriksson, 1988) in which some reduction of risk estimates was produced by the use of cancer
controls. But then this result could be the result of a general cancer effect as well.
Confounding bias is a tenable explanation for an association between an exposure and a
disease if the hypothetical confounder can be named and if a good case can be made that it is a
cause of the disease, that it was associated with the exposure in the study population, and that it
was not adequately controlled in the study design or data analysis. This review will explicitly
mention only those potential confounders that meet all of these criteria.
As stressed in previous EPA reviews (1985, 1988), concomitant exposures present a
special problem of potential confounding in the literature on TCDD and related chemicals. As a
noteworthy example, an association between 2,4,5-T exposure and a given cancer, if causal,
could be due to 2,4,5-T itself, to TCDD, or to some other contaminant. The problem multiplies
when it is recognized that, historically, many phenoxy acid herbicide preparations were mixtures
of 2,4,5-T and 2,4-D and that many persons who manufactured, processed, and used these
preparations were exposed to other chemicals as well. Nevertheless, it may be possible, by
examining studies of persons exposed to different combinations of chemicals, to identify
"threads" of commonality and differences in the results, especially when specific cancers are
considered separately.
Publication bias, sometimes considered a form of selection bias, is the tendency for the
results of a study to influence a judgment as to whether or not it will be published. The direction
and magnitude of publication bias is difficult, if not impossible, to quantify. It is expected to be
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a much greater problem in literature reviews and in studies relying on existing records than in
original research in which substantial resources are devoted to collection of data of relatively
high quality. The level of effort required for such studies creates a strong incentive to publish the
results. There is a tendency to publish studies with positive results as opposed to studies with
nonpositive findings.
Strength of association, as measured by the magnitude of the estimated relative risk, is an
important feature of a study's results. The stronger the association, the stronger a bias or
confounding factor would have to be to explain it. Because questions of bias and confounding
are study-specific, no defensible criteria can be set up in advance to place relative risk values into
categories of strength of association.
Trends in increased risk by degree of exposure and by time since first exposure (latency)
are also important. Different hypothetical causal mechanisms might predict different exposure-
response and latency patterns. Hypotheses of steadily increasing effect with increasing exposure
(i.e., monotonic exposure-response functions) and hypotheses of effects early in the carcinogenic
process (e.g., for factors that operate at the initiation stage) predict that increases in risk will be
greatest among persons with relatively high degrees of exposure and after relatively long latency
periods. Hypotheses of tumor promotion and/or initiation will be discussed in the appropriate
sections.
Replication of results is important in all scientific research. When several studies have
shown a positive association of effect with the same exposure but were conducted under different
circumstances, the possibility that an unknown confounder or chance produced the observed
elevated effect is minimized. When different investigators working with different populations
using different methods confirm an original finding, the results are more believable.
The statistical aggregation of results from different studies (meta-analysis) has become a
popular feature of epidemiologic literature reviews. In this review, results from separate studies
are aggregated only when all key methodologic features and results are reasonably similar. The
method of aggregation used here is to take the ratio of the sum of the cause-specific observed
deaths to the sum of the cause-specific expected deaths for the individual studies. Because
investigators recognize the value of varying their methods to test methodologic hypotheses, and
because results often differ appreciably, aggregation of results is not often indicated and is done
here with caution.
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7.5. FOLLOW-UP STUDIES OF CHEMICAL MANUFACTURING AND
PROCESSING WORKERS
7.5.1. United States
Fingerhut and colleagues (1990, 1991) reported a study of 5,172 males who had worked
at 12 plants in the United States in the production of chemicals contaminated with TCDD. Five
thousand of the cohort members (97%) were identified in company records as having been
"assigned to a production or maintenance job in a process involving TCDD contamination"
(Fingerhut et al., 1991). The remaining 172 cohort members were "identified in a previously
published study on the basis of exposure to TCDD" (Fingerhut et al., 1991). This cohort
subsumed, and thereby supplanted, company-specific cohorts from Dow Chemical USA (Ott et
al., 1987; Cook, 1981) and the Monsanto Company (Zack and Gaffey, 1983; Zack and Suskind,
1980) that had been the subject of previous reports. This study was initiated in 1978 to
determine whether health effects were apparent in humans who were exposed to 2,4,5-T. In
1978, lexicological, teratogenic, and carcinogenic effects data were released that indicated a
cancer effect in animals. There was a concern about the potential effects of exposure to Agent
Orange on Vietnam veterans and workers who produced products that were contaminated with
dioxin (Fingerhut et al., 1992). Follow-up began in 1940 or on the date of the "first
systematically documented assignment to a process involving TCDD contamination" (Fingerhut
et al., 1991), whichever was later, and closed at the end of 1987. Comparisons were made with
the United States population.
The authors stated that approximately 13% of the cohort of 5,172 workers had records of
chloracne. The presence of a significant incidence of chloracne in a group of people is an
indicator of relatively intense exposure to TCDD. Chloracne can be caused by higher-
chlorinated PCDDs, PCDFs, and PCBs as well (O'Malley et al., 1990) and also by Ah receptor
agonists such as brominated congeners and with naphthalenes. It is a highly specific indicator of
exposure to these compounds because it virtually never occurs among unexposed persons. It is a
nonsensitive indicator, however, because many highly exposed persons do not develop it (Manz
et al., 1991; Caramaschi et al., 1981; Mocarelli et al., 1991). Exposure to other dioxin-like
chemicals that can be found in the workplace produces a form of chloracne that could be
indistinguishable from that produced by dioxin (Ott et al., 1993). These chemicals can be dioxin-
like in their effects and act through the aryl hydroxylase (Ah) receptor.
Although all members of the cohort had specific assignments to TCDD exposure areas in
common, exposures to multiple chemicals generally occur in the chemical industry. At one
plant, for instance, considerable overlap existed among persons involved in the production of
chlorophenols, 2,4,5-T, and 2,4-D (Ott et al., 1987; Bond et al., 1988, 1989a), and thus exposed
to TCDD and higher- and lower-chlorinated PCDDs. Presumably, many persons throughout the
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cohort had contact with other chemicals. Comprehensive surveys of chemical exposures were
conducted in plant-specific cohorts and may be available through the authors.
Special attention was paid to results for the 3,036 workers who were followed for at least
20 years after first exposure. This group was again divided into those with less than 1 year
(N=l,516) and those with more than 1 year (1,520) of exposure (referred to below as the long
duration/latency subcohort). One year was chosen as the criterion for duration of exposure
because an analysis of 253 workers from 2 plants showed that every worker with 1 or more years
of exposure had a lipid-adjusted serum TCDD level greater than the mean value (7 ppt) in a
comparison group of unexposed workers (Fingerhut et al., 1990). Although the average level for
all 253 workers was 233 ppt, the average increased to 418 ppt in those 119 who were exposed for
1 or more years (Fingerhut et al., 1991). The average serum TCDD level in those exposed less
than 1 year was 69 pg/g. The researchers described a plan, utilized by Steenland et al. (1999),
that replaced this duration-based exposure scale by using "a dioxin exposure matrix constructed
from historic process descriptions, analytic measurements of TCDD and industrial hygiene data .
. . to develop the relative ranking of workers exposed to TCDD" (Fingerhut et al., 1990).
The cohort as a whole experienced an estimated 15% (95% CI = 1.0-1.3) elevation of
mortality from all cancers combined, with a 46% elevation (95% CI = 1.2-1.8) among those in
the long duration/latency subcohort (Table 7-1). An excess of deaths from cancers of connective
and soft tissues (STSs) was apparent in the total cohort (RR = 3.4, CI = 0.9-8.6) and in the long
duration/latency subcohort (RR = 9.2, CI = 1.9-27.0), but these results were based on only four
deaths and three deaths, respectively, from two different plants. A 40% overall elevation in
deaths (CI = 0.7-2.5) from NHL was confined to workers in the total cohort and was not seen in
the long duration/latency subcohort. Results for Hodgkin's disease were highly imprecise, based
on only three deaths (vs. 2.5 expected) in the total cohort. Lung cancer was elevated by 10%
overall but by 40% (CI = 1.0-1.9) in the long duration/latency subcohort. A similar 40% excess
of stomach cancer (CI = 0.4-3.5) in this subcohort was based on only four deaths; no excess was
seen in the total cohort.
The investigators conducted a special study of connective and soft tissue cancers. A review
of all available hospital records and tissue specimens failed to confirm the indications of STSs on
two of the four death certificates that had been assigned to this cause-of-death category
(Fingerhut et al., 1990). The review also provided evidence that two persons in plant 8 whose
deaths had been assigned to other causes of death had actually had soft-tissue sarcomas. Because
only the exposed cohort's death certificates were subjected to detailed review, the analytic
comparisons with the United States population were required to be based strictly on death
certificate information. The basic and well-known rule in such situations is that, absent evidence
to the contrary, erroneous information on death certificates must be considered to have been
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equally frequent in the two groups being compared. But Suruda et al. (1993), in a study of STS
diagnoses in cohorts exposed to dioxins and chlorinated naphthalenes, found that death
certificates are "relatively insensitive"for detecting STS and that the power of life-table analysis
to detect excess risks of STS may be reduced compared with its utility in correctly estimating the
risk of other cancers, such as colon cancer or rectal cancer. However, the correct identification of
STS as the underlying cause of death on death certificates appears to be much better (82%),
based on medical records, than first thought according to the results of this study. Medical
records on the remaining 18% could not be found. If a death certificate gives as an underlying
cause of death a STS and it is coded as 171x, it is very likely correctly coded, according to the
authors. In an earlier study, this figure was estimated by Percy et al. (1981) to be 55%. This
nondifferential misdiagnosis could potentially bias risk estimates downward (underestimate the
true risks).
Four of the STSs that are discussed in Fingerhut's study (two are included in Fingerhut's
life-table analysis while two others are discussed but did not qualify for inclusion in the life-table
analysis) are actually from the Nitro, West Virginia, plant. The remaining two cases that were
included in Fingerhut's life-table analysis are from a different plant in the study. One of these
cases also suffered chloracne. The four cases from the Nitro, West Virginia, plant are also the
subject of a later study by Collins et al. (1993). The two cases included in Fingerhut's life-table
analysis had previously suffered chloracne from an incident in which 121 workers developed
chloracne as a result of a trichlorophenol process accident on March 8, 1949 (Zack and Gaffey,
1983). Collins et al. (1993) included one more STS not previously discussed by Fingerhut from
the same plant. The Collins et al. (1993) study is reviewed later.
If the properly diagnosed STSs were correctly assigned to their appropriate plants and the
two incorrectly diagnosed STSs were removed, there would be one STS in plant 9 and three in
plant 8. No STSs were found at the other plants. This unusually skewed distribution might
possibly be related to the accident that happened in plant 8 in 1949. Or perhaps physicians in
Nitro, West Virginia, are more likely to diagnose STS than are physicians in other areas of the
country. Because of the rarity of STS, there was inadequate statistical power to expect to see
STSs in the other individual plants. If an excess risk is associated with exposure to TCDD, then
it might be several more years before this cohort will produce STSs at the other facilities that are
part of the study.
Cases of STS include a diverse group of histological entities. All STSs arise only from
mesenchymal tissue, and all share common features that make them alike in their basic
intercellular and intracellular composition rather than different in their morphology and location.
Characterizing them as fundamentally different because they are found in different sites of the
body is perhaps inadequate for determination of the risk of cancer (Enzinger and Weiss, 1988).
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The histological classification of STS is centered on a dozen distinctly different classes of
mesenchymal cells that form six relatively well-defined but widely distributed organ systems. By
considering the growth pattern and cell morphology with an evaluation of intracellular and
extracellular products of the tumor cells, fairly precise histogenetic classification of STSs is
possible (Hajdu, 1981). For human cancer risk assessment, all connective tissues developing
from the same mesodermal tissue, expressing the same set of "proto-oncogenes"and surrounded
by the same chemical milieu of the extracellular matrix, are expected to develop cancer following
exposure to certain carcinogens. The grouping of these end-target organs together is therefore
necessary to evaluate the risk from exposure to dioxin.
Confounding by cigarette smoking must be considered in interpreting the approximate 40%
excess of lung cancer deaths in the long duration/latency subcohort (Table 7-2). For the United
States as a whole, the authors (Fingerhut et al., 1990) computed age-adjusted proportions of 24%
never smokers, 19% former smokers, and 57% current smokers in 1965 (roughly midway
through the follow-up period). The corresponding proportions were 28% never smokers, 14%
former smokers, and 59% current smokers among the 87 workers from the study of serum TCDD
levels who were members of the long duration/latency subcohort as well. Assuming relative
risks of lung cancer of 4.7 for former smokers and 10.9 for current smokers, the authors used a
standard technique (Axelson and Steenland, 1988) to adjust the number of expected lung cancer
deaths and found essentially no change in the results. It should be kept in mind that the sample
of smoking histories was taken from only two plants but the excess in lung cancer risk was
chiefly in two other plants. The generalization of smoking habits of employees in 2 of the 12
participating plants to that of the entire cohort may not be representative of the true smoking
impact on the risk of lung cancer to the cohort. Most of the lung cancers (56 of 89 observed lung
cancer deaths) came from 3 facilities. The remaining 7 plants contributed the other 33 lung
cancers to the total because of the small sizes of the respective subcohorts and insufficient
latency. It then would be possible to evaluate the effect of smoking on the risk of lung cancer at
each individual plant. It should be remembered that national U.S. rates were used to derive
expected deaths in each of the 12 plants. It is possible that local or regional rates may be a more
appropriate comparison population for the cancer sites examined by the authors, although local
rates may be unstable. In addition, if biomonitoring could be extended to the remaining 10
plants, a better idea could be derived concerning the dose levels associated with those plants
experiencing higher lung cancer rates. The authors point out that deaths from other diseases
associated with smoking, such as diseases of the heart and circulatory system and emphysema,
were either not increased or significantly decreased in this cohort (Fingerhut et al., 1990).
Although the possible contribution of factors such as smoking and occupational exposure cannot
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be excluded, there is no evidence that smoking patterns in this cohort are entirely the reason for
the elevated risk of lung cancer (Fingerhut et al., 1991).
One possible explanation for the increase is that the 87 surviving members of the long
duration/latency subcohort did not show the well-known tendency for smoking to be more
common among blue-collar workers than in the general population. A possible reason is that
because smoking appreciably elevates the overall death rate, fewer and fewer smokers will
remain in a fixed group of persons as time goes by. Thus, the use of the 87 surviving members of
the long duration/latency subcohort may have underestimated the proportions of former and
current smokers in the subcohort as a whole over the course of mortality follow-up. However,
this same phenomenon is also present in the population from which expected deaths were
generated, so the effect is probably nullified. The effect on the lung cancer risk as the proportion
of smokers increased in the cohort is shown in Table 7-2. The lack of increased mortality from
cardiovascular diseases as well as cancer of the buccal cavity and pharynx in this cohort,
however, makes this explanation less likely. Furthermore, as the authors point out, mortality
from nonmalignant respiratory disease (standard mortality ratio [SMR] = 96), which is often
associated with smoking, was less than expected. This strengthens the argument that exposure to
dioxin causes lung cancer.
On the other hand, the authors report a correlation coefficient of 0.72 between length of
exposure and serum TCDD tissue level. This negative bias is potentially much greater than the
positive bias that could possibly be produced by smoking. And because nondifferential bias is
the only type of bias that could occur in the Fingerhut study, risk estimates are more than likely
lower than the true risk. The only way that dioxin exposure could have a positive bias is if it
prevents cancer and the exposure classification is 100% wrong.
The authors of this study also report that two of these cancer deaths were from
mesothelioma, a finding that more than likely indicates exposure to asbestos. Although these
two mesotheliomas occurred at plants 9 and 12, it is not known whether these persons were
exposed in that job or in some previous job. Only one death from asbestosis was noted in the
nonmalignant respiratory deaths, and this occurred in Plant 1. These three asbestos-related
deaths were more than likely due to exposure in a previous occupation.
Fingerhut et al. (1991) has been updated by Steenland et al. (1999), of the same research
group. The SMR for all cancers combined was 1.13 (95% CI = 1.02-1.25). The SMR for all
cancers combined for the highest exposure group was 1.60 (95% CI = 1.15-1.82). Recent
analyses support the finding that high exposure to TCDD results in an excess of cancer without
any marked specificity. Steenland and colleagues' later analyses differ from Fingerhut's in that
the authors applied a "job-exposure matrix"to a subcohort of 3,538 workers from the original
5,172 male workers and followed the entire cohort for 6 more years. This job exposure matrix is
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not based upon ambient air levels of TCDD or even blood TCDD serum levels. It is based upon
the concentration of TCDD (|ig/g) present in the process materials to which the workers are
exposed times the fraction of the day the worker was employed in that process times the
"contacf'level (meaning the likelihood the product will reach the skin or be inhaled) times the
period of time the worker worked in proximity to TCDD. Steenland et al. did not consider
workers and plants where records on the degree of exposure to TCDD were lacking, a detailed
work history was lacking, or there was concurrent exposure to pentachlorophenol contaminated
by higher chlorinated PCDDs/PCDFs. They also analyzed a subcohort of 608 workers with frank
chloracne without regard for any of the exceptions mentioned above, and analyzed a subset of
393 of the 608 who fell into the subgroup of 3,538 workers above.
The results indicated that as cumulative exposure increased through seven exposure
categories based upon their "job-exposure matrix," the calculated risk of overall cancer mortality
tended to increase (all sites combined). In the chloracne cohort of 393 men mentioned, in the
two highest septiles of cumulative exposure the risk was significant at 1.68 (CI = 1.19-2.30).
In the larger subcohort of 3,538, the results tended to be similar but less pronounced.
Utilizing either the life-table analytical method or the Cox regression method, trends tended to be
positive, although in an inspection of the seven categories, the increasing trends are not
monotonic using either method, lagged or unlagged (Table 7-3).
Although the authors believe that this lack of linear trend in cancer with cumulative
exposure as a continuous variable is due to the extreme skewedness of the data, this may have
more to do with the way in which cumulative exposure is determined in the job-exposure matrix
and the fact that the authors use seven exposure categories to generate risks. The fact that
quantity of TCDD in process materials is used, rather than ambient air levels of TCDD or even
blood serum levels of TCDD, to determine basic exposure potentially removes the estimation
process for exposure even further from the truth. There is likely considerable variability in the
cumulative exposure indices calculated for individuals in this cohort. Furthermore, the use of so
many categories of exposure also makes individual risk calculations within each septile less
stable and perhaps even unreliable, because they are based upon smaller numbers.
Despite these problems the authors were still able to detect significantly positive trends in
total cancer, several site-specific cancers such as lung cancer, and even ischemic heart disease,
especially when they considered the logarithm of cumulative exposure (the more appropriate test
statistic for this kind of distribution of exposures). The finding concerning an increased risk of
ischemic heart disease seems consistent with what is known about the interaction of serum
dioxin with endogenous cholesterol and high-density lipoproteins. There appears to be an
inverse relationship between serum TCDD level and high-density lipoprotein and a positive
relationship with total cholesterol (Grubbs et al., 1995; Calvert et al., 1996).
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Another observation is the finding that the risk of bladder cancer (16 cases) was increased
by exposure to 4-aminobiphenyl at the one plant where 10 cases occurred. This is consistent
with the fact that 4-aminobiphenyl is a strong bladder carcinogen. This could potentially bias the
elevated overall cancer risk because these deaths are included. However, they will probably not
affect site-specific cancers such as lung cancer, except that bladder cancer "competes" with other
potential causes for the distinction of being designated the "underlying" cause of death. The
authors also point out that exposure-related trends remained unchanged for smoking-related
cancers when bladder cancers were omitted. This implies that the findings for all cancers
remained unchanged when bladder cancers were omitted. The excess is about eight cases over
expected.
The analysis done by Fingerhut in her first paper, showing that there is a high correlation
between years of exposure to TCDD and in vivo serum levels of TCDD, based upon 253
workers, is strong evidence of a likely dose-response relationship. Endogenous markers of
exposure are superior to exogenous man-made constructs. Although markers of exposure that
are closer to the target organ for carcinogen!city are better indicators of the actual dose received
by the organism at some given point in time, they still tell us little of what may have occurred by
way of exposure in the past and exposures in the future. However, indexes of exposure such as
"cumulative exposure" are likely to be more accurate and reliable when based upon actual dose
measures.
In summary, Steenland et al. (1999) continues to support the hypothesis that dioxin is
causally associated with an increased risk of cancer punctuated by increased selected site-specific
risks such as lung cancer, STS, etc. There is a significant positive trend for cancer mortality with
increasing exposure, with a 60% excess mortality from cancer in general in the highest exposure
group. But in addition, the data suggest a potential for an added risk of ischemic heart disease.
The positive association of lung cancer in male workers observed in this study is also
consistent with an excess of pulmonary tumors found in male mice and rats exposed to TCDD
(see Chapter 6). These same animal data suggest the possibility of a protective hormonal effect
from TCDD and the risk of pulmonary cancer in female rats. Because this study dealt with only
male workers, this hypothesis could not be verified in female workers. On the other hand, no
elevated risk of liver cancer is evident in male workers even in the long duration/latency
subcohort. This is also consistent with rat data, where the tumors were only observed in female
rats. If there is a promoting effect on liver cancer in human females due to hormonal effects, as
suggested by the rat studies, it could not be verified.
Collins et al. (1993) suggested that there is an association of STS with exposure to 4-
aminobiphenyl. The authors reported that workers who developed chloracne from an accident in
which a chemical mix containing TCDD was scattered throughout a 2,4,5-trichlorophenol plant
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had increased mortality from STS, bladder cancer, and respiratory cancer. All individuals who
were identified in this study of 754 chemical employees as having STSs or lung cancers, and who
were employed at the time of the 1949 accident, were potentially exposed to 4-aminobiphenyl as
well as to TCDD. However, it is not known how many were actually working inside the plant
when the accident occurred. 4-Aminobiphenyl is thought to be a bladder carcinogen from
previous studies. However, this chemical has not been shown to be associated with STS or lung
cancer in humans. Unfortunately, no tissue measurements are available to substantiate exposure
to TCDD or exposure to 4-aminobiphenyl. It is also of interest that no significant increase in
STSs was noted in the "chloracne-free" subgroup exposed to 4-aminobiphenyl. However, the
authors report that an additional 106 persons also had indications of chloracne-type conditions
noted in their medical files, presumably as a result of exposure to TCDD and not as a result of
the accident. Although these individuals probably were heavily exposed to dioxin as well, these
workers were included in the "no chloracne" subgroup for the purpose of analysis. It would be of
interest to see what effect would occur to the risk estimates if these workers were included in the
"chloracne" subgroup. Furthermore, plant employees who left work before March 8, 1949, or
began work after November 22, 1949, were not included even if they had received exposure to
dioxin or developed chloracne as a result of exposure. The major interest, according to the
authors, was in the 122 workers who developed chloracne from the 1949 accident. The authors
point out that the numbers are small and that confounding factors, such as misclassification of
exposure, cannot be ruled out. 4-Aminobiphenyl has not been reported in plant 9, where one
STS was diagnosed. This study presents an interesting explanation that has not been
substantiated anywhere else.
It was also noted by Collins that toxicological evidence is available that supports the idea
that STSs (i.e., angiosarcomas) have resulted from exposure to 4-aminobiphenyl (Schieferstein et
al., 1985). Angiosarcomas and bladder cancer in females were found to be dose-related with oral
consumption of 4-aminobiphenyl in drinking water. However, angiosarcomas of the liver arise
mainly in the endothelial lining of blood vessels. This type of tissue is more likely susceptible to
a hydrophilic carcinogen such as 4-aminobiphenyl during its passage through the blood vessel.
Hydrophobic carcinogens such as TCDD might be expected to exert an influence on
mesenchymal tissue from which most STSs arise, i.e., fibrosarcoma, histiocytoma, liposarcoma,
leiomyosarcoma, rhabdomyosarcoma, synovial sarcoma, schwannoma, myxoid neurogenic
sarcoma, and others. Therefore, the author's assumption that 4-aminobiphenyl can cause other
types of STSs remains unproven and highly unlikely.
Ramlow et al. (1995) updated the study of a portion of an earlier cohort of workers
studied by Ott et al. (1987). Some 770 workers involved in the manufacture of
pentachlorophenol (PCP) were identified for a mortality study. PCP, a broad-spectrum pesticide,
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is contaminated with hexachlorinated, septachlorinated, and octachlorinated dioxins but allegedly
little or no TCDD as well as PCDFs. Follow-up continued until December 31, 1989. Expected
deaths were estimated based upon U.S. white male death rates. Separately, a reference
comparison population of employees from the same company but presumably without exposure
to PCP was used to develop site-specific relative risk (RR) estimates. Fifty cancer deaths were
observed, whereas 52.6 were expected based upon U.S. death rates. When stratified according to
a 15-year lag time after initial exposure, and further stratified into subjectively determined
cumulative H/OCDD high, medium, and low categories, there appeared to be little evidence of
any consistently increased site-specific cancer risks that could be attributed to exposure to PCP
other than those induced by the small numbers. The same could be said of the comparison with
the nonexposed company cohort. Calculated relative risks by 15-year lag time in each of the
three categories of exposure according to site revealed little that could be attributable to PCP
exposure. The few significant cancer risks seen were based upon small numbers (kidney cancer,
two deaths in the high-exposure category; gastric and duodenal ulcer, four deaths in the medium-
exposure category; and accidents, nine deaths, high-exposure category). The authors, however,
concluded that the few significantly increased site-specific deaths that were apparent could not be
attributed conclusively to PCP exposure. This study could benefit by having TEQ-determined
blood serums taken in order to substantiate if and how much exposure actually did occur to
members of the cohort, and also a longer follow-up to accumulate additional deaths.
7.5.2. Germany
Manz and colleagues (1991) reported a study of 1,583 persons (1,184 men and 399
women) employed at a German chemical manufacturing facility that produced 2,4,5-T and its
precursor, 2,4,5-trichlorophenol. In 1954, a chloracne outbreak had occurred in the working
population of the plant, and after that, production of the TCDD contaminant was reduced.
Cohort members worked at least 3 months from 1952 through 1984. The start of follow-up was
not stated in the report, but presumably began on the date of accumulation of 3 months of
employment. The follow-up period closed at the end of 1989. The cohort's mortality experience
was compared with that of the West German population and with that of a cohort of workers at a
gas supply company. Because limited data on the gas workers forced that comparison to be
based on a subset of the TCDD-exposed cohort, and because the results did not differ materially
between the analyses, only the results of the comparisons with West Germany are reported here.
The cohort was postdivided by duration of employment and by a three-category exposure
scale based on TCDD measurements "in nonsystematic samples of precursor materials, products,
waste, and soil from the grounds of the plant, mainly after the plant had closed in 1984" (Manz et
al., 1991). This scale was validated to some extent by adipose tissue TCDD levels in 48
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volunteers (mean = 296 ng/kg in 37 persons from the highest group, 83 ng/kg in 11 persons from
the other two groups). On the basis of these results, the low and intermediate groups are
combined for the present analysis.
For the males, this study, with 75 total cancer deaths expected and 24 expected in the
high-exposure subcohort, was considerably smaller than the study by Fingerhut et al. (1991),
which had 230 total cancer deaths expected and 78 in its long duration/latency subcohort
(Table 7-4). Manz et al. presented detailed analyses only for all cancers combined. The high-
exposure subcohort, and especially those with longer employment duration, experienced an
excess of total cancer deaths (RR = 1.4, CI = 1.0-2.0 for the high-exposure group and RR = 2.6,
CI = 1.2-4.9 for the high exposed/long duration subcohort) (Table 7-4). The authors concluded
that "the increase in (total) cancer risk of 1.24-1.39 . . . cannot be explained completely by
confounding factors, and ... is associated with exposure to TCDD" (Manz et al., 1991). In a
later abstract of an update of this same paper, Dwyer (1992) reported that after using a Cox
regression analysis of nine major areas of employment within the plant, the
area of work with the strongest relative risk for cancer mortality was 2,4,5-T production (RR =
2.7, CI = 1.7-4.2). These findings are similar to those of Fingerhut et al. (1991).
For the cohort as a whole, the estimated relative risk of lung cancer was 1.4 (CI = 1.0-2.0,
30 observed deaths). Smoking as an explanation for the observed increase is less likely because
a comparison using the gas worker reference actually leads to an increased RR of 1.7 (CI =1.1-
2.4). Although smoking histories were not available for the entire Boehringer cohort, of the 361
men, 73% reported that they smoked. Similarly, 76% of 2,860 gas workers smoked. Substantial
confounding based on smoking does not appear to be present because smoking seems to be
similar in both plants. The estimate for stomach cancer was 1.2 (CI = 0.7-2.1,12 observed
deaths). Three deaths were observed from NULs and none from connective and soft tissue
cancers. (The authors described an additional three deaths from chronic lymphocytic leukemia as
NHL deaths, but these deaths would not have been classified as non-Hodgkin's lymphomas in the
other studies in this review.) Dr. Lennart Hardell pointed out (letter to David Bayliss, January
10, 1995) that under the most recent classification the category "non-Hodgkin's lymphoma"
includes chronic lymphocytic leukemia as one type. Expected numbers of deaths from these
cancers were not given. Based on the proportions of expected cancer deaths in the Fingerhut
study, one might estimate that approximately 2.4 NHL deaths and 0.4 connective and soft tissue
cancer deaths would have been expected in this cohort as a whole, and about 0.1 connective and
soft tissue cancer deaths in the high-exposure subcohort. (The numbers of expected deaths from
lung cancer, stomach cancer, or NHL in the high-exposure subcohort were not estimated because
information is lacking on how many of the observed deaths from these cancers were in that
subcohort.)
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The authors reported exposure to other industrial chemicals, such as benzene and
dimethylsulphite. In addition, manual laborers were "probably" exposed to asbestos to some
extent. However, the authors maintain that this exposure explains neither the increased mortality
from all cancers nor the patterns of associations with TCDD exposure groups.
Other possible sources of bias include a potential lack of comparability between cause of
death ascertainment based on medical records (in some, perhaps many, cohort members) versus
only use of death certificates for cause of death certification in the derivation of German national
death rates. This is somewhat alleviated by the use of gas workers as a second comparison
group. In these workers, the same methods were used for medical certification, making
comparison of cause of death somewhat more accurate. However, this is offset by the fact that
the gas workers may have somewhat better mortality experience because they had to work a
minimum of 10 years to obtain entrance into their cohort, whereas the dioxin cohort had to work
only a minimum of 3 months. This could have introduced survivorship bias in this group and
consequently lower mortality and higher risk estimates.
Furthermore, the lack of an analysis of mortality data by time since first exposure for
individual causes such as lung cancer makes it impossible to assess latent effects.
Of the 399 female cohort members, only 7% worked in high-exposure departments. In
total, there were 54 deaths and an overall RR = 0.8 (CI = 0.6-1.0). The RR for all cancers was
0.9 (CI = 0.6-1.4), but the RR = 2.2 (CI = 1.0-4.1) for breast cancer was significantly increased
based on 9 deaths. Kogevinas et al. (1997) and Saracci et al. (1991) also report a borderline
significantly elevated risk of breast cancer based on nine cases who were exposed to TCDD or
high chlorinated dioxins. This is an interesting result in view of a suggestion of reduced
mammary cancer based on mechanistic studies and animal bioassays. However, at this point the
data do not provide a sufficient basis for any conclusions.
In another update of the Manz et al. (1991) cohort, Flesch-Janys et al. (1995) extended the
followup period to 1992. Lifetime exposure to the PCDDs and PCDFs (total toxic equivalencies)
was estimated quantitatively for the entire cohort from a subcohort of the workers (n = 190) in
the plant as well as estimated TCDD exposure alone. The Cox regression analysis provided
estimated RR by dose generated according to successive quintiles of TCDD levels and total toxic
equivalencies (TOTTEQ) or (PCDDs/DFs) matched by birth (5-year intervals). Gas workers
constituted the first set of controls. The second set of controls were internal. They were formed
from the first two quintiles and, lastly, the analysis was repeated excluding workers in the opiate
department. TOTTEQs as discussed by Flesch-Janys et al. (1995) are not to be confused with the
newer World Health Organization definition of TEQs. These Flesch-Janys TOTTEQs do not
include any PCBTEQs or TCDD.
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For TCDD alone, the highest relative risks for total cancer occurred in the highest quintile
(divided into deciles) of TCDD exposure (RR = 3.30, 95% CI = 2.05-5.31). Estimated levels
were between 344.7 and 3,890.2 ng/kg of blood fat, although mortality was elevated
nonsignificantly in lower quintiles as well. These were dose-related. For TOTTEQ exposure,
significant dose-related relative risks of total cancer were observed for the highest decile of
TOTTEQ exposure as well (RR = 3.27, 95% CI = 2.04-5.26), although the concentrations were
between 545.1 and 4,362 ng/kg of blood fat.
The authors concluded that the findings indicate a "strong dose-related relationship
between mortality due to cancer" and exposure to polychlorinated dioxins and furans. One of the
limitations to this kind of analysis is that it does not provide information regarding the
contribution of the specific and most potent congener TCDD to the increased risk of cancer from
exposure to TOTTEQ. The tables in the text of this paper suggest that when internal controls are
used, the risk of cancer is greater for exposure to TCDD alone than with TOTTEQ, which
includes TCDD. These data suggest that TCDD could be a confounding influence. However,
there are many assumptions about estimates of the parameters utilized in this methodology.
Limitations in the calculation of risk estimates based upon exposure to TOTTEQ in this
study are likely. Because the program computes estimates of the half lives of different congeners
of the PCDDs and PCDFs from exposure data collected from 1986 to 1994, and these estimates
are used in the calculaton of change in dose with time in job, the calculation of potentially
inaccurate individual estimates of exposure based upon work histories that date back to as early
as 1952 is possible.
Furthermore, no measurements are included from individuals who have died, some 414
(35%). Estimates of exposure from 190 live members of the cohort are generalized to the entire
cohort through the regression techniques utilized. If any deaths are dose-related to TCDD,
TCDF, or their congeners, their absence from the totals could underestimate doses for such
individuals. This in turn could bias risk estimates toward the null, and this could be the
explanation for the suggestion that the dose-response relationship for total cancer and increasing
TOTTEQs is not stronger.
A corrected Table 4 (Erratum, Flesch-Janys et al., 1996) indicates that for cancer the RR
is 2.69 (95% CI = 1.67-4.35). This did not change the bottom-line conclusion that there was a
strong dose-dependent relation between mortality from cancer and exposure to polychlorinated
dioxins and furans. This study was criticized in a letter to the editor (Swain, 1997) on the
grounds that the exposure assessment was inadequate, the choice of reference population was
inappropriate, and the statistical analysis was faulty. The author provided evidence in a rebuttal
companion letter to the editor (Flesch-Janys, 1997) that the choices he made in the study were
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not likely to produce any substantial biases in the results of the kind that were described by Swain.
In an effort to relate the risk of cancer to dose, Flesch-Janys et al. (1998) developed a
lifetime cumulative exposure index on each of the 1,189 male German herbicide and insecticide
workers discussed above and followed the cohort for another 3 years to the end of 1992. Cancer
mortality based upon 124 cases was significantly increased (SMR = 1.41, 95% CI = 1.17-1.68).
Mortality from lung cancer was also significantly increased (SMR = 1.51, 95% CI = 1.07-2.08)
based upon 38 cases, as well as hematopoietic and lymphatic cancer (SMR = 2.16, 95% CI =
1.11-3.77) based upon 12 cases. Production department-specific dose rates that were used to
develop exposure dose levels for all members of the cohort were derived from blood levels and
working histories of 275 workers. Four categories of cumulative exposure were developed for
TCDD as well as their corresponding TEQs. The cumulative PCDD/F (TEQs) levels were
expressed as nanograms/kilograms blood fat times years of exposure and based upon some 382
blood samples as follows:
I. 1.0 < TCDD < 125.2 ng/kg-years; 1.0 < TEQ < 360.9
II. 125.2 < TCDD < 627.1; 360.9 < TEQ < 1,614.4
IE. 627.1 < TCDD < 2,503.0; 1,614.4 < TEQ < 5,217.7
IV. 2,503.0< TCDD 5,217.7 < TEQ
A significant trend of increasing risk with increasing cumulative PCDD/F was evident.
The SMR for cancer was significantly increased (SMR = 1.73, 95% CI = 1.21-2.40) at the
highest quartile (greater than 2,503 ng/kg-years) (Table 7-5). The authors concluded that these
results indicated an elevated risk of total cancer mortality in a cohort with high exposure to
PCDD/F, that a dose effect was evident for estimated TCDD levels on total cancer SMR analysis,
and that these data could be used for quantitative cancer risk assessment for dioxin.
The authors provided an update to the mortality experience of this subgroup of females
from the Manz et al. (1991) study by following them through 1995 (Flesch-Janys et al., 1999).
For individuals known to be alive in 1995, mailed questionnaires were sent to elicit information
on any cancer diagnoses within the period to 1995. If cancer had been diagnosed during this
period, efforts were then made to secure medical records to substantiate the cancer diagnosis.
Cancers at particular sites were added to the cancer deaths at that same site to develop incidence
data in the cohort. Standardized incidence ratios (SIRs) were developed for all sites with two or
more cases. The authors found that the risk of breast cancer was elevated (SIR = 1.55, 95% CI
0.98-2.32) based upon 23 cases. The risk of cancer in general was elevated only slightly (SIR =
1.10, 95% CI = 0.83-1.42). The finding of an increased risk of breast cancer is somewhat
contradictory to the suggestion that TCDD/estrogen interactions decrease tumor incidence in
organs such as in the mammary gland of Sprague-Dawley female rats (Chapter 6, p. 6-36;
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Carcinogenicity of TCDD in Animals). The authors also found a dose-response relationship with
I-TEQ (ng/kg blood lipid) as follows:
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I-TEO RR 95% CI
0-1,900.9 0.99 0.4-2.05
1,901.0 -2,823.9 1.5 0.55-3.28
> 2,824.0 2.56 1.23-4.71
The authors point out that this cohort is very small, and they maintain that no definite
conclusions on individual contributions to the elevated risk can be drawn. They recommend an
extension of the follow-up, including additional determinations of blood levels .
These criticisms are not meant to discourage further work in this area. This is one
approach that could be taken to analyze whether TCDDs, TCDFs, and their congeners other than
TCDD are associated with increasing risks of cancer and other adverse health effects. All of
these potential problems could be reduced if exposure data could be collected over a longer span
of time and specimens could be included from deceased members who died from diseases that
might be attributable to exposure to TCDDs, TCDFs, and their congeners.
Becher et al. (1996) updated the earlier study by Manz et al. (1991). They included three
additional cohorts of workers exposed to TCDD in three different factories in Germany, although
at lower levels. The cohort of 399 women was not updated and was not studied further.
However, the remaining workers within that production facility and two of the three cohorts were
followed until the end of 1989. The fourth cohort, which he calls Cohort II, was followed until
December 1992. The authors compared mortality within these cohorts to that based upon
German national mortality rates. Altogether, some 2,479 workers from the 4 plants were studied.
A total of 138 deaths from all malignant diseases produced a borderline significant SMR of 119
(95% CI = 100 - 141). The risk increased with latency and was highest in cohort I, the original
study by Manz et al. (1991). It was within this plant that the highest TCDD blood levels were
recorded; i.e., 3 to 2,252 ng/kg blood fat. An increased mortality from respiratory cancer (SMR =
154, 95% CI = 115-202), cancer of the buccal cavity and pharynx (SMR = 295, 95% CI = 135-
560), and NHL (SMR = 326, 95% CI =119-710) was found in the total cohort. The authors
conclude that their findings are consistent with results from other cohorts, which showed an
increased overall cancer mortality and mortality of respiratory cancer after long-term exposure to
these phenoxy herbicides and dioxin. Combining several cohorts in order to increase the size of
the study population could lead to underestimating risks if the dioxin exposures as evidenced by
blood fat measurements are varied over a wide range from one plant to the next. On the other
hand, this latter update is an improvement over the earlier study, which relied on gas workers
who had to have worked a minimum of 10 years before they could be included in the comparison
dioxin-exposed cohort. This eliminated potential survivorship bias, although it increased the
potential for the healthy worker effect to play a role.
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In another investigation in Germany, Zober and colleagues (1990) studied persons
employed at another German chemical manufacturing facility where 2,4,5-trichlorophenol was
produced. An uncontrolled decomposition reaction in 1953 and subsequent cleanup activities
resulted in substantial TCDD exposures. The cohort contained 247 persons who had worked at
the plant from 1953 through 1987, 51% of whom had developed chloracne or erythema (a skin
condition that may be suggestive of chloracne), with mortality follow-up covering the same
calendar period. Seventy-eight persons had died (RR = 0.95); 23 had died of cancer (RR = 1.2)
(CI = 0.8-1.7). Expected deaths were based on national mortality rates in the Federal Republic of
Germany. When workers with chloracne (N=l 14 chloracne plus 13 erythema) were looked at
separately, the risk of cancer as expressed by the SMR rose to 1.4 (OBS= 16, CI = 0.9-2.1).
Again, within this highly exposed subgroup, if the analysis is restricted to only those workers
who were observed 20 or more years after first employment, the SMR was significant at 2.0
(OBS=14, CI = 1.2-3.2). For lung cancer, the SMR is of borderline significance at 2.5 (OBS=5,
CI = 1.0-5.3). The authors report that the results "do not support a strong association between
cancer mortality and TCDD, but they do suggest that some hazard may have been produced."
Three subcohorts were defined on the basis of the potential for varying degrees of
exposure. Subcohort CI contained 69 persons known to be exposed to TCDD during the
accident period. Cohorts C2 (84 persons) and C3 (94 persons) contained workers thought to be
exposed to lesser amounts of TCDD. Recent TCDD levels in blood samples from small numbers
of persons in each group suggested that exposures were higher in CI (median 24.5 ppt, 11
samples) than they were in C2 (median 9.5 ppt, 7 samples) or C3 (median 8.4 ppt, 10 samples).
Thus, C2 and C3 are grouped together in this review. A second stratification by the authors
divides the total cohort into 127 persons with chloracne (N = 114) and erythema (N = 13) versus
120 persons with neither. The average serum TCDD levels in the two subcohorts are 15 ppt and
5.8 ppt, based on 16 samples (with chloracne) and 12 samples (without chloracne), respectively.
The two stratifications provide similar results. Only the results of the first stratification, i.e., CI,
C2, and C3, are presented here.
This study, with only 20 expected cancer deaths in the total cohort and 4 expected cancer
deaths in the members of subcohort CI with 20 or more years of latency, is much smaller than
the studies by Fingerhut et al. (1991) and Manz et al. (1991). The authors, however, did provide
detailed analyses of data on specific cancers (Table 7-6).
Elevated mortality rates from lung cancer, stomach cancer, and all cancers combined
were confined largely to the members of subcohort CI with long latency. The confidence
intervals for the relative risk estimates are extremely wide, however. No deaths from cancers of
connective and soft tissues or from NHL were observed, and expected numbers of deaths from
these cancers were not reported. However, one mesothelioma was reported in a plant supervisor
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with known asbestos exposure. Based on the proportions of all expected cancer deaths due to
these cancers in the study by Fingerhut et al. (1991), one might estimate that approximately 0.6
NHL deaths and 0.1 connective and soft tissue cancer deaths would have been expected in this
cohort as a whole, and about 0.2 NHL deaths and less than 0.1 connective and soft tissue cancer
deaths among the members of subcohort Cl with long latency. This study lacks power to detect
a significant site-specific cancer risk at most sites because of its small size.
In an update of this study, Ott and Zober (1996) added an additional 4 years of follow-up
and reduced the size of the cohort slightly to just the 243 male members. They subsequently
divided the cohort into four exposure categories based upon estimated TCDD dose expressed in
ng/kg body weight. The development of this surrogate involved an appproach that included
detailed accounts of each employee's work activities, analyses of TCDD in blood lipid of 138
employees back calculated to time of initial exposure utilizing a half-life estimate of 5.8 years for
internal TCDD dose, and internally derived estimates of elimination rates of TCDD. Four
categories of cumulative exposure were developed as follows: under 0.1 ng/kg body wieght
estimated TCDD dose, 0.1 to 0.99, 1.0 to 1.99, and 2.0+ ng/kg body wieght. They calculated
standard mortality ratios (SMRs) for total cancer in each category as well as site-specific cancer
mortality after a 20-year latency. By the end of 1992, 47 men were diagnosed with cancer while
31 had died. The risk of cancer tended to increase with dose, although not significantly, because
of the reduced power to detect a significant risk based upon decreasing size of the subcategories
in the higher dose categories. At TCDD cumulative doses greater than 1 ng/kg, the SMR is 1.6
(95% CI = 0.9-2.6) (Table 7-7).
However, after 20 years' latency, the risk of cancer became significant in the exposure
category > 1 |ig/kg body weight (SMR = 1.97, 95% CI = 1.05-3.36). Of the 13 cancer deaths in
this category, 6 of these were due to lung cancer. The risk of lung cancer was also significant in
this category (SMR = 3.06, 95% CI = 1.12-6.66). The same pattern of increasing risk with
increasing dose was true based upon standard incidence ratios, although the increase was less
pronounced. In the higher exposure category, greater than 1 ng/kg body weight, the SIR without
regard to latency was 1.3 (95% CI = 0.8-2.0). For lung cancer, based on 8 cases the SIR was
significantly elevated at 2.2 (95% CI = 1.0-4.3). The authors concluded that their findings were
consistent with a carcinogenic effect induced by TCDD at the high dose category (> 1 ng/kg
body weight). The authors did note that because the cohort was small, the risk estimates could be
affected by selection bias and/or confounding bias.
7.5.3. Ten-Country Study by International Agency for Research on Cancer
A historical cohort study of cancer mortality in 18,390 production workers or sprayers
exposed to chlorophenoxy herbicides and/or chlorophenols was reported by Saracci et al. (1991).
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Exposure was reconstructed through questionnaires, factory or spraying records, and job
histories. Workers were classified as exposed (N = 13,482), probably exposed (N = 416),
exposure unknown (N = 541), and nonexposed (N = 3,951). The exposed group contained
everyone known to have sprayed chlorophenoxy herbicides and everyone who had worked in any
of certain specified departments at factories producing chlorophenoxy herbicides. The criteria
for duration or level of exposure required for selection was reported for only 3 of the 10 countries
and only 4 of the 20 cohorts; these ranged from at least 1 month to 1 year. For all the other
cohorts, the criterion for inclusion was to have ever been employed in production or spraying of
these herbicides. The cohort contained 1,537 female workers, but results were not presented
separately, except for female breast and genital organ cancers by phenoxy herbicide exposure.
Average follow-up for the cohort was 17 years; 5% of eligible workers were lost to follow-up.
Three of the cohorts comprising over 10,000 workers have also been reported in separate
publications but for different follow-up periods (Lynge, 1985, 1987, 1993; Coggon et al., 1986;
Kogevinas et al., 1993); these are discussed briefly in this report following this discussion.
Several other occupational cohorts discussed in this report are also included.
Also included in the analysis was a division of the cohort (probable vs. unlikely) by
whether or not exposure to TCDD occurred. No definition is given for "probable" exposure to
TCDD; exposure to phenoxy herbicides does not necessarily imply exposure to TCDD. The
"probably exposed" category includes production workers at two plants producing PCP, 2-(2,4-
dichlorophenoxy)propanoic acid (2,4-DP; dichlorprop), 4-(2,4-dichlorophenoxy)butanoic acid
(2,4-DB), (4-chloro-2-methylphenoxy)acetic acid (MCPA), or 2-(4-chloro-2-methylphenoxy)-
propanoic acid (MCPP; mecoprop). There is not likely to be any TCDD in these processes.
Those "unlikely exposed" appear to be so classified because they worked in different factories.
There were 181 cases of chloracne among workers in the cohort.
The results are presented below by each of the two divisions of the total cohort: phenoxy
herbicide (PH) and/or chlorophenols, and probable TCDD exposure. For the cohort division by
PH and chlorophenols, no excess was observed for all-cause mortality, for all malignant
neoplasms, for most common epithelial cancers, or for lymphomas. The four STS deaths were
all in the "exposed to phenoxy herbicides and chlorophenols" subcohort (RR = 2.0, CI = 0.5-5.2)
and all appeared 10 to 19 years after first exposure (RR = 6.1, CI = 1.6-15.5), with the excess risk
limited to exposed sprayers (RR = 8.8, CI = 1.8-25.8) based on three observed deaths. None
were observed in the 20 years or more category. Increases were also noted in the exposed group
for mortality from thyroid cancer (RR = 3.7, CI = 1.0-9.4) based on four deaths, cancer of the
testis (RR = 2.2, CI = 0.9-4.6) based on seven deaths, other endocrine glands (RR = 4.6, CI =
0.9-13.5) based on three deaths, and nose and nasal cavities (RR = 2.9, CI = 0.6-8.5) based on
three deaths. An increase in lung cancer mortality was limited to the "probably exposed" (to
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phenoxy herbicide and chlorophenols) group (RR = 2.2, CI = 1.1-4.0) based on 11 observed
deaths.
The authors provided an additional analysis of STS, including five additional cases who
were either alive at the end of follow-up or who had died from another cause. They concluded
that the results suggest that STS in these workers "is compatible with a causal role for
chlorophenoxy herbicides, though not specifically for those probably contaminated with TCDD."
The authors present only a limited analysis based on 215 and 294 expected total cancer
deaths in the "probable" vs. "unlikely" exposed groups, respectively. There was a slight increase
in mortality from all cancers for the probably versus unlikely exposed groups (RR = 1.1, CI =
1.0-1.2 versus RR = 0.9, CI = 0.8-1.1), but no increase in either STS or NHL based on 4 and 11
total cases, respectively. There was also an increased mortality for testicular cancer in the group
probably exposed to TCDD versus those probably not exposed (RR = 3.0 vs. 1.6) based on seven
total deaths, and for thyroid cancer (RR = 4.3 vs. 3.1) based on four total deaths. These latter
two differences are not significant and, while interesting because of TCDD's known effects on
these organs, add little to the information base.
While the Saracci et al. cohort was significantly larger than the other three worker cohorts
(Steenland et al., 1999; Fingerhut et al., 1991; Manz et al., 1991; Zober et al., 1990), the lack of
both a clear definition of exposure and uniformity of exposure classification between and within
plants makes the results difficult to interpret and lessens the confidence in these results. When
several studies have shown a positive association of effect with the same exposure but were
conducted under different circumstances, the possibility that an unknown confounder or chance
produced the observed elevated effect is minimized. When different investigators working with
different populations using different methods confirm an original finding, the results are more
believable. TCDD tissue levels were available only from a sample of 9 of the 181 workers with
chloracne (median = 340 ng/kg, range 98 to 659 ng/kg). For 17 external controls, the median
was 16 ng/kg while the range was 0 to 23.3 ng/kg. This suggests that some of the controls were
exposed to TCDD. Unfortunately, no further analysis was presented on these workers.
There are several problems with this study. A portion of the Saracci et al. cohort consists
of Danish workers from the Lynge (1985) study. None of them are reported by Saracci et al.
(1991) as having had any exposure to 2,4,5-T. Lynge indicates that 2,4,5-T was produced at the
Kemisk Vaerk K0ge (KVK) facility in Denmark from 1951 until the end of 1980. However, in a
later update (Lynge, 1993), the author maintains that the excess is due to exposure to phenoxy
herbicides other than 2,4,5-T because only 5.3 tons of 2,4,5-T were produced in 1951-1952. This
statement is somewhat contradicted in the author's methods paper (Lynge, 1987), where the
author discloses that 350 tons of 2,4,5-T esters were produced during the period 1951-1981, on
the basis of purchased 2,4,5-T acid. Perhaps more exposure to 2,4,5-T occurred than was
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asserted by the author. This suggests the possibility that exposure misclassification may be
present in the Saracci et al. study. There may be potentially as many as 3,844 workers who had
exposure to 2,4,5-T and consequently TCDD. Many if not all of them were considered as
unexposed in the Saracci et al. study.
Lynge in her studies reported on five histologically confirmed cases of STS. These are
listed as cases 2, 3, 4, 5, and 9 in Table IV of the Saracci et al. study. Two were considered alive
in the Saracci et al. study, even though in the later 1993 Lynge study these same two are listed as
deceased. The remaining three are reported by Saracci et al. as deceased. Two of these three are
coded to cancer sites other than STS; only one is correctly coded to STS. This suggests that
underreporting of STS as the underlying cause of death is a problem in this study, which is
consistent with the findings of Suruda (1993) that STS is underreported generally on death
certificates. Added evidence of underreporting of STS is provided by the death certificate's cause
of death for the two who were deceased after 1984. Both were coded to a cancer site other than
STS. Altogether, four out of five of the confirmed STSs in the Lynge study were coded to causes
other than STS.
In a followup study of this same cohort (Kogevinas et al., 1995), two nested case-control
studies were conducted on 11 identified cases of STS and 32 NHL cases from this cohort. Four
STSs and 20 NHLs were included in the earlier IARC study as deceased with the given
diagnosis. These were matched with 55 and 158 controls, respectively, by country of residence,
sex, and age through the use of incidence density sampling.
A panel of 3 industrial hygienists carried out the assessment of exposure to 21 chemicals
or mixtures without knowledge of the subject's case control status. Live cases were included as
well as deceased cases that were coded to causes other than code 171, "sarcomas of connective
and other soft tissue," of the 9th Revision of the International Classification of Diseases and
Causes of Death. Few actual tissue measurements of serum TCDD or other contaminants were
available on any of the cases or controls.
The authors found a significantly elevated risk of STS from exposure to "any dioxin or
furan" (OR = 5.6; 95% CI = 1.1-28); a nonsignificant increased risk with respect to TCDD (OR =
5.2; 95% CI = 0.85-32), and a significantly increased risk from exposure to "any phenoxy
herbicide" (OR = 10.2; 95% CI = 1.2-91). There is also the suggestion of an increasing risk of
STS with increasing intensity of exposure to TCDD. However, these are based upon small
numbers and are subject to much variability. Additionally, there may be a problem with random
misclassification in the estimates of intensity of exposure because the designation of intensity
was a subjective decision by the three industrial hygienists.
However, these findings tend to support the likelihood that exposure to any dioxin or
furan as well as TCDD alone is responsible for the elevated risk of STS seen in these workers
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from many different countries. There is a suggestion of a weak increased risk of NHL from
exposure to "any dioxin or furan" (OR = 1.84; CI = 0.8-4.3) and to TCDD (OR = 1.93; CI = 0.7-
5.1). These studies suggest an association with total PCDD/DF TEQ exposure.
In a recent update of this IARC study, Kogevinas et al. (1997) expanded the study group
to 26,976 workers by adding additional cohorts of workers from 12 plants in the United States
(Fingerhut et al., 1991) and 4 plants in Germany (Becher et al., 1996; Manz et al., 1991; Flesh-
Janys et al., 1995). A core protocol was developed by the participating countries to find
appropriate study populations of workers who produced or sprayed phenoxy herbicides or
chlorophenols. The study coordination was handled by IARC. The enlarged cohort includes
almost all of the phenoxy herbicide production workers who have ever been studied. Vital status
follow-up has been updated for most of the cohort.
The authors separated from the cohort those workers who were exposed to phenoxy
herbicides believed not to be contaminated with TCDD on the basis of information abstracted
from individual job records and company exposure questionnaires. This includes some 4,160
workers, mainly from Denmark and the Netherlands. Thus 1,012 women and 20,851 men remain
who presumably were exposed to varying amounts of TCDD in their jobs. Of the 36 cohorts
examined, measurements of serum TCDD have been done on 573 workers from 10 companies in
7 countries. Measured mean blood serum TCDD levels (pg/g) range from a mean low of 3.2
pg/g in one German plant to a high of 401.7 pg/g in another German plant. Some of these blood
serum levels could be considered so low as to be indistinguishable from population levels outside
of the plants where the measurements were taken. However, it should not be assumed that these
current levels reflect exposures received by the members of the cohorts at the time of exposure in
the past. They could either reflect very little exposure received over the years during and after
employment in the industry (background levels) or they could represent reduced levels following
reductions expected after the 7-year half life of dioxin in the body has been factored in.
Conditions have probably improved at these plants over the years.
The authors report that among those exposed to TCDD containing phenoxy herbicides,
mortality from malignant neoplasms (710 deaths; SMR = 1.12, 95% CI = 1.04-1.21) was slightly
but significantly elevated. Incidence of STS, lung cancer, and NHL was also elevated, but not
significantly so. The authors conclude that exposure to herbicides contaminated with TCDD and
higher chlorinated dioxins may be associated with a small increase in overall cancer risk and in
risk for specific cancers.
However, the same problems that plagued the Saracci et al. (1991) study also appear to
plague this update. If the mean blood serum TCDD data and the confidence intervals provided
around these means are any indication, it would appear that exposures varied considerably from
one plant to the next. Many if not most workers had exposures similar to those of the
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comparison populations from which the expected deaths were derived. Following such workers
through time to determine their risk for the development of diseases related to exposure to TCDD
will produce few if any significantly elevated risks that could be attributable to exposure to
TCDD and will only serve to depress the SMRs.
Furthermore, although it was noted above that Kogevinas and his colleagues separated
some 4,160 workers (mostly Danish) believed not to be exposed to phenoxy herbicides
containing TCDD, some of these workers may actually have had exposure to 2,4,5-T and
therefore its contaminant TCDD. In an earlier methods paper (Lynge, 1987) discussing job
departments and production processes in the two plants that are the subject of her study of
Danish workers (Lynge, 1985; 1993), she describes how "limited amounts of 2,4,5-T have been
processed" in the Kemisk Vaerk K0ge plant, "mainly in the formation of esters based on a
purchased acid." The handling of 2,4,5-T lasted from 1951 to 1981. However, in Table 1 of
Kogevinas et al. (1997), some 2,118 of the 2,341 workers at the two Danish plants are listed as
having no exposure to "TCDD or higher chlorinated dioxins." Coincidentally, several STSs were
identified from that same plant (Lynge, 1985, 1993).
In summary, this study is very little improved from the earlier study by Saracci et al.
(1991). The few blood serum TCDD samples that have been measured differ so much that they
indicate great variability in what the actual exposures might have been. And because many if not
most of these workers have very likely always had blood serum TCDD levels close to
background, the inclusion of such workers in the study cohort could introduce a potential bias in
the results and could serve to drive estimated risk ratios toward the null. Furthermore, the
definitions utilized in the feeder studies to decide who should be included or excluded cannot be
easily "retrofitted" to meet the rigor required by the core protocol of the present study.
Theoretically, all cohort members should enter the study at the same time. The study should
have only one ending date and there should be only one qualifying period of employment (or
exposure), not several, before inclusion as a member of the cohort, say 3 months or 6 months.
There are questions that can be raised concerning the quality of the follow-up in each study, and
whether the vital statistics and comparison populations are similar from one country to the next.
The potential problems that need to be addressed are numerous and overwhelming.
The nested soft tissue case-control sarcoma study and the non-Hodgkin's lymphoma case-
control study by Kogevinas et. al. (1995) discussed earlier in this chapter are methodologically
superior to the conglomerate larger cohort study by Kogevinas et al. (1997). They have none of
the design problems of the cohort study, although they are still limited by the lack of endogenous
measurements of exposure similar to the parent cohort study. This chief drawback, i.e., the use
of occupational data regarding proximity of the subjects to materials contaminated with dioxin as
a surrogate for exposure, raises the possibility that misclassification of exposure led to a
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reduction in risk estimates. Although this appears not to be the case with the STS study, where
the risk ratios were significantly elevated, it could be a problem with the lymphoma study and
other planned studies of site-specific cancer where no elevated risk was found. However, it is
noteworthy that a significantly elevated risk of STS was found in association with exposure to
materials contaminated with dioxin. Tallying the STS cases across all study cohorts in
Kogevinas et. al. (1997) and matching them with members from the same international cohort to
produce the excess significant risk estimates despite the drawbacks mentioned lends support to
the theory that dioxin activates the Ah receptor to produce the STSs as well as other site-specific
cancers.
7.5.4. Other Studies
Four studies containing portions of the same cohort reported above were reported
elsewhere (Lynge, 1985, 1987, 1993, 1998; Coggon et al., 1986; Kogevinas et al., 1993; Bueno
de Mesquita et al., 1993; Hooiveld et al., 1996, 1998). Lynge (1985) reported a study of cancer
incidence among persons employed in the manufacture of phenoxy herbicides in Denmark. The
cohort consisted of 4,459 persons from two factories. One factory contributed 615 cohort
members who had worked in the years 1951-1981. The only phenoxy acids manufactured and
packaged at this plant were MCPA and mecoprop, unlikely to contain TCDD. The other factory,
Kemisk Vaerk K0ge, contributed 3,844 cohort members who had worked in the years 1933-1981.
At this plant, MCPA, 2,4-D, and lesser amounts of mecoprop, dichlorprop, and 2,4,5-T were
manufactured and packaged. The investigators were unable to classify cohort members by the
specific types of phenoxy herbicides to which they were exposed. However, in this plant, where
exposure to TCDD-contaminated 2,4,5-T probably did occur, a significant excess risk of STS (4
observed vs. 1.00 expected, CI = 1.09-10.24) was noted by the author in those workers who had
achieved a minimum 10 years of latency. Unfortunately, individual tissue measurements of
TCDD were not included in this study.
In an update of the earlier study, Lynge (1993) continues to report an increase in the risk
of STS with four cases reported to be in persons exposed to phenoxy herbicides (standardized
incidence ratio [SIR] = 2.3, CI = 0.6-5.8). Just as before, this excess occurred in workers
employed for more than 1 year in the Kemisk Vaerk K0ge factory (SIR = 6.4, CI = 1.3-18.7).
The author concluded that her study continues to provide evidence that exposure to phenoxy
herbicides increases the risk of STS.
However, Lynge maintains that only small amounts of 2,4-D and "negligible" amounts of
2,4,5-T were produced at the KVK factory. That this amount of 2,4,5-T was negligible is
somewhat at odds with data from an earlier paper in which she discussed the design of her
ongoing cohort study (Lynge, 1987). In the 1987 paper, she reported that although 5.3 tons of
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2,4,5-trichlorophenol were produced in 1951 and 1952, 350 tons of 2,4,5-T esters were produced
from 1951 to 1981 based on purchased 2,4,5-T acid. This varied from zero to as much as 63 tons
in any one year. Very likely, the term "negligible" is used in a relative sense—relative to the
other herbicides in the KVK factory, which were produced in much larger amounts. Actual
exposure to 2,4,5-T may have been greater than the impression given in Lynge's 1993 study.
Most of the potential exposure was to MCPA, MCPP, 2,4-DP, and various dyes and pigments.
MCPA, MCPP, 2,4-DP, and the nondioxin-containing phenoxy herbicides have not heretofore
been seriously thought of as possible causes of cancer in humans.
Lynge also found that the risk of NHL was not elevated in persons potentially exposed to
phenoxy herbicides. She did find what she calls a "puzzling" 3.5-fold excess risk in employees
of KVK employed in other manufacturing departments. No detailed information on production
in these areas was included in the study.
Little additional information is provided concerning any increased risks of other forms of
cancer, except for a statement that multiple myeloma and cervical cancer in women and
malignant melanoma in men were significantly increased. No numbers are given for these
statements. A significant excess risk of lung cancer seen in the earlier study is now borderline
significant in this study (obs = 13, SIR = 1.6, CI = 0.9-2.8). The author had planned to have
serum tissues in some of her subjects analyzed for their PCDD and PCDF content although none
have been forthcoming.
In a later update, Lynge (1998) identified 2,119 workers from the two factories described
above who had exposure to phenoxy herbicides, 940 in the manufacture and packaging of
phenoxy herbicides and 1,179 in manual service functions. These workers were followed until
December 31, 1993, and all tumors diagnosed during this time were tallied and compared with
cancer incidence rates for the Danish population for sex, 5-year age, and calendar-time period.
Standard incidence ratios were calculated for numerous tumor sites including STS and NHL.
The overall cancer incidence was lower than expected (SIR = 0.87) based upon 204 observed
cases. The SIR for STS was 1.62 (95% CI = 0.4-4.1) based upon four observed cases, all among
men employed at the KVK, where the SIR was 2.38 (95% CI = 0.7-6.1). On the other hand, there
were only six cases of NHL, and the SIR was 1.10 (CI 0.4-2.6). The author concluded that on
the basis of small numbers there is a suggestion that an increased risk of STS is associated with
exposure to MCPA and related phenoxy herbicides. However, the author maintains that there is
no indication of an increased risk of NHL or of other cancer diseases as well. The author does
caution the reader that these findings are based upon small numbers. Few cancer cases
(including deaths) have been identified thus far. It may require several more years of follow-up
before any conclusive findings can be derived from this data.
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Coggon and colleagues (1986) conducted a study of 5,754 workers at a British plant that
manufactured and formulated MCPA from 1947 until 1982 and operated its own aerial and
tractor-mounted spraying service from 1947 until 1972. The authors stated that other phenoxy
acids were handled "at times" and that "in comparison with MCPA, 2,4,5-T was handled only on
a small scale." Overall mortality was less than that of the national population, as was mortality
from cancer. Among workers whose jobs meant potential exposure to MCPA, there was a deficit
of deaths from cancer, all sites (297 observed versus 314.0 expected), but with one STS
occurring when 0.6 were expected. If a rural adjustment factor were applied, the expected deaths
would be 276.3. This would produce an SMR of 106. No significant site-specific deaths were
reported. As MCPA contains no 2,4,5-T, these persons would not have exposure to dioxin.
However, because no exposure information was collected it would be difficult to confirm this.
Coggon et al. (1991) conducted a study of four British cohorts of manufacturers of
phenoxy herbicides, including 2,4,5-T, comprising 2,239 men employed sometime during the
period 1963 to 1985. All four of these cohorts were included in the Saracci et al. study
previously discussed. Follow-up was to the end of 1987 through the National Health Service
Central Register and the National Insurance Index. Comparisons were with the national
population. Factory A produced 2,4,5-T beginning in 1968, while the remaining three factories
formulated it beginning in 1959, 1960, and 1970, respectively. No tissue measurements were
conducted on any members of the cohort. A slight excess of lung cancer was noted (19 observed,
14.2 expected). Two NHLs also were observed (0.87 expected). No STSs were observed (0.18
expected). Total cancer also was not increased (37 observed, 36.85 expected).
This cohort has not been followed for a long enough time to expect latent effects to
manifest themselves. The authors assumed that the slight increase in lung cancer was probably
due to cigarette smoking or a chance occurrence based on the observation that most of the lung
cancer deaths occurred less than 10 years after first exposure to phenoxy compounds. Phenoxy
herbicides produced or formulated at these factories included 2,4-D, MCPA, 2,4-DP, 2-methyl-4
chlorophenoxy butyric acid (MCPB), MCPP, phenoxybutyric acid (PBA),
parachlorophenoxyacetic acid (PCPA), and phenoxyacetic acid (PAA), as well as other
herbicides. The author says they were exposed to a multiplicity of chemicals. Except for the
slight increase in lung cancer, which is in the same direction as the findings from the earlier
cohort studies, this study contributes little to the elucidation of the risk of cancer from exposure
to TCDD.
Kogevinas and colleagues (1993) studied a group of 701 occupationally exposed women
enrolled in LARC's International Registry of Persons Exposed to Phenoxy Herbicides and Their
Contaminants. These workers are also included in the Saracci et al. (1991) study as well as the
Kogevinas et al. (1997) study. The likelihood of exposure to TCDD was based on individual job
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histories, company records, and company exposure questionnaires. Actual measurements of
TCDD serum levels in women were not available, according to the authors, so that confirmation
of exposure could not be accomplished. Both national cancer incidence rates and national death
rates were used to generate expected cases and deaths utilizing the methods of the Saracci IARC
study.
The overall cancer risk did not exceed expected (SIR = 96, CI = 0.6-1.4) based on 29
cases. However, the group with the greatest potential for exposure to TCDD-contaminated
chlorophenoxy herbicides produced a significant excess risk of cancer of all sites (SIR = 222, CI
= 1.0-4.2) based on nine cases. The risk was observed within the first 10 years of exposure, with
no elevated risk appearing after the 10th year of observation. For those women who had
probable exposure to TCDD, the risk of dying from cancer was slightly elevated as well (SMR =
165, CI = 0.4-4.8) based on three deaths.
This study suffers from many of the same problems as the Saracci et al. (1991) and
Kogevinas et al. (1997) studies. In addition, it is a study of a small population and as such
cannot be considered sensitive to the detection of small risks. These same workers were also
subject to exposure to other toxic chemicals in the workplace, which may also have an effect on
the risk of cancer.
However, the elevated cancer risk in women exposed to TCDD-contaminated phenoxy
herbicides is consistent with a hypothesis of overall increased cancer risk seen in other studies
from exposure to TCDD or TCDD-like contaminants.
Recently, another study was published (Bueno de Mesquita et al., 1993) of a cohort of
2,310 workers in two plants involved in the manufacture and preparation of phenoxy herbicides
(not necessarily 2,4,5-T) in the Netherlands. These workers were also included in the IARC
International Registry of Persons Exposed to Phenoxy Herbicides and Their Contaminants and
hence were part of the Saracci et al. (1991) study. Some 963 were considered by the author to be
exposed to phenoxy herbicides, while 1,111 were considered not exposed. The follow-up
periods were somewhat skewed between the two subcohorts as well. The workers of one plant
were followed from 1955 to 1985, and those at the other were followed from 1965 to 1986.
Only a slight increase occurred in total cancer mortality based on 31 deaths (SMR = 107,
95% CI = 73-152) utilizing The Netherlands' national rates. A slightly higher risk of total cancer
was seen based on 10 deaths (SMR = 137, CI = 66-252) in 139 workers probably exposed to
dioxins during or immediately after a 1963 industrial accident in which dioxin was released into
the atmosphere.
When compared with nonexposed workers, mortality due to all cancers was
insignificantly elevated (RR = 1.7, 95% CI = 0.9-3.4) while that due to respiratory cancer was
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also insignificantly elevated (RR = 1.7, 95% CI = 0.5-6.3). This group was too small to provide
enough power to detect significant site-specific cancers.
Although the size of the cohort seems large, actually only about 549 workers in Factory A
had a potential for exposure to TCDD-contaminated 2,4,5-T and the higher chlorinated dioxins.
No one at Factory B was exposed to 2,4,5-T because it was not produced there. At Factory A,
the SMR for lung cancer was elevated insignificantly to 165 based on 6 deaths in the 20-year
latent category. No serum dioxin measurements are available to substantiate exposure to dioxin.
However, the authors did conclude that the SMR of 73 for Factory A, the SMR of 118 for
Factory B, and the SMR of 137 for the cohort exposed to the accident are not inconsistent with
the possibility of a carcinogenic effect of TCDD in humans.
Hooiveld and co-workers (1996) updated the earlier study by adding additional years of
follow up to December 31, 1991. Cancer mortality remained statistically significantly high
(SMR = 146, 95% CI = 109-192) in factory A. By latency after 20 years, a statistically
significant SMR = 160 (95% CI = 110-225) is evident. On the other hand, no unusually high
mortality occurred in factory B, possibly because of the small size of the cohort and the fact that
few deaths from cancer were expected. The authors concluded that relative risks were highest in
the highest exposure category, indicating a dose-response relationship with TCDD exposure
level.
Hooiveld et al. (1998) in still another update studied the 562 workers exposed to 2,4,5-T
that were discussed earlier. The authors followed the cohort to December 31, 1991, and
identified 139 deaths to the 549 males of the cohort. In addition, serum TCDD levels were also
gathered on 47 surviving workers of this cohort. Of these, 14 were exposed in the accident of
1963. Seventeen were exposed to phenoxy herbicides or chlorophenols but were not involved in
the accident. The remaining 16 of the 47 were never exposed to phenoxy herbicides or
chlorophenols. The arithmetic mean serum TCDD levels of the three groups defined as accident,
exposed but no accident, and nonexposed were, respectively, 96.3, 16.6, and 7.6 ppt. These data
were collected in 1993. On the basis of these data the authors extrapolated back in time to get
some idea of what the maximum potential serum TCDD levels could have been at the time of the
accident. These estimated values were 1,841.8, 244.1, and 7.6 ppt, respectively. The SMRs in
140 male workers who were exposed during the accident and who presumably had higher levels
of serum TCDD also exhibited a significantly higher risk of malignant neoplasms (SMR = 1.7,
95% CI = 1.1-2.7) while in the larger exposed group of 549 male workers, with lower levels of
serum TCDD, the risk of malignant neoplasms was significant as well (SMR =1.5, 95% CI =
1.1-1.9).
However, when mortality in the 549 exposed workers was contrasted against mortality in
the 482 nonexposed workers from the larger cohort, the exposed group exhibited a significantly
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increased risk of death from cancer (RR = 4.1, 95% CI = 1.8-9.0) and specifically, respiratory
cancer (SMR = 7.5, 95% CI = 1.0-56.1). The authors concluded that the results of this cohort
study support the evidence of an exposure-related high risk of cancer in workers exposed to
phenoxy herbicides, chlorophenols, and their contaminants.
Wiklund and Holm (1986) studied a massive cohort of 354,620 Swedish men who were
recorded as having an agriculture or forestry job according to the census of 1960, versus
1,725,845 Swedish men in all other industries. The primary exposure in those jobs was
postulated to be primarily MCPA, and 2,4-D and 2,4,5-T to a lesser extent. The authors found
that the relative risk of STS was 0.9. This study has several deficiencies that reduce its
usefulness in determining the risk of STS due to exposure to TCDD: (1) a lack of individualized
exposure data (men were classified into six major subgroups by occupation from census data);
(2) only 15% of Swedish agricultural and forestry workers were estimated to be exposed to
phenoxyacetic acids (a smaller percentage was exposed to dioxin-contaminated phenoxy
herbicides) and 2% to chlorophenols; (3) Swedish agricultural workers have a decreased cancer
risk and tend to use health services less frequently; (4) classifying workers according to a 1-week
employment status in October of 1960 as reported in a census invites the possibility of
misclassification; and (5) the crude rate of STS in agricultural and forestry workers based on data
in the study was 5.45 per 100,000 person-years, versus 5.00 per 100,000 person-years in the
remaining workers. Both rates are high compared with rates from other nations (1 to 3 per
100,000 person-years).
A few years later, Wiklund et al. (1988, 1989) produced two new cohort studies that
superficially appear to contradict the earlier findings of Hardell and Eriksson. Wiklund et al.
followed some 20,245 licensed pesticide applicators in Sweden from date of license in 1965 or
after until December 31, 1984. Some 72% were estimated to have been exposed to phenoxy
herbicides 1 day or longer (based on questionnaire data sent to a random sample of 273 persons
in the cohort). The relative risk for STS reported in the first study was found to be 0.9, with a
mean follow-up time of 13.9 years. Even after a 10-year latency, the risk for STS was only 1.0
based on four deaths. With respect to the second study of all other cancer sites, major significant
deficits were found in several sites followed for an average of 12.2 years until December 31,
1982. No report is given concerning loss to follow-up or vital status.
The authors describe a major disadvantage of these studies to be a "lack of individual
exposure data" and that information is available "for only a sample of the cohort." Even the
length of exposure of individual applicators to phenoxy herbicides is not available. What is
presented is information that herbicide use in the 1950s was only 19%, and in the 1960s it
increased to just 49%. By the 1970s, however, it was up to 67%. On the other hand, pesticide
use is reported to be 92% during the same period. This seems to indicate that perhaps less than
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half had any exposure to the phenoxy herbicides at the time of licensing and perhaps for many
years afterwards. Furthermore, no information is presented about the presence or absence of
chloracne, another marker of exposure to TCDD.
In addition to missing important information regarding the vital status of this cohort by
the end of the follow-up, no information is available concerning the distribution of person-years
"at risk" generated by the "lost to follow-up" group. Among the cancer sites reported to have
significantly reduced risks are total cancer, liver, pancreas, lung, and kidney.
Furthermore, the extent of exposure to the agent of concern, TCDD, may not be extensive
among licensed applicators in Sweden. The entire discussion is centered on exposure to
"phenoxy herbicides." The authors state that the most widely used phenoxy herbicides in
Sweden are MCPA, mecoprop, and dichlorprop. None of these contain TCDD as a contaminant.
2,4-D and 2,4,5-T have also been used to a "lesser extent" according to the authors. Sweden
prohibited the use of 2,4,5-T in 1977. If, as the authors state, only 19% used herbicides in the
1950s, increasing to 49% in the 1960s, it suggests that far fewer applicators were exposed to
small quantities of TCDD for a long enough period of time to produce any effects. Furthermore,
only 68.2% of the cohort could have attained the age of 59 by the close of the study in 1984.
This hints at the likelihood that the full impact of exposure on mortality has not yet been
achieved. In fact, the authors report that the "latency time may anyhow be too short to detect
increasing risks of cancer . . . ."
7.5.5. Summary
The cohorts assembled by Fingerhut et al. (1991), Steenland et al. (1999), Manz et al.
(1991), Becker et al. (1996), Flesch-Janys et al. (1995, 1998, 1999), Zober et al. (1990), and Ott
et al. (1996) are important because they contain sizable proportions of persons with substantial
TCDD exposures. These exposures were documented, at least in subsets of the cohorts, by blood
and/or adipose tissue measurements, workplace measurements, and the occurrence of chloracne.
The Saracci et al. (1991) cohort and later Kogevinas et al. (1997), while significantly larger, were
assembled with nonuniform exposure criteria for TCDD exposure, leading to less confidence in
the results.
The exposures and methods in the three studies in males (Fingerhut et al., 1991; Manz et
al., 1991; Sober et al., 1990) were similar enough to warrant aggregating the results. Within each
study, relative risks were estimated by summing the observed and expected numbers of deaths
across categories of age, race, and calendar time, and then dividing the totals to produce relative
risk estimates in the form of standardized mortality ratios. Thus, aggregate relative risks can be
obtained simply by summing the observed and expected numbers of deaths across the studies.
Alternatively, the aggregate relative risk could have been derived by weighting the individual
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relative risks from each study by the inverse of the variance. A separate analysis of the three
studies using estimates of lifetime dose intake is presented in Section 8.5. As shown in Table 7-
8, the studies by Manz et al. (1991), Becker et al. (1996), Flesch-Janys et al. (1995, 1998, 1999),
Zober et al. (1990), and Ott et al. (1996) add to the information provided by the study by
Fingerhut et al. (1991) and Steenland et al. (1999), i.e., they increase the precision of the relative
risk estimates (as indicated by a narrowing of the confidence intervals). They suggest increased
risk—especially among persons with relatively high exposure and relatively long latency—for
connective and soft tissue cancers, for lung cancer, and for all cancers combined.
The elevations for these cancers also appear to be more pronounced in the subcohorts of
relatively high exposure and relatively long latency than in the total cohorts. Because they come
from comparisons between blue-collar workers and national populations, it is reasonable to
suspect that these estimates—especially for lung cancer—are influenced to some degree by
confounding from cigarette smoking. However, the limited analyses presented suggest that the
association is not a chance occurrence (Table 7-2). The counter-influences of the healthy worker
effect, exposure misclassification, and/or diagnostic error would tend to force risk estimates
downward.
The results in males are consistent with results from animal studies. In Chapter 6, it was
shown that in a lifetime TCDD bioassay male rats developed lung cancer, and in an initiation-
promotion study ovariectomized rats exposed to TCDD developed lung tumors, while intact rats
similarly exposed did not. Furthermore, the mice in the NCI study developed fibromas and
fibrosarcomas. Also, TCDD affects the immune system and has been shown to be a tumor
promotor in animal liver, lung, and skin assays. Either or both of these actions could lead to
increased total cancer. With respect to health effects in females, the study by Saracci et al.
(1991) and its update by Kogevinas et al. (1997) suggest a possible increase in breast cancer, but
the results are considered preliminary in view of the small numbers and less certain exposure.
On the other hand, although Kogevinas et al. (1993) reported an increase in cancer incidence
from all causes among women who were exposed to chlorophenoxy herbicides contaminated
with TCDD, no excess was observed for breast cancer. Still, in another study by Bertazzi et al.
(1993, 1997, 1998) to be discussed later in the section on Seveso, Italy, a nonsignificant deficit of
breast cancer and endometrial cancer was seen in women living in geographical areas
contaminated by dioxin. TCDD exposure might be expected to result in decreased breast cancer
in females, on the basis of similar observations in rats and on TCDD's action on downregulation
of the estrogen receptor in the mammary gland. However, this is species-, tissue-, and age-
specific.
In conclusion, these studies in occupationally exposed workers are highly supportive of a
causal relationship between exposure to phenoxy herbicides and the risk of cancer.
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7.6. CASE-CONTROL STUDIES IN GENERAL POPULATIONS
In this section on case-control studies, the discussion will focus chiefly on the cancer
sites, i.e., STS and NHL, that have been suggested by the earlier Swedish studies as being
associated with exposure to the phenoxy herbicides. This is a reflection of the intense interest
shown in these two cancer sites over the past decade. Few, if any, case-control studies have been
completed on other cancer sites. And of course, if other cancer sites are not studied, the risk of
cancer cannot be evaluated.
7.6.1. Sweden
Hardell, Eriksson, and colleagues conducted four studies of STSs (Hardell and
Sandstrom, 1979; Eriksson et al., 1981, 1990; Hardell and Eriksson, 1988) and one study of
malignant lymphomas (Hardell et al., 1981) among men living in different parts of Sweden. In
all studies, cases and their matched controls were considered exposed if they reported phenoxy
acid or chlorophenol exposures lasting at least 1 day and occurring at least 5 years before the
case's date of diagnosis. These studies were initiated by clinical observations in 1976 made by
Dr. Hardell and his colleagues at the Center of Oncology, University Hospital, Umea, Sweden,
and documented in his 1977 case report (Hardell, 1977).
The nature of the phenoxy acid exposures differed across the Swedish study locales. In
northern Sweden, most exposures occurred in the use of 2,4,5-T and 2,4-D in combination in
forestry applications, often by knapsack spraying (Hardell and Sandstrom, 1979; Hardell and
Eriksson, 1988; Hardell et al., 1981; Hardell, 1981a,b). Phenoxy acid exposures not involving
2,4,5-T became progressively more common, on a proportional basis, in the central and southern
regions in which agricultural herbicide uses predominated (Eriksson et al., 1981, 1990).
Although exposures not involving 2,4,5-T made up only 22% of all phenoxy acid exposures in
the first northern sarcoma study (Hardell and Sandstrom, 1979; Hardell, 198la, b), they
accounted for 27% in the study in central Sweden (Eriksson et al., 1990) and 58% in the study in
southern Sweden (Eriksson et al., 1981). Exposures defined only as phenoxy acid exposures are
therefore less useful as indicators of exposure to TCDD and related compounds in southern
Sweden than in the central and northern parts of the country. Furthermore, none of these studies
provide information on how much exposure each subject may have had.
The reports contain little information on the specific chlorophenol preparations to which
the cases and controls were exposed. Occasional statements in some of the manuscripts suggest
that most chlorophenol exposures occurred in the sawmill and pulp industries, that they primarily
involved pentachlorophenol, and that they seldom involved trichlorophenols. Thus, most of the
reported chlorophenol exposures entailed exposures to the higher-chlorinated PCDDs and PCDFs
but not to TCDD.
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Because exposure prevalences were generally low and because phenoxy acids and
chlorophenols tend to be used in different occupations, very few persons reported joint
exposures. Thus, it is efficient to control potential confounding in the analysis of data from these
studies by comparing each exposure category (phenoxy acids vs. chlorophenols) with the
category composed of all persons who reported no exposure to phenoxy acids or chlorophenols.
Based on this method of analysis, relative risk estimates from all five studies are presented in
Table 7-9, with the sarcoma studies arranged in order of publication. The measure of effect here
is the "odds ratio" that is an estimate of the RR when the cancer is relatively rare and henceforth
will be called the RR.
The results for 2,4,5-T are the only results pertinent to TCDD exposures only. Because of
the small number of cases and controls reporting 2,4,5-T use in southern Sweden, the confidence
interval for the relative risk estimate from the study in that part of the country (Eriksson et al.,
1981) is extremely wide. A separate relative risk estimate for 2,4,5-T could not be computed
from the data in the second northern sarcoma study (Hardell and Eriksson, 1988). The published
report, however, did state that all of the cases and most of the controls
exposed to phenoxy acids were exposed to preparations including 2,4,5-T (Hardell and Eriksson,
1988). The report also gave a relative risk of 3.5 for TCDD exposure, but no confidence interval
or counts of cases and controls were provided.
The studies were conducted in two phases. The lymphoma study (Hardell et al., 1981),
the first northern sarcoma study (Hardell and Sandstrom, 1979), and the southern sarcoma study
(Eriksson et al., 1981) were published between 1979 and 1981. The remaining two sarcoma
studies (Hardell and Eriksson, 1988; Eriksson et al., 1990) appeared about a decade later. The
relative risk estimates from these more recent studies are consistently lower than those from the
earlier studies (Table 7-9). Thus, systematic differences between the two sets of studies may be
an explanation for the heterogeneity of results.
The first set of studies received a considerable amount of criticism concerning the
methods by which the exposure information had been obtained (Hardell, 1981b; Cole, 1980).
The basic concern was the possibility of bias from differential exposure misclassification
between cases and controls (sometimes called "observational bias" or "interviewer and recall
bias"), with false-negative reports of exposure suspected as being more common among the
controls and false-positive reports more common among the cases. Much of the discussion
focused on telephone interviews that were conducted by research staff who were aware of the
purpose of the study and of the case or control status of the respondents. These interviews were
conducted with selected participants to confirm reported exposures and to resolve uncertainties
on postal questionnaires, which were the primary sources of exposure information. For living
cases, controls were selected from the Swedish National Population Registry. For deceased
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cases, controls were selected from the Swedish National Registry for Causes of Death. Hardell
controlled for age by stratifying his cases and controls into four age groups and calculated
Mantel-Haenszel point estimates of the odds ratios. As the criticisms of these procedures have
echoed through the years (Bond et al., 1989b; Colton, 1986), no quantitative analysis has been
made of the degree of bias that would have been required to produce the very strong associations
reported in the first three studies (Table 7-9). Of greater importance, analyses by Hardell based
solely on the questionnaire information (Hardell, 1981b) have been largely overlooked. These
analyses produced relative risk estimates very similar to those obtained when the information
from the supplemental interviews was used.
Hardell also enrolled a series of colon cancer patients (Hardell, 1981) as a sort of
"positive control" group. In contrast to STSs and malignant lymphomas, colon cancer turned out
not to be associated strongly with phenoxy acid or chlorophenol exposures. Hardell and Eriksson
made a similar finding in one of the newer studies (Hardell and Eriksson, 1988) when they
included a control group consisting of a variety of cancers along with a set of general population
controls. The cancer controls were drawn at random from the Swedish Regional Cancer
Registry.
Despite Hardell's conclusion that "the previously reported associations . . . cannot to any
essential degree be explained by observational bias in the studies" (Hardell, 1981b), he and his
colleagues imposed procedures designed to reduce the potential for such bias in their subsequent
studies (Eriksson et al., 1990; Hardell et al., 1981). When lower relative risk estimates were
produced (Table 7-9), the researchers suggested that one explanation might have been the
improved methods of exposure assessment. Again, controls were from national population
registries. A second explanation suggested by the authors is that the use of cancer referents could
have reduced recall bias.
The investigators suggested that another explanation for the reduced relative risk estimate
for phenoxy acids and STSs in the study in central Sweden (Table 7-9) "could be the decade in
which exposure occurred" (Eriksson et al., 1990), with the implication that exposures were
higher in earlier decades. They supported this suggestion with an analysis in which only those
phenoxy acid exposures occurring in the 1950s were considered. This analysis yielded a higher
relative risk estimate of 2.3 (95% CI = 1.0-5.4). Unfortunately, no basis of comparison exists
because analyses by calendar time of exposure were not conducted in any of the other studies.
However, this is reasonable given what we know about processes.
As an alternative explanation for the lack of an elevated relative risk estimate in
connection with chlorophenols in the second northern sarcoma study (Table 7-9), the authors
offered "random variation" due to a low number of exposed subjects (Hardell and Eriksson,
1988). The prevalence of chlorophenol exposures among the controls in that study (10.9%) was
several times higher than in the earlier northern sarcoma study (2.9%) (Hardell and Sandstrom,
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1979) and virtually identical to the prevalence in the lymphoma study (10.4%) (Hardell et al.,
1981). The phenoxy acid exposure prevalences were highly uniform in all three northern studies:
7.2% in the lymphoma study (Hardell et al., 1981), 6.8% in the first sarcoma study (Hardell and
Sandstrom, 1979), and 7.1% in the second sarcoma study (Hardell and Eriksson, 1988). The
chlorophenol exposure prevalence among the controls in the first northern sarcoma study
(Hardell and Sandstrom, 1979) seems to have been low. However, the overall consistency of
some excess risk is perhaps more important than actual levels of the excess.
For three of the studies by Hardell and colleagues, relative risk estimates can be
computed restricting the data to persons who had worked in agriculture and the other
occupational categories in which the exposures of interest tended predominantly to occur
(Table 7-10). For the lymphoma study (Hardell et al., 1981) and the sarcoma study in
southern Sweden (Eriksson et al., 1981), the results for exposure to phenoxy acids,
chlorophenols, or both in the restricted analyses are virtually identical to those obtained with the
data for all subjects (Table 7-9). For the sarcoma study in central Sweden (Eriksson et al.,
1990), however, the relative risk for phenoxy acids was higher (2.3) within the special
occupational categories than among all subjects.
Interestingly, Eriksson and his colleagues stated that the association with the risk of STS
seemed to strengthen with exposure to the higher-chlorinated dioxin isomers. His conclusion
was that not only may TCDD be a risk factor for STS, but also that other higher-chlorinated
dioxins may be risk factors.
The risk calculated for exposure to 2,4,5-T in the 1950s was somewhat higher at 2.94
(95% CI = 1.1-8.0). However, exposure to 2,4,5-T during the span of the study was
nonsignificant at 1.8 (95% CI = 0.9-3.9), excluding the chlorophenols. Exposure to dioxin-
containing phenoxyacetic acids or chlorophenols, excluding nondioxin-containing herbicides,
produced a significant risk estimate of 2.4 (95% CI = 1.3-4.5). Exposure to high-grade
pentachlorophenols produced a risk ratio of 3.9 (95% CI = 1.2-12.9).
These analyses are important because many of the mechanisms by which biases might
occur would be related to occupation. For instance, biases in case identification, control
selection, or nonparticipation that might be related to occupational status (e.g., by its link to
socioeconomic status) would not be expected to be as great in analyses conducted within the
occupational categories as in analyses of the overall data. The potential for confounding by
occupational exposures encountered in the same lines of work would also be reduced in the
occupationally restricted analyses. Several researchers and reviewers (Johnson, 1990; Pearce et
al., 1985; Blair et al., 1985) have noted reports of farmers being at increased risk of malignant
lymphomas and other cancers and have mentioned a wide range of potentially responsible
exposures, "including pesticides, solvents, oils and fuels, dusts, paints, welding fumes, zoonotic
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viruses, microbes, and fungi" (Blair et al., 1985). (Studies of farmers and other agricultural
workers are not included in this review because mere membership in these occupational
categories is insufficient as an indicator of exposure to such substances as 2,4,5-T or
chlorophenols.)
In the original reports, occasional attempts to assess exposure-response trends produced
mixed results. In general, the reported exposure periods were short in all of the studies. In the
first northern sarcoma study, for instance, 93% of all reported phenoxy acid exposures lasted 1
year or less, 74% lasted 6 months or less, and 33% lasted 30 days or less (Hardell and Sandstrom,
1979; Hardell, 1981b). Reported exposures in southern Sweden were even briefer, with 53%
lasting 30 days or less (Eriksson et al., 1981).
Hardell et al. recently aggregated the four STS studies in a re-analysis examining
exposures to herbicides contaminated with TCDD and other dioxins (Hardell et al., 1991).
Increasing trends in risk with duration of exposure (<1 year and >1 year) and "latency" (5-19
years and >20 years since first exposure) were numerically impressive, being based on the totals
of 434 cases and 948 controls from all the studies (Table 7-11). The problem of concomitant
exposures was not solved in these analyses, however, and an analysis of the aggregated data
obscured the pronounced heterogeneity of results among the individual studies (Table 7-10).
Regardless of the exposure definition, considerable heterogeneity exists among the
relative risk estimates from the four STS studies (Table 7-10). (Tests of homogeneity yield two-
tailed/^-values of 0.002 for phenoxy acids, chlorophenols, or both; 0.02 for phenoxy acids; 0.03
for 2,4,5-T; and 0.01 for chlorophenols.) In this circumstance, aggregation of results across
studies is not indicated and, instead, a search should be made for explanations for the
heterogeneity.
Two additional studies of malignant lymphomas and one study of STSs were conducted
by independent research teams in southern Sweden. Olsson and Brandt's (1988) study consisted
of 167 men diagnosed with NHL in the years 1978-1981 and 140 controls from the Swedish
National Population Registry. Men who reported handling phenoxy acids or chlorophenols for at
least 1 day were considered exposed. However, the main focus of the study was to evaluate the
contribution of organic solvent exposure to the risk of NHL. Persson et al. (1989) studied 54
cases of Hodgkin's disease, 106 cases of NHL, and 275 controls of both genders from the
population registry of Sweden. The cases were diagnosed in the years 1964-1986, but only those
who were still alive in 1986 were included. The authors did not ask specific questions about
phenoxy acid use. Wingren et al. (1990) studied 96 men with STSs diagnosed in the years 1975-
1982, 450 general population controls, and 200 cancer controls from the regional cancer registry.
Because the results did not differ substantially between the two control groups, only those
obtained from analyses with the general population controls are reported here. The authors had
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to resort to job-associated uses, of which one was called "unspecified chemical work, potential
exposure to phenoxy herbicides and chlorophenols," because only limited information could be
obtained about specific chemical exposures from postal questionnaires and selected supplemental
telephone interviews. Results from these three studies are summarized in
Table 7-12. Persson et al. (1989) found strong associations, Wingren et al. (1990) found an
association of intermediate strength, and Olsson and Brandt (1988) found very little association.
These studies are limited by the lack of specificity in their exposure information.
7.6.2. United States
Zahm, Cantor, and colleagues from the National Cancer Institute have reported results
from three case-control studies of exposure to 2,4,5-T or 2,4-T as well as other pesticides and
herbicides in four Great Plains States (Hoar et al., 1986; Zahm et al., 1990; Cantor et al., 1992).
The first study was conducted in Kansas (Hoar et al., 1986). It included STSs, Hodgkin's
disease, and NHLs, but detailed analyses were confined to the NHLs. The two subsequent
studies, one conducted in eastern Nebraska (Zahm et al., 1990) and the other in Iowa and
Minnesota (Cantor et al., 1992), evaluated NHLs. Cancer risks at other sites from exposure to
2,4-D calculated from these groups are the subject of later studies. These studies did not
consider chlorophenol exposures, and only those persons who ever lived or worked on
a farm were asked questions about pesticide exposures. Farmers and nonfarmers were asked
about home and garden use of pesticides. Thus, all nonfarmers were considered unexposed. As
in southern Sweden (Table 7-10), the vast majority of phenoxy acid exposures did not involve
2,4,5-T, and those that did virtually always involved 2,4-D as well. The relevance of these
studies to the focus on TCDD and related compounds in this review is therefore somewhat
limited. Only 3 out of 299 cases and 18 out of 1,005 controls were exposed to 2,4,5-T, the
herbicide known to be contaminated with dioxin.
Results for 2,4,5-T from the three studies are summarized in Table 7-13. Among all
subjects and among farmers, only the study in eastern Nebraska (Zahm et al., 1990) suggests an
increase in risk. All three studies were conducted with virtually identical methods, and no
information on herbicide application methods in any of the reports indicate any exposure
conditions peculiar to eastern Nebraska.
The third set of relative risk estimates in Table 7-13 was computed using the
investigators' procedure of including only the exposed farmers and the unexposed nonfarmers,
with the unexposed farmers excluded. Comparing exposed farmers with unexposed nonfarmers
makes it possible that risk estimates could be influenced by confounding effects that are germane
to farmers, i.e., exposure to other pesticides or herbicides in large quantities. Furthermore, if the
results of follow-up efforts are markedly different in farmers than in nonfarmers, this difference
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also might add some uncertainty to the accuracy of risk estimates. In these analyses, the relative
risk estimates from the studies in Kansas, Iowa, and Minnesota are somewhat higher and the
estimate from the eastern Nebraska study is somewhat lower than in the two more conventional
analyses.
Formal homogeneity tests across the three studies yield two-tailed ^-values of 0.4 in the
analysis of all subjects, 0.3 in the analysis restricted to farmers, and 0.6 in the third analysis.
Ordinarily, especially considering the virtually identical methods used in the three studies, these
results would be considered sufficient justification to compute summary estimates. Summary
(maximum likelihood) estimates of relative risk are virtually identical in all three groups of
subjects, with point estimates of 1.2, lower 95% confidence limits of 0.8, and upper 95%
confidence limits of 1.7 to 1.8.
Woods and colleagues (1987) conducted a study of STSs and NHLs in western
Washington State. In this study, the principal method of phenoxy acid and chlorophenol
exposure assessment was to place job titles, activities, and chemical preparations reported during
interviews into categories of potential exposure. The categories were created "in consultation
with local industrial and university representatives who had long-term experience with forestry,
wood products, and agricultural industries in the Pacific Northwest" (Woods et al., 1987). No
statement is given about the relative prevalence of 2,4-D and 2,4,5-T among the phenoxy acids
used in this region. The authors did not present any information regarding tissue levels of TCDD
in either cases or controls.
The results (Table 7-14) show no association between STSs or NHLs and estimated
potential for exposure to phenoxy acids or chlorophenols. The authors report, however, that the
relative risk of NHLs associated with more than 15 years of potential exposure to phenoxy acids
increased with time since the accumulation of that exposure. The relative estimates were 1.3
(95% CI = 0.9-2.2) for exposures more than 5 years before diagnosis, 1.7 (95% CI = 1.0-2.8) for
exposures more than 15 years before, and 2.5 (95% CI = 0.5-13.0) for exposures more than 25
years before. The authors stated that similar trends were not seen in any of the analyses of STSs
and phenoxy acids or of either cancer in connection with chlorophenol exposures. It is not
possible with the available data from this study to conduct analyses restricted to persons who
worked in forestry, agriculture, and the wood products industry, and in which the exposed
persons are those who reported specific exposures to phenoxy acids or chlorophenols.
The western Washington State study reported two unique results. One consisted of
elevated relative risks in connection with a history of chloracne based on personal interviews:
3.3 (95% CI = 0.8-14.0) for STSs and 2.1 (95% CI = 0.6-7.0) for NHLs. However, the diagnoses
were not medically confirmed, and because only 1% of all cases and controls reported chloracne
histories, the confidence intervals were extremely wide. The other intriguing result consisted of
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elevated relative risks of STSs among persons with Scandinavian surnames (12% of the cases
and controls). The estimates from this analysis were 2.8 (95% CI = 0.5-15.6) for "high"
estimated potential for phenoxy acid exposure and 7.2 (95% CI = 2.1-24.7) for "high" estimated
potential for exposure to chlorophenols. The authors noted that similarly elevated relative risks
were not found for NHLs.
In a later study of the same study population, Woods and Polissar (1989) found a
significant excess risk (OR = 1.33, 95% CI = 1.03-1.7) of NHL among farmers compared with
nonfarmers. When further examined to determine if 2,4-D or 2,4,5-T was responsible, risks from
both tended to be nonsignificantly decreased. However, frequency of use of these herbicides was
not considered by the authors.
Brown et al. (1990) conducted a population-based, case-control interview study of 578
white males with leukemia in Iowa and Minnesota matched to 1,245 controls living in those
same States. The purpose of the study was to investigate potential agricultural hazards that may
be related to a diagnosis of leukemia. The cases were derived from the Iowa Tumor Registry and
a network of hospitals and pathology laboratories in Nebraska between March 1981 and October
1983. Areas with little farm activity were excluded from the study. There
was a slight but marginally significant elevation of leukemia risk (OR = 1.2, CI = 1.0-1.5) in
farmers versus nonfarmers. But for those who mixed, handled, or applied 2,4,5-T, the risk was
slightly but nonsignificantly elevated (OR = 1.3, CI = 0.7-2.2).
Eriksson and Karlsson (1992), in a population-based case-control study of 275 myeloma
cases matched with 275 controls in 4 counties of northern Sweden, found a significant excess of
myeloma (OR = 2.22, CI = 1.15-4.66) in persons who worked with phenoxy herbicides.
Significant associations were also found with "farming," DDT, and certain domestic animals.
Specific exposures to dioxins or dibenzofurans were not determined.
In another case-control study of Iowa agricultural influences on multiple myeloma,
Brown et al. (1993), using similar methodology as in her earlier study, matched 173 white males
with multiple myeloma to 650 controls from Iowa. Although a slight nonsignificant elevated risk
(OR = 1.2, CI = 0.8-1.7) was seen in farmers, the risk of multiple myeloma from exposure to
2,4,5-T was found to be nonsignificant (OR = 0.9, CI = 0.4-2.1). The same was true for
numerous other herbicides, pesticides, and insecticides.
The authors concluded that there was little evidence to suggest any association of
multiple myeloma with farming or pesticides. Neither of these studies on leukemia or multiple
myeloma has shown that exposure to dioxin occurred. This is only presumed; no actual
measurements were taken. Both of these studies could be considered hypothesis-generating
studies because they involved multiple exposures to many different chemicals used in farming.
The major problem with U.S. case-control studies is that specific exposure to TCDD and
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related compounds is not identified or quantified, although information on the use of 2,4,5-T and
2,4-D is available in some studies. In some, only potential exposure to phenoxy herbicides is the
exposure surrogate. This limits the usefulness of these studies.
7.6.3. New Zealand
Smith, Pearce, and colleagues conducted two studies of STSs (Smith et al., 1982a, 1983,
1984; Smith and Pearce, 1986) and one study of NHLs (Pearce et al., 1986, 1987) among men in
New Zealand. In these studies, persons were first asked whether or not they "had worked in
particular occupations in which there was potential for exposure to phenoxyherbicides or
chlorophenols" (Smith et al., 1984). If the response was affirmative, "a series of subsidiary
questions were asked to clarify the work done and the actual potential for exposure, firstly in
general terms, and then in specific terms, seeking the identity of the chemicals used" (Pearce et
al., 1986). The authors indicated that 2,4,5-T was widely used as a phenoxy acid herbicide in
New Zealand over the years pertinent to these studies (i.e., prior to the early 1980s) (Smith et al.,
1984). Thus, in these studies, the phenoxy acid exposure designation was considered a suitable
indicator of exposure to 2,4,5-T and, thus, to TCDD. Typical uses of 2,4,5-T were in the
spraying of gorse, blackberry, pasture, cereal, and peas. No actual measurements of TCDD were
made in these studies.
In the analyses of phenoxy acids, the authors distinguished between "potential" and
"probable or definite" exposure. The latter category was created by deleting persons with only
"possible" exposures from those with "potential" exposures. It is not clear whether the "probable
or definite" designation included inferences from job titles, activities, and the like, or whether it
was based solely on affirmative responses to specific questions about phenoxy acid exposures.
For chlorophenols, only the "potential" designation was employed.
In these studies, the controls were patients diagnosed with other cancers. Unlike a control
group selected from the entire study population, a cancer control group offers less certainty about
the degree to which its exposure distribution represents that of the study population, but greater
certainty that differential exposure misclassification through elimination of interviewer bias and
recall bias is negligible. However, inclusion of cancer sites in the controls that may be associated
with the exposure could potentially bias the risk estimate toward the null. In these particular
studies, the cancer controls had an additional advantage in minimizing any bias that might have
resulted from the inability of the researchers to include patients diagnosed at private hospitals.
Private hospitals in New Zealand have only recently been contributing to the National Cancer
Registry. In an interim report of the NHL study, a second control group was drawn from the
New Zealand electoral roll. The authors concluded that this control group "gave very similar
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findings to those obtained with the main control group of other cancer patients" (Pearce et al.,
1986).
Another unique feature of the New Zealand STS studies is that, like the mortality follow-
up studies of chemical manufacturing and processing workers previously reviewed, they included
only those cases classified to the International Classification of Diseases (World Health
Organization, 1977) category 171, malignant neoplasms of the soft and connective tissues. This
category, which does not include STSs occurring in parenchymatous organs such as the stomach
or uterus, accounted for about 60% of the STS cases in the studies in Sweden (Fingerhut et al.,
1984). There is no indication from the Swedish studies, however, that the associations with
phenoxy acids or chlorophenols differed between STSs that would be classified in category 171
and those that would be classified in the categories for the involved organs (Hardell and
Sandstrom, 1979; Eriksson et al., 1981; Hardell and Eriksson, 1988; Eriksson et al., 1990;
Hardell, 1981a,b).
In the New Zealand study, investigators divided their STS research into two studies with
very similar, but not identical, methods. The first study (Smith et al., 1982a, 1983, 1984)
consisted of patients and controls with cancer registrations in the years 1976-1980. The second
study (Smith and Pearce, 1986) extended case finding through 1982 and was the subject of an
extremely abbreviated report. The controls in the second study consisted of 315 of the 338
cancer controls from the NHL study (Pearce et al., 1985) whose cancer registrations were during
the period 1977-1981. (The results for the additional 23 controls, who were interviewed near the
end of the NHL study, evidently were unavailable at the time the analyses for the second STS
study were conducted.)
The first sarcoma study (Smith et al., 1982a, 1983, 1984) reported very similar results for
phenoxy acids and chlorophenols when all subjects were included in the analyses, with relative
risk estimates of 1.3 for any "potential" exposure and 1.6 for exposures ("definite or probable"
for phenoxy acids, "potential" for chlorophenols) lasting more than 1 day and occurring more
than 5 years prior to diagnosis (Table 7-14). For phenoxy acid exposures classified by the latter
definition, sufficient data were presented to permit an analysis restricted to farmers. Thirty of the
82 cases, 13 of the 17 exposed cases, 44 of the controls, and 9 of the 13 exposed controls were
farmers. Thus, the estimated relative risk is 3.0 (95% CI = 1.1-8.3) among farmers. Controlling
for farming by ("indirect") standardization yields an estimated relative risk of 1.9 (95% CI = 0.8-
4.5). Thus, as in some studies previously reviewed, accounting for the farmer/nonfarmer
distinction has a material impact on the results from this study.
Very few details were presented for the second sarcoma study (Smith and Pearce, 1986).
In comparison with a relative risk of 1.6 in the first study, the second study reported a relative
risk of 0.8 for the principal measure of phenoxy acid exposure (Table 7-14, homogeneity-test/?-
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value = 0.2 contrasting the two studies). The exposure prevalences in the two control groups
were virtually identical (14.1% in the first study and 14.6% in the second), but the prevalences in
the two case groups differed (exposure odds ratio = 2.0, 95% CI = 0.7-5.4). Because of this
difference, and because a relative risk estimate restricted to farmers cannot be computed with the
data available from the second study, aggregation of the results would not be warranted.
The NHL study (Pearce et al., 1986, 1987) reported little or no association with phenoxy
acids and a somewhat stronger association with chlorophenols (Table 7-15). The latter
association did not increase when the more restrictive measure of exposure was used. The
various activities involving exposure to chlorophenols include the treatment offence posts as
well as treating pelts in meat works tanneries. Data that would permit an analysis restricted to
farmers were not reported.
The authors continue to maintain that herbicide spraying is a full-time occupation in New
Zealand and that none of the STS or malignant lymphoma cases had been commercial sprayers.
Smith et al. (1984) estimated the prevalence of current and former commercial sprayers at
approximately 1,500, which would be 0.17% of the male population of New Zealand in the early
1970s (Waterhouse et al., 1982). On the null hypothesis, therefore, only about 0.1 commercial
sprayers would be expected among the cases in each of the two STS studies, and about 0.3
commercial sprayers would be expected among the NHL cases. Thus, STS risk could have been
increased manyfold and NHL risk could have been increased about threefold before even one
commercial sprayer would be expected in any of the case groups. As a consequence, the absence
of commercial sprayers in any of the case groups is not strong evidence against an effect.
In a letter to the editor, Pearce (1989) produced tabular data from his earlier case-control
study by duration of use and by frequency of use. Although he maintained that his data exhibited
little evidence of an association with NHL, a nonsignificant increase in the risk was seen in the
category 10-19 days of use per year (OR = 2.2, 95% CI = 0.4-12.6) before dropping back to 1.1
in the category greater than 19 years.
In a study of nine selected applicators in New Zealand who had sprayed herbicides (and
hence 2,4,5-T) for a minimum of 180 months, Smith et al. (1992) found a high correlation
between tissue levels of TCDD and months sprayed. This is analogous to Fingerhut's finding
that tissue levels of TCDD correlate well with duration of employment in the herbicide
manufacturing industry. TCDD serum levels ranged from 131.0 ppt in a sprayer with 31 years of
spraying to a low of 3.0 ppt in a sprayer who sprayed for only 7 years. The average was 53 ppt
for the nine sprayers who sprayed an average of 16 years. Actually, the mean average TCDD
serum level in Fingerhut's lowest exposure group who worked less than 1 year was higher (69
ppt) than the mean average of sprayers in the Smith et al. study. Smith's conclusions were based
on his analysis that brief exposures to TCDD probably do not contribute to the increased cancer
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risks seen in studies in other countries. Although it is an interesting inference, this conclusion
may be somewhat overstated without some information regarding what the tissue levels of
TCDD were in the individual cases and controls of those other studies, information that only
recently is becoming available and not for all studies.
Unfortunately, the finding suggested by several occupational accidents, such as in Seveso,
Italy, and Nitro, West Virginia, that one-time large doses of exposure to TCDD could, in fact,
lead to residual high tissue levels of serum TCDD years later, could not be tested in the studies of
Swedes. Hardell and colleagues have provided little information regarding tissue levels of
TCDD, past or present, in individuals who were participants in their studies. One study by
Nygren et al. (1986) is cited frequently as evidence that Swedish subjects who were involved in
spraying phenoxy herbicides have low levels of TCDD in adipose tissue samples. Thirty-one
patients from the Regional Hospital in Umea were each relieved of a sample of adipose tissue for
analysis of dioxin content. After "careful interviewing," it was determined that 13 of these
patients had "sprayed" herbicide at some time during their past. Adipose tissue measurements
indicated a mean of 2 ppt of TCDD. The remaining 18 nonsprayers revealed a mean of 3 ppt.
However, in correspondence with C. Rappe (1987) regarding this study, it was
determined that in the only three cases that were STSs, adipose tissue measurements of TCDD
are reported to be 2, 2, and 9 ppt. The one STS with the highest TCDD level (9 ppt) of any of the
31 subjects is stated by the authors to have had only 10 days of "knapsack spraying" some 25 to
29 years earlier. What is striking about these 13 "cases" is that the total levels of all chlorinated
dioxins are considerably greater, i.e., from 168 ppt to as much as 936 ppt per patient, and that
TCDD levels are a mere fraction of the total. There is no TEQ conversion here. The vast
majority consist of the higher-chlorinated PCDDs. It is not clear that spraying herbicides is or
ever was a major occupational endeavor of this group of 13 or that any of these patients was
exposed to TCDD in large quantities. Based on recent correspondence with Hardell (1993), none
of the patients reported by Nygren were members of any of his case-control studies. Nor does
Nygren claim in his study that any of the cases he considered are from Hardell or Eriksson's
studies. The total PCDD levels in the three STSs ranged from 674 ppt to 792 ppt (Rappe, 1987).
These were nearly all highly chlorinated. Although the cases with tissue samples came from
Hardell's clinic, they did not come from any of his case-control studies. In fact, since these case-
control studies were done in the late 1970s and early 1980s, most, if not all, subjects were
deceased by the time that the technology became available to measure serum TCDD levels.
This analysis provides little information on tissue levels in Swedish applicators with STS.
Furthermore, there may be some differences in applicator practices between New Zealand and
other countries such as Sweden. Professional applicators in New Zealand are registered with the
New Zealand Agricultural Chemicals Board (Smith et al., 1982b, 1992). And although it might
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appear that they could be expected to receive a great deal of exposure to TCDD, more than half
of the applicators show serum TCDD levels below 50 ppt (Smith et al., 1992). Considering that
they were spraying 2,4,5-T for 7 to 31 years until just recently, it seems remarkable that the
distribution of serum TCDD levels is as low as it is. The authors report that professional
pesticide applicators in New Zealand are "perhaps the group most heavily exposed to agricultural
use of 2,4,5-T in the world." In other studies, shorter exposures to large quantities of TCDD-
containing herbicides have occurred to a few personnel such as in the Ranch Hands cohort;
Seveso, Italy; and the Nitro, West Virginia, accident. In Fingerhut's study, employees with less
than 1 year of exposure to phenoxyacetic acids had mean serum levels averaging 69 ppt TCDD.
7.6.4. Italy
Vineis and colleagues conducted a case-control study of STSs in three provinces in
northern Italy (Vineis et al., 1986). Phenoxy acid exposure classifications were based on job
information provided on interviews or questionnaires. The assessments were made by "two
experts with experience in chemical aspects of agriculture." Cases and controls were classified
into three categories: "certainly unexposed," "exposure could not be ruled out" (abbreviated
below as "possibly exposed"), and "certainly exposed." The authors implied that phenoxy acid
herbicides of all types (2,4-D, 2,4,5-T, and MCPA) were used in the area during the periods of
interest, but were able to document only the use of 2,4-D and MCPA. Thus, this study may have
limited relevance to TCDD exposures.
The study indicated an inverse association between possible or certain phenoxy acid
exposure and STS risk among men and a positive association among women (Table 7-16). This
latter association was restricted to women who were alive at the time the exposure information
was collected. (In the other studies in this review, in which the results were stratified by vital
status at the time of the interview, no appreciable differences were found.) As shown in Table 7-
16, when the analysis is restricted to persons who had ever worked in agriculture ("farmers"), the
relative risk among all women is reduced from 1.9 to 1.1. Sufficient data are not available for an
analysis that is both restricted to farming women and stratified by vital status.
The authors offered overmatching by location of residence as an explanation for the lack
of association among deceased subjects. It would be extraordinary for overmatching or
nondifferential misclassification (the latter being the usual explanation when reduced relative
risks are obtained with exposure information from proxy respondents) to be so strong as to bias a
relative risk of 2.4 to 0.8.
Rice is the principal agricultural crop in the study area, and rice weeding was historically
a predominantly female occupation. (Of 29 rice weeders in the study, all but two were women.)
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Rice weeding during the period 1950-1955 was manual and contact with the phenoxy herbicides
was mainly through the skin.
Among all women in this study, rice weeding during the early 1950s is associated with a
relative risk of 2.3 (95% CI = 0.7-7.7). When the analysis is restricted to women who were
farmers, however, the relative risk drops to 1.4 (95% CI = 0.3-6.5).
7.6.5. Finland
Lampi et al. (1992) completed a case-control study of colon cancer, bladder cancer, soft
tissues, lymphoma, and leukemia in the municipality of Karkola, Finland, where residents
consumed fish from a local lake that was contaminated with chlorophenols. The main employer
in Jarvela, the industrial center of Karkola, is a sawmill that has been operating since before the
1940s. Large amounts of chlorophenol have been found in the ground water between the sawmill
and the intake plant for the drinking water. A person was considered exposed if he answered
positively to any one of a series of questions about personal exposure asked on a questionnaire.
These include sawmill work, farming, source and duration of drinking, and quantity offish
consumed. Four controls per case were randomly selected from the national population registry
of the Tiirismaa health-care district which includes Karkola. However, exclusion of controls
who failed to reply as well as nonparticipant cases resulted in a final 3 to 1 ratio of matching.
One hundred and twenty-three cases were matched by age, sex, and residing in the Tiirismaa
health-care district at the time the cancer was diagnosed, with up to 494 controls depending on
whether a reply was received for each of the questions asked.
No increased risk of colon or bladder cancer was associated with any of the exposure
categories listed. However, an elevated risk of NHL from exposure to consumption offish
and/or drinking water was found (RR = 6.9, 95% CI = 1.1-70.0). The authors concluded that this
could be attributable to chlorophenol exposure through the consumption offish or the drinking
water. The authors also noted a nonsignificant slightly increased risk of STS (RR = 4.0, CI =
0.3-55) in persons exposed to "drinking water" and/or "residence" and/or "fungicide"
(tetrachlorophenol, the main ingredient of the fungicide, has been used by the sawmill since the
1940s to inhibit the growth of bluestain fungus in timber) and/or "fish" based upon only three
cases versus two controls.
Levels of chlorophenol found in the drinking water of Jarvela ranged from 70 to 140
l-ig/L. This is reported by the authors to be near the maximum allowable levels. Fish from
nearby Lake Valkjarvi, which are caught for local consumption, contain high levels of
chlorophenols as well, i.e., 175 pg/kg in perch and 925 |_ig/kg in zander per net weight.
Although PCDDs and PCDFs have been found in technical and commercial products, this
population was not likely to be exposed to these substances in great quantities if at all, according
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to the authors. It was reported by the authors that none were found in contaminated drinking
water. No actual personal measurements of any of the chlorophenols or the phenoxy herbicides
were taken during the conduct of this study. Additionally, the number of cases per cancer site is
small, thus providing little power to detect significantly elevated risks where they are not now
seen. Efforts should be undertaken to determine if fish are contaminated by PCDDs or PCDFs.
7.6.6. Summary
From the standpoint of exposures to TCDD, the most important results from general-
population case-control studies come from those studies conducted in northern Sweden (Hardell
and Sandstrom, 1979; Hardell and Eriksson, 1988; Hardell et al., 1981), central Sweden
(Eriksson et al., 1990), and New Zealand (Smith et al., 1982a, 1983, 1984; Smith and Pearce,
1986; Pearce et al., 1986, 1987). These studies were conducted in areas in which high
proportions of phenoxy acid exposures involved 2,4,5-T. The exposure-assessment methods in
these studies included the posing of specific questions about particular chemicals and herbicide
preparations. Moreover, for all but the NHL study in New Zealand (Pearce et al., 1986, 1987),
available data permit analyses restricted to farmers and the other occupational categories within
which the relevant exposures predominantly occur.
For STSs, the Swedish studies are perhaps best represented by a relative risk of 2.3 (95%
CI = 1.0-5.4) for phenoxy acids among workers in agriculture, horticulture, and forestry in the
study in central Sweden (Table 7-10) (Eriksson et al., 1990). This is justified by the following
factors: the proportion of exposures to 2,4,5-T was high in this study, the methods of assessment
were better, and there were analyses within relevant occupational categories. The authors in their
current studies have redesigned their methods to accommodate readers' criticisms of their earlier
studies and have made an effort to present risk estimates that are adjusted to reflect these
criticisms.
The relative risk estimate of 3.0 (95% CI = 1.1-8.3) for phenoxy acid exposure among
farmers in the first STS study in New Zealand (Smith et al., 1982a, 1983, 1984) seems to indicate
that farming may be a confounder in this study. Indirect standardization for farming produces a
relative risk of 1.9 (95% CI = 0.8-4.5).
For malignant lymphomas, the case-control studies provide less evidence of a positive
association. The relative risk estimates from the study by Hardell and colleagues (Hardell et al.,
1981) were very high, even among persons employed in the special occupational groups, but this
study was conducted before the researchers had improved their data collection methods. The
studies in New Zealand (Pearce et al., 1986, 1987), Kansas (Hoar et al., 1986), eastern Nebraska
(Zahm et al., 1990), and Iowa and Minnesota (Cantor et al., 1992) are more consistent with a
much smaller increase in risk, or no increase at all, from exposures to TCDD.
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The remaining case-control studies (Eriksson et al., 1981; Olsson and Brandt, 1988;
Persson et al., 1989; Wingren et al., 1990; Woods et al., 1987) offer mixed results, some
suggesting increases in the risk of STS or malignant lymphoma and others suggesting little or no
increase. The informativeness of each of these studies, however, is limited by one or more of the
following important drawbacks: study areas in which most phenoxy acid exposures did not
involve 2,4,5-T, a lack of information on specific chemicals and preparations to which cases and
controls were exposed, and an inability with available data to conduct analyses restricted to
farmers and the other occupational groups in which the exposures of interest primarily occur.
Apparently, farming as an occupation appears to affect risk estimates based on the findings from
several studies where occupation is considered and should be considered as a potential
confounder.
Vineis et al. (1992) presents the hypothesis that the excess risk of NHL seen among
farmers exposed to phenoxy herbicides may be caused by viruses. Such viruses induce
proliferation and immortalization of B-cells, followed by T-cell impairment leading to cell-
mediated immunity. Increased risks of NHL have been observed in immunologically deficient
individuals. Hypothetically, the same effect could be the result of exposure to TCDD, as
suggested in some mouse studies (see Chapter 4, Immunotoxicity).
Lampi et al. (1992) conclude that the role of contamination of drinking water and fish due
to chlorophenol from sawmills must be considered as a possible cause of the significantly
elevated risk of NHL seen in Finland.
7.7. STUDIES OF PULP AND PAPER MILL WORKERS
Table 7-17 summarizes results for cancers of interest from three follow-up studies of pulp
and paper mill workers. A fourth study, not summarized in the table, produced inconsistent
results based upon choice of analytical method used. Both potentially produce biases that are
contradictory and will redue accuracy in the estimates. These studies are important because of
the potential for exposure to PCDDs and PCDFs in this line of work. The study by Robinson et
al. (1986) was of 3,572 persons who had worked for at least 1 year between 1945 and 1955 at any
of five mills in the States of California, Oregon, or Washington. The study by Jappinen et al.
(1987) was of 3,454 workers in the Finnish pulp and paper industry who had worked
continuously for at least 1 year between 1945 and 1961. The study by Henneberger et al. (1989)
was of 883 persons who had worked for at least 1 year at a mill in New Hampshire. Jappinen et
al. (1987) studied cancer incidence. The other two studies were mortality studies.
Individually and in the aggregate, these studies give little indication of appreciable
increases in the risk of NHLs, lung cancer, or stomach cancer among pulp and paper mill
workers. Overall, the rate of all cancers combined was somewhat lower than expected. None of
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the studies examined connective and soft tissue cancers specifically. Analyses of specific
cancers by work location, duration of employment, and latency were only occasionally
conducted in these studies. No consistent results were found that would alter substantially the
impression given by the results for the total cohorts. These studies do not specifically mention
exposure to the PCDDs/PCDFs and are not designed to evaluate the risk of cancer from
PCDDs/PCDFs.
Other studies of cancer among paper and pulp mill workers have been restricted to
information on deaths, using either proportional mortality ratios (Milham, 1976; Milham and
Demers, 1984; Schwartz, 1988; Solet et al., 1989) or mortality odds ratios (Wingren et al., 1991)
as measures of relative risk. These studies are not highly informative because they usually rely
on minimal information in death records and because they are subject to an upward bias due to
the "healthy worker effect" (i.e., a tendency for employed groups to have favorable total
mortality experience and causes of death other than cancers, when compared with the general
population). The degree of bias in such studies varies, but it can be appreciable. For instance, in
the cohort studied by Robinson et al. (1986), 915 deaths from all causes were observed and
1,150.3 were expected. If the relative risk estimates for stomach cancer and NFILs had been
computed as proportional mortality ratios or mortality odds ratios, they would have been 1.5 and
1.7, respectively, instead of the values of 1.2 and 1.3 that were obtained from the authors' more
valid comparisons of mortality rates (Table 7-17).
A more recent mortality and cancer incidence cohort study of 26,000 British Columbia
(B.C.) sawmill workers by Hertzman et al. (1997) offered mixed results regarding the cancer-
causing potential of exposure to chlorophenates that are contaminated with hexa-, hepta-, and
octa-chlorinated dioxin isomers but not TCDD. An analysis was accomplished in two parts.
First, B.C. vital statistics were used to generate expected deaths while B.C. incidence rates (from
the B.C. Cancer Agency) were used to generate expected cases. However, person-years were
generated in two ways. The first was to truncate the accumulation of person-years at the time the
subject was lost to follow-up; in the second analysis person-years were accumulated until the end
of the study period, which was 1990 even if the subject could not be traced to the end of 1992.
This treatment of the data served to produce conflicting results. In the former method multiple
significantly elevated site-specific cancer risks as well as significant total cancer appeared,
whereas in the latter analysis site-specific standard mortality ratios (SMRs) and total cancer were
all nonsignificant and close to what would be expected if there were no risk from exposure.
Presumably, this discrepancy in the findings is due to an inability of the authors to identify vital
status on 3,791 sawmill workers (14.3% of the total). Furthermore, those members of the cohort
who were diagnosed with cancer outside of British Columbia could not be included in the
analysis because these cases would not be known to the B.C. Cancer Agency. The authors
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assumed this loss would be around 4.4%, on the basis of deaths that were recorded outside of
British Columbia. A combination of these two factors could explain the disparity. Perhaps if the
remaining 14.3% could be followed until vital status was determined, and the underlying cause
of death could be identified on the 4.4%, more accurate estimates of the true site-specific cancer
risks would be known and, consequently, the studies's two sets of results would converge.
Another problem with this study is that the observed deaths do not appear to add up from
one table to the next where they should and no explanation is provided. The authors conclude
that their results are "consistent with the borderline positive associations seen in other recently
reported studies of chlorophenate-exposed workforces." This conclusion may be somewhat
overstated given the potential problems with this study, which appears to have ended prematurely
prior to the completion of vital status followup.
7.8. OTHER STUDIES
Studies of pesticide applicators are not informative because they contain little information
on specific compounds and preparations to which individual persons were exposed, and so there
is no evidence of exposure to TCDD. Studies with no information of this type include studies of
licensed pesticide applicators by Wang and MacMahon (1979), Barthel (1981), Blair et al.
(1983), Wiklund et al. (1987), Corrao et al. (1989), and a study of gardeners by Hansen et al.
(1992). These studies contribute little or nothing to the discussion of TCDD or compounds like
TCDD.
Axelson and Sundell assembled a cohort of 348 Swedish railroad workers who had
applied amitrol, 2,4-D, and 2,4,5-T (Axelson and Sundell, 1974). In the most recent report
(Axelson et al., 1980), 17 deaths from tumors were observed (11.85 expected, relative risk 1.43,
p = .09). The relative risk estimate for lung cancer was 1.4 (three observed deaths,/* = .37) and
the estimate for stomach cancer was 2.2 (three observed deaths, p = .15). Again, as in most
studies, no actual measurements of TCDD are available from this paper. Only potential exposure
to the herbicides 2,4-D and 2,4,5-T are mentioned without any effort to quantify the exposure.
Riihimaki et al. (1982, 1983) followed a cohort of 1,971 Finnish men who had applied
2,4-D and 2,4,5-T. With allowance for a 10-year latency period, 20 cancer deaths were observed
(24.3 expected, RR= 0.8, 95% CI = 0.5-1.2). The relative risk for lung cancer was 1.1 (12
deaths observed, 95% CI = 0.6-1.8). The author points out that because of limitations in the
study materials, only powerful carcinogenic effects are likely to be seen.
7.8.1. Vietnam Veterans
Distributions of TCDD levels in serum and adipose tissue are typically indistinguishable
between Vietnam veterans and comparison populations unless the Vietnam veterans group has
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been carefully defined on the basis of military records to have engaged in activities known to
have involved herbicide exposure (Centers for Disease Control Veterans Health Studies, 1988;
Devine et al., 1990; Gross et al., 1984; Kahn et al., 1988; Kang et al., 1991; Pirkle et al., 1989;
Schecter et al., 1989). Thus, the mere designation "Vietnam veteran" is insufficient as an
indicator of exposure to 2,4,5-T or TCDD exposure. This conclusion is also supported by
Stellman and Stellman's (1986) review of military records for the purpose of developing an
Agent Orange exposure index. Stellman and Stellman drew the further conclusion that "it is
impossible to give any credence to any health effects study in which assignment of herbicide
exposure levels to individual veterans is based solely on self-reports" (Stellman and Stellman,
1986). It is also insufficient to base an exposure index among Vietnam veterans on such crude
information as military branch (Army, Marine, etc.), corps, or region of duty within Vietnam.
Thus, a large number of studies of cancer experience among Vietnam veterans are uninformative
from the standpoint of hypothetical effects of TCDD. These include studies by Breslin et al.
(1988), the Centers for Disease Control (1987), Dalager et al. (1991, 1995a,b), Fett et al. (1987),
Greenwald et al. (1984), Kang et al. (1986), Kogan and Clapp (1988), Lawrence et al. (1985),
O'Brien et al. (1991), and Watanabe et al. (1995).
One Vietnam veteran study by Kang et al. (1987) that further examined mortality in a
subgroup of veterans who had ventured into areas at the time when Agent Orange was being
sprayed reported a nonsignificant odds ratio of 8.64 (CI = 0.77-111.84) for STS. The number of
cases was not provided. Presumably, these would be ground troops with a high likelihood of
exposure.
The only study of cancer among Vietnam veterans at present with information on
activities involving TCDD exposure is a small mortality study by Michalek et al. (1990) of 1,261
Air Force veterans of Operation Ranch Hand. These persons were responsible for the aerial
herbicide spraying missions in Vietnam. The researchers compared the Ranch Hand group with
a group of 19,101 other Air Force veterans who were mainly involved in cargo missions in
Southeast Asia and who did not have herbicide exposure. A total of 12 cancer deaths were
observed in the Ranch Hand cohort (17.0 expected, RR = 0.7, 95% CI = 0.3-1.1) by December
31, 1987, the cutoff date for followup. Interestingly, one of these was a STS. Out of 229 deaths
reported in the comparison population, one STS death was also found. Calculated death rates of
all specific cancers of interest in this review were equal to or less than the rates in the comparison
group, with the exception of bone, connective tissue, skin, breast, and genitourinary organs.
These numbers are too small for any meaningful comparisons.
Serum TCDD measurements were taken on 888 Ranch Hands (total). A few of the Ranch
Hands, who were enlisted ground crew, exhibited serum levels above 200 ppt of TCDD, but the
median serum level was 12.4 ppt (range 0 to 618 ppt) for the entire group. The median serum
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level in the controls averaged 4.2 ppt (Wolfe et al., 1990). The subgroups of Ranch Hands that
appear to have had the greatest exposure are nonflying enlisted personnel. The median serum
TCDD levels in 407 of them was 23.6 ppt. The next highest levels were in flying enlisted
personnel with a median of 17.2 ppt. The remaining Ranch Hands exhibited levels that were not
much elevated (under 10 ppt) from background (flying officers, both pilots and navigators, as
well as nonflying officers). Based upon these findings, it is likely that the majority of Ranch
Hands received little exposure to TCDD. Not a great deal of cancer mortality can be expected in
this relatively youthful group, which had not reached the 20-year latency milestone.
In a followup report through 1992 published in 1994 on the Ranch Hands (Wolfe et al.
1994), the authors report 111 deaths in the Ranch Hands versus 111.47 expected in a cohort of
1,261. Twenty-six deaths from cancer were reported versus 30.68 expected. Again no increased
risk of any site-specific cancer was found over expected. The comparison population from which
expected deaths were generated is very likely the same 19,080 Air Force veterans discussed
above who flew or serviced C-130 cargo aircraft in Southeast Asia during the same period that
the Ranch Hands were active in Vietnam, although it is not specifically stated in that report. It
would be of greater interest and more useful to continue follow-up of the enlisted Ranch Hands
only. They appear to be the subgroup with the greatest exposure, although their TCDD levels
cannot be considered high. Further follow-up of officers probably will not reveal useful
information that could be attributed to exposure to TCDD.
A more appropriate group in which to observe effects are members of the South
Vietnamese Army who did the mainstay of the spraying around the perimeters of the military
bases in Vietnam.
In another update of the Ranch Hands study, Ketchum et al. (1996) reported on mortality
through December 31, 1993. The number of reported cancer deaths increased to 30 versus 33.22
expected (SMR = 0.90, 95% CI = 0.63-1.27). Presumably, the comparison population is the
same as the one discussed above, i.e., 19,101 other Air Force veterans, but not stated in the report
by Ketchum. No unusual excess cancer risks occurred to any of the three main subgroups of
Ranch Hands discussed above. But then, it is still a relatively youthful cohort and few person-
years have aggregated beyond the 20th year since exposure, too few to do an adequate latency
analysis. It is reported by the authors that some 991 Ranch Hands have quantifiable dioxin levels
(presumably blood levels, although not stated). The authors maintain that survival time was not
significantly associated with dioxin levels. There was apparently no effort to assess levels of
dioxin in each of the three main subgroups, but only between those who were deceased and alive.
The levels overall were somewhat higher in the deceased (X = 35.0 ppt) versus the living (X
= 26.7 ppt). The number of deceased Ranch Hands with dioxin levels was 23, precluding
assessment of dioxin level averages in deceased veterans by occupational category. Overall,
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there is little in this report to support the hypothesis that exposure to dioxin is or is not causally
related to an increased risk of cancer. But of course, this study is subject to the same limitations.
The levels of dioxin found are not much greater than background and the mortality is still rather
low. Furthermore, the analysis should be confined to that group with the highest levels of serum
dioxin, i.e., the enlisted nonflying Ranch Hands.
In a slightly different analysis of the same cohort of Ranch Hands that was followed for
the same period of time until December 31, 1993, Michalek and colleagues (1998) assessed
mortality in the different occupational subgroups—pilots and navigators, administrative officers,
enlisted flight engineers, and finally, enlisted ground personnel—observed for less than 20 years
since service and after 20 years since service in Vietnam for certain site-specific cancers. The
authors found no significant increase in the risk of death from cancer, all sites combined (SMR =
1.1), for persons who survived more than 20 years since military service, while they reported a
nonsignificant increase in the number of deaths due to cancers of the bronchus and lung (SMR =
1.3) in Ranch Hands in that group. The authors reported that this latter finding was consistent
with an increase in respiratory cancer mortality seen in Fingerhut et al. (1991) in workers
observed for 20 years. Michalek recommends that followup should continue to determine
whether these slight increases persist. A nonsignificant increase in deaths due to digestive
diseases (SMR = 1.7, CI = 0.9-3.2) was reported but not evaluated. A single STS occurred to a
Ranch Hand officer (OBS = 1, Exp = 0.3). Again, this study is subject to the same limitations as
in earlier renditions. It is based on small numbers and very few deaths from cancer. Dioxin
levels were summarized but not analyzed.
Ketchum et al. (1999) simulated what the risks of cancer would be in Ranch Hands on the
basis of expected dioxin exposures extrapolated from current dioxin measurements back to the
time of exposure as if such hypothetical exposures were real at the time. The authors assumed a
first order model for dioxin elimination from the body and a half-life of 8.7 years in constructing
synthetic exposure levels at the time when military service ended in Vietnam. The comparison
population again was the same group of Air Force veterans that served in Southeast Asia at the
same time as the Ranch Hands but did not serve in Vietnam. The Ranch Hands were subdivided
into three groups designated as having "background," "low," or "high" exposure. The
"background" Ranch Hands consisted of only those personnel whose current measurements of
dioxin level never exceeded 10 ppt. The "low" and "high" categories consisted of personnel
whose current levels were over 10 ppt. After extrapolation to the supposed "initial" levels at the
time they left the service, if the "initial" level exceeded 94 ppt then the Ranch Hands were
considered to have "high" exposure. If the "initial" level was under 94 ppt then the Ranch Hands
were considered to have had only "low" exposure.
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The risk of cancer at sites other than skin in Ranch Hands with less than 20 years of
observation from end of service was significantly elevated only in the "low" exposure group
(OR=3.4, CI = 1.5-8.0). Although the "high" exposure category also was elevated, it was not
significant (OR=2.7, CI = 0.9-8.0). The risks calculated in the more-than-20-years-since-service
group were even less remarkable. Based upon 39 individuals diagnosed with cancer who fell
into this category, the risks were all under 1.0 and nonsignificant. The findings seem to resemble
an inverse relationship of cancer with that of latency and dose. The authors believed that their
results were inconsistent with that of the Fingerhut et al. (1991) study, and felt that the increased
risks seen within 20 years from service may not have been due to dioxin exposure.
The most important question concerns the possibility that misclassification of exposure
may have contributed to the supposed inverse relationship of exposure with risk. The
assumption that Ranch Hands with a current dioxin measurement under 10 ppt should be placed
in a "background" category may be inaccurate. It seems possible that with a lapse of as much as
25 years since service in Vietnam, the actual exposure in these individuals could have been
anywhere from 40 ppt to more than 80 ppt. Furthermore, without checking to determine if
exposure to dioxins had not occurred following service in Vietnam, the authors cannot be certain
that the high current levels measured were not due to post-Vietnam exposure to dioxin, in which
case the "initial" exposure determinations may be exaggerated.
Furthermore, considering the tabular data, were person-years allocated back to the higher
extrapolated exposure and latency categories when the expected cases were calculated for each
category? This could explain the apparent inverse relationship of risk to exposure.
Lastly, the authors stated that their results differed from those of Fingerhut and her
colleagues discussed earlier. Actually, the "current" measurements that were derived in the
Fingerhut study were considerably greater than the "current" measurements seen in this study. As
it is likely that most of the Ranch Hands were exposed to only low levels of dioxin, it would be
inappropriate to compare the results of this study with those of Fingerhut and her colleagues.
7.8.2. Residents of Seveso, Italy
Residents of Seveso, Italy, were exposed to TCDD in a chemical accident in 1976.
Nearly 200 cases of chloracne reported (Caramaschi et al., 1981). Children (Bertazzi et al.,
1992) and adults (Bertazzi et al., 1989a,b) who were exposed at the time of the accident are being
studied separately. The group residing in the zone (zone A) of highest potential exposure
(determined by levels of dioxin found in the soil) consists of 556 adults and 306 children. The
group residing in the zone of intermediate estimated exposure (zone B) is larger, with 3,920
adults and 2,727 children. The group with lowest estimated exposure (zone R) is larger still,
with 26,227 adults and 16,604 children. The accuracy of the three estimated exposure zones has
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been questioned (Caramaschi et al., 1981; Merlo et al., 1986; Ratti et al., 1987; Merlo and
Puntoni, 1986), especially because the ranking does not correspond to the occurrence of
chloracne in the area. Some parts of region R are almost adjacent to the site of the accident and
the factory where the accident happened, while region B begins about 1 kilometer away. Even
the nearest part of the "referent" region to the site of the accident is located about the same
distance as the nearest part of region B. Only region A and region R appear to come closest to
the site. It is entirely likely that many persons residing in region R were exposed to TCDD.
There is no question that at least some of the residents of the most heavily contaminated
area (zone A) received considerable exposure to TCDD (Mocarelli et al., 1991). The 1990
analysis, based on tissue specimens taken in 1976, found that the highest detected levels were
recorded just after the accident. Six children at the time who subsequently developed severe
chloracne had serum TCDD levels ranging from 12,100 ppt to 56,000 ppt. Four other persons
with slightly less severe chloracne exhibited levels ranging from 828 ppt to 17,300 ppt. These
were similar to those of nine other residents of zone A who did not develop chloracne whose
serum TCDD levels ranged from 1,770 to 10,400 ppt (Mocarelli et al., 1991). None of the latter
group of nine were reported to be ill at the time of sampling.
Support for the original division of the area of exposure into the three zones (A, B, R)
was strengthened by data from studies of tissue levels of plasma TCDD by Landi et al. (1996,
1998). Twenty years after the accident, randomly selected residents of the three areas affected
were chosen to have their tissue plasma TCDD levels measured. Plasma TCDD levels were
measured in 62 subjects from zones A and B. Their mean tissue levels ranged from 1.2 ppt to
89.9 ppt with a geometric mean value of 53.2 ppt (n = 7) in zone A. In zone B (n = 51) the mean
was 11.0. In the non-ABR region (n = 52), it was 4.9 ppt. In the most polluted areas of the three
zones, in adults over 13 years of age the estimated median TCDD levels were 443 ppt (zone A,
177 residents), 87 ppt (zone B, 54 residents), and 15 ppt (zone R, 17 residents), respectively
(Bertazzi et al., 1998).
The authors report that women have significantly higher TCDD levels than men in the
entire study area which they maintain are not due to location, consumption of meat, age, body
mass index, or even smoking. Levels decrease by distance from the accident site, according to
the authors. However, none of these levels appear to be excessively large. The authors caution
that "elevated TCDD levels in women may contribute to adverse reproductive, developmental,
and cancer outcomes."
The population in zones A, B, and R around Seveso were initially followed for 10 years
(Bertazzi et al., 1992, 1989a,b) to 1986. Ten cancer deaths, too few to support a meaningful
analysis of specific cancers, have been observed among children (Bertazzi et al., 1992).
(Bertazzi et al., 1989b). No excesses of mortality from lung cancer, stomach cancer, or all
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cancers combined were apparent. A moderate and statistically imprecise elevation in the death
rate from a subset of the cancers that make up the NHLs is evident in the second 5-year period of
follow-up. An excess of greater relative magnitude, but even more imprecisely estimated, in
mortality from cancers of connective and soft tissues appears to have occurred in the same time
period.
In a preliminary study of cancer incidence in the same Seveso population (Pesatori et al.,
1992), the relative risk estimate of connective, subcutaneous STS of males living in zone R is
reported to be significantly elevated at 2.81 based on six cases (CI = 1.1-7.4). In zones A and B,
none were observed, but 0.4 were expected to occur in males and 0.2 in females. For females,
the risk in zone R of STS was 1.43 based on two cases. Other cancer sites that are also elevated
are certain hematologic neoplasms in males (lymphoreticulosarcoma) and hepatobiliary tract
cancers in both males and females.
One year later, in a cancer incidence study (Pesatori et al., 1993) of a population of young
persons (ages 0 to 19 years) with some small changes in the definition parameters, the number of
identified cancer cases equaled 17, although the followup period remained unchanged. The
authors observed a slight tendency toward increased leukemia (5 observed versus 2.6 expected)
and cancer of the thyroid ((2 observed versus 0.4 expected) based upon a small number of cases.
These results are equivocal.
Bertazzi et al. (1993) refined his earlier study to include a more complete vital status
ascertainment without adding additional years of follow-up to the cancer incidence data in the
contaminated areas surrounding the factory where the accident took place. Cancer occurrence
ascertainment was confined chiefly to the Lombardy region of Italy (with a population of 9
million persons) because only there can be found an efficient hospital and discharge registration
system, according to the authors. Lombardy hospitals routinely provide hospitalization data to
the regional health department.
Of note in this update is the information that only 14 cases of cancer were reported to
have occurred in zone A. This is not unexpected, based on population estimates. This number
was based on the assignment of addresses by the municipal vital statistics offices. But it is far
too small to produce any meaningful results. However, in the more populated zone B, with about
4,800 residents, and zone R, with some 32,000 residents, several findings were noted (Table 7-
18). In zone B, hepatobiliary cancer in females (RR = 3.3, 95% CI = 1.3-8.1),
lymphoreticulosarcoma in men (RR = 5.7, 95% CI = 1.7-19.0), and multiple myeloma in women
(RR = 5.3, 95% CI = 1.2-22.6) were significantly elevated.
In zone R, STSs in men (RR = 2.8, 95% CI = 1.0-7.3) were the only site-specific cancers
that were significantly elevated. Cancer of the genitourinary system in women was significantly
depressed (RR = 0.8, 95% CI = 0.6-1.0) chiefly because of a low risk of cancer of the uterus.
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This is consistent with the animal data that suggest estrogen-induced protective effects in female
rats.
The authors explain that the absence of cancer among the chloracne victims is not
unexpected at this time because of the relatively young age of the group and the small number of
individuals affected. They state that at this time only 0.5 total cancer deaths could be expected in
this subgroup.
The fact that elevated risks of cancers have appeared within a relatively short period of
time following the accident may have been the result of exposures received from a 2,4,5-T
production plant that existed in that area many years prior to the accident. Earlier potential
exposure to dioxin from the preexisting plant could have been the initiating event for the cancers.
Furthermore, hematopoietic tumors have a shorter latency than most carcinomas.
In their update of their earlier studies of the Seveso population, Bertazzi et al. (1997,
1998) continue to report excess risks of cancer mortality resulting from the accident. The
populations around Seveso were followed for 15 years until December 31, 1991, with the
following results: There was no increase in overall cancer mortality in any zone utilizing the
population outside of zones A, B, and R as a comparison. Zone A had the fewest number of
exposed persons. Only 6 male (13.5 expected) and 10 female (8.5 expected) cancer deaths have
been reported after 15 years of observation in zone A, too few to provide any meaningful
information regarding potential increased cancer risks. On the other hand, in zone B significant
excess cancer mortality risks occurred at four sites in males (rectum, pleura, lymphohemopoietic,
and leukemia) but only one in females (myeloma) (Table 7-19).
In zone R significant excess mortality risks were observed at two sites: esophagus in
males and bone cancer in females. These significantly elevated site-specific mortality risks are
based on a sufficient number of deaths as to rule out the possibility of a small-numbers effect.
Only males in zone R reported any deaths from STSs (4 observed versus 1.9 expected) and this
was not significant. The authors conclude that the specific excesses seen here could not be
explained by bias or confounding and that their association with dioxin exposure is plausible.
There appears to be little consistency in the reported findings. Again, not much can be concluded
based upon mortality data after only 15 years' follow-up from the time of the accident. The
authors pointed out that the study had several limitations linked to exposure categorization, time
elapsed since exposure, and small size.
A more recent follow-up (Bertazzi et al., 2001a) of the same group of residents in Zones
A and B was completed after 20.5 years on December 31, 1996. No reports of findings in Zone
R and in the reference region are presented although the authors report that results from the "least
contaminated zone R failed to suggest increased cancer risks." The residents of zones A and B
continued to exhibit effects similar to those reported in the earlier updates.
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All cancer mortality was modestly increased. When latency is considered from the time
of the accident, the risk increases modestly in males (RR =1.1, 95%CI = 1.0, 1.3) in regions A &
B combined, but not in females. This analysis assumes that all effects may have been induced by
exposure from the explosion despite the fact that the factory occupied the site for many years.
Specific cancer sites that also exhibit elevated risks, both sexes combined, are cancer of the
rectum (RR = 1.8; 95%CI = 1.0-3.3); lymphatic and hemopoietic cancer (RR = 1.7; 95%CI = 1.2-
2.5) andHodgkin's disease (RR = 3.1; 95% CI= 1.1-8.6). After 15 years, the risks for non-
Hodgkin's lymphoma and myeloid leukemia were significantly elevated (RR = 2.8; 95% CI =
1.1-7.0) and (RR = 3.8; 95% CI= 1.2-12.5), respectively.
Female mortality was significantly elevated for lymphohemopoietic system (RR =1.8;
95%CI = 1.1-3.2) and multiple myeloma (RR = 3.2; 95%CI = 1.2-8.8). No latent trends are
evident in females (Table 7-20).
However, in males all cancer mortality was significantly elevated (RR =1.1; 95%CI =
1.0-1.3). After 15 years latency, all cancer mortality the risk increased significantly elevated (RR
= 1.3; 95%CI = 1.0-1.7). Rectal cancer (RR = 2.4; 95%CI = 1.2- 4.6) and lung cancer (RR =
1.3; 95%CI = 1.0- 1.7) were also significantly elevated after 15 years latency. Risks were
elevated in the shorter latent categories of these same two site-specific cancers. No apparent
trend of increasing or decreasing risks were evident when latency was considered at other
selected sites as well from the time of the accident. But then, the potential influence of
exposures received by the residents of the community from the preexisting factory cannot be
assessed for its effect on the relative risks and latency.
No soft tissue sarcomas were observed in zones A and B. However, less than one case
would have been expected to occur by the end of the followup. For instance, in Zone A, where
exposure was highest, the expectation of a STS was only 0.1, there was little power to detect a
significant risk in that region.
In a special study group of 182 persons exhibiting chloracne, mostly children, and who
might be expected to have had much greater exposure to dioxin than most, only two had died by
the end of the follow-up extension. Interestingly, 114 of these individuals with chloracne resided
in zone R and the reference region. Efforts should be made to confirm the TCDD dioxin levels
by blood lipid analyses on all 182 members of this group. They should form a separate group for
study.
Estimated exposure levels in the blood of random samples of residents of zones A and B
at different time intervals are provided in Table 7-22. During the 16- to 18-year lapse from the
initial blood lipid measurements in 1976-1977 until 1993-1994, there appears to have been a near
7-fold drop in blood levels of TCDD. This is consistent with what would be expected given the
approximate 7 year half life expectation of TCDD in the body.
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The results of this latest update continue to support the finding of an increased risk of
certain site-specific cancers in the population exposed by the industrial accident in 1976 although
all cancer risk is only modestly increased. This can be explained by the fact that the most heavily
exposed members of the population (Zone A) make up only 12% of the population of the two
zones examined by the authors and that the blood levels over time as evidenced in Table 7-22
have fallen as expected during the 20.5 year span of time since the accident. Furthermore, the
observed blood lipid levels of TCDD initially were, on average, much less than those observed
by Fingerhut et al. (1991) in industrial workers in the U.S. (Section 7.5.1.) who were involved in
the making of chemicals contaminated with TCDD. The accident in Seveso produced an intense
exposure to the population of that region that may have supplemented potential earlier exposures
received by workers at the preexisting factory and residents living nearby but, in general, body
burden levels were within an order of magnitude of background levels at the time when total
TEQ is considered.
After the accident, little additional exposure would have been expected to occur directly
except as part of the contamination of the surrounding crops and animals in the region, possibly
through the food chain. Any additional exposure received in this way probably would be
minuscule compared with the greater exposure received by the industrial chemical workers over
a long period of time. This is reflected in the blood lipid levels seen in such workers.
Furthermore, there is no follow-up of this group beyond the 20.5th year. Hence, latent effects, if
any, may not have had a chance to be expressed yet. Not so with the industrial workers in
Fingerhut et al. (1991) who were followed for over 30 years. The Seveso population should
continue to remain under surveillance for a longer follow-up period. More attention should be
given toward identifying exposed persons in Zone R and in the reference region through blood
lipid analyses so that they can be included for study. Despite these issues concerning estimates
of dose, the authors have stated that their results support the evaluation of TCDD as a human
carcinogen, especially with the increased estimates of relative risk for several causes of cancer in
the >15 year latency period.
In a commentary on this study by Smith and Lopipero (2001), two "key" problems were
identified. The "likely" exposure levels back-calculated to the time when the exposures had
occurred indicate that the weighted average for the two highest exposure zones in Seveso is only
136 ng/kg TCDD versus a mean of 3,600 ng/kg TCDD in the combined U.S. industrial cohorts.
Smith and Lopipero concluded that one would not expect to find detectable increases in all-
cancer mortality in the Seveso cohort for any latency. Thus, in their opinion, the results do not
add to or subtract from the findings from the industrial cohorts. Secondly, they point out,
smoking in the Seveso population may have influenced risk estimates of several causes of death
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that are associated with smoking, thus presenting the potential of a confounding effect. Slightly
elevated risks of lung cancer, myocardial infarction, and chronic respiratory disease are evident.
Bertazzi et al. (2001b) in a rebuttal agreed that exposures appear to be less than those of
the industrial cohorts, but they are still two orders higher than background environmental
exposures to TCDD, hence the increase in cancer mortality cannot be considered to be totally
unexpected. These authors also argue that smoking is not necessarily the cause of the increase in
the cancer mortality. Dioxin exposure also has been related to increased cardiovascular mortality
in recent studies and that if smoking were a cause of the increased cancer, then one would see an
increase in the risk of cancer of the larynx, esophagus, pancreas, and bladder and these risks were
not elevated in Seveso males after 15 years of observation.
Pesatori et al. (1999) briefly reported on the 20-year followup of the incidence of cancer
in the same cohort but in combined zone A and B. The risk of lymphatic and hematopoietic
neoplasms was borderline significantly increased in adult females (RR = 1.7; 95% CI = 1.0-2.9)
and in adult males (RR = 1.6; 95% CI = 1.0-2.7). The authors also report that biliary tract (four
cases) and central nervous system neoplasms (six) were also elevated in females. Rectal (12) and
pleural (3) tumors were elevated among males. Significance levels are not provided. STSs were
elevated only in R-zone males (RR = 2.2; 95% CI = 0.9-5.1) based on seven cases. The RR for
sarcomas any site was 1.3 based upon 15 cases. No other information is available from this
sketchy description.
7.8.3. Rice Oil Poisonings in Taiwan and Japan Involving Compounds Structurally
Related to Dioxin
This section discusses two similar incidents involving ingestion of rice cooked with oils
accidently poisoned with PCBs and PCDFs. PCBs and PCDFs are structurally similar to the
polychlorinated dioxins, and some of these are considered to be dioxin-like in their activity. The
dioxin-like effects of these compounds are mediated through a cytosolic receptor (Chapter 2).
The dioxin-like polychlorinated biphenyl congeners, chlorinated dibenzofurans, and dioxins that
have a high affinity to bind the Ah receptor induce similar effects in both animals and humans
but appear to differ quantitatively in toxicity (Ahlborg, Chapter 3 and 4). They appear to harm
growth and reproduction, they damage the immune system, and they also appear to cause cancer.
These same effects have been observed in a number of different species, including humans.
Two accidents involving ingestion of food contaminated with PCBs and dibenzofurans,
in Yusho (Japan) and Yu-Cheng (Taiwan), have been reported. The Yusho incident involved
1,900 people who in 1968 accidentally consumed up to 2 grams each of PCBs that had leaked
into the rice oil at the facility where the rice oil was canned. The PCBs were primarily Kanechlor
400 that had been used as a heat exchange medium thousands of times. Commercial-preparation
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Kanechlor 400 had a concentration that was 49% chlorinated. The use of this medium for
exchange of heat resulted in an increase in the dibenzofuran contamination of approximately 250
times. The final mixture that was actually present in the rice oil had a ratio of one molecule of
PCDFs to every 200 molecules of PCBs.
These victims suffered many ill effects from their massive exposure that lasted only a few
months. Tissue studies by the Japanese of the victims indicated that some of the PCBs and
PCDFs were retained for many years after the initial exposure. Certain congeners of the PCDFs
are eliminated at a slower rate than PCBs. Concentrations measured several years following the
Yusho accident indicated that the mass ratio of PCDFs to PCBs remaining in the adipose tissue
of the victims was about 1 to 4 (Kuratsune et al., 1975). Japanese researchers have attributed
most of the noncancer toxic effects to the presence of the PCDFs, although these effects are
consistent with PCB exposure. These toxic effects include comedo formation, acneform
eruptions, hyperpigmentation, and hyperkeratosis. In addition, ocular lesions such as swollen
meibomian glands filled with yellow infarct-like material and pigmentation of the conjunctiva
were seen, similar to effects of TCDD. For further discussion of these and other effects, see Part
B of Chapter 7, which addresses noncancer health effects in humans.
Kuratsune et al. (1988) reported a significantly increased risk of liver cancer in male
victims (9 observed vs. 1.6 expected; SMR = 559,/><.01) and a nonsignificantly increased risk in
female victims (2 observed vs. 0.66 expected; SMR = 304), as well as a significantly increased
risk of lung cancer in male victims (8 observed vs. 2.45 expected; SMR = 326,/X.Ol). Some
1,761 patients (887 males and 874 females) were followed from date of registration to the end of
1983, 15 years after the accident in October of 1968. Thirty-three male cancer deaths had
occurred by this time versus 15.51 expected. In female victims, 8 cancer deaths had occurred
while 10.55 were expected. Comparisons were with the age-, sex-, and cause-specific death rates
of Japan and, separately, of Nagasaki and Fukuoka prefectures in 1970, 1975, and 1980. The
author reports that the risk of liver cancer remained elevated even after the influence of latency,
alcohol consumption, and liver disease had been evaluated. Kuratsune said that because there
was an uneven distribution of deaths in the provinces where most of the victims lived, it was too
early to draw any conclusions. Apparently, most of the liver cancers occurred in Fukuoka
prefecture. A statistically significant excess mortality was still present in males of Fukuoka even
when liver cancer deaths occurring less than 9 years after the accident were eliminated. The
author stated, "Such a markedly uneven geographical distribution of deaths can hardly be
explained by exposure to the toxic rice oil alone." However, he cautioned that his findings
suggest that the poisoning might have caused liver cancer at least in male patients. He
concludes, "Our findings should not be disregarded, however, because the hepatocarcinogenicity
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of PCBs in animals has been well documented." Deaths from chronic liver diseases and cirrhosis
were also elevated but not significantly.
An outbreak of illness similar to Yusho was reported among some 2,000 persons in the
Taichung and Changhwa provinces of Taiwan in March 1979. The illness consisted of
chloracne, hyperpigmentation, and meibomian gland dilatation. In October 1979, the illness was
found to be the result of the ingestion of cooking oil contaminated with PCBs and PCDFs. Chen
et al. (1980) reported on the blood PCB levels of 66 victims for which gas chromatograms had
been prepared. Basically, blood concentration residues ranged from 11 ppb to 720 ppb in these
patients. The mean value was 49 ppb; most values were under 100 ppb. In only two instances
were the concentrations greater, at 120 ppb and 720 ppb. The authors reported that the higher
value of 720 ppb occurred in a patient who had difficulty metabolizing and excreting PCB
components. They also maintain that blood PCB levels of these patients are "much higher" than
those of 72 Japanese Yusho patients (Koda and Masuda, 1975). Koda and Masuda reported the
mean PCB value in Yusho patients was 5.9 ppb with a standard deviation of 4.5 ppb in 1973 and
1974. Chen et al. (1980) maintained that this difference is due to a lengthy time lapse from the
exposure to PCB in Yusho patients before measurements were taken compared with a much
shorter time lapse in Yu-Cheng patients. Furthermore, the Yu-Cheng patients consumed a larger
proportion of higher-chlorinated PCBs compared with those of Yusho and, as a result, the
substance would be retained longer in the body, according to the authors.
7.9. SUMMARY AND CONCLUSIONS
The strongest evidence that exposure to TCDD leads to an increased risk of generalized
cancers at multiple organ sites, including lung cancer, comes from the four occupational cohort
studies (Fingerhut et al., 1991; Steenland et al., 1999; Manz et al., 1991; Flesch-Janys et al.,
1995, 1998, 1999; Becher et al., 1996; Zober et al., 1990; Ott et al., 1996; Bueno de Mesquita et
al., 1993; Hooiveld et al., 1996, 1998) discussed earlier as well as studies of the victims of the
1976 Seveso accident in Italy (Caramaschi et al., 1981; Bertazzi et al., 1989a,b, 1992, 1993,
1997, 1998, 2001a; Landi et al., 1996, 1998; Pesatori et al., 1993, 1999). See Table 7-20 and 7-
21. These studies provide evidence of in vivo exposure to TCDD, with actual measurements of
TCDD serum levels in exposed individuals or their surrogates positively correlated with
significantly increased risks of cancer mortality of between 40% and 100% (SMRs range
generally from 1.4 to 2.0). Although it is clear that congeners of the PCDDs/PCDFs are also
present in the blood serum of exposed subjects, TCDD predominates. Flesch-Janys et al. (1995,
1998, 1999), in fact, have provided measurement data showing that when the PCDD/PCDF
congeners were converted to their toxic equivalences (TOTTEQs) they were found to be dose-
related to increasing risks of cancer, cardiovascular disease, and ischemic heart disease. In the
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Yusho accident (Kuratsune, 1988), victims exposed to PCBs and dibenzofurans that were similar
in structure to TCDD also reported a significant risk of lung cancer, as did the subjects of the
occupational cohort studies mentioned in this summary. At the highest levels of exposure, the
cancer risks are statistically significant. These data, together with the presence of dose-response
relationships in the occupational studies, lend support to the concept of a likely causal
relationship between cancer at multiple sites and exposure to TCDD, its congeners, and dioxin-
like congeners of the PCBs, based on epidemiological studies.
Although some confounding or synergism by tobacco smoke cannot be excluded entirely,
the limited analyses conducted and the lack of an excess risk of other smoking-related noncancer
diseases suggests that smoking as a confounder cannot entirely explain the significantly elevated
cancer risks seen in these studies. Although it is likely that exposure to asbestos fibers occurred
prior to employment in the herbicide manufacturing industry in a few subjects, it is improbable
that the two or three cases of asbestos-related diseases seen in these cohorts indicate widespread
confounding from asbestos. Furthermore, if other chemicals and hazardous agents are present in
the workplace of the chemical industry, that could be responsible for the excess cancer risks seen
in these studies whose sources have not been identified, except for 4-aminobyphenyl. The 4-
aminobyphenyl exposures were identified in one plant only, in one cohort studied, and were
suggested as a cause of the bladder cancers observed in these studies. But this observation does
not explain the increased risks of total cancer seen in the remaining cohorts, where it has either
not been looked for or not been found. The idea that other hazardous agents present in the
phenoxy herbicide industry are causing the increases in the risk of all cancer in these cohorts is
unlikely because this is a rare event in occupational studies, and there is no reported association
between exposure to other hazardous cancer agents and the increase in the risk of all cancers that
have been reported in this industry despite extensive study. Furthermore, in the Seveso accident,
residents living in the local area received almost exclusive exposure to TCDD, and have
exhibited some significantly elevated site-specific risks of cancer that appears to be dose-related
to TCDD.
Some studies discussed in this chapter report little or no increased risk of cancer from
exposure to TCDD or its congeners. These studies generally suffer from one or more
deficiencies that render them not relevant to provide information that could assist in determining
the carcinogenicity of dioxins. These deficiencies fall into the following categories that impact
the statistical power to detect an effect, if it was present: no measurements of in vivo exposure to
TCDD, leading to misclassification of exposure between subjects and controls; the measured
exposures are lower than those seen in the studies cited above and similar to those of the
comparison population; and finally, there has been no consideration of latency. In short, these
non- or weakly positive studies lack one or more strengths of the cohort studies discussed above.
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From case-control and follow-up studies, STS has previously been reported to provide
some evidence of an association with PCDD and its congeners. The original reports by Hardell
(1977); Hardell and Sandstrom (1979), Eriksson et al. (1981, 1990), and Hardell and Eriksson
(1988) of an association between STS and exposures involving TCDD-contaminated phenoxy
herbicides have been extensively questioned (see Section 7.6.1). The degree of risk, as estimated
in later studies by Hardell's research group, does not appear to be as great as originally
suggested. The results of the recent Lynge (1998) study suggest that exposure to MCPA and
related phenoxy herbicides may by itself increase the risk of STS. The results from the cohort
study by Fingerhut et al. (1991) of 5,000 chemical production and processing workers exposed to
TCDD are supportive of an association between TCDD exposure and STS, although the
association is not statistically significant. These findings are similar to those from the 10- and
15-year follow-up studies of victims exposed to TCDD in Seveso (Bertazzi et al., 1989a,b, 1993,
1997, 1998) in Region R, although the numbers are small and no in vivo exposure information is
available. The large IARC Registry cohort mortality study by Kogevinas et al. (1997) and
Saracci et al. (1991) also suggests an association between STS and TCDD and the higher
chlorinated dioxins. In a recently published nested case-control study by Kogevinas et al. (1995)
based upon diagnosed cases of STS from the Saracci Study, support is also given to the finding
of an association of exposure to other dioxin congeners, although the association with TCDD is
stronger. The first New Zealand sarcoma study (Smith et al., 1983, 1984) also appeared to
produce positive results when the analysis, presented above, was restricted to farmers to
minimize bias. But because of conflicting data or even contradictory evidence regarding the
likelihood of exposure to TCDD, a direct linkage could not be made that exposure specifically
increases the risk of STS. Therefore, the epidemiologic findings regarding an association
between exposure to TCDD or other dioxin-like compounds and STS do not add significantly to
the weight of the evidence regarding human cancer hazard of these compounds.
Earlier suggestions of an increased risk of malignant lymphomas from exposure to TCDD
have not been substantiated, but recent evidence suggests an association between NHL and
exposure to the herbicide 2,4-D (Zahm and Blair, 1992), which may contain dioxins other than
2,3,7,8-TCDD. The evidence from two large industrial cohort studies (Fingerhut et al., 1991;
Steenland et al., 1999; Saracci et al., 1991; Kogevinas et al., 1997), except for the Seveso
accident, suggest little, if any, evidence of increased risk of NHL. In the Bertazzi et al. (2001a)
study, a statistically significant excess of NHL occurs in the latent category (15+ years). The
limited evidence of TCDD exposure that can be extracted from the extensive case-control studies
on NHL by the National Cancer Institute (Hoar et al., 1986; Zahm et al., 1990; Cantor et al.,
1992) also does not indicate a consistent and pronounced increase in risk. At the present time,
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existing studies do not present a consistent picture of increased risks of malignant lymphoma
among persons probably exposed to TCDD.
Few studies of females are to be found in the case-control and cohort studies of the
effects of exposure to dioxin. The only reported female cohort with good TCDD exposure
surrogate information was that of Manz et al. (1991), which reported a borderline statistically
significant increase in breast cancer. Although Saracci et al. (1991) did report reduced female
breast and genital organ cancer mortality, this was based on few observed deaths and on exposure
to chlorophenoxy herbicide, rather than TCDD. In the later update and expansion of this cohort
Kogevinas et al. (1997) provided evidence of a reversal of this deficit and produced a borderline
significant excess risk of breast cancer in females. Bertazzi et al. (1993, 1997, 1998, 2001a)
reported nonsignificant deficits in the risk of breast and endometrial cancers in women living in
geographical areas around Seveso contaminated by dioxins. Although Kogevinas et al. (1993)
noted an increase in cancer incidence among female workers most likely exposed to TCDD, no
increase in breast cancer incidence was observed in a small cohort studied. In sum, TCDD
cancer experience for women may differ from that of men, but currently there are few
epidemiologic data to support this conclusion.
Animal studies suggest that males and females respond differently to TCDD and its
congeners. Dioxins have been shown to reduce estrogen levels in reproductive tissues and are
also known to reduce estrogen-receptor binding in rat and mouse liver. The female mouse liver
is more sensitive than the male mouse liver. These antiestrogenic effects might be responsible
for decreased tumor incidences seen in the mammary gland, uterus, and pituitary of TCDD-
treated female rats, although the decreases occurred in the high-dose group and may be due to
decreased body weight. Antiestrogenic effects may also be partially responsible for increased
liver cancer seen in female but not male rats (see Part II, Chapter 6, Sections 6.4.1 and 6.5.4).
These female rat liver tumors may be ovary-dependent, while at the same time the ovaries appear
to protect against TCDD-mediated tumor promotion in the rat lung (see Section 6.4.2). Thus,
these complex mechanisms might very well affect human carcinogenicity of dioxin-like
compounds in males and females differently.
TCDD-related receptor-mediated responses on cell differentiation and proliferation,
hormonal effects, and immune suppression probably produce multiorgan sensitivity and
contribute to the overall increased mortality from all malignancies combined seen in the four
production worker subcohorts and Seveso victims with higher estimated PCDD exposures. The
increased relative mortality risks (SMRs =1.4 to 2.0) seen in the most exposed subcohorts are
consistent and statistically significant. This is important because significant increases in common
tumors are difficult to demonstrate in epidemiological studies, even when dealing with relative
risks generated from incidence data. With mortality data, which do not include those who
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survived their cancer, it is even more unlikely that an effect will be detected when present, given
the insensitive nature of the epidemiological method. Therefore, a significantly elevated risk of
mortality from generalized cancer should be considered a significant contributor to the evaluation
of cancer hazard, particularly when the mode of action of the subject compounds is consistent
with promotion of existing lesions and/or immune suppression and multisite carcinogenesis.
In conclusion, although there are uncertainties associated with the epidemiologic
evidence that could have influenced the risk estimates rendering these data "limited," the overall
weight of evidence from the epidemiologic studies suggests that the generally increased risk of
overall cancer is more likely than not due to exposure to TCDD and its congeners. The
consistency of this finding in the four major cohort studies and the Seveso victims is
corroborated by animal studies that show TCDD to be a multisite, multisex, and multispecies
carcinogen with a mechanistic basis.
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o
O
to
O
O
Table 7-1. Relative risks of selected cancers in study of chemical manufacturing workers
exposed to TCDD in United States, by exposure duration and latency
O
O
2
O
H
O
O
?d
O
c
O
H
W
Latency ;>20 years
Cancer
Connective and
soft tissues
Hodgkin's
disease
Non-Hodgkin's
lymphomas
Lung cancer
Stomach cancer
All combined
Measure3
Observed deaths
Relative risk
95% confidence interval
Observed deaths
Relative risk
95% confidence interval
Observed deaths
Relative risk
95% confidence interval
Observed deaths
Relative risk
95% confidence interval
Observed deaths
Relative risk
95% confidence interval
Observed deaths
Relative risk
95% confidence interval
Latency <20 years
1
1.4
0.1 -7.0
2
1.1
0.2-3.5
6
1.6
0.7-3.4
32
0.9
0.7-1.3
O
J
0.6
1.5-1.6
103
1.05
0.8-1.2
Exposure <1 year
0
0.02
0.0-15.0
0
0.0
0.0-15.0
2
1.5
0.2-4.9
17
1.0
0.6-1.5
3
1.8
0.4-5.2
48
1.02
0.76 - 1.4
Exposure ;> 1 year
O
J
9.22
1.90-27.0
1
2.8
0.1 -15.3
2
0.9
0.1 -3.30
40
1.4
1.0-1.9
4
1.4
0.4-3.5
114
1.46
1.2-1.8
Total cohort
4
3.4
0.9-8.6
3
1.2
0.3-3.3
10
1.4
0.7-2.5
89
1.1
0.9-1.4
10
1.0
0.5-1.9
265
1.15
1.0-1.3
aRelative risks and confidence intervals are based on rounded values and may differ slightly from those in the original reports (Fingerhut et al., 1990, 1991).
Source: Fingerhut etal., 1991.
-------
o
O
to
O
O
Table 7-2. Relative risks of lung cancer in subcohort of chemical manufacturing workers
exposed to TCDD in United States for at least 1 year and with at least 20 years latency,
adjusted for alternative hypotheses about its smoking distribution
Proportion
Never smokers
24
28
25
20
15
10
15
10
of subcohort
Former
smokers
19
14
10
15
15
20
10
15
(percent)3
Current
smokers
57
59
65
65
70
70
75
75
Expected lung
cancer deaths
(40 observed)
28.8
29.2
30.5
31.2
33.2
33.9
34.4
35.1
Relative risk
1.4
1.4
1.3
1.3
1.2
1.2
1.2
1.1
95% confidence
interval
1.0-1.9
1.0-1.8
0.9-1.8
0.9-1.7
0.9-1.6
0.9-1.6
0.8-1.6
0.8-1.5
to
O
O
2
O
H
O
HH
H
W
The first set of proportions assumes no difference between the subcohort and the U.S. population. The second set is basedon 87 surviving members of the
subcohort (Fingerhut et al., 1990). The remaining sets are hypothetical values used to test thesensitivity of the results. Relative risks of lung cancer are assumed to
be 4.7 for former smokers and 10.9 for current smokers (Fingerhut et al., 1990).
Source: Fingerhut et al., 1991.
O
-------
Table 7-3. Life-table results for exposure-level subcohorts: standardized mortality
ratios (SMRs) for total cancer by cumulative exposure categories to TCDD content
in process materials with and without 15-year lag time for total cancer; U.S.
population as referent
SMR (No. of observed deaths)
Category
Septile 1
Septile 2
Septile 3
Septile 4
Septile 5
Septile 6
Septile 7
Two-sided p for
trend CE
LCE
Exposure level3 No lag
Oto<19 1.14(34)
19to<139 1.15(39)
139to<581 0.85(29)
581to 20,200 1.60b(40)
0.02
.10
Exposure level3 15 year lag
Oto<39 0.98(67)
39 to <224 0.90 (27)
224to<791 1.14(31)
791to<2,120 1.18(30)
2,120 to <6,140 1.33(34)
6,140 to <15,800 1.69b(33)
> 15,800 1.54b(34)
0.02
.002
a Estimated exposure = product of concentration of TCDD (jig/g) in process materials x contact level x fraction
of day exposed x time.
bp < . 05, two-sided.
CE = Cumulative exposure.
LCE = Logarithm of cumulative exposure.
Source: Steenland et al, 1999.
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December 2003
•<\
s*.
Table 7-4. Relative risks of all cancers combined in study of chemical manufacturing
workers exposed to TCDD in Germany, by duration and category of exposure
Exposure
duration Measure
<20 years Observed deaths
Relative risk
95% confidence
interval
>20 years Observed deaths
Relative risk
95% confidence
interval
Total Observed deaths
Relative risk
95% confidence
interval
Exposure category (median
level)
Low and medium (60
ng/kg)
49
1.1
0.8- 1.4
10
1.5
0.8-2.7
59
1.2
0.9- 1.5
adipose TCDD
High (137
ng/kg)
26
1.2
0.8- 1.8
8
2.6
1.2-4.9
34
1.4
1.0-2.0
Total
75
1.1
0.9- 1.4
18
1.9
1.1 -2.9
93
1.2
1.0- 1.5
Source: Manz et al., 1991.
O
O
2
o
H
O
HH
H
W
O
V
O
c
o
H
W
-------
Table 7-5. Relative risks of all cancers combined in study of chemical
manufacturing workers exposed to TCDD (ng/kg) in blood in Germany
by industry of exposure
TCDD content (ng/kg)
Oto<125.2
125.2 to < 627.1
627.1 to < 2,503.0
> 2,503.0
Total
Observed deaths
28
29
31
36
124
Relative risk
1.24
1.34
1.34
1.73
1.41
95% confidence
interval
0.82 to 1.79
0.9 to 1.92
0.91 to 1.90
1.21 to 2.40
1.17 to 1.68
Source: Flesch-Janys et al, 1998.
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Table 7-6. Relative risks of selected cancers in study of chemical manufacturing workers
exposed to TCDD in Germany, by median blood TCDD level and latency
Subcohort
(median
blood TCDD
level)
Cl (24.5 ppt)
C2(9.5ppt)and
C3 (8.4 ppt)
Total cohort
Cancer Measure
Lung Observed deaths
Relative risk
95% confidence interval
Stomach Observed deaths
Relative risk
95% confidence interval
All combined Observed deaths
Relative risk
95% confidence interval
Lung Observed deaths
Relative risk
95% confidence interval
Stomach Observed deaths
Relative risk
95% confidence interval
All combined Observed deaths
Relative risk
95% confidence interval
Lung Observed deaths
Relative risk
95% confidence interval
Stomach Observed deaths
Relative risk
95% confidence interval
All combined Observed deaths
Relative risk
95% confidence interval
Time
<20 years
1
1.2
0.1-6.2
1
2.0
0.1-9.7
2
0.7
0.1 -2.4
0
0.0
0.0-1.8
0
0.0
0.0-3.2
5
0.8
0.3-1.9
1
0.4
0.0-2.0
1
0.7
0.0-3.4
7
0.8
0.4-1.6
since first exposure
^20 years
3
2.5
0.6-6.9
2
4.0
0.7-13.2
7
1.7
0.7-3.3
2
1.0
0.2-3.4
0
0.0
0.0-3.9
9
1.3
0.6-2.4
5
1.6
0.6-3.5
2
1.6
0.3-5.2
16
1.5
0.9-2.3
Total
4
2.0
0.6-4.8
3
3.0
0.8-8.1
9
1.3
0.6-2.4
2
0.5
0.1-1.8
0
0.0
0.0-1.7
14
1.1
0.6-1.8
6
1.1
0.4-2.2
3
1.1
0.3-3.0
23
1.2
0.8-1.7
Source: Zober et al, 1990.
-------
o
O
to
O
O
oo
Table 7-7. Relative risks of selected cancers in study of chemical manufacturing workers exposed to TCDD in
Germany, by TCDD dose (ng/kg body weight), 1992
Cause of
death
Category
Malignant
neoplasms
Respiratory
system
Residual sites
Category
Malignant
neoplasms
Respiratory
system
Digestive
organs
Total
n
31
11
5
n
47
13
12
SMR
1.2
1.4
1.6
SIR
1.2
1.2
1.1
(95% CI)
(0.8 to 1.7)
(0.7 to 2.5)
(1.6 to 3.6)
(95% CI)
(0.8 to 1.5)
(0.6 to 2.0)
(0.6 to 1.9)
TCDD < 0.1 ng/kg
body weight
n
8
3
2
n
15
1
3
SMR
0.8
1.0
1.6
SIR
1.0
0.7
0.7
(95% CI)
(0.4 to 1.6)
(0.2 to 2.9)
(0.2 to 5.7)
(95% CI)
(0.5 to 1.6)
(0.2 to 2.1)
(0.2 to 2.2)
TCDD 0.1- 0.99 ng/kg body
weight
n
8
1
1
n
13
2
4
SMR
1.2
0.5
1.2
SIR
1.2
0.7
1.4
(95% CI)
(0.5 to 2.3)
(0.0 to 2.7)
(0.0 to 6.4)
(95% CI)
(0.6 to 2.1)
(0.1 to 2.5)
(0.4 to 3. 6)
TCDD ;> 1 jig/kg body weight
n
15
7
2
n
19
8
5
SMR
1.6
2.4
1.9
SIR
1.3
2.0
1.2
(95% CI)
(0.9 to 2.6)
(1.0 to 5.0)
(0.2 to 6.7)
(95% CI)
(0.8 to 2.0)
(0.9 to 3. 9)
(0.4 to 2.9)
fe
H
O
O
O
H
O
Source: Ottetal., 1996.
O
&
O
c
O
H
W
-------
o
O
to
O
O
Table 7-8. Summary of results for selected cancers from follow-up studies of chemical
manufacturing and processing workers exposed to TCDD
Total cohorts
Cancer
Connective and
soft tissue
cancers
Non-Hodgkin's
lymphomas
Lung cancer
Stomach cancer
All cancers
combined
Study
Fingerhut(1991)
Manz(1991)
Zober(1990)
Total
Fingerhut(1991)
Manz(1991)
Zober(1990)
Total
Fingerhut(1991)
Manz(1991)
Zober(1990)
Total
Fingerhut(1991)
Manz(1991)
Zober(1990)
Total
Fingerhut(1991)
Manz(1991)
Zoberfl990)
Total
Observed
deaths
4
0
0
4
10
3
0
13
89
30
6
125
10
12
3
25
265
93
23
381
Expected
deaths
1.2
0.4a
O.la
1.7
7.3
2.4a
0.6a
10.3
80.1
21.3
5.6
107.0
9.7
9.9
2.7
22.3
229.9
75.2
19.7
324.8
Relative
risk
3.3
0.0
0.0
2.4
1.4
1.2
0.0
1.3
1.1
1.4
1.1
1.2
1.0
1.2
1.1
1.1
1.2
1.2
1.2
1.2
95%
Confidence
interval
1.1-
0.0-
0.0-
0.7-
0.7-
0.3-
0.0-
0.7-
0.9-
1.0-
0.4-
1.0-
0.5-
0.7-
0.3-
0.7-
1.0-
1.0-
0.8-
1.1-
8.0
7.5
30.0
5.7
2.4
3.4
5.0
2.1
1.4
2.0
2.2
1.4
1.8
2.1
3.0
1.6
1.3
1.5
1.7
1.3
Subcohorts with high exposure, long latency, or both
Observed
deaths
3
0
0
3
2
NA
0
2
40
NA
3
43
4
NA
2
6
114
34
7
155
Expected
deaths
0.3
O.la
0.0a
0.4
2.1
NA
0.2a
2.3
28.8
NA
1.2
30.0
2.9
NA
0.5
3.4
78.0
23.9
4.2
106.1
Relative
risk
10.0
0.0
0.0
7.5
1.0
NA
0.0
0.9
1.4
NA
2.5
1.4
1.4
NA
4.0
1.8
1.5
1.4
1.7
1.5
95%
Confidence
interval
2.5-
0.0-
0.0-
1.9-
0.2-
NA
0.0-
0.1-
1.0-
NA
0.6-
1.1-
0.4-
NA
0.7-
0.7-
1.2-
1.0-
0.7-
1.2-
27.3
30.0
99.9
20.4
3.1
15.0
2.9
1.9
6.8
1.9
3.3
13.2
3.7
1.8
2.0
3.3
1.7
oo
O
O
2
O
H
O
""Estimated as a proportion of expected deaths from all cancers combined (see text).
NA, not available.
O
&
O
c
O
H
W
-------
o
O
to
O
O
Table 7-9. Relative risks of soft tissue sarcomas and malignant lymphomas in relation to
phenoxy acid and chlorophenol exposures in five case-control studies in Sweden
Exposure category
and measure
Not exposed to phenoxy
acids or chlorophenols
Cases
Controls
Exposed to phenoxy acids,
chlorophenols, or both
Cases
Controls
Relative risk (95% CI)
Exposed to phenoxv acids
Cases
Controls
Relative risk (95% CI)
Exposed to 2,4,5-T
Cases
Controls
Relative risk (95% CI)
Exposed to chlorophenols
Cases
Controls
Relative risk (95% CI)
Malignant
lymphoma
Northern Sweden
1974-1978 (Hardell
et al., 1981)
108
303
61
32
5.3(3.3-8.7)
41
24
4.8(2.8-8.3)
29
23
3.5(2.0-6.4)
50
35
4.0(2.5-6.5)
Soft tissue sarcoma
Northern Sweden
1970-1977 (Hardell
and Sandstrom,
1979)
33
187
19
19
5.7(2.7-11.8)
13
14
5.3(2.3-12.2)
11
10
6.2(2.5-15.8)
7
6
6.6(2.1-20.9)
Soft tissue sarcoma
Southern Sweden
1974-1978 (Eriksson
et al., 1981)
85
206
25
13
4.7(2.3-9.5)
14
5
6.8(2.4-19.4)
7
1
17.0(2.1-140.0)
11
8
3.3(1.3-8.6)
Soft tissue sarcoma
Northern Sweden
1978-1983 (Hardell
and Eriksson, 1988)
41a
255a
13a
56a
1.4(0.7-2.9)
9
22
2.5(1.1 -5.9)
NA
NA
NA
4
34
0.7(0.2-2.2)
Soft tissue sarcoma
Central Sweden
1978-1986
(Eriksson et al.,
1990)
171b
179b
47b
33b
1.5(0.9-2.4)
23
18
1.3(0.7-2.6)
19
11
1.8(0.8-3.9)
15
3
5.2(1.5-18.4)
O
O
2
O
H
O
O
&
O
c
O
H
W
aAssuming no joint exposure to phenoxy acids and chlorophenols.
Computed from Table 3 in the original report (Eriksson et al., 1990) with the assumption of no phenoxy acid exposures among persons with low-grade
chlorophenol exposures.
NA, not available.
-------
o
O
to
O
O
Table 7-10. Relative risks of malignant lymphoma and soft tissue sarcomas in relation
to phenoxy acid and chlorophenol exposures in three case-control studies in Sweden3
Cancer and study locale
Malignant lymphoma,
northern Sweden (Hardell et
al., 1981; Hardell, 1981)b
Soft tissue sarcoma,
southern Sweden (Eriksson
etal., 1981)c
Soft tissue sarcoma,
central Sweden (Eriksson et
al., 1990)d
Exposure category
Exposed to phenoxy acids,
chlorophenols, or both
Unexposed
Exposed to phenoxy acids,
chlorophenols, or both
Unexposed
Exposed to phenoxy acids
Unexposed
Cases
51
49
14
17
22
33
Controls
28
145
8
39
15
51
Relative risk (95% confidence
interval)
1.0
5.4(3
1.0
4.0(1
1.0
2.3(1
1-9.5)
4-11.3)
0-5.0)
oo
o
Restricted to persons who worked in the occupational categories in which these exposures predominantly occur.
bAnalysis restricted to persons employed in agriculture, forestry, or the wood products industry.
°Analysis restricted to persons employed in agriculture or forestry.
dAnalysis restricted to persons employed in agriculture, horticulture, or forestry.
O
O
2
O
H
O
O
&
O
c
o
H
W
-------
o
O
to
O
O
Table 7-11. Mantel-Haenszel odds ratios for soft tissue sarcoma among persons exposed
to all dioxins, TCDD, and dioxins other than TCDD in four case-control studies involving
434 cases and 948 controls'1
Substance and
variable
All dioxins
No. of cases
No. of controls
OR
90% CI
TCDD
No. of cases
No. of controls
OR
90% CI
Other dioxins
No. of cases
No. of controls
OR
90% CI
Exposure < 1 vrb Exposure > 1 vr
Latency Latency Latency Latency
No exposure 5 - 19 yr ;> 20 yr 5 - 19yr ;> 20 yr
352 24 34 3 21
865 22 52 0 9
1.0 2.4 6.4
1.7-3.4 3.5-12
352 18 22 1 5
865 14 25 0 2
1.0 3.0 7.2
2.0-4.5 2.6-20
352 6 12 2 16
865 8 27 0 7
1.0 1.7 6.2
0.98-2.9 2.9-13
oo
O
O
2
O
H
O
O
&
O
c
O
H
W
aOR denotes odds ratio and CI confidence interval.
bAll subjects were exposed for at least 1 day. Data for latency periods were combined to determine the odds ratios.
Source: Hardelletal., 1991.
-------
o
O
to
O
O
Table 7-12. Relative risks of soft tissue sarcomas, non-Hodgkin's lymphomas, and Hodgkin's disease in
relation to phenoxy acid and chlorophenol exposures in two case-control studies in southern Sweden
Authors, study period
Cancer
Exposure
Relative risk (confidence
interval)51
Olsson and Brandt, 1978-1981
(Olsson and Brandt, 1988)
Persson et al., 1964-1986
(Persson et al., 1989)
Wingrenetal., 1975-1982
(Wingrenetal., 1990)
Non-Hodgkin's lymphomas
Non-Hodgkin's lymphomas
Hodgkin's disease
Soft tissue sarcomas
Phenoxy acids
Chlorophenols
Herbicides
Herbicides
Unspecified chemical work,
potential exposure to phenoxy
herbicides or chlorophenols
1.3 (0.8-2.1)
1.2(0.7-2.0)
4.9(1.3 -18)
3.8(0.7-21)
1.6(0.8-3.3)
oo
to
O
O
2
O
H
O
aConfidence intervals are 95% in the Olsson and Brandt study and 90% in the other two studies.
O
&
O
c
O
H
W
-------
o
O
to
O
O
Table 7-13. Relative risks of non-Hodgkin's lymphomas in relation to farm use of 2,4,5-T in case-control
studies in Kansas, eastern Nebraska, Iowa, and Minnesota
Eastern Nebraska
Occupational
category
All subjects
Farmers
Exposed farmers
and unexposed
nonfarmers
Exposure category and
measure
Exposed
Unexposed
Relative risk
95% confidence interval
Exposed
Unexposed
Relative risk
95% confidence interval
Exposed
Unexposed
Relative risk
95% confidence interval
Kansas
Cases
3
167
3
130
3
37
(Hoar et al.. 1986)
Controls
18
930
0.9
0.3-3.2
18
644
0.8
0.2-2.8
18
286
1.3
0.4-4.6
(Zahm et al.
Cases
13
188
1.8
0.9-3.
13
134
1.8
0.9-3.
13
54
1.6
0.8-3.
, 1990)
Controls
27
696
5
27
512
7
27
184
4
Iowa and Minnesota
(Cantor et al., 1992)
Cases Controls
25
597
1.0
0.6-1
25
331
1.0
0.6-1
25
266
1.1
0.6-1
48
1,197
.7
48
650
.7
48
547
.8
oo
O
O
2
O
H
O
Sources: Hoaretal., 1986; Zahmetal., 1990; Cantor etal., 1992.
O
&
O
c
O
H
W
-------
^ Table 7-14. Relative risks of soft tissue sarcomas and non-Hodgkin's lymphomas in relation
2> to phenoxyacetic acid and chlorophenol exposure in a case-control study in western
g- Washington State, 1981-1984
to
o
o
O
O
2
O
H
O
O
&
O
c
o
H
W
Exposure measure
Estimated potential for phenoxvacetic acid exposure"
Low
Medium
High
Any
Estimated potential for chlorophenol exposure"
Low
Medium
High
Any
Soft tissue
0.6 (0.3 -
1.0 (0.6 -
0.9 (0.4 -
0.8 (0.5 -
0.9 (0.5 -
0.9 (0.6 -
0.9 (0.5 -
1.0 (0.7 -
sarcomas
1.1)
1.7)
1.9)
1.2)
1.6)
1.5)
1.8)
1.5)
Non-Hodgkin's
0.9 (0.6 -
0.9 (0.7 -
1.2(0.8-
1.1(0.8-
1.0(0.7-
0.9 (0.7 -
0.9(0.9-
1.0(0.8-
lymphomas
1.3)
1.3)
1.9)
1.4)
1.3)
L2)b
1.4)b
1.2)
aThe reference category had no estimated potential for exposure (relative risk 1.0 by definition).
bEither the point estimate or the lower confidence limit appears to have been a typographical error in the original report. (On a logarithmic scale, the point
estimate should be centered between the two confidence limits.)
Source: Woods et al., 1987.
-------
, Table 7-15. Relative risks of soft tissue sarcomas and non-Hodgkin's lymphomas in relation
!> to potential exposure to phenoxy acids
f
t>
-i
O
>J
1
^1
>
»
yi
0
>
Tj
H
3
D
2
and chlorophenols
in case-control studies
Soft tissue sarcomas
First study
Measure et al.
Cases
Controls
Phenoxv acids
Any potential exposure
Cases
Controls
Relative risk (95% CI) 1.3 (0
Probable or definite exposure >1 day, >5
years before cancer registration
Cases
Controls
Relative risk (95% CI) 1.6 (0
Chlorophenols
Any potential exposure
Cases
Controls
Relative risk (95% CI) 1.3 (0
Potential exposure >1 day, >5 years before
cancer registration
Cases
Controls
Relative risk (95% CI) 1 .6 (0
(1976-1980) (Smith
, 1983, 1984)
82
92
21
19
7-2.7)
17
13
7-3.5)a
8
7
5-3.8)
8
6
5-4.7)
Second study (1981-1982)
(Smith and Pearce, 1986)
51
315
NR
NR
NR
6
46
0.8(0.3- 1.9)
NR
NR
NR
NR
NR
NR
in New Zealand
Non-Hodgkin's lymphomas
(1978-1981)
(Pearce et al., 1987)
183
338
44
72
1.2(0.8-1.8)
29
50
1.1(0.7-1.8)
21
27
1.5(0.8-2.7)
20
27
1.4(0.8-2.6)
"Among farmers, 3.0 (1.1-8.3); controlling for farming by standardization, 1.9 (0.8-4.5).
-H CI, confidence interval; NR, not reported.
il
D
0
D
-H
3
H
n
-------
o
O
to
O
O
Table 7-16. Relative risks of soft tissue sarcomas in relation to phenoxy acid exposure
in case-control study in northern Italy, 1981-1983
Men
Category
Living
Deceased
Total
Total, farmers
only
Measure
Cases
Controls
Relative risk
(95% confidence interval)
Cases
Controls
Relative risk
(95% confidence interval)
Cases
Controls
Relative risk
(95% confidence interval)
Cases
Controls
Relative risk
(95% confidence interval)
Unexposed
21
54
1.0
NA
13
17
1.0
NA
34
71
1.0
NA
12
12
1.0
NA
Possibly or
certainly exposed
2
8
0.6
(0.1 -3.3)
1
6
0.2
(0.0 - 2.0)
3
14
0.4
(0.1 -1.7)
3
14
0.2
(0.0 - 0.9)
Women
Unexposed
16
53
1.0
NA
6
7
1.0
NA
22
60
1.0
NA
5
8
1.0
NA
Possibly or
certainly exposed
5
7
2.4
(0.7 - 8.5)
4
6
0.8
(0.1 -4.1)
9
13
1.9
(0.7 - 5.0)
9
13
1.1
(0.3 -4.5)
oo
O
O
2
O
H
O
NA, not applicable.
Source: Vineis etal., 1986.
O
&
O
c
O
H
W
-------
o
O
to
O
O
Table 7-17. Relative risks for selected cancers from follow-up studies of paper and pulp
mill workers
Cancer
Non-Hodgkin's
lymphomas
Lung cancer
Stomach cancer
All cancers
combined
Study
Robinson (1986)
Jappinen(1987)
Henneberser(1989)
Total
Robinson (1986)
Jappinen(1987)
Henneberser(1989)
Total
Robinson (1986)
Jappinen(1987)
Henneberser(1989)
Total
Robinson (1986)
Jappinen(1987)
Henneberser(1989)
Total
Observed deaths
12
2
4
18
50
78
25
153
17
24
5
46
160
196
97
453
Expected deaths
8.9
3.5
3.8
16.2
62.1
62.6
28.0
152.7
13.8
28.8
4.2
46.8
211.5
203.8
87.9
503.2
Relative risk
1.3
0.6
1.1
1.1
0.8
1.2
0.9
1.0
1.2
0.8
1.2
1.0
0.8
1.0
1.1
0.9
95% confidence
interval
0.7-2.3
0.1-1.9
0.3 -2.5
0.7-1.7
0.6-1.1
1.0-1.5
0.6-1.3
0.8-1.2
0.7-1.9
0.5-1.2
0.4-2.6
0.7-1.3
0.6-0.9
0.8-1.1
0.9-1.3
0.8-1.0
oo
O
O
2
O
H
O
O
&
O
c
O
H
W
-------
o
O
to
O
O
Table 7-18. Relative risks for selected cancers among adults exposed to TCDD in Seveso, Italy, in
contaminated areas B and R
Cancer
Region B
All malignancies
Trachea, bronchus, lung
Hepatobiliary
Liver
Hematopoietic system
Non-Hodgkin's lymphoma
Lymphoreticulosarcoma
Hodgkin's disease
Multiple myeloma
Leukemia
Myeloid leukemia
Region R
All malignancies
Trachea, bronchus, lung
Hepatobiliary
Liver
Connective & soft
Non-Hodgkin's lymphoma
Lymphoreticulosarcoma
Multiple myeloma
Myeloid leukemia
Genitourinary organs
Breast
OBS
76
18
5
4
8
3
3
1
2
2
1
447
96
11
3
6
12
4
1
5
75
1
Males
RR
1.1
1.1
1.8
2.1
2.1
2.3
5.7
1.7
3.2
1.6
2.0
0.9
0.8
0.5
0.2
2.8
1.3
1.1
0.2
1.4
1.0
1.2
95%
0.9-
0.7-
0.7-
0.8-
1.0-
0.7-
1.7-
0.2-
0.8-
0.4-
0.2-
0.9-
0.7-
0.3-
0.1 -
1.0-
0.7-
0.4-
0.0-
0.5-
0.8-
0.1-
CI
1.4
1.8
4.4
5.8
4.3
7.4
19.0
12.8
13.3
6.5
14.6
1.0
1.0
1.0
0.7
7.3
2.5
3.2
1.6
3.8
1.3
10.2
OBS
36
0
5
0
6
1
1
1
2
2
2
318
16
12
2
2
10
6
2
2
106
Females
RR
0.8
3.3
—
1.9
0.9
2.3
2.1
5.3
1.8
3.7
0.9
1.5
0.9
0.5
1.6
1.2
1.7
0.6
0.5
1.1
95%
0.6-
1.3-
—
0.8-
0.1-
0.3-
0.3-
1.2-
0.4-
0.9-
0.8-
0.8-
0.5-
0.1 -
0.3-
0.6-
0.7-
0.2-
0.1-
0.9-
CI
1.1
_
8.1
-
4.4
6.4
16.9
15.7
22.6
7.3
15.7
1.1
2.5
1.7
2.1
7.4
2.3
4.2
2.8
2.1
1.3
oo
oo
O
O
2
O
H
Q Source: Bertazzi et al., 1993.
O
&
O
c
O
H
W
-------
o
O
to
O
O
Table 7-19. Standard mortality ratios (SMRs) for selected cancers among adults exposed
to TCDD in Seveso, Italy, in contaminated areas B and R
Cancer- cause of death
Region B
All malignancies
Trachea, bronchus, lung
Pleura
Hepatobiliary
Liver
Rectum
Lymphohemepoietic
Non-Hodgkin's lymphoma
Lymphatic
Hodgkin's disease
Multiple myeloma
Leukemia
Myeloid leukemia
Region R
All malignancies
Trachea, bronchus, lung
Esophagus
Hepatobiliary
Liver
Connective and soft
Non-Hodgkin's lymphoma
Lymphohemopoietic
Bone
Multiple myeloma
Myeloid leukemia
Genitourinary organs
Breast
OBS
104
40
3
4
4
7
12
2
2
2
1
7
3
607
208
30
35
31
4
10
27
2
5
4
73
Males
SMR
1.1
1.2
5.3
0.6
0.6
2.9
2.4
1.5
2.9
1.5
1.1
3.1
3.3
0.9
0.9
1.6
0.7
0.7
2.1
1.1
0.8
0.5
0.8
0.6
1.0
95% CI
0.9-1.3
0.9-1.7
1.1-15.5
0.2-1.5
0.2-1.7
1.2-5.9
1.2-4.1
0.2-5.3
0.3- 10.6
0.2-5.3
0-6.2
1.3-6.4
0.7- 9.6
0.9-1.0
0.8-1.1
1.1-2.3
0.5-1.0
0.5-1.0
0.6-5.4
0.5-2.0
0.5-1.2
0.1-1.7
0.3-1.9
0.2-1.6
0.8-1.3
OBS
48
2
4
3
2
7
0
2
4
1
-
401
35
5
25
12
0
8
29
7
5
4
65
67
Females
SMR
0.9
0.5
1.1
1.3
1.3
1.8
0
6.5
6.6
0.6
-
0.9
1.1
0.9
0.8
0.6
0
0.9
1.0
2.4
1.0
0.6
1.1
0.8
95% CI
0.7-1.2
0.1-1.8
0.3-2.9
0.3-3.8
0.1-4.5
0.8-3.7
0-3.0
1.8-16.8
0-3.1
-
0.8-1.1
0.8-1.5
0.3-2.2
0.5-1.3
0.3-1.1
0-2.4
0.4-1.7
0.6-1.4
1.0-4.9
0.3-2.3
0.2-1.6
0.8-1.4
0.6-1.0
oo
VO
O
O
2
O
H
O
O
&
O
c
O
H
W
Source: Bertazzietal., 1997, 1998.
-------
o
O
to
O
O
Table 7-20. Standard mortality ratios (SMRs) for selected cancers among males and females exposed
to TCDD in Seveso, Italy, in contaminated areas A and B combined
Cancer- cause of death
Region A&B Combined*
All malignancies
Trachea, bronchus, lung
Pleura
Hepatobiliary
Liver
Rectum
Lymphohemopoietic
Non-Hodgkin's lymphoma
Lymphatic
Hodgkin's disease
Multiple myeloma
Leukemia
Myeloid leukemia
OBS
166
64
3
6
6
9
15
3
2
2
1
9
5
Males
SMR
1.1
1.3
5.3
0.5
0.5
3.8
1.7
1.2
1.6
2.6
0.6
2.1
3.4
95% CI
1.0-1.3
1.0-1.6
1.1-15.5
0.2-1.0
0.2-1.1
1.2^.6
1.0-2.8
0.4-3.9
0.4-6.8
0.6-10.9
O.l^t.3
1.1^.1
1.3-8.4
OBS
83
5
-
7
6
3
13
4
-
2
4
3
1
Females
SMR
0.9
0.7
-
1.0
1.3
1.1
1.8
1.8
-
3.7
3.2
1.0.6
-
95% CI
0.7-1.1
0.3-1.7
-
0.5-2.2
0.6-2.9
0.4-3.5
1.1-3.2
0.7^1.9
-
0.9-16.0
1.2-8.8
0.3-3.0
0.1-5.1
O
O
2
O
H
O
Source: Bertazzi et al., 2001a*.
O
&
O
c
o
H
W
-------
Table 7-21. Standard mortality ratios (SMRs) and relative risks (RRs) of cancer in cohorts with evidence (in vivo) of exposure to
dioxin
Population/industry
12 plants producing chemicals
contaminated with TCDD
German factory workers who
produced phenoxy herbicides
(N)
5,172
1,520
Not provided
1,189 men
399 women
689 men
459 men
Blood fat 275
workers
Exposure
Current serum TCDD level
(lipid adjusted)
n = 253
Mean = 233 ppt
>1 year exposed and >20
years latency, 95 sampled =
462 ppt
Highest exposed group >
20,200
cumulative exposure score
(concentration x fraction of
day exposed x contact level
x years exposed)
<1 yr, mean =11 Ippt
l-<5 yr, mean = 413 ppt
5 - 15 yr, mean = 738 ppt
>15 yr, mean = 2218 ppt
Current adipose tissue
TCDD levels used to obtain
cumulative exposure during
years when working
Medium and low
11 sampled
Mean = 83
Median = 60 ppt
High
37 sampled
Mean = 296 ppt
Median =137 ppt
Highest TCDD quartiles >
2,503 ng/kg - years
Highest TEQ quartile
>5217.7 ng/kg years
Effects (95% CI)
Total cancer
SMR =115 (102-130)
Unspecified sites
SMR = 162 (104-241)
Total cancer
SMR =146 (121-176)
Resp. system
SMR =142 (103-192)
Total cancer = 1.60 (1.2-1.8)
Lung cancer SMR = 1 .65
Trachea,
bronchus,
All cancers lung
102(75-133) 96(56-147)
165(119-198) 26(73-192)
138(97-186) 146(79-232)
115(68-175) 156(71-272)
All cancer
SMR =1.41 (1.2-1. 7)
Lung cancer
SMR =1.51 (1.1-2.1)
Nonsignificant exposure >20
years
SMR =1.54 (0.8-2.7)
All cancer
exposure duration > 20 years
SMR = 2.54 (1.1-5.0)
Total cancer
SMR 1.73 (1.2-2.4)
Total cancer
SMR =1.64 (1.2-2.3)
Strengths/weaknesses
Strengths
High power, multiple sites (plants) exposure data, dose-
response relationship, in vivo exposure information
Weaknesses
Possible confounding: smoking, other agents such as
asbestos. Only 2 plants of 12 sampled for exposure
data
Possible nondifferential misclassification of exposure
could underestimate true risks.
Strengths
Generally increasing risk with dose. Good evidence of
in vivo exposure to TCDD congeners. Actual
measurements from blood serums. Smoking apparently
not a confounder
Weaknesses
Measurements are current. Possible misclassification
of exposure by TEQ quartile
References
Fingerhut et al.,
1991
Steenland et al.,
1999
Aylward et al.,
1996
Becher et al.,
1998
Becher et al.,
1996
Flesch-Janys et
al., 1998
Manzetal., 1991
-------
o
O
Table 7-21. Standard mortality ratios (SMRs) and relative risks (RRs) of cancer in cohorts with evidence (in vivo) of exposure to
dioxin (continued)
Population/industry
Uncontrolled decomposition reaction
on 1 1/17/53 resulting in exposure to
TCDD in trichlorophenol production
unit
Phenoxy herbicide manufacturing
and preparation accident, 1963
(N)
243
108
66
69
<69
127 with
chloracne and
erythema
140 exposed in
accident
259 exposed
compared with
482 nonexposed
workers
242 exposed
compared with
482 nonexposed
workers
549 exposed
compared with
482 nonexposed
Exposure
Full group- measurement of
blood lipid TCDD+
duration of exposure to
obtain dose
n= 138
<0.1 ug/kg
body weight
<1.00
0.1 to0.99ng/kg
body weight
>1.00 ug/kg body
weight
> 1 .00 ug/kg body weight +
>20 years latency
body weight
>20 years latency
Current measurements
mean = 96.3 ppt serum
TCDD,
extrapolated to 1,841. 8 ppt
serum TCDD max at time
of accident, n =14
Medium exposure,
extrapolated range (7.7 to
124.1 ppt) at time of
accident (lipid adjusted)
High exposure,
extrapolated range (124.2
to 7,307.5 ppt) at time of
accident (lipid adjusted)
Exposed but not in
accident. 244.1 ppt
extrapolated, n = 17
Effects (95% CI)
Total cancer
SMR=1.2(0.8-1.7)
SMR = 0.8(0.4-1.6)
SMR= 1.2 (0.5-2.3)
SMR= 1.6 (0.9-2.6)
SMR= 1.97 (1.05-3.36)
Lung cancer = 3.06 (1.12-
6.66)
SMR = 2.01 (1.22-3. 15)
Malignant neoplasms
RR= 1.7 (1.1-2.7)
Malignant neoplasms RR =
5.0(2.2-11.5)
Malignant neoplasms
RR = 5.6(2.5- 12.7)
RR = 4.1 (1.8-9.0) adjusted
Strengths/weaknesses
Strengths
In vivo exposure assessment. 40-year follow-up
Weaknesses
Possible synergistic effect with smoking, small size of
cohort
Strengths
Current in vivo TCDD serum lipid measurements.
Wide range of exposure and adequate latency, dose
response relationship
Weaknesses
Small cohort, lack of power
References
Ott and Zober,
1996
Zober etal., 1990
Bueno de
Mesquita et al.,
1993
Hooiveld et al.,
1998
-------
o
O
to
O
O
Table 7-21. Standard mortality ratios (SMRs) and relative risks (RRs) of cancer in cohorts with evidence (in vivo) of exposure to
dioxin
Population/industry
Chemical explosion of phenoxy
herbicide factory in Seveso, Italy,
July 1976
(N)
Zones A, B, and
R
A-Zone
N=862
population
B-zone
N=6,647
population
R-zone
N=42,831
population
Exposure
Exposure as measured by
blood serum. TCDD ranged
as high as 56,000 ppt in one
child
Geometry means were as
follows:
A-53.2 ppt (N=7) plasma
TCDD current
230.0 ppt extrapolated to
time of accident
B-11.0ppt(N=51)
plasma TCDD current
47.5 ppt extrapolated to
time of accident
R-4.9 ppt (N=52)
plasma TCDD current
200 cases of chloracne were
reported
Effects (95% CI)
Significant excess risks in
males without regard for
latency
No significant findings
*Zone A and B
Total cancer RR = 1.10
(0.9-1.2)
Males
Total cancer RR=1.1 (1.0-1.3)
Respiratory cancer RR=1 .3
(1.0-1.6)
Lymphohematopoietic
RR=1. 7 (1.0-2.8)
Leukemia RR=2.1 (1.1-4.1)
Females (etc.)
Myeloma RR=3.2 (1.2-8.8)
"Zone R
Males
Esophagus RR=1.6 (1.1-2.3)
**Zone R
Females
Bone -RR=2.5 (1.0-4.9)
No separated finding yet
Strengths/weaknesses
Strengths
Relatively pure exposure to TCDD. Its distribution in
the environment was measured. This exposed
population stable. In vivo plasma TCDD serum
available on a large number of subjects.
Biases and confounding not likely to explain the
unusual RRs
Weaknesses
Time lapsed since measurement of exposure. Small
size of population in most heavily exposed region.
Small numbers of deaths from which to measure risks.
Possible misclassification of exposure. 20 year follow-
up is too short
References
Caramaschi et al.,
1981
Bertazzi et al.,
1989b, 1992,
1993, 1997, 1998,
1999, 200 la
Landi et al.,
1996, 1998
Pesatori et al.
1993, 1999
O
O
2
O
H
O
HH
H
W
*Bertazzi et al., 2001a.
"Bertazzi et al., 1997, 1998.
O
-------
Table 7-22. TCDD concentrations in the blood of selected residents in the zones
contaminated after the Seveso, Italy, industrial accident in 1976a
Study area
Zone A
Zone B
Zone R
Reference Zone
Lipid-adjusted plasma concentration
Number of Subjects Median
296 (1976-1977)
7 (1993-1994)
80 (1976-1977)
51 (1993-1994)
48 (1976-1977)
52(1993-1994)
447.0 ppt
73.3 ppt
94.0 ppt
12.4 ppt
48 ppt
5.5 ppt
aAdapted from Bertazzi et al., 2001a.
December 2003
7A-94
DRAFT—DO NOT CITE OR QUOTE
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PART 7B: EFFECTS OTHER THAN CANCER
7.10. INTRODUCTION
Human exposure to 2,3,7,8-TCDD has been associated with noncancer effects in most
systems. The majority of effects have been reported among occupationally exposed groups, such
as chemical production workers, pesticide users, and individuals who handled or were exposed to
materials treated with 2,3,7,8-TCDD-contaminated pesticides, and among residents of
communities contaminated with tainted waste oil (Missouri, USA) and industrial effluent (Seveso,
Italy).
These effects represent a complex network of responses ranging from changes in hepatic
enzyme levels which, based on current evidence, do not appear to be related to clinical disease, to
observable alterations in the character and physiology of the sebaceous gland, as in chloracne
(Calvert et al., 1992; Taylor, 1979). This section of Chapter 7 describes, by system , the
noncancer effects associated with exposure to 2,3,7,8-TCDD. The characterization of the effects
by system provides a context within which to compare the results of the various studies.
However, it is important to recognize that the observed effects are not independent events but
rather may be one outcome in a series of interrelated outcomes, some of which we may be
incapable of measuring with the present technology or which we currently do not recognize as an
outcome of exposure to 2,3,7,8-TCDD.
The information describing human effects attributed to exposure to 2,3,7,8-TCDD-
contaminated materials is derived from a wide variety of sources, including clinical assessments
(case reports) of exposed individuals and analytic epidemiologic studies using case-control,
cross-sectional, and cohort designs. The case reports describe the acute outcomes of exposure to
2,3,7,8-TCDD and provide the basis for hypothesis generation for controlled epidemiologic
studies; however, they are not suitable for testing causal relationships between exposure and
related effects (Ashe and Suskind, 1950; Suskind et al., 1953; Bauer et al., 1961; Goldman, 1972).
As described in the previous section, cohort and case-control studies have been used to
investigate hypothesized increases in malignancies among the various 2,3,7,8-TCDD-exposed
populations (Fingerhut et al., 1991a, b; Steenland et al., 1999; Manz et al., 1991; Eriksson et al.,
1990). Cross-sectional studies have been conducted to evaluate the prevalence or extent of
disease in living 2,3,7,8-TCDD-exposed groups (Suskind and Hertzberg, 1984; Moses et al.,
1984; Lathrop et al., 1984, 1987; Roegner et al., 1991; Grubbs et al. 1995; Sweeney et al., 1989;
Centers for Disease Control Vietnam Experience Study, 1988a; Webb et al., 1989; Ott and Zober,
1994). Many of the earliest studies were unable to define exposure-outcome relationships owing
to a variety of shortcomings, including small sample size, poor participation, short latency
periods, selection of inappropriate controls, and the inability to quantify exposure to
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2,3,7,8-TCDD or to identify confounding exposures. In more recent cross-sectional studies of
U.S. chemical workers (Sweeney et al., 1989), U.S. Air Force Ranch Hand personnel (Roegner et
al., 1991; Grubbs et al., 1995), and Missouri residents (Webb et al., 1989), serum or adipose tissue
levels of 2,3,7,8-TCDD were measured to evaluate 2,3,7,8-TCDD-associated effects in exposed
populations. The ability to measure tissue or serum levels of 2,3,7,8-TCDD for all or a large
sample of the subjects confirmed exposure to 2,3,7,8-TCDD and permitted the investigators to
test hypothesized dose-response relationships.
7.11. CROSS-SECTIONAL STUDIES: USES AND LIMITATIONS
Most of the studies that describe nonmalignant effects were designed as cross-sectional
medical studies. These types of studies are useful for assessing the current status of the surviving
study population; however, they are inherently limited by a number of factors, including survivor
and participation biases, exposure and disease misclassification, recall bias, and interobserver
variability. Survivor and participation biases may have occurred because the studies included
only those who were living at the time of the study, and did not or could not obtain similar
information on those who died or were too ill to participate. Studies of groups exposed to agents
that contribute to early deaths or cause severe illnesses may exclude the populations who were at
highest risk. Exclusion of the sick and deceased whose condition was associated with 2,3,7,8-
TCDD may erroneously cause the risk estimate to be closer to the null than the true risk.
Disease misclassification may be introduced in a variety of ways. Medical tests in many
reviewed studies were most often performed once, without follow-up. For some disorders,
multiple testing is preferred to obviate normal variations in some test parameters, e.g.,
immunologic tests or hormone levels. In other situations, self-reported medical histories were
collected and, by design, were not or could not be confirmed by medical records. When the
exposed group incorrectly reports more disease than the unexposed group, recall and reporting
biases may falsely raise the risk estimate.
Exposure misclassification, particularly in the early studies, was a major limitation. In the
earlier studies of production workers or community residents, exposure to 2,3,7,8-TCDD was
determined only by an individual's presence (residing or working) in an area that was
contaminated with 2,3,7,8-TCDD (Suskind and Hertzberg, 1984; Moses et al., 1984; Hoffman et
al., 1986; Poland et al., 1971; May, 1973, 1982; Martin, 1984; Bond et al., 1983, 1989; Filippini
et al., 1981; Ideo et al., 1985; Mocarelli et al., 1986). The lack of a measurement to quantify
exposure hindered the ability to confirm exposure and to assess the magnitude of an exposure-
response relationship. If the misclassification is nondifferential, it tends to bias the measure of
effect toward the null.
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As described above, in studies conducted a decade later (Roegner et al., 1991; Grubbs et
al., 1995; Sweeney et al., 1989; Centers for Disease Control Veterans Health Studies, 1988; Webb
et al., 1989) researchers were able to confirm and quantify exposure to 2,3,7,8-TCDD in serum or
adipose tissue. This breakthrough helped establish that certain previously exposed populations
had 2,3,7,8-TCDD levels well above the background level of less than 20 picograms per gram of
lipid (pg/g) (Patterson et al., 1989) and that the nonexposed comparison group was truly not
exposed to greater than background levels. Yet, because the occupational populations were
exposed to 2,3,7,8-TCDD-contaminated substances for as many as 40 years before being tested,
levels attained at the time of exposure can only be estimated. It appears that for some populations
with higher exposures, such as workers, the estimate reflects continuous exposure over an
extended period. For example, despite the intervening period between last exposure to 2,3,7,8-
TCDD and the determination of serum 2,3,7,8-TCDD levels in workers employed in the
production of 2,4,5-TCP and 2,4,5-T (15 to 37 years after last occupational exposure), the
duration of occupational exposure was highly correlated to serum levels of 2,3,7,8-TCDD
obtained at the time of the study (1987-1988) (Pearson product moment correlation coefficient [r]
= 0.7) (Sweeney et al., 1989). These data suggest a strong relationship between length of
occupational exposure and serum 2,3,7,8-TCDD regardless of the length of the intervening period.
The deposition, metabolism, and excretion of high doses of 2,3,7,8-TCDD in the human
system have not been fully described. A study by Pirkle et al. (1989) suggests that 2,3,7,8-TCDD
decays by one-half in approximately 7.1 years, based on a one-compartment model and using a
standard half-life equation. If this is true, exposures to trichlorophenol production workers may
have been as high as 30,000 pg/g (Fingerhut et al., 1991a) and in excess of 50,000 pg/g in some
residents of Seveso (Mocarelli et al., 1991). The data may be limited by the lack of complete
information on the manner in which human metabolism handles 2,3,7,8-TCDD exposure. In a
separate analysis, Michalek et al. (1996) estimated the half-life among veteran Ranch Hands to be
8.7 years (95% CI = 8.0-9.5 years). This calcuation was based on a mean decay rate of 0.0797 per
year. In this analysis half-life increased with increasing body fat, but not age.
This section of Chapter 7 is a selective review of studies that, to date, provide the most
information on the relationship between nonmalignant outcomes and exposure to 2,3,7,8-TCDD-
contaminated materials. Animal studies have been reviewed in other chapters of this document
and will not be discussed in detail. Case reports will not be reviewed, but will be used to provide
support for the analytic studies. In the assessment of mortality from nonmalignant causes of
death, only cohort studies in which standardized mortality ratios (SMR) or equivalent population-
based risk ratios were calculated will be discussed.
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7.12. DESCRIPTION OF PRINCIPAL STUDIES
In the following section, we have provided a summary of the population description and
methods of the studies that reported results for two or more systems or effects. Studies that are
referenced only once will be described when cited. Results of these studies will be described in
subsequent sections.
7.12.1. Occupational Studies
7.12.1.1. U.S. Chemical Workers: West Virginia
In March 1949, an explosion of a TCP reaction kettle in a chemical plant in Nitro, West
Virginia, and the subsequent cleanup exposed approximately 450 workers to 2,3,7,8-TCDD-
contaminated substances. Examination of the workers in 1949 revealed a number of acute
symptoms "characterized by skin, eye and respiratory tract irritation, headache, dizziness and
nausea" (Suskind and Hertzberg, 1984). The acute symptoms subsided but were followed within
a week or two by "acneform eruption, severe muscle pain affecting the extremities, thorax and
shoulders, fatigue, nervousness and irritability, dyspnea, complaint of decreased libido and
intolerance to cold" (Suskind and Hertzberg, 1984). Thirty years later, two independent, cross-
sectional medical studies were conducted to evaluate the long-term consequences of exposure to
2,3,7,8-TCDD-contaminated substances among the surviving workers (Suskind and Hertzberg,
1984; Moses et al., 1984).
In a study by Suskind and Hertzberg (1984), a group of 204 (of a total of 419) active and
retired white male workers exposed between 1948 and 1969 to the 2,4,5-T production process and
to the reactor release were included in a clinical examination program. The control group
consisted of 163 (46% participation) current or former employees of the same plant who had no
self-reported exposure to 2,4,5-T production or maintenance of the facility. The study collected
demographic and medical histories and performed clinical chemistries, urinalysis, pulmonary
function tests, dermatologic examinations, and conduction velocities of the sural sensory and
peroneal motor. Multiple linear regression analysis was used to compare the exposed and
nonexposed groups.
For participation in a separate study of workers at the Nitro plant, Moses et al. (1984)
invited all workers documented in union records to have worked in 2,4,5-T production and a
systematic random sample of workers with no known 2,4,5-T production exposure. Fifty-five
percent (N = 226) of the persons invited participated in the study. Lifetime occupational and
medical histories were collected and clinical chemistries, urinalysis, and dermatologic
examinations were conducted. Exposure to 2,3,7,8-TCDD could not be discerned because of
irreconcilable inconsistencies in self-reported work histories and the lack of good company
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records to estimate and confirm the likelihood of exposure. Although the authors recognized that
absence of chloracne did not preclude exposure, they compared the results of the group with
chloracne with those without chloracne. Thus, the study design was revised to explore the
differences in health status in individuals with and without chloracne. Because exposed workers
may have been included in the group diagnosed without chloracne, the usefulness of the data to
quantify exposure-disease relationships is limited.
Two studies of this cohort also examined cancer and noncancer mortality of subsets of
workers from this plant (Zack and Suskind, 1980; Collins et al., 1993).
7.12.1.2. U.S. Chemical Workers: The NIOSHStudy
The study conducted by the National Institute for Occupational Safety and Health is a
cross-sectional medical study of living workers who were previously employed for at least 1 day
in one of two plants located in Newark, New Jersey, and Verona, Missouri. From 1951 to 1969,
490 workers employed at the New Jersey plant produced sodium 2,4,5-trichlorophenate (NaTCP),
2,4,5-trichlorophenoxy acetic acid (2,4,5-T), and 2,4-dichlorophenoxy acetic acid (2,4-D). A high
proportion of chloracne and other dermatologic abnormalities and cases of porphyria and
hypomania were reported among the workers at the New Jersey facility (Poland et al., 1971;
Bleiberg et al., 1964), which produced some of the most heavily 2,3,7,8-TCDD-contaminated
NaTCP and 2,4,5-T among production facilities whose products were surveyed (Fee et al., 1975).
At the Missouri plant, NaTCP and 2,4,5-T were produced intermittently for 4 months in 1968, and
NaTCP and hexachlorophene were produced continuously for 22 months between April 1970 and
January 1972.
For comparison, unexposed neighborhood referents were recruited using a random
sampling procedure described by Sweeney et al. (1989). Referents were selected if they reported
no prior history of occupational exposure to 2,3,7,8-TCDD and matched the worker by age
(within 5 years), race, and gender. A total of 586 workers were eligible for inclusion in the study,
of which 400 (68.3%) were living, 142 (24.2%) were deceased, and 44 (7.5%) could not be
located. All 400 living workers were invited to participate in the study; 281 (70%) were
examined. A description of the study population is included in the results.
Worker and referent health and exposure status were assessed in 1987-1988 through an
interviewer-administered medical and occupational history and comprehensive physical and
psychological examinations (Sweeney et al., 1989). A lifetime medical history was obtained from
each participant by interviewers who were blind to the exposure status of the respondent. Results
of the pulmonary, hepatic, gastrointestinal, porphyria, mood dysfunction, and neurologic
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examinations have been published or accepted for publication (Calvert et al., 1991, 1992, 1993,
1994, 1996, 1998, 1999; Sweeney et al., 1993; Egeland et al., 1994; Halperin et al., 1995, 1998).
As a surrogate for cumulative exposure, serum 2,3,7,8-TCDD levels were measured in 237
workers and a random sample of 79 referents. Procedures for sample collection, preparation,
adjustment for lipids, and statistical analysis were described in earlier reports (Fingerhut et al.,
1989; Patterson et al., 1986a; Sweeney et al., 1990). The mean lipid-adjusted serum 2,3,7,8-
TCDD level for workers was 220 pg/g, median 80 pg/g, ranging to 3,400 pg/g. The mean level
was statistically significantly greater than that for referents (7 pg/g) (p<0.001). Analyses of other
congeners of dioxins and dibenzofurans were also conducted; only the 2,3,7,8-TCDD levels were
different in the two exposure groups (Piacitelli et al., 1992).
7.12.1.3. BASF Accident Cohort
"On 17 November 1953, an uncontrolled decomposition reaction occurred during the
production of 2,4,5-trichlorophenol at a BASF AG facility in Ludwigshafen, Germany" (Ott et al.,
1994). The reactor contents, which contained 2,3,7,8-TCDD, contaminated the building in which
the TCP autoclave was housed. A series of studies have documented the effects and mortality
experience of the workers present at the time of the decomposition reaction and exposed during
the initial cleanup and equipment maintenance (May 1954 medical department list) (Cohort Cl,
N = 69), individuals present during subsequent clean-demolition activities between 1954 and 1969
(Cohort C2, N = 84), and a mixed group of workers identified as of December 1987 through
interviews that include individuals who worked in the laboratory as safety inspectors and others
who participated in the 1968-1969 demolition activities (Cohort C3, N = 101) (Zober et al., 1990;
Ott et al., 1993). Two hundred forty-seven study subjects were included in a mortality study that
found a significantly elevated SMR for all malignant neoplasms among workers with chloracne
and 20 or more years since first exposure to 2,3,7,8-TCDD-contaminated chemicals (Zober et al.,
1990).
Among 79% of the living subjects, lipid-adjusted serum 2,3,7,8-TCDD levels were
measured in 138 (54%) of 254 study subjects during the period 1988-1992 (Ott et al., 1993). The
geometric mean of the 2,3,7,8-TCDD levels in the entire group was 15.4 ppt (ranging from <1 to
553.0 ppt) (Ott et al., 1993) or 43 pg/g of lipid (M. G. Ott, personal communication, 1993).
Geometric means for the cohorts are as follows: Cl = 1,009.5 pg/g lipid; C2 = 48.8 pg/g of lipid;
and C3 = 83.7 pg/g of lipid. Background levels were determined in separate analyses of 102
unexposed individuals from Germany (Papke et al., 1992). The geometric mean for 2,3,7,8-
TCDD of the external referent group was 3.0, ranging from 0.6 to 9.1 pg/g of lipid. On the basis
of regression analyses, serum 2,3,7,8-TCDD levels were highly correlated (R2=0.65) to duration
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of exposure and location of exposure. Chloracne severity was positively and significantly related
to 2,3,7,8-TCDD concentrations.
Comprehensive batteries of clinical chemistry measurements were also measured for the
138 subjects between 1988 and 1993 (Ott et al., 1994). Referents were selected from among
BASF employees between the ages of 50 and 69 who participated in routine occupational medical
examinations from 1989 to 1991. For some tests, there were as many as 6,000 referent values.
For the immunologic parameters, the referent values were obtained from a group of 42 unexposed
BASF employees who participated in a separate study that examined the immunologic function of
21 extruder personnel exposed to 2,3,7,8-tetrabrominated dibenzo-p-dioxin (2,3,7,8-TBDD) and -
furan (2,3,7,8-TBDF) (Ott and Zober, 1996b). Cause-specific mortality and cancer incidence was
also evaluated in the 243 male study subjects who were followed through 1992 (Ott and Zober,
1996a).
7.12.2. Studies of Community Residents
7.12.2.1. The Missouri Experience
During 1971, 2,3,7,8-TCDD-contaminated stillbottoms were removed from a
hexachlorophene production facility and mixed with waste oil. This mixture was deposited on 45
residential, recreational, and industrial sites in southeastern Missouri in 1971 and 1972 (Daryl
Roberts, personal communication). Waste oil mists were commonly used in the summer for dust
control on roadways, horse arenas, truck depots, and other unpaved surfaces. Estimated
contamination of the areas ranges from 1 to 2,200 ppb (Hoffman and Stehr-Green, 1989). A
listing of potentially exposed persons was created (volunteers), and a survey was conducted to
obtain baseline information to identify persons at high risk of exposure. Beginning in 1984, a
series of studies were conducted to evaluate potential effects (Hoffman et al., 1986; Evans et al.,
1988; Webb et al., 1989), including reproductive events, of residential exposure to 2,3,7,8-TCDD
(Stockbauer et al., 1988).
The study by Hoffman et al. (1986) was conducted on 154 individuals (74% of total
eligible) who were residents of the Quail Run Mobile Home Park between 1971 and 1983,
because soil concentrations around the site were 2,200 ppb 2,3,7,8-TCDD. The comparison group
of 155 (77% of total eligible) individuals was recruited from residents of a nearby mobile home
park. The examination included tests for delayed hypersensitivity (the multitest Cornell Medical
Index [CMI]; Merieux Institute, Miami, Florida) and neurobehavioral effects, blood chemistries,
urinalysis, height, weight, vital signs, and examination of the skin, peripheral pulses, lymph
nodes, abdomen, and peripheral nervous system. The results of this study were plagued by the
exclusion of skin test results of 150 of 294 participants because of high reader error. Furthermore,
information on subject exposure to 2,3,7,8-TCDD was limited because the study was based on a
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minimum residence of 6 months in areas with contaminated soil. Actual contact with
contaminated soil was not assessed.
In the follow-up study by Evans et al. (1988), 50 persons from the initial study who did not
respond to the delayed hypersensitivity skin tests were retested. These subjects were thought to
have impaired immune function. The multitest CMI was reapplied to all test subjects.
Webb and colleagues (1989) examined 41 of 51 persons with various histories of exposure
to 2,3,7,8-TCDD (residential, recreational, and occupational exposure) and for whom adipose
tissue levels of 2,3,7,8-TCDD were measured. Of the 41 participants, 16 had adipose 2,3,7,8-
TCDD levels less than 20 pg/g (within background range), 13 had levels between >20 and 60
pg/g, and 12 subjects had levels above 60 pg/g. Standard medical examinations were conducted,
and complete blood count with differential, a panel of automated chemistry tests, serum
immunoglobulins, tests for porphyrins, and the multitest CMI were performed.
7.12.2.2. Seveso, Italy
In 1976, an explosion of a trichlorophenol reactor in a 2,4,5-T production facility
in Medina, Italy, caused the contamination by 2,3,7,8-TCDD of the neighboring city of Seveso,
Italy. Several reports (Biscanti et al., 1978; Homberger et al., 1979; Pocchiari, 1980a; Pocchiari et
al., 1980b; Reggiani, 1978, 1980; Rehder et al., 1978; Tuchmann-Duplessis, 1977) compared four
potentially affected communities (Seveso, Meda, Cesano, and Desio) to nearby unexposed
communities. The contaminated area was subdivided into three zones (A, B, and R) of decreasing
mean soil levels of 2,3,7,8-TCDD (Mocarelli et al., 1988). The mean 2,3,7,8-TCDD
concentration in zone A was 230 ng/m2; in Zone B, 3.0 ng/m2; and in zone R, 0.9
Mean serum 2,3,7,8-TCDD concentrations among a sample of residents who were 13
years and older at the time of the explosion were: zone A, 443 pg/g lipid (N = 177); zone B, 87
pg/g lipid (N = 54); zone R, 15 pg/g (N = 17) (IARC, 1997). Geometric mean serum 2,3,7,8-
TCDD concentrations measured were: zone A, 53.2 pg/g lipid (N = 7); zone B, 1 1 pg/g lipid (N =
51); zone R, 4.9 pg/g (N = 55) (Landi et al., 1997).
In 1979, Pocchiari et al. (1979) reported on initial efforts to screen residents in zones A, B,
and R for 2,3,7,8-TCDD-related effects. Since then, a series of cross-sectional medical studies
have reported the final results of the screening (Caramaschi et al., 1981; Ideo et al., 1985;
Mocarelli et al., 1986; Assennato et al., 1989). Within 1 year of the reactor release, 193 cases of
chloracne were identified among residents of zones A, B, and R, most of which resolved with
time (Assennato et al., 1989). For Seveso residents, a standard diagnosis of chloracne was
developed, in which all cases were stratified by severity: 0, no lesions; 1, a few comedones (up to
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10, minimum stage); 2, numerous comedones and cysts (light stage); 3, comedones and cysts in
specific regions (medium stage); and 4, comedones and cysts spreading from the face to other
regions of the body (serious stage) (Caramaschi et al., 1981). Four studies investigated possible
biochemical changes, particularly liver enzyme induction and lipid levels, among the 170 children
diagnosed with chloracne and control groups (Caramaschi et al., 1981; Ideo et al., 1985; Mocarelli
et al., 1986; Assennato et al., 1989).
In addition to chloracne, several other studies also evaluated peripheral neuropathy. In an
early report by Pocchiari et al. (1979), tests for presence of peripheral nerve dysfunction were
conducted for Seveso residents and for workers at the Icmesa production facility. Assennato et al.
(1989) and Filippini et al. (1981) assessed the prevalence of peripheral neuropathy, comparing
residents with and without chloracne (Assennato et al., 1989) or comparing residents having
chloracne or abnormal serum hepatic enzyme levels with residents with no manifestations of
2,3,7,8-TCDD exposure (Filippini et al., 1981).
Two mortality studies, one of children ages 1-19 years and another of adults 20 years and
older, examined death rates in residents of zones A, B, and RIO years after the explosion (1976-
1986) (Bertazzi et al., 1989, 1992). The comparison population was composed of approximately
100,000 inhabitants of uncontaminated areas surrounding Seveso. Follow-up for both the young
and older cohorts was 99%. A third study examined the mortality of the three cohorts from July
1976 through June 1991 (Pesatori et al., 1998).
To date, although there are a few individual measurements of exposure among residents of
the exposure zones, the general limitation of the studies conducted in Seveso residents is the
classification of exposure of subjects by residence in zones A, B, or R, which is based on mean
soil concentration of 2,3,7,8-TCDD. The weaknesses of using soil 2,3,7,8-TCDD levels to classify
extent of exposure to 2,3,7,8-TCDD were aptly described by Bertazzi et al. (1989): "This (use of
soil contamination) is a rather poor surrogate of exposure, and by no means an indicator of intake,
since it does not take into consideration all the possible sources and ignores interindividual
variability." This is the same problem encountered by some researchers investigating 2,3,7,8-
TCDD-related effects among Missouri residents. One might also expand this limitation to each of
the studies where environmental rather than personal levels of 2,3,7,8-TCDD contamination were
used for exposure classification.
7.12.2.3. Developmental Studies From The Netherlands
In the early 1990s, scientists from several Dutch communities collected data on postnatal
developmental outcomes and related them to total PCB-dioxin-furan TEQ from breast milk, and
PCBs in cord blood and maternal blood. The results from these studies will be addressed by
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system in Section 7.13. These studies are similar in design; a number come from the same study
group including women and infants from Rotterdam, and from both Rotterdam and Grongenin.
The outcomes studied in both communities included a neurological examination (Prechtl/Touwen)
at 10 days and 18 months of age and Obstetrical Optimality score at 10 days of age. In Rotterdam
only, mental and psychomotor development (Bayley Scales of Infant Development) was measured
at 3, 7, and 18 months of age, and visual recognition memory (Pagan Infant test) at 3 and 7
months of age. At 42 months, these children were assessed for cognitive abilities (Kaufman
Assessment Battery) and a subgroup was assessed for verbal comprehension (Reynell Language
Developmental Scales). Another group in Amsterdam has also examined postnatal developmental
outcomes and breast milk level; this study will be discussed at the end of this section.
The reports that covered Rotterdam, or Rotterdam and Groningen together, include the
same infants. Data were collected beginning in June 1990 and continued until February 1992 or
June 1992, depending on the outcome examined. Women were introduced to the project by their
obstetricians or midwives, and details were explained to the women at home. First contact took
place in the third trimester (32-34 weeks), and women were screened for their intentions to breast
feed for at least 6 weeks ("exposed") or for their intention not to breast feed at all (comparisons).
Only women with full-term deliveries to first- or second-born infants, among other characteristics,
were selected for inclusion in the study. Originally 489 women who were willing to participate
were identified; 71 of these were lost to the final study when they were not able to breast feed for
the required 6 weeks.
Formula was supplied to those women in the study who did not intend to breast feed. In
each community, the goal was to get approximately 100 in each group. The investigators did not
describe the response rate, or the comparability of respondents to nonrespondents. All the reports
included blood samples from the mother during the last month of pregnancy, cord blood, and
breast milk collected in the second week after delivery. PCB levels (congeners 118, 138, 153,
180) were measured in the blood samples and used to estimate the prenatal exposures to the
children, and PCB and dioxin-furan levels were measured in the breast milk (seventeen 2,3,7,8-
substituted PCDDs and PCDFs, 3 coplanar PCBs, and 23 nonplanar PCB congeners) (Table 7-
21b). These data were then used to rank exposure in the women and infants into categories to
examine exposure outcome. The total breast milk values were calculated using the levels
measured and multiplying this by the number of weeks of breast feeding. This approach assumes
only small changes in the levels of these exposures over the duration of breast feeding when
actual numbers are used, or that the relative magnitude of exposure remains consistent. As long
as all the women are breast feeding, this is a useful approach to determine relative magnitude.
Breast milk samples collected on the second week and about the sixth week after delivery were
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compared for the following exposure groupings: dioxins-furans: IUPAC 48, 54, 66, 67, 70, 73, 75,
83, 94, 114, 118, 121, 124, 130, 131, 134, 135), coplanarPCBs (IUPAC 77, 126, 169), mono-
orthoPCBs (IUPAC 105, 118, 156), di-ortho PCBs (IUPAC 170, 180), and total PCB-dioxin-
furans, which includes all of the above. This terminology will be used in the descriptions of
individual reports. With continued breast feeding, a drop in the levels of these would be expected
as the body burden decreases. Decreases in levels were observed for a number of these; not all
were statistically significant, in part because of the small number of women evaluated: decrease in
dioxin-furans (no. women = 27, p = 0.07), coplanar PCBs (no. women = 44, p = 0.91), mono-
ortho PCBs (no. women = 180, p = 0.002), di-ortho PCBs (no. women = 180, p = 0.001), and
total PCB-dioxin-furans (no. women =l9,p = 0.10) (Koopman-Esseboom et al., 1994b). It seems
likely that the measured levels in breast milk would continue to drop with an extended period of
breast feeding. Thus, the studies' assumption of a steady-state level of dioxins, furans and PCBs
in the breast milk, with the women's different lengths of breast feeding, could overestimate the
actual exposure levels to varying degrees. Therefore, any effects observed might occur at a lower
level of exposure than reported. Effects of exposure in these studies were assessed by analyses of
the dioxin-furan TEQ or total PCB-dioxin-furan TEQ (for dioxins, furans and dioxin-like PCBs)
based on levels observed in breastmilk, and ZPCBcordblood or ZPCBmatemal blood (for IUPAC 118,
138, 153, 180), controlling for such items as sociodemographic indicators and personal habits.
Because data from other sources have shown breast milk levels to be well correlated with adipose
tissues, the breast milk level during the second week after delivery is a reasonable estimate for
prenatal exposures (at least the relative magnitude) to these agents (Jensen, 1987).
About half the 418 mothers and infants were in Rotterdam and half in Gronginen. A
comparison of a variety of demographic factors, personal habits, and health characteristics for the
two communities showed no differences for a number of them (e.g. maternal age, weight,
smoking status), and significantly higher parental education, maternal alcohol consumption, and
birth weight of the child in Groningen; the length of gestation was one-half week longer in
Groningen. A number of factors, socioeconomic status (SES) indicators, health, and exposure
were different in the breast-fed versus formula-fed group (Huisman, 1995b): for example, more
than 60% of both parents in the breast-fed group had higher education versus 31% or less in the
formula-fed group. In addition, breast-feeding mothers were more likely to have consumed
alcohol during pregnancy (37% versus 18%) and less likely to have smoked (16% versus 35%).
The breast-fed pregnancies were also more highly exposed prior to breast-feeding: £PCBcordblood
and £PCBmatemal blood were significantly higher in this group. Analysis of breast milk levels in the
Rotterdam area and in Groningen (Koopman-Esseboom et al, 1994a) showed that the dioxin-furan
TEQ and some individual dioxin and congener levels (D66, D67, F83, Fl 18, F121, F130, PCB66,
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PCB118, PCB137, and PCB187) were significantly higher in the more urbanized Rotterdam area.
For some of the analyses, the infants were placed into a "low" or "high" group based on the
median value for the dioxin-furan TEQ (at 30.8 pg TEQ/g fat, for the total PCB-dioxin-furan TEQ
(at 72.4 pg TEQ/g fat) or into "low," "medium," or "high" based on the PCB-dioxin-furan TEQ
times the number of weeks breast fed. In addition, in one report different congeners were
analyzed separately. These different analysis strategies make integrating the data from the various
reports difficult.
Two individuals performed some of the tests (e.g., Neurologic Optimality Score in
Huisman et al., 1995a and Huisman et al., 1995b; Fluency Cluster Score in Huisman et al.,
1995b), one each in Rotterdam versus Grongenin. The authors reported that their comparison of
the data from the two cities suggested a systematic difference between the two for some outcomes.
They controlled for this potential discrepancy in the logistic regressions. As there was no direct
comparison of the scoring on the same children, the above is a possible explanation, as is the
possibility that there are community differences (for some unknown reason) for these specific
outcomes.
A smaller series of women and their infants were studied by another research team in
Amsterdam; between June 1990 and May 1991 these women were identified while still pregnant.
Only women who intended to breast feed for 12 weeks were included in this study group. The
reports from this group (Pluim et al., 1993; Pluim et al., 1994; Pluim et al., 1996) had a different
final number of subjects (38 versus 35), but from the general description, they do appear to be
from the same general group. Maternal blood was collected around the time of delivery, cord
blood was collected, infant blood samples were collected at 1 and 11 weeks of age, and three
weeks after delivery, two breast milk samples were collected. The authors assumed that the breast
milk levels also reflected in utero exposures to the infants. They did not include bottle-fed
children as comparisons for the following reasons: first, the SES of women who choose to breast
feed tends to be different from those who choose to bottle-feed; and second, comparison of breast
milk levels is not possible for the mothers of bottle-fed infants, either directly or using these levels
as a surrogate for the in utero exposures. The 17 most toxic congeners, 7 dioxins and 10
dibenzofurans, were used to develop a dioxin-furan TEQ (see Table 7-2 Ib for the listing of these
congeners and the mean levels for the study group). The final score for consumption for each
child used the amount of milk consumed (assumed to be 700 g/day while the child received only
breast milk, and half that amount later), the amount of milk fat in the milk (assumed to be an
average of 2.5%), and the levels of the 17 congeners. The levels determined were split at the
median into "low" and "high" groups (28.0 pg dioxin-furan TEQ g/fat; high = 29.2-62.7 pg
TEQ/g and low was less than 28.0 pg TEQ/g). These reports compared differences in the high
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and low groups (1) with thyroid hormone concentrations (Pluim et al., 1993) (see Section
7.13.4.2); (2) with alanine aminotransferase and aspartate aminotransferase activities, and platelet
count in the infants' plasma (Pluim et al., 1994) (see Section 7.13.2.5.2); and (3) against several
growth measures (neonatal weight and length, liver size and quetelet index) over the first six
months of life (Pluim et al., 1997) (see Section 7.13.12.8).
The Amsterdam reports are based on small groups of infants with undescribed selection
procedures. Thus it is not possible to evaluate volunteer bias. These authors did attempt to more
closely estimate the dioxin-furan TEQ, using changes in feeding patterns as well as the measured
levels in the early sample, but did not evaluate the potential for decreasing levels with increasing
length of breast feeding.
Both sets of studies split the group of breast-fed infants at the median of the TEQ into high
and low exposure groups, and used these for logistic regression and other analyses. This approach
for quantification of exposure is a reasonable estimation of the general magnitude of exposures.
Mean levels of exposure over the total period may be somewhat lower than reported, since the
breast milk evaluations occurred early in lactation (1-2 weeks after delivery), while levels were
likely to decrease with longer periods of breast feeding (see Part I, Vol. n Chapter 6, of
"Estimating Exposure to Dioxin-Like Compounds"). For both of these Dutch study groups, it is
important to note that the levels of dioxins, furans, and PCBs were within common/background
environmental ranges.
7.12.3. Studies of Vietnam Veterans
7.12.3.1. The Vietnam Experience Study
The Vietnam Experience Study is described by its authors as a "multidimensional
assessment of the health of Vietnam veterans" (Centers for Disease Control Vietnam Experience
Study, 1988a-d). This study was designed to examine effects among men who served in Vietnam.
The study population was composed of a random sample of men who enlisted in the U.S.
Army from 1965 through 1971, whose military occupational status was other than "duty soldier,"
who enlisted for a single term with a minimum of 16 weeks active duty and who were discharged
at pay grades of E-l to E-5. The controls were selected from among veterans enlisting during the
same period but whose duty station was the United States, Germany, or Korea. Participation
involved completion of a telephone survey of current and past health status by 7,924 veterans who
served in Vietnam and 7,364 veterans who served outside of Vietnam. A random subsample of
2,940 Vietnam and 1,972 non-Vietnam veterans participated in the health evaluation component.
In a separate study, serum 2,3,7,8-TCDD levels were measured in a subset of the
examined population: 646 Vietnam veterans who served in Vietnam during 1967 and 1968 and
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97 non-Vietnam veterans (Centers for Disease Control Veterans Health Studies, 1988). The mean
serum 2,3,7,8-TCDD level was not different between Vietnam (mean = 4.1 pg/g lipid [standard
deviation (SD) ± 2.3]) and non-Vietnam (4.2 pg/g, SD ± 2.6) veterans. Two Vietnam veterans
had levels above the background level of 20 pg/g: 25 pg/g and 45 pg/g.
The overall strengths of this study are that it is a large study, with good power to detect
many common disorders; participation in the questionnaire part of the study was good (87% for
Vietnam veterans; 84% for non-Vietnam veterans); there was good comparability between the two
cohorts in demographic characteristics, although there were differential participation rates in the
examination; and validation of selected self-reported effects was conducted. This study is limited
primarily by the differential participation rates in the examination (75% for Vietnam veterans;
63% for non-Vietnam veterans). The low level of 2,3,7,8-TCDD in the sample of veterans made
it impossible to conduct dose-response analyses.
7.12.3.2. U.S. Air Force Ranch Hand Study
One of the largest epidemiologic studies of U.S. military personnel stationed in Vietnam is
being conducted by the U.S. Air Force. The study population consists of Air Force personnel who
served in Operation Ranch Hand units in Vietnam from 1962 to 1971 and who were employed in
the dissemination of Agent Orange through aerial spraying. Comparisons included Air Force
personnel who flew or maintained C-130 aircraft in Southeast Asia during the same time period.
The study design includes a series of cross-sectional medical studies conducted at 5-year
intervals beginning with the baseline study in 1982 (N = 1,045 exposed, 1,224 unexposed). Two
follow-up evaluations were conducted in 1985 (N = 1,016 exposed, 1,293 unexposed) and 1987
(N = 995 exposed, 1,299 unexposed). Each cross-sectional study included comprehensive
physical and psychological evaluations. In the 1982 baseline and 1985 and 1987 follow-up
studies, exposure was based on the comparison of the Ranch Hand group versus the comparison
group. An additional analysis approximated exposure (low, medium, high) for the Ranch Hand
group by using historical military data and herbicide procurement and usage records. The results
of these analyses were prepared by Lathrop and colleagues (1984 and 1987). In 1988, serum
2,3,7,8-TCDD levels were measured for a sample of the 1987 Ranch Hand group (N = 866) and
the 1987 comparison group (N = 804). The 1987 examination data were then reanalyzed using
lipid-adjusted serum 2,3,7,8-TCDD levels as the relative measure of exposure. The median serum
2,3,7,8-TCDD level adjusted for lipids for the Ranch Hand group was 12.8 pg/g, ranging to 618
pg/g. For the comparison group, the median level was 4.2, ranging to 54.8 pg/g (Roegner et al.,
1991). For later studies, veterans who refused to give serum samples in 1987 or received a
nonquantifiable result were resampled in 1992.
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The overall strengths of this study are that it is a large study, with good power to detect
many common disorders; follow-up was very good, as is continued participation of Ranch Hand
and comparison populations. The physical and psychological examinations are extensive, planned
to evaluate most, if not all, outcomes hypothetically associated with 2,3,7,8-TCDD. Continued
reevaluation of the subjects (every 5 years) permits investigators to monitor the development of
chronic diseases and to test for additional outcomes as new biochemical and toxicological data
become available. Finally, the determination of serum 2,3,7,8-TCDD levels permitted validation
of the exposure matrix based on historical records and the subsequent development of disease-
specific dose-response models. Repeated measures of serum 2,3,7,8-TCDD over time will also
provide valuable information on its half-life in humans.
Noteworthy caveats in the study include the fact that the majority of the population had
serum levels under the background level of 20 pg/g (median = 12.8 pg/g, range to 600 pg/g in
1987). These data suggest that, although there are some Ranch Hands who were exposed to very
high levels of 2,3,7,8-TCDD, most of the study group had lower exposures, if any at all. In
addition, serum 2,3,7,8-TCDD levels indicated that the exposure matrix used in the analysis of the
baseline and 1984 studies did not appropriately describe the potential for exposure. Therefore,
data described in this chapter will refer only to the baseline and 1984 results where Ranch Hands
as a group were compared to the comparisons and, most often, to the reanalysis of the 1987 data
(plus relevant 1992 data) using serum levels. The adjusted odds ratios for the three categories of
serum 2,3,7,8-TCDD selected by Roegner and colleagues (1991) are presented and discussed.
The categories are: <10 pg/g 2,3,7,8-TCDD; 15-<33.3 pg/g 2,3,7,8-TCDD; and >33.3 pg/g
2,3,7,8-TCDD.
In the categorical analysis of the 1992 followup examination results (Grubbs et al, 1995),
results were presented in relation to the current 2,3,7,8-TCDD concentration (current dioxin level)
and to the concentration estimated since time in duty in southeast Asia (initial level) in the
following categories: comparison, current dioxin level ^.10 pg/g of lipid; background (Ranch
Hand), current dioxin level<10 pg/g of lipid; low (Ranch Hand), current dioxin > 10 pg/g of lipid,
10 pg/g 10 pg/g of lipid, 10
pg/g143 pg/g. In subsequent analyses, for the low and high categories the
definitions were changed to the following: low (Ranch Hand), 10 current and initial >94 pg/g (Michalek et al., 1997).
A consequence of conducting a comprehensive study in which a large number of statistical
tests are performed is an increased possibility of spurious findings. The reader should be
cognizant of this limitation, looking for consistencies with the results of other studies and in the
toxicological literature rather than statistical significance alone.
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7.12.3.3. Studies of the Effect oflngestion of Rice Oil Contaminated With Polychlorinated
Dibenzofurans, Quaterphenyls, and Biphenyls in Japan (YUSHO) and Taiwan (YU-
CHENG)
This section also briefly reviews the noncancer effects observed in Yusho (Japan) and Yu-
Cheng (Taiwan) victims, individuals exposed by ingestion to large concentrations of compounds
structurally related to dioxins, namely polychlorinated dibenzofurans, quaterphenyls, and
biphenyls. In addition, other reviews have summarized the numerous papers dedicated to Yusho
and Yu-Cheng (Lu and Wong, 1984; Kuratsune, 1989; Rogan, 1989).
Reports describing effects among individuals who ingested the contaminated rice oil both
in Taiwan and Japan were generally limited descriptions of acute rather than general effects.
However, because more than 25 years have passed since Yusho and 15 years since Yu-Cheng,
more studies are evaluating potential chronic effects.
Recent epidemiologic studies have concentrated on the development of offspring of Yu-
Cheng mothers. These children were exposed in utero at the time the contaminants were ingested
or were conceived after the poisoning and were exposed to residual contaminants transplacentally
or through breast milk (Chen et al., 1992; Lai et al., 1993, 1994; Hsu et al., 1993; Guo et al., 1992,
1993,1994a,b, 1995a,b,1996; Chao et al., 1997; Yu et al., 1994, 1998, 2000).
7.12.3.3.1. Yusho. The initial recognition of the effects of the ingestion of contaminated rice oil
by the Yusho population occurred in 1968. As of 1983, a total of 2,060 individuals were
identified as part of the affected Yusho population (Masuda et al., 1985). Five years after
exposure ended, the mean concentrations of PCBs in the adipose tissue, liver, and blood of Yusho
cases were 1.9 ppm, 0.08 ppm, and 6.7 ppb (Masuda et al., 1985), respectively, which were about
twice the levels in the control group. Adipose tissue levels of PCDFs ranged from 6 to 13 ppb
(Masuda et al., 1985). Sixteen years after exposure, mean PCQ level in adipose tissue of Yusho
cases was 207 ppb, approximately 100 times the level in Japanese controls (Kashimoto et al.,
1985).
7.12.3.3.2. Yu-Cheng. The initial recognition of Yu-Cheng occurred in 1979. As of 1983,
approximately 2,000 individuals were found to have been exposed to the contaminated rice oil.
Within the first year of exposure, mean serum PCB, PCDF, and PCQ levels in blood for 15 cases
were 60 ppm (range 4-188 ppm), 0.14 ppb (range <0.005-0.27 ppb), and 19.3 ppb (range 0.9-
63.8), respectively (Kashimoto et al., 1985). Analysis of PCB levels in blood in 1980-1981 in 165
cases (mean 38 ppb, range 10-720) (Rogan, 1989) and in 1985 in 32 cases (mean 15.4 ppb, range
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0.6-86.8) (Lundgren et al., 1988) suggested that some PCBs were being eliminated. It is not clear
from the reports if the samples were drawn from distinctly different individuals or included some
of the same individuals.
7.13. REVIEW OF EFFECTS ASSOCIATED WITH EXPOSURE TO 2,3,7,8-TCDD
The following section contains a review of the case reports and epidemiologic studies that
describe effects associated with exposure to materials contaminated with 2,3,7,8-TCDD.
7.13.1. Dermal Effects
7.13.1.1. Chloracne
The most widely recognized dermal effect of exposure to 2,3,7,8-TCDD-contaminated
substances is chloracne. Chloracne is a persistent acneiform condition characterized by
comedones, keratin cysts, and inflamed papules with hyperpigmentation and a unique anatomic
distribution, occurring subsequent to acute and chronic exposure to a variety of chlorinated
aromatic compounds (Crow et al., 1978; Moses and Prioleau, 1985). This acne-like condition is
reported to have occurred with and without other TCDD-related health effects in at least a few
workers after all reported accidents at TCP production facilities (Ashe and Suskind, 1950;
Suskind et al., 1953; Goldman, 1972; May, 1973; Zober at al., 1990), among individuals involved
in daily production of 2,3,7,8-TCDD-contaminated products (Bleiberg et al., 1964; Poland et al.,
1971; Pazderova-Vejlupkova et al., 1981; Moses et al., 1984; Moses and Prioleau, 1985; Suskind
and Hertzberg, 1984; Bond et al., 1989), among three laboratory workers exposed to pure
2,3,7,8-TCDD (Oliver, 1975), and among at least 193 (0.6%) Seveso residents, mostly children
(Reggiani, 1978; Caramaschi et al., 1981; Ideo et al., 1985; Mocarelli et al., 1986; Assennato et
al., 1989). Chloracne was not found among Missouri residents (Hoffman et al., 1986; Webb et al.,
1989) examined 10 years after exposure or among Ranch Hand personnel (Burton et al., 1998;
Roegner et al., 1991). In U.S. Army Vietnam veterans, chloracne-like skin lesions were rarely
observed on examination (0.9% in Vietnam veterans versus 0.8% in non-Vietnam veterans,
OR = 1.4, 95% CI = 0.7-2.9) (Centers for Disease Control Vietnam Experience Study, 1988a).
Based on reports from Seveso and studies of chemical workers, chloracne appeared shortly
after exposure to 2,3,7,8-TCDD-contaminated chemicals (Caramaschi et al., 1981; Zober et al.,
1990). The eruption of blackheads, usually accompanied by cysts, was observed between 2 weeks
and 2 months after the reactor release (Reggiani, 1980). In Seveso, within 6 months of the
explosion, 34 cases of chloracne were identified among children, whereupon a more intensive
search was undertaken among schoolchildren (Caramaschi et al., 1981). In chemical workers
involved in the TCP reactor release at BASF Ludwigshafen, Germany, most cases of chloracne
(that were also diagnosed with cancer) developed within 2 days after first exposure (Ott et al.,
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1994; Zober et al., 1990). One case of chloracne did not develop for 2 years, but the authors
suggest that the etiology of this case is unclear.
For many affected individuals, the condition disappeared after discontinuation of exposure
(Assennato et al., 1989) despite high serum 2,3,7,8-TCDD levels (Mocarelli et al., 1991). But for
a few, the chloracne remained for many years (Suskind and Hertzberg, 1984; Moses and Prioleau,
1985). Of the 204 exposed workers in the study by Suskind and Hertzberg (1984), 52% had
persistent chloracne for at least 10 years after the TCP and 2,4,5-T processes ceased, 34% reported
a history of chloracne, and 14% reported no history of chloracne. Moses et al. (1984) reported
that the mean duration for persistent chloracne was 26.1±5.9 years.
There are very few human data from which to determine definitively the threshold level of
2,3,7,8-TCDD at which chloracne occurs or who is at greatest risk to develop chloracne. Data
from analyses of chloracne cases among chemical workers and chloracne's relationship to serum
and adipose tissue levels of 2,3,7,8-TCDD and hexachlorinated (HxCDD) dioxins provide some
basic yet useful information on the characteristics of chloracne cases, particularly the inter-
individual susceptibility to chloracne (Bond et al., 1989; Ott et al., 1987; Beck et al., 1989;
Mocarelli et al., 1991). Bond et al. (1989) described 325 cases (15%) of chloracne among 2,192
workers exposed to 2,3,7,8-TCDD and HxCDDs or octachlorinated (OCDD) dioxins between
1938 and 1982 during chemical production activities (not as a result of a reactor accident). Cases
were identified through company-maintained medical records; age- and calendar year-specific
incidence rates were estimated based on age- and calendar year-specific person-years of
employment contributed by the cohort; and risk factors were adjusted for by logistic regression.
The analysis found that risk of chloracne was highest among workers who were exposed at
younger ages, among those who had the longest length of exposure to 2,4,5-trichlorophenol or
pentachlorophenol production operations, and among jobs rated at the highest intensity of
exposure (Ott et al., 1987). These characteristics of chloracne cases in Michigan workers are
consistent with those observed in chloracne cases from the BASF accident cohort (Ott et al.,
1993). Although this study may underestimate the incidence rate of chloracne owing to possible
underreporting or misdiagnosis of cases, and misclassification of exposure may have occurred,
this study was the first of its kind to explore analytically the risk factors associated with
occupationally acquired chloracne. It is unfortunate that these exposure estimates have not been
validated by serum or adipose tissue 2,3,7,8-TCDD, HxCDD, and OCDD levels.
Serum levels of 2,3,7,8-TCDD and HxCDD have been measured in chloracne cases of
Seveso residents (Mocarelli et al., 1991) and German chemical workers (Beck et al., 1989; Ott et
al., 1993). Mocarelli et al. (1991) described chloracne in persons from zone A who had very high
serum 2,3,7,8-TCDD levels ranging from 820 to 56,000 pg/g measured within 1 year of the
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reactor release (Table 7-22). The study also included other individuals from Zone A, but without
chloracne, who had serum 2,3,7,8-TCDD levels that ranged from 1,770 to 10,400 pg/g. With the
exception of one person with chloracne who was 16 years old at the time of the accident, all of the
cases were in children under age 11. Those without chloracne, for the most part, were over age
30. It is not clear whether the children were more susceptible to the chloranegenic effects or
whether they had greater exposure to 2,3,7,8-TCDD-contaminated soil or airborne effluent.
As documented by others, adult TCP production workers also developed chloracne (Beck
et al., 1989; Suskind and Hertzberg, 1984; Bond et al., 1989). Adipose tissue levels of 2,3,7,8-
TCDD and HxCDD measured in adult chloracne cases of German chemical production workers
suggest that these cases may have been a function of the combined exposure, making it difficult to
isolate the contribution of the different chlorinated compounds. All cases had estimated adipose
levels of greater than 200 pg/g 2,3,7,8-TCDD and in excess of 2,000 pg/g lipid HxCDD at the
time of diagnosis. Estimated levels were based on the half-life extrapolation of the adipose tissue
level measured in 1986 to the date of last occupational exposure, which may have occurred
between 1949 and 1984. Similarly, Ott (1993) found that 80% of the severe chloracne cases had
estimated (back-calculated) lipid-adjusted serum 2,3,7,8-TCDD levels of 250 pg/g. Yet 26% of
nonchloracne cases had estimated 2,3,7,8-TCDD concentrations of 250 pg/g.
Data from studies of Seveso residents conducted from 1982 to 1985 indicate that, despite
high serum 2,3,7,8-TCDD levels, the chloracne resolved in all but one person by 1983 (Assennato
et al., 1989). The fact that the cases of chloracne in Seveso residents resolved within 10 years
may explain why no chloracne was observed in the Ranch Hand group, although some serum
2,3,7,8-TCDD levels exceeded 600 pg/g in 1988 (Roegner et al., 1991) and may have been as high
as 2,400 pg/g at the time of last occupational exposure, assuming 21 years since last exposure and
a 7-year half-life. Nevertheless, residual chloracne was observed 30 years after first exposure
among workers from Nitro, West Virginia, which may suggest that chronic high exposure to
2,3,7,8-TCDD or exposures higher than experienced by the Ranch Hands may account for long-
term persistence of chloracne.
7.13.1.2. Dermatologic Disorders Other Than Chloracne
Dermal effects other than chloracne attributed to 2,3,7,8-TCDD exposure include a variety
of symptoms and conditions that occurred less frequently than chloracne but appeared in several
groups subsequent to acute and continuous exposure to 2,3,7,8-TCDD-contaminated TCP and
2,4,5-T. Two reports indicated that after acute episodes of exposure, e.g., accidents, individuals
complained of red and irritated eyes, conjunctivitis, and blepharitis (inflammation of the eyelids)
(Ashe and Suskind, 1950; Baader and Bauer, 1951). Other investigators also found cases of
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eyelid cysts several months after acute exposure (Suskind et al., 1953; Kimmig and Schulz,
1957a,b; Poland et al., 1971; Reggiani, 1980) and up to 25 years after exposure (Moses et al.,
1984; Suskind and Hertzberg, 1984).
Hyperpigmentation and hirsutism (also known as hypertrichosis or abnormal distribution
of hair) were diagnosed among chemical workers in the United States (West Virginia and New
Jersey) (Ashe and Suskind, 1950; Suskind et al., 1953; Bleiberg et al., 1964; Poland et al., 1971),
Germany (Bauer et al., 1961; Goldman, 1972), and Czechoslovakia (Jirasek et al., 1974) who
were exposed to 2,3,7,8-TCDD-contaminated TCP during manufacturing processes or industrial
accidents and among laboratory workers in England exposed while synthesizing pure
2,3,7,8-TCDD (Oliver, 1975). Upon reexamination 25 years later, hypertrichosis was observed in
exposed workers (5.4% exposed vs. 1.8% unexposed) from the West Virginia plant, particularly
among workers with persistent chloracne on clinical examination (10.3% with persistent
chloracne vs. 0% with history of chloracne only) (/X0.001) in one of two independent studies
(Suskind and Hertzberg, 1984). A second study by Moses et al. (1984) found no evidence of
hypertrichosis, although 31% of the exposed workers had evidence of residual chloracne (Moses
et al., 1984). Studies of Vietnam veterans have reported no significant increase in the prevalence
of either hyperpigmentation or hypertrichosis (Roegner et al., 1991; Centers for Disease Control
Vietnam Experience Study, 1988a). Three cases of hypertrichosis but not hyperpigmentation
were observed among Missouri residents, one with serum levels less than 20 pg/g and two with
levels between 20 and 60 pg/g (Webb et al., 1989). Neither disorder was noted on examination
among residents of the Quail Run Mobile Home Park (Hoffman et al., 1986).
Actinic or solar elastosis was found to be more prevalent among West Virginia workers
diagnosed with active chloracne at the time of their examinations in 1979 (Suskind and Hertzberg,
1984) (exposed = 59.1% vs. unexposed = 30.1%,/X0.01). No significant difference was
observed in the age-adjusted prevalence of actinic elastosis in workers with or without chloracne
in the study by Moses et al. (1984). Actinic elastosis is known to be directly related to sun
exposure; however, the amount of sun exposure, skin type, or other factors contributing to the
sensitivity of the skin to sunlight were not assessed in the report. No other studies of TCP
production workers, the Ranch Hands, or U.S. Army Vietnam veterans have found an increase in
the prevalence of actinic elastosis.
Among the group of workers studied by Suskind and Hertzberg (1984), three cases of
Peyronie's disease were noted. Peyronie's disease is a rare condition characterized by progressive
scarring of the penile membrane. No explanation for this finding was expressed, nor has the
condition been noted (or perhaps looked for) in other studies (Bond et al., 1989; Moses et al.,
1984; Roegner et al., 1991).
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In 1984, a statistically significant excess of nonmelanotic skin cancer was reported among
Ranch Hand personnel involved in the aerial spraying of herbicides over Vietnam compared with
a matched comparison group (Lathrop et al., 1984). The comparison group was composed of Air
Force personnel assigned to cargo missions outside the sprayed areas of Vietnam. A follow-up
study of the same cohorts in 1987 confirmed the excess of basal cell carcinoma and attributed the
increase to sunlight exposure (Lathrop et al., 1987). However, in the reanalysis of the 1987
examination data, skin neoplasms of any kind were not related to serum 2,3,7,8-TCDD level
(Roegner et al., 1991).
7.13.1.3. Comment
From an epidemiologic perspective, chloracne is a common consequence of exposure to
chemicals contaminated with 2,3,7,8-TCDD and some other polyhalogenated hydrocarbons.
Available data on serum or adipose tissue levels of 2,3,7,8-TCDD have not determined the
threshold at which a case of chloracne occurs. Evidence from Bond et al. (1989) suggests that for
chemical production workers the risk of chloracne may be related to the part of the process in
which the workers were engaged, the amount of time spent in the contaminated region, and the
intensity of the exposure while in the area. Chloracne is also related to short-term high-intensity
exposures, as observed in Seveso residents (Reggiani, 1980) and occupational cohorts (Ott et al.,
1993). Few studies have been successful in evaluating the relationship between history of
chloracne and long-term nonmalignant effects. However, Zober et al. (1990) noted that the SMR
for all malignant neoplasms for workers with chloracne and who had at least 20 years of latency
(time since first exposure) was statistically significantly elevated (SMR 201; 90% CI = 122, 135).
Future studies that are able to evaluate the association between history of chloracne and effects
would provide useful information in this regard.
Other conditions, such as hyperpigmentation and hypertrichosis, may be more acute effects
of 2,3,7,8-TCDD exposure that resolve over time, because they were not observed in studies
where the cohorts were examined years after the cessation of exposure. Actinic keratosis,
Peyronie's disease, and basal cell carcinoma may not be due to 2,3,7,8-TCDD because actinic
keratosis and Peyronie's disease have been observed in a single cohort. Likewise, the excess basal
cell carcinoma was noted only in one study group, and the results could not be replicated when a
better indicator of personal exposure to 2,3,7,8-TCDD, serum 2,3,7,8-TCDD, was used as a
surrogate for exposure in the statistical models.
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7.13.2. Gastrointestinal Effects
7.13.2.1. Hepatic Effects
Changes in liver function and structure after exposure to 2,3,7,8-TCDD are commonly
observed in experimental animals (Greig et al., 1973; Vos et al., 1974; Jones and Greig, 1975;
McConnell et al., 1978a,b; Jones et al., 1981; Zinkl et al., 1973; Kociba et al., 1976, 1978;
Gasiewicz et al., 1980; DeCaprio et al., 1986). The changes are not always consistent from one
species to another, but they have prompted examination of hepatic effects among exposed human
populations. As with animals, there is wide variation in the type and degree of hepatic effects
reported in humans after exposure to 2,3,7,8-TCDD-contaminated materials. This section
describes selected hepatic effects associated with 2,3,7,8-TCDD exposure observed in humans,
including hepatomegaly and hepatic enzyme changes.
7.13.2.2. Liver Size
Increased liver size is consistently reported in treated animals after exposure to
2,3,7,8-TCDD (Vos et al., 1974; Allen et al., 1977; McConnell et al., 1978a,b; Kociba et al.,
1978; Gasiewicz et al., 1980). Among exposed human populations, four case reports in three
populations, but not controlled epidemiologic studies, described evidence of enlarged livers or
hepatomegaly. Liver size was reportedly increased among two TCP production workers in West
Virginia within a few months after a TCP reactor explosion (Ashe and Suskind, 1950; Suskind et
al., 1953) and among "several" production workers in Czechoslovakia exposed to TCP, the butyl
ester of 2,4,5-T, and sodium pentachlorophenol (Jirasek et al., 1974). Temporary liver
enlargement was observed in 5 of 22 Seveso residents who had severe chloracne (Reggiani,
1980). The hepatomegaly lasted "several" months without concomitant elevation in hepatic
enzymes. Fortunately, the effect appeared to be transient. Cross-sectional medical studies of TCP
production workers (Bond et al., 1983; Suskind and Hertzberg, 1984; Moses et al., 1984; Calvert
et al., 1992), Vietnam veterans (Centers for Disease Control Vietnam Experience Study, 1988a;
Roegner et al., 1991), and Missouri residents (Webb, 1989; Hoffman et al., 1986) have found little
evidence of excess hepatomegaly in the exposed populations. Additionally, no dose-response
relationship was observed between serum levels of 2,3,7,8-TCDD and physical findings of an
enlarged liver for the 1987 or 1992 examinations of Ranch Hands (< 10 pg/g 2,3,7,8-TCDD,
RR = 0.39, 95%CI = 0.11-1.33; 15-<33.3 pg/g 2,3,7,8-TCDD, RR= 1.47, 95% CI = 0.57-3.79;
>33.3 pg/g 2,3,7,8-TCDD, RR = 1.69, 95% CI = 0.60-4.75) (Roegner et al., 1991) (Background:
RR = 0.51 95% CI = 0.19, 1.38; Low: RR = 0.26, 95% CI = 0.06, 1.09; High: RR = 1.02,
95% CI = 0.43, 2.44) (Grubbs et al., 1995) or the NIOSH study of TCP production workers (two
workers; four referents; OR = 0.46, 95% CI = 0.09, 2.43) (Calvert et al., 1992).
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One Missouri resident was found to have hepatomegaly, but he also was suffering from
diabetes mellitus. His adipose tissue 2,3,7,8-TCDD level was 430 pg/g (Webb et al., 1989). The
differences in findings between the case reports and the controlled epidemiologic studies suggest
that hepatomegaly may be a resolvable, acute effect as a result of exposure to high levels of
2,3,7,8-TCDD.
7.13.2.3. Enzyme Levels
Laboratory studies have demonstrated changes in hepatic enzyme levels after 2,3,7,8-
TCDD exposure, although there is considerable interspecies variation in the observed effect
(Zinkl et al., 1973; Kociba et al., 1976, 1978; Gasiewicz et al., 1980; Olson et al., 1980).
Epidemiologic studies and case reports describe elevated liver enzymes among exposed TCP
production workers, Ranch Hand veterans (Roegner et al., 1991), and among Seveso residents
(Mocarelli et al., 1986; May, 1982; Martin, 1984; Moses et al., 1984; Calvert et al., 1992; Ott et
al., 1994).
7.13.2.4. GGT
Increased levels of gamma glutamyl transferase (GGT) may suggest activity such as
cholestasis, liver regeneration, or drug or xenobiotic metabolism (Table 7-23). The studies of
Seveso children demonstrate an increase in GGT levels occurring shortly after the explosion and
then a gradual decline to near normal levels within 5 years. In one of the earliest studies of
Seveso children with (N = 141) and without chloracne (N = 138), 2.8% of the children with
chloracne had out-of-range GGT levels, but none of the children without chloracne had an out-of-
range level (/X0.001) (Caramaschi et al., 1981). These results were echoed in a study of children
from zones A, B, and R of Seveso, in which enzyme levels were measured yearly between June
1977 and June 1982 (Mocarelli et al., 1986). GGT levels were elevated in children of zone A,
particularly in boys, during the first 2 years after the explosion (1977: exposed = 9.73 U/L;
unexposed = 7.28 U/L;/X0.01; 1978: exposed = 9.88 U/L; unexposed = 8.26 U/L;/X0.05)
(Mocarelli et al., 1986). Levels in girls during the same years were elevated but did not achieve
statistical significance. For the next 4 years of the study, GGT levels remained elevated in boys
and girls from zone A compared to unexposed children, but the values declined to normal levels
with time.
GGT was found to be elevated among TCP production workers from one plant in Great
Britain and in workers from West Virginia, Missouri, and New Jersey up to 30 years after last
occupational exposure to 2,3,7,8-TCDD-contaminated chemicals (May, 1982; Martin, 1984;
Moses et al., 1984; Calvert et al., 1992). The findings of two studies of British workers were
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similar. Mean GOT levels were increased, but not statistically elevated, in workers with
chloracne compared to unexposed controls tested 10 years after exposure to 2,3,7,8-TCDD-
contaminated chemicals as a result of a TCP reactor explosion (chloracne, GGT = 39 U/L;
controls, 27.7 U/L [May 1982]) (chloracne, GGT = 32 U/L; controls, 32 U/L [Martin, 1984]).
Similarly, a statistically significant excess in the proportion of individuals with abnormally high
GGT levels was found among West Virginia workers with chloracne who were examined as many
as 30 years after exposure (Moses et al., 1984) (chloracne, mean GGT = 26.3 U/L, 23% abnormal;
no chloracne, mean GGT = 17.4 U/L, 9% abnormal; /KO.003). Yet, compared to controls, GGT
was not elevated in another study of West Virginia workers (Suskind and Hertzberg, 1984).
In a study by Calvert et al. (1992), the mean GGT level and the proportion of workers with
out-of-range levels were statistically significantly elevated among TCP workers from New Jersey
and Missouri (workers, mean GGT = 58.5 U/L; unexposed referents, mean GGT = 47.4 U/L,
/K0.03; workers, 11% abnormal; referents, 5% abnormal, OR = 2.27, 95% CI = 1.17-4.39).
Based on the logistic regression model in Table 7-24, the increases in GGT were limited to
workers with high serum 2,3,7,8-TCDD levels (>100 pg/g) and high lifetime alcohol consumption
(>30 alcohol years) (alcohol year = 1 alcoholic beverage/day for 1 year). The contribution of
other potentially confounding exposures that may have affected GGT levels was not explored in
this study. Other studies of TCP production workers in Michigan and West Virginia or the BASF
accident cohort did not report elevations in GGT levels (Bond et al., 1983; Suskind and
Hertzberg, 1984; Ottetal., 1994).
Both the Vietnam Experience Study and the U.S. Air Force Ranch Hand Study found
statistically significant elevations in GGT levels (Centers for Disease Control Vietnam Experience
Study, 1988a; Roegner et al., 1991). In Army Vietnam veterans, mean GGT levels were 43.2 U/L
compared with 41.1 U/L in non-Vietnam veterans (OR for out-of-range value = 1.3, 95%
CI = 1.0-1.8) (Centers for Disease Control Vietnam Experience Study, 1988a). In the 1987
follow-up study, the comparison of the adjusted mean GGT level in the comparison group and in
each of the three Ranch Hand groups defined by 2,3,7,8-TCDD level found statistically significant
increases in the Ranch Hand population (<10 pg/g 2,3,7,8-TCDD,p<0.017; 15-<33.3 pg/g
2,3,7,8-TCDD,/X0.043; >33.3 pg/g 2,3,7,8-TCDD,/X0.001) (Roegner et al., 1991). In the 1992
followup, significant increseases were noted in Ranch Hands classified as either low or high
exposure (Grubbs et al., 1995).
7.13.2.5. AST and ALT
7.13.2.5.1. Adult measures. Abnormal levels of aspartate aminotransferase (AST) and alanine
aminotransferase (ALT) may indicate liver cell damage from a number of causes, including
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hepatic necrosis, metastatic carcinoma, or obstructive jaundice (AST and ALT) or infectious or
toxic hepatitis and cirrhosis (AST). Elevated levels of these enzymes may also be due to
nonhepatic origins, such as myocardial infarction, acute pancreatitis (AST and ALT), or skeletal,
cerebral, or renal necrosis (AST).
With respect to exposure to 2,3,7,8-TCDD, elevations in serum ALT and AST appear to
be transient effects of acute exposure (Table 7-25). Case reports note that some populations have
increased serum ALT levels shortly after exposure (Seveso children, British and Czechoslovakian
TCP production workers) (May, 1973; Jirasek et al., 1974; Caramaschi et al., 1981; Mocarelli et
al., 1986). Whereas epidemiologic studies conducted 10 to 30 years after last exposure reported
no effects in exposed workers, Vietnam veterans, and Missouri residents compared to unexposed
control groups (Table 7-25) (Suskind and Hertzberg, 1984; May, 1982; Martin, 1984; Bond et al.,
1983; Calvert et al., 1992; Centers for Disease Control Vietnam Experience Study, 1988a;
Roegner et al., 1991; Grubbs et al., 1995; Hoffman et al., 1986; Webb et al., 1989; Ott et al.,
1994), or workers with and without chloracne (Moses et al., 1984). Furthermore, it appears from
a single study that current exposure to 2,3,7,8-TCDD-contaminated substances must exceed some
threshold to produce detectable enzyme elevations (Poland et al., 1971). Normal levels of AST
(AST = 10.6 U/L) were found in workers who volunteered to participate in a medical study
conducted concurrently with their employment in a New Jersey chemical facility producing TCP
and 2,4,5,-T (Poland et al., 1971). This was the same plant included in a later cross-sectional
medical study of workers that found, in 1988, a high mean serum 2,3,7,8-TCDD level for the
group (220 pg/g) but no elevations in AST or ALT (Calvert et al., 1992; Fingerhut et al., 1991a).
Similarly, no increases in AST were noted in the BASF accident cohort (Ott et al., 1994).
Several reports illustrate the probable transiency of ALT and AST elevations after heavy
2,3,7,8-TCDD exposure. Both enzymes were reported to be elevated among TCP production
workers employed in Czechoslovakia who were described as exhibiting symptoms of "chemical
intoxication" (Jirasek et al., 1974). The authors reported that AST and ALT levels were
"different" in 11 (20%) of the 55 examined workers. These workers were exposed to 2,3,7,8-
TCDD-contaminated chemicals, pentachlorophenol, and its production by-products, which
include hexa-, hepta-, and octachlorinated dioxins and dibenzofurans. The exposures occurred
between 1965 and 1968, with the ensuing effects beginning shortly thereafter. The observation
period lasted from 1967 to 1973. In a follow-up report of the same population conducted in
approximately 1977, Pazderova-Vejlupkova et al. (1981) did not report liver enzyme levels but
suggested that the levels were not abnormal.
Similarly, ALT was increased in 5 of 14 TCP workers from Great Britain who were in the
manufacturing building at the time of a TCP reactor explosion in 1968 (May, 1973). Levels for
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AST were not reported. In 1977, workers from the same facility were reevaluated. No elevations
in AST or ALT were found in production and laboratory workers with chloracne (May, 1982).
Finally, during the first year after the TCP reactor explosion, Caramaschi et al. (1981)
evaluated AST and ALT among Seveso children with and without chloracne. Only ALT was
statistically significantly elevated in children with chloracne. In a larger study, Mocarelli et al.
(1986) tested liver enzyme levels yearly from 1977 to 1982 in male and female children from
Seveso and from the unexposed surrounding area. In the 1977, 1979, 1980, and 1981 test series
ALT, but not AST, was statistically significantly (p<0.05) elevated among male children in
Seveso compared with unexposed comparisons. Female children had normal levels compared
with controls for all years. In 1982, ALT levels in the exposed boys returned to normal.
None of the studies reporting elevations in ALT or AST identified clinical evidence of
liver disease in the study populations. Therefore, in the absence of reports of hepatic or
nonhepatic diseases related to changes in ALT or AST levels among exposed individuals, it is
possible that the increases in ALT and AST are related to high-level, acute exposure to 2,3,7,8-
TCDD-contaminated chemicals and that, barring additional exposure, the enzyme levels decrease
with time.
7.13.2.5.2. Developmental measures. One report (Pluim et al., 1994) has examined blood
measures in 35 babies (the study and exposure groupings are described in detail in Section
7.12.2.3.). Four blood samples were taken: maternal blood around delivery, cord blood, and the
infant's blood at 1 and 11 weeks of age. These samples were used to measure leucocytes (WBC),
platelets, and differential, along with plasma, activity of gamma glutamyltransferase (GOT), AST,
and ALT, and levels of cholesterol and bilirubin. Dioxin and furan levels were measured in breast
milk collected about 3 weeks post-delivery. None of the maternal blood measurements were
outside the normal range. A statistically significant inverse correlation was observed in an
uncorrected comparison of the number of polynuclear neutrophils and dioxin-furan levels in
breast milk r = -0.53, p = 0.022); this disappeared when regression analysis compared these and
controlled for gestational age. None of the other factors compared in the cord blood or at 1 week
or 11 weeks of age were statistically significant. The next set of analyses used the estimated
cumulative dioxin-furan intake from breast feeding at 11 weeks of age; the PCDD/PCDF TEQs
ranged from 5.7 to 123.7 pg TEQ/g fat, with a mean of 44.7 pg. The statistically significant
correlations are presented in Table 7-26. These results are unadjusted, but remain significant after
adjusting for maternal age, gestational age, and birth weight (regression coefficients were not
presented). The author proposed that the changes in ALT and AST suggest an effect on the liver,
associated with the cumulative exposure to dioxin-furans, and notes that all but three of the
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children had ALT and AST within "normal" ranges, but the distribution of some of these findings
(e.g., an increase in platelets) did vary. From this study, it is not possible to determine the
reversibility of these changes.
7.13.2.6. D-Glucaric Add
In five studies of Seveso residents, TCP production workers, and Vietnam veterans,
urinary excretion of D-glucaric acid was measured to determine if exposure to 2,3,7,8-TCDD
induced hepatic microsomal activity (Table 7-27). D-glucaric acid excretion is an indirect but
valid indicator of enzyme induction.
Ideo and colleagues (1985) measured urinary D-glucaric acid in adults and children from
all zones of Seveso and from nearby uncontaminated towns. Of adults tested in 1978, D-glucaric
acid excretion was significantly elevated in adults residing in Seveso, Italy, at the time of the
reactor explosion compared with residents of unexposed communities (Seveso = 27.1 nmol/g of
creatinine vs. unexposed = 19.8 nmol/g of creatinine,/><0.05). No further studies of adults have
been published. A series of studies evaluated D-glucaric acid excretion in Seveso children (Ideo
et al., 1985) (Table 7-27). In 1976, the levels in children from zone A with chloracne (39 nmol/g
of creatinine) were significantly greater than in children without chloracne (20.5 nmol/g of
creatinine). Additional studies, conducted until 1981, found significant yearly decreases in
urinary D-glucaric acid excretion. By 1981, levels were within normal range.
These early analyses of D-glucaric acid levels were conducted without the benefit of
individual exposure measurements. In 1989-1994, serum 2,3,7,8-TCDD concentrations were
analyzed in blood samples collected in 1976-77 from the original study group. In a comparison of
D-glucaric acid levels and serum 2,3,7,8-TCDD concentrations, Cassaniga et al. (1999) found the
median D-glucaric acid levels of the 26 controls and of the 11 exposed children with 2,3,7,8-
TCDD concentrations below 1,000 pg/g lipid were similar (Controls = 22.8 nmol/g creatinine;
<1,000 pg/g lipid TCDD = 21.6 nmol/g creatinine); the median D-glucaric acid levels of the five
children with 2,3,7,8-TCDD concentrations above 1,000 pg/g were twice as high (59.2 |_imol/g
creatinine). These data suggest that D-glucaric acid levels may be a useful marker of recent, very
high exposure to 2,3,7,8-TCDD because within 5 years after exposure, D-glucaric acid levels
returned to normal.
D-glucaric acid-creatinine ratios were used to assess urinary excretion in TCP production
workers tested within a year of suspension of TCP production and 10 years after a TCP reactor
explosion. For exposed workers with or without chloracne at the time of the study, the D-glucaric
acid-creatinine ratio was significantly higher than that of the unexposed controls (exposed = 2.09;
unexposed controls = 1.59;/><0.05) (Martin, 1984).
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Other studies of Air Force Ranch Hands or TCP production workers that examined
D-glucaric acid excretion did not find increases in exposed populations 10 to 37 years after last
exposure to 2,3,7,8-TCDD-contaminated chemicals (Roegner et al., 1991; Calvert et al., 1992).
7.13.2.6.1. Comment. The evidence presented by the large number of case reports and
epidemiologic studies of groups exposed to 2,3,7,8-TCDD-contaminated chemicals suggests that
hepatic enzyme induction occurred in some populations within a short time after high-level
2,3,7,8-TCDD exposure. In most cases, enzyme levels decreased as the time from exposure
increased. However, even after more than 15 years since last exposure, levels of GGT continue to
be significantly elevated in relation to serum 2,3,7,8-TCDD in TCP production workers with
above-average alcohol consumption (Calvert et al., 1992) and in Air Force Ranch Hands (Roegner
et al., 1991; Grubbs et al., 1995). Other Vietnam veterans (U.S. Army ground troops) also have
significantly increased GGT levels compared with non-Vietnam veterans, but this increase in the
Army veterans is probably not due to exposure to high levels of 2,3,7,8-TCDD. In the population
of Army Vietnam veterans studied, the mean serum 2,3,7,8-TCDD was approximately 4 pg/g
(Centers for Disease Control Veterans Health Studies, 1988), compared to a mean of 220 pg/g
(range to 3,400 pg/g) in the production workers and a median of 12 pg/g (range to 600 pg/g) in the
Ranch Hands.
The finding of continued elevation of GGT in the NIOSH and Army veterans may be a
spurious result or it may reflect activity related to the continued presence of above-background
levels of 2,3,7,8-TCDD in exposed individuals.
7.13.2.7. Porphyrin Metabolism
In rats and mice, exposure to 2,3,7,8-TCDD has been clearly shown to produce alterations
in porphyrin metabolism (Goldstein et al., 1973, 1982; Smith et al., 1982; Jones et al., 1981;
DeVerneuil et al., 1983; Cantoni et al., 1981). Whether 2,3,7,8-TCDD is associated with
porphyrin changes in humans, particularly porphyria cutanea tarda (PCT), is a subject of some
debate. PCT is a form of acquired or inherited porphyria caused by a deficiency of the enzyme
uroporphyrinogen decarboxylase and the resulting overproduction and excretion of uroporphyrin
(Sweeney, 1986). The predominant characteristics of PCT include skin fragility, blistering upon
sun exposure, dark pigmentation, excess hair growth, hepatomegaly, reddish-colored urine, and
urinary excretion of uro- and heptacarboxylicoporphyrins (Strik, 1979). PCT has been associated
with excessive alcohol intake, oral estrogens, iron overload, hepatomas, and exposure to
polyhalogenated hydrocarbons (Strik, 1979). A particularly large outbreak of PCT occurred after
consumption of grain treated with hexachlorobenzene (HCB) (Cam and Nigogosyan, 1963).
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Cases of PCT were described in two populations of TCP production workers (Bleiberg et al.,
1964; Jirasek et al., 1974) and among members of a family with inherited uroporphyrin
decarboxylase deficiency who were living in Seveso at the time of the reactor explosion (Strik,
1979).
In 1964, Bleiberg reported that based on the Watson-Schwartz test, 11 of 29 New Jersey
TCP production workers with chloracne had PCT as a result of increased urinary uroporphyrins,
coproporphyrins, and urobilinogen. In a later study of 73 workers from the same plant in New
Jersey, including four of the individuals that Bleiberg et al. (1964) found to have elevated urinary
porphyrins, Poland et al. (1971) identified one individual with uroporphyrinuria. The report did
not explain if this individual was one of the four described by Bleiberg et al. (1964). In the
NIOSH study that examined workers from the same plant in New Jersey, the pattern of urinary
porphyrin excretion for each participant was assessed to determine the presence of PCT (Calvert
et al., 1994). No difference in the prevalence of PCT was found between workers and an
unexposed control group (OR = 0.93, 95% CI = 0.19, 4.54). Furthermore, there were no
differences in the risk between workers and the control group for an out-of-range uroporphyrin
concentration or an out-of-range coproporphyrin concentration. Because this study was conducted
at least 15 years after last occupational exposure to TCDD, it was not possible to determine
whether porphyrinuria occurred during the years more proximal to occupational 2,3,7,8-TCDD
exposure.
Jirasek et al. (1974) found 11 of 55 Czechoslovakian TCP production workers to have
elevated urinary uroporphyrins that decreased during the observation period; the authors did not
describe the test used to measure urinary uroporphyrins or coproporphyrins. Ten years later in a
follow-up study, Pazderova-Vejlupkova et al. (1981) found no evidence of increased excretion of
uroporphyrins or dermatological indications of PCT in the same group of workers.
There is some question that the porphyria noted in the New Jersey (Bleiberg et al., 1964)
and Czechoslovakian workers was due to 2,3,7,8-TCDD exposure. Jones and Chelsky suggest
that the observed cases of PCT in both plants may be due to exposure to HCB or a combination of
both 2,3,7,8-TCDD and HCB (Jones and Chelsky, 1986). HCB was manufactured at the New
Jersey plant from 1951 until 1960 and was produced at the facility in Czechoslovakia during the
production of pentachlorophenol and TCP (Jirasek et al., 1973). In addition, there is some
question as to the appropriateness of the clinical test Bleiberg et al. (1964) used to measure
porphyrin levels. The Watson-Schwartz test was capable of measuring only the presence of
porphobilinogen. The test was rarely positive in cases of exposure to hepatotoxins. Bleiberg's
findings suggest that either other unspecified tests were used to measure uro- and coproporphyrin
levels or the authors misinterpreted the function of the Watson-Schwartz test.
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Evidence of porphyria in other studies of individuals exposed to 2,3,7,8-TCDD-
contaminated substances is minimal. Although Suskind and Hertzberg (1984) sampled urine for
porphyrins in the examination of West Virginia TCP workers, the authors report that the data are
not valid. However, there was no dermatologic evidence of porphyria identified among the
exposed workers. Moses et al. (1984) found no difference in porphyrin levels when comparing
TCP workers with and without chloracne. Finally, no PCT was reported among three laboratory
workers exposed to pure 2,3,7,8-TCDD (Oliver, 1975), the only humans known to have
documented unintentional exposure to uncontaminated 2,3,7,8-TCDD.
In 1977, 60 Seveso residents were tested for elevated porphyrins, exclusive of the family
with inherited deficiency of uroporphyrinogen decarboxylase. None of the 60 residents developed
PCT; however, 13 (22%) exhibited secondary coproporphyrinuria, 5 of whom showed a slight
increase of urocarboxyporphyrins, heptacarboxyporphyrins, and coproporphyrins classified as a
"transition constellation to CHP type A" (Doss et al., 1984). Porphyrin levels were retested in
1980. Porphyrin levels returned to normal in 12 individuals. In three of those with transition
CHP, porphyrin levels were higher than those in 1977 and were attributed to liver damage and
alcohol consumption. Doss et al. (1984) suggested that in the Seveso family with
uroporphyrinogen dicarboxylase deficiency, the exposure to 2,3,7,8-TCDD-contaminated effluent
caused an exacerbation of a preexisting enzyme deficiency.
7.13.2.7.1. Comment. It is possible that the PCT and elevated urinary porphyrins observed in the
New Jersey and Czechoslovakian workers were a direct result of exposure to hexachlorobenzene.
In the follow-up studies, urinary porphyrin levels in workers were not elevated (Pazderova-
Vejlupkova et al., 1981; Poland et al., 1971) or did not differ from levels in the control group
(Calvert et al., 1992). The transient elevations in coproporphyrins among 22 Seveso residents
described by Doss et al. (1984) may be a direct result of acute exposure to 2,3,7,8-TCDD.
The association is not clear. However, 2,3,7,8-TCDD is a potent porphyrigen in rats and
mice and, therefore, high acute exposures may have contributed to the observed changes in
porphyrin levels in these populations.
7.13.2.8. Lipid Levels
Animal studies provide conflicting evidence on the relationship between exposure to
2,3,7,8-TCDD and serum lipid levels. Some studies suggest that short-term high exposure to
2,3,7,8-TCDD increases serum cholesterol (Greig et al., 1973; Zinkl et al., 1973; Poli et al., 1980;
Gasiewicz et al., 1980; Schiller et al., 1986; Gasiewicz and Neal, 1979; Olson et al., 1980) and
triglyceride fractions (Schiller et al., 1986; McConnell et al., 1978a,b; Gasiewicz and Neal, 1979),
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whereas other studies suggest a decrease (Gasiewicz et al., 1980; Olson et al., 1980) or no change
in triglyceride levels (Poli et al., 1980). The human data appear to be similarly confusing. A
number of case reports and epidemiologic studies have described increases in the levels of serum
lipid fractions, particularly total cholesterol and triglycerides, in TCP production workers,
laboratory workers, Seveso and Missouri residents, and Vietnam veterans. Others report no
differences between subject and reference levels. A summary of the reported levels is included in
Tables 7-28 and 7-29.
7.13.2.9. Total Cholesterol
Two case reports of workers with presumably high exposures to 2,3,7,8-TCDD-
contaminated chemicals described elevations in total cholesterol. In 50% of 55 Czechoslovakian
TCP production workers examined between 1968 and 1969 who exhibited signs of "chemical
intoxication," total cholesterol was noted as elevated (Jirasek et al., 1974). In a follow-up study
10 years later, lipid levels among workers removed from exposure were not significantly different
from referent levels, but total cholesterol levels remained significantly increased
(Pazderova-Vejlupkova et al., 1981). In a separate report, three laboratory workers who were
exposed during the synthesis of pure 2,3,7,8-TCDD developed serum cholesterol levels in excess
of 7.7 mmol/L (Oliver, 1975). No information on pre-exposure cholesterol levels was provided in
either report.
The results of epidemiologic studies conflict. Among British TCP production workers
whose last exposure to 2,3,7,8-TCDD-contaminated chemicals was less than 1 year at the time of
the study, total cholesterol levels in exposed workers with (6.02 mmol/L) and without (6.14
mmol/L) chloracne were significantly elevated compared to unexposed controls (5.6 mmol/L)
(Martin, 1984) (Table 7-28), whereas May (1982) found unexposed workers (6.6 mmol/L) to have
cholesterol levels higher than those of exposed workers with chloracne (5.97 mmol/L). Martin
(1984) also found reduced, but not significantly, HDL cholesterol among exposed workers with
chloracne (1.19 mmol/L) compared to unexposed controls (1.25 mmol/L). The differences in the
results may be due to differences in the control groups and inclusion of different workers in the
exposed groups.
Cholesterol levels in West Virginia TCP production workers were compared with
unexposed workers from the same plant (Suskind and Hertzberg, 1984). No difference was
identified in mean cholesterol levels between workers and controls. However, when lipid
fractions were examined, there was a larger but nonsignificant percentage of exposed workers
with elevated LDL cholesterol (7.7%) compared to unexposed controls (6.3%). A comparison of
workers with persistent chloracne, no chloracne, or a history of chloracne found a significant
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association (p<0.05) between the proportion of out-of-range LDL cholesterol values and persistent
chloracne. An out-of-range LDL was defined as above the 90th percentile of the total range of
values. In a second study that compared West Virginia workers with and without chloracne, no
difference was found in mean cholesterol levels (Moses et al., 1984). In the NIOSH study, there
was little difference between the adjusted mean total cholesterol levels for workers (5.7 mmol/L)
and referents (5.6 mmol/L) and no relation to increasing serum 2,3,7,8-TCDD levels (Calvert et
al., 1996). The mean levels were adjusted for age, body mass index, age, and gender.
Mean cholesterol levels were no different between workers in the BASF accident cohort
(6.14 mmol/L) and the referent population (6.37 mmol/L) and were not related to current or log
TCDD back-calculated levels (Ott et al., 1994). In addition, no significant differences were noted
between the exposed and unexposed populations for HDL and LDL levels.
In general, cholesterol levels among exposed community residents were not increased.
Despite their high exposure to 2,3,7,8-TCDD-contaminated TCP, neither children nor adults from
Seveso were found to have elevated serum cholesterol levels compared to controls (Mocarelli et
al., 1986; Assennato et al., 1989). Evaluated from 1976 through 1985, cholesterol levels in this
population remained constant throughout the study period (Table 7-28). Similarly, among
Missouri residents, serum cholesterol was not related to residence in the Quail Run Mobile Home
Park (Hoffman et al., 1986) or to adipose tissue 2,3,7,8-TCDD levels (Webb et al., 1989).
Among U.S. Army veterans, there was no difference in total cholesterol levels between
groups serving in Vietnam or other arenas (Centers for Disease Control Vietnam Experience
Study, 1988a). In contrast, there was a statistically significant positive relationship between
Ranch Hands with serum 2,3,7,8-TCDD levels above 33.3 pg/g and total cholesterol in Air Force
Ranch Hands (Roegner et al., 1991). The total cholesterol-HDL ratio was also highest in this
serum 2,3,7,8-TCDD category. In the 1992 analysis, although cholesterol concentrations
remained higher in the High Ranch Hand category compared to the Low, Background and
Comparison group, the difference was not great enough to achieve statistical significance (Grubbs
etal., 1995).
7.13.2.10. Trigfycerides
Elevated triglyceride levels were reported in only three of the studied populations. Among
British TCP workers, triglycerides were significantly higher in exposed workers with (1.97
mmol/L) and without (1.90 mmol/L) chloracne compared to unexposed controls (1.41 mmol/L)
(Martin, 1984). TCP production workers from West Virginia who had chloracne had a
statistically nonsignificant increase in mean triglyceride levels (chloracne = 1.69 mmol/L; without
chloracne = 1.46 mmol/L) (Moses et al., 1984). In addition, compared to the unexposed
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Comparison population, triglyceride levels in Air Force Ranch Hands were significantly elevated
for all serum 2,3,7,8-TCDD categories (Table 7-29). Among workers in the NIOSH study there
appeared to be a small rise in triglyceride levels with increasing serum 2,3,7,8-TCDD (Calvert et
al., 1996). The mean adjusted triglyceride levels and the percent of abnormal triglyceride values
increase with increasing serum 2,3,7,8-TCDD level (<158 femtograms/liter [fg/L], mean = 1.04
mmol/L, % abnormal = 5.7; 158-520 fg/L, mean = 1.26 mmol/L, % abnormal = 6.1; 521-1,515
fg/L, mean = 1.23 mmol/L, % abnormal = 6.1; 1,516-19,717 fg/L, 1.35 mmol/L, % abnormal =
1.7,/><0.05 compared to referents [1.15 mmol/L]). Odds ratios and 95% confidence intervals for
the quartiles are OR = 0.7 (95% CI = 0.2, 1.9), OR = 1.1 (95% CI = 0.4, 3.2), OR = 0.9 (95%
CI = 0.3, 2.9), and OR = 1.7 (95% CI = 0.6, 4.6), respectively. The authors suggest that despite
this small rise with 2,3,7,8-TCDD level, the influence of factors such as gender, body mass index,
use of beta-blocker medication, and smoking had far greater effects on lipid concentration than
did 2,3,7,8-TCDD level. Likewise, triglyceride levels in the BASF accident cohort were similar
to those in the referent cohort and not related to 2,3,7,8-TCDD level (Ott et al., 1994).
Triglyceride levels were not elevated in Missouri (Hoffman et al., 1986; Webb et al.,
1989) or Seveso residents (Mocarelli et al., 1986; Assennato et al., 1989) or in U.S. Army
Vietnam veterans (Centers for Disease Control Vietnam Experience Study, 1988a). However, in
the 1987 examination, mean adjusted triglyceride concentrations were statistically significantly
higher among Ranch Hands whose serum 2,3,7,8-TCDD levels were above 15 pg/g of lipid
(Roegner et al., 1991). In the 1991 examination, mean adjusted triglyceride concentrations only in
the high group were significantly different from the comparison group (Grubbs et al., 1995).
7.13.2.10.1. Comment. The effect of exposure to 2,3,7,8-TCDD-contaminated chemicals on
cholesterol or triglyceride levels is not consistently well defined in the available studies. It is
possible the transient elevations in total cholesterol and triglyceride levels may have occurred
after high 2,3,7,8-TCDD exposure, as in the experience of the British and Czechoslovakian TCP
workers and British laboratory workers. However, this scenario does not concur with the
evidence from Seveso or among Ranch Hands. Despite their very high exposure to 2,3,7,8-
TCDD-contaminated chemicals, neither adults nor children from Seveso had lipid levels above
the referent level. In contrast, Ranch Hands continue to have elevated lipid levels despite the
extended length of time between exposure and testing. Other factors, such as dietary fat intake,
familial hypercholesterolemia, alcohol consumption, and exercise, which also affect cholesterol
and other lipid levels, may be factors that were not considered in many of these studies.
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7.13.3. Other Gastrointestinal Disorders
A variety of gastrointestinal disorders other than liver conditions were reported among
TCDD-exposed groups. After heavy, acute, or chronic exposure, chemical workers in West
Virginia (Ashe and Suskind, 1950), West Germany (Baader and Bauer, 1951; Bauer et al., 1961),
and Czechoslovakia (Jirasek et al., 1974) consistently reported transient episodes of right upper
quadrant pain, loss of appetite, and nausea. None of the reports suggest an etiology for these
symptoms, nor were the symptoms reported in later follow-up studies of any cohorts (Suskind and
Hertzberg, 1984; Moses et al., 1984; Pazderova-Vejlupkova et al., 1981).
Three investigations of TCP production workers reported an increased prevalence of a
history of upper gastrointestinal tract ulcer across all age strata of West Virginia workers (exposed
= 20.7% vs. unexposed = 5.5%) (Suskind and Hertzberg, 1984) and all digestive system diseases
(type not specified) among workers employed in a plant in Midland, Michigan (prevalence:
exposed = 1.5% vs. unexposed = 0.5%) (Bond et al., 1983). The factors contributing to these
conditions have not been examined fully. Neither the Ranch Hand study (Roegner et al., 1991;
Grubbs et al., 1995) or the NIOSH study (Calvert et al., 1992) found increased risk of upper
gastrointestinal tract ulcers with increasing serum TCDD level.
7.13.4. Thyroid Function
7.13.4.1. Adult Effects
The thyroid plays an essential role in the maintenance of metabolic rate, food intake, and
differentiation and maturation of various cell types. Because many of the toxic effects of 2,3,7,8-
TCDD noted in animals resemble the signs of thyroid dysfunction, researchers considered the role
of the thyroid in 2,3,7,8-TCDD toxicity (Neal et al., 1979). Some studies found a single high dose
of 2,3,7,8-TCDD resulted in decreased levels of serum thyroxine (T4), indicating hypothyroidism,
but no consistent findings were reported for alterations in 3,5,3'-triiodothyronine (T3); researchers
report decreases, no change, and increases in levels of T3 (Bastomsky, 1977; Potter et al., 1983;
Pazdernik and Kozman, 1985; Potter et al., 1986; Henry and Gasiewicz, 1987; Roth et al., 1988;
Muzi et al., 1989). Furthermore, hypothyroidism induced in rats was protective against 2,3,7,8-
TCDD-induced weight loss, immunotoxicity, and mortality (Rozman et al., 1984; Pazdernik and
Kozman, 1985). This protective effect was reversed when T4 supplements were given to these
animals (Rozman et al., 1985). Henry and Gasiewicz (1987), however, found that in hamsters
serum T3 and T4 levels increased after 2,3,7,8-TCDD administration, putting in question the role
of the thyroid in 2,3,7,8-TCDD-induced toxicity.
These animal findings suggest that if the thyroid plays a role in human toxicity,
hypothyroidism would be manifested as a reduction in serum T4; in extreme cases of 2,3,7,8-
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TCDD toxicity, however, one may experience hyperthyroidism. Only three studies of production
workers examined this issue (Suskind and Hertzberg, 1984; Ott et al., 1994; Calvert et al., 1999).
Suskind and Hertzberg (1984) performed T4 radioimmunoassay and thyroxine-binding globulin
(TBG) tests and found no significant differences between exposed and unexposed workers.
Quantitative results were not presented. Similarly, thyroid stimulating hormone (TSH), T4, and
TBG levels were within normal range in the BASF accident workers and the means were not
statistically different (Table 7-30). However, TGB and T4 levels were positively related to
2,3,7,8-TCDD levels in regression analyses (Ott et al., 1994). In the NIOSH study, TSH, total T4,
and thyroid binding resin were measured. Overall, workers had a significantly higher adjusted
mean free T4 index; however, there was no trend with serum 2,3,7,8-TCDD concentrations
(Calvert et al., 1999). TSH was not different between workers and referents.
The 1987 Ranch Hand study indicated a nonsignificant reduction of T3% uptake but this
was not measured in the 1991 study (Table 7-31). A slight increase in the mean level of TSH
(Table 7-32) with increasing serum 2,3,7,8-TCDD level was noted in both 1987 and 1991; these
results, however, did not reach statistical significance (Roegner et al., 1991; Grubbs et al., 1995).
Among Army Vietnam veterans, mean TSH levels, but not mean free thyroxine index (FTI)
levels, were statistically significantly higher than among non-Vietnam veterans, after adjustment
for the six entry characteristics of age and year of enlistment, race, enlistment status, general
technical test score, and primary military occupation (Table 7-31) (Centers for Disease Control
Vietnam Experience Study, 1988a). However, the percent of values that were out of reference
range did not differ significantly for TSH (Vietnam veterans, 1.0%; non-Vietnam veterans, 0.6%,
OR = 2.0, 95% CI = 0.9-4.3) and FTI (Vietnam veterans, 5.4%; non-Vietnam veterans, 4.6%,
OR = 1.2, 95% CI = 0.9-1.5). The exposure levels were low, based on the sample for which
2,3,7,8-TCDD was measured.
7.13.4.1.1. Comment. Few human studies examined the relationship between 2,3,7,8-TCDD
exposure and thyroid function, and the results of present research are equivocal. These studies
examined individuals with lower exposure to 2,3,7,8-TCDD than the animals, and exposure was
chronic. The data from the Ranch Hand and the NIOSH studies, which measured serum 2,3,7,8-
TCDD concentrations, suggest that in adults there are few long-term effects on adult thyroid
function.
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7.13.4.2. Developmental Effects
Two series of studies, both in The Netherlands but conducted in different communities,
examined thyroid function in infants and related this to dioxin, furans and/or PCB levels in breast
milk, cord blood, or third-trimester materal serum samples.
The first study, among infants in Amsterdam (Pluim et al., 1992; Pluim et al, 1993),
examined thyroid function among 38 full-term breast-fed infants in relation to the total toxic
equivalents per kg of breast milk fat (TEQ/kg) of dioxins and furans (listed in Table 7-2 Ib and
described in detail in Section 7.12.2.3) . The authors measured total T4, thyroxine-binding
globulin (TBG), and thyroid stimulating hormone (TSH) levels sequentially in cord blood, infants
at 1 week of age, and infants at 11 weeks of age (Tables 7-30 and 7-33). Total T3 was measured
in cord blood and at 11 weeks, and free T4 was measured in cord blood. Infants were classified
into "high" and "low" groups at the median of the range. At 1 week and 11 weeks postnatally,
total T4 and total T4/TBG ratios were significantly higher among infants in the high group. At 11
weeks, TSH was also significantly higher for the high group. The authors suggest that exposure to
high levels of dioxins and furans, either in utero or through breast milk, modulates the
hypothalamic-pituitary-thyroid regulatory system of the infant (Pluim et al., 1992; Pluim et al.,
1993).
A study conducted over approximately the same time period in Rotterdam, The
Netherlands, examined thryoid function in 105 mother-infant pairs (Koopman-Esseboom et al.,
1994c). Exposure was estimated using breast milk collected in the 1-2 weeks after delivery. As
in the other study, the mother-infant pairs were split into two groups at the median dioxin-furan
TEQ based on the congeners listed in Table 7-21b (see details in Section 7.12.2.3.). The authors
measured total T4, total T3, free T4, and TSH levels in the mother during the last month of
pregancy and 9-14 days post delivery, in cord blood, and in infants at 9-14 days and three months
after birth (Tables 7-30 and 7-33). Of those enrolled in the study, 78 mother-infant pairs met all
criteria and were included in the final analyses. All the thyroid measures were within normal
ranges, with the exception of TSH for one woman. All of the TEQs (dioxin-furan TEQ, coplanar
PCB TEQ, nonplanar PCB TEQ, and total PCB-dioxin-furan TEQ) were significantly correlated
with infant plasma levels of TSH at the second week and third month, and inversely correlated
with total T3 pre-delivery and with total T3 and total T4 post-delivery for the mothers. The only
exception is that the nonplanar PCB TEQ was not significantly correlated with the mothers total
T4 after delivery and the infants' third month TSH. Measures from the infants during their second
week of life showed a significant increase in TSH (Table 7-33) and a significant decrease for total
T4 and free T4 (Table 7-30) for infants in the "high" group.
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7.13.4.2.1. Comment. Two studies of nursing infants suggest that ingestion of breast milk with a
higher dioxin-furan TEQ may alter thyroid function (Pluim et al., 1993; Koopman-Esseboom et
al., 1994c). Both studies had similar exposure groupings and some findings in common: both had
significant increases in TSH at about 3 months of age with higher TEQs, and in one report,
significant increases at about 2 weeks of age and in the cord blood. Significant changes were
found with high TEQ for total T4, but they were in opposite directions, increased in neonates at 1
and 11 weeks of age (Pluim et al., 1993) and decreased for infants during the second postnatal
week (Koopman-Esseboom et al., 1994c). Koopman-Esseboom and her colleagues also noted a
significant increase in T4/TBG and a significant decrease in free T4. Both studies covered a short
observation period, which limits the examination of persistent or long-term changes in thyroid
status, and analyses did not control for other factors that might affect thyroid status. These
findings suggest a possible shift in the distribution of thyroid hormones, and point out the need for
collection of longitudinal data to assess the potential for long-term effects associated with
developmental exposures.
These two developmental studies investigated relatively small numbers of individuals with
thyroid parameters in the normal range. However, the high group, at about 3 months of age, had
increased TSH levels in comparison to the low group. Total T4 levels and total T4 to thyroid
binding globulin (TBG) ratio were generally elevated in the high infants.
The exact processes accounting for these observations in humans are unknown, but when
put in perspective of animal responses, the following might apply: dioxin-furan increases the
metabolism and excretion of thyroid hormone, mainly T4, in the liver. Reduced T4 levels
stimulate the pituitary to secrete more TSH, which enhances thyroid hormone production. Early
in the disruption process, the body can overcompensate for the loss of T4, which may result in a
small excess of circulating T4 to the increased TSH. In animals, given higher doses of dioxin, the
body is unable to maintain homeostasis, and TSH levels remain elevated and T4 levels decrease.
7.13.5. Adult Reproductive System
7.13.5.1. Hormones
In laboratory rats, high doses of 2,3,7,8-TCDD have been related to decreased testosterone
levels, with evidence that dioxin decreases testosterone synthesis (Kleeman et al., 1990; Mebus et
al., 1987; Moore and Peterson, 1988; Moore et al., 1985).
A reported symptom of men who were exposed to 2,3,7,8-TCDD-contaminated materials
as a result of daily exposure and industrial accidents is reduced libido (Baader and Bauer, 1951;
Bauer et al., 1961; Suskind et al., 1953). Two independently conducted studies of West Virginia
TCP workers noted that exposed study subjects also reported this condition approximately 50%
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more often than either the unexposed controls or individuals without chloracne (Moses et al.,
1984; Suskind and Hertzberg, 1984). Endocrine studies or evaluations of conditions or situations
that may lead to a reduction in libido were not conducted.
In the NIOSH study of TCP production workers, questions regarding libido were not
asked; however, reproductive hormone levels were measured and related to serum 2,3,7,8-TCDD
levels. In linear regression analyses, serum 2,3,7,8-TCDD was positively and significantly related
to serum levels of luteinizing hormone (LH) and follicle-stimulating hormone (FSH) and
inversely related to total testosterone after adjustment for potential confounders (p<0.05) (Egeland
et al., 1994). The prevalence of abnormally low testosterone was two to four times higher among
workers with serum 2,3,7,8-TCDD levels of 20-75 pg/g (OR = 3.9, 95% CI = 1.3, 11.3), 76-243
pg/g (OR = 2.7, 95% CI = 0.9, 8.2), or >244 pg/g (OR = 2.1, 95% CI = 0.8, 5.8) than among
unexposed referents (4.8%) (mean serum 2,3,7,8-TCDD = 7 pg/g). Workers in these same serum
2,3,7,8-TCDD quartiles had a higher prevalence of abnormally high LH than workers with serum
2,3,7,8-TCDD levels of 244 pg/g to 3,400 pg/g, but the differences between each serum 2,3,7,8-
TCDD category and referents were not significant.
The Ranch Hand veterans study provides the only other human data available that evaluate
the relationship between serum 2,3,7,8-TCDD and testosterone (Roegner et al., 1991). Ranch
Hand veterans with current serum dioxin levels exceeding 33.3 pg/g were reported to have a lower
mean total serum testosterone level (515.0 ng/dL) than the nonexposed comparison group (525.2
ng/dL), but the difference was statistically nonsignificant. No association was observed with FSH
and LH. In additional analysis by Henriksen et al. (1997) no association was found between
abnormally high or abnormally low testosterone level and dioxin exposure category. Testicular
abnormality was assessed by physician palpation at the 1987 physical examination (Roegner et al.,
1991). Because of this finding, testicular volume was measured by ultrasound at the 1992
physical examination (Grubbs et al., 1995). No association was found between testicular volume
measured by ultrasound and dioxin exposure category in 1992 (Grubbs et al., 1995).
Testosterone, FSH, and LH were also measured in U.S. Army veterans and non-Vietnam
veterans (Centers for Disease Control Vietnam Experience Study, 1988a). No significant
differences in hormone means were noted between the two groups. Additionally, the proportions
of values outside the reference range were also similar.
7.13.5.1.1. Comment. The human data offer some evidence of alterations in male reproductive
hormone levels associated with substantial occupational exposure to 2,3,7,8-TCDD. The results
support the animal literature, in which dioxin-related effects have been observed on the
hypothalamic-pituitary-Leydig-cell axis and on testosterone synthesis.
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7.13.5.2. Endometriosis
After noting the prevalence and severity of endometriosis in rhesus monkeys chronically
exposed to 2,3,7,8-TCDD (Rier et al. 1993), investigators started looking at endometriosis in
humans. The first reported effort was a case-control study (Mayani et al., 1997) comparing 79
women, all treated in an infertility clinic during 1991-1995, some with endometriosis (N = 44),
and the comparisons with tubal infertility (N = 35). All women underwent laparoscopic
examination for diagnosis and scoring of endometriosis. Altogether, 9 women had 2,3,7,8-TCDD
above the limits of detection: 2.9% of the controls (N = 1 of 35), 12.5% of those women with
Stage I-II endometriosis (N = 3 of 24), and 25% of those with Stage IE-TV endometriosis (N = 5
of 20). Logistic regression was used to control for potential effects of the different racial/ethic
compositions of the cases and controls. The results of this analysis, compared to other unadjusted
analyses, are not explicitly identified, but are probably OR = 7.6 (95% CI = 0.87-169.7). The
authors did not present sufficient information on their data analyses to evaluate them (for
example, whether actual levels of 2,3,7,8-TCDD were entered, or whether detectable levels were
observed), but did note the limited power of this effort. An exposure-severity relationship was not
observed. However, the frequency of exposure increased with increasing severity (not statistically
tested).
A recent report (Pauwels et al., 1999) examined 101 infertile women treated at a
collaborating Center for Reproductive Medicine in Belgium between 1995 and 1998. The couple
were defined as infertile after attempting pregnancy for at least 1 year without success. Using
laproscopic examination, 42 women were diagnosed with endometriosis; 25 women had
mechanical infertility (e.g., tubal disease), 8 husbands of 20 without diagnosis were found to be
infertile. Fourteen women were excluded from analysis because of ovulatory dysfunction.
CALUX TEQs were generated using serum (N = 101), adipose tissue (N = 46) and follicular fluid
(N = 8) based on major PCB congeners and chlorinated pesticides. In preliminary analyses and
using a cut point of 100 pg TEQ/g serum lipid weight, the investigators observed proportionately
more women with endometriosis with high TEQs (17%) compared to the controls (4%) (OR =
4.0, NS).
Finally, a study of endometriosis looked at each woman's history of breast milk
consumption (Ikezuki et al., 1999). The authors hypothesized that women who were breast fed as
infants would have higher dioxin levels and subsequently higher rates of endometriosis than
would those who had been formula fed. A total of 2,848 women were queried: 2,281 from 8
companies participating in the project, and 567 women in the Japanese Endometriosis Association
or who had surgery at Tokyo University Hospital. The proportions observed were the reverse of
those hypothesized: more controls had been breast fed (68%) than had women with endometriosis
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(51%). These data are of questionable use because of limited ascertainment of exposure, lack of
knowledge about potential cases missed by the recruitment methods, and lack of detail about the
comparability of the case and comparison groups.
7.13.5.2.1. Comments. The first two reports of infertility patients (Mayani et al., 1997; Pauwels
et al., 1999) raised the potential for an association between endometriosis and 2,3,7,8-TCDD or
TEQ exposure. These studies are small and of limited power. Studies currently underway will
greatly add to the database on this outcome. The study comparing breast-fed women to those who
were bottle-fed contained little concrete data on exposure and, most likely, incomplete and
potentially biased selection of cases, and thus are of limited use for examining the relationship of
dioxin to endometriosis.
7.13.6. Diabetes
Diabetes and fasting serum glucose levels were evaluated in cross-sectional medical
studies because of the apparently high prevalence of diabetes and abnormal glucose tolerance tests
in one case report of 55 TCP workers (Pazderova-Vejlupkova et al., 1981). In this group,
evaluated 10 years after exposure ended, approximately 50% of the subjects had either confirmed
cases of diabetes or abnormal glucose tolerance tests.
The results of later medical studies are mixed. Cross-sectional studies of workers from
Nitro, West Virginia (Suskind and Hertzberg, 1984; Moses et al., 1984), found no difference in
glucose levels between the exposed and control populations, although no quantitative values were
presented in either study. Similarly, the adjusted odds ratio for out-of-range fasting glucose levels
comparing Vietnam veterans to non-Vietnam veterans was not statistically significant (OR = 1.0,
95% CI = 0.4-2.2) (Centers for Disease Control Vietnam Experience Study, 1988a). But a
comparison of the adjusted mean fasting glucose levels between the two groups was marginally
significant (Vietnam veterans, 5.2 mmol/L; non-Vietnam veterans, 5.1 mmol/L,/><0.05). Mean
fasting glucose levels in the BASF accident cohort were marginally elevated compared to the
referent population and were associated with current levels of 2,3,7,8-TCDD (p = 0.062), but not
the back-extrapolated level (Ott et al., 1994). In the NIOSH study, 9.3% of workers and 7.0% of
age-matched comparisons met the case definition for diabetes. Although 60% of workers with
serum 2,3,7,8-TCDD concentrations above 1,500 pg/g lipid met the case definition for diabetes,
prevalence of diabetes mellitus was not related to serum 2,3,7,8-TCDD concentrations (Calvert et
al., 1999). However, adjusted mean serum glucose levels (5.45 mmol/l,/><0.03) were
significantly elevated for workers with half-life extrapolated 2,3,7,8-TCDD concentrations
between 1,860 and 30,000 pg/g lipid.
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Results from the Ranch Hand study (Henriksen et al., 1997) suggest that serum 2,3,7,8-
TCDD levels may be positively and significantly related to diabetes and 2-hour postprandial
glucose levels. Every participant who underwent at least one examination (1982-1992) was
considered for inclusion in this analysis. Diabetic status was assessed by measuring 2-hour
postprandial glucose and by using a case definition of diabetes. Diabetes was defined as having a
verified history of diabetes mellitus by diagnosis or an oral glucose tolerance test of > 11.1
mmol/L (200 mg/dL). In this analyses, the cohort was categorized by current and half-life
extrapolated serum 2,3,7,8-TCDD concentrations (in pg/g lipid): Comparisons current < 10;
Ranch Hands—background current < 10, low 10 < current and half-life extrapolated < 94, high
10 < current and half-life extrapolated > 94. Median serum 2,3,7,8-concentrations in pg/g lipid
for the Ranch Hand groups are: low: 15.0 (range 10.0 - 26.6); high: 46.2 (range 18.0 - 617.8).
Risk of diabetes mellitus was moderately increased for both low (RR = 1.3, 95% CI = 1.0-1.7) and
high (RR =1.5, 95% CI = 1.2-2.0) groups but not for the background group. For Ranch Hands
participating in the 1992 examination, the risk for being classified as having an impaired fasting
glucose level (140 mg/dl-200 mg/dl based on the 2-hour postprandial glucose test) was also
moderately increased for the high group (RR = 1.6, 95% CI = 1.2-2.2). The data also suggest that
Ranch Hands in the high group have a shorter time to diabetes onset and are more likely to use
some kind of control for their diabetes, with a greater percentage using oral medication.
The outcome measures used in the Ranch Hand study, presence of diabetes, 2-hour
postprandial, and fasting, did not permit a determination of the type of diabetes involved.
However, it shows that nondiabetic Ranch Hands with higher current (>10 pg/g lipid) or past
(>94 pg/g lipid half-life extrapolated) serum 2,3,7,8-TCDD concentration may be at a slightly
greater risk for developing diabetes than individuals with background levels of 2,3,7,8-TCDD.
These individuals also appear to have a greater prevalence of elevated fasting glucose levels,
which may be a precursor to conversion to a diabetic state.
Mortality from diabetes was assessed in the NIOSH and IARC occupational cohorts
(Steenland et al., 1999; Vena et al., 1998) and among adult residents of zones A, B and R in
Seveso, Italy (Pesatori et al., 1998). In the NIOSH cohort, mortality due to diabetes was slightly
but nonsignificantly elevated when considering diabetes only as an underlying cause of death
(SMR =118, 95% CI = 77-173) or if there was any mention of diabetes on the death certificate
(multiple causes of death analysis) (SMR=89, 95% CI = 87-133). The results were the same in a
cohort of 608 workers with chloracne (SMR=106, 95% CI = 29-271). When compared to an
internal control group, there was a inverse dose-response relationship between mortality from
diabetes and exposure score.
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In the subset of workers in the IARC cohort exposed to 2,3,7,8-TCDD or chlorophenols
N=13,831), there were modest, nonsignificant elevations in the risk of death from diabetes
controlling for any workplace exposure to TCDD/HCDD (RR = 2.25, 95% CI = 0.53-9.50 N = 33)
for duration of exposure (RR = 2.52, 95% CI = 0.89-7.11, N = 10 for 10-19 years), time since first
exposure (latency) (RR = 2.34, 95% CI = 0.56-9.83, N = 14 for 10-19 years) and year of first
exposure (1955-1964, 1965+) (RR= 1.76, 95% CI = 0.58-5.31, N = 10 for 1965+) using Poisson
regression analysis.
In the Seveso cohort, very modest, statistically significant elevations in mortality from
diabetes were observed only in females of zones B (RR = 1.9, 95% CI = 1.1-3.2, N = 13) and R
(RR = 1.2, 95% CI = 1.0-1.6, N = 74) but not in females of zone A or males from any zone
(Pesatori et al., 1998). Among males, statistically nonsignificant increased mortality was seen in
zone B (RR = 1.3, 95% CI = 0.6-2.9) and in zone R (RR = 1.1, 95% CI = 0.8-1.6). No deaths
from diabetes occurred in males of zone A. Given the small number of individuals in zone A,
there is little power to detect an effect of TCDD exposure on mortality from diabetes.
Analysis of the risk of alteration in glucose metabolism among individuals exposed only to
environmental levels of 2,3,7,8-TCDD was conducted using subjects who participated as
comparisons in the 1992 examination phase of the Ranch Hand study (Longnecker and Michalek,
2000). Diabetes prevalence and serum insulin and glucose levels were compared to serum
2,3,7,8-TCDD concentration. Median serum 2,3,7,8-TCDD concentration was 4.0 ng/kg (or 4
pg/g) lipid, which included 108 subjects with levels below the limit of detection and who were
assigned a level of 0.625 mg/kg. Multivariate regression analysis (adjusted for age, race, body
mass index, waist size, family history of diabetes, body mass index at the time the dioxin was
measured, military occupation, and triglycerides) suggests a slight increase in prevalence of
diabetes with increasing 2,3,7,8-TCDD serum concentration; however, the risk did not rise
monotonically with increasing serum concentration. All odds ratios calculated were compared to
subjects in Quartile 1 who had < 2.8 ng/kg [Quartile 2, 2.8 - <4.0 ng/kg, OR 0.91, 95% CI = 0.50-
1.68; Quartile 3, 4.0 - <5.2 ng/kg, OR 1.77 95% CI = 1.04-3.02; Quartile 4, < 5.2 ng/kg OR 1.56,
95% CI = 0.91-2.6]. Similar results were found when comparing serum 2,3,7,8-TCDD level and
the estimated change in level of insulin: [Quartile 2, 2.8 - <4.0 ng/kg, 0.13, 95% CI = 0.00-0.25;
Quartile 3, 4.0 - <5.2 ng/kg, 0.11 95% CI = -0.02 - 0.24; Quartile 4, < 5.2 ng/kg 0.14, 95% CI =
0.01-0.27].
7.13.6.1. Comment
Results of the available epidemiologic studies provide a patchwork of evidence that limits
a conclusion of a strong causal relationship between occupational or environmental exposure to
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2,3,7,8-TCDD and alterations in glucose metabolism. Diabetes is a particularly difficult outcome
to study using the designs described above. Although the cross-sectional studies attempted to
limit methodologic biases by using standard case definitions for diabetes and eliminating cases
that occurred prior to exposure, cases that did not participate in the study or died prior to the study
were lost to the analysis. This may have the effect of depressing the outcome measure, which may
have been the case in the NIOSH study (Calvert et al., 1999). Mortality studies commonly miss
diabetes as a cause of death because the many potentially fatal conditions manifested as a result of
the diabetes are recorded as the primary cause of death. This should not introduce differential bias
into the study because it is assumed that the death certificates of both the worker and comparison
populations are treated in the same way. Steenland et al. (1999) attempted to remedy this problem
by including in the analysis all recorded causes of death, yet they did not find excess mortality
from diabetes among workers, even the group thought to be more highly exposed. This approach,
called "multiple cause of death analysis" was not used in the analysis of the IARC or Seveso
cohorts. In addition, it appears that in none of the above mentioned mortality studies was onset of
diabetes in relationship to exposure considered in the analysis. Therefore, workers whose onset of
diabetes occurred prior to exposure were included in the analysis, potentially overestimating the
risk of exposure.
Furthermore, the patchwork of findings may also be attributed, in part, to the
heterogeneous etiology of diabetes and, perhaps, the interrelationship between the intrinsic factors
and exposure to 2,3,7,8-TCDD .
In addition, the National Academy of Sciences' Institute of Medicine (IOM) conducted a
review of the scientific evidence on the relationship between exposure to 2,3,7,8-TCDD and Type
2 diabetes (IOM, 2000). In this review, the committee examined the results of four mortality
studies of chemical workers (Steenland et al, 1999; Vena et al, 1998) and Seveso residents
(Pesatori et al., 1998) and also morbidity studies of chemical workers (Calvert et al., 1999), Ranch
Hands (Longnecker and Michalek, 2000; Air Force Health Study, 2000) and Vietnam veterans
from Australia (Commonwealth Department of Veterans' Affairs, 1998a,b). The committee
concluded that based on the collective evidence of the data and not on a single study that"...
there is limited/suggestive evidence of an association between exposure to the herbicides used in
Vietnam or the contaminant dioxin and Type 2 diabetes." They also noted that the observed
increased risk of Type 2 diabetes in Vietnam veterans related to herbicide exposure was small and
that, in this population, known risk factors, including family history, obesity and physical
inactivity were more strongly related to the prevalence of diabetes.
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7.13.7. Inuminologic Effects
7.13.7.1. Adult Effects
Animal toxicologicalal studies have demonstrated numerous immunologic effects after
exposure to 2,3,7,8-TCDD (see Chapter 4 for a more comprehensive review). In humans, the
information with which to assess the immunologic consequences of exposure is sparse. A number
of epidemiologic studies and one case report have described the immunologic function of
populations exposed to 2,3,7,8-TCDD (Evans et al., 1988; Hoffman et al., 1986; Webb et al.,
1989; Centers for Disease Control Vietnam Experience Study, 1988a; Roegner et al., 1991;
Jennings et al., 1988; Reggiani, 1978; Ott et al., 1994; Tonn et al., 1996; Ernst et al., 1998;
Halperin et al., 1998; Jung et al., 1998; Michalek et al., 1999). One study of extruder personnel
exposed to brominated dioxins and furans was also reported (Zober et al., 1992).
Evaluation of the immunologic status in exposed residential populations has not found a
relationship between exposure and impaired status. Immunocompetence was tested twice in 44
children who were residents of the region of Seveso with the highest 2,3,7,8-TCDD contamination
and in 43 age-matched children who did not reside in the contaminated area (Reggiani, 1978).
Twenty of the exposed children had chloracne and 24 had no skin lesions. The tests included
serum immunoglobulins, complement levels, lymphocyte subpopulations, and lymphocyte activity
analysis. Although no data were presented, the authors reported that the various measures were
within normal range and that there was no difference between the two groups.
Initial studies of Missouri residents who had the potential for exposure to 2,3,7,8-TCDD-
contaminated soil suggested that 2,3,7,8-TCDD caused depression in cell-mediated immunity
(delayed hypersensitivity), as demonstrated by a statistically significant increase in anergy
(exposed vs. nonexposed: 11.8% vs. 1.1%) (Hoffman et al., 1986). This study, however, was
limited by the exclusion from test results of 61% and 32% of the exposed and unexposed groups,
respectively. A follow-up study confirmed the presence of substantial bias in the first study.
Evans et al. (1988) retested 28 of the 50 exposed residents and 15 of the 27 unexposed residents
who did not respond (anergic) or responded weakly (relatively weakly) to an antigen challenge in
the first study. A follow-up study could not confirm the presence of anergy. Both studies found
in the exposed residents increased frequencies in CD4/CD8 ratio of less than 1.0 (Table 7-34).
No other abnormalities were noted by Hoffman et al. (1986) (Tables 7-35 to 7-40).
In a later study of Missouri residents, Webb et al. (1989) found no clinical evidence of
immunosuppression in 40 individuals whose adipose 2,3,7,8-TCDD levels ranged from under 20
pg/g to over 430 pg/g (top of range not given). Tests included serum immunoglobulins, T-cell
surface markers OKT3, OKT4, OKT8, OKT11, Leullc, CD4/CD8 ratio, (CD4 + CD8)/CD3, and
Bl and B2 cells. In logistic regression, significant (p<0.05) relationships were noted for IgG,
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%CD3, %T11, %CD8, and %CD4 + LEU8 POS, controlling for age and sex (Tables 7-35 to 7-40).
The effect of past occupational exposure on immunologic function was examined in 18
British workers who were evaluated 17 years after accidental industrial exposure to chemicals
contaminated with 2,3,7,8-TCDD (Jennings et al., 1988). It is not clear from the article when
occupational exposure to 2,3,7,8-TCDD ended for these workers. Exposed workers and
unexposed controls were matched for age, race, sex, smoking habits, alcohol consumption, and
percent of ideal body weight. There were no significant differences in the levels of
immunoglobulins, T and B lymphocytes, responsiveness to phytohemagglutinin A, and in the
number of CD4 and CDS counts. Three measures were found to be statistically significantly
(p<0.05) higher in workers than in controls: antinuclear antibodies (ANA) (8 workers vs. 0
controls,/><0.01) (when Hep2 cells were used as substrate but not when rat liver cells were used),
immune complexes (workers = 11 vs. 3 controls, /KO.05), and natural killer cells (NK) (workers =
0.21 x 106/L vs. controls = 0.59 x 106/L,/><0.002) identified by the monoclonal antibody Leu-7
(Table 7-41). No evidence of a consistent relationship between dioxin exposure and immune
system alteration, as indicated by antinuclear antibodies, were found in the Ranch Hands Study
(Michalek at al., 1999). In the discussion, the authors could not explain the physiologic basis of
their findings and suggested that further research was needed.
Among participants in the BASF accident study cohort, with the exception of natural killer
cells and helper-inducer cells, the proportions of some lymphocyte populations (B cells, T-cells, T
helper cells, T suppressor cells) were lower among workers, but the distribution of cells in
referents and workers was equivalent (Ott et al., 1994). Levels for IgA, IgG, IgM, and
complement C4 and C3 were slightly higher in workers than in the unexposed referent population.
There also appeared to be slight dose-related increases in IgA, IgG, and complement C4 with
2,3,7,8-TCDD levels measured in October 1988 and February 1992. IgA and IgG were related to
the half-life extrapolated 2,3,7,8-TCDD levels. It must be noted, however, that the statistically
significant relationship between IgA and 2,3,7,8-TCDD is most likely due to a case of liver
cirrhosis and the association with IgG due to a liver carcinoma.
Eleven German workers, exposed to chemicals contaminated with 2,3,7,8-TCDD and
other polychlorinated dioxins between 1966 and 1976 during production of 2,4,5-T or
maintenance activities were studied by Tonn et al. (1996). Between 1989-1992, serum 2,3,7,8-
TCDD concentrations were measured. The levels ranged from 43 to 874 pg/g lipid. No
differences were noted between the exposed and control groups for the lymphocyte subsets tested
or response to mitogen stimulation. However, in TCDD-exposed workers, the response of T-
cells to irradiated stimulator cells or IL-2 was statistically significantly decreased.
In the NIOSH study, measures of immunologic status and function were evaluated
(Halperin et al., 1998). Of all the parameters examined, only a slight decrease in the number and
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proportion of activated T-cells appeared to be related to 2,3,7,8-TCDD serum concentration:
CD3/Tal k/mm3 (number of activated cells) (TCDD 52-125 pg/g, OR = 2.7, 95% CI = 1.4-1.5;
TCDD 126-297 pg/g, OR = 2.6, 95% CI = 1.4-4.9; TCDD 298-3389 pg/g, OR = 2.4, 95% CI =
1.3-4.6), and CD3/Tal (%) (TCDD 52-125 pg/g, OR = 2.5, 95% CI = 1.3-4.8; TCDD 126-297
pg/g, OR = 2.3, 95% CI = 1.2-4.3; TCDD 298-3389 pg/g, OR=2.4, 95% CI = 1.3-4.5).
Jung et al. (1998) and Ernst et al. (1998) evaluated the immunologic status and function of
workers exposed to PCDDs and PCDFs during production of 2,4,5-T and other chlorophenols.
The cohort of exposed workers was previously described by Flesch-Janys et al. (1992). Jung et al.
(1998) included all 192 workers in their analysis. The median serum TCDD concentration of this
group was 36.1 pg/g lipid, with a range from 1.2 to 893.2 pg/g lipid. With the exception of a
significant negative correlation between CD3+/CD8+ cells and logTEQ, no other parameters were
found to be exposure-related.
Workers included in the analysis by Ernst et al. (1998) had a median serum TCDD level of
217 pg/g lipid, ranging from 33.6 to 2,732 pg/g lipid. The serum TCDD concentrations in the 28
age-matched control approximated normal at 4 pg/g lipid. Exposed subjects worked a minimum
of 7 years in the tricholorophenol and 2,4,5-T departments. Compared to controls, the exposed
group had significantly increased proportions of CD8+CA45RO+ (cytotoxic memory T-cells) and
activated CD8dimCA57+, and a significantly lower number of lymphocytes with naive phenotype
CD45RO+. The authors also report that interferon release in diluted whole-blood cultures, but not
in isolated peripheral blood mononuclear cells, was statistically significantly decreased in TCDD-
exposed workers.
Among the 21 extruder personnel exposed to both 2,3,7,8-TBDD and 2,3,7,8-TBDF, of
the 16 parameters tested, only complement C4 was statistically significantly (p<0.01) associated
with concentrations of 2,3,7,8-TBDD and 2,3,7,8-TBDF (Zober et al., 1992). Borderline
associations were noted between 2,3,7,8-TBDF and decreases in total lymphocyte count
(p = 0.056), T-cell count (p = 0.045), T-helper cell count (CD4) (p = 0.045) and an increase in
complement C3 (0.054), and between 2,3,7,8-TBDD and a decreased percent lymphocyte count
(p = 0.054). However, with the exception of complement C3, the associations appear to be driven
by a single individual with the highest 2,3,7,8-TBDD levels (478 pg/g of lipid) who, at the time of
the study, exhibited no evidence of clinical immunodeficiency.
Two studies extensively evaluated immunologic function in Vietnam veterans. No
significant differences were noted among U.S. Army ground troops and the comparison
population in lymphocyte subset populations, T-cell populations, or serum immunoglobulins
(Tables 7-34 to 7-40) (Centers for Disease Control Vietnam Experience Study, 1988a).
Comprehensive immunologic profiles were developed for each participant of the U.S. Air Force
Ranch Hand Study (Tables 7-34 to 7-40) (Roegner et al., 1991). Significant positive associations
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were found between IgA and serum 2,3,7,8-TCDD. The authors suggest that the rise in IgA is
consistent with a subclinical inflammatory response, but the authors could not explain the source
of the inflammatory response. In analysis of the 1992 examination round, Michalek et al. (1999)
found no relationship between category of serum TCDD concentration and immunologic changes.
The study population was classified by median serum TCDD concentration: Comparison: 4.0 pg/g
lipid, range 1-10 (current level); Ranch Hand background: 5.7 pg/g lipid, range 1-10 (current
level); Ranch Hand low: 52.8 pg/g lipid, range 28-94 (half-life extrapolated); Ranch Hand high:
194.7 pg/g lipid, range 94-3,290 (half-life extrapolated). Of all the tests performed, only
prevalence of an abnormal skin test, represented as a composite score and the proportion of CD20
cells in the background group, and the absolute count for CD16+CD56+CD3" were significantly
different than the referent population.
7.13.7.1.1. Comment. The information on immunologic function in humans, children or adults,
relative to exposure to 2,3,7,8-TCDD is scarce. All but one of the epidemiologic studies are
restricted to adults and do not describe a consistent pattern of effects among the studies. Natural
killer cells (NK) were increased among one population of 2,3,7,8-TCDD chemical workers
examined 17 years after exposure ended (Jennings et al., 1988). These findings were not
corroborated in Ranch Hands (Roegner et al., 1991; Michalek et al, 1999), the BASF accident
cohort (Ott et al., 1994), the NIOSH cohort (Halperin et al., 1998), the Hamburg cohort (Jung et
al., 1998; Ernst et al., 1998), or workers exposed to 2,3,7,8-TBDD and TBDF (Zober et al., 1992).
Dose-related elevations in IgA were observed in Ranch Hands in relation to current levels and in
the BASF accident cohort with respect to both current and half-life extrapolated 2,3,7,8-TCDD
levels. Yet, IgA was not higher in adult Missouri residents with adipose 2,3,7,8-TCDD levels
above background (Webb et al., 1989). IgG was also significantly related to 2,3,7,8-TCDD in the
BASF accident cohort but not in Ranch Hands (Michalek et al., 1998). While complement C3
and C4 were elevated in both the BASF accident cohort and the extruder personnel, no other study
examined these endpoints.
The effect of acute, high exposure to 2,3,7,8-TCDD among children from Seveso was
reportedly negative within 2 years after exposure (Reggiani, 1978). Although no data have been
published illustrating the values obtained from the tests of immunologic function in these
children, the author indicates that the measured parameters were no different in the exposed and
unexposed children after two series of tests.
More advanced functional analyses have been conducted relating to the ability of T-cells
to respond to intercellular stimulators such as the interleukins and interferon (Tonn et al., 1996;
Ernst et al., 1998). These studies are suggestive of a decreased ability of T-cells to respond in
individuals more heavily exposed to PCDDs and PCDFs. More work needs to be done in
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similarly exposed populations to confirm these findings and to determine the mechanism of
action.
More comprehensive evaluations of immunologic function with respect to 2,3,7,8-TCDD
exposure are necessary to assess more definitively the relationships observed in nonhuman
species. Longitudinal studies of the maturing human immunologic system may provide the
greatest insight, particularly because animal studies have found many of their significant results in
immature animals and breast milk is a source of 2,3,7,8-TCDD and other related compounds.
Additional studies of highly exposed adults may also shed light on the effects of long-term
chronic exposures. Therefore, there appears to be too little information to suggest definitively that
2,3,7,8-TCDD, at the levels observed, is an immunotoxin in humans.
7.13.7.2. Developmental Effects
One report (Weisglas-Kuperus et al., 1995) has examined direct and surrogate measures
of immune status in 207 babies in Rotterdam (study described in detail in Section 7.12.2.3.). The
surrogate measures were derived from questionnaires given to the mothers, covering incidence of
rhinitis, bronchitis, tonsillitis, and otitis in children up to 18 months of age. Almost all of the
children (205) were immunized against measles, rubella, and mumps; the children's antibody
levels to these were used to assess humoral antibody production. For the purposes of this report,
cord bloods from 48 of these children were analyzed to assess prenatal TEQ levels; at age 3
months, 47/48 bloods were drawn from the original group, with another child, randomly selected,
added to this group. At 18 months, 37 of the original children gave blood, and 6 other children,
randomly selected, were added to this group. In these samples, the following were measured:
monocytes, granulocytes, and lymphocytes in whole blood; lymphocyte subpopulations were
determined using monoclonal antibodies. No relationship was found between pre- and postnatal
total TEQ levels and respiratory tract symptoms (i.e., number of periods with rhinitis, bronchitis,
tonsillitis, and otitis) or humoral antibody production at 18 months to vaccination against mumps,
measles, and rubella at 14 months. A higher prenatal exposure, estimated by cord blood levels,
was associated with alterations in T cell subsets, with an increased number of TcRy§+ T cells;
increased total numbers of T cells, CD8+ cells, and TcRy§+ T cells at 18 months of age were
associated with higher TEQ levels (Table 7). Higher TEQ levels were also associated with a
decreased number of monocytes (Total TEQ, Dioxin-furan TEQ, mono-ortho PCB TEQ, and Di-
ortho PCB TEQ) and granulocytes (Total TEQ only) at 3 months. All values were found to be
within clinically "normal" ranges. The authors suggested that the subtle changes in the number of
blood leukocytes do not necessarily mirror alterations in the cell composition of lymphoid and
nonlymphoid organs, nor do they necessarily reflect functional defects (Weisglas-Kuperus et al.,
1995).
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In an update to the report described above (Weisglas-Kuperus et al. 2000), 193 children
were examined at 42 months of age. Questionnaires were completed for 175, including questions
on infection and allergic disease in the children. Blood samples were collected on a subsample of
85 children, limiting data on concurrent PCB levels (PCBs 118, 138, 153, 180) in plasma and
immunologic marker analyses in lymphocytes. Examination of questionnaire reports of infectious
diseases and allergies, only reduced levels of "attacks of shortness of breath with wheeze was
associated with prenatal PCB exposure (n=175; OR=0.44; 95% CI: 0.18-0.99); more associations
were observed with current PCB levels (n=85): increases in recurrent middle ear infections
(OR=3.05; 95% CI: 1.17-7.98); chicken pox (OR=7.63; 95%CI:1.21-48.54); and reduced allergic
reactions (OR=0.01; 95%CI:0.01-0.37). Dioxin-furan TEQ at birth was associated with increased
coughing, chest congestion, and phlegm (OR=1.06; 95%CI: 1.00-1.11). Total PCB levels at 42
months were significantly lower for formula fed children (0.21 ug/1 versus 0.75 ug/1, p<0.05);
however, no significant differences were observed for recurrent middle ear infections, chicken
pox or allergic reaction. Interestingly, when these were examined by duration of breast feeding,
infections and chicken pox were lower and allergic reactions were higher with longer breast
feeding (all were borderline non-significant at p=0.06 or 0.07), even though the total PCB levels
at 42 months were over 70% higher (not significant). Positive associations were observed
between the total PCB levels at birth (cord blood, maternal blood or both) and lymphocytes,
CD3+, CD3+CD8+, CD4+CD45RO+, and CD3+HLA-DR+ T-cells; no associations were
observed with the current PCB levels or with the dioxin-furan TEQ at birth. The authors
concluded that effects of perinatal exposures persist, and are associated with a greater likelihood
of infectious disease but less likelihood of allergic conditions. In addition, they concluded that the
benefits of longer periods of breast feeding helped to counteract the effects of exposure.
7.13.7.2.1. Comment. These data suggest some long term effects are occurring. Repeating these
investigations in other groups would be informative.
7.13.8. Neurologic Effects
Although there are few studies reporting neurologic abnormalities related to
2,3,7,8-TCDD exposure in adult animal models, neurologic effects are reported to have occurred
shortly after exposure in occupationally exposed individuals (Ashe and Suskind, 1950; Baader and
Bauer, 1951; Bauer et al., 1961; Goldman, 1972; Jirasek et al., 1974; Oliver, 1975; Pocchiari et
al., 1979) (Table 7-43) and in Seveso residents (Filippini et al., 1981) (Table 7-44). Previous case
reports and studies found symptoms referable to the central (CNS) and peripheral (PNS) nervous
systems among workers and community residents exposed to 2,3,7,8-TCDD-contaminated
materials. Although human studies reveal a wide spectrum of effects due to 2,3,7,8-TCDD
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(Sweeney et al., 1989), very few toxicological studies have focused on the nervous system. Singer
et al. (1982) reviewed the following animal studies, which examined the relationship of CNS
dysfunction and 2,3,7,8-TCDD exposure: Elovaara et al. (1977) found anomalous CNS function
in some rats exposed to a single dose of 2,3,7,8-TCDD, and Creso et al. (1978) reported CNS
symptoms of irritability, restlessness, and increased aggression in rats administered
2,3,7,8-TCDD.
7.13.8.1. Adult Neurobehavioral Assessments
Numerous case reports cite symptoms referable to the nervous system occurring after acute
exposure among occupationally exposed individuals (Creso et al., 1978; Ashe and Suskind, 1950;
Suskind et al., 1953; Goldman, 1972), as well as chronic exposure to 2,3,7,8-TCDD-contaminated
materials (Oliver, 1975; Baader and Bauer, 1951; Kimmig and Schulz, 1957a,b; Bauer et al.,
1961; Poland et al., 1971). Symptoms include headache (Ashe and Suskind, 1950; Bauer et al.,
1961; Jirasek et al., 1974; Oliver, 1975; Kimmig and Schulz, 1957a,b; Poland et al., 1971),
insomnia (Ashe and Suskind, 1950; Suskind et al., 1953; Oliver, 1975; Kimmig and Schulz,
1957a,b), nervousness or irritability (Ashe and Suskind, 1950; Suskind et al., 1953; Bauer et al.,
1961; Oliver, 1975), depression and anxiety (Bauer et al., 1961; Jirasek et al., 1974), loss of
libido, and encephalopathy (Jirasek et al., 1974; Kimmig and Schulz, 1957a) (Table 7-43).
Some reports indicate that symptoms referable to the CNS and PNS may persist in some
exposed individuals for as long as 25 years (Suskind et al., 1953; Poland et al., 1971; Jirasek et
al., 1973; Jirasek et al., 1974; Creso et al., 1978; Ashe and Suskind, 1950; Suskind et al., 1953).
In 1953, Suskind et al. reported a variety of CNS-related symptoms in 36 workers from a plant in
Nitro, West Virginia, who had developed chloracne and other symptoms after exposure to
contaminants subsequent to a TCP reactor explosion in March 1949 (N = 11) or during normal
production process of TCP and 2,4,5-T (N = 25) between 1948 and 1953 (Suskind et al., 1953).
Such symptoms reported among this relatively young group (average age = 36 years, range =
22-63 years) included fatigue (N = 21), nervousness and irritability (N = 17), and decreased libido
(N = 13). No attempt was made to determine whether these symptoms also occurred among
exposed individuals without chloracne or among the nonexposed plant population.
Between 1968 and 1969, Jirasek et al. (1974) "observed very closely" (by clinical
evaluation) a group of 55 workers exposed to 2,3,7,8-TCDD in the production of 2,4,5-T,
hexachlorobenzene, and pentachlorophenol. As described in the report, intoxication with
2,3,7,8-TCDD "occurred gradually from 1965 to 1968," although further quantification of
exposure is not described. Psychiatric examination revealed the following: severe neurotic
symptoms (64%), neurasthenia syndrome with depressive component (11%), depressive
syndromes (8%), pseudoneurasthenia syndromes in patients with arteriosclerosis of the CNS
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(14%), and normal psychiatric examination (3%). One patient died at age 57 years with rapidly
progressive dementia secondary to an atypical arteriosclerosis involving brain and other organs.
Ten years after the initial examination, Pazderova-Vejlupkova et al. (1981) evaluated the
health status of 44 of the original 55 workers. They found the following on psychiatric
examination: 58% continued to have neurotic symptoms without depressive or anxiety
components, 18% developed severe neurasthenia syndromes with signs of dementia, and 24%
were normal.
Poland et al. (1971) administered the Minnesota Multiphasic Personality Inventory
(MMPI) to 52 male production workers who were exposed at that time to TCP, 2,4,5-T, 2,4-D,
and other chemicals. Severity of acne correlated significantly with a high score on the hypomania
scale of the MMPI. When production workers were compared with 17 presumably unexposed
administrative workers, the two groups differed on only one MMPI symptom scale; exposed
production workers scored higher on the hypochondriasis scale.
Table 7-45 describes the results of neurologic and neurobehavioral assessments of TCP
production workers, Ranch Hands, and U.S. Army Vietnam veterans.
Moses et al. (1984) found a significant excess among workers with chloracne compared to
those without lesions for the following symptoms: insomnia, decreased libido, and difficulties
with ejaculation or erection. There was no difference between the two groups for the symptoms
of fatigue, irritability, nervousness, depression, or personality changes.
In the study by Suskind and Hertzberg (1984), psychological symptoms were evaluated by
interview and peripheral nerve function by nerve conduction velocity of the peroneal motor and
sural sensory nerve fibers. The complaint of loss of libido was more frequent among exposed
than unexposed, even after stratification by age. The complaint of nervousness, depression, or
anxiety was not significantly related to exposure (16.3% vs. 11.7%) in the crude analysis, even
though the sample size was sufficient to detect a twofold increase. However, in the group over 50
years old there was a significant exposure effect (19% vs. 6.4%) for the complaint of nervousness,
depression, or anxiety. The complaint of impotence was significantly related to exposure in the
crude analysis, but after stratification by age this effect disappeared.
The effects of exposure to 2,3,7,8-TCDD on measures of current symptoms of depression
were evaluated (Alderfer et al., 1992) as part of the NIOSH cross-sectional medical study
(Sweeney et al., 1989). Symptoms of depressed mood were measured by the Beck Depression
Inventory and the depression subscale of the Self-Report Symptom Checklist-90-Revised (SCL-
90-R). Neither serum 2,3,7,8-TCDD levels nor status as a worker was associated with depressed
mood as assessed by either the Beck Depression Inventory or the SCL-90-R depression subscale
(Alderfer et al., 1992). This finding supports the conclusion that current serum levels of 2,3,7,8-
TCDD are not associated with current depression among a population of workers that was highly
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exposed to TCDD. However, because this cross-sectional study was conducted many years after
2,3,7,8-TCDD exposure, this analysis could not address the question of whether 2,3,7,8-TCDD is
associated with past depression that resolved before the study was performed. These findings are
consistent with those of the U.S. Air Force study (Roegner et al., 1991) of personnel who applied
Agent Orange during the Vietnam War: serum 2,3,7,8-TCDD was not associated with the
depression subscale score of the SCL-90-R after controlling for covariates.
The Air Force study conducted neurologic and psychological assessments of the
participating Ranch Hands and comparisons (Lathrop et al., 1984). In the first examination series
published in 1984 (baseline), the psychological assessment included a self-report of psychological
or emotional illness, the Diagnostic Interview Schedule (DIS), Cornell Medical Index (inventory
of psychophysiologic symptoms), MMPI, Halstead-Reitan Battery (HRB), and Wechsler Adult
Intelligence Scale (WAIS). In the 1987 follow-up examination, the psychological assessment
included an interviewer-administered questionnaire in which each participant was asked about the
occurrence of mental or emotional disorders and sleep disorders. The presence of posttraumatic
stress disorder was based on a subset of questions from the MMPI; the WAIS IQ assessment was
deleted and the Millon Clinical Multiaxial Inventory (MCMI) and the Symptom Checklist-90-
Revised (SCL-90-R) were added (Lathrop et al., 1987).
Results of the 1984 baseline study revealed no difference between Ranch Hands and the
comparison group for self-reported psychological or emotional illness. The DIS revealed
significantly more fatigue, anger, erosion, and anxiety for high school-educated but not
college-educated Ranch Hands. These outcomes were highly related to education. The Cornell
Medical Index found 4 of 10 parameters abnormal for Ranch Hands: startle, psychosomatic,
gastrointestinal nervousness, and anxiety. These parameters were inversely related to education
level. On the MMPI, high school-educated Ranch Hands showed significant differences (more
deficits) on subscales for hypochondria, masculinity/femininity, and mania/hypomania, but
comparisons scored higher on the subscale for denial, a finding that might undermine the deficits
noted for Ranch Hands. Again, MMPI scores were influenced by education level (p<0.01) but not
exposure level. For both the HRB and WAIS, there was no difference between Ranch Hands and
comparisons, and the scores were related to education level.
In the 1987 reanalysis with serum 2,3,7,8-TCDD (Roegner et al., 1991), there was no
significant difference between groups or relationship with serum 2,3,7,8-TCDD levels on reported
(and verified) data on lifetime psychological illness or sleep disorders or any SCL-90-R. Some of
the MCMI parameters appeared to be related to serum 2,3,7,8-TCDD levels (significantly higher
mean schizoid and schizotypal scores and significantly lower mean histrionic score in the group
above 33.3 pg/g than in comparisons). However, these findings were inconsistent with similar
variables in the SCL-90-R and the self-reported histories.
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Comprehensive neurologic and psychological assessments were conducted on participants
of the Vietnam Experience Study (Centers for Disease Control Vietnam Experience Study, 1988a,
b). The neurobehavioral tests evaluated aptitude, concept formation and problem-solving,
memory, manual dexterity, verbal skills, visuomotor skills, attention, and mental control. Among
Vietnam veterans there was a significantly greater prevalence of alcohol abuse or dependence
(Vietnam veterans, 13.7%; non-Vietnam veterans, 9.2%; OR = 1.5, 95% CI = 1.2-1.8), depression
(Vietnam veterans, 4.5%; non-Vietnam veterans, 3.2%; OR = 2.0, 95% CI = 1.4-2.9), and a higher
prevalence of poor psychological status (Centers for Disease Control Vietnam Experience Study,
1988b). The poor psychological status tended to be most prevalent in Vietnam veterans who were
not white, who enlisted before their 19th birthday, and whose enlistment test scores fell below the
group median.
In the study of residents of the Quail Run Mobile Home Park, neurobehavioral tests
evaluated reaction time, mood, memory, visuo-motor coordination, intelligence, and indicators of
psychological stress (Hoffman et al., 1986). Differences between Quail Run residents and
controls were observed in the vocabulary subtest of the WAIS (Quail Run = 34.7, controls = 41.1,
raw scores;p<0.01), tension/anxiety raw score (Quail Run = 13.7, controls = 11.1;/><0.01), and
anger/hostility scale (Quail Run = 11.6, controls = 8.9;/><0.05) of the POMS inventory, and for
the depression/dejection and fatigue/inertia scales (no data provided).
7.13.8.2. Adult Neurologic Status
On neurologic examination, 11 of 60 West Virginia workers with chloracne exhibited
decreased sensitivity to pinprick, whereas none of the 34 subjects without chloracne had
decreased pinprick sensation (p<0.01) (Moses et al., 1984). There were no other differences in
performance of the neurologic examination noted in the text. When examined by Suskind and
Hertzberg (1984), no significant differences were noted in the conduction velocities of either
nerve fiber (sural sensory: exposed workers, mean = 42.06±0.49; unexposed workers, mean
41.49±0.54; peroneal motor: exposed workers, mean = 41.77±0.47; unexposed workers, mean =
42.62±0.52).
Among New Jersey and Missouri TCP workers, the overall neurologic status and
peripheral nerve function were assessed for all 281 workers and 260 referents by self-reported
medical history; neurologic examination; electrophysiologic tests of nerve conduction velocity,
amplitude, and latency; and vibratory and thermal threshold (Sweeney et al., 1993). No
differences in neurologic status or nerve function between workers or referents were detected.
Additionally, although the mean serum 2,3,7,8-TCDD level in the workers was 220 pg/g, there
was no relationship between neurologic function and levels of serum 2,3,7,8-TCDD.
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The neurologic examination in the 1987 follow-up study evaluated cranial, CNS, and PNS
function in participating Ranch Hands (Lathrop et al., 1987). In general, there was no difference
in the prevalence of neurologic abnormalities in Ranch Hands and comparisons. However, Ranch
Hands with serum 2,3,7,8-TCDD levels above 33.3 pg/g tended to have a higher proportion of
individuals with abnormal coordination than the comparisons (Ranch Hands, 2.7%; comparisons,
0.4%; adjusted RR = 18.3,/K0.001) (Roegner et al., 1991).
Overall neurologic status of Army Vietnam veterans did not differ from that of non-
Vietnam veterans (Vietnam veterans, 1.0%; non-Vietnam veterans, 0.8%; OR = 1.2, 95% CI =
0.6-2.3) (Centers for Disease Control Vietnam Experience Study, 1988b). Only self-reported
symptoms related to nerve disorders were significantly more prevalent among Vietnam veterans
than non-Vietnam veterans (Vietnam veterans, 8.2%; non-Vietnam veterans, 6.5%; OR = 1.2,
95%CI= 1.0-1.6).
Table 7-44 describes the results of neurologic and neurobehavioral studies of Seveso and
Missouri residents. While three studies evaluated the neurologic status of residents of Seveso
(Pocchiari et al., 1979; Filippini et al., 1981; Assennato et al., 1989), no studies evaluated
neurobehavioral changes. In an effort to quantify exposure-related neurologic disorders among
Seveso residents and among workers from the Icmesa plant, two government-sponsored
screenings were conducted in 1977 and 1978 on 308 residents of Seveso and 200 workers.
Among these workers, approximately 4% (N = 8) were found to have damage to nerve fibers of
multiple (unspecified) nerves, controlling for confounding factors such as alcohol abuse, diabetes,
kidney disease, and neurotoxic medication use (Pocchiari et al., 1979). The report did not
describe the extent of worker exposure to 2,3,7,8-TCDD. Other potential neurotoxic occupational
exposures do not appear to have been considered. Three workers were described as having
polyneuropathies of the lower limbs.
In 1981, prevalence risk ratios (PRR) for neuropathy were calculated separately for the
308 Seveso residents (Filippini et al., 1981). PRRs for neuropathy were determined for residents
who exhibited clinical indication of 2,3,7,8-TCDD exposure, defined as the presence of elevated
liver enzyme levels (GGT, ALT, AST) (which are also indicative of nonspecific insults to the
liver) or chloracne, and for those who exhibited conditions that are risk factors for neuropathy,
e.g., alcoholism, inflammatory disease, diabetes, or potential occupational exposure to
neurotoxins. Seveso residents who had clinical indication of 2,3,7,8-TCDD exposure (chloracne
or elevated liver enzymes GGT, AST, ALT) or who had risk factors for neuropathy were found to
have significantly greater prevalence of neuropathy than residents without either manifestation
(PRR exposure = 2.8, 95% CI = 1.2-6.5; PRR for possible 2,3,7,8-TCDD-predisposing factors =
2.6, 95% CI = 1.2-5.6) (Filippini et al., 1981). Additional analysis showed that individuals who
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met the definition for chloracne or abnormal levels of hepatic enzymes were significantly more at
risk than residents without either condition.
Residents of Seveso who developed chloracne after the reactor release (N = 193) were
invited to a series of three follow-up screenings in 1982-1983, 1983-1984, and 1985 (Assennato et
al., 1989). A control group from a nearby but uncontaminated area was also examined.
Conduction velocities of the median motor, peroneal motor, and sural sensory fibers were
conducted in 1982-1983 and 1985 (Assennato et al., 1989). No increases in the prevalence of
abnormal electrophysiologic measures were observed in the chloracne group when compared with
controls without chloracne. In addition, there was no change in the conduction velocities for each
fiber from the 1982-1983 to 1985 studies.
Quail Run residents reported significantly more "numbness" or "pins and needles" in the
hands or feet (28.6%) than the controls (18.1%) (p<0.05), but there were no differences in mean
threshold scores for the more objective neurosensory tests. Residents also reported more
persistent severe headaches (Quail Run, 26.0%; control, 14.2%;/?<0.05).
Participants in the study by Webb et al. (1989) did not complete neurobehavioral tests, but
they were examined by a neurologist. The results were unremarkable. Of the 38 participants, two
with levels above background had abnormal pinprick sensitivity (<20 pg/g, N = 2; 20-60 pg/g, N
= 1; >60 pg/g, N = 1), three had abnormal vibration thresholds (<20 pg/g, N = 2; 20-60 pg/g, N =
1; >60 pg/g, N = 2), and four had abnormal reflexes (<20 pg/g, N = 2; 20-60 pg/g, N = 2; >60
pg/g, N = 2). The results of other components of the neurologic examination were not reported
and are assumed to be normal.
7.13.8.2.1. Comment on adult studies. The overall results of these case reports and
epidemiologic studies demonstrate that exposure to 2,3,7,8-TCDD-contaminated materials is
associated with symptoms referable to the central and peripheral nervous systems shortly
following exposure and, in some cases, lasting many years (Tables 7-44 and 7-45). Symptoms
include fatigue, nervousness, anxiety, and decreased libido (Ashe and Suskind, 1950; Suskind et
al., 1953; Oliver, 1975; Kimmig and Schulz, 1957a,b; Bauer et al., 1961; Jirasek et al., 1974).
One case report found mania/hypomania on the MMPI (Poland et al., 1971). These symptoms are
consistent with mood disorder. Although one study reported neurasthenia with signs of dementia
lasting 10 or more years following exposure (Jirasek et al., 1974), the U.S. Air Force study of
Vietnam veterans used neurobehavioral testing but was unable to demonstrate cognitive or other
functional CNS deficits. However, this negative study did not investigate the relationship
between serum 2,3,7,8-TCDD levels and neurobehavioral deficits. NIOSH investigated measures
of depressed mood many years after exposure to 2,3,7,8-TCDD among production workers and
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found no relationship between depressive symptoms and serum 2,3,7,8-TCDD levels (Alderfer et
al., 1992).
Overall neurologic status of workers, community residents, and Vietnam veterans exposed
to 2,3,7,8-TCDD and evaluated from 5 to 37 years after last exposure appears to be normal.
These data suggest that, although exposure to 2,3,7,8-TCDD may have been extensive, as in the
case of the exposed workers, Ranch Hands, and Seveso residents, and case reports describe many
related symptoms, the effects may have been transient. If so, studies conducted years after the last
exposure would not detect such changes. These results suggest that, in adults, no long-term
neurologic effects were caused by even high exposure to 2,3,7,8-TCDD-contaminated materials.
However, there is very little information with which to examine the effects of exposure on the
developing human neurologic system.
7.13.8.3. Developmental Neurobehavioral Effects
Five recent reports from The Netherlands have examined neurologic/behavioral outcomes.
Outcomes are summarized in Table 7-46. The overall design of these studies and the
classification of biological exposure data are described in detail in Section 7.12.2.3.
A pair of studies examined the same group of children from Rotterdam (Table 7-46).
These infants were tested for (1) psychomotor and mental development indices (PDI and MDI)
based on the Dutch standardized verion of Bayley Scales of Infant Development (Koopman-
Esseboom et al., 1996) at ages 3, 7, and 18 months; (2) Visual Recognition Memory Scores based
on the Pagan Test of Infant Intelligence (Koopman-Esseboom et al., 1995b) at ages 3 and 7
months; and (3) use of the Prechtl neonatal neurologic examination (Koopman-Esseboom et al.,
1995a) to classify infants (about 2 weeks of age) as to neurologic normality.
The first study (Koopman-Esseboom et al., 1995b) demonstrated a significant increase in
the visual recognition memory test (Pagan Test of Infant Intelligence) for breast feeding and
length of breast feeding when examined with maternal serum PCB levels. Neither the prenatal
PCBs nor the non-dioxin-like PCBs in breast milk were associated with the Pagan outcome at
either time period. In this report and another (Koopman-Esseboom et al., 1996), reported below, a
cumulative score was developed for "low," "medium," and "high" exposure which multiplied the
pg total PCB-dioxin-furan TEQ/g fat times weeks breast feeding (see Table 7-46). In the final
regression analyses, significant differences were not observed for total PCB-dioxin-furan TEQ
with outcomes at 3 months of age. However, at 7 months, there was a dose-related increase in
scores with "medium" and "high" total PCB-dioxin-furan TEQ (Table 7-46). The authors
suggested that these benefits resulted from (1) increased breast-feeding and (2) the high total
PCB-dioxin-furan TEQ being an artifact of its correlation with the higher level of lipids or
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lipophilic factors (e.g. hormones, long-chain polyunsaturated fatty acids [LCPUFAs]) beneficial
to this aspect of development.
The second study (Koopman-Esseboom et al., 1996) observed a beneficial effect on PDI at
7 months of age (PDI-7) for breast feeding versus formula when the total PCB-dioxin-furan TEQ
was low (Table 7-46). There were statistically significant deficits observed for PDI-7 in the
regression analysis of "medium" levels of total PCB-dioxin-furan TEQ, and for medium and high
levels of total PCB-dioxin-furan TEQ combined. MDI-7 showed a significant increase with
duration of breastfeeding, but the total PCB-dioxin-furan TEQ did not have a significant effect.
The other endpoints (MDI-3, PDI-3, MDI-18 and PDI-18) were not significantly associated with
either duration of breast feeding or total PCB-dioxin-furan TEQ. In the analysis of prenatal PCB
(using maternal blood levels collected late in pregnancy), PDI scores were lower at 3 months with
higher PCB levels. The authors also examined thyroid hormone levels because they are necessary
for brain development, and found no significant effects of thyroid hormone levels on PDI or MDI.
The third study (Koopman-Esseboom et al., 1995a) used the Prechtl neonatal neurologic
examination to classify infants (about two weeks of age) as to neurologic normality: "normal,"
"mildly abnormal" (e.g., mild hypotonia or tremor), or "definitely abnormal" (e.g., hyper-
excitability, hypotonia, hypertonia, or a hemisyndrome). Two infants in each location were
classified as "definitely abnormal," and 20 total were classified as "mildly abnormal" (11 in
Groningen and 9 in Rotterdam). One "definitely abnormal" child was eliminated from further
analyses because of a birth trauma. Because of the small numbers, the remaining 23 children were
grouped together and termed "neurologically abnormal." These groups were examined for
obstetric optimality scores and thyroid levels (these thyroid levels were discussed in Section
7.13.4.2) and no significant findings were observed. The categorization of neurologically normal
or abnormal, as expected, was highly correlated with the neurologic optimality scores (postural
tone cluster and reflex cluster) (see Huisman et al., 1995a,b). The levels of coplanar PCB TEQ
and total PCB-dioxin-furan TEQ were different in the two groups (Table 7-46). Only free T4 was
significantly different in the two groups (total T3, total T4, free T4, and TSH were tested). The
authors concluded that there was no signficant relationship of dioxins, furans, and PCBs with
these "clinically relevant" outcomes and recommended follow-up of these children as they aged.
Two more studies examined the Rotterdam children and the Groningen children together
(Table 7-46). These studies covered (1) neonatal neurologic development at 10-21 days post-birth
(Huisman et al. 1995a) and (2) neurological condition at 18 months (Huisman, 1995b).
In the first report (Huisman et al., 1995a), infants were examined 10-21 days after birth,
and several evaluations were made: (1) neonatal neurological condition (394 infants were normal,
20 suspect, and 4 abnormal); (2) Prechtl's Neurologic Optimality Scores (NOS), based on 21
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items; and (3) these 21 items grouped to develop postural tone cluster scores and reflex cluster
scores. The NOS and the cluster scores were then dichotomized for use in the statistical analyses:
the NOS was divided at the median (a score of 57), the postural tone cluster score (less than or
equal to 9 (43% of the children) versus greater than 9, and the reflex cluster score (less than or
equal to 10 (22% of the children) versus greater than 10. Prenatal PCB measures (maternal blood
and cord blood) were not associated with NOS or the clusters. Many individual PCBs, dioxins
and furans in breast milk were associated with NOS (Table 7-46), as were most of the summary
measures based on breast milk (total PCB-dioxin-furan TEQ, dioxin-furan TEQ, £PCBbreastmilk,
mono-ortho PCB TEQ, and di-ortho PCB TEQ.) Coplanar PCB TEQ was associated with
hypotonia (measured through the postural tone cluster score): OR: 1.64 (1.03-2.63). Because the
data suggested observer differences in the two communities (by a shift in the distribution between
them), analyses controlled for community. However, the scoring of the two observers was not
compared for some common subjects.
The second report (Huisman et al., 1995b) examined the same groups of infants at 18
months of age. The infants were assessed during an observation of motor functions using
techniques described by Touwen, Hempel, and colleagues (1992-3). Of the 418 children scored,
408 were considered "normal" and the remainder were "mildly abnormal" (9) or "abnormal" (1).
Only the prenatal PCB exposure (estimated by either £PCBcord Or £PCBmatemal blood) was associated
with it at 18 months (Table 7-46) The authors observed an interaction with paternal smoking, so
that the adverse outcome with exposure was observed only in children with nonsmoking fathers.
The authors noted that maternal smoking was only collected during pregnancy, and so the
association of maternal postnatal smoking could not be evaluated. None of the measures of PCB,
dioxin, or furans were associated with the fluency cluster score, but breast-fed children in general
did have a higher score than did formula -fed children.
These children were again assessed at 42 months of age (Patandin et al., 1999). In this
round, 395 children (94% of the original study group) were assessed for cognitive abilities using
the Kaufman Assessment Battery for Children (K-ABC), and a subgroup of 193 (the Rotterdam
children) were assessed for verbal comprehension using the Reynell Language Developmental
Scales (RDLS). £PCB was calculated using PCBs (IUPAC numbers 118, 138, 153 and 180)
from the mother's blood, cord blood, and plasma from the 42-month-old children. PCB, dioxin,
and furan levels were available for the breast milk samples collected 2 weeks after delivery from
breast-feeding mothers. Exposure metrics included £PCB, total TEQ (dioxin, furan and PCB),
and the sum of 20 non-dioxin-like PCBs. Statistically significant deficits were associated with the
natural log of the £PCBmatemal blood for K-ABC for the entire group, and for those children who
were formula fed. Significant deficits for the RDLS were noted only in the formula-fed children.
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Analyses of the current body burden in the children were not associated with any cognitive
deficits. Statistically significant changes were not observed in the breast-fed children, possibly
because of the higher SES status, parental education, and parental verbal IQs. Another possibility
is the beneficial effects of breast feeding in general.
7.13.8.4. Comment on Developmental Studies
One factor demonstrated in this series of studies is the benefit derived from breast feeding.
Even though the level of environmental toxicants reaching the child through early dietary
exposure may be greater with breast feeding, formula-fed children did not do as well overall on
many behavioral and neurological measures in these studies. This may not be true with
environmental "accidents," which could result in much higher levels to the child. These
differences could also be attributed to the association of breast feeding with socioeconomic status
of the households, parental education levels, etc.
A large number of dioxins, furans, and PCBs were evaluated at different developmental
stages. Given the smaller volume in the collection of third-trimester blood from the mother and
cord blood at birth, only four PCBs were measured (IUPAC 118, 138, 153, 180). Thus, prenatal
dioxin-furan levels can only be approximated in these data. The statistically significant
correlations between the different agents and biological sources suggest that it would be difficult
to sort out effects of any individual group or class of agents.
In some of these studies, total breast-feeding time and breast milk levels were used to
estimate the total exposure via breast feeding. This model is a reasonable relative estimate of
broad categories, but may be problematic for estimation for women with widely different lengths
of breast feeding. The levels in breast milk are likely to decrease over time, and the consumption
of breast milk is likely to drop gradually as other food sources are increased. Thus the general
levels of the broad groupings are useful, but the individual estimates should be used with caution.
Several of these studies based their results on crude (unadjusted) analyses. Given that
there were significant differences between the breast-feeding parents and the bottle-feeding
parents as to socioeconomic status (e.g., education, profession) and other lifestyle factors (e.g.,
smoking and drinking patterns), these results could change with a more in-depth analysis. The
observation of hypotonia and prenatal PCB exposures is consistent with another study from the
1980s (Rogan et al., 1986). This study found effects of prenatal exposure (but not postnatal
through breast feeding) on hypotonia, as did one of the Dutch studies (Huisman et al., 1995a).
These associations with prenatal exposure have persisted up to 42 months of age (Patandin et al.,
1999). These findings are consistent with findings of cognitive deficits in 11-year old children
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exposed prenatally to PCBs in Michigan; as with the Dutch studies, deficits were not associated
with exposures through breast feeding (Jacobson and Jacobson, 1996).
7.13.9. Circulatory System
The relationship between human exposure to 2,3,7,8-TCDD-contaminated chemicals and
disorders of the circulatory system has been explored. A number of early case reports have
described effects on the cardiovascular system among individuals reportedly exposed to chemicals
contaminated with 2,3,7,8-TCDD. Myocarditis (Goldman, 1972), myocardial infarctions (Walker
and Martin, 1979; Bauer et al., 1961), ectasia of the coronary arteries (England, 1981), and rapidly
progressive atherosclerosis (Jirasek et al., 1974; Pazderova-Vejlupkova et al., 1981) have been
reported.
Some of the earliest studies described mortality from diseases affecting the circulatory
system among worker populations exposed to 2,3,7,8-TCDD (Bond et al., 1987; Coggon et al.,
1991; Fingerhut et al., 1991b; Zober et al., 1990; Bueno de Mesquita et al., 1993; Bertazzi et al.,
1989, 1992; Collins et al., 1993) (Table 7-47). The circulatory system includes ICD-9 codes 390-
459 (International Classification of Diseases 9). Most early studies were designed primarily to
test hypotheses relating to cancer and, secondarily, to characterize mortality compared to the
general population from causes other than cancer, and without detailed characterization of
confounders related to the circulatory system.
In many of the earlier studies, mortality from all diseases of the circulatory system among
TCP production workers was similar to or less than mortality in the general population, as
described by an SMR of 100 (Table 7-47). Examples include The Netherlands (Plant A) (SMR =
98, 95% CI = 65-142) (Bueno de Mesquita et al., 1993); the United States (Nitro, West Virginia)
(SMR = 90, 95% CI = 80-100) (Collins et al., 1993); and Great Britain (SMR = 116, 95% CI =
91-146) (Coggon et al., 1991). In studies of workers with chloracne, mortality from circulatory
diseases was decreased in U.S. workers (SMR = 95, 95% CI = 79-113 (Bond et al., 1987), and
moderately increased in German workers, SMR = 121, 90% CI = 83-170 (Zober et al., 1990). In
the follow-up study of the same cohort of German workers with chloracne —data not presented—
this excess disappeared (Ott and Zober, 1996a).
More recent studies, many of which are updates of earlier reports (Flesch-Janys et al.,
1995, Ott and Zober, 1996a; Hooiveld et al., 1998; Vena et al., 1998; Steenland et al., 1999), more
thoroughly examined mortality from noncancer endpoints, including diseases of the circulatory
system. In these studies, researchers employed more sophisticated analytical methods, such as
adjustment for confounders, use of internal control groups, and measurement or estimation of
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exposure to 2,3,7,8-TCDD or related compounds to refine calculated risk estimates for mortality
from diseases of the circulatory system and other outcomes.
In a cohort of German chemical workers (Hamburg) who manufactured 2,4,5-TCP; and
2,4,5-T and chemicals contaminated with higher chlorinated PCDDs and PCDFs (Flesch-Janys et
al., 1995), mortality for all circulatory diseases was positively related to estimated 2,3,7,8-TCDD
concentrations and significantly related to estimated total TEQ concentrations above 39 ng/kg,
lipid adjusted. Lipid-adjusted 2,3,7,8-TCDD concentrations and total TEQ estimates for the
cohort were based on PCDD and PCDF measurements of 190 male workers.
In contrast, Ott and Zober (1996a) found no dose-dependent relation between estimated
2,3,7,8 -TCDD dose and mortality from diseases of the circulatory system (Conditional risk ratio
= 0.93, 95% CI = 0.70, 1.24) when adjusted for cigarette smoking, body mass index, and age in
workers from Ludwigshafen, Germany. Similarly, Vena et al. (1998) found significant deficits in
mortality from circulatory diseases (SMR = 94, 95% CI = 88, 99) among workers in the expanded
IARC international cohort exposed only to phenoxy herbicides and chlorophenols when compared
to country-, gender-, age-, calendar period-, and cause-specific national death rates using the
World Health Organization Mortality Data Bank. However, when compared to an internal control
group of workers not exposed to TCDD/HCDD, mortality from all circulatory diseases among
workers exposed to TCDD or HCDD was statistically significantly elevated (RR = 1.51, 95% CI =
1.17, 1.96). This significant increase occurred for workers exposed for 5-9 years and 10-19 years,
but not for periods over 20 years.
In an update of the Dutch cohort, when compared to the general population (SMR = 100,
95% CI = 80, 140) or to an internal comparison group (adjusted RR = 1.4, 95% CI = 0.8, 2.5),
little risk for mortality from circulatory disease was observed among the 549 workers exposed
between 1955 and 1991 to 2,4,5-T; 2,4,5-TCP; and contaminants, or among the 140 workers
exposed in a 1963 reactor release (SMR =110, 95% CI = 60, 170) (Hooiveld et al., 1998).
Mortality from more specific endpoints, such as ischemic heart disease, all heart diseases,
and cerebrovascular disease, was noted in a few studies. The SMR from ischemic heart disease
was decreased (SMR= 96, 95% CI = 51-164) in U.S. workers (Midland, Michigan) (Bond et al.,
1987) and in German workers from Ludwigshafen (SMR = 70, 95% CI = 40-110) (Ott and Zober,
1996a). Overall mortality from ischemic heart disease was not elevated in a cohort of chemical
workers from Hamburg, but it was significantly increased in workers only in the highest 2,3,7,8-
TCDD quintile (RR = 2.48, 95% CI = 1.32-4.66). In an update of the Dutch cohort study through
1991, mortality from ischemic heart disease was elevated but did not achieve statistical
significance (SMR = 190, 95% CI = 90-360) (Hooiveld et al., 1998). For the 29 workers with a
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history of chloracne who were also exposed as a result of the 2,4,5-TCP reactor release, mortality
was significantly elevated (SMR = 3.7, 95% CI = 1.4-8.1, N = 6).
For workers included in the IARC cohort exposed to phenoxy herbicides or chlorophenols,
mortality from ischemic heart disease was decreased relative to the WHO comparison population
(SMR = 97, 95% CI = 90-104) but significantly increased relative to an internal control group
using Poisson regression (RR = 167, 95% CI = 123-226) (Vena et al., 1998). Using estimated
cumulative exposure scores (Piacitelli et al., 1997), Steenland et al. (1999) found mortality from
ischemic heart disease moderately increased with increasing exposure score, with an SMR = 93 in
the lowest septile to an SMR = 123 for workers in the highest septile (P ,es,fortrend = 0.14). In an
analysis using Cox regression and an internal control group, only the rate ratio for the category
with the highest exposure score (7th septile) was statistically significantly elevated, the test for
trend overall exposure score categories was statistically significant (p = 0.5). In the subcohort
with chloracne mortality from ischemic heart disease was marginally increased (SMR =117, 95%
CI = 94-144).
Cerebrovascular disease mortality was increased by more than twofold in Michigan TCP
production workers with chloracne (SMR = 208, 95% CI = 57,539) (Bond et al., 1987).
In an update of the 1993 study of Dutch TCP production workers (Bueno de Mesquita et al.,
1993), mortality from cerebrovascular disease remained slightly elevated when compared both to
the national population (SMR = 140, 95% CI = 60-260) and to an internal control group (SMR =
140, 95% CI = 40-510) (Hooiveld et al., 1998). Steenland et al. (1999) found a deficit of
mortality from cerebrovascular disease in an update of the NIOSH cohort (SMR = 96, 95% CI =
74-121), as did Vena et al. (1998) in his study of nonneoplastic mortality in the IARC expanded
cohort (SMR = 84, 95% CI = 71-98, international comparison population). When compared to
the internal reference population, mortality was significantly elevated in the IARC expanded
cohort (RR = 1.55, 95% CI = 0.83-2.28).
Mortality from fflD was lower than expected (SMR = 82, 95% CI = 0.67-1.02) in a cohort
of 1,909 herbicide sprayers followed from 1972 through 1989. Workers eligible for study applied
a mixture of 2,4-D and 2,4,5-T for 2 weeks or longer anytime during the period 1951-1971 (Asp
et al., 1994). Similar trends were observed for other diseases of the circulatory system.
In veteran populations, among 1,261 Ranch Hand personnel, mortality from circulatory
disease was nonsignificantly elevated (SMR = 110, 95% CI = 60-150) compared with that of a
comparison population of 19,101 other Air Force veterans who were not exposed to herbicides
(Michalek et al., 1990). These results were repeated in an updated mortality analysis (SMR =
105, 95% CI = 96-142) (Wolfe et al., 1994). After 15 years of followup, overall mortality from
circulatory disease was as expected (SMR = 100, 95% CI = 70-130); only the SMR for enlisted
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ground personnel, the subgroup with the highest dioxin levels, was greater than expected (SMR =
100, 95% CI = 90-150) (Michalek et al., 1998). Of the 118 total deaths, 3 were due to
cerebrovascular disease (SMR = 2.7, 95% CI = not calculated). Similar nonsignificant increases
in the relative mortality ratio (RMR) were observed for circulatory diseases (RMR = 1.6, 95% CI
= 0.8-3.2) in Australian Vietnam veterans (N = 19,205; 260 deaths) compared to 25,677 (263
deaths) non-Vietnam veterans who served only in Australia (Fett et al., 1987). In contrast, the
unadjusted relative risk of 0.49 (95% CI = 0.25-0.99) for all circulatory diseases suggested a
deficit of deaths from this cause among 9,325 Vietnam Army veterans (246 deaths) compared to
8,989 non-Vietnam veterans (200 deaths) (Centers for Disease Control Vietnam Experience
Study, 1988c).
Bertazzi and colleagues examined the mortality experience of Seveso residents ages 1-19
years (Bertazzi et al., 1992) and ages 20-74 years (Bertazzi et al., 1989) 10 years after the TCP
reactor release. In the younger population, two deaths from circulatory diseases occurred only in
female residents (RR = 1.63, 95% CI = 0.3-8.1). In the older population, circulatory disease
mortality of residents from zone A (the most highly contaminated region) was elevated in both
males (RR = 1.75, 95% CI = 1.0-3.2) and females (RR = 1.89, 95% CI = 0.8-4.2). In males, the
highest death rate occurred during the first 5 years after the release, 1976-1981 (RR = 2.04, 95%
CI = 1.0-4.2), and in females, the highest death rate occurred during the second 5 years post
release, 1982-1986. As the authors suggest, the study was limited by a small number of subjects,
a crude measure of 2,3,7,8-TCDD exposure, and a short followup period. The authors could not
attribute the increased mortality from circulatory disease to 2,3,7,8-TCDD exposure, but
suggested that the "high stress and pollution" imposed on the residents of zone A may have been a
contributing factor.
An update of the 15-year (1976-1981) mortality experience of the cohorts of zones A, B
and R was conducted by Pesatori et al. (1998). In zone A males, moderate increases in mortality
from all circulatory diseases combined was observed (RR = 1.6, 95% CI = 1.1-2.5, N = 21) due,
in part, to the threefold increase in mortality from chronic ischemic heart disease (RR = 3.0, 95%
CI = 1.2-7.3, N = 5). Among females in zone A, rheumatic heart disease (RR = 15.8, 95% CI =
4.9-50.4, N = 3) and hypertension (RR = 3.6, 95% CI = 1.2-11.4, N = 3) were in excess. No
significantly increased risks were observed for circulatory disease among residents of zone B,
however, in zone R males and females, small but statistically significant elevations occurred for
all circulatory diseases (RR= 1.1, 95% CI= 1.0-1.2, N = 719 males; RR= 1.1, 95% CI= 1.0-1.2,
N = 759 females). Among males, chronic ischemic heart disease was 40% higher than expected
(RR= 1.4, 95% CI= 1.1-1.7, N= 126) and 30% higher in females (RR= 1.3, 95% CI= 1.0-1.5,
N = 133). Only in zone R was the risk for mortality from cerebrovascular disease increased.
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Many cross-sectional medical studies also examined the association between 2,3,7,8-
TCDD exposure and effects on the cardiovascular system (Suskind and Hertzberg, 1984; Moses et
al., 1984; Bond et al., 1983; Centers for Disease Control Vietnam Experience Study, 1988a;
Roegner et al., 1991; Grubbs et al., 1995; Calvert et al., 1998). In the NIOSH morbidity study
cohort, after controlling for important confounders, no associations were observed between serum
2,3,7,8-TCDD concentrations and increased risk for myocardial infarction, angina, arrhythmia,
hypertension, systolic or diastolic hypertension at the time of the study, or abnormal peripheral
arterial flow (Calvert et al., 1998).
Statistically significant associations relative to 2,3,7,8-TCDD exposure were found in the
Ranch Hand study only for diastolic blood pressure, arrhythmias detected on the
electrocardiogram (ECG), and peripheral pulse abnormalities (Roegner et al., 1991). However,
there is some doubt that the significant findings are dose related because significant increases in
the mean diastolic blood pressure were found in Ranch Hands with serum 2,3,7,8-TCDD levels
from 15 to 33.3 pg/g but not in the Ranch Hands with higher serum levels. The adjusted odds
ratios for ECG-diagnosed arrhythmias among Ranch Hands were not entirely consistent with a
dose-response relationship. For each serum 2,3,7,8-TCDD category the odds ratios were: serum
2,3,7,8-TCDD levels <10 pg/g, OR = 1.33 (95% CI = 0.68-2.58, p = 0.40); 15-33.3 pg/g, OR =
0.83 (95% CI = 0.31, 2.23, p = 0.71); and for serum 2,3,7,8-TCDD levels above 33.3 pg/g the OR
was 2.34 (95% CI = 1.00-5.51, p = 0.051). The proportion of individuals in this group with
arrhythmias (5.2%) was not much higher than in Ranch Hands whose serum levels were < 10 pg/g
(4.7%). Finally, relative to the comparison group, the proportion of abnormal peripheral pulses in
all Ranch Hands, regardless of serum level, was elevated.
In the 1995 followup analysis, the findings closely reflect those of the 1992 analysis,
although some results did vary (Grubbs et al., 1995). The prevalence of hypertension of
hypertension continued to be significantly related to serum 2,3,7,8-TCDD levels, although there
was no evidence for a dose-response association for either diastolic or systolic blood pressure.
Overall ECG abnormalities were not different between Ranch Hands and comparisons. However,
dose-related increases in risk were noted for RBBB, nonspecific ST and T-waves, and
arrhythmias. Finally, no consistent evidence was found for a dose-related increase in the
prevalence of abnormal peripheral pulses; however, the prevalence was statistically significant
when Ranch Hands were compared to the comparisons.
No excess abnormalities or disorders of the circulatory system or heart were found in
several groups of TCP production workers, although their potential for exposure to 2,3,7,8-
TCDD-contaminated chemicals was high (Suskind and Hertzberg, 1984; Moses et al., 1984; Bond
et al., 1983). The prevalence of hypertension or coronary artery disease (both self-reported),
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abnormal ECG findings, atherosclerotic changes (not specified) on chest X-ray, or blood pressure
elevation was not elevated in West Virginia TCP production workers (Suskind and Hertzberg,
1984). Similarly, when Moses et al. (1984) examined TCP production workers with chloracne
and compared them with workers not affected by chloracne, they found no increased risk for self-
reported abnormal ECG, self-reported angina, or self-reported myocardial infarction and no
difference in the physical examination of the cardiovascular system. Finally, Bond et al. (1983)
found no increased risk for self-reported hypertension among workers involved in the production
of trichlorophenol and 2,4,5-T.
7.13.9.1. Comment
The SMRs for circulatory system diseases reported in the studies occupational are close to
100, suggesting that the "healthy worker effect" is diminished in these cohorts. Because
employed workers are healthier than the general population, the SMR for cardiovascular disease
in employed populations tends to be less than 100 (McMichael, 1976; Fox and Collier, 1976). It
is also possible that because most of the cohorts exposed to TCDD and related compounds are
composed mainly of retired or deceased workers, the healthy worker effect is reduced.
The picture relating exposure to 2,3,7,8-TCDD and diseases of the circulatory system is
mixed. Animal data indicate that high doses of 2,3,7,8-TCDD affect cardiac and vascular
integrity (Allen and Carstens, 1967; Allen et al., 1977; Norback and Allen, 1973). Data from a
few animal studies suggest that relatively high doses of 2,3,7,8-TCDD cause damage to the
myocardium and heart valves in rats (Kociba et al., 1978; Buu-Ho'i, 1972) and to the arterial walls
in rabbits (Brewster et al., 1987). Other research found that 2,3,7,8-TCDD may alter cardiac
function in rats and guinea pigs (Hermansky et al., 1988; Kelling et al., 1987; Canga et al., 1988),
causing reduced spontaneous and isoproterenol-induced heart contractility; this suggests that
2,3,7,8-TCDD may increase the risk of arrhythmias among the dosed animals. The authors
postulate that 2,3,7,8-TCDD alters cyclic-AMP concentrations, altering the responsiveness of
cardiac cells to p-adrenergic stimuli (Brewster et al., 1987). In contrast, histopathologic changes
were not observed in the cardiovascular system of hamsters, which appear to be resistant to the
effects of 2,3,7,8-TCDD at levels of 3,000 |ig/kg of (Olson et al., 1980). Other experimental
studies suggest an association between 2,3,7,8-TCDD and alterations in lipid levels (Poli et al.,
1980; Albro et al., 1978; Bombick et al., 1984; Swift et al., 1981).
Findings from mortality and morbidity studies of production workers are not definitive,
and suggest the need for analyses more specifically examining possible effects on the circulatory
system and heart. The outcomes examined in the animal and human studies are different. Animal
studies describe morphologic and chemical changes in the vascular and cardiac cells caused by
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2,3,7,8-TCDD. On the other hand, the diseases and causes of death in the human studies assessed
the possible consequences of exposure on long-term pathologic changes to the tissues, which
cause cell and, sometimes, organ and system failure. Using the animal data, it is possible to
project the long-term consequences of exposure to the organ or system. However, the animal
studies do not account for the possibility of intervening events, such as the repair of tissue after
exposure ends, or other events that reduce the hypothesized endpoint to a much less drastic
outcome.
In recent studies, circulatory diseases and diseases of the heart were investigated as
hypothesized health outcomes of exposure to 2,3,7,8-TCDD. A few mortality studies considered
the possible confounding effect of other variables, including smoking, lipid levels, and other
conditions that influence circulatory diseases and disorders of the heart. However, a general
limitation of mortality studies is that only those conditions that ultimately caused the death of the
individual are enumerated. Events such as myocardial infarctions, which may debilitate the
individual but not cause death, may be missed if death is caused by another circumstance.
Therefore, the effect of 2,3,7,8-TCDD on the coronary arteries might be missed because it was not
coded as the underlying cause of death on the death certificate.
With the exception of the studies of the Ranch Hand and NIOSH cohorts (Roegner et al.,
1991; Michalek et al., 1998; Calvert et al., 1999), cross-sectional analyses of other more highly
exposed groups were limited by a lack of good exposure data and an inability to examine the
relationship between serum 2,3,7,8-TCDD levels and diseases of the circulatory system or heart.
Such studies may also be limited by the fact that they include a survivor population, as described
in the introduction.
Further research would be useful to define the relationship between the pathologic
endpoints observed in animals after high single doses of 2,3,7,8-TCDD and the disease outcomes
observed in humans after high long-term exposure. To identify whether 2,3,7,8-TCDD has an
effect on the human vasculature, additional work is needed to determine whether certain doses of
2,3,7,8-TCDD cause changes in the human vascular system; to determine whether there are
changes in the action of chemicals associated with human cardiac muscle contraction caused by
2,3,7,8-TCDD exposure; and to assess mortality and morbidity in individuals with potential for
2,3,7,8-TCDD exposure while carefully controlling for other risk factors and using more accurate
measures of exposure.
7.13.10. Pulmonary Effects
Studies of long-term exposure to 2,3,7,8-TCDD in Sprague-Dawley rats (Kociba et al.,
1979; van Miller et al., 1977), B6C3F1 mice (NTP, 1982a), Swiss-Webster mice (NTP, 1982b),
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and rhesus monkeys (Allen et al., 1977) have reported changes in bronchiolar or alveolar tissue
ranging from epithelial hyperplasia and metaplasia to squamous cell carcinomas. The
hyperplastic and metaplastic changes observed in exposed animals are similar to the pathologic
picture of chronic bronchitis in humans (American Thoracic Society, 1962).
Case reports have described temporary respiratory irritation (Zack and Suskind, 1980) and
tracheobronchitis (Goldman, 1972) among chemical workers exposed to 2,3,7,8-TCDD-
contaminated herbicides following industrial accidents. In addition, Baader and Bauer (1951)
reported chronic bronchitis in seven workers involved in pentachlorophenol production, which
resolved in all but two workers within 2 weeks after production was discontinued.
There is conflicting evidence from controlled epidemiologic studies regarding an
association between chronic respiratory system effects and human exposure to substances
contaminated with 2,3,7,8-TCDD. One study of workers involved in the production of TCP and
2,4,5-T suggested that 2,3,7,8-TCDD exposure increases the risk for abnormal ventilatory
function (Suskind and Hertzberg, 1984). This study found a statistically significantly increased
risk for an abnormal forced expiratory volume at 1 second (FEVj) (p<0.01), an abnormal forced
vital capacity (FVC) (p<0.001), and an abnormal FEVj/FVC ratio (/X0.05) among workers who
were smoking at the time of the study. For workers, the percent predicted spirometric parameters
for FEVi, FVC, and FE^/FVC were 99.4%, 92.7%, and 76.5%, and for referents, 104.4%,
97.6%, and 79.9%, respectively. The only other study of TCP and 2,4,5-T production workers
that reported ventilatory function findings found no association between serum 2,3,7,8-TCDD
levels and declines in ventilatory function (Calvert et al., 1991). The disparity in results between
the two studies may be related to the age of the unexposed populations and potential exposures of
the exposed. In the Suskind and Hertzberg study, the exposed workers were, on average, 10 years
older than the unexposed workers. Although the authors indirectly adjusted for age by analyzing
age-adjusted ventilatory measures, it is not clear if these adjustments can completely control for a
10-year difference in age. In the study by Calvert et al. (1991), the difference in mean age
between the exposed and unexposed groups was 0.6 years. The second difference involves the
potential for exposure to 2,4,5-T acid dust at the plants studied. The 2,4,5-T acid that was
produced at the plant studied by Suskind and Hertzberg was finished as a powder. At the plants
studied by Calvert et al. (1991), the 2,4,5-T acid was finished as a liquid. Therefore, the potential
for exposure to 2,4,5-T acid dust was greater at the plant studied by Suskind and Hertzberg
(1984). Although we are not aware of any published reports supporting an association between
ventilatory function and 2,4,5-T acid exposure, a respiratory burden of particles, in the absence of
a specific toxic agent, can be a probable cause of ventilatory function declines (Becklake, 1985).
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The Ranch Hand study also examined the association between serum 2,3,7,8-TCDD level
and respiratory system effects (Roegner et al., 1991). This study found significant declines in the
mean FEVj and the mean forced expiratory volume (FVC) for Ranch Hands with serum 2,3,7,8-
TCDD levels above 33.3 pg/g (adjusted mean FEVj = 91.3%; mean FVC = 87.4) compared to a
nonexposed comparison group (adjusted mean FEVj = 93.5%; mean FVC = 91.7) (Roegner et al.,
1991). The 2,3,7,8-TCDD-related declines were small and were interpreted by the authors to be
"subtle" and "not clinically significant." As expected, smoking appeared to have the greater
influence on lung function although this has not been considered by the Air Force in their
interpretation. In the followup examination conducted in 1992, no consistent relationship was
found between serum 2,3,7,8-TCDD concentrations and respiratory parameters (Grubbs et al.,
1995).
Mortality from respiratory diseases among the various exposed populations as is mixed.
In productions workers, no excess mortality was observed from all diseases of the respiratory
system among subcorhort of workers in the IARC study exposed to phenoxy herbicides or
chlorophenols (SMR = 86, 95% CI = 73-101, N = 151) (Vena et al., 1999) or among German
accident workers (N = 1) (Ott and Zober, 1996a).
7.13.10.1. Other Mortality Studies of Production Workers
Overall mortality from respiratory diseases among the Seveso population was less than
expected for all exposure zones except males in zone A (Pesatori et al., 1998). However, chronic
obstructive lung disease was twofold higher than expected in females in zone B (N = 7) and
threefold higher in males of zone A (N = 4).
7.13.10.2. Comment
In conclusion, case reports indicate that intense acute exposure to 2,3,7,8-TCDD can
produce respiratory irritation. However, the findings from controlled epidemiologic studies do
not support an association between 2,3,7,8-TCDD exposure and chronic noncancer effects on the
respiratory system.
7.13.11. Renal Effects
There is little evidence in the animal or human data to suggest that exposure to
2,3,7,8-TCDD is related to renal or bladder dysfunction. In a single case report, a child exposed
to 2,3,7,8-TCDD after contact with soil sprayed with contaminated waste oil was diagnosed with
focal pyelonephritis (Kimbrough et al., 1977). After diagnosis and treatment, the condition
resolved with no reported recurrence. No major renal or bladder dysfunctions were noted among
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Air Force Ranch Hands (Lathrop et al., 1984, 1987; Roegner et al., 1991) or among TCP
production workers from West Virginia (Suskind and Hertzberg, 1984) or New Jersey (Poland et
al., 1971).
7.13.12. Developmental/Reproductive Effects
Animal studies of reproductive effects have focused primarily on maternal 2,3,7,8-TCDD
exposure and developmental toxicity. Prenatal exposure to 2,3,7,8-TCDD has been associated
with increased pre- and postnatal mortality, cleft palate and kidney abnormalities, altered sexual
development, and reduced fertility in studies of maternal exposure in a number of species. Fewer
studies have focused on the effects of dioxin on the male reproductive system or on the results of
matings in which only the males were exposed to dioxin. Studies of male exposures have not
provided evidence of paternally mediated effects on the offspring. (Chapter 5 contains a detailed
account of the animal literature.)
Thus, experimental research has emphasized maternally mediated developmental effects of
2,3,7,8-TCDD, while in humans, studies of paternal exposures have predominated. Moreover,
assessment of exposed male animals has most commonly examined the effects of 2,3,7,8-TCDD
on spermatogenesis, fertility, and sex organ development (Theobold and Peterson, 1984), whereas
studies of human males have mainly targeted studies of congenital malformations and recognized
spontaneous abortions. Experimental research designed to corroborate human investigations may
provide critically needed data to plug the gaps in our understanding of the mechanisms through
which 2,3,7,8-TCDD exposure may operate to produce adverse reproductive events in humans.
The origin of concerns regarding a potential link between exposure to chlorinated dioxins
and adverse developmental events can be traced to early animal studies reporting increased
incidence of developmental abnormalities in rats and mice exposed early in gestation to 2,4,5-T
(Courtney and Moore, 1971). This was of grave concern, as the U.S. military's most widely used
herbicide during the Vietnam conflict, Agent Orange, was composed of approximately equal
proportions by weight of the n-butyl esters of 2,4-D and 2,4,5-T. The latter is contaminated by
2,3,7,8-TCDD during manufacture.
One dilemma encountered when attempting to review the epidemiologic literature dealing
with dioxins and reproductive effects is the categorization of studies of sufficient similarity to
allow for comparative analysis. These studies vary greatly in the nature (occupational,
environmental) and route (inhalation, ingestion, absorption) of exposure; in the reproductive and
developmental outcomes examined (often multiple endpoints were considered, and case definition
differed across studies); in the assessment of parental exposure (maternal, paternal, or both); and
in the timing of exposure relative to the pregnancy.
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The examination of reproductive and developmental disorders poses several challenges to
the researcher compared to other health outcomes. First, to understand both normal and
pathologic reproduction, evaluation should include paternal and maternal, and sometimes fetal,
contributions. Increased interest in male-mediated reproductive toxicity emphasizes the need to
consider the couple as the unit of analysis in many reproductive study settings (Olshan and
Mattison, 1994).
The second challenge to researchers is the interrelatedness of the spectrum of reproductive
endpoints available for study. Fecundity (the joint potential to conceive), fertility (the production
of live children), and very early pregnancy loss (those conceptions that do not survive to be
recognized by usual diagnostic methods) as related to 2,3,7,8-TCDD exposure have not been
evaluated in the same population. Clearly, these endpoints affect the rates of reproductive
outcomes occurring later in the reproductive spectrum.
Another feature of developmental effects is the changing vulnerability of the developing
organism throughout gestation. Exposure to a single developmental toxicant throughout
pregnancy may result in different effects at various stages of gestation. The window of
susceptibility varies; therefore, knowledge of the timing of exposure is critical in these studies.
Finally, although not restricted to studies of developmental events, care must be given to
the collection and analysis of data on potential confounders. These factors may need to be
obtained for both mother and father, with attention to the timing of specific characteristics, such
as changes in smoking or occupation during pregnancy.
The reproductive effects of dioxins in humans were succinctly and elegantly reviewed by
Hatch in 1984 (Hatch, 1984b). In her review, Hatch employed type of exposure, i.e., populations
that were exposed occupationally, environmentally, or through industrial accident or military
service, as the classification scheme for the research presented. The same approach has been
followed in this review. The earlier investigations of 2,3,7,8-TCDD and reproductive effects, i.e.,
those conducted prior to 1984, are presented separately from the more recent studies. The
development of assays in the mid-1980s to quantitate 2,3,7,8-TCDD in serum and adipose tissue,
allowing individual measurements of exposure, warrants this dichotomy of the research.
Review of the Literature Prior to 1984
7.13.12.1. Occupational Studies
Epidemiologic studies of occupational dioxin exposure and reproductive effects have
focused on paternally mediated effects, with exposures occurring at varying intervals relative to
conception. Townsend et al. (1982) interviewed 370 wives of employees exposed to dioxins at
the Dow Michigan Division in Midland, Michigan (63% of those eligible for the study), and 345
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control wives of Dow employees who were not exposed to dioxin (62% of the eligible control
pool). Exposure classification was determined by an industrial hygienist familiar with the
processes performed at the plant. Employees were considered exposed to dioxin if they had been
assigned for at least 1 month to specific jobs associated with chlorophenol processes between
1939 and 1975. All outcomes were reported by the employee's spouse.
There was no systematic attempt to ascertain the reason(s) for the high refusal rate in both
cohorts. "Unsolicited reasons" for refusal included divorce, death of spouse, or no pregnancies;
no breakdown by cohort was provided. The possibility of differential rates of infertility or early
pregnancy loss, as reflected by the reported absence of pregnancy, was not addressed in this study.
For the multiple endpoints of spontaneous abortion, stillbirth, birth defects, infant
mortality, and childhood morbidity, no significant association between dioxin exposure and any
adverse event was identified (Table 7-48). This study had a very long interval during which the
exposure and event could have occurred, and a minimum requirement for paternal exposure of 1
month at any time during that interval. This exposure definition essentially assumes "irreparable"
damage that would persist long after the father's active occupational exposure had terminated or a
very long half-life, resulting in an elevated biological level up to 35 years later. Thus, an effect of
exposure, if one existed, would have been diluted by this approach. The authors do not discuss
whether time since last exposure was considered in the analysis.
In another study of occupational exposure to 2,4,5-T, Smith et al. (1982) identified 616
male chemical applicators from a list maintained by New Zealand's Agricultural Chemicals Board
and 531 comparison workers at small agricultural contracting companies. Of these, 548 sprayers
and 441 agricultural contractors and their spouses participated, with impressive response rates of
89% and 83%, respectively. Mailed questionnaires ascertained spraying activities from the males
and reproductive histories from their spouses. The investigators noted that wives of the chemical
sprayers anecdotally reported assisting their husbands in spraying activities, some performing this
task during their pregnancy.
The questionnaires were completed in 1980 and elicited information on spraying activities
and reproductive events that occurred between 1969 and 1980. Reported pregnancy outcomes
were categorized into three groups based on whether the fathers had sprayed any chemicals at any
time during, or prior to, the calendar year in which the pregnancy occurred and whether 2,4,5-T
had been used. There were 1,122 births reported among the 441 control spouses and 1,172 among
the 548 spouses of the exposed sprayers. If all other factors were comparable in both cohorts
(maternal age, socioeconomic status, and maternal smoking histories were shown to be similar),
then there appear to be 220 fewer births observed in the exposed group than might be expected.
No associations between herbicide exposure and spontaneous abortion (OR = 0.89, 95% CI = 0.6-
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1.3) or congenital malformations (OR = 1.2, 95% CI = 0.6-2.4) were identified. Other risk factors
did not appear to be controlled in the analysis of these data: for example, the analysis of
miscarriages did not appear to control for maternal age or occurrence of prior fetal loss. Multiple
pregnancies per family unit were studied; however, the authors did not address the problem of
nonindependent events.
This study is limited by the high probability of exposure to chemicals other than 2,4,5-T.
Although exposure levels were not quantitated in this study, a later study of a subset from this
same population was conducted to estimate 2,3,7,8-TCDD exposure.
In 1988, Smith and colleagues selected nine pesticide applicators from the above group
with the greatest number of years and months per year of pesticide application and evaluated their
serum 2,3,7,8-TCDD levels (Table 7-49). The mean serum 2,3,7,8-TCDD level, adjusted for total
lipids, among the cases (53.3 pg/g) was 10 times that of age-matched controls (5.6 pg/g).
However, exposure in this study was based on self-reports of pesticide application, and research
has demonstrated that self-reports do not correlate with documented serum 2,3,7,8-TCDD levels
(Needham et al., 1991). Therefore, it would be helpful if serum levels could be obtained from a
subset of those applicators with lower self-reported exposures.
A clinical epidemiology study conducted in 1979 examined workers involved with the
manufacture of 2,4,5-T between 1948 and 1969 in Nitro, West Virginia (Suskind and Hertzberg,
1984). All active and retired plant employees exposed to the 2,4,5-T process during that 22-year
interval comprised the eligible pool of "exposed" subjects. The control group consisted of current
and former plant employees who were never associated with the 2,4,5-T process, according to
company records. The response rates for these cohorts were 61% (N = 204) and 46% (N = 163),
respectively.
A reproductive history was obtained from these male employees during an interview and
clinical examination. No attempt was made to verify reports of live births, infant deaths,
miscarriages, birth defects, and stillbirths with the spouses or through medical records. There
were no significant differences in rates of any adverse outcome by exposure status. Given the
poor response rates, crude measure of exposure, and lack of verification of paternally reported
reproductive histories, this study was not likely to detect an association between 2,3,7,8-TCDD
and reproductive events if one existed.
7.13.12.2. Environmental Studies
The problem of documentation of exposure is perhaps of even greater concern in studies of
subjects environmentally exposed to dioxins but lacking individual 2,3,7,8-TCDD measures. The
route of exposure (inhalation, ingestion, absorption), length and intensity of exposure, and the
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timing of the exposure are more difficult to estimate in free-living populations compared with
workers in an occupational setting.
Selection bias is also a critical concern in environmental studies because there is no
environmental equivalent to company records for defining the exposure status of the study group.
Proximity of residence to a source (e.g., a toxic waste site or an industrial source) was generally
the best option available for identifying the population at risk of exposure. Issues such as length
of time living at that residence, the amount of time spent in the home, other potential sources of
exposure (e.g., occupational), and the timing of the contamination episode relative to the outcome
of interest may have greatly affected the degree of exposure.
The potential for volunteer bias, which is likely in studies that rely on subjects recruited
through publicized requests for participation, is an additional concern in studies of this nature.
Moreover, it is extremely difficult to conduct epidemiologic investigations under crisis situations
such as the industrial accident that occurred in Seveso, Italy. Given these limitations, the efforts
of these investigators have provided valuable impetus to the refinement of study designs and the
development of more sophisticated techniques to explore this issue.
7.13.12.2.1. The Seveso, Italy, dioxin accident of 1976. In 1976, during the production of
trichlorophenol at the ICMESA plant in Seveso, Italy, a runaway reaction resulted in an explosion
that ultimately contaminated 700 acres in the surrounding community. Environmental levels of
2,3,7,8-TCDD were determined by using wipe tests, evaluating toxic effects in small animals, and
analyzing grass samples. Approximately two weeks later, more than 200 families were evacuated
from high-contamination areas. Exposures were sufficiently high for chloracne to be observed in
this environmentally exposed population. Following the reactor release, an extensive surveillance
system was initiated to monitor the health of the exposed population. Changes in fetal loss rates
occurring during the last quarter of 1976 and first quarter of 1977 were found in both exposed and
unexposed communities. An estimated 150 women were in the first trimester of pregnancy at the
time of the accident (Rehder et al., 1978); of these, 125 women wished therapeutic abortions by
October 1976. Therapeutic abortions were approved for 30. Another estimate
(Tuchmann-Duplessis, 1980a,b) reports a total of 108 (50 in 1976 and 58 in 1977) therapeutic
abortions in the four affected communities. Several reports (Biscanti et al., 1978; Pocchiari,
1980; Pocchiari et al., 1980) suggested that a large number of women obtained unapproved, and
therefore not reported, therapeutic abortions. This supposition was supported by a steep decrease
in birth rates in the first 6 months of 1977, that was primarily observed in the exposed
communities. (All communities had had decreases over time; however, the decrease in exposed
communities at this key time was much larger.)
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The Seveso Congenital Malformations Registry enrolled all live births and stillbirths
occurring January 1, 1977, through December 31, 1982, to women residents of zones A, B, R, and
non-ABR (Mastroiacovo et al., 1988). A total of 15,291 births (live and stillbirths) and 742 birth
defects were recorded: zone A, 26 births and no birth defects; zone B, 435 births and 25 birth
defects (57.5/1,000 births); zone R, 2,439 births and 110 birth defects (45.1/1,000 births); zone
non-ABR, 12,391 births and 605 birth defects (48.8/1,000 births). Birth defects were confirmed
by medical records. Relative risk estimate for total defects comparing zones A+B with zone non-
ABR is 1.2 (90% CI = 0.88-1.64) (Table 7-48). The rate of birth defects occurring early in the
observation period (first quarter of 1977) did not differ from the rate of birth defects occurring
later in the observation period (data not shown), and there were no discernible patterns of defects
within or between the exposure groups.
To document an increase in the rate of an event, sound baseline information is required.
No reliable information regarding rates of birth defects in this area was available until the
establishment of the Seveso Congenital Malformations Registry 6 months after the incident. Thus
the potential contribution of 2,3,7,8-TCDD to the congenital malformation rate cannot be
separated from improved case ascertainment. Prior to this time, only certain birth defects were
required to be reported to Italian health officers (Reggiani, 1980b). In addition to the limitations
due to legal requirements, there were other reasons for the underreporting of malformations:
"Traditionally, Italian physicians have under-reported congenital malformations because of their
severe negative social implications." (Tuchmann-Duplessis, 1980a) Although physicians and
midwives were encouraged to do more complete recording, Reggiani (1978) concluded that the
malformation data are "missing" for 1976, and "incomplete" for the first trimester of 1977. The
influence of spontaneous abortions on the birth defect rate is likewise unknown. As with
congenital malformations, reliable background data for spontaneous abortion in the study area
were not available. The small number of conceptions available for study limits the power of these
studies to detect an association between 2,3,7,8-TCDD exposure and spontaneous abortion and
birth defects.
Another adverse outcome that has not been described in most epidemiologic studies is the
effect of 2,3,7,8-TCDD on the chromosome. Pocchiari and colleagues described one study in
which 30 therapeutic and 4 spontaneous abortions from the Seveso area were examined
(Pocchiari, 1979). There were no indications of mutagenic, teratogenic, or embryotoxic effects
that could be attributed to 2,3,7,8-TCDD exposure. However, it was difficult to determine
maternal exposure status for any of the cases. A later study examined the association between
2,3,7,8-TCDD and cytogenetic abnormalities in fetuses from abortions induced shortly after the
Seveso accident (Tenchini et al., 1983). The frequency of aberrant cells and the mean number of
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aberrations per damaged cell were significantly higher in exposed versus unexposed fetuses.
However, the paper omits important data, such as the timing of conception relative to the accident,
how long after exposure the abortion occurred, the zone in which the mother and father resided at
the time of conception and pregnancy, and tissue 2,3,7,8-TCDD concentration in the fetuses. No
other studies have examined fetal tissue to corroborate these data. This approach offers exciting
possibilities for future research, as genetic techniques in the area of DNA-adduct analyses become
more widely available.
7.13.12.3. Studies of Exposure to Agent Orange by Military Veterans and Vietnamese
Civilians
The problem of exposure documentation has also been a highly controversial issue in
studies of potential exposure to Agent Orange in Vietnam and adverse health effects among both
the Vietnam veterans and residents of Vietnam, and their offspring. An early study among the
Vietnamese population encompassed a 10-year period in Vietnam from 1960 to 1969 (Cutting et
al., 1970). Exposure was dichotomized into pre- or light-spraying years from 1960 to 1965 and
heavy-spraying years from 1966 to 1969. A total of 480,087 births, 16,166 stillbirths, 2,866
hydatidiform moles, and 2,355 congenital malformations of all types were examined in this study.
Pregnancy outcome data were collected from 22 hospitals.
Increases in the rates of stillbirths, molar pregnancies, and congenital malformations were
noted in the coastal plain and delta areas following heavy spraying, although the authors
emphasized the slight downward trend observed for all outcomes in the countrywide rates.
Several biases in this sampling approach severely limit the interpretation of the study's findings.
The births examined were not representative of the births in the country during that period. In
addition, the hospital records were incomplete, and transport of the mothers to the selected
hospitals resulted in uncertainty regarding maternal residence during the pregnancy.
A second investigation conducted in Vietnam used the HERBS tape (military records
detailing Agent Orange spraying missions) covering the period from 1965 to 1971 to determine
maternal exposure status according to area of residence (Kunstadter, 1982). HERBS data were
matched to hospital records with the date of birth to generate an "estimated date of conception"
and to maternal residence at birth to assign "potential for maternal exposure." Birth outcome data
were collected from hospital records, which were subject to incomplete ascertainment,
inaccuracies, and incompleteness.
No association between spraying of Agent Orange and any type of birth defect or perinatal
mortality was noted. Although cleft lip defects increased in proportion to other malformations
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during the heavy spraying period, the total number of birth defects declined and continued to
decrease after spraying activities ceased.
Finally, Australian investigators examined the relationship between service in Vietnam
during 1962-1972 and birth defects (Report to the Minister for Veterans' Affairs, 1983). Cases
were infants born with any of a defined set of congenital malformations in any of 34 hospitals in
New South Wales, Victoria, and the Australian Capital Territory from 1966 to 1979. Control
infants were matched to cases on maternal age, hospital, and time of birth, yielding 8,500 matched
pairs for analysis.
Fathers of case and control infants were matched against a list of members who had served
in the Australian Army during the specified time period. No associations were detected for
Vietnam service and total birth defects (OR = 1.02, 95% CI = 0.8-1.3) or for any of the
approximately 100 birth defects examined. Length of service in Vietnam, time between
deplanement and conception, and Vietnam service prior to and following conception were
considered in the analysis.
A critical point that should be emphasized regarding the Australian study is the assessment
of service in Vietnam as the exposure of interest. The author clearly stated that investigations had
indicated that exposure to herbicides was "infrequent and probably very low in Australian troops
in Vietnam; the study does not exclude possible effects of herbicides in situations of substantial
exposure" (Report to the Ministry for Veterans' Affairs, 1983).
A series of unpublished studies conducted by Vietnamese investigators was reviewed by
Hatch (1984a) and should be mentioned in this review. While these reports also have limitations,
including incomplete background data for rates of reproductive events and sparsity of
epidemiologic details provided in the studies, the assessment of Vietnamese populations offered
the opportunity to evaluate pregnancies with various patterns of parental exposure.
Investigations conducted in northern Vietnam assessed pregnancies with no maternal
exposure to spraying activities; paternal exposure was presumed to have occurred only when the
father had performed military service in the south. Studies of couples in southern Vietnam
compared reproductive outcomes observed in sprayed versus nonsprayed areas and represented
potential associations between either maternal and/or paternal herbicide exposure and risk of
adverse pregnancy outcome.
Three studies examined presumed paternal herbicide exposure and birth defects among
pregnancies in northern Vietnam. Lang and van compared the frequency of birth defects during
the period 1975-1978 as a function of the father having served in south Vietnam (Hatch, 1984a).
Among 2,547 offspring whose fathers had never served in the south, the birth defect rate was 6
per 1,000 (N = 15). Among 511 offspring whose fathers had served in the south, the rate was 29
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per 1,000 (N = 15). Similar results were obtained in a study by Lang and colleagues at
unspecified agricultural and handicraft cooperatives in northern Vietnam (Hatch, 1984a). The
congenital malformation rate among "exposed" pregnancies (paternal service in southern
Vietnam) was 23-26 per 1,000 (71 or 82 of 3,147). In comparison, the rate among the unexposed
pregnancies was 5 per 1,000 (10 of 2,172).
In what is perhaps the most stringent of the Vietnamese studies by Can et al. (reported in
Hatch, 1984a), 40,064 women from three rice-growing districts in northern Vietnam were
assessed (although few details are provided on the method of selection). All of the women were
married and pregnant at least once during the war and had no history of tuberculosis, syphilis, or
malaria, or of using antibiotics or hormones during pregnancy. "Detailed" interviews were
conducted by physicians and midwives, and district health records were consulted in an attempt to
validate reported pregnancies and outcomes. Only pregnancies conceived during the conflict were
considered in this study. There were a total of 121,993 pregnancies among the 29,041 women
whose spouses were "nonexposed" and 32,069 pregnancies among the 11,023 women whose
spouses had served in the south. Service in south Vietnam was associated with an increased rate
of spontaneous abortion (p = 0.05). The increased rate of congenital malformations among the
exposed pregnancies (0.6%) compared with the unexposed (0.4%) did not achieve statistical
significance (p = 0.10).
In attempt to further assess this difference, these investigators conducted a case-control
study in which they examined a random sample of 61 families of children who had survived with
a birth defect. A control group of 183 families of normal children (matched on maternal age,
number of deliveries, living environment, and age) was selected. Forty-nine percent of children
with a birth defect had a father who had served in south Vietnam, compared with 21% of the
children without malformations, yielding an odds ratio of 3.6 (95% CI = 1.9-6.7).
Additional unpublished studies conducted in Vietnam by Vietnamese investigators were
reviewed by Constable and Hatch (1985). The reader is referred to this source for details of the
studies. It was concluded that studies of presumptive paternal "herbicide" exposure prior to or at
conception were suggestive of a relationship with congenital malformations. The evidence for an
association with spontaneous abortion was "less convincing," and for molar pregnancies no
relationship appeared to exist. Those studies that examined both maternal and paternal
"herbicide" exposure were also suggestive of a relation to birth defects as well as spontaneous
abortion, stillbirths, and molar pregnancies. Follow-up studies by these Vietnamese investigators
supported these earlier findings (Huong et al., 1989; Phuong et al., 1989b).
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In the next section, the impact of assays to measure individual levels of 2,3,7,8-TCDD is
discussed, and studies of 2,3,7,8-TCDD exposure and reproductive effects that have been
published since 1984 are presented.
Review of the Literature From 1984 Through 1995
During the interval since 1984, assays that had been developed to measure TCDD in serum
and adipose tissue were being tested and refined. Several investigators have since used these
assays in (usually small) subsets of their study populations to estimate exposure to 2,3,7,8-TCDD
in their total sample and also in attempts to validate their assumptions regarding magnitude of
exposure for their study subjects. Subsets were generally selected to represent subjects designated
as "high" versus "low" exposure by the study investigators, utilizing various information sources
including self-reports, company or military records, etc. Table 7-49 presents the results of the
exposure analyses (Mocarelli et al., 1991; Patterson et al., 1986b; Smith et al., 1992; Centers for
Disease Control Veterans Health Studies, 1988; Fingerhut et al., 1991a; Schecter et al., 1989;
Kahn et al., 1988; Kang et al., 1991; Roegner et al., 1991; Phuong et al., 1989b).
These data illustrate wide variability in groups presumed to have been exposed to 2,3,7,8-
TCDD at levels above background (<20 pg/g). For example, the mean and median serum levels
of Vietnam ground combat troops with service in areas heavily sprayed with Agent Orange did not
exceed the levels found in the U.S. general population. There is evidence for higher exposure to
2,3,7,8-TCDD among certain subgroups of Vietnam veterans ( et al., 1988; Roegner et al., 1991)
as well as residents of Vietnam (Phuong et al., 1989b), Seveso (Mocarelli et al., 1991), and
Missouri (Patterson et al., 1986b), and occupational groups (Fingerhut et al., 1991a; Beck et al.,
1989).
With the exception of the Ranch Hand study (Roegner et al., 1991), these subsets were
selected to describe 2,3,7,8-TCDD exposure in the total study sample and not for an examination
of the relationship between 2,3,7,8-TCDD and reproductive events. In addition, the data from the
Ranch Hand population indicated a serum 2,3,7,8-TCDD half-life of 7.1 years (Pirkle et al.,
1989), but serum samples in this group were collected and analyzed at 11- and 15-year intervals
following exposure. Questions regarding the impact of initial dose, age, gender, and pregnancy
itself on half-life in humans remain unanswered at this time.
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7.13.12.4. Environmental Studies
7.13.12.4.1. The Times Beach, Missouri, 2,3,7,8-TCDD episode. In 1971, a waste oil dealer in
Missouri disposed of waste sludge containing approximately 29 kg 2,3,7,8-TCDD by mixing it
with waste oils as a dust control spray, which was subsequently distributed throughout the State.
General media announcements from health officials were made, urging persons potentially
exposed to 2,3,7,8-TCDD to participate in a survey and health screening process. People were
warned of their potential exposure by virtue of their residence, employment, or engagement in
recreational activities in the contaminated sites. A pilot study was initiated in 1983, in which a
subset of those persons who had responded to the media announcements was assessed (Stehr et
al., 1986). Approximately 800 completed questionnaires were screened to select participants; it
was not clear if this number represents all of the questionnaires that had been returned up to that
time.
This phase of the study was intended to identify potential problems for future
investigations. Persons determined to be at "high" versus "low" risk for dioxin exposure, based
on their completed surveys, were selected. "High risk" was defined as either (1) reported
residence or occupation in areas with TCDD levels between 20 and 100 ppb for at least 2 years or
in areas with TCDD levels >100 ppb for at least 6 months, or (2) participation in activities
requiring close contact with soil in areas with TCDD concentrations as described for similar
periods of time. "Low-risk" persons were determined to have had no access to, or "regular high-
soil-contact activities" in, any known contaminated area. Controls were frequency matched with
the high-exposed group on type of exposure site, age, sex, race, and socioeconomic status. Sixty-
eight "high-risk" persons (83% of those eligible) and 36 "low-risk" persons (90% response) were
evaluated through physical, neurological, and dermatological examinations; laboratory tests; and
interviews.
Information on reproductive outcomes was obtained during the interview administered to
the subjects "or their nearest relative." None of the outcomes observed differed significantly by
exposure status (Table 7-48), although high-risk women had a later mean age at menarche
(p = 0.06). A total of 30 births were available for assessment in this sample.
The following year, a more intensive study was undertaken to test the results found in the
pilot study (Hoffman et al., 1986). The exposed group consisted of residents of the Quail Run
Mobile Home Park in Gray Summit, Missouri, where TCDD levels in the soil were measured at
up to 2,200 ppb. Data on 95 of the approximately 207 households in the park were available;
154 persons (74%) who were "both eligible and interested" agreed to participate. The unexposed
group consisted of 155 residents of a nonexposed trailer park, representing 77% of those "both
eligible and interested" in participation.
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The protocol was similar to that employed in the pilot study described above. The authors
reported that "no differences were found between the exposed and unexposed groups in the
frequency of reproductive disorders or adverse pregnancy outcomes, such as fetal deaths,
spontaneous abortions, and children with congenital malformations." No sample sizes or
statistical test results for any of the outcomes were reported. Clearly, these studies of Missouri
residents were not designed to investigate the relationship between dioxin exposure and
reproductive outcomes, and not very much can be learned about this association from these
results.
In a retrospective cohort study by Stockbauer et al. (1988), the association between
2,3,7,8-TCDD and reproductive outcomes was examined among residents of contaminated areas
in Missouri. All live births and stillbirths that occurred in the nine residential areas identified as
contaminated with TCDD during the period 1972-1982 were identified through Missouri vital
statistics records. A total of 402 births were examined from six residential areas. TCDD levels in
the soil from these six areas ranged from 241 to 2,200 ppb. 2,3,7,8-TCDD levels were not
reported for the three areas where no births occurred during this interval, which would have been
of interest to note.
A reference group of 804 unexposed births, matched for maternal age and race, hospital
and year of birth, and plurality, was selected from the vital statistics records. Medical records
were abstracted to ascertain birth defects in the matched sets. In addition, the births were linked
to a statewide birth defects register that had been recently initiated. Data on several potentially
confounding variables were obtained from birth certificates.
The exposed mothers tended to be less educated, had more children, were more likely to
be in the extremes of the prepregnant weight distribution, and were more likely to smoke
cigarettes. Statistical testing for these differences was not reported. Moreover, statistical
adjustment for these potential confounders was performed only in the birth weight analyses,
although there was little change in the birth weight risk ratios after adjustment.
Increased risk ratios were reported in the exposed group for infant death (OR = 2.0), fetal
death (OR = 1.6), perinatal death (OR = 1.3), low birth weight (AOR = 1.5), and several
subcategories of malformations, although none of these findings achieved statistical significance
(Table 7-48).
Two approaches were used to determine a dose-response relationship. In the first, the
study data set was matched against the Missouri central listing of dioxin-exposed persons, which
yielded 98 of the exposed mothers and none of the control mothers. These 98 women were then
dichotomized into "high" (N = 20) or "low" (N = 78) exposure groups. High exposure was
defined as residence for at least 6 months in areas with > 100 ppb TCDD in the soil or >2 years in
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areas with TCDD levels from 20 to 100 ppb. Low exposure was defined as residence in areas
with similar TCDD levels as described above, but for less than the required time period or
residence at sites with 1-19 ppb TCDD. No evidence for a dose-response relationship was
observed with this analysis.
The second approach categorized the births into two different intervals relative to the
spraying of the soil with the TCDD-contaminated sludge in 1971 to 1973. When births in the
1972-1974 period were compared with births from 1975 to 1982, the authors reported that "the
only birth outcomes with higher risk ratios in the earlier time period were very low birth weight,
birth defects, and major birth defects." None of these observations were statistically significant,
but sample sizes and testing results were not provided in the paper.
The authors acknowledged the possibility of exposure misclassification and also the
"modest" power of the study to detect associations due to the small sample size.
7.13.12.5. Studies of Vietnam Experience in Ground Troops and Ranch Hands Published
1984-1995
7.13.12.5.1. Evaluation of exposure. Evidence from earlier studies to determine if Agent Orange
exposure increased the risk of adverse pregnancy outcomes among Vietnam veterans has been
described as "sparse, sometimes off the point, sometimes conflicting..." (Hatch and Stein, 1986).
The general dissatisfaction with these studies had a common factor: the lack of a valid measure of
dioxin exposure. Once the assays to document individual 2,3,7,8-TCDD exposure became
available and served as the gold standard for assessing the exposure assumptions made by study
investigators, this concern regarding exposure misclassification was shown to be justified. Until
1992, when the first study to examine reproductive outcomes among Vietnam veterans based on
individual exposure measurements of 2,3,7,8-TCDD was published, this remained the major
criticism of the research. Even though 2,3,7,8-TCDD serum levels were available for reanalysis
of the 1984 data of the Ranch Hand study, they were not used in that report. A later reanalysis (in
1995) used estimated 2,3,7,8-TCDD level at the time of conception. Exposure indices were
developed for several studies to aid in analysis of exposure outcome relationships.
7.13.12.5.2. Exposure indices: The CDC case-control study's exposure opportunity index. In
the early 1980s, the CDC conducted a large case-control study that examined the relationship
between service in Vietnam and risk of congenital malformations (Erickson et al., 1984).
Although service in Vietnam was the major exposure variable, two additional measures of
exposure were assessed. Vietnam veterans were asked if they believed they had been exposed to
Agent Orange. In addition, an exposure opportunity index (EOI), developed by the Army Agent
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Orange Task Force, assigned a score estimating the likelihood of exposure based on places and
times of Vietnam service. These scores ranged from minimal (1) to high (5) opportunity for
Agent Orange exposure.
The CDC then reported on a study, using the serum assay as the standard, to evaluate the
validity of both self-reported exposure to Agent Orange and use of military records to fomulate
the EOT used in the case-control study of military service in Vietnam and birth defects. The
results revealed a poor correlation (no correlation coefficient was provided) between both of these
exposure estimates and serum TCDD levels (Centers for Disease Control Veterans Health Studies,
1988). In addition, the distributions of serum 2,3,7,8-TCDD levels were nearly identical (median
= 3.8 ppt) among 646 ground combat troops who had served in heavily sprayed areas compared
with 97 veterans who had never served in Vietnam.
In a separate study, Kahn et al. (1988) measured serum 2,3,7,8-TCDD levels in 10
Vietnam veterans who reported that they had handled Agent Orange "regularly" while in Vietnam,
10 Vietnam veterans with little or no exposure to Agent Orange, and 27 Vietnam-era veterans.
The levels of serum 2,3,7,8-TCDD among those men who had handled Agent Orange were
significantly elevated (median = 25.1 pg/g blood fat) compared with the Vietnam control veterans
(median = 5.3 pg/g) and the Vietnam-era veterans (median = 3.9 pg/g) (p<0.01).
The Baseline Ranch Hand study's exposure index. In 1984, the U.S. Air Force released
preliminary results of a 20-year study designed to examine the personnel responsible for
conducting the aerial spraying of herbicides in Vietnam (Lathrop et al., 1984). The analyses in
this baseline study were based on cohort status, i.e., Ranch Hands ("exposed") versus controls
("nonexposed"). In an attempt to determine a link between exposure and clinical endpoints, an
exposure index was developed to estimate individual 2,3,7,8-TCDD exposure. The exposure
index was later found to be uncorrelated with dioxin levels in Ranch Hand veterans (Michalek,
1989).
In 1988, a report describing a U.S. Air Force and CDC collaborative pilot study utilizing
the serum 2,3,7,8-TCDD assay among 200 Air Force Ranch Hand personnel was published. It
was noted that the Ranch Hand personnel had significantly higher serum 2,3,7,8-TCDD levels
than did controls (Wolfe et al., 1988). These data also indicated that the Ranch Hands as a whole
were not as highly exposed to 2,3,7,8-TCDD as was the NIOSH cohort (Piacitelli et al., 1992) and
the Seveso population (Mocarelli et al., 1991).
In 1992, the report describing reproductive outcomes among Ranch Hand personnel
became available (Wolfe et al., 1992b). In addition to outcome verification, serum 2,3,7,8-TCDD
levels were measured in a sample of Ranch Hands (N = 791) and the comparison population
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(N = 942). A comparison of the exposure index used in the baseline Ranch Hand study with
individual 2,3,7,8-TCDD levels revealed "considerable misclassification" among the study
subjects.
As a result of these investigations, it became clear that the likelihood of exposure
misclassification in studies of the relationship between 2,3,7,8-TCDD and reproductive events,
without direct measures of individual exposure, casts considerable doubt as to the validity of the
early findings.
An equally important finding resulting from these investigations was the inability of the
exposure indices to classify exposure status in individuals (Needham et al., 1991). 2,3,7,8-TCDD
analyses in small subsets of the total study sample, selected on the basis of presumed exposure as
defined by an "exposure index" (either military or government records, self-reports, or 2,3,7,8-
TCDD levels in soil samples), have demonstrated that the exposure classification schemes
employed in these studies were not valid. Therefore, studies that defined exposure as paternal
military service in Vietnam should be evaluated without inference to 2,3,7,8-TCDD exposure.
7.13.12.5.3. Study results: Atlanta Congenital Defects Program study. In 1984, the CDC
released the findings from the large case-control study that examined the relationship between
service in Vietnam and risk of congenital malformations (Erickson et al., 1984). Case-group
babies were infants with serious structural malformations born between 1968 and 1980 and
registered with the Metropolitan Atlanta Congenital Defects Program (MACDP). Of 7,133
eligible cases, maternal interviews were obtained for 4,929 (69%). Control babies were selected
from Georgia vital statistics records and were frequency matched to cases on race and year and
hospital of birth. Of 4,246 eligible controls, maternal interviews were obtained for 3,029 (71%).
Paternal interview rates were 56% and 57%, respectively.
While response rates were similar by case status overall, among nonwhites significantly
more cases were not interviewed. The major reason for nonparticipation was the inability to
locate subjects rather than subjects refusing to enroll.
Among the children with congenital malformations, 428 (9%) were fathered by Vietnam
veterans and 4,387 (91%) were fathered by non-Vietnam veterans; identical percentages were
noted among the control infants. The odds ratio for service in Vietnam and birth defects of any
type was 0.97 (95% CI = 0.83-1.14). Odds ratios were also calculated for 96 separate categories
of birth defects, with no significant associations observed.
The EOT developed for this study (as described above) also was not associated with total
birth defects. However, significant associations were observed for spina bifida, cleft lip with or
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without cleft palate, neoplasms, and coloboma. With the exception of the last defect, all three
also showed evidence of a dose-response relationship.
For the analysis that examined potential associations between self-reported Agent Orange
exposure and birth defects, the frequency of exposure among fathers and each type of
malformation was compared with the frequency among fathers and all other defects in an attempt
to reduce recall bias. Twenty-five percent (N = 74) of the Vietnam veterans reported that they
believed they were exposed to Agent Orange during their service in Vietnam. The analysis of
self-reported exposure and birth defects was negative on all counts, in contrast to the EOT
analysis, which found significant associations as described above. However, the small numbers of
cases of many individual defects resulted in a virtual lack of power to detect any associations for
these specific defects.
CDC Vietnam Experience study. As part of a separate, large, multifaceted study mandated by
Congress, the CDC evaluated the risk of service in Vietnam and adverse reproductive outcomes
(Centers for Disease Control Vietnam Experience Study, 1988d, 1989). The Vietnam Experience
Study protocol involved two phases. In the first phase, a random sample of male veterans who
met eligibility criteria related to military service was selected for a telephone interview.
Of the eligible "exposed" group, i.e., those veterans who had served in Vietnam, 84% (N =
7,924) agreed to participate, and 84% (N = 7,364) of the nonexposed (those who had not served in
Vietnam) were enrolled. Of these 15,288 veterans who completed the telephone interview, a
random sample was selected for the second phase, which consisted of a comprehensive medical
examination. For this phase, the response rates were 75% (N = 2,490) for the Vietnam veterans
and 63% (N = 1,972) for the non-Vietnam veterans group.
During the telephone interview, veterans were questioned about miscarriage, induced
abortion, ectopic pregnancy, live births, stillbirths, birth defects, as well as leukemia and other
childhood cancers, and major health problems or impairments during the first 5 years of life.
A preliminary analysis of the interview data revealed that the Vietnam veterans had
reported 40%-50% more birth defects than the non-Vietnam veterans. In addition, a difference
between the cohorts for certain subcategories of malformations, including spina bifida, cleft lip
with or without cleft palate, and hydrocephalus, was noted. Therefore, a substudy was conducted
to compare the rates of total birth defects in Vietnam and non-Vietnam veterans as these events
were recorded on hospital records.
The eligible sample for this substudy consisted of 2,282 veterans who had not yet received
their medical examinations, as it would be easier to collect the additional information required and
permission to obtain hospital records for their offspring from this group. Hospital records were
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obtained for 92% (N = 1,791) of the offspring of Vietnam veterans and 91% (N = 1,575) of the
offspring of non-Vietnam veterans.
When birth defects were identified from hospital records, there was no association of
Vietnam service with total, major, minor, or suspected birth defects. From the telephone
interview data, the odds ratio for Vietnam service and total birth defects was 1.3 (95% CI = 1.2-
1.4); in the hospital records substudy, the odds ratio was 1.1 (95% CI = 0.7-1.8). It was concluded
that this finding supported the explanation of differential reporting in the telephone interviews.
The odds ratios for selected categories of birth defects calculated from both the telephone
interview and hospital records study are presented in Table 7-50. The rate of birth defects
increased for both cohorts when malformations were identified in the medical records.
The extent of differential reporting between the two cohorts has also been described
(Centers for Disease Control Vietnam Experience Study, 1989). Overall, the authors concluded
that agreement between veterans' reports and hospital records for the presence of a birth defect
was "relatively poor" for both cohorts. Positive predictive value, sensitivity, and the kappa
statistic were slightly lower among Vietnam veterans (Table 7-51). It was further stated that there
was no evidence of selection bias or participation bias in this substudy because no differences
were noted between cohorts in health histories and demographic or military covariates, and the
participation of both groups of veterans was high. However, the subjects in this substudy were
selected from the group of veterans who completed the examination. The response rate for this
phase of the study was not high, as noted above, with 75% of Vietnam veterans and only 63% of
the non-Vietnam veterans participating in the medical examination. Although characteristics of
the subset who agreed to undergo physical examination did not differ from the telephone
interview sample, no data on those who refused the exam and no reasons for refusing to
participate were provided in this paper.
Adjusted odds ratios for two additional outcomes verified in the hospital records substudy,
low birth weight (AOR =1.1, 95% CI = 0.8-1.4) and perinatal mortality (AOR = 1.6, 95% CI =
0.8-3.1), did not differ by Vietnam service status. A race-specific analysis of total, major, and
minor defects showed an increased risk for total and minor birth defects among black veterans.
The adjusted odds ratio was 3.3 (95% CI= 1.5-7.5) for total defects and 2.9 (95% CI= 1.1-8.0)
for minor malformations. This finding is based on very small numbers, however, and on multiple
occurrences of two minor defects in two families.
From data obtained during the telephone interview, the adjusted odds ratio for Vietnam
service and spontaneous abortion was 1.3 (95% CI = 1.2-1.4). Although an excess among
Vietnam veterans was noted across all three trimesters, only the association in the first trimester
was significant. There was no attempt to confirm this endpoint by using hospital records. No
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significant differences were observed for the reproductive outcomes of induced abortion
(AOR= 1.0, 95% CI= 0.9-1.2), stillbirths (AOR = 0.9, 95% CI = 0.7-1.1), ectopic pregnancies
(AOR= 1.0, 95% CI = 0.7-1.2), or childhood cancers (OR= 1.5, 95% CI = 0.8-2.8).
In addition to the reported excess of total birth defects in the telephone interview, Vietnam
veterans also reported more neural tube defects and hydrocephalus than the non-Vietnam veterans.
A second substudy was undertaken to examine the increase in cerebrospinal malformations
(CSMs). In this substudy, an attempt was made to obtain hospital records for all of the offspring
identified in the telephone interview as meeting one of the following criteria: (1) offspring with a
CSM as reported by a veteran, (2) those with a reported condition that suggested a CSM, or (3) all
reported stillbirths.
Of the 403 offspring reported to have a CSM, 109 were ineligible, 14% (N = 58) because
of conception prior to father's military service and 12.6% (N = 51) classified as miscarriages.
Again, the issue of the impact of spontaneous abortion on rates of birth defects is introduced.
The CSM substudy was limited by a poor response rate among the non-Vietnam veterans.
The sample thus consisted of 127 offspring of Vietnam veterans (82.5%) and 94 children of the
non-Vietnam veterans (67%). Compared with fathers who participated in the CSM study,
nonparticipants were more likely to be nonwhite, less educated, unmarried, younger at the time of
their child's birth, and have lower general technical test scores. There were also differences in
paternal covariates between the participating cohorts that could confound the findings. Compared
with Vietnam veterans, non-Vietnam veterans were better educated, had higher general technical
test scores, were more likely to be married, were older when their child was born, were less likely
to consume alcohol, and were more likely to have had a nontactical primary military occupational
specialty (MOS) in the Army and to have served in both the later and earlier time periods.
The number of CSMs reported by the veterans that were verified by hospital records was
examined separately by stillbirth and live birth status. Among the reported stillbirths, five CSMs
were documented among children of Vietnam veterans and six among the non-Vietnam veterans.
Ten of these 11 CSMs had not been reported by the veterans. Positive predictive values derived
from this analysis were 6.5% for Vietnam veterans and 8.1% for non-Vietnam veterans.
Among the live births, 21 of 49 reported CSM cases were noted on hospital records for the
Vietnam veterans; 6 of the reported 20 cases were observed among the non-Vietnam veterans
group, yielding positive predictive values of 42.9% and 30%, respectively.
Tables 7-35 and 7-36 in the unpublished technical report of the study list the fathers'
descriptions of the birth defects in their offspring obtained through the telephone interview by
cohort (Centers for Disease Control Vietnam Experience Study, 1989). It was intriguing to note
that among Vietnam veterans, for 22 of the 55 reported cases of birth defects, the hospital record
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finding was "none." In five additional cases, the hospital record finding was "none of the nervous
system." Of these 27 nondocumented reports of birth defects, 3 cases were reported as having
died within the first year of life. Death during the first year of life was also reported for one of the
eight unverified CSMs in the non-Vietnam veterans cohort.
In summary, the CDC investigators concluded that for "most reproductive and child health
outcomes studied, Vietnam veterans were more likely to report an adverse event than were non-
Vietnam veterans." In the two substudies conducted to compare rates that were identified through
hospital records, no significant differences in adverse outcomes between the two cohorts were
determined. However, the ability of this study to address the issue of 2,3,7,8-TCDD exposure and
reproductive outcome is severely limited. The question of bias still remains in the two substudies.
In addition, exposure was defined as service in Vietnam, which does not provide much insight
into the question of the reproductive toxicity of 2,3,7,8-TCDD.
American Legion study. The relation of self-reported exposure to Agent Orange and reproductive
outcomes was part of a study conducted among 6,810 American Legionnaires who had served
during the Vietnam War (Stellman et al., 1988). Information was obtained through a
questionnaire mailed to 2,858 veterans (42%) who had served in Southeast Asia and 3,933
veterans (58%) who had served elsewhere. No association between Agent Orange exposure and
difficulty with conception, time to conception of the first child, or infant birth weight was
observed. However, the proportion of spontaneous abortion was significantly higher among the
spouses of veterans who served in Vietnam (7.6%) compared with controls (5.5%) (p<0.001).
These figures were below the background rate for recognized spontaneous abortion (10%-15%) in
the general population.
The significance of these findings is limited by the lack of verification of self-reported
exposure, the low response rate, the lack of outcome verification through medical records, and the
selection of veterans from the American Legion organization, as they may not be representative of
all veterans who served during the Vietnam conflict.
Boston Hospital study. A case-control study to investigate the relationship between paternal
military service in Vietnam and risk of spontaneous abortion was conducted at Boston Hospital
for Women (Aschengrau and Monson, 1989). Cases identified through hospital records were
spontaneous abortions <27 weeks gestation that occurred between July 1976 and February 1978
(N = 201). Paternal identity information from the hospital birth records was linked with national
and State military records to identify those fathers who had served in Vietnam. Frequency of
service in Vietnam was compared for cases and all full-term live birth controls (N = 1,119) born at
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the hospital during this time period. No association was detected (OR = 0.88, 95% CI = 0.42-
1.86).
The same investigators conducted a subsequent study that expanded the outcomes
examined to include adverse outcomes in late pregnancy (Aschengrau and Monson, 1990). Case
infants identified through hospital records included 857 congenital malformations, 61 stillbirths,
and 48 neonatal deaths that occurred at the hospital during August 1977 and March 1980.
"Exposure" was defined by using the same method as in the previous study. Frequency of
paternal service in Vietnam was compared for cases and 998 normal-term infant controls. Again,
no associations with any of these later adverse outcomes were detected (Table 7-48).
The Ranch Hand study — reproductive outcomes: Baseline study - 1984. This initial report of
the health of Ranch Hand personnel used cohort status (Ranch Hand vs. comparisons) as the basis
for evaluating effects and exposure. This group of exposed veterans included those who served in
Vietnam during 1962-1965, when Herbicides Purple, Pink, and Green were sprayed. These
herbicides had higher TCDD concentrations (33 ppm, 66 ppm, and 66 ppm, respectively) than
Herbicide Orange with 2 ppm TCDD (Lathrop et al., 1984).
The protocol consisted of a comprehensive personal and family health questionnaire and a
physical examination, including an in-depth laboratory analysis. The response rates for each
phase of the protocol were quite different both within and between cohorts. Participation in the
questionnaire phase was 97% (N = 1,174) for the Ranch Hands and 93% (N = 956) for controls.
In the physical examination phase, participation dropped to 87% (N = 1,045) for the Ranch Hands
and 76% (N = 773) for controls.
Nonresponders were "on the average" younger than participants. Ranch Hand enlisted
personnel had higher participation rates than officers, and black Ranch Hand officers had lower
participation rates than nonblack officers. The difference in the response rates for the physical
examination phase of the protocol was ascribed partially to the active encouragement of the Ranch
Hand Association for participation and the intense media coverage that the study received. The
authors stated that the "majority" of reasons given for nonparticipation were "no time-no interest"
and passive refusal.
The reproductive outcomes evaluated in this phase of the study were ascertained through
questionnaires obtained from both the veterans and their spouses or partners. A total of 7,399
conceptions were analyzed in this report. There were 3,293 conceptions among 1,174 Ranch
Hands and 4,106 among the 1,531 controls.
No significant differences were reported for four "measures of fertility": (1) the number of
childless marriages, (2) the number of couples having achieved their desired family size, (3) the
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number of childless marriages per total number of marriages, and (4) the number of conceptions
per years spent together, which included nonmarital relationships. The fertility analysis was
performed on the total number of conceptions reported and was not adjusted for any confounding
variables. Moreover, exposure in this analysis was defined by a simple dichotomy of Ranch
Hands versus controls.
To examine the relationship between Ranch Hand status and spontaneous and induced
abortion, stillbirths, and live births, exposure was stratified by pre- and post-Southeast Asia (SEA)
service. The unadjusted analysis indicated that Ranch Hands had increased spontaneous abortion
rates in both pre-SEA duty (p = 0.06) and post-SEA duty (p = 0.13). The report qualified this by
stating that these inferences were based on analyses that were not adjusted and that "key factors
affecting pregnancy outcome are of questionable value," although no similar qualification was
given for the fertility analysis. After adjustment for maternal age, smoking, and alcohol use and
paternal age, no significant difference was observed for spontaneous abortion.
Among the live births with complete data obtained to allow for adjustment of cofactors, no
difference in risk of prematurity was noted. However, the estimate of gestational age, and all
other outcomes studied, were based on parental report, which may be subject to errors in recall,
and it was not clear whether prematurity was analyzed as only a dichotomous variable
(<37 weeks, >37 weeks) or as a continuous variable. No analyses for birth weight differences by
exposure status were performed in this effort; more recent data on IUGR and other pregnancy
outcomes (e.g., infant death) are presented in Section 7.13.12.8.
Unadjusted analyses were conducted to examine the relationship between exposure and
neonatal death, infant death, physical handicaps, birth defects, and learning disabilities. These
analyses were stratified by pre- and post-SEA service periods. The results indicated that Ranch
Hands were borderline statistically significantly more likely to report physical handicaps
(p = 0.07), birth defects (p = 0.08), and neonatal deaths (p = 0.02) in the post-SEA analysis. After
adjustment for the maternal and paternal covariates described above, the relationship with birth
defects achieved statistical significance (p = 0.04); the other relationships were not statistically
significant.
Twelve of the 76 birth defects reported to have occurred among the Ranch Hands after
post-SEA service were skin anomalies (ICD Code 757). When these anomalies are excluded, this
relationship is no longer statistically significant (p = 0.14), although "still of interest."
Finally, semen samples from Ranch Hands (N = 560) and controls (N = 409) were
analyzed for sperm count and morphology. The response rates for this parameter were 72.5% and
76.5%, respectively, although some of the samples submitted were ineligible for analysis because
of prior vasectomies and orchiectomies. Linear regression techniques examined sperm count as a
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continuous variable and percentage of sperm with abnormal morphology as a dependent variable.
Independent variables were age and exposure to industrial chemicals. No differences in either
parameter were identified.
This finding contrasts with the semen analysis results obtained among 324 Vietnam
veterans and 247 non-Vietnam veterans in the Vietnam Experience Study (Centers for Disease
Control Vietnam Experience Study, 1988a). That analysis indicated that Vietnam veterans had
significantly lower sperm concentrations (OR = 2.3, 95% CI = 1.2-4.3), below the clinical
reference value (20 million cells/mL), than the non-Vietnam veterans. In addition, Vietnam
veterans had a significantly lower average proportion of "normal" sperm heads. These analyses
were adjusted for six covariates, although industrial chemicals were not among them.
The Ranch Hand study - 1992. The significant association between Ranch Hand status and birth
defects found in the previous study was of sufficient interest to launch a massive project to verify
all reported conceptions and pregnancy outcomes through medical record abstraction. In addition,
in 1987 serum 2,3,7,8-TCDD levels were obtained from a subset of Ranch Hands and controls. In
1992, the Air Force released the results of the first study that examined the relationship between
direct measure of individual serum 2,3,7,8-TCDD levels and verified reproductive outcomes
(Wolfe et al., 1992b). A total of 4,607 conceptions were examined in this study; 2,533 were
contributed by 791 Ranch Hands, and 2,074 were contributed by 768 controls.
Ranch Hand personnel were shown to have significantly higher 2,3,7,8-TCDD levels
compared with the controls. The median values were 12.8 pg/g and 4.2 pg/g, respectively. The
98th percentile for Ranch Hands was 166.4 pg/g; for controls, 10.4 pg/g. 2,3,7,8-TCDD levels
were determined in 1987. These results were used to estimate initial doses received during the
veterans' tour in Southeast Asia but not the 2,3,7,8-TCDD level at the time of conception.
The fertility analysis performed in the earlier study was not repeated according to level of
serum 2,3,7,8-TCDD (an omission currently being addressed; personal communication, J.
Michalek, 2000). There was a significant variation in the association between 2,3,7,8-TCDD and
miscarriage with time since SEA tour (<18.6 years or >18.6 years) and time of conception (pre- or
post-SEA tour) among Ranch Hands with current 2,3,7,8-TCDD levels >10 ppt (p = 0.01) (Table
7-52). This was attributed to the low miscarriage rate among the pre-SEA Ranch Hands with
current 2,3,7,8-TCDD levels >33.3 pg/g lipids. In examining post-SEA conceptions only, a linear
trend can be seen for spontaneous abortions and increasing 2,3,7,8-TCDD levels among Ranch
Hands who had "late tours" in SEA, i.e., less than or equal to 18.6 years had elapsed between their
tour of duty and current 2,3,7,8-TCDD levels. The opposite trend is noted in Ranch Hands with
"early tours," i.e., more than 18.6 years had elapsed between the end of duty and the 1987 blood
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draw. It was concluded that 2,3,7,8-TCDD did not affect the rates of miscarriage because it
seemed "implausible that dioxin would act differently in the two groups."
An alternative explanation might be that there is a relationship, but it cannot be detected
by this type of analysis. To evaluate the relationship between 2,3,7,8-TCDD level and
spontaneous abortion, 2,3,7,8-TCDD level at the time of conception must be considered.
Assuming a half-life of 7 years in humans (Pirkle et al., 1989), it would seem reasonable, for
example, to assume that the two groups of Ranch Hands with 2,3,7,8-TCDD levels of 10-14.9
pg/g of lipids with post-SEA conceptions may have had very different 2,3,7,8-TCDD levels at the
time their children were conceived. This is possible because the early-tour veterans had more
time to decrease their body burden of 2,3,7,8-TCDD before their bloods were drawn in 1987 than
did their late-tour counterparts. Paternal TCDD level at the time of conception was estimated in a
subsequent analysis of these data (Wolfe et al, 1995).
Table 7-48 illustrates the risk estimates for reproductive outcomes in the Ranch Hand
study. Interestingly, the only statistically significant associations between 2,3,7,8-TCDD and
adverse events (total birth defects, genital anomalies, and urinary system anomalies) occurred
among Ranch Hands with 2,3,7,8-TCDD levels of 15-33.3 pg/g of lipids and not among those in
the >33.3 pg/g group.
The report stated that the "expected dose-pattern" for 2,3,7,8-TCDD and total adverse
reproductive outcomes (miscarriage, tubal pregnancy, other noninduced abortive pregnancy, or
stillbirth) is the "linear one in which the highest anomaly rate occurs at the highest levels of
dioxin." This statement raises at least two questions. If a linear response is assumed, might this
imply that very early pregnancy losses occur at the highest 2,3,7,8-TCDD levels, so that the
conceptus would not survive long enough to be clinically recognized? Or are very early
pregnancy losses and clinically recognized spontaneous abortions two separate entities with
different thresholds? Such a scenario has been suggested to explain changes in spontaneous
abortions observed after exposure to radiation in Hiroshima (Miller and Blot, 1972).
These questions are of interest because the rate of each of these endpoints may directly
affect the rates of all subsequent reproductive outcomes that are available for examination. The
miscarriages do not include the very early losses, as 99.6% of reported miscarriages were verified
through medical records. Very early losses are unlikely to be identified by the woman or
clinically.
An analysis in which the 1987 dioxin levels are used to estimate dioxin level at time of
conception would be a worthwhile effort. If a relationship between paternal 2,3,7,8-TCDD level
and adverse reproductive outcome does exist, this may help determine the dose-response pattern
of the relationship.
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No evidence was found to support an association between 2,3,7,8-TCDD and total adverse
outcomes. These findings should be viewed with caution in view of the above comments
concerning the unexplored area of events early in gestation.
Overall, there was little convincing evidence to support an association between birth
weight, examined as both a continuous variable and dichotomized (<2,500 g or >2,500 g), and
paternal 2,3,7,8-TCDD level. Analyses that adjusted for covariates included maternal and
paternal age, maternal alcohol use and smoking, and race of the father. No assessment of 2,3,7,8-
TCDD and prematurity was reported.
The potential association between cohort status and birth defects was examined for all
defects combined and 12 additional categories of malformations. The only categories with
sufficient numbers of verified post-SEA cases to detect a relative risk of 2 were total birth defects
(229 cases among 1,045 Ranch Hands and 289 cases among 1,602 controls) and musculoskeletal
deformities (132 cases among Ranch Hands and 180 among controls).
A significant variation was observed in the association between total birth defects
(p = 0.03), defects of the respiratory system (p = 0.03), and urinary system abnormalities
(p = 0.04) by Ranch Hand versus control status with time of conception (pre- or post-SEA). All
of these findings were due to a lower rate among Ranch Hands in the pre-SEA conceptions and a
higher rate among the post-SEA conceptions for the Ranch Hands.
Analyses of birth defects by 2,3,7,8-TCDD level did not find any "consistent patterns" to
support an association. For example, among children of enlisted flying and enlisted ground
personnel, children of Ranch Hands with 2,3,7,8-TCDD levels < 10 pg/g lipids had higher rates
(433 per 1,000 and 317 per 1,000) than children of controls with background 2,3,7,8-TCDD levels
<10 pg/g lipids (229 per 1,000). However, rates of children of enlisted ground personnel with
2,3,7,8-TCDD levels >33.3 pg/g lipids were not significantly elevated. Again, these analyses
were not based on 2,3,7,8-TCDD level at time of conception. Moreover, if the higher 2,3,7,8-
TCDD levels were related to early pregnancy loss, these results would make more biological
sense, as the abnormal conceptuses due to 2,3,7,8-TCDD exposure would have been lost before
the pregnancy was recognized.
There was also a significant association between 2,3,7,8-TCDD levels and neonatal death
(OR = 5.5, 95% CI = 1.5-20.7). Insufficient numbers (N = 13) precluded the calculation of an
adjusted odds ratio for this finding.
Finally, no association was detected between 2,3,7,8-TCDD level and either sperm count
or percentage of abnormal sperm. These analyses were based on semen samples that had been
collected in 1982.
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The Ranch Hand study - 1995. This study includes pregnancies to those men described above;
but the group has been restricted to confirmed pregnancies occurring after the beginning of service
in Vietnam in those men who participated in the 1987 physical, gave blood to evaluate serum
dioxin levels, and had usable laboratory measurements (Wolfe et al., 1995). This group potentially
included 872 Ranch Hand veterans and 1,036 comparison subjects. In fact, 454 Ranch Hand
veterans (RH) yielded 1,006 recognized conceptions and 419 veterans yielded 792 live births,
while 570 comparison subjects yielded 1,235 recognized conceptions and 531 controls yielded
981 live births. Paternal dioxin level at the time of conception was used to generate four exposure
groupings: (1) comparison with current level < 10 ppt, (2) RH with current level < 10 ppt, (3) RH
with current level > 10 ppt and estimated initial level < HOppt, and (4) RH with current level
> 10 ppt and estimated initial level > 110 ppt. Comparisons with > 10 ppt dioxin were eliminated
from the group, as having higher than "background" levels without an understanding of the
probable source of the exposure. The children in the second group (RH with < 10 ppt) were
considered separately because the fathers' levels could not be used to estimate exposure levels at
the time of pregnancy. Levels at conception were estimated using a fixed 7.1 year half-life with a
first-order decay rate. As the time between measurement (1987) and conception varied from 15 to
26 years, the number of half lives ranges from about two to a little over three.
All analyses were adjusted for paternal race, age and military occupation and maternal age,
and smoking and drinking during pregnancy. In addition to these, analyses of spontaneous
abortion were adjusted for spontaneous abortions occurring prior to service. The proportion of
men who fathered recognized pregnancies or live births were about the same in both groups,
Ranch Hand or comparison.
Analyses show modest, borderline significant increases in spontaneous abortion (Table 7-
48), defects of the circulatory system and heart (OR: 2.3; 95% CI = 1.0-5.1), all anomalies (OR:
1.3; 95% CI= 1.0-1.6), major birth defects (OR: 1.7; 95% CI= 1.1-2.7), and some developmental
delays (OR: 1.5; 95% CI = 1.0-2.3), all of these for the low RH group only. Dose-response
patterns were not observed, and more detailed analyses were not possible because of the small
number of adverse outcomes in each grouping.
7.13.12.5.4. Comment. The data described above do not present strong evidence for an
association of paternal dioxins with developmental effects, occurring pre-conceptionally,
prenatally, or identified around the time of birth. These studies are limited by the ability to
accurately define exposure at the critical windows for the events. The developmental studies are
retrospective, typically using a surrogate measure of exposure (recall, records of work place or
service in Vietnam, etc.). Overall, only a few of the developmental studies have collected
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biological measurements of dioxin levels at the time of the study (e.g., the Ranch Hand studies,
the CDC studies of Vietnam experience in ground troops). Several efforts have compared
surrogate exposure measures with serum levels: (1) a comparison of serum levels with CDC's
exposure opportunity index and with the subject's self-report of exposure (CDC, 1988) showed
that the surrogates were not a good estimate of the measured levels; (2) a rather small comparison
of Vietnam veterans (10 reported exposed, 10 reported little or no exposure) and 27 Vietnam-era
veterans (Kahn et al., 1988) showed a significant elevation for the exposed men compared to the
other two groups; and (3) in the Ranch Hand study, comparisons of serum levels from a sample of
men to the exposure index revealed "considerable misclassification" (Wolfe, 1992b). Given that
the second study was rather small, and confidence intervals included background exposure, those
data are of limited use. In general, the results described above suggest that many of the studies
reported have subjects misclassified by exposure. If so, effects of exposure could be masked.
Few of the results in the studies give an indication of an effect of dioxin on pregnancy
outcomes; some did show increased birth defects, but many of these were based on small
numbers. As mentioned above, the potential effects of fetal loss on the examination of
malformations is difficult to address, but might affect these results, especially in the higher
exposure groups.
7.13.12.6. New Issues - Developmental Outcomes (1996-1999)
7.13.12.6.1. Dental effects. An investigation of dioxin exposure and tooth development was
done in Finnish children as a result of studies of dental effects in dioxin-exposed rats, mice, and
nonhuman primates (Chapter 5), and in PCB-exposed children (Rogan et al., 1988). The Finnish
investigators examined enamel hypomineralization of permanent first molars in 6-7 year old
children (Alaluusua et al., 1996; Alaluusua et al., 1999). These molars were mineralized during
the postnatal period, when the children are exposed through breast feeding. The population was
first identified in Helsinki and Kuopio as part of an international effort, coordinated by
WHO/EURO, to evaluate possible health effects associated with levels of PCDDs and PCDFs in
breast milk. Approximately 150 women were recruited from each area, with the commitment to
provide a breast milk sample at 4 weeks postpartum, if still lactating. A total of 167 samples were
obtained, with about a 50% response rate in Helsinki (77 women provided samples) and about
60% from Kuopio (90 women provided samples). Exposure to each child was estimated using the
TEQ for PCDDs and PCDFs and their elimination constant, plus the length of time the child was
breast fed. At age 6 or 7, 102 children's teeth were examined (61% of those with breast milk
samples). Defects were scored as to severity and size, blind to exposure score. Duration of
lactation ranged from 1 to 36 months (mean 10.5 +/-5.S sd) and TEQs from 3.8 to 99.4 pg/g milk
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fat (mean 19.8 +/-10.9 sd). The length of time breast feeding was not significantly associated with
mineralization changes, nor was the TEQ alone. However, when the levels and length of breast
feeding were combined in an overall score, a statistically significant association was observed (r =
0.3, p = 0.003, regression analysis). The beta from the analysis was not presented, so the slope of
the relationship is unknown. The levels in breast milk might also be a surrogate for in utero
exposure; these samples were collected after 4 weeks of breast feeding, and so might not be as
good a surrogate as samples collected earlier in lactation.
7.13.12.6.2. Comments. These data present interesting findings relating hypomineralization of
permanent first molars and TEQ exposure through breast feeding. Unfortunately, the presentation
of the results is incomplete: They present limited information on adjustment for other risk factors/
confounders, have a small number of subjects and consequently low power, and since the beta was
not presented for the regression analyses, the potential biological significance cannot be
examined. This would be an interesting outcome to examine in additional studies.
7.13.12.7. Sex Ratio at Birth
Sex ratio has been reported to vary with a great number of factors, including race, timing
of conception within the cycle, certain parental diseases, and gestational age (James, 1987;
Kellokumpu-Lehtinen and Pelliniemi, 1984). The sex ratio is defined by demographers as
(number of male births)/(number of female births)* 100. However, many of the papers covered in
this section present the proportion of male births of the total, rather than an actual ratio. In this
discussion, while the endpoint will still be called "sex ratio," all data will be presented as the
proportion for consistency with the original literature and ease of comparison among the studies.
In response to a report of hormonal variations in men occupationally exposed to dioxin
(Egeland et al., 1994), James (1995, and repeated later [1997a,b]) proposed that with high
gonadotropin and low testosterone levels, sex ratios could be lowered (fewer male births
compared to female births). A 1996 letter (Mocarelli et al., 1996) reported an excess in female
births conceived following the Seveso accident. This included births from April 1977 to
December 1984, a time period approximating the half-life of dioxin. Seventy-four births occurred
during this time within the A-zone; 26 (35.1%) were males and 48 females (65.9%), compared to
the expected value (51.4% males [James, 1987]) used by the investigators. Since 1988, these
investigators measured dioxin levels in archived serum samples. Of the 74 births, 17 occurred in
families with both parents in the A-zone. "Elevated" dioxin level was defined in this report as >
100 ppt (lipid adjusted), and ranged from 104 to 2,340 pg/g lipid in fathers and 126-1,650 pg/g
lipid in mothers; 100 ppt or less was considered within normal range. Of this group, 100% (N =
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12) births were female. Eighty percent of those with "low" dioxin levels were male (N = 5). In an
unadjusted analysis, the overall sex ratio (0.235) was significantly different from the "expected
value" (X2 = 12.68, /X0.001), in an analysis unadjusted for other factors related to variations in
sex ratio. After this time period, from 1985 to 1994, the sex ratio increased to 0.484 (N = 124), a
value not significantly different from the "expected value." The authors mentioned that the
reduced sex ratio in this small series of births could have resulted from excess males in
spontaneous abortions (a theory that cannot be assessed with existing data), or that changes in sex
ratio could result from the changes in hormonal balance.
The investigators revisited the Seveso Cohort, identifying all births for those who lived in
A, B, or R contaminated at the time of the 1976 explosion (Mocarelli et al., 2000). Parents with
less than or equal to 15 ppt TCDD were compared to those above (all together, or split into
categories: >15-80 ppt, >80 ppt). After comparing levels in mothers and fathers in 1976, for a
total of 674 births in 452 families, a pattern of reduced birth ratios was noted for paternal
exposure only, or where both parents were exposed, but not where only the mother was exposed.
More detailed analyses focused on paternal exposure, with the observation that the reduction in
sex ratio was greater for those fathers who were less 19 years old at exposure in 1976 (sex ratio:
0.382; 95% confidence intervals: 0.30-0.47) versus those who were older (sex ratio: 0.469; 95%
confidence intervals: 0.41-0.53). In additional, the sex ratio in offspring to both groups of
exposed fathers were significantly less than unexposed fathers of all ages (sex ratio: 0.557; 95%
confidence interval: 0.50-0.62). The authors did not present data on age specific sex-ratio among
the unexposed fathers, thus leaving the comparison incomplete. An additional analysis would
have been more informative: the interaction of paternal age by paternal exposure level for sex
ratio. Also, details of factors considered and controlled in the multivariable analyses were not
presented, limiting the examination of this interesting study. This study presents intriguing data
on the possible relationship of sex ratio and age at dioxin exposure.
Dimich-Ward (1996, 1998) and colleagues examined 23,829 production and maintenance
workers employed in 11 sawmills in British Columbia between 1950 and 1985. These sawmills
used dioxin-contaminated chlorophenate as a fungicide for lumber. These workers were linked
with vital records on live and stillbirths for 1952-1988, obtaining data on the birth date and sex of
the 19,675 children born to the workers after beginning employment. The sex ratio of the entire
group was 51.6% male and 48.6% female, proportions consistent with those typically observed.
Exposures in this study were estimated by 10 experienced workers, because no measurements
were available. These estimated measures were then combined with cumulative hours worked
during different time periods. The exposure breakdowns, however, were not used in examination
of these data; the proportion of males and females were erroneously presented (reversed) in the
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heading of a table. Then James (1997) tested these overall proportions, found them statistically
significant, and stated that they supported his hypothesis. A correction of the headers (Dimich-
Ward, 1998), stating that the numbers had been transposed, resulted in a retraction of James' letter
(1998). This study does not contribute much to the discussion, given the lack of specific exposure
data, either on dioxins or other potential exposures.
Michalek and co-workers (1998) examined the Operation Ranch Hand study group for
differences in sex ratio. Men were grouped in one of four exposure categories (comparison
[n=1254] and background [n=346] both less than 10 ppt, low [n=277], and high [n=280] with >79
ppt) based on serum blood levels in 1987 or 1992 extrapolated to the time of conception, and
using a fixed 8.7 year half-life for dioxin. No maternal data on dioxin exposure were available,
but the investigators suspected that mothers had "background" levels. The analyses looked at
those children conceived within 1 month, 1 year, or 5 years, and any time post-service.
Confidence intervals (95%) were calculated using the binomial distribution. No significant
differences were observed in any analyses. The authors suggest that the findings in Seveso might
be associated with maternal exposure.
Rogan et al. (1999) evaluated sex ratio in their population of Taiwanese children whose
mothers were affected by exposure to dioxin-like compounds (PCBs and PCDFs) after
consumption of contaminated cooking oil (Yu-Cheng). Health effects identified in this population
included developmental delays and ectodermal effects in children born to affected mothers.
Overall the proportion of males in live births (n=137) from 1978-1985 was 0.496. When the first
year was eliminated, to examine only those births conceived at the time the oil was first sold (June
1998), the proportion of males was 0.508, not different from that number used as a comparison on
other analyses.
Examination of 44 (of 59) primiparous mothers in a cotton-growing region in Kazakhstan
(Hooper et al., 1999), showed that those living near a reservoir with agricultural runoff (zone A)
had higher levels of dioxin in breast milk than those located >10 miles away (zone B). This
information was then used to group all live births in the region occurring 2-8 weeks before the
sampling period in 1997. Women in zone A (N = 17) had mean breast milk levels of 53 pg/g
versus those in zone B (N = 24) with mean levels of 21 pg/g. When the data were examined by
zone, or by TCDD level (>30 pg/g versus < 30 pg/g), statistically significant differences were not
observed. The numbers were small, limiting the power, but in all the subgroups except zone B
(45.8%), more males were born (proportions ranging from 54.5% to 70.6%).
More recently, NIOSH (Schnorr et al., in press 2001) has examined its occupational cohort
study for altered sex ratio at birth. The study compared births of male workers' mates (292 births
with <20 ppt TCDD; 104 with 20-254 ppt TCDD; 88 with 255-1119 ppt TCDD; and 60 with
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1120+ ppt TCDD) to 647 births to never-exposed referents (<20 ppt TCDD). The exposed
proportions range from 0.51 to 0.55; none were significantly different from the referent
pregnancies (proportion=0.54), either in unadjusted or adjusted analyses (adjusted for maternal
education and paternal race). The pregnancies in this occupational setting experienced higher
paternal TCDD exposures than the environmental studies; even in the highest exposure group,
while limited in size, did not experience a change from the referents (1120+ ppt TCDD:
proportion=0.55; 95% CI: 0.49-0.61 versus <20 ppt TCDD: proportion=0.54; 95%CI: 0.52-0.56)
7.13.12.7.1. Comments. Many of these analyses provide limited data for one or more of the
following reasons: limited exposure data, no or limited adjustment for other risk
factors/confounders, assumption of a gold standard of 51.4% males (and not having comparison
groups) or small numbers. The recent NIOSH analysis, of occupationally exposed (adult) men,
with a broad range of exposure, an appropriate comparison group and adjustment of the analyses,
did not observe any differences.
Sex ratio at birth was significantly depressed in a group of 17 children in zone A of
Seveso in the years shortly following the industrial accident. This pattern disappeared a few years
later. However, a recent expanded effort suggests that paternal age at the time of exposure may be
a key factor. Sex ratio differences were not observed in other groups examined. If effects are
restricted to offspring of fathers less than 19 years old, as suggested in the new Seveso study, the
lack of effect elsewhere could be explained by the groups examined: maternal levels of dioxin in
community studies or studies of men older than 19. The findings in the most recent Seveso study
emphasize the need for more attention on male-mediated development effects, and the potential
importance of exposures prior to and during puberty.
7.13.12.8. Growth, Malformations, and Infant Mortality
In recent years, those investigating developmental outcomes have started looking at a
variety of measures of prenatal and postnatal growth. Outcomes considered have included birth
weight and size, intrauterine growth retardation (IUGR), and postnatal measures up to the age of
42 months. IUGR, also known as small-for-gestational-age, basically combines information on
birth weight with the length of gestation. Children with low birth weight are not necessarily IUGR
because of different expected birth weights at different gestational ages.
New analyses of growth of children in the smaller Dutch study (38 children, Pluim et al.,
1997) have been made. Birth weight data were recorded at delivery by the obstetrician or
midwife. Weight and length were recorded at 10 and 20 weeks during postnatal examination, and
used to calculate the Quetelet index (weight/length2). In addition to these measures, the
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circumference of the head was measured (1, 11, and 26 weeks) and area of the liver determined by
ultrasound (10 days and 11 weeks). No differences were found between low and high exposure
for any of the growth measures (using Student's t-test).
The Rotterdam study also examined birth weight and growth (Patandin et al., 1998). Birth
weight was only evaluated in relation to PCBs and so will not be discussed here. Postnatal growth
was examined in relation to TEQs for dioxins, furans and PCBs in breast milk multiplied by
weeks breast fed. Using multivariable regression, and controlling for other factors potentially
related to growth, no significant differences were observed at 3 months. A statistically significant
decrease in growth in length was observed (beta = -0.21, p = 0.04) with TEQ, but not with weight
or head circumference between 3 and 7 months of age. No differences were observed between 7
to 18 months or 18 to 42 months.
Another study examined the effects of background levels of PCDD and PCDF levels and
birth weight in all consecutive births from January-May 1987 in one maternity clinic in an urban
area (Helsinki, Finland) and in a rural area (Kuopio province) (Vartiainen et al., 1998).
Approximately 150 women were recruited from each area, with the commitment to provide a
breast milk sample at 4 weeks postpartum, if still lactating. A total of 167 samples were obtained,
with about a 50% response rate in Helsinki (77 women provided samples, representing 26% of
births) and about 60% from Kuopio (90 women, representing 30% of births). TEQs of breast
milk were significantly higher in the urban area (26.3 pg/g TEQ versus 20.1 pg/g TEQ in Kuopio
province). Correlation analyses (Pearson's correlation) were significant for all births and all male
births. Regression analyses of all children showed a decreasing relationship of birth weight with
TEQ of milk (beta = -0.00228), which appeared to be primarily in male births (beta = -0.00302;
versus in females, beta = -0.00107). Statistical significance of the regressions was not presented,
nor were details on other characteristics included in the models. When restricted to examination
of first-born children (N = 84), no significant relationships were observed.
One report examines placental AH receptor binding of TCDD in IUGR, preterm birth, and
structural malformation (Okey et al., 1997). The study group, 86 births, included 21 preterm
births, 20 with IUGR (8 of these were preterm), and 7 infants with structural malformations. The
Bmax (concentration of AH receptor sites for TCDD) and Kd (affinity for binding of TCDD) were
not significantly different for the different pregnancy outcomes. Some modest increases were
observed for Bmax (and less so for Kd) with IUGR (N = 10) and structural malformations (N = 5)
over normal deliveries (N = 23), but the power was limited by small numbers.
Michalek and colleagues (1998) examined IUGR in their study of the veterans of
Operation Ranch Hand. The analyses included 2,082 liveborn, singleton births occurring during
or after the father's service in Southeast Asia, for whom paternal serum measures of dioxin were
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available. Of the 2,082, 859 were in the Ranch Hand group and 1,223 were comparisons. If
serum dioxin levels in 1987 or 1992 were >10 pg/g lipid, the investigators modeled the father's
level at the time of conception of the child. For those at or under 10 pg/g lipid, levels at
conception were considered to be "background." Levels greater than 10 and less than 79 were
"low," and above that were "high." Length of gestation and birth weight were obtained from
labor and delivery records. Included births occurred between 1959 and 1992; the earliest births
were from comparison subjects. No differences were observed in IUGR across the exposure
groups. Small, nonsignificant increases were seen in preterm birth (<37 weeks gestation) for
Ranch Hand background and high groups (RR = 1.4, 95% CI = 0.9-2.3 and RR = 1.3, 95% CI =
0.8-2.3, respectively). Significant increases were observed in these groups for neonatal death
(within the first 28 days of life): RR = 3.2, 95% CI = 1.0-10.3 for background, and RR = 4.5, 95%
CI = 1.5-14.0 for high (for the low group: RR = 1.5, 95% CI = 0.3-7.5). Most of the Ranch Hand
deaths were due to short gestation and low birth weight, but only a third in the comparison group.
While these numbers were relatively small, the proportions in the background and high groups
were much higher: comparison: 3.7% of 54 preterm births; background: 25% of 20; low: 0% of 6;
high: 31.3% of 16. An examination of these data using occupation, so as to include all births, not
just those with serum measures, also showed elevated proportions of infant deaths in preterm
births in the exposed versus the comparison group. However, the proportions did not follow the
relative exposures observed among the categories.
7.13.12.9. Comment
At this time, the data relating growth measures and neonatal death are limited. For
example, in one study, decrements in length (but not other measures of growth) were observed
early, but disappeared with increasing age (Patandin et al., 1998). Some changes were observed
in the Ranch Hand study for preterm birth and neonatal death, but these did not follow an
exposure-response relationship (Michalek et al., 1998). The Finnish data are interesting because
birth weight did decrease in males, with increasing TEQ, but the lack of detail on the statistical
analyses makes interpretation difficult.
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7.14. NONCANCER EFFECTS OF INGESTION OF RICE OIL CONTAMINATED
WITH POLYCHLORINATED DIBENZOFURANS, QUATERPHENYLS, AND
BIPHENYLS IN JAPAN (YUSHO) AND TAIWAN (YU-CHENG)
This section briefly reviews the noncancer effects observed in Yusho (Japan) and Yu-
Cheng (Taiwan) victims, individuals exposed by ingestion to large concentrations of compounds
structurally related to dioxins, namely polychlorinated dibenzofurans, quaterphenyls, and
biphenyls. The history of each incident, the chemicals in question, and levels of exposure are
described in this chapter. In addition, other reviews have summarized the numerous papers
dedicated to Yusho and Yu-Cheng (Lu and Wong, 1984; Kuratsune, 1989; Rogan, 1989).
Reports describing effects among individuals who ingested the contaminated rice oil both
in Taiwan and Japan are limited to acute rather than chronic effects. Studies have not
comprehensively evaluated long-term effects even though over 30 years have passed since the
Yusho incident and over 20 years since the Yu-Cheng incident and that serum levels of some
contaminants are available for both populations. Recent epidemiologic studies have concentrated
on the development of offspring of Yu-Cheng mothers. These children were exposed in utero at
the time the contaminants were ingested, or were conceived after the poisoning and were exposed
to residual contaminants transplacentally or through breast milk (Chen et al., 1992; Lai et al., 1993,
1994; Hsu et al., 1993; Guo et al., 1993,1994a,b, 1995a,b,1996; Chao et al., 1997; Yu 1994, 1998).
7.14.1. Acute Effects in Adults and Children Directly Exposed to Contaminated Rice Oil
In both groups, the most notable acute effects are dermatologic and neurologic signs and
symptoms of fatigue, headaches, and gastrointestinal distress (nausea, vomiting, abdominal pain)
(Kuratsune, 1989; Rogan, 1989).
7.14.1.1. Yusho
The initial recognition of Yusho occurred in 1968. As of 1983, a total of 2,060 individuals
were identified as part of the Yusho population (Masuda et al., 1985). Five years after exposure
ended, the mean concentrations of PCBs in the adipose tissue, liver, and blood of Yusho cases
were 1.9 ppm, 0.08 ppm, and 6.7 ppb (Masuda et al., 1985), respectively, which were about twice
the levels in the control group. Adipose tissue levels of PCDFs ranged from 6 to 13 ppb (Masuda
et al., 1985). Sixteen years after exposure, mean PCQ level in adipose tissue of Yusho cases was
207 ppb, approximately 100 times the level in Japanese controls (Kashimoto et al., 1985).
In addition to the major health effects, other possible outcomes were examined. Effects
observed shortly after exposure included elevated triglyceride levels and effects on female
reproductive hormones, noted by changes in menstrual and basal body temperature patterns and
lowered excretion of estrogens and pregnanediol in exposed women (Kuratsune, 1989). However,
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fertility and other measures of reproductive function were not evaluated. Evidence of chronic
bronchitis and respiratory infections still remained 14 years after exposure ended (Nakanishi et al.,
1985). However, more than 10 years postexposure, PCB levels were not related to levels of serum
triiodothyronine (T3), thyroxine (T4), and thyroxine-binding globulin (TBG) (Murai et al., 1987).
Although the liver is the suspected target organ for halogenated hydrocarbons, and marked
proliferation in the endoplasmic reticulum was observed, clinical evidence of liver damage, such as
alterations in liver enzymes or liver disease, was not observed (Kuratsune, 1989).
Dermatologic effects were the most evident signs, characterized by hyperpigmentation of
the nails, gingivae, and face, and by nail deformities, horny plugs, comedones, acneform eruptions,
cysts, and other abnormal keratotic changes (Urabe and Asahi, 1985). Acneform eruptions were
observed on the face, cheeks, auricles, retroauricular areas, inguinal regions, and external genitalia
(Urabe and Asahi, 1985). More than 80% of Yusho cases experienced one or more dermatologic
effects (Kuratsune, 1989), which diminished in severity over time (Urabe and Asahi, 1985).
Ophthalmologic effects were characterized by swelling and hypersecretion of the
meibomian glands and pigmentary changes of the conjunctiva (Kuratsune et al., 1972). More than
80% of Yusho cases exhibited ocular changes, which, in some cases, appeared to persist 15 years
after exposure ended (Kuratsune, 1989).
Thirty percent of the cases reported having at least one symptom consistent with neurologic
involvement, such as limb parasthesia and spasms, weakness, headaches, and fatigue (Kuratsune,
1972). As summarized by Kuratsune (1989), Kuriowa et al. (1969) found mostly sensory deficits,
identified through slowed nerve conduction velocities, in 23 cases. Follow-up of these cases
indicated that the neurologic symptoms disappeared over time; however, conduction velocities
were not repeated.
A number of studies examined the immune status of Yusho cases (Kuratsune, 1989).
Significant decreases in mean IgA and IgM and increases in IgG were noted in 28 cases tested in
1970 (p<0.05) (Nakanishi et al., 1985). Within 2 years, mean levels of all three immunoglobulins
returned to normal. Small increases in the percentage of CD4 cells, small decreases in the
percentage of CDS cells, and enhanced lymphocyte stimulation were also noted in Yusho cases
(Nakanishi et al., 1985).
Studies of offspring of Yusho cases have been limited to descriptions of effects on
newborns exposed in utero. An early description of 13 children born to exposed mothers noted
two stillborn infants, one of whom was diffusely and deeply hyperpigmented (Rogan, 1982).
Neonates described in other reports were darkly pigmented and had marked secretions of the
conjunctival palpebra, gingival hyperplasia, hyperkeratosis, calcification of the skull, low birth
weight, and natal teeth (Yamashita and Hayashi, 1985). The abnormal pigmentation disappeared
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after 2 to 5 months. No other physical abnormalities (neurologic, cardiovascular, or
malformations) were identified.
7.14.1.2. Yu-Cheng
The initial recognition of Yu-Cheng occurred in 1979. As of 1983, approximately 2,000
individuals were found to have been exposed to the contaminated rice oil. Within the first year of
exposure, mean serum PCB, PCDF, and PCQ levels for 15 cases were 60 ppm (range 4-188 ppm),
0.14 ppb (range <0.005-0.27 ppb), and 19.3 ppb (range 0.9-63.8), respectively (Kashimoto et al.,
1985). Analysis of PCB levels in 1980-1981 in 165 cases (mean 38 ppb, range 10-720) (Rogan,
1989) and in 1985 in 32 cases (mean 15.4 ppb, range 0.6-86.8) (Lundgren et al., 1988) suggested
that some PCBs were being eliminated. It is not clear from the reports if the samples were drawn
from distinctly different individuals or included some of the same individuals.
The ophthalmologic and dermatologic changes observed in Yu-Cheng cases were very
similar in character and anatomical distribution to those noted in Yusho cases (Lii and Wu, 1985).
In 89 cases followed for up to 17 months, dermatologic conditions of 38% of the cases improved,
54% remained the same, and 7% showed deterioration of their conditions (Lii and Wong, 1984).
Like Yusho cases, Yu-Cheng cases examined within 2 years of exposure for nerve function
exhibited slowing of sensory nerve conduction. They also exhibited motor nerve slowing and
mixed deficits (Chen et al., 1981, 1983, 1985; Chia and Chu, 1984). Twenty percent of a
population of 27 individuals also had abnormal EEGs (Chia and Chu, 1984). However, the authors
suggest that any correlation between PCB exposure and the abnormal EEGs may be spurious
because of low PCB levels in the cerebrospinal fluid (0.5-2.3 ppb) (measured in four subjects),
despite much higher blood PCB levels of 48-64 ppb. A sample of 28 individuals with peripheral
neuropathy in 1980 was reexamined in 1982 and was found to have normal EEGs and some
recovery of sensory and motor nerve conduction velocity (Chia and Chu, 1985).
In 1981, immunologic function was assessed on different subsets of Yu-Cheng cases and
summarized by Lii and Wong (1984). In 30 cases compared with unexposed controls, both IgA
and IgM were significantly decreased, while IgG did not differ from controls. In this same group,
percentages of active T-cells and T-cells were significantly increased (p<0.05), while total
lymphocyte count and percentage of B cells were unchanged. Significant increases in helper T-
cells (T4) but not suppressor T-cells (T8) were also observed. In another group of cases, response
to lymphocyte-stimulating mitogens was mixed and the findings unclear. In 143 cases, reaction to
streptococci antigen appeared to be significantly (p<0.05) depressed relative to controls.
Alterations in porphyrin levels and liver enzymes have been identified as acute reactions to
exposure to halogenated polycyclic hydrocarbons, including PCBs. Porphyrin levels were
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measured in two exposed groups (Chang et al., 1980; Gladen et al., 1988). In 1980, statistically
significant elevations in 24-hour urinary excretion of uroporphyrin (exposed = 41.23 ng ± 24.56;
unexposed = 13.5?ng± 11.76,p<0.01) and oc-aminolevulinic acid (exposed = 1.002 mg± 0.600;
unexposed = 0.715 ± 0.337,/?<0.05) were noted among 69 subjects (Chang et al., 1980).
Coproporphyrin and porphobilinogen levels were increased in the exposed group but were not
significantly elevated. The second study group was composed of 75 children born between June
1978 and March 1985 to mothers who ingested contaminated rice oil (Gladen et al., 1988). Spot
urines were collected in 1985. Mean total porphyrin (exposed = 95.2 ng/L; unexposed = 80.7
and coproporphyrin (exposed = 72.4 n/L; unexposed = 59.8 n/L) excretion was elevated in the
exposed subjects, possibly because of extremely high levels (>200 ng/L) in eight exposed children
and two controls (Rogan et al., 1988). However, no porphyria cutanea tarda, a severe form of
porphyria, was observed in either group of children. Moderate, but statistically significant,
increases were observed in AST and ALT levels in 23 cases tested 1 year after exposure (Lii and
Wong, 1984). LDH and bilirubin levels were not significantly elevated. As in Yusho cases,
triglyceride levels were significantly increased by approximately twice the level in unexposed
controls.
Recently, a follow-up study interviewed 59% of 600 surviving women who were 30+ years
of age and less than 60 years of age in 1993. Drawing from registration data, women who lived in
the same areas in 1979 and were within 3 years of age of cases were selected for comparison. Of
these, 312 of 594 (53%) participated. The low response rate meant that not all case respondents
had matches in the controls, and the reverse. The authors chose not to maintain matching in the
analyses (unpaired X2 and t-tests), which may affect the results reported. In comparisons of the
Yu-Cheng women and their controls, Yu-Cheng women were more likely to have abnormal
menstrual flow (16.6% vs 7.5%, p O.068), to have had a stillborn infant since 1979 (4.2% vs
1.7%, p <0.05), to have had a child die before adolescence (10% vs 6.1%, p<0.05), and decided to
limit childbearing for health reasons (6.9% vs 2.0%, p <0.05). On the last point, the Yu-Cheng
women who did not decide to limit childbearing did not have a difference in family size from the
controls.
7.14.1.3. Effects Observed in Off spring of Yu-Cheng Cases
A concerted effort has been made to evaluate the overall development of children exposed
prenatally to contaminated rice oil ingested by their mothers. Two studies are currently being
conducted: one to evaluate children and controls born between 1978 and 1985 (termed "early-
born"), and the second to evaluate children born in 1985 and later. Controls were matched within
30 days of age (15 days if less than one year of age), neighborhood, sex, mother's age within 3
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years, and combined parental education (within 3 years). The purpose of the two studies was to
determine if there are differences among children exposed in utero or shortly after birth by the
mothers' ingestion of the contaminated rice oil and children born 7 to 12 years after direct exposure
to either parent ended (July 1, 1985 to December 31, 1991). Only a small sample of these children
(n=31) have had serum levels of PCB and PCDF determined, so all the analyses are based on broad
groupings, reducing their ability to look at exposure levels and health endpoints. The studies
comparing the Yu-Cheng children and controls typically analyzed data using paired t-tests, with
out adjusting for other potential risk factors (except for the matching of controls).
In terms of musculoskeletal development, several studies have documented delays and
abnormalities (Yu et al., 1991; Rogan, 1989; Chen et al., 1993; Guo et al., 1994a). In one of the
first studies conducted in 1985, Rogan and colleagues (1988) examined 117 children born since the
mothers' exposure in 1979 and 107 unexposed controls. In this study, babies of exposed mothers
were consistently smaller and shorter at birth than controls and had similar characteristics: natal
teeth, neonatal conjunctivitis, and pigmentation. Exposed mothers reported a mean birth weight
479 g lower than that reported by control mothers; no validation of these reports using medical
records was undertaken. As older children, they exhibited a variety of signs and symptoms: fragile
chipped teeth and gum hypertrophy, pigmented and deformed fingernails and toenails, and
abnormal lung auscultation. In this same study, neurologic developmental assessments were also
conducted to evaluate development (Yu et al., 1991). Forty-nine percent of Yu-Cheng children
compared with 22% of controls were developmentally delayed in 32 of 33 developmental
milestones, 12% had clinical evidence of developmental or psychomotor delays compared with 2%
of controls, and 7% of Yu-Cheng children versus 3% of controls had speech problems. These
delays were noted at all ages and persisted over 2 years of testing. Delay was greater in children of
smaller size and in children who had exhibited neonatal symptoms of intoxication.
In 1991, the musculoskeletal development of 56 Yu-Cheng children (age range 6-10 years)
and their matched controls was again assessed (Guo et al., 1994a). Only children born first after
the mother's exposure were shorter in stature (-3.4 cm, p = 0.02) and had decreased lean muscle
mass (-2.9 gm, p = 0.04) and soft tissue content (-5.3 gm, p = 0.06). Another examination of 110
Yu-Cheng children and 108 controls, ages 8 through 14 years, found Yu-Cheng girls to be
significantly shorter than controls, matched to the exposed children by age, sex, maternal age,
parents' combined educational level, occupation, and neighborhood (Guo et al., 1993). As
measured by the Tanner scale, sexual maturation was not slower in Yu-Cheng boys or girls.
However, Yu-Cheng boys aged 11-14 had significantly shorter penis length, but testicular and
scrotal development did not differ from the controls. Penile length was not related to sexual
development as measured by the Tanner scale.
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With a validated and standard battery of tests, cognitive and behavioral development of Yu-
Cheng offspring were studied yearly from 1985 through 1991. Throughout the testing period, Yu-
Cheng children scored consistently lower in the Stanford Binet IQ (SB-IQ) and 4-5 points lower
than controls (with the same matching criteria as the above study) in three subscales of the
Wechsler Intelligence Scale for Children, Revised (WISC-R): verbal IQ (VIQ), with significant
differences observed in 1990 and 1991; performance IQ (PIQ); with significant differences in
1987-1991; and full-scale IQ (FIQ), with significant differences in 1989-1991 (Chen et al., 1992;
Lai et al., 1993; Lai et al., 1994). When the PIQ, VIQ, and FIQ were examined by the age of the
child, significant deficits were observed for ages 6-8 (Lai et al., 1994). The Yu-Cheng children
scored lower on the mental development index (MDI) and the psychomotor development scale
(PDI) of the Bailey scale, with a significant difference at 2 years of age (Lai et al., 1994).
Yu-Cheng children are also reported to exhibit more health, habit, and behavior problems
as reported by parents responding to the Rutter's scale, and to manifest higher activity based on
teachers' responses to the Teacher's Activity Check List (Hsu et al., 1993). Rutter's Child Behavior
Scale A was used to screen children for their likelihood for social, health and behavioral problems
(Yu et al, 1994). The scale, validated in Britain of children ages 9-12, assesses these outcomes
through a questionnaire-checklist on each of the three topic areas. This study compared the 113 of
the 118 Yu-Cheng children born between July 1978 and June 1985 to exposed women who
participated in the seven year follow-up to their matched controls. Only those three years old or
older were included in this evaluation. The results showed that, at all ages, the exposed children
scored 14-38% worse on mean Rutter scores than did the controls. All age groups, except the 10-
12 year olds were significantly different. For the health subscores, all age groups, except 5 year
olds and 10 year olds were significantly different; for habits, all were significantly different except
for 8, 11, and 12 year olds; and for behavior subscores, ages 4, 6, 7, 8, and 9 were significantly
different.
In addition to these developmental measures, Raven's Colored Progressive Matrices (CPM
- used for those 6 to 8 years of age) and Standardized Progressive Matrices (SPM - used for 9 year
olds) were used to assess cognitive development (Guo et al., 1995a). Analyses used Wilcoxin one-
sample test for comparison of exposed children to their matched controls, and an examination of
differences for offspring born longer after exposure, regression analysis was used on year of birth.
Significant deficits were observed for children aged 6 through 8, with no pattern of differential
effects with year of birth. When the results were examined by sex, the significant effects were
found only in males.
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In a follow-up of these children, a random sample of 27 case-comparison pairs were
selected from those pairs whose case was between 7 and 12 years of age (Chen and Hsu, 1994).
These children were assessed for neuropsychologic changes (including cognitive — WISC-R and
auditory event-related potentials — P300), and neurophysiological changes (including pattern visual
evoked potentials - P-VEP, and short-latency somatosensory evoked potentials - SSEP). The
exposed children had significantly lower verbal and full-scale IQs (VIQ and FIQ) and P300
latencies at Cz and Pz. No differences were observed for P-VEP or for SSEP.
In October 1991, researchers began analysis of physical and cognitive development of 104
children whose mothers were exposed and 109 children whose fathers but not mothers were
exposed, and of three matched controls born after 1985 (Guo et al., 1993). Like children born
before 1985, the later-born children were shorter in stature and lower in weight than controls,
although the authors indicate that the differences were no longer statistically significant. Yu-
Cheng children are reported to have higher activity levels but do not have temperament, physical,
habit, or behavioral problems. Overall, scores on all tests in paternally exposed children were
similar to those of the controls. However, maternally exposed children scored lower on the SB-IQ
and on all subscales of the WISC-R.
Another report (Guo et al., 1994b), probably of the same children (born between 1985 and
1991) gave more details: Local household registration were used to identify these children and two
comparisons from the same neighborhood, age, sex, mother's age within three years, and combined
parental education (within three years). Only one comparison was used in this report. Researchers
began analysis of physical and cognitive development of 120 children of 79 exposed mothers, 75
children of 52 exposed fathers, and 4 children of exposed mothers and fathers. The children's
development was assessed using the Chinese Child Developmental Inventory (CCDI). The CCDI
is a modification of the Minnesota Child Developmental Inventory (MCDI), a tool used to evaluate
children from six month to six years of age. The scales in the CCDI include gross and fine motor
activity, language skill, comprehension, self-help activities and personal-social skills. Of these,
children of Yu-Cheng mothers had significantly lower scores for self help and general development
(a summary scale); no significant differences were observed in children of exposed fathers. When
the children of exposed mothers were examined by gender in comparison to their controls, Yu-
Cheng girls scored significantly lower on self help, general development and on conceptual
comprehension. Other borderline differences were observed for fine motor activity and situational
comprehension. No significant differences were observed for sons of exposed mothers. Rutter's
Child Behavior Scale A was used to screen children for their likelihood for health and behavioral
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problems. No significant differences were reported for this measure in these later born children
(versus effects observed in those born before July 1985 - Yu et al., 1994).
A report examined middle ear abnormalities in 1993 in 110 Yu-Cheng children and 96
matched controls (Chao and Hsu, 1994), born before July 1985. Ages ranged from 8 to almost 16
years. Of the 220 tympanic membranes evaluated in the Yu-Cheng children, 49 were abnormal
compared to 34 out of 192 tympanic membranes in the comparison group (p«0.01). A second
report, apparently resulting from these examinations (Chao et al., 1997), presented the same results
with slightly different numbers: In this 44 of 103 children (206 tympanic membranes assessed)
were abnormal in the Yu-Cheng children, versus 18 of 96 control children (192 tympanic
membranes assessed) (p«0.01). In this second report, 30 Yu-Cheng children with serum levels
of PCBs and PCDFs measured in 1991 were compared: this group was divided into those with and
without middle ear diseases. In analyses unadjusted for other factors, significantly higher serum
levels of 1,2,3,4,7,8-hexachloro-dibenzofuran (p=0.046) and 2,3,4,7,8-pentachloro-dibenzofuran
(p=0.022) were observed for Yu-Cheng children with disease compared to Yu-Cheng children
without. Differences were not observed for serum PCB levels.
The authors attributed the association with middle ear disease with, at least in part,
immunologic effects. Yu and colleagues (1998) recently published a report directly examining
immunologic function in Yu-Cheng children. In this effort, 105 Yu-Cheng children of exposed
mothers and 101 control children, born between July 1978 and June 1987, were assessed. The Yu-
Cheng children had significantly elevated rates of influenza during the 6 months preceding the
1995 physical examination. Blood was drawn for immunologic analyses, and compared for the
exposed and control children. Due to laboratory problems, 29 samples from exposure, and 22 from
controls were examined. In this limited comparison, no significant differences were observed in
leukocyte classification and immune markers.
7.14.1.4. Mortality Among Yu-Cheng Population
Cause-specific mortality through December 31, 1991, for the 2,000 Yu-Cheng poisoning
cases was assessed (Yu et al., 1997). Eighty-three deaths were identified from among the 1,837
persons whose vital status was known. Compared to the age, gender, and calendar-time specific
mortality rates of the Taiwan general population, the overall SMR was significantly lower among
the Yu-Cheng population (SMR = 80 (95% CI = 70-100). However, mortality from chronic liver
disease and cirrhosis was statistically significantly increased in the Yu-Cheng population (SMR =
2.7, 95%CI= 1.3, 4.9) (Yu et al., 1997).
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7.14.1.5. Comment
Data from Yusho and Yu-Cheng strongly implicate direct ingestion of contaminated rice oil
with numerous acute effects of the skin and peripheral and central nervous systems. The data on
immunologic function suggest possible effects, but the numbers of subjects in the various studies
were too small to determine an exposure-response relationship. Similarly, data on elevated
triglyceride and liver enzyme levels are from a small number of cases and, therefore, the
relationship between exposure and the effect is unclear. Furthermore, there are few data to
evaluate the long-term effects of these very high exposures. Because many of the 4,000
individuals who were exposed in these two episodes were children, longitudinal studies would be
invaluable in assessing the long-term health effects of these exposures.
One difficulty in evaluating the various reports relating to Yusho and Yu-Cheng is the
inability to determine if the effects are generalizable to the entire exposed population. It may be
that the cases reported in the literature were those who tended to have the severest signs and
symptoms and probably had the highest body burden of contaminants. Some reports included
small numbers of cases and controls, relative to the size of the exposed population, and others had
no controls. Finally, while much work has gone into determining severe acute effect, it would be
interesting to know what chronic, age, and gender-specific effects are now being exhibited in the
approximately 4,000 individuals directly exposed to the contaminated rice oil.
Another difficulty is the presence of several chlorinated hydrocarbons in the contaminated
oil, which results in uncertainty as to which contaminant or combination of contaminants is
responsible for the noted effects. The data on the offspring of exposed Yu-Cheng mothers and
fathers are fascinating and disturbing. It appears that parental exposure, specifically, maternal
exposure, to PCBs, PCDFs, and PCQs is directly linked to in utero exposure of the fetus, affecting
neurodevelopment, selective musculoskeletal, and possibly sexual development of the offspring.
The ongoing assessment of development in the Yu-Cheng children will contribute to the
understanding of long-term consequences of these exposures on the children's future quality of life
and highlights the importance of parental exposures to their children's well-being. Prospective
studies of fertility and reproductive experience in these offspring would provide insight into
possible intergenerational effects of exposure to these compounds.
7.15. SUMMARY
The data presented in this chapter describe nonmalignant effects in epidemiologic studies of
populations with the potential for exposure to chemicals contaminated with 2,3,7,8-TCDD. The
purpose of this review is to highlight the salient results of the studies and to assess whether the
observed effect was related to exposure to 2,3,7,8-TCDD.
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In summary, based on the results of two or more studies, recent evidence suggests that, in
adults, chloracne, elevated GOT levels, and altered testosterone levels appear to be long-term
consequences of exposure to 2,3,7,8-TCDD (Table 7-53). In contrast, multiple studies show
possible acute effects but few chronic exposure-related effects for dermatologic endpoints other
than chloracne, such as eyelid cyst, hypertrichosis, hyperpigmentation, actinic keratosis, and
Peyronie's disease; for liver diseases such as cirrhosis, liver enlargement, and hepatic enzyme
levels (LDH, AST, ALT, and D-glucaric acid) other than GGT; and for lipid concentrations,
porphyrias, and thyroid function; as well as renal, neurologic, and pulmonary disorders. Although
the available data are suggestive of an association between TCDD exposure and other adverse
outcomes, circulatory and heart diseases, diabetes and glucose metabolism, reproductive and
developmental outcomes, and immunologic disorders require further study before their respective
relationships to 2,3,7,8-TCDD can be more definitively assessed.
In the best of circumstances when reviewing studies, it would be ideal if all studies
examined the same endpoints in the same manner, had sufficient statistical power to detect truly
positive findings, had good estimates of extent of exposure, and had consistent exposure-response
relationships. In the absence of ideal situations, epidemiologists examine the evidence of studies
using "six tenets of judgment" (Hatch and Stein, 1986; Hill, 1965) to assess the collective wisdom
of the study results. These tenets are temporality (sequence of events); degree of exposure;
strength, consistency, and specificity of association; and biological plausibility.
In evaluating many of the studies that examined the relationship between serum 2,3,7,8-
TCDD and, in some cases, dioxins, furans, and PCBs, there are several common threads that bear
noting. They will be discussed first to avoid repetition throughout the summary.
In terms of temporality, all studies reviewed in this chapter were conducted after the
presumed exposure occurred. Some of the studies obtained exposure data at (approximately)
relevant time for the outcomes (e.g., Dutch developmental studies of dioxins, furans, and PCBs) or
shortly after the exposure, as in Seveso; others were conducted many years after the groups' last
exposure to evaluate more chronic health outcomes. One dilemma in assessing the effect of past
exposures is ascertaining whether an effect observed many years postexposure is due to the
exposure itself or to an exposure or event that occurred during the intervening period. Another
problem is determining what, in the analysis, the investigator considered the most important of the
possible confounding exposures. Finally, restricting examination of events to those that occurred
after the exposure does not in and of itself satisfy this time-order criterion. Several factors must be
considered, such as the half-life of the contaminant in the body and the concentration at the time of
the event. Consistency in the results of similarly designed studies of exposed populations should
help strengthen the conclusion of an effect or no effect.
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Determination of the extent of exposure throughout the studies was varied. When the risk
of disease increases with the dose or gradient of exposure, the evidence for causation is
strengthened. It should be emphasized that there are many possible dose-response patterns, which
may result in different threshold levels for different endpoints. Because of the exposure
misclassification bias present in most dioxin research, with the exception of a few studies, it is not
valid to attempt to determine dose-response relationships. To summarize, six studies evaluated the
relationship between nonmalignant effects and body levels of 2,3,7,8-TCDD (or a mixture of
dioxins, furans, and PCBs): the Ranch Hand study of U.S. Air Force personnel (Roegner et al.,
1991; Grubbs et al., 1995); the study of 50 Missouri residents (Stockbauer et al., 1988); the
evaluation of the BASF accident cohort (Ott et al., 1994) and the cohort of Hamburg chemical
workers (Flesch-Janys et al., 1995); the NIOSH study of 281 TCP production workers (Egeland et
al., 1994); and the two Dutch developmental series of studies (Series 1: Huisman et al., 1995a,b;
Koopman-Esseboom et al., 1994a-c, 1995a,b, 1996; Weisglas-Kuperus et al., 1995, 2000; Series 2:
Pluim et al., 1992, 1993, 1994, 1996). In 1988, the workers in the NIOSH study had the highest
serum 2,3,7,8-TCDD concentrations (mean = 220 pg/g) of these six study groups. Of these six sets
of studies, only the Dutch developmental studies examined common environmental exposures.
Because the Dutch series examined a variety of dioxins, furans and PCBs (see Table 7-21b), the
data are not strictly comparable to the other five studies.
In U.S. Army veterans (Centers for Disease Control Veterans Health Studies, 1988), serum
2,3,7,8-TCDD levels were measured, but the levels were not used to examine dose-response
relationships. In the Veterans Health Studies, more than 99% of the serum 2,3,7,8-TCDD levels of
the sample of both Vietnam and non-Vietnam veterans were at the background level (4 pg/g).
Therefore, comparisons were made between the two groups as a whole.
Serum levels of Seveso residents were obtained for a small proportion (N = 20) of the total
number of residents of zone A within 1 year of the reactor release (Mocarelli et al., 1991). The
data suggest that the levels may be related to a number of factors, including age (younger children
were outside at the time of the release), whether the resident was inside or outside, ingestion of
local produce, or number of days of residence in the area after the release, to name a few. The data
suggest that the potential for substantial exposure was high for individuals residing in the area.
The range of levels in the 20 zone A residents was 820 pg/g to 56,000 pg/g (median = 7,400 pg/g).
Thirty years after the explosion, Landi et al. measured serum 2,3,7,8-TCDD in 62 individuals
randomly selected from zones A, B and non-ABR (Landi et al., 1997). Geometric mean serum
concentrations were 53.2 pg/g lipid in zone A; 11.0 pg/g lipid in zone B, and 4.9 pg/g lipid in zone
non-ABR. These data suggest that elevated levels persist but are decreased from the original levels
of 1976.
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The majority of the remaining studies examined the differences between individuals
identified as exposed or unexposed, or with or without chloracne. Most of these studies did not
evaluate other parameters that might explain differences in effects between exposed and
unexposed, for example, the length of exposure. However, one study assessed dose-response
relationships based on a statistical algorithm of intensity and highest dose of TCDD exposure
(Bondetal., 1989).
In terms of the magnitude (or strength) of the association, this criterion refers to the degree
to which the measure of association (e.g., odds ratio or relative risk) exceeds the null value of 1.
The stronger the association between exposure and effect, the more convincing is the argument for
causation. There is no definitive cut point to numerically define a meaningful measure of
association. Other factors, such as the prevalence of the exposure in the population, affect the
significance of the measure.
A critical element that should always accompany the effect measure is a confidence
interval. Placement of an interval around the measure enables quantitation of the result for a more
meaningful interpretation. An odds ratio of 30 is quite impressive, but if the 95% confidence
interval is 0.9-200, the magnitude of the association is less impressive.
If an association between a factor and a disease is demonstrated across a variety of studies
employing different designs and different populations (consistency), then the argument for
causation is strengthened. Replication of an association under different conditions decreases the
likelihood that confounding is responsible for the observed association. Consistency is a powerful
criterion for causation, but only when "the variables under test (exposure, outcomes) are similar
enough" to justify the comparison of the various studies' findings (Hoffman et al., 1986).
It should also be determined a priori that each study included in the critical evaluation
process is in adherence to basic epidemiologic principles governing study design and analysis.
Deficient studies with suspect results should be excluded. While this is not to imply that such
studies have no worth, as invaluable information has often been derived from those studies that
improve on subsequent examinations of the issue, they have no place in the evaluation process.
Unfortunately, in studies of 2,3,7,8-TCDD and effects in humans, the probability of exposure
misclassification forces exclusion of much of the research to date.
Specificity refers to the uniqueness of the association between a factor and an outcome. If
the relationship were absolute, then only factor X would be related to only effect Y. It is indeed
rare to encounter this type of association, which renders this criterion generally less useful in the
evaluation process.
Finally, according to the criterion of biological plausibility, the observed association
between exposure and effect should be consistent with existing theory and information from other
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scientific disciplines. Certainly one would feel more secure in the causation debate if the
biological basis for an observed association could be explained. However, biological
implausibility may simply reflect gaps in existing scientific knowledge that could explain the
relationship.
7.15.1. Effects Having a Positive Relationship With Exposure to 2,3,7,8-TCDD
The following section describes those endpoints for which there is good evidence from two
or more studies suggesting an effect of exposure to 2,3,7,8-TCDD.
7.15.1.1. Chloracne
7.15.1.1.1. Temporality. Chloracne is one of the best known of the medical consequences of
exposure to 2,3,7,8-TCDD-contaminated substances. In general, it has been observed in most
incidents where substantial exposure has occurred, particularly among TCP production workers
(Goldman, 1972; May, 1973; Bleiberg et al., 1964; Bond et al., 1987; Suskind and Hertzberg,
1984; Moses et al., 1984; Zober et al., 1990) and Seveso residents (Reggiani, 1978; Caramaschi et
al., 1981; Ideo et al., 1985; Mocarelli et al., 1986; Assennato et al., 1989). As previously stated,
chloracne appears within several weeks to months from the time of exposure, often resolving after
discontinuation of exposure (Moses et al., 1984; Suskind and Hertzberg, 1984), although for some
it may remain for extended periods after exposure ended (Moses et al., 1984).
7.15.1.1.2. Degree of exposure, consistency of the association. The amount of exposure
necessary for development of chloracne has not been resolved, but studies suggest that high
exposure (both high acute and long-term exposure) to 2,3,7,8-TCDD increases the likelihood of
chloracne, as evidenced by chloracne in TCP production workers and Seveso residents who have
documented high serum 2,3,7,8-TCDD levels (Beck et al., 1989; Fingerhut et al., 1991a; Mocarelli
et al., 1991; Neuberger et al., 1991) or in individuals who have a work history with long duration
of exposure to 2,3,7,8-TCDD-contaminated chemicals (Bond et al., 1989). The absence of
substantial chloracne in U.S. Army Vietnam veterans whose mean serum 2,3,7,8-TCDD levels
were at background (4 pg/g) (Centers for Disease Control Vietnam Experience Study, 1988d) and
U.S. Air Force Ranch Hands whose serum 2,3,7,8-TCDD levels fell intermediate to those of
workers and Army Vietnam veterans (Roegner et al., 1991; Burton et al., 1997) suggests that there
is a higher incidence of the disorder among those with higher serum 2,3,7,8-TCDD levels.
7.15.1.1.3. Strength of the association. In earlier studies, chloracne was considered to be a
"hallmark of dioxin intoxication" (Suskind, 1985). However, only in two studies were risk
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estimates calculated for chloracne. Both were studies of different cohorts of TCP production
workers (Suskind and Hertzberg, 1984; Bond et al., 1989); one group was employed in a West
Virginia plant, the other in a plant in Michigan. Of the 203 West Virginia workers, 52.7%
(p<0.001) were found to have clinical evidence of chloracne, and 86.3% reported a history of
chloracne (p<0.001) (Suskind and Hertzberg, 1984). None of the unexposed workers had clinical
evidence or reported a history of chloracne. Among the Michigan workers, the relative risk for
cases of chloracne was highest for individuals with the longest duration of exposure (>60 months;
RR = 3.5, 95% CI = 2.3-5.1), those with the highest cumulative dose of TCDD (based on duration
of assignment across and within 2,3,7,8-TCDD-contaminated areas in the plant) (RR = 8.0, 95% CI
= 4.2-15.3), and those with the highest intensity of 2,3,7,8-TCDD exposure (RR = 71.5, 95%
CI=32.1-159.2) (Bond et al., 1989).
7.15.1.1.4. Specificity of the association. Chloracne is associated with exposure to other
polyhalogenated chemicals, including dibenzofurans, PCBs, naphthalenes, and others (Taylor,
1979). The likelihood of exposure to other polyhalogenated chemicals in the populations studied is
probably low, particularly among the Seveso children, whose exposure was to TCP reactant
effluents that were primarily contaminated with 2,3,7,8-TCDD. The issue is more relevant in
chemical workers, who by virtue of their occupation, have the potential for exposure to other
chemicals. Yet, much of the documented chloracne appeared shortly after TCP reactor releases
(Ashe and Suskind, 1950; Goldman, 1972; May, 1973) or during TCP or 2,4,5-T production (Bond
et al., 1989), suggesting that 2,3,7,8-TCDD was the chloranegenic agent.
7.15.1.1.5. Biological plausibility. Animal studies have been effective in describing the
relationship between 2,3,7,8-TCDD and chloracne, particularly in rhesus monkeys (McNulty,
1977; Allen et al., 1977; McConnell et al., 1978). Subsequent to exposure to 2,3,7,8-TCDD,
monkeys developed chloracne and swelling of the meibomian gland, a modified sebaceous gland.
The histologic changes in the meibomian gland are physiologically similar to those observed in
human chloracne (Dunagin, 1984).
In summary, the evidence provided by the various studies convincingly states what is
already presumed, that chloracne is a common sequela of high levels of exposure to 2,3,7,8-TCDD.
More information is needed to determine the level and frequency of 2,3,7,8-TCDD exposure
needed to cause chloracne and whether personal susceptibility plays a role in the etiology. Finally,
it is important to recall that the absence of chloracne does not imply lack of exposure (Mocarelli et
al., 1991).
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7.15.1.2. Gamma Glutamyl Trans/erase (GGT) Levels
7.15.1.2.1. Temporality, degree of exposure, and strength and consistency of association. There
appears to be a consistent pattern of increased GGT levels among individuals exposed to 2,3,7,8-
TCDD-contaminated chemicals. Elevated levels of serum GGT have been observed within a year
after exposure in Seveso children (Caramaschi et al., 1981; Mocarelli et al., 1986) and 10 or more
years after cessation of exposure among TCP and 2,4,5-T production workers (May, 1982; Martin,
1984; Moses et al., 1984; Calvert et al., 1992) and among Ranch Hands (Roegner et al., 1991;
Grubbs et al., 1995). All of these groups had a high likelihood of substantial exposure to 2,3,7,8-
TCDD. In addition, for those studies that evaluated dose-response relationships with 2,3,7,8-
TCDD levels, the effect was observed only at the highest levels or categories of 2,3,7,8-TCDD.
In contrast, although background levels of serum 2,3,7,8-TCDD suggested minimal
exposure to Army Vietnam veterans, GGT was increased, at borderline significance, among
Vietnam veterans compared to non-Vietnam veterans (Centers for Disease Control Vietnam
Experience Study, 1988a). In addition, despite the increases observed in some occupational
cohorts, other studies of TCP production workers from West Virginia or Missouri residents
measured but did not report elevations in GGT levels (Suskind and Hertzberg, 1984; Webb et al.,
1989).
7.15.1.2.2. Specificity. In clinical practice, GGT is often measured because it is elevated in almost
all hepatobiliary diseases and is used as a marker for alcoholic intake (Guzelian, 1985). In
individuals with hepatobiliary disease, elevations in GGT are usually accompanied by increases in
other hepatic enzymes, e.g., AST and ALT, and metabolites, e.g., uro- and coproporphyrins.
Significant increases in hepatic enzymes other than GGT and metabolic products were not
observed in individuals whose GGT levels were elevated 10 or more years after exposure ended,
suggesting that the effect may be GGT-specific. However, in the Seveso male children and those
with chloracne (both sexes), ALT was significantly increased concomitantly with GGT within 1
year of the reactor release (Mocarelli et al., 1986; Caramaschi et al., 1981). In a longitudinal
analysis, both enzymes returned to normal levels within 5 years after exposure (Mocarelli et al.,
1986; Assennato et al., 1989). In the NIOSH cohort, elevated GGT levels occurred only among
workers with both high 2,3,7,8-TCDD concentrations and lifetime alcohol consumption. Yet, no
other enzymes were found to be outside of normal ranges. Likewise, GGT was the single enzyme
found to be significantly elevated in Ranch Hands compared to nonexposed referents (Roegner et
al., 1991; Grubbs etal., 1995).
These data suggest that in the absence of increases in other hepatic enzymes, elevations in
GGT are associated with exposure to 2,3,7,8-TCDD, particularly among individuals who were
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exposed to high 2,3,7,8-TCDD levels. The fact that investigators observed a decline in enzyme
levels in Seveso children but a continued elevation in TCP workers may reflect (1) differences in
how exposure occurred (i.e., acute but high doses in Seveso versus continuous or frequent long-
term, medium to high doses in TCP workers), (2) differences in the metabolism of the maturing
versus mature system, (3) the fact that children grow rapidly, thus "diluting" a peak exposure
within that group, or (4) some combination of these.
7.15.1.2.3. Biological plausibility. The animal data with respect to 2,3,7,8-TCDD-related effects
on GGT are sparse. Statistically significant changes in hepatic enzyme levels, particularly AST,
ALT, and ALK, have been observed after exposure to 2,3,7,8-TCDD in rats and hamsters
(Gasiewicz et al., 1980; Kociba et al., 1978; Olson et al., 1980). Only one study evaluated GGT
levels (Kociba et al., 1978). Moderate but statistically nonsignificant increases were noted in rats
fed 0.10 ng/kg 2,3,7,8-TCDD daily for 2 years, and no increases were observed in control animals.
Among humans, increased levels of GGT may suggest activity such as cholestases, liver
regeneration, or drug or xenobiotic metabolism. In human adults, most of 2,3,7,8-TCDD is stored
in the adipose tissue and has a half-life of approximately 7 years (Pirkle et al., 1989). Continued
GGT activity in adults with serum 2,3,7,8-TCDD levels many times over background levels may
reflect continuous, low-level metabolism of 2,3,7,8-TCDD.
In summary, GGT is the only hepatic enzyme examined that was found in a number of
studies to be chronically elevated in adults exposed to high levels of 2,3,7,8-TCDD. The
consistency of the findings in a number of studies suggests that the finding may reflect a true effect
of exposure but for which the clinical significance is unclear. Long-term pathologic consequences
of elevated GGT have not been illustrated by excess mortality from liver disorders or cancer, or in
excess morbidity in the available cross-sectional studies.
It must be recognized that the absence of an effect in a cross-sectional study, for example,
liver enzymes, does not obviate the possibility that the enzyme levels may have increased
concurrent to the exposure but declined after cessation. The apparently transient elevations in ALT
levels among the Seveso children suggest that hepatic enzyme levels other than GGT may react in
this manner to 2,3,7,8-TCDD exposure.
7.15.1.3. Reproductive Hormones
7.15.1.3.1. Strength and consistency of association. Levels of reproductive hormones have been
measured with respect to exposure to 2,3,7,8-TCDD in three cross-sectional medical studies.
Testosterone, LH, and FSH were measured in TCP and 2,4,5-T production workers (Egeland et al.,
1994), in Army Vietnam veterans (Centers for Disease Control Vietnam Experience Study, 1988d),
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and in Ranch Hands (Thomas et al., 1990; Grubbs et al., 1995). The risk of abnormally low
testosterone was two to four times higher in exposed workers with serum 2,3,7,8-TCDD levels
above 20 pg/g than in unexposed referents (Egeland et al., 1994). In both the 1987 and 1992
examinations, mean testosterone concentrations were slightly, but not significantly higher in Ranch
Hands (Roegner et al., 1991; Grubbs et al., 1995). FSH and LH concentrations were no different
between the exposed and comparison groups. No significant associations were found between
Vietnam experience and altered reproductive hormone levels (Centers for Disease Control Vietnam
Experience Study, 1988d). Only the NIOSH study found an association between serum 2,3,7,8-
TCDD level and increases in serum LH.
7.15.1.3.2. Specificity. The NIOSH study excluded from analysis participants who had conditions
that might have influenced gonadotropin and/or testosterone levels: history of prostate cancer,
thyroid or other hormone usage, or liver cirrhosis. Similarly, in the Ranch Hand study, individuals
with orchiectomies or who were taking testosterone medication were excluded from the analysis of
testosterone; no participants were excluded from the analyses of FSH. The CDC study of Vietnam
veterans did not describe the exclusions.
7.15.1.3.3. Biological plausibility. In rats, 2,3,7,8-TCDD has been shown to decrease testosterone
levels (Moore et al., 1985; Moore and Peterson, 1988; Mebus et al., 1987) through a decrease in
testosterone synthesis (Kleeman et al., 1990) or by decreasing the production of pregnenolone from
cholesterol (Ruangwies et al., 1991). In addition, 2,3,7,8-TCDD has been shown in rats to reduce
the responsiveness of the pituitary to testosterone (Bookstaff et al., 1990a) and of the Leydig cells
to LH stimulation (Moore et al., 1991).
The findings of the NIOSH and Ranch Hand studies are plausible given the
pharmacological and toxicologicalal properties of 2,3,7,8-TCDD. A mechanism responsible for
the effects may involve the ability of 2,3,7,8-TCDD to influence hormone receptors. The aryl
hydrocarbon (Ah) receptor to which 2,3,7,8-TCDD binds can cross-talk with steroid hormone
receptors in both structure and mode of action. Studies suggest that 2,3,7,8-TCDD modulates
hormone receptors, including estrogens (Romkes and Safe, 1988; Romkes et al., 1987), prolactin,
and its own Ah receptor (Poland and Glover, 1980; Morrow et al., 1986). However, the effect of
2,3,7,8-TCDD on testosterone receptors has not been evaluated.
In summary, the results from both the NIOSH and Ranch Hand studies are limited by the
cross-sectional nature of the data and the type of clinical assessments conducted. However, the
available data provide evidence that alterations in human male reproductive hormone levels are
associated with serum 2,3,7,8-TCDD.
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7.15.2. Possible Effects of Exposure to 2,3,7,8-TCDD or Mixtures of Dioxins, Furans, and
PCBs
The following section describes outcomes that may be related to 2,3,7,8-TCDD exposure.
Further research would assist in the final evaluation of the effects of dioxin for the following
outcomes.
7.15.2.1. Possible Adult Effects
7.15.2.1.1. Lipid concentrations. Animal studies indicate that 2,3,7,8-TCDD is associated with
generally increased serum cholesterol and serum triglyceride levels. The effect of exposure to
2,3,7,8-TCDD-contaminated chemicals on lipids is not consistent in the available epidemiology
studies. Elevations in total cholesterol and triglyceride levels were reported after high 2,3,7,8-
TCDD exposure in TCP workers (Pazderova-Vejlupkova et al., 1981; Martin, 1984) and laboratory
workers (Oliver, 1975). Despite their very high exposure to 2,3,7,8-TCDD-contaminated
chemicals, neither adults nor children from Seveso had lipid levels above the referent level. Risk
factors such as dietary fat intake, familial hypercholesterolemia, alcohol consumption, and
exercise, which also affect cholesterol and other lipid levels, may be factors that were not
considered in these studies.
Ranch Hands and the NIOSH cohort continue to have marginally elevated lipid
concentrations despite the extended length of time between exposure and testing (Grubbs et al.,
1995; Calvert et al. 1995). These most recent data suggest that high exposure to 2,3,7,8-TCDD
contaminated substances are not related to significantly increased lipid concentrations, specifically
total cholesterol and triglycerides. Nevertheless, slight but chronic elevations in serum lipids may
put an individual at increased risk for disorders such as atherosclerosis and other conditions
affecting the vascular system.
7.15.2.1.2. Diabetes and fasting serum glucose levels. Diabetes mellitus is a heterogeneous
disorder that is a consequence of alterations in the number or function of pancreatic beta cells
responsible for insulin secretion and carbohydrate metabolism. Depending on its type, diabetes has
been attributed to endogenous factors such as genetic predisposition, to autoimmune processes, and
to exogenous factors such as viral infections (Yoon et al., 1987) and chemical exposures, notably a
rat poison (Miller et al., 1978) and some medications (Wilson and LeDoux, 1989), environmental
toxins (Diabetes Epidemiology Research International, 1987), age, obesity, reduced physical
exercise, diet, socioeconomic status, increased insulin resistance by the beta cells, and possibly
parity (Pareschi and Tomasi, 1987).
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The long-term risk of developing diabetes or other alterations in glucose metabolism after
exposure to 2,3,7,8-TCDD is not well addressed by the available toxicological data. The results of
the animal studies suggest that glucose levels are altered, generally decreased, by short-term, high-
dose exposure to 2,3,7,8-TCDD, and that the response may be species-specific. Studies of rats and
rhesus monkeys showed consistent decreases in serum glucose levels after daily doses administered
over 30 days (Zinkl et al., 1973) or after a single dose of 2,3,7,8-TCDD (McConnell et al., 1978a;
Gasiewicz et al., 1980; Schiller et al., 1986; Ebner et al., 1988) (Table 7-54). In one study, glucose
levels continued to drop up to 3 weeks postexposure (Gorski et al., 1990). Dose-related decreases
were also noted in CD rats fed 0.1, 1.0, or 10.0 ng/kg daily for 30 days (Zinkl et al., 1973). In
contrast, lower daily doses of approximately 0.004 ng/kg/day administered to guinea pigs over a
90-day period produced no significant changes in either glucose or insulin levels (DeCaprio et al.,
1986). Wasting syndrome, observed in many species after high dose exposure to 2,3,7,8-TCDD, is
hypothesized to be related to various changes in the glucose transport mechanism and is mediated
through the Ah receptor. Differences among the results of human and most animal studies may,
hypothetically, be due to a number of factors, such as the species studied, the length of the
exposure and the short observation periods of the toxicology studies, the rate of insulin metabolism
after 2,3,7,8-TCDD exposure, possible differential effects of 2,3,7,8-TCDD on the various types of
islet cells, and the high, usually single, 2,3,7,8-TCDD dose administered to the animals.
Additionally in humans, with some exceptions, onset of diabetes occurs later in life; unfortunately,
these characteristics were not evaluated in the chronic toxicity studies. Long-term feeding studies
to evaluate the relationship among glucose levels, the development of diabetes, and 2,3,7,8-TCDD
dose would be helpful in assessing the effect of exposure on the physiologic integrity of the islet
cells. In addition, such studies may identify other factors that affect either directly or indirectly the
function of the islet cells and the effect of 2,3,7,8-TCDD on the glucose transport mechanism.
More discussion of glucose transport system effects and their potential relationship to diabetes is
found in Part II: Chapter 3. Acute Subchronic, and Chronic Toxicity, Section 3.5. Also, a study by
Matsumura and his colleagues is underway to determine whether there is a biological basis for the
association between type II diabetes mellitus and dioxin levels in Ranch Hand veterans (personal
communication, J. Michalek, 2000).
The results of the Ranch Hand morbidity study are in direct contrast with the results of the
NIOSH mortality study, which included workers who were more highly exposed, with greater
frequency and severity of exposure. The prevalence of diabetes in the exposed, and referent groups
was similar and there was no significant positive trend between prevalence of diabetes and serum
glucose levels. What was notable is that 60% of the workers who had serum 2,3,7,8-TCDD
concentrations >1,500 pg/g lipid at the time of the study met the case definition for diabetes, and
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that the adjusted geometric mean glucose concentration measured at the time of the study was
statistically significantly higher than referent levels only in the half-life adjusted TCDD category
1,860-30,000 pg/g lipid. However, the analysis of the mortality data by exposure score, found
decreasing prevalence of mortality from diabetes with increasing exposure score. The exposure
score incorporated duration and intensity of exposure, among other factors. This puzzling picture
suggests that in the NIOSH cohort, diabetes may not be well correlated with serum 2,3,7,8-TCDD
concentration or duration of exposure.
The studies of Seveso populations and the NIOSH and IARC occupational cohort found
very modest and generally statistically nonsignificant increases in mortality from diabetes. The
results of each of these studies should be evaluated within the context of the limitations of the
studies. Although the Seveso study is limited by a short follow-up period (15 years), all of the
studies have small numbers of deaths from diabetes. It is well known that diabetes has a complex
etiology ranging from genetic susceptibility, viral infections, and chemical insult to physical states
such as obesity or pregnancy. The various mortality studies that examined the relationship
between rates of death from diabetes and 2,3,7,8-TCDD exposure evaluated no data on important
confounders such as the body mass index of cases, family history of diabetes, or the relationship
between year of onset of the diabetes and exposure to 2,3,7,8-TCDD or chlorophenols. In addition,
although the studies have reasonably good estimates of exposure to 2,3,7,8-TCDD,
misclassification of exposure for many subjects is possible.
The analyses by Longnecker and Michalek (2000) provide only weak evidence to support a
causal relationship between very low levels of serum 2,3,7,8-TCDD and increased risk of diabetes
or changes in serum glucose or insulin. The risk measures are generally not strong or statistically
significant, nor do they increase monotonically with increasing dose.
The current epidemiologic and toxicological data to date do not support a strong
relationship between exposure to 2,3,7,8-TCDD and diabetes or alterations in glucose metabolism.
However, there is some evidence to suggest that, particularly at high doses, 2,3,7,8-TCDD may
perturb glucose metabolism in some species, a fact which needs further exploration.
7.15.2.2. Possible Postnatal Developmental Effects
Given that postnatal developmental effects of dioxin have been studied only in one human
population (with the exception of some of the thyroid measures), these studies are being place in
the "potential" category. Additional studies in other groups are recommended, as well as followup
of these findings over time to evaluate whether these changes are temporary, with no long-term
health effects, or an early indication of chronic effects. All the effects in this section were part of
one or both of the Dutch developmental studies. The exposures assessed here are different from
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the more typical "dioxin" study: the first series of studies (in Amsterdam) examined dioxins and
furans, while the second (in Rotterdam and Grongenin) examined dioxins, furans, and PCBs.
Thus, any effects observed could be from one agent or some mixture. Even though the studies
may have picked out certain exposures as statistically significant, this does not mean that other
factors not selected are not associated. For example, in the Rotterdam/Grongenin studies, only
PCBs were evaluated prenatally and at birth, but these values were significantly correlated with
dioxins, furans, and PCBs collected about 2 weeks after delivery. Many of the findings in the
Rotterdam/Grongenin studies were associated with in utero PCB exposures measured in maternal
blood (IUPAC 118, 138, 153, 180). However, of these, only 118 is considered dioxin-like. As the
technology improves to measure dioxin levels in smaller samples, direct measurements will help
clarify the issues related to surrogate measures (e.g. PCBs).
7.15.2.2.1. Neurobehavioral effects. Of the various endpoints covered by the series of reports on
the Dutch population, the most interesting findings related the neurobehavioral endpoints.
Prechtl's Neurologic Optimality Scores (NOS) and the related postural tone cluster scores and
reflex cluster scores were collected at 18 months of age (Huisman et al., 1995a) and, at 42 months
of age, children were assessed for cognitive abilities using the Kaufman Assessment Battery for
Children (K-ABC) and for verbal comprehension using the Reynell Language Developmental
Scales (RDLS) (Patandin et al., 1999).
The NOS scores were somewhat arbitrarily divided at the median and compared to the
individual dioxins, furans, and PCBs, as well as their summary measures (Huisman et al., 1995a).
A number of the levels of the above agents in breast milk were associated with the NOS, while the
prenatal PCBs were not (Table 7-46). Coplanar PCB TEQ was associated with hypotonia
(measured through the posture tone cluster score). This observation of hypotonia and prenatal PCB
exposure is consistent with Rogan et al. (1986). An evaluation of motor function was associated
only with the prenatal PCB levels. Because of the small volume of materal and cord blood
collected, dioxins and furans were not measured during the prenatal period. An interaction
observed with paternal smoking suggests that this issue should be examined further by collecting
postnatal materal smoking data.
Statistically significant deficits in K-ABC were associated with £PCBmatemal blood for the
entire group, and in RDLS only in the formula-fed children (Patandin et al., 1999). Importantly,
the current body burdens in the 42-month-old children were not associated with any cognitive
deficits. Statistically significant changes were not observed in the breast-fed children, possibly
because of the higher SES status, parental education, and parental verbal IQs. Another possibility
is the beneficial effect of breast feeding in general.
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Even though studies of Yu-Cheng children are not directly comparable to the above studies,
they also showed neurobehavioral delays: increased psychomotor delays (Yu et al., 1991) and
lower scores on IQ tests (Chen et al., 1992; Lai et al., 1993; Guo et al., 1993; Chen and Hsu, 1994).
Many of the other outcomes in the Dutch population were "better" in those with exposure:
fluency cluster score (Huisman et al., 1995b), mental development index — MDI (Koopman-
Esseboom et al., 1996), and visual recognition memory test at 7 months of age (Koopman-
Esseboom et al., 1995b). This may be a result of the inherent benefit of breast feeding (and length
of breast feeding) over formula for those measures, or may be due to the way women select to
breast fed (e.g., higher SES women, parents with higher educational levels). As noted above, this
later notion is supported in a recent report by Patandin et al. (1999).
Transplacental exposures of mice demonstate neurobehavioral effects of dioxin and dioxin-
like compounds. These include effects on postural endpoints, motor function, visual abilities, and
learning. Perinatally exposed monkeys showed a deficit in cognitive function. Exposures are
presented by dietary levels or dose given, and thus are difficult to compare to the exposure
measures used in human studies. More details on these studies are presented in Chapter 5
(Developmental and Reproductive Toxicity).
Endpoints varied in the above studies, as did the components and levels of the exposures,
overall; in spite of this, the data suggest the relationship between dioxin and dioxin-like
compounds and neurobehavioral outcomes. Examination of other human populations and long-
term follow-up of these study groups will greatly benefit this database.
7.15.2.2.2. Thyroid function. Two series of studies, both in The Netherlands but conducted in
different groups, have examined thyroid function (Pluim et al., 1993; Koopman-Esseboom et al.,
1994c). The two reports did have a finding in common: both observed higher TSH at 3 months of
age with higher TEQs. They both had significant findings for T4, but they were in opposite
directions. All these findings, plus other changes found in the second report (an increase in
T4/TBG and a decrease in free T4) strongly suggest that more work be done in this area. These
findings suggest a possible shift in the distribution of thyroid hormones, and point out the need for
collection of longitudinal data to assess the potential for long-term effects associated with these
changes.
7.15.2.2.3. AST and ALT. One study looked at blood measures in 35 perinatally exposed children
in The Netherlands (Pluim et al., 1994). AST, ALT, and platelets all varied with exposure (Table
7-26). Even though all but three children had values within "normal" ranges, the distributions of
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has shifted (e.g., an increase in platelets), which could have some currently unknown/unrecognized
short- or long-term effect on health.
7.15.3. Effects for Which Further Research Is Needed
The following section describes endpoints for which the animal data have demonstrated
exposure-related effects, but the human data are inconclusive and require further study.
7.15.3.1. Diseases of the Circulatory System
In general, the results of the cohort mortality studies of TCP production workers were
remarkably similar. For all of the early studies, the SMRs for diseases of the circulatory system
(ICD-9: ICD 390-459) were approximately 100, meaning that the death rate in the exposed
population was nearly the same as that in the general population, controlling for age, race, gender,
and calendar year (Fingerhut et al., 1991b; Zober et al., 1990; Bueno de Mesquita et al., 1993;
Bertazzi et al., 1989, 1992; Collins et al., 1993; Bond et al., 1989; Coggon et al., 1991). None of
the SMRs above 100 were statistically significantly elevated.
In the only study of its kind, Flesch-Janys and colleagues (1995) estimated exposure to
PCDDs, PCDFs, and total TEQs for all members of the Hamburg chemical worker cohort based on
a sample of workers with either serum or adipose tissue exposure measurements. Mortality from
all cardiovascular diseases, and specifically from ischemic heart disease, was related to increasing
2,3,7,8-TCDD concentrations. More striking was the positive relationship betweeen Total TEQs
and cardiovascular disease as a whole.
Mortality from circulatory system diseases among Ranch Hands (SMR =110, 95% CI =
60-150) (Michalek et al., 1990) and Australian Vietnam veterans (RR = 1.7, 95% CI = 0.9-3.0)
(Fett, 1987b) was nonsignificantly elevated. In an update of the Ranch Hand data, the SMR for all
circulatory diseases combined among all Ranch Hands was not elevated (SMR = 100, 95% CI =
70-130) (Michalek et al., 1998). However, a significant increase in cardiovascular mortality was
noted in enlisted ground crew (SMR=150, 95% CI =1.0-2.2). There was a deficit of deaths from
this cause among U.S. Army Vietnam veterans compared to non-Vietnam veterans (Centers for
Disease Control Vietnam Experience Study, 1988c). Elevated mortality from circulatory diseases
among Seveso residents is considered by the authors to be the result of environmental stresses and
possibly other risk factors rather than exposure to 2,3,7,8-TCDD (Bertazzi et al., 1989; Pesatori et
al., 1998). The mortality pattern from circulatory and cardiovascular disease among the three
zones does not suggest a pattern of effect. Perhaps further followup will clarify the story.
Diseases of the circulatory system, particularly heart disease, are the leading causes of death
among populations of most developed nations. Leading risk factors include age, cigarette
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smoking, elevated lipid levels, obesity, hypertension, diabetes, and physical inactivity (Smith et al.,
1992). Among the studies that examined mortality from circulatory system diseases, none directly
adjusted SMRs for known risk factors or attempted to evaluate jointly the contribution of known
risk factors and 2,3,7,8-TCDD to the mortality rates (Fingerhut et al., 1991b; Zober et al., 1990;
Bueno de Mesquita et al., 1993; Bertazzi et al., 1989, 1992; Collins et al., 1993; Bond et al., 1989;
Coggon et al., 1991). More recent mortality studies attempted to control for known confounders;
when possible, most used internal control groups. Still, the picture is inconsistent among the
various cohorts. Therefore, given the strong contribution of these risk factors, it is not possible to
rule out physical and personal risk factors in the etiology of diseases of the circulatory system and
heart in these populations. However, the absence of a "healthy worker effect" for these causes of
death suggests that future research be directed specifically at the relationship between circulatory
and heart disease and exposure to 2,3,7,8-TCDD.
Cross-sectional morbidity studies have not found increases in the prevalence of circulatory
or heart disease among TCP workers, Ranch Hands, or U.S. Army Vietnam veterans (Suskind and
Hertzberg, 1984; Bond et al., 1987; Moses et al., 1984; Centers for Disease Control Vietnam
Experience Study, 1988a; Calvert et al., 1998). In some cross-sectional studies, risk estimates
were adjusted for some risk factors, depending on the study (Suskind and Hertzberg, 1984; Poland
et al., 1971; Moses et al., 1984; Bond et al., 1983; Centers for Disease Control Vietnam Experience
Study 1988a; Roegner et al., 1991). Ranch Hands were the only group to experience marginal
differences in diastolic blood pressure, arrhythmias, and peripheral pulse abnormalities after
adjusting for selected risk factors (Roegner et al., 1991).
The animal data suggest that at high levels of 2,3,7,8-TCDD, the vascular system, cardiac
muscle, and valves and function may be affected by exposure (Kociba et al., 1978; Buu-Ho'i et al.,
1972; Brewster et al., 1988; Hermansky et al., 1988; Kelling et al., 1987; Canga et al., 1988).
However, with the exception of the long-term feeding study (Kociba et al., 1978), the exposures in
animals were single high doses and the human exposures (except Seveso) were chronic, medium to
high doses.
In summary, the animal studies suggest that 2,3,7,8-TCDD causes pathologic changes that
may lead to later circulatory system disease. However, long-term studies of mature and aged
animals have not been carried out to evaluate these hypotheses and to correlate the results of the
animal with the human studies. Few epidemiologic studies were designed to control for many of
the risk factors known to cause circulatory system and heart disease, but a consistent absence of the
healthy worker effect for circulatory disorders and heart disease in numerous mortality studies, and
the positive relationship observed in one study between total TEQs and circulatory diseases,
suggest the need for additional research in this area. These studies should also include methods to
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quantify subject exposure to 2,3,7,8-TCDD and control of confounders related to circulatory
diseases.
7.15.3.2. Immunologic Effects
The available epidemiologic studies on immunologic function in humans relative to
exposure to 2,3,7,8-TCDD do not describe a consistent pattern of effects among the examined
populations. Two studies of German workers, one exposed to 2,3,7,8-TCDD and the other to
2,3,7,8-tetrabrominated dioxin and furan, observed dose-related increases of complements C3 or
C4 (Zober et al., 1992; Ott et al., 1994), while the Ranch Hands continue to exhibit elevations in
IgA (Roegner et al., 1991; Grubbs et al., 1995). Other studies of groups with documented
exposure to 2,3,7,8-TCDD have not examined complement components to any great extent or
observed significant changes in IgA. Suggestions of immunosuppression in a small group of
exposed workers as a result of a single test should be confirmed in other cohorts of similarly
exposed populations (Tonn et al., 1996).
Comprehensive evaluation of immunologic status and function of the NIOSH, Ranch Hand,
and Hamburg chemical worker cohorts found no consistent differences between exposed and
unexposed groups for lymphocyte subpopulations, response to mitogen stimulation, or rates of
infection (Halperin et al., 1998; Michalek et al., 1999; Jung et al., 1998; Ernst et al., 1998).
However, in a single study, T-cell response to Inferon-y in TCDD-exposed workers was negative
in isolated peripheral blood mononuclear lymphocytes and positive in diluted whole blood (Ernst
etal., 1998).
More comprehensive evaluations of immunologic function with respect to exposure to
2,3,7,8-TCDD and related compounds are necessary to assess more definitively the relationships
observed in nonhuman species. Longitudinal studies of the maturing human immunologic system
may provide the greatest insight, particularly because animal studies have found significant results
in immature animals, and human breast milk is a source of 2,3,7,8-TCDD and other related
compounds. Data from a longitudinal study of children ( Weisglas-Kuperus et al., 1995, 2000)
suggest long term effects. Additional follow-up and investigations of other populations would be
informative.
7.15.3.3. Adult Reproductive Outcomes
7.15.3.3.1. Semen changes. The Vietnam Experience Study found a significant relationship
between service in Vietnam and sperm abnormalities, whereas the Ranch Hand study did not
confirm these results when exposure was defined by both cohort status and 2,3,7,8-TCDD levels.
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However, the data on alterations in male reproductive hormone levels associated with occupational
exposure to 2,3,7,8-TCDD emphasize that further research in these areas is required.
7.15.3.3.2. Endometriosis. Two published reports of infertility patients (Mayani et al., 1997;
Pauwels et al., 1999) have raised the potential for an association between endometriosis and
TCDD/TEQ exposure. These studies are small and of limited power. Studies of women from
Seveso and New York State are currently underway and will add to the database on this outcome.
7.15.3.4. Developmental Effects
This section includes all developmental effects except those postnatal developmental
effects which are covered by outcome (thyroid, neurobehavioral outcomes, and AST and ALT).
Outcomes related to fertility are also reported (e.g., time to pregnancy, birth rates, semen changes).
7.15.3.4.1. Consistency. A variety of study designs, including case-control, ecologic, cross-
sectional, and historical cohort designs, have addressed the issue of 2,3,7,8-TCDD and
reproductive effects in humans. Unfortunately, the different criteria for case definitions across
studies make it difficult to compare the results. In addition, the method of case ascertainment for
certain endpoints influences the rate of events observed. The Vietnam Experience Study
substudies of veteran-reported birth defects compared with those identified through hospital
records demonstrated that rates of self-reported outcomes differed by exposure status. Moreover,
predictive value of self-reported events was poor in both cohorts. In contrast, rates of birth defects
in the Ranch Hand study were similarly reported by the Ranch Hands and controls. Both groups
underreported 7% of birth defects in children conceived prior to their SEA tour and 14% after their
tour of duty.
7.15.3.4.2. Strength. No developmental effect measure greater than 2 was noted in any of these
investigations. This is not surprising, given the limitations of the studies, particularly with regard
to exposure misclassification. Therefore, the trends across these studies carry more import than
"statistically significant" results.
7.15.3.4.3. Temporality, dose-response. Although these studies have restricted inclusion of
reproductive events to those that occurred after exposure to 2,3,7,8-TCDD was suspected, no study
has evaluated 2,3,7,8-TCDD levels at the time of the outcome. Determination of a 2,3,7,8-TCDD
dose-response relationship with adverse reproductive outcomes would not be valid unless
individual 2,3,7,8-TCDD levels were available. The recent Ranch Hand study estimated the
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2,3,7,8-TCDD levels at the time of the developmental outcome, which is an important contribution
toward understanding this phenomenon. However, with regard to early losses, this analysis would
not be able to address those occurring very early in gestation, before recognition of the pregnancy
by the woman or her physician, or their subsequent effect on identification of adverse outcomes
identified later in pregnancy or at birth.
7.15.3.4.4. Biological plausibility. A growing body of animal research described in Chapter 5
lends biological plausibility to the association between dioxin and most of the reproductive
endpoints evaluated in these studies, with the notable exception of molar pregnancies. There is
growing evidence that dioxin affects testis and accessory gland weight, testicular morphology,
spermatogenesis, and fertility in males. A model for a paternally mediated dioxin effect on
congenital malformations has not been reported; however, increased interest in this area (Olshan
and Mattison, 1994) may yield more information on this topic. Among female animals, the
primary reproductive endpoints that have been examined include decreased fertility and pregnancy
loss.
The mechanism by which 2,3,7,8-TCDD causes adverse reproductive and developmental
effects has not been well described, although considerable insight has been gained from research
focusing on the Ah receptor. Although the Ah receptor has been linked with birth defects in
several mouse strains, it appears that the mechanism of effect may be dependent on the outcome
evaluated, as well as other dioxin congeners to which the population is exposed. Clearly, these
relationships in humans have not been adequately investigated.
The discovery in the Times Beach, Missouri; CDC; and Ranch Hand studies that self-
reported dioxin exposure and exposure indices developed from the analyses of 2,3,7,8-TCDD in
soil and military records are poorly correlated with serum 2,3,7,8-TCDD levels aids in
understanding the inconsistencies of the research to date regarding 2,3,7,8-TCDD and effects.
Thus, because of the likelihood of exposure misclassification in those studies lacking direct
measures of exposure, the findings have historically been severely limited.
7.15.3.4.5. Spontaneous abortions. Miscarriages were investigated in several studies with
different designs and varied patterns of parental exposure. Events were generally ascertained by
self- or spousal report. When case ascertainment was through medical records, such as in the
Ranch Hand study or the Vietnamese investigations, the events are by definition restricted to those
miscarriages that were clinically recognized.
Research in the area of pregnancy loss indicates that 30%-50% of all conceptions are lost
prior to or during implantation (Hertig et al., 1959). The rate of loss between implantation and
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expected first menstrual period ranges from 22% to 30% (Wilcox et al., 1988; Sweeney et al.,
1988). Thus it is clear that restriction of the examination of pregnancy loss to those events that are
ascertained through medical records, or even self-reports, results in excluding a large proportion of
the outcome of interest. In studies of environmental factors and spontaneous abortion where
information is lacking concerning conception, "the conflation of different doses with different
effects can mislead" (Kline et al., 1989). Because of these discrepancies, it would not be
meaningful to pool the results of the research on the association between dioxin exposure and
miscarriage to judge the "consistency" of the association.
Overall, it must be acknowledged that the data compiled to date are inadequate to address
this issue. To simply enumerate and compare the number of "positive" versus "negative" studies
to ascertain consistency in the research would be inappropriate. The reasons for this have been
described above in detail, with emphasis on the high (40%-50%) exposure misclassification that
has been documented in the majority of these investigations, the small sample sizes evaluated, lack
of data on dioxin levels at the time of conception, and the unknown impact of early pregnancy loss
on identification of birth defects. The animal and human evidence for a 2,3,7,8-TCDD-pregnancy
loss relationship is sufficiently suggestive to warrant further investigation. Several studies of
various designs and populations have demonstrated weak but consistent associations (Reggiani,
1978; Hatch, 1984b; Constable and Hatch, 1985; Huong et al., 1989; Phuong et al., 1989a;
Stellman et al., 1988), whereas others have not (Townsend et al., 1982; Smith et al., 1982; Cutting
et al., 1970; Kunstadter, 1982; Report to the Minister for Veterans' Affairs, 1983; Stockbauer et al.,
1988; Erickson et al., 1984; Centers for Disease Control Vietnam Experience Study, 1989). (These
studies include those that should be restricted to the assessment of military service in Vietnam and
reproductive events.) Only two studies, one an analysis of the Ranch Hand developmental data
(Wolfe et al., 1995) and the other a recent analysis of the NIOSH cohort (Schnorr et al., in press
2001), have used biological measurements and estimated the 2,3,7,8-TCDD levels present around
the time of conception. The first study did note a modest increase in recognized spontaneous
abortions and stillbirths but only at the low, but not the high, level. The Ranch Hand study leaves
several questions unanswered, including the determination of a dose-response level and the impact
of very early pregnancy losses on rates of recognized fetal death and birth defects that survive long
enough to be "counted." The NIOSH study did not observe increases at any level.
7.15.3.4.6. Congenital malformations/birth defects. The confusing evidence regarding the
relationship between dioxin exposure and birth defects suffers not only from the limitations
described above for the studies of miscarriage but also from the lack of power to evaluate specific
types of malformations. To increase the power to detect a potential relationship, the studies have
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combined all birth defects together and calculated an odds ratio for total birth defects. Given
evidence for etiologic heterogeneity among subgroups of birth defects (Khoury, 1989), it is
probable that this approach dilutes the effect measure.
These studies also should more carefully examine type of parental exposure, i.e., paternal,
maternal, or both. Timing of exposures and potential biological mechanisms for birth defects are
different for maternal and paternal exposures. The field of paternally mediated effects is rather
new, but future research may assist in the interpretation of these results (Olshan and Mattison,
1994). If dioxin exposure is related to malformations among the offspring conceived after paternal
service in Vietnam, then the effect must occur either premeiotically, or anew with continuing
circulating levels of TCDD. Some animal studies have found that spermatogonia and
spermatocytes (premeiotic spermatogenic cells) were able to repair DNA after exposure to toxic
agents, whereas spermatids and spermatozoa did not have this capability (Lee and Dixon, 1978).
A few studies (Hatch, 1984a; Constable and Hatch, 1985; Huong et al., 1989; Phuong et al.,
1989a), including those investigations of the Yusho and Yu Cheng incidents (Rogan, 1982; Yen et
al., 1989; Rogan et al., 1988) have suggested an association. Many studies have failed to find a
relationship between dioxin and birth defects (Townsend et al., 1982; Smith et al., 1982; Cutting et
al., 1970; Kunstadter, 1982; Stockbauer et al., 1988; Erickson et al., 1984; Centers for Disease
Control Vietnam Experience Study, 1989; Mastroiacovo et al., 1988). Again, however, in view of
the serious limitations of these studies of 2,3,7,8-TCDD and developmental and reproductive
events, it must be concluded that the relationship between paternal dioxin exposure and congenital
malformations remains unknown. However, if military service in Vietnam is the exposure of
interest, there is only modest evidence to support an association with birth defects. Most of the
data on grouped birth defects have very small numbers.
7.15.3.4.7. Dental effects. Finnish investigators examined the association of enamel
hypomineralization of permanent first molars in 6-7-year-old children and TEQ exposure through
breast feeding (these teeth develop during the first 2 years of life) (Alaluusua et al., 1996;
Alaluusua et al., 1999). These data present interesting findings. Unfortunately, the presentation of
the results is incomplete, so the potential biological significance cannot be assessed. This would be
an interesting outcome to examine in additional studies.
7.15.3.4.8. Sex ratio at birth. Sex ratio at birth was significantly depressed in a group of 17
children in zone A, Seveso, in the years shortly following the industrial accident. This pattern
disappeared a few years later. However, a recent expanded effort suggests that paternal age at the
time of exposure may be a key factor. Sex ratios differences were not observed in other groups
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examined. If effects are restricted to offspring to fathers less than 19 years old, as suggested in the
new Seveso study, the lack of effect elsewhere could be explained by the groups examined:
maternal levels of dioxin in community studies or studies of men older than 19. The findings in
the most recent Seveso study emphasize the need for more attention on male-mediated
development effects, and the potential importance of exposures prior to and during puberty.
Because this outcome can easily be collected in studies of developmental effects, more thorough
examination of this outcome could be useful.
7.15.3.4.9. Growth measures. Growth measures include endpoints such as intrauterine growth
retardation (IUGR), low birth weight, and postnatal growth. Available evidence does not support
an association between paternal dioxin level and low birth weight (Centers for Disease Control
Vietnam Experience Study, 1989; Wolfe et al., 1992b, 1995; Michalek et al., 1998). In the
Rotterdam study, decrements in length (but not other measures of growth) were observed early
(months 3-7 postnatally) and associated with PCB levels, but disappeared with increasing age
(Patandin et al., 1998). The Finnish data (Vartiainen et al., 1998) are interesting because birth
weight did decrease in males with increasing TEQ, but the lack of detail on the statistical analyses
makes interpretation difficult.
7.15.3.4.10. "Miscellaneous" endpoints. Additional reproductive outcomes that were evaluated
in a subset of the studies include molar pregnancies (in the Vietnamese studies), neonatal and
infant death, and childhood cancer and mortality. Mainly because of small sample sizes, it is
difficult to reach conclusions regarding neonatal, infant, and child mortality and childhood cancers.
Recently, the increased risk for neonatal death observed in the Ranch Hand study, the only study
with individual TCDD levels, was investigated. Some changes were observed in the Ranch Hand
study for preterm birth and neonatal death, but these did not follow an exposure-response
relationship (Michalek et al., 1998).
Conclusion
In conclusion, the research to date has been successful in resolving some confusion
surrounding the evidence for an association of dioxin exposure and various developmental and
reproductive endpoints in humans. High occurrence of exposure misclassification, differences in
case definitions across studies, and small sample sizes have severely limited the power of these
studies to address these questions. Additional research that includes a measure of dioxin level at
the time of conception for both the father and mother is necessary if the effect of dioxins on the
spectrum of reproductive outcomes is to be understood.
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7.15.4. Acute Effects
The following section reviews endpoints that were described in groups shortly after
exposure to 2,3,7,8-TCDD but were not observed as chronic effects in studies conducted many
years after exposure ceased. Also reviewed are endpoints observed as long-term effects in single
studies.
7.15.4.1. Dermatologic Conditions Other Than Chloracne
Dermatologic conditions other than chloracne, such as hyperpigmentation, hypertrichosis,
and eyelid cysts, have been related to exposure to 2,3,7,8-TCDD in early case reports (Ashe and
Suskind, 1950; Suskind et al., 1953; Bleiberg et al., 1964; Poland et al., 1971; Bauer et al., 1961;
Goldman, 1972; Jirasek et al., 1974; Oliver, 1975). However, these conditions may have been
acute effects of 2,3,7,8-TCDD exposure that resolved over time or may be residual effects of
chloracne, because they appear to occur more frequently in individuals with persistent chloracne
(Suskind and Hertzberg, 1984). These conditions were not observed in studies in which the
cohorts were examined years after cessation of exposure, in individuals with the potential for high
exposure, or in those with high adipose or serum 2,3,7,8-TCDD levels (Moses et al., 1984; Webb,
1989; Roegner et al., 1991; Burton et al. 1998).
Actinic keratosis, Peyronie's disease, and basal cell carcinoma may not be associated with
2,3,7,8-TCDD. All three conditions were observed in only one study (Suskind and Hertzberg,
1984; Lathrop et al., 1984) and were not observed in studies of individuals with similar potential
for exposure (Ott et al., 1994).
7.15.4.2. Liver Enzymes Other Than GGT and Hepatomegaly
A number of studies reported elevated liver enzymes, particularly AST and ALT, among
individuals who were being exposed at the time of the measurement (May, 1973) or whose
exposure was within a few years of the measurement (Jirasek et al., 1974; Mocarelli et al., 1986;
Caramaschi et al., 1981). Follow-up studies or longitudinal analyses of exposed cohorts suggest
that the increase in enzyme level resolves over time (Mocarelli et al., 1986; Assennato et al., 1989;
Pazderova-Vejlupkova et al., 1981; May, 1982). In studies of exposed populations tested many
years after exposure ceased, levels of AST and ALT were within normal range (Calvert et al.,
1992; Webb et al., 1989; Roegner et al., 1991; Suskind and Hertzberg, 1984; Moses et al., 1984).
D-glucaric acid was tested in a number of 2,3,7,8-TCDD-exposed populations as an
indicator of enzyme induction (Ideo et al., 1985; Martin, 1984; Roegner et al., 1991; Calvert et al.,
1992). Shortly after the TCP reactor release, D-glucaric acid levels in Seveso children were
elevated (Ideo et al., 1985). No other studies of exposed groups tested 5 to 37 years after exposure
ceased found elevations of this enzyme (Martin, 1984; Roegner et al., 1991; Calvert et al., 1992).
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These data suggest that certain hepatic enzymes are increased as a response to high,
exogenous exposure to 2,3,7,8-TCDD. Once the exposure ends, the enzyme levels seem to
decrease over time, as observed in the Seveso populations (Mocarelli et al., 1986; Ideo et al.,
1985). Additional evidence of the acute nature of AST, ALT, and D-glucaric acid elevations is
demonstrated by the lack of such increases in studies of highly exposed groups conducted long
after exposure ceased (Calvert et al., 1992; Roegner et al., 1991; Grubbs et al., 1995; Martin,
1984).
As in the case of ALT, AST, and D-glucaric acid, hepatomegaly appears to be a condition
reported in case reports after high exposure to 2,3,7,8-TCDD-contaminated chemicals, particularly
among TCP production workers examined after a TCP reactor explosion and among Seveso
residents (Ashe and Suskind, 1950; Suskind et al., 1953; Jirasek et al., 1974; Reggiani, 1980a).
Later studies conducted after exposure ceased failed to find excess dose-related hepatomegaly in
the exposed populations (Bond et al., 1983; Suskind and Hertzberg, 1984; Moses et al., 1984;
Calvert et al., 1992; Centers for Disease Control Vietnam Experience Study, 1988a; Roegner et al.,
1991; Webb et al., 1989; Hoffman et al., 1986). However, the absence of an effect in cross-
sectional studies does not confirm the lack of an effect in the past.
7.15.4.3. Pulmonary Disorders
Early case reports suggest that exposure to 2,3,7,8-TCDD chemicals may cause temporary
respiratory irritation (Zack and Suskind, 1980) and tracheobronchitis (Goldman, 1972). The data
from two cross-sectional medical studies provide weak evidence of slightly decreased lung
function among exposed individuals (Suskind and Hertzberg, 1984; Roegner et al., 1991). In these
studies, the effects may be due more to smoking (Roegner et al., 1991) or to a substantial age
difference between the exposed and unexposed groups (Suskind and Hertzberg, 1984). One study
of highly exposed TCP production workers found no relationship between serum 2,3,7,8-TCDD
levels and chronic obstructive pulmonary disease, bronchitis, or decreased pulmonary function
(Calvert et al., 1992).
In conclusion, case reports indicate that intense acute exposure to 2,3,7,8-TCDD can
produce respiratory irritation. However, the findings from controlled epidemiologic studies
conducted many years after exposure do not convincingly support an association between 2,3,7,8-
TCDD exposure and chronic effects on the respiratory system.
7.15.4.4. Neurologic Disorders
The results of case reports and epidemiologic studies demonstrate that exposure to
2,3,7,8-TCDD-contaminated materials is associated with symptoms referable to the central and
peripheral nervous systems shortly following exposure and, in some cases, lasting many years
December 2003 7B-132 DRAFT—DO NOT CITE OR QUOTE
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(Filippini et al., 1981; Ashe and Suskind, 1950; Moses et al., 1984). Overall, however, the
neurologic status of workers, community residents, and Vietnam veterans exposed to 2,3,7,8-
TCDD and evaluated from 5 to 37 years after last exposure appears to be normal (Centers for
Disease Control of Vietnam Experience Study, 1988a; Lathrop et al., 1984; Sweeney et al., 1993).
The data suggest that, although exposure to 2,3,7,8-TCDD may have been extensive as in exposed
workers, Ranch Hands, and Seveso residents, the effects described in case reports may have been
transient (Filippini et al., 1981; Lathrop et al., 1984; Centers for Disease Control Vietnam
Experience Study, 1988a,b; Assennato et al., 1989; Alderfer et al., 1992; Sweeney et al., 1993).
The findings of recent studies suggest that in adults there are no long-term neurologic effects
caused by even high exposure to 2,3,7,8-TCDD-contaminated materials, but there is very little
information with which to examine the effects of exposure on the developing human neurologic
system.
7.15.4.5. Porphyrias
In rats and mice, exposure to 2,3,7,8-TCDD has been clearly shown to alter porphyrin
metabolism (Goldstein et al., 1973; Smith et al., 1981; Jones and Chelsky, 1986; DeVerneiul et al.,
1983; Cantoni et al., 1981; Goldstein et al., 1982). Whether 2,3,7,8-TCDD is associated with
porphyrin changes in humans, particularly PCT, is a subject of unresolved debate. It has been
suggested that the PCT and elevated urinary porphyrins observed in the New Jersey and
Czechoslovakian workers during the years of operation of the plants were the result of exposure to
hexachlorobenzene, which was produced at the same time as TCP (Pazderova-Vejlupkova et al.,
1981; Jones and Chelsky, 1986). These statements have not been corroborated with strong studies.
In the follow-up studies, urinary porphyrin levels of these TCP production workers were not
elevated (Pazderova-Vejlupkova et al., 1981; Poland et al., 1971) or did not differ from levels in
the control group (Calvert et al., 1993). Doss et al. (1984) also described transient elevations in
coproporphyrins among 22 Seveso residents exposed to 2,3,7,8-TCDD.
Because 2,3,7,8-TCDD is a porphyrigen in rats and mice, it has been of interest to
determine whether exposures may have contributed to the observed changes in porphyrin levels in
human populations. The NIOSH study could not address the question of etiology or transient
porphyria, but it did not find porphyria in highly exposed workers many years after their
occupational exposure.
However, although porphyria was not found after 2,3,7,8-TCDD exposure, it may be an
outcome of exposure to PCBs when there is a co-exposure to chlorophenols, as suggested by the
study of pentachlorophenol workers (Hryhorczuk et al., 1998). Further research is recommended
to examine these findings.
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7.15.4.6. Thyroid Function
Many effects of 2,3,7,8-TCDD exposure in animals resemble signs of thyroid dysfunction
or significant alterations of thyroid-related hormones. In the few human studies that examined the
relationship between 2,3,7,8-TCDD exposure and hormone concentrations in adults, the results are
mostly equivocal (Centers for Disease Control Vietnam Experience Study, 1988a; Roegner et al.,
1991; Grubbs et al., 1995; Suskind and Hertzberg, 1984). However, concentrations of thyroid
binding globulin (TBG) appear to be positively correlated with current levels of 2,3,7,8-TCDD in
the BASF accident cohort (Ott et al., 1994). Little additional information on thyroid hormone
levels has been reported for production workers and none for Seveso residents, two groups with
documented high serum 2,3,7,8-TCDD levels.
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Table 7-21b. Overview of biologic measurements in Dutch studies of postnatal developmental effects
Compound
PCDDs
2,3,7,8-TCDD
1,2,3,7,8-
PECDD
1,2,3,4,7,8-
HXCDD
1,2,3,6,7,8-
HXCDD
1,2,3,7,8,9-
HXCDD
1,2,3,4,6,7,8-
HPCDD
1,2,3,4,6,7,8,9-
OCDD
PCDFs
2,3,7,8-TCDF
1,2,3,7,8-
PECDF
2,3,4,7,8-
PECDF
IUPACa
48
54
66
67
70
73
75
83
94
114
TEF"
1
0.5
0.1
0.1
0.1
0.01
0.001
0.1
0.05
0.5
Rotterdam/
Groningen0
Breast milk
[plasma/
cord blood]*1
4.0
10.6
8.7
47.4
6.7
63.2
799.6
0.8
0.3
22.7
Amsterdam"
Breast milk
3.8
10.6
1.3
49.1
6.5
54.3
297.5
2.0
0.2
21.9
Compound
PCDFs (cont.)
1,2,3,4,7,8-
HXCDF
1,2,3,6,7,8-
HXCDF
1,2,3,7,8,9-
HXCDF
2,3,4,6,7,8-
HXCDF
1,2,3,4,6,7,8-
HPCDF
1,2,3,4,7,8,9-
HPCDF
1,2,3,4,6,7,8,9-
OCDF
Planar PCBs
3,3',4,4'-PCB
3,3',4,4',5-PCB
3,3',4,4',5,5'-PCB
IUPAC
118
121
124
130
131
134
135
77
126
169
TEF
0.1
0.1
0.1
0.1
0.01
0.01
0.001
0.0005
0.1
0.01
Rotterdam/
Groningen
Breast milk
[plasma/
cord blood]
6.6
5.7
3.6
0.3
7.9
0.2
2.2
19.3
152.0
84.3
Amsterdam
Breast milk
7.0
6.2
3.2
BDL
6.1
BDL
1.3
-------
o
O
to
O
O
Table 7-21b. Overview of biologic measurements in Dutch studies of postnatal developmental effects (continued)
Compound
IUPACa
TEF"
Rotterdam/
Groningen0
Breast milk
[plasma/
cord blood]*1
Amsterdam"
Breast milk
Nonplanar PCBs
2,4,4'
2,2', 5,5'
2,3',4,4'
2,3', 4', 5
2,2',4,4',5
2,2',4,5,5'
2,3,3',4,4'
2,3',4,4',5
2,2',3,3',4,4'
2,2',3,4,4',5
2,2', 3, 4,4', 5'
2,3,4,5,2',5'
28
52
66
70
99
101
105f
118f
128
137
138
141
0.0001
0.0001
12.1
2.6
11.6
18.5
19.7
1.5
9.4
35.5
[0.16/0.04]
4.0
16.8
129.9
[0.60/0.13]
1.1
Compound
IUPAC
TEF
Rotterdam/
Groningen
Breast milk
[plasma/
cord blood]
Amsterdam
Breast milk
Nonplanar PCBs (cont.)
2,2',3,5,5',6
2,2',4,4',5,5'
2,3,3',4,4',5
2,2',3,3',4,4',5
2,2',3,3',4',5,6
2 2' 3 4 4' 5 5'
2,2',3,4,4',5',6
2 2' 3 4' 5 5' 6
2,2',3,3',4,4',5,5'
2,2',3,3',4,4',5,6
2,2',3,3',5,5',6,6'
151
153
156f
170g
177
180g
183
187
194
195
202
0.0005
0.0001
0.00001
0.9
186.3
[0.91/0.18]
21.0
37.1
6.3
76.8
[0.54/0.10]
12.2
20.0
8.6
2.9
0.9
td
O
O
2
O
H
O
HH
H
W
O
c
O
H
W
a IUPAC: International Union of Pure and Applied Chemistry.
b TEF:Toxic Equivalence Factor (WHO, 1993).
0 Measurements for this series of studies from Koopman-Esseboom et al..
1994.
d Mean values: breast milk in pg/g fat, blood in ng/g plasma.
e Measurements for this series of studies from Pluim et al., 1994.
fMono-orthoPCBs.
g Di-ortho PCBs.
-------
Table 7-22. Serum 2,3,7,8-tetrachlorodibenzo-/>-dioxin (TCDD) levels in Seveso
residents with chloracne and adipose tissue levels of 2,3,7,8-TCDD and
hexachlorinated (HxCDD) dioxins in German chemical workers
Author
Beck et al,
1989
Mocarelli et
al., 1991
Population
Chemical
workers0
Seveso
residents
2,3,7,8-TCDD
level (pg/g)a
174
99
147
61
50
16
1,280
49
50
2,252
158
6
56,000d
27,800
26,400
15,900
12,100
17,300
7,420
1,690
828
HxCDD
level
(pg/g)a
247
166
5,101
172
517
58
1,019
3,442
9,613
3,087
1,191
283
—
—
—
—
—
—
—
—
Year of
chloracne
diagnosis
1955
1955
1963
1957(?)
1969
1955
1978
1974(?)
1972(?)
1984
1977(?)
1970
1976-1977
1976-1977
1976-1977
1976-1977
1976-1977
1976-1977
1976-1977
1976-1977
1976-1977
Half-life
extrapolated
2,3,7,8-
TCDD"
750
4,010
2,350
2,470
380
650
3,380
210
260
2,850
460
40
Half-life
extrapolated
HxCDD"
10,000
6,720
81,620
6,970
3,940
2,360
2,690
14,760
50,740
3,910
3,490
1,970
"pg/g of lipid (parts per trillion).
bHalf-life extrapolation calculated by authors (Beck et al., 1989) using the formula C0 = Ct x 2n
where C0 = original concentration of 2,3,7,8-TCDD or HxCDD, Ct = concentration at time t, n = number
of half-life periods, and t = half-life period of 5.8 years. Exposures occurred between 1949 and 1986.
°Measured in 1984.
dMeasuredin 1976.
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Table 7-23. Mean serum levels of gamma glutamyl transferase (GGT) among Seveso
and Missouri residents, TCP production workers, BASF accident cohort, and
Vietnam veterans
Author
Mocarelli et al.,
1986
Assennato et al.,
1989
Webbetal.,
1989
Hoffman et al.,
1986
May, 1982
Martin, 1984
Moses et al. ,
1984
Calvert et al. ,
1992
Population
Seveso residents
(1977) (Boys)
(Girls)
(1982) (Boys)
(Girls)
1976
1982
1984
1985
Missouri residents
<208
20-60
>60
Missouri residents in Quail
Run Mobile Home Park
TCP production workers in
Great Britain
TCP production workers in
Great Britain
TCP and 2,4,5-T production
workers in West Virginia
TCP and 2,4,5-T production
workers in Missouri and
New Jersey
Exposed
Mean
N leveP (SD)
52b 9.73 (4.72-20.04)
32b 9.10 (3.62-22.85)
106b 9.70 (4.29-21.93)
117b 9.04 (4.25-19.23)
193d 11.94 (16.80)
152d 11.67 (7.94)
142d 10.53 (7.07)
141d 10.57 (7.38)
16 24.0 (15.5)
13 17.7 (7.23)
12 32.8 (23.90)
140 30.011 (88.3)
41 39.0 -
41 40.0 (14-91)
22d 26.3C (27.0)
280 58.5C'' (73.7)
Unexposed
Mean
N level* (SD)
42b 7.28 (3.71-14.26C)
43b 8.05 (2.99-21.68)
138b 8.99 (4.45-18.20)
140b 8.59 (3.85-19.14)
e
123f 11.23 (7.41)
196f 11.19 (7.05)
167f 10.94 (4.68)
141 20.1 (23.4)
31 27. T -
120 32.0 (11-90)
9f 17.4 (11.0)
259 47.4 (41.1)
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Table 7-23. Mean serum levels of gamma glutamyl transferase (GGT) among
Seveso and Missouri residents, TCP production workers, BASF accident cohort, and
Vietnam veterans (continued)
Author
Ottetal., 1993b
Centers for
Disease Control
Vietnam
Experience
Study, 1988a
Roegner et al.,
1991
Grubbs et al.,
1995
Population
BASF accident cohort
U.S. Army ground troops
U.S. Air Force Ranch Hand
personnel
Unknown < 101>m
Low l5-<33.3l-m
High >33.3lm
U.S. Air Force Ranch Hand
personnel
Background 1'm'n
Low 1>m'n
High 1-m-n
Exposed
Mean
N leveP (SD)
133 30.5 (58.4)
2,490 43. 2J
338 31.49c-° -
191 38.28c-° -
182 40.82c-° -
362 32.60° -
251 36.99C'° -
502 37.67c-° -
Unexposed
Mean
N level* (SD)
6,708 29.9 (43.5)
1,972 41. lk -
777 34.64° -
1025 34.34° -
aUnits = U/L.
bNumber of samples.
><0.05.
Chloracne.
eNo data for controls in 1976.
fNo chloracne.
8Adipose tissue level of 2,3,7,8-TCDD in pg/g of lipid.
h% abnormal: exposed = 3.6; unexposed = 3.6.
'% abnormal: exposed = 10.7; unexposed = 5.0; OR=227 (95% CI=1.17, 4.39).
jGeometric mean.
k% Abnormal (Vietnam veterans, 5.5%; non-Vietnam veterans, 4.4; OR=1.3, 95% CI=1.0-1.8).
1 Serum 2,3,7,8-TCDD level in pg/g of lipid.
"Contrasted to unexposed comparison population.
"Background: current dioxin <10 pg/g of lipid.
Low: current dioxin > 10 pg/g of lipid, 10 pg/g< initial dioxin <143 pg/g of lipid.
High: current dioxin > 10 pg/g of lipid, 10 pg/g < initial dioxin > 143 pg/g of lipid.
"Adjusted mean.
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Table 7-24. Logistic regression model for an out-of-range serum gamma-glutamyl
transferasea (GGT) level using the categorical TCDDb exposure measure0
Variable
Intercept
Exposure
(worker = 1, referent = 0)
Per alcohol-year
Current alcohol drinker
(yes = 1, no = 0)d
Alcohol-years/exposure interaction6''
Triglyceride level
Beta
-4.12
0.37
-2.4 X 106
0.90
7.2 X 103
3.8 X 103
Standard error of
the estimate
0.50
0.43
2.6 x 103
0.42
3.5 x 103
1.1 x 10 3
X2
67.01
0.74
0.00
4.70
4.24
11.94
P
< 0.001
0.195
0.999
0.030
0.039
< 0.001
"Reference value: 96 IU/L; a level was considered out of range if it exceeded the reference value. Reference
values were defined as the 95th percentile for the referent cohort.
bTCDD = 2,3,7,8-tetrachlorodibenzo-/7ara-dioxin.
CN=536 observations.
dThe results from this logistic regression analysis change little when this term is dropped from the model.
Interaction between alcohol-years and exposure.
'Exposure odds ratios (ORs) for an abnormal y-glutamyltransferase level among workers by selected
alcohol-year levels, adjusting for all variables in the model, are as follows: OR=2.96 (95% confidence
interval [CI] = 1.34-6.54) for 100 alcohol-years; OR=1.79 (95% CI=0.81-3.97) for 30 alcohol-years;
OR=1.45 (95% CI=0.63-3.36) for 1 alcohol-year; andOR = 1.44 (95% CI=0.62-3.34) for 0 alcohol-years.
Source: Calvert et al., 1992b. Used with permission from the author.
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Table 7-25. Serum alanine aminotransferase (ALT) among Seveso children and
Missouri residents, TCP production workers, BASF accident cohort, and Vietnam
veterans
Author
Mocarellietal., 1986
Caramaschi et al.,
1981
Moses et al., 1984
Calvert et al., 1992
Ott et al., 1994
Hoffman et al. , 1986
Webbetal., 1989
Centers for Disease
Control Vietnam
Experience Study,
1988a
Roegner et al., 1991
Grubbs et al. ,
1995
Population
Seveso children
1977 Boys
Girls
1982 Boys
Girls
Seveso children
TCP production workers;
10-30 years postexposure
TCP production workers;
15-37 years postexposure
BASF accident cohort
Missouri residents in Quail
Run Mobile Home Park
Missouri residents
<20i
20-60
>60
U.S. Army ground troops
U.S. Air Force Ranch Hand
personnel
Unknown < 101
Low 15-S33.31
High >33.3'
U.S. Air Force Ranch Hand
personnel
Background1
Low1
High1
Exposed
Mean
N level* (SD)
46b 12.25° (7.14-20.99)
100 12.97 (6.68-25.18)
33 10.74 (5.09-22.65)
119 12.27 (6.5-23.17)
141d 3.5e'c
105d 15.9 (13.0)
280« 33.8 (22.6)
133 14.8 (8.4)
134 — h
16 22.7 (2.76)
12 22.5 (2.39)
12 23.3 (3.06)
2,490 26.4j-k
338m 19.16 -
19ml 20.83 —
182c-m 20.09 -
367m 26.25 -
254m 27.47 —
254m 27.09 -
Unexposed
Mean
N level* (SD)
45b 9.33° (3.73-23.33)
141 11.99 (5.51-26.12)
39 10.74 (5.09-22.65)
136 12.19 (6.46-23.01)
Of Oe
101f 15.7 (13.0)
259 33.0 (21.2)
6,721 15.1 (10.0)
135 — h
1,972 25.8
777 20.34
1,025 27.56
"ALT; units U/L.
bNumber of samples.
cp<0.05.
dChloracne.
e% abnormal.
'No chloracne.
8% abnormal: workers 4.3; unexposed 5.0; OR=0.85
(95%CI=0.38, 1.89).
h% abnormal: exposed 6.0; unexposed 3.0.
Adipose 2,3,7,8-TCDD level in pg/g of lipid.
jGeometric mean.
k% abnormal; Vietnam veterans, 5.3; non-Vietnam veterans,
4.4;OR=1.2,95%CI=0.9-1.5.
'Serum 2,3,7,8-TCDD in pg/g of lipid.
Background: current dioxin^ 10 pg/g of lipid.
Low: current dioxin > 10 pg/g of lipid, 10 pg/g< initial dioxin
^143 pg/g of lipid.
High: current dioxin > 10 pg/g of lipid, 10 pg/g<
initial dioxin >143 pg/g of lipid.
"Adjusted mean.
December 2003
7B-141 DRAFT—DO NOT CITE OR QUOTE
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Table 7-26. Blood measures and cumulative dioxin-furan TEQs
from 11 weeks of breast feeding (Pluim et al., 1994)
Blood measure
ALT
AST
Platelets
Correlation
coefficient
0.40
0.44
-0.48
/j-value
0.02
0.009
0.011
December 2003
7B-142 DRAFT—DO NOT CITE OR QUOTE
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Table 7-27. Mean D-glucaric acid levels among Seveso residents, TCP production
workers, and Vietnam veterans
Author
Ideo et
al., 1985
May,
1982
Martin,
1984
Calvert
et al. ,
1992
Roegner
et al. ,
1991
Population
Seveso adults
Levels measured in
1978 (Zone B)
Seveso children
Zone A, 1976
ZoneB
1979
1980
TCP production
workers in Great
Britain
TCP production
workers in Great
Britain
TCP and 2,4,5-T
production workers
in Missouri and
New Jersey
U.S. Air Force
Ranch Hand
personnel
Unknown < Iff
Low 15-<33.3
High >33.3
Exposed
N
117
14e
26.8
17.0
41
39
273
317
180
173
Mean
27.r>b>c
oga.b.c
g
—
2.0711
2.09C
14. 11
13.99k-'
14. 431
15. 221
(SD)
—
—
—
(0.7-7.9)
(11.1)
—
Unexposed
N
127
17f
—
—
31
126
256
727
Mean
19.8a'b'd
20. 5b
—
—
1.5211
1.59
13.21
14.11k
(SD)
—
—
—
(0.8-8.3)
(7.9)
—
"Units: /rniol/g of creatinine.
bMedian level.
c/7<0.05.
Residents of unexposed community.
eSkin lesions.
fNo skin lesions.
8d-glucaric acid levels measured in 1979 were significantly higher than levels measured in 1980 (p<0.05), no data
presented.
hd-glucaric acid/creatinine ratio.
'Units = jug/g of creatinine.
jSerum 2,3,7,8-TCDD levels inpg/g of lipid.
kUnits = juM.
'Contrasted to unexposed comparison population.
"Adjusted mean.
December 2003
7B-143 DRAFT—DO NOT CITE OR QUOTE
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Table 7-28. Mean total cholesterol levels among Seveso and Missouri residents, TCP
production workers, BASF accident cohort, and Vietnam veterans
Author
Mocarelli et
al, 1986
Caramaschi
etal, 1981
Assennato et
al., 1989
May, 1982
Martin, 1984
Poland et al.,
1971
Moses et al.,
1984
Suskind and
Hertzberg,
1984
Ottetal,
1994
Calvert et al.,
1996
Population
Seveso children
1977
1982
Seveso children
Seveso residents
1976
1982
1984
1985
TCP production
workers in Great
Britain
TCP production
workers in Great
Britain
TCP production
workers in New
Jersey
TCP production
workers in West
Virginia
TCP production
workers in West
Virginia
TCP production
workers: chloracne
vs. never chloracne
BASF accident
cohort
TCP and 2,4,5-T
production workers
in Missouri and
New Jersey
Exposed
N
16b
182b
138
193d
152d
142d
141d
41
39
71
105d
200
105d
135
278
Mean3
level
4.62
4.48
15.2c'd
4.78
4.06
4.09
4.14
5.97
6.02g
6.12
5.38
5.46
5.44
6.14m
5.7n
(SD)
3.26-5.98
2.47-5.99
_
(0.99)
(0.80)
(0.88)
(0.91)
_
_
(0.82)
(0.88)
(0.07)
(0.08)
(1.01)
Unexposed
N
28b
250b
120
f
123e
196e
167e
31
126
_
101e
163
28e
6,581
259
Mean3
level
4.45
4.41
12.5c'e
—
4.14
4.12
4.13
6.6
5.6
_
5.37
5.28
5.30
6.37m
5.6n
(SD)
(3.12-5.77)
(2.99-5.83)
_
—
(0.77)
(0.86)
(0.78)
_
_
_
(0.85)
(0.08)
(0.18)
(1.17)
December 2003
7B-144 DRAFT—DO NOT CITE OR QUOTE
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Table 7-28. Mean total cholesterol levels among Seveso and Missouri residents, TCP
production workers, BASF accident cohort, and Vietnam veterans (continued)
Author
Hoffman et
al., 1986
Webbetal,
1989
Centers for
Disease
Control
Vietnam
Experience
Study, 1988a
Roegner et
al., 1991
Grubbs et al.,
1995
Population
Missouri residents
in Quail Run
Mobile Home Park
Missouri residents
<20h
20-60h
>60h
U.S. Army Vietnam
veterans
Air Force Ranch
Hand personnel
Unknown <10k
Lowl5-<33.3k
High>33.3k
U.S. Air Force
Ranch Hand
personnel
Background0
Low0
High0
Exposed
N
142
16
12
12
2,490
3381
191
182
362
251
251
Mean3
level
4.97g
5.88
6.60
6.76
5.431J
5.53P
5.55P
5.68g-p
5.7P
5.68P
5.76P
(SD)
(0.96)
(1.10)
(0.93)
(0.97)
—
—
—
—
—
—
Unexposed
N
148
—
—
—
1,972
777
—
—
1,025
Mean3
level
5.2
—
—
—
5.36
5.51
—
—
5.69
(SD)
(1.09)
—
—
—
—
—
—
—
aUnits = mmol/L.
bNumber of samples.
°% abnormal.
dChloracne.
eNo chloracne.
'No data for controls in 1976.
gp<0.05.
hAdipose tissue levels of 2,3,7,8-TCDD in pg/g of lipid.
'Geometric mean.
J% abnormal: Vietnam veterans, 5.1; non-Vietnam veterans, 4.7; OR=1.1, 95% CI=0.8-1.5.
kSerum 2,3,7,8-TCDD levels in pg/g of lipid.
'Contrasted to unexposed comparisons.
"Adjusted for age, body mass index, smoking history.
"Adjusted for age, body mass index, smoking, gender.
"Serum 2,3,7,8-TCDD levels in pg/g of lipid.
Background: current dioxin <10 pg/g of lipid.
Low: current dioxin > 10 pg/g of lipid, 10 pg/g< initial dioxin <143 pg/g of lipid.
High: current dioxin > 10 pg/g of lipid, 10 pg/g< initial dioxin >143 pg/g of lipid
pAdjusted mean.
December 2003
7B-145
DRAFT—DO NOT CITE OR
QUOTE
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Table 7-29. Mean triglyceride levels among Seveso and Missouri residents,
TCP production workers, BASF accident cohort, and Vietnam veterans
Author
Mocarelli
etal, 1986
Assennato
etal., 1989
May, 1982
Martin, 1984
Moses
etal., 1984
Suskind and
Hertzberg, 1984
Ottetal, 1994
Calvert
etal., 1996
Hoffman
etal., 1986
Webb
etal,. 1989
Population
Seveso children
1977
1982
Seveso residents
1976
1982
1984
1985
TCP production
workers, Great
Britain
TCP production
workers, Great
Britain
TCP production
workers, West
Virginia
TCP production
workers, West
Virginia
BASF accident
cohort
TCP and
2,4,5-T
production
workers, Missouri
and New Jersey
Missouri residents
in Quail Run
Mobile Home
Park
Missouri residents
<20h
20-6011
>60h
Exposed
N
38b
207b
193
152
142
141
41d
39d
93d
200
135
273
141
16
12
12
Mean (SD)
0.97 (0.60-1.50)
0.91 (0.52-.60)
0.99 (0.43)
0.87 (0.40)
0.94 (0.59)
0.84 (0.44)
2.03 —
1.97f (0.4-4.0)
1.69B (1.26)
1.65 (0.08)
1.91h (1.19)
1.201 —
1.07 (0.73)
2.17 (2.08)
1.81 (1.19)
2.69 (1.06)
Unexposed
N
36b
257b
123
196
167
31e
126e
93e
163
4,471
259
146
—
Mean (SD)
0.95(0.63-1.51)
0.86(0.47-1.56)
0.85 (0.37)
0.88 (0.46)
0.87 (0.55)
1.83 —
1.41 (0.3-3.2)
1.46 (0.73)
1.76 (0.08)
1.97h (1.65)
1.151 —
1.19 (1.07)
— —
December 2003
7B-146 DRAFT—DO NOT CITE OR QUOTE
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Table 7-29. Mean triglyceride levels among Seveso and Missouri residents, TCP
production workers, BASF accident cohort, and Vietnam veterans (continued)
Author
Centers for
Disease
Control
Vietnam
Experience
Study, 1988a
Roegner
etal, 1991
Grubbs
etal., 1995
Population
U.S. Army
Vietnam
veterans
Air Force Ranch Hand
personnel
Unknown <10k
Lowl5-<33.3k
High>33.3k
U.S. Air Force
Ranch Hand personnel
Background
Low1
High1
Exposed
N
2,490
3381
191
182
362
191
182
Mean (SD)
1.06^ —
1.02^'° —
1.37 _
1.35^° —
L43m,n _
1.49m'n —
1.35m'n —
Unexposed
N
1,972
777
1,025
Mean (SD)
1.05
1.16 —
1.47
aUnits = mmol/L.
bNumber of samples.
'Adipose tissue 2,3,7,8-TCDD levels in pg/g of lipid.
dChloracne.
eNo chloracne.
f/K0.01.
hAdjusted for age, body mass index, smoking history.
'Adjusted for age, body mass index, smoking, gender.
J% abnormal: Vietnam veterans, 4.7; non- Vietnam veterans, 5.3; OR=0.9, 95% CI=0.7-1.2.
""Geometric mean.
'Serum 2,3,7,8-TCDD levels in pg/g of lipid.
"Contrasted to unexposed comparison group.
"Serum 2,3,7,8-TCDD levels in pg/g of lipid.
Background: current dioxin < 10 pg/g of lipid.
Low: current dioxin > 10 pg/g of lipid, 10 pg/g< initial dioxin <143 pg/g of lipid.
High: current dioxin > 10 pg/g of lipid, 10 pg/g< initial dioxin >143 pg/g of lipid.
"Adjusted mean.
December 2003
7B-147 DRAFT—DO NOT CITE OR QUOTE
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o
O
to
O
O
Table 7-30. Levels of thyroxine-binding globulin (TBG), thyroxine (T4), free thyroxine (FT4), or T4/TBG
Outcome
Author
Population
Exposed3
N
Mean level
Standard
deviation
Unexposedb
N
Mean level
Standard
deviation
Nursing infants
Total T4 (nmol/L)
Pluimetal., 1992
Pluimetal., 1993
Koopman-Esseboom,
1994
Neonates;
Amsterdam,
The Netherlands
At birth/cord blood
1 wk postnatal
1 1 wks postnatal
Neonates;
Rotterdam,
The Netherlands
2nd wk postnatal
15
19
16
39
134.3
178.7C
122.2C
159.9C
4.8d
5.5
3.0
31.6
18
19
18
39
122.5
154.5
111.1
177.5
4.1d
6.3
4.0
39.2
Free T4 (nmol/L)
Koopman-Esseboom,
1994
Neonates;
Rotterdam,
The Netherlands
2nd wk postnatal
39
23.0C
3.3
39
24.6
3.5
TBG (nmol/L)
Pluimetal., 1992
Pluimetal., 1993
Neonates;
Amsterdam,
The Netherlands
At birth/cord blood
1 wk postnatal
1 1 wks postnatal
15
19
16
589.5
546.2
500.7
30.5d
19.1
13.0
18
19
18
520.1
532.6
519.0
27.2d
16.3
29.4
td
oo
O
O
*
O
H
O
HH
H
W
O
&
O
c
O
H
W
-------
o
O
to
O
O
td
O
O
2
O
H
O
HH
H
W
Table 7-30. Levels of thyroxine-binding globulin (TBG), thyroxine (T4), free thyroxine (FT4), or T4/TBG
(continued)
Outcome
Author
Population
Exposed3
N
Mean level
Standard
deviation
Unexposedb
N
Mean level
Standard
deviation
Nursing infants (continued)
T4/TBG
Pluimetal., 1992
Pluimetal., 1993
Neonates;
Amsterdam,
The Netherlands
At birth/cord blood
1 wk postnatal
1 1 wks postnatal
15
19
16
0.232
0.332C
0.247C
0.008d
0.011
0.009
18
19
18
0.240
0.291
0.220
0.007d
0.009
0.008
BASF accident cohort
TBG (mg/L)
T4 Gig/dL)
T4/TBG (mU/L)
Ottetal., 1994
Ottetal., 1994
Ottetal., 1994
BASF cohort
BASF cohort
BASF cohort
131
131
131
12.7
7.8
6.3
3.2
1.9
1.3
141
141
141
12.7
8.3
6.7
2.9
1.5
1.6
"High-exposure group: 29.2-62.7 ng toxic equivalents/kg (dioxin-furan TEQ per kg milk fat) (Pluim et al, 1992 and 1993); >30.75-76.43 pg dioxin-furan-TEQ/g fat
(Koopman-Esseboometal., 1994).
bLow-exposure group: 8.7-28 ng TEQ/kg (Pluim et al, 1992 and 1993); 12.44-30.75 pg dioxin-furan TEQ/g fat (Koopman-Esseboom et al., 1994).
°p<0.05 compared to the unexposed group.
dStandard error of the mean.
O
-------
o
O
to
O
O
Table 7-31. Levels of triiodothyronine percent (T3%) uptake or free thyroxine index in
Vietnam veterans
Author
Population
Exposed
N
Mean level
Adjusted RR
(95% CI)
Unexposed
N
Mean level
T3% uptake
Roegner
etal., 1991
U.S. Air Force
Ranch Hand
personnel
Unknown < 10a
Low 15-<33.3a
High>33.3a
All Ranch Hand vs.
all comparisons
338
194
181b
937
30.7
30.4
30.0
30.6
1.1
(0.6,2.1)
0.9
(0.4, 2.2)
0.5
(0.1, 1.5)
1.14
(0.7-1.8)
772
1,198
30.7
30.6
Free thyroxine index
Centers for
Disease
Control
Vietnam
Experience
Study, 1988a
U.S. Army
Vietnam veterans
2,490
2.2C
Adjusted OR
(95% CI)
1.2
(0.9,1.5)
1,972
2.2C
td
I
O
O
O
2
O
H
O
HH
H
W
aSerum 2,3,7,8-TCDD in pg/g of lipid.
b/?<0.05 comparison of veterans at background level with > 3 3.3 pg/gTCDD.
GGeometric mean.
O
c
o
H
W
-------
o
O
to
O
O
Table 7-32. Levels of thyroid-stimulating hormone (TSH) in Vietnam veterans
Author
Population
Exposed
N
Mean level"
Adjusted RR
(95% CI)
Unexposed
N
Mean level"
Vietnam veterans
Centers for Disease
Control Vietnam
Experience Study,
1988a
Roegner
etal., 1991
Grubb
etal., 1995
U.S. Army Vietnam
veterans
U.S. Air Force Ranch
Hand personnel, 1988
Unknown < 10°
Low 15-<33.3C
High >33.3C
U.S. Air Force Ranch
Hand personnel, 1992
Comparison < 10e
Background RH < 10e
Low 10-<143e
High > 143e
Low + High
2,490
338
194
181
365
254
255
509
1.6b
0.948
0.978
1.026d
1.64
1.60
1.64
1.62
Adjusted OR
(95% CI)
2.0 (0.9-4.3)
1.45 (0.62-3.40)
0.88 (0.24-3.02)
2.15(0.80-5.79)
0.88(0.40-1.92)
0.50(0.15-1.67)
1.72(0.78-3.80)
1.06(0.53,2.15)
1,972
616
1,027
1.6b
0.920
1.58
td
fe
H
O
O
O
H
O
O
V
O
c
O
"Units: /iU/mL.
bGeometric mean.
cSerum 2,3,7,8-TCDD level in pg/g (ppt) of lipid.
dp< 0.05 compared to the unexposed group.
Comparisons: current dioxin < 10 ppt.
Background (Ranch Hand): current dioxin < 10 ppt.
Low (RH): current > 10 ppt, 10 ppt < initial dioxin < 143 ppt.
High (RH): current > 10 ppt, initial dioxin > 143.
-------
o
O
to
O
O
Table 7-33. Levels of thyroid-stimulating hormone (TSH) in nursing infants
and BASF accident cohort
td
to
O
O
2
O
H
O
HH
H
W
O
V
O
c
O
H
W
Author
Population
Exposed
N
Mean level"
SD
Unexposed
N
Mean level"
SD
Nursing infants
Pluimetal., 1992
Pluimetal., 1993
Koopman-Esseboom,
1994
Neonates;
Amsterdam,
The Netherlands
At birth/cord blood
1 wk postnatal
1 1 wks postnatal
Neonates;
Rotterdam,
The Netherlands
At birth/cord blood
2nd wk postnatal
3 months
llb
11
12
f
39b
39
11.9
2.56
2.50e
11.6e
2.6s
2.3e
1.9C
0.41
0.26
8.0
1.5
1.0
14d
15
18
39d
39
10.4
2.93
1.81
8.5
1.9
1.6
1.3C
0.41
0.19
6.0
0.8
0.6
BASF accident cohort
Ottetal., 1993b,1994
BASF chemical
workers
130
1.19
0.9
^
—
—
aUmts: uU/mL.
bHigh-exposure group: 29.2-62.7 ng toxic equivalents/kg (TEQ per kg milk fat) (Pluim et al., 1992 and 1993);
>30.75-76.43 pg dioxin-furan-TEQ/g fat (Koopman-Esseboom et al., 1994).
°Standard error of the mean.
dLow-exposure group: 8.7-28 ng TEQ/kg (Pluim et al., 1992 and 1993); 12.44-30.75 pg TEQ/g fat (Koopman-Esseboom et al., 1994).
ep<0.05 compared to low-exposure group.
fTotal for both high and low = 75.
^o referent values.
-------
o
O
to
O
O
Table 7-34. CD4/CD8 ratios in Missouri residents, Vietnam veterans, and BASF accident cohort
Author
Roegner
etal., 1991
Centers for Disease
Control Vietnam
Experience Study,
1988a
Hoffman
etal., 1986
Webb
etal., 1989
Ott et al., 1994
Zoberet al., 1992
Grubbsetal., 1995
Population
U.S. Air Force Ranch Hand
personnel
Unknown <10a
Low 15-<33.3a
High >33.3a
U.S. Army Vietnam
veterans
Missouri residents
Missouri residents
<20C
20-60°
>60C
BASF accident cohort
BASF personnel exposed to
TBDD and TBDFe
U.S. Air Force
Ranch Hand personnel
Background*
Low
High
Exposed
N
126
72
72
2,490
135
16
12
12
132
21
139
94
106
Mean level (SD)
1.72
1.91
1.99
1.8"
1.9 (0.8)
2.0(0.7)
2.1 (1.0)
1.4(0.7)
1.6 (0.94)
1.6 (0.5)
1.50
1.58
1.57
Ratio
OR < reference range
0.9
OR > reference range
1.1
% abnormal
8.2
—
—
—
Unexposed
N
301
1,972
142
—
42d
42
399
Mean level (SD)
1.89
1.8"
1.9 (0.6)
—
1.5 (0.6)
1.5 (0.6)
1.48
Ratio
% abnormal
6.3
—
—
—
td
O
O
2
O
H
O
HH
H
W
O
&
O
c
O
H
W
"Serum 2,3,7,8-TCDD level in pg/g of lipid.
'Geometric mean.
°Adipose 2,3,7,8-TCDD level in pg/g of lipid.
dFromZoberetal., 1992.
"TBDD = 2,3,7,8-tetrabrominated dibenzo-/>-dioxins and TBDF = 2,3,7,.
tetrabrominated dibenzofurans.
Comparison: current dioxin <10 pg/g of lipid.
Background: current dioxin > 10 pg/g.
Low: current dioxin > 10 pg/g, 10 pg/g < initial dioxin < 143 pg/g.
High: current dioxin > 10 pg/g, 10 pg/g < initial dioxin > 143 pg/g.
-------
Table 7-35. Total lymphocytes in 2,4,5-T production workers, Missouri residents, Vietnam veterans, and
BASF accident cohort
Author
Roegner
etal., 1991
Centers for
Disease Control
Vietnam
Experience Study,
1988a
Hoffman et al.,
1986
Webb et al., 1989
Jennings et al.,
1988
Ottetal., 1994
Zoberetal., 1992
Grubbs et al.,
1995
Population
U.S. Air Force
Ranch Hand
personnel
Unknown < 10°
Low 15-<33.3C
High>33.3c
U.S. Army
Vietnam veterans
Missouri residents
Missouri residents
<20e
20-60e
>60e
2,4,5-T production
workers exposed
17yrspriorto
the study
BASF accident
cohort
BASF personnel
exposed to TBDDg
&TBDFg
U.S. Air Force
Ranch Hand
personnel
Background11
Lowh
High11
Exposed
N
127
73
74
2,490
135
16
12
12
18
133
21
141
95
108
Mean level3 (SD)1
1,954 — d
2,011 —
2,032 —
1,973 —
2,465 (724)
2,200 (830)
2,300 (600)
2,200 (720)
1,980 (840)
1,978.3 (805)
2,179.5 (678)
2,066.7'
1,988.6
2,034.4
Ratio
—
ORreference range
1.2
—
% Lymphocytes
32
32
28
% Lymphocytes
33.4(9.4)
% Lymphocytes
33.4(8.4)
—
Unexposed
N
301
1,972
142
—
15
42
42
400
Mean level (SD)
1,972 —
1,936 —
2,311(634)
—
2,020 (470)
2,267.6 (837.5)
2,267.6 (837.5)
2,022.4
Ratio
% Lymphocytes
36(12.4)
% Lymphocytes
36(12.4)
—
aUnits: counts/mm3.
bSD = standard deviation.
cSerum 2,3,7,8-TCDD level in pg/g of lipid.
d— = data not presented.
'Adipose 2,3,7,8-TCDD level in pg/g of lipid.
•From Zoberetal., 1992.
gTBDD = 2,3,7,8-Tetrabrominated dibenzo-p-dioxins;
TBDF = 2,3,7,8-Tetrabrominated dibenzofurans.
h Comparison: current dioxin < 10 pg/g of lipid.
Background: current dioxin > 10 pg/g.
Low: current dioxin > 10 pg/g, 10 pg/g 10 pg/g, 10 pg/g 143 pg/g.
-------
o
O
to
O
O
'Adjusted mean.
Table 7-36. Bl levels in production workers, 2,4,5-T
Missouri residents, Vietnam veterans, BASF accident cohort, and extruder personnel
Author
Roegner
etal., 1991
Grubbs
etal., 1995
CDC Vietnam
Experience
Study, 1988a
Webb etal., 1989
Jennings et al, 1988
Ott etal., 1994
Zoberetal, 1992
Population
U.S. Air Force Ranch
Hand personnel
Unknown <10b
Lowl5-<33.3b
High>33.3b
U.S. Air Force Ranch
Hand personnel
Backgound6
Low6
High6
U.S. Army Vietnam
veterans
Missouri residents
<20d
20-60d
>60d
2,4,5-T production
workers exposed 17 yrs
prior to the study
BASF accident cohort
BASF personnel
exposed to TBDD8
and TBDFg
Exposed
N
127
71
73
140
95
106
2,490
16
12
12
18
133
21
Mean level3 (SD
176 —
183 —
191 —
245f —
224 —
220 —
240C
190 (865)
189 (983)
171 (573)
210 (110)
10.46 (6.0)
276.3 (156)
) Ratio
—
—
ORref. range 1.2
%B1 cells
9.1
8.3
7.8
—
—
% B cells
12.5 (4.4)
Unexposed
N
301
400
1,972
—
15
42
42
Mean level3 (SD)
172 —
214 —
230C —
—
160 (80)
12.3e>f (5.1)
286.4(199.3)
Ratio
—
—
—
—
—
—
12.3 (5.1)
td
O
O
2
O
H
O
HH
H
W
O
&
O
c
O
H
W
aUnits: cells/mm3.
"Serum 2,3,7,8-TCDD level in pg/g of lipid.
'Geometric mean.
dAdipose 2,3,7,8-TCDD level in pg/g of lipid.
e%Bl cells.
'From Zoberetal., 1992.
gTBDD = 2,3,7,8-tetrabrominated dibenzo-p-dioxin.
TBDF = 2,3,7,8-tetrabrominated dibenzofuran.
-------
o
O
to
O
O
Table 7-37. CD4 levels in production workers, Missouri residents, Vietnam veterans, and BASF
chemical workers
Author
Roegner
etal., 1991
Grubbs
etal., 1995
CDC Vietnam
Experience
Study, 1988a
Hoffman
etal., 1986
Webb
etal., 1989
Jennings
etal., 1988
Tonn
etal., 1996
Population
U.S. Air Force
Ranch Hand
personnel
Unknown < 10b
Low 15-<33.3b
High>33.3b
U.S.A.F.
Ranch Hand
personnel
Background8
Low
High
U.S. Army
Vietnam veterans
Missouri
residents
Missouri
residents
<20d
20-60d
>60d
2,4,5-T
production
workers exposed
17yrspriorto the
study
2,4,5-TCP
production &
maintenance
workers
Ex
N
127
72
72
141
95
108
2,490
135
16
12
12
18
11
Mean level3 (SD)
867 —
945 —
929 —
96 lh —
917 —
962 —
1,020C —
1,021 (353)
1,084 (485)
1,198 (391)
963 (403)
950 (340)
—
posed
Ratio
—
ORref. range 1.4
% Abnormal
0.7
% T4 cells
48
51
42
"
% of total
47.6(8.1)
Unexposed
N
301
403
1,972
142
—
15
10
Mean level (SD)
907 —
923 —
990C —
1,033 (346)
—
1,040 (290)
—
Ratio
—
—
—
% Abnormal
0.0
—
"
% of total
48.5 (10.6)
td
fe
H
O
O
O
H
O
O
V
O
c
O
-------
o
O
to
O
O
Table 7-37. CD4 levels in production workers, Missouri residents, Vietnam veterans, and
BASF chemical workers (continued)
Author
Ottetal, 1994
Zober
etal., 1992
Population
BASF chemical
workers
BASF personnel
exposed to
TBDDf
and TBDFf
Ex
N
133
21
Mean level3 (SD)
—
973.3 (381.9)
posed
Ratio
% T4 cells
42.5 (10.4)
% CD4
44.8(6.1)
Unexposed
N
42e
42
Mean level (SD)
—
1,032 (444.6)
Ratio
% T4 cells
45.1 (8.9)
% CD4
45.1(8.9)
td
aUnits: counts/mm.
bSerum 2,3,7,8-TCDD level in pg/g of lipid.
cAdipose 2,3,7,8-TCDD level in pg/g of lipid.
dAdipose 2,3,7,8-TCDD level in pg/g of lipid.
Trom Zober etal., 1992.
fTBDD = 2,3,7,8-tetrabrominated dibenzo-p-dioxins;
TBDF = 2,3,7,8-tetrabrominated dibenzofurans.
fe
H
O
O
O
H
O
O
V
O
c
O
-------
o
O
to
O
O
Table 7-38. CDS levels in 2,4,5-T production workers, Missouri residents, Vietnam veterans, and
BASF accident cohort
Author
Roegner
etal., 1991
Grubbs
etal., 1995
CDC Vietnam
Experience
Study, 1988a
Hoffman
etal., 1986
Webb
etal., 1989
Jennings
etal., 1988
Ott
etal., 1994
Zober
etal., 1992
Population
U.S. Air Force Ranch
Hand personnel
Unknown <10b
Lowl5-<33.3b
High>33.3b
U.S. Air Force Ranch
Hand personnel
Background11
Low
High
U.S. Army Vietnam
veterans
Missouri residents
Missouri residents
<20d
20-60d
>60d
2,4,5-T production
workers exposed
17 yrs prior to the study
BASF accident cohort
BASF personnel
exposed to TBDD8 and TBDF8
Exposed
N
126
71
73
140
95
106
2,490
135
16
12
12
18
132
21
Mean level3 (SD)
485 —
465 —
475 —
645 —
606 —
618 —
560C —
592 (223)
562 (215)e
645 (225)
807 (381)
630 (280)
665 (282.6)
Ratio
—
ORref. range 0.9
% Abnormal
1.5
% T8 cells
26
28
35
% T8 cells
31.9(10.4)
% CDS
30.8(7.9)
Unexposed
N
301
400
1,972
142
15
42f
42
Mean level3 (SD)
473 —
634 —
550 —
578 (198)
590 (230)
717.2 (282.4)
Ratio
—
—
% Abnormal
0.0
% T8 cells
32.0(7.1)
% CDS
32.0(7.1)
td
-------
Table 7-39. IgG levels in Missouri residents, Vietnam veterans, and BASF accident cohort
O
O
to
O
O
oo
Author
Roegner et al., 1991
Grubbsetal., 1995
Centers for Disease
Control Vietnam
Experience Study,
1988a
Webb et al., 1989
Ottetal., 1994
Zoberetal., 1992
Population
U.S. Air Force Ranch
Hand personnel
Unknown <10b
Low 15-<33.3b
High>33.3b
U.S. Air Force Ranch
Hand personnel
Background11
Lowh
High11
U.S. Army Vietnam
veterans
Missouri residents
<20d
20-60d
>60d
BASF accident cohort
BASF personnel
exposed to TBDDg
and TBDFg
Ex
N
335
190
175
364
243
251
2,490
16
12
12
132
21
Mean level3 (SD)
1,087 —
1,122 —
1,122 —
1,126' —
1,111 —
1,115 —
1,078°
l,064e (273)
1,146 (193)
1,151 (223)
1,1 99f (226)
1,057.7 (199.0)
posed
Ratio
OR < reference range
1.0
OR > reference range
1.0
—
—
Unexposed
N
757
1,035
1,972
—
194
42
Mean level3 (SD)
1,120 —
1,1139 —
1,077C
—
1,182.6(310.0)
1,102.9(207.1)
td
O
O
2
O
H
O
HH
H
W
aUnits: mg/dL.
"Serum 2,3,7,8-TCDD in pg/g of lipid.
°Geometric mean.
dAdipose 2,3,7,8-TCDD in pg/g of lipid.
cp< 0.05 trend.
'Significant positive relationship between IgG and current 2,3,7,8-TCDD
level and back-extrapolated 2,3,7,8-TCDD (p<0.01).
8TBDD = 2,3,7,8-tetrabrormnateddibenzo-/Mlioxms;
TBDF = 2,3,7,8-tetrabrominateddibenzofurans.
h Comparison: current dioxin < 10 pg/g lipid.
Background: current dioxin > 10 pg/g.
Low: current dioxin > 10 pg/g, 10 pg/g 10 pg/g, 10 pg/g 143 pg/g.
1 Adjusted mean.
O
c
O
H
W
-------
Table 7-40. IgM levels in Missouri residents, Vietnam veterans, BASF accident cohort, and extruder personnel
O
O
to
O
O
Author
Roegner et al.,
1991
Grubbs et al.,
1995
Centers for
Disease Control
Vietnam
Experience
Study, 1988a
Webbetal.,
1989
Ottetal., 1994
Zober et al.,
1992
Population
U.S. Air Force
Ranch Hand
personnel
Unknown < 10b
Low 15-<33.3b
High >33.3b
U.S. Air Force
Ranch Hand
personnel
Background8
Low8
High8
U.S. Army
Vietnam veterans
Missouri residents
<20d
20-60d
>60d
BASF accident
cohort
Personnel exposed
to TBDDe and
TBDFe
Exposed
N
335
190
175
365
253
251
2,490
16
12
12
132
21
Mean level"
(SD)
107 -
96 -
106 -
Data not
presented
121C —
128(89)
157(57)
114(44)
139.6 (65.1)
142f(52.6)
Ratio
—
OR< reference range 1.0
OR> reference range 1.0
—
—
—
Unexposed
N
757
1,035
1,972
—
192
42
Mean level"
(SD)
103
Data not
presented
121C
—
134.7 (70)
114.7f(46.5)
td
i
o
O
O
O
2
O
H
O
HH
H
W
O
c
O
H
W
"Units: mg/dL.
bSerum 2,3,7,8-TCDD level in pg/g of lipid.
GGeometric mean.
dAdipose 2,3,7,8-TCDD level in pg/g of lipid.
eTBDD = 2,3,7,8-tetrabrominated dibenzo-p-dioxins;
TBDF = 2,3,7,8-tetrabrominated dibenzofurans.
f/?=0.04.
gComparison: current dioxin < 10 pg/g of lipid.
Background: current dioxin > 10 pg/g.
Low: current dioxin > 10 pg/g, 10 pg/g 10 pg/g, 10 pg/g 143 pg/g.
-------
Table 7-41. Levels of natural killer cells in Missouri residents, Vietnam veterans, and extruder personnel
O
O
to
O
O
td
fe
H
O
O
O
H
O
O
^
O
c
O
Author
Roegner et al., 1991
Grabbsetal., 1995
Tonnetal., 1996
Jennings et al., 1988
Zoberetal., 1992
Population
U.S. Air Force
Ranch Hand
personnel
Unknown <10a
Low 15-<33.3a
High>33.3a
U.S. Air Force
Ranch Hand
personnel
Background8
Lowg
High5
2,4,5-TCP
production and
maintenance
workers
2,4,5-T production
workers exposed
17yrspriorto the
study
BASF personnel
exposed to TBDDf
and TBDFf
Exposed
N
126
70
72
139h
94
106
11
18
21
Mean level (SD)
455b'c
378
386
CD16+CD56
242 —
219 —
237 —
%CD56
5.4(1.9)
400e (210)d
280.5 (175.2)
Unexposed
N
291
399
10
15
42
Mean level
(SD)
414b
248
%CD56
5.6(1.6)
590e(230)
250.8(149.9)
aSerum 2,3,7,8-TCDD level in pg/g of lipid.
bUnits: cpm.
°Net response.
VO.05.
eUnits: lOVmm3.
fTBDD = 2,3,7,8-tetrabrominated dibenzo-p-dioxins;
TBDF = 2,3,7,8-tetrabrominated dibenzofurans.
Comparison: current dioxin < 10 pg/g of lipid.
Background: current dioxin > 10 pg/g.
Low: current dioxin > 10 pg/g, 10 pg/g 10 pg/g, 10 pg/g 143 pg/g.
hAdjusted mean.
-------
Table 7-42. Developmental immunologic outcomes in a study of Dutch Children
Outcome
Exposure(s)
Correlation
coefficient
/7-value
Weisglas-Kuperus et al., 1995
Monocytes
At 3 months:
Granulocytes
At 3 months:
TCRy8+ T-cells
At birth:
TCR«8+ T-cells
at 18 months:
CD3+ CD8+ cells
at 18 months:
Total TEQ
Dioxin-furan TEQ
Mono-ortho PCB TEQ
Di-ortho PCB TEQ
Total TEQ
Total TEQ
Dioxin-furan TEQ
Total TEQ
Dioxin-furan TEQ
Di-ortho PCB TEQ
Total TEQ
Dioxin-furan TEQ
Planar PCB TEQ
Di-ortho PCB TEQ
-0.64
-0.55
-0.67
-0.51
-0.47
0.50
0.57
0.57
0.71
0.61
0.65
0.80
0.71
0.68
<0.01
<0.01
<0.01
<0.05
<0.05
<0.05
<0.01
<0.05
<0.01
<0.05
<0.05
<0.01
<0.01
<0.05
Weisglas-Kuperus et al., 2000
Lymphocytes
at 42 months
CD3+
at 42 months
CD3+CD8+
at 42 months
CD4+CD45RO+
at 42 months
TCR«P+ T-cells
at 42 months
CD3+HLA-DR+
at 42 months
Total Maternal PCB
Total Cord PCB
Total Maternal PCB
Total Cord PCB
Total Maternal PCB
Total Cord PCB
Total Maternal PCB
Total Cord PCB
Total Maternal PCB
Total Cord PCB
Total Maternal PCB
Total Cord PCB
0.25
0.22
0.25
0.21
0.27
0.24
0.25
0.26
0.25
0.20
0.26
0.31
0.02
0.05
0.02
0.07
0.01
0.04
0.02
0.02
0.02
0.08
0.02
0.005
December 2003
7B-162 DRAFT—DO NOT CITE OR QUOTE
-------
Table 7-43. Case reports of psychological and neurologic effects among individuals exposed to 2,3,7,8-TCDD-
contaminated materials
Author
Ashe and
Suskind, 1950
West Virginia
Suskind etal.,
1953
West Virginia
Baader and
Bauer, 1951
Nordheim-
Westfalen, West
Germany
Exposure
Precursor and reaction
products of TCP
process TCP,a NaOH,b
2,4,5-T
TCP
2,4,5-T
PCPd
TCP
Population
4 employees involved in
cleanup after 1949
explosion or worked
with equipment used
prior to explosion;
studied 6 mos and 1 yr
after explosion
36 workers with
chloracne after TCP
reactor explosion
25 persons exposed
during production of
TCP and 2,4,5-T
1948-1953
lOmeninPCP
production experimental
TCP production
Evaluation
Clinical history
and examination
Clinical history
and examination
Record review
case history
Findings
Headaches
Insomnia
Social withdrawal
Nervousness
Fatigue
Crying spells
Numbness in feet
Decreased libido,
muscle strength,
sensory ability,
myelin sheath and fiber
destruction of sural
nerve
Fatigue
Nervousness
Irritability
Reduced libido
Back and limb pain
Vertigo
Paresthesia
Serf-reported: pain and
weakness
Paresthesia and pain in
gluteal and femoral
region
o
O
to
O
O
oo
td
O
O
2
O
H
O
HH
H
W
O
c
O
H
W
-------
o
O
to
O
O
Table 7-43. Case reports of psychological and neurologic effects among individuals exposed to 2,3,7,8-TCDD-
contaminated materials (continued)
Author
Bauer et al.,
1961
Hamburg, West
Germany
Goldman, 1972
Ludwigshafen,
West Germany
Exposure
TCP
2,4,5-T
TCP
2,4,5-T
Population
3 1 men in TCP and
2,4,5-T production with
residual chloracne,
continual
"neuromuscular
weakness," vasovagal
disturbances,
psychopathological
disturbances" 5 yrs after
first exam
42 BASF workers with
chloracne after TCP
reactor explosion in
1953
Evaluation
Clinical history
and examination
Hamburg-
Wechsler Test
Rorschach
Psychogram
Clinical history
and examination
Findings
Reduced libido
Headaches, dizziness
Decreased libido
Irritability
Depression
Sleep disturbances
Anorexia
Paresthesia
Tremor
Muscle weakness
Hamburg- Wechsler
indicative of acquired
decrease in mental
efficiency
Rorschach Psychogram
indicative of decreased
emotional reactivity,
slowed thinking
process for
concentration
Neurasthenia
td
fe
H
O
O
O
H
O
O
V
O
c
O
-------
o
O
to
O
O
oo
Table 7-43. Case reports of psychological and neurologic effects among individuals exposed to 2,3,7,8-TCDD-
contaminated materials (continued)
Author
Jiraseket al.,
1974
Spolano,
Czechoslovakia
Oliver, 1975
England
Exposure
TCP
2,4,5-T
PCP, HCBe
Pure 2,3,7, 8-TCDD
Population
55 workers in TCP and
2,4,5-T production
Followed: 1969-1973
3 scientists synthesis of
2,3,7, 8-TCDD
Evaluation
Clinical
examination
history and
examination
Not described
Findings
35 patients with
"neurasthenic or
depressive syndrome"
Subject A:
Fatigue and headache
Subject B:
Headaches
Loss of vigor and
fatigue
Irritability and anger
Poor concentration
Subject C:
Loss of concentration
(apathy and fatigue)
"Loss of energy and
drive"
Some difficulty
sleeping
td
O
O
2
O
H
O
O
V
O
C
O
-------
H
W
O
Table 7-43. Case reports of psychological and neurologic effects among individuals exposed to 2,3,7,8-TCDD-
contaminated materials (continued)
Author
Pazderova-
Vejlupkova et
al., 1981
Spolano,
Czechoslovakia
Exposure
TCP
2,4,5-T
PCP, HCB
Population
4-year followup of 44
production workers
Evaluation
Clinical
psychiatric
examination
Findings
Psychiatric changes at
start of "intoxication"
83 % with neurotic
symptoms;
neurasthenia syndromes
with depressive
component; depressive
syndrome with
endogenous component
(N=55)
16% with
pseudoneurasthenia
syndrome and CNS
arteriosclerosis
3 % with no psychiatric
signs or symptoms
Psychiatric changes in
exposed individuals at
followup (1973-1979)
58 % with neurotic
symptoms and no
depressive or anxiety
symptoms
18% with severe
pseudoneurasthenia and
signs of dementia
(usually in patients
over 50 years old but
dementia occurred in
30-year-old patient)
O
H
W
-------
o
O
to
O
O
Table 7-43. Case reports of psychological and neurologic effects among individuals exposed to 2,3,7,8-TCDD-
contaminated materials (continued)
Author
Pazderova-
Vejlupkova et
al., 1981 (cont.)
Kimmig and
Schultz, 1957b
Hamburg,
Germany
Poland et al.,
1971
New Jersey
Exposure
TCP
2,4,5-T
TCP
2,4,5-T
2,4-Df
HCB
TCP reactor explosion
in 1960
Daily exposure 1951-
1969
Population
31 production workers
with chloracne (1953-
1954)
73 male workers in
production,
maintenance, office
areas
Evaluation
Clinical history
and examination
Noted histories
of smoking,
alcohol,
medications
MMPP given to
52 production
and 17
unexposed
administrative
staff
Findings
24% with no
psychiatric signs or
symptoms.
Tiredness (N = 3)
Headaches (N=5)
Headaches (N=8)
Severity of acne
significantly correlated
with high score on the
mania scale of MMPI.
td
fe
H
O
O
O
H
O
HH
H
W
aTCP = 2,4,5-trichlorophenol.
bNaOH = sodium hydroxide.
C2,4,5-T = 2,4,5-trichlorophenoxyacetic acid.
dPCP = pentachlorophenol.
eHCB = hexachlorobenzene.
f2,4-D = 2,4-dichlorophenoxyacetic acid.
8Minnesota Multiphasic Personality Inventory.
O
c
O
H
W
-------
o
O
to
O
O
oo
Table 7-44. Cross-sectional studies of psychological and neurologic effects among residents of Missouri and Seveso
exposed to 2,3,7,8-TCDD-contaminated materials
Author
Webbetal.,
1989
Missouri
Pocchiari
etal., 1979
Seveso, Italy
Exposure
2,3,7,8-
TCDD-
contaminated
waste oil 1971
TCP
TCP reactor
accident
July 1976
Study
population
68 volunteers
in State
Dioxin
Registry;
estimated
exposure to
20-100 ppb
TCDD for 2
yrs or 100
ppb for 6
months
446 residents
of Seveso
and Meda,
Italy
200 workers
from Icmesa
plant
Comparison
population
36 volunteers in
State Dioxin
Registry with no
history of
exposure to
2,3,7, 8-TCDD
255 residents of
nearby towns
without
contamination
None
Evaluation
Missouri
Dioxin Health
Studies.
Progress
Report (1983)
Not detailed
but included
clinical exam
and NCVb
Exam, EMGC,
NCV
Findings
No differences in
neurologic exam or
in feeling of pins-
needles; loss of
sensation in
extremities, tingling
in fingers and toes,
diminished VIB 256,
diminished VIB 64,
diminished pin
sensation, diminished
thermal sensation
(PRR=2%)a
1977: neurologic
damage in 6. 7% of
Seveso residents and
in 1.2% unexposed
residents
1978: neurologic
damage in 11.7% of
Seveso residents
Workers: 4%
neuropathic clinical
signs exam, EMG,
NCV
td
oo
O
O
*
O
H
O
HH
H
W
O
&
O
c
O
H
W
-------
o
O
to
O
O
oo
td
Table 7-44. Cross-sectional studies of psychological and neurologic effects among residents of Missouri and Seveso
exposed to 2,3,7,8-TCDD-contaminated materials (continued)
Author
Filippini et al.,
1981
Seveso, Italy
Exposure
See
description
above
Study
population
308 residents
of Seveso,
Italy
Comparison
population
305 residents of
nearby towns
without
contamination
Evaluation
Symptoms:
pain, tingling,
numbness
Sensory
review
Muscle tone
and strength
NCVofulnar
and peroneal
nerves
Findings
Elevated PRR for
peripheral
neuropathy in Seveso
residents with
indicators of
exposure (high GGT,
ALT, AST, or
chloracne)
(PRR=2.8, 95%
CI = 1.2-6.5) Seveso
residents with
predisposing factors
(alcohol or
inflammatory
disease) (PRR=2.6,
95% CI = 1.2-5.6)
"Prevalence risk ratio.
bNerve conduction velocity.
GElectromyography.
fe
H
O
O
O
H
O
HH
H
W
O
c
O
H
W
-------
o
O
to
O
O
Table 7-45. Cross-sectional studies of psychological and neurologic effects among TCP production workers and
Vietnam veterans exposed to 2,3,7,8-TCDD-contaminated materials
Author
Moses et al.,
1984
West Virginia
Suskind and
Hertzberg,
1984
West Virginia
Exposure
TCP3
2,4,5-Tb
1949 TCP
reactor
explosion
TCP
2,4,5-T
Study
population
226 workers
invited based on
union record of
employment in
production of
TCP or 2,4,5-T
Definition of
exposure =
"current or
history of
chloracne"
204 workers or
maintenance
workers in TCP
or 2,4,5-T dept.
Comparison
population
Workers without
chloracne
(N = 109)
Conduction
velocities
163 workers
never in TCP or
2,4,5-T
Mean age of
exposed
population was
younger (56.7
vs. 46.2)
(p< 0.0001).
Evaluation
Review of
symptoms
(ROS)
obtained by
examining
physician
Interviews
and clinical
examination
Findings
Significant diff. with
and without chloracne
for: insomnia;
decreased libido,
erection, or
ejaculation; no change
for fatigue,
irritability,
nervousness,
depression, or
personality changes.
Decreased pinprick
sensation in 18.3 %
with chloracne; no
decreased pinprick
sensation without
chloracne
Decreased libido was
more frequent among
exposed by Mantel-
Haenszel Chi by age.
Nervousness/depressi
on/ anxiety not
increased in exposed
(sample size sufficient
to detect a twofold
increase)
td
O
O
2
O
H
O
HH
H
W
O
-------
o
O
to
O
O
Table 7-45. Cross-sectional studies of psychological and neurologic effects among TCP production workers and
Vietnam veterans exposed to 2,3,7,8-TCDD-contaminated materials (continued)
Author
Sweeney
etal., 1993
Alderfer
etal., 1992
Missouri and
New Jersey
Lathrop etal.,
1984
Exposure
TCP 2,4,5-T
Vietnam
service
1962-1971
Study
population
280 production
workers with
daily exposure
New Jersey
1951-1969
Missouri 1968-
1972
1,208 Ranch
Hands assigned
to aerial
spraying
herbicides and
insecticides
Republic of
Vietnam
Comparison
population
261 unexposed
community
residents
matched for age,
race, gender
1,238 men who
flew cargo
missions in
Southeast Asia
Matched by
month of birth,
race, and
occupational
code
Evaluation
Symptom and
medical
history;
neurologic
examination;
neurophysiolo
gic tests of
vibration and
thermal
sensitivity;
Beck
Depression
Scale; SCL-
90-R
Self-report of
psychological
or emotional
illness
Diagnostic
Interview
Schedule
(modified)
Self-reported
depression
Findings
No significant
difference for
symptoms or any
examination variable
No significant
difference in mood
disorders
No difference
Significantly more
fatigue, anger,
erosion, anxiety,
closest for high
school-educated
Ranch Hands; no
difference for college-
educated
Greater for Ranch
Hands
td
fe
H
O
O
O
H
O
HH
H
W
O
c
O
H
W
-------
Table 7-45. Cross-sectional studies of psychological and neurologic effects among TCP production workers and
Vietnam veterans exposed to 2,3,7,8-TCDD-contaminated materials (continued)
Author
Lathrop et al.,
1984 (cont.)
Exposure
Study
population
Comparison
population
Evaluation
Cornell Index (self-
administered inventory of
neuropsychiatric
symptoms)
(psychophysiologic)
MMPI
Halstead-Reitan Battery
Wechsler Adult
Intelligence Scale
(WAIS)
Findings
After adjustment for
education 4/10
parameters abnormal
(nervousness, anxiety,
startle, psychosomatic,
gastrointestinal
system); abnormal
parameters inversely
related to education
level.
High school-educated
Ranch Hands showed
significant deficits on
scales for
hypochondria,
masculinity/femininity;
mania/hypomania but
comparisons show
more denial; MMPI
scores influenced by
education (p<0.0l)
No impairment in
Ranch Hands
Scores related to
educational level
o
O
to
O
O
td
to
O
O
2
O
H
O
HH
H
W
O
&
O
c
O
H
W
-------
o
O
to
O
O
Table 7-45. Cross-sectional studies of psychological and neurologic effects among TCP production workers and
Vietnam veterans exposed to 2,3,7,8-TCDD-contaminated materials (continued)
Author
Roegner et al.,
1991
Exposure
Vietnam
service
Study
population
720 USAF Ranch
Hands; serum
2,3,7,8-TCDD levels
were measured
(lipid adjusted)
Comparison
population
779 who flew
cargo missions
in Southeast
Asia; serum
2,3,7,8-TCDD
levels were
measured
Evaluation
Serf-report
SCL-90-RC
Cornell
Medical Index
(CMI)
Neurologic
exam
Findings
No significant mental,
emotional, or sleep
disorders
No significant
differences with
increasing serum levels
Significantly higher
mean schizoid and
schizotypal scores in
Ranch Hands over 33.3
pg/g 2,3,7,8-TCDD,
but they did not relate
to similar scales in the
SCL-90-R
Overall, no consistent
relationship between
neurologic
abnormalities and
TCDD level.
td
O
O
2
O
H
O
HH
H
W
aTCP = 2,4,5-trichlorophenol.
b2,4,5-T = 2,4,5-trichlorophenoxyacetic acid.
CSCL-90-R: Symptom Checklist Revised.
O
-------
o
O
to
O
O
Table 7-46. Studies of neurologic and behavioral effects among Dutch infants
Author
Koopman-
Essebaum
etal., 1996
Exposure
Total PCB-dioxin-furan
TEQ from breast milk
CATEGORIES for total TEQb:
"Low" 3 mos.= 168-617
7+18 mos. 168-769
"Med." 3 mos. = 618-810
7+18 mos. = 770-1,289
"High" 3 mos = 81 1-1,860
7+18 mos = 1,290-4,340
EPCB^^-ng/g'
Study groups
Exposed: 105
breast-fed infants
and their mothers
Not exposed:
102 bottle-fed
infants and their
mothers
All in the
Rotterdam area
N 182
N=198
N=206
Evaluation
From Bayley Scales of Infant
Development:
Psychomotor Dev. Index (PDI)
of infant at
3 months,
7 months, and
18 months of age
Mental Development Index
(MDI) of infant at
3 months,
7 months, and
18 months of age
PDI at 7 months'
Medium exposure
High exposure
Breast feeding -duration
PDI at 3 months
Breast feeding -duration
MDI at 7 months
Ln(£ PCBplaamil)
Breast feeding -duration
Findings
Formula-fed
N Mean(SD) N Mean(SD)
99 118(12)
105 115(15)'
105 110(17)
101 128(31)
105 115(11)'
105 113(18)d
100 117(12)
102 111(13)
101 108(14)
100 126(13)
102 112(9)
102 107(17)
Regression analysis
Coefficient Std. error
-9.5d
-7.7
6.9d
-4.8'
0.91
2.0
2.0'
3.9
4.9
2.3
2.0
0.91
1.7
0.9
td
O
O
*
O
H
O
HH
H
W
O
V
O
c
O
H
W
-------
Table 7-46. Studies of neurologic and behavioral effects among Dutch infants (continued)
Author
Koopman-
Essebaum
et al.,
1995b
Koopman-
Essebaum
et al.,
1995a
submitted
for
publication
Exposure
Total PCB-dioxin-faran TEQ
from breast milk
CATEGORIES for total TEQb:
Low: 3mos.= 168-617
7mos. 168-769
Med.: 3 mos. = 618-810
7 mos. = 770-1,289
High: 3 mos. = 811-1860
7 mos. = 1,290-4,340
LPCBplasmil~ng/ge
PCB-dioxin-furan TEQ from
breast milk and thyroid status
Study groups
Exposed: 105
breast-fed infants
and their mothers
Not exposed:
102 bottle-fed
infants and their
mothers
All in Rotterdam
N = 182
N= 181
Exposed:
Breast-fed infants
104 in
Groningen
105 in
Rotterdam
Comparison:
Bottle-fed infants
107 in
Groningen
102 in
Rotterdam
Evaluation
From Fagan Test of Infant
Intelligence:
Visual Recognition
Memory Test
3 months of age
7 months of age
Visual Recognition Memory
Test
at 7 months0
Medium exposure
Fligh exposure
Breast feeding -duration
or
Ln(L PCBptaJ
Breast feeding -duration
Prechtl neurologic exam (2
wk):23 neuro. abnorm.
394 neurologically normal
LPCBcor(lbloo(1e
if^^matmai bloo(1
Dioxin-furan TEQ
Planar PCB TEQ
Mono-ortho PCB TEQ
Di-ortho PCB TEQ
Total PCB-dioxin-furan TEQ
Findings
Formula-fed
N Mean(SD) N Mean(SD)
105 61.5(9.0)
105 59.9(5.9)°
101 62.2(10.7)
102 57.3 (5.9)
Regression analysis
Coefficient Std. error
4.4"
4.6"
-0.46
-0.50
1.63d
Neuro. Normal
N Mean (SD)
352 0.5(0.3)
393 2.2(0.9)
189 30.0(10.2)
194 16.3 (7.4)'
189 14.8(5.3)
189 4.4(2.2)
182 65.3 (21.6)'
1.7
2 2
1.0
1.07
0.56
Neuro. Abnormal
N Mean (SD)
20 0.4(0.1)
21 2.0(0.6)
12 25.4(7.9)
12 11.7(3.8)
12 12.6(4.6)
12 3.7(1.5)
12 52.5(15.6)
o
rt>
o
rt>
CT*
rt>
to
o
o
td
O
O
2
O
H
O
HH
H
W
O
&
O
c
o
H
W
-------
o
O
to
O
O
Table 7-46. Studies of neurologic and behavioral effects among Dutch infants (continued)
td
Author
Huisman et al.,
1995a
Exposure
Dioxins,
dibenzo-furans,
and PCBs in
breast milk
(No effects
observed in
maternal or cord
blood)
Study groups
Exposed:
Breast-fed infants
104 in
Groningen
105 in
Rotterdam
Comparison:
Bottle-fed infants
107 in
Groningen
102 in
Rotterdam
Evaluation
Prechtl's exam at 10-21 days:
Neurologic Optimality Score (NOS)
ORs from logistic regression
Total PCB-dioxin-furan TEQ"
Dioxin-furan TEQ
D54 (TEF=0.5)
D66 (TEF=0.1)
D67 (TEF=0.1)
D70 (TEF=0.1)
D73 (TEF=0.01)
F83 (TEF=0.1)
F114 (TEF=0.5)
Planar PCB TEQ
PCB169 (TEF=0.01)
Non-planar PCB
PCB70
PCB 99
PCB118 (TEF=0.0001)
PCB138
PCB153
PCB156 (TEF=0.0005)
PCB170 (TEF=0.0001)
PCB 177
PCB 183
PCB 187
LPCBmilk
Mono-ortho PCB TEQ
Di-orlho PCB TEO
Findings
Odds 95% confidence
ratio interval
3.21
3.12
2.23
1.64
3.03
1.23
1.61
1.31
2.48
1.67
2.33
1.88
2.01
2.21
2.73
2.76
2.55
2.33
1.96
2.32
1.87
2.93
2.78
2.38
1.37-7.48
1.36-7.18
1.10-4.52
1.08-2.50
1.34-6.81
1.03-1.48
1.01-2.56
1.04-1.66
1.16-5.32
0.97-2.87
1.13-4.80
1.29-2.72
1.19-3.40
1.24-3.95
1.40-5.35
1.39-5.48
1.31-4.95
1.19-4.56
1.14-3.39
1.21-4.46
1.07-3.28
1.45-5.90
1.40-5.59
1.21-4.71
-------
o
O
to
O
O
Table 7-46. Studies of neurologic and behavioral effects among Dutch infants (continued)
Author
Huisman
etal., 1995b
Exposure
£PCBcoriblood
No effects
observed with
breast milk
Study groups
Exposed:
Breast-fed infants
104 in Groningen
105 in Rotterdam
Comparison:
Bottle-fed infants
107 in Groningen
102 in Rotterdam
Evaluation
Neurologic development in 1 8-
month-old children:
Neurologic optimality score
Log(EPCBcori/0.8)
Paternal smoking
Log(£PCBcori/0.8) x paternal
smoking
Above analysis presented by
paternal smoking status:
Children w/nonsmoking fathers
Children w/smoking fathers
Fluency cluster score
Log(£PCBcori)
Breast (0) vs bottle (1) fed
Findings
Coefficient
(std error) p-value
-0.149
(0.049)
-0.402
(0.130)
0.200 (0.078)
-0.149
-0.051
-0.295
(0.175)
-0.450
m \-in\
0.003
0.002
0.011
0.003
0.402
0.093
0.012
td
b pg PCB-dioxin-furan TEQ/g fat times weeks breast feeding.
°overallp for high + medium = 0.05.
Vo.oi.
e£PCBmaltnllllblood(ng/g) — maternal blood level sometime during 36-40 weeks of gestation. This and cord blood are measured in ng/g plasma.
f All summary logistic results presented; for individual chemicals, only those with ORs significantly greater than 1 are presented.
O
O
2
O
H
O
HH
H
W
O
c
O
H
W
-------
Table 7-47. Mortality from disease of the circulatory systems in populations exposed to 2,3,7,8-TCDD
O
O
to
O
O
Author
Steenland
et al.,
1999
Hooiveld
etal.
1998
Vena et
al., 1998
Population
2,4,5-TCP and
2,4,5-T
production
workers,
USA
2,4,5-TCP,
2,4,5-T, MCPA
production
workers,
The
Netherlands
IARC
expanded
international
cohort; 36
cohorts from
12 countries;
workers
employed in
the production
of phenoxyacid
herbicide and
chlorophenol
& sprayers
Outcomes
Ischemic heart disease
(ICD410-414)
Cerebrovascular disease
(ICD 430-438)
Circulatory system
(ICD390-459)
Ischemic heart disease
(ICD 4 10-4 14)
Cerebrovascular disease
(ICD 430-438)
Circulatory system
(ICD 390-459)
Ischemic heart disease
(ICD 4 10-4 14)
Cerebrovascular disease
(ICD 430-438)
No. of deaths
456
922
69
45
16/(45)
33
10/(33)
9
3/9
1,738
l,170e
l,151f
1,179
789e
l,151f
254
162e
l,151f
SMR
109
RR=1.75"
117b
96
100
RR=1.4C
RR=1.5dMetlium
RR=1 jdHigh
120
RR=1.8C
RR=1 jdMedium
RR=2.3dHi8h
140
RR=0.6C
RR=2.0dMetlium
RR=0.8dHi8h
91
94e
RR=1.51f
92
97e
RR=1.67f
254
84e
RR=1.54f
95% CI
100-120
1.07-2.87'
94-144b
74-121
80-140
0.8-2.5°
0.8-2.8d
0.8-2.9d
80-160
0.9-3.6C
0.7-3.6d
0.5-8.2d
60-260
0.4-5.1°
0.5-8.2d
0.2-4. ld
87-95
88-99e
1.17-1.96f
87-98
90-104'
1.23-2.26f
76-97
71-98e
0.83-2.88f
Cohort size
5,132
608b
5,132
562
549 (males)
562
549 (males)
562
549 (males)
21,863
13,831
Years
of
followup
1942-
1993
1955-
1991
Varies by
cohort
1939-
1992
td
oo
fe
H
O
O
O
H
O
O
V
O
c
O
-------
o
rt>
o
rt>
CT*
rt>
to
o
o
td
Table 7-47. Mortality from disease of the circulatory systems in populations exposed to 2,3,7,8-TCDD (continued)
Author
Ottand
Zober,
1996a
Michalek
et al.,
1998
Population
2,4,5,-T
production
workers or
involved in
cleanup after a
TCP reactor
release
(Germany)
US Air Force
Ranch Hands
Outcomes
Diseases of the circulatory
system (ICD not reported)
TCDDh<0.1 (ig/kgbw
TCDD 0.1-0. 99 (ig/kgbw
TCDD>1 (ig/kgbw
Ischemic heart disease
TCDDO.l (ig/kgbw
TCDD 0.1-0. 99 (ig/kgbw
TCDD>1 (ig/kgbw
Noncancer deaths: all causes
Diseases of the circulatory
system
Circulatory diseases
(ICD 380-459)
Pilots & navigators
Enlisted flight engineers
Administrative officers
Enlisted ground personnel
No. of deaths
37s
13
11
12
16
7
4
5
92
37
39
12
8
0
24
SMR
80
80
100
80
70
90
70
60
RR=1.03i
RR^.931
100
90
30
—
150
95% CI
60-120
40-140
50-170
40-120
40-110
30-180
20-170
20-130
0.88-1.22
0.70-1.24
70-130
50-160
—
—
100-220
Cohort size
243 males
4 females
1,261
veterans
19,080
comparisons
Years
of
followup
1953-
1992
1962-
1993
O
O
^
O
H
O
HH
H
W
O
c
o
H
W
-------
Table 7-47. Mortality from disease of the circulatory systems in populations exposed to 2,3,7,8-TCDD (continued)
Author
Pesatori
et al.,
1998
Population
Residents of
Seveso, Italy
Outcomes
Circulatory sys (ICD 390-459)
Zone A: males
Zone B
Zone R
Zone A: females
ZoneB
ZoneR
Ischemic heart disease
(ICD 4 10-4 14)
Zone A: males
Zone B
Zone R
Zone A: females
ZoneB
ZoneR
Chronic ischemic heart
disease
(ICD 412, 414)
Zone A: males
Zone B
ZoneR
Zone A: females
ZoneB
Zone R
Chronic rheumatic heart
disease
(ICD 390-398)
Zone A: females
Zone B
Zone R
Cerebrovascular disease
(ICD 430-438)
Zone A: males
Zone B
Zone R
Zone A: females
ZoneB
ZoneR
No. of deaths
5
18
126
0
15
133
9
36
316
1
24
210
5
18
126
0
15
133
3
0
11
5
30
190
2
26
258
SMR
Rel. risk
1.6
0.9
1.1
1.0
1.0
1.1
1.5
0.8
1.1
0.3
1.1
1.0
3.0
1.3
1.4
1.3
1.3
15.8
—
1.2
1.5
1.2
1.1
0.5
1.0
1.2
95% CI
1.1-2.5
0.7-1.1
1.0-1.2
0.6-1.7
0.8-1.2
1.0-1.2
0.8-2.9
0.6-1.2
0.9-1.2
0.0-2.0
0.7-1.6
0.9-1.2
1.2-7.3
0.8-2.1
1.1-1.7
0.8-2.2
1.0-1.5
4.9-50.4
—
0.6-2.3
0.6-3.7
0.8-1.7
0.9-1.3
0.1-2.0
0.7-1.5
1.0-1.3
Cohort size
PYAR
5,541
42,219
271,483
5,975
41,391
271,483
Years
of
followup
1976-
1991
-------
Table 7-47. Mortality from disease of the circulatory systems in populations exposed to 2,3,7,8-TCDD (continued)
O
ft>
g aRate ratio calculated using Cox regression for cumulative exposure categories compared to an internal control group. Rate ratio for 7 septile exposure
3 category.
of bChloracne cohort.
^j °Relative risks for 549 males, calculated using Poisson regression and using an internal control group.
g dRelative risks calculated using Poisson regression adjusted for age, calendar period at end of followup, and time since first exposure/employment,
w comparing workers with estimated low TCDDmax=7.1 ppt (lipid adjusted) to workers with estimated medium TCDDmax=7.7 ppt to!24.1 ppt (lipid adjusted)
and estimated high TCDDmax= 124.2 to 7,307.5 ppt (lipid adjusted).
eWorkers exposed to TCDD or higher chlorinated dioxins.
fRR=relative risk calculated using Poisson regression, adjusted for age, gender, calendar period, employment status and years since first exposure and
duration of exposure to phenoxy herbicides or chlorophenols. Comparison of workers exposed to TCDD or higher chlorinated dioxins to nonexposed
workers.
gExpected deaths based on age, sex, calendar-period-specific death rates of the former West Germany, 1952-1992.
hEstimated 2,3,7,8-TCDD concentration based on 138 employees.
'Rate ratio calculated using Cox regression; TCDD ng/kg bw forced into model.
JPYAR: Person years at risk.
^i
td
i
oo
o
O
2
O
H
O
HH
H
W
O
c
O
H
W
-------
Table 7-48. Results of studies examining the effect of dioxin on reproductive and developmental outcomes in humans,
1984-1992
Author
Smith
et al.,
1982
Townsend et
al., 1982
Exposed
group
548 male
pesticide
applicators
who
sprayed
2,4,5-T
and other
pesticides
370 male
chemical
workers
exposed to
2,4,5-T
only and
their spouses
Male chemical
workers
exposed to any
dioxins
Control
group
441
agricultural
contractors
345
employees
not exposed
to 2,4,5-T
and
their spouses
Type of
exposure
Spraying of
2,4,5-T
Working with
chlorophenol
processes
Data source:
exposure/
outcome
Mailed
survey/mailed
survey
Interviewer-
administered
questionnaire/
interviewer-
administered
questionnaire
Outcome
Total births
Congenital defect
Miscarriage1"
Stillbirth
# Conceptions
All fetal deaths
Stillbirth
Spontaneous
abortions
Infant deaths
Health defects
Congenital
malformation
Total conceptuses
All fetal deaths
Stillbirth
Spontaneous
abortions
Infant deaths
Health defects
Congenital
malformation
Outcome
inexp.
(N)
1,172
13
43
3
418
50
7
43
6
32
21
737
100
15
85
9
52
30
Outcome in
unexp. (N)
1,122
9
40
0
2,031
246
33
213
39
155
87
2,031
246
33
213
39
155
87
OR
1.19"
0.89
—
c
1.02
0.97
0.96
0.82
0.93
1.08
1.03"
1.06
1.03
0.63
0.85
0.85
95% CI
0.58-2.45
0.61-1.30
—
0.71-1.47
0.38-2.36
0.65-1.42
0.30-2.09
0.60-1.43
0.63-1.8
0.78-1.37
0.54-2.09
0.77-1.39
0.27-1.39
0.60-1.21
0.53-1.35
-------
Table 7-48. Results of studies examining the effect of dioxin on reproductive and developmental outcomes in humans,
1984-1992 (continued)
Author
Mastroia-
covo et al.,
1988
Stehr et al.,
1986
Hoffman and
Stehr-Green,
1989
Stockbauer et
al., 1988
Erickson et
al., 1984
Exposed
group
2,900 births in
zones A, B, and
R, Seveso,
Italy, 1977-
1981
68 persons
residing in
areas of high
TCDD cone.
154 residents of
Quail Run
Mobile Home
Park
402
pregnancies to
exposed
mothers
4,929 infants
from the Met.
Atlanta
Congenital
Defects
Program
Control
group
12,391 births
in study area
outside zones
A, B, and R
36 persons
with no
known contact
with contam.
soil
155 residents
nonexposed
mobile home
park
804
pregnancies to
unexposed
mothers
(matched on
maternal age
and race,
hospital and
year of birth,
plurality)
3,029 infants
from Georgia
vital statistics
records
Type of
exposure
TCDD cloud
released from
chemical plant
accident
Contact with
TCDD-contam.
soil
Contact with soil
sprayed with
TCDD for dust
control
Contact with soil
sprayed with
TCDD for dust
control
Vietnam military
service
Data source:
exposure/
outcome
TCDD soil analysis/
Seveso Congenital
Malformations
Registry
EPA soil analyses for
TCDD/ interview
EPA soil analyses for
TCDD/ interview
EPA soil analyses for
TCDD/vital statistics
and hospital records
Self-reported, and
Exposure Opportunity
Index/birth defects
registry and vital
statistics
Outcome
Total birth def.
Multiple birth
defects
Syndromes
Major birth
defects
Minor birth
defects
Infert. (males)
Impotence
Infert. (females)
Fetal deaths
Spontaneous
abortions
Congenital
malformations
Birth defects-all
Major birth
defects
Multiple birth
defects
Fetal deaths
Infant deaths
Perinatal deaths
Low birth wt.
Very low birth
weight
IUGR
Total birth defects
(96 subcategories
also examined)
Outcome
inexp.
(N)
137
10
5
67
70
—
—
—
17
15
2
4
5
6
27
1
14
428
Outcome in
unexp. (N)
605
38
29
343
262
—
—
—
42
35
11
5
5
9
36
4
26
4,387
OR
0.97
1.12
0.74
0.83
1.14
—
—
—
0.78
0.84
0.34
1.60
2.00
1.33
1.59
0.50
1.09
0.97
95% CI
0.83-1.13
0.63-2.02
0.33-1.63
0.67-1.04
0.92-1.42
NS
NS
NS
NS
NS
NS
0.40-1.47
0.40-1.66
0.03-1.65
0.32-7.43
0.46-8.69
0.39-4.20
0.89-2.81
0.01-5.05
0.50-2.28
0.83-1.14
-------
Table 7-48. Results of studies examining the effect of dioxin on reproductive and developmental outcomes in humans,
1984-1992 (continued)
Author
CDC, 1988d,
1989
Sub study #1,
CDC 1988d,
1989
Substudy #2,
CDC 1988d,
1989
Stellman et
al., 1988
Aschengrau
and Monson,
1989
Aschengrau
and Monson,
1990
Exposed
group
7,924 Vietnam
veterans
1,791 offspring
of Vietnam
veterans
127 offspring
of Vietnam
veterans
2,858 Vietnam
veterans
201
spontaneous
abortion cases
at Boston
Hospital for
Women
966 infants
with late adv.
preg. outcomes
at
Boston
Hospital for
Women
Control
group
7,364 non-
Vietnam
veterans
1,575
offspring of
non- Vietnam
veterans
94 offspring
of non-
Vietnam
veterans
3,933 non-
Vietnam
veterans
1,1 19 full-
term births at
Boston
Hospital for
Women
998 normal
term infants at
Boston
Hospital for
Women
Type of
exposure
Vietnam military
service
Vietnam military
service
Vietnam military
service
Vietnam military
service
Vietnam military
service
Vietnam military
service
Data source:
exposure/
outcome
Military records/self-
reports
Military records/self-
report and hospital
records verification
Military records/self-
report and hospital
record verification
Survey/
survey
Military records/
hospital records
Military records
Hosp. records
Outcome
Total birth def.
Spontaneous
abortion
Stillbirth
Low birth wt.
Childhood ca
Sperm
abnormalities:
concentration
motility
morphology
Total birth def
Low birth wt
Perinatal mort.
Suspected birth
defects
Cerebrospinal
malformations
Difficulty 349
conceiving
Time to
conception
Birth weight —
Spontaneous 231
abortion
Spontaneous abortion
Total birth def.
> 1 Major malf.
Minor malf
Stillbirths
Neonatal deaths
Outcome
inexp.
(N)
826
1,566
126
100
25
51
91
51
130
51
58
21
26
363
4.4 yrs
195
8
55
18
11
5
3
Outcome in
unexp. (N)
590
1,190
131
87
17
20
58
29
112
37
54
21
12
4.4 yrs
44
656
151
189
51
36
OR
1.3
1.3
0.9
1.1
1.5
2.3
1.2
1.6
1.0
1.1
1.0
0.9
1.8
1.2
1.3
0.9
1.3
1.8
0.9
1.5
1.2
95% CI
1.2-1.4
1.2-1.4
0.7-1.1
0.8-1.4
0.8-2.8
1.2-4.3
0.8-1.8
0.9-2.8
0.8-1.4
0.7-1.8
0.7-1.5
0.5-1.7
0.8-4.0
NS
NS
NS
1.4-2.0
0.42-1.9
0.9-1.9
1.0-3.1
0.5-1.7
0.4-3.9
0.2-4.2
-------
Table 7-48. Results of studies examining the effect of dioxin on reproductive and developmental outcomes in humans,
1984-1992 (continued)
Author
Wolfe et al,
1992b
Exposed
group
2,533
conceptions
among 791
Ranch Hand
personnel
Control
group
2,074
conceptions
among 768
non-Ranch
Hand
personnel
Type of
exposure
Spraying/
handling of
Agent Orange
Data source:
exposure/
outcome
Serum TCDD levels/
hospital and medical
records
Outcome
Total birth def.
<10k
15-<33.3
>33.3
Nervous system
anomalies
<10k
15-<33.3
>33.3
Respiratory sys.
anomalies
<10k
15-<33.3
>33.3
Digestive system
anomalies
<10k
15-<33.3
>33.3
Genital anomalies
<10k
15-<33.3
>33.3
Urinary system
anomalies
<10k
15-<33.3
>33.3
Musculoskeletal
deformities
<10k
15-<33.3
>33.3
Outcome
inexp.
(N)
202. 11
293.1
193.8
O.O1
5.7
13.2
7.1'
5.7
4.4
21.31
34.5
17.6
3.5'
51.7
13.2
14.21
34.5
22.0
120.61
143.7
105.7
Outcome in
unexp. (N)
208.0
3.1
2.0
24.51
18.31
12.21
134.61
OR
p.96m-
1 ^8
1. Jo
0.92
—
1 QQm,
1.88
4.37m-
3.5m'°
2.83
2.17
0.83
1.30
0.64
0.19
2 92
0.72
1.16
2.88
1.82
p.88m>
1.08
0.76
95% CI
0.69-1.34
1.10-2.27
0.64-1.32
0.20-18.3
0.87,21.8
0.49-25.0
0.26-31.4
0.20-24.0
0.31-2.23
0.48-3.51
0.21-1.91
0.03-1.43
1.29-6.61
0.21-2.46
0.37-3.63
1.07-7.79
0.63-5.22
0.59, 132
0.68-1.71
0.48-1.21
-------
o
O
to
O
O
oo
Table 7-48. Results of studies examining the effect of dioxin on reproductive and developmental outcomes in humans,
1984-1992 (continued)
Author
Wolfe et al.,
1995
Exposed
group
1,006
conceptions
among 454
Ranch Hand
personnel
Control
group
1,235
conceptions
among 570
non-Ranch
Hand
personnel
Type of
exposure
Spraying/
handling of
Agent Orange
Data source:
exposure/
outcome
Serum TCDD
levels/hospital and
medical
records
Outcome
Anomalies of the
skin
<10k
15-<33.3
>33.3
Circulatory system
and heart anomalies
<10k
15-<33.3
>33.3
Spontaneous
abortion:
Comparisons
Background
RHLow
RH Hiah
Stillbirth:
Comparisons
Background
RHLow
RH High
Developm'tal delays:
Comparisons
Background
RHLow
RH High
Major birth defects:
Comparisons
Background
RHLow
RH High
Outcome
inexp.
(N)
17.7'
34.5
8.8
14.21
46.0
8.8
57
56
44
7
6
1
24
26
21
17
23
19
Outcome in
unexp. (N)
21.41
16.3
172
13
71
56
OR
0.76
1.83
0.46
0.85
2.16
0.32
1
1.1
1.3
1.0
1
1.8
1.8
0.3
1
1.2
1.5
1.1
1
1.1
1.7
1.2
95% CI
0.25-2.26
0.72-4.70
0.11-1.95
0.27-2.69
0.81-5.74
0.04-2.52
0.8-1.5
1.0-1.7
0.7-1.3
0.7-4.5
0.7-4.7
0.0-2.3
0.8-1.8
1.0-2.3
0.7-1.7
0.6-1.8
1.1-2.7
0.8-2.1
td
oo
O
O
2
O
H
O
O
V
O
c
O
"Relative risk.
bRate: 86/1,000 births in applicators; 93/1,000 births in agricultural contractors
(controls).
"Adjusted for mother's age at time of birth, birth control methods, labor and delivery
complications, medical conditions and medications during
pregnancy, smoking and alcohol use during pregnancy, high job risk, and gravidity.
sRate: zone A: 0; zone B: 57.5/1,000 births; zone R: 45.1/1,000 births; zone non-ABR:
48.8/1,000 births.
'90% CI.
fZones AandB.
8Zones A and B vs. non-ABR.
'Controls matched on maternal age, race, hospital of birth, plurality, and year of birth.
'Odds ratio adjusted for veteran's age at birth, year of entry in army, enlistment status,
general technical test score, military occupational specialty, years between entry and
birth, maternal age, and gravidity .^Children born after the father was stationed in
Southeast Asia (SEA).
kLogit (p) = Po + PA + P2d2 + P3d3, where p = probability of an adverse reproductive
outcome; d1; d2, d3 are indicators for the dioxin
categories: Unknown (Ranch Hands with up to 10 pg/g current dioxin), Low (Ranch
-------
Hands with more than 15 pg/g of lipid and up to 33.3 pg/g
of lipid current dioxin), and High (Ranch Hands with more than 33.3 pg/g of lipid
current dioxin).
'Rate/1000 of abnormals.
"Unadjusted.
"Adjusted analysis not statistically significant.
"No adjusted analysis: total defects <10.
pComparions: < 10 ppt; Ranch Hand (RH) Background < 10; RH LOW with current
level> 10 ppt and estimated initial level< < 1 lOppt; and RH HIGH with current level> 10
ppt and estimated initial level > 110 ppt.
-------
Table 7-49. 2,3,7,8-TCDD levels (pg/g of lipid) for selected populations
O
O
to
O
O
td
oo
oo
O
O
2
O
H
O
HH
H
W
O
Author
Mocarelli et
al., 1991
Patterson et
al., 1986b
Smith etal.,
1992
CDC, 1988
Kahn, 1988
Schecter et
al., 1989
Study
population
Seveso, Italy
residents:
10 Zone A
10 former Zone
A
10 non-ABR
Zone
39 Missouri
residents with
history of TCDD
exposure
57 Missouri
residents with no
known TCDD
exposure
9 New Zealand
pesticide
applicators
9 controls
646 Vietnam
ground combat
troops with
service in
heavily sprayed
areas
97 non- Vietnam
veterans
10 "heavily
exposed"
Vietnam
veterans
10 Vietnam
veterans with
"little or no"
exposure
7 non- Vietnam
veterans
26 Vietnam
veterans
Specimen
Serum
Adipose tissue
Serum
Serum
Blood (per
lipids)
Adipose tissue
Blood (per
lipids)
Adipose tissue
Blood (per
lipids)
Adipose tissue
Adipose tissue
Range
828 - 56,000
1770 - 10,400
nd-137
2.8-750
1.4-20.2
3.0-131.0
2.4-11.3
nd-45
nd-15
—
—
—
—
—
nd-11
Mean
19,144
5,240
—
79.7
7.4
53.3
5.6
4.2
4.1
46.3
41.7
6.6
5.1
4.3
3.2
5.8
Median
14,000
4,540
—
17.0
6.4
37.6
9.3
3.8
3.8
25.1
15.4
5.3
5.4
3.9
3.5
-------
o
rt>
o
rt>
to
o
o
Table 7-49. 2,3,7,8-TCDD levels (pg/g of lipid) for selected populations (continued)
td
oo
VO
Author
Kang, 1991
Roegner et
al., 1991
Phuong,
1989b
Study
population
36 Vietnam
veterans
79 non- Vietnam
veterans
80 civilians
872 Ranch
Hands
1,060 controls
Vietnamese
populations:
9 OB/GYN
patients from a
South Vietnam
hospital
Specimen
Adipose tissue
Serum
Adipose tissue
Range
—
—
—
0-617.8
0-54.8
nd-103
Mean
13.4
12.5
15.8
—
—
23
Median
10.0
11.4
11.8
12.8
4.2
11.3
O
o
*
o
H
O
HH
H
W
O
&
O
c
o
H
W
-------
Table 7-50. Odds ratios for selected categories of birth defects for the telephone
interview and hospital records study in the Vietnam experience study, 1989
Birth defects
Vietnam veterans
rate/1,000
Controls
rate/1,000
OR
95% CI
Telephone interview
Total
64.6
49.5
1.3
1.2-1.4
Hospital records study
Total
Major
Minor
72.6
28.5
32.4
71.1
23.5
34.3
1.0
1.1
1.00
0.8-1.4
0.7-1.8
0.7-1.5
Adapted from the Centers for Disease Control Vietnam Experience Study, 1988d.
December 2003
7B-190 DRAFT—DO NOT CITE OR QUOTE
-------
Table 7-51. Results of the misclassification analyses for birth defects
in the hospital records substudy, Vietnam experience study, 1989
Vietnam veterans
PPV
NPV
Sensitivity
Specificity
% Agreement
Kappa index
24.8%
95.2%
27.1%
94.7%
90.6%
20.9%
Non- Vietnam veterans
PPV
NPV
Sensitivity
Specificity
% Agreement
Kappa index
32.9%
95.8%
30.3%
96.2%
92.4%
27.6%
PPV = Positive predictive value.
NPV = Negative predictive value.
Adapted from the Centers for Disease Control Vietnam Experience Study, 1989.
December 2003
7B-191 DRAFT—DO NOT CITE OR QUOTE
-------
Table 7-52. Rates of miscarriage (per 1,000) by pre- and post-Vietnam tour status
and time since tour of duty, among 1,475 Ranch Hands with > 10 pg/g serum
dioxin, Ranch Hand study, 1992a
Time of
conception
Pre -tour
Post-tour
Time since
tour (years)
<18.6
>18.6
<18.6
>18.6
Miscarriage rate per 1000
(no. In)
by current dioxin level
10-14.9 pg/g
142.0
(23/162)
123.9
(14/113)
92.1
(7/76)
237.3
(14/59)
15-33.3 pg/g
146.8
(32/218)
159.4
(33/207)
136.6
(22/161)
198.6
(29/146)
>33.3
Pg/g
48.8
(2/41)
166.7
(16/96)
168.5
(15/89)
121.5
(13/107)
/rvalue
0.014b
"Adapted from Wolfe et al., 1992b.
bComparison of pre- and post-tour data.
December 2003
7B-192 DRAFT—DO NOT CITE OR QUOTE
-------
Table 7-53. Summary of effects observed in adults exposed to 2,3,7,8-TCDD
System
Dermatologic
Liver
Liver enzymes
GI other than liver
Urinary porphyrins
Lipids
Cholesterol
Triglycerides
Thyroid
Diabetes
Immune
Neuro
Circulatory
Pulmonary
Renal
Reproductive hormones
Chromosome
Acute
Conjunctivitis
Red and irritated eyes
Blepharitis
Temporary enlargement
T GOT
T AST
T ALT
T D-glucaric acid excretion
+\- RUQC pain
Loss of appetite
Nausea
+/-b
PCT
Uroporphyrin
Urobilinogen
Coproporphyrin
T
T
T
TT4
T T/TBG in some studies
No data
No data
+/- I libido
T irritability
T nervousness
I pin prick sensation
—
Irritation
—
No data
+/-
Chronic
chloracne
a
T GGT
—
—
—
—
—
—
+/-
+/- TNK cells
+/- TlgA
—
+/-
—
—
+/- LH in cf
+/- FSH in c?
I testosterone in cf
no data
aNo effect noted.
bSome positive and some negative studies.
°Right upper quadrant.
December 2003
7B-193 DRAFT—DO NOT CITE OR QUOTE
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Table 7-54. Effects of exposure to 2,3,7,8-TCDD on serum glucose levels in
nonhuman mammalian species
Author
Zinkl et
al.,
1993
Gasiewi
czet
al.,
1980
Schiller
etal.,
1986
Gorski
etal.,
1990
McConn
ellet
al.,
1978a
DeCapri
o et al. ,
1986
Ebner et
al.,
1988
Species
CD rat
Rats
Fischer
rat
Sprague-
Dawley
rat
Rhesus
monkey
Hartley
guinea
Pig
New
Zealand
rabbits
Route
oral
ip
(TPN)
ip
(chow-
fed)
gavage
ip
gavage
oral
ip
Dose
(H/kg)
0.1
1.0
10.1
100C
30
60
90
180°
270C
360C
125C
70
350°
0.01
0.06
0.44
1
50
Duration
Ix/day for 30
days
1 time
1 time
1 time
1 time
90 days
1 time
Percentage of
serum
glucose levels in
control animals"
91b
71
51
29d
51d
74d,e
54d,e
30d-e
43d,e
38d-e
39d,e
day 4 75M
day 8 67M
day 16 50M
day 21 31M
(decreased)b'd
NCb,e,f
NCb,e,f
NCb,e,f
NC
15 min NC
1 hour 125d-e
2 day 87
10 day 80d-e
"Relative to control values.
Temale animals.
°Lethal dose.
dSignificantly different from controls.
"Male animals.
fData not displayed.
ip = intraperitoneal.
TPN = total parenteral nutrition.
NC = no change.
December 2003
7B-194 DRAFT—DO NOT CITE OR QUOTE
-------
REFERENCES FOR CHAPTER 7, PART B
Air Force Health Study. (1995) An epidemiologic investigation of health effects in Air Force personnel
following exposure to herbicides. 1992 Followup examination results. 10 vols. Brook AFB, TX: Epidemiologic
Research Division, Armstrong Laboratory.
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DRAFT NCEA-I-0835
DO NOT CITE OR QUOTE December 2003
NAS Review Draft
www.epa.gov/ncea
Chapter 8. Dose-Response Modeling for 2,3,7,8-TCDD
Exposure and Human Health Reassessment
of 2,3,7,8-Tetrachlorodibenzo-p-Dioxin (TCDD)
and Related Compounds
Part II: Health Assessment for 2,3,7,8-Tetrachlorodibenzo-/7-dioxin
(TCDD) and Related Compounds
NOTICE
THIS DOCUMENT IS A PRELIMINARY DRAFT. It has not been formally released by the U.S.
Environmental Protection Agency and should not at this stage be construed to represent Agency
policy. It is being circulated for comment on its technical accuracy and policy implications.
National Center for Environmental Assessment
Office of Research and Development
U.S. Environmental Protection Agency
Washington, DC
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DISCLAIMER
This document is a draft. It has not been formally released by the U.S. Environmental
Protection Agency and should not at this stage be construed to represent Agency policy.
Mention of trade names or commercial products does not constitute endorsement or
recommendation for use.
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TABLE OF CONTENTS - OVERVIEW
Exposure and Human Health Reassessment
of 2,3,7,8-Tetrachlorodibenzo-/7-Dioxin (TCDD)
and Related Compounds
Part I: Estimating Exposure to Dioxin-Like Compounds (Draft Final)
(EPA/600/P-00/001 Bb, Be, Bd) September 2000
Volume 1: Sources of Dioxin-Like Compounds in the United States (EPA/600/P-00/001Bb)
Chapters 1 through 13
Volume 2: Properties, Environmental Levels, and Background Exposures
(EPA/600/P-00/001Bc) Chapters 1 through 6
Volume 3: Site-Specific Assessment Procedures (EPA/600/P-00/001Bd)
Chapters 1 through 8
Part II: Health Assessment for 2,3,7,8-Tetrachlorodibenzo-p-dioxin (TCDD) and Related
Compounds (Draft Final)
(EPA/600/P-00/001Be) September 2000
Chapter 1. Disposition and Pharmacokinetics
Chapter 2. Mechanism(s) of Actions
Chapter 3. Acute, Subchronic, and Chronic Toxicity
Chapter 4. Immunotoxicity
Chapter 5. Developmental and Reproductive Toxicity
Chapter 6. Carcinogenicity of TCDD in Animals
Chapter 7. Epidemiology/Human Data
Chapter 8. Dose-Response Modeling for 2,3,7,8-TCDD
Chapter 9. Toxic Equivalency Factors (TEF) for Dioxin and Related Compounds
Part III: Integrated Summary and Risk Characterization for
2,3,7,8-Tetrachlorodibenzo-p-Dioxin (TCDD) and Related Compounds
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CONTENTS
8. DOSE-RESPONSE MODELING 8-1
8.1. INTRODUCTION 8-1
8.1.1. Overview 8-1
8.1.2. What Is Dose? 8-1
8.1.3. What Is Response? 8-2
8.1.4. What Is Modeling? 8-6
8.1.5. Empirical Modeling 8-8
8.1.6. Mechanism-Based and Mode-of-Action-Based Modeling 8-8
8.1.7. Elements of Chapter 8 8-11
8.2. DOSE METRICS 8-11
8.2.1. Introduction 8-11
8.2.2. Selection of Effective Dose Levels 8-14
8.2.3. Dose Corrections for Species Differences in Half-Lives 8-17
8.3. EMPIRICAL DOSE-RESPONSE MODELING OF INDIVIDUAL
DATA SETS 8-17
8.3.1. Introduction 8-17
8.3.2. Human Dose-Response Models 8-18
8.3.2.1. All Cancers Combined and Lung Cancer 8-18
8.3.2.2. Average Body Burden 8-23
8.3.2.3. NoncancerEndpoints 8-24
8.3.2.4. Uncertainties in Estimates From Human Epidemiology 8-25
8.3.2.5. Conclusions for Human Cancer Dose-Response
Modeling 8-28
8.3.2.6. Additional Knowledge Gaps in Human Cancer
Dose-Response Modeling 8-28
8.3.3. Rodent Dose-Response Models: Cancer Endpoints 8-29
8.3.3.1. Animal Cancer Studies for Dose-Response Modeling 8-29
8.3.3.2. Conclusions From Animal Cancer Dose-Response
Modeling 8-30
8.3.3.3. Knowledge Gaps in Animal Cancer Dose-Response
Modeling 8-30
8.3.4. Rodent Dose-Response Models: Noncancer Endpoints 8-31
8.3.4.1. Methodology 8-31
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CONTENTS (continued)
8.3.4.2. Multiple-Dose Studies 8-35
8.3.4.3. Single-Dose Studies: Adult Animals 8-37
8.3.4.4. Single-Dose Studies: Developmental Studies 8-38
8.3.4.5. Summary of the Dose-Response Modeling for
Noncancer Endpoints 8-39
8.4. MODE-OF-ACTION-BASED DOSE-RESPONSE MODELING 8-43
8.4.1. Introduction 8-43
8.4.2. Model Structures and Model Development 8-44
8.4.2.1. PBPK Models 8-44
8.4.2.2. Biochemical, Tissue, and Endocrine Response Models 8-52
8.4.3. Application of Models 8-58
8.4.3.1. ModelingPreneoplasticLesions 8-59
8.4.3.2. Estimation of Cancer Risks 8-62
8.4.4. Knowledge/Data Gaps 8-63
8.4.5. Summary 8-65
8.5. DATA GAPS 8-66
8.6. SUMMARY 8-67
8.7. CONCLUSIONS 8-71
REFERENCES FOR CHAPTER 8 8-112
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LIST OF TABLES
8-1. Estimated half-lives for species considered in the analyses to
follow and used for converting between daily exposures and
steady-state body burdens 8-74
8-2. Maximum likelihood (95% lower bound) estimates for average
body burden yielding 1% added risks for lung cancer and total
cancer response from three epidemiological studies 8-75
8-3. Doses yielding 1% excess risk (95% lower confidence bound)
based upon 2-year animal carcinogenicity studies using simple
multistage models 8-76
8-4. Noncancer endpoints used for comparing ED01 values 8-77
8-5. Ratio of ED01/lowest dose, categorized by study type and
endpointtype 8-78
8-6. Estimated shape parameters, categorized by study type and
endpoint type 8-79
8-7. Categorization of specific endpoints 8-80
8-8. Steady state ED01 values calculated using mechanism-based
dose-response models of dioxin-regulated responses 8-82
LIST OF FIGURES
8-1. Distribution of ED01 and BB01 values in multidose studies by endpoint 8-83
8-2. Distribution of ED01 values in single-dose studies by endpoint 8-84
8-1. Distribution of ED10s in multidose studies and single-dose studies by endpoint 8-85
8-4. Schematic representation of the linkage of current PBPK models
and biochemical/tissue response models for TCDD action 8-86
APPENDICES
Appendix I: Multiple-dose studies 8-87
Appendix II: Single-dose adult studies 8-99
Appendix HI: Single-dose developmental studies 8-106
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8. DOSE-RESPONSE MODELING
8.1. INTRODUCTION
8.1.1. Overview
This chapter describes concepts that embody the evaluation of dose-response relationships
for the dioxins and related compounds and examines dose-response models for
2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD). TCDD is the most potent form of a broad family of
xenobiotics that bind to an intracellular protein known as the aryl hydrocarbon receptor (AhR)
(Chapter 2). Other members of this family, in addition to the polychlorinated dibenzodioxins
(PCDDs), include polyhalogenated hydrocarbons such as the polychlorinated dibenzofurans
(PCDFs), polychlorinated biphenyls (PCBs), and polychlorinated naphthalenes (PCNs). In addition,
there are other classes of chemicals that bind to the AhR, such as polynuclear aromatic
hydrocarbons and naturally occurring compounds. A detailed discussion of the interactions of these
chemicals and the concept of TCDD equivalence is presented in Chapter 9. The biological and
toxicological properties of dioxins have been investigated extensively in more than 5,000
publications and abstracts since the identification of TCDD as a chloracnogen (Kimmig and Schulz,
1957). Some data sets on members of this family of compounds other than TCDD are clearly
amenable to dose-response modeling. However, this chapter focuses exclusively on studies in
laboratory animals that can be used to evaluate dose-response for TCDD. In addition, it evaluates
human data where exposure to TCDD has been estimated and dose-response can be modeled
quantitatively.
Most of the information presented in this introduction is found in more extensive detail later
in this chapter or in the other parts of this reassessment. This introduction sets the stage for
discussion of dose-response modeling of TCDD by briefly answering the questions, "what is dose?"
"what is response?" and "what is modeling?" It then goes on to describe and, to a limited degree,
compare different modeling approaches. This introduction also shows the reader the types of data
and information available for TCDD that may have an impact on the development of dose-response
models. Both in the introduction and throughout this chapter, gaps in knowledge relating to the
evaluation of TCDD dose-response are identified. Understanding these gaps and their impact on the
conclusions of this chapter can guide the design of new experiments that will add to our knowledge
of TCDD action and clarify issues related to its dose-response.
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8.1.2. What Is Dose?
When performing dose-response analyses, it is critical to understand what is meant by dose
and how it applies to the response. The dose, in dose-response modeling, is an inclusive term.
Examples of dose include the amount of TCDD given to an experimental animal by some specific
route at some specific frequency, measured tissue concentrations in laboratory studies, body burdens
attained in these studies, or daily exposure seen by workers in an occupational setting. In general,
units of dose should reflect the magnitude of the exposure and the frequency over which it applies.
Dose can be expressed in a multitude of metrics. Some of these metrics include daily intake
(ng/kg/day), total body burden (ng/kg), body burden averaged over a given period of time, or tissue
concentration. Depending on the particular endpoints to be compared, and in consideration of the
half-life of elimination of TCDD (see Section 8.2), it may be possible to express dose in a form that
allows comparison of response across various endpoints and species. Specific issues relating to
dosage and comparison across species and endpoints are discussed in Section 8.2.
Most, if not all, of the effects elicited by TCDD are mediated by the ability of this chemical
to bind to and activate the AhR. The activation of this protein leads to a series of molecular and
biochemical events that ultimately contribute to particular biological responses (see Part II, Chapter
2). It is clear from the available human and animal data that TCDD can elicit many types of
responses depending on the species, the age of the animal at exposure, and whether the exposure is
acute or chronic. These responses vary from biochemical alterations such as enzyme induction,
which may require only acute exposures, to developmental effects, which may require a level of
exposure at a particular window of tissue development, to more complex responses such as cancer,
which may require prolonged exposures (Section 8.1.3). To determine what might be the most
sensitive endpoints, the species variation in sensitivity to these endpoints, and how these differences
or similarities might be extrapolated to effects in humans, requires a comparison of the amount, or
dosage, of TCDD that is present in particular tissues and/or the whole organism.
Dose is not always a known quantity. For humans, the actual dose is rarely known and best
estimates are made on the basis of several assumptions and observations made at only a few time
points, often many years after what may be believed to be the period of highest exposures. For
these cases, models of exposure linked to response data may be used to develop a dose-response
model. However, limited knowledge of the events that control tissue distribution (especially in
humans at low levels of exposure) and those molecular and biochemical processes that ultimately
lead to particular responses contribute uncertainty in these analyses.
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8.1.3. What Is Response?
Response, in this context, generally relates to an observation seen in an animal or a human
following exposure to TCDD. These responses cover a broad range of observations, ranging from
early responses such as biochemical alterations that are closely coupled to activation of the AhR to
more complicated responses such as cancer and developmental defects. The responses are
sometimes species- and/or tissue-specific and have different degrees of variation across individuals.
However, there is some commonality across species and there are known linkages between some
responses (e.g., mRNA serves as a precursor molecule for the synthesis of protein). Dose-response
modeling can address endpoints separately, provide insight into their quantitative similarity across
species and tissues, and link responses in a mechanistically reasonable manner.
The binding of TCDD to the AhR is similar, although not identical, to the interaction of
many steroid hormones with their intracellular receptors (Poellinger et al., 1987; Cuthill et al., 1991;
DeVito et al., 1991; Lucier et al., 1993). An overall hypothesis for the mode of action of TCDD,
put forth by several groups, is based on the transcriptional activation of expression of specific
genes. This hypothesis has been most well characterized for transcriptional activation of the
cytochrome CYP1 Al gene. There is also some evidence to indicate that activation of the AhR by
TCDD may elicit responses by mechanisms that may not involve direct transcriptional activation of
genes. The biological basis for these models of AhR action is outlined in Part II, Chapter 2. It is
accepted by most researchers that most, if not all, cellular responses to TCDD require the initial
interaction between TCDD and the AhR.
Although gaps in our knowledge remain, evidence to date is consistent with the hypothesis
that binding of TCDD to the AhR and inappropriate activation of this protein represent the first
steps in a series of biochemical, cellular, and tissue changes that define the toxicity observed. These
changes are defined as responses to TCDD. Evidence to support this theory has been reviewed in
several sections of this document as well as in the peer-reviewed literature (Safe, 1990; Birnbaum,
1994; Poland and Knutson, 1982). Many of the known biological activities of related PCDDs and
PCDFs also appear to follow their rank order of binding affinity of the congeners and analogues to
the AhR (see Part II, Chapters 2 and 9). This rank order holds for toxic responses such as acute
toxicity and teratogenicity and for changes in concentration of several proteins, including the
induction of cytochromes P-450 1A1 (CYP1A1), 1A2 (CYP1A2), estrogen receptor, and epidermal
growth factor receptor (EGFR). The direct relationship between AhR binding and carcinogenicity
of TCDD is less clear, although limited structure activity relationship studies on tumor promotion
demonstrate a rank order in potency similar to binding to the Ah receptor (see Part II, Chapter 9).
The AhR has been identified in numerous mammalian species including humans (Okey et
al., 1994; Roberts et al., 1985, 1986; Abbott, 1995; Manchester et al., 1987; Lorenzen and Okey,
1991; Cook and Greenlee, 1989), several non-mammalian vertebrates including chicken embryos
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(Denison et al., 1986) and newts (Marty et al., 1989), and several aquatic species from whales to
teleosts and elasmobranchs (Hahn, 1998). The broad phylogenetic distribution in vertebrate
evolution (Hahn, 1998) and the phylogenetic conservation of this receptor also suggest that it has an
important role in regulating cellular function in vertebrate animals. However, the physiological role
or function of this receptor has yet to be determined.
Although the human data are limited, there is relatively good concordance for the
biochemical/molecular effects of TCDD between laboratory animals and humans, indicating that
animal models are generally appropriate for estimating human responses. Where wide species
differences exist, understanding the relative sensitivity of human responses may not be possible at
this time. However, many of the biochemical effects produced by TCDD and its analogues in
animals also occur in humans. Data on effects of TCDD and its analogues in humans are based on
in vitro (i.e., in cell culture) as well as epidemiological studies. Placentas from Taiwanese women
exposed to rice oil contaminated with dioxin-like PCBs and PCDFs have markedly elevated levels
of CYP1A1 (Lucier et al., 1987). Comparison of these data with induction data in rat liver suggests
that humans are at least as sensitive as rats to enzyme-inductive actions of TCDD and its structural
analogues (Lucier, 1991). Consistent with this contention, the in vitro EC50 for TCDD-mediated
induction of CYP1A1-dependent enzyme activities is -1.5 NM when either rodent or human
lymphocytes are used (Clark et al., 1992). The human AhR appears to have greater than a
twentyfold range in TCDD affinity (Okey et al., 1994). This range is comparable to that of the
sensitive and resistant mouse strains as well as that of rats (see Chapter 2). It does appear that
humans contain a fully functional AhR (Cook and Greenlee, 1989), as evidenced by significant
CYP1 Al induction in tissues from exposed humans, and that this response occurs with similar
sensitivity as observed in experimental animals.
One of the biochemical effects of TCDD that might have particular relevance to toxic effects
is the loss of plasma membrane EOF receptor. There is evidence to indicate that TCDD and its
structural analogues produce the same effects on the EOF receptor in human cells and tissues as
observed in experimental animals. Incubation of human keratinocytes with TCDD decreases
plasma membrane EGF receptor, and this effect is associated with increased synthesis of
transforming growth factor-cc (TGF-oc) (Choi et al., 1991; Hudson et al., 1985). Placentas from
humans exposed to rice oil contaminated with PCDFs also exhibited markedly reduced
EGF-stimulated autophosphorylation of the EGF receptor, and this effect occurred with similar
sensitivity as observed in rats (Lucier, 1991; Sunahara et al., 1989). The magnitude of the effect on
autophosphorylation was positively correlated with decreased birth weight of the offspring.
Chloracne, a well-known response observed in highly exposed humans, has also been shown
to occur in several animal species including nonhuman primates, rabbits, and hairless mice.
However, it should be noted that in populations exposed to similar amounts of TCDD (e.g., Seveso,
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Italy), some humans may exhibit chloracne while others do not. In mice, responsiveness to TCDD
and related chemicals can be modified by genes as well as the AhR. For example, mice congenic at
the hairless (hf) locus demonstrate altered sensitivity to the chloracnegenic and tumor-promoting
effects of TCDD (Poland et al., 1982). These data suggest that there may be multiple factors (e.g.,
genetics) that may contribute to the development of a particular response both within and between
species.
Several reports in the literature suggest that exposure of humans to TCDD and related
compounds may be associated with cancer at many different sites, including malignant lymphomas,
soft tissue sarcomas, hepatobiliary tumors, hematopoietic tumors, thyroid tumors, and respiratory
tract tumors. These studies are evaluated in Part n, Chapter 7a, including discussion of
confounding factors and strength of evidence. TCDD is a carcinogen in several species of
laboratory animals (mice, rats, hamsters, fish) and the tumor sites include liver, thyroid, and the
respiratory tract, as well as others.
Several noncarcinogenic effects of PCDDs and PCDFs show good concordance between
laboratory species and humans (DeVito et al., 1995). For example, in laboratory animals, TCDD
causes altered intermediary metabolism manifested by changes in lipid and glucose levels.
Consistent with these results, workers exposed to TCDD during the manufacture of trichlorophenol
showed elevated total serum triglycerides and cholesterol with decreased high density lipoprotein
(Walker and Martin, 1979), similar to results seen in Air Force personnel following exposure to
Agent Orange (Wolfe et al., 1990; Fallen et al., 1994). Another interesting finding of these studies
was a positive relationship between TCDD exposure and diabetes (see Part II, Chapter 7b).
There are also differences between human and animal effects associated with TCDD. For
example, chloracne has been observed in exposed humans but in only some animal species.
Similarly, increases in humans of certain cancers such as soft-tissue sarcoma have not been
observed in animals (see Part n, Chapters 6 and 7). Also, immunotoxic endpoints consistently seen
in animals have rarely been demonstrated, or looked for, in humans (see Part II, Chapter 4). The
recognition of these similarities and differences is essential when using animal data to estimate
human effects. Understanding of these similarities and differences can substantially improve
dose-response analysis.
The human-to-experimental-animal comparison is also complicated by several other factors:
(1) for most toxic effects produced by dioxin, there is marked species variation. An outlier
or highly susceptible species for one effect (i.e., guinea pigs for lethality or mice for
teratogenicity) may not be an outlier for other responses;
(2) human toxicity testing is based on epidemiological data comparing "exposed" to
"unexposed" individuals. However, the "unexposed" cohorts contain measurable amounts
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of background exposure to PCDDs, PCDFs, and dioxin-like PCBs. Also, the results of
many epidemiological studies are hampered by small sample size, and in many cases the
actual amounts of TCDD and related compounds in the human tissues were not examined;
and
(3) In addition, it is often difficult, if not impossible, to assess in humans the same
endpoints that might be determined in experimental animals (e.g., some immunotoxic
effects and altered liver enzymes).
In summary, for many of the biological responses elicited by TCDD, animal models appear
to be reasonable surrogates for estimating human risks. However, it must be kept in mind that the
animal-to-human comparison would be strengthened by additional mechanistic information,
especially the relevance of specific molecular/biochemical precursors to toxic responses. It is also
important to note that the key events leading to carcinogenesis may be quite different at different
sites (see Part II, Chapter 6).
8.1.4. What Is Modeling?
In the sciences, a model is a representation of how something works. Models are of several
types, such as conceptual (e.g., a mental image of how something works), biological (e.g.,
transgenic mice as a surrogate for a human system), physical (e.g., a three-dimensional model of the
human heart) and mathematical (e.g., a physiologically based pharmacokinetic model [PBPK]).
Any model is defined by a set of parameters that make up its key components, and usually has
inputs (e.g., dose) and outputs (e.g., response) that correspond to its real-world counterparts.
Mathematical models of dose-response generally can be classed into two broad areas: empirical
models and mechanism-based or mode-of-action models; these are described in the next two
sections.
Modeling involves the application of a mathematical model to data as a tool to allow for
analysis and prediction. Any modeling exercise requires the estimation of model parameters. The
tools used to estimate parameters range from very simple techniques, such as estimating a slope of a
straight line (linear regression), to extremely complicated approaches, such as estimation by
maximizing a statistical likelihood function comprising unknown model parameters. In some cases,
estimation of parameters in a model involves choosing a value based upon scientific judgment. The
quality of any parameter estimate is dependent on the available data to characterize the model. The
quality of the data and information used to develop a mathematical model is the major component
in determining the confidence placed in any conclusions or predictions from that mathematical
model.
Dose-response models for receptor-mediated events should use information on the
quantitative relationships among ligand concentration, receptor occupancy, and biological response.
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For example, Roth and Grunfeld (1985) state: "At very low concentrations of hormone receptor,
occupancy occurs but may be trivial; i.e., the curve approaches 0% occupancy of receptors. But if
there are 10,000 receptors per cell (a reasonable number for most systems), the absolute number of
complexes formed is respectable even at low hormone concentrations. One advantage of this
arrangement is that the system is more sensitive to changes in hormone concentration; at receptor
occupancy (occupied receptors/total receptors) below 10%, the concentration of occupied receptors
is linearly related to the concentration of hormone, whereas at occupancies of 10 to 90%, the
concentration of HR is linear with log hormone concentration, a given increase in the concentration
is more effective in generating occupied receptors at the lowest part of the curve than at the
middle."
It is clear that multiple dose-response models are possible when considering ligand-receptor
mediated events. For example, when there is a proportional relationship between receptor
occupancy and biological response, occupancy of any number of receptors would produce a
response, although it would be unlikely that the response could be detected if the number of
receptors occupied was very low. Given this proportionality, a simple model, describing the
response as a linear function of dose, may be adequate. However, such a simple relationship is
unlikely to explain the diversity of biological responses that can be elicited by a single hormone
utilizing a single receptor. For example, low concentrations of insulin produce much greater effects
on fat cells than on muscle cells because fat cells have more receptors. These differences are due to
cell-specific factors that determine the qualitative relationship between receptor occupancy and
response. Similarly, it is expected that there are markedly different dose-response relationships for
different effects of TCDD.
Coordinated biological responses, such as TCDD-mediated increases in cell proliferation,
likely involve other systems, which means that the dose-response relationships for relatively simple
responses (i.e., CYP1A1 induction) may not accurately predict dose-response relationships for
complex responses such as cancer. Thus, it is necessary to consider what is known and observed
regarding a biological response before a reasonable mathematical model can be applied to the data.
Responses that include coordination of multiple steps that have linear dose-response relationships
may ultimately produce markedly nonlinear dose-response relationships.
The goal of mathematical modeling should be to use as much data as possible to reduce
uncertainties and to identify the areas where data gaps exist. Several important concepts have been
generally accepted that may determine the types of mathematical models one might apply to
responses due to exposure to TCDD:
(1) TCDD is a member of a class of xenobiotics (and probably natural products) that is not
directly DNA reactive, binds to a cellular receptor, alters gene expression, and alters cell
growth and development;
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(2) a significant amount of information is available for estimating risks from exposure to
this compound, and these data should be used to their fullest extent; and
(3) the biology of receptor-mediated events should be included to the greatest extent
possible in any modeling exercise for TCDD, empirical or mechanism-based.
8.1.5. Empirical Modeling
By its very nature, data applicable to dose-response modeling can generally be expressed
through groups of individuals (cells, animals, humans) exposed to a common level of a toxic agent
(TCDD) for which some response is measured. Given sufficient numbers of exposure groups, it is
possible to see a pattern arise, which indicates a change of that response as a function of increasing
dose. Empirical dose-response modeling attempts to find a simple mathematical model that
adequately describes this pattern. Empirical models generally have little or no direct linkage to the
underlying mechanisms driving a given response, but instead focus on flexible mathematical forms
that can fit a broad spectrum of data and allow comparisons across individual data sets. However,
empirical models should be interpreted in light of information available on the biology of the
modeled response and, in doing so, can provide qualitative insights into underlying mechanisms.
Examples of empirical models include linear functions (such as those used in linear
regression), log-linear models, Poisson regression (commonly used in epidemiology), and Hill
models (commonly used to analyze ligand-receptor data). Empirical models have the advantage of
ease of use, the existence of "user-friendly" software tools capable of fitting these models to
dose-response data, and a formal framework for hypothesis testing and interpolation between data
points. In addition, empirical models can be used to estimate a point of departure for extrapolation.
The major disadvantage of empirical models is their inability to quantitatively link multiple data
sets in a mechanistically meaningful manner.
8.1.6. Mechanism-Based and Mode-of-Action-Based Modeling
In contrast to empirical modeling, mechanism-based modeling attempts to use an
understanding of the mechanistic relationship between exposure and multiple endpoints to
simultaneously describe the observed response. Mechanism-based modeling can be a powerful tool
for understanding and combining information on complex biological phenomena (Lucier et al.,
1993). Mechanism-based modeling commences from a series of experiments with a xenobiotic
agent. The experimental results (data) can indicate a mechanism supporting the creation of a
mathematical model. The predictions of that model are tested for consistency with the existing
knowledge base for the agent and effect under study. Defects in the fit can suggest new experiments
that may permit refinement of the model. On each iteration of this process, the model either gains
additional credibility by predicting the new experimental results or it is modified to fit the new as
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well as previous results. In either case, subsequent iterations of this process increase our confidence
in accepting or rejecting a final model, although it may be difficult or impossible to quantify this
confidence.
Mathematical models that incorporate parameters that correspond to actual biological
structures or processes do not automatically constitute "mechanism-based models." The types of
data available for the model and the method by which these data are incorporated into the model
determine if a model truly reflects the biology. A parameter that specifies the activity of a
xenobiotic metabolizing enzyme, for example, should have a biologically realistic value. Without
careful attention to the representation of biological detail, confidence in the model and use of its
results is reduced.
Ideally, the parameters in a mechanism-based model are derived from first principles in a
"bottom-up" fashion. In this case, the structure of the model is an accurate mathematical
representation of the known properties of the system being modeled, and the mechanistic
parameters in the model are estimated directly from data. Such a model can increase confidence in
extrapolating outside the range of the data as long as attendant uncertainties are carefully evaluated.
In practice, it is generally impossible to completely develop a mathematical model for biological
processes. At some point, processes by which the mechanistic events elicit the observed toxic
effects must be deduced in a "top down" approach that uses some curve fitting. The concept of
mode of action has been developed in response to this difficulty in implementing the "bottom up"
approach (U.S. EPA Guidelines for Carcinogen Risk Assessment, EPA/600/Z96001). The term
mode of action is defined as a series of key events and processes starting with interaction of an
agent with a cell, through operational and anatomical changes resulting in cancer formation and
other toxicities. "Mode" is contrasted with "mechanism" of action, which implies a more detailed
molecular description of events. Operationally, the description of the mode of action should convey
enough information to characterize the shape of the exposure-response curve. A risk assessment
model based on the mode of action is preferable to empirical modeling when making inferences
outside of the range of the effects data.
Without data (as is the case with extrapolated predictions), the statistical issue of the
accuracy of a prediction cannot be easily addressed. Thus, while there may be greater biological
confidence in extrapolated results, it is unlikely that an increased statistical confidence can be
demonstrated. However, for each level and type of data, there are ranges of exposure beyond which
it is impossible to demonstrate an effect because of limitations in the sensitivity of those assays. In
general, effects can be demonstrated at lower exposures for mechanistic data (e.g., gene expression)
than for toxicity data. Hence, use of a true mechanism-based approach should enable reliable and
scientifically credible extrapolations to lower exposures.
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Risk assessment typically involves extrapolations between species, from high to low doses,
and between different patterns of exposure. Uncertainty in risk assessment is reduced to the extent
that these extrapolations are based on mechanistic considerations. For TCDD, the mechanisms of
three processes are of primary interest: (1) the dosimetry of TCDD throughout the body and
specifically to target tissues; (2) the molecular interactions between TCDD and tissue proteins,
emphasizing the activation of gene transcription and increases in cellular concentrations of
growth-regulatory gene products and metabolic enzymes; and (3) the progressive tissue-level
alterations resulting from these interactions that lead, eventually, to toxicity. Mechanism-based
modeling for TCDD is the quantitative description of the mechanisms that define these processes.
A model based on mechanistic understanding of the biochemistry of TCDD-induced toxicity and
that accurately reproduces observed effects would permit more confident extrapolations to low
doses and more reliable resultant risk estimates. As previously stated (Greenlee et al., 1991),
"Neither the position taken by U.S. EPA or by Environment Canada (and several other countries
such as Germany and the Netherlands) is based on any detailed mechanistic understanding of
receptor-mediated interactions between TCDD and target tissues. In addition to their use in risk
assessment, models of these processes can aid in the design of future experiments to clarify
understanding of TCDD toxicity and support further risk estimation."
Several models ranging from very simple to complex have been developed to describe the
toxicity of TCDD. It is obvious that the biology governing the toxicity of TCDD, beyond a few
initial critical events, is not straightforward. These critical events, the first of which is binding to
the AhR, are generally response-independent. The response-dependent events are species-, sex-,
organ-, tissue-, cell- and developmental stage-specific. If binding to the AhR is essential but not
sufficient for effects to occur, then the dose-response curve for this event (as well as the rate
equations) should be a better predictor of biological action than external dose as long as the shapes
of the dose-response curves for these subsequent actions are similar to those of receptor binding
curves. In general, the available data indicate that receptor involvement is necessary for most if not
all low-dose actions of TCDD. However, it is clear that for many responses, the dose-response
curves are different from receptor binding curves. Furthermore, although the AhR has been
detected in many kinds of cells, not all of these exhibit toxic responses. These data suggest that
there must be other factors that are necessary for TCDD-induced toxicity. The roles of these
cell-specific factors and how they affect the ultimate response must be elucidated before there is a
complete understanding of TCDD action. However, a model may be developed for specific
endpoints by using available data and biologically plausible assumptions.
TCDD can be considered as a prototype for exploring and examining the ability of
mechanism-based modeling to improve the accuracy of quantitative risk assessment. The database
for a mechanistic modeling approach to TCDD is extensive and contains a considerable amount of
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information on low-dose behavior. In addition, there is some concordance between human data and
experimental evidence in animals (see Section 8.3). On the other hand, some aspects of the
mechanism by which TCDD induces its effects, such as binding of the AhR to accessory proteins,
have not been modeled extensively because of lack of data. Because of this deficiency, several
alternative mechanistic hypotheses may agree with the existing data. The role of mechanism-based
modeling in this case is to identify a set of candidate biologically plausible models, rather than to
provide a final description. This outcome is inevitable for the application of the technology of
mechanism-based modeling to a new area. Reduction in the size of the candidate set and,
eventually, identification of the preferred model must await additional results from the laboratory.
To reiterate an earlier point, mechanism-based modeling can aid in explaining and
understanding experimental results, beyond its proposed use in risk assessment.
8.1.7. Elements of Chapter 8
The following sections of this chapter discuss the underlying science related to selection of
appropriate dose metrics for dose-response modeling, empirical modeling of individual data sets,
and mechanism-based dose-response modeling for biochemical responses and tissue responses.
This modeling effort follows a natural progression related to the kind of information available at the
time these models were developed. In addition, knowledge gaps have been identified throughout
the chapter and have been consolidated in a section related to data gaps and research needed to
address critical uncertainties that remain in the dose-response modeling of TCDD. Discussion of
the strengths and weaknesses, assumptions and uncertainties, and implications of these TCDD
dose-response modeling efforts follows. Detailed tables containing the outputs of the empirical
dose-response modeling efforts are appended to this chapter for the benefit of those readers who
wish a more detailed view of the data and analyses supporting the discussion and conclusions of this
chapter. General conclusions are presented in a short summary statement that is found toward the
end of this chapter.
8.2. DOSE METRICS
8.2.1. Introduction
One of the more perplexing issues in toxicology is animal-to-human dose extrapolation. To
provide significant insight into differences in sensitivity among species, an appropriate
animal-to-human extrapolation of tissue dose is required. Chemicals can produce many different
types of responses depending on the exposure scenario and the response. Some responses are
reversible (enzyme induction) whereas others are irreversible (death, cancer). Some responses
require prolonged exposures (porphyria and cancer). Others have unique windows of susceptibility
where an adverse effect (e.g., cleft palate) occurs only after a critical window of exposure (e.g.,
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during development). The processes leading to particular toxic responses are highly divergent, with
some responses requiring a continued exposure over a prolonged period of time and some requiring
an exposure over only several hours. It is unlikely that a single dose metric will be adequate for
interspecies and intraspecies extrapolation for all of these endpoints.
Estimating risk to various human populations is complicated by differences in exposure
scenarios. Human exposures to high levels of dioxins have occurred in several different scenarios.
There have been industrial accidents that have resulted in high exposures over a very short period of
time, such as the explosion at the ICMESA trichlorophenol plant near Seveso, Italy, in 1976
(Ghezzi et al., 1982) and the BASF chemical plant in Ludwigshafen, Germany, in 1953 (Zober et
al., 1990). Increased daily exposures over background to dioxins have occurred in occupationally
exposed populations using some herbicides, for example, during the Vietnam War (Verger et al.,
1994) and in agricultural workers (Kogevinas et al., 1995). Routine occupational exposures have
occurred in several manufacturing facilities around the world. The final type of human exposure
occurs in the general population, which is exposed daily to TCDD in the diet at a dose rate of
approximately 0.14 to 0.4 pg/kg/day1 (see Part I). One of the difficulties in examining and
comparing these different populations is that the actual dose or exposure is rarely known. Estimates
are often based on present serum TCDD concentrations, with extrapolation back to the initial time
of exposure based on the half-life of TCDD in humans (Fingerhut et al., 1991; Scheuplein and
Bowers, 1995).
In contrast, the exposures in animal experimentation are controlled and well defined. Animal
studies use multiple dosing regimens including single acute exposures, chronic daily exposures, and
biweekly exposures. Comparison across species sometimes requires extrapolation from one
exposure scenario to another. Large differences between species and the half-life of TCDD, and
quantitative differences in the tissue distribution of TCDD, must be considered (van der Berg et al.,
1994).
Determining the most appropriate dose metric represents an additional difficulty when
different endpoints and species are compared. Comparison of responses across species requires the
expression of dose using an equivalent metric. Dose can be expressed in a multitude of metrics
(DeVito et al., 1995) such as daily intake (ng/kg/day), current body burden (ng/kg), average body
burden over a given period of time, plasma concentration, concentration of occupied AhR (Jusko,
1995), induced CYP1A2 (Andersen et al., 1997a; Kohn et al., 1993), and reduced EGFR (Portier
andKohn, 1996).
'Calculated from human daily dietary dose of 10 to 20 pg/day TCDD and human body weights between 50 and 70
kg; it should be noted that, on a total TCDD equivalents (TEQ) basis, total daily intake equals approximately 70
pg/day (see Part I) (see Chapter 9 for discussion of TCDD equivalents).
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Different dose metrics can lead to widely diverse conclusions. For example, the lowest dose
with an increased tumorigenic response (thyroid tumors) in a rat (NTP, 1982a) is 1.4 ng/kg/day and
the daily intake in humans is approximately 0.14 to 4 pg/kg/day. This implies that humans are
exposed to doses 3,500 to 10,000 times lower than the rat dose. However, 1.4 ng/kg/day in the rat
leads to a steady-state body burden of approximately 25 ng/kg, assuming a half-life of TCDD of 23
days and absorption from feed of 50%2. The current body burden of TCDD in humans is
approximately 5 ng/kg lipid or 1.25 ng/kg body weight (assuming about 25% of body weight is
lipid), suggesting that humans are exposed to about 20 times less than the minimal carcinogenic
dose for the rat. The difference between these two estimates is entirely due to the approximately
100-fold difference in the half-life between humans and rats. At least for this comparison, the most
appropriate metric for comparison is the steady-state body burden. (Note that current daily intake
for humans is likely lower than historical levels and is biased downward because of unknown
sources, leading to a discrepancy between body burdens and daily intake.
In addition to the uncertainty in the half-life of TCDD in humans, such calculations assume
exposure to TCDD at a constant rate rather than the actual episodic exposure scenarios generally
seen in the studied populations. In principle, a reliable PBPK model for humans could be used to
compute body burden, tissue dose, or any other desired dose metric for any dosing scenario.
However, as outlined in Section 8.4, the existing data are inadequate for this extrapolation. If time
courses of TCDD in human blood were available for widely different doses, metabolic parameters
for humans could be estimated. Inclusion of these quantities in a PBPK model would permit the
calculation of a tissue dose or body burden to be used for risk assessment.
The developing embryo represents a very different complication in choosing a correct dose
measurement. The susceptibility of a developing embryo or fetus to TCDD insult may be
dependent upon the stage of development. For example, susceptibility to TCDD-induced cleft
palate has a specific window of sensitivity. Once the palatal shelves fuse, cleft palates cannot be
induced by TCDD. These windows of susceptibility are on the orders of hours to days. One of the
difficulties is that the time span is often too short to clearly discriminate among dose metrics such as
peak concentration, steady-state body burden, or average body burden. When these types of
comparisons for TCDD are attempted, it appears that they are of equivalent utility, provided the
dose metric was determined only during the window of sensitivity. In both animals and humans, the
biological half-life of TCDD is much greater than the time span of the window of susceptibility.
Hence, an average measurement or a peak measurement can be used as an appropriate dose metric.
2 Steady-state body burden (ng/kg) = daily dose (ng/kg/day) [(half-life/ln(2)] (f where f is the fraction absorbed from
the exposure route (unitless) and half-life is the half-life in days.
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The windows of susceptibility for some of the developmental toxicities of TCDD have been
identified (i.e., induction of cleft palate and hydronephrosis). Peak body burden may be a more
appropriate dose metric for developmental effects because the window of susceptibility is undefined
for several endpoints.
Ideally, the best dose metric is that which is directly and clearly related to the toxicity of
concern by a well-defined mechanism. For mechanism-based cancer modeling, instantaneous
values of a dose metric are used because these can be used as surrogates for mutational rates and
growth rates within a two-stage cancer model. For epidemiology studies of lung cancer and all
cancers combined, there is not enough information to develop a mechanistic approach. In this case
the chronic exposures generally thought to be associated with the cancer process can be described
by metrics that integrate dose over a specific time period., and an average body burden dose metric
is acceptable for steady-state conditions. However, difficulties arise when this metric is applied to
accidental high acute exposures. To allow for comparison across studies, it is sometimes useful to
find a constant daily exposure or steady-state body burden that yields the same total exposure.
Comparability of response over multiple species for a given dose metric can be used to assess the
adequacy of that metric. It should be noted that for compounds like TCDD with very long
half-lives, relative differences between doses expressed as steady-state body burden versus those
expressed as total exposure may be small for humans, although the same may not be true in
experimental animals where the half-life is much shorter.
8.2.2. Selection of Effective Dose Levels.
Comparisons across multiple endpoints, multiple species, and multiple experimental
protocols are too complicated to be made on the basis of the full dose-response curve. Comparisons
of this sort can be made by either choosing a given exposure and comparing the responses, or
choosing a particular response level and comparing the associated exposures. In the analyses for the
presentations in this chapter, responses are compared using estimated exposures associated with a
given level of excess risk or response. To avoid large extrapolations, this common level of excess
risk or response was chosen such that for most studies, the estimated exposure is in or near the
range of the exposures in the studies being compared (Murrell et al., 1998; Gaylor and Zheng, 1996;
Barton and Das, 1996; Allen et al., 1994a,b; McGrath et al., 1995), with extra weight given to the
human data. A common metric for comparison is the effective dose, or EDp, which is the exposure
dose resulting in a excess risk in the studied population. Although effective dose reporting for the
2%, 5%, and 10% increased risks has been the suggested approach, these latter two levels are
actually higher than those typically observed in the exposed groups in studies in humans. To
illustrate, lung cancer mortality has a background lifetime risk of approximately 4% (smokers and
nonsmokers combined), so that even a relative risk of 2.0 represents approximately a 4% increased
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lifetime risk. On the basis of this observation, and recognizing that many of the endpoints studied
in the laboratory include 1% effect levels in the experimental range, the dose resulting in a 1%
effect above controls (ED01) is presented.
Different measures can be used to present risks above and beyond the background risks
encountered in the general environment or through genetic variables. For simplicity, a common
measure will be used; the excess risk, defined as the effective dose for risk (p*100%), satisfying the
relationship in equation (1):
P = RfdJ -R(0) (1)
R(oo)-R(0)
where R(dp) represents the response (either risk or other measure) atp at a given exposure or dose
level d, andR(°°) is the maximum response possible (e.g., R(°°) =1 for quantal responses, such as
cancer). In this exercise/? is equal to 0.01.
The relative risk commensurate with a one percent excess risk can be calculated by
rearranging the above formula:
0.01
Relative Risk (ED01) = 0.99 +
Multiplying the relative risk by R(0), the background risk, gives the value of the absolute risk. If
the background risk is 0 then the absolute risk equals the excess risk.
In the present analysis, the benchmark effect level has been specified as a 1% increase in the
extra risk. Quantal data is determined as a probability on a scale of zero to one. Hence the
difference between the probability of an adverse response at a given benchmark response level and
the probability of a response at background is already on a standardized scale. In contrast,
estimating the extra risk for continuous data is challenging. The changes in a continuous response
that are considered adverse depend on the nature of the response that is determined. The change in
effect that results in a significant public health problem is different for every response determined.
In addition, the study design can influence this value. In order to have a consistent response level
between endpoints, the measurement of response must be standardized between endpoints.
As outlined in Murrell et al (1998), there are several methods proposed to standardize
continuous data. One method uses a specified change relative to background and is calculated
according to the following equation:
Fe(d} -
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where Fe(J) is the function relating the response to dose d. There are some problems with this
approach. Erelative is now highly sensitive to the background response. For example, a small change
in a response with a small background may seem more important compared to the same effective
change in response with a large background. In addition, it is not clear that a certain percent change
from background is an equivalent risk for all endpoints. For example, a 30% change in a heart rate
may not be an equivalent risk as a 30% change in serum porphyrin concentrations. Therefore, using
Ereiative does not result in a standardized risk level by which one could compare across endpoints.
Another proposed standardization method is to divide the change in effect by the standard
deviation of the control group or from an assumed distribution of the mean effect for a particular
dose group (Crump, 1984; Slikker et al., 1996). Because the standard deviation may vary for a
variety of reason independent of the health importance of the effect, this method does not
necessarily standardize across a variety of endpoints and experimental conditions.
Ideally, the benchmark effect level is one that separates a normal or no-effect level from
abnormal or adverse effect levels (Crump, 1995). One of the difficulties in applying this approach
are the assumptions that are made in the determination of the benchmark level. Often, there is not a
clear consensus as to when a change in a continuous response becomes adverse. Because of the
lack of a consensus on adversity levels for many of the effects examined in this analysis, this
method was deemed inappropriate to use as a means of standardization across endpoints.
In the present analysis, the continuous data is standardized by the dynamic range of response
for each effect (Murrell et al., 1998). Similar to quantal data, continuous data also has maximal
response levels. Thus one can define extra effect as the change in the effect from background as
standardized to the total range of the response. Dividing the change in effect by the theoretical or
observed maximum produces a quantity that is standardized across endpoints with respect to scale.
8.2.3. Dose Corrections for Species Differences in Half-Lives
Considering the very large difference between half-lives of TCDD in various species, it is
best to compare across species using body burden rather than daily intake (DeVito et al., 1995).
Under steady-state conditions, it is possible to calculate total body burdens (ng/kg) for TCDD in
equation (2).
ED01(ng/kg body burden)=ED01(ng/kg/day)*half-life/ln(2)*f (2)
where f is the fraction of dose absorbed and is assumed to be 50% for absorption from food (Kociba
et al., 1976) and 100% for other routes. Half-lives for converting between daily exposures and
steady-state body burden are presented in Table 8-1.
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In summary, the unit(s) of dose should appropriately reflect the magnitude of exposure and
the frequency of this exposure. Given the various types of exposure scenarios and different types of
responses, it is difficult to determine a single dose metric for TCDD that can be used to compare all
endpoints and species. Nevertheless, for several types of specific endpoints, it is possible to express
the dose of TCDD in a form that allows for a comparison of responses across various endpoints and
species. For the analysis contained in this chapter, various measures of body burden will be used.
8.3. EMPIRICAL DOSE-RESPONSE MODELING OF INDIVIDUAL DATA SETS
8.3.1. Introduction
TCDD has been previously classified by EPA as a probable human carcinogen, and has
more recently been classified as a known human carcinogen by the International Agency for
Research on Cancer (IARC, 1997). In the Ninth Report on Carcinogens, the U.S. Department of
Health and Human Services describes TCDD as "known to be a human carcinogen" (HHS, 2001).
Epidemiological data have suggested increases in all cancers combined, respiratory system tumors,
and soft-tissue sarcomas (see Chapter 7 for a detailed discussion of these findings).
TCDD is a carcinogen in all species and strains of laboratory animals tested (e.g., mice, rats,
hamsters) with tumors detected in the liver, thyroid, respiratory tract, and other organs and tissues
(see Part n, Chapter 6). Long-term rodent carcinogenicity studies have shown that TCDD is a
potent carcinogen, with the most seriously affected organ being liver in female rodents (NTP,
1982a,b; Kociba et al., 1978; Portier et al., 1984).
8.3.2. Human Dose-Response Models
Despite the increasing amount of epidemiological data available for TCDD, it is generally
difficult to find human data with sufficient information to model dose-response relationships.
Unlike laboratory studies, human data can be affected by factors that are difficult to control. There
exists the possibility of disease misclassifications, and measurements of exposure are often
imprecise. However, risks studied in human populations do not require assumptions concerning
species extrapolation and, as such, should be used maximally in studying dose-response. TCDD is
no different in this regard, with several epidemiological studies providing varying degrees of utility
for dose-response assessment. This section discusses those studies and the models that have been
applied to them.
8.3.2.1. All Cancers Combined and Lung Cancer
There exist three studies of human occupational exposure that provide enough information
to perform a quantitative dose-response analysis. These are the NIOSH study (Fingerhut et al.,
1991; Steenland et al., 1999; Steenland et al., 2001), the Hamburg cohort study (Manz et al., 1991;
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Flesch-Janys et al., 1995, 1998a; Becher et al., 1998), and the BASF cohort study (Zober et al.,
1990).
8.3.2.1.1. NIOSHstudy. NIOSH conducted a cohort study of 5,172 male workers at 12 plants in
the United States that produced TCDD-contaminated chemicals (Fingerhut et al., 1991). They
reported increased mortality for total cancers and for respiratory cancers for workers with greater
than 1 year of exposure and more than 20 years latency since start of employment.
Steenland et al. (1999) performed an analysis of male workers from eight of the twelve
plants in the NIOSH study (the plants with sufficient information on work histories and TCDD
levels on the job) who had no exposure to pentachlorophenol. Exposure was measured using a job-
exposure matrix. Cumulative TCDD exposure scores per day were assigned by multiplying TCDD
concentration in industrial materials, fraction of work day spent working with TCDD-containing
materials, and a qualitative degree-of-contact measure. The exposure scores for each day were
added to get a cumulative exposure score, which cannot be interpreted as units of TCDD. Workers
were divided into septiles by levels of this exposure score (with or without a 15-year latency taken
into account).
SMRs were calculated by septile for all cancers and for lung cancer. SMRs for all cancers
for 0 or 15 years latency showed a statistically significant (p<=.05) positive trend with exposure
score, as did the SMRs for lung cancer with no latency. Cox regression analyses were also
performed to compare high-exposure groups to the lowest exposure group. For the data with zero
latency, rate ratios for all cancers did not show a significant positive trend with exposure. For data
with 15-year latency, the analysis was performed for all cancers combined, lung cancers, smoking-
related cancers, and non-smoking-related cancers. The rate ratios for all cancers and for non-
smoking-related cancers showed a significant positive trend; ratios for lung cancer and for smoking-
related cancer showed no significant trend with cumulative exposure, though they did show a
significant trend with logarithm of cumulative exposure. ("Smoking-related cancer" here means
cancer that has historically been associated with smoking; the smoker or nonsmoker status of
workers was not itself included in the analysis.)
Steenland et al. (2001) extended their analysis of these workers to include estimated dioxin
exposures. Serum lipid levels of TCDD in 1988 were measured in 193 workers at one of the eight
plants in the study. First-order kinetics with a constant 8.7 year half-life were used to extrapolate
back to serum level at time of last exposure. These serum levels were regressed on the exposure
scores, using a first-order model for exposure between first exposure and last exposure (the
resulting predicted serum level and the observed levels have correlation coefficient of 0.62). The
formula derived by regression was used to estimate serum TCDD levels for all 3538 workers in the
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cohort, and then to estimate serum TCDD areas under the lipid adjusted serum level curves over
time (AUC).
Several different dose-response models were fit to these data to provide estimates of risk for
dose-response assessment. The best-fitting model used the log(AUC) lagged by 15 years as the
exposure metric. The analysis used Cox regression and had date-of-birth as a categorical variable (4
categories). Excess risk of cancer for intake of Ipg/kg/day for 75 years of exposure was estimated
as 0.0094 for males and 0.0080 for females. This analysis assumes a background exposure of 0.5
pg/kg/day as background. The analysis was also carried out using log of TEQ AUC as a dose
variable. Lifetime excess cancer risk using the TEQ model with an intake of 10 pg/kg/day TEQ was
0.0018 for males and 0.0015 for females.
A piecewise linear model fit nearly as well as the model using log AUC. Its risk estimates
for an intake of 1 pg/kg/day TCDD for 75 years were 0.0005 for males and 0.0004 for females.
When the background exposure included TEQ's, the risk estimates for an intake of 10 pg/kg/day
TEQ for 75 years were 0.0005 for both males and females. These numbers appeared low and after
discussing this with Dr. Steenland, he noted an error in the calculation and the corrected numbers
are 0.0071 for males and O.;0060 for females; in line with the numbers for the TCDD only analysis.
Aylward et al. (1996) presented a dose-response analysis using data from Fingerhut et al.
(1991), considering only cancers occurring after 20 years of exposure. This analysis is superceded
by the extended follow-up and exposure matrix used in Steenland et al (2001).
8.3.2.1.2. Hamburg cohort study. Another cohort studied consisted of 1,189 men who worked at a
herbicide plant in Hamburg, Germany (Becher et al., 1998; Manz et al., 1991; Flesch-Janys et al.,
1995, 1998a). Flesch-Janys et al. (1995) used an estimate of TCDD levels in workers in their
analysis. Levels of TCDD were measured in blood or adipose tissue for 190 male workers in the
cohort. Levels at the end of employment were estimated using a first-order kinetic model, and the
contribution of each of several job areas was estimated by regression of the TCDD level on time
worked in the job areas. The regression results were used to calculate TCDD concentrations (ng/kg
of blood fat) at the end of the occupational exposure for each member of the entire cohort. The
cohort was divided into the lower four quintiles and ninth and tenth deciles of the calculated value.
Cox regression was used to calculate relative risks for cancer mortality. Relative risks were
calculated using either an external reference group (control group of gas workers) or the lowest two
quintiles of the Hamburg cohort combined as internal reference. Variables used in the regression
were TCDD level (categorized by quintiles), total duration of employment, age, and calendar year of
first employment. A test for trend of the relative risks with increasing TCDD concentration was
conducted. In the calculations using either reference group, the trend test was significant at/><0.05.
Standard mortality ratios (SMRs) were calculated on the basis of the national mortality data
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available from the German Federal Office of Statistics using standard methods (Breslow and Day,
1987). The SMRs for the tenth decile of TCDD concentration were significantly elevated, whereas
none of the SMRs for lower TCDD concentration categories were significantly elevated in the
comparison with the lowest two quintiles combined. In the comparison with the gas worker
controls, SMRs were 129 or higher. The increase was significant for three of the five categories.
Flesch-Janys et al. (1998a) extended this analysis using mortality up to 1992 and calculating
time courses for TCDD concentration in blood lipid. Workers were divided into quartiles by
integrated blood concentrations over time and SMRs were calculated. For total cancer mortality, the
mortality was significantly increased for the highest quartile (SMR 173; 95% CI=121-240) and for
all workers combined (SMR 141, 95% CI=117-168). The overall cancer SMR is increased over the
results of Manz et al. (1991), which included mortality only up to 1989. For all workers combined,
lung cancer mortality was significantly increased (SMR 151, 95% CI= 107-208), but the SMRs
were not significantly over 100 for any of the individual quartiles. A linear trend test on the SMRs
by quartile was significant for total cancer deaths (p=0.01) but not for lung cancer deaths.
Another recent article (Becher et al., 1998) gave a dose-response analysis of the Hamburg
cohort for all cancers combined. A Cox regression was used for the dose-response modeling. Three
response models were used: a multiplicative model, an additive model, and a power model. The
response variable in the analysis was SMR for total cancer mortality. The dose variable was the
integrated blood levels for TCDD concentration (AUC) as calculated by Flesch-Janys et al. (1998a).
Year of entry into employment, age at entry, duration of employment, and an exposure metric for
beta-hexachlorocyclohexane were also used as covariates in the model. The models were calculated
with latency times of 0 and 10 years. The dose-response was modeled using three classes of
models. The "multiplicative model" had relative risk (RR) equal to exp(pd), where the dose d is the
AUC. The "additive model" had RR=l+(3d, and the "power model" had RR= exp((3
log(kd+l))=(kd+l) P. The value (3 and k are estimated parameters. The multiplicative model gave
the best fit, but the fits for the three classes of models were so close that Becher et al. found no
statistical reason to select between them. In all cases, the value of (3 was significantly different from
0 (p<0.05). The model results were used to calculate unit risk estimates, i.e., estimates for (risk of
cancer death through age 70 given a daily dose of 1 pg/kg body weight of TCDD) - (risk given no
exposure to TCDD). These calculations were based on background German mortality rates. The
unit risks for intake of Ipg/kg/day of TCDD ranged from 0.0011 (risk for females under the
multiplicative model with 10-year lag) to 0.0084 (risk for males under the power model with no
lag).
Becher also gave results for a Cox regression for lung cancer deaths using the multiplicative
model. The resulting risk (value of (3) was close to that for the model of all cancer deaths.
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8.3.2.1.3. BASF cohort study. Zober et al. (1990) studied a cohort of 247 workers from a 1953
accident at a BASF factory in Germany that released TCDD into the factory. Overall cancer
mortality for all workers combined was not significantly increased. However, for the 127 workers
who developed either chloracne or erythema, and for a 20+ year latent period, mortality from all
cancers was increased (SMR=201; 90% CI=122-315). There was also an increase in cancer
mortality with a 20+ year latency for a subcohort of 153 workers who were considered most likely
to have been exposed to TCDD (SMR 198; 90% CI=122-305).
Another study of the BASF cohort (Ott and Zober, 1996a) included 243 male workers.
Chloracne status and estimated TCDD concentration (|ig/kg body weight) at time of exposure were
used as metrics of exposure. The concentration was calculated by a first-order kinetics model using
a regression procedure. Subjects were divided into 3 or 4 groups by concentration. SMRs were
calculated by dose group. Standardized incidence ratios were calculated by dose group for all
cancers and for cancers at various sites. Neither total cancer mortality nor respiratory system cancer
mortality was significantly increased overall, although respiratory cancer mortality was increased in
the highest of three TCDD concentration groups (SMR 240, 95% CI=100- 500). The incidence was
not significantly increased for all cancers or respiratory cancers, either overall or in any
concentration subgroup. This study also included a dose-response analysis by a Cox proportional
hazard model, which calculated relative risks, with cigarette smoking, body mass index, exposure to
asbestos, exposure to aromatic amines, age, and date of first exposure included as explanatory
variables. TCDD dose was found to be marginally significantly related to total cancer deaths
(conditional risk ratio for 1 |ig TCDD/kg body weight = 1.22; 95% CI=1.00-1.50), but not
significantly related to respiratory cancer deaths or to incidence of either. There also appeared to be
a trend for increasing total cancer deaths by TCDD level in smokers and in all workers, but not in
nonsmokers or ex-smokers.
8.3.2.1.4. Other studies. Hooiveld et al. (1998) studied former workers at an herbicide factory in
the Netherlands. A back-calculation and regression method was used to estimate peak TCDD
concentration for all workers. A total of 1,031 male workers were divided into groups of low,
medium, or high estimated peak TCDD level (cutpoints were 7.7 and 124.2 ppt). These groups
were approximately tertiles of the TCDD level. Relative risks (RR) of mortality were calculated for
the high and medium groups versus the low group, with adjustment for age, time of follow-up, and
time since first exposure. Relative risks for total cancer deaths were significantly increased for both
medium (RR 1.9, 95% CI=1.2-2.8) and high (RR 1.9, 95% CI=1.3-2.8) exposure groups, but with
no apparent trend. Some relative risks for specific cancer types were marginally significant, but
with no apparent trend from medium to high exposure. Not enough information is given in this
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study to calculate average body burden. In the cohort of residents from Seveso, Italy (Bertazzi et
al., 1993), a single episode of exposure to TCDD occurred following an explosion at a local
chemical plant. Men, women, and children from this community have been followed for cancer
mortality for 15 years. However, this study could not be included in this analysis because the
limited exposure information is not sufficient at present to calculate average body burden. Two
other studies were also not included in this analysis for various reasons. Kuratsune et al. (1998)
reported increased lung cancer mortality in male victims (SMR = 330, based on eight cases) from
the Yusho PCB and PCDF contaminated rice-oil poisonings. Although there are serum
measurements and 37 total TCDD equivalents (TEQ) estimates available for this cohort, there was
no TCDD in the contaminants reported. Because this chapter has focused primarily on the effects
of TCDD, this cohort will not be included in the modeling effort. In addition, Collins et al. (1993)
reported increased mortality for both lung cancer and all cancers combined for a subcohort of 122
U.S. workers who developed chloracne following exposure to TCDD at a chemical plant during a
1949 accident. Their analysis, however, attributes this increase in mortality to co-exposure to 4-
aminobiphenyl. As that chemical plant is included in the NIOSH study cohort (Fingerhut et al.,
1991), it is discussed in Chapter 7.
8.3.2.2 ED and Unit Risk Calculations
Life table data (total death risk by age and percent of deaths due to cancer by age)
from 1995-1997 were obtained from the National Center for Health Statistics (NCHS, 1999).
Cancer deaths through age 75 due to TCDD exposure of 1 ppt body burden over background
(background assumed to be a steady-state lipid concentration of 5 parts per trillion; 1 ppt body
burden above background equals 4 ppt lipid concentration over background) were calculated using
the best-fitting models for the NIOSH data (Steenland et al., 2001), the Hamburg data (Becher et al.,
1998), and the BASF data (Ott and Zober, 1996). In these calculations, exposure was assumed to be
at steady-state TCDD body burden.
The models literature used by Steenland et al. and Becher et al. use TCDD or TEQ lipid
concentration in calculating the AUC; lipid concentrations were converted to body burdens by
dividing by 4. For those models, the exposure levels were used to calculate AUCs with a time lag
included as specified by the model.
The model used by Ott and Zober (1996) gives risk in terms of conditional risk ratio per unit
TCDD dose. Units for TCDD were |lg/kg body weight at time of initial exposure to TCDD. For
purposes of the current analysis, it is necessary to convert from units of steady-state body burden to
Ott and Zober's units of initial dose. Assuming a constant half-life of 2593 (approximately 7.1
years) as in Table 8-1, an initial body burden of B0 will yield a body burden at time t of
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B (V) = B0e kft , where ke is an elimination constant equal to ln(2)/(half-life in years). This implies
D
that the AUC at time T after initial exposure is AUC = —2-(l-e~kJ } . Tm this case will be 39 years
ke
(time from the accident in 1953 to the followup in 1992). Dividing by a lifetime of 71 years (mean
age in 1954, 33 years, plus 38 years from 1954 to the followup in 1992) gives the lifetime mean
body burden as:
B
Ilk
In the risk calculations, therefore, the steady-state body burden will be converted
to units of equivalent initial dose by dividing by the constant (l-e~k'T }. With the given values
l\ke ^ '
for half-life and T, that constant is 0.1411 and l/(the constant) is 7.0851. The model from Ott and
Zober has risk proportional to e^Adose with (3=ln(1.22). The corresponding slope for the mean
(steady-state) body burden is 7.0851*log(1.22)*0.001 (the 0.001 converts nanograms to
micrograms) and the slope for steady-state lipid concentration is that value divided by 4.
ED01, ED05, and ED10 values were calculated by finding the dose giving the specified excess
risk. All estimates were calculated using the same methods as the original author with estimates of
risk from the background exposure subtracted from the mortality data.
ED values are given below in Table 8-2. The values are exposures above background
exposure which will produce the given level of excess risk. The table also gives unit excess risks:
the excess risk for a unit exposure above background, given for exposure of 1 ppt body burden
above background. Steenland et al. (2001) provide sufficient information to develop confidence
bounds for the calculations using their models. Confidence limits for the models using the
Hamburg data could not be calculated due to insufficient detail in the manuscript. Because the
lower confidence limit for the risk value in the Ott and Zober (1996) model is zero (conditional risk
ratio of 1.00), the lower confidence limits for unit risk are zero and the upper confidence limits for
the ED values are infinite. The power model from the Steenland et al. (2001) data predicted that an
unrealistically large fraction of the tumors seen in humans was due to background dioxin exposure.
8.3.2.3. Noncancer Endpoints
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8.3.2.3.1. Cardiovascular disease. A pattern of increased risk of cardiovascular and ischemic heart
disease mortality was observed by Flesch-Janys et al. (1995) across six exposure categories. There
was a statistically significant trend (p=0.04) in relative risk for mortality for all cardiovascular
diseases when gas workers were used as the reference population, but in no single class of TCDD
exposure was there a significantly increased relative risk. There was no statistically significant
trend for death from ischemic heart disease (p=0.1), but the highest TCDD group (344.7-3,890.2
ppt) showed a significant relative risk of 1.99 (CI=1.05-3.75). When national rates were used for
the reference population, there were no statistically significant trends for either disease, and all
confidence intervals included 1. Information about time-average body burden could be obtained
from Flesch-Janys et al. (1998 a,b). With these data, an excess body burden over background (95%
lower bound) for 1% excess risk was calculated as 11.2 ng/kg (3.1 ng/kg) for all cardiovascular
disease, assuming a lifetime risk of 25%. No statistically significant increase of cardiovascular
diseases was observed for the NIOSH cohort (Steenland et al., 1999) or for the BASF cohort (Zober
etal., 1990, 1994).
8.3.2.3.2. Effects on infants. One major public health concern is the potential effects of
environmental chemicals on the developing fetus, infants, and children. TCDD and related
chemicals produce a broad range of effects in experimental animals exposed in utero ranging from
alterations in biochemical parameters to overt toxicity and lethality (see Chapter 5 for a review).
Few studies have examined the effects of TCDD and related chemicals in humans following in
utero exposures. Studies in the Netherlands (Huisman et al., 1995; Koopman-Esseboom, 1996;
Weisglas-Kuperus et al., 1995) have examined infants for thyroid hormone status, mental and
psychomotor development, and immunological status. Exposures were assessed by determining the
concentrations of PCBs, PCDFs, and PCDDs in maternal and umbilical blood and maternal breast
milk. Exposures were then categorized by total TCDD equivalents (TEQs), Planar-PCB TEQ,
nonplanar-PCB TEQ and total dioxin-PCB TEQs. (For a discussion of the TCDD toxic
equivalency concept, refer to Chapter 9.) These studies are discussed in greater detail (design,
analysis, and limitations) in Chapter 7. There is an indication that these data would be amenable to
dose-response analysis for complex mixtures of PCDDs, PCDFs, and PCBs, but not for TCDD
exposure alone.
8.3.2.4. Uncertainties in Estimates From Human Epidemiology
There are many uncertainties associated with risk estimates derived from epidemiological
studies, both in hazard identification and in dose estimation. The estimates of dose, although based
on actual body measurements, may not be fully representative or precise. Although 253 subjects
were sampled in the Fingerhut et al. (1991) study, the blood samples were all taken decades after
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last exposure and were from 2 of a total of 12 plants. Subjects from the larger of these two plants
had the higher TCDD levels but a lung cancer SMR=72 based on seven deaths, whereas the smaller
plant had only one death from lung cancer (SMR=155). Thus, while serum TCDD levels correlated
well with duration of occupational exposure for the 253 individuals sampled, and cancer response
correlated well with duration of exposure for the 12 plants overall, correlation of serum TCDD
levels with cancer response in this study is far less certain. Analysis by plant in the Fingerhut et al.
(1991) study would have been possible if body measurements at these other 10 plants had been
available.
The choice of half-life is another element of uncertainty. In the literature, average body
burden was calculated on the basis of a one-compartment model with first-order elimination. Half-
life assumptions in the literature vary. Some data, however, suggest a shorter half-life of as little as
5.8 years (Ott and Zober, 1996b) while others suggest a longer half-life of 11.3 years (Wolfe et al.,
1994). A recent study (Portier et al., 1999) suggests a half-life of 9.5 years. However, the
assumption of a single half-life is uncertain because it is possible that in humans the apparent half-
life may be shorter at higher levels of exposure, as has been observed in rat liver (Walker et al.,
2000). If this were the case, the actual initial exposure may have been higher than predicted using a
single half-life. In addition, it is assumed that the apparent half-life for TCDD is independent of
exposure to other dioxin-like compounds. In the rodent, apparent half-life is in part determined by
binding to CYP1A2, which is inducible via the AhR. In humans, while neither the dose response
for induction of CYP1A2 by TCDD nor the effect this may have on disposition of TCDD is known,
it is likely that the half-lives for dioxin-like compounds are not independent.
The fraction of TCDD absorbed could have an impact on the risk estimates derived from the
epidemiological data. In our calculations, we have either directly assumed a 50% absorption
fraction or relied upon analyses by the original authors that used a 50% absorption fraction. In the
analyses applied in this chapter, changes in absorption fraction result in a proportional change in
steady-state body burden. Hence, a 10% change in absorption would result in a corresponding 10%
change in steady-state body burden.
Another uncertainty is possible interaction or confounding between TCDD and tobacco
smoking. In mice, TCDD and 3-methyl cholanthrene (3-MC, one of the many poly cyclic aromatic
hydrocarbons in tobacco smoke) have been shown to be cocarcinogenic (Kouri et al., 1978). Other
studies of mouse skin tumors have shown that TCDD can have anticarcinogenic properties when
administered before initiation with either 3-MC or benzo(a)pyrene. Furthermore, dioxin's tumor-
promoting ability suggests that two-stage models would be more appropriate if individual smoking
histories were known. Smoking histories and analyses are presented only for the Zober et al. (1990)
cohort; for the 37 cancer cases, only 2 were stated as being nonsmokers. Of the 11 men with lung
cancer, only 1 reported never smoking. The Ott and Zober (1996b) analysis, which includes
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smoking as a covariate, did appear to show an effect of smoking on TCDD dose-response.
Although similar SMRs from other smoking-related diseases in the two subcohorts in Fingerhut et
al. (1991) suggest similar smoking prevalence across this multifactory cohort, the effects with
higher levels of TCDD could be synergistic for cancer. Steenland et al. (1999) point out that
confounding by smoking is likely to be reduced in an exposure-response analysis comparing highly-
exposed workers to workers with lower exposure.
Other potential confounders in all three studies include exposures concomitant with TCDD
exposures, other chlorinated hydrocarbons in the case of Zober et al. (1990) and Manz et al. (1991)
and miscellaneous chemicals including 4-aminobiphenyl, a known human bladder carcinogen, in
the case of Fingerhut et al. (1991). These confounders raise the question of whether the increased
SMRs are due to exposure to TCDD or to the confounders. However, it is important to note that
within this context, 4-aminobiphenyl does not increase tumors overall, and there is no evidence that
TCDD induces the incidence of bladder cancers.
Another source of uncertainty is the choice of model for analysis. The Becher et al. (1998)
analysis of data from the Hamburg cohort used three models for dose-response for total cancer
mortality, of which only one was linear. The risk estimates they derived using different models
varied by as much as a factor of five. The risk estimates of Steenland et al. (2001) for their best-
fitting model (Cox proportional hazards model) fall above the range of risk estimates given by
Becher et al. The risk estimates for Steenland et al.'s second best fitting model (piecewise linear)
are more than an order of magnitude lower than those for their best fitting model.
When interpreting the risk estimates presented in this section, a few additional caveats and
potential biases must be kept in mind.
All observed risk is attributed to exposure to TCDD, even in the presence of exposure to
other confounding chemicals. In particular, this analysis ignores exposure to PCDDs, PCDFs, and
other dioxin-like chemicals. The extent to which exposure to other agents increases the total
exposure on a TEQ basis (see Chapter 9) also increases the potential bias of calculated risk
estimates. In general, exposure to these compounds is correlated with the exposure to TCDD,
although differences in relative contribution of different dioxin-like compounds to the total TEQ
have been observed and are briefly discussed for the epidemiological data. This issue is especially
important for agents with shorter half-lives than TCDD (some will be longer; some shorter).
Analysis of blood samples analyzed years after exposure may fail to adequately measure an initial
exposure to dioxin-like compounds with shorter half-lives. For example, a current lipid level of 1
ppt for an agent with a half-life of 7 years, e.g., TCDD, would imply a lipid level of a little less than
8 ppt 20 years ago. On the other hand, an isomer with a current lipid level of 1 ppt and a half-life of
2 years would imply a lipid level of 1,024 ppt 20 years ago.
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In any epidemiological study, misclassification can bias estimates of risk. In this case,
recent exposures to TCDD, changes in the lipid fraction of body weight or presence/absence of
genetic differences in humans that alter the distribution and metabolism of TCDD could cause
misclassification bias, resulting in higher or lower risk estimates depending upon the direction of
the misclassification.
Selection bias may be another factor. For example, it is possible that the subpopulation used
for the biomonitoring of TCDD levels in human blood is not representative of the entire cohort used
for risk estimation. There is also a potential bias due to a healthy worker effect in these
occupational populations.
8.3.2.5. Conclusions for Human Cancer Dose-Response Modeling
Epidemiological studies of occupational exposure suggest a TCDD-mediated increase
in all cancers and also suggest that the lung in the human male is a sensitive target for TCDD.
Smoking and other factors (discussed above) may be modifiers for these cancers. Caution should be
used in interpreting the overall risk estimates and care should be taken to understand them in the
context of the entire weight-of-evidence concerning the potential toxicity of TCDD. The data
obtained from two occupational studies were sufficient to calculate risk estimates. Estimates
derived from the human data suggest an ED01 based on body burden in the range of 1.4-62 ng/kg for
all cancers combined.
8.3.2.6. Additional Knowledge Gaps in Human Cancer Dose-Response Modeling
One major knowledge gap in the epidemiological data is a complete exposure history for
each individual in the cohort. This includes lack of a realistic exposure matrix (areas and their
exposure potency and time spent in such areas of occupational exposure) and TCDD concentrations
measured over time during exposure. At present, only a few measurements per individual are
available to estimate a time course ranging over many years of human life.
Different dose metrics have been discussed in Section 8.2, and others may arise if more
information about the exposure process becomes available. Neither comparisons of the dose
metrics applicable at present to available data sets nor simulation studies on artificial data sets have
been performed to clarify the strengths and weaknesses of different metrics under different
scenarios.
More information is needed on factors determining individual differences in half-life of
TCDD such that these can be included into the calculation of individual time-average body burdens.
Age, sex, and portion of body fat have been discussed and used as factors of influence. The
existence of a more complex model for TCDD kinetics in humans may be possible, but no
systematic usage of these factors in risk estimation has been made so far.
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Information about confounders of human carcinogenesis, such as smoking or other
behavioral cancer risk factors, was sparse in these studies. Future studies must reduce this lack of
information by use of appropriate design measures, or by inclusion of appropriate biomarkers of
coexposure. Exposure to related dioxin-like compounds clearly complicates the estimates of the
effective dose of TCDD. For example, in the Hamburg cohort, the mean TCDD concentration for
236 males was 108.3 ppt, whereas the mean TEQ concentration based on all other PCDDs and
PCDFs (except TCDD) was 142.0 ppt. Other coexposure-based confounders have been described
above. Although TEQ values can be calculated for each person using half-life estimates of each
individual PCDD and PCDF congener, it is unclear how an interaction of different congeners in the
individual organism determines the concentration levels over a long time period in humans. Long-
term studies, even of a small cohort of individual persons, would have the potential to clarify basic
pharmacokinetics of these complex mixtures. One question to be addressed would be potential
changes in half-life of TCDD in the presence of other dioxin-like compounds in different
concentrations.
The ED01s presented are based on simple dose-response models. The analyses uses the
crude endpoint of all cancers combined. No mechanistic information was available for these
cohorts to strengthen this analysis. This prohibited cancer modeling using parameters other than
TCDD blood serum concentration. For a mechanism-based cancer risk estimation, such
information would be required. If such information cannot be obtained for the entire cohort,
investigators should consider statistically appropriate subcohort sampling as a possible source of
information.
Risk estimates could not be calculated for infant or nonadult exposure. This is to some
extent due to insufficiencies in study design for risk estimation for the total population and missing
information in the reporting of the results. Similarly, it is not possible at present to identify
subpopulations that may be at increased risk. Effects of limited but high exposure at an early age
have not been investigated under conditions where dose-response analyses can be done. In addition,
dose-response data are almost completely missing for human noncancer endpoints. Although the
cohorts considered above are large (with a few thousand individuals), given the size of the effects to
be expected, the statistical power of some analyses is quite small and larger studies with thorough
epidemiological design consideration are required.
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8.3.3. Rodent Dose-Response Models: Cancer Endpoints
8.3.3.1. Animal Cancer Studies for Dose-Response Modeling
Mathematical modeling can be a powerful tool for understanding and combining
information on complex biological phenomena. Modeling of carcinogenicity can be accomplished
using simple techniques (Portier et al., 1984) and can be improved by taking the results of an
existing mechanism-based model on receptor-based effects of TCDD within the context of a
physiologically based pharmacokinetic (PBPK) model (Kohn et al., 1993) and using these results in
a detailed multistage model of carcinogenesis (Portier et al., 1996). Both approaches have been
attempted. For a mechanism-based approach see Section 8.4.3.2.
Portier et al. (1984) used a simple multistage model of carcinogenesis with up to two
mutation stages affected by exposure to model the five tumor types observed to increase in the 2-
year feed study of Kociba et al. (1978) (Sprague-Dawley rats) and the eight tumor types observed to
increase in the 2-year gavage cancer study conducted by the National Toxicology Program (1982a)
(Osborne-Mendel rats and B6C3FJ mice). The findings from this analysis are presented in Table 8-
3. The ED01s were calculated based on Portier et al. (1984). Excess risks were then calculated from
the ED01 using equation (1) in Section 8.2.2. All but one of the estimated ED01 values are above the
lowest dose used in the experiment (approximately 1 ng/kg/day) and are thus within the
experimental range. The exception, liver cancer in female rats from the Kociba study, is very near
the lowest dose used in this study. Steady-state body burden calculations were also used to derive
doses for comparison across species (see Section 8.2). Absorption was assumed to be 50% for the
Kociba et al. (1978) study (feed experiment) and 100% (Rose et al., 1976) for the NTP study
(1982a) (gavage experiment). Also presented in Table 8-3 are the shapes of the dose-response
curves as determined by Portier et al. (1984).
The predominant shape of the dose-response curve in the experimental region is linear; this
does not imply that a nonlinear model such as the quadratic or cubic would not fit these data. In
fact, it is unlikely that in any one case, a linear model or a quadratic model could be rejected
statistically (Hoel and Portier, 1994). These studies had only three experimental dose groups; hence
these shape calculations are not based upon sufficient doses to guarantee a consistent shape
estimate; they should be viewed with caution. The body burdens at the ED01 values range from a
low value of 14 ng/kg based upon the linear model associated with liver tumors in female rats, to as
high as 1,190 ng/kg based upon a cubic model associated with thyroid follicular cell adenomas in
female rats.
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8.3.3.2. Conclusions From Animal Cancer Dose-Response Modeling
The animal studies show an increase in cancer incidence in rats and mice at various sites.
The ED01 estimates of daily intake level obtained from an empirical linear model range from 0.8 to
43 ng/kg body weight/day depending on the tumor site, species, and sex of the animals investigated.
These are equivalent to steady-state body burdens of 14 to 1,190 ng/kg body weight. By way of
comparison, the ED01 estimate obtained from a linear mechanistic model of liver tumor induction in
female rats (Section 8.4.3.2) was 0.15 ng/kg body weight/day, equivalent to a steady-state body
burden of 2.7 ng/kg body weight (Portier and Kohn, 1996).
8.3.3.3. Knowledge Gaps in Animal Cancer Dose-Response Modeling
The dose-response data for cancer in animals following TCDD exposure are limited to three
exposure groups. Although nonlinear models could be applied to these data (Portier et al., 1994),
the estimates of the shape of the dose-response curve should be viewed with caution. Studies with
more dose groups and sufficient animals per dose group are needed for distinguishing between
different shapes of dose-response curves. Furthermore, mechanism-based cancer modeling could be
improved if physiological, biochemical, and tissue response information were obtained from the
same experiment.
Hepatocellular carcinomas have been the main focus for much of the research on the
carcinogenicity of TCDD, although there has been increased tumor incidence in other organs. With
respect to extrapolation to humans, the investigation of lung and thyroid cancer should be studied
further. Animal cancer studies using other PCDDs, PCDFs, PCBs and complex mixtures reflecting
human exposure patterns have rarely been done and may add information to the problem of
complex human exposure.
8.3.4. Rodent Dose-Response Models: Noncancer Endpoints
8.3.4.1. Methodology
Risk assessments for noncancer endpoints traditionally have not used endpoint-specific
mathematical models. Instead they have relied on safety assessment involving determination of a
dose that is likely to be without risk, taking both data and model uncertainties into account.
Although many of the same biochemical effects involved in carcinogenesis are also involved in
many other toxicities, biologically based mathematical models for noncancer endpoints are not as
developed as are the cancer risk models. In the interim, we will use a simple empirical modeling
scheme to estimate effective doses and to discuss dose-response curve shape for the biological and
toxicological effects induced by TCDD. The models and the statistical details follow similar
analyses done by McGrath et al. (1995) and Murrell et al. (1998). In brief, two different models
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were applied to the continuous data depending upon the number of dose groups used and the overall
quality of the data. First choice was to use a Hill model of the form
vcT (4)
R(d) = b+
V+cT
where R(d) is the response at dose d, and 6, v, &, and n are model parameters to be estimated from
the data. The parameters each describe a different aspect of the dose-response curve: b is the
background response, v is the maximum attainable response, A: is the dose yielding half of v, and n is
the Hill coefficient describing the curvature of the dose-response. As the shape of the
dose-response curve is critical for risk assessment, it is of interest to consider important
classifications based on n. When n is near or below 1, risk is predicted to be approximately
proportional to dose or climbing more rapidly than proportional. When n is much larger than 1 (n >
1.5), the dose-response is sigmoidal and has been described as appearing to have a threshold. For
these reasons, n will also be referred to as the shape parameter.
In the present exercise, n was not allowed to vary below 1, and thus the model as used does
not predict supralinearity. Estimates of n were restricted to be greater than 1 to avoid instability.
Estimates for the ED01 are sensitive to the slope of the dose-response curve evaluated at dose=0, and
when «<1, this slope becomes infinite. This infinite slope is not biologically realistic and is difficult
to tie down accurately to these data. This makes the estimates of the ED01 unstable and, worse,
makes their lower confidence bounds very unstable. The net effect of this restriction is a possible
bias towards higher-than-expected ED01 value and a truncation in the distribution of observed
shapes. The first effect cannot be avoided, but the second should not be a problem because
unrestricted estimates of n<\ will yield restricted estimates of n=l and the shape will be classified
into a grouping of risk approximately proportional to dose.
The second model used here is the power function:
R(d) = b + sd" (5)
where b and n have similar descriptions and s, referred to as the scale parameter, describes the
magnitude of the effect per unit of dose. Unlike the Hill model, this model has no fixed maximum
and is used in this chapter for data with either no experimentally evident maximal response or with
few dose groups. This poses a considerable problem in defining effective doses, and caution should
be used in applying effective doses derived from the power function model. Quantal data were
modeled using the Weibull model given by
R(d) =c + (1- c)[l - exp(-acf)J (6)
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where R(d) is the probability of response at dose d, c is the expected response in untreated animals
(0 < c < 1), a is the magnitude of response per unit dose raised to the ^h power (a > 0), and k is the
shape parameter (k > 1). The Weibull model as used in this analysis estimates threshold-like
behavior when k is large. In addition, k was not allowed to be less than 1 to avoid instability in the
analysis. The ED01 values from quantal data satisfy the excess risk relationship described in
equation (1) in Section 8.2.2 where R(°°) is equal to 1 for quantal endpoints.
The data sets examined in this exercise are found in the published literature. The studies
analyzed provided dose-response information on TCDD using at least three dose levels of TCDD
and a control. In addition, the mean and an estimate of the variance of the data had to be presented
in tabular form in the manuscript. Attempts to estimate the means and variances of data presented
in graphical forms proved unreliable, thus publications where the data were presented only in graphs
were not included in the analysis. Model fits, calculation of the ED01 and ED10 and the 95% lower
bound on the estimated ED01 were carried out using the U.S. Environmental Protection Agency
(EPA) Benchmark Dose Software (BMDS) version Lib (U.S. EPA, 1999). In some cases, the
BMDS software failed to locate a lower confidence bound on the ED01.
The model fits were evaluated with regard to the observed data. The goodness of the model
fit was determined as "good" if the model curve included nearly all of the data point means,
"marginal" if the model curve was within one standard deviation of the data point means, or "poor"
if model fit was not within one standard deviation of the means. There were 242 endpoints for
which dose-response analyses could be made (approximately 200 continuous endpoints and
approximately 30 quantal effects), obtained from more than 36 published manuscripts (see
Appendices). The number of data sets, categorized by species, gender and study type, is shown in
Table 8-4. Poor fits were not evaluated further and were not included in the overall assessment of
the ED01 values.
For the Hill model fits, the Vmax estimates from "good and "marginal" model fits were
subjectively evaluated for stability and biological plausibility with regard to the observed data. This
evaluation identified some potential problems with some of the Vmax estimates. In some cases the
error associated with the Vmax could not be calculated by the BMDS software. In these cases if the
Vmax model estimate was similar to the "observed Vmax" (i.e. the difference between the highest
dose response level and the control response level) then the Vmax estimate was considered
biologically plausible and was used for the calculation of an ED01. Otherwise the "observed Vmax"
was used for calculation of the ED01.
In other cases the error associated with the model Vmax estimate was high, indicating a
potentially unstable estimate that may not be biologically plausible. The Vmax estimate was
considered unstable if the error associated with the Vmax estimate was greater than the Vmax itself.
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In these cases an "alternate Vmax" was calculated as 3 standard deviations of the observed
control response. If the maximum response associated with the use of this "alternate Vmax" was
within 3 standard deviations of the observed highest dose response then this new Vmax value was
considered to be biologically plausible and was used for subsequent ED01 calculations.
However, if the maximum response was not within 3 standard deviations of the observed
highest dose response then this "alternate Vmax" value was considered to not be biologically
plausible and was not used. In these cases an "observed Vmax" was determined for use in the ED01
calculations. The "observed Vmax" was calculated as the difference between the observed highest
dose response and the observed control response.
In all cases where the model estimated Vmax was replaced with a new surrogate Vmax a
new ED01 was recalculated using this new Vmax. In all cases, the original shape, and ED50
parameters were used for calculation of the ED01, i.e. the data were not remodeled using the new
assigned Vmax as fixed parameter in the Hill model. Because the data were not remodeled,
estimates of the lower confidence interval are not available for these data sets.
There were 284 dose-response data sets found in the peer-reviewed literature that fit the
inclusion criteria described above. The Hill, Power or Weibull models were fit to these 284 data
sets. Good or marginal fits were attained for 242 of these data sets. The data sets that had poor fits
were not included in the synthesis of the modeling results. All fits to the 284 data sets are presented
in Appendices I through HI.
The analyses of the data are presented as summaries of the endpoint categories in Figure 8-1,
Figure 8-2, Figure 8-3, Table 8-5, and Table 8-6. The data are divided into several categories on the
basis of exposure regimen and endpoint. Exposure categories are grouped as either single
exposures or multiple exposures. For simplicity, effects were categorized as biochemical, hepatic,
immune, endocrine, tissue, or toxicity (Table 8-7). Biochemical changes included alterations in
mRNA, protein, or enzyme activities. The category of hepatic changes included responses of
hepatotoxicity, such as serum enzymes and histological effects. Immune responses included
alterations in lymphocyte phenotypes and functional alterations such as altered responses to antigen
challenge. Alterations in tissue and body weights were classified as a tissue response.
Developmental, reproductive, and tissue toxicities were classified as toxic responses. Finally, there
were limited studies on the effects of TCDD on serum thyroid hormone concentrations and
alterations in either serum or tissue retinoid concentrations; these studies were categorized as
endocrine effects.
Comparison of the ED01 between studies can be problematic for several reasons. The
effective dose is dependent upon the sensitivity of the endpoint examined and the dosing regimen
employed. For example, in studies examining the effects of TCDD following a single exposure, the
time after the initial exposure when the determinations were made varied from days to weeks. For
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some effects, the differences in the time after the initial exposure probably influence the effective
dose. Similarly, in studies employing multiple doses, investigators used a variety of regimens
including daily exposure, weekly exposures, and loading/maintenance regimens. These differences
in dosing regimens may influence the dose response relationships. In addition, investigators used a
variety of exposure routes including dietary, oral gavage, subcutaneous, and intraperitoneal. The
different routes and vehicles (diet vs. oil solution) have different absorption rates and percentage
absorbed. These differences may result in different tissue concentrations and may influence the
dose-response relationships. In order to compare the multiple-dose studies using different routes of
exposure, the average daily dose was estimated for each study by calculating the total dose
administered to the animal over the course of the study and dividing by the length of the study in
days. In addition, for the multiple-dose studies, average steady-state body burden at the ED01 was
calculated using the equation in Section 8.2.2 and the percentage of dose adsorbed and the half-lives
for TCDD in Table 8-1.
In applying a consistent modeling approach across all endpoints, some uncertainty is
introduced for those data sets where this approach provides only a marginally adequate fit. In some
cases, no trend was apparent below the highest dose examined, thus reducing the confidence that
can be placed in accurately estimating the dose associated with a change as small as 1%. In other
cases, it appeared that other models could provide a better fit to the data, with a significantly
different ED01. For example, sometimes the Hill model gave a dose-response curve with sharp
changes in slope, but a Weibull model could have provided a better fit to the data with a smoother
curve and a lower ED01. In addition, the ED01 and the 95% lower confidence interval (LED01) were
sometimes quite far apart (differing by more than tenfold), suggesting that little confidence can be
placed in some ED01 values as a precise index of toxicity. In such cases, it is useful to look at the
LED01 as a bound. Whenever the modeling results were problematic for these or other reasons, we
noted it and gave less emphasis to those results in our overall synthesis of the data. In this way, the
overall conclusions are based on the strongest results.
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8.3.4.2. Multiple-Dose Studies
Of 139 endpoints examined from multiple-dose studies, 108 data sets had fits described as
good or marginal. Thirteen of the data sets had statistically significant fits designated as poor and
18 data sets did not have statistically significant fits to the models. Data sets with poor fits or non-
statistically significant fits were not included in the following analysis. The estimates of the Vmax
were unstable in 43 of the 108 data sets with good and marginal fits. Of the 43 data sets with
unstable Vmax estimates, five of the modeled Vmax values were accepted as the estimate. The
Vmax was set at three standard deviations from controls for 25 of the data sets. For 13 data sets, the
Vmax was set as the difference between the response of the highest dose tested with those of the
controls.
In the studies examining the effects of TCDD following multiple exposures, the range of the
ED01 values is highly variable within and across response categories (Figure 8-1). For the multiple
dose exposure studies, the ED01 values were modeled using the average daily dose from each study.
When examined by category, the median values for the ED01 for biochemical responses are lower
than the median ED01 for other types of response by almost an order of magnitude. Biochemical
responses have a median ED01 of approximately 1 ng/kg/d and hepatic, immune and tissue
responses have median ED01 values of 10 ng/kg/d or greater. Of the 108 endpoints examined from
studies using multiple exposures, ten have ED01 values less than 0.1 ng/kg/day. Six of the ten
endpoints with an ED01 below 0.1 ng/kg/day are markers of immune response. However, the ED01
for markers of immune function range over six orders of magnitude, decreasing the confidence of
any particular ED01 value for this response. In general these ED01 values represent dose-response
information from female rats and mice, with few studies examining male rats and mice or other
species. These knowledge gaps decrease our confidence in making extrapolations between species
and gender.
ED01s for single dose exposures were also estimated using body burden as the dose metric
(Figure 8-2). Biochemical responses had a median ED01 of approximately 13 ng/kg. Hepatic,
immune and tissue response had median ED01s greater than 200 ng/kg. Background human
exposure to dioxin-like chemicals is approximately 5 ng TEQ/kg. The margin of exposure between
the median ED01s for humans is approximately 3 for biochemical effects and approximately 40 for
hepatic, immune and tissue responses. Of the 108 data sets for which ED01s were estimated, 42 are
less than 50 ng/kg and include responses in the biochemical, endocrine, immune and hepatic
categories. There are 11 responses with ED01s of 5 ng/kg or lower. Six of these responses are
immune, three are biochemical and one each is endocrine and tissue. These data indicate that a
number of the ED01s are at or below present background exposures.
The ED10 was also estimated for these data sets and similar trends were observed compared
to the ED01. The median ED10 for biochemical responses was approximately 200 ng/kg and the
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other response categories were 5-10 times higher (Figure 8-3). There were 14 data sets with ED10s
less than 50 ng/kg and 9 of these data sets had ED10s that were less than 5 ng/kg. This data suggests
that the ED10 for a number of endpoints is within an order of magnitude of background exposures.
In 44 of the multiple-dose data sets analyzed the ED10 was less than 2 times the ED01. In 28 of the
data sets the ED10 was between 10 and 2 times greater than the ED01.
One measure of the degree of confidence of the ED01 estimate is the ratio of the ED01 to the
lowest dose used in the study from which it was derived (Table 8-5). A ratio of 1 or greater
indicates that the ED01 is above the lowest dose examined. Ratios between 1 and 0.1 are within one
order of magnitude of the lowest dose tested and indicate that the ED01 may provide a realistic
value. Ratios less than 0.1 indicate that the estimate was more than an order of magnitude below
the lowest dose used in the study and should be viewed with caution. Forty-seven of the 108 values
had ratios of the ED01/lowest-dose less than 1. However, of these 47 only 37 were less than one
order of magnitude below the lowest dose used in the study. Another measure of the stability of the
ED01 is the ratio of the LED01 to the ED01 of the 59 data sets for which the LED01 was estimated, the
median ratio of the LED01/ED01 is 0.39. Only 17 of the 59 data sets had an LED01 that was an order
of magnitude or more less than the ED01.
In general, an estimated shape parameter that is less than 1.5 indicates that the shape of the
dose-response curve tends to be linear at low doses, and those with shape parameters greater than
1.5 tend to be threshold-like. Of the 108 endpoints for which an estimate was obtained, 48 had
shape parameters less than 1.5, indicating linear dose-response relationships (Table 8-6).
Approximately half of the biochemical and half of the tissue responses indicated a linear
dose-response relationship. In contrast, only 19% of the immune function responses were linear.
Although there is some consistency of shape within certain categories of these endpoints, in
general about half of the responses could be classed as either linear or nonlinear. These
observations do not strongly support linearity for TCDD dose-response, nor do they strongly
support the existence of thresholds within the observable range.
8.3.4.3. Single-Dose Studies: Adult Animals
There were 98 data sets examining the effects of dioxin in adult rats and mice following a
single exposure. Good or marginal fits were assigned to 75 of these data sets. The Hill model was
used in 58 of these data sets and the Weibull model was applied to 17 of these data sets. The Vmax
was considered unstable in 17 out of the 58 data sets with good or marginal fits to the Hill model.
The Vmax was set at three standard deviations from control for 5 data sets and the response at the
high dose minus the control response was used as the Vmax for 10 data sets. For two of the data
sets with unstable Vmax estimates, the modeled estimate was considered acceptable based on the
criteria outlined above.
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The median ED01 is above 1 ng/kg for all endpoints examined (Figure 8-2). Biochemical
and immune responses had the lowest median ED01 estimates, 207 and 133 ng/kg, respectively.
Hepatic and toxic responses gave median ED01s greater than 10,000 ng/kg. Once again there was
large variability in the ED01s for a given category. In general the ED01s varied over three orders of
magnitude within each category. There were 14 data sets from the immune, tissue and biochemical
response categories with ED01 values less than 50 ng/kg. The ED01 estimates were below the lowest
dose tested for 21 of the 74 endpoints for which an estimate was obtained. Of these 21 estimates,
the ED01 was less than one order of magnitude lower than the lowest dose tested for 13 of the values
(Table 8-5). An LED01 was estimated for 70 data sets. The median ratio of the LED01/ED01 was
0.36 with only 11 out of 70 data sets with ratios less than 0.1.
Estimates of the ED10 produced similar trends (figure 8-3). The immune and biochemical
response categories had the lowest median ED10 values. There are four data sets with ED10 values
less than 50 ng/kg and all are in the immune response category. There are four response categories
that overlap between the single acute studies and the multiple dose studies. The ED10s for the
biochemical, hepatic and tissue response categories are higher in the single dose studies than in the
multiple dose studies. In contrast, the ED10s in the immune response categories are approximately 4
times less in the single dose studies compared to the multiple dose studies.
In studies examining the effects of dioxin in adult rats and mice following a single exposure,
the median ED01 is above 1 ng/kg for all endpoints examined Figure 8-2. Biochemical and immune
responses had the lowest median ED01 estimates, 207 and 133 ng/kg, respectively. Hepatic and
toxic responses gave median ED01s greater than 10,000 ng/kg. Once again there was large
variability in the ED01s for a given category; in general they varied over three orders of magnitude
within each category. The ED01 estimates were below the lowest dose tested for 21 of the 74
endpoints for which an estimate was obtained. Of these 21 estimates, the ED01 was less than one
order of magnitude lower than the lowest dose tested for 13 of the values (Table 8-5).
Following a single exposure to TCDD, 30 of the 74 endpoints examined (40%) had shape
parameters less than 1.5, indicating linear dose-response relationships (Table 8-6). There was no
consistent pattern in the shape of the dose-response relationships for the biochemical, immune, and
tissue response categories. In these categories both linear and threshold-like dose-response
relationships were observed. In contrast, all endpoints in the toxicity category exhibited
threshold-like dose-response relationships.
8.3.4.4. Single-Dose Studies: Developmental Studies
There were 90 data sets classified as developmental studies following a single exposure.
The model fits were described as good or marginal for 60 data sets. The Hill model was fit to 55
data sets. Thirty data sets were not included in this analysis because the model fits were either not
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statistically significant or were described as poor. The model estimates of the Vmax was considered
unstable in 18 out of the 55 data sets with good or marginal fits to the Hill model. In 7 of these data
sets, the model estimate of the Vmax was considered acceptable based on the criteria describe
above. Vmax was set at 3 standard deviations from controls in 11 of the data sets and for 5 of the
data sets Vmax was set as the response at the high dose minus the response in the controls.
Following a single exposure, a number of developmental effects have been examined. These
effects have been categorized as biochemical, tissue, or toxic. The majority of the effects examined
were considered tissue responses. The range of ED01 values was more than five orders of
magnitude, and the median values for all response categories were greater than 10 ng/kg, with an
overall median of 139 ng/kg (Figure 8-2). The median ED01 values for the response categories were
lower for developmental effects following a single dose compared to ED01 estimates for effects
observed in adults after a single dose. The tissue response category was the only category with
sufficient studies to compare between the developmental studies and the multiple dose studies in
adults. In this case the median ED01 for the developmental effects was approximately an order of
magnitude less than the median ED01 for the multiple dose studies. There were 18 out of the 60 data
sets that had ED01 values of less than 50 ng/kg and 8 of these were less than 5 ng/kg. ED10s were
also estimated for the developmental effects and similar trends were observed. The ED10 values for
12 of the developmental data sets was below 50 ng/kg and only one of the data sets had an ED10
less than 5 ng/kg. Decreases in epididymal sperm counts on PND 49 had and ED01 of 1.7 ng/kg
based on data from Gray et al. (1997).
The ED01 values for developmental effects were below the lowest dose tested in 38 out of 60
endpoints for which an estimate was obtained. Of the 28 estimates that were below the
experimental range, approximately half (18) were less than an order of magnitude below the lowest
dose tested. There were 37 endpoints for which the LED01 was estimated. The median ratio of the
LED01/ED01 was 0.19 and there were 8 endpoints with ratios less than 0.1. The shape parameter for
the developmental effects was less than 1.5 (i.e. linear) for only 18 of 60 endpoints analyzed.
The results of the analysis of the single exposure studies had similarities to those of the
multiple dose studies. There was a large range of ED01 values within response categories which in
some instances reached over five orders of magnitude. Similar to the analysis of multiple-dose
studies, the biochemical and immune response categories had the lowest ED01s. The median values
for all response categories were greater than 10 ng/kg, with an overall median of 139 ng/kg (Figure
8-2).
8.3.4.5. Summary of the Dose-Response Modeling for Noncancer Endpoints
The activation of the AhR by TCDD initiates a cascade of events resulting in alterations in
growth factors and their receptors, hormones and their receptors, and proteins involved in numerous
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cellular functions such as cell cycle regulation and intermediary metabolism (see Chapter 2 for a
more detailed discussion of these processes). Many of these biochemical changes, particularly the
alterations in growth factors and their receptors, may mediate the toxic effects of TCDD. The role
of other biochemical changes, e.g., induction of aldehyde dehydrogenase, is less certain. One can
consider the biochemical and toxicological effects of dioxins as a continuum, starting with
biochemical changes leading to toxicological events. Hence, understanding the shape of the
dose-response relationship for the biochemical effects may provide insight into the shape of the
dose-response relationship for toxic responses, particularly in the low-dose region.
Consistent with the hypothesis that the biochemical effects are precursors of the toxic effects
is that, in general, the biochemical responses tend to have lower ED01 estimates than other types of
endpoints examined. However, few of the biochemical changes examined have been directly linked
to toxic responses. For example, the induction of CYP1A proteins is perhaps the best-characterized
response to TCDD and related chemicals. Despite their known role as modulators of intermediary
metabolism for a number of classes of environmental chemicals in both activation and elimination
pathways, the direct relevance of these proteins to the toxic effects of TCDD remains uncertain.
Induction of CYP1A proteins has been proposed as a dose surrogate for the carcinogenic effects of
TCDD (Portier and Kohn, 1996). One of the best examples of biochemical changes leading to
toxicities is the TCDD-induced decreases in circulating thyroid hormones. This is likely a result of
TCDD-mediated induction in hepatic glucuronosyltransferases (UGTs), which metabolize these
hormones and increase their elimination, van Birgelen et al. (1995a) determined total and free
plasma thyroxine concentrations and hepatic thyroxine glucuronidation (T4UGT) in rats exposed to
TCDD for 90 days in the diet. The ED01 values for total plasma thyroxine, free plasma thyroxine,
and T4UGT are 33, 4.9, and 1.6 ng/kg/day. The increased sensitivity of T4UGT is consistent with
the mechanism by which the plasma concentrations of these hormones are decreased. In female
Sprague-Dawley rats exposed biweekly to TCDD for 30 weeks, Sewall et al. (1995) examined the
effects of TCDD on UGT mRNA, serum total thyroxine, and serum TSH. All three responses had
shape parameters greater than 1.5 and the ED01 values were 0.37, 1.3, and 26 ng/kg/day for UGT
mRNA, total serum thyroxine, and serum TSH, respectively. Similar to the data of van Birgelen,
the induction of UGT is more sensitive than changes in total serum thyroxine, which in turn is more
sensitive than are changes in serum TSH. These data indicate that simple biochemical responses
have lower ED01 values than more complex phenomena such as decreases in thyroxine and
alterations in the homeostasis of thyroid hormones.
One concern in the interpretation of the data is whether the study design can affect the ED01
or the shape parameters. One example of this is the studies by Diliberto and co-workers. Diliberto
et al. (1995) examined both dose-response and time course for CYP1 Al-associated hepatic
ethoxyresorufm deethylase (EROD) activity at 7, 14, 21, and 35 days after a single exposure to
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TCDD. In these studies, the ED01 values and the shape parameters increased with time after dosing.
The increase most likely stems from the decreasing tissue concentrations of TCDD and the
subsequent decreases in enzyme induction from day 7 to day 35. The shape parameter ranged from
1 at 7 days after dosing to 6.5 at the 35-day time point. The ED01 increased from 27 ng/kg at 7 days
after dosing to 740 ng/kg at the 35-day time point. These data indicate that both the shape
parameter and the ED01 are sensitive to the study design. Comparisons of studies that determined
EROD activity within 7 days of administration of TCDD demonstrate considerable consistency.
Four studies examined EROD induction in rats or mice within 7 days of dosing and the ED01 values
ranged from 16 to 84 ng/kg. The estimated shape parameter is 1 for the Diliberto et al. (1995),
Abraham et al. (1988), and Narasimhan et al. (1994) studies and 1.8 for the van Birgelen et al.
(1995a) study. It should be noted that two of these studies are in mice and two are in rats,
suggesting similar dose-response relationships for enzyme induction between these species.
Another variation in study design that may affect dose-response modeling is dose selection.
The dose-response relationship for induction of hepatic EROD activity was modeled for six studies
(van Birgelen et al., 1995a,b; DeVito et al., 1994; Johnson et al., 1997; Schrenk et al., 1994; Vogel
et al., 1997). Only the data from DeVito et al. (1994) and Johnson et al. (1997) had shape
parameters greater than 1.5. While most of the ED01 values were approx 1 ng/kg/day, the data of
Vogel et al. (1997), resulted in an ED01 more than 100-fold lower. Vogel et al. (1997) used a
loading/maintenance dosing regimen, and the low dose used was 100 times lower than those of the
other studies. The highest dose in the Vogel study was approximately 50-100 times lower than the
highest dose used in the other studies. The much lower ED01 from this study may be a consequence
of the dose pattern and dose selection in this study compared to the other studies.
Another factor to consider is species and strain selection in the studies. The developmental
effects of TCDD have generated concern, particularly the developmental reproductive toxicities
observed in rats and hamsters (Mably et al., 1992a,c; Gray et al., 1997). These studies demonstrated
decreases in epididymal sperm counts on postnatal day 63. However, the shape parameters vary
between 1 and 11 and the ED01 values vary between 0.65 and 140 ng/kg. The studies used different
strains of rats, and perhaps this may account for some of the differences between the data sets. The
decreases in the epididymal sperm counts were greater in the Holtzman rat used by Mably et al.
(1992a) when compared to the Long Evans rat used by Gray et al. (1997) Overall, the study by Gray
et al. (1997) demonstrated smaller effects than the study by Mably et al. (1992a). Also, the data
from Gray et al. (1997) demonstrate highly nonlinear responses (shape parameters greater than 2 for
all but 3 out of 32 responses examined). In contrast, the effects observed in Mably et al. (1992a)
were larger, the shape parameters indicate a more linear dose-response, and the ED01 is almost two
orders of magnitude lower than those estimated from the data of Gray et al. (1997).
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One of the apparent observations of this exercise is the limited number of studies examined
compared to the vast literature on the health effects of 2,3,7,8-TCDD. There are thousands of
research articles examining health effects of TCDD. Of these articles, less than 50 were analyzed.
There are a variety of reasons why only a limited number of articles could be included in this
analysis. First, only studies in experimental animals were included, omitting many articles on in
vitro studies. Second, only studies providing dose-response data that included a minimum of three
dose levels and a control were included. Third, the data had to be presented in tabular form with
means, standard deviations or standard error, and the number of samples for which the mean was
calculated. It is likely that given the vast number of data sets available, some were inadvertently
excluded. However, most of the studies found in the literature did not fit these criteria, either
because of inadequate dose-response information or graphical presentation. For some studies that
provided adequate dose-response information but presented the data in graphical format, the authors
were asked to provide means and standard deviations and kindly did so. One of the conclusions of
this exercise is that when preparing data for publication, authors conducting dose-response studies
should consider the use of their data and present it in such a way that it is usable in future
independent analyses.
Care should be taken in interpreting these analyses. There tends to be a large variation in
both the shape parameter and the ED01 values for a given endpoint. Most of the studies examined
were designed to determine a no-observed-effect-level (NOEL) or lowest-observed-effect-level
(LOEL) and, as such, these data contain limited dose-response information. The limited
information contributes to the observed variation in the estimates of both the shape parameters and
the ED01 values. This should not be taken as a critique on the quality of the study designs. In
almost all instances, the authors of the studies used analysis of variance as a statistical tool and the
studies were designed for such an analysis. In contrast, the present exercise attempts to examine the
dose-response relationships using nonlinear regression analysis as a statistical tool. Because of the
limited dose-response data available, particular caution should be used when extrapolating to dose
levels outside the experimental design. If this situation is to be improved and uncertainties in data
interpretation reduced, studies will need to be designed and data produced that are more suitable for
nonlinear regression analysis. Second, and perhaps more disappointing, was the frequency of
inadequate reporting of the data. Many studies would present a mean and some measure of variance
without describing whether the variance was presented as a standard deviation, a standard error of
the mean, or some confidence interval. These variables can be adjusted for use in modeling if the
proper number of animals/group is provided. However, often the number of animals/group was
presented as a range.
Although ED01 values are intended as a common measure across studies and endpoints, they
must be interpreted in relation to their respective maximal responses. For example, if enzyme
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induction varies over a considerably greater range in one strain than another (for example, hepatic
EROD induction in the studies by DeVito et al. [1994] compared to that observed in the study of
Vogel et al. [1997]), then their respective ED01 values will represent different levels of induction.
The biological significance of these responses may not be commensurate with their respective ED01
values. In addition, comparisons across endpoints must proceed cautiously. A 1% increase in
response for decreased body weight may not necessarily be comparable to a 1% excess effect on
immune function or enzyme induction.
Several studies have demonstrated that control rats and mice have detectable amounts of
TCDD and related chemicals (Vanden Heuvel et al., 1994a; DeVito et al., 1998). The
concentrations of these chemicals are at or near the quantification limits. In the present analysis, the
background exposures of the control animals were not considered. The inclusion of background
exposure levels or tissue concentrations in the dose-response analysis may alter the shape of the
dose-response curves and is some cases may possibly increase the ED01 estimate and/or the model
estimate of the shape parameter. However, it is unlikely that any effect of the estimates would
substantially change the observed trends in the estimates or the main conclusions of this
dose-response chapter.
An important finding in this analysis is that the biochemical effects tend to have lower ED01
values compared to more complex effects such as immunotoxicity or tissue weight loss. This
finding is consistent with the hypothesis that the biochemical responses are precursors to the toxic
responses of these chemicals. Another difference between the biochemical and toxicological
responses is that the biochemical responses tend to have lower shape parameters. Thus, the
dose-response relationships for the biochemical responses tend to be linear more often than the
toxicological responses. Because of the limited dose-response data available for many of these
analyses, caution must be taken when making some of these generalizations. For example, the
decrease in thymus weight tends to have estimated shape parameters of 1.
Finally, the present analysis focused on the ED01. This effect level was chosen because it
would allow the comparison between the human epidemiological data and the animal data.
Typically, the ED01, ED05 or ED10 is used as the point of departure in risk assessments. Use of either
of these alternative risk estimates would result in some differences. Obviously choosing higher
effect levels will result in higher dose levels compared to the ED01. Also the estimates of the ED10
would most likely be more stable than the estimates of the ED01. However, in the present analysis
several data sets had ED01s and ED10s that were less than 50 ng TCDD/kg. Based on a
background human exposure at 5 ng TEQ/kg, using either the ED01 or ED10s would result in a
number of effects with margin of exposures less than an order of magnitude from background
human exposures.
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8.4. MODE-OF-ACTION-BASED DOSE-RESPONSE MODELING
8.4.1. Introduction
Mode-of-action-based modeling for TCDD encompasses PBPK models for estimating tissue
dose and biochemical/tissue response models that describe the consequences of tissue dose. The
distinction between tissue dose and response is often maintained in developing mechanism- or
mode-of-action-based models. A number of PBPK models for TCDD have been developed. These
models have provided insights into key determinants of TCDD disposition in TCDD-treated
animals, such as diffusion-limited movement of TCDD between blood and tissue and induction of
hepatic binding. PBPK models may be extended to generate predictions for biochemical
consequences of the tissue dosimetry of TCDD. The molecular steps leading to observed responses
form a causal sequence that describes the mode of action by which pathology is produced.
Examples of carcinogenic modes of action include enhanced mutation by direct DNA reactivity,
increased cell proliferation related to toxicity or mitogenic stimulation, or diminished apoptosis in a
population of altered cells. The predictions of a PBPK model can be used to describe parameters in
the mathematical representation of this mode of action. The goal of mode-of-acti on-based
modeling is to express quantitatively the relationships between TCDD exposure, TCDD tissue
kinetics, and the biochemical alterations leading to effects on these integrated responses. This
section discusses models for dosimetry, biochemical, and tissue responses, and how they ultimately
lead to adverse effects of TCDD.
Risk assessments where mechanistic dosimetry models have been used without any attempt
to describe the mechanism of tissue response are a viable intermediate stage in the development of
mechanism-based risk assessments. This approach to risk assessment also reflects the paucity of
mechanistic models of tissue response, relative to models of tissue dosimetry. The more ambitious
modeling of the entire exposure-tissue response continuum (Section 8.4.2) carries with it the greater
requirement for mechanistic understanding of tissue response. When our understanding of
mechanisms of tissue dosimetry and response are different, careful consideration should be given to
the sources of uncertainty in the overall modeling effort. The realization that dosimetry and
response submodels can contribute unequally to overall model uncertainty can help to guide the
choices made in developing the final risk model and the allocation of resources for additional
research.
8.4.2. Model Structures and Model Development
8.4.2.1. PBPKModeh
8.4.2.1.1. Issues pertaining to PBPK models. Tissue dosimetry encompasses the absorption of an
administered chemical and its distribution among tissues, metabolism, and elimination from the
body (ADME). TCDD dosimetry depends on physicochemical properties of TCDD (e.g., tissue
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permeation constants, partition coefficients, kinetic constants, and biochemical parameters) and
physiological parameters (e.g., organ volumes and blood flow rates). The mathematical structure
that describes the relationship between these factors and ADME constitutes a model for the tissue
dosimetry of dioxin. These models describe the pharmacokinetics of TCDD by a series of
mass-balance differential equations in which the state variables represent the concentration of
TCDD in anatomically distinct regions of the body. These tissue "compartments" are linked by a
physiologically realistic pattern of blood perfusion, called a PBPK model. Several research
documents discuss the development of PBPK models for general use (Gerlowski and Jain, 1998),
and use in risk assessment (Clewell and Anderson, 1985).
PBPK models have been validated in the observable response range for numerous
compounds in both animals and humans, making them useful for risk assessment, especially for
cross-species extrapolation. In addition, they aid in extrapolation from one chemical to other
structurally related chemicals because many of the components of the model are the same or can be
deduced for related compounds. The tissue concentrations of several cellular proteins are known to
be modified by TCDD, making them useful as dose metrics. A model can be used to predict the
concentrations of these proteins as well. If one of these proteins is mechanistically linked to a toxic
endpoint, the protein could also serve as a dose metric of toxic effects.
The time course of behavior in each compartment of a PBPK model is defined by an
equation containing terms for input and loss of chemical. The specific structure of a PBPK model
and the assumptions used to develop the model are encoded in the equations. A careful evaluation
of any PBPK model must involve the adequacy of its fit to the data, the relationship of its structure
to the underlying biology, and the mathematical details linking the two. Several PBPK models have
been developed for TCDD and related chemicals (see Part II. Chapter 1 for a brief overview).
Models have also been developed for polychlorinated biphenyls (Lutz et al., 1984; Matthews and
Dedrick, 1984; Parham et al., 1997, 1998) and polychlorinated dibenzofurans in several species
(King et al., 1983), including humans.
There are four levels of complexity in PBPK models for the effects of TCDD. First is the
traditional PBPK model by Leung et al. (1988) with the added complexity of protein binding to
CYP1A2 in the liver. The next level of complexity are the models by Andersen et al. (1993) and
Wang et al. (1997) using diffusion-limited modeling and protein induction by interaction of DNA
binding sites. The third level is represented by the model of Kohn et al. (1993) with extensive
hepatic biochemistry and the model for zonal induction of cytochromes P-450 (Andersen et al.,
1997b). Finally, there are the models that include coordination of responses in multiple organs
(Kohn et al., 1996) for hormonal interactions, and Roth et al. (1994) with its detailed description of
gastrointestinal uptake, lipoprotein transport, and mobilization of fat (Figure 8-4).
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8.4.2.1.2. Initial attempts to include protein induction. Leung et al. (1988) developed a PBPK
model for TCDD disposition in mice, for Sprague-Dawley rats (Leung et al., 1990a) and for
2-iodo-3,7,8-trichlorodi-benzo-/>-dioxin in mice (Leung et al., 1990b). These initial models
considered tissue partitioning, protein binding in blood, specific binding of TCDD to inducible
hepatic proteins, binding of TCDD to the AhR, and activation of gene transcription by the
Ah-TCDD complex. Subsequent PBPK models have refined the representations of these processes
as more biological information became available.
This early PBPK model (Leung et al., 1990a) contained five flow-limited tissue
compartments, including blood, liver, fat, and slowly perfused and richly perfused tissues. TCDD
binding in blood was described by an effective equilibrium between the bound and free TCDD
given by a constant ratio. TCDD also binds to two liver proteins: one corresponding to the
high-affinity, low-capacity AhR and the other to a lower affinity, higher capacity microsomal
protein inducible by TCDD, now known to be CYP1A2. The predictions from this modeling
exercise prompted a series of experiments to examine the nature of these binding proteins in mice
(Poland et al., 1989a,b). In the PBPK model (Leung et al., 1990a), the concentration of the AhR is
held constant and the concentration of CYP1A2 is calculated using a Michaelis-Menten equation
for the instantaneous extent of induction as a function of hepatic TCDD concentration.
In various studies, TCDD has been administered by intravenous, intraperitoneal, or
subcutaneous injection; feeding; or by oral intubation (gavage). In the PBPK modeling framework,
intravenous injection can be represented by setting the initial amount in the blood compartment
equal to the injected dose. Oral intubation and subcutaneous injection were modeled as first-order
uptake from the site of administration, with TCDD appearing in the liver blood after oral
administration and in the mixed venous blood after subcutaneous injection. Feeding was modeled
(Leung et al., 1988, 1990a) as a constant input rate on days that TCDD was included in the diet.
With 2-iodo-3,7,8-trichlorodibenzo-/7-dioxin, the estimated rate constant for oral absorption was
considerably larger in TCDD-induced than in naive animals. The physiological basis of this change
is unknown, but it may be a consequence of increased hepatic lipid synthesis and elevated plasma
lipid following TCDD treatment (Gorski and Rozman, 1987).
The descriptions of the routes of uptake are clearly not defined in specific physiological
terms, and this lack of detail represents a common limitation in all of the PBPK models for TCDD.
These descriptions of the oral, subcutaneous, and skin routes are simply empirical attempts to
estimate an overall rate of uptake of TCDD into the PBPK model. This is one area in which
additional research could improve dose-response modeling for TCDD.
Partition coefficients for TCDD were estimated from measurements of tissue and blood
concentrations in exposed animals. Leung et al. (1990a) also modeled metabolic clearance as a
first-order process with the rate constant scaled inversely with (body weight)03. In the mouse with
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the iodo-derivative, TCDD pretreatment at maximally inducible levels caused a threefold increase
in the rate of metabolism, probably through loss of iodine. However, Olson et al. (1994) found that
pretreatment of rats with 5 jig TCDD/kg body weight increased metabolism in isolated hepatocytes
only when at least 1 mM TCDD was present in the medium. Induction of its own metabolism by
TCDD appears to be a minor high-dose effect.
Leung et al. (1990a) kept all physiological parameters (e.g., organ perfusion rates and tissue
volumes) constant over the lifetime of the animal. Subsequent PBPK models have included growth
of the animals over time and changes in organ size due to growth and toxicity. TCDD and TCDD
analogues have dose- and time-dependent kinetics in both rodents (Kociba et al., 1976, 1978; Rose
et al., 1976; Abraham et al., 1978; Poland et al., 1989b; Tritscher et al., 1992) and humans (Carrier
and Brodeur, 1991; Pirkle et al., 1989). As the exposure level increases in single and short-duration
exposures, the proportion of total dose found in the liver increases. This initial model served as the
basis of later models as new data were published on dose and time dependence of TCDD tissue
concentrations (Abraham et al., 1988 Tritscher et al. 1992).
In discussing the components that form the basis for a mechanistic model for TCDD, we
focus on aspects of the model that could lead to nonproportional response for low environmental
doses (nonlinear behavior). The model of Leung et al. (1990a) predicted slight nonlinearity
between administered dose and tissue concentration in the experimental dose range. In the
low-dose range, the model predicts a linear relationship between dose and concentration. The
authors argue, however, that tissue dose alone should not be used for risk assessment for TCDD
because of the large species specificity in the ability of TCDD to elicit some toxic responses. They
suggest instead that use of time-weighted receptor occupancy linked with a two-stage model of
carcinogenesis is a better approach to risk estimation. The time-weighted receptor occupancy
predictions derived from the Leung et al. (1990a) model are linear in the low-dose region, reaching
saturation in the range of high doses used to assess the toxicity of TCDD. This discussion
represented one of the early attempts to define a dose metric for the carcinogenic action of TCDD.
8.4.2.1.3. Refinements with DNA binding ofAh-TCDD complexes. Andersen et al. (1993a)
modified the model of Leung et al. (1990a) to include Hill kinetics in the induction of CYP1 Al and
CYP1A2 and to treat tissue uptake of TCDD as diffusion limited instead of blood flow limited as
done by Leung et al. (1990a). Diffusion limitation was incorporated by replacing the blood flow
term in the expression for tissue uptake of TCDD by a permeability factor equal to the diffusion
coefficient times the cell membrane surface area accessible to the chemical. Andersen et al. (1993a)
assumed this quantity to be proportional to the tissue perfusion rate, with a constant of
proportionality less than 1. In the model used by Andersen et al. (1993a) each tissue has two
subcompartments, the tissue blood compartment and the tissue itself.
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This revised model eliminated allometric scaling of the metabolic rate constant used in the
model of Leung et al. Instead, it treats TCDD as inducing its own metabolism, with a maximal
increase of 100%. The increase is a hyperbolic function similar to that for binding of TCDD to the
AhR. This induction led to an improved fit to observed liver and fat TCDD concentrations.
Subsequent research (Olson et al., 1994; McKinley et al., 1993) revealed no induction of
metabolism of TCDD suggesting that this is likely to be a minor high-dose effect.
Most of the physiological constants and many of the pharmacological and biochemical
constants used by Leung et al. (1990a) were modified for the Andersen et al. (1993a) model because
Wistar rats instead of Sprague-Dawley rats were used in the experiments they simulated. The
parameters in the model were optimized to reproduce tissue distribution and CYP1 Al -dependent
enzyme activity in a study by Abraham et al. (1988) and liver and fat concentrations in a study by
Krowke et al. (1998). For the longer exposure regimens and observation periods, changes in total
body weight and the proportion of weight as fat compartment volume were included via piecewise
constant values (changes occurred at 840 hours and 1,340 hours).
Induction of CYP1A1 proteins in the model was modeled by including interaction between
the Ah-TCDD complex and presumed DNA binding sites. The concentrations of CYP1A2 and
CYP 1A1 were modeled as a function of hepatic AhR-TCDD concentration. Although the revised
model represented the kinetics with a Hill equation, the Hill exponent was 1, similar to the
Michaelis-Menten model used by Portier et al. (1993) for the independent induction of CYP1A2.
The Hill exponent for CYP1A2 (2.3) introduced marked sigmoidicity in the computed dose-
response of this protein.
Andersen et al. (1993a) noted that the liver/fat concentration ratio changes with dose
because of an increase in the amount of microsomal TCDD-binding protein (CYP1A2) in the liver.
For high doses in chronic exposure studies, this introduces a nonlinearity into the concentration of
TCDD in the liver. In the low-dose region, because the Hill coefficients for CYP1A2 concentration
and for TCDD binding to the AhR are equal to 1, the liver TCDD concentration as a function of
dose is still effectively linear. In the observable response range, there is a slight nonlinearity in the
concentration of TCDD in the liver as a function of dose under chronic exposure (Andersen et al.,
1993a). The dose-dependent changes in liver/fat ratio are consistent with animal data and limited
human data (Carrier and Brodeur, 1991), and are a necessary part of the modeling for TCDD.
Andersen et al. (1993b) provided a simple comparison of the induction of CYP1A1 and
CYP1A2, the concentration of free TCDD in the liver, and the total concentration of TCDD in the
liver to tumor incidence (Kociba et al., 1976) and to the volume of altered hepatic foci (Pitot et al.,
1987). The computed cumulative hepatic concentrations of TCDD and induced proteins were used
as summary metrics of internal exposure. Tumor promotion correlated more closely with predicted
induction of CYP1A1 than with the other dose metrics. The choice of an independent induction
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model for CYP1 Al and a Hill coefficient greater than 1 leads to nonlinear low-dose behavior.
These correlations were not based on any mechanistic considerations of the role of induction of
CYP1A1 in hepatocarcinogenesis.
8.4.2.1.4. Improving the physiological characteristics of the TCDD models. Kohn et al. (1993)
modeled the binding of TCDD to the AhR using explicit rate constants for association and
dissociation of ligand instead of dissociation equilibrium constants. However, large unidirectional
specific rates were used, leading to a predicted TCDD-AhR complex concentration similar to that
computed by Leung et al. (1990a) and Andersen et al. (1993a). Other binding reactions in the
model were handled similarly (e.g., TCDD binding to CYP1A2 and TCDD binding to blood
protein). This approach avoids having to solve for the concentration of TCDD in the liver using the
mass conservation relationship described in Leung et al. (1990a) as mass balance is automatically
achieved. The physiology described in the Kohn et al. (1993) model is dependent on the body
weight of the animal. Body weighs as a function of dose and age were recorded by Tritscher et al.
(1992) and directly incorporated into the model by cubic spline interpolation among the measured
values. Tissue volumes and flows were calculated by allometric formulas based on work by Delp et
al. (1991). To allow the model to fit data at both low and high doses (Tritscher et al., 1992), this
model includes loss of TCDD from the liver by lysis of dead cells, where the rate of cell death was
assumed to increase as a hyperbolic function of the cumulative amount of unbound hepatic TCDD.
This assumption is based on the observation of a dose-response for cytotoxicity in livers of
TCDD-treated rats (Maronpot et al., 1993) and is consistent with observed tissue burdens of TCDD.
No information regarding the rate of TCDD release from lysed cells is available; therefore, this
feature of the Kohn et al. (1993) model predicts a net contribution of TCDD clearance by
TCDD-induced cell death.
A further extension of this model, incorporating effects on thyroid hormones (Kohn et al.,
1996), included tissue blood compartments similar to those used by Andersen et al. (1993a). Blood
was distributed among these compartments and a compartment for the major blood vessels, instead
of supplementing a generalized blood compartment with the tissue blood. The GI tract was
separated from the rapidly perfused tissues compartment to permit a more realistic representation of
uptake of TCDD and perfusion of the liver. The allometrically scaled metabolic rate constant used
in the Kohn et al. (1993) model was replaced by a Hill rate law, and parameters were estimated to
reproduce the kinetic data of Abraham et al. (1988) and the dose-response data of Tritscher et al.
(1992).
Transthyretin (also known as prealbumin) can bind hydroxylated PCDDs, (McKinney et al.,
1985) and single doses of TCDD can cause prolonged decrease in this protein (Albro et al., 1978).
A dose-dependent decrease was included in the model and the algebraic equation for blood binding
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was replaced by a differential equation. The revised model, incorporating blood binding, correctly
predicted blood TCDD data not used in constructing the model. Ignoring production of binding
protein led to serious underestimation of the low-dose data, and ignoring inhibition led to
overestimation of the high-dose data. This revised model also differed from the earlier version in its
treatment of loss of TCDD from the liver consequent to cytotoxicity. Instead of simply
disappearing from the model, TCDD from lysed cells was assumed to pass via the bile into the gut,
where it was reabsorbed and redistributed to tissues. This model also explicitly accounted for
background exposures of TCDD equivalents in the feed, as observed by Vanden Heuvel et al.
(1994a).
The above models have been applied in developing dose metrics for biochemical and tissue-
response models. They do not necessarily include every aspect of the distribution of TCDD within
the mammalian organism. The following two efforts expand on issues related to TCDD
distribution. However, at this time they have not been included in the dose-response models and are
unlikely to dramatically change estimates of dose metrics.
8.4.2.1.5. Lipid metabolism and sequestration in blood. The above PBPK models empirically
represent sequestration of TCDD in blood without reference to the nature of the pools of TCDD in
the blood compartment. Animals exposed to high doses of TCDD and related compounds exhibit
alterations in lipid metabolism characterized by mobilization of fat stores and resulting in wasting,
hyperlipidemia, and fatty liver. Roth et al. (1993, 1994) constructed a PBPK model of the
distribution of TCDD in the rat over a 16-day period following an oral dose. The model did not
include tissue blood compartments but did consider diffusion limitation in uptake by multiplying
tissue perfusion rates by a fractional extraction, mathematically identical to the formulations of
Andersen et al. (1993a) and Kohn et al. (1996). A unique feature of this model was the division of
the GI tract into five subcompartments—stomach, duodenum, jejunum, cecum, and colon—with
sequential passage of ingested material. The model also separates the rapidly perfused tissues
compartment into its constitutive organs and separates white and brown adipose tissue because of
their different perfusion rates and differences in ability to mobilize lipid stores. The model included
an earlier submodel of fatty acid metabolism in liver and adipose tissues, triglyceride transport via
lipoprotein particles in blood plasma, and uptake of lipoprotein by liver and fat (Roth et al., 1994).
Regulation of food consumption and lipolysis in white adipose tissue were assumed to be regulated
by a cytosolic receptor that binds TCDD.
The model included the possibility for loss of body weight, muscle mass, and fat weight and
hypertrophy of the liver subsequent to TCDD administration. It matched data for the initial
increases and subsequent declines of TCDD in liver and brown and white fat. Fecal and urinary
excretion data also were reproduced. The model included induction of CYP1A2 binding sites for
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TCDD. The measured concentration of TCDD in white adipose tissue shows a paradoxical increase
at 16 days postdosing despite the fact that TCDD was being cleared from the body. The model of
Roth et al. (1994) failed to reproduce this effect, but the concentration in the lipid portion of the
tissue did increase because the mass of lipid was decreasing in highly exposed animals. Roth et al.
suggested that barriers to uptake and efflux of TCDD may not be symmetrical.
Roth et al. (1994) cited evidence that TCDD is absorbed from the gut, dissolved in dietary
fat, carried into the bloodstream by chylomicrons, and secreted into the gut lumen from the
intestinal mucosa. There does not appear to be a significant first-pass extraction of these
unprocessed lipoprotein particles by the liver. Several tissues (e.g., heart, spleen, and fat) have high
levels of receptors for such very-low-density lipoprotein vesicles. So TCDD transport may be
regulated by endocytosis of these particles and not be under equilibrium control, as has been
assumed in all other pharmacokinetic models. Such a process may reflect the mechanistic origin of
diffusion-limitation in TCDD tissue uptake. Further research may be required to resolve this point.
Another feature of the Roth et al. (1994) model that suggests additional research is the assumption
that white adipose tissue contains a cytosolic TCDD receptor (adipose tissue does express the AhR)
which mediates effects on lipid metabolism.
8.4.2.1.6. Diffusion limitations in multiple tissues. Assessment of diffusion limitation in tissue
uptake has been hampered by a lack of data at short times after dosing with TCDD. Wang et al.
(1997) obtained time-course data for TCDD in blood, several tissues, and the remaining carcass
following a single oral dose. They fit an eight-compartment (blood, lung, liver, kidney, spleen, fat,
skin, carcass) PBPK model to these data, estimating the values of gut absorption rate, tissue
permeability, partition coefficients, AhR concentrations, and CYP1A2 induction parameters by an
ad hoc method (no formal optimization). The terminal TCDD half-lives in liver and kidney were
assumed to reflect metabolism and were used to calculate an effective first-order rate constant.
Time courses in highly vascularized tissues (lung, spleen) could be fit with flow-limited kinetics,
but diffusion restriction was required for other tissues, especially kidney. The model by Wang et al.
was also used to predict induction of CYP1 Al and CYP1A2 protein in liver and CYP1 Al and
CYP1A2 enzyme activity in liver, kidney, lung, and skin (Santostefano et al., 1998). This model
has recently been shown to predict the TCDD tissue concentrations from a study by Krowke and
coworkers using a loading dose/maintenance dose exposure regimen (Wang et al., 2000). However,
it was not demonstrated that the model could reproduce responses to chronic exposure to TCDD.
8.4.2.1.7. Modeling of dose-dependent tissue disposition in humans. Carrier et al. developed a
simple empirical model to account for dose-dependent hepatic sequestration of dibenzofurans and
other TCDD-like compounds (Carrier et al., 1995a,b). This description had two primary
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parameters: a maximum proportion sequestration of body burden in the liver (Fmax) and a
half-saturation constant (Kd)(in units of jig TEQ/kg) for enhanced sequestration with increasing
dose. These two parameters were estimated by fitting the model to data on the dose-dependent
sequestration in the liver presumed to occur in the livers from human poisoning incidents in Japan
and China. The model was also used to derive similar empirical constants from the rat data
(Abraham et al., 1988). These two fitting parameters do not contain specific information about the
biology of TCDD and related compounds. A PBPK model for TCDD was used recently to infer the
relationship between specific biological factors and these two empirical parameters (Evans and
Anderson, 2000). With sensitivity analyses, the half saturation constant (Kd) was found to be
related to characteristics of the binding of TCDD to the AhR and the AhR-TCDD complex binding
to dioxin response elements on DNA. In contrast, the maximum proportion in liver is determined
by fatblood partition coefficients and binding parameters for the interaction of CYP1A2 with
TCDD. The composite parameters of Carrier's models (1995a,b) have no obvious relationship to
specific biological processes.
In principle, it is possible to convert a PBPK model of disposition of TCDD in a laboratory
rodent into one for a human by substituting human parameter values for rodent values. (Andersen
et al., 1997c). Although values for anatomical and physiological parameters are available for
humans, the biochemical parameters (e.g., TCDD metabolism, binding to the AhR and CYP1A2,
and induction of the various proteins cited above) are generally not available for humans.
Parameters for protein binding (Kd and basal Bmax) could be determined in vitro from samples of
human tissues obtained either postmortem or from surgical patients, but estimating parameters for
induction of proteins would require tissue samples from living individuals exposed to dioxin.
Alternatives to measuring human parameter values include allometric scaling of rodent values by
the 2/3 or 3/4 power of body weight. This tactic is suspect, as species differences in expression of
proteins do not follow a simple pattern for all proteins.
8.4.2.2. Biochemical, Tissue, and Endocrine Response Models
The next step after the modeling of the disposition of TCDD within the body is the modeling
of effects of TCDD on biological responses that are plausibly linked with activation of the AhR.
8.4.2.2.1. Generic receptor-mediated response models. Looking at one aspect of modeling of
TCDD's effects, Portier et al. (1993) examined the relationship between tissue concentration and the
response of three liver proteins by TCDD in intact female Sprague-Dawley rats. The effects studied
included the induction of two hepatic cytochrome P-450 isozymes, CYP1A1 and CYP1A2, and the
reduction in maximal binding of EGF to its receptor in the hepatic plasma membrane.
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Portier et al. (1993) modeled the rate-limiting step in the induction of CYP1A1 and
CYP1A2 following exposure to TCDD using a Hill equation. Hill equations are commonly used for
modeling ligand-receptor binding and enzymatic kinetics data. Consequently, these models could
be applied to other receptor-mediated effects and are not specific to TCDD and the AhR. The Hill
equation allows for both linear and nonlinear response below the maximal induction range. A
complete discussion of Hill kinetics and other models for ligand-receptor binding is given by
Boeynaems and Dumont (1980). Examples of the use of Hill kinetics for ligand-receptor binding
include the muscarinic acetylcholine receptors (Hulme et al., 1981), nicotinic acetylcholine
receptors, opiate receptors (Blume, 1981), the AhR (Gasiewicz and Rucci, 1984), estrogen receptors
(Notides et al., 1985), and glucocorticoid receptors (Sunahara et al., 1989). The Hill model can be
thought of as a very general kinetic model that reduces to hyperbolic kinetics when the Hill
exponent is 1. Portier et al. (1993) also modeled the reduction in maximal binding to the EGF
receptor with Hill kinetics, assuming that TCDD reduces expression of the receptor protein from the
rate observed in control animals. For all EGFR, CYP1A1, and CYP1A2, proteolysis was assumed
to follow Michaelis-Menten kinetics. The proposed models fit the data in the observable response
range. The major purpose of this paper by Portier et al. was to emphasize the importance of the
mechanism of basal (i.e., uninduced) expression on the curve shape of tissue concentration of
protein vs. dose of TCDD. For each protein, they considered two separate models of steady-state
protein production.
In the first model, the additional expression of protein induced by TCDD is independent of
the basal-level expression. In their second model, basal expression of these proteins is mediated by
a ligand of endogenous or dietary origin that competes with TCDD for binding sites on the AhR.
Using these simple models, Portier et al. (1993) see virtually no difference in predicted protein
concentrations between the independent and additive models in the observable response range, even
estimating almost equal Hill coefficients in the two models for all three proteins. In the low-dose
range where risk extrapolation would occur, the models differed depending on the value of the Hill
coefficient. An estimated Hill exponent exceeding 1 yielded a concave upwards dose-response
curve, especially for the independent model. This behavior implies diminished increases in
responses at very low doses followed by an accelerated response as the dose increases. For
CYP1A2, the Hill exponent was estimated to be about 0.5. When the estimated Hill exponent is
less than 1, the dose-response curve was convex upwards, indicating greater than linear increases in
response at low doses. Finally, for the EGF receptor, the Hill exponent was approximately 1, in
which case the two models are identical.
The additive model is expected to exhibit low-dose linearity because each additional
molecule of TCDD adds more ligand to the pool available for binding and, under subsaturating
conditions, proportionally increases the concentration of protein. Similar observations have been
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made with regard to statistical (Hoel, 1980) and mechanistic (Portier, 1987) models for tumor
incidence. Thus, even though these two basic models show almost identical response in the
observable response region, their low-dose behavior is remarkably different. If either CYP1A1 or
CYP1A2 levels had been used as dose surrogates for low-dose risk estimation, the choice of the
independent or additive model would yield differences of several orders of magnitude in the risk
estimates for humans. Using CYP1 Al as a dose surrogate, the independent model would predict
much lower risk estimates than the additive model. For CYP1A2, the opposite occurs. For EGF
receptor, there would be no difference.
8.4.2.2.2. Specific biochemical responses to TCDD. Kohn et al. (1993) have provided an
extensive model of the biochemistry of TCDD in the liver to explain TCDD-mediated alterations in
hepatic proteins in the rat, specifically considering CYP1 Al, CYP1A2, and the Ah, EGF, and
estrogen receptors over a wide dose range. The model describes the distribution of TCDD to the
various tissues, accounting for both time and dose effects observed by other researchers. A
description of the PBPK portion of this model is described above. Earlier PBPK models (Andersen
et al., 1993a, Leung et al., 1990a) relied on several single-dose data sets (Rose et al., 1976;
Abraham et al., 1988) and were validated against dosimetry results from longer term subchronic and
chronic dosing regimens (Kociba et al., 1976; Krowke et al., 1989). These and other studies
(Tritscher et al., 1992; Sewall et al., 1993) were used to model the pharmacokinetics and induction
of gene products in female Sprague-Dawley rats (Kohn et al., 1993). Among the data reported were
concentrations of TCDD in blood and liver, concentrations of hepatic CYP1A1 and CYP1A2, and
EGF receptor binding capacity in the hepatocyte plasma membrane. The tissue dosimetry for the
model (Kohn et al., 1993) was validated against single-dose and chronic dosing regimen
experimental data not used in estimation of model parameters.
In the biochemical effects portion of the model the AhR-TCDD complex upregulates four
proteins: CYP1A1, CYP1A2, the AhR, and an EGF-like peptide (treated nominally as transforming
growth factor-alpha, TGF-alpha). The induction of an EGF-like peptide is deduced from
observations on human keratinocytes (Choi et al., 1991; Gaido et al., 1992) and is quantified on the
basis of a presumed interaction with the EGF receptor, resulting in a downregulation and
internalization of the EGF receptor. However, TCDD-mediated induction of TGF-alpha or of other
EGF-like peptides has not been demonstrated in liver. For all four proteins, synthesis is defined
explicitly as a function of occupied AhR concentration. Constitutive rates of expression for
CYP1A2, AhR, and EGF receptor are substantial and were assumed independent of the induced
expression. The Hill coefficients for the induction of these proteins were estimated to be 1.0,
indicating low dose linearity in this response irrespective of the mechanism of basal expression.
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Estimated ED01 values for TCDD-regulated responses predicted from the dose-response model is
shown in Table 8-8.
The model included a background of dioxin-like AhR agonists, which compete with TCDD
for binding to the receptor. Induction of CYP1A1 was assumed to be based on additive induction
because this enzyme is poorly expressed in the absence of an inducer and expression in control
animals is likely due to the background exposure. Again, the Hill exponent was estimated to be 1,
leading to low-dose linearity under either additive or independent assumptions. This model predicts
that the induction of all gene products appears to be a hyperbolic function of dose without any
apparent cooperativity. The discrepancy in the estimates of the Hill exponents between this model
and the other models discussed (Andersen et al., 1993a,b; Portier et al., 1993; Kedderis et al., 1993)
is probably related to the inclusion only in the Kohn et al. (1993) model of induction of the AhR.
The effects of TCDD on the AhR concentration are uncertain. In acute studies, the AhR is
decreased following TCDD exposure (Pollenz et al., 1998), whereas in subchronic studies, there is
some evidence that the AhR is increased (Sloop and Lucier, 1987). Further studies are required to
better understand the regulation of the AhR following TCDD exposure.
The AhR-TCDD complex is assumed to downregulate the EGF receptor in the Kohn et al.
(1993) model. It was assumed that the estrogen receptor-estrogen complex synergistically reacts
with the AhR-TCDD complex to transcriptionally activate gene(s) that regulate synthesis of an
EGF-like peptide. This term was introduced to partially account for the observation of reduced
TCDD tumor-promoting potency in ovariectomized females as compared to intact female rats
(Lucier et al., 1991). This mechanism of TCDD regulation of these proteins, although supported by
some data (Sunahara et al., 1989; Clark et al., 1991), is speculative.
Vanden Heuvel et al. (1994b) provided data on the production of CYP1A1 mRNA and
protein following a single oral dose of TCDD. These observations were used to extend the Kohn et
al. model and resulted in a model that predicted two critical DNA binding sites for the liganded
AhR with different affinities (Vanden Heuvel et al., 1994; Kohn et al., 1994). Both sites had to be
occupied in order to activate transcription. This rate equation led to a sigmoidal dose-response
curve for the message. Protein synthesis on the mRNA template was modeled by a Hill equation.
The optimal Hill exponent was less than 1 and the computed overall dose-response was hyperbolic,
as in the Kohn et al. model. This result suggests that the supralinear response of protein to mRNA
production compensates for the sublinear response of the message to AhR-TCDD complex
formation. It is possible that this reflects the greater sensitivity of the RT-PCR method to detect
CYP1A1 mRNA than measurement of CYP1A1 protein. Within this context it is of note that there
are more than two DREs within the human CYP1 Al promoter region that may be occupied (Kress
etal., 1998).
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8.4.2.2.3. Tissue response models: zonal induction model. The mechanistic model of Kohn et al.
treats the TCDD-treated liver as a single homogeneous unit. With regard to the induction of
cytochromes P-450 in the liver, Tritscher et al. (1992) used antibody staining techniques, showing
that the induction of CYP1A1 and CYP1A2 by TCDD in the liver exhibits a regiospecific pattern
of induction characterized by increased areas of staining around the central vein of the liver lobule.
The size of the induced region in the centrilobular region increased with increasing dose of TCDD.
This sharp demarcation in observed induction within hepatocytes could be due to an insensitivity in
detection of low levels of CYP proteins in the cell using immunohistochemical techniques:
alternatively, it may indicate differences in the sensitivity of hepatocytes to TCDD across the liver.
In an attempt to model this regiospecific pattern of induction, Andersen et al. assumed that the
observed sharp demarcation in CYP1A expression between induced and noninduced regions
indicated that individual hepatocytes were either fully induced or noninduced (Anderson et al.,
1997a,b). In this model the liver lobular structure was divided the into five concentric zones with a
threefold difference between adjacent zones, in the affinity of DREs for the liganded AhR. The
model also further used Hill kinetics for induction, with a Hill exponent of 4. The model
reproduced the qualitative features of expanding zonal induction and, with parameters selected to
yield a fit to time-course data (Abraham et al., 1988) and CYP1 Al mRNA data (Vanden Heuvel et
al., 1994), produced a fit to P-450 data comparable to that obtained with the homogeneous liver
model of Kohn et al. (1993). The mRNA data were fitted without proposing multiple DRE binding
sites for transcriptional control of message. However, the low-dose extrapolated responses
predicted by the regional induction model exhibited greater low-dose sublinearity than a comparable
homogeneous liver model. The model predicted an 81-fold difference in AhR-TCDD binding
between periportal and centrilobular zones and utilized steep Hill kinetics; these two issues drive
the low-dose nonlinearity of this model and are important areas for further research.
8.4.2.2.4. Endocrine models: thyroid hormones. In addition to models of whole-tissue responses
such as that seen in the liver, attempts have also been made to model endocrine effects that
encompass changes that may occur in multiple tissues. This is demonstrated in the thyroid hormone
model of Kohn et al. (1994). TCDD induces thyroid tumors in male rats and female mice at lower
doses than those that induce liver tumors in female rats (NTP, 1982a). Sewall et al. (1995) found
increased circulating thyrotropin (TSH) and thyroid hypertrophy and hyperplasia in TCDD-treated
rats, suggesting that thyroid tumors may be a consequence of chronically elevated serum TSH (Hill
et al., 1989). Because this may be a sensitive endpoint for TCDD carcinogenesis, the Kohn et al.
(1993) model was extended (Kohn et al., 1996) to include effects of TCDD on thyroid hormones.
The extended model added compartments for tissues involved in the production (pituitary
and thyroid glands) and storage (e.g., kidney, brown fat) of thyroid hormones as well as equations
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for secretion and metabolism of the hormones. It reproduced the data used in the original model,
blood levels of thyroid hormones and TSH (Sewall et al., 1995), and mRNA (vanden Heuvel et al.,
1994b) for the thyroxine metabolizing enzyme UDP-glucuronosyl-transferase-l*6 (UDPGT). It
also reproduced experimental data for induction of this enzyme that were not used in the
construction of the extended model. In the model, induction of UDPGT by TCDD and subsequent
endocrine changes in thyroid hormone homeostasis can lead to chronically elevated serum TSH.
This may be related to increased thyroid cancer risk. The estimated dose-response relationships
were hyperbolic in the experimental range, supporting a linear dose-response at lower doses.
8.4.2.2.5. Dose-response behavior of biochemical/tissue dose-response models. The models of
Kohn et al. (1993, 1996) are based on the concept that tissue-level responses are emergent
properties that arise from the accumulated molecular effects of exposure to TCDD. Thus, the
models were constructed in a bottom-up fashion starting from these more elementary steps, e.g.,
binding to the AhR, transcript!onal activation, translation of mRNA, and the enzymatic functions of
the induced proteins. The calculated responses that can serve as dose metrics include altered
expression of CYP1A1, CYP1A2, and UDPGT. Because TCDD induces expression of the AhR,
lower computed doses are required to obtain the same responses as estimated by models that ignore
this effect. The critical steps are binding of the liganded AhR to DREs and translation of the
mRNA into protein. The most important lesson of this modeling exercise is that lack of significant
sigmoidicity in the dose-response curves calculated for these proteins arises from saturation of
protein synthesis at low concentrations of mRNA, compensating for possible sublinearity in
transcription. Similar compensatory effects led to low-dose linearity in the more complex responses
of EGF receptor internalization and elevation of plasma TSH.
Any of the above responses can serve as indices of toxicity or pathology, and which is
selected for such use depends on the hypothesized origin of the endpoint. Use of CYP1A2 as a
marker for indirect DNA damage is based on the hypothesis that the catalytic properties of this
enzyme lead to the generation of free radicals or DNA-reactive quinones (Yager and Liehr, 1996).
Use of the internalized EGF receptor as a marker for promotional effects in the liver is based on the
hypothesis that TCDD induces growth factors that are ligands of this receptor. Use of TSH as a
marker for promotional effects in the thyroid is based on the goitrogenic properties of this hormone.
Further experiments are required to determine if these postulated events are causally related to the
pathological responses. Nevertheless, if the computed responses are used as dose metrics, the
model indicates that linear extrapolation from the experimental dose range can be used to estimate
low-dose effects.
The main hepatic response motivating the regional induction model was the pattern of
staining within hepatic lobules in TCDD-treated rats (Tritscher et al., 1992). On the basis of
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geometric considerations, hepatic lobular structure was described as a series of concentric lobular
regions with differing affinities of DNA binding sites for the Ah-TCDD complex (Andersen et al.,
1997a). A main underlying assumption was a linear correspondence between mRNA
concentrations and protein levels, modeled by an inducible rate of synthesis and a first-order
degradation. The rate of message production was modeled with Hill kinetics with respect to
receptor complex concentration. The successful parameterization required differences in binding
affinity between adjacent zones and very steep dependence on TCDD and Ah-receptor complex
concentration (i.e., the estimated Hill coefficients were large) in order to reproduce experimental
data. A single-compartment liver model was also examined. It could reproduce all data except the
heterogeneous distribution and low-dose mRNA levels. The major inference drawn from this
analysis was that induction should be considered on the level of the cell, not the gene. The effects
appear to be coordinate, cooperative expression of a battery of gene products and emergence of new
cellular characteristics. This behavior, if true, might be regarded as a reversible differentiation of
TCDD-transformed phenotype, rather than induction of single genes in isolation. Overall linear
behavior in the entire liver arises from composite responses of individual cells with differing
thresholds for induction. The sensitivity of cells in the centrilobular region of the liver would
determine the low-dose behaviors.
In the present model the low-dose behavior of this small group of cells would be distinctly
nonlinear. The ED01 with this regional induction model was about 1.4 ng/kg/day (Table 8-8). This
value is close to the estimate of 0.34 for the induction of CYP1A2 estimated by Kohn et al. More
significant than the differences in ED01 values are the inferences drawn with regard to the shape of
the curve in the low-dose region by the two models. Specific studies on regional induction and
cellular level responses should be vigorously pursued to discriminate between these two model
structures. Regional induction of mRNA needs to be studied on a more quantitative level and
methods need to be developed for studying induction in primary hepatocytes. Recent data in rats
exposed to TCDD demonstrate that the hepatocytes in the centrilobular region accumulate TCDD to
a greater extent in the low-dose region and are more responsive to TCDD than are the periportal
hepatocytes (Santostefano et al., 1999).
8.4.3. Application of Models
The goal of biochemical response models is to link TCDD-regulated responses to adverse
effects associated with TCDD exposures. In principle, these models could be applied to a variety of
adverse responses. The focus of the application of these models has been to carcinogenic endpoints.
Much less attention has been given to the application of mathematical models to the development of
noncancer pathologies.
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TCDD is a potent carcinogen in all animal species tested (see Part n, Chapter 6). TCDD is
an operational promoter, as defined in assay systems of skin and/or liver in mice and rats (Schrenk
et al., 1994; Maronpot et al., 1993; Clark et al., 1991; Pitot et al., 1980; van Birgelen et al., 1999;
Buchman et al., 1994) (see Chapter 6). Mathematical modeling can be a powerful tool for
understanding and combining information on complex biological phenomena such as
carcinogenesis. For the analysis of tumor promotion by TCDD, much of the focus on the use of
mathematical and mechanistic models has been on understanding the mechanism of
hepatocarcinogenesis induced by TCDD. Specifically, the focus has been on modeling the
development of putatively preneoplastic altered hepatocellular foci (AHF) that exhibit altered
expression of marker enzymes such as placental glutathione-s-transferase (POST), or
gamma-glutamyl transpeptidase (GGT). Mechanism-based modeling of carcinogenicity can be
accomplished by incorporating linkages between cell growth and mutation and the
biochemical/tissue responses of TCDD, within the context of the quantitative dose-response models
described above. In addition, analysis of changes in hepatocyte replication has been used to
estimate of parameter values for in some models.
8.4.3.1. Modeling Preneoplastic Lesions
Within the framework of a two-stage model of carcinogenesis, these models treat AHFs as
an initiated phenotype produced by conversion of a normal cell by a mutational event. Models for
the numbers of normal and initiated cells also incorporate parameters related to the relative birth
rates and death rates of the respective cell populations. These growth and mutational parameters
may or may not be directly related to biological processes altered by TCDD. Three research groups
have evaluated growth and development of AHFs, using different mathematical approaches,
different assumptions of the phenotypic distribution of the AFIFs, and different linkages of
biological processes to the model parameters.
8.4.3.1.1. Models with a single initiated phenotype. Portier et al. (1996) estimated the parameters
in the first half of a two-stage mathematical model of carcinogenesis from the initiation-promotion
data (Maronpot et al., 1993) using previously developed methods (Dewanji et al., 1989). This
analysis used daily average dose as the dose metric for examining dose dependent effects of TCDD
on model parameters. Maronpot et al. (1993) quantified the number and size of liver AFIF lesions
expressing the placental form of glutathione-S-transferase (POST). The modeling results indicate
that TCDD stimulates the production of PGST-positive AFIF (which could indicate a mutational
effect) and promotes the growth of POST AHF (as a result of either increases in birthrate or
decreases in the death rate). Data on cell replication indices and liver weight could not explain the
mutational effect of TCDD. Following upon the work of Kohn et al. (1993), Portier et al. (1996)
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suggested this finding could be due to an increase in the metabolism of estrogens to catechol
estrogens, leading to subsequent increase in free oxygen radicals and eventually to mutations. The
analysis also indicated an interaction between DEN and TCDD that results in dose-related
formation of initiated cells throughout the study period. Portier et al. (1996) also found that
best-fitting curves (using maximum likelihood methods) for the effect of TCDD on the mutation
and birth rates reached saturation levels at doses below 3.5 ng/kg/day.
As a validation exercise, Portier et al. (1987) used the same methods to analyze focal lesion
data from Pitot et al. The two studies utilized different initiation protocols. In the Maronpot
experiments, a necrogenic DEN dose (175 mg/kg) was used, whereas in the Pitot experiments a
non-necrogenic dose of DEN (30 mg/kg) was given 24 hours after partial hepatectomy. These two
initiation protocols lead to differences in background tumor rates and differences in time course for
tumor development following TCDD exposure.
In the Pitot experiment, three types of enzyme-altered AHF were quantified using the marker
enzymes gamma-glutamyltranspeptidase (GOT), canalicular adenosine triphosphatase (ATP) and
glucose-6-phosphatase (G6P). Portier et al. (1996) found that all four types of AHF from the two
different studies produced similar qualitative results; TCDD had effects on both mutation and birth
rates. The effect of dose on the birth rates for both data sets produced similar patterns, with an
almost identical unexposed birthrate for all of the four lesion types, a maximal increase over the
background rate between 33% and 300%, saturation of the increased birthrate at low doses, and a
small increase in birthrate because of DEN initiation. The pattern of dose-related changes in the
mutation rate is slightly different in the ATP, GOT, and G6P AHF than for the PGST AHF, tending
more toward linearity than the hyperbolic response seen for the PGST AHF. However, for all four
lesions, the maximal induction rate tended to be the same.
Moolgavkar et al. (1996) analyzed data from Buchmann et al. (1994) on ATP AHF in
female Wistar rats exposed to 2,3,7,8-TCDD as well as l,2,3,4,6,7,8-heptachlorodibenzo-/?-dioxin
(HCDD). The initiation protocol was a non-necrogenic dose (10 mg/kg) for 5 consecutive days. In
addition to the mathematical analysis developed by Dewanji et al. (1989), Moolgavkar et al. (1996)
used a modification that allowed for cellular replication focused on the edge of the AHF. Although
Moolgavkar et al. (1996) did not have information on multiple dose groups, the results of their
analysis for TCDD concur qualitatively with those of Portier et al. (1996). In essence, they
observed no effect on the birthrate of initiated cells, a significant (sevenfold in noninitiated and
twofold in initiated) effect of TCDD on the mutation, and a prolonged effect of DEN following
initiation (similar to the interaction effect observed by Portier et al. [1996]). The observed lack of
change in birthrates is similar to that of the nonsignificant increase observed by Portier et al. (1996)
for PGST+, GGT, and G6P foci, but smaller than that for ATP foci in the Pitot et al. (1980) study.
In the DEN-initiated groups, the associated increases in the mutation rates were quantitatively
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similar to those observed for POST lesions in the Portier et al. (1996) study (2.2-fold at 100
ng/kg/day in Moolgavkar et al. (1996), 2.5-fold at 125 ng/kg/day for POST), but much smaller than
those observed for the ATP, GOT, and G6P lesions from the Pitot et al. (1980) study (9.9-fold for
ATP, 4.5-fold for GGT and 5.8-fold for G6P). The observed increase in the mutation rate in
noninitiated animals was much larger in the Moolgavkar et al. (1996) analysis than that for the
Portier et al. (1996) analysis. This study was conducted at a single dose and the comparison is
simply treated versus control.
8.4.3.1.2. Models with two initiatedphenotypes. Conolly and Andersen (1997) developed a model
for focal lesion growth based upon two types of initiated cells, applying the negative selection
mechanism for hepatic tumor promotion proposed by Jirtle et al. (1991a,b). In this model, even
though the two types of initiated cells express the same biochemical marker, they respond
differently to promotional stimulation in the liver. The model presumes that a promotional stimulus
to the liver is countered by mitoinhibitory signals generated by the liver to constrain proliferation.
One set of mutated cells is sensitive to this mitoinhibition whereas the other set of mutated cells is
insensitive and responds only to the promotional stimulus. The result is that, under increasing doses
of the promoter, one group of focal lesions is decreasing in size, and hence number of cells, while
the other group is increasing in size.
Conolly and Andersen's model is different from those of Portier et al. (1996) and
Moolgavkar et al. (1996) in that it can result in U-shaped dose-response curves for the total number
and mean size of observable focal lesions without using U-shaped parametric forms for the
mutation rates or the birthrates. Number and size of focal lesions were estimated using the
stochastic resampling methods outlined in Conolly and Kimbell (1994), with deterministic growth
replacing stochastic growth when colonies exceeded 1,000 cells. Twenty-five replicates for each
model output were compared to the data for the combination of all three focal lesion types from the
study by Pitot et al. (1980) to obtain parameter estimates for the birth and death rates of the two
types of mutated cells. This analysis used administered dose as the tissue dose metric.
The two-cell model adequately fit the data with biologically reasonable parameter values.
An alternative model including an effect of TCDD on mutation rates was not considered.
Similarly, the earlier analyses of Portier and Moolgavkar did not consider two types of initiated
cells, so comparisons between models with one type of initiated cell versus two types of initiated
cells relating to the issue of the effect of TCDD on mutation rates cannot be made. This is an area
that could use additional research. The birthrates (combined for the two mutated clones in the
Conolly and Andersen model) for all three sets of models (Portier et al., 1996; Moolgavkar et al.,
1996; Conolly and Andersen, 1997) are comparable in the control groups but differ substantially for
the higher dose groups, with the two clone models having much larger rates. This difference is
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partially due to the assumption in the Conolly and Andersen model that there is no increase in
mutation rate following initiation and partially due to the use of an increasing death rate with
exposure to TCDD. Portier et al. (1996) used a fixed death rate in their final model and
Moolgavkar et al. (1996) varied the death rate with the birth rate. Results from a study of
Stinchcombe et al. (1995) indicate a lack of significant effects of TCDD on cell replication in POST
foci, but remarkable suppression of apoptosis within PGST-positive AHF. This study, however
does not supply information on dose dependency of these parameters. Given the lack of sufficient
data, it is not possible to simultaneously estimate both the birthrates and death rates for the initiated
cell phenotypes.
8.4.3.1.3. Alternative dose metrics in promotion studies. In the above models, oral dose of TCDD
was essentially used as the dose metric. In contrast, Conolly and Andersen used the fraction of the
maximum possible induction of CYP1A1 and CYP1A2 calculated from the zonal induction model
(Andersen et al., 1997a) as a dose-surrogate for the effect of TCDD on the clonal expansion of both
mutated cell types within the framework of a two-cell multistage model. Andersen et al. (1997a) fit
their multicompartment geometric model of hepatic zonation (Andersen et al., 1997b) to data
derived from several studies on the expression of CYP1A2 in rats (Abraham et al., 1988; Tritscher
et al., 1992; van den Heuvel et al., 1994b). The zonal induction model is described previously in
this review. The model was linked to the previous PBPK model (Andersen et al., 1993a) with
modifications (Andersen et al., 1997b) to account for the regional induction of CYP1A2, rather than
to the original model which was based upon uniform expression throughout the liver. Formal
optimization methods were not used to obtain model parameters; however, graphical comparisons
of the model predictions to these data did not appear to be obviously different from previous
descriptions and provided adequate fits. The dissociation constants for binding of the TCDD-AhR
complex to dioxin-responsive elements for CYP1A1 (0.6 to 2 nM for compartment 3) and CYP1A2
(0.08 to 1.0 nM for compartment 3) were fit separately for each data set and varied by a factor of 3
from compartment to compartment. This produced a model that fit the fraction of liver volume
occupied by focal cells, but failed to fit the number of foci per volume of liver as well as the
original analysis. These analyses used percent of liver expressing CYP1A2 as an indicator of the
dose metric.
8.4.3.2. Estimation of Cancer Risks
Portier and Kohn (1996) combined the biochemical response model of Kohn et al. (1993)
with a single initiated phenotype two-stage model of carcinogenesis to estimate liver tumor
incidence in female Sprague-Dawley rats from the 2-year cancer bioassay of Kociba et al. (1978).
In the simplest of several models tested, the initial mutation rate to the initiated phenotype was
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proportional to the instantaneous concentration of CYP1A2 as predicted by the biochemical model
of Kohn et al. The birthrate of mutated cells was a linear function of loss of EGFR. All death rates
were held constant, as was the second mutation rate from the initiated to the malignant phenotype.
This model adequately fit the tumor data, although it overestimated the observed tumor response at
the lowest dose in the Kociba et al. (1978) study. The shape of the dose-response curve was
approximately linear and the estimated ED01 value for this model (0.15 ng/kg/day) is presented in
Table 8-8. The corresponding body burden giving a 1% increased effect was 2.7 ng/kg. The use of
CYP1A2 as a dose metric for the first mutation rate is consistent with its role as the major
TCDD-inducible estradiol hydroxylase in the liver (Hayes et al., 1996; Dannan et al., 1986) and
with the hypothesized role of estrogen metabolites leading to increased oxidative DNA damage and
increased mutation (Yager and Liehr, 1996; Roy et al., 1992; Cavalieri et al., 1997) .
Even though the thyroid hormone model of Kohn et al. (1996) has not been strictly used for
modeling of thyroid neoplasia induced by TCDD, it is important to note that the hypothesis for
induction of thyroid neoplasia consequent to growth stimulation by chronically elevated serum TSH
is highly plausible. In contrast there is weaker evidence in the liver that alteration in CYP1A2 and
EGFR are causally linked to carcinogenesis. Given that the alteration in thyroid hormone
homeostasis as a consequence of TCDD induction of UDPGT can be effectively modeled provides
an excellent opportunity to mechanistically link activation of gene expression by TCDD with
thyroid cancer risk.
8.4.4. Knowledge/Data Gaps
Knowledge gaps still exist with each of the models. All the PBPK models have biological
structure and encode hypotheses about the modulation of protein concentrations by TCDD.
However, each of them falls between curve fitting and mathematical representations of known
biology. Parameters in empirical equations representing overall production of the protein gene
products, for example, were estimated using dose-response data for protein concentrations and
enzyme activity. Although protein level is a direct consequence of gene expression, this empirical
approach constitutes curve fitting. In the cases of CYP1A1 and UDGPT induction, information
about both mRNA and protein levels was available, permitting a more realistic, although still
empirical, representation of the mechanism of induction. Similarly, equations for metabolism of
TCDD and thyroid hormones in the model of Kohn et al. (1996) and of lipids in the model of Roth
et al. (1994) are not based on detailed studies of the enzymatic kinetics but are greatly simplified
representations. Nonetheless, the structure of the physiological models was specified by
information on anatomy, physiology, and qualitative effects of TCDD. These PBPK models
reproduce protein concentrations in data sets that were not included in the construction of the model
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and that were obtained from experimental designs different from those used to define the model.
This constitutes at least a partial mechanistic validation of these models.
Models for tissue response including lipid metabolism and hepatic lobular effects also have
aspects that need confirmation. The Roth et al. (1994) model has not been validated for chronic
exposures or low doses. Even though the Wang et al. (1997) model has examined CYP1A1 and
CYP1A2 induction, it has not been validated for chronic exposures. The regional induction model
(Andersen et al., 1997a,b) creates a hypothesis concerning regional induction that should be further
studied. An alternative to altering the affinity of DREs to the liganded AhR is a gradient in the
receptor concentration across the liver acinus. The concentration of the receptor in centrilobular
hepatocytes was found to be more than 40 times that in periportal hepatocytes (Lindros et al., 1997).
The use of Hill kinetics to describe at least some of the binding (or metabolic) reactions is a
convenience to allow flexibility in estimating dose-response relationships.
The models for estimating values of the dose metrics for exposure or effects differ in their
mathematical representations of the same physiological processes while providing comparable fits
to the observed responses. The endocrine response model includes TCDD induction of the AhR,
binding to multiple DREs, and saturation kinetics for protein synthesis on the mRNA template.
This sequence of steps can potentially lead to nonlinear kinetics for the overall responses, but the
nonlinearities in the individual steps appeared to compensate for each other, leading to
approximately linear low-dose responses. The regional induction model (Andersen et al., 1997a)
collapses this sequence into a single overall process and uses Hill kinetics to represent the potential
overall nonlinearity. A high Hill exponent was required to reproduce the sharp edge detected for the
induced region of the liver, leading to sublinear predicted responses below the experimentally
accessible range of doses. Thus, emphasizing different aspects of the underlying biology leads to
different mathematical structures with different predicted low-dose behavior. Which of these
processes are most important in producing the overall responses cannot be resolved by existing data.
The biochemical and tissue response models were linked to a two-stage cancer model
(Portier and Kohn, 1996). Although TCDD is not a mutagen in in vitro systems commonly used to
detect mutation through DNA damage, inferences drawn from biochemical data and mechanistic
modeling supported a secondary mechanism for TCDD-induced mutations (Portier et al., 1996;
Moolgavkar et al., 1996). Another approach, with secondary pathways leading to mutations and
two cellular phenotypes, also fit these data but does not require this secondary effect on mutation
rate (Andersen et al., 1997a,b; Conolly and Andersen, 1997). Even though this secondary
mechanism of mutation is still speculative, these studies present challenges to the application of
general models for cancer risk assessment based on direct chemical mutagenesis as a fundamental
mechanism for chemically induced or radiation-induced cancer and the notion of a single cellular
phenotype as a precursor for cancer.
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8.4.5. Summary
The development of PBPK models describing the disposition of TCDD within experimental
animals has proceeded through multiple levels of refinement, with newer models incorporating
ever-increasing levels of biological complexity. The two most complete PBPK models give similar
predictions about TCDD tissue dose metrics. It is unlikely that additional refinement of the current
models will have a major impact on the model predictions within the observable dose range.
However, further work could better characterize the biological processes involved in disposition.
Despite their availability, these PBPK models have been highly underutilized in aiding
empirical dose-response analyses for the effects of TCDD observed in laboratory studies.
Differences in dosing regimens in experimental animals, such as exposure duration, route of
exposure, time after dosing to necropsy, use of maintenance-loading dose regimen, etc., complicate
the use of a simple metric based on administered dose for comparative analyses between studies
(Section 8.3). The use of the current PBPK models could provide a more scientifically credible
description of a body burden dose metric and may reduce some of the uncertainties introduced when
converting a daily averaged dose ED01 to a body burden dose metric.
Similarly, the application of these models to human dose-response data, while possible has
also not been pursued. The current level of detail in rodent PBPK modes for TCDD has not been
included in any current human PBPK model for TCDD. Human exposure assessment for use in
dose-response modeling utilizes either back-extrapolation based on a single measurement of a tissue
(plasma/serum) concentration or a dose metric based on an estimated external exposure. Although
extrapolation of the current generation of rodent PBPK models to humans would have uncertainties,
it is unlikely that predictions from such a model would be any less uncertain that current
methodologies used for estimating human body burdens.
With regard to the extension of PBPK models to biochemical response, tissue response, and
toxicological responses, the differences in interpretation of the mechanism of action of a
TCDD-dependent response lead to varying estimates of the dose-dependent behavior for similar
responses. In addition, the hypotheses and assumptions used in different models may restrict the
shape of the dose-response curves that are calculated and lead to differences in their low-dose
behaviors.
The use of specific biochemical/tissue responses as dose metrics for the evaluation of the
dose-response for toxicity are based upon hypotheses regarding specific linkages between these
responses and toxicity. A greater understanding of the mechanism of linkage of these dose metrics
to the toxicological endpoint of concern is required before an interpretation of the shape of the
dose-response curve or estimation of low-dose risk is credible.
In summary, the state of the science for mechanism-based modeling has been greatly
improved by these newer PBPK models and incorporation of knowledge of the mode of action of
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TCDD. These models may allow qualitative assessment of modes of action, i.e., low-dose
behavior; however, differences exist in the low-dose expectations of current models. Expanded use
of current PBPK models could reduce uncertainty in quantifying actual internal dose following
different dosing regimens.
8.5. DATA GAPS
This chapter identified several important data and knowledge gaps. Information to fill these
gaps would substantially improve dose-response analysis and risk assessment. The most substantial
gaps are summarized below.
There are similarities and differences, both qualitative and quantitative, in responses to
TCDD between laboratory animals and humans. These are due to a variety of factors, including
disposition of TCDD, AhR properties and regulation, and tissue- and species-specific biochemical
responses and specific factors regulating these responses. A better understanding of these factors
could substantially improve dose-response analysis and risk assessment.
There are differences between AhR binding curves and dose-response curves for specific
toxic endpoints. This suggests that factors in addition to the AhR contribute to these toxic
endpoints. For complex endpoints, including frank toxicities, there are likely to be earlier
biochemical events, initiated by receptor binding, that lead ultimately to the toxic responses.
Detailed quantitative knowledge of this sequence of events would increase reliability in response
and species extrapolation, mechanistic modeling, and extrapolation to lower doses.
Also, tissue disposition of TCDD plays a critical role in the approach to risk assessment for
this chemical. Knowledge about the disposition of TCDD at or near the background exposures
experienced by the general population is limited. PBPK models can make predictions about tissue
disposition at these low levels of exposure, though these predictions tend to be below the dose
ranges for which the models have been validated. Lack of knowledge of disposition of low doses is
especially applicable to human exposures and exposures that may occur in the embryo at critical
time points. Furthermore, there is uncertainty about half-life in humans and about the heterogeneity
in this half-life among individuals. These factors add to the difficulty in determining the proper
dose metric for different endpoints and across different species. PBPK modeling could help to
address this problem if the existing models developed for laboratory rodents were extrapolated to
humans. Although there would be uncertainty associated with this extrapolation, it would not
necessarily be greater than, nor even as great as, the uncertainty associated with the current
approach.
In animals, more information is needed about background levels of exposure and how they
may affect dose-response analyses. This is especially true because greater emphasis is being placed
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on low levels of exposure in animal experiments. Including background exposure data may alter the
shape of the dose-response curve and affect the estimate of the ED01.
Quantitative mechanism-of-action-based models can provide insights into the complex
interrelationships of the molecular and biochemical events that comprise a mechanism or mode of
action. However, the level of confidence in the models and their predictions should not be greater
than the level of confidence in the quality of the database and degree of scientific consensus about
the mechanism or mode of action that the model describes. This is particularly true when the model
is to be used for risk assessment. It is possible to use alterations in the concentrations of proteins
known to be altered by TCDD as potential dose metrics. However, more information is needed
about the mechanistic linkages of these proteins to toxic endpoints to improve estimations of shapes
of dose-response curves and estimates of low-dose risks.
8.6. SUMMARY
Data available for several biochemical and toxicological effects of TCDD, and on the
mechanism of action of this chemical, indicate that there is good qualitative concordance between
responses in laboratory animals and humans. For example, human data on exposure and cancer
response appear to be qualitatively consistent with animal-based risk estimates derived from
carcinogenicity bioassays. These data would suggest that animal models are generally an
appropriate basis for estimating human responses. Nevertheless, there are clearly differences in
responses between animals and humans, and recognition of these is essential when using animal
data to estimate human risk. The level of confidence in any prediction of human risk depends on
the degree to which the prediction is based on an accurate description of these interspecies
extrapolation factors.
Almost all data are consistent with the hypothesis that the binding of the TCDD to the AhR
is the first step in a series of biochemical, cellular, and tissue changes that ultimately lead to toxic
responses observed in both experimental animals and humans. As such, an analysis of
dose-response data and models should use, whenever possible, information on the quantitative
relationships between ligand (i.e., TCDD) concentration, receptor occupancy, and biological
response. However, it is clear that multiple dose-response relationships are possible when
considering ligand-receptor mediated events. For example, dose-response relationships for
relatively simple responses, such as enzyme induction, may not accurately predict dose-response
relationships for complex responses such as developmental effects and cancer. Cell-specific factors
may determine the quantitative relationship between receptor occupancy and the ultimate response.
Indeed, for TCDD there is much experimental data from studies using animal and human tissues to
indicate that this is the case.
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One of the most difficult issues in risk assessment is the dose metric to use for
animal-to-human extrapolations. The most appropriate dose metric should reflect both the
magnitude and frequency of exposure, and should be clearly related to the toxic endpoint of concern
by a well-defined mechanism. However, considering the variety of endpoints in different species, it
is unlikely that a single dose metric will be adequate for interspecies extrapolation for all of these
endpoints. Furthermore, the use of different dose metrics with respect to the same endpoint may
lead to widely diverse conclusions. Nevertheless, it is possible to express dose in a form that allows
for comparison of responses for selected endpoints and species. This can be done by either
choosing a given exposure and comparing responses or choosing a particular response level and
comparing the associated exposures. For particular endpoints, and considering the large differences
in half-lives for TCDD across multiple species, it is best to compare the dose metric as body burden
rather than daily intake. A useful and common metric for comparison is the 1% effective dose or
ED01, which is the exposure dose resulting in 1% change in a particular endpoint. The possibility
that existing PBPK models could be used to a greater extent to compare tissue doses across
experimental designs and between species deserves further study.
TCDD has been classified as a known human carcinogen, and is a carcinogen in all species
and strains of laboratory animals tested. However, it is generally difficult to find human data with
sufficient information to model dose-response relationships. For those data that are available, the
uncertainties involved in the modeling of these data are considerable, and notably include
extrapolation of occupational exposure many years after it took place, and the type and shape of the
curve for the dose-response model used in the extrapolation. A linear model is often used because
the number of exposure groups for analysis is too small to support more complex models. On the
other hand, analysis of animal data suggests that many complex responses to TCDD are nonlinear
(Figures 8-1, 8-2, 8-3). Nevertheless, with these qualifications, it is possible to apply simple
empirical models to studies in which exposure data for TCDD are available in human populations.
An analysis of epidemiological studies of two studies of occupationally-exposed individuals
suggests an effect of TCDD on all cancers at body burden ED01s for total cancers ranging from 1.4
ng/kg to 40 ng/kg. This was slightly smaller than the estimates from empirical modeling from the
animal studies which ranged from 14 ng/kg to 1190 ng/kg (most estimates were in the range from
14 to 500 ng/kg), and in most cases slightly above the 2.7 ng/kg estimate from the single
mechanism-based model. The two lowest human ED01 values (1.4 and 1.8 ng/kg) were associated
with the power model used by Steenland et al. (2001) which predicts an unrealistic risk for the
background exposure; the next lowest value was 6 ng/kg.
At this point, sufficient data are not available to model noncancer endpoints in humans.
Many studies are available to estimate ED01 values for noncancer endpoints in animals. However,
there are a number of difficulties and uncertainties that should be considered when comparing
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endpoints across species. Some of these include differences in sensitivity of endpoints, times of
exposure, exposure routes, species and strains, use of multiple or single doses, and variability
between studies even for the same response. The estimated ED01 values may be influenced by
experimental design, suggesting that caution should be used in comparing values from different
designs. In addition, caution should be used when comparing studies that give ED01 estimates
outside the experimental range. Furthermore, comparing values between different categories of
inducible responses may result in misleading estimates of a potential health risk. For example, the
human health risk for a 1% change of body weight may not be comparable to a 1% change in
enzyme activity. Finally, background exposures are not often considered in these calculations
simply because they were not known. The latter consideration is particularly important as the
inclusion of these may alter the shape of the dose-response curve, possibly increasing the shape
parameter so that the responses would demonstrate more threshold-like effects. Nevertheless, given
these considerations several general trends were observed. The lowest ED01 values tended to be for
biochemical effects, followed by hepatic responses, immune responses, and responses in tissue
weight. An analysis of shape parameters implies that many dose-response curves, for a variety of
responses, were consistent with linearity over the range of doses tested. This does not imply that
the curves would be linear outside this range of doses. The lower shape parameters, suggesting
linearity, were for biochemical responses, whereas the higher values for shape parameters,
suggesting nonlinearity, were for tissue responses. Overall, these data suggest that biochemical
responses to TCDD are more likely to be linear within the experimental dose range, while the more
complex responses including frank toxicity are more likely to assume a nonlinear shape. For
cancer, the shapes were split between linear (eight analyses) and nonlinear (five analyses).
The tissue weight changes seen for animals (using only data sets with good or moderate
empirical fits to the model) yielded a median ED01 of 510 ng/kg in the multidose studies (range 11
to 28,000 ng/kg) and a median ED01 of 160 ng/kg (range 0.0001 to 9,700 ng/kg) in the single- dose
studies. Toxicity endpoints from the single-dose studies resulted in a median value of 4,300 ng/kg
(range 1.3 to 1,000,000 ng/kg). For tissue weight changes, 43% of the dose-response curves
exhibited linear response. In contrast, the toxicity endpoints from the single-dose studies exhibited
predominantly nonlinear responses (80%). All multidose studies demonstrated a greater degree of
linear response (41%) than did single-dose studies (37%), especially for tissue weight changes and
toxicity endpoints (50% linear for multidose versus 34% for single dose). In general it is not
possible to specify the differences between cancer and noncancer dose-response as being due to
differences in endpoint response or to differences in the length of dosing and exposure. Also, a
greater percentage of the noncancer ED01 values were below the experimental dose range (42%)
than was the case for the cancer endpoints (8% in animals and no extrapolations in humans).
However, many more noncancer data sets were examined compared to the cancer endpoints.
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Empirical models have advantages and disadvantages relative to mechanism-based models.
Empirical models provide a simple mathematical model that adequately describes the pattern of
response for a particular data set and can also provide the means for hypothesis testing and
interpolation between data points. In addition, empirical models can provide qualitative insights
into underlying mechanisms. However, the major disadvantage is their inability to quantitatively
link data sets in a mechanistically meaningful manner. On the other hand, comprehensive
mechanism-based models can be powerful tools for understanding and combining information on
complex biological systems. Use of a truly mechanism-based approach can in theory enable reliable
and scientifically sound extrapolations to lower doses and between species. However, any scientific
uncertainty about the mechanisms that the models describe is inevitably reflected in uncertainty
about the predictions of the models.
PBPK models have been validated in the observable response range for numerous
compounds in both animals and humans. The development of PBPK models for disposition of
TCDD in animals has proceeded through multiple levels of refinement, with newer models showing
increasing levels of complexity by incorporating data for disposition of TCDD and its molecular
actions with the AhR and other proteins, as well as numerous physiological parameters. These have
provided insights into key determinants of TCDD disposition in treated animals. The most
complete PBPK models give similar predictions about TCDD tissue dose metrics. The PBPK
models have been extended to generate predictions for early biochemical consequences of tissue
dosimetry of TCDD such as induction of CYP1A1. Nevertheless, extension of these models to
more complex responses is more uncertain at this time. Differences in interpretation of the
mechanism of action lead to varying estimates of dose-dependent behavior for similar responses.
The shape of the dose-response curves governing extrapolation to low doses is determined by these
hypotheses and assumptions. In the observable range around 1% excess response, the quantitative
differences are relatively small. Below this response, the different mechanisms can diverge rapidly.
The use of predicted biochemical responses as a dose metric for toxic responses is considered a
potentially useful application of these models. However, greater understanding of the linkages
between these biochemical effects and toxic responses is needed to reduce the potentially large
uncertainty associated with these predictions.
8.7. CONCLUSIONS
Once an environmental agent has been deemed a health hazard, the two main questions to be
addressed in any dose-response assessment are: (1) What can be said about the shape of the
dose-response function in the observable range, and what does this imply about dose-response in the
range of environmental exposures? and (2) What is a reasonable limit (critical dose or point of
departure) at the edge of the observable range, and what risk is associated with this exposure? For
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the dose-response assessment of TCDD, these questions are complicated by the multiplicity of
responses observed and the complexity of the mechanisms known to impact upon those responses.
In the dose-response evaluation conducted for this chapter, we have attempted to use the best
available analytic procedures to provide insight into the answers to these questions. This includes
both the critical assessment of formal empirical dose-response analyses of the available data and,
where appropriate, predictions of dose-response behavior using mechanism-based models of
TCDD.
Many different shapes of dose-response curves were seen in the observable range. Although
human data were available, the data were not adequate for addressing curvature of the
dose-response relationship. Consequently, the main conclusions on the shape of the dose-response
for TCDD are based on animal models.
Under simple empirical dose-response models, about half of the cancer endpoints observed
in animals were linear in the observable range and about half were not. Noncancer endpoints had a
greater degree of nonlinearity, with only 40% of the observed responses being linear. Biochemical
endpoints (more closely coupled to activation of the AhR) tended to exhibit linear dose-response
curves, whereas TCDD-inducible responses, which are likely more complex and involve multigene
interactions, exhibited more nonlinear behavior. Mechanism-based modeling provided two
different answers depending upon the approach used in the analysis and the assumptions used in the
approaches. The variability in the available data for mechanism-based modeling did not allow us to
clearly decide upon any one given model in favor of another. For intermediate biochemical
endpoints and preneoplastic lesions in the rat liver, we saw model fits that strongly supported
nonlinear dose-response shapes in the observable range. This was based upon the assumptions of a
nonlinear expression of proteins in the liver and upon multiple types of focal lesions responding
differently to the effects of TCDD. In contrast, using an alternative model resulted in effectively
linear dose-response (defined as response proportional to dose in the low-exposure region, not
necessarily the higher experimental doses) for both endpoints and the proposition of a secondary
effect of TCDD on increasing mutations through changes in estrogen metabolism.
All humans tested contain detectable body burdens of TCDD and other dioxin-like
compounds that are likely to act through the same mode of action. This consideration, together with
the high percentage of observed linear responses, suggests that a proportional model should be used
when extrapolating beyond the range of the experimental data rather than using a
margin-of-exposure analysis. However, this decision would have to be based upon a policy choice
because this analysis dose not strongly support either choice.
Because we had human data for dose-response analysis and a strong desire to stay within the
range of responses estimated by these data, the risk chosen for determining a point of departure was
the 1% excess risk. Doses and exposures associated with this risk (the ED01s) were estimated from
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the available data using both mechanistic and empirical models. Comparisons were made on the
basis of body burdens (either averaged, steady-state or administered dose) to account for differences
in half-life across the numerous species studied. In humans, restricting the analysis to dose-
response models from the literature for two occupational cohorts resulted in body burden ED01s for
total cancers ranging from 1.4 ng/kg to 40 ng/kg. This was slightly smaller than the estimates, from
empirical modeling from the animal studies which ranged from 14 ng/kg to 1190 ng/kg (most
estimates were in the range from 14 to 500 ng/kg), and in most cases slightly above the 2.7 ng/kg
estimate from the single mechanism-based model. The two lowest human ED01 values (1.4 and 1.8
ng/kg) were associated with the power model used by Steenland et al. (2001) which predicts an
unrealistic risk for the background exposure; the next lowest value was 6 ng/kg. Estimates for non-
cancer endpoints showed much greater variability. In general, the noncancer endpoints displayed
lower body burdens at the ED01 for longer term exposures versus short-term exposures, and for
simple biochemical endpoints versus more complex endpoints such as tissue weight changes or
toxicity. In addition, the noncancer endpoints generally displayed higher estimated body burdens at
the ED01 than the cancer endpoints, with most estimates ranging from 100 ng/kg to 100,000 ng/kg.
For some endpoints, however, the body burdens at the ED01 were below the range of the cancer
endpoints. The mechanism-based models for noncancer endpoints gave a lower range of body
burdens at the ED01 (0.17 to 105 ng/kg). While most of these estimates were based upon a single
model, the estimate from the hepatic zonal induction model gave a body burden for the ED01 for
CYP1A2 induction of 51 ng/kg and hence was within the same range.
These estimates, although highly variable, suggest that any choice of body burden, as a
point-of-departure, above 100 ng/kg would likely yield greater than 1% excess risk for some
endpoints in humans. Also, choosing a point-of-departure below 1 ng/kg would in general only be
supported by analyses that gave estimates that were below the range of these data, and would likely
represent a risk of less than 1%. Any choice in the middle range of Ing/kg to 100 ng/kg, would be
supported although the data provide the greatest support in the range of 10 ng/kg to 20 ng/kg.
This Chapter has produced an extensive summary of dose-response relationships as is
feasible at this time. The analyses and discussions synthesize a considerable breadth of data and
model types, drawing upon this information to highlight strengths and weaknesses in the
information base, gaps in our qualitative and quantitative understanding and the uncertainties
inherent in making a decision concerning a point-of-departure for risk characterization. While such
an extensive evaluation may not be necessary for most environmental contaminants, the concepts
envisioned here can serve as a framework for evaluation in other settings. This unique document
hopefully marks the beginning of more objective, quantitative reviews of information pertaining to
risk decisions for environmental agents.
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Table 8-1. Estimated half-lives for species considered in the analyses to follow and used for converting
between daily exposures and steady-state body burdens
Species
Half-life (days)
C57BL/6N mice
All other mouse strains
Golden Syrian hamster
Wistar rats
All other rat strains
Human
10
11
12
22
25
2,593
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TABLE 8-2: Total cancer risk in humans through age 75 (units are constant body burden in ng/kg not adjusted for lipid).
Upper and lower 95% confidence limits (where available) are in parentheses after ED values
Study
Steenland
et. al, (2001)
Becher et al.,
(1998)
Ott and Zober
(1996)
Model and Sex
power male
power female1
piecewise linear
male
piecewise linear
female2
power-male
power-female4
additive-male
additive-female5
multiplicative-
male
multiplicative-
female6
multiplicative-
male
multiplicative-
female7
ED10
500
(46.4, 2.91 x 107)
1315
(84.4, 4.5 x 108)
•
(92.9, «3)
.8
(108.9, «3)
120.3
170.9
192.8
239.1
258.9
304.4
411.7
(201.9, D)
478.0
f234.4. l~h
ED05
33.9
(8.23, 1.59 x 104)
64.5
( 12.6, 2.50 x 104)
83.6
(51.8, -3)
100.7
(62.39, «3)
41.17
55.44
93.35
116.2
144.4
173.8
229.0
(112.3, D)
272.1
C133.4. Ill
ED01
1.38
(0.71, 8.95)
1.84
(0.92, 14.9)
18.6
(11.5,48.3)
23.1
(14.3, 59.8)
5.971
7.580
18.22
22.75
32.16
39.82
50.9
(25.0, D)
62.1
f30.5. l~h
Unit excess risk
for 1 ppt body burden
above background
0.0079
(0.0027, 0.0132)
0.0064
(0.0022, 0.0107)
0.00052
(0.00020, 0.00084)
0.00042
(0.00016, 0.00067)
0.0018
0.0014
0.00055
0.00044
0.00030
0.00024
0.00019
(0, 0.00039)
0.00015
rO. 0.000321
1 Relative risk RR proportional to (AUC) ° °97, with 15 year lag
2 Relative RR proportional to exp (0.000015 AUC). This is based on the linear
function in the lower range of the piecewise linear model.
3 When body burden exceeds 133 ng/kg, the AUC years exceeds 40,000 ppt years
and the model cannot achieve the prescribed risk level
4 Relative risk RR proportional to (0.00017 AUC +1)°-326
5 Relative risk RR proportional to (1+0.000016 AUC)
6 Relative RR proportional to exp (0.00000869 AUC).
7 Relative RR proportional to exp (0.0003522 x lipid concentration).
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Table 8-3. Doses yielding 1% excess risk (95% lower confidence bound) based upon
2-year animal carcinogenicity studies using simple multistage models
Tumor
Liver cancer in female rats (Kociba)
Squamous cell carcinoma of the tongue in male
rats (Kociba)
Squamous cell carcinoma of the nasal turbinates
or hard palate in male rats (Kociba)
Squamous cell carcinoma of the lung in female
rats (Kociba)
Squamous cell carcinoma of the nasal turbinates
or hard palate in female rats (Kociba)
Thyroid follicular cell adenoma in male rats
(NTP)
Thyroid follicular cell adenoma in female rats
(NTP)
Liver adenomas and carcinomas in female rats
(NTP)
Liver adenomas and carcinomas in male mice
(NTP)
Liver adenomas and carcinomas in female mice
(NTP)
Thyroid follicular cell adenomas and carcinomas
in female mice (NTP)
Subcutaneous tissue sarcomas in female mice
(NTP)
Leukemias and lymphomas in female mice
(NTP)
Shape
Linear
Linear
Cubic
Cubic
Linear
Linear
Cubic
Quadratic
Linear
Linear
Linear
Lin-Cubic
Linear
ED01
Intake for 1%
excess risk
(ng/kg/day)
0.77 (0.57)
14.1 (5.9)
41.4(1.2)
40.4 (2.7)
5.0 (2.0)
4.0(2.1)
33.0(3.1)
13.0(1.7)
1.3(0.86)
15.1 (7.8)
30.1 (14.0)
43.2(14.1)
10.0 (5.4)
Steady-state
body burden
(ng/kg) at EDn,
14(10)
254 (106)
746 (22)
730 (48)
90 (36)
144 (76)
1,190(112)
469 (61)
20.6(13.6)
239 (124)
478 (222)
686 (224)
159(86)
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Table 8-4. Noncancer endpoints used for comparing ED01 values
Species
Mouse
Rat
Hamster
Total
Gender
Female
Male
Unknown
Female
Male
Female
Male
Multi-dose
25
0
—
62
21
0
0
108
Single-dose
Adult
23
35
—
10
4
0
2
74
Developmental
5
20
3
0
32
0
0
60
Total
53
55
3
72
57
0
2
242
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Table 8-5. Ratio of ED01/lowest dose, categorized by study type and endpoint type*
Multi-dose
Category
Biochemical
Hepatic
Immune
Endocrine
Tissue
Toxicity
Subtotals
TOTALS
Out of range
21 (16)
4(4)
8(6)
6(4)
8(6)
—
47 (37)
In-range
7
9
8
3
34
—
61
108
Single-Adult
Out of range
1(1)
0
13(8)
—
7(4)
0
21 (13)
74
In-range
15
13
3
—
9
13
53
Single-Developmental
Out of range
1(1)
—
—
—
31 (17)
6 (0)
38 (18)
60
In-range
0
—
—
—
21
1
22
* These data do not include analyses where a poor fit of the model to the data was obtained. "Out of range"
indicates studies where the ED01 estimate was lower than the lowest dose used in the study. "In-range"
indicates the estimate was within the experimental dose range used in the study from which the estimate was
derived. Number of endpoints where the estimate was less than 1 order of magnitude lower than the lowest
dose used are shown in parentheses.
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Table 8-6. Estimated shape parameters, categorized by study type and endpoint type
Multi-dose
Category
Biochemical
Hepatic
Immune
Endocrine
Tissue
Toxicity
Subtotals
TOTALS
Linear*
15
3
3
5
21
—
47
Non-linear
13
10
13
4
21
—
61
108
Single-Adult
Linear
6
4
10
—
10
0
30
Non-linear
10
9
6
—
6
13
44
74
Single-Development
Linear Non-linear
0 1
— —
— —
— —
14 38
4 3
18 42
60
* "Linear" shape parameters are those where the Hill model coefficient n<1.5
These data do not include analyses where a poor fit of the model to the data was obtained.
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Table 8-7. Categorization of specific endpoints
Category
Biochemical
Hepatic
Immune
Endpoint
CYP 1A1 mRNA
CYP1A1 (Protein)
CYP1A1 EROD in liver, lung, and
skin
CYP1A2 (Protein)
CYP1A2 ACOH
CYP1A2 mRNA
CYP1A2 MROD
CYP1B1 mRNA
EGF dissociation (Kd)
EGFR autophosphorylation
EGFR maximum binding
Serum 5'-nucleotidase
Serum alkaline phosphatase
Serum ALT
Serum BUN
Serum bilirubin (total, indirect,
direct)
Serum esterified cholesterol
Serum glucose
CD4+/CD8+
CD8+/CD4-
CD8-/CD4-
CD4+/CD8-
Cells/spleen(xlO-6)
Liver benzopyrene hydroxylase (CYP1A1 activity)
Liver cytochrome P-450 (Total)
Renal retinol concentration
Renal RPH activity
Serum testosterone
Superoxide anion production by PLC
T4UGT
Total Ah Receptor binding
UGTmRNA
UGT1A1
Serum Not Esterified chloesterol
Serum S. Dehydrogenase
Serum SGPT
Serum TEA
Serum total cholesterol
Serum triglycerides
Immune footpad swelling (following SRBC)
Immune increment in ear thickness (following oxazalone)
PFC/106 splenocytes
PFC/spleen(xlO-4)
Total thymic cells/mouse
Immune titer
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Table 8-7. Categorization of specific endpoints (continued)
Endocrine
Tissue
Toxicity
Hepatic retinol
Thyroid-stimulating hormone
Thyroxine
Age at puberty
Body weight
Brain weight
Caput/corpus epid. sperm
numbers
Cauda epid. sperm numbers
Cauda epididymal weight
Coagulating glands
Daily sperm production
Dorsal prostate weight
DSP/gDday 120
Endometrial lesion diameter
Endometrial lesion weight
Cleft palate
Fertility index
Gestation period
Hydronephrosis
Litter size
Live birth index (%)
Plasma retinol
Thyroxine Free T4
Thyroxine Total T4
Epididymal sperm count
Epididymidis weight
Eye opening
Eye opening in F/M
Glans penis weight
Heart weight
Incisor eruption
Kidney weight
Liver weight
Ovarian weight
Ovulation (ova/rat)
Paired epididymal weight
Pituitary gland weight
Liver BDH
Liver fatty change
Liver HCC
Liver HCK
Number of copulatory plugs
Pinna detachment
Hepatic retinyl-palmitate
Relative kidney weight
Relative liver weight
Relative spleen weight
Relative thymus weight
Seminal vesicle weight
Spleen atrophy
Spleen cellularity
Testes weight
Thymus atrophy
Thymus weight
Uterine horn weight
Uterus weight
Ventral prostate weight
Sperm morphology
Stomach edema
Testes MNGC
Testes SFEN
Testis descent
Total testis sperm numbers
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Table 8-8. Steady-state ED01 values calculated using mechanism-based dose-response
models of dioxin-regulated responses
Response
CYP1A1 (nmol/g)b
CYP1A2 (nmol/g)b
CYP1A2 (% liver induced)0
Internalized-EGFR (pmol/g)b
T4 (nM)b
UGT RNA pmol/g
UDPGT (nmol/g)b
TSH pMb
Liver cancerd
Response value
Control
(0 (ig/kg/day)
0.0216
0.558
0
29.0
1.13
0.118
77.8
0.35
Maximum
(10 ng/kg/day)
6.09
7.17
2.09
3.96
14.1
0.416
179
1.00
ED01
(ng/kg/day)
0.0047
0.34
1.4
0.28
0.27
0.85
2.9
1.3
0.15
Body burden 01
(ng/kg)a
0.17
12.3
50.5
10.1
9.7
30.7
104.6
46.9
2.7
"Steady-state body burdens were calculated from the formula in Section 8.2.3. assuming 100% absorption, except for the liver cancer model,
which used 50% absorption.
bValues obtained using the extended thyroid hormone model.
"Values from the zonal induction model.
dMechanism-based cancer model.
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(a)
(b)
1 C3 •
1E4 -
1E3 -
1E2-
1E1 -
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^^t
i~j
o
immune
0
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0
truue
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biochemical endocrmt hepatc Immune tissue
Figure 8-1. Distribution of ED01 and BB01 values in multidose studies by endpoint.
(a) ED01 values, (b) Body burden values at the ED01. The distribution of individual values is presented as
box plots. The boxed region contains values within the 25th to the 75th percentiles of the sample distribution,
with the median value (50th percentile) shown as a line within the boxed region. The error bars represent
values within the 10th to the 90th percentiles. Values above the 90th percentile and below the 10th percentile
are shown as individual data points. Values are categorized according to Table 8-7.
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(a)
(b)
o
Q
i ca i
1E8 •
1E7 -
1 E6 -
1E5 i
1E4 -5
^ C"3
1 b J -
1E2 -3
1E1 i
1 -
1E-1 -
i
4r
„ ^
o
. T .
t -L
1 o
7T -?-
1
1
1 o
1E9
1E8
IE? ,
1E6 •
1E5 -.
1E3 t
1E2
1E1 •
1 i
1E-1
biochemical hepatic immune tissue toncfty
biochermceJ
tooMty
Figure 8-2. Distribution of ED01 values in single-dose studies by endpoint.
(a) Adult endpoints. (b) Developmental endpoints. The distribution of individual values is presented as
box plots. The boxed region contains values within the 25th to the 75th percentiles of the sample distribution,
with the median value (50th percentile) shown as a line within the boxed region. The error bars represent
values within the 10th to the 90th percentiles. Values above the 90th percentile and below the 10th percentile
are shown as individual data points. Values are categorized according to Table 8-7.
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(a)
o
LU
i c? •
1E4 •
1EJ -
1 C£. ]
1 El -
1E-1-
IE- 2 •<
•
-
j
<
-
•
fe
r
>
-r-
I
_L
0
X
n
^r
O
A
t
i
J_
8
o _
biochemical endocrine hepatic immune tissue
(b)
1E7
1E6
1ES
1E4
Q
QJ
1E2
1E1
1
o Adi*
[o] Developmental
1
T
btochemk:al hepatic Immune tissue toxlcity
Figure 8-3. Distribution of ED10s in multi-dose studies and single-dose studies by endpoint.
(a) Multi-dose studies, (b) Single-dose studies. The distribution of individual values is presented as box plots.
The boxed region contains values within the 25th to the 75th percentiles of the sample distribution, with the
median value (50th percentile) shown as a line within the boxed region. The error bars represent values within
the 10th to the 90th percentiles. Values above the 90th percentile and below the 10th percentile are shown as
individual data points. Values are categorized according to Table 8-7.
December 2003
8-83
DRAFT—DO NOT CITE OR QUOTE
-------
TCDD (food )
1
TCDD (tissue)
1
TCDD-AHR (Liver)
Zonal induction
model
1
Fraction of
liver induced
1
Two-cell
model
Biochemical Response
model
/
CYP1A2
\
\
EGFR
/
Two stage-
model
1
Thyroid
hormone
model
I
TSH
elevation
1
Follicular
cell
hyperplasia
Cancer risk
Figure 8-4. Schematic representation of the linkage of current PBPK models and
biochemical/tissue response models for TCDD action.
December 2003
8-84 DRAFT—DO NOT CITE OR QUOTE
-------
Appendix I: Multiple-dose studies
Study
description
Kociba et al.
(1976), male
Sprague-Dawley
rats
Dose regimen3
13 weeks,
5x/wk, 1 ng/kg
Endpointb
Body weight
Brain weight
Rel brain
weight
Heart weight
Kidney weight
Liver weight
Rel liver weight
Serum alkaline
phosphatase
Serum BUN
Serum direct
bilirubin
Serum indirect
bilirubin
Serum total
bilirubin
Spleen weight
Rel spleen
weight
Shape
parameter
18.0
5.7
4.9
6.4
7.4
1.0
1.0
6.2
6.9
NA1
NA
7.0
6.4
8.6
Daily ED01
(ng/kg/day)
9.1E+01
5.4E+01
3.2E+02
6.8E+01
6.2E+01
1.2E-01
1.1E+00
3.8E+02
5.1E+02
NA
NA
4.8E+02
5.4E+01
5.3E+02
Body
burden
ED01(ng/kg)
1.6E+03
9.8E+02
5.9E+03
1.2E+03
1.1E+03
2.3E+04
1.9E+01
6.8E+03
9.2
NA
NA
8.7E+03
9.8E+02
9.5E+03
Relative
ED01<
9.1E+01
5.4E+01
3.2E+02
6.8E+01
6.2E+01
1.2E-01
1.1E+00
3.8E+02
5.1E+02
NA
NA
4.8E+02
5.4E+01
5.3E+02
Daily ED10
(ng/kg/day)
l.OE+02
8.3E+01
5.2E+02
9.9E+01
8.5E+01
1.4E+00
1.2E+01
5.5E+02
7.1E+02
NA
NA
6.7E+02
7.9E+01
6.9E+02
Body
burden
ED10 (ng/kg)
1.9E+03
1.5E+03
9.4E+03
1.8E+03
1.5E+03
2.6E+01
2.1E+02
9.9E+03
1.3E+04
NA
NA
1.2E+04
1.4E+03
1.2E+04
Relative
ED10<
l.OE+02
8.3E+01
5.2E+02
9.9E+01
8.5E+01
1.4E+00
1.2E+01
5.5E+02
7.1E+02
NA
NA
6.7E+02
7.9E+01
6.9E+02
Quality
of fit"
M
M
M
M
M
G
G
M
M
NF
NF
G
M
M
8-85
-------
Appendix I: Multiple-dose studies (continued)
Study
description
Kociba et al.
(1976), female
Sprague-Dawley
rats
Dose regimen3
13 weeks,
5x/wk, 1 ng/kg
Endpoint"
Rel testes
weight
Thymus weight
Rel thymus
weight
Body weight
Brain weight
Rel brain
weight
Heart weight
Kidney weight
Liver weight
Rel liver weight
Serum alkaline
phosphatase
Serum direct
bilirubin
Serum indirect
bilirubin
Serum total
bilirubin
Shape
parameter
7.1
1.0
1.0
1.0
1.0
5.8
5.5
7.3
7.1
1.1
7.7
1.0
NA
18.0
Daily ED01
(ng/kg/day)
7.3E+01
3.7E+00
2.6E+00
4.8E+00
5.8E+00
6.8E+01
5.2E+01
5.1E+02
6.0E+00
5.4E-01
7.3E+00
6.8E+00
NA
7.7E+02
Body
burden
ED01(ng/kg)
1.3E+03
6.7E+01
4.8E+01
8.6E+01
1.1E+02
1.2E+03
9.4E+02
9.8E+03
1.1E+02
9.8E+00
1.3E+02
1.2E+02
NA
1.4E+04
Relative
ED01<
7.3E+01
3.7E+00
2.6E+00
4.8E+00
5.8E+00
6.8E+01
5.2E+01
5.1E+02
6.0E+00
5.4E-01
7.3E+00
6.8E+00
NA
7.7E+02
Daily ED10
(ng/kg/day)
l.OE+02
4.0E+01
3.0E+01
4.7E+01
6.5E+01
1.1E+02
8.1E+01
7.0E+02
8.4E+00
5.2E+00
9.9E+00
7.5E+01
NA
8.8E+02
Body
burden
ED10 (ng/kg)
1.9E+03
7.3E+02
5.4E+02
8.5E+02
1.2E+03
1.9E+03
1.5E+03
1.3E+04
1.5E+02
9.4E+01
1.8E+02
1.3E+03
NA
1.6E+04
Relative
ED10<
l.OE+02
4.0E+01
3.0E+01
4.7E+01
6.5E+01
1.1E+02
8.1E+01
7.0E+02
8.4E+00
5.2E+00
9.9E+00
7.5E+01
NA
8.8E+02
Quality
of fit"
M
M
M
G
M
M
M
M
M
G
M
M
NF
M
8-86
-------
Appendix I: Multiple-dose studies (continued)
Study
description
Clark et al.
(1981), male
C57B1/6 mice
Tritscher et al.
(1992), female
Sprague-Dawley
rats
Dose regimen3
4 weeks, Ix/wk,
1 week after last
dose, 400 ng/kg
31 weeks, lx/2
weeks, 3.5
ng/kg/day
Endpoint"
Serum SGPT
Thymus weight
Rel thymus
weight
Immune
footpad
swelling
(following
SRBC)
Immune
increment in ear
thickness
(following
oxazalone)
CYP1A1
(Protein) (DEN)
CYP1A1
(Protein)
(saline)
CYP1A2
(Protein) (DEN)
CYP1A2
(Protein)
(saline)
Shape
parameter
14.1
1.0
1.0
7.0
18.0
1.2
1.0
1.0
1.0
Daily ED01
(ng/kg/day)
2.3E+02
1.3E+00
l.OE+00
2.6E+03
1.6E+02
4.1E-01
3.5E-01
5.1E-01
3.6E-01
Body
burden
ED01(ng/kg)
4.2E+03
2.3E+01
1.9E+01
3.8E+04
2.3E+03
1.5E+01
1.3E+01
1.9E+01
1.3E+01
Relative
ED01<
2.3E+02
1.3E+00
l.OE+00
5.7E+01
3.4E+00
1.2E-01
10.0E-02
1.5E-01
l.OE-01
Daily ED10
(ng/kg/day)
2.8E+02
1.4E+01
1.1E+01
2.8E+04
1.6E+03
3.0E+00
3.8E+00
5.6E+00
3.9E+00
Body
burden
ED10 (ng/kg)
5.0E+03
2.5E+02
2.0E+02
4.1E+05
2.3E+04
1.1E+02
1.4E+02
2.0E+02
1.4E+02
Relative
ED10<
2.8E+02
1.4E+01
1.1E+01
6.1E+02
3.4E+01
8.6E-01
1.1E+00
1.6E+00
1.1E+00
Quality
of fit"
P
G
G
P
P
G
G
G
G
8-87
-------
Appendix I: Multiple-dose studies (continued)
Study
description
Fox etal. (1993),
female
Sprague-Dawley
rats
Fox etal. (1993),
female
Sprague-Dawley
rats
Dose regimen3
7 days, 5 ng/kg
(initial dose),
0.9 ng/kg/4
days until 0.03
ng/g steady-
state achieved
14 days, 5 ng/kg
(initial dose),
0.9 ng/kg/4
days until 0.03
ng/g steady-
state achieved
Endpoint"
Body weight
Body weight
change
Liver weight
Liver
weight:body
weight ratio
Body weight
Body weight
change
Liver weight
Liver
weight:body
weight ratio
Shape
parameter
15.2
2.5
11.2
1.0
1.0
2.7
1.0
1.0
Daily ED01
(ng/kg/day)
1.2E+03
7.9E+01
3.3E+01
9.6E-01
2.0E+00
5.5E+01
1.2E+00
1.9E+01
Body
burden
ED01(ng/kg)
2.2E+04
1.4E+03
6.0E+02
1.7E+01
3.6E+01
l.OE+03
2.2E+01
3.4E+02
Relative
ED01<
1.4E+03
9.4E+01
3.9E+01
1.1E+00
3.7E+00
l.OE+02
2.2E+00
3.5E+01
Daily ED10
(ng/kg/day)
1.4E+03
2.0E+02
4.1E+01
9.8E+00
2.1E+01
1.4E+02
1.3E+01
1.9E+02
Body
burden
ED10 (ng/kg)
2.5E+04
3.6E+03
7.3E+02
1.8E+02
3.8E+02
2.4E+03
2.4E+02
3.4E+03
Relative
ED10<
1.7E+03
2.4E+02
4.8E+01
1.2E+01
3.9E+01
2.5E+02
2.4E+01
3.5E+02
Quality
of fit"
M
M
M
G
G
G
G
M
H
W
O
c
O
H
W
8-88
-------
Appendix I: Multiple-dose studies (continued)
Study
description
Fox etal. (1993),
male
Sprague-Dawley
rats
Fox etal. (1993),
male
Sprague-Dawley
rats
Maronpot et al.
(1993), female
Sprague-Dawley
rats
Dose regimen3
7 days, 5 ng/kg
(initial dose),
0.9 ng/kg/4
days until 0.03
ng/g steady-
state achieved
14 days, 5 ng/kg
(initial dose),
0.9 ng/kg/4
days until 0.03
ng/g steady-
state achieved
3 1 weeks,
lx/2weeks, 3.5
ng/kg/day
(DEN-initiated)
Endpoint"
Body weight
Body weight
change
Liver weight
Liver
weightbody
weight ratio
Body weight
Body weight
change
Liver weight
Liver
weightbody
weight ratio
Serum
5'-nucleotidase
Shape
parameter
5.3
2.4
1.0
3.1
18
18
6.2
2.5
1.9
Daily ED01
(ng/kg/day)
9.2E-06
1.3E+02
2.8E+00
7.7E+01
1.2E-05
1.1E+03
6.3E+00
3.4E+01
8.3E-01
Body
burden
ED01(ng/kg)
1.7E-04
2.3E+03
5.0E+01
1.4E+03
2.2E-04
2.0E+04
1.1E+02
6.1E+02
3.0E+01
Relative
ED01<
1.1E-05
1.5E+02
3.3E+00
9.1E+01
2.3E-05
2.0E+03
1.1E+01
6.2E+01
2.4E-01
Daily ED10
(ng/kg/day)
1.5E-05
3.4E+02
3.1E+01
1.7E+02
1.4E-05
1.3E+03
9.2E+00
8.9E+01
3.0E+00
Body
burden
ED10 (ng/kg)
2.6E-04
6.2E+03
5.5E+02
3.0E+03
2.6E-04
2.3E+04
1.7E+02
1.6E+03
1.1E+02
Relative
ED10<
1.7E-05
4.1E+02
3.6E+01
2.0E+02
2.6E-05
2.3E+03
1.7E+01
1.6E+02
8.5E-01
Quality
of fit"
P
M
G
G
P
P
M
G
G
8-89
-------
Appendix I: Multiple-dose studies (continued)
Study
description
Maronpot et al.
(1993), female
Sprague-Dawley
rats
Sewall et al.
(1993), female
Sprague-Dawley
rats
Dose regimen3
31 weeks, lx/2
weeks, 3.5
ng/kg/day
(SALINE)
31 weeks, lx/2
weeks, 3.5
ng/kg/day
(DEN-initiated
and saline-
treated)
Endpoint"
Serum alkaline
phosphatase
Serums.
dehydrogenase
Serum total
cholesterol
Serum
triglycerides
Serum
5'-nucleotidase
Serum total
cholesterol
Serum
triglycerides
EOF
dissociation
(KJ (DEN)
EOF
dissociation
(Kd) (saline)
Shape
parameter
2.4
1.0
1.3
18.0
18.0
2.0
18.0
1.0
18.0
Daily ED01
(ng/kg/day)
7.6E+00
5.1E-01
4.2E-01
2.8E+01
2.6E+01
2.3E+00
9.1E+01
8.1E-01
1.4E+01
Body
burden
ED01(ng/kg)
2.7E+02
1.8E+01
1.5E+01
l.OE+03
9.2E+02
8.3E+01
3.3E+03
2.9E+01
5.0E+02
Relative
ED01<
2.2E+00
1.5E-01
1.2E-01
8.0E+00
7.3E+00
6.6E-01
8.0E+00
2.3E-01
4.0E+00
Daily ED10
(ng/kg/day)
2.0E+01
5.6E+00
2.6E+00
3.2E+01
2.9E+01
7.4E+00
1.1E+02
8.9E+00
1.6E+01
Body
burden
ED10 (ng/kg)
7.4E+02
2.0E+02
9.3E+01
1.2E+03
1.1E+03
2.7E+02
3.8E+03
3.2E+02
5.9E+02
Relative
ED10<
5.8E+00
1.6E+00
7.4E-01
9.2E+00
8.3E+00
2.1E+00
3.0E+01
2.6E+00
4.7E+00
Quality
of fit"
M
G
G
M
G
G
P
M
M
8-90
-------
Appendix I: Multiple-dose studies (continued)
Study
description
DeVito et al.
(1994), female
B6C3F1 mice
Schrenck et al.
(1994), female
Wistar rat
Dose regimen3
13 weeks,
5x/week, 1.5
ng/kg/day
13 weeks, lx/2
weeks, 2 ng/kg
Endpoint"
EGFR
autophospho-
rylation
EGFR
Maximum
binding (DEN)
EGFR
Maximum
binding (saline
CYP1A1 EROD
CYP1A1 EROD
lung
CYP1A1 EROD
skin
CYP1A2
ACOH
Body weight
CYP1A1 EROD
Relative liver
weight
Shape
parameter
1.0
1.6
1.5
1.6
1.3
NA
1.0
10.7
1.2
1.0
Daily ED01
(ng/kg/day)
4.9E-01
1.7E+00
3.8E-01
3.2E+00
6.1E-01
NA
1.2E-01
1.3E+01
8.2E-01
3.5E-01
Body
burden
ED01(ng/kg)
1.8E+01
6.1E+01
1.4E+01
5.1E+01
9.7E+00
NA
1.9E+00
4.2E+02
2.6E+01
1.1E+01
Relative
ED01<
1.4E-01
4.8E-01
1.1E-01
2.1E+00
4.1E-01
NA
8.2E-02
6.6E+00
4.1E-01
1.8E-01
Daily ED10
(ng/kg/day)
5.1E+00
7.7E+00
1.9E+00
1.5E+01
3.7E+00
NA
1.3E+00
1.7E+01
5.6E+00
3.9E+00
Body
burden
ED10 (ng/kg)
1.8E+02
2.8E+02
6.8E+01
2.3E+02
5.8E+01
NA
2.1E+01
5.3E+02
1.8E+02
1.2E+02
Relative
ED10<
1.5E+00
2.2E+00
5.4E-01
9.8E+00
2.5E+00
NA
9.0E-01
8.3E+00
2.8E+00
1.9E+00
Quality
of fit"
G
G
G
G
G
NF
G
G
G
G
8-91
-------
Appendix I: Multiple-dose studies (continued)
Study
description
Sewall et al.
(1995), female
Sprague-Dawley
rats
VanBirgelen et
al. (1995),
female
Sprague-Dawley
rats
VanBirgelen et
al. (1995b),
female
Sprague-Dawleyr
ats
Dose regimen3
31 weeks, lx/2
weeks, 3.5
ng/kg/day,
(DEN-initiated)
13 weeks,
Ix/day, 14
ng/kg/d
13 weeks,
Ix/day,
14ng/kg/d
Endpoint"
CYP1A1
mRNA
Thyroid-stimu-
lating hormone
Thyroxine
UGTmRNA
CYP1A1 EROD
T4UGT
Thyroxine ft4
Thyroxine tt4
UGT1A1
Body weight
CYP1A1 EROD
CYP1A2
ACOH
Shape
parameter
18.0
12.1
1.9
16.0
1.3
1.0
1.0
16.6
1.7
1.0
1.0
2.1
Daily ED01
(ng/kg/day)
2.6E+01
2.6E+01
1.3E+00
3.7E-01
l.OE+00
1.6E+00
4.9E+00
3.3E+01
1.5E+00
4.3E+00
6.1E-01
2.1E+00
Body
burden
ED01(ng/kg)
9.4E+02
9.3E+02
4.8E+01
1.3E+01
3.8E+01
5.8E+01
1.8E+02
1.2E+03
5.3E+01
1.6E+02
2.2E+01
7.4E+01
Relative
ED01<
7.4E+00
7.4E+00
3.8E-01
1.1E-01
7.5E-02
1.1E-01
3.5E-01
2.4E+00
l.OE-01
3.1E-01
4.3E-02
1.5E-01
Daily ED10
(ng/kg/day)
3.0E+01
3.1E+01
4.7E+00
4.3E-01
6.9E+00
1.8E+01
5.4E+01
3.8E+01
6.0E+00
4.7E+01
6.7E+00
6.5E+00
Body
burden
ED10 (ng/kg)
1.1E+03
1.1E+03
1.7E+02
1.6E+01
2.5E+02
6.3E+02
1.9E+03
1.4E+03
2.2E+02
1.7E+03
2.4E+02
2.3E+02
Relative
ED10<
8.5E+00
9.0E+00
1.3E+00
1.2E-01
4.9E-01
1.3E+00
3.8E+00
2.7E+00
4.3E-01
3.4E+00
4.8E-01
4.6E-01
Quality
of fit"
M
M
G
M
G
G
M
M
M
G
G
M
8-92
-------
Appendix I: Multiple-dose studies (continued)
Study
description
Rhile et al.
(1996), female
DBA/2 mice
Dose regimen3
11 days, Ix/day,
100 ng/kg
Endpoint"
Hepatic retinol
Hepatic retinyl-
palmitate
Liver weight
Liver
weightbody
weight ratio
Plasma retinol
Relative kidney
weight
Relative spleen
weight
Relative thymus
weight
Thymus weight
Thyroxine ft4
Thyroxine tt4
Total thymic
cells/mouse
CD8+ cells
CD8+/CD4+
CD8-/CD4-
Shape
parameter
1.0
1.0
18.0
1.0
1.2
1.0
0.9f
1.0
1.0
1.0
16.6
8.5
NA
18.0
NA
Daily ED01
(ng/kg/day)
2.8E-01
4.0E-02
2.2E+02
4.9E+00
2.3E+00
4.8E-01
4.9E+00
3.0E+00
2.5E+00
4.9E+00
3.3E+01
6.5E+02
NA
6.4E+03
NA
Body
burden
ED01(ng/kg)
l.OE+01
1.5E+00
8.0E+03
1.8E+02
8.2E+01
1.7E+01
1.8E+02
1.1E+02
8.9E+01
1.8E+02
1.2E+03
l.OE+04
NA
1.5E+05
NA
Relative
ED01<
2.0E-02
2.9E-03
1.6E+01
3.5E-01
1.6E-01
3.4E-02
3.5E-01
2.1E-01
1.8E-01
3.5E-01
2.4E+00
6.5E+00
NA
6.4E+01
NA
Daily ED10
(ng/kg/day)
3.1E+00
4.4E-01
2.5E+02
5.4E+01
1.7E+01
5.3E+00
7.4E+01
3.3E+01
2.7E+01
5.4E+01
3.8E+01
8.6E+02
NA
7.2E+03
NA
Body
burden
ED10 (ng/kg)
1.1E+02
1.6E+01
9.1E+03
2.0E+03
6.0E+02
1.9E+02
2.7E+03
1.2E+03
9.7E+02
1.9E+03
1.4E+03
1.4E+04
NA
1.1E+05
NA
Relative
ED10<
2.2E-01
3.2E-02
1.8E+01
3.9E+00
1.2E+00
3.8E-01
5.3E+00
2.3E+00
1.9E+00
3.8E+00
2.7E+00
8.6E+00
NA
7.2E+01
NA
Quality
of fit"
G
G
P
G
G
G
G
M
M
G
M
M
NF
M
NF
8-93
-------
Appendix I: Multiple-dose studies (continued)
Study
description
Rhile et al.
(1996), female
C57 BL/6 mice
Rhile et al.
(1996), female
C57BL/6 Ipr/lpr
mice
Vogel et al.
(1997), female
C57BL/6 mice
Dose regimen3
11 days, Ix/day,
100 ng/kg
11 days, Ix/day,
100 ng/kg
23 days, 1 ng/kg
(initial dose),
0.2 ng/kg/week
(3x total)
Endpoint"
CD4+
Total thymic
cells/mouse
CD8+ cells
CD8+/CD4+
CD8-/CD4-
Total thymic
cells/mouse
CD8+ cells
CD8+/CD4+
CD8-/CD4-
CD4+
Immune
CD4+/CD8- (23
d)
Immune
CD4-/CD8-
(23d)
Immune
CD4-/CD8+ (23
d)
Shape
parameter
17.5
15.0
13.5
11.2
1.0
1.0
18.0
18.0
15.3
18.0
6.1
1.0
6.1
Daily ED01
(ng/kg/day)
1.7E+02
7.5E+01
3.4E+03
3.2E+03
9.9E-01
1.6E+01
3.8E+03
2.8E+04
1.2E+04
3.6E+04
2.9E-02
1.3E-03
2.5E-02
Body
burden
ED01(ng/kg)
2.7E+03
1.2E+03
5.4E+04
4.9E+04
1.6E+01
2.5E+02
6.1E+04
4.5E+05
1.9E+05
5.7E+05
4.2E-01
1.8E-02
3.7E-01
Relative
ED01<
1.7E+00
7.5E-01
3.4E+01
3.1E+01
9.9E-03
1.6E-01
3.8E+01
2.9E+02
1.2E+02
3.6E+01
4.2E-01
1.8E-02
3.6E-01
Daily ED10
(ng/kg/day)
1.9E+02
8.9E+01
4.1E+03
3.8E+03
1.1E+01
1.8E+02
4.3E+03
3.3E+04
1.4E+04
4.1E+03
4.3E-02
1.4E-02
3.8E-02
Body
burden
ED10 (ng/kg)
3.0E+03
1.4E+03
6.5E+04
6.1E+04
1.7E+02
2.8E+03
6.9E+04
5.2E+05
2.3E+05
6.5E+04
6.3E-01
2.0E-01
5.5E-01
Relative
ED10<
1.9E+00
8.9E-01
4.1E+01
3.8E+01
1.1E-01
1.8E+00
4.3E+01
3.3E+02
1.4E+02
4.1E+01
6.2E-01
2.0E-01
5.4E-01
Quality
of fit"
M
M
M
G
G
G
M
P
P
M
G
M
G
8-94
-------
Appendix I: Multiple-dose studies (continued)
Study
description
Vogel et al.
(1997), female
C57BL/6 mice
Vogel et al.
(1997), female
C57BL/6 mice
Dose regimen3
79 days, 1 ng/kg
(initial dose),
0.2 ng/kg/week,
(7x total)
135 days, 1
ng/kg (initial
dose), 0.2
ng/kg/week
until 0.034
ng/kg
steady-state
reached
Endpoint"
ImmuneCD4+/
CD8+ (23 d)
Immune
CD4+/CD8- (79
d)
Immune
CD4-/CD8- (79
d)
ImmuneCD4-/C
D8+ (79 d)
CYP1A1 EROD
(135 d)
CYP1A1
mRNA (135 d)
CYP1A2
mRNA (135 d)
CYP1A2
MROD (135 d)
Shape
parameter
5.5
13.4
18.0
6.6
1.0
8.1
1.1
1.0
Daily ED01
(ng/kg/day)
2.7E-02
6.2E-02
7.9E-02
1.2E-02
7.4E-03
1.7E+00
3.0E-03
1.5E-02
Body
burden
ED01(ng/kg)
3.9E-01
8.9E-01
1.1E+00
1.7E-01
1.1E-01
2.5E+01
4.3E-02
2.2E-01
Relative
ED01<
3.8E-01
2.1E+00
2.6E+00
4.0E-01
2.2E-01
5.0E+01
8.7E-02
4.6E-01
Daily ED10
(ng/kg/day)
4.2E-02
7.4E-02
9.0E-02
1.7E-02
8.0E-02
2.3E+00
2.7E-02
1.6E-01
Body
burden
ED10 (ng/kg)
6.0E-01
1.1E+00
1.3E+00
2.5E-01
1.2E+00
3.3E+01
3.9E-01
2.4E+00
Relative
ED10<
5.9E-01
2.5E+00
3.0E+00
5.7E-01
2.4E+00
6.7E+01
7.9E-01
4.8E+00
Quality
of fit"
G
P
G
M
G
G
G
G
o
c
o
H
W
8-95
-------
o
O
to
O
O
Appendix I: Multiple-dose studies (continued)
Study
description
Johnson et al.
(1997), female
B6C3F1 mice
Walker et al.
(1999), female
Sprague-Dawley
Rats
Dose regimen3
18 weeks, lx/3
wks (5x total), 3
weeks after last,
1,000 ng/kg
31 weeks, lx/2
weeks, 3.5
ng/kg/day,
(DEN-initiated)
Endpoint"
CYP1A1 EROD
Endometrial
lesion diameter
Endometrial
lesion weight
Liver weight
Thymus weight
Ovarian weight
CYP1A1
mRNA
CYP1A2
mRNA
CYP1B1
mRNA
Shape
parameter
2.8
NA
NA
1.1
NA
15.2
2.0
3.0
3.1
Daily ED01
(ng/kg/day)
1.9E+01
NA
NA
7.7E+00
NA
3.5E+02
1.6E+00
7.6E+00
7.0E+00
Body
burden
ED01(ng/kg)
6.2E-03
NA
NA
1.2E+02
NA
5.5E+03
5.9E+01
2.7E+02
2.5E+02
Relative
ED01<
8.2E+00
NA
NA
1.6E-01
7.3E+00
NA
4.7E-01
2.2E+00
2.0E+00
Daily ED10
(ng/kg/day)
4.2E+01
NA
NA
6.2E+01
NA
4.1E+02
5.6E+00
1.7E+01
1.5E+01
Body
burden
ED10 (ng/kg)
6.6E+02
NA
NA
9.8E+02
NA
6.4E+03
2.0E+02
6.1E+02
5.4E+02
Relative
ED10<
8.8E-01
NA
NA
1.3E+00
NA
8.5E+00
1.6E+00
4.8E+00
4.3E+00
Quality
of fit"
G
NF
NF
G
NF
P
G
G
G
oo
O
O
*
O
H
O
HH
H
W
O
V
O
c
O
H
W
"Dose regimen is described by study duration, exposure frequency, and lowest dose used in the study.
bUnless noted otherwise, the Hill model was used to fit these data.
°Relative EDX effect is the ratio of daily EDX to the lowest daily doselevel used in the study from the study.
Qualitative assessment of fit: G=good (model curve goes through/near all data point mean); M=marginal (model within one std. deviation of mean); P=poor (model not
within one std. deviation of means).
eNR- In some cases, BMDS (U.S. EPA, 1999) fails to locate a lower confidence bound on the 1% effective dose.
^ower model was used for these data.
hNR- Quality of fit was not assessed for this endpoint.
8-96
-------
Appendix I: Multiple-dose studies (continued)
'NA-Models inBMDS (U.S. EPA, 1999) not applicable to these data.
JNF - Quality of fit not assessed for this endpoint.
8-97
-------
Appendix II: Single-dose adult studies
Study description
Kitchin & Woods
(1979), female
Sprague-Dawley rats
Olson etal. (1980), male
Golden Syrian hamsters
Vecchi etal. (1983),
female B6 mice
Vecchi etal. (1983),
female C3 mice
Vecchi etal. (1983),
female D2 mice
Dose regimen"
3 days, 0.6 ng/kg
50 days, 5,000
ng/kg
12 days, 1,200
ng/kg
12 days, 1,200
ng/kg
12 days, 1,200
ng/kg
Endpointb
Liver cytochrome P-450
(total)
Liver benzopyrene
hydroxylase (CYP1A1
activity)
Thymus weight
Spleen weight
Body weight
Thymus weight
PFC/1E+06 splenocytes
PFC/spleen
Body weight
Thymus weight
Body weight
Thymus weight
PFC/1E+06 splenocytes
PFC/spleen
Shape
parameter
1.0
17.7
1.1
3.5
12.0
1.4
1.0
1.0
11.1
1.0
17.8
1.0
1.0
1.3
Daily ED01
(ng/kg/day)
1.5E+01
1.4E+03
3.7E+03
1.5E+05
2.0E+04
1.5E+02
2.7E+00
3.9E+00
4.4E+03
3.9E+01
3.8E+05
3.5E+00
5.2E+01
1.3E+02
Relative
ED01'
2.6E+01
2.4E+03
7.3E-01
3.1E+01
1.6E+01
1.3E-02
2.3E-04
3.3E-04
3.6E-01
3.3E-03
3.1E+02
2.9E-03
4.3E-02
1.1E-01
Daily ED10
(ng/kg/day)
1.7E+02
1.6E+03
3.5E+04
3.0E+05
2.4E+04
8.3E+02
1.3E+02
2.1E+02
5.4E+03
4.3E+02
4.3E+05
3.8E+01
5.7E+02
8.5E+02
Relative
ED10<
2.8E+02
2.7E+03
6.9E+00
6.1E+01
2.0E+01
6.9E-01
1.1E-01
1.7E-01
4.5E+00
3.6E-01
3.6E+02
3.2E-02
4.7E-01
7.1E-01
Quality
offitd
G
P
G
M
G
G
G
G
P
G
P
M
G
G
8-98
-------
Appendix II: Single-dose adult studies (continued)
Study description
Vecchietal. (1983),
female B6D2F1 mice
Abraham etal, (1988),
female Wistar rats
Davis and Safe (1988),
male 657BL/6J mice
Birnbaum etal. (1990),
male C57BL/6J (Ahb/b)
mice
Birnbaum etal. (1990),
male C57BL/6J (Ahb/b)
mice
Dose regimen"
12 days, 1,200
ng/kg
7 days, 1 ng/kg
9 days, 1 nmol/kg
35 days, 50 ng
35 days, 50 ng
Endpointb
Thymus weight
PFC/1E+06 splenocytes
PFC/spleen
Liver EROD (CYP1A1
activity)
Liver cytochrome P450
(total)
Spleen cellularity
PFCs/spleen
PFCs/lE+06 viable cells
Serum TEA
Serum SDH
Serum ALT
Serum 5'-NUC
Serum glucose
Serum total cholesterol
Serum NEChol
Shape
parameter
1.0
1.0
1.0
1.1
1.0
18.0
4.2
4.0
18.0
2.8
2.4
18.0
18.0
18.0
4.7
Daily ED01
(ng/kg/day)
6.1E+01
1.4E+01
1.4E+01
1.6E+01
6.7E+00
4.5E+02
2.0E+02
2.1E+02
4.6E+04
1.7E+04
1.6E+04
8.8E+04
5.3E+04
3.5E+04
7.6E-04
Relative
ED01C
5.1E-02
1.2E-03
1.2E-03
1.6E+01
6.7E+00
1.4E+00
6.3E-01
6.5E-01
9.1E+02
3.4E+02
3.2E+02
1.8E+03
1.1E+03
6.9E+02
1.5E-05
Daily ED10
(ng/kg/day)
6.6E+02
1.6E+03
1.5E+03
7.3E+01
1.4E+02
5.2E+02
3.6E+02
3.8E+02
5.2E+04
3.9E+04
4.3E+04
l.OE+05
6.0E+04
4.0E+04
1.3E-03
Relative
ED10C
5.5E-01
1.3E+00
1.2E+00
7.3E+01
1.4E+02
1.6E+00
1.1E+00
1.2E+00
l.OE+03
7.8E+02
8.6E+02
2.0E+03
1.2E+03
7.9E+02
2.5E-05
Quality
of fit"
G
G
G
G
G
M
G
G
M
M
M
M
M
M
P
8-99
-------
Appendix II: Single-dose adult studies (continued)
Study description
Birnbaumetal. (1990),
male C57BL/6J (Ahd/d)
mice
Dose regimen"
35 days, 400 ng
Endpointb
Serum Echol
Liver Hepatocellular
cytomegaly
Liver Hepatocellular
karyomegaly
Fatty liver change
Liver bile duct
hyperplasia
Thymic atrophy
Splenic atrophy
Testes: multinucleated
spermatid giant cells
Testes: seminiferous
tubule epithelium
necrosis
Gland, stomach edema
Serum TEA
Serum SDH
Serum ALT
Serum 5'-NUC
Serum glucose
Shape
parameter
18.0
7.2g
5.8g
7.9g
2.6g
2.0g
1.9g
2.3g
6.9g
1.5g
2.3
7.1
1.0
18.0
18.0
Daily ED01
(ng/kg/day)
3.5E+04
8.5E+04
3.0E+04
5.8E+04
4.8E+04
2.3E+04
1.6E+04
3.7E+04
l.OE+05
1.8E+04
4.0E+05
1.1E+06
4.2E+04
3.2E+05
6.1E+05
Relative
ED01C
7.1E+02
1.7E+03
6.0E+02
1.2E+03
9.6E+02
4.6E+02
3.3E+02
7.4E+02
2.0E+03
3.7E+02
9.9E+02
2.1E+04
l.OE+02
8.1E+02
1.5E+03
Daily ED10
(ng/kg/day)
4.0E+04
1.2E+05
4.5E+04
7.8E+04
1.2E+05
7.6E+04
5.5E+04
l.OE+05
1.4E+05
8.6E+04
1.1E+06
1.5E+06
4.2E+05
3.7E+05
6.9E+05
Relative
ED10C
8.1E+02
2.3E+03
8.9E+02
1.6E+03
2.4E+03
1.5E+03
1.1E+03
2.1E+03
2.9E+03
1.7E+03
2.7E+03
3.8E+03
l.OE+03
9.2E+02
1.7E+03
Quality
of fit"
M
G
G
G
G
G
G
G
G
G
M
M
M
P
P
8-100
-------
Appendix II: Single-dose adult studies (continued)
Study description
Jureketal. (1990), male
Sprague-Dawley rats
Dose regimen"
12 days, 1 nmol/kg
Endpointb
Serum triglycerides
Serum total cholesterol
Serum NEChol
Serum Echol
Liver Hepatocellular
cytomegaly
Liver Hepatocellular
karyomegaly
Fatty Liver change
Liver BDH
Thymic atrophy
Splenic atrophy
Testes: seminiferous
tubule epithelium
necrosis
Gland, stomach edema
Body weight
Liver weight:body
weight ratio
Kidney weightbody
weight ratio
Shape
parameter
18.0
1.0
1.0
1.0
4.2g
3.1g
2.6g
1.6g
1.0g
1.0g
4.2g
4.2g
1.0
8.2
2.7
Daily ED01
(ng/kg/day)
1.8E+06
5.1E+02
l.OE+03
1.7E+03
1.5E+06
9.2E+04
6.9E+05
1.3E+06
4.7E+04
2.3E+04
1.9E+06
1.9E+06
9.2E+02
1.1E+06
3.4E-03
Relative
ED01C
4.6E+03
1.3E+00
2.5E+00
4.2E+00
3.8E+03
2.3E+02
1.7E+03
3.2E+03
1.2E+02
5.8E+01
4.9E+03
4.9E+03
2.9E+00
3.5E+03
1.1E-05
Daily ED10
(ng/kg/day)
2.1E+06
5.6E+03
1.1E+04
1.8E+04
2.7E+06
1.9E+05
1.7E+06
5.4E+06
4.9E+05
2.4E+05
3.4E+06
3.4E+06
l.OE+04
1.4E+03
8.3E-03
Relative
ED10C
5.3E+03
1.4E+01
2.8E+01
4.6E+01
6.7E+03
4.9E+02
4.3E+03
1.3E+04
1.2E+03
6.1E+02
8.5E+03
8.5E+03
3.1E+01
4.2E+00
2.6E-05
Quality
of fit"
P
G
G
G
M
M
M
M
M
M
G
G
M
P
P
8-101
-------
Appendix II: Single-dose adult studies (continued)
Study description
Alsharifetal. (1994),
female Sprague-Dawley
rats
Narasimhan et al.
(1994), female B6C3F1
mice
Harper etal. (1994),
male C57BL/6 mice
Smialowicz et al.
(1994), male F344 rats
Dose regimen"
1 day, 5 ng/kg
24 hrs., 5 ng/kg
4 days, 5 ng/kg
8 days, 0.6 mg/kg
Ix followed by
immunization with
SRBC 7 days later,
100 ng/kg
Endpointb
Renal retinol
concentration
Renal RPH activity
Superoxide anion
production by PLC
Liver EROD (CYP1A1
activity)
Liver CYP1A1 (mRNA)
Liver CYP1A2 (mRNA)
Spleen PFC/lE+06cells
Total AhR binding
Immune liter
PFC/1E+06 cells
PFC/1E+06 cells
PFC/spleen(xlO-4)
Cells/spleen(xlO-6)
Titer(log2)
Shape
parameter
12.3
18.0
5.4
1.1
1
3.2
1.0
3.8
4.8
6.1
18.0
18.0
18.0
1.4
Daily ED01
(ng/kg/day)
2.0E+03
1.5E+04
5.7E+04
8.4E+01
5.6E+00
1.7E+02
2.0E+00
3.5E+02
3.0E+02
3.3E+02
1.6E+04
2.3E+04
7.3E+03
1.2E+02
Relative
ED01C
6.3E+00
4.5E+01
1.1E+04
1.7E+01
1.1E+00
3.4E+01
4.1E-01
7.0E+01
5.0E-01
5.5E-01
1.6E+02
2.3E+02
7.3E+01
1.2E+00
Daily ED10
(ng/kg/day)
2.5E+03
1.7E+04
8.9E+04
7.2E+02
6.2E+01
3.7E+02
2.2E+01
6.5E+02
5.0E+02
4.9E+02
1.8E+04
2.6E+04
8.3E+03
6.9E+02
Relative
ED10C
7.6E+00
5.2E+01
1.8E+04
1.4E+02
1.2E+01
7.3E+01
4.5E+00
1.3E+02
8.3E-01
8.1E-01
1.8E+02
2.6E+02
8.3E+01
6.9E+00
Quality
of fit"
M
M
G
G
G
G
G
G
G
M
P
P
P
G
8-102
-------
Appendix II: Single-dose adult studies (continued)
Study description
Smialowicz et al.
(1994), female F344 rats
Smialowicz et al.
(1994), female B6C3F1
mice
Vanden Heuvel et al.
(1994a), female
Sprague-Dawley rats
Diliberto et al. (1995),
female B6C3F1 mice
Lietal. (1995),
female Sprague-Dawley
rats
Dose regimena
lx folio wed by
immunization with
SRBC 7 days later,
100 ng/kg
lx followed by
immunization with
SRBC 7 days later,
300 ng/kg
4 days, 0.1 ng/kg
S, 7, 14,21,35
days, 100 ng/kg
4 days, 300 ng/kg
Endpoint"
PFC/1E+06 cells
PFC/spleen(xlO-4)
PFC/1E+06 cells
PFC/spleen(xlO-4)
CYP1A1 mRNA
UGTmRNA
Liver EROD
(CYPlAl):7days
Liver EROD
(CYP1A1): 14 days
Liver EROD
(CYP1A1): 21 days
Liver EROD
(CYP1A1): 35 days
Body weight
Ovarian weight
Shape
parameter
1.0
1.0
1.0
1.1
3.6
1.4
1.0
3.5
2.8
6.5
3.7
1.0
Daily ED01
(ng/kg/day)
3.4E+02
3.6E+02
2.9E+00
4.4E+00
3.9E+02
3.5E+01
2.7E+01
2.8E+02
2.4E+02
7.4E+02
1.2E+03
1.7E+02
Relative
ED01C
3.4E+00
3.6E+00
9.6E-03
1.5E-02
3.9E+03
3.5E+02
2.7E-01
2.8E+00
2.4E+00
7.4E+00
3.9E+00
5.7E-01
Daily ED10
(ng/kg/day)
3.4E+03
3.6E+03
3.2E+01
4.0E+01
7.7E+02
1.9E+02
3.0E+02
5.5E+02
5.7E+02
1.1E+03
2.2E+03
1.9E+03
Relative
ED10C
3.4E+01
3.6E+01
1.1E-01
1.3E-01
7.7E+03
1.9E+03
3.0E+00
5.5E+00
5.7E+00
1.1E+01
7.4E+00
6.2E+00
Quality
of fit"
P
P
M
G
G
G
P
G
G
M
G
G
8-103
-------
o
O
to
O
O
Appendix II: Single-dose adult studies (continued)
Study description
VanBirgelen et al.
(1996), female B6C3F1
mice
Dose regimena
S, 7 days,
100 ng/kg
Endpoint"
Ovulation (ova/rat)
CYP1A1 EROD
Shape
parameter
1.4
1.8
Daily ED01
(ng/kg/day)
1.5E+02
7.1E+01
Relative
ED01C
4.9E-01
7.1E-01
Daily ED10
(ng/kg/day)
8.7E+02
2.7E+02
Relative
ED10C
2.9E+00
2.7E+00
Quality
of fit"
G
G
oo
I
o
aDose regimen is described by study duration (total days after single administration) and lowest dose used in the study.
bUnless noted otherwise, the Hill model was used to fit these data.
"Relative EDX is the ratio of the EDX to the lowest dose tested in the study.
Qualitative assessment of fit: G=good (model curve goes through/near all data point means); M=marginal (model within one std. deviation of means); P=poor (model
not within one std. deviation of means).
eNR- In some cases, BMDS (U.S. EPA, 1999) fails to locate a lower confidence bound on the 1% effective dose.
'Power model used to fit these data.
BWeibull model used to fit these data.
hNA - Models in BMDS (U.S. EPA, 1999) not applicable to these data.
'NF- Quality of fit not assessed for this endpoint.
O
O
*
O
H
O
HH
H
W
O
c
o
H
W
8-104
-------
Appendix III: Single-dose developmental studies
Study description
Birnbaumetal. (1989),
C57BL/6N mice
Mablyetal. (1992b,c),
pregnant female, male
offspring, Holtzman
Sprague-Dawley rats
Dose regimen"
GD 10 or 12, 8 or 6
days (sacrificed on
GD 18), 6,000
ng/kg
GD 15, postnatal
day (PND) 49, 63,
or 120, 64 ng/kg
Endpointb
Cleft palate GD-106
Cleft palate GD-12e
Hydronephrosis GD-10e
Hydronephrosis GD-12e
Sperm morph. - day 120
Fertility index
Cauda sperm count day
63
Cauda sperm count -
day 120
Cauda sperm count/g -
day 120
DSP/g - day 63
DSP/g - day 120
DSP/g -day 49
Shape
parameter
3.5
6.4
1.0
2.3
4.4
NAg
1.0
1.0
1.7
1.0
1.4
1.7
ED01
(ng/kg/day)
3.3E+03
4.4E+03
3.2E+01
2.1E+02
8.7E+01
NA
6.6E-01
7.6E-01
3.7E+00
5.6E-01
1.4E+00
6.6E+00
Reproductive outcomes of females:
Litter size
Live birth index (%)
18.0
NA
7.9E+01
NA
Relative
ED01c
3.3E+00
4.4E+00
3.2E-02
2.1E-01
1.4E+00
NA
l.OE-02
1.2E-02
5.8E-02
8.8E-03
2.2E-02
l.OE-01
1.2E+00
NA
ED10
(ng/kg/day)
6.4E+03
6.3E+03
3.3E+02
5.7E+02
1.5E+02
NA
7.2E+00
8.3E+00
1.5E+01
6.2E+00
7.9E+00
2.8E+01
Relative
ED10C
1.1E+00
1.1E+00
5.5E-02
9.5E-02
2.3E+00
NA
1.1E-01
1.3E-01
2.3E-01
9.7E-02
1.2E-01
4.4E-01
Quality
of fit"
G
G
M
P
G
NFh
G
G
G
G
G
G
9.1E+01
NA
1.4E+00
NA
P
NF
8-105
-------
Appendix III. Single-dose developmental studies (continued)
t>
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n
Study description
Dose regimena
Theobald etal. (1997),
pregnant female, male
and female offspring
ICR mice
Theobald etal. (1997),
pregnant female, male
and female offspring
ICR mice
GD 14, PND 44,
15,000 ng/kg
GD 14, PND 65,
15,000 ng/kg
Endpointb
Shape
parameter
ED01
(ng/kg/day)
Age of indices of dev. in pups:
Pinna detachment
Incisor eruption
Eye opening
Testis descent
Testes weight
Epididymidis wt.
Dorsal prostate wt.
Ventral prostate wt.
Coagulating glands
Seminal vesicles
Ovary weight
Uterus weight
Testes weight
Epididymidis wt.
Ventral prostate wt.
17.0
1.0
1.0
1.0
1.0
18.0
1.0
2.9
1.7
18.0
18.0
4.5
18.0
3.1
18.0
7.7E+02
1.3E+01
7.0E+00
1.3E+00
7.4E+02
4.8E+04
3.0E+02
1.2E-04
3.3E+03
4.5E+04
2.4E+04
9.8E+03
1.1E+04
1.4E-04
1.1E+04
Relative
ED01c
1.2E+01
2.0E-01
1.1E-01
2.1E-02
6.5E+00
3.2E+00
2.0E-02
8.0E-09
2.2E-01
3.0E+00
1.6E+00
6.5E-01
7.5E-01
9.5E-09
NR
ED10
(ng/kg/day)
Relative
ED10C
Quality
offitd
8.8E+02
1.3E+02
7.4E+01
1.4E+01
6.9E+03
5.4E+04
3.3E+03
2.8E-04
1.3E+04
5.2E+04
2.8E+04
1.7E+04
1.3E+04
9.4E-04
1.3E+04
1.4E+01
2.1E+00
1.2E+00
2.3E-01
4.6E-01
3.6E+00
2.2E-01
1.9E-08
8.7E-01
3.4E+00
1.8E+00
1.1E+00
8.5E-01
6.2E-08
8.6E-01
P
G
G
G
M
M
P
M
G
M
M
G
M
P
M
8-106
-------
Appendix III. Single-dose developmental studies (continued)
Study description
Theobald etal. (1997),
pregnant female, male
and female offspring
ICR mice
Theobald etal. (1997),
pregnant female, male
and female offspring
ICR mice
Dose regimena
GD 14,PND
114/128, 15,000
ng/kg
GD 14,PND
114/128, 15,000
ng/kg
Endpointb
Coagulating glands
Seminal vesicles
Sperm production: ESN
Sperm production: DSP
Pituitary gland wt.
(males) (PND 65)
Epididymidis wt.
Dorsal prostate wt.
Ventral prostate wt.
Coagulating glands
Seminal vesicles
Sperm production: ESN
(PND 114/128)
Female rep: ovary wt.
(PND 114)
Female rep: uterus wt.
(PND 114)
Pituitary gland wt.
(males) (PND 128)
Shape
parameter
18.0
1.0
13.4
18.0
11.5
NA
1.0
18.0
18.0
NA
NA
18.0
4.5
NA
ED01
(ng/kg/day)
1.1E+04
1.2E+03
l.OE+04
1.5E+04
3.0E+05
NA
5.0E+02
1.1E+04
1.1E+04
NA
NA
1.6E+04
2.1E+04
NA
Relative
ED01c
7.5E-01
7.5E-01
7.8E-02
6.8E-01
9.8E-01
2.0E+01
NA
3.4E-02
7.3E-01
7.6E-01
NA
NA
l.OE+00
1.4E+00
ED10
(ng/kg/day)
1.3E+04
1.2E+04
1.2E+04
1.7E+04
3.7E+05
NA
5.3E+03
1.3E+04
1.3E+04
NA
NA
1.8E+04
3.5E+04
NA
Relative
ED10C
8.6E-01
7.8E-01
8.2E-01
1.1E+00
2.5E+01
NA
3.6E-01
8.4E-01
8.6E-01
NA
NA
1.2E+00
2.4E+00
NA
Quality
offitd
M
M
M
M
P
NF
P
M
M
NF
NF
M
G
NF
8-107
-------
Appendix III. Single-dose developmental studies (continued)
Study description
Grayetal. (1997), Long
Evans Hooded rat male
offspring
Dose regimena
GD 15, PND 49,50
ng/kg
Endpointb
Pituitary wt. (females)
(PND 128)
Hydronephrosis
(females)
Eye opening (females)
Thymus weight
(females)
Hydronephrosis (males)
Eye opening (males)
Thymus weight (males)
Body weight (day 49)
Testes weight (49)
Paired epididymal
weight (49)
Cauda epididymus (49)
Epididymal sperm count
(49)
Ventral prostate weight
(49)
Seminal vesicle weight
(49)
Shape
parameter
18.0
l.le
1.0
1.0
1.0e
1.0
3.4
9.6
1.1
13.9
18.0
1.0
12.4
17.9
ED01
(ng/kg/day)
1.1E+04
1.2E+03
3.8E+01
3.2E+02
2.6E+02
7.6E+01
1.6E-04
1.4E+02
l.OE+01
1.4E+02
7.9E+01
1.5E-01
1.4E+02
1.5E+02
Relative
ED01c
NA
7.2E-01
8.0E-02
2.5E-03
2.1E-02
1.7E-02
5.1E-03
l.OE-08
2.7E+00
2.1E-01
2.9E+00
1.6E+00
3.0E-03
2.7E+00
ED10
(ng/kg/day)
1.2E+04
9.4E+03
4.2E+02
3.5E+03
2.7E+03
8.4E+02
3.2E-04
1.8E+02
8.4E+01
1.7E+02
9.0E+01
1.7E+00
1.6E+02
1.7E+02
Relative
ED10C
8.2E-01
6.3E-01
2.8E-02
2.3E-01
1.8E-01
5.6E-02
2.1E-08
3.5E+00
1.7E+00
3.4E+00
1.8E+00
3.4E-02
3.3E+00
3.5E+00
Quality
offitd
M
M
M
M
M
G
P
G
G
M
G
P
G
M
8-108
-------
Appendix III. Single-dose developmental studies (continued)
Study description
Grayetal. (1997), Long
Evans Hooded rat male
offspring
Dose regimena
GD 15, PND 63,50
ng/kg
Endpointb
Daily sperm production
(49)
Age at puberty (49)
Body weight at puberty
(49)
Pituitary (49)
Body weight (63)
Testes weight (63)
Paired epididymal
weight (63)
Cauda epididymus (63)
Epididymal sperm count
(63)
Ventral prostate weight
(63)
Seminal vesicle weight
(63)
Daily sperm production
(63)
Serum testosterone (63)
Pituitary (63)
Shape
parameter
14.1
2.8
13.6
8.9
17.5
10.8
14.2
12.1
11.2
14.0
11.3
13.6
10.3
8.7
ED01
(ng/kg/day)
5.9E+02
4.0E+01
1.4E+02
9.6E+01
1.6E+02
1.3E+02
1.4E+02
1.3E+02
1.4E+02
1.4E+02
1.6E+02
5.4E+02
3.3E+01
3.7E+01
Relative
ED01c
3.0E+00
1.2E+01
1.3E+03
7.9E-01
2.7E+00
1.9E+00
3.2E+00
2.6E+00
2.8E+00
2.6E+00
2.8E+00
2.8E+00
3.2E+00
1.1E+01
ED10
(ng/kg/day)
6.9E+02
9.4E+01
1.6E+02
1.3E+02
1.8E+02
1.6E+02
1.6E+02
1.6E+02
1.7E+02
1.7E+02
2.0E+02
6.4E+02
4.1E+01
4.9E+01
Relative
ED10C
1.4E+01
1.9E+00
3.2E+00
2.5E+00
3.6E+00
3.2E+00
3.3E+00
3.1E+00
3.5E+00
3.4E+00
4.0E+00
1.3E+01
8.2E-01
9.7E-01
Quality
offitd
M
P
M
M
P
G
P
G
G
P
G
M
M
M
8-109
-------
Appendix III. Single-dose developmental studies (continued)
Study description
Grayetal. (1997), Long
Evans Hooded rat male
offspring
Dose regimena
GD 15, offspring
examined 15
months, 50 ng/kg
Endpointb
Body weight
Seminal vesicle weight
Glans penis weight
Paired epididymal
weight
Cauda epididymal
weight
Epididymal sperm
numbers
Caput/corpus epid.
sperm numbers
Cauda epid. sperm
numbers
Number of copulatory
plugs
Total testis sperm
numbers
Pituitary weight
Shape
parameter
13.0
18.0
1.4
18.0
10.7
4.3
15.5
2.9
2.4
12.3
18.0
ED01
(ng/kg/day)
1.6E+02
7.8E+01
3.8E+00
7.3E+01
3.3E+01
3.8E+01
1.2E+02
1.4E+01
1.1E-06
1.6E+02
7.7E+01
Relative
ED01c
6.5E-01
7.4E-01
3.1E+00
5.5E+02
1.6E+00
7.6E-02
1.5E+00
6.5E-01
7.5E-01
2.5E+00
2.7E-01
ED10
(ng/kg/day)
1.9E+02
8.9E+01
2.2E+01
8.4E+01
4.1E+01
6.6E+01
1.4E+02
3.1E+01
3.2E-06
2.0E+02
8.8E+01
Relative
ED10C
3.8E+00
1.8E+00
4.5E-01
1.7E+00
8.2E-01
1.3E+00
2.9E+00
6.3E-01
6.3E-08
4.0E+00
1.8E+00
Quality
offitd
M
G
G
P
P
G
P
G
P
P
P
o
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o
rt>
CT*
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to
o
o
oo
O
O
2
O
H
O
O
&
O
c
o
H
W
a Dose regimen is described by specific time of single administration, duration or offspring examination day, and lowest dose used in the study.
b Unless noted otherwise, the Hill model was used to fit these data.
0 Relative EDX is the ratio of the EDX to the lowest dose tested in the study
d Qualitative assessment of fit: G=good (model curve goes through/near all data point mean); M=marginal (model within one std. deviation of means); P= poor (model
not within one std. deviation of means).
e The Weibull model was fit to these data.
8-110
-------
Appendix III. Single-dose developmental studies (continued)
f NR-In some cases, the BMDS (U.S. EPA, 1999) fails to locate a lower confidence bound on the 1% effective dose.
g NA-Models in BMDS (U.S. EPA, 1999) not applicable to these data.
h NF-Qualiry of fit was not assessed for this endpoint.
8-111
-------
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DRAFT NCEA-I-0836
DO NOT CITE OR QUOTE December 2003
NAS Review Draft
www.epa.gov/ncea
Chapter 9. Toxic Equivalency Factors (TEF) for Dioxin
and Related Compounds
Exposure and Human Health Reassessment
of 2,3,7,8-Tetrachlorodibenzo-p-Dioxin (TCDD)
and Related Compounds
Part II: Health Assessment for 2,3,7,8-Tetrachlorodibenzo-/7-dioxin
(TCDD) and Related Compounds
NOTICE
THIS DOCUMENT IS A PRELIMINARY DRAFT. It has not been formally released by the
U.S. Environmental Protection Agency and should not at this stage be construed to represent
Agency policy. It is being circulated for comment on its technical accuracy and policy
implications.
National Center for Environmental Assessment
Office of Research and Development
U.S. Environmental Protection Agency
Washington, DC
-------
DISCLAIMER
This document is a draft. It has not been formally released by the U.S. Environmental
Protection Agency and should not at this stage be construed to represent Agency policy.
Mention of trade names or commercial products does not constitute endorsement or
recommendation for use.
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TABLE OF CONTENTS - OVERVIEW
Exposure and Human Health Reassessment
of 2,3,7,8-Tetrachlorodibenzo-/7-Dioxin (TCDD)
and Related Compounds
Part I: Estimating Exposure to Dioxin-Like Compounds (Draft Final)
Volume 1: Sources of Dioxin-Like Compounds in the United States
Chapters 1 through 13
Volume 2: Properties, Environmental Levels, and Background Exposures
Chapters 1 through 6
Volume 3: Site-Specific Assessment Procedures
Chapters 1 through 8
Part II: Health Assessment for 2,3,7,8-Tetrachlorodibenzo-p-dioxin (TCDD) and Related
Compounds
Chapter 1. Disposition and Pharmacokinetics
Chapter 2. Mechanism(s) of Actions
Chapter 3. Acute, Subchronic, and Chronic Toxicity
Chapter 4. Immunotoxicity
Chapter 5. Developmental and Reproductive Toxicity
Chapter 6. Carcinogenicity of TCDD in Animals
Chapter 7. Epidemiology/Human Data
Chapter 8. Dose-Response Modeling for 2,3,7,8-TCDD
Chapter 9. Toxic Equivalency Factors (TEF) for Dioxin and Related Compounds
Part III: Integrated Summary and Risk Characterization for
2,3,7,8-Tetrachlorodibenzo-/>-Dioxin (TCDD) and Related Compounds
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CONTENTS
>D TOXIC EQUIVALENCY FACTORS (TEFs) FOR DIOXIN AND RELATED
COMPOUNDS 9-1
9.1. INTRODUCTION 9-1
9.2. HISTORICAL CONTEXT OF TEFs 9-1
9.2.1. TEFs for PCDDs and PCDFs 9-2
9.2.2. TEFs for PCBs 9-3
9.2.3. The Most Recent Evaluation of TEFs for PCDDs, PCDFs, and PCBs 9-5
9.2.4. Illustrative Examples of the Data Used for Deriving the TEF Values 9-8
9.2.5. Variability in the REPs Across Endpoint, Species, Dosing Regimen and
Laboratories 9-9
9.2.6. Critical Considerations in the Application of the TEF Methodology 9-10
9.3. SPECIFIC ISSUES 9-11
9.3.1. Ah Receptor and Toxicity Factors 9-11
9.3.2. The Role of the AhR in the Toxicity of Dioxin-Like Compounds 9-11
9.3.3. Species Comparison of the AhR 9-12
9.3.4. Mode of Action and Implications for the TEF Methodology 9-15
9.3.5. Ah Receptor Ligands 9-16
9.3.5.1. Industrial/Synthetic AhR Ligands 9-16
9.3.5.2. Naturally Occurring AhR Ligands 9-17
9.3.5.3. Industrial vs. Natural AhR Ligands 9-19
9.3.5.4. Limitations in Comparing the Quantitative Interactions between
Industrial/Synthetic and Natural AhR Ligands 9-21
9.4. TOTAL TEQ AND THE ADDITIVITY CONCEPT 9-22
9.4.1. Examination of Laboratory Mixtures of PCDDs and PCDFs 9-23
9.4.2. Examination of Commercial or Laboratory-Derived Mixtures
of PCDDs, PCDFs, and PCBs 9-26
9.4.3. Examination of Environmental Samples Containing PCDDs,
PCDFs, and/or PCBs 9-29
9.4.4. Nonadditive Interactions With Non-Dioxin-Like Compounds 9-30
9.4.5. Examination of the TEF Methodology in Wildlife 9-34
9.4.6. Toxic Equivalency Functions 9-36
9.4.7. Species and Endpoint Specific TEFs 9-36
9.5. APPLICATION OF UNCERTAINTY ANALYSIS TO THE TEF
METHODOLOGY 9-37
9.6. IMPLICATIONS FOR RISK ASSESSMENT 9-39
9.7. SUMMARY 9-40
REFERENCES FOR CHAPTER 9 9-47
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LIST OF TABLES
9-1. Estimated relative toxicity of PCDD andPCDF isomers to 2,3,7,8-T4CDD 9-42
9-2. Toxic equivalency factors (TEFs) 9-43
9-3. The range of the in vivo REP values for the major TEQ contributors 9-44
LIST OF FIGURES
9-1. Structures of polychlorinated dibenzo-p-dioxin, dibenzofurans,
and biphenyls 9-45
9-2. TEQ-based bioassay results 9-46
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9. TOXIC EQUIVALENCY FACTORS (TEF) FOR
DIOXIN AND RELATED COMPOUNDS
1 9.1. INTRODUCTION
2 Previous risk assessments of dioxin and dioxin-like compounds from around the world have
3 employed the Toxic Equivalency Factor (TEF) methodology. This method is also used
4 throughout EPA's dioxin reassessment. This chapter has been added to the EPA's dioxin
5 reassessment effort to address questions raised by the Agency's Science Advisory Board (SAB)
6 in 1995. In its Report to the Administrator (U.S. EPA, 1995), the Committee said it "supports
7 EPA's use of Toxic Equivalencies for exposure analysis..." However, the SAB suggested that, as
8 the toxic equivalent (TEQ) approach was a critical component of risk assessment for dioxin and
9 related compounds, the Agency should be explicit in its description of the history and application
10 of the process and go beyond reliance on the Agency's published reference documents on the
11 subject (U.S. EPA, 1987; 1989; 1991) to discuss issues raised in review and comment on this
1 2 approach. Significant additional literature is now available on the subject, and this chapter
1 3 provides the reader with a summary which is up-to-date through 2000. Future research will be
14 needed to address uncertainties inherent in the current approach. The World Health Organization
1 5 (WHO) has suggested that the TEQ scheme be reevaluated every 5 years and that TEFs and their
1 6 application to risk assessment be re-analyzed to account for emerging scientific information (van
17 den Berg et al., 1998).
18
19 9.2. HISTORICAL CONTEXT OF TEFs
20 A wide variety of polyhalogenated aromatic hydrocarbon (PHAH) compounds can be
21 detected as complex mixtures in both abiotic and biotic samples. Because of PHAHs' known
22 global environmental distribution and their toxicity to experimental animals (DeVito et al., 1995;
23 DeVito and Birnbaum, 1995; Grassman et al., 1998)(see Part II, Chapters 3-6), to wildlife (Giesy
24 and Kannan, 1998; Ross, 2000), and to humans (IARC, 1997) (see also Part II, Chapter 7 ),
25 hazard characterization and risk assessment activities have tended to focus on a subset of
26 polychlorinated dibenzo-^-dioxin (PCDDs), polychlorinated dibenzofurans (PCDFs), and
27 polychlorinated biphenyls (PCBs)(Figure 9-1). The subset of compounds, shown in Figure 9-1,
28 are known as "dioxin-like" and have been assigned TEF values by WHO. In this chapter, the
29 development of TEFs for these and other PHAHs is discussed.
30
31 9.2.1. TEFs for PCDDs and PCDFs
32 In 1983, the Ontario Ministry of the Environment produced a Scientific Criteria Document
33 for PCDDs and PCDFs which concluded, based on a review of available scientific information,
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1 that dioxin and dibenzofurans were structurally similar compounds that shared a common
2 cellular mechanism of action (activation of the aryl hydrocarbon receptor or AhR) and induced
3 comparable biological and toxic responses, and that the development of environmental standards
4 for human health concerns should be based on a "toxic equivalency" approach with 2,3,7,8-
5 tetrachlorodibenzo-^-dioxin (TCDD) as the prototype (OME, 1984). The final recommendation
6 divided all PCDD/PCDF congeners into their respective homologue groups and assigned to each
7 group a toxicity factor relative to TCDD (Table 9-1). These numerical factors could then be
8 applied to transform various concentrations of PCDDs and PCDFs into equivalent concentrations
9 of 2,3,7,8-TCDD. Shortly thereafter, the first use of a TEF-like method was described by Eadon
1 0 et al. (1986) as a means to estimate potential health risks associated with a PCB transformer fire
1 1 in Binghamton, NY.
1 2 Following up on an initial risk assessment methodology designed to address the emission of
1 3 dioxins and furans from waste incinerators, EPA also concluded that TEFs were the best
1 4 available interim scientific policy for dealing with complex mixtures of these contaminants.
1 5 With the mandate to develop active research programs that would address the limitations
1 6 inherent to this risk management technique, the Agency recommended TEFs for specific
1 7 congeners, rather than isomeric groups (Table 9-2; U.S. EPA, 1987). In an analogous fashion to
1 8 OME's approach, concentrations of PCDDs and PCDFs would be analytically determined, the
1 9 concentration of each congener would be multiplied by its respective TEF value, and all the
20 products would be summed to give a single 2,3,7,8-TCDD equivalent. This approach has been
2 1 described mathematically as:
k
22 Total Toxic Equivalency (TEQ) = ^ Cn * TEF«
n=l
23 Cn equals the concentration of the individual congener in the complex mixture under analysis.
24 TEFs were determined by inspection of the available congener-specific data and an assignment of
25 an "order of magnitude" estimate of relative toxicity when compared to 2,3,7,8-TCDD. In vitro
26 AhR binding and in vitro and in vivo toxicity studies were considered in setting individual TEFs.
27 Scientific judgment and expert opinion formed the basis for these TEF values. External review
28 of the toxicity and pharmacokinetic data utilized by EPA in setting these TEFs supported the
29 basic approach as a "reasonable estimate" of the relative toxicity of PCDDs and PCDFs (Olson et
30 al., 1989).
3 1 A 3-year study conducted by the North Atlantic Treaty Organization Committee on the
32 Challenges of Modern Society (NATO/CCMS) also concluded that the TEF approach was the
33 best available interim measure for PCDD/PCDF risk assessment. On the basis of examination of
34 the available data dealing with exposure, hazard assessment, and analytical methodologies
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1 related to dioxin and furans, an International Toxicity Equivalency Factor (I-TEF) scheme was
2 presented (Table 9-2; NATO/CCMS, 1988). This review also concluded that "data strongly
3 support the role of the AhR in mediating the biologic and toxic responses elicited by 2,3,7,8-
4 TCDD and related PCDDs and PCDFs and provide the scientific basis for the development of
5 TEFs for this class of compounds." Various refinements to previous efforts included selection of
6 TEF values based more on in vivo toxicities, assigning TEF values to octachlorodibenzo-/?-
7 dioxin and octachlorodibenzofuran, and removing any TEF values for all non-2,3,7,8-substituted
8 congeners. Although it was indicated that, theoretically, it may be possible to detect nearly all of
9 the 210 PCDD/DF isomers in the environment, seventeen 2,3,7,8-substituted congeners were
10 known to be preferentially retained and bioaccumulated. For example, when fish or a variety of
11 rodent species were exposed to a complex mixture of PCDDs/PCDFs from incinerator fly ash,
1 2 the 2,3,7,8-substituted congeners, which were minor components of the original mixture,
1 3 predominated in the analysis of their tissues (Kuehl et al., 1986; van den Berg et al., 1994). In
14 addition, when humans were exposed to a complex mixture of more than 40 different PCDF
1 5 congeners during the Oriental rice oil poisoning episodes, only the 2,3,7,8-substituted congeners
16 were detected in subsequent blood and adipose tissue analysis (Ryan et al., 1990). EPA, which
1 7 had participated in the NATO/CCMS exercise, officially adopted the revised I-TEFs in 1989,
1 8 with the caveat that this risk assessment approach remains interim and continued revisions
1 9 should be made (U.S. EPA, 1989; Kutz et al., 1990). The use of the TEF model for risk
20 assessment and risk management purposes has been formally adopted by a number of countries
21 (Canada, Germany, Italy, the Netherlands, Sweden, the United Kingdom, U.S.A.) (Yrjanheiki,
22 1992), and as guidance by international organizations such as the International Programme on
23 Chemical Safety, WHO.
24
25 9.2.2. TEFs for PCBs
26 During the period of TEF development for PCDDs/PCDFs, a considerable body of
27 experimental evidence was also being generated regarding the structure-activity relationships
28 between the different polychlorinated biphenyl homologue classes (Safe, 1990, 1994). Following
29 the synthesis of analytical standards for all 209 theoretical PCB congeners by 1984, subsequent
30 analysis of a variety of commercial samples was able to identify all but 26 (Jones, 1988).
31 However, once released into the environment, PCBs are subject to a variety of photolysis and
32 biodegradation processes, to the extent that only 50-75 congeners are routinely detected in higher
33 trophic level species (van den Berg et al., 1995). Initial structure-activity relationship studies
34 revealed that those congeners substituted in only the meta and para positions were approximate
35 isostereomers of TCDD. Subsequent toxicological studies confirmed that these non-ortho-
36 substituted, "coplanar" PCBs (e.g., PCB 77, 81, 126, 169) did induce a variety of in vitro and in
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1 vivo effects similar to TCDD (Leece et al., 1985). Maximum TCDD-like activity is obtained for
2 PCBs when there are no ortho, two or more meta, and both para positions occupied (Figure 9-1).
3 Introduction of a single ortho substituent to the biphenyl (mono-ortho "coplanars") results in a
4 diminishing, but not elimination, of TCDD-like activity and toxicological responses resembling
5 commercial mixtures of PCBs. The addition of a single ortho substituent also increases the non-
6 dioxin-like activity of the compound. Several congeners from this group are prevalent in both
7 commercial PCBs and a wide variety of environmental samples. Some of the more persistent
8 mono-ortho substituted PCBs (PCBs 105, 118, 156) can be found in human serum and adipose
9 samples at levels up to three orders of magnitude higher than the "coplanar" PCBs, PCDDs and
10 PCDFs (Patterson et al., 1994). In limited studies a third group of PCB congeners, the di-ortho
11 "non-coplanars," has exhibited only minor amounts of dioxin-like activity (if any), usually 4-6
1 2 orders of magnitude less potent than TCDD (Safe, 1990). Recent studies have demonstrated that
13 some of the earlier methods of preparation of these di-ortho non-coplanar PCBs had trace
14 contaminants of PCDFs, which may account for the weak dioxin-like activity of these
1 5 compounds (van der Kolk et al., 1992). In 1991, EPA convened a workshop to consider TEFs
16 for PCBs (Barnes et al., 1991). The consensus was that a small subset of the PCBs displayed
1 7 dioxin-like activity and met the criteria for inclusion in the TEF methodology. Such proposals
1 8 for the TEF methodology also seem to have utility in assessing risks to wildlife (van den Berg et
1 9 al., 1998; Giesey and Kannan, 1998; Ross, 2000).
20 PCBs are often classified into two categories: "dioxin-like" and "non-dioxin-like." The
21 dioxin-like PCBs bind to the AhR and produce dioxin-like effects in experimental animals. All
22 other PCBs then fall into the non-dioxin-like classification. Although the dioxin-like PCBs are
23 generally more potent at inducing biological effects, they constitute only a minor portion of the
24 mass of PCBs found in environmental and biological samples. The non-dioxin-like PCBs
25 account for a majority of the mass of the PCBs found in environmental and biological samples.
26 The use of the term non-dioxin-like PCBs is not necessarily useful. The PCBs not included in
27 the TEF scheme (i.e., the non-dioxin-like PCBs) are not a single class of compounds and have
28 multiple toxicities with separate structure-activity relationships (Barnes et al., 1991). Not enough
29 congener-specific research has been performed to adequately characterize or classify these
30 compounds. For example, the "neurotoxic" PCBs have been typically defined by structure-
31 activity relationships for decreasing dopamine concentrations or alterations in intracellular
32 calcium in cell culture (Shain et al., 1991; Kodavanti et al., 1996).
33 As part of the joint WHO European Centre for Environmental Health (WHO-ECEH) and the
34 International Programme on Chemical Safety (IPCS) project to harmonize TEF schemes for
35 dioxin-like compounds, a database was generated consisting of all available relevant
36 toxicological data for PCBs up to the end of 1993. Of almost 1,200 peer-reviewed publications,
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1 146 were selected and analyzed on the basis of the following criteria: at least one PCB congener
2 was investigated; TCDD or a reference coplanar PCB (77, 126, 169) was used during the
3 experiment or results were available from previous experiments (same author, laboratory,
4 experimental design); and the endpoint in question was affected by both the reference compound
5 and the PCB congener in question (i.e., dioxin-specific). TEFs were then determined from a total
Q of 60 articles/manuscripts on the basis of the reported results for 14 different
7 biological/toxicological parameters. Following scientific consultation by 12 experts from 8
8 different countries, interim TEF values were recommended for 13 dioxin-like PCBs (Table 9-2),
9 based on four inclusion criteria: (1) the compound should show structural similarity to PCDDs
10 and PCDFs; (2) it should bind to the Ah receptor; (3) it should induce dioxin-specific
11 biochemical and toxic responses; and (4) it should be persistent and accumulate in the food chain
1 2 (Ahlborg et al., 1994). Increased consideration was given to selection of a TEF value based on
13 repeat-dosing in vivo experiments, when available.
14 There is experimental evidence to suggest that a limited number of PCB congeners classified
1 5 as weak or non-AhR agonists could effect concentration-dependent nonadditive interactions with
1 6 dioxin-like compounds (Safe, 1990; 1994). Both antagonistic (Safe, 1990; Morrissey et al.,
1 7 1992; Smialowicz et al., 1997b) and synergistic (Safe, 1990; van Birgelen et al., 1996a,b; van
18 Birgelen et al., 1997) interactions between TCDD and PCBs have been observed in experimental
1 9 systems. The non-additive interactions of the PCBs are thought to be mediated through non-AhR
20 pathways (Smialowicz et al., 1997). These interactions usually occur at extremely high doses of
21 the PCBs that are not environmentally relevant, and thus the nonadditive interactions are thought
22 not to significantly detract from the TEF methodology (van den Berg et al., 1998; Birnbaum,
23 1999).
24
25 9.2.3. The Most Recent Evaluation of TEFs for PCDDs, PCDFs, and PCBs
26 An additional recommendation from the first WHO PCB TEF consultation was that the
27 current database should be expanded to include all relevant information on PCDDs, PCDFs, and
28 other dioxin-like compounds that satisfied the four inclusion criteria. Prior to the second WHO-
29 ECEH consultation in 1997, various terminologies or definitions applicable to TEFs were
30 reviewed and standardized. Whereas previously the term TEF had been used to describe all
31 scientific endpoints used in comparison with TCDD, it was noted that a variety of experimental
32 parameters may not be considered "toxic," but are considered as biological/biochemical
33 responses, such as AhR binding and alkoxyresorufin O-dealkylase induction. The decision was
34 that any experimental endpoint for which a numerical value of the relative potency compared to
35 TCDD had been generated from a single laboratory examining a single endpoint would be known
36 as a relative potency value, or REP. The term TEF would then be restricted to describe an order-
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1 of-magnitude consensus estimate of the toxicity of a compound relative to the toxicity of TCDD
2 that is derived using careful scientific judgment of all available data (van Leeuwen, 1997; van
3 den Berg et al., 1998).
4 At the second WHO-ECEH consultation in 1997, relative potency factors were calculated
5 based on the following methodology (van den Berg et al., 1998):
6
7 •Assigned as reported in the publication/manuscript (verified from available data).
8 •Calculated from the dose-response curves using linear interpolation of log doses comparing
9 the same effect levels with correction for different control levels.
10 •Calculated from ratios of low or no observed effect levels (LOELs, NOELs) and effect
11 concentration/dose 10%, 25% or 50% values (ED/EC 102550).
1 2 •Calculated from ratios of tumor promotion indexes or maximal enzyme induction levels.
1 3 • Calculated from ratios of Ah receptor binding affinities (Kd ).
14
1 5 Whereas the resulting range of in vitro/in vivo REP values for a particular congener may span
16 3-4 orders of magnitude, final selection of a TEF value gave greater weight to REPs from repeat-
1 7 dose in vivo experiments (chronic > subchronic > subacute > acute). As with the PCB TEF
1 8 consultation, dioxin-specific endpoints were also given higher priority. A rounding-off
19 procedure (nearest 1 or 5) was also employed for final TEF selection (Table 9-2). It should be
20 noted that the TEF was rounded up or down depending on the compound, the data, and scientific
21 judgment.
22 Notable amendments to the previous NATO/WHO TEF schemes include:
23
24 •On the basis of new REPs from in vivo tumor promotion and enzyme induction, a TEF of
25 1.0 was recommended for 1,2,3,7,8-PeCDD.
26 •Originally the TEF for OCDD was based on body burdens of the compound following
27 subchronic exposures; a TEF based on administered dose is reduced to 0.0001.
28 «New in vivo enzyme induction potency and structural similarity with OCDD support the
29 TEF change to 0.0001 for OCDF.
30 •REPs from an in vivo subchronic toxicity study (enzyme induction, hepatic retinol
31 decreases) support reducing the TEF to 0.0001 for PCB 77.
32 • A TEF value of 0.0001 was assigned for PCB 81. Even though PCB 81 was not assigned a
33 TEF value at the 1993 WHO consultation because of lack of human residue and
34 experimental data, more recent data demonstrate similar qualitative structural activity
35 results compared to PCB 77.
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1 •Because of the lack of in vivo enzyme induction (CYP 1A1/A2) and reproductive toxicity
2 with structurally similar congeners (PCB 47 and PCB 153), the previous interim TEF
3 values for the di-ortho-substituted PCBs 170 and 180 were withdrawn.
4
5 Although a number of uncertainties associated with the TEF concept have been identified
6 (nonadditive interactions with non-dioxin-like PCBs, natural ligands for the Ah receptor,
7 questionable low-dose linearity of REP responses), the 1997 WHO expert meeting decided that
8 an additive TEF model remained the most feasible risk assessment method for complex mixtures
9 of dioxin-like PHAHs.
10 The WHO working group acknowledged that there are a number of other classes of chemicals
11 that bind and activate the Ah receptor. The chemicals include, but are not limited to,
1 2 polyhalogenated naphthalenes, diphenyl ethers, fluorenes, biphenyl methanes, quaterphenyls, and
1 3 others. In addition, a number of brominated and chloro/bromo-substituted dioxin analogues of
14 the PCDDs and PCDFs have been demonstrated to cause dioxin-like effects. The WHO working
1 5 group concluded that "at present, insufficient environmental and toxicological data are available
16 to establish a TEF value for any of the above compounds" (van den Berg et al., 1998).
1 7 The development and refinement of the TEF methodology can be thought of as an iterative
18 process. As we accumulate more data on the biological effects of dioxin-like chemicals and a
19 better knowledge base of their mode of action, the TEF methodology is improved. The latest
20 evaluation of the TEF methodology for use in human health risk assessment by the WHO
21 working group provides the most accurate assessment of the TEFs for dioxin-like chemicals.
22 The WHO98 TEF values are recommended for use in human health risk assessment.
23 In January 1998, EPA and the U.S. Fish and Wildlife Service sponsored a meeting entitled
24 "Workshop on the Application of 2,3,7,8-TCDD Toxicity Equivalency Factors to Fish and
25 Wildlife." The major objective of the workshop was to address uncertainties associated with the
26 use of the TEF methodology in ecological risk assessment. Twenty-one experts from academia,
27 government, industry, and environmental groups participated in the workshop. The consensus of
28 the workgroup was that while there are uncertainties in the TEF methodology, the use of this
29 method decreases the overall uncertainty in the risk assessment process. However, quantifying
30 the decrease in the uncertainty of a risk assessment using the TEF methodology remains
31 ambiguous, as does the exact uncertainty in the TEF methodology itself (U.S. EPA, 2001).
32 This first section has outlined the process of assessing the relative potency of chemicals and
33 the assignment of a consensus TEF value. There are still many questions on the use of the TEF
34 method and the validity of some of the underlying assumptions. A detailed discussion and
35 review of the data supporting the development and use of the TEF method, as well as the data
36 relating to the issue of additivity, is included within the specific issues section that follows.
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1 9.2.4. Illustrative Examples of the Data Used for Deriving the TEF Values
2 The TEF scheme includes 17 PCDDs and PCDFs and 13 PCBs. However, in human tissue
3 samples and food products, only five of these congeners, TCDD, 1,2,3,7,8-PCDD, 1,2,3,6,7,8-
4 HxCDD, 2,3,4,7,8-PeCDF, and PCB 126, account for over 70% of the TEQ. There is
5 considerable data on the relative potency of these compounds in both in vitro and in vivo studies.
Q Table 9-3 provides a summary of the REPs from in vivo data available for the compounds that
7 account for approximately 80% of the TEQs in humans (see Part I, Volume 3, Section 4.2.). This
8 information was obtained from the WHO database used to derive TEF values for PCDDs,
9 PCDFs, and PCBs (Van den Berg et al., 1998). The WHO database contains duplicate
10 recordings of studies for several of the compounds. The data in Table 9-3 does not include the
11 duplicates. In addition, the WHO database also contains studies that used a single dose level of
1 2 the test chemical, and REP values were not estimated for these studies. For example, in the
13 WHO database for PCB 126, there are 144 in vivo endpoints. Of these 144, 50 do not have REP
14 values associated with the entry because the study used only a single dose level. In other cases,
1 5 for a given endpoint from a particular study, the REP value is presented as estimated by the
16 authors as well as by alternative analyses by members of the WHO workgroup. In total, there are
1 7 62 data sets that have dose-response relationships sufficient enough to estimate the relative
1 8 potency of PCB 126. These data sets examine enzyme induction, changes in organ and body
1 9 weights, immunotoxicity, developmental toxicity, thyroid hormones, renal and hepatic retinoids,
20 and tumor promotion. The WHO database for 1,2,3,7,8-PCDD contained studies examining
21 enzyme induction, changes in organ and body weights, hepatic porphyria, hepatic retinoids, and
22 tumor promotion. The WHO database for 2,3,4,7,8-PCDF contained studies examining enzyme
23 induction, changes in organ and body weights, immunotoxicity, developmental toxicity, thyroid
24 hormones, hepatic retinoids, hepatic porphyria, and tumor promotion. The data presented in
25 Table 9-3 for 1,2,3,6,7,8-HxCDD is from U.S. EPA (1989) because the WHO database contained
26 no new in vivo data for this compound. There are only three in vivo studies on the effects of
27 1,2,3,6,7,8-HxCDD, one of which is the NTP carcinogenicity study on a mixture of 31%
28 1,2,3,6,7,8-HxCDD and 67% l,2,3,7,8,9HxCDD (NTP, 1980).
29 The REPs for 1,2,3,7,8-PCDD in the in vivo studies vary by approximately a factor of five.
30 A TEF value was assigned to 1,2,3,7,8-PCDD based on the REP for tumor promotion which
31 ranged from 0.8-1.0. The REPs for 2,3,4,7,8-PCDF and PCB 126 have a greater variability, but
32 the assigned TEF values are similar to the means of the REP values. The mean±standard
33 deviation for all in vivo REP values for 2,3,4,7,8-PCDF is 0.4±0.7. If only subchronic studies
34 are examined, the mean±standard deviation of the REP values is 0.2±0.13. These REP values for
35 2,3,4,7,8-PCDF are similar to the TEF value of 0.5. The REPs for PCB 126 range over two
36 orders of magnitude with a mean for all in vivo responses of 0.2±0.2. The mean REP for
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1 subchronic studies examining PCB 126 is 0.13±0.13. The TEF for PCB 126 is 0.1, which is
2 slightly lower than the mean of the REP values. With the exception of 1,2,3,6,7,8-HxCDD, the
3 REPs are based on several studies from different laboratories examining different endpoints.
4
5 9.2.5. Variability in the REPs Across Endpoint, Species, Dosing Regimen and
6 Laboratories.
7 Using PCB 126 as an example, the variability of the REPs across endpoint, species,
8 laboratory and dosing regimen will be described. PCB 126 has the most in vivo studies
9 comparing the its relative potency to TCDD of all the chemicals in the WHO data base. Upon
10 examining this data base, it is apparent that within an endpoint there is considerable variability
11 (greater than an order of magnitude). For instance, the REPs for hepatic EROD induction in
1 2 mice following a single exposure to PCB 126 are 0.005, 0.012, 0.38 and 0.55. These studies use
1 3 similar dosing paradigms and time course for endpoint determinations so there is no clear reason
14 why these values should range over two orders of magnitude. In some cases, interlaboratory
1 5 variability appears to be a significant cause for variance in the estimates of the REPs. In order to
1 6 examine REPs across endpoints and control for interlaboratory variability, two studies were
1 7 examined. Hemming et al (1993) examined the REPs for tumor promotion, hepatic EROD
1 8 induction, and alterations in liver, thymus and body weights in rats compared to TCDD. In this
1 9 study, the REPs were 0.16, 0.3, 0.05, 0.07, and 0.1 for liver, thymus and body weight changes,
20 hepatic EROD induction and tumor promotion, respectively. While the range of these REPs is
21 0.05-0.3, the authors only provided point estimates of the REPs and no information was provided
22 on the variance of these values. Thus, it is impossible to determine if the REP values are
23 statistically different from one another. The study by Hemming et al (1993) is typical of the
24 literature estimating the REPs for dioxin-like chemicals in that no information on the variance of
25 these estimates are available. A recent study by DeVito et al (2000), demonstrated that the REPs
26 for PCB 126 for hepatic and dermal ethoxyresorufm-O-deethylase (EROD) activity, a marker for
27 CYP1A1 induction, and hepatic acetanilide 4-hydroxylase (ACOH) activity, a marker for
28 CYP1A2 induction, were equivalent. However in this study, the REP for pulmonary EROD
29 induction was an order of magnitude lower than the other endpoints.
30 The example described above suggests that the source of the variability in the REP values
31 remains uncertain. Most studies do not provide estimates of the variance of the REP values.
32 This decreases the ability to compare REP values across endpoints, species, dosing regimens and
33 laboratories. One of the few studies that did provide estimates of the variance around the REPs
34 examined only a single biochemical (ethoxyresorufm-O-deethylase activity ) endpoint in
35 different tissues and it is uncertain whether the results from this study are applicable to other
36 endpoints (DeVito et al., 2000).
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1
2 9.2.6. Critical Considerations in the Application of the TEF Methodology.
3 There are a number of underlying assumptions used in the development of the TEF
4 methodology and these assumptions have significant implications in the application of this
5 method. Some of these assumptions and there implications are listed below.
6
7 «D The Ah receptor mediates most if not all of the biologic and toxic effects of
8 TCDD.
9 *D The TEF methodology attempts to estimate the potential TCDD-like effects of a
10 chemical. Toxic effects of a chemical induced through mechanisms other than the
11 Ah receptor are not accounted for in this method.
12 «D Even though not all the molecular mechanisms following Ah receptor binding are
1 3 understood, the TEF methodology is still valid.
14 «D The chemical binds to Ah receptor and is a full agonist for endpoints of concern.
15 «D The relative potency of a chemical is equivalent for all endpoints of concern.
16 «D The relative potency of a chemical is equivalent for all exposure scenarios.
17 «D The relative potency of a chemical in rodents is predictive of its relative potency
1 8 in humans.
19 «D The toxicity of a mixture of dioxins is dose additive based on the relative
20 potencies or TEFs of the individual components .
21 «D The TEF methodology ignores the interactions of dioxins with other chemicals
22 present.
23 «D Naturally occurring chemicals with short half-lives and varying degrees of affinity
24 to the Ah receptor and intrinsic activity do not interfere with the predictions of
25 dioxin equivalents in the mixture.
26 «D TEFs are not calculated. They are assigned based on the following criteria:
27 - Greater weight is given to REPs from repeat-dose in vivo experiments
28 (chronic > subchronic > subacute > acute).
29 - Dioxin-specific or Ah receptor mediated effects were given also higher priority.
30 - A rounding-off procedure (nearest 1 or 5) was also employed for final TEF
31 selection (Table 9-2). It should be noted that the TEF was rounded up or down
32 depending on the compound, the data, and scientific judgment.
33
34 Many of the assumptions are necessary because of a lack of data. For example, TCDD
35 and a mixture of hexachlorinated dioxins are the only congeners which have been tested for
36 carcinogenicity. Thus, in order to estimate the carcinogenic potency of a mixture of dioxins, it
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1 must be assumed that the REPs for non-cancer endpoints approximate those for cancer. While
2 these assumptions lead to uncertainties, there is a consensus that the TEF methodology decreases
3 the overall uncertainty of a risk assessment (USEPA, 2001). More detailed discussion of these
4 points is presented in the following section.
5
6 9.3. SPECIFIC ISSUES
7 9.3.1. Ah Receptor and Toxicity Factors
8 Issues relating to the role of the Ah receptor as the common mediator of toxicity of
9 dioxin-like compounds and the cross-species comparability of AhR structure and function
10 frequently arise when the TEF approach is discussed. Recent data relating to each of these issues
11 are discussed below.
12
1 3 9.3.2. The Role of the AhR in the Toxicity of Dioxin-Like Compounds
14 The general basis for the TEF scheme is the observation that the AhR mediates most if
1 5 not all of the dioxin-like biological and toxic effects induced by compounds included in the TEF
1 6 scheme (Safe, 1990; Okey et al., 1994; Birnbaum, 1994; Hankinson, 1995). Binding to the
1 7 receptor is necessary, but not sufficient, to generate the wide variety of toxic effects caused by
1 8 dioxin-like halogenated aromatic hydrocarbons (Sewall and Lucier, 1995; De Vito and
19 Birnbaum, 1995) (for additional review references, see Part II, Chapter 2). There are several
20 lines of evidence that the Ah receptor is important in the toxicity of the dioxin-like compounds.
21 A brief discussion of this evidence shall be presented in the following section. Those wishing a
22 more detailed discussion of this issue are referred to Part II, Chapter 2.
23 Initial studies on the toxicity of PAHs demonstrated that the sensitivity to these
24 compounds varied by strain of mice and segregated with the Ah locus. The Ah locus was then
25 found to encode a receptor designated as the aryl hydrocarbon receptor or AhR. Sensitive strains
26 of mice expressed receptors with high binding affinity for these compounds, while the resistant
27 mice expressed a receptor that poorly bound the PAHs. One of the best ligands for this receptor
28 was TCDD. Shortly after the discovery of the AhR, structure-activity relationship studies
29 demonstrated a concordance between binding affinity to the Ah receptor and toxic potency in
30 vivo in mice. Further support of the role of the AhR in the toxicity of dioxin-like compounds
31 was demonstrated following the development of AhR knockout mice (Fernandez-Salguero et al.,
32 1995; Schmidt et al., 1996; Mimura et al., 1997; Lahvis and Bradfield, 1998). The Ah receptor
33 knockout mice are a strain of mice in which the Ah receptor has been genetically altered so that
34 the receptor is not expressed or "knocked-out" in these mice. Administration of TCDD at doses
35 more than 10 times the LD50 of wild-type mice has not produced any significant dioxin-like
36 effects, either biochemical or lexicological, in the AhR knockout mice (Fernandez-Salguero et
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1 al., 1996; Peters et al., 1999). These data as a whole demonstrate that the binding to the AhR is
2 the initial step in the toxicity of dioxin-like compounds.
3
4 9.3.3. Species Comparison of the AhR
5 Although binding to the AhR initiates a cascade of molecular and cellular events leading
Q to toxicity, the exact mechanism of action of dioxin-like compounds is not completely
7 understood. One difficulty in determining the mechanism is our limited understanding of the
8 normal physiological role of the AhR, which would aid in understanding of potential species
9 differences in response to dioxin-like chemicals. The available data indicate that the AhR does
10 play an important role in normal processes and that there are a number of similarities in the
11 action of the AhR between species. These data strengthen our confidence in species
1 2 extrapolations with these chemicals.
13 There are several lines of evidence suggesting that the AhR is an important factor in
14 developmental and homoeostatic processes. The AhR is a ligand-activated transcription factor
1 5 that is a member of the basic-helix-loop-helix-Per-Arnt-Sim (bHLH-PAS) superfamily. The
1 6 bHLH-PAS superfamily consists of a growing list of at least 32 proteins found in diverse
1 7 organisms such as Drosophila, C. elegans, and humans. Many of these proteins are transcription
1 8 factors that require either hetero- or homodimerization for functionality. These proteins regulate
1 9 circadian rhythms (per and clock) and steroid receptor signaling (SRC-1, TIF2, RAC3) and are
20 involved in sensing oxygen tension (Hif-1, EPAS-1/HLF) (Hahn, 1998). The AhR is also a
21 highly conserved protein that is present in all vertebrate classes examined, including modern
22 representatives of early vertebrates such as cartilaginous and jawless fish (Hahn, 1998). In
23 addition, an AhR homologue has been identified in C. elegans (Powell-Coffman et al., 1998).
24 The classification of the AhR as part of the bHLH-PAS superfamily and its evolutionary
25 conservation imply that this protein may play an important role in normal physiological function.
26 It has been proposed that understanding the function of the bHLH-PAS family of proteins and the
27 phylogenetic evolution of the AhR may lead to an understanding of the role of this protein in
28 normal processes (Hahn, 1998).
29 The process of development is a complex phenomenon that involves the specific
30 expression of numerous genes in a spatial and temporal pattern. The importance of a particular
31 gene in developmental biology is often inferred by its spatial and temporal expression during
32 development. The AhR is expressed in a tissue, cell, and temporal pattern during development
33 (Abbott et al., 1995). It is highly expressed in the neural epithelium, which forms the neural crest
34 (Abbott et al., 1995). The expression of the AhR at critical periods during development suggests
35 that this protein has important physiological functions.
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1 Further evidence of the role of the AhR in developmental processes is provided by the
2 development and study of AhR knockout mice. Three strains of AhR knockout mice have been
3 produced using a targeted disruption of the AhR locus (Fernandez-Salguero et al., 1995; Schmidt
4 et al., 1996; Mimura et al., 1998; Lahvis and Bradfield, 1998). The AhR -/- mice develop
5 numerous lesions with age (Fernandez-Salguero et al., 1995). Mortality begins to increase at
Q about 20 weeks, and by 13 months almost half of the mice either die or become moribund.
7 Cardiovascular alterations consisting of cardiomyopathy with hypertrophy and focal fibrosis,
8 hepatic vascular hypertrophy and mild fibrosis, gastric hyperplasia, T-cell deficiency in the
9 spleen, and dermal lesions are apparent in these mice and the incidence and severity increases
10 with age (Fernandez-Salguero et al., 1995). Although male and female AhR -/- mice are fertile,
11 the females have difficulty maintaining conceptus during pregnancy, surviving pregnancy and
1 2 lactation, and rearing pups to weaning (Abbott et al., 1999). It should be noted that the AhR
13 knockout mice are resistant to the toxic effects of TCDD.
14 Comparisons between the AhR of experimental animals (primarily rodents) and the
1 5 human AhR have revealed a number of similarities in terms of ligand and DNA binding
1 6 characteristics and biochemical functions. Tissue-specific patterns of expression of AhR mRNA
1 7 are similar in rats, mice, and humans, with highest levels generally detected in lung, liver,
1 8 placenta, and thymus (Dolwick et al., 1993; Dohr et al., 1996). Nuclear AhR complexes isolated
19 from human and mouse hepatoma cells (Hep G2 and Hepa Iclc7, respectively) have similar
20 molecular weights. Although the human AhR appears more resistant to proteolytic digestion by
21 trypsin or chymotrypsin, the major breakdown products were similar between the two species,
22 and photolabeling analysis with TCDD suggested common features in the ligand binding portion
23 of the receptors (Wang et al., 1992).
24 Limited analysis has suggested the average human AhR exhibits a lower binding affinity
25 for various HAHs than "responsive" rodent strains. However, similar to a variety of
26 experimental animals, human populations demonstrate a wide variability in AhR binding affinity
27 (Micka et al., 1997). Recent determination of AhR binding affinity (Kd) toward TCDD in 86
28 human placenta samples showed a greater than twenty-fold range in the binding affinity, and this
29 range encompasses binding affinities similar to those observed in sensitive and resistant mice
30 (Okey et al., 1997). Whereas the concentration of various ligands required to activate a human
31 AhR reporter gene construct was higher than required with rodent cell cultures, the actual rank
32 order of binding affinities was in agreement (Rowlands and Gustafsson, 1995). Although
33 comparisons have been made of the TCDD binding affinity to the AhR of different species,
34 caution should be used when attempting to predict species sensitivity to TCDD and related
35 compounds. For mice, the sensitivity to the biochemical and toxicological effects of TCDD and
36 related compounds is associated with the relative binding affinity of TCDD to the AhR in the
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1 different strains (Birnbaum et al., 1990; Poland and Glover, 1990). However, the relative
2 binding affinity of TCDD to the AhR across species does not aid in the understanding of
3 interspecies differences in the response or sensitivity to TCDD (DeVito and Birnbaum, 1995).
4 The human AhR also demonstrates other slight differences when compared to the AhR
5 from experimental animal species. The molecular mass of the human AhR ligand-binding
6 subunit appears to be greater than the AhR subunit from certain TCDD "responsive" mouse
7 strains but similar to the receptor molecular mass for rats (Poland and Glover, 1987). Currently
8 there has been no association established between differences in the molecular mass of the AhR
9 and sensitivity to a particular biochemical or toxicological response across species (Okey et al.,
10 1994). The non-liganded human AhR appears thermally more stable compared to AhR from
11 various rodent species, whereas the reverse situation exists with the liganded human AhR (Nakai
12 and Bunce, 1995). Transformation of the ligand-bound human AhR receptor (isolated from
1 3 colon adenocarcinoma cells) to the DNA-binding state, unlike rodent hepatic AhR, is
14 temperature dependent (Harper et al., 1992). The importance of these species differences in
1 5 transformation and stability of the AhR in the species sensitivity to TCDD remain uncertain.
1 6 However, in critical areas of receptor function such as ligand recognition, transformation, and
1 7 interaction with genomic response elements, the human AhR is comparable to the AhR isolated
1 8 from experimental animals.
1 9 Ligand-bound or transformed AhR from a variety of mammalian species, including
20 humans, bind to a specific DNA sequence or "dioxin response element" with similar affinities
21 (Bank et al., 1992; Swanson and Bradfield, 1993). The bHLH structure of receptor proteins such
22 as AhR ensures appropriate contact and binding with DNA recognition sites. Amino acid
23 sequence analysis between mouse and human AhR shows an overall sequence homology of
24 72.5%, whereas the bHLH domain shows 100% amino acid concordance (Fujii-Kuriyama et al.,
25 1995). In comparison, the deduced amino acid composition of the AhR from killifish was 78%-
26 80%, similar to the amino acid sequence of rodent and human AhR (Hahn and Karchner, 1995).
27 These studies demonstrate a concordance between the structure of the receptor and its function
28 across species.
29 The majority of scientific evidence to date supports the theory that binding to the AhR is
30 a necessary first step prior to dioxin-like compounds eliciting a response, as discussed in Part n,
31 Chapter 2. Current research has identified the AhR in a variety of human tissues and cells that
32 appear to function in a similar manner to the AhR from experimental animals, including fish,
33 birds, and mammals. When multiple endpoints are compared across several species, there exists
34 a high degree of homogeneity in response and sensitivity to TCDD and related compounds
35 (DeVito et al., 1995). Therefore, these data provide adequate support for the development of the
36 TEF methodology. However, these data also reflect the true complexity of intra- and interspecies
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1 comparisons of biochemical and toxicological properties. Continued research into the variety of
2 additional cytoplasmic and nuclear proteins capable of interacting with the AhR signaling
3 pathway will ultimately lead to a better understanding of the observed species and strain
4 variability in the response to dioxin-like chemicals and may be useful in further refining TEFs.
5
6 9.3.4. Mode of Action and Implications for the TEF Methodology
7 Many of the toxic effects of dioxins are mediated by disruption of normal growth and
8 differentiation processes. For example, TCDD alone is capable of producing cancer in
9 experimental animals. However, its genotoxicity is limited. From an operational point of view,
10 TCDD is a tumor promoter (See Part n, Chapter 6). Tumor promoters act by disrupting the
11 natural balance between cell replication and cell death. Similarly, many of the non-cancer
1 2 effects, such as immunotoxicity and developmental toxicities, are due to TCDD-induced
13 alterations in cell growth and differentiation. While these events are initiated by the activation of
14 the Ah receptor, the exact molecular and cellular alterations beyond receptor binding remain
1 5 uncertain. One criticism of the TEF methodology is that the exact molecular mechanisms for the
1 6 toxic effects of these chemicals is uncertain and thus one cannot apply this method to mixtures
1 7 with certainty. The uncertainties in understanding the exact molecular mechanism of dioxin
1 8 action is not unique and does not detract significantly from the utility of the TEF methodology.
19 The exact molecular mechanisms of the biochemical and physiological effects of estrogens are
20 also uncertain. This does not decrease our confidence that if a chemical binds to the estrogen
21 receptor and induces uterine growth in vivo that the chemical is estrogenic and that it can be
22 useful to describe its potency relative to estradiol. Similarly, if a chemical binds to the Ah
23 receptor and induces dioxin-like effects, we can classify the chemical as dioxin-like and describe
24 its relative potency to TCDD without understanding every molecular event leading to the
25 biological effect. For many of the chemicals assigned TEF values, there are in vitro Ah receptor
26 binding data and a number of in vivo studies estimating the REP of these chemicals for toxic and
27 biochemical effects.
28
29 9.3.5. Ah Receptor Ligands
30 A wide variety of structurally diverse anthropogenic and natural chemicals are capable of
31 interacting with the AhR. These chemicals also have a broad range of potencies at inducing
32 dioxin-like effects in experimental systems. One of the major differences between the
33 anthropogenic chemicals included in the TEF methodology and the natural AhR ligands is their
34 pharmacokinetics. The anthropogenic chemicals included in the TEF methodology are persistent
35 and bioaccumulate in wildlife and humans. In contrast, most if not all of the natural AhR ligands
36 are rapidly metabolized and eliminated from biological systems. The following section will
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1 examine the differences between the chemicals included in the TEF methodology and remaining
2 AhR ligands not included in this approach.
3
4 9.3.5.1. Industrial/Synthetic AhR Ligands
5 The synthetic compounds that bind to AhR include a number of different classes of
6 chemicals, most notably the PCDDs, PCDFs, and PCBs. Other synthetic AhR ligands include
7 industrial chemicals (polybrominated biphenyls, polychlorinated napthalenes, chlorinated
8 paraffins, etc.), pesticides (hexachlorobenzene), and contaminants (polybrominated dioxins,
9 dibenzofurans, and napthalenes) associated with various manufacturing, production, combustion,
10 and waste disposal processes. In addition, pyrolysis of organic material can produce a number of
11 non-halogenated polycyclic aromatic hydrocarbons (PAHs) with moderate to high affinity for
1 2 AhR (Poland and Knudson, 1982; Nebert, 1989; Chaloupka et al., 1993).
1 3 Not all of the anthropogenic sources of dioxin-like compounds are included in the TEF
14 methodology. Many of these chemicals, such as hexachlorobenzene and the brominated diphenyl
1 5 ethers, are only weakly dioxin-like and have significant toxicological effects that are not
16 mediated by the AhR. For these chemicals, it is not clear that adding them to the TEF
1 7 methodology would decrease the uncertainty in the risk assessment process. For other classes of
1 8 chemicals, such as the chlorinated napthalenes, environmental concentrations and human
1 9 exposures are largely uncertain.
20 The PAHs are one class of anthropogenic chemicals not included in the TEF scheme
21 despite evidence for AhR binding. The PAHs are not included in the TEF methodology because
22 of their short half-lives and relatively weak AhR activity. In addition, the role of the Ah receptor
23 in the toxicity of the PAHs is uncertain. For example, both benzo[a]pyrene and chrysene induce
24 CYP1A1 activity through an AhR-mediated mechanism (Silkworm et al., 1995). However,
25 while the Ah receptor also plays a role in the immune suppressive effects of benzo[a]pyrene it
26 does not appear to be involved in the immune suppression induced by chrysene (Silkworm et al.,
27 1995). Furthermore, PAHs are DNA reactive and mutagenic and these mechanisms play a large
28 role in both the carcinogenicity and immunotoxicity of the PAHs (Ross and Nesnow, 1999). In
29 contrast, TCDD and other dioxin-like compounds are not DNA reactive. While there are PAHs
30 that bind to the AhR, the role of AhR or other competing pathways in the toxicity of these
31 compounds has not been clearly defined.
32 Brominated dioxins, dibenzofurans, biphenyls, and napthalenes also induce dioxin-like
33 effects in experimental animals (Miller and Birnbaum, 1986; Zacherewski et al., 1988;
34 Birnbaum et al., 1991; Hornung et al., 1996; DeVito et al., 1997; Weber and Greim, 1997). The
35 brominated dioxins and dibenzofurans may be more or less potent than their chlorinated
36 orthologues, depending on the congener (Birnbaum et al., 1991; DeVito et al., 1997). The
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1 sources of the brominated dioxin-like compounds are not well characterized. Some of the
2 chemicals, such as the brominated biphenyls and their contaminants the brominated
3 naphthalenes, were synthesized and sold as commercial flame retardants. The manufacture and
4 use of polybrominated biphenyls has been prohibited. Brominated dibenzofurans are produced
5 as byproducts of synthesis and pyrolysis of some brominated flame retardants. There is some
6 evidence of human exposure to brominated dioxins and dibenzofurans from extruder operators
7 (Ott and Zober, 1996). Polybrominated, polychlorinated, and mixed bromo- and chloro- dioxins
8 and dibenzofurans have been found in soot from textile processing plants (Sedlak et al., 1998).
9 Although these chemicals have been found in humans, these studies are limited to a small
10 population and exposure to the general population remains undetermined. Future examinations
11 of the TEF methodology should include a more detailed discussion of the brominated dioxins and
12 dibenzofurans.
13
14 9.3.5.2. Naturally Occurring AhR Ligands
1 5 The evolutionary conservation of AhR and its biological function following activation by
1 6 dioxin-like compounds have led to the hypothesis that there must be an endogenous or
1 7 physiological ligand(s) for this receptor. Presently, the endogenous ligand remains
18 undetermined. However, efforts to discover the natural ligand have led to the discovery of a
1 9 number of naturally occurring AhR ligands. A number of naturally occurring chemicals present
20 in the diet are capable of binding to AhR and inducing some dioxin-like effects in experimental
21 animals (Bradfield and Bjeldanes, 1984; 1987) and humans (Michnovicz and Bradlow, 1991;
22 Sinha et al., 1994). The question of how the interaction of these chemicals relates to the toxicity
23 of those chemicals designated as dioxin-like has become the subject of much debate.
24 One class of naturally occurring chemicals that activate the AhR is the indole derivatives.
25 Indole derivatives, naturally present in a variety of cruciferous vegetables, are capable of
26 modulating the carcinogenicity of PAHs (Wattenberg and Loub, 1978). Indole-3-carbinol (I-3-C)
27 and 3,3'-diindolylmethane (DEVI) are major secondary metabolites found in cruciferous
28 vegetables and induce both phase I and II metabolic enzymes (CYP1 A-dependent glutathione and
29 glucuronyl transferases, oxidoreductases) in experimental animals (Bradfield and Bjeldanes,
30 1984, 1987), human cell lines (Bjeldanes et al., 1991; Kleman et al., 1994), and humans
31 (Michnovich and Bradlow, 1990, 1991). Although both compounds induce CYP450 enzymes
32 under AhR transcript!onal control, they exhibit relatively low binding affinity for the Ah receptor
33 (Gillner et al., 1985). Further investigation revealed that I-3-C is relatively unstable in the acidic
34 environment of the digestive tract and readily forms DEVI. In turn, DEVI can participate in acid
35 condensation reactions to form indolocarbazoles (ICZs) (Chen et al., 1995). ICZs are also
36 produced by bacterial metabolism of the common dietary amino acid tryptophan. ICZs, in
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1 particular indolo[3,2b]carbazole, exhibit high binding affinity for the rodent AhR, approximately
2 equipotent to 2,3,7,8-tetrachlorodibenzofuran, and can induce CYP1 Al activity in cultured cells
3 (Bjeldanes et al., 1991; Gillner et al., 1993; Chen et al., 1995). ICZ and a methylated derivative,
4 5,1 l-dimethylindolo[3,2b]carbazole (MICZ), are also capable of binding to and activating the
5 AhR in human hepatoma cells (HepG2) (Kleman et al., 1994). With considerably lower efficacy,
6 I-3-C and DEVI can partially displace TCDD from the AhR from human breast cancer cells
7 (T47D) (Chen et al., 1996). These results would suggest that this group of compounds may
8 represent a class of physiologically active AhR ligands derived from natural sources, which could
9 either mimic dioxin-like compounds in their action or act as competitors for AhR binding.
10 In addition to the plant-derived indoles, experimental animals consuming thermally
11 treated meat protein as well as humans fed cooked meat can exhibit induced CYP1A2 activity
1 2 (Degawa et al., 1989). High-temperature cooking (250°C, 22 minutes) of ground beef resulted in
1 3 the formation of a number of heterocyclic aromatic amines (HAAs) in part-per-billion levels,
14 which were thought to be responsible for the observed CYP1A2 induction in human volunteers
1 5 (Sinha et al., 1994). Mechanistic analysis of one particular HAA, 2-amino-3,8-
1 6 dimethylimidazo[4,5-f]quinoxaline (MelQx), has shown that it is capable of both interacting with
1 7 the AhR and inducing CYP1A1/A2 activity in rats (Kleman and Gustafsson, 1996). These data
18 should be viewed cautiously because recent data indicate that CYP1A2 can be induced through
19 non-AhR mechanisms (Ryu et al., 1996). Because there are multiple pathways to induce
20 CYP1A2, the increase in CYP1A2 activity following exposure to complex mixtures, such as
21 cooked meat, does not necessarily indicate the presence of dioxin-like compounds.
22 Other diet-derived chemicals that can interact with the AhR include oxidized essential
23 amino acids. UV-oxidized tryptophan is capable of inducing CYP1A1 activity in mouse
24 hepatoma cells through an AhR-dependent mechanism (Sindhu et al., 1996). Rats exposed to
25 UV-oxidized tryptophan in vivo also exhibited induction of hepatic and pulmonary CYP1A1
26 activity. Both in vitro and in vivo enzyme induction were transient, with the oxidized tryptophan
27 possibly being metabolized by CYP1A1 (Sindhu et al., 1996). Tryptanthrins, biosynthetic
28 compounds produced from the metabolism of tryptophan and anthranilic acid by yeast commonly
29 found in food, are agonists for the rat AhR (Schrenk et al., 1997). Various tryptanthrins also
30 induce CYP1 Al-related enzyme activity in mouse hepatoma cells with the approximate efficacy
31 of indolo[3,2b]carbazole.
32 Recent studies have demonstrated that physiological chemicals can bind to the AhR.
33 Bilirubin was recently found to transform the AhR from mouse hepatoma cells into its DNA-
34 binding state, resulting in CYP1A1 induction. Hemin and biliverdin can also be metabolically
35 converted to bilirubin, resulting in AhR-dependent gene activation (Sinai and Bend, 1997).
36 Despite these results, there is no clear evidence that these are the physiological ligands for the
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1 AhR, nor is there evidence that these compounds can modulate the activity of dioxin-like
2 compounds or lead to dioxin-like toxic effects in humans or animals.
3
4 9.3.5.3. Industrial vs. Natural AhR Ligands
5 There are a number of structurally diverse chemicals that bind to the Ah receptor. Some
6 of these chemicals are industrial chemicals produced intentionally (PCBs, PBBs, etc.). Others
7 are by-products of industrial processes (PCDDs and PCDFs). There are also a number of
8 "natural" AhR ligands that are either plant derived (i.e. I-3-C) or are synthesized endogenously,
9 such as bilirubin. It has been postulated that the natural ligands could be the major contributors
10 to the daily dose of TEQs, because of their higher estimated intakes (Safe, 1995). The natural
11 ligands tend to have short half-lives and do not accumulate. The PCDDs/PCDFs and PCBs
1 2 included in the TEF methodology clearly bioaccumulate. If contributions to the total TEQ are
13 estimated on steady-state body burdens of these chemicals instead of daily intake, then TCDD
14 and other PCDDs/PCDFs and PCBs contribute more than 90% of the total TEQ compared to the
1 5 natural ligands (DeVito and Birnbaum, 1996). The difference in the results of these analyses
16 demonstrates our uncertainty of the relative potencies, exposures and dose metrics used in the
1 7 comparisons of the synthetic dioxins vs. the natural AhR ligands.
18 When a comparison is attempted between the perceived relative risk from natural vs.
1 9 anthropogenic AhR agonists, a number of factors should be taken into consideration. The
20 potency of AhR ligands depends on several factors, including AhR binding affinity and
21 pharmacokinetic properties. When estimating the relative potency of a chemical compared to
22 TCDD, the larger the difference in pharmacokinetic properties, the greater the effect that study
23 design has on the relative potency. Initial studies comparing the potency of
24 indolo[3,2b]carbazole to TCDD demonstrate the importance of the pharmacokinetic differences
25 between these chemicals. In Hepa-1 cells exposed for 4 hours, the relative potency for induction
26 of CYP1A1 mRNA of indolo[3,2b]carbazole compared to TCDD is 0.1 (Chen et al., 1995). If
27 the relative potency is determined after 24 hours of exposure, the potency of
28 indolo[3,2b]carbazole drops 1,000-fold to 0.0001 (Chen et al., 1995). In addition, the dioxin-like
29 effects of low doses of indolo[3,2b]carbazole in Hepa-1 cells are transient. Similar transient
30 effects of other dietary-derived AhR ligands have also been reported (Xu and Bresnick, 1990;
31 Berghard et al., 1992; Riddick et al., 1994). These data demonstrate that the relative potencies of
32 these chemicals compared to TCDD are dependent upon the pharmacokinetic properties of the
33 chemicals and the experimental design used in the comparisons. In addition, these data also
34 demonstrate that for rapidly metabolizable AhR ligands, the effects are transitory and not
35 persistent like TCDD. It appears that the transient nature of the effect is due to the transient
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1 concentrations of these chemicals in these experimental systems. These data also demonstrate
2 our uncertainty of the relative potency of the dietary-derived AhR ligands.
3 The chemicals included in the TEF scheme are those that not only bind to AhR but also
4 bioaccumulate and have long biological half-lives in humans, typically on the order of years. In
5 contrast, the pharmacokinetics of the endogenous or natural group are not well studied, but these
6 chemicals tend to be short-lived, with half-lives on the order of minutes to hours. Although both
7 PAHs and the halogenated aromatics bind to AhR and induce cytochrome P450-related enzyme
8 activities, only the latter group produces the additional dioxin-like spectrum of toxicological
9 responses. These toxicities are thought to be due to the persistent exposures attributable to the
10 long half-lives of these chemicals (Riddick et al., 1994).
11 One caution when comparing the relative exposures to dietary AhR ligands and the
1 2 anthropogenic AhR ligands is that few in vivo studies have examined the relative potency of the
1 3 dietary or natural AhR ligands for toxic responses. Using the criteria of the WHO workgroup for
14 PCDDs, PCDFs, and PCBs results in only two in vivo studies of I-3-C which compared the REP
1 5 to TCDD (Wilker et al., 1996; Bjeldanes et al., 1991). In an in vivo study of the developmental
16 effects of I-3-C, in utero exposure of rats to I-3-C resulted in a number of reproduction-related
1 7 abnormalities in male offspring, only some of which resemble those induced by TCDD (Wilker
18 et al., 1996). Because of the different spectrum of effects of I-3-C compared to TCDD in these
1 9 developmental studies, it is likely that mechanisms other than AhR activation are involved in
20 these effects. I-3-C and some of its acid catalyzed oligomerization products alter androgen and
21 estrogen metabolism (Wilson et al., 1999; Telang et al., 1997), which may contribute to the
22 developmental effects of I-3-C. While a number of in vitro studies have demonstrated dioxin-
23 like enzyme induction of the indole derivatives, in order to have REP values that adequately
24 describe the in vivo potency of these chemicals, future in vivo studies examining toxic responses
25 are required.
26
27 9.3.5.4. Limitations in Comparing the Quantitative Interactions between Industrial/Synthetic
2 8 and Natural AhR Ligands
29 Although there are limited data on the in vivo biochemical and toxicological effects of
30 these ligands, the effects of mixtures of anthropogenic and natural AhR ligands is altogether
31 lacking. There is one study examining the interactions of I-3-C and DEVI on the effects of
32 TCDD in cell culture systems. However, it is uncertain how to extrapolate these in vitro
33 concentrations to present human in vivo exposures. The limited data available do not adequately
34 address the interactions between these chemicals. Future in vivo studies are required in order to
35 better understand the potential interactions between these classes of AhR ligands.
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1 Another limitation in comparing the natural AhR ligands to the dioxins is the multiple
2 effects induced by the natural AhR ligands. In vivo and in vitro studies of I-3-C indicate that it
3 induces a number of biochemical alterations that are not mediated through the AhR (Broadbent
4 and Broadbent, 1998). The activation of these additional pathways creates difficulties in making
5 direct comparisons with TCDD and related chemicals. Similarly, the PAHs also have non-AhR-
6 mediated biochemical and toxicological effects that also complicate direct comparisons with
7 TCDD and related dioxins. For example, co-exposure to TCDD and PAHs have demonstrated
8 both synergistic and antagonistic interactions in mice depending upon the endpoint examined
9 (Silkworm et al., 1993).
10 Presently, there are several limitations in our understanding of the importance of naturally
11 occurring dioxin-like compounds vs. the dioxin-like compounds included in the TEF
1 2 methodology. First is the limited data available on the dioxin-like toxicities of the natural
13 ligands. In addition, there is a lack of data on the interactions between these classes of
14 chemicals. Few if any mixtures of natural AhR ligands and PCDDs or PCDFs examining a toxic
1 5 response have been published. Many of the natural AhR ligands have multiple mechanisms of
1 6 action that presently cannot be accounted for in the TEF methodology. For example, I-3-C has
1 7 anticarcinogenic properties in tumor promotion studies, and these effects may or may not be
18 mediated through AhR mechanisms (Manson et al., 1998). The lack of data and the role of non-
1 9 AhR mechanisms in the biological effects of these chemicals prohibit a definitive conclusion on
20 the role of natural vs. anthropogenic dioxins in human health risk assessment. Though it is
21 important to address these issues, the available data do not lend themselves to an appropriate
22 quantitative assessment of these issues.
23 One of the most significant differences between the industrial Ah receptor ligands (i.e.
24 dioxins) and the natural Ah receptor ligands is the persistence of the dioxins in biological
25 systems. Because of their long half-lives, dioxins provide a persistent activation of the Ah
26 receptor. In contrast, the natural ligands are rapidly metabolized and the activation of the Ah
27 receptor is short-lived. Determining the relative potency of the natural ligands compared to
28 TCDD is not necessarily a trivial matter. The relative potency of these chemicals is due to their
29 ability to bind and activate the Ah receptor and the persistence of this signal. Most of the studies
30 examining the relative potency of the natural ligands are based on in vitro or short-term in vivo
31 studies. The estimates of the relative potencies of these chemicals is greatly exaggerated in these
32 short-term assays because of the bioaccumulative nature of TCDD. Studies comparing the
33 relative potency of TCDD to TCDF have demonstrated that due to the differences in the half-
34 lives of TCDF and TCDD, short-term studies overestimate the relative potency of TCDF
35 compared to the relative potency observed in longer-term studies (DeVito and Birnbaum, 1995).
36 The relative potencies of the natural ligands would best be estimated following long term
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1 exposures. These data are unavailable and thus the estimates of the relative potencies of these
2 chemicals is unreliable.
3 Although Safe has suggested that exposure to natural AhR ligands is 100 times that of
4 TCDD and other dioxin-like compounds (Safe, 1995), the impact of the natural AhR ligands
5 remains uncertain. Epidemiological studies suggest that human exposures to TCDD and related
6 chemicals are associated with adverse effects, such as developmental impacts and cancer. In
7 many of these studies, the exposed populations have approximatley 100 times more TCDD
8 exposure than background populations (see Part II, Chapter 7). If the exposure to natural AhR
9 ligands is included in these comparisons, then the exposed populations should have
10 approximatley double the total TEQ exposures than the background population. It seems
11 unlikely that epidemiological studies could discriminate between such exposures. These data
1 2 suggest that the estimates of the contribution of the natural AhR ligands to the total TEQ
13 exposure are overestimated. In addition, regardless of the background human exposure to
14 "natural" AhR ligands, the margin of exposure to TCDD and related chemicals between the
1 5 background population and populations where effects are observed remains a concern.
16
17 9.4. TOTAL TEQ AND THE ADDITIVITY CONCEPT
1 8 The issue of the scientific defensibility of additivity in determining total TEQ has been
19 raised since the onset of the use of TEFs. Arguments regarding this approach include the
20 presence of competing agonists or antagonists in various complex mixtures from environmental
21 sources, interactions based on non-dioxin-like activities (inhibition or synergy), and the fact that
22 dose-response curves for various effects may not be parallel for all congeners assigned TEFs.
23 Although comparative pharmacokinetics have also been raised as an issue, this has generally
24 been accounted for by the heavier weight accorded to in vivo studies in the assignment of TEFs.
25 Despite these concerns, empirical data support the use of the additivity concept, recognizing the
26 imprecise nature of the TEFs per se. A substantial effort has been made to test the assumptions
27 of additivity and the ability of the TEF methodology to predict the effects of mixtures of dioxin-
28 like compounds. These efforts have focused on environmental, commercial, and laboratory-
29 derived mixtures. In addition, endpoints examined ranged from biochemical alterations, such as
30 enzyme induction, to toxic responses such as tumor promotion, teratogenicity, and
31 immunotoxicity. A brief summary of some of the more important work is given and discussed in
32 the following section.
33 The TEF methodology has been examined by testing mixtures of chemicals containing
34 dioxins and sometimes other chemicals. These mixtures have either been combined and
35 produced in the laboratory or were actual environmental samples. Researchers have also used
36 different approaches in estimating the TCDD equivalents of the mixtures. Some researchers
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1 have determined the REP of the components of the mixture in the same system in which the
2 mixture was tested and have used these REPs to estimate TCDD equivalents. These studies can
3 provide insight into the validity of the assumption of additivity of the TEF methodology. Other
4 researchers have used consensus TEF values to estimate the TCDD equivalents of the mixture. It
5 is not clear if these studies can be considered true tests of the additivity assumption. The
6 consensus TEF values have been described as conservative estimates of the relative potency of a
7 chemical in order to protect humans and wildlife. If the consensus TEF values are conservative
8 and protective, then they should overestimate the potency of mixtures tested in an experimental
9 system. In essence, using the consensus TEF values should generally over predict the potency of
10 a mixture (and therefore under predict the response) when compared to the equivalent
11 concentrations of TCDD in an experimental system. In the following discussion of the studies
1 2 examining the assumption of additivity, these differences in study design and their implications
13 for interpretation of the data must be considered.
14
15 9.4.1. Examination of Laboratory Mixtures of PCDDs and PCDFs
16 Bock and colleagues evaluated the TEF methodology in several systems using both
1 7 individual congeners as well as laboratory-derived mixtures (Lipp et al., 1992; Schrenk et al.,
18 1991, 1994). REPs or toxic equivalents or "TEs" (as designated by the authors) were determined
1 9 for 2,3,7,8-substituted PCDDs based on enzyme induction in human HepG2 cells, rat H4IIE
20 cells, and primary rat hepatocytes. Three laboratory-defined mixtures (Ml, M2, and M3) were
21 prepared and then examined in these same cell culture systems. TCDD contributed between
22 6%-8% of the TEQs for Ml and M2, but was not present in M3. In Ml, 1,2,3,4,6,7,8-HpCDD
23 contributes approximately 60% of the TEQ, and 1,2,3,7,8-PCDD and 1,2,3,4,7,8-HxCDD
24 contribute 10% each. In M2, 1,2,3,4,6,7,8-HpCDD contributes 45%, 1,2,3,7,8-PCDD and
25 1,2,3,4,7,8-HxCDD contribute 15% each; and TCDD, 1,2,3,6,7,8-HxCDD, and 1,2,3,7,8,9-
26 HxCDD contribute less than 10% to the total TEQ. The TEQs in M3 are derived predominately
27 from 1,2,3,4,7,8-HxCDD (50%); 1,2,3,4,7,8-HxCDD (20%); and 1,2,3,6,7,8-HxCDD (18%).
28 These mixtures also contain up to 49 chlorinated dibenzo-p-dioxins. The TCDD equivalents of
29 the mixtures were determined on the basis of the assumption of additivity using the TEF
30 methodology and the laboratory derived REPs or TEs as well as experimentally by comparing the
31 EC50s of the mixtures with that of TCDD. According to the authors, in all three systems the data
32 demonstrated that the components of the mixture act in an additive manner (Lipp, 1991; Schrenk
33 et al., 1991). For example, in the human HepG2 cells the EC50 of a mixture of 49 different
34 PCDDs was determined experimentally at 0.034 pg TEQ/plate, compared to the calculated or
35 predicted EC50 of 0.028 pg TEQ/plate. Interestingly, the TEF methodology accurately predicted
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1 the effects of M3, a mixture containing predominately OCDD, some heptaCDDs and hexaCDDs,
2 and no pentaCDDs or TCDD (Schrenck et al., 1991).
3 Bock and colleagues also tested a mixture of 49 PCDDs in a rat liver tumor promotion
4 study. The mixture, designated as M2, was the same mixture used in the cell culture studies
5 described above and TCDD contributed approximatley 8% of the TEQs of this mixture. In
6 theses studies, rats received an estimated 2-200 ng TCDD/kg/d or 200-20,000 ng mixture/kg/d.
7 The doses of the mixture were equivalent to the TCDD doses using a TE of the mixture of 0.01
8 based on enzyme induction in rat hepatocytes (Schrenk et al., 1991). A comparison of the
9 relative potency of the mixture was based on liver concentrations of the chemicals followed by
10 TEQ calculations using the I-TEFs (NATO/CCMS, 1988). According to the authors, in the low-
11 dose region (2-20 ng TCDD/kg/d) the I-TEFs accurately predict the enzyme-inducing activity of
1 2 the mixture but tend to overestimate the potency of the mixture at the higher doses (20-200
1 3 ng/kg/d). Also, according to the authors, the I-TEFs provide a rough estimate of the tumor-
14 promoting potency of the mixture but overestimate the mixture's potency . However, the authors
1 5 did not quantify or qualify the magnitude of the overestimation.
16 In the studies by Schrenk and colleagues, the TEQs were based on tissue dose, not
1 7 administered dose. Recent studies by DeVito et al. (1997b, 2000) indicate that the REP for
18 dioxin-like compounds can differ when determined based on administered or tissue dose. The
19 higher chlorinated dioxins tend to accumulate in hepatic tissue to a greater extent than does
20 TCDD, and their REPs tend to decrease when estimated based on tissue dose (DeVito et al.,
21 1997b, 2000). Because the I-TEFs are based on an administered dose, they may not predict the
22 response when the TEQ dose is expressed as liver concentration. If the TEQ dose in the data by
23 Schrenk et al. (1994) is compared on an administered dose, then the dose-response relationship
24 for increases in relative volume of preneoplastic ATPase-deficient hepatic foci (% of liver) are
25 comparable between TCDD and the mixture, indicating that additive TEFs provided an
26 approximation of the tumor-promoting ability of a complex mixture of PCDDs (Schrenck et al.,
27 1994). In addition, because TCDD contributed less than 10% of the total TEQ in these mixtures,
28 these data indicate that the assumption of additivity reasonably predicts the response of complex
29 mixtures of dioxins.
30 In responsive mouse strains, induction of cleft palate and hydronephrosis by TCDD
31 occurs at doses between 3 and 90 //g TCDD/kg (Nagao et al., 1993; Weber et al., 1985;
32 Birnbaum et al., 1985, 1987, 1991). Several groups have examined the assumption of additivity
33 using teratogenic effects of dioxins as an endpoint. Birnbaum and colleagues examined TEF
34 methodology using mouse teratogenicity as an endpoint (Weber et al., 1985; Birnbaum et al.,
35 1985, 1987, 1991). REPs were derived for 2,3,7,8-TCDF, 2,3,4,7,8-PeCDF, 1,2,3,7,8-PeCDF,
36 and 1,2,3,4,7,8-HxCDF (Weber et al., 1984, 1985; Birnbaum et al., 1987). Analysis of the dose-
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1 response for these chemicals, based on administered dose, demonstrated parallel slopes.
2 According to the authors, dose-response analysis of two mixtures containing either TCDD and
3 2,3,7,8-TCDF or 2,3,4,7,8-PeCDF and 1,2,3,4,7,8-HxCDF demonstrated strict additivity
4 (Birnbaum et al., 1987; Weber et al., 1985).
5 Nagao et al. (1993) also examined the TEF methodology using teratogenicity in mice as
6 an endpoint. Mice were exposed to a single dose of TCDD (5-90 /wg/kg) or a mixture of PCDDs,
7 or one of two different mixtures of PCDFs. The mixtures contained no detectable TCDD. The I-
8 TEFs were used to determine the TEQ of the mixtures. According to the authors, the I-TEFs
9 predicted the potency of the PCDD mixture, and the dose-response relationship was consistent
10 with the assumption of additivity. The I-TEFs overestimated the potency of the PCDF mixtures
11 by two- or fourfold. All three mixtures contained significant concentrations of non 2,3,7,8-
1 2 chloro-substituted PCDDs and PCDFs in addition to the dioxin-like compounds present. In the
1 3 studies by Birnbaum and colleagues (Weber et al., 1985; Birnbaum et al., 1985, 1987, 1991) and
14 Nagao et al. (1993) examining the assumption of additivity using teratogenicity as an endpoint,
1 5 the TEF methodology proves useful in estimating the effects of these mixtures.
16 Rozman and colleagues have examined the assumption of additivity of PCDDs in both
1 7 acute and subchronic studies. In acute studies, TCDD (20-60 //g/kg), 1,2,3,7,8-PCDD (100-300
1 8 /"g/kg), 1,2,3,4,7,8-HxCDD (700-1,400 //g/kg), and 1,2,3,4,6,7,8-HpCDD (3,000-8,000 //g/kg)
19 were administered to male rats, and REP values were determined for lethality. A mixture of all
20 four chemicals at equally potent concentrations was then prepared and dose-response studies
21 were performed with the mixture at doses that would produce 20%, 50%, and 80% mortality.
22 The mixture studies demonstrated strict additivity of these four chemicals for biochemical and
23 toxicological effects (Stahl et al., 1992; Weber et al, 1992a,b). Following the acute studies,
24 Viluksela et al. (1998a,b) prepared a mixture of these chemicals and estimated the TEQ based on
25 the REPs from the acute studies. A loading/maintenance dose regimen was used for 90 days and
26 the animals were followed for an additional 90 days. According to the authors, the assumption of
27 additivity predicted the response of the mixture for lethality, wasting, hemorrhage, and anemia,
28 as well as numerous biochemical alterations such as induction of hepatic EROD activity and
29 decreases in hepatic phosphenolpyruvate carboxykinase and hepatic tryptophan 2,3-dioxygenase
30 (Viluksela et al., 1997; 1998). Increases in serum tryptophan concentrations and decreases in
31 serum thyroxine concentrations were also predicted by the TEF methodology (Viluksila et al.,
32 1998a).
33 Rozman and colleagues followed up these initial studies by examining the assumption of
34 additivity of the effects of PCDDs as endocrine disrupters (Gao et al., 1999). Ovulation is a
35 complex physiological phenomenon that requires the coordinated interaction of numerous
36 endocrine hormones. In a rat model, ovulation can be inhibited by TCDD at doses between 2 to
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1 32 //g/kg (Gao et al., 1999). Dose-response analysis of TCCD, 1,2,3,7,8-PeCDD, and
2 1,2,3,4,7,8-HxCDD demonstrate that the slopes are parallel and the REPs are 0.2 and 0.04,
3 respectively. According to the authors, the dose response for a mixture of these chemicals, in
4 which the components were at equally potent concentrations, further demonstrated the response
5 additivity of mixtures of PCDDs and the predictive ability of the TEF methodology (Gao et al.,
6 1999).
7 The research on the interactions between mixtures of PCDDs and PCDFs has taken two
8 approaches. The first is to derive REP values in the same system in which the mixtures shall be
9 tested. These studies confirm that the assumption of additivity can predict the response of
10 mixtures of PCDDs and PCDFs. A second approach is to use the I-TEFs to assess the potency of
11 a mixture. These studies tend to indicate that the I-TEFs overestimate the potency of a mixture
1 2 by factors of two to four. Recently, the WHO TEFs have been described as "order of magnitude"
1 3 estimates of the potency of dioxin-like compounds. However, the studies using consensus TEFs
14 demonstrate that for mixtures of PCDDs and PCDFs, the TEF methodology will predict within a
1 5 half-order of magnitude or less (Schrenck et al., 1994; Nagao et al., 1993). In either case, the
16 TEF methodology accurately predicts the responses of experimentally defined mixtures of
1 7 PCDDs and PCDFs. Furthermore, several of these studies described the effects of mixtures
1 8 containing either no TCDD or with TCDD contributing less than 10% of the TEQ in the presence
1 9 of significant concentrations of non-2378- CDDs and CDFs. These studies strongly support the
20 use of the TEF methodology.
21
22 9.4.2. Examination of Commercial or Laboratory-Derived Mixtures of PCDDs, PCDFs,
23 and PCBs
24 Commercial mixtures of PCBs elicit a broad spectrum of biological and toxicological
25 responses in both experimental animals and humans. Some of the observed effects resemble
26 those induced by dioxin and furans (enzyme induction, immunotoxicity, teratogenicity, endocrine
27 alterations, etc.). Attempts to expand the TEF approach to risk assessment of PCBs have
28 investigated the ability of both commercial PCBs and individual congeners, selected on the basis
29 of structure-activity relationships, to induce dioxin-like effects and to interact with TCDD. One
30 of the first studies to examine the interactions of individual PCB congeners with TCDD used
31 mouse teratogencity as an endpoint (Birnbaum et al., 1985, 1987). A mono-ortho PCB
32 (2,3,4,5,3',4'-HxPCB or PCB 156) at doses of 20 mg/kg or higher (Birnbaum, 1991) induced
33 hydronephrosis and cleft palate in mice. When mice were co-exposed to PCB 156 and 3.0 //g
34 TCDD/kg the interactions resulted in strict additivity.
35 The interaction of TCDD with dioxin-like PCBs has been examined by van Birgelen et al.
36 (1994a,b) in subchronic rat feeding studies. Concentrations of PCB 126 in the diet between 7
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1 and 180 ppb induced several dioxin-like effects, including CYP1A1 induction, thymic atrophy,
2 liver enlargement, and decreases in hepatic retinol concentrations, body weight gains, and plasma
3 thyroxine concentrations. The REP for PCB 126 was estimated by the authors at between 0.01
4 and 0.1 (van Birgelen et al., 1994a). Co-exposure to PCB 126 and TCDD (0.4 or 5.0 ppb) in the
5 diet demonstrated additivity for all responses except induction of CYP1A2 and decreases in
Q hepatic retinol, where antagonism occurred at the highest doses of PCB 126 and TCDD tested.
7 These nonadditive interactions were not observed at more environmentally relevant exposures,
8 according to the author. In a similar study design, PCB 156 also induced dioxin-like effects with
9 a REP estimated between 0.00004 and 0.001 (van Birgelen et al., 1994b). Similar to the
10 interactions between PCB 126 and TCDD, additive interactions were observed in animals
11 receiving mixtures of PCB 156 and TCDD in the low-dose region for all responses examined.
1 2 However, at the highest exposures of PCB 156 and TCDD, the authors reported slight
1 3 antagonistic interactions for decreases in hepatic retinol (van Birgelen et al., 1994b). For both
14 PCB 126 and PCB 156, antagonistic interactions were observed with TCDD only at exposures
1 5 that produced maximal CYP1 Al induction. The authors concluded that the antagonistic
1 6 interactions are unlikely to occur at relevant human exposures.
17 In a series of studies examining the TEF methodology, TCDD (1.5-150 ng/kg/d),
1 8 1,2,3,7,8-PeCDD; 2,3,7,8-TCDF; 1,2,3,7,8-PeCDF; 2,3,4,7,8-PeCDF; OCDF; the coplanar PCBs
1 9 77, 126, and 169; and the mono-ortho substituted PCBs 105, 118, and 156 were administered to
20 mice 5 days/week for 13 weeks. REPs were determined for EROD induction, a marker for
21 CYP1A1, in liver, lung, and skin; ACOH activity, a marker for CYP1A2, in liver; and hepatic
22 porphyrins (DeVito et al., 1997a; 2000; van Birgelen et al., 1996c). These data demonstrate that
23 the dose-response curves for the PCDDs and PCDFs were parallel (DeVito et al., 1997a). Dose-
24 response curves for some of the enzyme induction data for the individual PCBs displayed
25 evidence of non-parallelism in the high-dose region (DeVito et al., 2000). A laboratory-derived
26 mixture of these chemicals with congener mass ratios resembling those in food was administered
27 to mice and rats, and indicated that despite the evidence of non- parallelism for the PCBs at high
28 doses, the assumption of additivity predicted the potency of the mixture for enzyme induction,
29 immunotoxicity, and decreases in hepatic retinoids (Birnbaum and DeVito, 1995; van Birgelen et
30 al., 1996; 1997; DeVito et al., 1997; Smialowicz et al., 1996). In addition, the REPs estimated in
31 mice also predicted the response of the mixture in rats for enzyme induction and decreases in
32 hepatic retinyl palmitate concentrations (van Birgelen et al., 1997d; Ross et al., 1997; DeVito et
33 al., 1997b). These studies indicate that not only do the REPs for enzyme induction in mice
34 predict other responses, such as immunotoxicity and decreases in hepatic retinyl palmitate, they
35 also can be used to predict responses of mixtures in another species.
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1 The commercial PCB mixtures induce a variety of dioxin-like effects. Rats exposed to
2 commercial Aroclors and observed for 2 weeks exhibited dose-dependent induction of hepatic
3 CYP1A activity (EROD) but no thymic atrophy (Harris et al., 1993). Using REP values derived
4 for EROD induction in rats, the TEF methodology provided good agreement with experimental
5 estimates of the ED50 for enzyme induction. However, use of the conservative TEF values of
6 Safe (1990) overestimated the potency of the Aroclor mixutres (Harris et al., 1993). In contrast,
7 similar studies examining immunotoxicity as an endpoint demonstrate that both experimentally
8 derived REP values and the conservative TEF values of Safe (1990) overestimate the potency of
9 the Aroclor mixtures by a factor of 1.2 - 22 (Harper et al., 1995). These data demonstrate that
10 there are nonadditive interactions between dioxin-like compounds and the non-dioxin-like PCBs
11 and that these interactions are response specific and most likely are not due to AhR antagonism.
12 In in vitro systems, using H4IIe cells and rat hepatocytes, Schmitz et al. (1995, 1996)
1 3 examined the assumption of additivity for individual congeners as well as commercial mixtures.
14 After deriving REP values for enzyme induction, the authors concluded that a laboratory mixture
1 5 of PCBs 77, 105, 118, 126, 156, and 169 demonstrated perfect additive behavior in these cell line
1 6 systems (Schmitz et al., 1995). However, when the mixture was combined with a tenfold surplus
1 7 of a mixture containing non-dioxin-like PCBs (PCB 28, 52, 101, 138, 153 and 180), the mixture
18 demonstrated an approximate threefold higher TEQ than predicted. The authors concluded that a
1 9 moderate synergistic interaction is responsible for the increased enzyme-inducing potency of the
20 mixture containing dioxins and non-dioxin-like PCBs. Further studies by Schmitz et al. (1996)
21 also demonstrated a slight synergistic deviation (less than threefold) from strict additivity when
22 the calculated TEQ based on chemical analysis of Aroclor 1254 and Clophen A50 was compared
23 to the CYP1 A-induction TEQ derived in an established rat hepatoma cell line (H4IIE) (Schmitz
24 etal., 1996).
25 Recently, Mayes et al. (1998) compared the carcinogenicity of Aroclor 1016, 1242, 1254
26 and 1260 in Sprague-Dawley rats. All four mixtures increased the incidence of hepatic tumors in
27 female rats. The authors concluded that the female rats were more susceptible than the males to
28 the hepatocarcinogenic effects of these mixtures. In the two-year bioassay of TCDD in Sprague-
29 Dawley rats, the female rats were also more susceptible to the hepatocarcinogenic effects than the
30 males (Kociba et al., 1978). Mayes and colleagues(1998) performed congener specific analysis
31 of the Aroclor mixtures and calculated dioxin TEQ values for each of these mixtures. In order to
32 compare the cancer induction potential of dioxin TEQ in PCB mixtures (Mayes et al. 1998) with
33 that from TCDD (Kociba et al., 1978) in the same species of rat, the dose-response relationships
34 are graphed and presented in figure 9-2. The dose-response relationship for hepatic tumors in
35 female rats is similar between the Aroclor 1242, 1254, 1260 and TCDD dose regimen. This
36 analysis demonstrates that the TEF methodology qualitatively and quantitatively predicts the
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1 response of a complex mixture of PCBs. This is particularly important because the mass
2 concentration of dioxin equivalents in the mixture is approximately 100,000 times less than the
3 non-dioxin-like PCBs present in these mixtures. These data strongly support the ability of the
4 TEF methodology to estimate the carcinogenic potency of a complex mixture of PCBs even in
5 the presence of significant concentrations of non-dioxin-like PCBs.
6 Researchers have evaluated the applicability of the TEF methodology to mixtures
7 containing dioxin-like PCBs by examining the interactions of binary mixtures, laboratory-derived
8 mixtures, or commercial mixtures of PCBs. The studies examining the binary mixtures or
9 laboratory-derived mixtures have demonstrated that the assumption of additivity provides good
10 estimates of the potency of a mixture of PCBs and other dioxin-like compounds. In contrast,
11 studies using commercial mixtures of PCBs suggest that the assumption of additivity may be
1 2 endpoint specific, and that both synergistic and antagonistic interactions may occur for some
1 3 mixtures of dioxins and PCBs for certain endpoints. A more detailed examination of these issues
14 follows in the section on nonadditive interactions with non-dioxin-like compounds.
15
1 6 9.4.3. Examination of Environmental Samples Containing PCDDs, PCDFs, and/or PCBs
1 7 One of the first tests of the TEF methodology examined soot from a transformer fire in
1 8 Binghamton, NY (Eadon et al., 1986). Benzene extracts of soot from a PCB transformer fire
1 9 which contained a complex mixture of PCDDs, PCDFs, PCBs, and polychlorinated
20 biphenylenes were administered to guinea pigs as single oral doses, and LD50 values were
21 compared to TCDD. Relative potency values for the PCDDs and PCDFs based on guinea pig
22 LD50 values were used to estimate the TCDD equivalents of the mixture. Eadon and co-workers
23 exposed guinea pigs to either TCDD alone or the soot and determined their LD50s. With these
24 relative potency values, the soot extract had a TCDD equivalent concentration of 22 ppm.
25 Comparison of the LD50s for TCDD and the soot led to a TCDD equivalent of 58 ppm for the
26 mixture. Other endpoints examined included alterations in thymus weight, body weight, serum
27 enzymes, and hepatotoxicity. Experimentally the TCDD equivalents of the soot varied from 2 to
28 58 ppm. The authors concluded that because the benzene extract of the soot contained hundreds
29 of chemicals including PCDDs, PCDFs, and PCBs, the difference between the calculated TEQ of
30 22 ppm and the experimentally derived TEQs between 2 and 58 seems minimal. (Note: the
31 initial analytical TEQ value of soot [22 ppm] was calculated on the basis of guinea pig LD50
32 values of the respective components; using the current recommended TEF scheme [van den Berg
33 et al., 1998], the "calculated" TCDD TEQ would be approximately 17 ppm.)
34 Shortly after the studies on the Binghamton transformer fire soot, investigators applied the
35 TEF methodology to the leachate from Love Canal, NY. The organic phase of the leachate
36 consisted of more than 100 different organic compounds including PCDDs and PCDFs. The
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1 leachate did not contain PCBs or PAHs. The authors estimated the TEQ of the mixture on the
2 basis of REP values for teratogenicity (cleft palate and hydronephrosis in mice) for the PCDDs
3 and PCDFs present in the leachate. The authors state that the leachate contained the equivalent
4 of 3 //g TCDD/g and that more than 95% of the TEQ was contributed by TCDD. There were two
5 other PCDFs present in the leachate, and their contribution to the total TEQ was approximately
6 5% (Silkworm et al., 1989). When the TEQ of the mixture was based on dose-response analysis
7 of the mixture compared to TCDD, the leachate was estimated to contain between 6.6 and 10.5
8 ng TCDD/g (Silkworm et al., 1989). The authors concluded there was a good agreement
9 between the experimental TCDD equivalents (6.6-10.5 //g TCDD/g) and the analytical TEQs (3
10 //g TCDD/g). In addition, these studies illustrate that the non-AhR components of the leachate
11 did not interfere with receptor-mediated teratogenicity (Silkworm et al., 1989). Additional
1 2 investigations have shown that the same complex mixture of non-AhR agonists slightly
1 3 potentiated TCDD-induced thymic atrophy and immunosuppression (plaque-forming cells/spleen
14 response) while decreasing the hepatic CYP1 A-inducing ability of the TCDD component
15 (Silkworm et al., 1993).
1 6 The assumption of additivity was also examined using a PCDD/PCDF mixture extracted
1 7 from fly ash from a municipal waste incinerator (Suter-Hofmann and Schlatter, 1989). As a
18 purification step, rabbits were fed organic extracts from the fly ash. After 10 days the livers were
19 removed and analyzed for PCDDs and PCDFs. The rabbit livers contained predominately
20 2,3,7,8-substituted PCDDs/PCDFs. Based on the chemical analysis of the liver, lyophilized and
21 pulverized liver was added to the standard rat diet. This diet was fed to rats for 13 weeks and
22 body weights and terminal thymus weights were recorded. The authors concluded that the
23 mixture of PCDDs and PCDFs produced equivalent toxicities as TCDD, and the assumption of
24 additivity was confirmed.
25
26 9.4.4. Nonadditive Interactions With Non-Dioxin-Like Compounds
27 For a number of toxicological responses, there appears to be evidence for nonadditive
28 interactions in defined dose ranges by both commercial Aroclors and major congeners with little
29 if any AhR agonist activity (i.e., PCB 153). Both commercial Aroclors and a PCB mixture
30 comprised of major congeners found in human breast milk were shown to antagonize the
31 immunotoxic effects of TCDD in mice (Biegel et al., 1989; Davis and Safe, 1989; Harper et al.,
32 1995). When immunotoxicity-derived TEF values for a variety of PCB congeners were used in
33 an additive manner to estimate TCDD TEQs for commercial Aroclors, in comparison to the
34 experimental TEQs, they were approximately predictive for Aroclor 1254 and 1260 (Harper et
35 al., 1995). However, the TEF approach tended to overestimate the immunotoxicity of Aroclors
36 1242 and 1248, suggesting some antagonism.
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1 Typical responses to TCDD exposure in rodents include CYP1 enzyme induction and
2 thymic atrophy. Rats consuming a diet containing 5 ppb TCDD for 13 weeks exhibited a 33-fold
3 increase in hepatic CYP1A activity (EROD) and a greater than 50% reduction in relative thymus
4 weight. Addition of PCB 153 to the diet at concentrations up to 100 ppm had no significant
5 effect on either response (van der Kolk et al., 1992). Mice dosed simultaneously with TCDD and
6 up to a 106-fold molar excess of PCB 153 (1 nmol/kg vs. 1 mmol/kg) exhibited no significant
7 dose-dependent alteration in hepatic CYP1A1/A2 protein compared to the TCDD dose group
8 alone (De Jongh et al., 1995). There was, however, an approximate twofold increase in hepatic
9 EROD activity in the highest combined PCB 153:TCDD dose group. Subsequent tissue analysis
10 revealed that the increase in EROD activity appeared related to PCB 153 increasing hepatic
11 TCDD concentrations. The same PCB congener at high doses (358 mg/kg) is able to almost
1 2 completely inhibit TCDD-induced suppression of the plaque-forming cell (PFC) response toward
13 sheep red blood cells in male C57BL/6J mice (Biegel et al., 1989; Smialowicz et al., 1997).
14 However, as PCB 153 displays negligible AhR binding affinity, the exact mechanism(s) behind
1 5 these interactions is unknown. Recently, it has been shown that PCB 153 at high doses (greater
1 6 than 100 mg/kg) actually enhances the PFC response in female B6C3F1 mice, thereby raising the
1 7 "control" set point. When combined doses of TCDD and PCB 153 are then compared to the
18 elevated PCB 153 response, an apparent block of the immunosuppressive effect of TCDD is
1 9 observed (Smialowicz et al., 1997). The relevance of this functional antagonism is uncertain, as
20 the doses required to inhibit the TCDD-like effects are at least 100 mg/kg of PCB 153. These
21 doses of PCB 153 seem unlikely to occur in human populations except under extreme conditions.
22 Commercial PCBs and various PCB congeners have been shown to potentiate or
23 antagonize the teratogenicity of TCDD depending upon the dose ranges and response examined
24 (Biegel et al., 1989; Morrissey et al., 1992). Treatment of developing chicken embryos with
25 TCDD and dioxin-like PCBs induces a characteristic series of responses, including embryo
26 lethality and a variety of embryo malformations/deformities. Combined exposure of chicken
27 embryos to both PCB 126 and PCB 153 (2 //g/kg and 25-50 mg/kg, respectively) resulted in
28 protection from PCB 126-induced embryo malformations, edema, and liver lesions, but not
29 mortality (Zhao et al., 1997). In mice, doses of 125 mg PCB 153/kg or higher inhibit the
30 induction of cleft palate by TCDD (Biegel et al., 1989; Morrissey et al., 1992). The induction of
31 hydronephrosis by TCDD was slightly antagonized by PCB 153, but only at doses of 500 mg/kg
32 or higher. Once again, the environmental relevance of exposures of 100 mg/kg of PCB 153 or
33 higher remains quite speculative, and nonadditive interactions are not expected at environmental
34 exposures.
35 Nonadditive interactions have also been observed in rodents exposed to both TCDD and
36 mixtures of various PCB congeners for hepatic porphyrin accumulation and alterations in
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1 circulating levels of thyroid hormones. A strong synergistic response was seen with hepatic
2 porphyrin accumulation in female rats following the combined dietary exposure to TCDD and
3 PCB 153 (van Birgelen, 1996a). The mechanism accounting for the interaction was thought to
4 be a combination of both AhR-dependent (CYP1A2 induction) and AhR-independent
5 (6-aminolevulinic acid synthetase [ALAS] induction) events. Additionally, subchronic exposure
6 of mice to a mixture of PCDDs, PCDFs, and dioxin-like PCBs in a ratio derived from common
7 foods also resulted in a highly synergistic response, when compared to an equivalent dose of
8 TCDD alone, for both hepatic porphyrin accumulation and urinary porphyrin excretion (van
9 Birgelen et al., 1996b). PCB 153, although not porphyrinogenic alone, when added to the
10 mixture further enhanced the synergistic response of hepatic porphyrin accumulation. Non-AhR-
11 mediated induction of ALAS activity by both the dioxin-like mono ortho-substituted PCBs in the
1 2 mixture and by PCB 153 was hypothesized to partially explain the synergism.
13 Decreases in thyroid hormone levels have been observed in both experimental animals and
14 humans following exposure to both dioxin-like and non-dioxin-like compounds (Nagayama et
1 5 al., 1998; Koopman-Esseboom et al., 1997). It is currently thought that multiple mechanisms,
1 6 including induction of specific isozymes of hepatic UDP-glucoronyl transferase (UDPGT) and
1 7 binding to thyroid hormone transport proteins (thyroid binding globulin, transthryetin) could be
18 involved. Exposure of female rats to a food-related mixture of PCDDs, PCDFs, and dioxin-like
19 PCBs for 90 days resulted in an approximately 85% decrease in decrease in plasma levels of
20 thyroxine. In contrast, the TCDD equivalent dose produced no effect on serum thyroxine (van
21 Birgelen et al., 1997). Increased induction of several isoforms of UDPGT by the HAH mixture
22 as compared to TCDD was thought to only partially explain the observed response with
23 thyroxine levels.
24 Several studies examining the interactions of dioxins and non-dioxins for rat liver tumor
25 promotion and additive and nonadditive interactions have been reported. Synergistic interactions
26 for tumor promotion have been observed for combinations of PCB 77 and PCB 52 (2,2',5,5'-
27 tetrachlorbiphenyl) in rat liver (Sargent et al., 1992). Eager et al. (1995) reported greater than
28 additive interactions of PCBs 126 and 153 in a rat liver tumor promotion model.
29 The assumption of additivity was examined in a laboratory-derived mixture of PCDDs,
30 PCDFs, and PCBs in a rat liver tumor promotion model (van der Plas et al., 1999). The mixture
31 contained TCDD, 1,2,3,7,8-PeCDD, 2,3,4,7,8-PeCDF, and PCBs 126, 118, and 156. The
32 composition of the mixture was based on concentrations of these chemicals in Baltic herring.
33 PCB 126 and 1,2,3,7,8-PeCDD accounted for 65% of the TEQ in the mixture and TCDD
34 accounted for approximately 6.6%. Both TCDD and the TEQ mixture increased mean foci
35 volume and the volume fraction of foci in the liver. However, the response was statistically
36 significantly greater in the TCDD treated animals by approximately 2-fold. While the TEQ
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1 mixture did not produce the exact same response level as TCDD, it is difficult to quantify the
2 overestimation of the TEF methodology in this study since only a single dose level was
3 examined. The authors also did a dose-response study with the mixture. However, they added
4 PCB 153 to the mixture used for the dose response study. The concentration of PCB 153 was
5 20,000 times the concentration of TCDD in these mixtures. Dose levels of 0.5, 1, and 2 ug
6 TEQ/kg/week were administered to the animals. The presence of PCB 153 did not alter the
7 effects of the 1 ug TEQ/kg/week dose since there was no statistical difference between the
8 response of animals to the TEQ mixture with or without PCB 153. The highest dose examined, 2
9 ug TEQ/kg/week produced an effect that was statistically equivalent to the animals treated with
10 TCDD alone. Van der Plas et al (1999) also determined the concentration of chemicals in the
11 liver at the termination of the study. Their data suggest that the lower response level of the
1 2 mixture is due to pharmacokinetic interactions. Animals administered 1 ug TEQ/kg/week had
1 3 approximately one third of the liver TEQ concentrations as the animals treated with TCDD.
14 Animals treated with 2 ug/kg/week had equivalent TEQ concentrations in the liver and also had
1 5 similar responses as animal treated with 1 ug TCDD/kg/week. Van der Plas and colleagues
1 6 concluded that the TEF methodology predicted the tumor-promoting potency of the mixture quite
1 7 well, within a factor of two, but pharmacokinetic interactions between dioxins may alter the
18 accuracy of the methodology (van der Plas et al., 1999).
19 In another study, van der Plas and colleagues (2000) examined the interactions of co-
20 planar and non-coplanar components of Aroclor 1260 in a tumor promotion study. In these
21 studies, Aroclor 1260 was separated into planar (0-1 ortho chlorines) and non-planar (2-4 ortho
22 chlorines) components. Rats were then exposed to either 1 ug TCDD/kg/week, 1 mg 0-
23 lortho/kg/week, 9 mg 2-4 ortho/kg/week, 10 mg 0-4 ortho/kg/week or 10 mg aroclor
24 1260/kg/week. Mean foci volume and the volume fraction of the liver occupied by foci increased
25 in animals treated with either TCDD, the 2-4 ortho mixture, the 0-4 ortho mixture and aroclor
26 1260. The 0-1 ortho mixture did not alter foci development compared to the control animals.
27 Van der Plas et al (2000) concluded that the results from their study indicate that 80% of the
28 carcinogenicity of Aroclor 1260 is due to the non-dioxin congeners in the mixture.
29 In the study described above, Van der Plas et al (2000) used the CALUX assay to
30 determine the TEQ of the different mixtures. The lot of Aroclor 1260 used in this study had very
31 low TEQs based on the CALUX assay. For example, 10 mg Aroclor 1260/kg/week was
32 equivalent to 0.0012 ug TEQ/kg/week or approximately 0.12 ppm TEQ. In addition, the 1 mg 0-
33 1 ortho/kg/week dose is equivalent to 0.09 ng TEQ/kg/d. In contrast, the lot of Aroclor 1260
34 used by Mayes et al (1998) had 7.2 ppm TEQ concentrations using the WHO TEF values and
35 dose levels examined ranged from 10-42 ng TEQ/kg/d. The lot of Aroclor 1260 used by Mayes
36 et al (1998) has approximately 60 times more TEQs than the lot used by van der Plas et al
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1 (2000). In the Mayes et al (1998) studies the TEF methodology accurately predicts the
2 carcinogenic response of the mixture. The differences in the van der Plas et al (2000) and the
3 Mayes et al (1998) studies may be due to the different lots of Aroclor 1260 used by these two
4 groups.
5 The interactions of dioxins with non-dioxin-like compounds results in additive and
Q nonadditive responses. The antagonistic interactions, while endpoint specific, appear to occur at
7 dose levels that greatly exceed most human exposures and should not affect the overall use of the
8 TEF methodology. One of the difficulties in addressing the nonadditive interactions is
9 understanding the mechanism behind these interactions. For the greater than additive
10 interactions for induction of porphyria and decreases in serum thyroxine, there are hypotheses
11 that may explain these effects. The mechanism of the antagonistic interactions of non-dioxin-
1 2 like PCBs and TCDD on immunotoxicity and teratogenicity in mice is uncertain. For other
13 responses, such as developmental reproductive toxicity, the interactions of PCDDs, PCDFs, and
14 PCBs have not been examined. In addition, it has also been suggested that antagonism of Ah
1 5 receptor-mediated events may be species specific. For example, addition of PCB 52, a congener
1 6 commonly found in biotic samples, inhibited the TCDD-induced expression of a reporter gene
1 7 under the regulatory control of the Ah receptor in mouse and rat cells, but not in guinea pig or
1 8 human hepatoma cells (Aarts et al., 1995). Our limited understanding of the interactions
1 9 between dioxins and non-dioxins for a variety of responses requires further research before their
20 impact on the TEF methodology can be fully understood.
21
22 9.4.5. Examination of the TEF Methodology in Wildlife
23 Many wildlife species also exhibit toxic effects associated with exposure to halogenated
24 aromatic hydrocarbons. Early life stage (ELS) or sac fry mortality in fish, characterized by
25 edema, structural malformations, and growth reduction prior to fry mortality can be induced in
26 trout species following exposure to dioxin-like PCDDs, PCDFs, and PCBs (Walker and
27 Peterson, 1991). Binary combinations of a variety of PCDDs, PCDFs, and both dioxin and non-
28 dioxin-like PCB congeners injected into fertilized trout eggs were also capable of inducing ELS
29 mortality, with the majority of interactions between the congeners described as strictly additive
30 (Zabel et al., 1995). When a synthetic complex mixture of PCDDs, PCDFs, and PCBs, in
31 congener ratios that approximated Great Lakes fish residues, was tested in the ELS mortality
32 assay, the lethal potency observed for the mixture, compared to TCDD, deviated less than
33 twofold from an additivity approach (Walker et al., 1996). Recently, the TCDD TEQ of an
34 environmental complex mixture of PCDDs, PCDFs, and PCBs extracted from lake trout and
35 applied to the ELS bioassay could also be predicted by an additivity approach (Tillitt and Wright,
36 1997). These results suggest that additional halogenated aromatic compounds, including non-
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1 dioxin-like PCBs, present in fish do not significantly detract from an additivity response for this
2 AhR-mediated event.
3 There are also numerous studies that have examined the effects of environmental mixtures
4 in marine mammals and avian species (Ross, 2000; Giesy and Kannan, 1998; Ross et al., 1996;
5 Shipp et al., 1998a,b; Restum et al., 1998; Summer et al., 1996a,b). Ross and colleagues
Q examined captive harbor seals fed herring from either the Atlantic Ocean (low levels of
7 PCDDs/PCDFs/PCBs) or the Baltic Sea (high levels of PCDDs/PCDFs/PCBs). The seals fed
8 herring from the Baltic Sea displayed immunotoxic responses including impaired natural killer
9 cell activity and antibody responses to specific antigens. These effects were correlated with the
10 TEQ concentrations in the herring. Using mink as a model, Aulerich, Bursian, and colleagues
11 have also examined the TEF methodology. Minks were fed diets containing carp from Saginaw
1 2 Bay to provide exposures of 0.25, 0.5, or 1 ppm PCB in the diet. In a series of reports, the
1 3 authors demonstrated that the diet induced dioxin-like effects ranging from enzyme induction to
14 reproductive and developmental effects, and that these effects were correlated with the dietary
1 5 intake of TEQs (Giesy and Kannan, 1998). Similar studies in White Leghorn hens also
16 demonstrated that the TEQ approach provided accurate estimates of the potency of the mixtures
17 (Summer etal., 1996).
1 8 In summary, current experimental evidence suggests that for PCDDs, PCDFs, coplanar
19 dioxin-like PCBs, and strictly AhR-mediated events, the concept of TEF additivity adequately
20 estimates the dioxin-like toxicity of either synthetic mixtures or environmental extracts, despite
21 the variations in relative contributions of each congener. Addition of the more prevalent mono-
22 and di-ortho-substituted PCBs to a mixture, at least in the case of environmental extracts and
23 wildlife, does not seem to significantly detract from this assumption of additivity. Interactions
24 other than additivity (antagonism, synergism) have been observed with a variety of effects
25 (teratogenicity, immunotoxicity, hepatic porphyrin accumulation, thyroid hormone metabolism)
26 in both binary combinations and complex synthetic mixtures of dioxin and partial or non-Ah
27 receptor agonists (commercial PCBs, PCB 153). However, it appears that at these high-dose
28 exposures, multiple mechanisms of action not under the direct control of the Ah receptor are
29 responsible for these nonadditive effects.
30 Additional research efforts should focus on complex mixtures common to both
31 environmental and human samples and the interactions observed with biological and
32 toxicological events known to be under Ah receptor control. In the interim, the additive
33 approach with TEFs derived by scientific consensus of all available data appears to offer a good
34 estimation of the dioxin-like toxicity potential of complex mixtures, keeping in mind that other
35 effects may be elicited by non-dioxin-like components of the mixture.
36
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1 9.4.6. Toxic Equivalency Functions
2 The TEF methodology has been described as an "interim" methodology. Since this
3 interim method has been applied, there have been few proposed alternatives. One recent
4 proposal suggests that the TEF value be replaced by a toxic equivalency function (Putzrath,
5 1997). It has been proposed that the REPs for PCDDs/PCDFs are better described by a function
6 as compared to a factor or single-point estimate (Putzrath, 1996). The use of a factor to describe
7 the relative potency of a chemical implies that its relative potency is independent of dose.
8 Putzrath (1997) suggests that data exists which indicates that the REPs are dose dependent and
9 that the REPs must be described as a function of dose. Recent studies have examined this
10 possibility for a series of PCDDs/PCDFs and PCBs (DeVito et al., 1997; DeVito et al., 2000).
11 For the PCDDs/PCDFs, the data indicate that the REPs estimated from enzyme induction data in
1 2 mice are best described by a factor and not a function. For some of the PCBs examined, a
1 3 function fit better, but the change in the REP was within a factor of two to five for most of the
14 four enzymatic responses examined (DeVito et al., 2000). In addition, the dose dependency was
1 5 observed only at the high-dose and not in the low-dose region (DeVito et al., 2000).
16 Even though these studies suggest that a TE function may be useful, there are numerous
1 7 difficulties in applying this method. If the REPs are really functions and not factors, there must
18 be a mechanistic basis for these differences, and these mechanisms would most likely be
19 response specific and perhaps species specific. This would then require that for all critical
20 responses, every chemical considered in the TEF methodology would have to be examined.
21 Once again, it is highly unlikely that 2-year bioassays and multigenerational studies will be
22 performed on all the TEF congeners in the foreseeable future. The use of a TEF function
23 requires extensive data sets that are not available and are unlikely to be collected.
24
25 9.4.7. Species and Endpoint Specific TEFs
26 It is often suggested that species and endpoint TEFs may be required for the TEF concept
27 to provide accurate estimates of risk. In fact, the WHO does have class specific TEFs based on
28 fish, birds and mammals (van den Berg et al., 1998). The most notable differences are the lack
29 of effect of some mono-ortho PCBs in fish (van den Berg et al., 1998). Hahn and colleagues
30 recently examined the influence of affinity and intrinsic activity on the relative potency of PCBs
31 in PLHC-1 cells (Hestermann et al., 2000). Using this cell line derived from fish, Hahn and
32 colleagues demonstrated clear differences in the response of these cells to mono-ortho PCBs.
33 The insensitivity of these fish cells to the mono-ortho PCBs is due to both reduced affinity and
34 reduced intrinsic efficacy. Using information on affinity and intrinsic efficacy allowed for better
35 predictions of mixtures of these chemicals than did the application of the TEF methodology
36 (Hestermann et al., 2000). Future studies examining species differences applying the approach of
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1 Herstermann et al., (2000) may provide insight into species specific TEFs as well as alternative
2 approaches to the TEF methodology.
3 There are numerous examples of endpoint specific relative potencies for receptor mediated
4 pharmacological agents, such as the antiestrogen, tamoxifen. It is reasonable to assume that the
5 Ah receptor and its ligands would be no different from these other receptor systems.
6 Examination of the WHO data base suggests that even for the chemicals with the largest data sets
7 this question cannot be adequately addressed (See section 9.2.5). Endpoint specific TEFs would
8 require a much more complete data set than is available at this time. In addition, these studies
9 would have to be designed to test the hypothesis that the REPs are equivalent across endpoints.
10 This requires controlling the species and dosing regimen employed as well as other factors. One
11 of the reasons the TEF methodology was developed was because limited toxicity data was
1 2 available for the other dioxin-like chemicals and it was unlikely that all relevant chemicals would
1 3 be tested for all responses in all species, including humans. For example, it is extremely unlikely
14 that 2-year bioassays for carcinogenesis or multi-generational studies will be performed on all
1 5 chemicals included in the TEF methodology. Even though there are significant data
16 demonstrating that a number of chemicals produce dioxin-like toxic effects, clearly the data set is
1 7 not complete. For this reason, WHO recommends revisiting the TEF values every 5 years.
18
1 9 9.5. APPLICATION OF UNCERTAINTY ANALYSIS TO THE TEF METHODOLOGY
20 TEFs are presented as point estimates, in spite of the fact that variability in the REP values
21 estimated from the supporting experimental data can range several orders of magnitude for a
22 particular congener. It has been proposed that some of this variability in the REP values can be
23 attributed to differences in exposure regimens, test species, or purity of the test compound. In
24 addition, others have argued that the variability of the REPs may be due to differences in the REP
25 across endpoints. The reasons for much of this variability have not been adequately examined
26 experimentally and remain unknown. For example, in the WHO database, PCB 126 has the
27 largest data set of REP values. However, while there are numerous studies estimating the REPs
28 for this chemical, these individual studies were not designed to address the variability in the REP
29 values. Close examination of theses studies indicates that it is difficult to attribute the variability
30 of the REP to either species, endpoint, dosing regimen or laboratory differences. For example,
31 there are four studies that examined the REP of 126 for immune effects in mice in the WHO data
32 base (Harper et al., 1994; 1995; Mayura et al.,1993; Steinburg et al., 1993). The range of the
33 REPs from these studies is 0.05 - 0.99 with a mean of 0.23 ± 0.22. It is not clear why the range
34 is so large. In fact, three of the studies and the two extreme REPs (0.05 and 0.99) come from the
35 same laboratory (Harper et al., 1994; 1995; Mayura et al., 1993). Similarly, there are four studies
36 examining the REP of PCB 126 for hepatic EROD induction in mice following an acute
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1 exposure and the REPs are 0.0005, 0.012, 0.38 and 0.55. Once again, there is no clear reason for
2 the three order of magnitude range in the REPs for this endpoint. Because the experiments used
3 to estimate the REPs were not designed to address the variability, further studies will be required
4 to determine what is causing the variability.
5 One of the difficulties in quantitatively describing the uncertainties in the TEF
6 methodology is due to the method by which the TEF values are assigned. First and foremost is
7 the fact that TEFs are assigned and not derived. While there is a clear description of the
8 qualitative weighting scheme used in assigning the TEFs, quantitatively describing how the
9 actual committee actually assessed this weighting scheme is impossible. Consequently, the TEF
10 approach, as currently practiced, does not provide for a quantitative description of the uncertainty
11 for individual TEF values.
1 2 There has been several proposals for incorporating quantitative uncertainty descriptors into
13 TEFs. Suggestions have been made to use meta-analytic approaches or Monte Carlo techniques,
14 however (Finley et al., 1999), these approaches are only as good as the data available. For some
1 5 chemicals, such as PCB 126, PeCDD and 4-PeCDF, there are sufficient data to apply these
16 methods. In contrast, chemicals such as OCDD and OCDF have only a few studies and
1 7 application of these statistical methods would be inappropriate. Another shortcoming to the
18 application of meta-analytic approaches or Monte Carlo techniques is that they would also have
19 to incorporate the weighting scheme described by the WHO workgroup (van den Berg et al.,
20 1998). The weighting scheme gives qualitatively greater weight to studies that examine toxic
21 endpoints following repeated exposures. Because our concern is generally for potential toxic
22 effects following repeated exposures, this weighting scheme is appropriate. Incorporating a
23 quantitative description of the weighting scheme into a meta-analytic approaches or a Monte
24 Carlo approach to describe the uncertainty is not a trivial task (Finley et al., 2000). Future efforts
25 by WHO or USEPA which develop guidelines and approaches to incorporating these weighting
26 schemes into quantitative uncertainty analysis are an important step in understanding the
27 uncertainties of the TEF methodology.
28 Qualitative statements of confidence are embodied in the discussions associated with the
29 establishment and revision of TEFs. These qualitative judgments, when examined in the context
30 of a specific risk assessment, can provide valuable insight into the overall uncertainty of some
31 TEQ estimates. For example, using WHO TEFs (van den Berg et al., 1998) to look at
32 background exposure from a typical U.S. diet, it is clear that only a limited number of congeners
33 significantly contributed to the total TEQ. Approximately 80% of the TEQ-WHO98 associated
34 with background dietary exposure (1 pg/kg/d) comes from only five congeners: 2,3,7,8-TCDD,
35 1,2,3,7,8-PCDD, 2,3,4,7,8-PeCDF, and PCB 126 (see Part I, Volume 3). The variability of the
36 REP values found in the literature for these congeners is much lower than for congeners that are
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1 minor contributors to background TEQ. Furthermore, the assigned TEF values for the chemicals
2 contributing 80% to the TEQ intake are similar to the mean of their in vivo REP values. The
3 confidence in the TEF methodology is also increased by empirical examination. A number of
4 studies have examined complex mixtures of dioxin and non-dioxin-like compounds and the TEF
5 methodology consistently results in TEQ estimates within a factor 2-3 for these mixtures. Based
6 on these mixture studies it is unlikely that the estimated TEQ over or under estimates the "true"
7 TEQ by more than a factor of five. Finally, the uncertainty in TEQ estimates is only one
8 component of the overall uncertainty in a dioxin risk assessment. The TEQ uncertainty only
9 addresses the confidences associated in ascribing 2,3,7,8-TCDD equivalents to a mixture. It does
10 not address the uncertainty associated with quantitatively linking health effects to 2,3,7,8-TCDD
11 exposure, or the uncertainties associated with exposure estimates themselves.
12
13 9.6. IMPLICATIONS FOR RISK ASSESSMENT
14 The TEF methodology provides a mechanism to estimate potential health or ecological
1 5 effects of exposure to a complex mixture of dioxin-like compounds. However, the TEF method
1 6 must be used with an understanding of its limitations. This methodology estimates the dioxin-
1 7 like effects of a mixture by assuming dose-additivity and describes the mixture in terms of an
1 8 equivalent mass of 2,3,7,8-TCDD. Although the mixture may have the toxicological potential of
19 2,3,7,8-TCDD it should not be assumed for exposure purposes to have the same environmental
20 fate as 2,3,7,8-TCDD. The environmental fate of the mixture is still the product of the
21 environmental fate of each of its constituent congeners. Different congeners have different
22 physical properties such as vapor pressure, practical vapor partition, water octanol coefficient,
23 photolysis rate, binding affinity to organic mater, water solubility, etc. Consequently, both the
24 absolute concentration of a mixture in an environmental medium and the relative concentration
25 of congeners making up an emission will change as the release moves through the environment.
26 For some situations, treating emission as equivalent to exposure, which assumes that modeling
27 fate and exposure can be reasonably accomplished by treating a mixture as if it were all
28 2,3,7,8,-TCDD, is a useful but uncertain assumption. However, for many risk assessments the
29 differences in fate and transport of different congeners must be taken into consideration and TEQ
30 must be calculated at the point of exposure if more accurate assessments are to be achieved.
31 Similarly, many dioxin releases are associated with the release of non-dioxin-like compounds
32 such as pesticides, metals, and non-dioxin-like PHAHs, and their risk potential may also need to
33 be assessed in addition to dioxin-related risk.
34 There are instances where exposures to PCBs are the major problem. The TEF
35 methodology provides risk assessors with a useful tool to estimate potential dioxin-related health
36 risks associated with these exposures. Typically, the congener makeup of environmental
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1 exposures to PCBs does not resemble the congener profile of any of the commercial mixtures
2 produced. Because the environmental mixtures do not resemble the commercial mixtures, it is
3 not clear that using total PCB concentrations and comparing them to any of the commercial
4 mixtures provides an accurate assessment of the potential risks. However, the use of the TEF
5 methodology allows for the estimation of the risk associated with the dioxin-like effects of the
6 mixture and may provide a more accurate assessment of the risk in conjunction with the use of
7 total PCBs. The Agency has recently published an application of this approach to the evaluation
8 of PCB carcinogenicity (U.S. EPA, 1996; Cogliano, 1998)
9
10 9.7. SUMMARY
11 The AhR mediates the biochemical and toxicological actions of dioxin-like compounds
1 2 and provides the scientific basis for the TEF/TEQ methodology. In its 20-year history, this
1 3 approach has evolved, and decision criteria supporting the scientific judgment and expert opinion
14 used in assigning TEFs have become more transparent. Numerous countries and several
1 5 international organizations have evaluated and adopted this approach to evaluating complex
16 mixtures of dioxin and related compounds. It has become the accepted, interim methodology,
1 7 although the need for research to explore alternative approaches is widely endorsed. Although
18 this method has been described as a "conservative, order of magnitude estimate" of the TCDD
1 9 dose, experimental studies examining both environmental mixtures and laboratory-defined
20 mixtures indicate that the method provides a greater degree of accuracy when all effects are
21 considered and may not be as conservative as sometimes described. Clearly, basing risk on
22 TCDD alone or assuming all chemicals are as potent as TCDD is inappropriate on the basis of
23 available data. Although uncertainties in the TEF methodology have been identified, one must
24 examine the utility of this method in the broader context of the need to evaluate the public health
25 impact of complex mixtures of persistent bioaccumulative chemicals. The TEF methodology
26 decreases the overall uncertainties in the risk assessment process (U.S. EPA, 1999); however,
27 this decrease cannot be quantified. One of the limitations of the TEF methodology in risk
28 assessment is that the risk from non-dioxin-like compounds is not evaluated. This applies to
29 both industrial/synthetic as well as natural ligands which are not considered to be dioxin-like, in
30 addition to non-AhR ligands which may be interacting with dioxin-like chemicals in modulating
31 their impacts on biological systems. Future research should focus on the development of methods
32 that will allow risks to be predicted when multiple mechanisms are present from a variety of
33 contaminants.
34 Since TEFs were first proposed in the 1980's, there have been several expert panels
35 charged with evaluating and assigning TEF values to dioxin-like congeners. The development of
36 the TEF methodology can be seen as an iterative process in which as more data was collected and
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1 our knowledge base on the mode of action and biological effects of these chemicals accumulated,
2 the later panels provided more accurate assessments of the chemicals included in the TEF
3 methodology. For example, the initial TEF proposals assigned values to all tetra-, penta-, hexa-,
4 hepta- and octa-chlorinated dioxin and dibenzofuran congeners. Later evaluations assigned TEF
5 values only to the 2,3,7,8-chlorine substituted congeners. The most recent expert panal to re-
6 evaluate and assign TEF values to dioxin-like congeners was the WHO panel convened in 1997
7 (Van den berg, 1998). This group of experts assigned TEF values to dioxin-like PCBs and
8 revised TEF values for several of the chlorinated dioxins and dibenzofurans. The WHO98 TEF
9 values are based on the most recent data available and it is recommended that these values
10 supercede previous TEF values.
11 Thus, in summary, the WHO98 TEF values, which include dioxins, furans and dioxin-like
1 2 PCBs, are the recommended TEF values. These are the TEF values recommended for use in
1 3 human health risk analysis.
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Table 9-1. Estimated relative toxicity of PCDD and PCDF isomers to 2,3,7,8-
T4CDDa
Isomer groups
DD
MjCDD
D2CDD
T3CDD
T4CDDb
P5CDD
H6CDD
H7CDD
08CDD
DF
MjCDF
D2CDF
T3CDF
T4CDF
P5CDF
H6CDF
H7CDF
00CDF
Toxicity factor relative to 2,3,7,8-T4 CDD
nontoxic
0.0001
0.001
0.01
0.01
0.1
0.1
0.01
0.0001
nontoxic
0.0001
0.0001
0.01
0.5
0.5
0.1
0.01
0.0001
aOME, 1984.
b Excluding 2,3,7,8-T4CDD.
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Table 9-2. Toxic equivalency factors (TEFs)
Congener
PCDDs
2,3,7,8-TCDD
1,2,3,7,8-PeCDD
1,2,3,4,7,8-HxCDD
1,2,3,7,8,9-HxCDD
1,2,3,6,7,8-HxCDD
1,2,3,4,6,7,8-HpCDD
1,2,3,4,6,7,8,9-OCDD
PCDFs
2,3,7,8-TCDF
1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
1,2,3,4,7,8-HxCDF
1,2,3,7,8,9-HxCDF
1,2,3,6,7,8-HxCDF
2,3,4,6,7,8-HxCDF
1,2,3,4,6,7,8-HpCDF
1,2,3,4,7,8,9-HpCDF
1,2,3,4,6,7,8,9-OCDF
PCBs
IUPAC # Structure
77 3,3',4,4'-TCB
81 3,4,4',5-TCB
105 2,3,3',4,4'-PeCB
114 2,3,4,4',5-PeCB
118 2,3',4,4',5-PeCB
123 2',3,4,4',5-PeCB
126 3,3',4,4',5-PeCB
156 2,3,3',4,4',5-HxCB
157 2,3,3',4,4',5'-HxCB
167 2,3',4,4',5,5'-HxCB
169 3,3',4,4',5,5'-HxCB
170 2,2',3,3',4,4',5-HpCB
180 2,2',3,4,4',5,5'-HpCB
189 2,3,3',4,4',5,5'-HpCB
EPA/87 a
1
0.5
0.04
0.04
0.04
0.001
0
0.1
0.1
0.1
0.01
0.01
0.01
0.01
0.001
0.001
0
NATO/89 b
1
0.5
0.1
0.1
0.1
0.1
0.001
0.1
0.05
0.5
0.1
0.1
0.1
0.1
0.01
0.01
0.001
WHO/94 c
0.0005
-
0.0001
0.0005
0.0001
0.0001
0.1
0.0005
0.0005
0.00001
0.01
0.0001
0.00001
0.0001
WHO/98"
1
1
0.1
0.1
0.1
0.01
0.0001
0.1
0.05
0.5
0.1
0.1
0.1
0.1
0.01
0.01
0.0001
0.0001
0.0001
0.0001
0.0005
0.0001
0.0001
0.1
0.0005
0.0005
0.00001
0.01
-
_
0.0001
aU.S. EPA, 1987.
bNATO/CCMS, 1989.
cAlhlborgetal, 1994.
d Van den Berg, 1998.
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Table 9-3. The range of the in vivo REP values for the major TEQ contributors
Chemical
1,2,3,7,8-
PCDD
2,3,4,7,8-
PCDF
1,2,3,6,7,8-
HxCDD
PCB 126
Number of
in vivo
endpoints
22
40
O
62
Range of
REPs
(mean±std)
0.16-0.9
(0.5±0.22)
0.018-4.0
(0.4±0.7)
0.015-0.16
0.0024-0.98
(0.20±0.20)
Number of
end points
from
subchronic
studies
16
20
1
31
Range of
REPs
(mean±std)
0.19-0.9
(0.53±0.24)
0.018-0.6
(0.20±0.13)
0.04
0.004-0.18
(0.13±0.13)
TEF
1
0.5
0.1
0.1
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Cl
Cl
TCDD (2,3,7,8)
TCDF (2,3,7,8)
Cl
2,3,3',4,4'-PeCB
Cl
Cl
Cl Cl
2,2',4,4',5,5'-HCB
Figure 9-1. Structures of polychlorinated dibenzo-»-dioxins, dibenzofurans and biphenyls.
The prototype chemical 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD[2,3,7,8]), and example of a
dioxm-like dibenzofuran 2,3,7,8-tetrachlorodibenzfuran (TCDF[2,3,7,8J), a mono-ortho dioxin-
likePCB, 2,3,3',4,4'-pentachlorobiphenyl (2,3,3',4,4'-PeCB), a dioxin-like coplanarPCB,
3,3',4,4',5-pentachlorobiphenyl (3,3',4,4',5-PeCB) and an example of a non-dioxin-like di-ortho
substituted PCB, 2,2',4,4',5,5r-hexachlorobiphenyl (2,2',4,4',5,5V-HCB).
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2
Q.
0.6 -i
0.5 -
0.4 -
£ 0.3-
0.2 -
TCDD, Kociba rev. pathology
--+-- Aroclor 1260 TEQ; Mayes 1998
—n—Aroclor 1254 TEQ; Mayes 1998
— X —Aroclor 1242 TEQ; Mayes 1998
—*—Aroclor 1016 TEQ; Mayes 1998
0.02 0.04
0.06 0.08 0.1 0.12 0.14
TCDD-equivalents (ug/kg/day)
0.16 0.18
0.2
Figure 9-2: TEQ-based bioassay results. (Kociba et al.,1978 and Mayes et al.,1998)
Presentation of the comparison of the dose-response relationship for hepatic tumors for TCDD
(Kociba et al., 1978) with Aroclor 1016, 1242, 1254, and 1260 (Mayes et al., 1998) when dose is
expressed as TCDD equivalents using the TEF methodology (Ahlborg et al., 1994).
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DRAFT December 2003
DO NOT CITE OR QUOTE NAS Review Draft
www.epa.gov/ncea/dioxin
Exposure and Human Health Reassessment
of 2,3,7,8-Tetrachlorodibenzo-p-Dioxin (TCDD)
and Related Compounds
Part III: Integrated Summary and Risk Characterization for
2,3,7,8-Tetrachlorodibenzo-p-Dioxin (TCDD) and Related Compounds
NOTICE
THIS DOCUMENT IS A PRELIMINARY DRAFT. It has not been formally released by the
U.S. Environmental Protection Agency and should not at this stage be construed to represent
Agency policy. It is being circulated for comment on its technical accuracy and policy
implications.
National Center for Environmental Assessment
Research and Development
U.S. Environmental Protection Agency
Washington, DC
-------
DISCLAIMER
This document is a draft for review purposes only and does not constitute U.S.
Environmental Protection Agency policy. It has been provided for review to the National
Academy of Sciences (NAS). While the NAS review is being conducted and until a final agency
assessment has been released, the draft dioxin reassessment (2003 version or other draft versions)
remains draft, does not represent a final position, and is not intended to serve as the basis or
rationale for regulatory and other policy action. However, EPA will continue its work to reduce
human exposure to dioxin.
While the NAS review is underway and no final reassessment has been issued, in meeting
their regulatory responsibilities, the agency will continue its current practice of utilizing the best
available data that meet the EPA Information Quality Guidelines and the government-wide
Information Quality Guidelines issued by OMB. T he Agency will consider all such data and
associated uncertainty to determine the strength of the evidence in proposing regulatory actions
related to dioxin and dioxin-like compounds.
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Exposure and Human Health Reassessment
of 2,3,7,8-Tetrachlorodibenzo-p-Dioxin (TCDD)
and Related Compounds
TABLE OF CONTENTS-OVERVIEW
Part I: Estimating Exposure to Dioxin-Like Compounds (Draft Final)
Volume 1: Sources of Dioxin-Like Compounds in the United States
Chapters 1 through 13
Volume 2: Properties, Environmental Levels, and Background Exposures
Chapters 1 through 6
Volume 3: Site-Specific Assessment Procedures
Chapters 1 through 8
Part H: Health Assessment for 2,3,7,8-Tetrachlorodibenzo-/>-dioxin (TCDD) and
Related Compounds
Chapter 1. Disposition and Pharmacokinetics
Chapter 2. Mechanism(s) of Actions
Chapter 3. Acute, Subchronic, and Chronic Toxicity
Chapter 4. Immunotoxicity
Chapter 5. Developmental and Reproductive Toxicity
Chapter 6. Carcinogenicity of TCDD in Animals
Chapter 7. Epidemiology/Human Data
Chapter 8. Dose-Response Modeling for 2,3,7,8-TCDD
Chapter 9. Toxic Equivalency Factors (TEF) for Dioxin and Related
Compounds
Part HI: Integrated Summary and Risk Characterization for
2,3,7,8-Tetrachlorodibenzo-p-Dioxin (TCDD) and Related Compounds
(NAS Review Draft, December 2003)
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CONTENTS
LIST OF TABLES vii
LIST OF FIGURES ix
LIST OF ACRONYMS, ABBREVIATIONS, AND SYMBOLS x
AUTHORS xiii
1. INTRODUCTION 1-1
1.1. DEFINITION OF DIOXIN-LIKE COMPOUNDS 1-3
1.2. TOXIC EQUIVALENCY FACTORS 1-5
1.3. UNDERSTANDING EXPOSURE/DOSE RELATIONSHIPS FOR
DIOXIN-LIKE COMPOUNDS 1-10
1.3.1. Administered Dose 1-12
1.3.2. Area Under the Curve 1-13
1.3.3. Plasma or Tissue Concentrations 1-15
1.3.4. Steady-State Body Burdens 1-16
1.3.5. Mechanistic Dose Metrics 1-17
1.3.6. Summary 1-17
2. EFFECTS SUMMARY 2-1
2.1. BIOCHEMICAL RESPONSES 2-3
2.2. ADVERSE EFFECTS INHUMANS AND ANIMALS 2-7
2.2.1. Cancer 2-7
2.2.1.1. Epidemiologic Studies 2-7
2.2.1.2. Animal Carcinogenicity 2-14
2.2.1.3. Plausible Mode(s) of Carcinogenic Action 2-17
2.2.1.4. Other Data Related to Carcinogenesis 2-20
2.2.1.5. Cancer Hazard Characterization 2-21
2.2.2. Reproductive and Developmental Effects 2-23
2.2.2.1. Human Effects 2-23
2.2.2.2. Experimental Animal Effects 2-26
2.2.2.3. Other Data Related to Developmental and
Reproductive Effects 2-30
2.2.2.4. Developmental and Reproductive Effects Hazard
Characterization 2-31
2.2.3. Immunotoxicity 2-33
2.2.3.1. Epidemiologic Findings 2-33
2.2.3.2. Animal Findings 2-34
2.2.3.3. Other Data Related to Immunologic Effects 2-35
2.2.3.4. Immunologic Effects Hazard Characterization 2-36
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CONTENTS (continued)
2.2.4. Chloracne 2-37
2.2.5. Diabetes 2-39
2.2.6. Other Effects 2-40
2.2.6.1. Elevated GOT 2-40
2.2.6.2. Thyroid Function 2-41
2.2.6.3. Cardiovascular Disease 2-42
2.2.6.4. Oxidative Stress 2-43
3. MECHANISMS AND MODE OF DIOXIN ACTION 3-1
3.1. MODE VERSUS MECHANISM OF ACTION 3-2
3.2. GENERALIZED MODEL FORDIOXIN ACTION 3-3
3.2.1. The Receptor Concept 3-3
3.2.2. A Framework to Evaluate Mode of Action 3-6
3.2.3. Mechanistic Information and Mode of Action—Implications for Risk
Assessment 3-6
4. EXPOSURE CHARACTERIZATION 4-1
4.1. SOURCES 4-1
4.1.1. Inventory of Releases 4-3
4.1.2. General Source Observations 4-6
4.2. ENVIRONMENTAL FATE 4-10
4.3. ENVIRONMENTAL MEDIA AND FOOD CONCENTRATIONS 4-12
4.4. BACKGROUND EXPOSURES 4-15
4.4.1. Tissue Levels 4-15
4.4.2. Intake Estimates 4-18
4.4.3. Variability in Intake Levels 4-19
4.5. POTENTIALLY HIGHLY EXPOSED POPULATIONS OR
DEVELOPMENTAL STAGES 4-20
5. DOSE-RESPONSE CHARACTERIZATION 5-1
5.1. DOSE METRIC(S) 5-4
5.1.1. Calculations of Effective Dose 5-8
5.2. EMPIRICAL MODELING OF INDIVIDUAL DAT A SETS 5-9
5.2.1. Cancer 5-11
5.2.1.1. Estimates of Slope Factors and Risk at Current Background
Body Burdens Based on Human Data 5-19
5.2.1.2. Estimates of Slope Factors and Risk at Current Background
Body Burdens Based on Animal Data 5-20
5.2.1.3. Estimates of Slope Factors and Risk at Current Background
Body Burdens Based on a Mechanistic Model 5-22
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5.2.2. NoncancerEndpoints 5-24
5.3. MODE-OF-ACTION-BASED DOSE-RESPONSE MODELING 5-26
5.4. SUMMARY DOSE-RESPONSE CHARACTERIZATION 5-26
6. RISK CHARACTERIZATION 6-1
APPENDIX A-l
GLOSSARY G-l
REFERENCES R-l
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LIST OF TABLES
1-1. The toxic equivalency factor (TEF) scheme for I-TEQDF 1-19
1-2. The toxic equivalency factor (TEF) scheme for TEQDEP-WHO94 1-19
1-3. The toxic equivalency factor (TEF) scheme for TEQDEP-WHO98 1-20
1-4. The range of the in vivo relative potency estimates (REP) values for the major
TEQ contributors 1-21
1-5. Comparison of administered dose and body burden in rats and humans 1-22
2-1. Effects of TCDD and related compounds in different animal species 2-44
2.2. Some biochemical response to TCDD 2-45
2-3. Summary of the combined cohort and selected industrial cohort studies with
high exposure levels, as described by IARC (1997) 2-46
2-4. Tumor incidence and promotion data cited for the TEF-WHO98 for principal
congeners 2-47
3-1. Early molecular events in response to dioxin 3-15
4-1. Confidence rating scheme 4-25
4-2. Inventory of environmental releases (grams/year) of TEQDF-WHO98 in the
United States 4-26
4-3. Sources that are currently unquantifiable (Category E) 4-30
4-4. Summary of North American CDD/CDF and PCB TEQ-WHO98 levels in
environmental media and food 4-31
4-5. Background serum levels in the United States 1995-1997 4-33
4-6. Adult contact rates and background intakes of dioxin-like compounds 4-34
4-7. Variability in average daily toxic equivalent (TEQ) intake as a function of age 4-35
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LIST OF TABLES (continued)
5-1. Peak serum dioxin levels in the background population and epidemiological
cohorts 5-31
5-2. Published cancer epidemiology and bioassay data in dose-response
formulae 5-34
5-3. All cancer risk in humans through age 75 5-36
5-4. Summary of all site cancer ED01 and slope factor calculations 5-37
5-5. Doses yielding 1% excess risk (95% lower confidence bound) based upon
2-year animal carcinogenicity studies using simple multistage (Portier et al., 1984)
models 5-38
5-6. Body burdens for critical endpoints in animals with human equivalent
daily intake 5-39
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LIST OF FIGURES
1-1. Chemical structure of 2,3,7,8-TCDD and related compounds 1-23
2-1. Cellular mechanism for AhR action 2-48
4-1. Estimated CDD/CDF I-TEQ emissions to air from combustion sources in the
United States, 1995 4-36
4-2. Comparison of estimates of annual I-TEQ emissions to air (grams I-TEQ/yr)
for reference years 1987 and 1995 4-37
4-3. Blood levels (I-TEQ for CDD/CDF + WHO94) versus age of a subset of
participants in the CDC (2000) 4-38
4-4. Predicted distributions and average TEQDF - WHO98 concentrations within
an adult population for four years: 1965, 1985, 1995, and 2030 4-39
4-5. Demonstration of the model for evaluating impacts on lipid concentrations (A)
and body burdens (B) of infants resulting from various nursing scenarios
nursing scenarios during a lifetime 4-40
5-1. Comparison of lifetime average body burden and area under the curve in
hypothetical background and occupational scenarios 5-41
5-2. Peak dioxin body burden levels in background populations and
epidemiological cohorts (back-calculated) 5-42
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LIST OF ACRONYMS, ABBREVIATIONS, AND SYMBOLS
Ah
AHF
AhR
ALK
ALT
Arnt
AST
ATSDR
AUC
BaP
BDD
BDF
BMD
BW
CDC
CDD
CFD
CI
CTL
CYP1A1
CYP1A2
CYP1B1
DFP (subscript)
DEN
DHT
DNA
ED
ED01
EDC/VC
EOF
EGFR
EPA
FSH
g
GD
GOT
HAH
HCDD
HIF
HpCDD
aryl hydrocarbon
altered heptacellular foci
aryl hydrocarbon receptor
alkaline phosphatase
alanine aminotransferase
aryl hydrocarbon receptor nuclear translocator
aspartate aminotransferase
Agency for Toxic Substances and Disease Registry
area under the curve
benzo[a]pyrene
brominated dibenzodioxin
polybrominated dibenzofuran
benchmark dose
body weight
Centers for Disease Control and Prevention
chlorinated dibenzodioxin
chlorinated dibenzofuran
confidence interval
cytotoxic T lymphocyte
cytochrome P4501A1 enzyme
cytochromeP4501A2 enzyme
cytochrome P4501B1 enzyme
dioxins, furans, PCBs
diethylnitrosamine
5a-dihydrotestosterone
deoxyribonucleic acid
effective dose
effective dose at the 1% response level
ethylene dichloride/vinyl chloride
epidermal growth factor
epidermal growth factor receptor
U.S. Environmental Protection Agency
follicle-stimulating hormone
gram
gestation day
gamma glutamyl transferase
halogenated aromatic hydrocarbons
hexachl orodib enzo-p-di oxin
hypoxia-inducible factor
heptachlorodibenzo-p-dioxin
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LIST OF ACRONYMS, ABBREVIATIONS, AND SYMBOLS (continued)
hr hairless
IARC International Agency for Research on Cancer
ID immunosuppressive dose
IgA immunoglobulin A
I-P initiation-promotion
IPCS International Programme on Chemical Safety (WHO)
I-TEQ international TEF scheme adopted by EPA in 1989
kg kilogram
L liter
LABB lifetime average body burden
LED01 lower bound of the effective dose at the 1% response level
LH luteinizing hormone
LMS linearized multistage
LOAEL lowest-observed-adverse-effect level
MOE margin of exposure
mRNA messenger ribonucleic acid
MRL minimal risk level (ATSDR)
NHANES National Health and Nutrition Examination Survey
NHATS National Human Adipose Tissue Survey
ng nanogram
NIOSH National Institute for Occupational Safety and Health
NTP National Toxicology Program
NOAEL no-observed-adverse-effect level
NOEL no-observed-effect level
OCDD octachlorodibenzo-^-dioxin
pg picogram
PAH polycyclic aromatic hydrocarbon
PBPK physiologically based pharmacokinetic
PBDD polybrominated dibenzodioxin
PBDF polybrominated dibenzofuran
PCB polychlorinated biphenyl
PCDD polychlorinated dibenzodioxin
PCDF polychlorinated dibenzofuran
PCP pentachlorophenol
PCQ polychlorinated quaterphenyl
PeCDD pentachlorodibenzo-p-dioxin
PeCDF pentachlorodibenzo-p-furan
PK pharmacokinetic
POD point of departure
POTW publicly-owned treatment works
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LIST OF ACRONYMS, ABBREVIATIONS, AND SYMBOLS (continued)
ppt part per trillion
PVC polyvinyl chloride
REP relative potency
RfD reference dose (EPA)
RR relative risk
SAB U. S. EPA's Science Advisory Board
SMR standardized mortality ratio
SRBC sheep red blood cells
2,4,5-T 2,4,5-trichlorophenoxyacetic acid
TDG thyroid binding globulin
TCDD 2,3,7,8-tetrachlorodibenzo-p-dioxin
TCP trichlorophenol
TDI tolerable daily intake
TEF toxic equivalency factor
TEQ toxic equivalent
TEQ-WHO94 1994 WHO extension of the I-TEF scheme to include 13 dioxin-like PCBs
TEQ-WHO98 1998 WHO update to the previously established TEFs for dioxins, furans, and
dioxin-like PCBs
TPA tetradecanoyl phorbol acetate
TNP-LPS trinitrophenyl-lipopolysaccharide
TSH thyroid stimulating hormone
URL unit risk level
WHO World Health Organization
approximately
> greater than
< less than
> greater than or equal to
< less than or equal to
jig microgram
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AUTHORS
William H. Farland
Acting Deputy Assistant Administrator for Science
Office of Research and Development
U.S. Environmental Protection Agency
Washington, DC
Linda S. Birnbaum
Director
Experimental Toxicology Division
National Health and Environmental Effects Laboratory
Office of Research and Development
U.S. Environmental Protection Agency
Research Triangle Park, North Carolina
David H. Cleverly
Exposure Analysis and Risk Characterization Group
National Center for Environmental Assessment
Office of Research and Development
U.S. Environmental Protection Agency
Washington, DC
Michael J. DeVito
Chief, Pharmacokinetics Branch
Experimental Toxicology Division
National Health and Environmental Effects Laboratory
Office of Research and Development
U.S. Environmental Protection Agency
Research Triangle Park, North Carolina
Matthew N. Lorber
Exposure Analysis and Risk Characterization Group
National Center for Environmental Assessment
Office of Research and Development
U.S. Environmental Protection Agency
Washington, DC
Bruce D. Rodan
Medical Officer (Research)/Senior Health Scientist
National Center for Environmental Assessment
Office of Research and Development
U.S. Environmental Protection Agency
Washington, DC
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AUTHORS (continued)
John L. Schaum
Environmental Engineer
Immediate Office of the Division Director-Washington
National Center for Environmental Assessment
Office of Research and Development
U.S. Environmental Protection Agency
Washington, DC
Linda C. Tuxen
Special Assistant
National Center for Environmental Assessment
Office of Research and Development
U.S. Environmental Protection Agency
Washington, DC
Dwain L. Winters
Director
Dioxin Policy Project
Office of Pollution Prevention and Toxics
Office of Prevention, Pesticides, and Toxic Substances
U.S. Environmental Protection Agency
Washington, DC
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1. INTRODUCTION
1 This document presents an integrated summary of available information related to
2 exposure to and possible health effects of dioxin and related compounds. It also presents a short
3 risk characterization, which is a concise statement of dioxin science and the public health
4 implications of both general population exposures from environmental "background"1 and
5 incremental exposures associated with proximity to sources of dioxin and related compounds.
6 Even though this document is a summary of key findings developed in the exposure and health
7 assessment portions (Parts I and n, respectively) of the U.S. Environmental Protection Agency's
8 (EPA or Agency) dioxin reassessment, it is meant to be detailed enough to stand on its own for
9 the average reader. Readers are encouraged to refer to the more detailed documents, cited below,
10 for further information on the topics covered here and to see complete literature citations.
11
1 2 Estimating Exposure to Dioxin-Like Compounds: This document, hereafter referred to as
1 3 Part I, the Exposure Document, is divided into 3 volumes: (1) Sources of Dioxin-Like
14 Compounds in the United States; (2) Properties, Environmental Levels, and Background
1 5 Exposures; and (3) Site-Specific Assessment Procedures.
16
1 7 Health Assessment for 2,3,7,8-Tetrachlorodibenzo-p-dioxin (TCDD) and Related
18 Compounds: This document, hereafter referred to as Part n, the Health Document,
1 9 contains two volumes with nine chapters covering pharmacokinetics, mechanisms of
20 action, epidemiology, animal cancer and various noncancer effects, toxic equivalency
21 factors (TEFs), and dose-response.
22
23 Parts of this integrative summary and risk characterization go beyond individual chapter
24 findings to reach general conclusions about the potential impacts of dioxin-like compounds on
25 human health. This document specifically identifies issues concerning the risks that may be
26 occurring in the general population at or near population background exposure levels. It
The term "background exposure" has been used throughout this reassessment to describe exposure that
regularly occurs to members of the general population from exposure media (food, air, soil, etc.) that have dioxin
concentrations within the normal background range. Most (> 95%) background exposure results from the presence
of minute amounts of dioxin-like compounds in dietary fat, primarily from the commercial food supply. The origin
of this background exposure is from three categories of sources: naturally formed dioxins, anthropogenic dioxins
from contemporary sources, and dioxins from reservoir sources. The term "background exposure" as used in this
document should not be interpreted as indicating the significance or acceptability of risk associated with such
exposures.
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1 articulates the strengths and weaknesses of the available evidence for possible sources,
2 exposures, and health effects, and it presents assumptions made and inferences used in reaching
3 conclusions regarding these data. The final risk characterization provides a synopsis of dioxin
4 science and its implications for characterizing hazard and risk for use by risk assessors and
5 managers inside and outside the EPA and by the general public.
6 This document (Part ID) is organized as follows:
7
8 1. Introduction. This chapter describes the purpose/organization of and the process for
9 developing the report, defines dioxin-like compounds in the context of the EPA
10 reassessment, and explains the toxic equivalence (TEQ) concept.
11
1 2 2. Effects Summary. This chapter summarizes the key findings of the Health Document
13 and provides links to relevant aspects of exposure, mechanisms, and dose-response.
14
15 3. Mechanisms and Mode of Dioxin Action. This chapter discusses the key findings on
1 6 effects in terms of mode of action. It uses the "Mode-of-Action Framework" recently
1 7 described by the World Health Organization/(WHO) International Programme on
1 8 Chemical Safety (IPCS) Harmonization of Approaches to Risk Assessment Project and
1 9 contained in the Agency's draft guidelines for carcinogen risk assessment as the basis for
20 the discussions.
21
22 4. Exposure Characterization. This chapter summarizes the key findings of the
23 Exposure Document and links them to the effects, mechanisms, and dose-response
24 characterization.
25
26 5. Dose Response Characterization. This chapter summarizes approaches to dose-
27 response that are found in the Health Document and provides links to relevant aspects of
28 exposure and effects.
29
30 6. Risk Characterization. This chapter presents conclusions that are based on an
31 integration of the exposure, effects, mechanisms, and dose-response information. It also
32 highlights key assumptions and uncertainties.
33
34 The process for developing this risk characterization and companion documents has been
35 open and participatory. Each of the documents has been developed in collaboration with
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1 scientists from inside and outside the Federal Government. Each document has undergone
2 extensive internal and external review, including review by EPA's Science Advisory Board
3 (SAB). In September 1992, early drafts of all the background chapters underwent external peer
4 review. This was followed by extensive revision and re-review of the epidemiology chapter in
5 September 1993. In September 1994, drafts of each document, including an earlier version of
6 this risk characterization, were made available for public review and comment, which included a
7 150-day comment period and 11 public meetings around the country to receive oral and written
8 comments. These comments, along with those of the SAB, have been considered in the drafting
9 of this final document. The dose-response chapter of the Health Document underwent peer
1 0 review in 1997; an earlier version of this Integrated Summary and Risk Characterization
11 underwent development and review in 1997 and 1998, and comments have been incorporated.
1 2 In addition, as requested by the SAB, a chapter on toxic equivalency has been developed
1 3 and underwent external peer review in parallel with the Integrated Summary and Risk
14 Characterization in July 2000. Review by the SAB of the dose-response chapter, the toxic
1 5 equivalency chapter, and the Integrated Summary and Risk Characterization occurred in
1 6 November 2000. The report of that review was submitted to the EPA Administrator on May 31,
1 7 2001. These sections of the document, as well as a few of the other background chapters in Parts
1 8 I and U, have been revised to reflect the comments of the SAB and the public. The
1 9 comprehensive set of background documents and this integrative summary and risk
20 characterization are now being published as final reports to replace previous dioxin assessments
21 as the scientific basis for EPA decision making.
22
23 1.1. DEFINITION OF DIOXIN-LIKE COMPOUNDS
24 As defined in Part I of this document, this assessment addresses specific compounds in
25 the following chemical classes: polychlorinated dibenzo-/?-dioxins (PCDDs or CDDs),
26 polychlorinated dibenzofurans (PCDFs or CDFs), polybrominated dibenzo-p-dioxins (PBDDs or
27 BDDs), polybrominated dibenzofurans (PBDFs or BDFs), and polychlorinated biphenyls (PCBs);
28 these chemicals are described as "dioxin-like." Dioxin-like refers to the fact that these
29 compounds have similar chemical structure and physical-chemical properties, and they invoke a
30 common battery of toxic responses. Because of their hydrophobic nature and resistance towards
31 metabolism, these chemicals persist and bioaccumulate in fatty tissues of animals and humans.
32 The CDDs include 75 individual compounds; CDFs include 135 different compounds.
33 These individual compounds are referred to technically as congeners. Likewise, the BDDs
34 include 75 different congeners, and the BDFs include an additional 135 congeners. Only 7 of the
35 75 congeners of CDDs or of BDDs are thought to have dioxin-like toxicity: those with
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1 chlorine/bromine substitutions in, at a minimum, the 2, 3, 7, and 8 positions. Only 10 of the 135
2 possible congeners of CDFs or of BDFs are thought to have dioxin-like toxicity; also those with
3 substitutions in the 2, 3, 7, and 8 positions. This suggests that 17 individual CDDs/CDFs and an
4 additional 17 BDDs/BDFs exhibit dioxin-like toxicity. The database on many of the brominated
5 compounds regarding dioxin-like activity has been less extensively evaluated, and these
6 compounds are not explicitly considered in this assessment. (For a review of this topic see
7 Birnbaumetal., 2003.)
8 There are 209 PCB congeners, only 12 of which are thought to have dioxin-like toxicity:
9 PCBs with four or more lateral chlorines, with one or no substitution in the ortho position. These
10 compounds are sometimes referred to as coplanar, meaning that they can assume a flat
11 configuration, with rings in the same plane. Similarly configured polybrominated biphenyls
1 2 (PBBs) are likely to have similar properties. However, the database on these compounds with
13 regard to dioxin-like activity has been less extensively evaluated, and these compounds are not
14 explicitly considered in this assessment. Mixed chlorinated and brominated congeners of
1 5 dioxins, furans, and biphenyls also exist, increasing the number of compounds potentially
1 6 considered dioxin-like within the definitions of this assessment. The physical/chemical
1 7 properties of each congener vary according to the degree and position of chlorine and/or bromine
1 8 substitution. Very little is known about occurrence and toxicity of the mixed (chlorinated and
1 9 brominated) dioxin, furan, and biphenyl congeners. Again, these compounds are not explicitly
20 considered in this assessment.
21 Generally speaking, this assessment focuses on the 17 CDDs/CDFs and a few of the
22 coplanar PCBs that are frequently encountered in source characterization or environmental
23 samples. The Agency recognizes that other dioxin-like compounds exist in the chemical classes
24 discussed above (e.g., brominated or chlorinated/brominated congeners) or in other chemical
25 classes (e.g., polyhalogenated naphthalenes or benzenes, azo- or azoxybenzenes), but this
26 evaluation focuses on the two dozen chlorinated congeners that are generally considered to be
27 most associated with environmental and human health risks.
28 The chlorinated dibenzodioxins and dibenzofurans are tricyclic aromatic compounds with
29 similar physical and chemical properties. Certain of the PCBs (the so-called coplanar or mono-
30 ortho coplanar congeners) are also structurally and conformationally similar. The most widely
31 studied of this general class of compounds is 2,3,7,8-tetrachlorodibenzo-/?-dioxin (TCDD). This
32 compound, often simply called "dioxin," represents the reference compound for this class of
33 compounds. The structure of TCDD and several related compounds is shown in Figure 1-1.
34 Although sometimes confusing, the term "dioxin" is often also used to refer to the complex
35 mixtures of TCDD and related compounds emitted from sources or found in the environment or
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1 in biological samples. It can also be used to refer to the total TCDD "equivalents" found in a
2 sample. This concept of toxic equivalency is discussed extensively in Part n, Chapter 9, Section
3 9.4, and is summarized below.
4
5 1.2. TOXIC EQUIVALENCY FACTORS
6 CDDs, CDFs, and PCBs are commonly found as complex mixtures when detected in
7 environmental media and biological tissues or when measured as environmental releases from
8 specific sources. Humans are likely to be exposed to variable distributions of CDDs, CDFs, and
9 dioxin-like PCB congeners that vary by source and pathway of exposures. This complicates the
10 human health risk assessment that may be associated with exposures to variable mixtures of
11 dioxin-like compounds. In order to address this problem, the concept of toxic equivalency has
1 2 been considered and discussed by the scientific community, and TEFs have been developed and
13 introduced to facilitate risk assessment of exposure to these chemical mixtures.
14 On the most basic level, TEFs compare the potential toxicity of each dioxin-like
1 5 compound in the mixture to the well-studied and understood toxicity of TCDD, the most toxic
1 6 member of the group. The use of the TEF methodology has been EPA policy since 1987, when
1 7 the Agency "adopted an interim procedure, based on dioxin 'toxicity equivalence' factors
1 8 (TEFs), for estimating the hazard and dose-response of complex mixtures containing CDDs and
1 9 CDFs in addition to 2,3,7,8-TCDD" (EPA 1987, 1989a). The background and historical
20 perspective regarding this procedure is described in detail in Part n, Chapter 9, Section 9.1, 9.2,
21 and in Agency documents (U.S. EPA, 1987, 1989a, 1991a). This procedure involves assigning
22 individual TEFs to the 2,3,7,8-substituted CDD/CDF congeners and dioxin-like PCBs. To
23 accomplish this, scientists have reviewed the toxicological databases and considered chemical
24 structure, persistence, and resistance to metabolism and have agreed to ascribe specific "order of
25 magnitude" TEFs for each dioxin-like congener relative to TCDD, which is assigned a TEF of
26 1.0. The other congeners have TEF values ranging from 1.0 to 0.00001. Thus, these TEFs are
27 the result of scientific judgment of a panel of experts who used all of the available data, and they
28 are selected to account for uncertainties in the available data and to avoid underestimating risk.
29 In this sense, they can be described as "public health-conservative" values.
30 It is important to understand that this process results in values that represent the scientific
31 judgment of experts working with specified criteria. As described below, these values rely more
32 heavily on in vivo than in vitro data and on chronic or subchronic exposures rather than acute
33 exposures. Attempts to replicate or critique individual TEF values on the basis of distributional
34 analysis of relative potency (REP) estimates from individual endpoints or all data have been
35 undertaken (Finley et al., 2003), suggesting possible benefits from the analysis of REP
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1 distributions. It remains important, however, to recognize the emphasis placed by WHO on the
2 above noted weighting factors and on the expert scientific judgment used to derive the existing
3 TEF values.
4 The TEQ concept is applied by multiplying the TEF of each congener present in a
5 mixture by the respective mass concentration and the products are summed to represent the
6 2,3,7,8-TCDD TEQ of the mixture, as determined by equation 1-1.
7
X~" • \ F • ' *
g JEQ s > . ['..'-.yngeKS^ :< iMr:, I+^1,<:••••;gsne?,: .•: j £K\: j+ ; :-.OMge:»Y€?s xj^.^ J (1-1)
9
10 The TEF values for PCDDs and PCDFs were originally adopted by international
11 convention (U.S. EPA, 1989a). Subsequent to the development of the first international TEFs
1 2 for CDD/CDFs, these values were further reviewed and/or revised and TEFs were also developed
1 3 for PCBs (Ahlborg et al., 1994; van den Berg et al., 1998). A problem arises in that past and
14 present quantitative exposure and risk assessments may not have clearly identified which of three
1 5 TEF schemes was used to estimate the TEQ. This reassessment introduces a new uniform TEQ
1 6 nomenclature that clearly distinguishes between the different TEF schemes and identifies the
1 7 congener groups included in specific TEQ calculations. The nomenclature uses the following
1 8 abbreviations to designate which TEF scheme was used in the TEQ calculation:
19
20 1. I-TEQ refers to the International TEF scheme adopted by EPA in 1989 (U.S. EPA,
21 1989a). See Table 1-1.
22
23 2. TEQ-WHO94 refers to the 1994 WHO extension of the I-TEF scheme to include 13
24 dioxin-like PCBs (Ahlborg et al., 1994). The TEF values for the dioxins and furans
25 are identical to the I-TEQ. See Table 1-2.
26
27 3. TEQ-WHO98 refers to the 1998 WHO update to the previously established TEFs for
28 dioxins, furans, and dioxin-like PCBs (van den Berg et al., 1998). There are numerous
29 changes in the TEF values for the dioxins, furans and PCBs. See Table 1-3.
30
31 The nomenclature also uses subscripts to indicate which family of compounds is included
32 in any specific TEQ calculation. Under this convention, the subscript D is used to designate
33 dioxins, the subscript F to designate furans, and the subscript P to designate PCBs. For example,
34 "TEQDF-WHO98" would be used to describe a mixture for which only dioxin and furan congeners
35 were determined and where the TEQ was calculated using the WHO98 scheme. If PCBs had also
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1 been determined, the nomenclature would be "TEQDFP-WHO98." Note that the designations
2 TEQDF-WHO94 and I-TEQDF are interchangeable, as the TEFs for dioxins and furans are the same
3 in each scheme. Note also that in the current draft of this document, I-TEQ sometimes appears
4 without the D and F subscripts. This indicates that the TEQ calculation includes both dioxins
5 and furans.
6 This reassessment recommends that the WHO98 TEF scheme be used to assign toxic
7 equivalency to complex environmental mixtures for assessment and regulatory purposes. Later
8 sections of this document describe the mode(s) of action by which dioxin-like chemicals mediate
9 biochemical and toxicological actions. These data provide the scientific basis for the TEF/TEQ
10 methodology. In the 20-year history of the TEF/TEQ concept, the approach has evolved, and
11 decision criteria supporting the scientific judgment and expert opinion used in assigning TEFs
1 2 have become more transparent. Numerous states and countries and several international
1 3 organizations have studied and consequently adopted this approach to evaluating complex
1 4 mixtures of dioxin and related compounds (Part II, Chapter 9, Section 9.2). It has become the
1 5 accepted methodology, although the need for research to explore alternative approaches is widely
1 6 endorsed. Clearly, basing risk on TCDD alone or assuming that all chemicals are equally as
1 7 potent as TCDD is inappropriate on the basis of available data. Although uncertainties in the use
18 of the TEF methodology have been identified and are described later in this document and in
19 detail in Part II, Chapter 9, Section 9.5, one must examine the use of this method in the broader
20 context of the need to evaluate the potential public health and environmental impact of complex
21 mixtures of persistent, bioaccumulative chemicals.
22 It can be generally concluded that the use of TEF methodology for evaluating complex
23 mixtures of dioxin-like compounds decreases the overall uncertainties in the risk assessment
24 process, as compared to alternative approaches. Use of the latest consensus values for TEFs
25 assures that the most recent scientific information informs this "useful, interim approach" (U.S.
26 EPA, 1989a; Kutz et al., 1990) to dealing with complex environmental mixtures of dioxin-like
27 compounds. As stated by the EPA's SAB (U.S. EPA, 1995), "The use of the TEFs as a basis for
28 developing an overall index of public health risk is clearly justifiable, but its practical application
29 depends on the reliability of the TEFs and the availability of representative and reliable exposure
30 data." EPA will continue to work with the international scientific community to update these
31 TEF values to ensure that the most up-to-date and reliable data are used in their derivation and to
32 evaluate their use on a periodic basis.
33 A chemical is assigned a TEF value on the basis of all the available data comparing the
34 REP of a chemical to 2,3,7,8-TCDD. REP values are obtained from individual studies available
35 in the peer-reviewed literature. In addition, there are weighting criteria that place more emphasis
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1 on REP values from chronic and subchronic studies that examine toxic endpoints (van den Berg
2 et al., 1998). There is a broad range in the quantity and quality of the data available for
3 individual congeners. For example, the TEF forPCB 126 is based on over 60 REP values from
4 in vivo endpoints that examine responses as diverse as enzyme induction, developmental
5 toxicity, immunotoxicity, hepatic toxicity, alterations in hormones, and tumor promotion,
6 whereas the TEF for 3,4,4',5-tetrachlorobiphenyl (PCB 81) is based on REP values for in vitro
7 CYP1A induction and QSAR calculations. Fortunately, the uncertainty in the PCB 81 TEF
8 based on limited data has minimal effect on the risk characterization of complex mixtures of
9 dioxin-like compounds since it does not contribute significantly to human TEQ exposures.
10 Five congeners contribute approximately 80% of the total TEQ in humans: 2,3,7,8-
11 TCDD; 1,2,3,7,8-PCDD; 1,2,3,6,7,8-HxCDD; 2,3,4,7,8-PCDF; and PCB 126 (see Part I, Volume
12 2 and Section 4.4.3 of this document). With the exception of 1,2,3,6,7,8-HxCDD, the TEFs for
13 these chemicals are based on a number of different endpoints examined in multiple studies
14 performed in different laboratories (Table 1-4). The TEF for 1,2,3,6,7,8-HxCDD is based
1 5 heavily on a two-year bioassay in which rats were exposed to a mixture of 1,2,3,6,7,8-HxCDD
1 6 and 1,2,3,7,8,9-HxCDD. The TEFs for 2,3,4,7,8-PCDF and PCB 126 are similar to the mean
1 7 REP value for all in vivo endpoints and are similar to their REPs for tumor promotion. The TEF
1 8 for 1,2,3,7,8-PCDD is based largely on its REP for tumor promotion in rats, supported by studies
19 of its biochemical effects in a subchronic mouse study (DeVito et al., 1997).
20 From these data, it is clear that the chemicals that contribute approximately 80% to the
21 total human TEQ are well studied and that the assigned TEFs provide reasonable estimates of the
22 relative potency of these chemicals. In contrast, although some chemicals in the TEF
23 methodology have minimal data sets with which to reliably assess their relative potency, they do
24 not contribute substantially to the background human blood TEQ.
25 The ability of the TEF methodology to predict the biological effects of mixtures
26 containing dioxin-like chemicals has been evaluated in a number of experimental systems. These
27 studies generally demonstrate that the assumption of additivity provides a reasonable estimate of
28 the dioxin-like potential of a mixture (Part II, Chapter 9, Section 9.4). Hamm et al. (2003)
29 demonstrated that a mixture of TCDD, PeCDD, TCDF, 1-PeCDF, 4-PeCDF, OCDF, and PCBs
30 77, 126 and 169 at doses approximating the relative abundance in the food supply, as described
31 by Birnbaum and DeVito (1995), induced a similar spectrum of reproductive toxicity in rat
32 offspring as does TCDD, and that the TEF methodology did reasonably well at predicting the
33 dose-response relationship of the mixture. A close relationship was evident for maternal EROD
34 enzyme induction between TCDD and the equivalent TEQ mixture, with a slightly lowered dose-
35 response for fetal effects from the mixture (~2 fold lower), attributed to decreased transfer of
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1 mixture components to the offspring. A recent statistical modeling exercise of EROD enzyme
2 induction in the NTP bioassays (Toyoshiba et al., 2004) reported that from a statistical standpoint
3 the consensus WHO98 TEFs were "significantly different from the maximum likelihood-based
4 estimates, but not very different in actual magnitude." Graphing of the non-log-scaled summary
5 data reported in Toyoshiba et al. (2004) reveals differences of less than 2-3 fold from predicted
6 TEQ-based activities, for individual congeners and the mixture. There are examples of
7 nonadditive interactions between dioxins and nondioxins. Both greater-than-additive and less-
8 than-additive interactions have been observed in these studies. In general the nonadditive
9 interactions between the dioxins and nondioxins have been observed at doses that are
10 considerably higher than present background human exposures (Part II, Chapter 9, Section 9.4).
11 There are a number of natural chemicals that bind and activate the aryl hydrocarbon (Ah)
1 2 receptor (AhR) and induce some dioxin-like effects. It has been proposed by some scientists that
13 these chemicals contribute significantly to total TEQ exposures and that these exposures far
14 outweigh those from PCDDs, PCDFs, and PCBs (Safe, 1995a). There are several limitations to
1 5 these analyses, as detailed in Part II, Chapter 9, Section 9.3.5. The hypothesis is built on AhR
1 6 binding studies and a few other in vitro studies that compared natural ligands to the dioxin-like
1 7 chemicals. Under these circumstances, neither biological half-life nor toxicity profile is
18 considered.
1 9 The in vivo data on the natural AhR ligands is limited to enzyme induction and a single
20 developmental study. Few if any toxicology studies demonstrating clear dioxin-like toxicities
21 have been published. The natural AhR ligands are rapidly metabolized and result in both
22 transient tissue concentrations and transient effects. More recent data demonstrate that these
23 potent in vitro AhR agonists (e.g., indolo[2,3-b]carbazole) neither elicit dioxin-like toxicity nor
24 alter the effects of dioxin in vivo (Pohjanvirta et al., 2002). This may occur because of short
25 persistence times in target organs or inadequate/inappropriate conformational changes induced as
26 a result of AhR-ligand binding (Henry and Gasiewicz, 2003). The natural ligands also have their
27 own distinct biological effects that are independent of the AhR, and it is not clear as to the role of
28 the AhR in the biological effects of these chemicals. Because of the relative concentration of
29 these compounds in the daily diet, their in vitro binding characteristics, and the limited
30 toxicological information in vivo, this issue requires further research in order to better understand
31 the uncertainty surrounding the relative potential health effects of dioxin and related chemicals as
32 compared to natural AhR ligands.
33 One of the limitations of the use of the TEF methodology in risk assessment of complex
34 environmental mixtures is that the risk from nondioxin-like chemicals is not evaluated in concert
35 with that of dioxin-like chemicals. Another limitation of the TEF methodology is the application
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1 of TEFs to nonbiological samples. The fate and distribution of PCDDs, PCDFs, and PCBs are
2 not necessarily related to their TEFs. Thus, the use of the TEF for assessing potential hazard and
3 risk based on dioxin-like compounds passing through nonbiological media must be done
4 cautiously. Fate and transport of the mixture and likelihood and route of exposure will have
5 important impacts on such assessments. Future approaches to the assessment of environmental
6 mixtures should focus on the development of methods that will allow risks to be predicted when
7 multiple mechanisms are present from a variety of contaminants coming into contact with
8 humans and other environmental receptors through multiple routes.
9 There are a number of uncertainties in the application of the TEF methodology which are
1 0 discussed in greater detail in Part H, Chapter 9. In 1998, the U.S. EPA and the U.S. Department
1 1 of the Interior sponsored a workshop on the use of the TEF methodology in ecological risk
1 2 assessment. This workshop involved panel members from academia, industry and state and
1 3 federal governments. This panel concluded that "the uncertainties associated with using RePs or
1 4 TEFs are not thought to be larger than other sources of uncertainty within the [ecological] risk
1 5 assessment process (e.g., dose-response assessment, exposure assessment, and risk
1 6 characterization)" (U.S. EPA, 200la). In addition, despite the uncertainties in the TEF
1 7 methodology, the use of this methodology decreases the overall uncertainty of the risk
1 8 assessment. The panel had difficulty in quantitatively expressing the uncertainty in the TEF
19 methodology. While the panel supported the use of the TEF methodology, they also
20 recommended continued research focusing on a better understanding of the uncertainty in the
21 TEF methodology.
22
23 1.3. UNDERSTANDING EXPOSURE/DOSE RELATIONSHIPS FOR DIOXIN-LIKE
24 COMPOUNDS
25 Risk assessment requires the scaling of exposure/dose across endpoints and across
26 species. Given the many responses to TCDD and its congeners, the selection of dose metrics for
27 use in quantitative risk assessments is a complex problem. The biochemical and toxicological
28 responses to TCDD and related chemicals are initiated by their interaction with the Ah receptor.
29 Some responses, such as enzyme induction, require short periods (minutes to hours) of AhR
30 activation. Other responses, such as cancer, require prolonged (months to many years) activation
31 of this pathway. Still other responses, such as the developmental toxi cities, require receptor
32 activation during specific windows of sensitivity. Because of the different mechanisms involved
33 in these diverse responses, it is unlikely that a single dose metric will be adequate for all of these
34 endpoints.
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1 A number of studies have proposed a variety of dose metrics for a number of different
2 responses. These studies have taken different approaches, ranging from simple curve-fitting
3 exercises (Hurst et al., 2000; van Birgelen et al., 1996) to more complex physiologically based
4 pharmacokinetic (PBPK) modeling approaches (Jusko et al., 1995; Andersen et al., 1997; Kohn
5 et al., 1993; Portier and Kohn, 1996). Area under the curve (AUC) has been used traditionally in
6 the drug literature as a dose metric of choice when the dose and the time related to effects in
7 humans are known.
8 The choice of dose metric not only considers mechanistic data but must consider
9 pragmatic approaches as well. The use of the dose metric plays a role in its choice. Because of
1 0 differences in lifespan and uncertainties in the windows of sensitivity for various endpoints,
11 lifetime AUC may not be a useful dose metric for cross-species extrapolation in the risk
1 2 assessment of dioxin and related compounds. For instance, reported interspecies differences in
1 3 rat liver versus human lung cancer risks based on lifetime AUC are heavily influenced by
14 different lifespans of humans (-70 yrs) versus rats (~2 years), and are mitigated though the use of
1 5 peak levels or average concentrations (Aylward et al., 1996). Notably, there are no interspecies
16 differences in risk calculations between humans and rats when applying average body burden to
1 7 the same endpoint, all cancers combined, coupled with more detailed exposure data from the
18 epidemiology studies (see Table 5-4). Because cross-species scaling is not required when the
19 analysis is confined to humans, lifetime AUC has been used in the analysis of human cancer data
20 on TCDD (Becher et al., 1998) and may be a useful dose metric when applied to accidental or
21 occupational exposures.
2 2 The choice of dose metric is also dependent on the data available. A number of dose
23 metrics, such as AhR occupancy, induction of CYP1A2, and decreases in epidermal growth
24 factor (EGF) receptor (EGFR) have been proposed on the basis of PBPK models (Jusko et al.,
25 1995; Andersen et al.,1997; Kohn et al., 1993; Portier and Kohn, 1996). Although these dose
26 metrics have been useful in hypothesis testing in experimental systems, they are not useful in
27 animal-to-human extrapolations due to the difficulty in measuring these parameters in humans.
28 In the following section, the strengths and weaknesses of a variety of proposed dose metrics are
29 presented.
30
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1 1.3.1. Administered Dose
2 In experimental studies, animals are administered a defined dose through a variety of
3 routes. A default method used by EPA (U.S. EPA, 1992a, 1996) to estimate the human
4 equivalent dose when scaling across species is to use allometric scaling based on the following
5 equation:
6
7 0086^= Doserat(BWrat/BWhuman)°-25 (1-2)
8
9 where BW is the body weight in kilograms and Dose is the daily administered dose in rats or the
10 scaled human daily dose expressed as mg/kg/day, or in the case of TCDD ng/kg/day. This
11 method, in the absence of data to select a more appropriate dose metric, is thought to scale
1 2 administered dose in such a way as to result in equivalent effective doses in humans and
1 3 experimental animals (U.S. EPA, 1992). Using this equation, a dose of 1 ng TCDD/kg/day in a
14 0.35 kg rat would result in a scaled human dose of 0.27 ng TCDD/kg/day for a 70 kg human. If
1 5 this scaling method applies to TCDD and related chemicals, then 1 ng TCDD/kg/day in the rat
16 should produce similar effective doses in a human exposed to 0.27 ng TCDD/kg/day, some 3.8
1 7 times lower. However, this method fails to take into account differences in the elimination half-
1 8 life of the chemical in the two species. In the case of dioxin-like compounds, this is an important
19 consideration.
20 Assuming similar sensitivity between rats and humans at the tissue level, effective doses
21 should be a function of tissue concentration. Tissue concentrations of TCDD and related
22 chemicals are directly related to the concentration of TCDD in the body. The steady-state
23 concentration of TCDD in the body, or steady-state body burden, can be estimated in rats and
24 humans using the following equation.
25
26 Steady-state body burden (ng/kg) = [Dose (ng TEQ/kg)* half-life (days)] * F (1-3)
27 Ln(2)
28
29 where Dose is the daily administered dose, F is the fraction absorbed, and t,/2 is the species-
30 specific half-life of TCDD. In the present example, we will assume that the species-specific half-
31 life of TCDD is 25 days for rats and 2593 days for humans. We also assume for this illustration
32 that F is 50% for both human and animal studies. The fraction absorbed varies from -50-100%
33 of administered dose, depending on dosing matrix (pellets, oil, food, breast milk; greater
34 variability from soil) and study species. For standardization elsewhere in Part IE, Risk
35 Characterization, the Agency has adopted 50% absorption from animal food pellets and 80%
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1 from human dietary intake (see Part II, Chapter 1; Poiger and Schlatter, 1986; Abraham et al.,
2 1996). The fraction absorbed linearly impacts the calculation of resulting body burden, with 80%
3 absorption leading to a 1.6-fold higher value than 50% absorption.
4 Starting with an administered dose of 1 ng/kg/day in rats and the scaled human dose of
5 0.27 ng/kg/day, the steady-state body burdens are presented in Table 1-5. The steady-state body
6 burden of TCDD using the scaled human dose is approximately 28 times that of the steady-state
7 body burden in the rat (Table 1-5). Using equation 1-3 to estimate equivalent steady-state body
8 burdens (i.e., 18 ng/kg), a human equivalent administered dose comparable to 1 ng/kg/day
9 administered to the rat was estimated at 0.0096 ng/kg/day, over 100 times less.
10 Clearly, the default scaling method results in an estimated human equivalent dose that
11 produces much greater estimated human tissue concentrations (505 ng/kg) than the rat's tissue
1 2 concentration (18 ng/kg). The default scaling approach accounts for a difference of ~ 3.7 times,
1 3 based on allometric considerations, yet the half-life of TCDD in humans alone is approximately
14 100-fold greater than in rats. This exercise suggests that administered dose may not provide a
1 5 useful dose metric for cross-species extrapolation even if the dose is scaled using the EPA
1 6 default methodology. However, administered dose can be used to compare chronic exposures
1 7 between human populations in order to describe potential human health risks, because the species
1 8 differences in half-life would not exist in this case. Adjustments will still need to be made,
19 however, to compare short-term exposures expressed as intake as a function of body weight per
20 day to more typical daily intake values in the general population.
21
22 1.3.2. Area Under the Curve
23 AUC is frequently used as a dose metric for reversible responses of pharmaceutical
24 agents. Typically, these agents have half-lives on the order of minutes to hours. In addition, the
25 pharmacological actions of the drug and the length of time of the response is clearly defined in
26 both animals and humans. For example, for anesthetics, sleep time is used as the length of time
27 for determining the AUC. In essence, plasma concentrations are readily determined and the time
28 span is easily defined. In contrast, TCDD has a prolonged half-life in both humans and
29 experimental animals and some of the adverse effects that are of concern in the hazard
30 characterization are not reversible responses. Because of these differences it is unclear whether
31 the AUC is the best dosemetric.
32 Mechanistic considerations suggest that AUC may be a useful dose metric for
33 carcinogenesis. TCDD and related chemicals are thought to induce tumors through promotional
34 mechanisms as opposed to acting as direct initiators. The promotional effects of TCDD and
35 related chemicals are associated with altered gene expression, resulting in alterations in growth
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1 and differentiation. This promotional process requires sustained tissue concentrations of TCDD
2 sufficient to maintain increased gene expression. One recent study examined AUC as a dose
3 metric for the tumor promotional responses of TCDD. Kim et al. (2003) compared AUC and
4 peak concentrations in rats as a dose metric for liver tumor promotion. Animals receiving a
5 single high exposure to TCDD had greater numbers of altered hepatic foci than animals receiving
6 repeated low dose exposures, even though the AUC was equivalent between the two exposures.
7 These data suggest that the peak concentrations of TCDD may play a significant role in TCDD
8 carcinogenicity and that future dose-response modeling exercises should incorporate measures of
9 dose timing and peak concentrations.
10 It is possible that AUC could be an appropriate dose metric for cancer in humans, and it
11 may also involve the incorporation of a threshold concentration (Hays et al., 1997). However,
1 2 the use of AUC for species extrapolation for TCDD is more complicated. Although blood or
1 3 plasma concentrations of TCDD can be determined in both humans and animals, the
1 4 determination of the time span for which the AUC is to be calculated is much less certain. For
1 5 some of the toxic responses to TCDD, such as induction of cleft palate, the window of sensitivity
16 is clearly defined in rodents and humans. For other responses, such as the developmental
1 7 reproductive alterations observed in male rats, the window of sensitivity has been narrowed to
1 8 exposures between gestational day 15 and 20 in the rats, but the human window of sensitivity is
1 9 uncertain. For many of the chronic toxic effects of TCDD, the length of time required to induce
20 the response remains uncertain in both experimental animals and humans. In order to apply
21 AUC for species comparisons of sensitivity to TCDD, one must have a better understanding of
22 the species differences in the windows of sensitivity to the various biological effects of TCDD.
23 In addition, differences in lifespan also must be considered. Brody and Reid (1967)
24 proposed that the biological activity of a drug is related to its plasma concentrations. If animals
25 and humans had the same plasma concentrations for their entire lives, the human AUC would be
26 greater because humans have a longer half-life of elimination for TCDD. However, because the
27 plasma concentrations would be the same, according to Brody and Reid (1967), the responses
28 should be similar. Hence, in order to use AUC for chronic toxicities, such as cancer, a correction
29 for the difference in lifespan must be applied. Typically, this involves the derivation of a lifetime
30 average serum lipid concentration, which is calculated by dividing the AUC by the time period of
31 exposure (Aylward et al., 1996). An estimation of the average daily AUC is directly related to
32 steady-state body burdens. Hence, once the AUC is corrected for life-span differences, these
33 values are equivalent to steady-state body burdens.
34 Although AUC may not be an appropriate dose metric for animal-to-human
35 extrapolations, it may be a useful tool for comparing populations exposed to high concentrations
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1 of dioxins over a short period of time to the background population. Becher et al. (1998) and
2 Steenland et al. (2001) used this approach to examine dose-response relationships for cancer in
3 occupationally exposed cohorts. One difficulty in determining AUC is the accuracy of the intake
4 measurements. Past exposures through the diet are uncertain, although they have been estimated
5 (Pinsky and Lorber, 1998). Future exposures are thought to be decreasing, although the exact
6 magnitude of this decrease is uncertain. Hence, determination of AUC carries a number of
7 uncertainties that must be considered.
8
9 1.3.3. Plasma or Tissue Concentrations
10 Brodie and Reid (1967) have argued that the response to a drug is determined by the
11 amount bound to its biological receptor, and because the drug-receptor complex is in dynamic
1 2 equilibrium with the free drug in the plasma, the biological response of a drug will be related to
1 3 its plasma concentrations. There is no reason to believe that this relationship will not be true for
14 TCDD and related chemicals. However, there are several data gaps that may prohibit the use of
1 5 plasma or blood concentrations for species extrapolation. First, few animal studies have
1 6 determined blood or plasma concentrations of TCDD, particularly in the subchronic, chronic, and
1 7 lifetime exposures. PBPK models can be used to estimate blood concentrations and should
1 8 provide reasonable estimates of these values. In contrast, the human exposure data are based
1 9 predominantly on blood, serum, or plasma dioxin concentrations.
20 One limitation of the human data is that it is mostly presented on a lipid-adjusted basis.
21 Hence, in order to compare the human and animal plasma or blood concentrations, one would
22 have to first estimate the blood concentrations in the animals using a PBPK model. Then, either
23 the animal data would have to be expressed as a lipid basis or the human data would have to be
24 expressed as a wet-weight basis. In either case, assumptions of the percent lipid in the blood
25 would have to be applied, as would a number of other assumptions typically used in the
26 construction of PBPK models. Recent work by Salvan et al. (2001) has attempted to account for
27 some of these assumptions in an analysis of cancer mortality in the National Institute for
28 Occupational Safety and Health (NIOSH) cohort (Steenland et al., 1999, 2001) using data on age-
29 related body mass index (BMI) and historical background exposures and tissue half-lives from
30 the Ranch Hand cohort (Michalek and Tripathi, 1999).
31 The use of tissue concentrations as a dose metric has also been examined by van Birgelen
32 et al. (1996) and Hurst et al. (1998, 2000). van Birgelen et al. presented data demonstrating that
33 target tissue concentrations provided an accurate prediction of enzyme induction regardless of the
34 exposure scenario (i.e., acute vs. subchronic). Similarly, Hurst et al. (2000) presented data
35 demonstrating that fetal tissue concentrations of TCDD on gestation day 16 predicted decreases
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1 in sperm counts, delays in puberty in males, urethra-phallus distance, and the incidence of
2 vaginal threads in rats prenatally exposed to TCDD on either gestational day 9 or 15. These data
3 suggest that target tissue concentrations may be a reasonable dose metric for these responses.
4 Although target tissue concentrations may aid in estimating risks, these data are unlikely to be
5 collected in humans in sufficient numbers to be useful, particularly for fetal concentrations.
6 Plasma (or serum) concentrations are also a useful tool for comparing exposures in
7 different human populations. Application of plasma concentration as a dose metric for species
8 extrapolation requires some level of assumptions, as described above, but reasonable
9 comparisons could be made, particularly for steady-state in humans and animals. Comparing
10 plasma or blood concentrations following acute exposures in experimental animals directly to
11 steady-state human blood or plasma concentrations is problematic.
1 2 One problem with the use of plasma, blood, or target tissue concentrations as a dose
1 3 metric is the limitations of current human PBPK models to predict these values on the basis of
14 changes in intake patterns. Further work will be required to develop such models.
15
16 1.3.4. Steady-State Body Burdens
1 7 Body burden is defined as the concentration of TCDD and related chemicals in the body
18 and is typically expressed as ng/kg body weight. In animals, these values are calculated from
19 studies at or approaching steady-state. These values are calculated on the basis of knowledge of
20 the species-specific half-life and the exposure or they are estimated on the basis of the TCDD
21 tissue concentration, the size of the tissues, and the weight of the animal. In humans the values
22 are typically presented as steady-state body burdens and are estimated on the basis of an intake
23 rate and the half-life of TCDD in humans. Alternatively, body burdens in humans are estimated
24 on the basis of lipid-adjusted serum or adipose tissue TCDD or TEQ concentrations (See Part I,
2 5 Volume 2, Chapter 4).
26 Steady-state body burdens provide a useful dose metric for several reasons. First, tissue
27 and blood concentrations are directly related to body burdens. Thus, body burdens are surrogates
28 for tissue concentrations. Second, the differences in the half-life of TCDD between species are
29 accounted for, because these body burdens are estimated at steady-state conditions. Third,
30 DeVito et al. (1995) have demonstrated that for a multitude of in vitro, biochemical, and toxic
31 responses, including chloracne and cancer, species have similar rates of responses when dose is
32 expressed on a body burden basis. Finally, body burdens provide flexibility, because they can be
33 estimated on the basis of either intake rates or on measured tissue concentrations.
34 Use of steady-state body burdens also has some limitations. In order to estimate steady-
35 state body burdens from lipid-adjusted tissue concentrations, an assumption of the percent body
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1 fat must be used. In the reassessment, a value of 25% has been used for humans. It should be
2 noted that there are human populations with body fat compositions as low as 10% and greater
3 than 35%. Also, when estimating the body burden on the basis of intake rates and half-lives, the
4 uncertainty of these parameters should be considered. In the reassessment, the estimated current
5 steady-state body burden of approximately 5 ng TEQDFP-WHO98/kg is based on measured serum
6 concentrations from several populations in the mid 1990's.
7 Although measured concentrations should eliminate some of the uncertainties in
8 estimates using intake rates and half-life assumptions, it is likely that these measured values
9 represent a past history of higher exposure, and we must anticipate a continued downward trend
10 to represent a "true" lifetime average concentration associated with current dose intake rates.
11 Caution must be used when using body burden as a dose metric for species extrapolation when
1 2 comparing short-term animal studies to steady-state human exposures. Under acute exposure
1 3 conditions in the animals, the relationship between tissue concentrations and body burden may
14 not be the same as under the steady-state conditions.
15
1 6 1.3.5. Mechanistic Dose Metrics
1 7 Several groups have proposed a variety of dose metrics based on mechanistic
1 8 considerations, such as concentration of occupied AhR (Jusko, 1995), induced CYP1A2
1 9 (Andersen et al., 1997; Kohn et al., 1993) and reduced EGFR (Portier and Kohn, 1996).
20 Although these dose metrics are intellectually appealing, it must be kept in mind that they are
21 still hypothesized dose metrics and require further research to demonstrate their utility for cross-
22 species extrapolations. In addition, these dose metrics are unlikely to be measured in sufficient
23 human samples to be useful.
24
25 1.3.6. Summary
26 A variety of dose metrics have been proposed for estimating potential human health
27 effects following exposure to dioxins. Many of them, such as tissue concentrations and the
28 mechanistic dose metrics, have practical limitations that inhibit their use. Others, such as AUC,
29 have limited utility for species extrapolations because of our limited understanding of the concept
30 of physiological time. Some, such as AUC and administered dose, can be used to compare
31 different human exposures, but are not necessarily suitable for cross-species extrapolations.
32 Others, such as steady-state body burdens or blood concentrations, are useful for species
33 extrapolations because they are directly related to tissue concentrations and can be estimated in
34 both animals and humans. All of these dose metrics require more research to improve cancer and
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1 noncancer risk prediction. This research could include efforts to quantify impacts of dose timing,
2 peak concentrations, and AUC above a baseline.
3 The use of any of these dose metrics requires a number of assumptions, discussed above
4 and in various chapters in Parts I and II. The choice of dose metric requires an understanding of
5 the data available and their application in the intended use of the dose metric. Future research
6 efforts could provide better guidance in choosing the dose metrics for dioxins and related
7 chemicals. However, in the meantime, the use of steady-state body burdens can provide a
8 reasonable description of dose for use in species extrapolations and risk assessments for many
9 chronic effects and is clearly preferable to intake levels.
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1
2
4
5
6
7
8
9
10
Table 1-1. The toxic equivalency factor (TEF) scheme for I-TEQDFa
Dioxin congener
2,3,7,8-TCDD
1,2,3,7,8-PeCDD
1,2,3,4,7,8-HxCDD
1,2,3,6,7,8-HxCDD
1,2,3,7,8,9-HxCDD
1,2,3,4,6,7,8-HpCDD
1,2,3,4,6,7,8,9-OCDD
TEF
1.0
0.5
0.1
0.1
0.1
0.01
0.001
Furan congener
2,3,7,8-TCDF
1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
1,2,3,4,7,8-HxCDF
1,2,3,6,7,8-HxCDF
1,2,3,7,8,9-HxCDF
2,3,4,6,7,8-HxCDF
1,2,3,4,6,7,8-HpCDF
1,2,3,4,7,8,9-HpCDF
1,2,3,4,6,7,8,9-OCDF
TEF
0.1
0.05
0.5
0.1
0.1
0.1
0.1
0.01
0.01
0.001
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
aNote that the scheme does not include dioxin-like PCBs. The nomenclature for this scheme is I-TEQDF, where
"I" represents "International," TEQ represents the 2,3,7,8-TCDD toxic equivalence of the mixture, and the
subscript DF indicates that only dioxins (D) and furans (F) are included in the TEF scheme.
Table 1-2. The toxic equivalency factor (TEF) scheme for TEQDFP-WHO94a
Dioxin congener
2,3,7,8-TCDD
1,2,3,7,8-PeCDD
1,2,3,4,7,8-HxCDD
1,2,3,6,7,8-HxCDD
1,2,3,7,8,9-HxCDD
1,2,3,4,6,7,8-HpCDD
1,2,3,4,6,7,8,9-OCDD
TEF
1.0
0.5
0.1
0.1
0.1
0.01
0.001
Furan congener
2,3,7,8-TCDF
1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
1,2,3,4,7,8-HxCDF
1,2,3,6,7,8-HxCDF
1,2,3,7,8,9-HxCDF
2,3,4,6,7,8-HxCDF
1,2,3,4,6,7,8-HpCDF
1,2,3,4,7,8,9-HpCDF
1,2,3,4,,6,7,8,9-OCDF
TEF
0.1
0.05
0.5
0.1
0.1
0.1
0.1
0.01
0.01
0.001
Dioxin-like
PCB
PCB-77
PCB-126
PCB- 169
PCB- 105
PCB-118
PCB-123
PCB-156
PCB-157
PCB- 167
PCB-114
PCB- 170
PCB- 180
PCB- 189
TEF
0.0005
0.1
0.01
0.0001
0.0001
0.0001
0.0005
0.0005
0.00001
0.0005
0.0001
0.00001
0.0001
29
30
31
32
33
34
a The nomenclature for this TEF scheme is TEQDFP-WHO94, where TEQ represents the 2,3,7,8-TCDD toxic
equivalency of the mixture, and the subscript DFP indicates that dioxins (D), furans (F), and dioxin-like PCBs
(P) are included in the TEF scheme. The subscript 94 following WHO displays the year changes were made to
the TEF scheme.
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1
2
4
5
6
7
8
9
10
11
Table 1-3. The toxic equivalency factor (TEF) scheme for TEQDFP-WHO98a
Dioxin congener
2,3,7,8-TCDD
1,2,3,7,8-PeCDD
1,2,3,4,7,8-HxCDD
1,2,3,6,7,8-HxCDD
1,2,3,7,8,9-HxCDD
1,2,3,4,6,7,8-HpCDD
1,2,3,4,6,7,8,9-OCDD
TEF
1.0
1.0
0.1
0.1
0.1
0.01
0.0001
Furan congener
2,3,7,8-TCDF
1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
1,2,3,4,7,8-HxCDF
1,2,3,6,7,8-HxCDF
1,2,3,7,8,9-HxCDF
2,3,4,6,7,8-HxCDF
1,2,3,4,6,7,8-HpCDF
1,2,3,4,7,8,9-HpCDF
1,2,3,4,6,7,8,9-OCDF
TEF
0.1
0.05
0.5
0.1
0.1
0.1
0.1
0.01
0.01
0.0001
Dioxin-like
PCB
PCB-77
PCB-81
PCB-126
PCB- 169
PCB- 105
PCB-118
PCB-123
PCB-156
PCB-157
PCB- 167
PCB-114
PCB- 189
TEF
0.0001
0.0001
0.1
0.01
0.0001
0.0001
0.0001
0.0005
0.0005
0.00001
0.0005
0.0001
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
' The nomenclature for this TEF scheme is TEQDFP-WHO98, where TEQ represents the 2,3,7,8-TCDD toxic
equivalency of the mixture, and the subscript DFP indicates that dioxins (D), furans (F), and dioxin-like PCBs (P)
are included in the TEF scheme. The subscript 98 following WHO displays the year changes were made to the
TEF scheme. Note that the changes to the TEFs since 1994 are as follows:
• for 1,2,3,7,8-PeCDD, the new WHO TEF is 1 and the I-TEF is 0.5;
• for OCDD, the new WHO TEF is 0.0001 and the I-TEF is 0.001;
• for OCDF, the new WHO TEF is 0.0001 and the I-TEF is 0.001;
• for PCB 77, the new TEF is 0.0001;
• the addition of PCB 81 (i.e., 3,4,4',5-TCB); and
• for the two di-ortho substituted HpCBs in the 1994 TEF scheme (i.e., PCBs 170 and 180), no TEFs have
been assigned in the new WHO TEF scheme.
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1
2
3
Table 1-4. The range of the in vivo relative potency estimates (REP) values
for the major toxic equivalency contributors
Chemical
1,2,3,7,8-
PCDD
2,3,4,7,8-
PCDF
1,2,3,6,7,
8-HxCDD
PCB 126
Number of in
vivo endpoints
22
40
3
62
Range of RE Ps
(mean ± std)
0.16-0.9
(0.5 ± 0.22)
0.018-4.0
(0.4 ±0.7)
0.015-0.16
0.0024-0.98
(0.20 ± 0.20)
Number of
endpoints from
subchronic studies
16
20
1
31
Range of
REPs
(mean ± std)
0.19-0.9
(0.53 ± 0.24)
0.018-0.6
(0.20 ±0.13)
0.04
0.004-0.18
(0.13 ±0.13)
TEF
1
0.5
0.1
0.1
5
6
7
8
9
10
1 1
12
13
14
15
16
TEF = toxic equivalency factor
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1
2
3
Table 1-5. Comparison of administered dose and body burden in rats
and humans3
Dose
(ng/kg/day)
Body burden
(ng/kg)
(A)
Rat daily
administered
dose/body
burden
1
18
(B)
Human scaled
administered
dose/body
burdenb
0.27
505
(C)
Human
equivalent
administered
dose/body
burden0
0.0096
18
(A/B)
Ratio of
rat-to-
human
scaled
dose
3.7
0.036
(A/C)
Ratio of
rat-to-
human
equivalent
Dose
104
1
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
a This matrix compares the effects of different interspecies scaling factors between rats and humans. Column A
indicates that a dose of 1 ng/kg/day to a rat leads to a steady-state body burden (BB) of 18 ng/kg, using the
formula BB = half-life*dose*absorption fraction (0.5)/ln2. Columns B and C then use different interspecies
scaling factors to convert the rat dose to a human equivalent dose. Column B uses body weight to the 3/4 power
as the interspecies scaling factor to convert the rat dose of 1 ng/kg/day (from the column A dose row) to the
equivalent human scaled dose of 0.27 ng/kg/day, which in turn corresponds to a human body burden of 505 ng/kg
based on the human half-life of 7.1 years and f = 0.5 (used in this table for consistency). Column C uses body
burden as the interspecies scaling factor to convert the rat body burden of 18 ng/kg (from column A body burden
row) to the equivalent 18 ng/kg BB in humans, and then derives the human dose that would correspond with this
body burden, i.e., 0.0096 ng/kg/day. The fifth column divides column A results by column B results, revealing
that the BW3/4 interspecies factor leads to a rat/human ratio of 3.7-fold. The last column divides column A by
column C results, revealing that when body burden is used as the interspecies scaling factor the rat dose is over
100 times the equivalent human dose.
b Assumes administered dose scales across species as a function of BW3/4
0 Assumes administered dose scales across species as a function of equivalent body burdens
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2,3,7,8-Tetractil oradi benzo-p-dioxin
1,2,3,7,8-Pentach lorod ibenzo-p-dioxin
ci' •> ci
3,3-,4,41,5,51-Hexachlorobiphefi yl
2,3,7,8-Tetrachl orodi betizofurari
2,3,4,7,8-P enta chl orodi benzoftira n
ci
3,3',4,4', SPentach lor ob ip heny I
Figure 1-1. Chemical structure of 2,3,7,8-TCDD and related compounds.
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1 2. EFFECTS SUMMARY
2
3 Since the identification in 1957 of 2,3,7,8-TCDD as a chloracnegen, more than 5000
4 publications have discussed its biological and toxicological properties. A large number of the
5 effects of dioxin and related compounds have been discussed in detail throughout the chapters in
6 Part n of this assessment. These discussions illustrate the wide range of effects produced by this
7 class of compounds. The majority of effects have been identified in experimental animals; some
8 have also been identified in exposed human populations. Although past EPA risk assessments
9 have focused on cancer estimates based on extrapolation models as the major concern for dioxin
10 and related compounds, more recent data suggest that noncancer effects may be occurring at or
11 near human background steady-state body burden levels in animals and in humans. Evaluation
1 2 of noncancer effects and their relationship to past and current body burdens and intake levels is
1 3 an important feature of this reassessment. Direct comparisons between various noncancer effects
1 4 and cancer in animals and humans and exposures of interest are presented in the form of margins
1 5 of exposure (MOE).
16 Cross-sectional studies have been conducted to evaluate the prevalence or extent of
1 7 disease in living 2,3,7,8-TCDD-exposed groups (Suskind and Hertzberg, 1984; Moses et al.,
1 8 1984; Lathrop et al., 1984, 1987; Roegner et al., 1991; Grubbs et al., 1995; Sweeney et al., 1989;
1 9 CDC Vietnam Experience Study, 1988; Webb et al., 1989; Ott and Zober, 1994). The limitations
20 of the cross-sectional study design for evaluating hazard and risk are discussed in Part II, Chapter
21 7b, Section 7.11. Many of the earliest studies were unable to define exposure-outcome
22 relationships owing to a variety of shortcomings, including small sample size, poor participation,
23 short latency periods, selection of inappropriate controls, and the inability to quantify exposure to
24 2,3,7,8-TCDD or to identify confounding exposures.
25 Cohort and case-control studies have been used to investigate hypothesized increases in
26 malignancies among the various 2,3,7,8-TCDD-exposed populations (Fingerhut et al., 1991a, b;
27 Manz et al., 1991; Eriksson et al., 1990). In more recent analyses of occupational cohorts
28 (Steenland et al., 1999; Ott and Zober, 1996; Flesch-Janys et al., 1998), cross-sectional studies of
29 U.S. chemical workers (Sweeney et al., 1989), U.S. Air Force Ranch Hand personnel (Roegner et
30 al., 1991; Grubbs et al., 1995), and Missouri residents (Webb et al., 1989), serum or adipose
31 tissue levels of 2,3,7,8-TCDD were measured to evaluate 2,3,7,8-TCDD-associated effects in
32 exposed populations. The ability to measure tissue or serum levels of 2,3,7,8-TCDD for all or a
33 large sample of the subjects confirmed exposure to 2,3,7,8-TCDD and permitted the investigators
34 to test hypothesized dose-response relationships.
35
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1 A large number of effects of exposure to TCDD and related compounds have been
2 documented in the scientific literature. Although many effects have been demonstrated in
3 multiple species (see Table 2-1), other effects may be specific to the species in which they are
4 measured and may have limited relevance to the human situation. Although the potential
5 species-specific responses are an important consideration for characterizing potential hazard, all
6 the observed effects of 2,3,7,8-TCDD illustrate the multiple sequelae that are possible when
7 primary impacts are at the level of signal transduction and gene transcription. Even though not
8 all observed effects may be characterized as "adverse" (i.e., some may be responses within the
9 normal range or adaptive or compensatory and of unknown or neutral consequence), they
10 represent a continuum of response expected from the fundamental changes in biology caused by
11 exposure to dioxin-like compounds. As discussed in the following sections, the doses associated
1 2 with this plethora of effects are best compared across species using a common measurement unit
1 3 of steady-state body burden of 2,3,7,8-TCDD and other dioxin-like compounds, as opposed to
14 the level or rate of exposure/intake.
1 5 The low end of the range of experimental lowest-observed-adverse-effect levels
1 6 (LOAELs), no-observed-adverse-effect levels (NOAELs), and effective doses at the 1% response
1 7 level (ED01s) for critical endpoints from animal studies is compiled in Table 5-6 and Appendix
18 A. These selected endpoints cover a spectrum from overt toxicity (e.g., fetal mortality, cancer),
1 9 through developmental and reproductive toxicity endpoints, to enzyme induction as a marker of
20 intracellular dioxin activity. Many of the studies report multiple statistically significant effects
21 related to dioxin exposure. From these results, the values tabulated were selected on the basis of
22 the lowest dose at which significant effects occurred—findings that were generally highlighted
23 by the authors of the publication. In the event that multiple endpoints were elicited at the same
24 dose, the effect considered of most consistency across studies and relevance to human risk
25 assessment was selected (e.g., decreased sperm counts).
26 A variety of methods were employed to estimate body burdens corresponding to the
27 LOAELs/NOAELs/ED01s, including using measured body burden and lipid concentration data,
28 absorption adjustments for single-dose studies, and first-order pharmacokinetic modeling
29 estimates using absorbed dose and halflife. Additional details on study design, endpoint
30 selection, and calculation of body burdens are included in Appendix A and can also be found in
31 Sections 5.2 and 6.0 of this document and in other chapters of the dioxin reassessment. Human
32 equivalent intakes for the body burden endpoints were calculated according to formulae
33 discussed in Part II, Chapter 8 of this report and are displayed in order corresponding to the
34 preceding three results columns in Table 5-6 and Appendix A. These comparisons result in the
35 finding that, when animal data associated with effects at the low end of the range of experimental
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1 observation (NOAELs/LOAELs/ED01s) are compared to current average human body burdens of
2 approximately 5 ng TEQDFP-WHO98/kg—representing lifetime average intake values of
3 approximately 3 pg TEQDFP-WHO98/kg/day—or to current intake values of 1 pg TEQDFP-
4 WHO98/kg/day, relatively small MOEs are obtained. Similarly, some human noncancer effects
5 (e.g., developmental delay, neurobehavioral outcomes, and impact on thyroid function in Dutch
6 children) and cancer outcomes show comparatively small MOEs.
7 In the following sections which discuss these general effects, the focus is on developing
8 an understanding of dioxin hazard and risk. This discussion is, by its nature, selective of findings
9 that inform the risk assessment process. Readers are referred to the more comprehensive
10 chapters for further discussion of the broader epidemiologic and toxicologic database.
11
1 2 2.1. BIOCHEMICAL RESPONSES (Cross-reference: Part II, Chapters 2, 3, and 8)
13 As described later in Section 3, mechanistic studies can reveal the biochemical pathways
14 and types of biological events that contribute to adverse effects from exposure to dioxin-like
1 5 compounds. For example, much evidence indicates that 2,3,7,8-TCDD acts via an intracellular
1 6 protein, AhR, which is a ligand-dependent transcription factor that functions in partnership with
1 7 a second protein (known as the AhR nuclear translocator, or Arnt) to alter gene expression. In
1 8 addition, receptor binding may result in release of cytoplasmic proteins that, in turn, alter the
1 9 expression or activity of cell-regulatory proteins (e.g., increases in Src activity). Therefore, from
20 a mechanistic standpoint, TCDD's adverse effects appear likely to reflect alterations in gene
21 expression or protein activity that occur at an inappropriate time and/or for an inappropriate
22 length of time. Mechanistic studies also indicate that several other proteins (e.g. hif cc, Rb, relA,
23 src, sim, etc.) contribute to TCDD's gene-regulatory effects and that the response to 2,3,7,8-
24 TCDD involves a relatively complex interplay between multiple genetic and environmental
25 factors. This model is illustrated in Figure 2-1 (from Part n, Chapter 2). Comparative binding
26 studies and other data suggest that biochemical events observed in response to TCDD exposure
27 are also seen with other dioxin-like compounds in proportion to their TEFs.
28 Comparative data from animal and human cells and tissues suggest a strong qualitative
29 similarity across species in response to dioxin-like chemicals. This further supports the
30 applicability to humans of the generalized model of initial events in response to dioxin exposure.
31 These biochemical and biological responses are sometimes considered adaptive or reflective of
32 exposure to dioxin-like compounds. When they are seen within normal homeostatic limits, these
33 biochemical changes are often not considered adverse in and of themselves. However, many of
34 these changes are potentially on a continuum of dose-response relationships that leads to adverse
35 responses and, considering the potential to shift population distributions in response, may be of
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1 concern. Because of the distribution of responses and sensitivity within a population, it is
2 possible that adaptive responses for some are frankly adverse for those at the tails of the
3 distribution. For this reason, a balanced approach must be used when describing these events,
4 recognizing that they may be adaptive or simply biomarkers of exposure to dioxin-like
5 compounds, or they may represent early events in a pathway resulting in a risk of adverse effects
6 in some humans.
7 If, as we can infer from the evidence, 2,3,7,8-TCDD and other dioxin-like compounds
8 operate through these mechanisms, there are constraints on the possible models that can plausibly
9 account for dioxin's biological effects and also on the assumptions used during the risk
10 assessment process. For instance, the linear relationship expected between ligand concentration
11 and receptor binding may or may not be reflective of dose-response relationships for downstream
1 2 events requiring complex interactions of other regulatory proteins with the activated receptor.
1 3 Puga et al. (2000a) have shown that interactions of TCDD with the AhR alters expression of over
14 300 genes in a single cell line at one time point and one dose. These data suggest that
1 5 mechanisms of toxic action may be very complicated and that additional research will be
1 6 necessary to further unravel the mechanistic relationships underpinning dioxin' s toxicity.
1 7 Mechanistic knowledge of dioxin action may also be useful in other ways. For example,
1 8 knowledge of genetic polymorphisms that influence 2,3,7,8-TCDD responsiveness may also
1 9 allow the identification of individuals either refractory to or at particular risk from exposure to
20 dioxin. In addition, knowledge of the biochemical pathways that are altered by dioxin-like
21 compounds may help in the development of approaches to intervention or to drugs that can
22 prevent dioxin's adverse effects.
23 As described in Part II, Chapter 2, biochemical and genetic analyses of the mechanisms
24 by which dioxin modulates particular genes have revealed the outline of a novel regulatory
25 system whereby a chemical signal can alter cellular regulatory processes. Future studies of
26 dioxin action have the potential to provide additional insights into mechanisms of mammalian
27 gene regulation that are of relatively broad interest. Additional perspectives on dioxin action can
28 be found in several recent reviews (Birnbaum, 1994a, b; Schecter, 1994; Hankinson, 1995;
29 Schmidt and Bradfield, 1996; Rowlands and Gustafsson, 1997; Gasiewicz, 1997; Hahn, 1998;
30 Denison et al., 1998; Wilson and Safe, 1998; Schecter and Gasiewicz, 2003).
31 The ability of 2,3,7,8-TCDD and other dioxin-like compounds to modulate a number of
32 biochemical parameters in a species-, tissue-, and temporal-specific manner is well recognized.
33 Despite the ever-expanding list of these responses from the past 20 years and the elegant work on
34 the molecular mechanisms mediating some of these, there still exists a considerable gap between
35 our knowledge of individual biochemical changes and the degree to which they are related to the
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1 more complex biological and toxicological endpoints elicited by these chemicals. A framework
2 for considering these responses in a mode of action context is discussed later in this document.
3 TCDD-elicited activation of the AhR has been clearly shown to mediate altered
4 transcription of a number of genes, including several oncogenes and those encoding growth
5 factors, receptors, hormones, and drug-metabolizing enzymes. Table 2-2 provides an illustrative
6 list of gene products whose regulation or activity is modulated by 2,3,7,8-TCDD. Although this
7 list is not meant to be exhaustive, it demonstrates the range of potential dioxin impacts on
8 pathways with potential to lead to adverse effects.
9 As discussed in Part II, Chapter 2, it is possible that the TCDD-elicited alteration of
10 activity of these genes may occur through a variety of mechanisms. The transcription of some
11 genes may be directly regulated by the activated AhR. Other alterations in gene expression may
1 2 be secondary to the initial biochemical events directly regulated transcriptionally by the AhR.
13 Some of the changes may also occur by post-transcriptional processes such as messenger
14 ribonucleic acid (mRNA) stabilization or altered protein phosphorylation (Gaido et al., 1992;
15 Matsumura, 1994). Nie etal. (2001) described cross-talk between Arnt-requiring pathways
1 6 resulting in interactions between the AhR and the hypoxia signaling pathways. Thus, the
1 7 molecular mechanisms by which many if not most of the biochemical processes discussed herein
18 are altered by 2,3,7,8-TCDD treatment remain to be determined. Nevertheless, it is assumed,
19 based on the cumulative evidence available, that all of these processes are mediated by the
20 binding of 2,3,7,8-TCDD to the AhR. Although evidence has accumulated for the involvement
21 of the AhR in many but not all of these processes, structure-activity relationships, genetic data,
22 and reports from the use of biological models such as "knockout" mice that are lacking the AhR
23 (AhR"7") are consistent with the involvement of the AhR as the initial step leading to these
24 biochemical alterations. In fact, for every biochemical response that has been well studied, the
25 data are consistent with the particular response being dependent on the AhR.
26 The dioxin-elicited induction of certain drug-metabolizing enzymes such as CYP1A1,
27 CYP1A2, and CYP1B1 is clearly one of the most sensitive responses observed in a variety of
28 different animal species, including humans, and it occurs at body burdens as low as 3-8 ng
29 TCDD/kg in animals (see Part n, Chapter 8, Sections 8.3 and 8.4). These and other enzymes are
30 responsible for the metabolism of a variety of exogenous and endogenous compounds. Several
31 lines of experimental evidence suggest that these enzymes may be responsible for either
32 enhancing or protecting against the toxic effects of a variety of agents, including known
33 carcinogens as well as endogenous substrates such as hormones. These interactive effects are
34 dependent on the compounds and the experimental system examined.
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1 Several reports (Kadlubar et al., 1992; Esteller et al., 1997; Ambrosone et al., 1995;
2 Kawajiri et al., 1993) provide evidence that human polymorphisms in CYPIA1 and CYPIA2 that
3 result in higher levels of enzyme activity are associated with increased susceptibility to
4 colorectal, endometrial, breast, and lung tumors. Also, exposure of AhR-deficient ("knockout")
5 mice to benzo[a]pyene (BaP) results in no tumor response, suggesting a key role for the
6 AhR—and perhaps CYPIA1 and CYPIA2—in BaP carcinogenesis (Dertinger et al., 1998;
7 Shimizu et al., 2000). Modulation of these enzymes by dioxin may play a role in chemical
8 carcinogenesis. However, the exact relationship between the induction of these enzymes and any
9 toxic endpoint observed following dioxin exposure has not been clearly established.
1 0 In addition to what is known about the P450 isozymes (CYP1 Al, CYP1A2, and
11 CYP1B1), there exists some evidence from experimental animal data to indicate that the
1 2 alteration of certain other biochemical events might have a more direct relationship to sensitive
1 3 toxic responses observed following TCDD exposure. Some of these may be relevant to
14 responses observed in humans, and further work in these areas is likely to lead to data that would
1 5 assist in the risk characterization process. For example, changes in EGFR have been observed in
1 6 tissues from dioxin-exposed animals and humans (see Part II, Chapter 3, Section 3.5, and
1 7 Chapter 6, Section 6.5 ). EOF and its receptor possess diverse functions relevant to cell
1 8 transformation and tumorigenesis, and changes in these functions may be related to a number of
1 9 dioxin-induced responses, including neoplastic lesions, chloracne, and a variety of reproductive
20 and developmental effects. Likewise, the known ability of TCDD to directly or indirectly alter
21 the levels and/or activity of other growth factors and hormones, such as estrogen, thyroid
22 hormone, testosterone, and gonadotropin-releasing hormone and their respective receptors as
23 well as enzymes involved in the control of the cell cycle (Safe, 1995b), may affect growth
24 patterns in cells/tissues, leading to adverse consequences. In fact, most of the effects that the
25 dioxins produce at the cellular and tissue levels are due not to cell/tissue death but to altered
26 growth patterns (Birnbaum, 1994b). Many of these alterations may occur at critical times in
27 development and/or maturation and thus may be irreversible.
28 There does not yet exist a precise understanding of the relationships between the
29 alteration of specific biochemical processes and particular toxic responses observed in either
30 experimental animals or humans exposed to the dioxins. This is due predominantly to our
31 incomplete understanding of the complex and coordinated molecular, biochemical, and cellular
32 interactions that regulate tissue processes during development and under normal homeostatic
33 conditions. A further understanding of these processes and how 2,3,7,8-TCDD may interfere
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1 with them remains an important goal that would greatly assist in the risk characterization process.
2 In particular, knowledge of the causal association of these responses coupled with dose-response
3 relationships may lead to a better understanding of sensitivity to various exposure levels of the
4 dioxin-like compounds. Nevertheless, it is important to recognize that many of the biochemical
5 and biological changes observed are consistent with the notion that 2,3,7,8-TCDD is a powerful
6 growth dysregulator. This hypothesis may play a considerable role in the risk characterization
7 process by providing a focus on those processes, such as development, reproduction, immunity,
8 and carcinogenesis, that are highly dependent on coordinated growth regulation.
9
10 2.2. ADVERSE EFFECTS IN HUMANS AND ANIMALS
11 2.2.1. Cancer (Cross-reference: Part II, Chapters 6, 7, and 8)
12 2.2.1.1. Epidemiologic Studies
13 Since the last formal EPA review in 1988 of the human database relating to the
14 carcinogen!city of TCDD and related compounds, a number of new follow-up mortality studies
1 5 have been completed. This body of information is described in Part II, Chapter 7a, Section 7.5,
1 6 of this assessment, and summaries appear in an International Agency for Research on Cancer
1 7 monograph (IARC, 1997), the Agency for Toxic Substances and Disease Registry (ATSDR)
1 8 ToxProfile (ATSDR, 1999a), and the National Toxicology Program's report on carcinogens
1 9 (NTP, 2001). Among the most important of these are the ones by Fingerhut et al. (199 la) and
20 Steenland et al. (1999, 2001) from NIOSH of 5172 U.S. chemical manufacturing workers and the
21 independent analyses by Aylward et al. (1996) and Salvan et al. (2001) and followup of the Dow
22 sub-cohort by Bodner et al. (2003); a study of 2479 German workers involved in the production
23 of phenoxy herbicides and chlorophenols by Becher et al. (1996, 1998) and by others in separate
24 publications (Manz et al., 1991; Nagel et al., 1994; Flesch-Janys et al., 1995, 1998); a study of
25 more than 2000 Dutch workers in two plants involved in the synthesis and formulation of
26 phenoxy herbicides and chlorophenols (Bueno de Mesquita et al., 1993) and subsequent follow-
27 up and expansion by Hooiveld et al., 1998); a smaller study by Zober et al. (1990) of 247 workers
28 involved in a chemical accident cleanup and subsequent follow-up (Ott and Zober, 1996b); and
29 an international study by Saracci et al. (1991) of more than 18,000 workers exposed to phenoxy
30 herbicides and chlorophenols, with subsequent follow-up and expansion by Kogevinas et al.
31 (1997). Recent reports also indicate increased cancer risks among the Seveso population
32 (Bertazzi et al. 2001a, Warner et al. 2002).
33 Although uncertainty remains in interpreting these cohort results because not all potential
34 confounders have been ruled out and coincident exposures to other carcinogens are likely (see
35 Cole et al., 2003 for a critique), all provide support for an association between exposure to dioxin
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1 and related compounds and increased cancer mortality. Strong inference regarding carcinogenic
2 hazard often relies on the availability of studies with well-documented exposures. One of the
3 strengths of these studies is that each has some exposure information that permits an assessment
4 of dose response. Some of these data have, in fact, served as the basis for fitting the dose-
5 response models in Part II, Chapter 8, Section 8.4.
6 In addition, during the development of its monograph on PCDDs/PCDFs (IARC, 1997),
7 the IARC Working Group abstracted from the published literature data concerning the most
8 highly exposed populations in the world. The group focused its attention on the most exposed
9 subcohorts within cohorts with adequate latency. IARC suggests that if associations between
1 0 exposure and risk are truly causal, they will become more apparent in these highly exposed
11 subcohorts with adequate latency. Increased risk for all cancers combined and lung cancer
1 2 mortality were consistent findings in the occupational cohort studies. Although the increase was
1 3 generally low (20-50%), it was highest in the subcohorts with the presumed heaviest exposure.
14 The results of the IARC Working Group's analysis regarding all cancer and lung cancer mortality
1 5 in the recent studies are summarized in Table 2-3. Observed numbers of cases, standardized
16 mortality ratios (SMR) and 95% confidence intervals (CI) are given for each of these two
1 7 findings for each study.
18 In addition, the Working Group developed overall SMRs for the combined studies. The
1 9 group state clearly that, although these total SMRs are low (1.4, 95% CI = 1.2-1.6 for all cancers
20 and 1.4, 95% CI = 1.1-1.7 for lung cancer), these results are unlikely to be due to chance, nor can
21 confounding by cigarette smoking likely account for the increase in lung cancer. Positive dose-
22 response trends in the German studies and increased risk in the longer duration U.S. subcohort
23 and the most heavily exposed Dutch workers support this view. In the opinion of these experts,
24 increases of this magnitude in all cancers combined have rarely been found in occupational
25 cohorts. These results are also supported by significantly increased mortality from lung and liver
26 cancers subsequent to the Japanese rice oil poisoning accident where exposure to high levels of
27 PCDFs and PCBs occurred (Kuratsune et al., 1988; Kuratsune, 1989).
28 Although smoking as a confounder cannot be totally eliminated as a potential explanation
29 of the occupational studies results, analyses conducted to date (Fingerhut et al., 1991b; Ott and
30 Zober, 1996b) suggest that smoking is not likely to explain the entire increase in lung cancer and
31 may even suggest synergism between occupational exposure to dioxin and smoking. These
32 analyses have not been deemed entirely satisfactory by some reviewers of the literature. The
33 question of confounding exposures such as to asbestos and other chemicals in addition to
34 smoking has not been entirely ruled out and must be considered as potentially adding to the
35 observed increases. Although increases of cancer at other sites (e.g., non-Hodgkin's lymphoma,
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1 soft tissue sarcoma, gastrointestinal cancer) have been reported (see Part n, Chapter 7a, Section
2 7.5), the data for an association with exposure to dioxin-like chemicals are less compelling due to
3 the limited numbers of observed tumors at any specific site.
4 As discussed by IARC (McGregor et al., 1998) and Smith and Lopipero (2001), it is
5 unusual for a cancer hazard characterization to focus on the "all cancers combined" category of
6 epidemiological results, and continuing uncertainties regarding site-specific cancer increases
7 following dioxin exposure remain a factor in concluding that the epidemiological information is
8 limited. McGregor et al. (1998) note, however, that the predominant cancer promotion
9 mechanism of action for dioxin will theoretically elicit pre-existing initiated cell lines. These
10 promotional effects would be expected in multiple tissues, especially those most sensitive to the
11 effects of dioxin. In epidemiological studies, there may not be a statistically increased tumor
1 2 site(s), but rather a pattern of smaller increases that could vary across study populations because
1 3 of differences in life histories, exposures, and pre-existing initiating events.
1 4 The cancer-promotion mechanism may also serve to accentuate existing tumor rate
1 5 increases following other carcinogenic exposures, thereby acting in a synergistic manner. Timing
1 6 of tumor induction may differ between a cancer promoter and initiator, where the effects of a
1 7 promoter may not be monotonic with time, but rather may exhibit an earlier onset, harvesting
18 effect, where the total cancer burden may not have changed but the onset has been accelerated.
1 9 These timing issues are exacerbated by the pharmacokinetics of dioxin elimination, where initial
20 peak body burdens during employment or after accidental exposures decline gradually after
21 cessation of exposure.
22 Mathematically, a net carcinogenic effect in one or more organ sites must, by definition,
23 increase the "all cancers combined" risk for the exposed population if the exposed and control
24 groups are matched (i.e., they have the same background cancer rate absent the exposure). Thus,
25 an increase in the all cancers category should be considered an expected result of a carcinogen
26 exposure, not an unusual event. The statistical power of a study to detect such an effect is,
27 however, the limiting factor in the presence of stochastic events and imperfect matching. This
28 constraint is particularly applicable to rare tumor sites, but it also occurs for common tumor sites
29 such as lung, colon, breast (? ), and prostate (o*) or for mechanistically linked sites (e.g.,
30 hormonally related breast, ovary, uterus), where substantial increases in site-specific relative
31 risks are necessary to impact the all cancer category.
32 Ionizing radiation (a mutagenic carcinogen) provides an example where small increased
33 relative risks at multiple sites lead to a significantly increased relative risk for "all nonleukemic
34 cancers." In atomic bomb survivors, the relative risk for all nonleukemic cancers at 100 rads was
35 1.17 (p<0.01), comprised principally of small but statistically significant increases in stomach
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1 (relative risk [RR] = 1.11), lung (1.33), breast (1.69), ovary (1.52), and bladder-kidney (1.55)
2 cancers and nonstatistically significant increases in esophagus (1.23), liver (1.35), ovary (1.52),
3 and multiple myeloma (1.51). Although the relative risk for leukemia was 3.95 (p<0.01), the
4 excess cancer burden from nonleukemic sites in the exposed population was over twice that due
5 to the leukemias (Hoel, 1987).
6 Some studies that are discussed in Part II, Chapter 7a, report small or no increased risk of
7 cancer from exposure to 2,3,7,8-TCDD or its congeners. These studies generally suffer from one
8 or more deficiencies that limit their ability to determine the carcinogenic hazard of dioxins.
9 These deficiencies fall into the following categories: little statistical power to detect an effect of
10 exposure because the measured exposures are lower than those seen in the studies cited above
11 and are more similar to those of the comparison population; no measurements of internal
1 2 exposure to 2,3,7,8-TCDD and potential for misclassification of exposure; and inadequate
1 3 latency or follow-up.
1 4 The Ranch Hand study of U.S. Air Force personnel who sprayed the defoliant Agent
1 5 Orange during the Vietnam War provides an illustrative example of statistical power constraints
1 6 in the presence of low predicted relative risks. Statistical power is the ability of a study to detect
1 7 a real difference between two groups at pre-defined levels of statistical significance (usuallyp<
18 0.05) and relative risk. Statistical power analysis based on the detailed dosimetry and health
19 status data available for this cohort indicates insufficient statistical power to detect an elevated
20 all-cancers risk at levels consistent with the occupational dose-response data. A predicted
21 relative risk for all cancers combined can be estimated for the Ranch Hands by calculating the
22 difference between their dose and that of the control group (mean background of 4.25 ppt TCDD
23 in lipid) (Michalek et al., 1998) and then multiplying this dose increment by an estimated cancer
24 risk slope factor for TCDD. The median AUC increment value for the overall Ranch Hand group
25 is 468 ng TCDD/kg lipid * years, and for the high dioxin group the median is 2280 ng TCDD/kg
26 lipid * years. Using the Becher et al. (1998) linear formula (RR = 1 + 0.000016 x AUC ng-
27 TCDD/kg lipid * years, which equals ~ 3 x 10~3 risk/pg/kg/day) described in Section 5.3 and
28 Table 5-2 of this document, the estimated all-cancers relative risk for the overall Ranch Hand
29 cohort is approximately 1.01, and for the high-exposure group it is 1.04 as compared to the
30 control population. Using formulae in Fleiss (1981) and Cohen (1977) and assuming two-sided
31 testing at a significance level of 5%, the study has no power to detect 1 to 4% increases in
32 relative risk. Data on the overall prevalence of cancer in the comparison group (18.9%) and
33 sample sizes (all Ranch Hand 845 vs. 1224 controls; high category 241 vs. 1200 controls) used in
34 the above analysis were obtained from the 1997 Ranch Hand morbidity report
35 (http://www.brooks.af.mi1/AFRL/HED/hedb/afhs/.html).
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1 Recent suggestive cancer findings from the Ranch Hand database are consistent with
2 these calculations, both in the magnitude of the risk ratios under review and in the constraints on
3 statistical methods to detect such levels of incremental risk. Akhtar et al. (2003) provide results
4 that suggest exposure to dioxin-contaminated herbicides may be associated with cancer, based on
5 a statistically significant positive trend in "any site" cancer relative risk with exposure group,
6 accompanied by a non-significant increase in the any site cancer standardized incidence ratio of
7 1.09 (Obs. 134, Exp 123.34, p=0.34).
8 In addition, one of the earliest reported associations between exposure to dioxin-like
9 compounds in dioxin-contaminated phenoxy herbicides and increased cancer risk involved an
10 increase in soft tissue sarcomas (Hardell and Sandstrom, 1979; Eriksson et al., 1981; Hardell and
11 Eriksson, 1988; Eriksson et al., 1990). In this and in other recent evaluations of the
1 2 epidemiologic database, many of the earlier epidemiological studies that suggested an association
13 between dioxin exposure and soft tissue sarcoma have been criticized for a variety of reasons.
14 Arguments regarding selection bias, lack of exposure or differential exposure misclassification,
1 5 confounding, and chance in each individual study, which increases uncertainty around this
16 association, have been presented in the scientific literature. Nonetheless, the incidence of soft
1 7 tissue sarcoma is elevated, although not statistically so, in several of the most recent studies
1 8 (Bertazzi et al., 1993, 1997, 1999; Fingerhut et al., 1991a; Hertzman et al., 1997; Kogevinas et
1 9 al., 1997; Lampi et al., 1992; Lynge, 1998; Pesatori et al., 1999; Saracci et al., 1999; Vineis et al.,
20 1986). It is probable that soft tissue sarcomas are not unlike other site-specific cancers whose
21 risks from exposure to TCDD are difficult to define because of small numbers and lack of
22 measures of internal exposure.
23 The accidental exposure of the population at Seveso, Italy, serves as an example of a
24 more highly exposed group where, in previous assessments, latency was considered to be
25 inadequate. Although Bertazzi and coworkers published results of cancer mortality after 10 and
26 15 years of latency, results are suggestive but not definitive regarding an association between
27 exposure to TCDD and cancer deaths. Results of the analysis of 20 years of follow-up have
28 recently been published (Bertazzi et al., 2001). This more recent follow-up of the same group of
29 residents in zones A and B was completed after 20.5 years to December 31, 1996. The authors
30 stated that their results support the evaluation of TCDD as a human carcinogen, especially with
31 the increased estimates of relative risk for all cancer mortality and for several specific sites of
32 cancer in the >15 year latency period. No soft tissue sarcomas were observed in zones A and B.
33 However, less than one case would have been expected to occur by the end of the follow-up. In
34 Zone A, where exposure was highest, the expectation of a soft tissue sarcoma was only 0.1.
35 There was little power to detect a significant risk in that region.
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1 In a commentary by Smith and Lopipero (2001) on this study, two "key" problems were
2 identified. The "likely" exposure levels back-calculated to the time when the exposures occurred
3 indicate that the weighted average for the two highest exposure zones in Seveso is only 136
4 ng/kg TCDD (lipid adjusted) versus a mean of 3600 ng/kg TCDD (lipid adjusted) in the
5 combined U.S. industrial cohorts. This interpretation is consistent with the data in Figure 5-1 of
6 this document. On this basis, one would not expect to find significant increases in all cancers
7 combined based on extra risk estimates from the occupational cohorts. This situation is not
8 unlike the one described above for the Ranch Hand cohort. However, in this case, associations
9 with exposure to TCDD and cancer risk are being reported.
10 The other issue raised by these authors is the potential for smoking-related causes of
11 disease to be confounders in this study. The relatively low dioxin exposure and the increase in
1 2 major smoking-related causes of death raise questions regarding the attribution of these cancer
1 3 effects to TCDD exposure. Other data are consistent with potential dioxin hazard in this exposed
14 population, for example, the finding of increased diabetes mortality among women. Bertazzi
1 5 (200 Ib) takes exception to these interpretations and argues against the perception of "low"
16 exposure and smoking as a confounder. It is clear that the question of whether the Bertazzi
1 7 (200la) study contributes to the weight of evidence for carcinogenicity awaits further follow-up
1 8 and improved exposure assessment.
1 9 In general, both past and more recent human studies have focused on males. Although
20 males comprise all the case-control studies and the bulk of the cohort study analyses, animal and
21 mechanism studies suggest that males and females might respond differently to TCDD. There
22 are now, however, some limited data suggesting carcinogenic responses associated with dioxin
23 exposure in females. The only report of a female cohort that had good TCDD exposure surrogate
24 information was that of Manz et al. (1991), which found a borderline statistically significant
25 increase in breast cancer. Although Saracci et al. (1991) did report reduced female breast and
26 genital organ cancer mortality, the finding was based on few observed deaths and on
27 chlorophenoxy herbicide rather than TCDD exposures. In the later update and expansion of this
28 cohort, Kogevinas et al. (1997) provided evidence of a reversal of this deficit and reported a
29 borderline significant excess risk of breast cancer in females.
30 Bertazzi et al. (1993, 1997, 1998) reported nonsignificant decreases in breast cancer and
31 endometrial cancer in women living in geographical areas around Seveso that were contaminated
32 by dioxin. Breast cancer rates in women who had been exposed as infants at the time of the
33 Seveso explosion were increased. On the basis of 15 (1.5%) confirmed breast cancer cases in the
34 Seveso Women's Health Study, a Cox proportional hazard ratio for breast cancer of 2.1 fold
35 (95% CI1.0 - 4.6) was reported for a ten-fold increase in serum TCDD levels (Warner et al.,
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1 2002). Although Kogevinas et al. (1993) saw an increase in cancer incidence among female
2 workers most likely exposed to TCDD, no increase in breast cancer was observed in their small
3 cohort. In short, TCDD cancer experience for women may differ from that of men, but currently
4 there are few data to adequately address this question.
5 Both laboratory animal data and mechanistic inferences suggest that males and females
6 may respond differently to the carcinogenic effects of dioxin-like chemicals. Further data will be
7 needed to address this question of differential response between sexes, especially to hormonally
8 mediated tumors. In addition, studies by Brown et al. (1998) demonstrated that prenatal
9 exposure of rats to 2,3,7,8-TCDD enhances their sensitivity as adults to chemical carcinogenesis.
1 0 A mechanistic understanding of the impact of gestational dioxin exposure on mammary tissue
11 development has been provided by the work of Fenton and coworkers (Fenton et al., 2002;
1 2 Vorderstrasse et al., 2004). The experimental data in laboratory animals suggest that exposure to
13 women or perinatal exposures may result in carcinogenic responses. The epidemiological data
14 examining the association between exposure of adult women to dioxin and cancer is limited. No
1 5 epidemiological data are available to address the question of the potential impact of exposure to
16 dioxin-like compounds on childhood cancers or the effects of perinatal exposures on the
1 7 development of cancers later in life. The epidemiological data to date have not adequately
1 8 addressed these issues.
1 9 In summary, 2,3,7,8-TCDD and, by inference from more limited data, other dioxin-like
20 compounds are described as potentially multisite carcinogens in the more highly exposed human
21 populations—consisting primarily of adult males that have been studied. Although the
22 epidemiologic data by themselves are not sufficient to infer a causal association between
23 exposure to TCDD and other dioxin-like chemicals and increased cancer in humans (IARC,
24 1997; ATSDR, 1999a; DHHS, 2001), this "limited" epidemiologic database has been
25 strengthened by emerging data that reflect further follow-up and better exposure metrics.
26 Although uncertainty remains, the cancer findings in the epidemiologic literature are generally
27 consistent with results from studies of multiple laboratory animal species, where dioxin-like
28 compounds have clearly been identified as multisite carcinogens and tumor promoters.
29 2,3,7,8-TCDD has also been demonstrated to promote dose-dependent clonal expansion
30 and neoplastic transformation in human epidermal keratinocytes immortalized by simian
31 adenovirus SV40 exposure, leading to fixed alterations in regulatory gene expression (Yang et
32 al., 1999) and squamous cell carcinoma when inoculated into athymic nude mice (Yang et al.,
33 1992). These phenomena did not occur in the absence of SV40 virus induction or in control cell
34 lines, including the immortalized cell culture.
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1 Thus, the findings of increased risk at multiple sites in occupationally exposed humans
2 appear to be plausible, given what is known about mechanisms of dioxin action and the
3 fundamental level at which this class of compounds appears to act on gene expression and
4 cellular regulation in target tissues. Although several studies found a positive trend in dose-
5 response and have been the subject of empirical risk modeling (see Part n, Chapter 8, and Becher
6 et al., 1998, and Steenland et al., 2001), the epidemiologic data alone provide little insight into
7 the shape of the dose-response curve below the range of observation in these occupationally
8 exposed populations. However, Mackie et al. (2003) suggest that there is no evidence of a dioxin
9 cancer threshold from the epidemiology data. Steenland and Deddens (2003) also reported that
10 the results of quantitative exposure-response analyses for low environmental levels based on the
11 NIOSH cohort are consistent with the results from the Becher cohort and demonstrate that a
1 2 doubling of background levels of exposure will increase lifetime risk of cancer death between 0.1
13 and 1%. The issue of the shape of the dose-response curve in occupational cohorts is further
14 discussed in Section 5.2.1 of this document.
15
1 6 2.2.1.2. Animal Carcinogenicity (Cross-reference, Part II: Chapters 6 and 8)
1 7 An extensive database on the carcinogenicity of dioxin and related compounds in
18 laboratory studies exists and is described in detail in Part n, Chapter 6. There is adequate
1 9 evidence that 2,3,7,8-TCDD is a carcinogen in laboratory animals, based on long-term bioassays
20 conducted in both sexes of several strains of rats and mice, hamsters, and fish (U.S. EPA, 1985;
21 Huff et al., 1991; Zeise et al., 1990; IARC, 1997; DHHS, 2001). All the studies produced
22 positive results, leading to conclusions that TCDD is a multi-site carcinogen that increases the
23 incidence of tumors at sites distant from the site of treatment and at doses well below the
24 maximum tolerated dose. Since this issue was last reviewed by the Agency, in 1988, TCDD has
25 been shown to be a carcinogen in hamsters (Rao et al., 1988), which are relatively resistant to the
26 lethal effects of TCDD. Other preliminary data have also shown TCDD to be a liver carcinogen
27 in the small fishMedaka (Johnson et al., 1992).
28 In the past, limited attempts had been made to demonstrate the carcinogenicity of other
29 dioxin-like compounds. A mixture of two isomers of hexachlorodibenzo-p-dioxin (HCDDs)
30 produced liver tumors in both sexes of rats and mice when given by the gavage route (NTP,
31 1980), but not by the dermal route in Swiss mice (NTP, 1982a,b). Reports from Rozman (1999,
32 2000) and Rozman et al. (2000) demonstrated lung cancer in female rats given gavage exposures
33 of l^S^e^S-heptachlorodibenzo-^-dioxintHpCDD).
34 Recently, the National Toxicology Program (NTP, 2003 a-d) has conducted chronic
35 bioassays to test the relative carcinogenic potency of four dioxin-like congeners (TCDD,
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1 2,3,4,7,8-PeCDD, PCB 118, and PCB 126), both alone and in combination. In these studies,
2 TCDD, PCB 126 and 2,3,4,7,8-PeCDF, were tested individually or in an equally potent mixture
3 of all three chemicals in a 2-year bioassay in female Sprague-Dawley rats. The NTP study also
4 included PCB 118, but the results and interpretation of this bioassay remain under review due to
5 substantial contamination by PCB 126. Initial reports from the NTP study indicate that there is
6 clear evidence of carcinogenicity for both TCDD and PCB 126. In these studies, both TCDD and
7 PCB 126 exposures increases the incidence of cholangiocarcinoma of the liver, cystic
8 keratinizing epithelioma of the lung, and gingival squamous cell carcinoma of the oral mucosa.
9 Under the conditions of the 2-year study, there was some evidence of carcinogenic activity for
10 the 2,3,4,7,8-PeCDF based on increased incidences of cholangiocarcinoma of the liver, cystic
11 keratinizing epithelioma of the lung and gingival squamous cell carcinoma of the oral mucosa.
1 2 The results from the mixture study also indicate clear evidence of carcinogenicity as evidenced
1 3 by dose dependent increases in cholangiocarcinomas in the liver and cystic keratinizing
14 epitheliomas of the lung. The data on the three individual chemicals and mixtures demonstrate
1 5 consistent increases in the incidence of three tumor types. This evidence provides support that
1 6 the carcinogenicity of dioxin-like chemicals is mediated through their interactions with the Ah
1 7 receptor and that the TEF methodology may provide a useful tool in estimating the potential
1 8 carcinogenic risks of dioxin-like chemicals.
19 TCDD is characterized as a nongenotoxic carcinogen because it is negative in most
20 assays for DNA-damaging potential and is a potent "promoter" and a weak initiator or
21 noninitiator in two-stage initiation-promotion (I-P) models for liver, skin, and lung. The liver
22 response is characterized by increases in altered hepatocellular foci (AHF), which are considered
23 to be preneoplastic lesions because increases in AHFs are associated with liver cancer in rodents.
24 The results of the multiple I-P studies enumerated in Table 6-5 and in Part II, Chapter 6, Section
25 6.3, have been interpreted as showing that induction of AHFs by TCDD is dose-dependent
26 (Maronpot et al., 1993; Teeguarden et al., 1999), exposure-duration dependent (Dragan et al.,
27 1992; Teeguarden et al., 1999; Walker et al., 2000), and partially reversible after cessation of
28 treatment (Dragan et al., 1992; Tritscher et al., 1995; Walker et al., 2000).
29 Other studies indicate that other dioxin-like compounds have the ability to induce AHFs.
30 These studies showed that the compounds demonstrate a rank-order of potency for AHF
31 induction that is similar to that for CYP1A1 (Flodstrom and Ahlborg, 1992; Waern et al., 1991;
32 Schrenk et al., 1994). Non-ortho-substituted, dioxin-like PCBs have also induced the
33 development of AHFs according to their potency to induce CYP1 Al (Hemming et al., 1995; van
34 der Plas et al., 1999). It is interesting to note that liver I-P studies carried out in ovariectomized
35 rats demonstrated the influence that the intact hormonal system has on AHF development. AHF
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1 were significantly reduced in the livers of ovariectomized female rats (Graham et al., 1988;
2 Lucieretal., 1991).
3 I-P studies on skin have demonstrated that TCDD is a potent tumor promoter in mouse
4 skin as well as rat liver. Early studies demonstrated that TCDD is at least two orders of
5 magnitude more potent than the "classic" promoter tetradecanoyl phorbol acetate (Poland et al.,
6 1982), that TCDD skin tumor promotion is AhR dependent (Poland and Knutsen, 1982), that
7 TCDD had weak or no initiating activity in the skin system (DiGiovanni et al., 1977), and that
8 TCDD's induction of drug-metabolizing enzymes is associated with both metabolic activation
9 and deactivation of initiating agents, as described by Lucier et al. (1979). More recent studies
10 show that the skin tumor-promoting potencies of several dioxin-like compounds reflect relative
11 AhR binding and pharmacokinetic parameters (Hebert et al., 1990).
1 2 Although few I-P studies have demonstrated lung tumors in rats or mice, the study by
1 3 Clark et al. (1991) is particularly significant because of its use of ovariectomized animals. In
14 contrast to liver tumor promotion, lung tumors were seen only in initiated (diethylnitrosamine
1 5 [DEN]), TCDD-treated rats. No tumors were seen in DEN-only, TCDD-only, control, or
1 6 DEN/TCDD intact rats. Liver tumors are ovary dependent, but ovaries appear to protect against
1 7 TCDD-mediated tumor promotion in female rat lung. Perhaps the use of transgenic animal
1 8 models will allow further understanding of the complex interaction of factors associated with
1 9 carcinogenesis in rodents and, by extension, in humans. Several such systems are being
20 evaluated (Eastin et al., 1998; van Birgelen et al., 1999; Dunson et al., 2000).
21 The tumor-promoting ability of a number of dioxin-like chemicals has been examined. As
22 discussed in Part II, Chapter 6, Section 6, 1,2,3,7,8-PCDD; 1,2,3,4,6,7,8-HpCDD; 2,3,4,7,8-
23 PCDF; 1,2,3,4,7,8-HCDF; PCB126; and PCB105 all promote the development of AHF within
24 rodent liver, suggesting that they, like TCDD, are tumor promoters. (For a summary of positive
25 tumor-promotion studies for PCDDs and PCDFs in rats, see Part n, Chapter 6, Table 6-5). In
26 addition, complex mixtures of dioxins and furans and commercial PCB mixtures act as
2 7 promoters of liver AHF. For the five principle dioxins, furans, and coplanar PCBs that comprise
28 approximately 80% of the current, total dioxin/furan/PCB TEQ in human blood, all are positive
29 in either rodent bioassays or rodent liver tumor-promotion studies or mouse skin tumor-
30 promotion studies. Although the majority of dioxin-like congeners have not been tested for
31 carcinogenicity in chronic rodent bioassays, these data suggest that it is likely that those
32 individual congeners and mixtures of dioxin-like compounds that comprise the majority of the
33 dioxin-like activity in human tissues are likely to be carcinogenic to rodents.
34 van den Berg et al. (2000) present a summary of the data (their Table 1) relied on by
35 WHO'S European Centre for Environment and Health (WHO-ECEH) and IPCS in their joint
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1 consensus re-evaluation of the TEFs for PCDDs, PCDFs, and dioxin-like PCBs for mammals.
2 These TEFs were derived using a tiered approach in which in vivo toxicity data were given more
3 weight than in vitro data, toxicity more than biochemical endpoints, and chronic more than acute
4 data. Table 2-4 summarizes the tumor incidence and promotion data that were cited in the
5 development of these TEFsDFP-WHO98. The data presented are for those congeners that are
6 principal contributors to the background body burden of dioxin TEQs in the United States (see
7 Part I, Chapter 3). For 1,2,3,7,8-PeCDF and 2,3,4,7,8-PeCDF, the TEF was used to adjust the
8 dose from the studies by Waem et al. (1991), and for PCB 126 similar dose adjustments are
9 included from Hemming et al. (1995; their Fig. 4). For the comparison of TCDD to the
10 HxCDDs, the primary TCDD data points from the Kociba et al. (1978) bioassay were graphed for
11 both the original tumor count data and for the revised tumor counts from Goodman and Sauer
1 2 (1992). This presentation of both the original and the revised tumor counts for TCDD reflects
1 3 the contemporaneous performance and analysis of the HxCDD and TCDD bioassays and
14 pathology and the recognition that the HxCDD pathology has not been re-analyzed.
1 5 Table 2-3 illustrates the comparability of the TCDD and other congener data sets based
16 on TEFs. This analysis also demonstrates that the development of the TEFs for all of the
1 7 congeners that contribute substantially to the background dioxin TEQ appropriately reflect either
1 8 cancer bioassay or tumor promotion data. Furthermore, when one considers the impact of current
1 9 TEF values on compounds that made up the majority of the TEQ prior to 1990, it is clear that
20 more than 80% of the TEQ for either dioxins/furans or PCBs was made up of compounds for
21 which the current TEF is supported by data on relative potencies which included tumor
22 promotion or carcinogenic endpoints. This point is illustrated in Part II, Chapter 6, Table 6-10.
23
24 2.2.1.3. Plausible Mode(s) of Carcinogenic Action
25 Several potential mechanisms for TCDD carcinogenicity are discussed above and in Part
26 II, Chapter 6, Section 6.4. These include oxidative stress, indirect DNA damage, endocrine
27 disruption/growth dysregulation/altered signal transduction, and cell replication/apoptosis
28 leading to tumor promotion. All of these mechanisms are biologically plausible as contributors
29 to the carcinogenic process in humans, and none are mutually exclusive. Several biologically
30 based models that encompass many of these activities are described in Part n, Chapter 8, Section
31 8.4. Further work is needed to elucidate a detailed mechanistic model for any particular
32 carcinogenic response in animals or in humans; however, plausible modes of action with
33 probable relevance to human carcinogenicity are discussed below.
34 TCDD is a potent tumor promoter in rat and mouse liver and in initiated human skin
35 cells. In general terms, it is believed that cancer is likely due to the clonal expansion of damaged
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1 cells that have a heritable genetic defect. Increased growth and accumulation of damage in
2 critical genes ultimately aid in the progression into tumors. Consequently, promotion of
3 carcinogenesis by TCDD may occur at several steps: (1) increased formation of
4 initiated/susceptible cells through DNA mutation and/or increase rate of fixation of damaged
5 DNA into the genome, (2) reduced loss of initiated cells through a suppression of apoptosis, (3)
6 increase in growth rate and clonal expansion of initiated cells, and (4) accumulation of DNA
7 damage in critical genes resulting in the progression of clonally expanded cell populations into
8 tumors. Within this framework, it is hypothesized that TCDD may be acting as a tumor promoter
9 through multiple mechanisms. Primarily, the activation of the AhR leads to alteration in genes
10 that are involved in normal cell growth and differentiation pathways.
11 TCDD may contribute to the formation and accumulation of DNA damage via an indirect
1 2 mechanism involving the production of reactive oxygen species. These reactive oxygen species
1 3 may be formed as a result of autooxidation during futile metabolism of TCDD by the induction
14 of CYP1 enzymes or via the CYP1-dependent production of estrogen metabolites capable of
1 5 redox cycling. The clonal expansion of these damaged cells by TCDD and related chemicals is
16 likely to occur through the altered expression and activity of a number of genes that regulate the
1 7 cell-cycle. Activation of the AhR by TCDD results in altered expression or activity of the EGF
18 receptor, retinoblastoma protein, TGF-beta, and many others. These proteins all regulate the cell
19 cycle, and alterations of these proteins would alter cell growth properties.
20 The contribution of these two pathways in the carcinogenic actions of TCDD remains
21 uncertain. However, Portier et al. (1996) have proposed a model in which the contribution of
2 2 TCDD to the number of DNA damaged or initiated cells plays a significant role in its
23 carcinogenic response. In contrast, Conolly and Andersen (1997) have proposed a tumor
24 promotion model based on a negative selection mechanism in which the actions of TCDD are
25 focused on its ability to alter cell growth properties. Descriptions of these models are provided in
26 Part n, Chapter 8. Interestingly, the use of the model by Portier and colleagues leads to a result
27 that is consistent with low-dose linearity, whereas the Andersen and Conolly model predicts
28 highly nonlinear dose response relationships in the low-dose region. Presently, the available data
29 do not allow for adequate discrimination between these two models.
30 TCDD causes a dose-related increase in thyroid follicular cell adenomas and carcinomas
31 in rats and mice. One hypothesis for the induction of thyroid tumors involves the disruption of
32 thyroid hormone homeostasis via the induction of the phase n enzymes UDP-
33 glucuronosyltransferases (UGTs) (Hurley, 1998; Hill et al., 1998). Dioxin-like compounds
34 induce the synthesis of UDP-glucuronosyltransferase-1 (UGT1) mRNA by an AhR-dependent
35 transcriptional mechanism (Bock et al., 1998; Nebert et al., 1990). It is proposed that dioxin-like
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1 chemicals increase the incidence of thyroid tumors through an extrathyroidal mechanism.
2 Dioxin-like chemicals induce hepatic UGT, resulting in increased conjugation and elimination of
3 thyroxine (T4) and leading to reduced serum T4 concentrations. T4 production is controlled by
4 thyroid stimulating hormone (TSH), which is under negative and positive regulation from the
5 hypothalamus, pituitary, and thyroid by thyrotrophin releasing hormone (TRH), TSH itself,
6 thyroxine (T4), and triiodothyronine (T3). Consequently, the reduced serum T4 concentrations
7 would lead to a decrease in the negative feedback inhibition on the pituitary gland. This would
8 then lead to a rise in secreted TSH and stimulation of the thyroid. The persistent induction of
9 UGT by dioxins and subsequent prolonged stimulation of the thyroid would result in thyroid
10 follicular cell hyperplasia and hypertrophy of the thyroid, thereby increasing the risk of
11 progression to neoplasia.
1 2 In support of this hypothesis, Kohn et al. (1996) modeled the effect of 2,3,7,8-TCDD on
1 3 UGTs and thyroid hormones in female rats within the framework of a PBPK model. This
14 mathematical model described release and uptake of thyroid hormones, metabolism, 2,3,7,8-
1 5 TCDD induction of UGT 1, regulation of TSH release from the pituitary by T4, and feedback on
16 TRH and somatostatin, which inhibits TSH release. The model successfully reproduced the
1 7 observed effects of 2,3,7,8-TCDD on serum T3, T4, and TSH and UGT1 mRNA and enzyme
1 8 activity, suggesting that this is a plausible mechanism for an indirect role of 2,3,7,8-TCDD on the
1 9 thyroid. This model is supported by the more recent experimental work of Schuur et al. (1997),
20 which demonstrated the extrathyroidal effects of 2,3,7,8-TCDD on thyroid hormone turnover.
21 Although this discussion illustrates that there is no defined molecular mechanism leading
22 to cancer in either liver or thyroid, it does demonstrate the concept of "mode of action" as
23 defined in the Agency's proposed cancer guidelines (U.S. EPA, 1996, 1999, 2003). In each case,
24 critical "key events" that correlate with carcinogenicity can be identified and measured, and these
25 same events occur in both animals and humans. Although these relationships and linkages
26 remain to be detailed, they form plausible, testable hypotheses whose acceptance by the scientific
27 community is growing.
28 Despite this lack of a defined mechanism at the molecular level, there is a consensus that
29 2,3,7,8-TCDD and related compounds are receptor-mediated carcinogens in that (1) interaction
30 with the AhR is a necessary early event; (2) 2,3,7,8-TCDD modifies a number of receptor and
31 hormone systems involved in cell growth and differentiation, such as the EGFR and estrogen
32 receptor; and (3) sex hormones exert a profound influence on the carcinogenic action of 2,3,7,8-
33 TCDD.
34
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1 2.2.1.4. Other Data Related to Carcinogenesis
2 Despite the relatively large number of bioassays on 2,3,7,8-TCDD, those by Kociba et al.
3 (1978) and NTP (1982a), because of their multiple dose groups and wide dose range, continue to
4 be the focus of dose-response modeling efforts and of additional review. Goodman and Sauer
5 (1992) reported a re-evaluation of the female rat liver tumors in the Kociba study using the latest
6 pathology criteria for such lesions. The review confirmed only approximately one-third of the
7 tumors of the previous review (Squire, 1980). Although this finding did not change the
8 determination of carcinogenic hazard—as 2,3,7,8-TCDD induced tumors in multiple sites in this
9 study—it did have an effect on evaluation of dose-response and on estimates of risk at low doses.
10 These issues are discussed in a later section of this document.
1 1 One of the more intriguing findings in the Kociba bioassay was reduced tumor incidences
1 2 of the pituitary, uterus, mammary gland, pancreas, and adrenals in exposed female rats as
1 3 compared to controls. Although this finding, coupled with evaluation of epidemiologic data, has
14 led some authors to conclude that dioxin possesses "anticarcinogenic" activity (Kayajanian,
1 5 1997, 1999), it should be noted that in the Kociba study, the decreased incidence of tumors, with
1 6 the exception of mammary gland tumors, is associated with significant weight loss in these rats.
1 7 Examination of the data from NTP also demonstrates a significant decrease in these tumor types
18 when there is a concomitant weight loss in the rodents, regardless of the chemical administered
1 9 (Haseman and Johnson, 1996). It is also worth noting that the decrease in mammary tumors was
20 only observed in one of seventeen rodent carcinogenesis studies, and was not observed in the
21 recent NTP studies on TCDD, PCB 126, and 2,3,4,7,8-PeCDF (NTP, 2003 a-d).
22 As discussed in Section 3.2.3, under certain circumstances exposure to 2,3,7,8-TCDD
23 may elicit beneficial effects. For example, 2,3,7,8-TCDD protects against the subsequent
24 carcinogenic effects of polycyclic aromatic hydrocarbons (PAHs) in mouse skin, possibly
25 reflecting induction of detoxifying enzymes (Cohen et al., 1979; DiGiovanni et al., 1980). In
26 other situations, 2,3,7,8-TCDD-induced changes in estrogen metabolism may alter the growth of
27 hormone-dependent tumor cells, producing a potential anticarcinogenic effect (Spink et al., 1990;
28 Gierthy et al., 1993). While TCDD has been shown to inhibit the growth of certain breast cancer
29 cell lines, Warner et al. (2002) have demonstrated an increase in breast cancer in highly exposed
30 women from Seveso. Because the mechanism of the decreases in the tumor cells is unknown,
31 extrapolation of these effects to humans is premature.
32 In considering overall risk, one must take into account factors such as the range of doses
33 to target organs and hormonal state to obtain a complete picture of hazard and risk. Although
34 exposure to dioxins may influence cancer response directly or indirectly and positively or
35 negatively, it is unlikely that such data will be available to argue that dioxin exposure provides a
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1 net benefit to human health. It is also important to note that the doses at which the incidence of
2 certain tumors may decrease is in the same range at which adverse noncancer effects occur (see
3 Appendix A).
4
5 2.2.1.5. Cancer Hazard Characterization
6 TCDD, CDDs, CDFs, and dioxin-like PCBs are a class of well-studied compounds whose
7 human cancer potential is supported by a large database, including "limited" epidemiological
8 support, unequivocal animal carcinogenesis, and biologic plausibility based on mode of action
9 data. In 1985, EPA classified 2,3,7,8-TCDD and related compounds as "probable" human
10 carcinogens, based on the available data. During the intervening years, the database relating to
11 the carcinogen!city of dioxin and related compounds has grown and strengthened considerably.
1 2 In addition, EPA guidance for carcinogen risk assessment has evolved (U.S. EPA, 1996, 1999,
1 3 2003). Under EPA's current approach, complex mixtures of dioxin and related compounds are
14 considered "likely to be carcinogenic to humans," as are individual dioxin-like congeners other
1 5 than TCDD. This descriptor is based primarily on the concept of toxic equivalency but also on
16 the data available to support this characterization for individual congeners. Positive lifetime
1 7 bioassays are available for a number of the principal congeners contributing to human TEQ body
1 8 burden, specifically TCDD, 2,3,4,7,8-PeCDF, 1,2,3,6,7,8-HxCDD, 1,2,3,7,8,9-HxCDD, and PCB
1 9 126 (Kociba et al., 1978; NTP, 1980; NTP, 2003 a-d).
20 2,3,7,8-TCDD is best characterized as "carcinogenic to humans." This means that, based
21 on the weight of all of the evidence (human, animal, mode of action), 2,3,7,8-TCDD meets the
22 stringent criteria that allows EPA and the scientific community to accept a causal relationship
23 between exposure and cancer hazard. The guidance (see EPA, 2003, section 2.6) suggests that
24 "carcinogenic to humans" is an appropriate descriptor of carcinogenic potential when there is an
25 absence of conclusive epidemiologic evidence to clearly establish a cause-and-effect relationship
26 between human exposure and cancer but there is compelling carcinogenicity data in animals and
27 mechanistic information in animals and humans demonstrating similar modes of carcinogenic
28 action.
29 The "carcinogenic to humans" descriptor is suggested for 2,3,7,8-TCDD because all of
30 the following conditions are met:
31
32 • Occupational epidemiologic studies all show an association between 2,3,7,8-TCDD
33 exposure and increases in the all-cancers-combined category, in lung cancer, and
34 perhaps in cancers at other sites, but the data are insufficient on their own to
35 demonstrate a causal association.
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1
2 • There is extensive carcinogenicity in both sexes of multiple species of animals at
3 multiple sites.
4
5 • There is general agreement that the mode of 2,3,7,8-TCDD's carcinogenicity is AhR
6 dependent and proceeds through modification of the action of a number of receptor
7 and hormone systems involved in cell growth and differentiation, such as the EGFR
8 and estrogen receptors.
9
10 • The human AhR and the rodent AhR are similar in structure and function and, once
11 transformed, both bind to the same DNA response elements, designated DRE's.
12
13 • Human and rodent tissue and organ cultures respond to TCDD and related chemicals
14 in a similar manner and at similar concentrations.
15
1 6 Other dioxin-like compounds are characterized as "likely to be carcinogenic to humans,"
1 7 primarily because of the lack of epidemiological evidence associated with their carcinogenicity,
18 although there is a strong inference based on toxic equivalency that they would behave in humans
1 9 as 2,3,7,8-TCDD does. Each of the congeners that contributes substantially to human body
20 burden has been evaluated in vivo in cancer bioassays or tumor promotion assays. Each has a
21 large database demonstrating AhR-mediated dioxin-like activities. Each has physico-chemical
22 properties that contribute to their persistence. For each congener, the degree of certainty of
23 carcinogenic hazard is dependent on the available congener-specific data and its consistency with
24 the generalized mode of action that underpins toxic equivalency for 2,3,7,8-TCDD and related
25 compounds. For the congeners most frequently encountered in human blood, milk, and adipose
26 tissue, the database in support of 2,3,7,8-TCDD-like carcinogenic hazard is strong; those with
27 weaker data supporting 2,3,7,8-TCDD-like carcinogenicity contribute relatively little to total
28 TEQ.
29 On the basis of this logic, all complex environmental mixtures of 2,3,7,8-TCDD and
30 dioxin-like compounds would be characterized as "likely" carcinogens, but the degree of
31 certainty of the cancer hazard would be dependent on the major constituents of the mixture. For
32 instance, the hazard potential, although still considered "likely," would be characterized
33 differently for a mixture whose TEQ was dominated by octachlorodibenzo-p-dioxin as compared
34 to one dominated by other PCDDs.
35
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1 2.2.2. Reproductive and Developmental Effects
2 Several sections of this reassessment (Part n, Chapter 5 and Chapter 7b) have focused on
3 the variety of effects that dioxin and dioxin-like agents can have on human reproductive health
4 and development. The emphasis in each of these chapters has been on the discussion of the more
5 recent reports of the impact of dioxin-like compounds on reproduction and development. These
6 reports have been put into context with previous reviews of the literature applicable in risk
7 assessment (Hatch, 1984; Sweeney, 1994; Kimmel, 1988) to develop a profile of the potential for
8 dioxin and dioxin-like agents to cause reproductive or developmental toxicity, based on the
9 available literature. An earlier version of the literature review and discussion contained in Part
10 n, Chapter 5, has been previously published (Peterson et al., 1993).
11 The origin of concerns regarding a potential link between exposure to chlorinated dioxins
1 2 and adverse developmental events can be traced to early animal studies reporting increased
1 3 incidence of developmental abnormalities in rats and mice exposed early in gestation to 2,4,5-
14 trichlorophenoxyacetic acid (2,4,5-T) (Courtney and Moore, 1971). 2,4,5-T is a herbicide that
1 5 contains dioxin and related compounds as impurities. Its use was banned in the late 1970s, but
16 exposure to human populations continued as a result of past production, use, and disposal.
17
18 2.2.2.1. Human Effects
19 The literature base with regard to potential human effects is detailed in Part II, Chapter
20 7b, Section 7.13. In general, there is limited epidemiological evidence to make a direct
21 association between exposure to TCDD or other dioxin-like compounds and effects on human
22 reproduction or development. One effect that may illustrate this relationship is the altered sex
23 ratio (increased females) seen in the 6 years after the Seveso, Italy, accident (Mocarelli et al.,
24 1996, 2000), and in a heavily exposed occupational cohort in Russia (Ryan et al., 2002).
25 Particularly intriguing in these evaluations is the observation that exposure before and during
26 puberty is linked to this sex ratio effect, and predominantly through the paternal side. Other sites
27 have been examined for the effect of TCDD exposure on sex ratio with mixed results but with
28 smaller numbers of offspring. Data on these sites are still preliminary, but effects similar to the
29 Seveso findings are being reported. Continued evaluation of the Seveso population may provide
30 other indications of impacts on reproduction and development but, for now, such data are limited
31 and further research is needed.
32 Positive human data on developmental effects of dioxin-like compounds are limited to a
33 few studies of populations exposed to a complex mixture of potentially toxic compounds (e.g.,
34 developmental studies from the Netherlands and effects of ingestion of contaminated rice oil in
35 Japan [Yusho] and Taiwan [Yu-Cheng]). In the latter studies, however, all four manifestations
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1 of developmental toxicity (reduced viability, structural alterations, growth retardation, and
2 functional alterations) were observed to some degree following exposure to dioxin-like
3 compounds as well as other agents. Data from the Dutch cohort of children exposed to PCBs and
4 dioxin-like compounds (Huisman et al., 1995a, b; Koopman-Esseboom et al., 1994a-c; 1995a, b,
5 1996; Pluim et al., 1992, 1993, 1994; Weisglas-Kuperus et al., 1995; Patandin et al., 1998, 1999;
6 ten Tusscher et al., 2003; Vreugdenhil et al., 2002) suggest impacts of background levels of
7 dioxin and related compounds on neurobehavioral outcomes, thyroid function, immune function,
8 and liver enzymes aspartate aminotransferase (AST) and alanine aminotransferase (ALT).
9 Although these effects cannot be attributed solely to dioxin and related compounds,
10 several associations suggest that these are, in fact, likely to be AhR-mediated effects. Similarly,
11 it is highly likely that the developmental effects in human infants exposed to a complex mixture
1 2 of PCBs, PCDFs, and poly chlorinated quaterphenyls (PCQs) in the Yusho and Yu-Cheng
13 poisoning episodes may have been caused by the combined exposure to those PCB and PCDF
14 congeners that are AhR agonists (Lii and Wong, 1984; Kuratsune, 1989; Rogan, 1989).
1 5 However, it is not possible to determine the relative contributions of individual chemicals to the
16 observed effects.
1 7 The incidents at Yusho and Yu-Cheng resulted in increased perinatal mortality and low
18 birth weight in infants born to women who had been exposed. Rocker bottom heal was observed
1 9 in Yusho infants, and functional abnormalities have been reported in Yu-Cheng children. Not all
20 the effects that were seen are attributable only to dioxin-like compounds. The similarity of
21 effects observed in human infants prenatally exposed to this complex mixture with those reported
22 in adult monkeys exposed only to TCDD suggests that at least some of the effects in the Yusho
23 and Yu-Cheng children are due to the TCDD-like congeners in the contaminated rice oil ingested
24 by the mothers of these children. The similar responses include a clustering of effects in organs
25 derived from the ectodermal germ layer, referred to as ectodermal dysplasia, including effects on
26 the skin, nails, and Meibomian glands and developmental and psychomotor delay during
27 developmental and cognitive tests (Chen et al., 1992). Some investigators believe that because
28 some of the effects in the Yusho and Yu-Cheng cohorts do not correlate with TEQ, such effects
29 could be exclusively due to nondioxin-like PCBs or to an interaction between the dioxins and the
30 nondioxin-like congeners.
31 Of particular interest is the common developmental origin (ectodermal layer) of many of
32 the organs and tissues that are affected in humans. An ectodermal dysplasia syndrome involving
33 hyperpigmentation, deformation of the fingernails and toenails, conjunctivitis, gingival
34 hyperplasia, and abnormalities of the teeth has been clearly associated with the Yusho and Yu-
35 Cheng episodes, and in the non-human primate studies. Alaluusua et al. (1996, 1999)
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1 investigated dioxin exposure and tooth development in Finnish children as a result of studies of
2 dental effects in dioxin-exposed rats, mice, and nonhuman primates (Part n, Chapter 5, Section
3 5.2) and in PCB-exposed children (Rogan et al., 1988). The Finnish investigators examined
4 enamel hypomineralization of permanent first molars in 6-7-year-old children. The length of
5 time that infants breast-fed was not significantly associated with either mineralization changes or
6 with TEQ levels in the breast milk. However, when the levels and length of breast-feeding were
7 combined in an overall score, a statistically significant association was observed (r= 0.3,
8 />=0.003, regression analysis). These data are discussed further in Part n, Chapter 7b, Section
9 7.13. Follow-up mechanistic studies on tooth development in TCDD sensitive and resistant rats
10 revealed a relatively high dose impact on epithelial-mesenchymal interactions, particularly the
11 mesenchymal odontocytes. This effect that was not associated with differential resistance to
1 2 acute TCDD toxicity (Kiukkonen et al., 2002).
1 3 Other investigations into noncancer effects of human exposure to dioxin have provided
14 human data on TCDD-induced changes in circulating reproductive hormones. This was one of
1 5 the effects judged as having a positive relationship with exposure to TCDD in Part II, Chapter
16 7b, Section 7.13. Levels of reproductive hormones have been measured with respect to exposure
1 7 to 2,3,7,8-TCDD in three cross-sectional medical studies. Testosterone, luteinizing hormone
1 8 (LH), and follicle-stimulating hormone (FSH) were measured in trichlorophenol (TCP) and
1 9 2,4,5-T production workers from the NIOSH cohort (Egeland et al., 1994), in Army Vietnam
20 veterans (CDC Vietnam Experience Study, 1988), and in Air Force Ranch Hands, who handled
21 and/or sprayed Agent Orange during the Vietnam War (Roegner et al., 1991; Grubbs et al.,
22 1995). A recent study also demonstrated an inverse correlation between TCDD levels and
23 prolactin in 2,4,5,-T herbicide sprayers (Johnson et al., 2001). Alterations in breast development
24 have been reported in young women, where a doubling of the serum dioxin concentration
25 (CALUX assay) increased the odds of not having reached the adult stage of breast development
26 by 2.3 fold (PO.02) in the women (-17 yo) studied (Den Hond et al., 2002). Alterations in
27 menstrual duration and flow have been reported in women exposed as premenarcheal girls 20
28 years previously as a result of the Seveso incident (Eskenazi et al., 2002a).
29 The risk of abnormally low testosterone was two to four times higher in exposed workers
30 who had serum 2,3,7,8-TCDD levels above 20 ng/g than in unexposed referents (Egeland et al.,
31 1994). In both the 1987 and 1992 examinations, mean testosterone concentrations were slightly
32 but not significantly higher in Ranch Hands (Thomas et al., 1990; Grubbs et al., 1995). FSH and
33 LH concentrations were no different between the exposed and comparison groups. No
34 significant associations were found between Vietnam experience and altered reproductive
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1 hormone levels (CDC Vietnam Experience Study, 1988). Only the NIOSH study (Egeland et al.,
2 1994) found an association between serum 2,3,7,8-TCDD level and increases in serum LH.
3 The findings of the NIOSH and Ranch Hand studies are plausible, given the
4 pharmacological and toxicological properties of 2,3,7,8-TCDD in animal models, which are
5 discussed in Part II, Chapters 5 and 7. One plausible mechanism responsible for the effects of
6 dioxins may involve their ability to influence hormone receptors. The AhR, to which 2,3,7,8-
7 TCDD binds, and the hormone receptors are signaling pathways that regulate homoeostatic
8 processes. These signaling pathways are integrated at the cellular level, and there is considerable
9 "cross-talk" between these pathways. For example, studies suggest that 2,3,7,8-TCDD
1 0 modulates the concentrations of numerous hormones and/or their receptors, including estrogen
11 (Romkes and Safe, 1988; Romkes et al., 1987), progesterone (Romkes et al., 1987),
1 2 glucocorticoid (Ryan et al., 1989), and thyroid hormones (Gorski and Rozman, 1987; Pavuk et
13 al., 2003).
14 In summary, the results from both the NIOSH and the Ranch Hand studies are limited by
1 5 the cross-sectional nature of the data and the type of clinical assessments conducted. However,
16 the available data provide evidence that small alterations in human male reproductive hormone
1 7 levels are associated with serum 2,3,7,8-TCDD.
18
19 2.2.2.2. Experimental Animal Effects
20 The extensive experimental animal database with respect to reproductive and
21 developmental toxicity of dioxin and dioxin-related agents is discussed in Part n, Chapter 5.
22 Dioxin exposure has been observed to result in both male and female reproductive effects as well
23 as developmental effects. These latter effects are among the most responsive health endpoints to
24 dioxin exposure (see Part II, Chapter 8, Section 8.3). In general, the prenatal and developing
25 postnatal animal is more sensitive to the effects of dioxin than is the adult. In several instances
26 (e.g., fetotoxicity in hamsters, rats, mice, and guinea pigs), the large species differences seen in
27 acute toxicity are greatly reduced when developing animals are evaluated. Most of the data
28 reviewed are from studies of six genera of laboratory animals. Although much of the data come
29 from animals exposed only to TCDD, more recent studies of animals exposed to mixtures of
30 PCDD/PCDF/ PCB congeners provide results that are consistent with the studies of TCDD
31 alone.
32
33 2.2.2.2.1. Developmental toxicity. Dioxin exposure results in a wide variety of developmental
34 effects; these are observed in three different vertebrate classes and in several species within each
35 class. All four of the manifestations of developmental toxicity have been observed following
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1 exposure to dioxin: reduced viability, structural alterations, growth retardation, and functional
2 alterations. As summarized previously (Peterson et al., 1993), increased prenatal mortality (rat
3 and monkey), functional alterations in learning (rat, mouse, and monkey) and sexual behavior
4 (rat), and changes in the development of the reproductive system (rat, hamster, and mouse) occur
5 at the lowest exposure levels tested (see also Part n, Chapter 8, Section 8.3).
6 Dioxin exposure has resulted in reduced prenatal or postnatal viability in virtually every
7 species in which it has been tested. Previously, increased prenatal mortality appeared to be
8 observed only at exposures that also resulted in maternal toxicity. However, the studies of Olson
9 and McGarrigle (1990) in the hamster and Schantz and Bowman (1989) in the monkey suggested
10 that this was not the case in all species. Although the data from these two studies were limited,
11 prenatal death was observed in cases where no maternal toxicity was evident. In the rat,
1 2 Peterson's laboratory (Bjerke et al., 1994a, b; Roman et al., 1995) reported increased prenatal
1 3 death following a single exposure to TCDD during gestation that did not cause maternal toxicity,
14 and Gray et al. (1995a) observed a decrease in postnatal survival under a similar exposure
1 5 regimen. Although identifying the presence or absence of maternal toxicity may be instructive as
16 to the specific origin of the reduced prenatal viability, it does not alter the fact that pre- and
1 7 postnatal deaths were observed. In either case, the Agency considers these effects as being
1 8 indicators of developmental toxicity in response to the exposure (U.S. EPA, 1991b).
1 9 Some of the most striking findings regarding dioxin exposure relate to the effects on the
20 developing reproductive system in laboratory animals. Only a single, low-level exposure to
21 TCDD during gestation is required to initiate these developmental alterations. Mably et al.
22 (1992a-c) originally reported that a single exposure of the Holtzman maternal rat to as little as
23 0.064 ng/kg could alter normal sexual development in the male offspring. A dose of 0.064 ng/kg
24 in these studies resulted in a maximal body burden in the maternal animal of 64 ng/kg during
25 critical windows in development. More recently, these findings of altered normal sexual
26 development have been further defined (Bjerke et al., 1994a, b; Gray et al., 1995a; Roman et al.,
27 1995) and extended to female offspring and other strains (Faqi et al., 1998; Ohsako et al., 2001)
28 and species (hamsters and mice) (Gray et al., 1995b; Theobald et al., 1997). In general, the
29 findings of these later studies have produced qualitatively similar results that define a significant
30 effect of dioxin on the developing reproductive system.
31 In the developing male rat, TCDD exposure during the prenatal and lactational periods
32 results in delay of the onset of puberty, as measured by age at preputial separation. There is a
33 reduction in testis weight, sperm parameters, and sex accessory gland weights. In the mature
34 male exposed during the prenatal and lactational periods, there is an alteration of normal sexual
35 behavior and reproductive function. Males exposed to TCDD during gestation are
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1 demasculinized. Feminization of male sexual behavior and a reduction in the number of
2 implants in females mated with exposed males have also been reported, although these effects
3 have not been consistently found. These effects do not appear to be related to reductions in
4 circulating androgens, which were shown in the most recent studies to be unaffected by TCDD.
5 Most of these effects have occurred in a dose-related fashion, some at doses of 0.05 i-ig/kg and
6 0.064 i-ig/kg, the lowest doses tested (Mably et al., 1992c; Gray et al., 1997a).
7 In Part n, Chapter 8, ED01 values were estimated from the Mably et al. (1992a-c) and
8 Gray et al. (1997a) reports. In these two studies more than 44 data sets were modeled, and 17 of
9 these data sets had body burden ED01s lower than 50 ng/kg. For the 12 endpoints in the Mably et
10 al. studies that were modeled in Part II, Chapter 8, the median body burden ED01 estimate is 5.2
11 ng TCDD/kg. Although not modeled in Part E, Chapter 8, the data from Faqi et al. (1998) and
1 2 Ohsaka et al. (2001) have LOAELs and NOAELs for developmental reproductive effects of
1 3 TCDD in male rats ranging from body burdens of 12.5-200 ng TCDD/kg, which is consistent
14 with the Mably et al. and Gray et al. studies.
1 5 In the developing female rat, Gray and Ostby (1995) demonstrated altered sexual
16 differentiation in both the Long Evans and Holtzman strains. The effects observed depended on
1 7 the timing of exposure. Exposure during early organogenesis altered the cyclicity, reduced
18 ovarian weight, and shortened the reproductive lifespan. Exposure later in organogenesis
19 resulted in slightly lowered ovarian weight, structural alterations of the genitalia, and a slight
20 delay in puberty. However, cyclicity and fertility were not affected with the later exposure. The
21 most sensitive dose-dependent effects of TCDD in the female rat were the structural alterations
22 of the genitalia that occurred at 0.20 ng TCDD/kg administered to the dam (Gray et al., 1997b).
23 As described above, studies demonstrating adverse health effects from prenatal exposures
24 often involved a single dose administered at a discrete time during pregnancy. The production of
25 prenatal effects at a given dose appears to require exposure during critical times in fetal
26 development. This concept is well supported by a recent report (Hurst et al., 2000) that
27 demonstrated the same incidence of adverse effects in rat pups born to dams with a single
28 exposure of 0.2 ng TCDD/kg body weight on gestation day 15 versus 1.0 ng TCDD/kg body
29 weight on gestation day 8. Both of these experimental exposure paradigms resulted in the same
30 fetal tissue concentrations and body burdens during the critical window of sensitivity. For
31 example, exposure to 0.2 ng TCDD/kg on day 15 resulted in 13.2 pg TCDD/g fetal tissue on
32 day!6; exposure to 1.0 ng TCDD/kg on day 8 resulted in 15.3 pg TCDD/g fetus on day 16. This
33 study demonstrates the appropriateness of the use of body burden to describe the effects of
34 TCDD when comparing different exposure regimens. The uncertainties introduced when trying
35 to compare studies with steady-state body burdens with single-dose studies may make it difficult
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1 to determine a lowest effective dose. Application of pharmacokinetic models (described in Parts
2 I and n) to estimate body burdens at the critical time of development is expected to be a sound
3 method for relating chronic background exposures to the results obtained from single-dose
4 studies.
5 Structural malformations, particularly cleft palate and hydronephrosis, occur in mice
6 administered TCDD. The findings, although not representative of the most sensitive
7 developmental endpoints, indicate that exposure during the critical period of organogenesis can
8 affect the processes involved in normal tissue formation. The TCDD-sensitive events appear to
9 require the AhR. Mouse strains that produce AhRs with relatively high affinity for TCDD
1 0 respond to lower doses than do strains with relatively low-affinity receptors. Moreover,
11 congeners that have a greater affinity for the AhR are more developmentally toxic than those that
1 2 have a lower affinity. This is consistent with the rank ordering of toxic potency based on affinity
1 3 for the receptor, as discussed in Part n, Chapter 9, Section 9.3. In addition, mice in which the Ah
14 receptor has been knocked out do not develop cleft palate.
1 5 Recent work, not elaborated upon here, has demonstrated that developmental exposure of
16 rodents to dioxin also permanently alters the development of the prostate in wild type but not
1 7 AhR null mutant mice (Lin et al., 2003), and mammary development in rats and mice (Fenton et
18 al, 2002; Vorderstrasse et al., 2003). The key role of the Ah receptor has also been demonstrated
1 9 in the developing heart of AhR null mice (Lund et al., 2003).
20
21 2.2.2.2.2. Adult female reproductive toxicity. The primary effects of TCDD on female
22 reproduction in animals appear to be decreased fertility, inability to maintain pregnancy for the full
23 gestational period, and, in the rat, decreased litter size. In some studies of rats and of primates,
24 signs of ovarian dysfunction such as anovulation and suppression of the estrous cycle have been
25 reported (Kociba et al., 1976; Barsotti et al., 1979; Allen et al., 1979; Li et al., 1995a, b). Although
26 the majority of reproductive effects are associated with high-dose exposures in experimental
27 animals, the induction of endometriosis in primates occurs at body burdens near background
28 human exposures. This effect is discussed further below.
29
30 2.2.2.2.3. Adult male reproductive toxicity. TCDD and related compounds decrease testis and
31 accessory sex organ weights, cause abnormal testicular morphology, decrease spermatogenesis,
32 and reduce fertility when given to adult animals in doses sufficient to reduce feed intake and/or
33 body weight. In the testes of these different species, TCDD effects on spermatogenesis are
34 characterized by loss of germ cells, the appearance of degenerating spermatocytes and mature
35 spermatozoa within the lumens of seminiferous tubules, and a reduction in the number of tubules
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1 containing mature spermatozoa (Allen and Lalich, 1962; Allen and Carstens, 1967; McConnell et
2 al., 1978; Chahoud et al., 1989). This suppression of spermatogenesis is not a highly sensitive
3 effect when TCDD is administered to postweanling animals, as an exposure of 1 |ig/kg/day over
4 a period of weeks appears to be required to produce these effects.
5
6 2.2.2.3. Other Data Related to Developmental and Reproductive Effects
7 2.2.2.3.1. Endometriosis. The association of dioxin with endometriosis was first reported in a
8 study of rhesus monkeys that had been exposed for 4 years to dioxin in their feed and then held
9 for an additional 10 years (Rier et al., 1993). There was a dose-related increase in both the
10 incidence and severity of endometriosis in the exposed monkeys as compared to controls.
1 1 Follow-up on this group of monkeys revealed a clear association with total TEQ. A study in
1 2 which rhesus monkeys were exposed to PCBs for up to 6 years failed to show any enhanced
1 3 incidence of endometriosis (Arnold et al., 1996). However, many of these monkeys were no
14 longer cycling, and the time may not have been adequate to develop the response. In the TCDD
1 5 monkey study, it took 7 years before the first case of endometriosis was noted (Rier et al., 1993).
1 6 A recent study in Cynomolgus monkeys showed promotion of surgically induced
1 7 endometriosis by TCDD within 1 year after surgery (Yang et al., 2000). Studies using rodent
1 8 models for surgically induced endometriosis have also shown the ability of TCDD to promote
1 9 lesions in a dose-related manner (Cummings et al., 1996, 1999; Johnson et al., 1997; Bruner-
20 Tran et al., 1999). This response takes at least 2 months to be detected (Cummings et al., 1996,
21 1999; Johnson et al., 1997). Another study in mice that failed to detect dioxin promotion of
22 surgically induced endometriosis held the mice for only 1 month, not long enough to detect a
23 response (Yang et al., 1997). Prenatal exposure of mice also enhanced the sensitivity of the
24 offspring to the promotion of surgically induced endometriosis by TCDD (Cummings et al.,
25 1999).
26 The effects of TCDD in the murine model of endometriosis appear to be AhR-mediated,
27 as demonstrated in a study in which AhR ligands were able to promote the lesions, whereas non-
28 AhR ligands, including a nondioxin-like PCB, had no effect on surgically induced endometriosis
29 (Johnson et al., 1997). Dioxin has also been shown to result in endometriosis with human
30 endometrial tissue implanted in nude mice (Bruner-Tran et al., 1999).
31 Data on the relationship of dioxins to endometriosis in humans is intriguing, but
32 preliminary. Studies in the early 1990s suggested that women who had higher levels of persistent
33 organochlorines were at increased risk for endometriosis (Gerhard and Runnebaum, 1992). This
34 was followed by the observation that Belgian women, who have the highest levels of dioxins in
35 their background population, had higher incidences of endometriosis than those reported from
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1 other populations (Koninckx et al., 1994). A study from Israel then demonstrated that there was
2 a correlation between detectable TCDD in women who had surgically confirmed endometriosis
3 in comparison to those who had no endometriosis (Mayani et al., 1997).
4 Recent studies from Belgium indicate that women with higher body burdens, based on
5 serum TEQ determinations, are at greater risk for endometriosis (Pauwels et al., 1999). No
6 association was seen with total PCBs in this study. A small study in the United States that did
7 not involve surgically confirmed endometriosis saw no association between TCDD and
8 endometriosis (Boyd et al., 1995). Likewise, a study in Canada saw no association between total
9 PCBs and endometriosis (Lebel et al., 1998). The lack of an association with total PCBs is not
10 surprising, because the rodent studies have indicated that this response is AhR-mediated
11 (Johnson et al., 1997). The Seveso Women's Health Study reported "...a doubled, non-
1 2 significant risk for endometriosis among women with serum TCDD levels of 100 ppt or higher,
1 3 but no clear dose-response. Unavoidable disease misclassification in a population-based study
14 may have led to an underestimate of the true risk of endometriosis"(Eskenazi et al., 2002b).
1 5 The animal results lend biological plausibility to the epidemiology findings (Birnbaum
1 6 and Cummings, 2002). Endometriosis is not only an endocrine disorder, it is also associated with
1 7 immune system alterations (Rier et al., 1995; Rier and Foster, 2002). Dioxins are known to be
1 8 potent modulators of the animal immune system and to affect estrogen homeostasis. Further
19 studies are clearly needed to provide additional support to this association of endometriosis and
20 dioxins, as well as to demonstrate causality.
21
22 2.2.2.3.2. Androgenic deficiency. The effects of TCDD on the male reproductive system when
23 exposure occurs in adulthood are believed to be due in part to an androgenic deficiency. This
24 deficiency is characterized in adult rats by decreased plasma testosterone and
25 5a-dihydrotestosterone concentrations, unaltered plasma LH concentrations, and unchanged
26 plasma clearance of androgens and LH (Moore et al., 1985, 1989; Mebus et al., 1987; Moore and
27 Peterson, 1988; Bookstaff et al., 1990a). The cause of the androgenic deficiency was believed to
28 be due to decreased testicular responsiveness to LH and increased pituitary
29 responsiveness to feedback inhibition by androgens and estrogens (Moore et al., 1989, 1991;
30 Bookstaff et al., 1990a, b; Kleeman et al., 1990). The single dose used in some of those earlier
31 studies (15 ng TCDD/kg body weight) is now known to affect Leydig cells (Johnson et al.,
32 1994).
33
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1 2.2.2.4. Developmental and Reproductive Effects Hazard Characterization
2 There is limited direct evidence addressing the issues of how or at what levels humans
3 will begin to respond to dioxin-like compounds with adverse impacts on development or
4 reproductive function. The series of published Dutch studies suggest that pre- and early postnatal
5 exposures to PCBs and other dioxin-like compounds may impact developmental milestones at
6 levels at or near current average human background exposures. Although it is unclear whether
7 these measured responses indicate a clearly adverse impact, if humans respond to TCDD
8 similarly to animals in laboratory studies, there are indications that exposures at relatively low
9 levels might cause developmental effects and at higher levels might cause reproductive effects.
10 There is especially good evidence for effects on the fetus from prenatal exposure. The Yusho
11 and Yu-Cheng poisoning incidents are clear demonstrations that dioxin-like compounds can
1 2 produce a variety of mild to severe developmental effects in humans that resemble the effects of
1 3 exposure to dioxins and dioxin-like compounds in animals.
14 Humans do not appear to be particularly sensitive or insensitive to effects of dioxin
1 5 exposure in comparison to other animals. Therefore, it is reasonable to assume that human
1 6 responsiveness would lie across the middle ranges of observed responses. This assumption still
1 7 does not address the issues surrounding the potentially different responses that humans (or
1 8 animals) might have to the more complex and variable environmental mixtures of dioxin-like
1 9 compounds. One additional key point is that most of the epidemiology studies have focused on
20 TCDD, and not the total TEQ. Eskenazi et al. (2004) have shown that background exposure to
21 dioxins, furans and PCBs in the referent population (zone non-ABR) cohort at Seveso was
22 substantial,with non-ABR residents having average serum 2,3,7,8-TCDD and TEQ levels of 20.2
23 ppt and 100.4 ppt, respectively. The exposure zone A median serum TCDD level was 272 ppt
24 and zone B was 47 ppt. The authors suggest that previous Seveso studies "that considered only
25 TCDD exposure, may have underestimated health effects due to total TEQ concentrations."
26 TCDD and related compounds have reproductive and developmental toxicity potential in
27 a broad range of wildlife and domestic and laboratory animals. Many of the effects have been
28 shown to be TCDD dose-related. The effects on perinatal viability and male reproductive
29 development are among the most sensitive effects reported, occurring at a single prenatal
30 exposure range of as little as 0.05-0.075 i-ig/kg, resulting in calculated fetal tissue concentrations
31 of 3-4 ng/kg in the rat (Hurst et al., 2000). In these studies, effects were often observed at the
32 lowest exposure level tested, thus a NOAEL has not been established for several of these
33 endpoints. In general, the structure-activity results are consistent with an AhR-mediated
34 mechanism for the developmental effects that are observed in the low-dose range. The structure-
35 activity relationship in laboratory mammals appears to be similar to that for AhR binding. This
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1 is especially the case with cleft palate in the mouse, but has also been seen with hydronephrosis
2 in the mouse, and developmental reproductive effects in rats.
3 It is assumed that the responses observed in animal studies are indicative of the potential
4 for reproductive and developmental toxicity in humans. This is an established assumption in the
5 risk assessment process for developmental toxicity (U.S. EPA, 1991b). It is supported by the
6 number of animal species and strains in which effects have been observed. The limited human
7 data are consistent with an effect following exposure to TCDD or TCDD-like agents. In
8 addition, the phylogenetic conservation of the structure and function of the AhR also increases
9 our confidence that these effects may occur in humans.
10 There is extensive evidence in experimental animals (mice, rats, monkeys) that exposure
11 to dioxin-like chemicals during development produces neurobehavioral effects. In fact, recent
1 2 studies in rodents demonstrate effects on brain development (Zareba et al., 2002), attention
1 3 (Markowski et al., 2002), and behavior (Hojo et al., 2002) at doses close to current human body
1 4 burdens. The situation in humans is more complex. Studies in humans demonstrate associations
1 5 between dioxin exposure and alterations in neurological development. These same studies often
16 show similar associations between exposure to nondioxin-like PCBs and these same effects. On
1 7 the basis of the human studies, it is possible that the alterations in neurological development are
1 8 due to an interaction between the dioxins and the nondioxin-like PCBs. At present there are
19 limited data that define the roles of the dioxins versus the nondioxin-like PCBs in these effects
20 on neurological development.
21 In general, the structure-activity results on dioxin-like compounds are consistent with an
22 AhR-mediated mechanism for many of the developmental effects that are observed. The
23 structure-activity relationship in laboratory mammals appears to be similar to that for AhR
24 binding. This is especially the case with teratogenesis in the mouse. However, a direct
2 5 relationship with AhR binding has not yet been proven for those involving the developing
26 nervous system.
27
28 2.2.3. Immunotoxicity
29 2.2.3.1. Epidemiologic Findings
30 The available epidemiologic studies on immunologic function in humans relative to
31 exposure to 2,3,7,8-TCDD do not describe a consistent pattern of effects among the examined
32 populations. Two studies of German workers in which one cohort was exposed to 2,3,7,8-TCDD
33 (Ott et al., 1994), and the other to 2,3,7,8-tetrabrominated dioxin and furan (Zober et al., 1992),
34 found dose-related increases of complements C3 or C4, whereas the Ranch Hands have
35 continued to exhibit elevations in immunoglobulin A (IgA) (Roegner et al., 1991; Grubbs et al.,
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1 1995). Other studies of groups with documented exposure to 2,3,7,8-TCDD have not examined
2 complement components to any great extent or observed significant changes in IgA. Suggestions
3 of immunological disturbances have been observed in a small group of exposed workers (Tonn et
4 al., 1996) and in perinatally exposed children (ten Tusscher et al., 2003), providing support for a
5 testable hypothesis to be evaluated in other exposed populations.
6 Comprehensive evaluation of immunologic status and function of the MOSH (Halperin
7 et al., 1998), Ranch Hand (Michalek et al., 1999b), and Hamburg chemical workers (Jung et al.,
8 1998; Ernst et al., 1998) cohorts found no consistent differences between exposed and unexposed
9 groups for lymphocyte subpopulations, response to mitogen stimulation, or rates of infection.
10 However, recent data from the Seveso experience demonstrate subtle effects on immune function
11 (Baccarelli et al., 2002).
1 2 More comprehensive evaluations of immunologic function with respect to exposure to
1 3 2,3,7,8-TCDD and related compounds are necessary to assess more definitively the relationships
14 observed in nonhuman species. Longitudinal studies of the maturing human immune system may
1 5 provide the greatest insight, particularly because animal studies have found significant results in
1 6 immature animals, and human breast milk is a source of 2,3,7,8-TCDD and other related
1 7 compounds. The studies of Dutch infants (ten Tusscher et al., 2003) described earlier provide an
1 8 example of such a study design. Additional studies of highly exposed adults may also shed light
1 9 on the effects of long-term chronic exposures through elevated body burdens. Therefore, there
20 appears to be too little information to suggest definitively that 2,3,7,8-TCDD, at the levels
21 observed, causes long-term adverse effects on the immune system in adult humans.
22
23 2.2.3.2. Animal Findings
24 Cumulative evidence from a number of studies indicates that the immune system of
25 various animal species is a target for toxicity of TCDD and structurally related compounds,
26 including other PCDDs, PCDFs, and PCBs. Both cell-mediated and humoral immune responses
27 are suppressed following TCDD exposure, suggesting that there are multiple cellular targets
28 within the immune system that are altered by TCDD. Evidence also suggests that the immune
29 system is indirectly targeted by TCDD-induced changes in nonlymphoid tissues. TCDD
30 exposure of experimental animals results in decreased host resistance following challenge with
31 certain infectious agents, which likely result from TCDD-induced suppression of immunological
32 functions.
33 The primary antibody response to the T cell-dependent antigen, sheep red blood cells
34 (SRBCs), is the most sensitive immunological response that is consistently suppressed in mice
35 exposed to TCDD and related compounds. The degree of immunosuppression is related to the
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1 potency of the dioxin-like congeners. There is remarkable agreement among several different
2 laboratories for the potency of a single acute dose of TCDD (i.e., suppression at a dose as low as
3 0.1 \ig TCDD/kg with an average 50% immunosupressive dose [ID50] value of approximately
4 0.7 \ig TCDD/kg) to suppress this response in Ah-responsive mice. Results of studies that have
5 compared the effects of acute exposure to individual PCDDs, PCDFs, and PCB congeners
6 (which differ in their binding affinity for the AhR) on this response have provided critical
7 evidence that certain dioxin-like congeners are also immunosuppressive. The degree of
8 immunosuppression has been found to be related to potency of the dioxin-like congeners.
9 Antibody responses to T cell-independent antigens such as trinitrophenyl-lipopolysaccharide and
10 the cytotoxic T lymphocyte (CTL) response are also suppressed by a single acute exposure to
11 TCDD, albeit at higher doses than those that suppress the SRBC response. Although a thorough
1 2 and systematic evaluation of the immunotoxicity of TCDD-like congeners in different species
1 3 and for different immunological endpoints has not been performed, it can be inferred from the
14 available data that dioxin-like congeners are immunosuppressive.
1 5 Perinatal exposure of experimental animals to TCDD results in suppression of primarily
1 6 T cell immune functions, with suppression persisting into adulthood. In mice, the effects on T
1 7 cell functions appear to be related to the fact that perinatal TCDD exposure alters thymic
1 8 precursor stem cells in the fetal liver and bone marrow and thymocyte differentiation in the
19 thymus. These studies suggest that perinatal development is a critical and sensitive period for
20 TCDD-induced immunotoxicity. Further efforts should be made to determine the consequences
21 of perinatal exposure to TCDD and related compounds and mixtures on immune system
22 integrity.
23
24 2.2.3.3. Other Data Related to Immunologic Effects
25 In addition to the TCDD-like congener results, studies using strains of mice that differ in
26 the expression of the AhR have provided critical evidence to support a role for Ah-mediated
27 immune suppression following exposure to dioxin-like compounds. Recent in vitro work also
28 supports a role for Ah-mediated immune suppression. Other in vivo and in vitro data, however,
29 suggest that non-Ah-mediated mechanisms may also play some role in immunotoxicity induced
30 by dioxin-like compounds. However, more definitive evidence remains to be developed to
31 support this latter view.
32 The immunosuppressive potency of individual dioxin-like compounds in mice is related
33 to their structural similarity to TCDD. However, the immunotoxicity of TCDD and related
34 congeners can be modified by co-exposure to nondioxin-like PCBs in simple binary or more
35 complex mixtures, resulting in additive or antagonistic interactions. There is a need for the
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1 generation of dose-response data of acute, subchronic, and chronic exposure to the individual
2 congeners in a mixture and for the mixture itself in order to fully evaluate potential synergistic,
3 additive, or antagonistic effects of environmentally relevant mixtures. A preliminary report
4 demonstrating that the immunotoxicity of a food-like mixture of dioxins was well-predicted by
5 the TEQ has been presented (Smialowicz et al., 1997).
6 Animal host resistance models that mimic human disease have been used to assess the
7 effects of TCDD on altered host susceptibility. TCDD exposure increases susceptibility to
8 challenge with bacteria, viruses, parasites, and tumors. Mortality is increased in TCDD-exposed
9 mice challenged with certain bacteria. Increased parasitemia occurs in TCDD-exposed mice and
10 rats challenged with parasitic infections. Low doses of TCDD also alter resistance to virus
11 infections in rodents. Increased susceptibility to infectious agents is an important benchmark of
1 2 immunosuppression; however, the role that TCDD plays in altering immune-mediated
1 3 mechanisms important in murine resistance to infectious agents remains to be elucidated. Also,
14 because little is known about the effects that dioxin-like congeners have on host resistance, more
1 5 research is recommended in this area.
1 6 Studies in nonhuman primates exposed acutely, subchronically, or chronically to
1 7 halogenated aromatic hydrocarbons (HAH) have revealed variable alterations in lymphocyte
1 8 subpopulations, primarily T lymphocyte subsets. In three separate studies in which monkeys
1 9 were exposed subchronically or chronically to PCBs, the antibody response to SRBC was
20 consistently found to be suppressed. These results in nonhuman primates are important because
21 they corroborate the extensive database of HAH-induced suppression of the antibody response to
22 SRBC in mice and thereby provide credible evidence for immunosuppression by HAHs across
23 species. In addition, these data indicate that the primary antibody response to this T cell-
24 dependent antigen is the most consistent and sensitive indicator of HAH-induced
25 immunosuppression.
26 The available database derived from well-controlled animal studies on TCDD
27 immunotoxicity can be used for the establishment of NOELs. As the antibody response to
28 SRBCs has been shown to be dose-dependently suppressed by TCDD and related dioxin-like
29 compounds, this database is best suited for the development of dose-response modeling.
30
31 2.2.3.4. Immunologic Effects Hazard Characterization
32 Accidental or occupational exposure of humans to TCDD and/or related compounds
33 variably affects a number of immunological parameters. Unfortunately, the evaluation of
34 immune system integrity in humans exposed to dioxin-like compounds has provided data that are
35 inconsistent across studies. The broad range of "normal" responses in humans due to the large
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1 amount of variability inherent in such a heterogenous population, the limited number and
2 sensitivity of tests performed, and poor exposure characterization of the cohorts in these studies
3 compromise any conclusions about the ability of a given study to detect immune alterations.
4 Consequently, there are insufficient clinical data from these studies to fully assess human
5 sensitivity to TCDD exposure. Nevertheless, based on the results of the extensive animal work,
6 the database is sufficient to indicate that immune effects could occur in the human population
7 from exposure to TCDD and related compounds at some dose level. At present, it is EPA's
8 scientific judgment that TCDD and related compounds should be regarded as nonspecific
9 immunosuppressants and immunotoxicants until better data to inform this judgment are
10 available.
11 It is interesting that a common thread in several human studies is the observed reduction
1 2 in CD4+ T helper cells, albeit generally within the "normal" range, in cohorts exposed to dioxin-
1 3 like compounds. Even though these reductions may not translate into clinical effects, it is
1 4 important to note that these cells play an important role in regulating immune responses and that
1 5 their reduction in clinical diseases is associated with immunosuppression. It is also important to
16 realize that those at the extremes of the population distribution may be at special risk of such
1 7 alterations. Another important consideration is that a primary antibody response following
1 8 immunization was not evaluated in any of the human studies. Because this immune parameter
19 has been revealed to be the most sensitive in animal studies, it is recommended that TCDD and
20 related compounds be judged immunosupressive and that this parameter be included in future
21 studies of human populations exposed to TCDD and related compounds. It is also recommended
22 that research focused on delineating the mechanism(s) underlying dioxin-induced
23 immunotoxicity and immunosuppression continue.
24
25 2.2.4. Chloracne
26 Chloracne and associated dermatologic changes are widely recognized responses to
27 TCDD and other dioxin-like compounds in humans. Along with the reproductive hormones
28 discussed above and gamma glutamyl transferase (GGT) levels, which are discussed below,
29 chloracne is one of the noncancer effects that has a strong positive association with exposure to
30 TCDD in humans (see Part II, Chapter 7b, Section 7.13). Chloracne is a severe acne-like
31 condition that develops within months of first exposure to high levels of dioxin and related
32 compounds. For many individuals, the condition disappears after discontinuation of exposure,
33 despite initial serum levels of dioxin in the thousands of parts per trillion; for others, it may
34 remain for many years. The duration of persistent chloracne is on the order of 25 years, although
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1 cases of chloracne persisting for more than 40 years have been noted (see Part n, Chapter 7b,
2 Section 7.13).
3 In general, chloracne has been observed in most incidents where substantial dioxin
4 exposure has occurred, particularly among TCP production workers and Seveso residents (see
5 Part II, Chapter 7b). The amount of exposure necessary for development of chloracne has not
6 been resolved, but studies suggest that high exposure (both high acute and long-term exposure) to
7 2,3,7,8-TCDD increases the likelihood of chloracne, as evidenced by chloracne in TCP
8 production workers and Seveso residents who had documented high serum 2,3,7,8-TCDD levels
9 (Beck et al., 1989; Fingerhut et al., 1991a; Mocarelli et al., 1991; Neuberger et al, 1991) or in
1 0 individuals who had a work history with long duration of exposure to 2,3,7,8-TCDD-
11 contaminated chemicals (Bond et al., 1989).
1 2 In earlier studies, chloracne was considered to be a "hallmark of dioxin intoxication"
1 3 (Suskind, 1985). However, in only two studies were risk estimates calculated for chloracne.
1 4 Both were studies of different cohorts of TCP production workers, one of which was employed
15 in a West Virginia plant (Suskind and Hertzberg, 1984), the other in a plant in Michigan (Bond et
1 6 al., 1989). Of the 203 West Virginia workers, 52.7% (p<0.001) were found to have clinical
1 7 evidence of chloracne, and 86.3% reported a history of chloracne (p<0.001). None of the
1 8 unexposed workers had clinical evidence or reported a history of chloracne. Among the
1 9 Michigan workers, the relative risk for cases of chloracne was highest for individuals with the
20 longest duration of exposure (> 60 months; RR = 3.5, 95% CI = 2.3-5.1), those with the highest
21 cumulative dose of TCDD (based on duration of assignment across and within 2,3,7,8-TCDD-
22 contaminated areas in the plant) (RR = 8.0, 95% CI = 4.2-15.3), and those with the highest
23 intensity of 2,3,7,8-TCDD exposure (RR = 71.5, 95% CI = 32.1-159.2).
24 Studies in multiple animal species have been effective in describing the relationship
25 between 2,3,7,8-TCDD and chloracne, particularly in rhesus monkeys (McNulty, 1977; Allen et
26 al., 1977; McConnell et al., 1978). Subsequent to exposure to 2,3,7,8-TCDD, monkeys
27 developed chloracne and swelling of the meibomian glands, the modified sebaceous glands in the
28 eyelid. The histologic changes in the meibomian glands are physiologically similar to those
29 observed in human chloracne (Dunagin, 1984).
30 In summary, the evidence provided by the various studies convincingly supports what is
31 already presumed—that chloracne is a common sequel of high levels of exposure to 2,3,7,8-
32 TCDD and related compounds. More information is needed to determine the level and frequency
33 of exposure to dioxin-like compounds needed to cause chloracne and whether personal
34 susceptibility plays a role in the etiology. Finally, it is important to recall that the absence of
35 chloracne does not imply lack of exposure (Mocarelli et al., 1991).
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1 2.2.5. Diabetes
2 Diabetes mellitus is a heterogeneous disorder that is a consequence of alterations in the
3 number or function of pancreatic beta cells responsible for insulin secretion and carbohydrate
4 metabolism. Diabetes and fasting serum glucose levels were evaluated in more recent cross-
5 sectional medical studies because of the apparently high prevalence of diabetes and abnormal
6 glucose tolerance tests in one case report of 55 TCP workers (Pazderova-Vejlupkova et al.,
7 1981). Recent epidemiology studies, as well as early case reports, have indicated a weak
8 association between serum concentrations of dioxin and diabetes. This association was first
9 noted in the early 1990s when a decrease in glucose tolerance was seen in the NIOSH cohort.
10 This was followed by a report of an increase in diabetes in the Ranch Hand cohort (Michalek et
11 al., 1999a; Longnecker and Michalek, 2000). An increase in diabetes in other occupational
1 2 cohorts (Steenland et al., 1999; Vena et al., 1998) as well as in the Seveso population (Pesatori et
13 al., 1998) has also been reported. There was not a significant increase in diabetes in the NIOSH
14 mortality study, although 6 of the 10 most highly exposed workers did have diabetes (Calvert et
1 5 al., 1999). However, mortality studies are limited in their ability to assess risk from diabetes
16 mellitus because the prevalence of disease may not be available from death certificates.
1 7 A paper by Longnecker and Michalek (2000) found a pattern suggesting that low levels of
18 dioxin may influence the prevalence of diabetes. However, these results did not show an
19 exposure-response relationship. Because it is the only study of its type to have been published,
20 additional population-based studies are warranted to validate its findings. A recent update of the
21 Ranch Hand study shows a 47% excess of diabetes in the most heavily exposed group of veterans
22 (Michalek et al., 1999a).
23 Most of the data suggest that the diabetes observed in the studies is Type n, or adult-onset
24 diabetes, rather than insulin dependent, or Type I. Aging and obesity are the key risk factors for
25 Type II diabetes. However, dioxins may shift the distribution of sensitivity, putting people at risk
26 at younger ages or when they have less weight. Dioxin alters lipid metabolism in multiple
27 species, including humans (Sweeney et al., 1997; Pohjanvirta and Tuomisto, 1994), and it also
28 alters glucose uptake into both human and animal cells in culture (Enan and Matsumura, 1994;
29 Olsen et al., 1994). Mechanistic studies have demonstrated that dioxin affects glucose transport
30 (Enan and Matsumura, 1994), a property under the control of the hypoxia response pathway
31 (Ouiddir et al., 1999). A key regulatory protein in this pathway is the partner of the AhR, Arnt
32 (also known as HIFl-beta) (Gu et al., 2000; Taylor and Zhulin, 1999). Activation of the AhR by
33 dioxin may compete with other pathways for Arnt, such as the hypoxia-inducible factor (HIF)
34 pathway (Gradin et al., 1992). Dioxin has also been shown to downregulate the insulin growth
35 factor receptor (Liu et al., 1992). These three issues—altered lipid metabolism, altered glucose
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1 transport, and alterations in the insulin signaling pathway—all provide biological plausibility to
2 the association of dioxins with diabetes.
3 A causal relationship between diabetes and dioxin has not been established, although both
4 the toxicologic and epidemiological data are suggestive of a plausible association (Remillard and
5 Bunce, 2002). Many questions have yet to be answered. For example, does diabetes alter the
6 pharmacokinetics of dioxin? Diabetes is known to alter the metabolism of several drugs in
7 humans (Matzke et al., 2000) and may also alter dioxin metabolism and kinetics. Because adult-
8 onset diabetes is also associated with being overweight, and body composition has been shown to
9 modify the apparent half-life of dioxin, could the rate of elimination of dioxins be lowered in
10 people who have diabetes, causing them to have higher body burdens? This may be relevant to
11 the background population, but it is hardly likely to be an explanation in highly exposed
12 populations.
13 Key research needs are twofold. The first is to develop an animal model with which to
14 study the association between dioxins and diabetes and glucose perturbation. Several rodent
1 5 models for Type n diabetes exist and may be used. The second is to conduct population-based
1 6 incidence studies that take into account dioxin levels as well as the many known factors
1 7 associated with diabetes. Although diabetes may cause the underlying pathology leading to
18 death, it is often not attributed as the cause of death and thus limits the utility of mortality
19 studies.
20
21 2.2.6. Other Effects
22 2.2.6.1. Elevated GGT
23 As mentioned above, there appears to be a consistent pattern of increased GGT levels
24 among individuals exposed to 2,3,7,8-TCDD-contaminated chemicals. Elevated levels of serum
25 GGT were observed within a year after exposure in Seveso children (Caramaschi et al., 1981;
26 Mocarelli et al., 1986) and 10 or more years after cessation of exposure among TCP and 2,4,5-T
27 production workers (May, 1982; Martin, 1984; Moses et al., 1984; Calvert et al., 1992) and
28 among Ranch Hands (Roegner et al., 1991; Grubbs et al., 1995). All of these groups had a high
29 likelihood of substantial exposure to 2,3,7,8-TCDD. In addition, for those studies that evaluated
30 dose-response relationships with 2,3,7,8-TCDD levels, the effect was observed only at the
31 highest levels or categories of 2,3,7,8-TCDD and, in the NIOSH study, only in workers who
32 reported drinking high levels of alcohol.
33 In contrast, although background levels of serum 2,3,7,8-TCDD suggested minimal
34 exposure in Army Vietnam veterans, GGT was increased at borderline significance among
35 Vietnam veterans as compared to non-Vietnam veterans (CDC Vietnam Experience Study,
12/23/03 2-40 DRAFT—DO NOT CITE OR QUOTE
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1 1988). In addition, despite the increases observed in some studies of occupational cohorts, other
2 studies of TCP production workers from West Virginia or Missouri residents measured but did
3 not report elevations in GOT levels (Suskind and Hertzberg, 1984; Webb et al., 1989).
4 In clinical practice, GOT is often measured because it is elevated in almost all
5 hepatobiliary diseases and is used as a marker for alcoholic intake (Guzelian, 1985). In
6 individuals with hepatobiliary disease, elevations in GGT are usually accompanied by increases
7 in other hepatic enzymes, for example, AST and ALT, and metabolites, for example, uro- and
8 coproporphyrins. Significant increases in hepatic enzymes other than GGT and metabolic
9 products were not observed in individuals whose GGT levels were elevated 10 or more years
10 after exposure ended, suggesting that the effect may be GGT-specific. These data suggest that in
11 the absence of increases in other hepatic enzymes, elevations in GGT are associated with
1 2 exposure to 2,3,7,8-TCDD, particularly among individuals who were exposed to high levels.
13 The animal data with respect to 2,3,7,8-TCDD-related effects on GGT are sparse.
14 Statistically significant changes in hepatic enzyme levels, particularly AST, ALT, and alkaline
1 5 phosphatase, have been observed after exposure in rats and hamsters (Gasiewicz et al., 1980;
16 Kociba et al., 1978; Olson et al., 1980). Only one study evaluated GGT levels (Kociba et al.,
1 7 1978); moderate but statistically nonsignificant increases were noted in rats fed 0.10 |_ig/kg
1 8 2,3,7,8-TCDD daily for 2 years, and no increases were observed in control animals.
1 9 In summary, GGT is the only hepatic enzyme examined that was found in a number of
20 studies to be chronically elevated in adults exposed to high levels of 2,3,7,8-TCDD. The
21 consistency of the findings in a number of studies suggests that the elevation may reflect a true
22 effect of exposure, but its clinical significance is unclear. Long-term pathological consequences
23 of elevated GGT have not been illustrated by excess mortality from liver disorders or cancer or in
24 excess morbidity in the available cross-sectional studies.
25 It must be recognized that the absence of an effect—for example, liver enzymes—in a
26 cross-sectional study does not obviate the possibility that the enzyme levels may have increased
27 concurrently with the exposure but declined after cessation. The apparently transient elevations
28 in ALT levels among the Seveso children suggest that hepatic enzyme levels other than GGT
29 may react in this manner to 2,3,7,8-TCDD exposure.
30
31 2.2.6.2. Thyroid Function
32 Many effects of 2,3,7,8-TCDD exposure in animals resemble signs of thyroid dysfunction
33 or significant alterations of thyroid-related hormones. In the few human studies that have
34 examined the relationship between 2,3,7,8-TCDD exposure and hormone concentrations in
35 adults (CDC Vietnam Experience Study, 1988; Roegner et al., 1991; Grubbs et al., 1995;
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1 Suskind and Hertzberg, 1984), the results are mostly equivocal. Cross-sectional analysis of the
2 Ranch Hand cohort (Pavuk et al., 2003) found signs of elevated TSH means among the high
3 TCDD exposure group in the 1985 and 1987 follow-ups, with an increasing trend across the
4 decade 1982 - 1992, but no association with the occurrence of thyroid disease. Concentrations of
5 thyroid binding globulin also appeared to be positively correlated with current levels of 2,3,7,8-
6 TCDD in the BASF accident cohort (Ott et al., 1994). Little additional information on thyroid
7 hormone levels has been reported for production workers and none for Seveso residents, two
8 groups with documented high serum 2,3,7,8-TCDD levels.
9 Thyroid hormones play important roles in the developing nervous system in all vertebrate
10 species, including humans—to the extent that all infants in the United States are tested for
11 hypothyroidism shortly after birth. Several studies of nursing infants suggest that ingestion of
1 2 breast milk with a higher dioxin TEQ may alter thyroid function (Pluim et al., 1993; Koopman-
13 Esseboom et al., 1994c; Nagayama et al., 1997). These findings suggest a possible shift in the
1 4 distribution of thyroid hormones, particularly T4, and point out the need for collection of
1 5 longitudinal data to assess the potential for long-term effects associated with developmental
16 exposures.
1 7 The exact processes that account for these observations in humans are unknown, but
1 8 when put in perspective of animal responses, the following might apply: dioxin increases the
1 9 metabolism and excretion of thyroid hormone, mainly T4, in the liver, and reduced T4 levels
20 stimulate the pituitary to secrete more TSH, which enhances thyroid hormone production. Early
21 in the disruption process, the body can overcompensate for the loss of T4, which may result in a
22 small excess of circulating T4 to the increased TSH. In animals given higher doses of dioxin, the
23 body is unable to maintain homeostasis, TSH levels remain elevated, and T4 levels decrease.
24 A plausible mode of action for thyroid effects is described in Section 2.2.1.3.
25
26 2.2.6.3. Cardiovascular Disease
27 Elevated cardiovascular disease has been noted in several occupational cohort studies
28 (Steenland et al., 1999; Sweeney et al., 1997; Flesch-Janys et al., 1995) and in the Seveso
29 (Pesatori et al., 1998) and the rice oil poisoning studies. This appears to be associated with
30 ischemic heart disease and in some cases with hypertension. Recent data from the Ranch Hand
31 study indicate that dioxin may be a possible risk factor for the development of essential
32 hypertension (Grubbs et al., 1995). Elevated blood lipids have also been seen in several cohorts.
33 The association of dioxins with heart disease in humans has biological plausibility, given the data
34 in animals. First is the key role of hypoxia in heart disease and the potential for involvement of
35 the activated AhR in blocking an hypoxic response (Gradin et al., 1996; Gu et al., 2000). Dioxin
12/23/03 2-42 DRAFT—DO NOT CITE OR QUOTE
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1 has been shown to perturb lipid metabolism in multiple laboratory species (Pohjanvirta and
2 Tuomisto, 1994). The heart—in fact the entire vascular system—is a clear target for the adverse
3 effects of dioxin in fish and birds (Hornung et al., 1999; Cheung et al., 1981). Recent studies
4 have demonstrated that the heart is also a target in mammals (Lund et al., 2003; NTP 2003a). In
5 mammals, dioxin has been shown to disturb heart rhythms at high doses in guinea pigs (Gupta et
6 al., 1973; Pohjanvirta and Tuomisto, 1994).
7
8 2.2.6.4. Oxidative Stress
9 Several investigators have hypothesized that some of the adverse effects of dioxin and
10 related compounds may be associated with oxidative stress. Induction of CYP1A isoforms has
11 been shown to be associated with oxidative DNA damage (Park et al., 1996). Altered
1 2 metabolism of endogenous molecules such as estradiol can lead to the formation of quinones and
1 3 redox cycling. This has been hypothesized to play a role in the enhanced sensitivity of female
1 4 rats to dioxin-induced liver tumors (Tritscher et al., 1996). Lipid peroxidation, enhanced DNA
1 5 single-strand breaks, and decreased membrane fluidity have been observed in liver as well as in
1 6 extrahepatic tissues following exposure to high doses of TCDD (Stohs, 1990). A dose- and time-
1 7 dependent increase in superoxide anion in peritoneal macrophages following exposure to TCDD
1 8 (Alsharif et al., 1994). A recent report that low-dose (0.15 ng TCDD/kg/day) subchronic
19 exposure can lead to oxidative changes in several tissues in mice (Slezak et al., 2000) suggests
20 that this mechanism or mode of toxicity deserves further attention.
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1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
Table 2-1. Effects of TCDD and related compounds in different animal species
Effect
Presence of AhR
Binding of TCDD:
AhR complex to the
DRE (enhancer)
Enzyme induction
Acute lethality
Wasting syndrome
Teratogenesis/fetal
toxicity, mortality
Endocrine effects
Immunotoxicity
Carcinogenicity
Neurotoxicity
Chloracnegenic
effects
Porphyria
Hepatotoxicity
Edema
Testicular atrophy
Bone marrow
hypoplasia
Teeth
Humans
+
+
+
0
+
+/-
+/-
+/-
+/-
+
+
+
+
+
Monkey
+
+
+
+
+
+
+
+
+
0
+
+
+
+
+
Guinea
Pig
0
+
+
+
+
+
+
0
+/-
0
+
+
Rat
+
+
+
+
+
+
+
+
+
+
+
+
0
+
+
Mouse
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+/-
Hamster
+
+
+
+
+
+
+
+
0
+/-
+
Cow
+
+
+
+
+
+
+
Rabbit
+
+
+
+
+
+
+
+
Chicken
+
+
+
+
+
+
+
+
+
+
+
Fish
+
+
+
+
+
+
+
+
+
+
+
+
Avian
wildlife
+
+
+
+
+
+
+
Marine
mammals
+
+
+
+
+
+
+
+
Mink
+
+
+
+
+
+
+
+ = observed.
+/- = observed to limited extent, or +/- results.
0 = not observed.
Blank cells = no data.
-------
1
2
Table 2-2. Some biochemical responses to TCDD
CYP1A1
CYP1A2
CYP1B1
GSTYa
GSTYb
GSTYc
UDP glucuronyl transferase
QR quinone reductase/ Nmo
Aldehyde dehydrogenase
Ornithine decarboxylase
Malic enzyme
Phospholipase A2
60kDa microsomal esterase
Aminolevulinic acid synthetase
Choline kinase
EctoATPase
Prostaglandin synthetase -2 (COX-2)
Plasminogen activator inhibitor-2
Urokinase plasminogen activator
Nedd-4-like ubiquitin protein ligase
PEPC kinase
Terminal transferase
Testosterone 7alpha hydroxylase
Human chorionic gonadotrophin
Interleukin-lbeta
Gastrin
TNF alpha
TGF-beta
EGF
Fibrinogen
Plastin
EGFR
c-erbA related hormone receptor
Estrogen receptor
25Dx-putative progesterone receptor
MDR-1 multidrug resistance
Aryl hydrocarbon binding protein
c-fos
c-jun
Cy statin-like protein
MHC-Q1
Protein kinase C
pp60 c-src protein kinase
p21 ras
p27/Kipl
bcl-2
Note: This list is not a comprehensive list of all responses known to be affected by TCDD.
Source: Sutler et al., 1992; Lai et al., 1996.
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1
2
3
Table 2-3. Summary of the combined cohort and selected industrial cohort
studies with high exposure levels, as described by IARC (1997)a
Reference
All cancers
Observed
SMR
95% CI
Lung cancer
Observed
SMR
95% CI
International cohort
Kogevinas et al.
(1997)b
394
1.2
1.1-1.3
127
1.2
1.0-1.4
Industrial populations (high-exposure subcohorts)
Fingerhut et al.
(1991a)c (USA)
Becher et al.
(1996)d (Germany)
Hooiveld et al.
(1996)e
(Netherlands)
Ott and Zober
(1996b)f
(BASF accident)
TOTAL
p value
114
105
51
18
[288]
1.5
[1.3]
1.5
1.9
[1.4]
1.2-1.8
[1.0-1.5]
1.1-1.9
1.1-3.0
[1.2-1.6]
<0.001
40
33
14
7
[94]
1.4
[1.4]
1
2.4
[1.4]
1.0-1.9
[1.0-2.0]
0.5-1.7
1.0-5.0
[1.1-1.7]
<0.01
6
7
8
9
10
1 1
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
a Adapted from IARC; Table 38 (1997); non-Hodgkin's lymphoma, soft-tissue sarcoma, and gastrointestinal results
not shown. TOTALs were calculated by the IARC Working Group.
b Men and woman > 20 years since first exposure. These data include the cohorts of Fingerhut et al. (1991a,b),
Becher et al. (1996), Hooiveld et al. (1996a), the original IARC cohort (Saracci et al., 1991), and other cohorts.
0 Men > 20 years latency and > 1 year exposure.
dMen, cohorts I and II, summed (Boehringer-Ingelheim, Bayer-Uerdingen cohorts).
e Men and women, Factory A.
fMen, chloracne subgroup, > 20 years latency. Data presented for lung cancer are all respiratory tract
cancers combined.
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1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
Table 2-4. Tumor incidence and promotion data cited
for principal congeners
% of adipose
TEF-WHO98 tumor TEQDFP-
incidence/promotion WHO98 tissue
Congener citation3 TEF-WHO98 conc.b
2,3,7,8- TEF Standard 1 8
TCDD
1,2,3,7,8- Waernetal. (1991) 1 15
PeCDD
2,3,4,7,8- Waernetal. (1991) 0.5 7
PeCDF
1,2,3,6,7,8- NTP(1980); 0.1 10
HxCDD 1,2,3,6,7,8-HxCDD/
1,2,3,7,8,9-HxCDD;
1:2 mixture;
long-term bioassays,
Osborne-Mendel rats
in NTP studies,
Sprague-Dawley rats
123789 *n Kociba et al. (1978) Q , ~
HxCDD
PCB 126 Hemming et al. (1995) 0.1 33
a van den Berg et al., 2000. Hexa-CDD referenced to previous TEF reviews.
b See Part II, Chapter 4, Tables 4-46, 4-47
for the TEF-WHO98
Dose-response graphs: dose
adjusted to reflect TEF multiplier
1
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Differentiation
and
Proliferation
chromatln
Co-activators
Co-rqiressors
Changes in protein levels
(e.g.,CYPIAl,IL-1p,
Altered gene expression
mRNA
2 Figure 2-1. Cellular mechanism for AhR action. TCDD, 2,3,7,8-tetrachlorodibenzo-p-dioxin;
3 AhR, aryl hydrocarbon receptor; AIP, associated immunophilin-like protein; hsp90, 90 kilodalton
4 heat shock protein; p, sites of phosphorylization; Arnt, AhR nuclear translocator protein; RB,
5 retinoblastoma protein; NF-kB, nuclear transcription factor; HIF, hypoxia inducible factor; DRE,
6 dioxin-responsive element; BTFs, basal transcription factors; TATA, DNA recognition sequence.
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1 3. MECHANISMS AND MODE OF DIOXIN ACTION
2
3 Mechanistic studies can reveal the biochemical pathways and types of biological and
4 molecular events that contribute to dioxin's adverse effects (See Part II, Chapter 2, for a detailed
5 discussion). For example, much evidence indicates that TCDD acts via an intracellular protein
6 (the AhR), which functions as a ligand-dependent transcription factor in partnership with a
7 second protein (Arnt). Therefore, from a mechanistic standpoint, TCDD's adverse effects appear
8 likely to reflect alterations in gene expression that occur at an inappropriate time and/or for an
9 inappropriately long time. Mechanistic studies also indicate that several other proteins contribute
10 to TCDD's gene regulatory effects and that the response to TCDD probably involves a relatively
11 complex interplay between multiple genetic and environmental factors. If TCDD operates
1 2 through such a mechanism, as all evidence indicates, then there are certain constraints on the
13 possible models that can plausibly account for TCDD's biological effects and, therefore, on the
14 assumptions used during the risk assessment process (e.g., Poland, 1996; Limbird and Taylor,
15 1998).
1 6 Mechanistic knowledge of dioxin action may also be useful in other ways. For example,
1 7 a further understanding of the ligand specificity and structure of the AhR will likely assist in the
1 8 identification of other chemicals to which humans are exposed that may add to, synergize, or
1 9 block the toxicity of TCDD. Knowledge of genetic polymorphisms that influence TCDD
20 responsiveness may also allow the identification of individuals at greater risk from exposure to
21 dioxin. In addition, knowledge of the biochemical pathways that are altered by TCDD may help
22 identify novel targets for the development of drugs that can antagonize dioxin's adverse effects.
23 As described below, biochemical and genetic analyses of the mechanisms by which
24 dioxin may modulate particular genes have revealed the outline of a novel regulatory system
25 whereby a chemical signal can alter cellular regulatory processes. Future studies of dioxin action
26 have the potential to provide additional insights into mechanisms of mammalian gene regulation
27 that are of a broader interest. Additional perspectives on dioxin action can be found in several
28 reviews (Birnbaum, 1994a, b; Schecter, 1994; Hankinson, 1995; Schmidt and Bradfield, 1996;
29 Gasiewicz, 1997; Rowlands and Gustafsson, 1997; Denison et al., 1998; Hahn, 1998; Wilson and
30 Safe, 1998; Schecter and Gasiewicz, 2003; Matsumura, 2003; Carlson and Perdew, 2002).
31 Knowledge of the mode(s) of action by which the broad class of chemicals known as
32 dioxins act may facilitate the risk assessment process by contributing to the weight of the
33 evidence for hazard characterization and by imposing bounds on the models used to describe
34 possible responses of humans resulting from exposure to mixtures of these chemicals (see
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1 Sections 2 and 5 of this document). The relatively extensive database on TCDD, as well as the
2 more limited database on related compounds, has been reviewed, with emphasis on the role of
3 the specific cellular receptor for TCDD and related compounds—the AhR—in the postulated
4 mode(s) of action. This discussion focuses on summarizing the elements of the mode(s) of
5 dioxin action that are relevant for understanding and characterizing dioxin risk for humans.
6 These elements include:
7
8 • Similarities between humans and other animals with regard to receptor structure
9 and function;
10
11 • The relationship between receptor binding and toxic effects; and
12
1 3 • The extent to which the purported mechanism(s) or mode(s) of action might
1 4 contribute to the diversity of biological responses seen in animals and, to some
1 5 extent, in humans.
16
1 7 In addition, this section identifies important and relevant knowledge gaps and
1 8 uncertainties in the understanding of the mechanism(s) of dioxin action and indicates how these
1 9 may affect the approach to risk characterization.
20
21 3.1. MODE VERSUS MECHANISM OF ACTION
22 In the context of revising its carcinogen risk assessment guidelines, EPA has proposed
23 giving greater emphasis to use of all of the data in hazard characterization, dose-response
24 characterization, exposure characterization, and risk characterization (U.S. EPA, 1996, 1999,
25 2003). One aid to the use of more information in risk assessment has been the definition of mode
26 versus mechanism of action. Mechanism of action is defined as the detailed molecular
2 7 description of key events in the induction of cancer or other health endpoints. Mode of action
28 refers to the description of key events and processes, starting with interaction of an agent with the
29 cell through functional and anatomical changes, resulting in cancer or other health endpoints.
30 Despite a desire to construct detailed biologically based toxicokinetic and toxicodynamic
31 models to reduce uncertainty in characterizing risk, few examples have emerged. Use of a mode
32 of action approach recognizes that, although all of the details may not have been worked out,
33 prevailing scientific thought supports moving forward using a hypothesized mode of action
34 supported by data. This approach is consistent with advice offered by the National Academy of
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1 Sciences' National Research Council in its report entitled Science and Judgment in Risk
2 Assessment (NASINSiC, 1994).
3 Mode of action discussions help to provide answers to the questions: How does the
4 chemical produce its effect? Are there mechanistic data to support this hypothesis? Have other
5 modes of action been considered and rejected? In order to demonstrate that a particular mode of
6 action is operative, it is generally necessary to outline the hypothesized sequence of events
7 leading to effects, to identify key events that can be measured, to outline the information that is
8 available to support the hypothesis, and to discuss those data that are inconsistent with the
9 hypothesis or support an alternative hypothesis. Following this, the information is weighed to
10 determine whether there is a causal relationship between key precursor events associated with the
1 1 mode of action and cancer or other lexicological endpoint in animals, and ultimately whether this
1 2 inference can be extended to humans.
13
1 4 3.2. GENERALIZED MODEL FOR DIOXIN ACTION
1 5 Dioxin and related compounds are generally recognized to be receptor-mediated
16 toxicants. The generalized model has evolved over the years to appear as illustrated in Table 3-1
17 and Figure 2-1.
18
19 3.2.1. The Receptor Concept
20 One of the fundamental concepts that influences our approach to risk assessment of
21 dioxin and related compounds is the receptor concept. The idea that a drug, hormone,
22 neurotransmitter, or other chemical produces a physiological response by interacting with a
23 specific cellular target molecule, that is, a "receptor," evolved from several observations. First,
24 many chemicals elicit responses that are restricted to specific tissues. This observation implies
25 that the responsive tissue (e.g., the adrenal cortex) contains a "receptive" component whose
26 presence is required for the physiologic effect (e.g., cortisol secretion). Second, many chemicals
27 are quite potent. For example, picomolar to nanomolar concentrations of numerous hormones
28 and growth factors elicit biological effects. This observation suggests that the target cell contains
29 a site(s) to which the particular chemical binds with high affinity. Third, stereoisomers of some
30 chemicals (e.g., catecholamines, opioids) differ by orders of magnitude in their ability to produce
31 the same biological response. This observation indicates that the molecular shape of the
32 chemical strongly influences its biological activity. This, in turn, implies that the binding site on
33 or in the target cell also has a specific, three-dimensional configuration. Together, these types of
34 observations support the prediction that the biological responses to some chemicals involve
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1 stereospecific, high-affinity binding of the chemicals to specific receptor sites located on or in the
2 target cell. Many of these characteristics have been noted for TCDD and related compounds.
3 The availability of compounds of high specific radioactivity has permitted quantitative
4 analyses of their binding to cellular components in vitro. To qualify as a potential receptor, a
5 binding site for a given chemical must satisfy several criteria: (1) the binding site must be
6 saturable, that is, the number of binding sites per cell should be limited; (2) the binding should be
7 reversible; (3) the binding affinity measured in vitro should be consistent with the potency of the
8 chemical observed in vivo; (4) if the biological response exhibits stereospecificity, so should the
9 in vitro binding; (5) for a series of structurally related chemicals, the rank order for binding
10 affinity should correlate with the rank order for biological potency; and (6) tissues that respond to
11 the chemical should contain binding sites with the appropriate properties.
1 2 The binding of a chemical ("ligand") to its specific receptor is assumed to obey the law of
1 3 mass action; that is, it is a bimolecular, reversible interaction. The concentration of the liganded,
14 or occupied, receptor [RL] is a function of both the ligand concentration [L] and the receptor
1 5 concentration [R] as shown in equation 3-1:
16 kj
17 _^ (3-1)
18 [L] + [R] -- [RL]
19 k2
20
21 Inherent in this relationship is the fact that the fractional occupancy (i.e., [RL]/[RJ) is a
22 function of ligand concentration [L] and the apparent equilibrium dissociation constant KD, which
23 is a measure of the binding affinity of the ligand for the receptor, that is, [RL]/[RJ = [L]/(KD+
24 [L]), where KD = [L] [RJ/[LR] = k2/kj. Therefore, the relationship between receptor occupancy
25 and ligand concentration is hyperbolic. At low ligand concentrations (where [L]«KD), a small
26 increase in [L] produces an approximately linear increase in fractional receptor occupancy. At
27 high ligand concentration (where [L]»KD), the fractional occupancy of the receptor is already
28 very close to 1, that is, almost all receptor sites are occupied. Therefore, a small increase in [L]
29 is likely to produce only a slight increase in receptor occupancy. These issues are discussed in
30 regard to TCDD binding to the AhR and dose-response in Part II, Chapter 8.
31 Ligand binding constitutes only one aspect of the receptor concept. By definition, a
32 receptor mediates a response, and the functional consequences of the ligand-receptor binding
33 represent an essential aspect of the receptor concept. Receptor theory attempts to quantitatively
34 relate ligand binding to biological responses. The classic "occupancy" model of Clark (1933)
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1 postulated that (1) the magnitude of the biological response is directly proportional to the fraction
2 of receptors occupied, and (2) the response is maximal when all receptors are occupied.
3 However, analyses of numerous receptor-mediated effects indicate that the relationship between
4 receptor occupancy and biological effect is not as straightforward as Clark envisioned.
5 In certain cases, no response occurs even when there is some receptor occupancy. This
6 suggests that there may be a threshold phenomenon that reflects the biological "inertia" of the
7 response (Ariens et al., 1960). In other cases, a maximal response occurs well before all
8 receptors are occupied, a phenomenon that reflects receptor "reserve" (Stephenson, 1956).
9 Therefore, one cannot simply assume that the relationship between fractional receptor occupancy
10 and biological response is linear. Furthermore, for a ligand (such as TCDD) that elicits multiple
11 receptor-mediated effects, one cannot assume that the binding-response relationship for a simple
1 2 effect (such as enzyme induction) will necessarily be identical to that for a different and more
13 complex effect (such as cancer).
14 The cascades of events leading to different complex responses (e.g., altered immune
1 5 response to pathogens or development of cancer) are likely to be different, and other rate-limiting
16 events likely influence the final biological outcome, resulting in different dose-response curves.
1 7 Thus, even though ligand binding to the same receptor is the initial event leading to a spectrum
18 of biological responses, ligand-binding data may not always mimic the dose-effect relationship
19 observed for particular responses.
20 Another level of complexity is added when one considers different chemical ligands that
21 bind to the same receptor. Relative potencies are determined by two properties of the ligand:
22 affinity for the receptor and capacity to confer a particular response in the receptor (e.g., a
23 particular conformational change), also called efficacy (Stephenson, 1956). Ligands with
24 different affinities and the same degree of efficacy would be expected to produce parallel dose-
25 response curves with the same maximal response within a particular model system. However,
26 ligands of the same affinity with different efficacies may result in dose-response curves that are
27 not parallel or that differ in maximal response. These issues relate particularly to Ah receptor
28 ligands that are not "dioxins," where different efficacies or an inability to elicit the suite of
29 dioxin-like responses compound differences in binding affinity for the Ah receptor. This
30 complicates the use of the toxic equivalency approach, particularly for extrapolation purposes
31 beyond the closely related congener groups. As described previously, this argues strongly for the
32 use of all available information in setting TEFs and highlights the important role that scientific
33 judgment plays in addressing uncertainty in the face of incomplete mechanistic understanding.
34
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1 3.2.2. A Framework to Evaluate Mode of Action
2 In its revised proposed guidelines for carcinogen risk assessment (U.S. EPA, 1999, 2003),
3 EPA recommends the use of a structured approach to evaluating mode of action. This approach
4 is similar to and builds upon an approach developed within the WHO/IPCS Harmonization
5 Project (WHO, 2000). Fundamentally, the approach uses a modification of the "Hill Criteria"
6 (Hill, 1965), which have been used in the field of epidemiology for many years to examine
7 causality between associations of exposures and effects. The framework calls for a summary
8 description of the postulated mode of action, followed by the identification of key events that are
9 thought to be part of the mode of action. These key events are then evaluated as to strength,
10 consistency, and specificity of association with the endpoint under discussion. Dose-response
11 relationships between the precursor key events are evaluated and temporal relationships are
1 2 examined to be sure that "precursor" events actually precede the induction of the endpoint.
13 Finally, biological plausibility and coherence of the data with the biology are examined and
14 discussed. All of these "criteria" are evaluated and conclusions are drawn with regard to
1 5 postulated mode of action.
16 In the case of dioxin and related compounds, elements of such an approach are found for
1 7 a number of effects, including cancer, in Part II. Application of the framework to dioxin and
1 8 related compounds may now proceed in a step-wise fashion to evaluate the association between
1 9 the chemical or complex mixture and clearly adverse effects. The approach can be applied
20 sequentially to early events, for example, receptor binding and intermediate events such as
21 enzyme induction or endocrine impacts. Additional data will be required to extend the
22 framework to most effects, but several have data that would support a framework analysis, a
23 number of which are discussed below.
24
25 3.2.3. Mechanistic Information and Mode of Action—Implications for Risk Assessment
26 A substantial body of evidence from investigations using experimental animals indicates
27 that the AhR mediates the biological effects of TCDD. The key role of the AhR in the effects of
28 dioxin and related compounds is substantiated by four lines of research: (1) structure/activity
29 relationships, (2) responsive versus nonresponsive mouse strains, (3) mutant cell lines, and (4)
30 the development of transgenic mice in which the gene for the AhR has been "knocked out"
31 (Birnbaum, 1994a; Fernandez-Salguero et al., 1996; Lahvis and Bradfield, 1998). Dioxin
32 appears not to cause effects in the AhR knockout mouse (Fernandez-Salguero et al., 1996; Lahvis
33 and Bradfield, 1998; Peters et al., 1999).
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1 It is clear that the AhR is necessary, but not sufficient, for essentially all of the well-
2 studied responses to dioxin. The AhR functions as a ligand-activated transcription factor,
3 controlling the expression of specific genes via interaction with defined nucleotide sequences in
4 the promoter regions. In order to control transcription, the TCDD-AhR complex interacts with
5 another protein, Arnt, to bind to the dioxin response element. This complex is also bound by
6 other nuclear coactivators and/or corepressors to bind to the transcriptional complex and initiate
7 transcription (Gu et al., 2000). However, Arnt has many other partners that control hypoxia
8 response, neuronal differentiation, morphological branching, etc. (Gu et al., 2000).
9 It is possible that there are other mechanisms that impact how dioxin initiates its toxic
1 0 effects, apart from its direct transcriptional activation of drug metabolizing genes. It may be that
11 the adverse effects of dioxin may result from competition of the ligand-activated AhR with other
1 2 Arnt partners (Gradin et al., 1996). The AhR, Arnt, and Arnt partners are all members of the Per-
1 3 Arnt-Sim (PAS) family of basic helix-loop-helix proteins that function as nuclear regulatory
14 proteins (Gu et al., 2000). The PAS proteins are highly conserved, with homologous proteins
1 5 being present in prokaryotes. They play key roles in circadian rhythms and development. The
1 6 embryolethality of Arnt knockout mice, as well as the reduced fertility and viability of the AhR
1 7 knockout mice (Abbott et al., 1999), point to a key role of these proteins in normal physiology.
1 8 Another potential mechanism by which TCDD can cause effects involves the
1 9 protein/protein interactions of the AhR. When not bound to a ligand, the AhR exists in a
20 multimeric protein complex that involves two molecules of heat shock protein 90 as well as other
21 proteins, including AIP/XAP2/ara9, ara3, ara6, src, rel, and Rb (Carver et al., 1998; Enan and
22 Matsumura, 1996; Puga et al., 2000b). AIP/XAP2/ara9 is a 37 kilodalton protein that is related
23 to known immunophilins and is involved in the control of signal transduction processes. C-src
24 has been shown to be associated with the AhR in several tissues and is a tyrosine kinase (Enan
25 and Matsumura, 1996). Dioxin has been shown to cause a rapid increase in phosphorylation
26 upon exposure. Recent studies have shown that rel, which is a key component of the NF-kappaB
27 complex that controls apoptosis, binds to the AhR complex (Tian et al., 1999; Puga et al.,
28 2000c). Similarly, several investigators have demonstrated an association between the AhR and
29 the retinoblastoma protein; this has been shown to affect cell cycling (Puga et al., 2000b).
30 Thus, the AhR may act as a negative regulator of key regulator molecules involved in
31 phosphorylation, cell cycling, and apoptosis in its unliganded state. Upon binding of TCDD,
32 these other proteins are now able to exert their effects. In addition, dioxin may act by competing
33 for Arnt, thus blocking key roles of other PAS regulatory proteins. Both of these mechanisms for
34 the effects of dioxin are in addition to the direct role of the ligand-bound form of the receptor in
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1 control of transcription via the well-studied mechanism of binding to a dioxin-response element
2 in DNA.
3 Although studies using human tissues are much less extensive, it appears reasonable to
4 assume that dioxin' s mode of action to produce effects in humans includes receptor-mediated key
5 events. Studies using human organs and cells in culture are consistent with this hypothesis. A
6 receptor-based mode of action would predict that, except in cases where the concentration of
7 TCDD is already high (i.e., [TCDD]~KD), incremental exposure to TCDD will lead to some
8 increase in the fraction of AhRs occupied. However, it cannot be assumed that an increase in
9 receptor occupancy will necessarily elicit a proportional increase in all biological response(s),
10 because numerous molecular events (e.g., cofactors, other transcription factors, genes) that
11 contribute to the biological endpoint are integrated into the overall response. That is, the final
1 2 biological response should be considered as an integration of a series of dose-response curves,
1 3 with each curve dependent on the molecular dosimetry for each particular step.
14 Dose-response relationships that will be specific for each endpoint must be considered
1 5 when using mathematical models to estimate the risk associated with exposure to TCDD. It
16 remains a challenge to develop models that incorporate all the complexities associated with each
1 7 biological response. Furthermore, the parameters for each mathematical model may apply only
18 to a single biological response within a given tissue and species.
1 9 Given TCDD's widespread distribution, its persistence, and its accumulation within the
20 food chain, it is likely that most humans are exposed to some level of dioxin; thus, the population
21 at potential risk is large and genetically heterogeneous. By analogy with the findings in inbred
22 mice, polymorphisms in the AhR probably exist in humans. Therefore, a concentration of TCDD
23 that elicits a particular response in one individual may not do so in another. For example, studies
24 of humans exposed to dioxin following an industrial accident at Seveso, Italy, failed to reveal a
25 simple and direct relationship between blood TCDD levels and the development of chloracne
26 (Mocarelli et al., 1991). These differences in responsiveness to TCDD may reflect genetic
27 variation either in the AhR or in some other component of the dioxin-responsive pathway.
28 Therefore, analyses of human polymorphisms in the AhR and Arnt genes have the potential to
29 identify genotypes associated with higher (or lower) sensitivities to dioxin-related effects. Such
30 molecular genetic information may be useful in the future for accurately predicting the health
31 risks posed by dioxin to humans.
32 Complex responses (such as cancer) probably involve multiple events and multiple genes.
33 For example, a homozygous recessive mutation at the hr (hairless) locus is required for TCDD's
34 action as a chloracnegen and tumor promoter in mouse skin (Poland et al., 1982). Thus, the hr
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1 locus influences the susceptibility of a particular tissue (in this case, skin) to a specific effect of
2 dioxin (tumor promotion). An analogous relationship may exist for the effects of TCDD in other
3 tissues. For example, TCDD may produce porphyria cutanea tarda only in individuals who have
4 inherited uroporphyrinogen decarboxylase deficiency (Doss et al., 1984). Such findings suggest
5 that, for some adverse effects of TCDD, the population at risk may be limited to individuals who
6 have a particular genetic predisposition.
7 Other factors can influence an organism's susceptibility to TCDD. For example, female
8 rats are more prone to TCDD-induced liver neoplasms than are males; this phenomenon is
9 related to the hormonal status of the animals (Lucier et al., 1991). In addition, hydrocortisone
10 and TCDD synergize in producing cleft palate in mice (Abbott et al., 1992). Retinoic acid and
11 TCDD produce a similar synergistic teratogenic effect (Couture et al., 1990). These findings
1 2 indicate that, in some cases, TCDD acts in combination with hormones or other chemicals to
13 produce adverse effects. Such phenomena might also occur in humans. If so, the difficulty in
14 assessing risk is increased, given the diversity among humans in hormonal status, lifestyle (e.g.,
1 5 smoking, diet), and chemical exposure.
1 6 Dioxin's action as a tumor promoter and developmental toxicant presumably reflects its
1 7 ability to alter cell proliferation and differentiation processes. There are several plausible
1 8 mechanisms by which this could occur. First, TCDD might activate a gene (or genes) that is
1 9 directly involved in tissue proliferation. Second, TCDD-induced changes in hormone
20 metabolism may lead to tissue proliferation (or lack thereof) and altered differentiation secondary
21 to altered secretion of a trophic hormone. Third, TCDD-induced changes in the expression of
22 growth factor or hormone receptors may alter the sensitivity of a tissue to proliferative stimuli.
23 Fourth, TCDD-induced toxicity may lead to cell death, followed by regenerative proliferation.
24 These mechanisms likely differ among tissues and period of development, and they may be
25 modulated by different genetic and environmental factors.
26 The parallels between animal and human data relating to dioxin's tumor-promotion
27 potential can assist in informing determinations of human risk, recognizing that the complexity
28 of these intracellular processes limits our current mechanistic understanding. Using a weight-of-
29 evidence approach, the Agency considers the cancer promotion data from in vitro and in vivo
30 animal studies to be relevant and informative to humans. Although the specific mechanism(s) by
31 which dioxin causes cancer remains to be established (as, indeed, for cancer in general), the
32 intracellular factors and mechanistic pathways involved in dioxin's cancer-promotion mode of
33 action all have parallels between animals and humans. No qualitative differences have been
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1 reported to indicate that humans should be considered fundamentally different from the multiple
2 animal species in which bioassays have demonstrated dioxin-induced neoplasia. Notably:
3
4 • the intracellular molecular protein, DNA, and RNA factors and mechanisms
5 postulated in dioxin cancer promotion are common to animals and humans,
6 reflecting intracellular functions that have been preserved phylogenetically over
7 millions of years. These factors include the AhR, Arnt heterodimerization,
8 cellular growth and differentiation functions, dioxin responsive elements, DNA
9 transcription mechanisms, and oxidative enzyme induction; and,
10
11 • similar dioxin-induced toxic outcomes are evident between animals and humans
1 2 across a variety of endpoints, progressing from enzyme induction, altered
1 3 intracellular regulatory proteins, dermal lesions, and liver function and porphyria
1 4 through to in vitro neoplastic cell promotion and clonal expansion following viral
1 5 or chemical induction (in addition to the epidemiological cancer results following
16 occupational exposures).
17
18 As detailed in Part II, Chapter 2 (mechanism of action), the mode of action parallels
19 between humans and animals can be traced through dioxin's impacts at the subcellular level, as
20 follows:
21
22 AhR binding: The AhR has been phylogenetically retained over hundreds of millions of
23 years of evolution in humans and animals (Hahn, 1998) and is highly expressed in developing
24 tissues (Abbott et al., 1995), pointing to a fundamental role in cellular growth, differentiation
25 and/or endogenous/xenobiotic metabolism. Species-specific AhR molecular structures reveal
26 them to be members of a family of transcription-activating proteins that exhibit a basic helix-
27 loop-helix (bHLH) DNA binding motif, PAS domain for dimerization and ligand binding, and a
28 C-terminal transactivation domain related to transcription induction and associated with a variety
29 of toxic endpoints.
30 Notable similarities exist in the AhR across animal taxa, particularly at the bHLH and
31 PAS sites (Fujii-Kuriyama et al., 1995), with human AhR being structurally most closely related
32 to that of the guinea pig (75% base homology) and other sensitive animal strains (Korkalainen et
33 al., 2001). Dioxin-resistant strains of rats and hamsters exhibit mutations in the AhR and/or
34 increased homology differences, particularly in the C-terminal transactivation domain and Q-rich
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1 subdomain (Korkalainen et al., 2001). Human AhR binding affinities vary -20-fold (Kd ~
2 0.3-38.8 nM) (Okey et al. 1997), encompassing the range from sensitive C57BL/6 mice (0.27
3 nM) to relatively resistant DBA/2 mice (1.5 nM) (Ema et al., 1994). Evidence suggests that
4 within species, the AhR binding affinity correlates with biochemical effects and toxicity
5 (Birnbaum et al., 1990, Poland and Glover, 1980), whereas between species, relative AhR
6 binding affinities do not determine dioxin sensitivity because multiple downstream events
7 intercede (DeVito and Birnbaum, 1995). Differences in conformational changes in the AhR
8 following ligand binding are also likely to impact toxicity (Henry and Gasiewicz, 2003).
9
10 TCDD-AhR binding to Arnt: Following ligand binding, the TCDD-Arnt complex
11 translocates to the nucleus, where it heterodimerizes (joins) with the bHLH-PAS transcription
1 2 partner protein, Arnt. Arnt has been phylogenetically retained over evolutionary time in both
1 3 humans and animals in several related forms and is essential for fetal survival. Arnt molecular
14 weights vary across species from 85 kDa for the mouse, 87 kDa for humans, and 88 kDa for the
1 5 rat (Pohjanvirta et al., 1999). The Arnt protein also dimerizes with other receptor/transcription
1 6 pathways in the cell nucleus, indicating its importance and fundamental role in regulating DNA
1 7 transcription (Schmidt and Bradfield, 1996; Zaher et al., 1998; Ge and Elferink, 1998; Tian et al.,
18 1999).
19
20 Cross-talk among intracellular regulatory proteins: As noted, cancer is inherently a loss
21 of the regulation of normal cell growth, differentiation, and death (apoptosis) that is locked into
22 the genetic coding through clonal expansion. Central to the control of cell cycling and
23 programmed cell death are numerous regulatory proteins (e.g., EGF, HIF-lcc, TNF-cc, TGF-pj,
24 NF-xB, RB), whose functional roles, although being rapidly elucidated, remain uncertain. These
25 regulatory proteins are expressed in humans and animals and can be impacted by dioxin
26 exposure, as in the role of EGF in dioxin-induced cleft palate in mice (Bryant et al., 2001). The
27 Arnt protein is a common co-transcription factor for many bHLH-PAS regulatory proteins in
28 addition to its role in the TCDD-AhR transcription pathway. The potential exists, therefore, for
29 prolonged, inappropriate TCDD-AhR induction to impact multiple Arnt-related functions in the
30 nucleus, thereby altering other regulatory pathways.
31 Competition for the Arnt protein has been demonstrated regarding the hypoxia inducible
32 factor 1 (HIF-lcc) pathway following dioxin administration and Arnt cross-talk (Gradin et al.,
33 1996; Nie et al., 2001). In addition, dioxin-induced clonal expansion in human and animal cell
34 cultures has resulted in fixed changes to the intranuclear expression of plasminogen activation
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1 inhibitor (PAI-2), tumor necrosis factor alpha (TNF-cc), and transforming growth factor p: (TGF-
2 Pj), although it remains to be determined whether these changes were cause or effect of the
3 dioxin-promoted clonal expansion (Yang et al., 1999).
4
5 Dioxin response elements (DREs): In the well-studied pathway of cytochrome mixed
6 function oxidase induction (e.g., CYP1A1, 1A2), the ligand-AhR-Arnt heterodimer binds 1:1 to
7 DREs upstream of the DNA gene battery transcription site (Denison et al., 1989). This
8 mechanism is common to the mouse (six DREs) (Lusska et al. 1993), the rat (three DREs), and
9 humans (two DREs) (Swanson and Bradfield, 1993), and is based on the 3'A-CGCAC5' DNA
10 sequence. Subsequent to DRE binding, the C-terminal transactivation domain of the AhR alters
11 hi stone proteins and causes unwinding of the chromatin, exposing the dioxin promoter and aryl
1 2 hydrocarbon hydroxylase (AHH) gene battery to constitutively expressed DNA transcription
1 3 proteins (Whitlock et al., 1996).
14
1 5 Enzyme induction: At least seven enzyme genes, and likely more, are included in the
1 6 AhR-Arnt induced gene battery: three oxidative P450 cytochromes (CYP1 Al, 1A2 , 1B1) and
1 7 four non-P450 enzymes responsive to reactive oxygenated metabolites and oxidative stress (for
18 example, a quinone oxidoreductase, aldehyde dehydrogenase, glucuronosyltransferase, and
19 glutathione transferase [Nebert et al., 2000; Zhang et al., 1998]). These enzymes are expressed in
20 humans and animals. Similar EC50s were reported for CYP1 Al induction in lymphocytes in
21 mice (1.3 nM) and humans (1.8nM) (Clark et al., 1992). However, substantial interspecies
22 differences have been noted between cultured human and mouse embryonic palatal cells
23 regarding CYP1 Al induction and morphological effects. Paralleling a -200-fold lower
24 sensitivity for morphological and cellular effects on embryonic palatal tissue, human cell cultures
25 expressed -350-fold fewer receptors and exhibited -1500-fold lower dioxin-induced CYP1A1
26 m-RNA induction than mice (Abbott et al., 1999). Notably, though, effects on human and rat
27 embryonic palatal shelf tissue occur at similar in vitro concentrations as compared to the much
28 higher sensitivity shown in mice, suggesting that mice may exhibit a particular sensitivity to
29 effects on palatal differentiation (Abbott and Birnbaum, 1990, 1991; Couture et al., 1990).
30 For CYP1A2 there is a -40-fold variability in protein and enzyme activity levels in the
31 human population (Eaton et al., 1995; Nebert et al., 1996). The importance of CYP1A2 to dioxin
32 toxicity in rodents has been demonstrated in knockout mice, where dioxin-induced porphyrin
33 changes did not occur in the absence of CYP1A2, and hepatic toxicity was substantially reduced
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1 (Smith et al., 2001). This is likely due to the lack of hepatic sequestration in the absence of
2 CYP1A2 (Diliberto et al., 1999).
3 Recent human epidemiological data have reported long-term hepatic enzyme and
4 porphyrin ratio changes many years after industrial dioxin exposure (Neuberger et al., 1999).
5 The prolonged up-regulation of mixed-function oxidase (MFO) enzymes has been postulated to
6 impact the carcinogenic potential of xenobiotics that are metabolically activated, such as the
7 PAHs. Indeed, carcinogenicity from PAHs is absent in AhR-knockout mice, presumably from
8 lack of induction of the mixed-function oxidases. In a related mechanistic postulate, emphasis
9 has been placed on the existence of both MFCs (CYP1A1, 1A2) and detoxifying/scavenging
10 phase II transferase enzymes in the dioxin-induced gene battery, suggesting an evolutionary
11 mechanism that creates reactive oxidative products through the MFOs (possibly as a result of
1 2 endogenous ligand metabolism) yet provides a protective mechanism for mitigating the resulting
1 3 oxidative stress through the phase n transferase enzymes. Abnormal regulation of this
14 mechanism could cause oxidative stress that is related both to DNA damage and cell
1 5 cycling/apoptosis regulation (Nebert et al., 2000).
16
17 Toxic effects and clonal proliferation: A spectrum of toxic effects has been demonstrated
1 8 in both animals and humans following dioxin exposure, including developmental impacts,
19 hormonal changes, skin lesions, and liver damage (DeVito et al., 1995). Dioxin has also been
20 demonstrated to promote neoplastic changes and clonal expansion in human and animal cell
21 cultures following viral induction. Exposure of normal human keratinocytes in vitro leads to
22 accelerated differentiation, increased cell proliferation, and decreased senescence in
23 differentiating cells (Ray and Swanson, 2003). These changes were accompanied by decreased
24 levels of a number of cell regulatory proteins, including p53, supporting the concept that dioxin
25 may exert its tumor promoting effects, in part, through this mechanism.
26 In Yang et al. (1992), human epidermal keratinocytes immortalized by adenovirus 12 -
27 simian virus 40 exposure (SV40) underwent neoplastic transformation after 2 weeks of dioxin
28 exposure in vitro at > 0.1 nM, exhibiting increased saturation density, colony formation on soft
29 agar, and squamous cell carcinoma when inoculated into athymic nude mice. These phenomena
30 did not occur in the absence of SV40 virus induction or in control cell lines, including the
31 immortalized cell culture. Both the neoplastic cell transformation and AHH induction in the
32 untransformed cells were dose dependent. Follow-up analyses demonstrated alterations in
33 growth regulatory gene expression (PAI-2, TNF-cc, and TOF-PJ that became fixed in the genome
34 following successive division in TCDD-damaged cells (Yang et al., 1999).
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1 Conversely, under certain circumstances, exposure to TCDD may elicit beneficial effects
2 in selected tissue or cells. For example, TCDD protects against the subsequent carcinogenic
3 effects of PAHs in mouse skin, possibly reflecting induction of detoxifying enzymes (Cohen et
4 al., 1979; DiGiovanni et al., 1980). In other situations, TCDD-induced changes in estrogen
5 metabolism may alter the growth of hormone-dependent tumor cells, producing a potential
6 anticarcinogenic effect (Spink et al., 1990; Gierthy et al., 1993). However, several recent studies
7 in mice indicate that the AhR has an important role in the genetic damage and carcinogenesis
8 caused by components in tobacco smoke, such as BaP, through its ability to regulate CYP1 Al
9 gene induction (Dertinger et al., 1998; Shimizu et al., 2000). TCDD's biological effects likely
10 reflect a complicated interplay between genetic and environmental factors. These issues
11 complicate the risk assessment process for dioxin.
1 2 Thus, it is clear that the robust database on mode(s) of dioxin action related to
13 biochemical effects and to clearly adverse effects supports an understanding of dioxin's impact
14 on biological and cellular processes. This database is among the best available for xenobiotic
1 5 chemicals. The short-comings described above will stimulate additional research to further
1 6 elucidate details in this understanding of the impact of dioxins, but they should not detract from
1 7 the recognition that, among the data available to aid hazard characterization and risk assessment,
18 these are remarkably consistent and useful findings.
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1
Table 3-1. Early molecular events in response to dioxin"
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
Diffusion into the cell
Binding to the AhR protein
Impacts on cytoplasmic phosphorylation
Dissociation from hsp90
Active translocation from cytoplasm to nucleus
Association with Arnt protein
Competition for Arnt with other nuclear cofactors
Conversion of liganded receptor to the DNA-binding form
Binding of liganded receptor heteromer to enhancer DNA
Enhancer activation
Altered DNA configuration
Histone modification
Recruitment of additional proteins
Nucleosome disruption
Increased accessibility of transcriptional promoter
Binding of transcription factors to promoter
Enhanced mRNA and protein synthesis
1 These events are discussed in detail in Part II, Chapter 2.
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1 4. EXPOSURE CHARACTERIZATION
2
3 This section summarizes key findings developed in the exposure portion of the Agency's
4 dioxin reassessment. These findings are developed in the companion document entitled Parti:
5 Estimating Exposure to Dioxin-Like Compounds, which is divided into three volumes: (1)
6 Sources of Dioxin in the United States, (2) Properties, Environmental Levels, and Background
7 Exposures, and (3) Site-Specific Assessment Procedures. Readers are encouraged to examine the
8 more detailed companion document for further information on the topics covered here and to see
9 complete literature citations. The characterization discussion provides cross-references to help
10 readers find the relevant portions of the companion document.
11 This discussion is organized as follows: (1) sources, (2) fate, (3) environmental media
1 2 and food concentrations, (4) background exposures, (5) potentially highly exposed populations,
1 3 and (6) trends. The key findings are presented in italics.
14
15 4.1. SOURCES (Cross-reference: Parti, Volume 1: Sources of Dioxin-Like
1 6 Compounds in the United States)
1 7 CDD/CDFs have never been intentionally produced other than on a laboratory-scale basis
1 8 for use in scientific analysis. Rather, they have been generated as unintended by-products in
1 9 trace quantities in various combustion, industrial, and biological processes. PCBs, on the other
20 hand, were commercially produced in large quantities, but they are no longer commercially
21 produced in the United States. EPA has classified sources of dioxin-like compounds into five
2 2 broad categories:
23
24 1. Combustion Sources. CDD/CDFs are formed in most combustion systems, which can
25 include waste incineration (such as municipal solid waste, sewage sludge, medical
26 waste, and hazardous wastes), burning of various fuels (such as coal, wood, and
27 petroleum products), other high temperature sources (such as cement kilns), and
28 poorly or uncontrolled combustion sources (such as forest fires, building fires, and
29 open burning of wastes). Some evidence exists that very small amounts of dioxin-like
30 PCBs are produced during combustion, but they appear to be a small fraction of the
31 total TEQs emitted.
32
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1 2. Metals Smelting, Refining, and Processing Sources. CDD/CDFs can be formed
2 during various types of primary and secondary metals operations, including iron ore
3 sintering, steel production, and scrap metal recovery.
4
5 3. Chemical Manufacturing. CDD/CDFs can be formed as by-products from the
6 manufacture of chlorine-bleached wood pulp, chlorinated phenols (e.g.,
7 pentachlorophenol, or PCP), PCBs, phenoxy herbicides (e.g., 2,4,5-T), and
8 chlorinated aliphatic compounds (e.g., ethylene dichloride).
9
10 4. Biological and Photochemical Processes. Recent studies suggest that CDD/CDFs
11 can be formed under certain environmental conditions (e.g., composting) from the
1 2 action of microorganisms on chlorinated phenolic compounds. Similarly, CDD/CDFs
1 3 have been reported to be formed during photolysis of highly chlorinated phenols.
14
15 5. Reservoir Sources. Reservoirs are materials or places that contain previously formed
1 6 CDD/CDFs or dioxin-like PCBs and have the potential for redistribution and
1 7 circulation of these compounds into the environment. Potential reservoirs include
18 soils, sediments, biota, water, and some anthropogenic materials. Reservoirs become
1 9 sources when they have releases to the circulating environment.
20
21 The development of national estimates of annual environmental releases to air, water, and
22 land is complicated by the fact that only a few facilities in most industrial sectors have been
23 evaluated for CDD/CDF emissions. Thus, an extrapolation is needed to estimate national
24 emissions. The extrapolation method involves deriving an estimate of emissions per unit of
25 activity (i.e., an emission factor) at the tested facilities and multiplying this by the total activity
26 level in the untested facilities.
27 In order to convey the level of uncertainty in both the measure of activity and the
28 emission factor, EPA developed a qualitative confidence rating scheme. The confidence rating
29 scheme, presented in Table 4-1, uses qualitative criteria to assign a high, medium, or low
30 confidence rating to the emission factor and activity level for those source categories for which
31 emission estimates can be reliably quantified. The overall "confidence rating" assigned to a
32 quantified emission estimate was determined by the confidence ratings assigned to the
33 corresponding "activity level" and "emission factor." If the lowest rating assigned to either the
34 activity level or the emission factor terms is "high," then the category rating assigned to the
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1 emission estimate is high (also referred to as "A"). If the lowest rating assigned to either the
2 activity level or emission factor terms is "medium," then the category rating assigned to the
3 emission estimate is medium (also referred to as "B"). If the lowest rating assigned to either the
4 activity level or emission factor terms is "low," then the category rating assigned to the emission
5 estimate is low (also referred to as "C").
6 For many source categories, either the emission factor information or the activity level
7 information were inadequate to support development of reliable quantitative release estimates for
8 one or more media. For some of these source categories, sufficient information was available to
9 make preliminary estimates of environmental releases of CDD/CDFs or dioxin-like PCBs;
10 however, the confidence in the activity level estimates or emission factor estimates was so low
11 that the estimates cannot be included in the sum of quantified emissions from sources with
1 2 confidence ratings of A, B, or C. These estimates were given an overall confidence class rating
13 of D. For other sources, some information exists suggesting that they may release dioxin-like
14 compounds; however, the available data were judged to be insufficient for developing any
1 5 quantitative emission estimate. These estimates were given an overall confidence class rating of
16 E.
17
18 4.1.1. Inventory of Releases
1 9 This dioxin reassessment has produced an "inventory" of sources of environmental
20 releases of dioxin-like compounds for the United States (Table 4-2). The inventory was
21 developed by considering all sources identified in the published technical and scientific literature
22 and by the incorporation of results from numerous individual emissions test reports of individual
23 industrial and combustion source facilities. In order to be representative of the United States,
24 data generated from U.S. sources of information were always given first priority for developing
25 emission estimates. Data from other countries were used for making estimates in only a few
26 source categories where foreign technologies were judged similar to those found in the United
27 States and the U.S. data were judged to be inadequate. The inventory is limited to sources whose
28 releases can be reliably quantified (i.e., those with confidence ratings of A, B, or C, as defined
29 above). As discussed below, this document does provide preliminary estimates of releases from
30 Class D sources, but they are presented separately from the inventory.
31 The inventory presents the environmental releases in terms of two reference years: 1987
32 and 1995. The year 1987 was selected primarily because little empirical data existed for making
33 source-specific emission estimates prior to this time; 1995 represents the latest year that could
34 reasonably be addressed within the timetable for producing the rest of this document. EPA
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1 expects to conduct periodic revisions and updates to the source inventory in the future to track
2 changes in environmental releases over time.
3 Figure 4-1 displays the emission estimates to air for sources included in the inventory and
4 shows how the emission factors and activity levels were combined to generate emission
5 estimates. Figure 4-2 compares the annual mean I-TEQ emission estimates to air for the two
6 reference years (1987 and 1995).
7 The following conclusions are made for sources of dioxin-like compounds included in the
8 inventory:
9
10 • EPA 's best estimates of releases ofCDD/CDFs to air, water, and land from
11 reasonably quantifiable sources were approximately 3300 g TEQDF-WHO98 (3000 gl-
1 2 TEQ) in 1995 and 14,000 g TEQDF-WHO98 (12,800 g I-TEQ) in 1987. This finding is
1 3 derived directly from Table 4-2.
14
1 5 • The inventory indicates that, between 1987 and 1995, there was approximately a 76%
1 6 decrease in total environmental releases of CDDs/CDFs from known sources in the
1 7 United States. EPA is currently evaluating source releases for the year 2000.
1 8 Preliminary indications support the observation of a continued reduction in total
1 9 environmental releases from 1995 levels. The inventory updated for the year 2000
20 will undergo scientific peer review.
21
22 • The environmental releases ofCDD/CDFs in the United States occur from a wide
23 variety of sources, but they are dominated by releases to the air from combustion
24 sources. The current (1995) inventory indicates that emissions from combustion
25 sources are more than an order of magnitude greater than emissions from the sum of
26 emissions from all other categories. Approximately 70% of all quantifiable
2 7 environmental releases were contributed by air emissions from just three source
28 categories in 1995: municipal waste incinerators (representing 38% of total
29 environmental releases); backyard burning of refuse in barrels (19%); and medical
30 waste incinerators (14%).
31
32 • The decrease in estimated releases ofCDD/CDFs between 1987 and 1995
33 (approximately 76%) was due primarily to reductions in air emissions from
34 municipal and medical waste incinerators, and further reductions are anticipated.
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1 For both categories, these emission reductions have occurred from a combination of
2 improved combustion and emission controls and from the closing of a number of
3 facilities. EPA's regulatory programs estimate that full compliance with recently
4 promulgated regulations should result in further reductions in emissions from the
5 1995 levels of more than 1800 I-TEQ. These reductions will occur in the following
6 source types: municipal waste combustors, medical waste incinerators, and various
7 facilities that burn hazardous waste (see Part I, Volume 1, for further details about
8 these reductions). No federal regulations are in place or currently under development
9 for limiting dioxin emissions from backyard burning of refuse in barrels. A number
10 of states have general restrictions on the practice of backyard trash burning.
11
1 2 • Insufficient data are available to comprehensively estimate point source releases of
1 3 dioxin-like compounds to water. Sound estimates of releases to water are available
14 only for chlorine bleached pulp and paper mills (356 g I-TEQDF or TEQDF-WHO98 for
15 1987 and 20 g I-TEQDF or TEQDF-WHO98 for 1995) and the manufacture of ethylene
1 6 dichloride (EDC)/vinyl chloride monomer (VCM) (< 1 g I-TEQDF or TEQDF-WHO98
1 7 in 1995). Other releases to water bodies that cannot be quantified on the basis of
1 8 existing data include effluents from publicly owned treatment works (POTW) and
1 9 most industrial/commercial sources. EPA's Office of Water estimates that when full
20 compliance with limitations on effluent discharges of CDD/CDF from chlorine
21 bleached pulp and paper mills is achieved, annual emissions will be reduced to 5 g I-
2 2 TEQDF or TEQDF-WHO98.
23
24 • Based on the available information, the inventory includes only a limited set of
25 activities that result in direct environmental releases to land. Total releases to land
26 quantified in the national inventory are estimated at 110 g TEQDF-WHO98 in 1995 and
27 are principally from municipal wastewater treatment sludge (76.6 g) and the use of
28 2,4-D (28.9 g). Not included in the inventory's definition of an environmental release
29 is the disposal of sludge and ashes into approved landfills.
30
31 • Significant amounts of dioxin-like compounds produced annually are not considered
32 environmental releases and, therefore, are not included in the national inventory.
33 Examples include dioxin-like compounds generated internal to a process but
34 destroyed before release, waste streams that are disposed of in approved landfills and
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1 are therefore outside the definition of annual environmental releases, and products
2 that contain dioxin-like compounds but for which environmental releases, if any,
3 cannot be estimated.
4
5 The procedures and results of the U.S. inventory may have underestimated releases from
6 contemporary sources. A number of investigators have suggested that national inventories may
7 underestimate emissions because of the possibility of unknown sources. This claim has been
8 supported with mass balance analyses that suggest that deposition exceeds emissions (Rappe,
9 1991; Harrad and Jones, 1992; Bruzy and Kites, 1995); however, the uncertainty, in both the
10 emissions and deposition estimates for the United States prevents the use of this approach for
11 reliably evaluating the issue.
1 2 A variety of other arguments indicate that the inventory could underestimate emissions of
1 3 dioxin-like compounds:
14
15 • A number of sources lacked sufficient data to include in the inventory but
16 there were limited evidence indicating that these sources can emit CDD/CDFs.
1 7 These sources are listed in Tables 4-3 and 4-4 and include various components
18 of the metals industries, such as electric arc furnaces and foundries and
1 9 uncontrolled or minimally controlled combustion practices (e.g., accidental
20 fires at landfills).
21
22 • The possibility remains that truly unknown sources exist. Many of the sources
23 that are well-accepted today were discovered only in the past 10 years. For
24 example, CDD/CDFs were found unexpectedly in the wastewater effluent
25 from bleached pulp and paper mills in the mid 1980s. Ore sintering is now
26 listed as one of the leading sources of CDD/CDF emissions in Germany, but it
27 was not recognized as a source until the early 1990s.
28
29 4.1.2. General Source Observations
30 For any given time period, releases from both contemporary formation sources and
31 reservoir sources determine the overall amount of the dioxin-like compounds that are being
32 released to the open and circulating environment. Because existing information is incomplete
33 with regard to quantifying contributions from contemporary and reservoir sources, it is not
34 currently possible to estimate the total magnitude of release for dioxin-like compounds from all
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1 sources into the U.S. environment. For example, in terms of 1995 releases from reasonably
2 quantifiable sources, this document estimates releases of 3300 g TEQDF-WHO98 (3000 g I-
3 TEQDF) for contemporary formation sources and 2900 g I-TEQDF or TEQDF-WHO98 for reservoir
4 sources.
5 In addition, there remain a number of unquantifiable and poorly quantified sources. No
6 quantitative release estimates can be made for agricultural burning or for most CDD/CDF
7 reservoirs or for any dioxin-like PCB reservoirs. The preliminary 1995 estimate of releases from
8 poorly characterized contemporary formation sources is 1400 g I-TEQDF or TEQDF-WHO98.
9 The preliminary release estimates for contemporary formation sources and reservoir sources are
10 presented in Table 4-2. Table 4-3 lists all the sources that have been reported to release dioxin-
11 like compounds but cannot be characterized on even a preliminary basis.
1 2 Additional observations and conclusions about all sources of dioxin-like compounds are
13 summarized below:
14
1 5 • The contribution of dioxin-like compounds to waterways from nonpoint source
1 6 reservoirs is likely to be greater than the contribution from point sources. Current
1 7 data are only sufficient to support preliminary estimates of nonpoint source
1 8 contributions of dioxin-like compounds to water (i.e., from urban storm water runoff
1 9 and rural soil erosion). These estimates suggest that, on a nationwide basis, total
20 nonpoint releases are significantly larger than point source releases.
21
22 • Current emissions of CDD/CDFs to the U.S. environment result principally from
23 anthropogenic activities. Evidence that supports this finding includes matches in
24 time of rise of environmental levels with time when general industrial activity began
25 rising rapidly (see trend discussion in Part I, Volume 2, Chapter 6), the lack of any
26 identified large natural sources, and observations of higher CDD/CDF body burdens
27 in industrialized versus less industrialized countries (see discussion on human tissue
28 levels in Part I, Volume 2, Chapter 4).
29
30 • Although chlorine is an essential component for the formation of CDD/CDFs in
31 combustion systems, the empirical evidence indicates that for commercial-scale
32 incinerators, chlorine levels in feed are not the dominant controlling factor for rates
33 of CDD/CDF stack emissions. Important factors that can affect the rate of CDD/CDF
34 formation include the overall combustion efficiency, post-combustion flue gas
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1 temperatures and residence times, and the availability of surface catalytic sites to
2 support CDD/CDF synthesis. Data from bench-, pilot- and commercial-scale
3 combustors indicate that CDD/CDF formation can occur by a number of mechanisms.
4 Some of these data, primarily from laboratory and pilot-scale combustors, have shown
5 direct correlation between chlorine content in fuels and rates of CDD/CDF formation.
6 Other data, primarily from commercial-scale combustors, show little relation between
7 availability of chlorine in feeds and rates of CDD/CDF formation.
8
9 • The conclusion that chlorine in feed is not a strong determinant of CDD/CDF
10 emissions applies to the overall population of commercial-scale combustors. For any
11 individual commercial-scale combustor, circumstances may exist in which changes in
1 2 chlorine content of feed could affect CDD/CDF emissions. For uncontrolled
1 3 combustion, such as open burning of household waste, the chlorine content of the
1 4 waste may play a more significant role in rates of CDD/CDF formation and release
1 5 than is observed at commercial-scale combustors. The full discussion on this issue is
1 6 presented in Part I, Volume 1, Chapter 2.
17
1 8 • Dioxins are present in some ball clays, but insufficient data are available to estimate
1 9 whether environmental releases occur during mining and use. Recent studies in the
20 United States and Europe have measured dioxins (principally CDDs) in some ball
21 clays and other related clays. As discussed in Part I, Volume 1, Chapter 13, it is likely
22 that the dioxin present in ball clay is of a natural origin. Ball clay is principally used
23 in the manufacture of ceramics, which involves firing the clay in high-temperature
24 kilns. This activity may cause some portion of the CDDs contained in the clay to be
25 released into the air, but emission tests have not yet been conducted that would allow
26 characterizing these releases.
27
28 • Data are available to estimate the amounts of CDD/CDFs contained in only a limited
29 number of commercial products. No systematic survey has been conducted to
30 determine levels of dioxin-like compounds in commercial products. The available
31 data do, however, allow estimates to be made of the amounts of dioxin-like
32 compounds in bleached pulp (40 g I-TEQDF or TEQDF-WHO98 in 1995), POTW sludge
33 used in fertilizers (3.5 g I-TEQDF or 2.6 g TEQDF-WHO98 in 1995),
34 pentachlorophenol-treated wood (8400 g I-TEQDF or 4800 g TEQDF-WHO98 in 1995),
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1 dioxazine dyes and pigments (< 1 g I-TEQDF or TEQDF-WHO98 in 1995), and 2,4-D
2 (18.4 g I-TEQDF or 28.9 g TEQDF-WHO98 in 1995).
3
4 • No significant release of newly formed dioxin-like PCBs is occurring in the United
5 States. Unlike CDD/CDFs, PCBs were intentionally manufactured in the United
6 States in large quantities from 1929 until production ceased in 1977. Although it has
7 been demonstrated that small quantities of coplanar PCBs can be produced during
8 waste combustion, no strong evidence exists that the dioxin-like PCBs make a
9 significant contribution to TEQ releases during combustion. The occurrences of
10 dioxin-like PCBs in the U.S. environment most likely reflect past releases associated
11 with PCB production, use, and disposal. Further support for this finding is based on
1 2 observations of reductions since the 1980s in PCBs in Great Lakes sediment and other
13 areas.
14
15 'It is unlikely that the emission rates of CDD/CDFs from known sources correlate
1 6 proportionally with general population exposures. Although the inventory shows the
1 7 relative contribution of various sources to total emissions, it cannot be assumed that
18 these sources make the same relative contributions to human exposure. It is quite
19 possible that the major sources of dioxin in food (see the discussion in Part I, Volume
20 2, Chapter 2, indicating that diet is the dominant exposure pathway for humans) may
21 not be those sources that represent the largest fractions of current total emissions in
22 the United States. It is important to consider the geographic locations of sources
23 relative to the areas from which much of the beef, pork, milk, and fish come. That is,
24 many of the agricultural areas that produce dietary animal fats are not located near or
25 directly downwind of the major sources of dioxin and related compounds.
26
27 • The contribution of reservoir sources to human exposure may be significant. Several
28 factors support this finding:
29 1. Because the magnitude of releases from current sources of newly formed PCBs
30 are most likely negligible, human exposure to the dioxin-like PCBs is thought to
31 be derived almost completely from reservoir sources. Key pathways involve
32 releases from both soils and sediments to both aquatic and terrestrial food chains.
33 As discussed in Part I, Volume 2, Chapter 4, one-third of general population
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1 TEQDEP exposure is due to PCBs. Thus, at least one-third of the overall risk from
2 dioxin-like compounds comes from reservoir sources.
3
4 2. CDD/CDF releases from soil via soil erosion and runoff to waterways may be
5 significant. These releases appear to be greater than releases to water from the
6 primary sources included in the inventory. CDD/CDFs in waterways can
7 bioaccumulate in fish, leading to human exposure via their consumption. As
8 discussed in Part I, Volume 2, Chapter 4, fish consumption makes up about one-
9 fifth of the total general population CDD/CDF TEQ exposure. This suggests that
10 a significant portion of the CDD/CDF TEQ exposure could be due to releases
11 from the soil reservoir. It is not known, however, how much of the soil erosion
1 2 and runoff represents recently deposited CDD/CDFs from primary sources or
1 3 longer-term accumulation. Much of the eroded soil comes from tilled agricultural
14 lands, which would include a mix of CDD/CDFs from various deposition times.
1 5 The age of CDD/CDFs in urban runoff is less clear.
16
17 3. Potentially, soil reservoirs could have vapor and particulate releases that deposit
1 8 on plants and enter the terrestrial food chain. The magnitude of this contribution,
1 9 however, is unknown.
20
21 Collectively, these three factors suggest that reservoirs are a significant source of current
22 background TEQ exposure, perhaps contributing half or more of the total.
23
24 4.2. ENVIRONMENTAL FATE (Cross-reference: Part I, Volume 2, Chapter 2)
2 5 The estimates of environmental releases are presented above in terms of TEQs. This is
26 done for convenience in presenting summary information and to facilitate comparisons across
2 7 sources. For purposes of environmental fate modeling, however, it is important to use the
28 individual CDD/CDF and PCB congeners values rather than TEQs because the physical/chemical
29 properties of individual dioxin congeners vary and will behave differently in the environment.
30 For example, the relative mix of congeners released from a stack cannot be assumed to remain
31 constant during transport through the atmosphere and deposition to various media. The full
32 congener-specific release rates for most sources are given in an electronic database that is
33 available as a companion to this document (U.S. EPA, 1998) Database of Sources of
34 Environmental Releases of Dioxin-Like Compounds in the United States. EPA/600/P-98/002Ab.
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1 In Part I, Volume 3, site-specific procedures are provided for estimating the impact of
2 emissions on local populations, and this section emphasizes that congener specific emission
3 values should be used in modeling their environmental fate. Finally, it is important to recognize
4 that this document does not use source release estimates to generate background population
5 intake/risk estimates; rather, these estimates are derived primarily from food levels and
6 consumption rates.
7 Dioxin-like compounds are widely distributed in the environment as a result of a number
8 of physical and biological processes. The dioxin-like compounds are essentially insoluble in
9 water, they are generally classified as semivolatile, and they tend to bioaccumulate in animals.
10 Some evidence has shown that these compounds can degrade in the environment, but in general
11 they are considered to be very persistent and relatively immobile in soils and sediments. These
1 2 compounds are transported through the atmosphere as vapors or attached to airborne particulates
1 3 and can be deposited on soils, plants, or other surfaces (by wet or dry deposition). The dioxin-
1 4 like compounds enter water bodies primarily via direct deposition from the atmosphere or by
1 5 surface runoff and erosion. From soils, these compounds can reenter the atmosphere as either
16 resuspended soil particles or vapors. In water, they can be resuspended into the water column
1 7 from sediments, they can be volatilized out of the surface waters into the atmosphere, or, they
18 can become buried in deeper sediments. Immobile sediments appear to serve as permanent sinks
1 9 for the dioxin-like compounds. Although anthropogenic materials (such as PCP) are not always
20 considered an environmental compartment, dioxin-like compounds are also found in such
21 materials, and from there they have the potential to be released into the broader environment.
22 Atmospheric transport and deposition of the dioxin-like compounds are a primary means
23 of their dispersal throughout the environment. The dioxin-like compounds have been measured
24 in wet and dry deposition in most locations, including remote areas. Numerous studies have
25 shown that they are commonly found in soils throughout the world. Industrialized countries tend
26 to show similar elevated concentrations in soil, and detectable levels have been found in
27 nonindustrialized countries. The only satisfactory explanation available for this distribution is air
28 transport and deposition. Finally, by analogy these compounds would be expected to behave
29 similarly to other compounds that have similar properties, and this postulated mechanism of
30 global distribution is becoming widely accepted for a variety of persistent organic compounds.
31 The two primary pathways for the dioxin-like compounds to enter the ecological food
3 2 chains and human diet are air-to-plant-to-animal and water/sediment-to-fish. Vegetation
33 receives these compounds via atmospheric deposition in the vapor and particle phases. The
34 compounds are retained on plant surfaces and bioaccumulated in the fatty tissues of animals that
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1 feed on these plants. Vapor phase transfers onto vegetation have been experimentally shown to
2 dominate the air-to-plant pathway for the dioxin-like compounds, particularly for the lower
3 chlorinated congeners. In the aquatic food chain, dioxins enter water systems via direct
4 discharge or deposition and runoff from watersheds. Fish accumulate these compounds through
5 their direct contact with water, suspended particles, and bottom sediments and through their
6 consumption of aquatic organisms.
7 Although these two pathways are thought to normally dominate contribution to the
8 commercial food supply, others can also be important. Elevated dioxin levels in cattle resulting
9 from animal contact with PCP-treated wood have been documented by the U. S. Department of
10 Agriculture. Animal feed contamination episodes have led to elevations of dioxins in poultry in
11 the United States, milk in Germany, and meat/dairy products in Belgium (see Part I, Volume 2,
1 2 Chapter 5).
13
14 4.3. ENVIRONMENTAL MEDIA AND FOOD CONCENTRATIONS (Cross-reference:
1 5 Part I, Volume 2, Chapter 3)
1 6 Background levels of dioxin-like compounds in various environmental media, including
1 7 food, are presented in Table 4-4 in terms of means, variability, and sample sizes used to support
1 8 the estimates. Estimates for background levels of dioxin-like compounds in environmental
19 media are based on a variety of studies conducted at different locations in North America. Of the
20 studies available for this compilation, only those conducted in locations representing
21 "background" were selected. The amount and representativeness of the data vary, but in general
22 they were derived from studies that were not designed to estimate national background means.
23 The environmental media concentrations were similar to those in studies from Western Europe.
24 These data are the best available for comparisons with site-specific values. Because of the
25 limited number of locations examined, it is not known whether these estimates adequately
26 capture the full national variability. As new data are collected, these ranges are likely to be
27 expanded and refined. The limited data on dioxin-like PCBs in environmental media are
28 summarized in Part I, Volume 2, Chapter 3.
29 Estimates for levels of dioxin-like compounds in food are based on data from a variety of
30 studies conducted in North America. Beef, pork, and poultry estimates were derived from
31 statistically based national surveys. Milk estimates were derived from a survey of a nationwide
32 milk sampling network. Dairy estimates were derived from milk fat concentrations, coupled with
33 appropriate assumptions for the amount of milk fat in dairy products. The background egg
34 concentrations were based on an analysis of 15 egg samples collected from retail stores in eight
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1 states (CA, OH, GA, NY, PA, OR, MN, WS; two samples per state except one in OR), where
2 each sample was a composite of 24 individual eggs (i.e., 15 samples represented 360 eggs). The
3 fish data, as discussed below, were derived from multiple studies, with samples collected both
4 directly from water bodies and from retail outlets. All fish concentrations were expressed on the
5 basis of fresh weight in edible tissue. As with other environmental media, food levels found in
6 the United States were similar to levels found in Europe.
7 The procedure to evaluate background fish exposures emphasizes the use of both species-
8 specific consumption rates and species-specific concentrations. EPA's national bioaccumulation
9 study (U.S. EPA, 1992b) provides some species-specific information on freshwater/estuarine fish
10 caught in the wild at various locations in the United States. Additional species-specific data on
11 store-bought fish are available from studies conducted by the U.S. Food and Drug Administration
1 2 (FDA) during the mid to latter 1990s (Jensen and Bolger, 2000; Jensen et al., 2000). An
13 important aspect of the FDA studies is that they include data on store-bought catfish, tuna,
14 shellfish, and salmon, which are some of the most highly consumed species. Accordingly, the
1 5 data used to characterize CDD/CDF fish levels are much improved over previous estimates, with
1 6 more than 300 individual samples and good representation of the most highly consumed species.
1 7 However, the levels of dioxins in fish remain more uncertain than those in the other foods.
1 8 The compilation of data from different studies still lacks the geographic coverage and
19 statistical power of the other food surveys. The EPA and FDA studies did not address dioxin-
20 like PCBs; rather, these are based on a much smaller data set derived from the open literature.
21 Also, the estimates of dioxin intake resulting from fish consumption do not include consumption
22 offish oils. Currently, insufficient data are available to support estimates of dioxin intake from
23 direct fish oil consumption.
24 The general population dioxin intake calculations used in this document are a function of
25 both consumption rate and dioxin concentration in food. The concentration data used in this
26 document were measured in raw foods; therefore, if cooking significantly alters the dioxin
27 concentration in consumed portions it must be accounted for in estimating dioxin intake.
28 This issue has been examined in a number of studies that measured the effects of cooking
29 on the levels of CDDs, CDFs, and PCBs in foods (see Part I, Volume 2, Chapter 3). These
30 studies have a range of results, depending on food type and cooking method. Most of the
31 cooking experiments suggested that cooking reduces the total amount of dioxins in food but
32 causes relatively little change in its concentration.
33 Although some cooking experiments have shown increases and others have shown
34 decreases in dioxin concentrations, the relative prevalence of these impacts have not been
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1 established. Therefore, given that most experiments show little change and others show change
2 in both directions, the most reasonable assumption that can be made from the existing data is that
3 dioxin concentration in uncooked food is a reasonable surrogate for dioxin concentration in
4 cooked food. Although cooking in general does not reduce dioxin concentration in food, some
5 specific food preparation practices can be adopted that can reduce dioxin intake by significantly
6 reducing overall animal fat consumption. For example, carefully trimming fat from meat,
7 removing skin from chicken and fish, and avoiding cooking in animal fats should reduce both
8 animal fat and dioxin intake.
9 Some evidence from Europe suggests that during the 1990s a decline occurred in
10 concentrations of dioxins and furans in food products, particularly dairy products (see Part I,
11 Volume 2, Chapter 6). For example, the United Kingdom's Ministry of Agriculture, Fisheries,
1 2 and Food collected milk samples in 1990 and again from similar locations in 1995. In 1990, the
1 3 I-TEQDF ranged from 1.1 to 3.3 ppt, whereas the 1995 I-TEQDF ranged from 0.7 to 1.4. In
14 Germany, a sampling of 120 dairy products in 1994 found I-TEQDF concentrations that were 25%
1 5 lower than those in a similar sampling program in 1990. Liem et al. (2000) reports on a
16 European cooperative study coordinated by the National Institute of Public Health and the
1 7 Environment in the Netherlands and the Swedish National Food Administration. Ten countries
18 supplied data on food concentrations, food consumption patterns, and other data used to evaluate
1 9 exposure to dioxins in Europe. Some of the data suggested reductions in concentrations over
20 time, but the available information was insufficient to draw general conclusions.
21 No systematic study of temporal trends in dioxin levels in food has been conducted in the
22 United States. Although not statistically based, one U.S. study examined dioxin levels in 14
23 preserved food samples from various decades in the 20th century (Winters et al., 1998). It was
24 found that meat samples of the 1950s through the 1970s had concentrations that were two-three
25 times higher for the CDD/CDF TEQs and about 10 times higher for the PCB TEQs, as compared
26 to current meat concentrations.
27 The food data and associated exposure estimates presented here reflect a mid-1990's time
28 frame. New studies underway now or recently completed could be used in future updates to this
29 report to make exposure estimates for a new reference year, such as 2000. The following studies
30 on dioxin levels in food were not completed in time to be included in this document and should
31 be considered in future updates:
32
33 • The milk levels used in Tables 4-4 and 4-6 are based on a study by Lorber et al.
34 (1998) where milk samples were collected in 1996. A very similar milk survey was
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1 conducted by Schaum et al. (2003) involving the collection and analysis of TEQDFP in
2 cow milk samples from 45 dairy plants in July of 2000 and again in January 2001.
3 This study reported TEQDFP levels in whole milk which were about half the levels
4 found by Lorber et al. (1998). Follow-up work by Schuda et al. (2004), which
5 addressed CDD/Fs only, allowed estimation of 2000/2001 TEQDF milk levels on a
6 lipid basis. This approach showed similar TEQDF levels in milk lipid, or perhaps a
7 slight decrease, when comparing CDD/F TEQs in the two sampling times (0.71 pg
8 TEQDF/g lipid in 2000/2001 compared to 0.82 pg TEQDF/g lipid in 1996).
9
10 • USDA is currently conducting a nationwide survey of dioxin levels in beef, pork and
11 poultry. Samples were collected in 2002 and 2003 and data analysis is now
1 2 underway. The survey design and data analysis are structured in a similar way to the
1 3 earlier USDA surveys used in this report and should allow for trend analysis.
14
1 5 • The Institute of Medicine of the National Academies published a review of dioxin
1 6 levels in foods in 2003 (Institute of Medicine of the National Academies, 2003). This
1 7 document presents policy options for reducing dietary exposure to dioxins in food and
18 related research recommendations. Appendix B of the Institute of Medicine's report
1 9 summarizes FDA's Total Diet Survey of dioxin levels in food collected in 2001. A
20 wide variety of foods were sampled including dairy products, eggs, meats, fish, fruits,
21 vegetables and fats/oils.
22
23 The food consumption rates used here are based primarily on USDA's 1994-1996 Continuing
24 Survey of Food Intakes by Individuals. As new USDA survey data come available, these should
25 be incorporated into future updates of this report.
26
2 7 4.4. BACKGROUND EXPOSURES (Cross-reference: Part I, Volume 2, Chapter 4)
28 4.4.1. Tissue Levels
29 The average CDD/CDF/PCB tissue level for the general adult U.S. population appears to
30 be declining, and the best estimate of current (late 1990s) levels is 25 ppt (TEQDFP-WHO98, lipid
31 basis).
32 The tissue samples collected in North America in the late 1980s and early 1990s showed
33 an average TEQDFP-WHO98 level of about 55 pg/g lipid. This finding is supported by a number of
34 studies—all conducted in North America—that measured dioxin levels in adipose, blood, and
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1 human milk. However, the number of participants in most of these studies was relatively small
2 and they were not statistically selected in ways that ensure their representativeness of the general
3 U.S. adult population. One study, the 1987 National Human Adipose Tissue Survey, involved
4 more than 800 individuals and provided broad geographic coverage, but it did not address
5 coplanar PCBs. Similar tissue levels of these compounds have been measured in Europe and
6 Japan during similar time periods.
7 Because dioxin levels in the environment have been declining since the 1970s (see the
8 trends discussion in Part I, Volume 2, Chapter 6), it is reasonable to expect that levels in food,
9 human intake, and, ultimately, human tissue have also declined over this period. The changes in
10 tissue levels are likely to lag the decline seen in environmental levels, and the changes in tissue
1 1 levels cannot be assumed to occur proportionally with declines in environmental levels.
1 2 CDC (2000) summarizes levels of CDDs, CDFs, and PCBs in human blood collected
1 3 between 1995 to 1997 from 316 U.S. residents (ages 20-70 years). The individuals sampled had
1 4 no known exposures to dioxin other than normal background. Although the samples in this data
1 5 set were not collected in a manner that can be considered statistically representative of the
16 national population and they lack wide geographic coverage, they are judged to provide a better
1 7 indication of current tissue levels in the United States than the earlier data.
1 8 PCBs 105, 118, and 156 are missing from the blood data for the comparison populations
1 9 reported by CDC (2000). These congeners account for 62% of the total PCB TEQ estimated in
20 the early 1990s. Assuming that the missing congeners from the CDC study data contribute in the
21 same proportion to the total PCB TEQ as in earlier data, they would increase the estimate of
22 current body burdens by another 3.3 pg TEQ/g lipid, for a total PCB TEQ of 5.3 pg/g lipid and a
23 total of 25.4 pg TEQDFP-WHO98/g lipid (i.e., the TEQDF-WHO98 concentration was 20.1 pg/g
24 lipid, and the TEQP-WHO98 concentration was estimated at 5.3 pg/g lipid). A summary of the
25 CDC (2000) data is shown in Table 4-5.
26 A portion of the CDC blood data were plotted as a function of age. This plot, shown in
27 Figure 4-3, indicates that blood levels generally increase with age, as does the variability in blood
28 levels.
29 The calculation of a current tissue level of 25.4 pg/g lipid TEQDFP-WHO98 is further
30 supported by the observation that this mean tissue level is consistent with the best estimate of
31 current adult intake, 66 pg TEQDFP-WHO98/d. Using this intake in a one-compartment, steady-
32 state pharmacokinetic model yields a tissue level estimate of about 11.3 pg TEQDFP/g lipid
33 (assumes TEQDFP has an effective half-life of 7.1 years, 80% of ingested dioxin is absorbed into
34 the body, and lipid weight is 25% of the adult assumed body weight of 70 kg, or 17.5 kg).
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1 Because intake rates appear to have declined in recent years, and steady-state is not likely to have
2 been achieved, it is reasonable to observe higher measured tissue levels, such as the 25.4 pg
3 TEQ/g lipid, than those predicted by the model.
4 Characterizing national background levels of dioxins in tissues is uncertain because the
5 current data cannot be considered statistically representative of the general population. It is also
6 complicated by the fact that tissue levels are a function of both age and birth year. Because
7 intake levels have varied over time, the accumulation of dioxins in a person who turned 50 years
8 old in 1990 is different than that in a person who turned 50 in 2000. As discussed in Part I,
9 Volume 2, Chapter 6, exposure to dioxin-like compounds peaked during the 1960s, with
10 declining exposures since then. Therefore, a person born in 1910 will see a rise in body levels
11 that peaks at 50 to 70 years old. At the other end of the spectrum, a person born in 1970 will
1 2 experience a higher body concentration very early in life, with declining levels in later years.
1 3 A pharmacokinetic (PK) modeling framework was developed to study trends in
14 population body burdens of CDDs/CDFs throughout the 20th century and into the 21st century
1 5 (Lorber, 2002). It was assumed that individuals within a population were exposed to doses rising
1 6 from 0.50 pg WHO98-TEQDF/kg-day during the 1940s to about 6.5 pg WHO98-TEQDF/kg-day by
1 7 the late 1960s, down to 1.0 pg WHO98-TEQDF/kg-day by 1980, and finally to 0.50 pg WHO98-
1 8 TEQDF/kg-day by 2000, remaining constant at that level into the 21st century. It was found that a
19 modeled population tissue level distribution will vary, depending on the year the modeled
20 population is sampled. The results of this analysis are presented in Figure 4-4, which shows
21 modeled population tissue level distributions for four years. An "age trend" is seen in the figure
22 for modeled populations sampled in 1985 and 1995, as was seen in the CDC monitoring study of
23 actual blood measurements of WHO98-TEQDFP (see Fig. 4-3). Figure 4-4 also suggests that this
24 age trend will disappear in the 21st century and that the CDD/CDF tissue level will drop below 10
25 ppt TEQDF-WHO98 lipid basis by 2030.
26 Monitoring studies which are currently underway should help determine whether the
27 decline in body burdens has been continuing into the 21st century, as suggested by modeling.
28 Results from the National Health and Nutrition Examination Survey of 1999-2000 (NHANES
29 1999-2000) were recently made available (CDC, 2003). NHANES 1999-2000 included data on
30 dioxin-like compounds in the blood of 1921 sampled individuals, aged 12 and higher, and
31 sampled from numerous locations around the country. These compounds included the 17 dioxin
32 and furan congeners, as well as PCB congeners 126, 77, 169, and 81.
33 The current estimate of background body burden is based on 6 different studies totaling
34 316 individuals around the country which measured concentrations of these compounds in
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1 populations characterized as "background" (CDC, 2000). Often these populations were selected
2 the "background" population for studies which targeted other potentially exposed populations.
3 The dates of these surveys, as noted above, were from about 1995 to 1997. In addition to being
4 more recent, the NHANES 1999-2000 sampled population was much larger, but perhaps most
5 importantly, NHANES was statistically designed to be representative of U.S. background after
6 several years of data collection while the merged population from the 6 studies was not.
7 However, the amount of blood serum available for individual measurements in NHANES
8 1999-2000 was too small to be able to detect and characterize current levels of dioxin like
9 compounds in the population. A large majority of the measurements were nondetects. For this
1 0 reason, an effort is underway to pool remaining, available individual samples from NHANES and
11 measure them for dioxin-like compounds, which would provide an updated measure of average
1 2 concentrations of these compounds in the blood of U.S. citizens (ages 12 and greater, circa 1999-
1 3 2000, and with all other delimiters relevant to the pooled samples, of course).
14
1 5 4.4.2. Intake Estimates
1 6 Adult daily intakes ofCDD/CDFs and dioxin-like PCBs are estimated to average 43 and
1 7 23pg TEQDFP-WHO98/day, respectively, for a total intake of66pg/day TEQDFP-WHO98. Daily
1 8 intake is estimated by combining exposure media concentrations (food, soil, and air) with contact
19 rates (ingestion, inhalation). Table 4-6 summarizes the media concentrations, contact rates, and
20 resulting intake estimates.
21 The intake estimate is supported by an extensive database on food consumption rates and
22 estimates of dioxin-like compounds in food (as discussed above). PK modeling provides further
23 support for the intake estimates. Applying a simple steady-state PK model to an adult average
24 blood level of 25 ppt TEQDEP-WHO98 (on a lipid basis) yields a daily intake of 146 pg TEQDFP-
25 WHO98/day (assumes TEQDFP has an effective half-life of 7.1 years, 80% of ingested dioxin is
26 absorbed into the body, and lipid weight is 25% of the adult assumed body weight of 70 kg, or
27 17.5 kg). This PK-modeled CDD/CDF/PCB intake estimate is about 2.2 times higher than the
28 direct intake estimate of 66 pg TEQDFP-WHO98/day. This difference is to be expected with this
29 application of a simple steady-state PK model to current average adipose tissue concentrations.
30 Current adult tissue levels reflect intakes from past exposure levels, which are thought to be
31 higher than current levels (Lorber, 2002; also in Part I, Volume 2, Chapter 6). Because the
32 direction and magnitude of the difference in intake estimates between the two approaches are
33 understood, the PK-derived value is judged supportive of the pathway-derived estimate. It
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1 should be recognized, however, that the pathway-derived value will underestimate exposure if it
2 has failed to capture all the significant exposure pathways.
3
4 4.4.3. Variability in Intake Levels
5 CDD/CDF and dioxin-like PCB intakes for the general population may extend to levels at
6 least three times higher than the mean. Variability in general population exposure is primarily
7 the result of the differences in dietary choices that individuals make. These are differences in
8 both quantity and types of food consumed. An increased background exposure can result from
9 either a diet that favors consumption of foods high in dioxin content or a diet that is
10 disproportionately high in overall consumption of animal fats.
11 The best data available to determine the variability of total fat consumption come from
1 2 several analyses of the Bogalusa Heart Study (Cresanta et al., 1988; Nicklas et al., 1993, 1995,
13 Nicklas, 1995; Frank et al., 1986). These data show that the 95th percentile of total fat
14 consumption is about twice the mean and the 99th percentile is approximately three times the
1 5 mean. For a diet that has a broad distribution of animal fats (as does the typical U. S. diet), this
1 6 same distribution can be assumed for dioxin intake.
1 7 Although body burden data cannot be assumed to be perfectly representative of current
18 intakes (because they reflect past exposures as well as current ones), they also provide some
1 9 support for this finding, based on the observation that the 95th percentile blood level in the CDC
20 (2000) study was almost twice the mean level.
21 Intakes ofCDDs/CDFs and dioxin-like PCBs are more than three times higher for a
22 young child than for an adult, on a body-weight basis. This figure is based on combining age-
23 specific food consumption rate and average food concentrations, as was done above for adult
24 intake estimates (see Table 4-7).
25 Only 4 of the 17 toxic CDD/CDF congeners and 1 of the 11 toxic PCBs account for most
2 6 of the toxicity in human tissue concentrations: 2,3,7,8-TCDD, 1,2,3,7,8-PCDD, 1,2,3,6,7,8-
27 HxCDD, and2,3,4,7,8-PCDFandPCB 126. This finding is derived directly from the data
28 described earlier on human tissue levels and is supported by intake estimations that indicate that
29 these congeners are also the primary contributors to dietary dose. These five compounds make
30 up about 80% of the total TEQDFP-WHO98 tissue level.
31
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1 4.5. POTENTIALLY HIGHLY EXPOSED POPULATIONS OR DEVELOPMENTAL
2 STAGES (Cross-reference: Part I, Volume 2, Chapter 5)
3 As discussed earlier, background exposures to dioxin-like compounds may extend to
4 levels at least three times higher than the mean. This upper range is assumed to result from the
5 normal variability of diet and human behaviors. Exposures from local elevated sources or
6 exposures resulting from unique diets would be in addition to this background variability. Such
7 elevated exposures may occur in small segments of the population, such as individuals living
8 near discrete local sources. Nursing infants represent a special case: for a limited portion of their
9 lives, these individuals may have elevated exposures on a body-weight basis when compared
10 with nonnursing infants and adults.
11 Dioxin contamination incidents involving the commercial food supply have occurred in
1 2 the United States and in other countries. For example, in the United States, contaminated ball
13 clay was used as an anticaking agent in soybean meal, which resulted in elevated dioxin levels in
14 some poultry and catfish. This incident, which occurred in 1998, involved a small faction of the
1 5 national poultry production, and the use of contaminated ball clay has since been eliminated.
1 6 Elevated dioxin levels have also been observed in a few beef and dairy animals, where the
1 7 contamination was associated with contact with pentachlorophenol-treated wood. Evidence of
1 8 this kind of elevated exposure was not detected in the national beef survey. Consequently, its
1 9 occurrence is likely to be low, but it has not been determined.
20 These incidents may have led to small increases in dioxin exposure to the general
21 population. However, it is unlikely that they have led to disproportionate exposures to
22 populations living near where they occurred because in the United States meat and dairy products
23 are highly distributed on a national scale. If contamination events were to occur in foods that are
24 predominantly distributed on a local or regional scale, then such events could lead to more highly
25 exposed local populations (see Part I, Volume 2, Chapter 5).
26 Elevated exposures associated with the workplace or with industrial accidents have also
27 been documented. U.S. workers in certain segments of the chemical industry had elevated levels
28 of TCDD exposure, with some tissue measurements in the thousands of part per trillion TCDD.
29 There is no clear evidence that elevated exposures are currently occurring among U.S. workers.
30 Documented examples of past exposures for other groups include certain Air Force personnel
31 exposed to Agent Orange during the Vietnam War and people exposed as a result of industrial
32 accidents in Europe and Asia.
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1 Consumption of breast milk by nursing infants leads to higher levels of exposure and
2 higher body burdens ofdioxins during early years of life as compared with those ofnonnursing
3 infants (Part I, Volume 2, Chapter 5).
4 Kreuzer et al. (1997) and Abraham et al. (1994, 1995, 1998, 2000) compared dioxin
5 levels in infants who were breast-fed with those who were formula-fed. All the studies showed
6 elevations in the concentrations of dioxins in the breast-fed infants. Collectively, these studies
7 included more than 100 infants, and they found that blood levels in infants aged 4-12 months
8 were generally higher than 20 pg TEQDF-WHO98/g lipid in nursing infants and lower than 5 pg
9 TEQDF-WHO98/g lipid in formula fed infants. Limited data suggest a similar difference for
10 dioxin-like PCBs. Abraham et al. (1995) reported that at 11 months a breast-fed infant had a
1 1 concentration of 31.4 pg TEQp-WHO98/g lipid, compared to 2.5 pg TEQp-WHO98/g lipid for the
1 2 formula-fed infant.
13 U.S. dioxin intakes from nursing were calculated using time-dependent values for breast
1 4 milk concentrations, consumption rates, and body weights. These calculations estimated an
1 5 intake immediately after birth of 242 pg TEQDFP-WHO98/kg/day. This level dropped to 18 pg
1 6 TEQDFP-WHO98/kg/day after 12 months of nursing. The average intake over 1-year of nursing
1 7 was calculated to be 87 pg TEQDFP-WHO98/kg/day. The cumulative intake for a 1 year nursing
18 scenario represented about 13% of the total lifetime cumulative intake (see Lorber and Phillips,
19 2002, and Part I, Volume 2, Chapter 5, for details on these calculations).
20 CDC (1997) reported that in 1995, 5 5% of all babies experienced some breast-feeding,
21 with about half of those breast-feeding beyond 5 months. The average duration of breast-feeding
22 was 28.7 weeks. In a policy statement, the American Academy of Pediatrics (1997) stated that
23 exclusive breast feeding provides ideal nutrition and is sufficient to support optimal growth and
24 development for 6 months after birth. It recommended that breast-feeding continue for at least
25 12 months and thereafter for as long as mutually desired.
26 To better evaluate the impact of nursing on infants, changes in body burden were
27 calculated using a one-compartment, first-order pharmacokinetic model (Lorber and Phillips,
28 2002). First, the model was validated using data from Abraham et al. (1998). Dioxin and furan
29 concentrations for six mother/infant pairs were provided, including two breast milk
30 measurements while the mother was feeding her infant and a blood measurement for the infant
31 at about 1 year. These mothers' milk concentrations were used as the independent source term
32 for the model, and the infant blood concentrations served as dependent model prediction. Other
33 required parameters included the infant's body weight and lipid fraction over time (assigned
34 average male and female infant values), absorption fraction (assigned a constant value of 0.80),
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1 and, most importantly, an assumption of a rapid dissipation rate of TEQs in the infant (half-life
2 < 1 year) during the early months of life. This dissipation rate was developed by Kreuzer et al.
3 (1997), and it contrasts the more typical 7-year half-life found in adults for TCDD.
4 The average observed infant concentration was 24 pg TEQDF-WHO98/g lipid, compared to
5 a predicted concentration of 26 pg TEQDF-WHO98/g lipid. The observed high and low
6 concentrations were 5 and 44 pg TEQDF-WHO98/g lipid, compared to predicted high and low
7 concentrations in these infants of 10 and 36 pg TEQDF-WHO98/g lipid. When the model was
8 rerun at a higher TEQ dissipation rate of 7 years, the average predicted concentration rose to 39
9 pg TEQDF-WHO98/g lipid. This demonstrated the appropriateness and importance of the
1 0 assignment of a rapid dissipation rate of TEQs in infants.
11 This framework was used to evaluate various nursing scenarios: formula only and 6
1 2 weeks, 6 months, 1 year, and 2 years nursing. These scenarios reasonably capture the range of
1 3 current nursing practices. This modeling effort required using the intake assumptions described
14 earlier—242 pg TEQDFP-WHO98/kg/day at birth and an average of 87 pg TEQDFP-WHO98/kg/day
1 5 over a year of breast-feeding—and other parameters noted above including the fraction of the
16 oral dose that is absorbed into the body, changes in body weight over time, and changes in body
1 7 fat fraction over time. For the infant, the half-life was less than 1 year, and during adulthood the
18 half-life increased as the fraction of body fat increased. The longer half-life during the later
19 years of life was based on a model presented in Michalek et al. (1996). The complete set of input
20 values is listed in Lorber and Phillips (2002) as well as in Part I, Volume 2, Chapter 5.
21 The modeling results in terms of changes in lipid concentrations and body burdens as a
22 function of age are shown in Figure 4-5. Some key observations include:
23
24 • For the 6-month, 1-year, and 2-year nursing scenarios, lipid concentrations peaked at
25 around 9 weeks at 44 ppt TEQDFP-WHO98. For the formula-fed infants they peaked at
26 less than 10 ppt after the first year.
27
28 • In all four scenarios, the lipid concentrations merged at about 10 years of age at a
29 concentration of about 13 ppt TEQDFP-WHO98. Lipid and body burdens declined
30 slightly from age 10 to about age 20 and then rose gradually through adulthood. This
31 rise was due to the increase in half-life with age. At age 70, the modeled lipid and
32 body burden concentrations were 13 ppt TEQDFP-WHO98 lipid and 5 ppt TEQDFP-
33 WHO98 whole body weight.
34
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1 • Breast-feeding leads to higher total lifetime exposures to TEQs as compared to
2 formula feeding. Using an AUC approach, 70-year cumulative lifetime exposures
3 were evaluated. The results suggest that breast-feeding added between 3% (for the 6-
4 week breast-feeding scenario) and 18% (for the 2-year scenario) more accumulated
5 exposure to TEQs as compared to formula-feeding.
6
7 The above analysis indicates that the average annual infant intake resulting from 1 year of
8 nursing, 87 pg TEQDFP-WHO98/kg/day, significantly exceeds the currently estimated adult intake
9 of 1 pg TEQDFP-WHO98/kg/day. The impact of nursing on infant body burdens, however, is much
10 less, that is, infant body burdens will not exceed adult body burdens by 87 times. Rather, the
11 modeling suggests that peak infant body burdens are only about two times the current adult body
1 2 burdens (44 vs. 25 pg TEQDFP-WHO98/g lipid). The reduced impact on body burden levels in
1 3 nursing infants (relative to the intake) is due to the rapidly expanding infant body weight and
14 lipid volume, and the faster elimination rate in infants. Body burden levels in nursing infants
1 5 should decline in the future if, as discussed earlier, general population exposures decline.
1 6 Consumption offish, meat, or dairy products containing elevated levels ofdioxins and
1 7 dioxin-like PCBs can lead to elevated exposures in comparison with the general population.
1 8 The above discussion identified the general population distribution as extending up to roughly
19 three times the mean. Most people will have exposures within this range even if they have
20 unusual diets in terms of meat and dairy products. This is because (1) most people eat food from
21 multiple sources, which tends to average out the contamination levels, and (2) meat and dairy
22 products have similar dioxin levels, so substitution of one type of meat for another should not
23 have a great impact on total exposure. Clearly, elevated exposures are possible in unusual
24 situations, such as when an individual consumes large quantities of meat or dairy products that
25 have significantly increased dioxin levels.
26 Elevated exposures resulting from fish consumption can occur in different situations.
27 Concentrations in freshwater fish are significantly greater than in meat and dairy products;
28 therefore, individuals who consume large quantities of freshwater fish at background
29 contamination levels may have intakes higher than the general population distribution. A simple
30 scenario was devised to evaluate this hypothesis. Through a review of the literature, EPA (U.S.
31 EPA, 1997) concluded that a range of consumption of 59 to 170 g/day describes subsistence fish
32 consumption behavior. These consumption rates were adopted to characterize the range of
33 exposures in this scenario. Further, it is assumed that freshwater fish is the primary source of
34 protein, that is, no meat or eggs are consumed. Assuming that all other exposure pathways stay
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1 the same and using background exposure media concentrations, adult daily intake in this
2 subsistence fisher scenario is calculated to range from 2.2 to 5.7 pg TEQDEP-WHO98/kg-day.
3 These intakes are about two to six times higher than the adult general population mean daily
4 intake of 0.93 pg TEQDFP-WHO98/kg-day. If subsistence fishers obtain their fish from areas
5 where the concentration of dioxin-like chemicals in the fish is elevated, their exposure could be
6 higher. Although this scenario appears reasonable, no clearly supportive data could be found to
7 confirm that such highly exposed subpopulations exist in the United States.
8 One study that measured dioxin-like compounds in the blood of sport fishers in the Great
9 Lakes area showed elevations over mean background but within the range of normal variability.
1 0 However, another study that measured 90 PCB congeners (seven of which were dioxin-like
1 1 PCBs, although PCB 126 was not measured) in the blood of sport fishers who consume high
1 2 amounts of fish caught from Lake Michigan (> 26 pounds of sport fish per year) did find
1 3 significant elevations of PCBs in their blood as compared to a control population (individuals
14 consuming < 6 pounds of sport fish per year). The average total concentration of PCBs in the
1 5 blood of the sport fishers was more than three times higher than that of the control population.
16 Similarly, elevated levels of coplanar PCBs have been measured in the blood of fishers on the
1 7 north shore of the Gulf of the St. Lawrence River who consume large amounts of seafood.
18 Elevated CDD/CDF levels in human blood have been measured in Baltic fishermen. For further
19 details on these studies see Part I, Volume 2, Chapter 5.
20 High exposures to dioxin-like compounds as a result of consuming meat and dairy
21 products would most likely occur in situations where individuals consume large quantities of
22 these foods and the level of these compounds is elevated. Most people eat meat and dairy
23 products from multiple sources, and even if large quantities are consumed they are not likely to
24 have unusually high exposures. Individuals who raise their own livestock for basic subsistence
25 have the potential for higher exposures if local levels of dioxin-like compounds are high. One
26 study in the United States showed elevated levels in chicken eggs near a contaminated soil site.
27 European studies at several sites have shown elevated CDD/CDF levels in milk and other animal
28 products near combustion sources, and some of these studies have also documented elevations in
29 the levels of dioxin-like compounds in blood from families who consume their own home
30 products.
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1
2
3
4
5
6
Table 4-1. Confidence rating scheme
Confidence
category
Confidence
rating
Activity level estimate
Emission factor estimate
Categories/media for which emissions can be reasonably quantified
A
B
C
High
Medium
Low
Derived from comprehensive
survey
Based on estimates of average
plant activity level and number of
plants or limited survey
Based on data judged possibly
nonrepresentative.
Derived from comprehensive survey
Derived from testing at a limited but
reasonable number of facilities
believed to be representative of
source category
Derived from testing at only a few,
possibly nonrepresentative facilities
or from similar source categories
Categories/media for which emissions cannot be reasonably quantified
D
E
Preliminary
estimate
Not quantified
Based on extremely limited data,
judged to be clearly
nonrepresentative.
No data.
Based on extremely limited data,
judged to be clearly
nonrepresentative.
(1 ) Argument based on theory but no
data
(2) Data indicating dioxin formation
but not in a form that allows
developing an emission factor
8
9
10
11
12
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1
2
3
Table 4-2. Inventory of environmental releases (grams/year) of
TEQDF-WHO98 in the United States
Emission source category
Confidence rating3
reference year 1995
A
B
C
D
Confidence rating3
reference year 1987
A
B
C
Releases (g TEQ/yr) to air
Waste incineration
Municipal waste incineration
Hazardous waste incineration
Boilers/industrial furnaces
Medical waste/pathological
incineration
Crematoria
Sewage sludge incineration
Tire combustion
Pulp and paper mill sludge
incinerators0
Power/energy generation
Vehicle fuel combustion
- leadedd
- unleaded
- diesel
Wood combustion
- residential
- industrial
Coal combustion
- utility boilers
- residential
- commercial/Industrial
Oil combustion
- industrial/utility
- residential
Other high temperature sources
Cement kilns (hazardous
waste burning)
Lightweight aggregate kilns
burning hazardous waste
Cement kilns (nonhazardous
waste burning)
Petroleum refining catalyst
regeneration
1250
5.8
14.8
27.6
60.1
0.39
488
9.1b
0.11
2
5.6
33.5
62. 8b
10.7
156.1
3.3b
17.8
2.21
30
40
6
8877
5
6.1
26.4
50.8
0.78
2590
5.5b
0.11
37.5
3.6
27.8
89. 6b
17.8
117.8
2.4b
13.7
2.24
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Table 4-2. Inventory of environmental releases (grams/year) of
TEQDF-WHO98 in the United States (continued)
Emission source category
Confidence rating3
reference year 1995
A
B
C
D
Confidence rating3
reference year 1987
A
B
C
Releases (g TEQ/yr) to air (continued)
Other high temperature sources
(continued)
Cigarette combustion
Carbon reactivation furnaces
Kraft recovery boilers
Combustion of landfill gas
Biogas combustion
Minimally controlled or
uncontrolled combustion6
Backyard barrel burningf
Landfill fires
Accidental fires (structural)
Accidental fires (vehicles)
Forest and brush fires
Metallurgical processes
Ferrous metal
smelting/refining
- sintering plants
- electric arc furnaces
- foundries
Nonferrous metal
smelting/refining
- primary copper
- secondary aluminum
- secondary copper
- secondary lead
- primary magnesium
Coke production
Drum and barrel reclamation
Chemical
manufacturing/processing
sources
Ethylene dichloride/vinyl
chloride
TOTAL RELEASES TO AIRg
2.3
28
<0.5b
1.72
11.2b
0.8
0.08b
628
29.1
271
0.08
7
< 1
1000
<20
30
200
40
20
15
7
3125
2
<0.5b
1.29
1
0.06b
604
32.7
16.3
983
0.08
13515
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Table 4-2. Inventory of environmental releases (grams/year) of
TEQDF-WHO98 in the United States (continued)
Emission source category
Confidence rating3
reference year 1995
A
B
C
D
Confidence rating3
reference year 1987
A
B
C
Releases (g TEQ/yr) to water
Chemical manufacturing/
processing sources
Bleached chemical wood pulp
and paper mills
POTW (municipal)
wastewater
Ethylene dichloride/vinyl
chloride
Reservoir sources
Urban runoff to surface water
Rural soil erosion to surface
water
TOTAL RELEASES TO
WATER8
19.5
0.43b
10
190
2700
19.93
356
356
Releases (g TEQ/yr) to land
Chemical manufacturing/
processing sources
Bleached chemical wood pulp
and paper mill sludge
Ethylene dichloride/vinyl
chloride
Municipal wastewater
treatment sludge
Commercially marketed
sewage sludge
2,4-D ichloro phenoxy
acetic acid
TOTAL RELEASES TO
LAND8
OVERALL RELEASES (g/yr)
TO THE OPEN AND
CIRCULATING
ENVIRONMENT
1.4
76.6
2.6
28.9
0.73b
110.23
3255
(SUM OF COLUMNS A, B, C )
14.1
76.6
2.6
33.4
126.7
13,998
(SUM OF COLUMNS
A, B, C )
a The most reliable estimates of environmental releases are those sources within Categories A, B, and C, which are
defined as:
A = Characterization of the Source Category judged to be Adequate for Quantitative Estimation
with High Confidence in the Emission Factor and High Confidence in Activity Level.
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Table 4-2. Inventory of environmental releases (grams/year) of
TEQDF-WHO98 in the United States (continued)
1 B Characterization of the Source Category judged to be Adequate for Quantitative Estimation
2 with Medium Confidence in the Emission Factor and at least Medium Confidence in Activity
3 Level.
4 C = Characterization of the Source Category judged to be Adequate for Quantitative Estimation
5 with Low Confidence in either the Emission Factor and/or the Activity Level.
6 D Preliminary Indication of the Potential Magnitude of I-TEQDF Emissions from "Unquantified"
7 (i.e., Category D) Sources in Reference Year 1995. Based on extremely limited data, judged to
8 be clearly nonrepresentative.
9
1 0 b Congener-specific emissions data were not available; the I-TEQ estimate was used as a surrogate for the TEQDF-WHO98
1 1 emissions estimate.
1 2 c Included within estimate for Wood Combustion - industrial.
13 d Leaded fuel production and the manufacture of motor vehicle engines requiring leaded fuel for highway use have been
1 4 prohibited in the United States. (See Section 4.1 for details.)
1 5 e This refers to conventional pollutant control, not dioxin emissions control. Very few of the sources listed in this inventory
1 6 control specifically for CDD/CDF emissions.
1 7 f This term refers to the burning of residential waste in barrels.
18 g TOTAL reflects only the total of the estimates made in this report.
19
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1
2
3
4
Table 4-3. Sources that are currently unquantifiable (Category E)a
Category
Combustion sources
Metal smelting and refining
Chemical manufacturing
Biological and photochemical processes
Reservoir sources
Unquantified sources
Uncontrolled combustion of PCBs
Agricultural burning
Primary aluminum
Primary nickel
Mono- to tetrachlorophenols
Pentachlorophenol
Chlorobenzenes
Chlorobiphenyls (leaks/spills)
Dioxazine dyes and pigments
2,4-Dichlorophenoxy acetic acid
Tall oil-based liquid soaps
Composting
Air
Sediments
Water
Biota
PCP -treated wood
7
8
9
10
11
' There exist no or insufficient data characterizing environmental releases from these sources. Therefore, it
is currently not possible to arrive at an estimate of annual environmental releases.
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1
2
3
Table 4-4. Summary of North American CDD/CDF and PCB TEQ-WHO98
levels in environmental media and food8
Media
Urban soil, ppt
Rural soil, ppt
Sediment, ppt
Urban air, pg/m3
Rural air, pg/m3
Freshwater fish and
shellfish, ppf
Marine fish and shellfish,
ppf
Water, ppq
Milk, ppt
(Note: each composite for
CDD/F/PCB comprised of
40+ U.S. regional
samples)
Dairy, ppf
Eggs, ppt
(Note: each composite for
CDD/F data comprised of
24 eggs)
CDD/CDFsb
n=270
9.3 ± 10.2
Range = 2-21
n = 354
2.7
Range = 0.1 1-5. 7
n=ll
5. 3 ±5. 8
Range = <1-20
n=106
0.12 ±0.094
Range = 0.03-0.2
n=60
0.013
Range = 0.004-0.02
n=222
1.0 (NAd)
n=158
0.26 (NAd)
n=236
0.00056 ± 0.00079 (NAd)
n=8 composites
0.0186
n = 8 composites
0.12e
n=15 composites
0.0816
PCBsb
n = 99
2.3
n = 62
0.59
n=ll
0.53 ±0.69
n=53
0.0009
n=53
0.00071
n = 1 composite of 10
samples plus 6 composites
1.2e(NAd)
n = 1 composite of 13
samples plus 5 composites
0.25 (NAd)
NAd
n = 8 composites
0.00886
n = 8 composites
0.0586
n = 18 plus 6 composites
0.10e(NAd)
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Table 4-4. Summary of North American CDD/CDF and PCB TEQ-WHO98
levels in environmental media and food (continued)
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
Media
Beef ppt
Pork, ppt
Poultry, ppt
Vegetable fats, ppt
CDD/CDFsb
n=63
0.18±0.11
Range = 0.1 1-0.95
n=78
0.28 ±0.28
Range = 0.1 5-1. 8
n=78
0.068 ±0.070
Range = 0.03-0.43
n=30
0.056 ± 0.24s (NAd)
PCBsb
n = 63
0.084
n = 78
0.012
n = 78
0.026
n = 5 composites
0.0376
a Whole-weight basis; concentrations provided in parenthesis for food products are calculated at ND = 0.
b Values are the arithmetic mean TEQs and standard deviations. Nondetects were set to one-half the limit
of detection, except for soil and CDD/CDFs in vegetable fats for which nondetects were set to zero.
0 The TEQdf fish concentrations reported here are species-specific ingestion rate weighted averages.
dNA = not available; congener-specific PCB data and data to calculate TEQ concentrations at ND = 0 are limited.
e Standard deviations could not be calculated due to limitations associated with the data (i.e., composite analyses).
fTEQ calculated by setting nondetects to zero.
BDairy concentration calculated from milk lipid concentrations and then assuming a fat fraction for dairy.
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1
2
3
4
5
6
7
8
9
10
11
12
13
Table 4-5. Background serum levels in the United States 1995-1997
Value
Median
Mean
95th Percentile
TEQDFP-WH098 (pg/g lipid)
18.7
22. r
38.8
2,3,7,8-TCDD (pg/g lipid)
1.9
2.1
4.2
a After adjusting to account for missing PCBs, the mean is 25.4 pg/g lipid.
Source: CDC, 2000
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1
2
3
4
Table 4-6. Adult contact rates and background intakes of dioxin-like
compounds
Exposure
route
Soil
ingestion
Soil dermal
Freshwater
fish and
shellfish8
Marine fish
and
shellfish8
Inhalation
Milk
Dairy
Eggs
Beef
Pork
Poultry
Other meats
Vegetable
fat
Water
Contact rate
50 mg/d
12g/d
5.9 g/d
9.6 g/d
13.3mVd
175 g/d
55 g/d
0.24 g/kg-d
0.67 g/kg-d
0.22 g/kg-d
0.5 g/kg-d
0.35 g/kg-d
17 g/d
1.4L/d
Dioxins and furans
Concentration
TEQDF-WHO,8
9.3 pg/g
9.3 pg/g
1.0 pg/g
0.26 pg/g
0.12pg/m3
0.018 pg/g
0.12 pg/g
0.081 pg/g
0.1 8 pg/g
0.28 pg/g
0.068 pg/g
O.lSppt
0.056 pg/g
0.0005 pg/L
Total
Intake
(pg TEQDF-
WH098/kg-d)
0.0066
0.0016
0.084
0.036
0.023
0.045
0.094
0.019
0.13
0.062
0.034
0.062
0.014
0.000011
0.61
(43 pg/d)
Dioxin-like PCBS
Concentration
TEQP-WHO98
2.3 ppt
2.3 ppt
1.2 pg/g
0.25 pg/g
NA
0.0088 pg/g
0.058 pg/g
0.10 pg/g
0.084 pg/g
0.012 pg/g
0.026 pg/g
0.041 pg/g
0.037 pg/g
NA
Intake
(pg TEQp-
WH098/kg-d)
0.0016
0.00039
0.1
0.034
NA
0.022
0.046
0.024
0.06
0.0026
0.013
0.014
0.009
NA
0.33
(23 pg/d)
Total
Intake
(pg TEQDFP-
WH098 /kg-d)
0.0082
0.002
0.18
0.07
0.023
0.067
0.14
0.043
0.19
0.065
0.047
0.076
0.023
0.000011
0.94
(66 pg/d)
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
a The TEQ^fish concentrations reported here are species-specific ingestion rate weighted averages.
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1
2
3
4
5
6
7
8
9
10
11
Table 4-7. Variability in average daily toxic equivalent (TEQ) intake as
a function of age
Age range
1-5 years
6-11 years
12-1 9 years
Adult
Intake, mass basis
pgTEQnFP-WH(Vd
50
54
61
66
Intake, body weight basis
pg TEQDFP-WH098/kg-d
3.3
1.8
1.1
0.9
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Emission Source
(tested/total units)
Best Estimate of
I-TEQ Emission Factor
(ng/kg or ng/L)
Total Annual "Activity"
(thousand metric tons/yr or
million L/yr)
Annual I-TEQ Emission
(g l-TEQ/yr)
Municipal Solid Waste Incineration (39/13O)
Medical Waste Incineration (2O/2,4OO)
Secondary Copper Smelting (2/3)
Cement Kilns Burning Haz Waste (1O/34)
Residential Wood Burning (7/25,OOO,OOO)
Utility / Industrial Coal Combustion (11/7)
On-Road Diesel Fuel Combustion (NA)
Secondary Aluminum Smelting (6/76)
Industrial Wood Burning (B/7)
Iron Ore Sinter Plants (2/11)
Cement Kilns Not Burning Haz Waste (15/17B)
Sewage Sludge Incineration (13/257)
Manufacture of EDC/VC (?/?)
Utility / Industrial ON Combustion (>2/7)
Crematoria (16/1.555)
Hazardous Waste Incineration (17/182)
On-Road Unleaded Gas Fuel Combustion (7/7)
On-Road Leaded Gas Fuel Combustion (7/7)
The figures include sources with annual I-TEQ emission estimates greater than 5 g l-TEQ/yr in
one or both of Reference Year 1995 and Reference Year 1987. Derivations of emission factors
and annual "activity1 estimates (e.g., kg of waste incinerated) are presented in the following
chapters of this report. The difference in bar shading indicates the degree of confidence in
the estimate. The set of numbers following the source categories indicates the number of
facilities/sites for which emission test data are available versus the number of facilities/sites
in the category. A question mark (?) indicates that the precise number of facilities/sites could
not be estimated.
Legend
Low Confidence
Medium Confidence
High Confidence
1
2
3
Figure 4-1. Estimated CDD/CDF I-TEQ emissions to air from combustion sources
in the United States, 1995.
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Municipal Solid Waste Incineration
Backyard Barrel Burning
Medical Waste Incineration
Secondary Copper Smelting
Cement Kilns Burning Haz Waste
Residential Wood Burning
Utility/Industrial Coal Combustion
On-Road Diesel Fuel Combustion
Secondary Aluminum Smelting
Industrial Wood Burning
Iron Ore Sinter Plants
Cement Kilns Not Burning Haz Waste
Sewage Sludge Incineration
Manufacture of EDC/VC
Utility/Industrial Oil Combustion
Crematoria
Hazardous Waste Incineration
On-Road Unleaded Gas Fuel Combustion
On-Road Leaded Gas Fuel Combustion
10
100
1000
10000
1995 D 1987
2
3
Figure 4-2. Comparison of estimates of annual I-TEQ emissions to air (grams I-
TEQ/yr) for reference years 1987 and 1995.
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1
2
Figure 4-3. Blood levels (I-TEQ for CDD/CDF + WHO94) versus age of a subset of participants in the CDC (2000).
Source: ATSDR, 1999b
-------
90 T
80-
1-60-
^40H
a
iiJ 30 H
20 i
10
Sample Avg
Year TEQ
77
_.1985 49
.--1995
..2030
-)-
-t-
-h
-t-
-)-
-t-
-h
20 25 30 35 40 45 50 55 60 65 70
Age within a sampled population, yrs
28
1
2
3
4
5
Figure 4-4. Predicted distributions and average TEQDF - WHO98 concentrations
within an adult population for four years: 1965,1985,1995, and 2030. (CDD/CDFs
only, not PCBs).
Source: Adapted from Lorber, 2002
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1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
(A)
(B)
o
LLJ
Q.
Q.
O
-------
1 5. DOSE-RESPONSE CHARACTERIZATION
2
3 Previous sections of this integrated summary focused on characterizing the hazards of and
4 exposure to dioxin-like compounds. In order to bring these issues together and provide an
5 adequate characterization of risk, the relationships of exposure to dose and, ultimately, to
6 response must be evaluated. Key questions to be asked include: (1) What can be said about the
7 shape of the dose-response function in the observable range and what does this imply about
8 dose-response in the range of environmental exposures? and (2) What is a reasonable limit
9 (critical dose or point of departure [POD]) at the lower end of the observable range and what risk
10 is associated with this exposure? In addition, one can address the issue of extrapolation beyond
11 the range of the data in light of the answers to the above questions. Although extrapolation of
1 2 risks beyond the range of observation in animals and/or humans is an inherently uncertain
13 enterprise, it is recognized as an essential component of the risk assessment process (NAS/NRC,
14 1983). The level of uncertainty is dependent on the nature (amount and scope) of the available
1 5 data and on the validity of the models that have been used to characterize dose-response. These
1 6 form the bases for scientific inference regarding individual or population risk beyond the range of
1 7 current observation (NAS/NRC, 1983, 1994).
18 Dose-response analysis can be implemented in a variety of ways in risk assessment,
19 depending on the extent and quality of the available data. At the basic level, dose-response
20 information comes from a comparison of doses or levels at which there are no observed adverse
21 effects with those at which the lowest adverse effect is observed. Such an analysis can be
22 enhanced through the application of mathematical models to interpolate between empirically
23 measured data points (plus incorporating their statistical variability), with the option for
24 extrapolation below these data points subject to model shape assumptions when going beyond
25 the range of known data. One such form of modeling is the benchmark dose (BMD) analysis,
26 where a mathematical model is used to calculate the dose necessary to elicit a predetermined
27 response rate (e.g., an effective dose [ED] for a 1% response: ED01). Ultimately, the
28 development and use of physiologically-based pharmaco kinetic PBPK models and biologically-
29 based dose response models goes beyond the mathematical replication of data points by linking
30 the model to relevant and measurable biological parameters in the species of interest, and
31 potentially between species (Kim et al., 2002).
32 These dose-response concepts are developed in Part II, Chapter 8, where the body of
33 literature concerning dose-response relationships for TCDD is presented. Among other things,
34 this chapter addresses the important concept of selecting an appropriate metric for cross-species
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1 scaling of dose and presents the results of empirical modeling for many of the available data sets
2 on TCDD exposures in humans and in animals. Although not all human observations or animal
3 experiments on TCDD are amenable to this level of dose-response modeling, more than 200 data
4 sets were evaluated for shape, leading to an effective dose value expressed as a percent response
5 being presented for each endpoint being evaluated.
6 The analysis of dose-response relationships for TCDD, considered within the context of
7 toxic equivalency, mechanism of action, and background human exposures, helps elucidate the
8 common ground and the boundaries of the science and science policy components inherent in
9 this risk characterization for the broader family of dioxin-like compounds. For instance, the
10 dose-response relationships provide a basis to infer a POD for extrapolation for cancer and
11 noncancer risk for a complex mixture of dioxin-like congeners given the assumption of toxic
1 2 equivalency as discussed in Part II, Chapter 9, Section 9.6. Similarly, these relationships provide
1 3 insight into the shape of the dose-response at the POD, which can help inform choices for
14 extrapolation models for both TCDD and total TEQ. Dose-response modeling also provides a
1 5 perspective on the relationship between the level at which effects are seen in experimental
1 6 systems or epidemiologic studies and background exposures and body burdens for dioxin and
1 7 related compounds.
18 In evaluating the dose-response relationships for TCDD as a basis for assessing this
1 9 family of compounds, both empirical dose-response modeling approaches and mode of action
20 based approaches have been developed and applied (see Part n, Chapter 8, Section 8.3 and 8.4;
21 Portier et al., 1996; Kim et al., 2003). Empirical models have advantages and disadvantages
22 relative to more ambitious mechanism-based models. Empirical models provide a simple
23 mathematical model that adequately describes the pattern of response for a particular data set;
24 they can also provide the means for hypothesis testing and interpolation between data points. In
25 addition, they can provide qualitative insights into underlying mechanisms. However, the major
26 disadvantage of empirical models is their inability to quantitatively link data sets in a
27 mechanistically meaningful manner. On the other hand, mechanism-based modeling can be a
28 powerful tool for understanding and combining information on complex biological systems. Use
29 of a truly mechanism-based approach can, in theory, enable more reliable and scientifically sound
30 extrapolations to lower doses and between species. However, any scientific uncertainty about the
31 mechanisms that the models describe is inevitably reflected in uncertainty about the predictions
32 of the models.
33 PBPK models have been validated in the observable response range for numerous
34 compounds in both animals and humans. The development of PBPK models for disposition of
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1 TCDD in animals has proceeded through multiple levels of refinement, with newer models
2 showing increasing levels of complexity by incorporating data for disposition of TCDD and its
3 molecular actions with the AhR and other proteins, as well as numerous physiological parameters
4 (Part II, Chapter 1). These models have provided insights into key determinants of TCDD
5 disposition in treated animals. Development of such models continues and the current generation
6 of dioxin PBPK models are being submitted for publication (DeVito et al., personal
7 communication). Pharmacokinetic models have been extended to generate predictions for early
8 biochemical consequences of tissue dosimetry of TCDD, such as induction of CYP1A1, and are
9 being developed to address the impacts of enzyme induction (e.g., CYP1A2) on TCDD storage
10 and half-life. It is anticipated that these enhanced PBPK models will improve the understanding
11 of early phase human distributional and half-life kinetic data. However, extension of these
1 2 models to more complex responses is more uncertain at this time, particularly regarding selection
1 3 of the appropriate tissue metric to link to the effect(s) under consideration. Differences in
14 interpretation of the mechanism of action embodied in these pharmacodynamic models lead to
1 5 varying estimates of dose-dependent behavior for similar responses. The shape of the
16 dose-response curves governing extrapolation to low doses are determined by these hypotheses
1 7 and assumptions.
1 8 At this time, the knowledge of the mechanism of action of dioxin, receptor theory, and
19 the available dose-response data do not firmly establish a scientific basis for replacing a linear
20 procedure for estimating cancer potency. Consideration of this same information indicates that
21 the use of different procedures to estimate the risk of exposure for cancer and noncancer
2 2 endpoints may not be appropriate. Both the cancer and noncancer effects of dioxin appear to
23 result from qualitatively similar modes of action. Initial steps in the process of toxicity are the
24 same, and many early events appear to be shared. Thus, the inherent potential for low dose
25 significance of either type of effect (cancer or noncancer) should be considered equal and
26 evaluated accordingly. In the observable range around 1% excess response, the quantitative
27 differences are relatively small. Below this response, the different mechanisms can diverge
28 rapidly. The use of predicted biochemical responses as dose metrics for toxic responses is
29 considered a potentially useful application of these models. However, greater understanding of
30 the linkages between these biochemical effects and toxic responses is needed to reduce the
31 potentially large uncertainty associated with these predictions.
32
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1 5.1. DOSE METRIC(S)
2 One of the most difficult issues in risk assessment is determining the dose metric to use
3 for animal-to-human extrapolations. An appropriate animal-to-human extrapolation of tissue
4 dose is required to provide significant insight into differences in sensitivity among species. As
5 noted in Section 1.3, the most appropriate dose metric should reflect both the magnitude and
6 frequency of exposure, and it should be clearly related to the toxic endpoint of concern by a
7 well-defined mechanism. However, this is often difficult, because human exposures with
8 observable responses may be very different from highly controlled exposures in animal
9 experiments. In addition, comparable exposures may be followed by very different
10 pharmacokinetics (absorption, distribution, metabolism and/or elimination) in animals and
11 humans. Finally, the sequelae of exposure in the form of a variety of responses related to age,
1 2 organ, and species sensitivity complicate the choice of a common dose metric. Despite these
13 complexities, relatively simple default approaches, including body surface or body weight scaling
14 of daily exposures, have often been recommended (U.S. EPA, 1992a, 1996; ATSDR, 1999).
1 5 As discussed in Section 1.3, dose can be expressed in a number of ways. For TCDD and
16 other dioxin-like compounds, attention has focused on the consideration of dose expressed as
1 7 daily intake (ng/kg/day), body burden (ng/kg), or AUC (DeVito et al., 1995; Aylward et al.,
1 8 1996). The concept of physiological time (lifetime of an animal) complicates the extrapolation,
19 as the appropriate scaling factor is uncertain for toxic endpoints. Because body burden
20 incorporates differences between species in TCDD half-life (these differences are large between
21 rodent species and humans [see Part n, Chapter 8, Table 8.2]), this dose metric appears to be the
22 most practical for many effects of this class of compounds (DeVito et al., 1995).
23 Average lifetime body burden is best suited for steady-state conditions, with difficulties
24 arising when this dose metric is applied to the evaluation of acute exposures, such as those
25 occurring in the 1976 accidental exposure in Seveso, Italy (Bertazzi and di Domenico, 1994). In
26 cases such as this one, increased body burden associated with the acute exposure event is
27 expected to decline (half-life for TCDD is approximately 7 years) until it begins to approach a
28 steady-state level associated with the much smaller daily background intake. In general, daily
29 excursions in human exposure are relatively small and have minor impact on average body
30 burden. Instead, PBPK models suggest that human body burdens increase over time and begin to
31 approach steady-state after approximately 25 years with typical background doses. Occupational
32 exposures represent the middle ground where daily excursions during the working years can
33 significantly exceed daily background intakes for a number of years, resulting in elevated body
34 burdens.
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1 The relationship between occupational exposures and body burden and between body
2 burden and AUC are demonstrated in Figure 5-1. This figure graphs two hypothetical body
3 burden scenarios during the 70-year lifespan of an individual. The first is a continuation to 70
4 years of age of the background body burden scenario discussed—with caveats and
5 assumptions— in Part I, Volume 3, Chapter 5. In this scenario, an infant is breast-fed for 6
6 months by a mother who has a background dioxin body burden level and is subsequently exposed
7 to the average current level of dioxin in the food supply (1 pg/kg/day). This background scenario
8 leads to a 70 year lifetime area under the curve (AUC) of 184 ng/kg* Y, equivalent to a lifetime
9 average body burden (LABB) of 2.6 ng/kg (~184/70 years).
10 In the second scenario, the same individual incurs an additional occupational exposure
11 between 20 and 30 years of age of 100 pg/kg/day—100 times background—which then ceases.
1 2 The buildup of dioxin body burden is evident in the peak level and shark fin appearance. AUC in
1 3 this occupational scenario is 3911 ng/kg*Y, and LABB is 55.9 ng/kg. Note that in the
14 occupational scenario the AUC and LABB are only 21 times background.
1 5 Table 5-1 and Figure 5-2 summarize literature on average levels of dioxin TEQs in the
1 6 background human population and peak levels in commonly cited epidemiological cohorts.
1 7 Table 5-1 collates data on tissue lipid levels (ppt lipid adjusted) in populations, principally from
18 serum, and tabulates either current levels for the background population or back-calculated peak
19 levels for the exposed cohorts. Figure 5-2 graphs the estimated range and central tendency of the
20 total TEQDEP body burden (ng/kg whole body), combining the range of measured 2,3,7,8-TCDD
21 values with the estimate of the background non-2,3,7,8-TCDD TEQ level from the U.S.
22 population in the late 1980s/early 1990s. TEQ levels are calculated for PCDD, PCDF, and
23 PCBs, based on TEQDFP-WHO98 values, and assume a constant 25% body fat ratio when
24 converting from serum lipid ppt to ng/kg body burden. Total TEQ values for the Hamburg
25 cohort women were calculated by the authors, but did not include a dioxin-like PCB contribution.
26 Seveso values reported by Needham et al. (1999) are based on stored serum samples from
27 subjects undergoing medical examinations contemporaneous with the exposure and were not
28 back-calculated. Additional information consistent with Figure 5-2 has recently been published
29 (Eskenazi et al., 2004) that demonstrate similar Seveso Zones A and B initial levels, with an
30 important further measurement of background 2,3,7,8-TCDD (20.2 ppt serum lipid) and other
31 congener TEQ contributions (80.2 ppt) in the unexposed background population (non-ABR
32 women) in this time period.
33 As discussed earlier, using background total body burden (TEQDFP-WHO98) as a point of
34 comparison, these often-termed "highly exposed" populations have peak body burdens that are
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1 relatively close to general population backgrounds at the time. When compared with background
2 body burdens of the late 1980s, many of the median values and some of the mean values fall
3 within a range of one order of magnitude (factor of 10) and all fall within a range of two orders
4 of magnitude (factor of 100). General population backgrounds at the time are likely to have been
5 higher than present background body burdens.
6 One uncertainty in comparing peak body burdens is the use of a first-order elimination
7 rate with an overall half-life of 7.1 years. Recent evidence suggests that the elimination of
8 TCDD may be dependent on the level of exposure, in addition to an early distributional or
9 sequestration phase. Populations with high exposures may have half-lives significantly less than
10 7.1 years. Relatively rapid early elimination was noted in two highly exposed Austrian women
11 (initial half-lives of-1.5 and 2.9 years; Geusau et al., 2002). Supportive data are also available
1 2 through an analysis of the Seveso populations (Michalek et al., 2002). In this analysis, a period
1 3 of fast elimination within the first 0.27 years after the exposure in Seveso was observed, followed
14 by a period of slower elimination between 3 and 16.35 years from exposure. The mean TCDD
1 5 half-life in the first 0.27 years after exposure in the Seveso cohort was 0.34 years in males (n=6)
1 6 and 0.43 years in females (n=10). From 3 years onward in the Seveso cohort, the half-life in
1 7 males was 6.9 years (n=9) and 9.6 years in females (n=13). For Ranch Handers, the half-life was
1 8 7.5 years (n=97) between 9 and 33 years after exposure. This analysis indicates that dioxin body
1 9 burdens and elimination kinetics may be more complex at higher doses than represented by a
20 single first-order half-life, including issues of tissue distribution and dose-dependent elimination.
21 This is consistent with the limited data available in rodents that also indicates a dose-dependent
22 elimination.
23 There are a number of physiologically-based pharmacokinetic models of TCDD in both
24 experimental animals and humans. Several of the rodent models assume that the elimination rate
25 of TCDD is a constant (Wang et al., 1997; 2000; Emond et al., 2004). One model by Anderson
26 et al. (1993) has a dose dependent doubling of the elimination rate which is dependent upon Ah
27 receptor occupancy. Kohn et al. (1993; 1996) has the elimination rate increasing in proportion to
28 body weight and includes an increased elimination of TCDD from the liver at high doses due to
29 hepatocyte cell death. The Carrier et al. (1995a, b) model describes a dose-dependent
30 elimination of TCDD and other dioxins due to a dose-dependent hepatic sequestration of these
31 chemicals. While these models use different approaches, they all provide reasonable fits to the
32 available experimental data.
33 Attempts to develop pharmacokinetic models for TCDD in humans have also resulted in
34 a variety of mathematical descriptions of the elimination rate. Maruyama et al. (2002, 2003)
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1 have assumed that the elimination rate is constant. Van der Molen et al. (1998; 2000) multiply a
2 constant elimination rate by the ratio of liver fat/body fat. This results in an overall change in the
3 elimination of TCDD based on body composition and body weight. Gentry et al. (2003) and
4 Clewell et al. (2004) describe the elimination of TCDD in proportion to hepatic CYP1A2
5 expression. Aylward et al. (2004) modified the Carrier et al. (1995a, b) model to include an
6 elimination of dioxins directly into the large intestine based on lipid partitioning. This model
7 provided reasonable fits to data from Seveso patients as well as three Austrian patients. Finally,
8 Michalek et al. (2002) used a classical pharmacokinetic approach to describe the Seveso data.
9 This work suggests that there is an early distribution phase that results in a rapid loss of TCDD
10 from the blood (half-life of 0.37 years) followed by a prolonged terminal elimination phase (half-
11 life approximately 6.9 years).
1 2 Hence, there are a number of pharmacokinetic models available that describe the
1 3 absorption, distribution and elimination of TCDD in animals and humans. While these models
14 provide reasonable fits to the available data, they employ a wide range of descriptions of the
1 5 elimination of TCDD. Some assume first order elimination, while others assume dose-dependent
1 6 pharmacokinetics. Others suggest that body composition significantly influences the elimination
1 7 of dioxins. Presently, it is difficult to determine which of these model structures provides the
1 8 most accurate description of the pharmacokinetics of TCDD and other dioxins.
19 Advances in understanding the dose-dependency of the pharmacokinetics of TCDD and
20 related chemicals will improve our ability to describe the relationship between exposure, dose
21 and response. The development of more accurate models may affect both exposure group
22 assignment in epidemiology studies and the calculation of dose-response curves, although the
23 magnitude and direction of these postulated impacts remains to be quantified. Estimates of back-
24 calculated doses are important because the ability to detect effects in epidemiologic studies is
25 dependent on a sufficient difference between control and exposed populations. Using published
26 first-order back-calculation procedures, the relatively small difference (< 10-100-fold) in body
27 burden between exposed and controls in the dioxin epidemiology studies makes exposure
28 characterization in the studies a particularly serious issue. This point also strengthens the
29 importance of measured blood or tissue levels in the epidemiologic analyses, despite the
30 uncertainties associated with calculations extending the distribution of measured values to the
31 entire cohort and assumptions involved in back-calculations.
32 As a bounding exercise on the impact of half-lives on back-extrapolated exposure
33 estimates, EPA has compared the impacts of varying half-life values on back-calculated peak and
34 AUC results. This scenario is constructed by calculating the peak body burden 20 years prior to a
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1 terminal level for various half-lives versus a 7.1 year fixed half-life, assuming first order kinetics
2 (Ct = C0e"rt). A constant dosing regimen is then constructed to simulate an occupational exposure
3 that would achieve these same peak body burdens following 10 years exposure, maintaining the
4 same half-life as in the 20 year follow-up. For each half-life value, a different dose level is
5 necessary and was mathematically derived to reach the required peak level after ten years
6 occupational exposure.
7 In this occupational scenario, peak and AUC ratios (AUCvanable half_llfe/AUC7 lyears) varied in
8 a non-linear manner depending on the input half-life. Half-life values of 4 years and longer had
9 low, single digit numerical impacts on the peak and AUC ratios compared to the 7.1 year half-life
10 results (e.g., at a 4 year half-life, the ratio for the peak value = 4.6, the AUC ratio = 3.8; at a 5
11 year half-life, the ratio for peak = 2.3, AUC = 2). At half-lives below 4 years, peak and AUC
1 2 ratios rose dramatically to approximately 1 and 2 orders of magnitude for 3 and 2 year half-lives,
13 respectively. The terminal body burden did not influence the ratio because the mathematical
14 function remained constant. More complex PBPK models, where half-life varies with body
1 5 burden, are under development and will be more influenced by the terminal body burden for each
1 6 individual. This bounding exercise suggests that impacts on back-calculated peak and AUC
1 7 values may become significant if the models predict prolonged periods with half-lives of less
1 8 than 4 years.
19 5.1.1. Calculations of Effective Dose
20 Comparisons across multiple endpoints, multiple species, and multiple experimental
21 protocols are too complicated to be made on the basis of the full dose-response curve. As
22 discussed above, comparisons of this sort can be made by either choosing a given exposure and
23 comparing the responses or choosing a particular response level and comparing the associated
24 exposures. In the analyses contained in Chapter 8, Section 8.3, and elsewhere in the
25 reassessment, emphasis is placed on comparing responses using estimated exposures associated
26 with a given level of excess response or risk. To avoid large extrapolations, this common level
27 of excess risk was chosen such that for most studies the estimated exposure is in or near the
28 range of the exposures seen in the studies being compared, with extra weight given to the human
29 data. A common metric for comparison is the effective dose, which is the dose resulting in an
30 excess response over background in the studied population. This excess response rate can be
31 calculated as a fraction of the minimum to maximum response (e.g., 1% increase in risk).
32 Alternatively, for continuous data the dose can be calculated as the amount necessary to move an
33 additional percentage of distribution of the response past a predetermined "effect" level. EPA
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1 has suggested this approach in calculating BMDs (Allen et al., 1994) and in its proposed
2 approaches to quantifying cancer risk (U.S. EPA, 1996, 1999, 2003).
3 Although effective dose evaluation at the 10% response level (ED10 or lower bound on
4 ED10 [LED10]) is somewhat the norm, given the power of most chronic toxicology studies to
5 detect an effect, this level is actually higher than those typically observed in the exposed groups
6 in studies of TCDD impacts on humans. To illustrate, lung cancer mortality has a background
7 lifetime risk of approximately 4% (smokers and nonsmokers combined), so that even a relative
8 risk of 2.0 (two times the background lifetime risk) represents approximately a 4%, or 4 in 100,
9 increased lifetime risk (see Chapter 8 for a comprehensive elaboration of formulae). On the basis
10 of this observation, and recognizing that many of the TCDD-induced endpoints studied in the
11 laboratory include 1% effect levels in the experimental range, Chapter 8 presents effective doses
1 2 of 1%, or ED01 and 10%, or ED10, values.
13 The use of effective dose values below 10% is consistent with the Agency's guidance on
14 the use of mode of action in assessing risk, as described in the proposed carcinogen risk
1 5 assessment guidelines (U.S. EPA, 1996, 1999, 2003) and in the evaluation framework discussed
1 6 in Section 3.3, in that the observed range for many "key events" for TCDD extends down to or
1 7 near the 1% response level. Determining the dose at which key events for dioxin toxicity begin
18 to be seen in a heterogeneous human population provides important information for decisions
1 9 regarding risk and safety.
20
21 5.2. EMPIRICAL MODELING OF INDIVIDUAL DATA SETS
22 As described in Chapter 8, Section 8.3, empirical models have advantages and
23 disadvantages relative to more ambitious mechanism-based models. Empirical models provide a
24 simple mathematical model that adequately describes the pattern of response for a particular data
25 set and that can also provide the means for hypothesis testing and interpolation between data
26 points. In addition, they can provide qualitative insights into underlying mechanisms. However,
27 the major disadvantage is their inability to quantitatively link data sets in a mechanistically
28 meaningful manner.
29 Data available for a number of biochemical and lexicological effects of TCDD and for its
30 mechanism of action indicate that there is good qualitative concordance between responses in
31 laboratory animals and humans (see Table 2-1). In addition, as described below, human data on
32 exposure and cancer response appear to be qualitatively consistent with animal-based risk
33 estimates derived from carcinogenicity bioassays. These and other data presented throughout this
34 reassessment would suggest that animal models are generally an appropriate basis for estimating
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1 human responses to dioxin-like compounds. Nevertheless, there are clearly differences in
2 exposures and responses between animals and humans, and recognition of these is essential when
3 using animal data to estimate human risk. The level of confidence in any prediction of human
4 risk depends on the degree to which the prediction is based on an accurate description of these
5 interspecies extrapolation factors. See Chapter 8, Section 8.3, for a further discussion of this
6 point.
7 Almost all dioxin research data are consistent with the hypothesis that the binding of
8 TCDD to the AhR is the first step in a series of biochemical, cellular, and tissue changes that
9 ultimately lead to toxic responses observed in both experimental animals and humans (see Part II,
10 Chapter 2, Section 2.3). Therefore, an analysis of dose-response data and models should use,
11 whenever possible, information on the quantitative relationships among ligand (i.e., TCDD)
1 2 concentration, receptor occupancy, and biological response. However, it is clear that multiple
13 dose-response relationships are possible when considering ligand receptor-mediated events. For
1 4 example, dose-response relationships for relatively simple responses, such as enzyme induction,
1 5 may not accurately predict dose-response relationships for complex responses such as
16 developmental effects and cancer.
1 7 Cell- or tissue-specific factors may determine the quantitative relationship between
18 receptor occupancy and the ultimate response. Indeed, for TCDD there are much experimental
19 data from studies using animal and human tissues to indicate that this is the case. This serves as
20 a note of caution, as empirical data on TCDD are interpreted in the broader context of complex
21 exposures to mixtures of dioxin-like compounds as well as to nondioxin-like toxicants.
22 As for other chemical mechanisms where high biological potency is directed through the
23 specific and high-affinity interaction between chemical and critical cellular target, the
24 supposition of a response threshold for receptor-mediated effects is a subj ect for scientific
25 debate. The basis of this controversy has been summarized by Sewall and Lucier (1995).
26 Based on classic receptor theory, the occupancy assumption states that the magnitude of
27 biological response is proportional to the occupancy of receptors by drug molecules. The
28 "typical" dose-response curve for such a receptor-mediated response is sigmoidal when plotted
29 on a semilog graph or hyperbolic if plotted on an arithmetic plot. Implicit in this relationship is
30 low-dose linearity (0-10% fractional response) through the origin. Although the law of mass
31 action predicts that a single molecule of ligand can interact with a receptor, thereby inducing a
32 response, it is also widely held that there must be some dose that is so low that receptor
33 occupancy is trivial and, thus, no perceptible response is obtainable.
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1 Therefore, the same receptor occupancy assumption of the classic receptor theory is
2 interpreted by different parties as support for and against the existence of a threshold. It has been
3 stated that the occupancy assumption cannot be accepted or rejected on experimental or
4 theoretical grounds (Goldstein et al., 1974). To determine the relevance of receptor interaction
5 for TCDD-mediated responses, one must consider (1) alternatives as well as limitations of the
6 occupancy theory, (2) molecular factors contributing to measured endpoints, (3) limitations of
7 experimental methods, (4) contribution of measured effect to a relevant biological/toxic
8 endpoint, and (5) background exposure.
9 Throughout this reassessment, each of these considerations has been explored within the
10 current context of the understanding of the mechanism of action of TCDD, of the methods for
11 analysis of dose-response for cancer and noncancer endpoints, and of the available data sets of
1 2 TCDD dose and effect for several rodent species, as well as humans who were occupationally
1 3 exposed to TCDD at levels exceeding the exposure of the general population.
14
1 5 5.2.1. Cancer
1 6 As discussed in Section 2.2.1.4, TCDD is characterized as carcinogenic to humans when
1 7 using a weight-of-evidence approach, and is a carcinogen in all species and strains of laboratory
18 animals tested. The epidemiological database for TCDD, described in detail in Part II, Chapter
19 7a, suggests that exposure may be associated with increases in all cancers combined and
20 respiratory cancer and with the possibility of elevated risks at other sites. Although there are
21 sufficient data in animal cancer studies to model dose-response for a number of tumor sites, as
22 with many chemicals it is generally difficult to find human data with sufficient information to
23 model dose-response relationships. For TCDD, three studies of human occupational exposure
24 have sufficient information to perform a quantitative dose-response analysis: Becher et al. (1998)
25 (the Hamburg cohort); Ott and Zober (1996) (the BASF cohort); and Steenland et al. (2001) (the
26 NIOSH cohort).
27 The all-cancer mortality ED01/LED01 results from these three studies are detailed in Part
28 II, Chapter 8, Section 8.3, and tabulated and graphed in Table 5-2, along with the bioassay results
29 for liver cancer in female Sprague-Dawley rats (Kociba et al., 1978). Table 5-2 includes only the
30 results and mathematical formulae that were published by the primary authors in the peer-
31 reviewed literature. These calculations and formulae were chosen because they are based on the
32 full primary data set and not on secondary analyses using summary results. In order to graph
33 results for the occupational cohort studies, the central points for data ranges were requested from,
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1 and kindly provided by, the authors (Drs. Steenland, Zober and Becher) and are included in the table.
2 Slightly different approaches are used for modeling cancer in humans than are used for
3 modeling in animal studies. The modeling approach used in the analysis of the human
4 epidemiology data for all cancers combined and lung cancer involves applying the estimated
5 human body burden-to-cancer response and estimating parameters in a mathematical risk model
6 for each data set. For the three occupational cohort studies, exposure subgroups were defined by
7 the authors using measured and then back-extrapolated TCDD levels in a subset of workers to
8 inform exposure calculations for the remainder of the cohort. None of the studies sampled
9 TCDD blood serum levels for more than a fraction of its cohort, and these samples were
10 generally taken decades after the last known exposure. In each study, serum fat or body fat levels
11 of TCDD were back-calculated using a first-order model. The assumed half-life of TCDD used
1 2 in the model varied from study to study.
13 Steenland et al. and Becher et al. used the measured and back-extrapolated TCDD
14 concentrations to refine and quantitate job exposure matrices, which were then used to estimate
1 5 dioxin cumulative dose for each member of their entire cohort. Ott and Zober (1996a) used
16 regression procedures with data on time spent at various occupational tasks to estimate TCDD
1 7 levels for all members of the cohort. The cohorts were then divided into exposure groups on the
18 basis of the estimated TCDD levels. As noted, central measures of the ranges from the primary
19 data were provided to the Agency by the authors, removing the need to estimate this parameter
20 from the upper and lower range points in the literature.
21 Risk outcomes in these cohorts were expressed as standardized mortality ratios (SMRs)
22 or rate ratios. SMRs are calculated by comparing the cancer rates in the subcohorts to the age-
23 and gender-matched general community in that time period. SMR results are usually expressed
24 as a ratio, with SMR =100 set as the community, or expected, cancer death rate. Rate ratios are
25 calculated from within cohort data using the lowest exposed group as the control value for both
26 dose and risk. Although the lowest exposed group is defined to have a risk equal to unity (rate
27 ratio = 1), this low group may not, in fact, have an SMR equal to the general community (it could
28 be either lower or higher).
29 The three occupational cohort studies provide best fit dose-response models within the
30 range of their data. These models and the resulting formulae allow for the calculation of
31 ED01/LED01 values, from which a linear extrapolation can be performed, consistent with the
32 EPA's draft cancer guidelines. There are several assumptions and uncertainties involved in
33 modeling these data, including extrapolation of dosage (both in back-calculation and in
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1 elimination kinetics), the type of extrapolation model employed, and whether the origin point
2 should be fixed (i.e., SMR = 100) or allowed to float.
3 Based on the model formulae using the full data set as provided in the primary literature
4 (Steenland et al., 2001; Ott and Zober, 1996; Becher et al., 1998; detailed in Chapter 8), the
5 calculated ED01 central estimates for all cancers combined range from 1.4 to 62 ng TCDD/kg
6 LABB (Table 5-3). The lower bounds on these doses (based on a modeled 95% CI) range from
7 0.71 ng TCDD/kg to 30.5 ng TCDD/kg (not available for models published by Becher et al.,
8 1998, due to the absence of statistical parameter measures). A parallel measure of unit excess
9 risk per one part per trillion TCDD body burden above background (assumed 5 ppt) is also
10 tabulated. These values are strongly dependent on the study chosen and the model used, and it
11 must be recognized that the risks posed to some members of the population from TCDD may be
1 2 zero, depending on the model chosen to extrapolate results below the range of observation. Male
1 3 and female values do not match because of differences in the input variable of background
14 lifetime all-cancer mortality risk.
1 5 Analysis of model results indicates that the power model applied to the Steenland et al.
16 (2001) data leads to unreasonably high risks at low exposure levels, based on calculations of the
1 7 attributable risk that this model would predict from background dioxin levels in the general
1 8 population. This result is due to the very steep slope of this power curve at low environmental
19 levels. The steep dose-response curve also makes the power model very sensitive to the
20 background dose that is incorporated into the calculations and the location of the calculation
21 point on the dose-response curve. Exclusion of the Steenland et al. power model reduces the
22 ED01 range to 6-62 ng TCDD/kg LABB and the LED01 range to 11.5-31 ng TCDD/kg LABB
23 (lower confidence values were unavailable for the Becher et al. 1998 data). For the purposes of
24 this assessment, the piecewise linear formula published by Steenland et al. (2001) is the preferred
25 model from this data set.
26 These epidemiologically derived ED01 values are summarized in Table 5-4 (additional
27 details in Part II, Chapter 8), along with the resulting cancer slope factors. The results of the
28 Kociba et al. (1978) cancer bioassay are also included in Table 5-4 for comparison purposes,
29 using the Goodman and Sauer (1992) revision to the liver tumor pathology results. Dose-
30 response modeling for this bioassay used the EPA Benchmark Dose software and multistage
31 model to calculate the ED01/LED01. The similarity between the cancer bioassay ED01 results in
32 rodents (Kociba et al. 1978) and the human epidemiology results is noteworthy when the
33 exposure metric is based on lifetime average body burden (LABB). LABB is calculated as the
34 AUC divided by lifetime years, and it equilibrates tissue doses across species.
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1 The epidemiological data and dose-response models have stimulated considerable
2 contemporary interest and statistical analysis, particularly the option of performing a pooled or
3 meta-analysis on the entire occupational cohort data set. In reviewing this literature, care should
4 be taken to note which published analyses form the basis for the statistical tests, the recent
5 provision of data-derived central dose estimates for the ranges given in the literature (courtesy of
6 the primary authors), and the availability of more detailed primary dose-response literature
7 (Steenland et al., 2001; Becher et al., 1998), which supercede studies used previously (Aylward
8 et al., 1996; Flesch-Janys et al., 1998). For instance, the dose-response pattern for the NIOSH
9 cohort summary data, as published by Aylward et al. (1996), demonstrates a different high dose
10 point from the more recent and detailed analysis of the full dataset, as published by Steenland et
11 al. (2001).
1 2 Starr (2001, 2003) reviewed meta-analysis data and results that were included in the
1 3 external review draft of the EPA dioxin reassessment, and the analysis performed by Crump et al.
14 (2003; see below). The draft EPA meta-analysis was based on summary results published by
1 5 Aylward et al. (1996; NIOSH), Ott and Zober (1996; BASF), and Flesch-Janys et al. (1998).
1 6 Exposure range midpoints were either obtained from the original publication (Aylward et al.,
1 7 1996) or were based on a log-normal fit to the data ranges to estimate the midpoint (for Ott and
18 Zober, 1996; Flesch-Janys et al., 1998). On the basis of these earlier data sets and the application
19 of a linear model, Starr concluded that the assumption of a fixed origin at an SMR =100 should
20 be rejected on statistical grounds. Although a significantly increased cancer risk was evident in
21 these cohorts, the overall results using an unconstrained linear model (not fixed to the SMR =
22 100 point) were concluded to be consistent with the null hypothesis of no dose-response
23 relationship between TCDD and the cancer rate.
24 In a subsequent dioxin meta-analysis performed as part of the Joint European
25 Commission on Food Additives, Crump et al. (2003) performed similar and expanded statistical
26 analyses on a more recent data set using data-derived central estimates of exposure levels for Ott
27 and Zober (1996; Hamburg cohort) and from Steenland et al. (2001; NIOSH cohort). Fitting a
28 linear model to the data again indicated that the baseline SMR =100 assumption could be
29 rejected, based on statistical tests.
30 Goodness of fit trend tests for this linear model were statistically significant both with the
31 background SMR set equal to 100 and with the background SMR estimated (p=0.01). A further
32 series of trend tests were performed by successively removing the highest cumulative exposure to
33 determine the lowest exposure for which there remained statistically significant evidence for an
34 effect. This progressive analysis of the data was considered by Crump et al. to provide a more
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1 robust test for trends than a linear goodness of fit test. The analysis demonstrated an increase in
2 total cancer at cumulative TEQ serum levels that would result from a lifetime average intake of 7
3 pg TEQ/kg body weight/day (assuming 50% uptake, t1/2 7.6 years, 25% body fat), with no trend
4 for increase at 6 pg/kg/day.
5 The pooled analysis of the Ott and Zober (1996), Flesch-Janys et al. (1998), and
6 Steenland et al. (2001) data yielded ED01 estimates of 51 ng/kg body burden (baseline SMR fixed
7 at 100) and 91 ng/kg body burden (baseline SMR estimated), corresponding to ED01 daily intake
8 estimates of 25 and 45 (95% CI = 21-324) pg/kg/day, respectively, above current background
9 TCDD-TEQ for all cancers combined (calculated using the half-life and absorption assumptions
10 in Crump et al.). These results are consistent with the range of ED01s in Part II, Chapter 8, and
11 Tables 5-3 and 5-4. On the basis of their results and comparison to other published analyses,
1 2 Crump et al. (2003) concluded that they could not see a clear choice between their ED01 estimate
13 of 45 pg/kg/day and the Steenland et al. (2001) estimate of 7.7 pg/kg/day, citing advantages to
14 each study.
1 5 The choice of model is central to the above statistical analyses of the individual studies
1 6 and the meta-analysis. The epidemiological data are not sufficient to mandate the selection of
1 7 any particular model shape. The published literature includes power, linear, piecewise linear,
18 and multiplicative models (see Table 5-2). The EPA's draft carcinogen risk assessment
1 9 guidelines (U.S. EPA, 1999) propose applying a standard curve-fitting procedure within the
20 range of the data (e.g., Benchmark Dose software), recognizing that more elaborate models will
21 be appropriate for more complex information and that, ultimately, biologically based
22 pharmacokinetic models would be preferred.
23 The curve-fitting procedure is used to determine a POD, generally at the 10% response
24 level, but where more sensitive data are available, a lower point for linear extrapolation can be
25 used to improve the assessment (e.g., 1% response for dioxin, ED01). Extrapolation from the
26 POD to lower doses is conducted using a straight line drawn from the POD to the origin—zero
27 incremental dose, zero incremental response—to give a probability of extra risk. The linear
28 default is selected on the basis of the agent's mode of action when the linear model cannot be
29 rejected and there is insufficient evidence to support an assumption of nonlinearity. Additional
30 important uncertainties in the human epidemiological data are discussed in Part II, Chapter 8,
31 Section 8.3, and include the representativeness and precision of the dose estimates that were
32 used, the choice of half-life and whether it is dose dependent, and potential interactions between
33 TCDD and smoking or other toxicants.
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1 For the animal data, both empirical and mechanistic models have been applied to examine
2 cancer dose-response. Portier et al. (1984) used a simple multistage model of carcinogenesis
3 with up to two mutation stages affected by exposure to model the five tumor types observed to be
4 increased in the 2-year feed study by Kociba et al. (1978) (Sprague-Dawley rats) and the eight
5 tumor types observed to be increased in the 2-year gavage cancer study conducted by NTP
6 (1982a) (Osborne-Mendel rats and B6C3Fj mice). The findings from this analysis, which
7 examined cancer dose-response within the range of observation, are presented in Part n, Chapter
8 8, Table 8.3., which is reproduced with slight modifications as Table 5-5. All but one of the
9 estimated ED01s are above the lowest dose used in the experiment (approximately 1 ng
10 TCDD/kg/day in both studies) and are thus interpolations rather than extrapolations. The
11 exception, liver cancer in female rats from the Kociba study, is very near the lowest dose used in
1 2 this study and is only a small extrapolation (from 1 ng TCDD/kg/day to 0.77 ng TCDD/kg/day).
13 Steady-state body burden calculations were also used to derive doses for comparison across
14 species. Absorption was assumed to be 50% for the Kociba et al. (feed experiment) and 100%
1 5 for the NTP study (gavage experiment).
1 6 The shapes of the dose-response curves as determined by Portier et al. (1984) are also
1 7 presented in Table 5-5. The predominant shape of the dose-response curve in the experimental
1 8 region for these animal cancer results is linear. This does not imply that a nonlinear model such
19 as the quadratic or cubic—or for that matter a "J-shaped" model—would not fit these data. In
20 fact, it is unlikely that in any one case a linear model or a quadratic model could be rejected
21 statistically. These studies had only three experimental dose groups; hence, these shape
22 calculations are not based on sufficient doses to guarantee a consistent estimate, and they should
23 be viewed with caution.
24 The ED01 steady-state body burdens range from a low value of 14 ng/kg, based on the
25 linear model associated with liver tumors in female rats, to as high as 1190 ng/kg, based on a
26 cubic model associated with thyroid follicular cell adenomas in female rats. Lower bounds on
27 the steady-state body burdens in the animals range from 10 ng TCDD/kg to 224 ng/kg. The
28 corresponding estimates of daily intake level at the ED01 obtained from an empirical linear model
29 range from 0.77 to 43 ng TCDD/kg body weight/day, depending on the tumor site, species, and
30 sex of the animals investigated. Lower confidence bounds on the estimates of daily intake level
31 at the ED01 in the animals range from 0.57 to 14 ng TCDD/kg body weight/day.
32 In addition, using a mechanistic approach to modeling, Portier and Kohn (1996)
33 combined the biochemical response model by Kohn et al. (1993) with a single initiated-
34 phenotype two-stage model of carcinogenesis to estimate liver tumor incidence in female
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1 Sprague-Dawley rats from the 2-year cancer bioassay by Kociba et al. (1978). By way of
2 comparison, the ED01 estimate obtained from this linear mechanistic model was 0.15 ng
3 TCDD/kg body weight/day, based on intake, which is equivalent to 2.7 ng TCDD/kg steady-state
4 body burden. No lower bound on this modeled estimate of steady-state body burden was
5 provided.
6 As discussed in Part II, Chapter 8, Section 8.2, the use of different dose metrics can lead
7 to widely diverse conclusions. For example, the ED01 intake for the animal tumor sites presented
8 above ranges from less than 1 to tens of ng/kg/day, and the lowest dose with an increased
9 tumorigenic response (thyroid tumors) in a rat is 1.4 ng TCDD/kg/day (NTP, 1982a). The daily
10 intake of dioxins in humans is estimated at approximately 1 pg TEQ/kg/day. This implies that
11 humans are exposed to doses 1400 times lower than the lowest tumorigenic daily dose in rat
1 2 thyroid. However, 1.4 ng TCDD/kg/d in the rat leads to a steady-state body burden of
1 3 approximately 25 ng TCDD/kg, assuming a half-life of TCDD of 25 days and absorption from
14 feed of 50%2. If the body burden of dioxins in humans is approximately 20 ng TEQ/kg lipid, or 5
1 5 ng TEQ/kg body weight (assuming about 25% of body weight is lipid), "average" humans are
16 exposed to about five times less TCDD than the minimal carcinogenic dose for the rat. The
1 7 difference between these two estimates is entirely due to the approximately 100-fold difference in
1 8 the half-life of TCDD in humans and rats. At least for this comparison, if cancer is a function of
19 average levels in the body, the most appropriate metric for comparison is the average or steady-
20 state body burden, because this accounts for the large differences in animal and human half-lives.
21 Comparisons of human and animal ED01s from Part II, Chapter 8, Section 8.3, for cancer
22 response on a body burden basis show similar potential for the carcinogenic effects of TCDD. In
23 humans, cancer ED01s ranged from approximately 6 ng/kg to 62 ng/kg (excluding the Steenland
24 et al., 2001, power model). This is similar to the empirical modeling estimates from the animal
25 studies, which ranged from 14 ng/kg to 1190 ng/kg (most estimates were in the range of 14 to
26 500 ng/kg). The lower bounds on the human body burdens at the ED01s (based on a modeled
27 95% CI) ranged from 11.5 ng TCDD/kg to 31 ng TCDD/kg (again, the lower values that would
28 have resulted from the Becher et al., 1998, analysis could not be included because error bounds
29 on the models were unavailable). Lower bounds on the steady-state body burdens in the animals
30 ranged from 10 ng TCDD/kg to 224 ng/kg. The estimate for the single mechanism-based model
31 presented earlier (2.7 ng/kg) is below the lower end of the human ED01 estimates.
Steady-state body burden (ng/kg) = (daily dose (ng/kg/day) * (half-life)/Ln(2)) ( f ), where f is the fraction absorbed
from the exposure route (unitless) and half-life is the half-life in days.
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1 Using human and animal cancer ED01s, their lower bound estimates, and the value of 2.7
2 ng TCDD/kg from the single mechanism-based model, slope factors and comparable risk
3 estimates for a human background body burden of approximately 5 ng TEQ/kg (20 ng TEQ/kg
4 lipid) can be calculated using the following equations:
5 Slope factor (per pg TEQ/kgBW/day) = risk at ED01 / intake (pg TEQ/kgBW/day)
6 associated with human equivalent steady-state body burden at ED01 where:
7
8 Risk at ED01 = 0.01; and
9
10 Intake (pg TEQ/kg BW/day) = [body burden at ED01 (ng TEO/kg^)* Ln(2YI * 1000 (pg/ng)
11 half-life (days) x f (5-1)
12
1 3 half-life = 2593 days in humans and 25 days in rats (see Table 8.1 in Part II, Chapter 8)
14 f = fraction of dose absorbed; assumed to be 0.8 (80%)
15 and
1 6 Upper bound on excess risk at human background body burden = (human (5-2)
1 7 background body burden (ng/kg))(risk at ED01)/lower bound on human
1 8 equivalent steady-state body burden (ng/kg) at ED01, where:
19 Risk at ED01 =0.01
20
21 Use of these approaches reflects methodologies being developed within the context of the
22 revised draft carcinogen risk assessment guidelines (U.S. EPA, 1999, 2003). Under these draft
23 guidelines (EPA, 2003, section 5.4), risk estimates may be based on linear extrapolation or
24 nonlinear hazard quotients, depending on the mode of action, accompanied by a statement on the
2 5 extent of extrapolation generally expressed as the margin of exposure (MOE = POD/exposure).
26 The formulae used in this quantitative linear analysis for dioxin are approximate for a number of
27 the cancer slope factors derived from human data in Table 5.4 because of the calculation of risk
28 for Ipg TCDD/kg body weight/day above background, the use of lifetable analysis to derive the
29 expected cancer rates, and the changing gradient of the dose-response curves as body burden
30 increases, especially for the power formulae. As discussed below, these methods can be
31 compared to previous approaches using the linearized multistage (LMS) procedure to determine
32 whether the chosen approach has significantly changed the estimation of slope. The estimates of
33 ED01/LED01 represent the human-equivalent body burden for 1% excess cancer risk based on
34 exposure to TCDD and are assumed for purposes of this analysis to be equal for TCDD
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1 equivalents (total TEQ). This assumption is based on the toxic equivalency concept discussed
2 throughout this report and in detail in Part II, Chapter 9. All cancer slope factors can be
3 compared to the Agency's previous slope factor of 1.6 x 10"4 per pg TCDD/kg body weight/day,
4 which is equivalent to 1.6 x 105 per mg TCDD/kg body weight/day (U.S. EPA, 1985).
5
6 5.2.1.1. Estimates of Slope Factors and Risk at Current Background Body Burdens
7 Based on Human Data
8 Traditionally, EPA has relied on central estimates of risk from epidemiological studies
9 rather than on upper bound estimates, which can exhibit substantial statistical spread in these
10 results. This practice developed because epidemiological data were most often from high-end
11 occupational exposures—as with the principal dioxin literature—where the data were likely to
1 2 provide upper estimates of cancer slope and where all excess cancer increases were attributed to
1 3 the single exposure of interest, amidst a variety of other potential carcinogenic exposures. For
14 the analyses conducted herein, the Agency has presented both central (e.g., ED01) and upper
1 5 bound (e.g., LED01) estimates where these are available.
16 The estimates of slope factors (risk per pg TCDD/kg body weight/day) calculated from
1 7 the human ED01s presented in Part II, Chapter 8, Table 8.3.1, range from 5.1 x 10~3 if the ED01 for
18 all cancer deaths in the Hamburg cohort is used to 0.57 x io~3 if the ED01 for all cancer deaths in
19 the smaller BASF cohort is used. All of the other slope factors for all cancer deaths in the three
20 cohorts fall within this range (Table 5-4). The meta-analysis by Crump et al. (2003) leads to
21 similar results, with the reported ED01 of 46 ng/kg (95% lower bound = 31 ng/kg) BB, resulting
22 in a cancer slope factor of 0.65 (95% upper confidence limit = 0.97) x 10"3 risk per pg
23 TCDD/kgBW/day (adopting the EPA assumptions of baseline SMR = 100, halflife = 7.1 years,
24 80% absorption; alternatively, adopting a floating SMR results in a CSF = 0.37 (0.69) x 10'3).
25 There is no compelling reason to choose one slope factor over the next from among those
26 calculated, given that each study had particular strengths and weaknesses (See Part II, Chapter
27 7a). The results cluster around a cancer slope factor of 10"3 risk/pgTCDD/kg body weight/day
28 above background, which represents EPA's most current upper bound slope factor for estimating
29 human cancer risk based on human data. By inference, this risk value could also apply to total
30 TEQ intake. As described in Section 4.4.2, current intakes in the United States are
31 approximately 1 pg TEQDFP-WHO98/kg body weight/day, and body burdens are approximately 5
32 ng TEQDFP-WHO98/kg body weight (which equates to a serum level of approximately 20 pg/g
33 lipid). Uncertainties associated with these estimates from human studies are discussed in Part II,
34 Chapter 8, Section 8.3, and in Becher et al. (1998).
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1 These estimates compare well with the published estimates of cancer slope and risk for
2 the Hamburg and NIOSH cohorts by Becher et al. (1998) and Steenland et al. (2001),
3 respectively. The risk estimates by Becher et al. were derived from data on TCDD exposure to
4 male workers with a 0 or 10-year latency. These estimates range from 1.3 x 10"3 to 5.6 x 10"3 per
5 pg TCDD/kg body weight/day, and were calculated using German background cancer rates. The
6 fraction of dioxin assumed absorbed is not stated by Becher et al. but, presumably, if the
7 absorption fraction was set at 100%, this would contribute to the slight differences to the EPA
8 values in Table 5.5. The Steenland et al. calculations were performed for either no lag or a 15-
9 year lag. The authors calculated a lifetime all cancer excess risk above background of between 5
10 x 10"4 (piecewise linear) to 9.4 x 10"3 (power model) per pgTCDD/kg/day. The Steenland et al.
11 results are lower than those presented in Table 5-4 because the authors assumed 50% absorption
1 2 and a lower additional dose (i.e., incorporating a two-fold doubling of dose over background into
13 the Steenland et al. results reproduces their calculations).
14 In both analyses, all excess cancers are attributed to TCDD exposure, despite significant
1 5 levels of other dioxin-like compounds in blood measurements. Notable, though, is the Becher et
16 al. determination of a very similar slope coefficient for total TEQ and TCDD, based on their
1 7 measured data, which is consistent with the TEF methodology. The results from Steenland et al.
18 are more consistent with a reduced cancer slope factor when based on TEQ. Although risk
19 estimates using TCDD alone in these cohorts might suggest an overestimate of risk because dose
20 is underestimated, no evidence for this has emerged from the analysis because TCDD dominates
21 the total TEQ in these occupational cohorts.
22
23 5.2.1.2. Estimates of Slope Factors and Risk at Current Background Body Burdens Based
24 on A nimal Data
2 5 Upper bound slope factors (per pg TCDD/kg body weight/day) for human cancer risk
26 calculated from lower bounds on ED01s (LED01s) for the animal cancers presented in Table 5-5
27 range from 3 x 10"3 to 0.1 x 10"3, that is, from 19 times greater than the previous upper bound
28 estimate on cancer slope (1.6 x io~4 [U.S. EPA, 1985]) to less than 50% of this value. The
29 highest slope factor is derived from the same study as the 1985 estimate; that is, the slope factor
30 derived from the female liver cancer in the Kociba et al. (1978) study continues to give the
31 highest slope factor.
32
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1 5.2.1.2.1. Reconciling the Portier (1984) and EPA (1985) slope estimates. In attempting these
2 comparisons, two issues became apparent. First, the body burden and the intake at the ED01 from
3 Portier et al. (1984) does not result in the same slope factor as EPA's (U.S. EPA, 1985). Despite
4 the use of the same study results, a slope factor of 1.8 x 10~5 per pg TCDD/kg body weight/day
5 results when using the LMS approach in Portier et al. (1984), which is approximately a factor of
6 10 lower than EPA's estimate of the slope (U.S. EPA, 1985). The differences are attributable to
7 the aims of the respective calculations at the time. Portier et al. calculated "virtually safe doses"
8 assuming that rodent and human doses scaled on a mg/kg basis, and they used the original tumor
9 counts from the study. EPA, on the other hand, used (body weight) 3 to arrive at a human
10 equivalent dose and the pathology results from a reread of the original Kociba study (U.S. EPA,
11 1980). In addition, EPA adjusted tumor counts for early mortality in the study. The factor to
1 2 adjust for (body weight) 3 scaling in the rat is 5.8. The correction for early mortality can be
13 accounted for with a factor of 1.6 (this is the ratio of the intake values at the ED01 with and
14 without the early mortality correction). If the Portier et al. slope factor (1.8 x 10"5 per pg
1 5 TCDD/kg body weight/day) is multiplied by these two factors, a slope of 1.7 x 10~4per pg
1 6 TCDD/kg body weight/day is calculated. This is essentially equivalent to the EPA estimate of
17 1.6 x 10"4per pg TCDD/kg body weight/day. Reconciling these issues is important to ensuring
18 appropriate comparisons of slope factor estimates.
19
20 5.2.1.2.2. Calculating a revised estimate of cancer slope from Kociba et al. (1978). Of greater
21 consideration is the calculation of slope factor estimates using current methods of analysis that
22 recognize the importance of the dose metric and the differences in half-life of dioxins in the
23 bodies of laboratory animals and humans (see Part II, Chapter 8, Section 8.2, for detailed
24 discussion). The major difference between the approaches used to calculate risks in the mid-
25 1980s (Portier et al., 1984; U.S. EPA, 1985) and the current approach is the use of body burden
26 as the dose metric for animal-to-human dose equivalence. The decision to use body burden
27 accounts for the approximately 100-fold difference between half-lives of TCDD in humans and
28 rats (2593 days vs. 25 days [see Part E, Chapter 8, Table 8.1]).
29 The use of equation 5-1 results in an estimated body burden at the LED01 of 6.1 ng
30 TEQ/kg, derived from the EPA (U.S. EPA, 1985) Kociba et al. tumor counts. This compares
31 favorably with the Portier estimate of 10 ng TEQ/kg found in Table 5-5. The difference is
32 entirely accounted for by the early deaths adjustment by EPA. Use of these body burdens at the
33 LED01 results in slope factor estimates of 3.3 x 10"3perpg TCDD/kg body weight/day and 4.9 x
34 10'3 per pg TCDD/kg body weight/day for the Portier at al. (1984) (10 ng/kg) and the newly
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1 derived body burden (6.1 ng/kg), respectively. Again, the difference is due solely to the
2 adjustment for early mortality, which EPA considers a better estimate of upper bound lifetime
3 risk than the unadjusted estimate. EPA's revised slope factor (4.9 x io~3 per pg TCDD/kg body
4 weight/day) would be 31 times greater than the slope factor from 1985.
5 However, a second issue with the modeling of the Kociba et al. data relates to the use of
6 the appropriate tumor counts. As mentioned in Section 2.2, Goodman and Sauer (1992) reported
7 a second re-evaluation of the female rat liver tumors in the Kociba et al. study using the latest
8 pathology criteria for such lesions. Results of this review are discussed in more detail in Part II,
9 Chapter 6, Section 6.2. The review confirmed only approximately one-third of the tumors seen
10 in the previous review (U.S. EPA, 1980). Although this finding did not change the determination
11 of carcinogenic hazard, because TCDD induced tumors in multiple sites in this study, it does
1 2 have an effect on evaluation of dose-response and on estimates of risk. Because neither the
1 3 original EPA slope factor estimate (U.S. EPA, 1985) nor that of Portier et al. (1984) reflect this
14 reread, it is important to factor these results into the estimate of the ED01 and slope factor.
1 5 Using the LMS procedure used by EPA in 1985 and the tumor counts as reported in Part
16 II, Chapter 6, Table 6.2, the revised slope factor is reduced by approximately 3.6-fold to yield a
1 7 slope factor of 4.4 x 10~5 per pg TCDD/kg body weight/day. However, because the original
18 estimates used a (body weight)273 scaling, an adjustment must also be made to remove this
1 9 interspecies scaling factor in order to obtain a correct result when comparing with body burden as
20 the interspecies metric. When dose is adjusted and equation 5-1 is used, an LED01 of 22.2 ng
21 TEQ/kg and a slope factor of 1.4 x 10~3 per pg TCDD/kg body weight/day are derived. This
22 represents EPA's most current upper bound slope factor for estimating human cancer risk based
23 on animal data. It is 8.7 times larger than the slope factor calculated in U.S. EPA (1985). This
24 number reflects the increase in slope factor based on the use of the body burden dose metric (31
25 times greater) and the Goodman and Sauer (1992) pathology (3.6 times less). These results can
26 also be obtained using EPA's Benchmark Dose software and entering adjusted tumor counts and
27 dose data to obtain a BMDL01 from which an LED01 body burden of 22 ng/kg can be derived (see
28 Tables 5-2, 5-4).
29
30 5.2.1.3. Estimates of Slope Factors and Risk at Current Background Body Burdens Based
31 on a Mechanistic Model
32 As discussed above, Portier and Kohn (1996) combined the biochemical response model
33 of Kohn et al. (1993) with a single initiated-phenotype two-stage model of carcinogenesis to
34 estimate liver tumor incidence in female Sprague-Dawley rats from the Kociba et al. (1978)
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1 bioassay. The model is described in more detail in Part II, Chapter 8, Section 8.4. This model
2 adequately fit the tumor data, although it overestimated the observed tumor response at the
3 lowest dose in the Kociba et al. study. The shape of the dose-response curve was approximately
4 linear, and the estimated ED01 value for this model was 1.3 ng/kg/day. The corresponding body
5 burden giving a 1% increased effect was 2.7 ng/kg.
6 The model authors believe that the use of CYP1A2 as a dose metric for the first mutation
7 rate is consistent with its role as the major TCDD-inducible estradiol hydrolase in liver and with
8 its hypothesized role in the production of estrogen metabolites leading to increased oxidative
9 DNA damage and increased mutation (Yager and Liehr, 1996; Hayes et al., 1996; Dannan et al.,
10 1986; Roy et al., 1992). Although no lower bound estimate of the ED01 is calculated, a
11 maximum likelihood estimate of the slope factor of 7.1 x 10~3 per pg TCDD/kgBW/day can be
1 2 calculated. This estimate represents an example of the type of modeling based on key events in a
1 3 mode of action for carcinogenesis that is consistent with the future directions in dose-response
14 modeling described in EPA's revised proposed cancer risk assessment guidelines (U.S. EPA,
1 5 1999). Although a number of uncertainties remain regarding structure and parameters of the
16 model, the slope estimate is consistent with those derived from humans and animals. More
1 7 details on this model can be found in Part II, Chapter 8, Section 8.4.
1 8 An alternative mechanistic model has been proposed (Conolly and Andersen, 1997). This
19 model was developed for focal lesion growth, based on two types of initiated cells and applying
20 the negative selection mechanism for hepatic tumor promotion proposed by Jirtle et al. (Jirtle and
21 Meyer, 1991; Jirtle et al., 1991). In this model, even though the two types of initiated cells
22 express the same biochemical marker, they respond differently to promotional stimulation in the
23 liver. The model presumes that a promotional stimulus to the liver is countered by mito-
24 inhibitory signals generated by the liver to constrain proliferation. One set of mutated cells is
25 sensitive to this mito-inhibition, whereas the other set of mutated cells is insensitive and
26 responds only to the promotional stimulus. The result is that, under increasing doses of the
27 promoter, one group of focal lesions is decreasing in size—and hence, number of cells—whereas
28 the other group is increasing in size.
29 The Conolly and Andersen model is different from the Portier and Kohn (1996) model in
30 that it can result in U-shaped dose-response curves for the total number and mean size of
31 observable focal lesions without using U-shaped parametric forms for the mutation rates or the
32 birthrates. Conolly and Andersen did not apply their model to cancer risk estimation. Presently,
33 there are insufficient experimental data to support or refute the use of either the Portier and Kohn
34 or the Conolly and Andersen model.
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1
2 5.2.2. Noncancer Endpoints
3 The analysis of noncancer endpoints following dioxin exposure uses the same dose
4 metrics as for the preceding cancer analysis, although with increased emphasis on LOAELs and
5 NOAELs. Summarized here are noncancer results based on the 200+ ED01 calculations
6 performed in Part II, Chapter 8, combined with a tabulation (Table 5-6; Appendix A) of the
7 lower range of measured, empirical, LOAEL/NOAEL results. Noncancer endpoints following
8 dioxin exposure present similar—lower for some effects—PODs as compared to cancer ED01s,
9 with many of the PODs falling in a range of-10-50 ng/kg BB and lower still for subclinical
10 endpoints.
11 Before presenting these results, consideration should be give to a number of difficulties
1 2 and uncertainties associated with comparing the same or different endpoints across species, such
13 as differences in sensitivity of endpoints, times of exposure, exposure routes, and species and
14 strains; the use of multiple or single doses; and variability between studies even for the same
1 5 response. The estimated ED01s may be influenced by experimental design, suggesting that
1 6 caution should be used when comparing values from different designs. Caution should also be
1 7 used when comparing studies that extrapolate ED01s outside the experimental range.
1 8 Furthermore, it may be difficult to compare values across endpoints. For example, the human
1 9 health risk for a 1% change of body weight may not be equivalent to a 1% change in enzyme
20 activity. Similarly, a 1% change in response in a population for a dichotomous endpoint is
21 different from a 1% change in a continuous endpoint, where the upper bound of possible values
22 may be very large, leading to a proportional increase in what constitutes the 1% effect level.
23 Finally, background exposures are often not considered in these calculations simply because they
24 were not known.
25 Part II, Chapter 8, presents estimated ED01s for more than 200 data sets. These data sets
26 were categorized by exposure regimen (single exposure vs. multiple exposures), effect
27 (biochemical, hepatic, tissue, immune, and endocrine) and developmental stage (adult vs.
28 developmental). The Hill model was fit to a majority of the data sets. This model not only
29 provides estimates of the ED01, it also provides insight into the shape of the dose-response curve
30 in the form of a shape parameter. The shape parameter, or the Hill coefficient, can be used to
31 determine whether the dose-response curve is linear or threshold-like. An analysis of the shape
32 parameters for the different response categories implies that many dose-response curves are
33 consistent with linearity over the range of doses tested. This analysis does not imply that the
34 curves would be linear outside this range of doses, but it does inform the choices for
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1 extrapolation. This is particularly true when body burdens or exposures at the lower end of the
2 observed range are close to body burdens or exposures of interest for humans, which is the case
3 with dioxin-like chemicals and biochemical effects.
4 Several general trends were observed and discussed in Part n, Chapter 8, relating to the
5 ED01 results. The lowest ED01s tended to be for biochemical effects, followed by hepatic
6 responses, immune responses, and responses in tissue weight. However, there was a wide range
7 of ED01s within each category. For example, in the immune category, there was a range of
8 almost six orders of magnitude in the ED01 estimates. In addition, some of the lowest ED01
9 estimates were for changes in immune function in adult mice, with ED01s ranging from 2 to 25 ng
10 TCDD/kg. Overall shape parameter data suggest that biochemical responses to TCDD are more
11 likely to be linear within the experimental dose range. The more complex responses are more
1 2 likely to assume a nonlinear shape. Nonetheless, a large number (> 40%) of the more complex
1 3 responses have shape parameters that are more consistent with linearity than with nonlinearity.
14 Table 5-6 summarizes the range of experimental LOAEL, NOAEL, and ED01 values for
1 5 critical endpoints from animal studies. The published data supporting these values are presented
16 in Appendix A. These endpoints were chosen because they are considered adverse (e.g.,
1 7 developmental or reproductive toxicity) or are on the critical path for cancer and noncancer
18 effects. In addition, these effects were chosen because the body burdens at which the effects
1 9 occur are approximately 50 ng/kg or lower. The use of ED01s and NOAELs and/or LOAELs in
20 this analysis provides a "point of departure" for a discussion of margins of exposure for a variety
21 of health endpoints. No one endpoint has been chosen as the "critical effect," as is often done in
22 RfD calculations. For the effects listed in Table 5-6 and Appendix A, the MOE is approximately
23 10 or less. In some cases, particularly for ED01 values for the developmental toxicities of TCDD
24 in rats (Mably et al., 1992a-c; Gray et al., 1997a, b; Faqi et al., 1998; Markowski et al., 2001), the
25 MOE is less than 1. These estimates of the MOE assume a background human body burden of 5
26 ng TEQ/kg b ody weight.
27 Results from the analysis of ED01s and an examination of LOAELs in additional studies
28 suggest that noncancer effects can occur at body burden levels in animals equal to or less than
29 body burdens calculated for tumor induction in animals. This is especially true when considering
30 biochemical changes that may be on the critical path for both noncancer and cancer effects, such
31 as enzyme induction or impacts on growth factors or their receptors. Although human noncancer
32 effects were not modeled in Part n, Chapter 8, the observation of effects in the Dutch studies
33 (discussed in Section 2.2.2 in this document) suggest that subtle but important noncancer human
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1 effects may be occurring at body burden levels equivalent to those derived for many
2 biochemical—and some clearly adverse—effects in animals.
3
4 5.3. MODE-OF-ACTION-BASED-DOSE-RESPONSE MODELING
5 As described in Part II, Chapter 8, Section 8.3, mechanism-based modeling can be a
6 powerful tool for understanding and combining information on complex biological systems. Use
7 of a truly mechanism-based approach can, in theory, enable reliable and scientifically sound
8 extrapolations to lower doses and between species. However, any scientific uncertainty about the
9 mechanisms that the models describe is inevitably reflected in uncertainty about the predictions
10 of the models. The assumptions and uncertainties involved in the mechanistic modeling
11 described in Chapter 8 are discussed at length in that chapter and in cited publications.
1 2 The development and continued refinement of PBPK models of the tissue dosimetry of
1 3 dioxin has provided important information concerning the relationships between administered
14 dose and dose-to-tissue compartments (Part n, Chapter 8, Section 8.2). Aspects of these models
1 5 have been validated in the observable response range for multiple tissue compartments, species,
1 6 and class of chemical. These models will continue to provide important new information for
1 7 future revisions of this health assessment document. Such information will likely include
18 improved estimates of tissue dose for liver and other organs where toxicity has been observed,
19 improved estimates of tissue dose(s) in humans, and improved estimates of tissue dose for
20 dioxin-related compounds.
21 In this reassessment, the development of biologically based dose-response models for
22 dioxin and related compounds has led to considerable and valuable insights regarding both
23 mechanisms of dioxin action and dose-response relationships for dioxin effects. These efforts,
24 described in some detail in Part II, Chapter 8, Section 8.3, have provided additional perspectives
25 on traditional methods such as the linearized multistage procedure for estimating cancer potency
26 or the uncertainty factor approach for estimating levels below which noncancer effects are
27 unlikely to occur. These methods have also provided a biologically based rationale for what had
28 been primarily statistical approaches. The development of models like those in Chapter 8 allows
29 for an iterative process of data development, hypothesis testing, and model development.
30
31 5.4. SUMMARY OF DOSE-RESPONSE CHARACTERIZATION
32 All humans tested contained detectable body burdens of TCDD and other dioxin-like
33 compounds that are likely to act through the same mode of action. The receptor modeling theory
34 outlined in Chapter 8 indicates that xenobiotics that operate through receptor binding
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1 mechanisms, such as dioxin, will follow a linear dose-response binding in the 1-10% receptor
2 occupancy region. This theoretical basis suggests—and this is supported by empirical
3 findings—that the proximal biochemical and transcription reactions for dioxins, such as effects
4 on DNA transcription and enzyme induction, may also follow linear dose-response kinetics.
5 More distal toxic effects could be linear or sublinear/threshold depending on (1) the toxic
6 mechanism, (2) location on the dose-response curve, and (3) interactions with other processes
7 such as intracellular protein binding and co-factor induction/repression.
8 Empirical data provide dose-response shape information down to approximately the 1%
9 effect level for many toxic endpoints. Many examples of adverse effects experienced at these
10 low levels have too much data variability to clearly distinguish on a statistical basis (goodness of
11 fit) between dose-response curve options and whether dose-response follows linear,
1 2 supra/sublinear, power curve, or threshold kinetics. Toxic effects seen only at higher doses are
1 3 presumably more likely to result from multiple cellular perturbations and are thus less likely to
14 follow linear relationships.
1 5 Empirical dose-response data from cancer studies—both human epidemiological and
1 6 bioassays—do not provide consistent or compelling information supportive of either threshold or
1 7 supralinear models (see Tables 2-3 and 5-2) and are insufficient to move from EPA's default
1 8 linear extrapolation policy in the proposed carcinogen risk assessment guidelines (U.S. EPA,
19 1996, 1999, 2003). This policy indicates that, for cancer dose-response, the data are to be
20 modeled within the observed range and a POD calculated from which a linear extrapolation to
21 the origin is generated. For noncancer endpoints, EPA proposes using an MOE approach, rather
22 than an RfD approach, due to the inability to determine levels that are likely to be without
23 appreciable effects of lifetime exposure to the population (including susceptible subpopulations)
24 for all adverse effects, particularly given the current level of background exposure and human
25 body burdens. Data on background levels of dioxins, furans and coplanarPCBs (see Part I,
26 Volume 3, and Section 4.4 in this document) indicate that current levels in humans are already
27 substantially along the dose-response curve. Thus, theoretical issues regarding increases from
28 zero body burden levels are moot, and assessments must consider both background and
29 additional increments of dose to this background level.
30 MOEs between population levels and the empirically observed (not modeled) 1% effect
31 levels for a number of biochemical/toxic endpoints are on the order of less than 1 to 2 orders of
32 magnitude. Thus, the extrapolation between observed effects and background levels is not large,
33 with any increments to background further advancing along the dose-response curve through or
34 toward the observed range. This further reduces the level of uncertainty when evaluating the
12/23/03 5-27 DRAFT—DO NOT CITE OR QUOTE
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1 significance of MOEs. It is possible that any additional exposure above current background body
2 burdens will be additive to ongoing responses. The magnitude of the additional response will be
3 a function of the toxic equivalency of the incremental exposure. This observation, the relatively
4 small MOE for "key events" potentially on the pathway to cancer and noncancer effects, and the
5 high percentage of observed linear responses suggest that a proportional model should be used
6 when extrapolating beyond the range of the experimental data. Short of extrapolating linearly
7 over one to two orders of magnitude to estimate risk probabilistically for cancer and noncancer
8 effects in the face of the uncertainties described above, a simple MOE approach may be useful to
9 decision makers when discussing risk management goals. However, this decision would have to
10 be based on a policy choice, because this analysis does not strongly support either approach.
11 Because human data for cancer dose-response analysis were available and because of a
1 2 strong desire to stay within the range of responses estimated by these data, the risk chosen for
13 determining a POD was the 1% excess risk. Doses and exposures associated with this risk (the
14 ED01s) were estimated from the available data using both mechanistic and empirical models.
1 5 Comparisons were made on the basis of body burdens to account for differences in half-life
16 across the numerous species studied.
1 7 In humans, restricting the analysis to log-linear models resulted in cancer ED01s ranging
1 8 from 6.0 ng/kg to 62 ng/kg. These were similar to the estimates from empirical modeling of the
1 9 animal studies, which ranged from 14 ng/kg to 1190 ng/kg (most estimates were in the range of
20 14 to 500 ng/kg), and 2.7 ng/kg for the single mechanism-based model. Lower bounds on these
21 ED01 estimates were used to calculate upper bound slope factors and risk estimates for average
22 background body burdens.
23 Table 5-4 summarizes the ED01/LED01 and slope factor calculations for the occupational
24 cohort and bioassay studies. The slope factor calculations are performed by linearly
25 extrapolating the ED/LED01 values to the background response rates, consistent with procedures
26 outlined in the draft proposed guidelines for carcinogen risk assessment (U.S. EPA, 1996, 1999,
27 2003). A slope factor estimate of approximately 1 x 10"3 per pg TCDD/kg body weight/day
28 represents EPA's most current upper bound slope factor for estimating human cancer risk based
29 on human data. A slope factor of 1.4 x 10"3 per pg TCDD/kg body weight/day represents EPA's
30 most current upper bound slope factor for estimating human cancer risk based on animal data.
31 Details on the specific procedures and calculations are provided in the footnotes. Additional
32 details on the study characteristics and dose-response data and graphs are available in Section 5.2
33 and Table 5-2. The Agency, although fully recognizing the range and the public health-
34 conservative nature of the slope factors that make up the range, suggests the use of 1 x 1Q-3 per
12/23/03 5-28 DRAFT—DO NOT CITE OR QUOTE
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1 pg TEQ/kg body weight/day as an estimator of upper bound cancer risk for both background
2 intakes and incremental intakes above background.
3 Upper bound slope factors allow the calculation of the high end (greater than 95%) of the
4 probability of cancer risk in the population. This means that there is a greater than 95% chance
5 that cancer risks will be less than the upper bound. Use of the ED01 rather than the LED01 to
6 provide more likely estimates based on the available epidemiological and animal cancer data
7 result in slope factors and risk estimates that are within a factor of 2 from the upper bound
8 estimates. Even though there may be individuals in the population who might experience a
9 higher cancer risk on the basis of genetic factors or other determinants of cancer risk not
10 accounted for in epidemiologic data or animal studies, the vast majority of the population is
11 expected to have less risk per unit of exposure, and some may have zero risk. On the basis of
1 2 these slope factor estimates (per pg TEQ/kg body weight/day), upper bound cancer risk at
1 3 average current background body burdens (5 ng TEQ/kg body weight) exceed 10~3 (1 in 1000).
14 Current background body burdens reflect higher average intakes from the past (approximately 3
1 5 pg TEQ/kg body weight/day). For a very small percentage of the population (< 1%), estimated
16 upper bound risks may be two to three times higher than this upper bound, based on average
1 7 intake, if their individual cancer risk slope is represented by the upper bound estimate and they
1 8 are among the most highly exposed (among the top 5%), based on dietary intake of dioxin and
19 related compounds.
20 Estimates for noncancer endpoints show greater variability. In general, when compared
21 on a body burden basis, the noncancer endpoints displayed lower ED01s and NOAELs and/or
22 LOAELs for short-term exposures versus longer-term exposures and for simple biochemical
23 endpoints versus more complex endpoints such as tissue weight changes or toxicity. A number
24 of significant, adverse, noncancer responses occurred at LOAEL/NOAEL/ED01s of < 10-50
25 ng/kg, levels that are similar to the ED01s estimated for cancer effects (see Tables 5-4, 5-6 and
26 Appendix A). The mechanism-based models for noncancer endpoints gave a lower range of
27 ED01s (0.17 to 105 ng/kg) when compared to the broader noncancer data set. Although most of
28 these estimates were based on a single model, the estimate from a different model—the hepatic
29 zonal induction model—gave an ED01 for CYP1A2 induction of 51 ng/kg and, hence, was within
30 the same range.
31 Although highly variable, these estimates suggest that any choice of body burden of more
32 than 100 ng/kg as a POD would likely yield > 1% excess risk for some endpoint in humans,
33 including those with clear clinical significance. Also, choosing a POD of less than 1 ng/kg
34 would likely be an extrapolation below the range of these data. Any choice in the middle range
12/23/03 5-29 DRAFT—DO NOT CITE OR QUOTE
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AUTHORS (continued)
John L. Schaum
Environmental Engineer
Immediate Office of the Division Director-Washington
National Center for Environmental Assessment
Office of Research and Development
U.S. Environmental Protection Agency
Washington, DC
Linda C. Tuxen
Special Assistant
National Center for Environmental Assessment
Office of Research and Development
U.S. Environmental Protection Agency
Washington, DC
Dwain L. Winters
Director
Dioxin Policy Project
Office of Pollution Prevention and Toxics
Office of Prevention, Pesticides, and Toxic Substances
U.S. Environmental Protection Agency
Washington, DC
12/23/03 xiv DRAFT—DO NOT CITE OR QUOTE
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1
2
Table 5-1. Peak serum dioxin levels in the background population and epidemiological cohorts
Cohort
CDC comparison
population, USA
1995-1997; CDC
(2000)
Background, Dioxin
Assessment, USA
-1990s
No.
316
pooled
results
Total TEQ
(ppt lip id)
Lower
2a
30
Central
Tendency
25.4 meanb
52.8 mean
55 median
Upper
50a
70
2,3,7,8-TCDD
(ppt lipid)
Central Tendency
2.1 mean
1.9 median
(95%UCL = 4.2)
5.2 mean
SD~1.32C
PCBs
Mean
TEQ
5.3 (est.)b
18.8 mean
20 median
Non-2,3,7,8-TCDD
TEQ (ppt lipid)
Central Tendency
23.3 mean
47.6 mean
Comment
TEQDFP-WH098;
serum; missing PCBs
105, 118, 156
estimated
TEQDFP-WH098;
serum, adipose,
breast milkd
Back-calculated
Ranch Hand, low;
Ketchum et al.
(1999)
Ranch Hand, high;
Ketchum et al.
(1999)
Hamburg cohort,
women; Flesch-Janys
etal. (1999)
NIOSH,Fingerhutet
al. (1991b), NTIS
BASF, severe
chloracne; Ott et al.
(1993)
276
283
6 5 2,3,7,8
64TEQ
253
56
19.3
811.2 mean6
172.8 median6
6789.1
52.3 median
(range 27-94)
195.7 median
(range 94-3,290)
506.8 mean
125.8 median
(range 2.4-6397.4)
2,000 mean
(rangef2-32,000)
1008 geom. mean
(range* 20-1 3360)
304.4 mean6
serum
serum
I-TEQs, dioxin and
furan TEQ only;
serum
serum
serum
4
5
6
7
8
9
10
1 1
12
13
14
15
16
17
18
19
20
21
22
23
24
25
-------
Table 5-1. Peak serum dioxin levels in the background population and epidemiological cohorts (continued)
Cohort
BASF, moderate
chloracne; Ott et al.
(1993)
BASF, no chloracne;
Ottetal. (1993)
Seveso Zone A;
Landietal. (1998)
Seveso Zone A,
medical; Needham et
al.(1999)h
Seveso Zone B;
Landietal. (1998)
Seveso Zone B,
medical; Needham et
al.(1999)h
Seveso Zone R,
medical; Needham et
al. (1999)h
Seveso NonABR;
Landietal. (1998)
No.
59
139
7
296
51
80
48
52
Total TEQ
(ppt lipid)
Lower
Central
Tendency
Upper
2,3,7,8-TCDD
(ppt lipid)
Central Tendency
420.8 geom. mean
(range8 2.72-49 15)
38.4 geom. mean
(range8 2. 72-2981)
230 geom. mean
325.9 median
(range 41. 2-399. 7)
381-489 median
(range 1.5-56,000)
47.5 geom. mean
52.5 median
(range 5.3-273)
87-147 median
(range 1.8-725)
15-89 median
(range 1-545)
4.9 geom. mean
5.5 median
(range 1.0-18.1)
PCBs
Mean
TEQ
Non-2,3,7,8-TCDD
TEQ (ppt lipid)
Central Tendency
Comment
serum
serum
serum
Samples taken 1976,
not back-calculated;
serum;
using '/2 DL
serum
Samples taken 1976,
not back-calculated;
serum;
using '/2 DL
Samples taken 1976;
not back-calculated;
serum;
using '/2 DL
serum
-------
Table 5-1. Peak serum dioxin levels in the background population and epidemiological cohorts (continued)
Cohort
Dutch Accident;
Hooiveld et al.
(1996)
Dutch Main
Production;
Hooiveld et al.
(1996)
No.
14
5
Total TEQ
(ppt lipid)
Lower
Central
Tendency
Upper
2,3,7,8-TCDD
(ppt lipid)
Central Tendency
1841.8 arith. mean
1433.8 geom. mean
(range 301-3683)
608.2 arith. mean
285.9 geom. mean
(range 17-1160)
PCBs
Mean
TEQ
Non-2,3,7,8-TCDD
TEQ (ppt lipid)
Central Tendency
Comment
serum
serum
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
a Estimated from ATSDR (1999b) Calcasieu comparison population graph.
b CDC data scaled upward to adjust for missing dataonPCB congeners 105, 118 and 156 by matching to PCB
congener ratios measured in the early 1990s.
0 SD approximated from unweighted estimate.
d Weighted average levels for the subset of serum lipid TEQs were 4.54 ng/kg for 2,3,7,8-TCDD and 55.4 ng/kg
for total TEQ (PCB contribution not adjusted for missing congeners).
e PCDD- and PCDF-derived TEQ only, using I-TEFs.
f Lower interval on current level.
g Range estimated from exponential log distribution graph.
h Ranges for median values for Seveso result from age groupings in original publication (Needham et al.,
1999; Tables 1,2, 5)
-------
1
2
Table 5-2. Published cancer epidemiology and bioassay data and dose-response formulae
4
5
6
7
Study
Hamburg
cohort,
Becher et al.
(1998)
Exposure
groups
0-1
1-4
4-8
8-16
16-64
64+
Years
n= 11 89 male;
measured = 275;
cancer deaths =
124
Central estimate
of range
(ng/kg fat x
years)"
0
2000
5657
11314
32000
96000
All cancer deaths
observed
(latency)
1.00 RR
1.12 (0-yrlag)b
1.42 P trend = 0.03
1.77
1.63
2.19
Harmonic mean, 1.5 x
upper limit
Power: p=0.026
RR =
(0.00017x+l)A0.326
Additive: p=0.031
RR= 1+0.000016 x
Multiplicative:
p=0.043
RR =
exp(0.00000 869 x)
129
D 20000 40000 60000 80000 100)00
QjnJatiue TCDO ngkg I ip d x jears
8
9
10
11
NIOSH
cohort,
Steenland et
al. (2001)
<335
335-<520
520-<1212
1212-<2896
2896-<7568
7568-<20455
>20455
ppt lipid *Years
n = 3538 male;
measured = 199;
cancer deaths
= 256
260
402
853
1895
4420
12125
59838
Median
1.00 RR
1.26 (15-yrlag)
1.02
1.43
1.46
1.82
1.62
Power: p=0.003
RR =
(x/background)A0.097
Piecewise linear,
<40000:c
RR = exp(0.000015
D ZmUD 4Oj[I]
Qjmulatw TicDDngJUgllpli] I ysan
12
13
14
15
BASF
cohort, Ott
and Zober
(1996)
0.1-0.99
1.0-1.99
2.0+
Hg/kg bw. peak;
n = 243 male;
measured = 138;
cancer deaths =31
598
19407
55057
148800
Arithmetic mean
0.80 SMR
1.2 (0-yrlag)
1.4
2.0
Conditional risk ratio
= 1.22 (95% CI
1.00-1.50)d
RR =
exp(0.00000503 x)
DJS
. 0II End ZtfiEr
D sum in mm ismrn
Cum ula 1 w TCDDng^hg Npld E yean
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Table 5-2. Published cancer epidemiology and bioassay data and
dose-response formulae (continued)
1
2
3
4
5
6
7
Study
Exposure
groups
S-D Rats,
Kociba et al.
(1978);
Goodman
and Sauer
(1992)
pathology
0
0.001
0.01
0.1
Hg/kg/day
Central estimate
of range
(ng/kg fat x
years)"
0
540
1700
8100
ng/kg lipid,
not AUC
All cancer deaths
observed
(latency)
2/86 Tumors
1/50
9/50
18/45
e Model mtiDJecarridenix In I
ae
as
Bo.«
K
•*. D3
S
i°2
D.1
D
a onz on*
DCS n.i
8
9
10
11
12
13
' Central estimates provided courtesy of Drs. Steenland, Zober, and Becher.
b RR data provided only for 1he zero-lag analysis in Becher et al. (1998)
c Coefficient for the piecewise linear model (0.000015) provided by Dr. Steenland. The initial slope in the
piecewise regression is applicable only to 40,000 ng/kg lipid years.
d Slope factor calculated from the conditional risk ratio, CR=1.22; see Chapter 8
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1
2
3
4
5
6
7
8
Table 5-3. All cancer risk in humans through age 75a
Study
Steenland et al.
(2001)
Becher et al.
(1998)
Ott and Zober
(1996)
Model and Sex
power male
power female
piece wise linear male
piece wise linear female
power-male
power-female
additive-male
add iti ve-fema le
multiplic ati ve-m ale
multiplicative-female
multiplicative-male
multiplic ative-female
ED01
1.38
1.84
18.6
23.1
5.971
7.58
18.22
22.75
32.16
39.82
50.9
62.1
95% CI
(lower,
upper)
0.71, 8.95
0.92, 14.9
11.5,48.3
14.3,59.8
25.0,~
30. 5, ~
Unit excess risk for 1 ppt
body burden above
background
0.0079 (0.0027, 0.0132)
0.0064(0.0022,0.0107)
0.00052 (0.00020, 0.00084)
0.00042 (0.00016, 0.00067)
0.0018
0.0014
0.00055
0.00044
0.0003
0.00024
0.00019(0,0.00039)
0.00015 (0,0.00032)
9
10
11
12
13
' Units are constant body burden in ng/kg not adjusted for lipid: see Part III, Chapter 8, Table 8-2, for details.
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1
2
3
4
5
6
7
8
9
10
1 1
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
Table 5-4. Summary of all site cancer ED01 and slope factor calculations
Study
Hamburg cohort,
Hamburg cohort,
Hamburg cohort,
Becher et al. (1998), power
Becher et al. (1998), additive
Becher et al. (1998), multiplicative
NIOSH cohort, Steenland et al. (2001), piecewise linearb
BASF cohort, from Ott and Zober (1996), multiplicative
ED01 (LED01)
(ng/kg)
6
18.2
32.2
18.6(11.5)
50.9 (25.0)
Cancer slope factor
for 1 pg/kg/day above
background3
(UCL)
5.1 E-3
1.6 E-3
0.89 E-3
1.5 E-3 (2. 5 E-3)
0.57 E-3 (1.2 E-3)
Sprague-Dawley rats, Kociba et al.(1978);
Goodman and Sauer (1992), pathology
31.9(22)c
BMD dose
38(27.5)
BMD adipose
0.97 E-3 (1.4 E-3)
0.8 E-3 (1.1 E-3)
a Assumes 25% of body weight is lipid; 80% of dioxin dose is absorbed from the normal diet in humans; the TCDD
half-life is 7.1 years in humans. Background all cancer mortality rate calculated through lifetable analysis to 75
years. Summary results are for male all cancer risk, because the male lifetime (to 75 years) all cancer risk is greater
than for females, leading to correspondingly higher cancer slope factors. As detailed in Part III, Chapter 8,
RelRisk^pQ!) = 0.99 + O.Ol/Risk^^,,^. Based on the manner in which the dose-response data were calculated using
Cox Regression rate ratio analyses, risks are given as cancer slope factors for 1 pg/kg/day above background,
assumed 5 ppt TCDD in lipid.
b Steenland et al. (2001) power model results are not included, as this formula predicts unreasonably high
attributable risks at background dioxin levels in the community due to the steep slope of the power curve formula
at very low levels.
0 Modeled using U.S. EPA Benchmark Dose Software, version 1.2, with either dose or adipose concentration as the
metric. Absorption from food pellets in animals is assumed to be 50%. BMD = 0.00176849 ug/kg/day. BMDL =
0.00122517 ug/kg/day. Therefore, ratLED01 = 1.2251 x25 x 0.5/ln2 = 22 ng/kg; human equivalentLED01 = 22 x
In2x 1000/2593/0.8= 7.38 pg/kg/day; slope factor = 0.01/7.38 = 1.4 E-3 risk/pg/kg/day.
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1
2
3
4
Table 5-5. Doses yielding 1% excess risk (95% lower confidence bound)
based upon 2-year animal carcinogenicity studies using simple multistage
(Portier et al., 1984) models3
Tumor
Liver cancer in female rats (Kociba)
Squamous cell carcinoma of the tongue in male
rats (Kociba)
Squamous cell carcinoma of the nasal turbinates
or hard palate in male rats (Kociba)
Squamous cell carcinoma of the lung in female
rats (Kociba)
Squamous cell carcinoma of the nasal turbinates
or hard palate in female rats (Kociba)
Thyroid follicular cell adenoma in male rats
(NTP)
Thyroid follicular cell adenoma in female rats
(NTP)
Liver adenomas and carcinomas in female rats
(NTP)
Liver adenomas and carcinomas in male mice
(NTP)
Liver adenomas and carcinomas in female mice
(NTP)
Thyroid follicular cell adenomas and carcinomas
in female mice (NTP)
Subcutaneous tissue sarcomas in female mice
(NTP)
Leukemias and lymphomas in female mice (NTP)
Shape
Linear
Linear
Cubic
Cubic
Linear
Linear
Cubic
Quadratic
Linear
Linear
Linear
Lin-Cubic
Linear
ED01
Animal intake for 1%
excess risk in
ng/kg/day (95% lower
confidence bound)
0.77(0.57)
14.1 (5.9)
41.4 (1.2)
40.4(2.7)
5.0(2.0)
4.0(2.1)
33.0(3.1)
13.0(1.7)
1.3 (0.86)
15.1 (7.8)
30.1 (14.0)
43.2(14.1)
10.0(5.4)
Steady-state body
burden in ng/kg at
ED01 (95% lower
confidence bound)
14 (10)
254 (106)
746 (22)
730(48)
90(36)
144 (76)
1190(112)
469(61)
20.6 (13.6)
239 (124)
478 (222)
686 (224)
159(86)
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
' Reprinted with slight modifications from Part II, Chapter 8, Table 8.3.2.
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1
2
3
Table 5-6. Body burdens for critical endpoints in animals with human
equivalent daily intake
Animal
Rats
Rhesus
monkeys
Rats
Rats
Rats
Rats
Mice
Rats
Mice
Rats
1 ml point
Cancer
Fetal mortality
Developmental
neurotoxicity
Endometriosis
Reproductive tox.
(multigenerational)
Developmental/
reproductive toxicity
Developmental
immunotoxicity
Developmental
Neurotoxicity
Immunological effects
(adult)
Thyroid effects
CYP1A1/1A2 enzyme
induction
CYP1A1/1A2 enzyme
induction
Study
Kocibaetal. (1978)
Bowman et al. (1989)
Schantzet al. (1992)
Rier et al. (1993)
Murray et al. (1979)
Mablyetal. (1992)
Grayetal. (1997)
Faqi et al. (1998)
Ohsakoetal. (2001)
Gehrs and Smialowicz
(1999)
Markowski et al. (2001)
Burleson et al. (1996)
Smialowicz et al. (1994)
Narasimhan et al. (1994)
Vecchietal. (1983)
Sewall et al. (1995)
DeVito et al. (1994)
Diliberto et al. (2001)
Vogeletal. (1997)
Narasimhan et al (1994)
van Birgelen et al. (1995)
Schrenketal. (1994)
Sewall et al. (1995)
Walker et al. (1999)
Estimated body burden (ng/kg)
LOAEL
180
90
21
21
180
38
30
25
30
60
108
6
300
100
1200
76
24
2.8
5.1
25
243
72
8
76
NOAEL
18
21
-
-
18
-
-
-
8
-
36b
3
-
50b
-
22
-
-
0.51
10
-
-
2
-
ED01
32
NC
NC
NC
NC
0.34
0.08
0.6
NC
NC
0.7
NC
2.9
1.5
7
26
22
67
0.003
3
19
26
3.5
59
Human equiv."
intakes
(pg/kg/day)
60; 6; 11
30; 7
7
7
60; 6
13; 0.1
10; 0.03
8; 0.2
10; 3
20
36; 12; 0.2
2; 1
100; 1
33; 17; 0.5
401; 2
25; 7; 8
8; 7
0.9; 22
1.6; 0.16; 0.001
8; 3; 2; 1
81;6
24; 9
3; 0.7; 1
25; 20
16
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1 Table 5-6. Body burdens for critical endpoints in animals with human equivalent daily
2 intake (continued)
3
4 a Human equivalent intakes were estimated according to the following equation: daily intake (pg/kg/day) =
5 (body burden (ng/kg)*Ln2*1000)/(t!/2*absorption) where t!/2 = 2593 days and absorption fraction = 0.8 (Poiger
6 and Schlatter 1986; see Section II). Corresponding human equivalent intake values are arranged in sequence
7 from the previous three columns.
8 b NOAEL values are based on the highest individual dose group in which there are no statistically significant
9 changes. Statistically significant dose response trends plus apparent declines are also evident at all dose
1 0 levels—20 and 60 ng/kg orally—in all fixed-ratio test groups in Markowski et al. (2001) and in the 50 ng/kg
1 1 dose group in Narasimhan et al. (1994).
12
13 - - = no NOAEL value, as effects seen in the lowest dose group in the study.
1 4 NC = Not calculated due to insufficient dose response information (less than three doses and a control) or due to
1 5 presentation of the data in graphical form without tabulation of mean and variance estimates.
1 6 Note: This table is reproduced in Appendix A with explanatory details of study design, results, and
1 7 calculation procedures, formulae, and assumptions.
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1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
2SO.O -r
2QD.O --
150.0 --
100.0 --
c
CB
0.0
PeakBB
-Lifetime Ave. Body
50.0 -fBurdaTOccup=~5T9 ng/k;
LABB AUC
ng/kg ng/kg *Y
Background 2.6 134
Occupational 55.9 3911
LABB= AUC/Yrs
Area Under the Curve
- Occupational
= 3911 ng/kg* Y
= lS4ng/kj*Y
D.O 10,0 20,0 300 40.0
Age, years
50.0
GOO
70.0
Figure 5-1. Comparison of lifetime average body burden and area under the curve in
hypothetical background and occupational scenarios.
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10000
1000
o
J3
(D
100 -
u
3
PQ
o
PQ
10
E3 2378-TCDD H Est. Background Non-TCDD TEQs
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
Figure 5-2. Peak dioxin body burden levels in background populations and
epidemiological cohorts (back-calculated) (See Table 5-1). For the background U.S.
populations (CDC; USA -1990s), the bars represent the range of total TEQ measured in the
population. The lower shaded portion represents the variability from non-2,3,7,8-TCDD-
derived TEQs, the upper shaded portion the variability in the 2,3,7,8-TCDD. Note that the
respective bar sizes do not represent the total non-2,3,7,8-TCDD TEQ or 2,3,7,8-TCDD
contributions, because a portion of each of these contributions is contained within the region
between the x-axis and bottom of the bar, namely the minimum estimated body burden. For
each of the back-calculated epidemiological cohort exposures, the bar was estimated on the
basis of the combination of two distributions: the 2,3,7,8-TCDD levels measured in the
respective cohort plus the estimated range of background non-2,3,7,8-TCDD-derived TEQs
from the U.S. population. The lower estimate is the combination of the lower 2,3,7,8-
TCDD and lower non-2,3,7,8-TCDD TEQ contributions; the shading junction represents the
variability in background U.S. population non-2,3,7,8-TCDD levels that have been added to
this bar; the mean/median/geometric mean indicators represent the addition of the measured
2,3,7,8-TCDD central estimate with the mean background U.S. population non-2,3,7,8-
TCDD TEQ level (-47.6 ppt lipid, 11.9 ng/kg body burden at 25% body fat); and the upper
limit is the combination of the upper 2,3,7,8-TCDD and upper non-2,3,7,8-TCDD TEQs.
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1 6. RISK CHARACTERIZATION
2
3 Characterizing risks from dioxin and related compounds requires the integration of
4 complex data sets and the use of science-based inferences regarding hazard, mode of action, dose
5 response, and exposure. It also requires consideration of incremental exposures in the context of
6 an existing background exposure that, for the majority of the population, is independent of local
7 sources and dominated by exposure through the food supply. Finally, this characterization must
8 consider risks to special populations and developmental stages (subsistence fishers, children,
9 etc.) as well as to the general population. It is important that this characterization convey the
10 current understanding of the scientific community regarding these issues, highlight uncertainties
11 in this understanding, and specify where assumptions have been used or inferences made in the
1 2 absence of data. Although characterization of risk is inherently a scientific exercise, it must by
13 nature go beyond empirical observations and draw conclusions in untested areas. In some cases,
14 these conclusions are, in fact, untestable, given the current capabilities in analytical chemistry,
1 5 toxicology, and epidemiology. This situation should not detract from one's confidence in the
16 conclusions of a well-structured and well-documented characterization of risk, but it should serve
1 7 to confirm the importance of considering risk assessment as an iterative process that benefits
18 from evolving methods and data collection and is subject to change as the knowledge base
19 improves.
20
21 Dioxin and related compounds can produce a wide variety of effects in animals and may
22 produce many of the same effects in humans.
23 There is adequate evidence, based on all the available information, as discussed in Parts I
24 and II of this Reassessment and in this Integrated Summary, to support the inference that the
25 potential exists for humans to respond with a broad spectrum of effects from exposure to dioxin
26 and related compounds, depending on the magnitude and duration of exposure. This inference is
27 based on the similarities in receptor and receptor binding and their sequellae observed in animals
28 and in humans. Effects will likely range from detection of biochemical changes at or near
29 background levels of exposure to detection of adverse effects with increasing severity as body
30 burdens increase above background levels. Data presented in Part II, Chapter 8, and illustrated in
31 Table 5-6 and Appendix A support this general conclusion.
32 Enzyme induction, changes in hormone levels, and indicators of altered cellular function
33 seen in humans and laboratory animals represent effects of unknown clinical significance but that
34 may be early indicators of toxic response. Induction of activating/metabolizing enzymes at or
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1 near background levels, for instance, may be adaptive and, in some cases, beneficial, or it may be
2 considered adverse. Induction may lead to more rapid metabolism and elimination of potentially
3 toxic compounds, or it may lead to increases in reactive intermediates and may potentiate toxic
4 effects. Examples of both of these situations are available in the published literature, and events
5 of this type formed the basis for a biologically based model discussed in Part in, Section 5.
6 Subtle effects, such as the impacts on neurobehavioral and developmental outcomes in
7 laboratory animals and humans, the thyroid function and immune system alterations seen in the
8 Dutch children exposed to background levels of dioxin and related compounds, or the changes in
9 circulating reproductive hormones in men exposed to TCDD, illustrate the types of responses
10 that support the finding of subtle yet arguably adverse effects at or near background body
11 burdens. Clearly adverse effects, including, perhaps, cancer, may not be detectable until
1 2 exposures contribute to body burdens that exceed current background by one or two orders of
1 3 magnitude (10 or 100 times). MOEs in this range are considerably less than those typically seen
14 for environmental contaminants of toxicologic concern, particularly when the health endpoint is
1 5 cancer, as observed in epidemiologic studies.
16 Clear mechanistic relationships between biochemical and cellular changes seen at or near
1 7 background body burden levels and production of adverse effects detectable at higher levels
1 8 remain uncertain, but modes of action consistent with available data have been discussed in
1 9 several chapters in Part n. Information on these mechanistic relationships and modes of action is
20 useful in hazard characterization, and data are accumulating to suggest refined mode of action
21 hypotheses for further testing.
22 It is well known that individual species vary in their sensitivity to any particular dioxin
23 effect. Laboratory rodents (typically strains of rats and mice) are not necessarily the most
24 sensitive responders for several well-studied effects. However, the evidence available to date
25 indicates that humans most likely fall in the middle rather than at either extreme of the range of
26 sensitivity for individual effects among animals. In other words, evaluation of the available data
27 suggests that humans, in general, are neither extremely sensitive nor insensitive to the individual
28 effects of dioxin-like compounds.
29 Human data provide direct or indirect support for evaluation of likely effect levels for
30 several of the endpoints observed in laboratory studies (e.g., cancer and neurobehavioral and
31 endocrine endpoints), although the influence of variability among humans remains difficult to
32 assess. Discussions have highlighted certain prominent, biologically significant effects of TCDD
33 and related compounds. In TCDD-exposed men, subtle changes in biochemistry and physiology,
34 such as enzyme induction, altered levels of circulating reproductive hormones, or reduced
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1 glucose tolerance and, perhaps, diabetes, have been detected in a limited number of
2 epidemiologic studies.
3 These findings, coupled with the knowledge derived from animal experiments, suggest
4 the potential for adverse impacts on human metabolism and developmental and/or reproductive
5 biology and, perhaps, other effects in the range of current human exposures. These biochemical,
6 cellular, and organ-level endpoints have been shown to be affected by TCDD, but specific data
7 on these endpoints do not generally exist for other congeners. Despite this lack of congener-
8 specific data, there is reason to infer that these effects may occur for all dioxin-like compounds,
9 based on the concept of toxic equivalency.
10 In this document, dioxin and related compounds are characterized as developmental,
11 reproductive, immunological, endocrinological, and carcinogenic hazards. The deduction that
1 2 humans are likely to respond with noncancer effects from exposure to dioxin-like compounds is
13 based on the finding that these compounds impact cellular regulation at a fundamental level and
14 on the demonstration of adverse effects among a broad range of species. For example, because
1 5 developmental toxicity following exposure to TCDD-like congeners occurs in fish, amphibians,
1 6 reptiles, birds, and mammals, it is likely to occur at some level in humans.
17 It is not currently possible to state exactly how or at what levels individuals will respond
18 with specific adverse impacts on development or reproductive function, but the analyses of the
1 9 Dutch cohort data and laboratory animal studies suggest that some effects may occur at or near
20 background levels. Fortunately, there have been few human cohorts identified with TCDD
21 exposures high enough to raise body burdens significantly over background levels (see Table 5-1
22 and Figure 5-2 in this document), and when these cohorts were examined, relatively few
23 clinically significant effects were detected. However, the power of these studies to detect these
24 effects remains an issue. The lack of sufficient exposure gradients and adequate human
25 information and the focus of most currently available epidemiologic studies on occupationally
26 TCDD-exposed adult males make it difficult to evaluate the inference that noncancer effects
27 associated with exposure to dioxin-like compounds may be occurring in the broader human
28 population. It is important to note, however, that when exposures to very high levels of dioxin-
29 like compounds have been studied—such as in the Yusho and Yu-Cheng cohorts—a spectrum of
30 adverse effects have been detected in men, women, and children. Many of these effects are
31 similar to what has been observed not only in small laboratory animals, but in wildlife and in
32 nonhuman primates.
33 Some have argued that in the absence of better human data, deducing that a spectrum of
34 noncancer effects will occur in humans overstates the science; however, most of the scientists
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1 involved as authors and reviewers in the reassessment have indicated that such inference is
2 reasonable, given the weight of evidence from available data. As presented, this logical
3 conclusion represents a testable hypothesis that may be evaluated by further data collection.
4 EPA, its federal colleagues, and others in the general scientific community are continuing to fill
5 critical data gaps, which will reduce our uncertainty regarding both hazard and risk
6 characterization for dioxin and related compounds. However, as discussed by EPA's SAB (U.S.
7 EPA, 200Ib) "neither knowledge breakthroughs nor fully developed techniques for producing
8 more unbiased risk assessments can be expected to be available in the near future."
9
10 Dioxin and related compounds are structurally related and elicit their effects through a
11 common mode of action.
1 2 The scientific community has identified and described a series of common biological
13 steps that are necessary for most, if not all, of the observed effects of dioxin and related
14 compounds in vertebrates, including humans. Binding of dioxin-like compounds to a cellular
1 5 protein called the aryl hydrocarbon receptor (AhR) represents the first step in a series of events
16 attributable to exposure to dioxin-like compounds, including biochemical, cellular, and tissue-
1 7 level changes in normal biological processes. Binding to the AhR appears to be necessary for all
18 well-studied effects of dioxin, but it is not sufficient in and of itself to elicit these responses.
1 9 There remains some uncertainty as to whether every dioxin response is AhR-mediated.
20 Some data from the use of sensitive biological tools, such as AhR-deficient (AhR"7") mice,
21 suggest a small residual of effects from exposure to TCDD, and, thus, we cannot rule out
22 receptor-independent alternative pathways. However, these reported non-AhR-mediated
23 responses occur in animals at doses that are orders of magnitude higher than current human
24 exposures and require much higher doses than other AhR-mediated effects in animals. Thus,
25 these putative non-AhR-mediated mechanisms are unlikely to impact any of the assumptions
26 made in this reassessment.
27 Exposure of animals—and in some cases humans—to chemicals whose structure and
28 AhR binding characteristics are similar to those of 2,3,7,8-TCDD can elicit similar effects. In the
29 past 5 years, significant data have accumulated that support the concept of toxic equivalence, a
30 concept that is at the heart of risk assessment for the complex mixtures of dioxin and related
31 compounds encountered in the environment. These data have been analyzed and summarized in
32 Part II, Chapter 9. This chapter was added to EPA's dioxin reassessment to address questions
33 raised by the SAB in 1995. The SAB suggested that, because the TEQ approach was a critical
34 component of risk assessment for dioxin and related compounds, the Agency should be explicit
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1 in its description of the history and application of the process and go beyond reliance on the
2 Agency's published reference documents on the subject (U.S. EPA, 1987, 1989a).
3 The analyses in Parts II and HI of this document demonstrate that, although variability in
4 the data underpinning the scientific judgments regarding toxic equivalency exists, when data are
5 restricted to longer exposure and in vivo data, the empirical analysis strongly supports the
6 judgment of experts in setting TEF values. This is particularly true for the use of TEFs for
7 assessing the animal cancer endpoint but will likely apply even more strongly to noncancer
8 effects as additional congener-specific data are collected. A focus on the five congeners that
9 make up greater than 80% of human body burden on a TEQ basis reveals rather robust data sets,
10 which form the basis for assigned TEFs. This focus reduces the impact of the uncertainties in
11 TEFs assigned to less-studied congeners. In its recent review (U.S. EPA, 2001b), EPA's SAB
1 2 agreed that the general framework for calculating TEFs and applying them to obtain a TEQ is
13 well described in Part II, Chapter 9. The Board recognized that uncertainties remained regarding
14 toxi cities of joint exposures that are not dominated by well-studied congeners, and recommended
1 5 further development of the TEF methodology (e.g., development of probability density functions
1 6 around experimental results to assist future expert judgment in reviewing and revising TEFs) (see
1 7 Finley et al., 2003).
18
1 9 EPA and the international scientific community have adopted toxic equivalency of dioxin
20 and related compounds as prudent science policy.
21 Dioxin and related compounds always exist in nature as complex mixtures. As discussed
22 in the exposure document, these complex mixtures can be characterized through analytic
23 methods to determine concentrations of individual congeners. Dioxin and related compounds
24 can be quantified and biological activity of the mixture can be estimated using relative potency
25 values and an assumption of dose additivity. Such an approach has evolved over time to form
26 the basis for the use of TEQ in risk assessment for this group of compounds. Although such an
27 approach is dependent on critical assumptions and scientific judgment, it has been characterized
28 by the SAB as a "useful, interim" way to deal with the complex mixture problem, and it has been
29 accepted by numerous countries and several international organizations. Alternative approaches,
30 including the assumption that all congeners carry the toxic equivalency of 2,3,7,8-TCDD or that
31 all congeners other than 2,3,7,8-TCDD can be ignored, have been rejected as inadequate for risk
32 assessment purposes.
33 Significant additional literature is now available on the subject of toxic equivalency of
34 dioxin and related compounds, as summarized (through 2000) in Part n, Chapter 9. An
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1 international evaluation of all of the available data (van den Berg et al., 1998) reaffirmed the
2 TEQ approach and provided the scientific community with the latest values for TEFs for PCDDs,
3 PCDFs, and dioxin-like PCBs. Consequently, we can infer with greater confidence that humans
4 will respond to the cumulative exposure of AhR-mediated chemicals. This reassessment
5 recommends that the WHO98 TEF scheme be used to assign toxic equivalency to complex
6 environmental mixtures for assessment and regulatory purposes. Further research is needed to
7 address remaining uncertainties inherent in the current approach, in particular those regarding the
8 impact of actual exposures compared to measured body burdens of highly persistent congeners
9 and the continuing debate regarding the role of other Ah-agonists in the diet on the toxicity of
10 dioxin-like compounds. WHO has suggested that the TEQ scheme be reevaluated on a periodic
11 basis and that TEFs and their application to risk assessment be reanalyzed to account for
1 2 emerging scientific information. EPA supports this suggestion and intends to participate in
13 future re-evaluations.
14
1 5 Complex mixtures of dioxin and related compounds are highly potent, "likely"
16 carcinogens.
1 7 A weight-of-evidence evaluation suggests that mixtures of dioxin and related compounds
1 8 (CDDs, CDFs, and dioxin-like PCBs) are strong cancer promoters and weak direct or indirect
19 initiators and that they are likely to present a cancer hazard to humans. Because dioxin and
20 related compounds always occur in the environment and in humans as complex mixtures of
21 individual congeners, it is appropriate that the characterization apply to the mixture. According
22 to the Agency's revised proposed guidelines for carcinogen risk assessment, the descriptor
23 "likely to be carcinogenic to human" is appropriate when the available tumor effects and other
24 key data are adequate to demonstrate carcinogenic potential to humans (U.S. EPA, 1999, 2003)
25 yet are not sufficient to infer a cause-and-effect relationship.
26 "Adequate data" are recognized to span a wide range. Even though the database from
27 cancer epidemiologic studies remains a point of scientific discussion, it is the view of this
28 reassessment that this body of evidence is supported by the laboratory data that indicate that
29 TCDD increases cancer mortality of several types. Although not all confounders were ruled out
30 in any one study, positive associations between surrogates of dioxin exposure, either length of
31 occupational exposure or proximity to a known source combined with some information based
32 on measured blood levels, and cancer have been reported.
33 These epidemiologic data strongly suggest a role for dioxin exposure to contribute to a
34 carcinogenic response but are not sufficient to confirm a causal relationship between exposure to
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1 dioxin and increased cancer incidence. Available human studies alone cannot demonstrate
2 whether a cause-and-effect relationship between dioxin exposure and increased incidence of
3 cancer exists. Therefore, evaluation of cancer hazard in humans must include an evaluation of all
4 of the available animal and in vitro data as well as the data from exposed human populations.
5 The data for complex mixtures of dioxin and related compounds represent a case that,
6 according to discussions in the draft guidelines, would approach the strong-evidence end of the
7 adequate data spectrum. Epidemiologic observations of an association between exposure and
8 cancer responses (TCDD); unequivocal positive responses in both sexes, multiple species,
9 multiple sites, and different routes in lifetime bioassays or initiation-promotion protocols or other
10 shorter-term in vivo systems such as transgenic models (TCDD plus numerous PCDDs, PCDFs,
11 dioxin-like PCBs); and mechanistic or mode-of action data that are assumed to be relevant to
1 2 human carcinogenicity, including, for instance, initiation-promotion studies (PCDDs, PCDFs,
1 3 dioxin-like PCBs) all support the description of complex mixtures of dioxin and related
14 compounds as likely to be human carcinogens. On the basis of these observations, complex
1 5 environmental mixtures of TCDD and dioxin-like compounds should be characterized as "likely"
16 carcinogens, with the degree of certainty of the characterization being dependent on the
1 7 constituents of the mixture, when known. For instance, the hazard potential, although "likely,"
18 would be characterized differently for a mixture whose TEQ was dominated by octaCDD as
19 compared with one dominated by pentaCDF.
20 As discussed in Section 2.2.1.5, under EPA's current approach for carcinogen risk
21 assessment, individual congeners can also be characterized as to carcinogenic hazard. 2,3,7,8-
22 Tetrachlorodibenzo-^-dioxin (TCDD) is best characterized as "carcinogenic to humans." This
23 means that, on the basis of the weight of all of the evidence (human, animal, mode of action),
24 TCDD meets the criteria that allow EPA and the scientific community to accept a causal
25 relationship between TCDD exposure and cancer hazard. The guidance suggests that
26 "carcinogenic to humans" is an appropriate descriptor of human carcinogenic potential when
27 there is an absence of conclusive epidemiologic evidence to clearly establish a cause-and-effect
28 relationship between human exposure and cancer but there is compelling evidence of
29 carcinogenicity in animals and mechanistic information in animals and humans demonstrating
30 similar modes of carcinogenic action. The "carcinogenic to humans" descriptor is suggested for
31 TCDD because all of the following conditions are met:
32
33 • There is strong and consistent evidence from occupational epidemiologic studies for an
34 association between TCDD exposure and increases in cancer at all sites, in lung cancer
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1 and, perhaps, at other sites, but the data are insufficient on their own to support a causal
2 association. This point was discussed in detail by the International Agency for Research
3 on Cancer (IARC, 1997).
4
5 • There is extensive carcinogenicity in both sexes of multiple species at multiple sites.
6
7 • There is general agreement that the mode of TCDD's carcinogenicity is as an AhR-
8 dependent promoter and proceeds through gene expression and/or a modification of the
9 action of a number of receptor and hormone systems involved in cell growth and
10 differentiation, such as the epidermal growth factor receptor and the estrogen receptor.
11
1 2 • The human AhR and the rodent AhR are similar in structure and function and, once
13 activated, both bind to the same DNA response elements, designated DREs.
14
1 5 • Human and rodent tissue and organ cultures respond to TCDD and related chemicals in a
1 6 similar manner and at similar concentrations. TCDD has the ability to transform
1 7 immortalized human and rodent cells that then have demonstrable tumorigenicity.
18
1 9 Other individual dioxin-like compounds are characterized as "likely to be carcinogenic to
20 humans" primarily because of the lack of epidemiological evidence associated with their
21 carcinogenicity, although the inference based on toxic equivalency is strong that they would
22 behave in humans as TCDD does. Other factors, such as the available congener-specific chronic
23 bioassays, also support this characterization. For each congener, the degree of certainty is
24 dependent on the available congener-specific data and their consistency with the generalized
25 mode of action that underpins toxic equivalency for TCDD and related compounds.
26 Although uncertainties remain regarding quantitative estimates of upper-bound cancer
27 risk from dioxin and related compounds, efforts of this reassessment to bring more data into the
28 evaluation of cancer potency have resulted in evaluation of the slope of the dose-response curve
29 at the low end of the observed range (using the LED01) using a simple proportional (linear) model
30 and a calculation of both upper-bound risk and MOE based on human equivalent background
31 exposures and associated body burdens. Evaluation of shape parameters (used to estimate degree
32 of linearity or nonlinearity of dose-response within the range of observation) for biochemical
33 effects that can be hypothesized as key events in a generalized dioxin mode-of-action model do
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1 not argue for significant departures from linearity below a calculated ED01, extending down to at
2 least one to two orders of magnitude lower exposure.
3 Risk estimates for intakes associated with background body burdens or incremental
4 exposures based on this slope factor represent a plausible upper bound on risk, based on the
5 evaluation of animal and human data. The slope factors, based on the most sensitive cancer
6 responses calculated by authors of peer-reviewed publications and presented in Part II, Chapter 8,
7 and Section 5 for both animals and humans, fall in a range of approximately 0.6 x 10~3 to 5 x 10~3
8 per pg TEQ/kg body weight/day.
9 The ranges of estimates of upper-bound cancer potency calculated from the human and
10 animal data overlap. The range above is bounded on the upper end by the estimate of slope from
11 the Hamburg cohort epidemiology study and on the lower end by the estimates from the Ott and
1 2 Zober epidemiology study, with the NIOSH piece-wise linear epidemiology model and the
1 3 reanalyzed Kociba rat study falling intermediate in this range. Consequently, the Agency,
14 although fully recognizing this range and the public health-conservative nature of the slope
15 factors that make up the range, suggests the use of 1 x ip-3 per pg TEQ/kg body weight/day as an
16 estimator of upper-bound cancer risk for both background intakes and incremental intakes above
17 background.
1 8 This decision reflects the weight given to the individual estimates from the human studies
19 and the comparability of the revised estimate from the animal data. A recently published meta-
20 analysis (Crump, 2003) is consistent with this estimate. In addition, this decision reflects the
21 judgment that, because ED01 estimates require little extrapolation from the range of observation
22 and current body burdens are within a factor of 10 of the ED01 estimates, use of a linear model is
23 both consistent with the data and unlikely to require more than an order of magnitude
24 extrapolation. This bounding on extrapolation would apply to both estimates of risk at current
25 background exposures and to additional increments above current background. Application of
26 upper-bound slope factors allows the calculation of a high-end bounding estimate of the
27 probability of cancer risk in the population. This means that there is greater than a 95% chance
28 that "true" population cancer risks will be less than the upper-bound estimate.
29 Use of the human ED01s rather than the LED01s to provide more likely upper-bound
30 estimates based on the available epidemiological data is a matter of EPA science policy and
31 compares well with upper-bound animal cancer data. Use of either ED01 or LED01 results in
32 slope factors and risk estimates that are within a factor of 2; well within the inherent uncertainty
33 of these estimates. Although there may be individuals within a population who may experience a
34 higher cancer risk on the basis of genetic factors or other determinants of cancer risk not
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1 accounted for in epidemiologic data or animal studies, the vast majority of the population is
2 expected to have less risk per unit of exposure than the bounding estimate would suggest, and
3 some may have zero risk.
4 On the basis of these slope factor estimates (per pg TEQ/kg body weight/day), upper-
5 bound risks at average current background body burdens (5 ng TEQ/kg body weight) that result
6 from historical average intakes of approximately 3 pg TEQ/kg body weight/day may exceed 10~3
7 (1 in a 1000). A very small percentage of the population (< 1%) has estimated risks that are a
8 few times higher than an upper bound based on average intake if their individual cancer risk
9 slope is represented by the upper bound estimate and they are among the most highly exposed
10 (among the top 5%), based on dietary intake of dioxin and related compounds. This estimate of
11 the range of upper-bound risk for the general population has increased by approximately an order
1 2 of magnitude from the estimate described at background exposure levels in EPA's earlier draft of
13 this reassessment (10~4-10~3) (U.S. EPA, 1994). This has occurred because, despite the fact that
14 average intakes and body burdens are going down, estimates of upper-bound risk per unit dose
1 5 have gone up by a factor of approximately 6 over the Agency's 1985 estimate and the range of
16 exposure through the diet has been characterized.
1 7 EPA's approach to the development of an upper-bound estimate on cancer risk is
18 consistent with its own past practices described above and with FDA's approach. In its recent
19 report (U.S. EPA, 2001b), the SAB agreed that the treatment of the range of upper-bound risks
20 obtained for the general population in this assessment is consistent with past EPA practice.
21 FDA's past estimates of a risk-specific dose associated with a one-in-a-million risk (0.057 pg/kg
22 body weight/day) (FDA 1990) have been based on animal data and have differed from EPA's
23 only in minor ways regarding tumor counts and in the approach to cross-species scaling. In 1992,
24 while EPA's reassessment was underway, FDA's risk-specific dose was adopted by the U.S.
25 Public Health Service's Committee to Coordinate Environmental Health and Related Programs
26 (CCEHRP) as the risk-specific dose for TEQ. In 1998, ATSDR used this risk-specific dose as a
27 line of support for its policy guideline on dioxin and dioxin-like compounds in soil.
28 WHO and a number of individual countries have taken a different science-policy
29 approach and have treated dioxins as nongenotoxic carcinogens and assumed that a safety factor
30 approach, based on noncancer effects observed at lower doses than cancer in animals, would be
31 adequate to account for concerns for both cancer and noncancer effects. This approach assumes
32 that there is a virtual threshold for cancer effects above those for many noncancer effects. This
33 position has been reiterated as recently as June 2001 by the Joint FAO/WHO Expert Committee
34 on Food Additives (JECFA). The differences between EPA (plus a number of other U.S. federal
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1 agencies) and these international organizations in their approach to assessing potential cancer
2 risk reflect differences in science policy.
3 Despite EPA's use of the epidemiology data to describe an upper bound on cancer risk,
4 the peer panels who met to review earlier drafts of the cancer epidemiology chapter suggested
5 that the epidemiology data alone were not adequate to support the characterization of dioxin and
6 related compounds as "known" human carcinogens but that the results from the human studies
7 were largely consistent with observations from laboratory studies of dioxin-induced cancer and,
8 therefore, should be weighed in the assessment. Other scientists, including those who attended
9 the peer panel meetings, felt either more or less strongly about the weight of evidence from
1 0 cancer epidemiology studies, representing the range of opinions that still exists on the
11 interpretation of these studies. Similar opinions were expressed in the comments documented in
1 2 the SAB's reports in 1995 and in 2001 (U.S. EPA, 1995, 2001b).
1 3 In its reevaluation of the cancer hazard of dioxin and related compounds, IARC (1997)
14 found that whereas the epidemiologic database for 2,3,7,8-TCDD was still "limited," the overall
1 5 weight of the evidence provided by human, animal and mechanistic data was sufficient to
1 6 characterize 2,3,7,8-TCDD as a Category 1 "known" human carcinogen. Other related members
1 7 of the class of dioxin-like compounds were considered to have "inadequate" epidemiologic data
1 8 to factor into hazard categorization. A similar classification of 2,3,7,8-TCDD as a "known"
1 9 carcinogen has been published within the context of the Department of Health and Human
20 Services' report on carcinogens (NTP, 2001). Here, too, the characterization is based on the
21 weight of the human, animal, and mode of action information in humans and animals.
22 Therefore, given that 2,3,7,8-TCDD is contained in complex mixtures of dioxin and
23 related compounds and that the TEQ approach has been adopted as a reasonable approach to
24 assessing risks of these complex mixtures, it is also reasonable to apply estimates of upper-bound
25 cancer potency derived from epidemiology studies where 2,3,7,8-TCDD was associated with
26 excess cancer risk to complex mixtures of dioxin and related compounds.
27 The current evidence suggests that both receptor binding and most early biochemical
28 events such as enzyme induction demonstrate linearity of dose-response within the range of
29 observation. The mechanistic relationship of these early events to the complex process of
30 carcinogenesis remains uncertain, although modes of dioxin action have been proposed. If these
31 findings imply low-dose linearity in biologically based cancer models under development, then
32 the probability of cancer risk may also be linearly related to exposure to TCDD. Until the
33 mechanistic relationship between early cellular responses and the parameters in biologically
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1 based cancer models is better understood, the shape of the dose-response curve for cancer below
2 the range of observation can be inferred only with uncertainty.
3 Initial attempts to construct a biologically based model for certain dioxin effects as
4 described in this reassessment will need to be continued and expanded to accommodate more of
5 the available biology and to apply to a broader range of potential health effects associated with
6 exposure to dioxin-like compounds. Associations between exposure to dioxin and certain types
7 of cancer have been noted in occupational cohorts with average body burdens of TCDD
8 approximately one to three orders of magnitude (10 to 1000 times) higher than average TCDD
9 body burdens in the general population. In terms of TEQ, the average body burden in these
10 occupational cohorts level is within one to two orders of magnitude (10 to 100 times) of average
11 background body burdens in the general population (see Table 5-1 and Figure 5-2). Thus, there
12 is no need for large-scale, low-dose extrapolations when applying models based on curve-fitting
13 empirical data in order to evaluate background intakes and body burdens, and there are few if any
14 data to suggest large departures from linearity in this somewhat narrow window between the
1 5 lower end of the range of observation and the range of general population background exposures.
1 6 Nonetheless, the relationship of apparent increases in cancer mortality in these worker
1 7 populations to calculations of general population risk remains a source of uncertainty.
18
19 Use of a "margin of exposure" approach to evaluate risk for noncancer and cancer
20 endpoints.
21 The likelihood that noncancer effects may be occurring in the human population at
22 environmental exposure levels has received increased attention in recent years and is a major
23 focus of this reassessment. This likelihood is often evaluated using an MOE approach. AnMOE
24 is calculated by dividing a "point of departure" at the low end of the range of observation in
25 human or animal studies (the human-equivalent LOAEL, NOAEL, BMD, or effective dose
26 [EDxx]) by the comparable surrogate of human exposure at the level of interest. It differs from a
27 reference dose (RfD), which establishes a level of exposure below which the Agency considers it
28 unlikely that any adverse effects will occur. The Agency has used the MOE approach for a
29 number of years in its noncancer assessment of the safety of pesticides. The MOE concept has
30 also been incorporated into the Draft Final Guidelines for Carcinogen Risk Assessment (U.S.
31 EPA, 2003) as an alternative approach to dose-response analysis if the shape of the dose-
32 response curve is uncertain. These draft cancer guidelines recommend differing approaches and
33 default assumptions for linear versus nonlinear cancer data, where linear data can be
34 approximated through the cancer slope factor and nonlinear data through an RfD and Hazard
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1 Index approach. For both linear and nonlinear approaches to cancer characterization, the Agency
2 recommends a statement of the extent of extrapolation of risk estimates from observed data to
3 exposure levels of interest and its implications for certainty or uncertainty in quantifying risk.
4 The extent of this extrapolation can be expressed as a margin of exposure (MOE).
5 As the exposure of interest approaches the range of observation of effects and MOEs get
6 smaller, reaching any conclusion regarding the certainty of no harm is much more difficult and
7 relies heavily on scientific judgment regarding the adequacy of the available data. In order for a
8 decision relying on the MOE to be adequately protective of health, information is provided to
9 allow the decisionmaker, to the extent information allows, to take into account the nature of the
10 effect at the POD; the shape and slope of the dose-response curve; the adequacy of the overall
11 database to assess human hazard; interindividual variability in the human population with regard
1 2 to exposure, metabolism, and toxic response; and other factors. Background exposures should be
13 factored into the calculation. Considering MOEs based on estimates of incremental exposure
14 alone divided by the human exposure of interest is not considered to give an accurate portrayal of
1 5 the implications of that exposure unless background exposures are insignificant.
16 One of the difficulties in assessing the potential health risk of exposure to dioxins is that
1 7 background exposures are often a significant component of total exposure when based on TEQ.
18 The average levels of background intake and current average body burdens of dioxin-like
1 9 compounds in terms of TEQs in the general population (1 pg TEQ/kg body weight/day and 5 ng
20 TEQ/kg body weight, respectively) are within a factor of 10 of human-equivalent levels
21 associated with NOELS, LOAELs, or ED01 values derived from studies in laboratory animals
22 exposed to TCDD or TCDD equivalents for both cancer and noncancer toxic effects (see Table
23 5-6 and Appendix A). Therefore, in many cases, the MOE compared to background using these
24 toxic endpoints is a factor of 10 or less. These estimates and others are presented and discussed
25 in Part II, Chapter 8.
26 As discussed in Chapter 8, these data, although variable, suggest that choosing a human-
27 equivalent body burden associated with an ED01 value above 100 ng/kg as a point of departure
28 would likely yield a greater than 1% excess risk for some toxicity endpoint in humans. Also,
29 choosing a POD below 1 ng/kg would likely be an extrapolation below the range of these data.
30 Given the nature of the data and the range of uncertainty around individual data sets, any choice
31 for a 1% effect point of departure in the middle range of 1 ng/kg to 100 ng/kg would be
32 supported by the analyses, although the data provide the greatest support for defining a point of
33 departure consistent with principles of safety assessment in the range of 10 ng/kg to 50 ng/kg.
34 This range also includes body burdens consistent with the empirically derived NOAELs and
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1 LOAELs for many of the effects that have traditionally been used as a POD for safety assessment
2 by WHO, JECFA, and ATSDR.
3 Although somewhat dependent on experimental design or the model chosen to derive the
4 ED01, NOAEL, and LOAEL values, this range provides a perspective on the nature and variety of
5 effects that have been evaluated within approximately an order of magnitude, from biochemical
6 markers of exposure to more clearly adverse effects in animals. This range of body burdens
7 should also provide a useful point of comparison when evaluating impacts of risk management
8 on average body burdens in the general population or on estimates of impact of incremental
9 exposures above background on the range of individual body burdens at various ages.
10 Because of the relatively high background levels as compared to effect levels, the Agency
11 is not recommending the derivation of a reference dose (RfD) for dioxin and related compounds.
1 2 Although RfDs are often useful because they represent a health risk goal below which there is
1 3 likely to be no appreciable risk of noncancer effects over a lifetime of exposure, their primary use
14 by the Agency is to evaluate increments of exposure from specific sources when background
1 5 exposures are low. Any RfD that the Agency would recommend using a traditional approach for
1 6 setting an RfD using uncertainty factors to account for limitations of knowledge is likely to be
1 7 below—perhaps significantly below (by a factor of 10 or more)—current background intakes and
18 body burdens. Because exceeding the RfD is not a statement of risk, comparing an incremental
19 exposure to an RfD when the RfD has already been exceeded by average background exposures
20 has little value for evaluating possible risk management options. In addition, the calculation of
21 an RfD (with its traditional focus on a single "critical" effect) distracts from the large array of
2 2 effects associated with similar body burdens of dioxin.
23 The Agency's SAB, in its comments on an earlier draft of this document, remarked that
24 there might be value in calculating an RfD, despite a recognition of these concerns. The RfD
25 could be used for purposes of comparison with other chemical-specific RfDs, to ensure that
26 proper emphasis was given to noncancer effects and to set a goal for future exposure reductions.
27 These comments notwithstanding, the Agency feels that all of these ends can be accomplished
28 without the establishment of an RfD.
29 As discussed earlier, a range of values has been presented that indicates that dioxin and
30 related compounds can produce effects, some of which are indicative of a biological response to
31 dioxin exposure and some of which are arguably adverse, at or near current background body
32 burdens or intake levels. Several of the studies within this range could logically be chosen as the
33 "critical" effect upon which an RfD could be set. No one effect provides the obvious choice, as
34 evidenced by approaches taken by WHO, JECFA and ATSDR, all of which chose different
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1 effects upon which to base their tolerable or minimal risk levels. A range of ED01s has been
2 described in Chapter 8 and a summary of NOAELs, LOAELs, and ED01s for low-dose effects is
3 presented in Table 5-6 and Appendix A.
4 Depending on the choice of the endpoint, a composite uncertainty factor would need to be
5 determined in order to set an RfD. This composite uncertainty factor should account for, at a
6 minimum, pharmacodynamic aspects of cross-species scaling (traditionally, a factor of
7 3)—because pharmacokinetic factors are assumed to be accounted for by cross-species scaling on
8 the basis of body burden—and interindividual human variability (traditionally, a factor of 10). In
9 addition, selection of a LOAEL within the range would suggest an additional factor of
10 uncertainty as large as 10. Recently published results also indicate neurobehavioral impacts on
11 adult rats exposed perinatally at levels that yield body burden ED01s below current average
1 2 human body burdens and as low as the lowest noncancer effects previously evaluated
13 (Markowski et al., 2001). In addition, many of the developmental reproductive effects observed
14 in rats (Mably et al., 1992a-c) have ED01 values less than current background exposures. These
1 5 results suggest that there may be additional database needs regarding risks to children. The
1 6 above considerations would traditionally yield a composite uncertainty factor in the range of 30
17 to 100 or more.
18 Coupled with the relatively narrow range of possible "critical" effects discussed above,
19 the range of plausible composite uncertainty factors make the selection of any particular value as
20 the Agency's RfD more difficult than usual and probably unnecessary, particularly in light of the
21 fact that any value that the Agency might choose using traditional approaches would be below
22 current background body burden or intake levels.
23 When evaluating incremental exposures associated with specific sources, knowing the
24 increment relative to background may help in understanding the impact of the incremental
25 exposure. For instance, it would be misleading to focus on only the incremental exposure in
26 evaluating the potential impact on human health when a relatively large background body burden
27 of dioxin already exists in the exposed population. In these circumstances, the incremental
28 exposure needs to be evaluated in the context of these background levels to aid in determining
29 whether these incremental exposures have regulatory significance. This approach would parallel
30 the Agency's approach to evaluating lead exposures. Other parallel science and management
31 issues between dioxin-like compounds and lead are under discussion within the Agency.
32 Providing guidance on the how to judge the significance of incremental increases to background
33 using the MOE approach is beyond the science scope of the reassessment and will have to be
34 addressed elsewhere by EPA. However, it is clear, in light of relatively high background
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1 exposures, that the MOE approach is more useful than an RfD for characterizing dioxin
2 noncancer risks.
3 Other national and international bodies have chosen to define "safe" or "tolerable" levels
4 for dioxin and related compounds (e.g., WHO, 1998; ATSDR, 1999a; SCF, 2000). These
5 estimates cluster within a factor of 4 of current average intake levels, although estimates in the
6 past have spanned many orders of magnitude. Some commenters on earlier drafts of this
7 reassessment have suggested that EPA's approach is inconsistent with these efforts and overly
8 "conservative." Two distinctions can help in understanding these apparent differences. First, in
9 its reassessment, EPA has not tried to establish a tolerable or acceptable level of risk. Rather, it
10 has tried to provide a science-based description of hazard and potential risk without making a
1 1 policy judgment of acceptability. Second, whether one is providing a risk descriptor or an
1 2 acceptable risk determination, a number of judgments need to be made as one moves from
1 3 experimental observation to conclusion. Apparently subtle differences in these judgments can
1 4 result in significantly different conclusions. These differences in judgment fall into three major
1 5 areas: (1) the original focus on cancer rather than noncancer effects as the primary endpoint of
1 6 regulatory concern and the assumption by some that all nongenotoxic compounds have
1 7 thresholds below which cancer risk is minimal or nonexistent; (2) the use of intake as the cross-
1 8 species dose metric despite the large difference in half-life in animals versus humans (for TCDD,
19 for instance, the difference between rats and humans is over a factor of 100); and (3) the size of
20 the "safety" factor or "uncertainty" factors used to derive a "safe or "tolerable" level.
21 The latter factor is currently the most widely divergent. More recent assessments have
22 taken noncancer endpoints into account and have applied a range of uncertainty factors. For
23 instance, ATSDR (1999a) set a minimal risk level (MRL), which is defined similarly to EPA's
24 RfD, for dioxin and related compounds of 1.0 pg TEQ/kg body weight/day. The ATSDR
25 assessment is based on the results of Schantz et al. (1992), a study that is included in Table 5-6
26 and Appendix A. ATSDR used intake as the interspecies dose metric and a composite
27 uncertainty factor of 90, accounting for intraindividual human variability (10), a minimal
28 LOAEL/NOAEL (3), and residual pharmacodynamic differences (3).
29 Hypothetically, had ATSDR relied on the TCDD body burdens measured during this
30 series of rhesus monkey experiments (see Bowman et al., 1989) and had all other factors been
31 equal, the MRL would likely have been determined to be in the range of 0.07 pg TEQ/kg body
32 weight/day (see Table 5-6 and Appendix A), or more than 10 times lower than the existing
33 ATSDR MRL and current average intake levels. The ATSDR assessment, however, selects a
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1 single "critical" effect from among a number of choices and uses "traditional" uncertainty
2 factors, but it uses intake rather than body burden as the dose metric.
3 Several recent assessments have recognized the value of body burden rather than daily
4 intake as the preferred dose metric. WHO (1998) has set a tolerable daily intake (TDI) of 1-4 pg
5 TEQ/kg body weight/day using a range of effects and body burden and has indicated that,
6 although current exposures in that range are "tolerable" (a decision taking into account risk
7 management in addition to traditional hazard assessment), efforts should be made to ultimately
8 reduce intake levels to the lower end of the range and perhaps further. Findings in this
9 reassessment and comments made by the SAB (U.S. EPA, 2001b) are consistent with this
10 recommendation. The WHO assessment relied on an evaluation of the most sensitive effects that
11 are considered adverse (hormonal, reproductive, and developmental effects) and were seen at low
1 2 doses in animal studies (rats and monkeys). Body burden was used as a dose metric, and a
1 3 composite uncertainty of 10 was recommended to account for a number of factors, including the
1 4 use of a LOAEL rather than a NOAEL, differences in animal-to-human susceptibility, and
1 5 differences in half-lives of elimination for the different components of the TEQ mixture.
1 6 In May 2001, the European Commission Scientific Committee on Food (SCF, 2000)
1 7 established a tolerable weekly intake of 14 pg TEQ/kg body weight/week (equivalent to a TDI of
18 2 pg TEQ/kg body weight/day), based on several new studies, which are also now included in
19 EPA's range of low-dose effects, and on a composite uncertainty factor of 9.6. This factor
20 accounts for interindividual variability in toxicokinetics (a factor of 3.2) and marginal effects
21 close to a NOAEL (a factor of 3). The committee concluded that no uncertainty factor needed to
22 be applied for differences in toxicodynamics between experimental animals and humans and for
23 interindividual variation among humans. In June 2001, WHO JECFA determined a provisional
24 tolerable monthly intake (PTMI) of 70 pg TEQ/kg body weight/month (equivalent to 2.33 pg
25 TEQ/kg body weight/day), based on an approach similar to that used by the SCF. The same two
26 studies and safety factors of 3.2 or 9.6 were used, but two models were used to extrapolate the
27 maternal body burden at the NOEL/LOEL of the studies. The committee chose the PTMI as the
28 mid-point of the range of values from its analysis.
29 It should be clear from the discussion above that there is a consensus that sensitive animal
30 responses falling within a relatively narrow range of body burdens can be used as a POD for
31 regulatory guidance, but the choice of individual studies varies. The EPA assessment is the only
32 one to bound the full range of effects (from arguably adaptive and questionably adverse to
33 arguably adverse to clearly adverse) observed through the application of a uniform modeling
34 approach, as well as through evaluating experimental LOAELs and NOAELs. There is also an
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1 emerging consensus that body burden should often be used as a cross-species dose metric. This
2 has implications for ATSDR's current MRL derivation. Finally, there is no consensus on the size
3 or nature of uncertainty factors to be applied. Traditional approaches that might be applied by
4 EPA or that have been applied by ATSDR would likely require additional information to support
5 the choice or removal of uncertainty factors as performed by WHO, SCF, and JECFA. In
6 particular, the focus on accounting for residual toxicodynamic differences in cross-species
7 scaling and interindividual variability in the general population to account for sensitive
8 individuals, including children, would suggest larger uncertainty factors than have been proposed
9 by these groups if EPA were to set an RfD.
10 The choice of any composite uncertainty factor greater than 10 applied to effect levels
11 based on body burden in any of the analyses described above would result in TDIs or MRLs
1 2 below current background intakes. The use of uncertainty factors in the range of 30 to 100 or
1 3 more, as traditionally used by EPA, would result in values even further below some current
14 background body burdens or intake levels than the values presented by other organizations.
1 5 Given the range of choices for a POD, the range of potential composite uncertainty factors and
1 6 the uninformative nature of an RfD below current background levels, the Agency has chosen to
1 7 continue to focus on MOE analyses and to not establish an RfD for dioxin and related
18 compounds.
19
20 Children's risk from exposure to dioxin and related compounds may be increased, but
21 more data are needed to fully address this issue.
22 The issue of children's risk from exposure to dioxin-like compounds has been addressed
23 in a number of sections throughout this reassessment. Data suggest a sensitivity of response in
24 both humans and animals during the developmental period, both prenatal and postnatal.
25 However, these data are limited. Because evaluation of the impacts of early exposures on both
26 children's health and health later in life is important for a complete characterization of risk,
27 collection of additional data should be a high priority in order to reduce uncertainties in future
28 risk assessments.
29 Data from the Dutch cohort of children exposed to PCBs and dioxin-like compounds
30 suggest subtle impacts on neurobehavioral outcomes, thyroid function, and immune system
31 alterations from prenatal—and perhaps postnatal—exposure to 1980s background levels of
32 dioxin and related compounds. Although these effects cannot be attributed solely to dioxin and
33 related compounds, several associations suggest that these effects are, in fact, likely to be Ah-
34 mediated. An investigation of background dioxin exposure and tooth development was done in
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1 Finnish children as a result of studies of dental effects in dioxin-exposed rats, mice, and
2 nonhuman primates and in PCB-exposed children. The Finnish investigators examined enamel
3 hypomineralization of permanent first molars in 6- and 7-year-old children. The length of time
4 that infants breast fed was not significantly associated with either mineralization changes or with
5 TEQ levels in the breast milk. However, when the levels and length of breast feeding were
6 combined in an overall score, a statistically significant association was observed.
7 In addition, effects have been seen in cases where significantly elevated exposure
8 occurred. The incidents at Yusho and Yu-Cheng resulted in increased perinatal mortality and
9 low birth weight in infants born to women who had been exposed. Rocker bottom heal was
10 observed in Yusho infants, and functional abnormalities have been reported in Yu-Cheng
11 children. The similarity of effects observed in human infants prenatally exposed to the complex
1 2 mixture in Yusho and Yu-Cheng and those reported in adult monkeys exposed perinatally to only
13 TCDD suggests that at least some of the effects on children are due to the TCDD-like congeners
14 in the contaminated rice oil ingested by the mothers of these children. The similar responses
1 5 include a clustering of effects in organs derived from the ectodermal germ layer, referred to as
16 ectodermal dysplasia, including effects on the skin, nails, and Meibomian glands, and
1 7 developmental and psychomotor delay during developmental and cognitive tests.
1 8 Some investigators believe that because all of the effects in the Yusho and Yu-Cheng
1 9 cohorts do not correlate with TEQ, some of the effects are due exclusively to nondioxin-like
20 PCBs or to a combination of all the congeners. In addition, on the basis of these data, the extent
21 of the association between overt maternal toxicity and embryo/fetal toxicity in humans is still not
22 clear. Further studies in the offspring as well as follow-up of the Seveso incident may shed
23 further light on this issue. In addition to the chloracne and acute responses to TCDD exposure
24 seen in Seveso children, elevated levels of serum GGT have been observed within a year after
25 exposure in some of the more highly exposed Seveso children. Long-term pathologic
26 consequences of elevated GGT have not been illustrated by excess mortality from liver disorders
27 or cancer or in excess morbidity, but further follow-up is needed. It must be recognized that the
28 absence of an effect thus far does not obviate the possibility that the enzyme levels increased
29 concurrently with the exposure but declined after cessation. The apparently transient elevations
30 in ALT levels among the Seveso children suggest that hepatic enzyme levels other than GGT
31 may react in this manner to TCDD exposure. Recent studies in Seveso have also demonstrated
32 an altered sex ratio in the second generation (Mocarelli et al., 2000).
33 Impacts on thyroid hormones provide an example of an effect of elevated postnatal
34 exposure to dioxin and related compounds. Several studies of nursing infants suggest that
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1 ingestion of breast milk that has a higher dioxin TEQ may alter thyroid function. Thyroid
2 hormones play important roles in the developing nervous system of all vertebrate species,
3 including humans. In the United States, all infants are tested for hypothyroidism shortly after
4 birth. Results from the studies mentioned above suggest a possible shift in the population
5 distribution of thyroid hormone levels, particularly T4, and point out the need for collection of
6 longitudinal data to assess the potential for long-term effects associated with developmental
7 exposures.
8 A large number of studies in animals, including studies of single congeners and exposures
9 to complex mixtures, have addressed the question of effects of dioxin-like chemicals after in
10 utero or lactational exposure. However, the vast majority of the data are derived from studies of
11 2,3,7,8-TCDD, single congeners (e.g., PCB 77), or commercial mixtures of PCBs. Exposure
1 2 patterns have included single doses to the dams as well as dosing on multiple days during
1 3 gestation beginning as early as the first day of gestation. These studies are discussed in detail in
14 Part n, Chapter 5. The observed toxic effects include developmental toxicity, neurobehavioral
1 5 and neurochemical alterations, endocrine effects, and developmental immunotoxicity. For
1 6 instance, results of this body of work suggest that 2,3,7,8-TCDD clearly has the potential to
1 7 produce alterations in male reproductive function (rats, mice, hamsters), male sexual behavior
18 (rats), and female genitalia (rats, hamsters) after prenatal exposure. In addition, impacts on
19 neuromotor and cognitive behavior as well as on development of the immune system have been
20 indicated in a number of studies.
21 No epidemiological data and limited animal data are available to address the question of
22 the potential impact of exposure to dioxin-like compounds on childhood cancers or on cancers of
23 later life. The direct impacts of increased early postnatal exposure on the carcinogenic process
24 may be small, noting the limited impact of nursing on total body burden (see the discussion of
25 breast milk exposures and body burdens below), the assumption that cancer risk is a function of
26 average lifetime body burden, and the possibility that, because dioxin is a potent cancer promoter
27 rather than a direct initiator of the cancer process, exposures later in life might be more important
28 than those received earlier. However, recent studies of Brown et al. (1998) suggest that prenatal
29 exposure of rats to dioxin and related compounds may indirectly enhance their sensitivity as
30 adults to chemical carcinogenesis from other chemical carcinogens. Further work is needed to
31 evaluate this issue.
32 Fetuses, infants, and children are exposed to dioxins through several routes. The fetus is
33 exposed in utero to levels of dioxin and related compounds that reflect the body burden of the
34 mother. It is important to recognize that the greatest impact on the mother's body burden is from
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1 of her lifetime exposure history rather than from the individual meals she eats during pregnancy.
2 Good nutrition, including a diet with appropriate levels of fat, has consequences on dietary intake
3 and consequent body burdens of dioxin and related compounds. Nursing infants represent
4 special cases because for a limited portion of their lives they may have elevated exposures on a
5 body-weight basis when compared with non-nursing infants and with adults (see discussion
6 below).
7 In addition to breast milk exposures, intakes of CDD/CDFs and dioxin-like PCBs are
8 more than three times higher for a young child than for an adult, on a body-weight basis. Table
9 4-7 in Section 4 of this document describes the variability in average intake values as a function
10 of age using age-specific food consumption rates and average food concentrations, as was done
11 for adult intake estimates. However, as with the nursing infants, the differences in body burden
1 2 between children and adults are expected to be much less than the differences in daily intake.
13 Assuming that body burden is the relevant dose metric for most if not all effects, there is some
14 assurance that these short-term increased intake levels will have limited additional impact on risk
1 5 as compared with overall lifetime exposure.
16
1 7 Background exposures to dioxin and related compounds need to be considered when
18 evaluating both hazard and risk.
19 The term "background exposure" has been used throughout this reassessment to describe
20 exposure of the general population to environmental media (food, air, soil, etc.) that have dioxin
21 concentrations within the normal background range. Adult daily intakes of CDD/CDFs and
22 dioxin-like PCBs are estimated to average 43 and 23 pg TEQDFP-WHO98/day, respectively, for a
23 total intake of 66 pg/day TEQDFP-WHO98. On a body-weight basis, this corresponds to
24 approximately 1 pg TEQDFP-WHO98/kg-day. Daily intake is estimated by combining exposure
25 media concentrations (food, soil, air) with contact rates (ingestion, inhalation). Table 4-6
26 summarizes the intake rates derived by this method. The intake estimate is supported by an
27 extensive database on food consumption rates and food data. Pharmacokinetic modeling
28 provides further support for the intake estimates. Current adult tissue levels reflect intakes from
29 past exposure levels, which are thought to be higher than current levels.
30 CDD/CDF and dioxin-like PCB intakes for the general population may extend to levels at
31 least three times higher than the mean. Variability in general population exposure is primarily a
32 result of differences in the dietary choices that individuals make in terms of both quantity and
33 types of food consumed. A diet that is disproportionately high in animal fats will result in an
34 increased background exposure over the mean. Data on the variability of fat consumption
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1 indicate that the 95th percentile is about twice the mean and the 99th percentile is approximately
2 three times the mean. Additionally, a diet that substitutes meat sources that are low in dioxin
3 (e.g., beef, pork, or poultry) with sources that are high in dioxin (e.g., freshwater fish) could
4 result in elevated exposures.
5 Evidence of widespread background exposure can also be seen by examining data on
6 human tissue. These data indicate that the average CDD/CDF tissue level for the general adult
7 U.S. population appears to be declining. A pharmacokinetic modeling evaluation of this
8 declining trend suggests that the CDD/CDF tissue level will drop below 10 ppt TEQDF-WHO98,
9 lipid basis, by 2030 (Lorber, 2002). The best estimate of current (mid to late 1990s) levels is 25
10 ppt (TEQDEP-WHO98, lipid basis). The tissue samples collected in North America in the late
11 1980s and early 1990s showed an average TEQDFP-WHO98 level of about 55 pg/g lipid. This
1 2 finding is supported by a number of studies, all conducted in North America, that measured
1 3 dioxin levels in adipose tissue, blood, and human milk. However, the number of people in most
14 of these studies is relatively small, and the participants were not statistically selected in ways that
1 5 ensured their representativeness of the general U.S. adult population. One study, the 1987
1 6 National Human Adipose Tissue Survey (NHATS), involved more than 800 individuals and
1 7 provided broad geographic coverage, but it did not address coplanar PCBs. Similar tissue levels
1 8 of these compounds were measured in Europe and Japan during similar time periods.
1 9 Because dioxin levels in the environment have been declining since the 1970s, it is
20 reasonable to expect that levels in food, human intake, and, ultimately, human tissue have also
21 declined over this period. The changes in tissue levels are likely to lag the decline seen in
22 environmental levels, and the changes in tissue levels cannot be assumed to occur proportionally
23 with declines in environmental levels. CDC (2000) summarized levels of CDDs, CDFs, and
24 PCBs in human blood collected between 1995 and 1997. The individuals sampled were all U.S.
25 residents who had no known exposures to dioxin other than normal background. The blood was
26 collected in six different locations from 316 individuals ranging in age from 20 to 70 years. All
27 TEQ calculations were made assuming that nondetects were equal to half the detection limit.
28 Although these samples were not collected in a manner that can be considered statistically
29 representative of the national population and they lack wide geographic coverage, they are judged
30 to provide a better indication of current tissue levels in the United States than the earlier data (see
31 Table 4-5).
32 PCBs 105, 118, and 156 are missing from the blood data for the comparison populations
33 reported by CDC (2000). These congeners account for 62% of the total PCB TEQ estimated in
34 the early 1990s. Assuming that the missing congeners from the CDC study data contribute the
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1 same proportion to the total PCB TEQ as in earlier data, they would increase the estimate of
2 current body burdens by another 3.3 pg TEQ/g lipid, for a total PCB TEQ of 5.3 pg/g lipid and a
3 total TEQDFP-WHO98 of 25.4 pg/g lipid.
4 As noted, characterizing national background levels of dioxins in tissues is uncertain
5 because the current data cannot be considered statistically representative of the general
6 population. The task is also complicated by the fact that tissue levels are a function of both age
7 and birth year. Because intake levels have varied over time, the accumulation of dioxins in a
8 person who turned 50 in 1990 is different from that in a person who turned 50 in 2000. Future
9 surveys should help to characterize national levels of CDD/CDF/PCBs during the last years of
10 the 20th century and into the 21st century. The National Health and Nutrition Examination Survey
11 (NHANES) conducted in 1999-2000 included measurements of dioxin blood levels in 1921
1 2 individuals, aged 12 and higher, from numerous locations around the country (CDC, 2003).
13 Unfortunately, not enough blood serum was available per individual to be able to quantify the
14 dioxin concentrations at low background levels, so the majority of measurements were
1 5 nondetects. An effort is currently underway to pool remaining NHANES 1999-2000 samples and
1 6 reanalyze them. This will allow for an estimate of average background body burdens of dioxin-
1 7 like compounds representative of the turn of the century, and in future years should provide a
1 8 picture of dioxin levels in the general U.S. population.
19 As described above, current intake levels from food sources are estimated in this
20 reassessment to be approximately 1 pg TEQ/kg body weight/day. Certain segments of the
21 population may be exposed to additional increments of exposure by being in proximity to point
22 sources or because of dietary practices. These types of exposure are described below.
23
24 Evaluating the exposure of "special" populations and developmental stages is critical to
25 risk characterization.
26 As discussed above, background exposures to dioxin-like compounds may extend to
27 levels at least three times higher than the mean. This upper range is assumed to result from the
28 normal variability of diet and human behaviors. Exposures from local elevated sources or unique
29 diets would be added to this background variability. Elevated exposures may occur in small
30 segments of the population, such as individuals living near discrete local sources or subsistence
31 or recreational fishers. Nursing infants represent a special case. For a limited portion of their
32 lives, they may have elevated exposures on a body-weight basis when compared to non-nursing
33 infants and to adults. This exposure will be discussed in a separate section.
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1 Dioxin contamination incidents involving the commercial food supply have occurred in
2 the United States and other countries. For example, in the United States, contaminated ball clay
3 was used as an anticaking agent in soybean meal, resulting in elevated dioxin levels in some
4 poultry and catfish. This incident involved only a small fraction of national poultry production
5 and the practice has since been eliminated. Elevated dioxin levels have also been observed in a
6 few beef and dairy animals, where the contamination was associated with contact with
7 pentachlorophenol-treated wood. This type of elevated exposure was not detected in the national
8 beef survey; consequently, its occurrence is likely to be low, although it has not been determined.
9 These incidents may have led to small increases in dioxin exposure to the general
10 population; however, it is unlikely that they have led to disproportionate exposures to
11 populations living near where they occurred because, in the United States, meat and dairy
1 2 products are highly distributed on a national scale. If contamination events were to occur in
1 3 foods that are predominantly distributed on a local or regional scale, then such events could lead
1 4 to higher exposure among local populations.
1 5 Elevated exposures associated with the workplace or with industrial accidents have also
1 6 been documented. U.S. workers in certain segments of the chemical industry had elevated levels
1 7 of TCDD exposure, with some tissue measurements in the thousands of parts per trillion TCDD.
18 There is no clear evidence that elevated exposures are currently occurring among U.S. workers.
1 9 Documented examples of past exposures for other groups include certain Air Force personnel
20 exposed to Agent Orange during the Vietnam War and individuals exposed as a result of
21 industrial accidents in Europe and Asia.
22 The discussion in Section 4.5 identified the general population distribution of exposure as
23 extending up to roughly three times the mean. Most people will have exposures within this range
24 even if they have unusual diets in terms of meat and dairy products because most people eat food
25 from multiple sources, which tends to average out the contamination levels, and meat and dairy
26 products have similar dioxin levels, so substitution of one type of meat for another should not
27 have a great impact on total exposure. Clearly elevated exposures are possible in unusual
28 situations where an individual consumes high quantities of meat or dairy products that have
29 significantly increased dioxin levels. Elevated exposures resulting from fish consumption can
30 occur in different situations because concentrations in freshwater fish are significantly greater
31 than in meat and dairy products. Therefore, people who consume large quantities of freshwater
32 fish at background contamination levels may have intakes elevated above the general population
33 distribution.
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1 Consumption offish, meat, or dairy products containing elevated levels of dioxins and
2 dioxin-like PCBs can lead to elevated exposures in comparison to the general population. Most
3 people eat some fish from multiple sources, both fresh and salt water. If individuals obtain their
4 fish from areas where the concentration of dioxin-like chemicals is elevated, they may constitute
5 a highly exposed subpopulation. Although this scenario seems reasonable, very little supporting
6 data could be found for such a highly exposed subpopulation in the United States. One study that
7 measured dioxin-like compounds in blood of sports fishers in the Great Lakes area showed
8 elevations over mean background but within the range of normal variability.
9 Another study that measured 90 PCB congeners—of which 7 were dioxin-like mono-
10 ortho PCBs (although PCB 126 was not measured)—in Lake Michigan "sport-fish eaters"
11 showed a significant elevation in these PCBs versus a control group (little or no sport fish
1 2 consumption). Significantly elevated concentrations of dioxins, furans, and coplanar PCBs were
1 3 measured in Great Lakes fish by the Ontario Ministry of the Environment, although this study
14 was conducted in known or suspected hot spots for the purpose of setting consumption
1 5 advisories. It is not known to what extent individuals would be consuming fish at the high
16 concentrations measured. Elevated CDD/CDF levels in human blood have been measured in
1 7 Baltic fishermen. Similarly, elevated levels of coplanar PCBs have been measured in the blood
1 8 of fishers on the north shore of the Gulf of the St. Lawrence River who consume large amounts
19 of seafood.
20 High exposures to dioxin-like chemicals as a result of consuming meat and dairy products
21 would most likely occur in situations where individuals consume large quantities of these foods
22 and the level of these compounds is elevated. Most people eat meat and dairy products from
23 multiple sources, and even if large quantities are consumed, unusually high exposures are not
24 likely. Individuals who raise their own livestock for basic subsistence have the potential for
25 higher exposures if local levels of dioxin-like compounds are high. One study in the United
26 States showed elevated levels in chicken eggs near a contaminated soil site. European studies at
27 several sites have shown elevated CDD/CDF levels in milk and other animal products near
28 combustion sources.
29 In summary, in addition to general population exposure, some individuals or groups of
30 individuals may also be exposed to dioxin-like compounds from local discrete sources or
31 pathways within their environment. Examples of these "special" exposures include
32 contamination incidents, occupational exposures, direct or indirect exposure to local populations
33 from discrete sources, or exposures to subsistence or recreational fishers.
34
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1 Breast-feeding infants have higher intakes of dioxin and related compounds for a short but
2 developmentally important part of their lives; however, the benefits of breast feeding are
3 widely recognized to outweigh the risks.
4 Three studies have compared dioxins in infants who were breast fed with those who were
5 formula fed, and all have shown elevations in the concentrations of dioxins in infants being
6 breast fed. Formula-fed infants had lipid-based concentrations < 5 ppt TEQDF-WHO98, whereas
7 breast-fed infants had average lipid-based concentrations > 20 ppt TEQDF-WHO98. A similar
8 disparity is seen in more limited data on dioxin-like PCBs.
9 The dose to the infant varies as a function of infant body weight, the concentration of
10 dioxins in the mother's milk, and the trend of dioxins in the mother's milk to decline over time.
11 Using typical values for these parameters, dioxin intakes at birth were estimated to equal 242 pg
1 2 TEQDFP-WHO98/kg/day, which would drop to 18 pg TEQDFP-WHO98/kg/day after 12 months. The
13 average infant dose over a year was calculated to be 87 pg TEQDFP-WHO98/kg/day. Although this
14 dose exceeds the currently estimated adult dose of 1 pg TEQDFP-WHO98/kg/day, the effect on
1 5 infant body burdens is expected to be less dramatic, that is, infant body burdens will not exceed
1 6 adult body burdens by 87 times. This is due to the rapidly expanding infant body weight and
1 7 lipid volume, the decrease in concentration of dioxins in the mother's milk over time, and more
1 8 rapid elimination in infants.
1 9 A pharmacokinetic exercise comparing 6-month, 1-year, and 2-year nursing scenarios
20 with formula feeding showed peak infant lipid concentrations of 44 ppt TEQDFP-WHO98 at 9
21 weeks of age, compared with peak lipid concentrations of less than 10 ppt for the formula-fed
22 infants and average adult lipid concentrations of 25 ppt TEQDFP-WHO98. The dioxin
23 concentrations in breast-fed and formula-fed children were predicted to merge at about 10 years
24 of age, at a lipid concentration of about 13 ppt TEQDFP-WHO98. Breast feeding for 1 year was
25 predicted to result in a lifetime accumulated exposure about 13% higher as compared to formula
26 feeding only.
27 The American Academy of Pediatrics (1997) has made a compelling argument for the
28 diverse advantages of breast feeding for infants, mother, families, and society. These include
29 health, nutritional, immunologic, developmental, psychological, social, economic, and
30 environmental benefits. Breast milk is the point of comparison for all infant food, and the breast-
31 fed infant is the reference for evaluation of all alternative feeding methods. In addition,
32 increasing the rates of breast-feeding initiation is a national health objective and one of the goals
33 of the United States Government's Healthy People 2010. WHO (1988) maintained that the
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1 evidence did not support an alteration of its recommendations that promote and support breast
2 feeding. A more recent consultation in 1998 (WHO, 2000) reiterated these conclusions.
3 Although it is important that the recommendations of these groups continue to be
4 reevaluated in light of emerging scientific information, the Agency does not believe that the
5 findings contained in this reassessment provide a scientific basis for initiating such a
6 reevaluation. This conclusion is based on the fact that stronger data have been presented that
7 body burden, not intake, is the best dose metric; that many of the noncancer effects, particularly
8 those seen in children, are more strongly associated with prenatal exposure and the mother's
9 body burden than with postnatal exposures and breast milk levels; and that dioxin-like
10 compounds are strong promoters of carcinogenicity, a mode of action that depends on late-stage
11 impacts rather than on early-stage impacts on the carcinogenic process.
12
1 3 Many dioxin sources have been identified and emissions to the environment are being
14 reduced.
1 5 Current emissions of CDDs/CDFs/PCBs to the United States environment result
1 6 principally from anthropogenic activities. Evidence for this finding includes matches in time of
1 7 the rise of environmental levels with the rise in general industrial activity (see discussion in
1 8 Section 4.1), lack of any identified large natural sources, and observations of higher
1 9 CDD/CDF/PCB body burdens in industrialized versus less industrialized countries (see
20 discussion on human tissue levels in Section 4.4).
21 The principal identified sources of environmental releases are (1) combustion and
22 incineration sources; (2) chemical manufacturing/processing sources; (3) industrial/municipal
23 processes; (4) biological and photochemical processes; and (5) reservoir sources. Development
24 of national estimates of annual environmental releases to air, water, and land is complicated by
25 the fact that only a few facilities in most industrial sectors have been evaluated for CDD/CDF
26 emissions. Thus, an extrapolation is needed to estimate national emissions. The extrapolation
27 method involves deriving an estimate of emissions per unit of activity (i.e., an emission factor) at
28 the tested facilities and multiplying this by the total activity level in the untested facilities.
29 In order to convey the level of uncertainty in both the measure of activity and the
30 emission factor, EPA developed a qualitative confidence rating scheme. The confidence rating
31 scheme, presented in Section 4, Table 4-1, uses qualitative criteria to assign a high, medium, or
32 low confidence rating to the emission factor and activity level for those source categories for
33 which emission estimates can be reliably quantified. The dioxin reassessment has produced an
34 inventory of source releases for the United States (Table 4-2). The inventory is limited to
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1 sources whose releases can be reliably quantified (i.e., those with confidence ratings of A, B, or
2 C, as defined in Table 4-1). The inventory presents the environmental releases in terms of two
3 reference years: 1987 and 1995. For both of these periods, emissions from combustion and
4 incineration sources dominated total releases. EPA's best estimates of releases of CDD/CDFs to
5 air, water, and land from reasonably quantifiable sources were approximately 3300 g (7 pounds)
6 TEQDF-WHO98 in 1995 and 14,000 g (31 pounds) TEQDF-WHO98 in 1987. The decrease in
7 estimated releases of CDD/CDFs between 1987 and 1995 (approximately 76%) was due
8 primarily to reductions in air emissions from municipal and medical waste incinerators.
9 Although this inventory is one of the most comprehensive and well-documented in the
10 world, it is likely to underestimate total releases because a number of known sources lacked
11 sufficient data to be included in the inventory and the possibility remains that truly unknown
1 2 sources exist.
1 3 Further reductions in environmental releases since the inventory for 1995 can be
14 anticipated as a result of EPA regulations for waste combustion sources and pulp and paper
1 5 facilities. EPA's regulatory programs estimate that, under full compliance with these regulations,
1 6 an additional 1800 g I-TEQ reduction in CDD/CDF emissions should occur. With these
1 7 anticipated emission reductions, uncontrolled burning of household waste would become the
18 largest quantifiable source. Although the full magnitude of reservoir releases remains uncertain,
19 their relative contribution to total annual releases be can reasonably anticipated to increase as
20 contemporary formation sources continue to decrease.
21 No significant release of newly formed dioxin-like PCBs is occurring in the United
22 States. Unlike CDD/CDFs, PCBs were intentionally manufactured in the United States in large
23 quantities from 1929 until production was banned in 1977. Although it has been demonstrated
24 that small quantities of coplanar PCBs can be produced during waste combustion, no strong
25 evidence exists that the dioxin-like PCBs make a significant contribution to TEQ releases during
26 combustion. The occurrences of dioxin-like PCBs in the U.S. environment most likely reflect
27 past releases associated with PCB production, use, and disposal. Further support for this finding
28 is based on observations of reductions since the 1980s in PCBs in Great Lakes sediment and in
29 other areas.
30 As described in Section 4.1, combustion appears to be the most significant process of
31 CDD/CDF formation today. Important factors that can affect the rate of dioxin formation include
32 overall combustion efficiency, post-combustion flue gas temperatures and residence times, and
33 the availability of surface catalytic sites to support dioxin synthesis. Although chlorine is an
34 essential component for the formation of CDDs/CDFs in combustion systems, the empirical
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1 evidence indicates that, for commercial-scale incinerators, chlorine levels in feed are not the
2 dominant controlling factor for rates of CDD/CDF stack emissions. The conclusion that chlorine
3 in feed is not a strong determinant of dioxin emissions applies to the overall population of
4 commercial scale combustors. For any individual commercial-scale combustor, circumstances
5 may exist in which changes in chlorine content of feed could affect dioxin emissions. For
6 uncontrolled combustion, such as open burning of household waste, chlorine content of wastes
7 may play a more significant role than commercial-scale combustors in levels of dioxin emissions.
8
9 Dioxins are widely distributed in the environment at low concentrations, primarily as a
10 result of air transport and deposition.
11 The dioxin-like compounds are essentially insoluble in water, they are generally classified
1 2 as semivolatile, and they tend to bioaccumulate in animals. Once introduced into the
1 3 environment, they are widely distributed in the environment as a result of a number of physical
14 and biological processes. There is some evidence that these compounds can degrade in the
1 5 environment, but in general they are considered very persistent and relatively immobile in soils
16 and sediments.
1 7 The dioxin-like compounds are transported through the atmosphere as vapors or attached
18 to airborne particulates and they can be deposited on soils, plants, or other surfaces (by wet or dry
19 deposition).
20 They enter water bodies primarily via direct deposition from the atmosphere or by surface
21 runoff and erosion. From soils, these compounds can reenter the atmosphere as resuspended soil
22 particles or as vapors. In water, they can be resuspended into the water column from sediments,
23 volatilized out of the surface waters into the atmosphere, or buried in deeper sediments.
24 Immobile sediments appear to serve as permanent sinks for the dioxin-like compounds.
25 Anthropogenic materials (such as pentachlorophenol), although not always considered an
26 environmental compartment, may also contain these compounds, and they have the potential to
27 be released from these materials into the broader environment.
28 The two primary pathways by which dioxin-like compounds enter the ecological food
29 chains and human diet are air to plant to animal and water/sediment to fish. Vegetation receives
30 these compounds via atmospheric deposition in the vapor and particle phases. The compounds
31 are retained on plant surfaces and bioaccumulated in the fatty tissues of animals that feed on
32 these plants. In the aquatic food chain, dioxins enter water systems via direct discharge or
33 deposition and runoff from watersheds. Fish accumulate these compounds through direct contact
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1 with water, suspended particles, and bottom sediments and through the consumption of aquatic
2 organisms.
3 Although these two pathways are thought to normally dominate contribution to the
4 commercial food supply, others can also be important. Animal feed contamination episodes have
5 led to elevations of dioxins in poultry in the United States, in milk in Germany, and in meat/dairy
6 products in Belgium. Gaining a quantitative understanding of how dioxin moves in the
7 environment will be particularly important in understanding the relative contributions of
8 individual point sources to the food chain and assessing the effectiveness of control strategies to
9 reduce human exposure. Although the emissions inventory shows the relative contribution of
10 various sources to total emissions, it is unlikely that these sources make the same relative
11 contributions to human exposure.
12 It is quite possible that the major contributors of dioxin to food may not be those sources
13 that represent the largest fractions of total emissions in the United States (see discussion in
1 4 Section 4.4 indicating that the diet is the dominant exposure pathway for humans). The
1 5 geographic locations of sources relative to the areas from which much of the beef, pork, milk,
16 and fish are produced should be considered. Most of the agricultural areas that produce dietary
1 7 animal fats are not located near or directly downwind of the major sources of dioxin and related
18 compounds.
1 9 The contribution of reservoir sources to human exposure is likely to be significant.
20 Several factors support this finding. First, human exposure to the dioxin-like PCBs is thought to
21 be derived almost completely from reservoir sources. Because approximately one-third of
22 general population TEQ intake is due to PCBs, then at least one-third of the calculated overall
23 risk from dioxin-like compounds comes from reservoir sources. Second, CDD/CDF releases
24 from soil via soil erosion and runoff to waterways appear to be greater than releases to water
25 from the primary sources included in the inventory. CDD/CDFs in waterways can bioaccumulate
26 in fish, leading to human exposure via consumption offish. This suggests that a significant
27 portion of the CDD/CDF TEQ exposure could be due to releases from the soil reservoir. Finally,
28 soil reservoirs could have vapor and particulate releases that deposit on plants and enter the
29 terrestrial food chain. However, the magnitude of this contribution is unknown. Collectively,
30 these three factors suggest that reservoirs are a significant source of current background TEQ
31 exposure, perhaps contributing half or more of the total.
32
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1 Environmental levels, emissions, and human exposures have declined during recent
2 decades.
3 The most compelling supportive evidence of a general decline in environmental levels for
4 CDD/CDF/PCBs comes from dated sediment core studies. CDD/CDF/PCB concentrations in
5 sediments began to increase around the 1930s and continued to increase until about 1970.
6 Decreases began in 1970 and have continued to the time of the most recent sediment samples
7 (about 1990). Sediment studies in lakes located in several European countries have shown
8 similar trends.
9 It is reasonable to assume that sediment core trends are driven by a similar trend in
10 emissions to the environment. The period of increase generally matches the time when a variety
11 of industrial activities began rising, and the period of decline appears to correspond with growth
1 2 in pollution abatement. Decreases in dioxin emissions will presumably have resulted from many
1 3 of these abatement efforts, which included elimination of most open burning, particulate controls
14 on combustors, phase-out of leaded gas, and bans on PCBs, 2,4,5-T, hexachlorophene and
1 5 restrictions on the use of pentachlorophenol. Also, the national source inventory of this
1 6 assessment documented a significant decline in emissions from the late 1980s to the mid-1990s.
1 7 Evidence of declines in human exposure can be inferred from the overall declines in
1 8 environmental levels and emissions, and it is directly supported by limited data on concentrations
1 9 in food and human tissues (see Sections 4.3 and 4.4). Because of the lag between environmental
20 levels and body burdens, it is anticipated that further declines in tissue concentrations should
21 occur as individuals with higher body burdens from past exposure age out of the population. A
22 pharmacokinetic modeling exercise suggested that levels of TEQDF-WHO98 in the U.S.
23 population should decline from levels of about 20 ppt lipid-basis measured in the mid-1990s
24 CDC study to below 10 ppt lipid-basis by 2030. This analysis includes CDD/CDFs only, not
25 PCBs. Dioxin-like PCBs currently make up approximately 20% of the current total TEQ body
26 burden but may increase in percentage as CDD/CDFs decline. This modeling result is based on
27 the assumption that current CDD/CDF intakes remain the same into the 21st century.
28
29 Risk Characterization Summary Statement
30 2,3,7,8-Tetrachlorodibenzo-p-dioxin (TCDD; "dioxin") is highly toxic to many animal
31 species, producing a variety of noncancer and cancer effects. Other 2,3,7,8-substituted
32 polychlorinated dibenzo-p-dioxins and dibenzofurans and coplanar polychlorinated biphenyls
33 (PCBs) exhibit similar effects, albeit at different doses and with different degrees of confidence
34 in the database.
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1 The similarities in toxicity between species and across different dioxin congeners stem
2 from a common mode of action via initial binding to the aryl hydrocarbon (AhR) receptor. This
3 common mode of action is supported by the consistency in effects evident from multiple
4 congener databases, although uncertainty remains due to data gaps for some congeners. The
5 databases supportive of dioxin-like toxicity, both cancer and noncancer, are strongest for those
6 congeners that are the major contributors to the risk to human populations. This has led to an
7 international scientific consensus that it is prudent science policy to use the concept of toxic
8 equivalency factors (TEFs) to sum the contributions of individual PCDD, PCDF, and coplanar
9 PCB congeners with dioxin-like activity.
10 In addressing receptor-mediated responses resulting from complex mixtures of dioxin-
11 like congeners, this assessment has provided a basis for the use of integrated measures of dose
1 2 such as lifetime average body burden as more appropriate default metrics than average lifetime
13 daily intake. Although average body burden over a lifetime appears to be the most useful dose
14 metric for chronic effects, average body burden during the window of sensitivity may be the most
1 5 appropriate metric for developmental effects. The Agency recognizes, therefore, that the final
16 choice of the appropriate metric may depend on the endpoint under evaluation.
1 7 Dioxin and related compounds have been shown to be developmental, reproductive,
1 8 immunological, endocrinological, and cancer hazards, among others in multiple animal species.
1 9 There is no reason to expect, in general, that humans would not be similarly affected at some
20 dose, and indeed, a growing body of data supports this assumption. On the basis of the animal
21 data, current margins of exposure are lower than generally considered acceptable, especially for
22 more highly exposed human populations. The human database supporting this concern for
23 potential effects near background body burdens is less certain. Occupational and industrial
24 accident cohorts exposed at higher levels show correlations with exposure for cancer and a
25 number of noncancer effects consistent with those seen in the animal studies.
26 For cancer outcomes, the epidemiological evidence provides consistent findings of
27 statistically significant elevations, with dose-response trends for all cancers combined and lung
28 cancer risk in occupational cohorts along with evidence of possible additional tissue-specific
29 cancer rate elevations. Given this substantial yet still not definitive epidemiological data, the
30 positive cancer bioassays at multiple sites and in all animal species tested, in vitro studies, and
31 the mechanistic considerations common to animals and humans for dioxin carcinogenicity, EPA
32 characterizes 2,3,7,8-tetrachlorodibenzo-p-dioxin as "carcinogenic to humans." On the basis of
33 similarities of response in multiple positive animal bioassays for non-TCDD congeners and
34 mixtures, mode of action studies, and consistent with the concept of toxic equivalency, complex
35 mixtures of dioxin and related compounds are considered highly potent "likely" carcinogens.
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1 The calculated body burdens of dioxin and dioxin-like substances leading to an estimated
2 1% increase (ED01) in the lifetime risk of cancer in the three occupational studies with the best
3 exposure information fall within a 10-fold range, and those calculated from the animal bioassay
4 data fall in the middle of this range. The ED01 for all cancers combined from the three
5 occupational cohorts range from 6 to 62 ngTCDD/kg body weight (excluding the NIOSH power
6 model calculation), depending on the study and the model used. By comparison, current
7 background body burdens in the United States are approximately 5 ngTEQ/kg body weight,
8 suggesting little margin of exposure (MOE) at today's body burden levels.
9 From these same occupational and animal cancer studies, EPA estimates an upper bound
10 on the lifetime risk of all cancers combined of 1 x 10~3 per pgTEQ/kg/day. This cancer slope
11 factor is based on a statistical estimate of risks from occupational exposures—principally to
1 2 healthy, adult, male workers—and it must be coupled with a recognition that a small number of
13 people may be both more susceptible and consume up to three times the average level of fat per
1 4 day (the principal exposure pathway for dioxins in the general population). Conversely, this risk
1 5 estimate is based on assumptions that the extra cancer risk seen in the occupational cohorts is
16 attributable to dioxin and not other chemical agents present; that the appropriate metric for
1 7 cancer risk is lifetime average body burden and not a measure of peak exposure, which would
18 tend to mitigate risks at low exposures; and that the dose-response model curve continues below
1 9 the range of statistically significant data and does not then exhibit some nonlinearity. Using the
20 best available estimates of cancer risks, the upper bound on general population lifetime risk for
21 all cancers might be on the order of 1 in 1000 or more. Upper-bound risk estimates allow the
22 calculation of the high end of the probability of cancer risk in the population. This means that
23 there is greater than a 95% chance that cancer risks will be less than the upper bound, and it
24 could be as low as zero in some individuals.
25 For noncancer effects, EPA generally calculates an RfD/RfC value that represents an
26 estimate (with uncertainty spanning perhaps an order of magnitude) of a daily exposure to the
27 human population (including sensitive subgroups) that is likely to be without an appreciable risk
28 of deleterious effects during a lifetime. RfD/RfCs are generally calculated by estimating a point
29 of departure dose just below the lower end of the range of observed adverse effects, and dividing
30 this by uncertainty factors to account for extrapolation issues and database deficits. Applying
31 these standard procedures to the data reviewed in this assessment would result in an RfD/RfC
32 below the current estimated average dose to the U.S. population (~1 pgTEQ/kg/day), and would,
33 therefore, be uninformative for a safety assessment.
34 EPA has chosen instead to characterize the MOEs for noncancer endpoints in order to
35 better inform risk management decisions. The MOE is the ratio of the effect level in the
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1 comparison species (ED01 or low effect level; animal or human) to the human body burden. For
2 the most sensitive endpoints identified, MOEs range from, for example, less than 1 for enzyme
3 induction in mice and rats, < 4 for developmental effects, and 4 for endometriosis in non-human
4 primates. In evaluating MOEs, consideration should be given to uncertainties in distinguishing
5 between adaptive biochemical changes and adverse effects, both on an individual level and as
6 these changes impact whole populations. The risks from dioxin and related compounds may be
7 greater for children than for adults, but more data are needed to fully address this issue.
8 Releases of dioxins to the environment from characterized sources have decreased
9 significantly over the last decade and are expected to continue to decrease. Other sources are still
10 poorly characterized, and an environmental reservoir of dioxins from both man-made and natural
11 sources has been recognized. Human body burdens have also declined and are anticipated to be
1 2 further reduced as additional, recently implemented, dioxin emission controls impact
1 3 environmental and food levels and, ultimately, human exposure, although the relationship with
14 reservoir sources remains uncertain.
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1
2
3
4
5
APPENDIX A
Table A-l. Body burdens for critical endpoints in animals with human
equivalent daily intake
Animal
Rats
Rhesus
monkeys
Rats
Rats
Rats
Rats
Mice
Rats
Mice
Rats
Endpoint
Cancer
Fetal Mortality
Developmental
Neurotoxicity
Endometriosis
Reproductive Tox.
(multigenerational)
Developmental/
Reproductive Toxicity
Developmental
Immunotoxicity
Developmental
Neurotoxicity
Immunological Effects
(adult)
Thyroid Effects
CYP1A1/1A2 Enzyme
Induction
CYP1A1/1A2 Enzyme
Induction
Study
Kociba et al. (1978)1
Bowman et al. (1989)z
Schantzetal. (1992)3
Rier et al. (1993)4
Murray et al. (1979)5
Mably etal. (1992a, b, c)6
Grayetal. (1997)7
Faqi et al. (1998)8
Ohsakoetal. (2001)9
Gehrs and Smialowicz (1999)10
Markowski et al. (200 1)11
Burleson et al. (1996)12
Smialowicz et al. (1994)13
Narasimhan et al. (1994)14
Vecchietal. (1983)15
Sewalletal. (1995)16
DeVitoetal. (1994)17
Diliberto et al. (2001)18
Vogel et al. (1997)19
Narasimhan et al (1994)14
van Birgelen et al. (1995)20
Schrenk et al. (1994)21
Sewall et al. (1995)ls
Walker etal. (1999)22
Estimated body burden
(ng/kg)
LOAEL
180
90
21
21
180
38
30
25
30
60
108
6
300
100
1200
76
24
2.8
5.1
25
243
72
8
76
NOAEL
18
21
-
-
18
-
-
-
8
-
36b
3
-
50b
-
22
-
-
0.51
10
-
-
2
-
ED01
32
NC
NC
NC
NC
0.34
0.08
0.6
NC
NC
0.7
NC
2.9
1.5
7
26
22
67
0.003
3
19
26
3.5
59
Human
equiv.a
intakes
(pg/kg/day)
60; 6; 11
30; 7
7
7
60; 6
13; 0.1
10; 0.03
8; 0.2
10; 3
20
36; 12; 0.2
2; 1
100; 1
33; 17; 0.5
401; 2
25; 7; 8
8; 7
0.9; 22
1.6; 0.16; 0.001
8; 3; 2; 1
81; 6
24; 9
3; 0.7; 1
25; 20
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1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
Table A-l. Body burdens for critical endpoints in animals with human
equivalent daily intake (continued)
a Human equivalent intakes were estimated according to the following equation: daily intake (pg/kg/day)
= (body burden (ng/kg)*Ln2*1000)/(t1/2*absorption) where tlA = 2593 days and absorption fraction = 0.8
(Poiger and Schlatter 1986; see Section II). Corresponding human equivalent intake values are arranged
in sequence from the previous three columns.
bNOAEL values are based on the highest individual dose group in which there are no statistically significant
changes. Statistically significant dose response trends plus apparent declines are also evident at all dose
levels—20 and 60 ng/kg orally—in all fixed-ratio test groups in Markowski et al. (2001) and in the 50 ng/kg
dose group inNarasimhan et al. (1994).
- - = No NOAEL value, as effects seen in the lowest dose group in the study.
NC = Not calculated due to
insufficient dose response information (less than three doses and a control) or due to presentation of the data
in graphical form without tabulation of mean and variance estimates.
1. Kociba et al. (1978). Increased cancer in female Sprague-
Dawley rats exposed for 2 years to TCDD in the food matrix.
Statistical LOAEL and NOAEL body burden estimates modeled
assuming 50% absorption from the food matrix and a 25-day half-
life. Compare to measured lipid levels in the Kociba et al. (1978)
rats of 540 and 1700 ppt at 1 and 10 ng/kg/day dose rates and to
measured body burdens in the Hurst et al. (2000) subchronic 5/7 day
gavage study in female Long-Evans rats of 19 and 120 ng/kg at 1 and
10 ng/kg/day dose rates. ED01 calculated for female rat tumors using
a multistage formula and EPA Benchmark Dose Software result in an
ED01 (LED01) of 31.9 (22) ng/kg body burden using Kociba et al.
(1978) data and Goodman and Sauer (1992) pathology.
Kociba et al. 1978: Tumors
PJMttS&ge IJo*l«jraOS6COI1HM« Uutl
lineage —
'•*,-•
0.1
3t
10
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Table A-l. Body burdens for critical endpoints in animals with human
equivalent daily intake (continued)
1 2. Bowman et al. (1989). Offspring per cohort significantly
2 reduced at the 25 ppt dose group in cohorts I and II (LOAEL) but
3 not in the 5 ppt dose group (NOAEL; publication Fig. 5 attached).
4 Estimated maternal body burdens are calculated at parturition of the
5 25 ppt cohort II group for the LOAEL (lowest value of 25 ppt
6 cohorts I and II) and the 5 ppt cohort I for the NOAEL (highest
7 value of 5 ppt cohorts I and II). Maternal TCDD fat levels are
8 estimated according to the empirical formula and data supplied in
9 Bowman et al. (1989; see publication figures 3 and 5): y = 14.9 +
1 0 4.29 x (r=0.924), where y=PCDD/fat ppt infant at weaning and
1 1 x=TCDD/fat ppt mother at parturition. The measured TCDD fat
1 2 levels in offspring ("y" value) of the 5 ppt cohorts I and II at
1 3 parturition were 377±141 ppt and 323±70 ppt, respectively,
1 4 resulting in estimated maternal fat levels at parturition of cohorts I
1 5 and II of 84 and 72 ppt, respectively. Following the authors'
1 6 recommendation, the fat level in the 25 ppt dose group is calculated
1 7 following a 5:1 ratio to the 5 ppt groups, i.e., 420 and 360 ppt for
18 cohorts I and II respectively. Measured maternal data in the 25 ppt
1 9 dose group at the time of birth of cohort III (488 days post cessation
20 of TCDD dose) were 335±119 ppt (3 non-bred females) and 219±75
21 ppt (all 7 monkeys) in fat. A 25% body lipid was assumed in
22 converting to human equivalent body burden.
23
24 3. Schantz et al. (1992). Increased rough-tumble play (publication
25 Fig. 2 attached), fewer retreats during play bouts, and fewer
26 displacements from preferred positions in the 5 ppt cohort I
27 offspring. Maternal TCDD fat levels are estimated according to the
28 empirical formula and data supplied in Bowman et al. (1989; see
29 Figs. 3 and 5): y = 14.9 + 4.29 x (r=0.924), where y=PCDD/fat ppt
30 infant at weaning and x=TCDD/fat ppt mother at parturition. The
3 1 measured TCDD fat level in offspring ("y" value) of the 5 ppt cohort
32 I group at parturition was 377±141 ppt, resulting in an estimated
33 maternal fat level at parturition of 5 ppt cohort I of 84 ppt. Fat level
34 converted to body burden by dividing by 4, approximating 25% body
35 fat in a human equivalent comparison.
Bowman et al. 1989: Infant Survival
*
290 300 400
'.i TC i:. rji, ppi
Schantz et al. 1992: Rough-tumble
Play
3S-
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Table A-l. Body burdens for critical endpoints in animals with human
equivalent daily intake (continued)
1 4. Rier et al. (1993). Increased incidence, severity and dose-
2 response for rhesus monkeys with endometriosis in the 5 and 25 ppt
3 dose groups (rAFS classification; publication figure 2 attached, *
4 /K0.17, **p<0.05). LOAEL (no NOAEL) body burden adopted
5 from the highest maternal fat level calculated according to the
6 formula supplied by Bowman et al. (1989; see footnote 2) of 84 ppt
7 for the 5 ppt dose group occurring at the parturition of cohort I. For
8 comparison, the average of eight measured maternal fat levels at the
9 birth of the 5 ppt cohort III (488 days post cessation of TCDD) was
10 54±llpptfat. A 25% body lipid was assumed in converting to
1 1 human equivalent body burden.
Rier et al. 1993: Endometriosis
1
1
12 5. Murray et al. (1979). Significant reductions in fertility (graph
1 3 of publication table 1 data attached), litter size, gestation survival,
1 4 and neonatal survival and growth in the 10 ng/kg/day food matrix
1 5 maternal dose group in a three-generation reproduction study in
16 Sprague-Dawley rats. Mathematically estimated body burden of
17 180 ng/kg at 10 ng/kg/day (half-life = 25 days, 50% absorption from
1 8 food matrix). Comparison empirical measurements from a similar
1 9 dose regimen in the related cancer study by Kociba etal. (1978)
20 were 1700 ppt TCDD in lipid in the 10 ng/kg/day dose group, and
2 1 the measured body burden in Hurst et al. (2000) subchronic 5/7 day
22 gavage study in female Long-Evans rats was 120 ng/kg at the 10
23 ng/kg/day dose rate. The fertility index in the f0 generation was so
24 low that further studies with this dose group were discontinued.
25 Thus, the study is essentially two dose levels and a control and was
26 not modeled because of the limited dose response relationship data.
Murray et al. 1979: Rat Fertility
Index
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Table A-l. Body burdens for critical endpoints in animals with human
equivalent daily intake (continued)
1 6. Mably et al. (1992a,b,c). Decreased daily sperm production
2 (publication Fig. 5 attached), cauda epididymal sperm, epididymis
3 weights and altered sexual behavior in offspring at 64 ng/kg orally to
4 Holtzman rat dams on gestation day 15. LOAEL (no NOAEL) body
5 burden based on Hurst et al. (2000) GDI6 body burden fraction of
6 60% following single GD15 50 ng/kg gavage dose to female Long-
7 Evans rats. ED01 value modeled for caudal sperm count of 0.34
8 ng/kg body burden at day 63 using EPA Benchmark Dose Software
9 Version 1.3, 60% absorption. ED01 modeling of Mably etal. (1992)
1 0 using EPA Benchmark Dose Software Version 1.3 results in a broad
1 1 range of ED01s, from 0.34 ng/kg for daily sperm production on PND
12 63 to 461 ng/kg for pinna detachment, with a median value of 3.1
1 3 ng/kg for 15 different endpoints.
14
Mably et al. 1992: Epididymal
Sperm
0.1 1,0
MATERNAL TCDD DOSE
(ua/ka)
15 7. Gray et al. (1997). Decrease in ejaculated sperm numbers in
1 6 male offspring, pooled results from two studies (see publication Fig.
17 1; results pooled with Gray et al. 1995; attached graph of data from
18 publication text p.15) at 50 ng/kg single dose, day 15 of gestation to
1 9 female Long-Evans rats. LOAEL (no NOAEL) body burden based
20 on Hurst et al. (2000) GDI6 body burden fraction of 60% following
21 single GDIS 50 ng/kg gavage dose to female Long-Evans rats. ED01
22 modeling of Gray et al. (1997) using EPA Benchmark Dose
23 Software Version 1.3, 60% absorption, results in a broad range of
24 EDois from 0.08 ng/kg for epididymal sperm count on D49 to 327
25 ng/kg for daily sperm production onD49, with a median value of 80
26 ng/kg for 32 different endpoints.
27
Gray et al. 1997: Ejaculated Sperm
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Table A-l. Body burdens for critical endpoints in animals with human
equivalent daily intake (continued)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
8. Faqi et al. (1998). Decreased daily sperm production (graph
attached of data from publication Table 3), cauda epididymus sperm,
sperm transit rate, and percent abnormal sperm in offspring of
25/5 ng/kg (loading/weekly maintenance) maternal Wistar rat
group. Maintenance dose of 5 ng/kg/week subcutaneous
administered to maintain body burden of 25 ng/kg. Additional
data on TCDD levels measured in maternal fat at gestation day 21 g
estimated from publication Figure 1 at 150 ng/kg in 25/5 group. ~
Decreases in cauda epididymal sperm numbers (PND170) and £
daily sperm production (PND 70 and 170) were observed at all Q
doses. In addition, increases in sperm transit rate and percent
abnormal sperm were observed at all dose levels at PND 170.
ED01 value of 0.6 ng/kg for decreases in daily sperm production,
on PND70 and modeled using EPA Benchmark Dose Software
Version 1.3.
9. Ohsako et al. (2001). Decreased ano-genital distance in male
offspring ofHoltzman rat dams receiving 50 ng/kg single dose or
greater on gestation day 15 (publication Fig. 7 attached). NOAEL at
12.5 ng/kg single dose. Dose-dependent decreases in androgen
receptor mRNA levels in ventral prostate in all dose groups
(publication Fig. 8 attached). No changes in daily sperm production
or sperm reserve. LOAEL/NOAEL body burdens based on Hurst et
al. (2000) gestation day (GD) 16 body burden fraction of 60%
following single GDIS 50 ng/kg gavage dose to female Long-Evans
rats. ED01 values for this study were not calculated because the
significant data were not presented in tabular format.
Ohsako et al. 2001: Androgen Receptor
2 *] 5ctR.1l H AR
Faqi et al. 1998; Daily Sperm
Production
Ohsako et al.2001:
Ano-genital Distance
PMM9
s
4 "
PNDTZfl
id ia 1^
i7E SI ifld MS 6 U* TO
V. i::.,... TCOU :-J-_V III-: k'il
STO
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Table A-l. Body burdens for critical endpoints in animals with human
equivalent daily intake (continued)
1 10. Gehrs and Smialowicz (1999). Decreased delayed-type
2 hypersensitivity (DTK; publication Fig. 2a attached; dose units for
3 columns are 0, 100, 300, and 1000 ng/kg) in male offspring
4 following single maternal oral dose of 100 ng/kg on gestation day 14
5 to F344 rats. LOAEL (no NOAEL) body burden based on Hurst et
6 al. (2000) GD16 body burden fraction of 60% following single
7 GDI5 50 ng/kg gavage dose to female Long-Evans rats. Benchmark
8 dose analysis was not performed on this study because the data were
9 presented in graphical format.
Gehrs and Smialowicz 1999:
Delayed-Type Hypersensitivity
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
11 Markowski et al. (2001). Perinatal TCDD exposure produced a significant dose-related reduction in the
number of earned opportunities to run, lever response rate, and total number of revolutions in the wheel in
offspring of Holtzman rats exposed to single oral TCDD doses on GDIS. Statistically significant dose group
effects at 180 ng/kg dose (LOAEL). NOAEL at 60 ng/kg dose group, where apparent declines are not statistically
significant (see publication Fig. 2 attached; publication Table 3). ED01 results modeled by the authors. Table
includes result for total wheel revolutions. Body burdens based on 180 and 60 ng/kg single oral doses and Hurst
et al. (2000) GD16 body burden fraction of 60% following single GDIS 50 ng/kg gavage dose to female Long-
Evans rats.
Markowski et al. 2001: Operant Conditioning
12. Burleson et al. (1996). Increased susceptibility to influenza
infection challenge in B6C3F1 mice following 10 ngTCDD/kg
(LOAEL) and higher single oral gavage dose to 8-week-old mice
(publication Fig. 1 attached). No significant effects seen at 1 and 5
ng/kg doses (NOAEL). Assume 60% absorption. Benchmark dose
analysis was not performed on study because the data were not
presented in tabular format.
Burleson et al. 1996: Influenza
Susceptibility
-
* "
.^_^^
^V^_
* ">i:i*-*-r-.i.. »—
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Table A-l. Body burdens for critical endpoints in animals with human
equivalent daily intake (continued)
1 13. Smialowicz et al. (1994). Dose-related suppression of antibody
2 plaque forming cell (PFC; publication Fig. 1 attached) response in
3 adult female B6C3F1 mice at 300 ng/kg single intraperitoneal
4 injection and higher. PFC increases reported in high-dose-group
5 male F344 and female Long-Evans rat species tested, accompanied
6 by alterations to splenic CD4"CD8+ lymphocytes. ED01 values for
7 mice calculated for plaque forming cells per million cells of 2.9
8 ng/kg body burden using EPA Benchmark Dose Software Version
9 1.3.
Smialowicz et al. 1994: PFC
Immune Response
0 1 2
DOSE i>,-A' TCHO)
10
11
12
13
14
15
16
17
18
19
20
21
22
14. Narasimhan et al. (1994). Decreased splenic antibody plaque-
forming cell (PFC; graph of publication Table 5 data attached)
response following single intraperitoneal dose administered to
female B6C3F1 mice (7-9 weeks old). LOAEL for decreased SRBC
and splenic PFC responses at 100 ng/kg, nonstatistically significant
decrease evident at 50 ng/kg, NOAEL at 25 ng/kg. CYP1A1
LOAEL (NOAEL) at 25 (10) ng/kg dose (graph of publication table
1 data attached). ED01 values calculated for spleen PFC/million cells
of 1.5 ng/kg body burden and for CYP1A1 mRNA induction of 3
ng/kg using EPA Benchmark Dose Software Version 1.3.
Narasimhan et al. 1994:
PFC Immune Response
Cnn 1
i ir^
Q. n
i
B-DJ.
nz.
n-
5
\
^
— -t
TCCOdois
CYP1A1 mRNA Induction
&O.S
ittJP
T ODD a
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Table A-l. Body burdens for critical endpoints in animals with human
equivalent daily intake (continued)
1 15. Vecchi et al. (1983). Decreased plaque-forming cells (PFC) per
2 million and PFC/spleen (graph of publication table 2 data attached)
3 at all doses tested in aryl hydrocarbon hydroxylase sensitive mouse
4 strains (B6, C3) following single intraperitoneal doses. Less
5 sensitivity in other strains (e.g. DBA/2 and AKR). LOAEL (no
6 NOAEL) of 1200 ng/kg. ED01 calculated for PFC/million
7 splenocytes of 7 ng/kg for B6 mice using EPA Benchmark Dose
8 Software Version 1.3.
Vecchi et al. 1983: PFC Immune
Response
9 16. Sewall et al. (1995). Statistically significant decreased ratio of
1 0 thyroid parenchymal area to thyroid follicle area (publication Fig. 6
1 1 attached) was reported in female Sprague-Dawley rats following oral
1 2 gavage biweekly dosing for 30 weeks at daily equivalent doses of
1 3 0.1-125 ng/kg/day. LOAEL (NOAEL) of 3.5 (1) ng/kg/day for
1 4 thyroid parenchyma/follicle ratio, calculating to approximate body
1 5 burdens of 76 and 22 ng/kg for the LOAEL and NOAEL,
1 6 respectively. These calculations assume a half-life of 25 days and
1 7 60% body burden fraction following gavage dose, based on Hurst et
1 8 al. (2000). Significant increases were also reported for thyroid
1 9 stimulating hormone, with a LOAEL of 3.5 ng/kg/d and a NOAEL of
20 1 ng/kg/d. Serum thyroxine was significantly decreased at 10.5
21 ng/kg/day and at higher doses. ED01 values were not calculated for
22 thyroid parenchymal/follicle ratio. ED01 for decreases in serum
23 thyroxin of 43 ng/kg body burden using EPA Benchmark Dose
24 Software Version 1.3. The ED01 for increased serum thyroid
25 stimulating hormone is 26 ng/kg. LOAEL(NOAEL) for CYP1A1
26 mRNA induction of 0.35 (0.1) ng/kg/day, approximating to 8 (2)
27 ng/kg body burden (assuming 60% absorption, 25 day halflife).
28 ED01 for increases in CYP1A1 mRNA was 3.5 ng/kg using EPA
29 Benchmark Dose Software Version 1.3.
30
Sewall et al. 1995: Thyroid
Histology
rcoo oott
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Table A-l. Body burdens for critical endpoints in animals with human
equivalent daily intake (continued)
1 17. DeVito et al. (1994). LOAEL (no NOAEL) of 1.5 ng/kg/day
2 for induction of CYP1A1 and CYP1A2 (publication Fig. 2 attached)
3 and increased phosphorylation of phosphotyrosyl proteins in female
4 B6C3F1 mice gavage fed 1.5-150 ng/kg/day, 5 days per week, for
5 13 weeks. Approximate body burden after 13 weeks at 1.5
6 ng/kg/day of 24 ng/kg, based on Diliberto etal. (2001). ED01 value
7 calculated at 22 ng/kg for CYP1A1 induction in the liver using EPA
8 Benchmark Dose Software Version 1.3.
DeVito et al. 1994: Enzyme
Induction
-If
9 18. Diliberto et al. (2001). Dose response relationship for
1 0 CYP1 Al induction in female B6C3F1 mice (60 days old) at all oral
1 1 gavage doses from 0.15 ng/kg/day (5/7 days, 13 weeks) and higher,
1 2 corresponding to a radiolabel measured body burden of 2.75 ng/kg.
1 3 Hepatic CYP1 Al activity (publication Table 5, graph of liver EROD
1 4 data attached) modeled using EPA BMDS Software Version 1.3
1 5 results in an ED01 of 9.7 ng/kg/day. Body burden interpolated using
1 6 linear regression of data from Diliberto et al. (2001; Table 4) with
1 7 formula: body burden = 6.8782 * daily dose (M-F) (R2 = 0.9994;
1 8 Microsoft Excel), resulting in estimated ED01 body burden of 67
1 9 ng/kg.
Diliberto et al. 2001:
EROD Induction
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Table A-l. Body burdens for critical endpoints in animals with human
equivalent daily intake (continued)
1 19. Vogeletal. (1997). LOAEL(NOAEL) for CYP1A1 EROD
2 induction (graph of publication Table 3 data attached) at 0.34
3 (0.034) ng/kg/day to C57 female mice administered 1, 10, 100 ng/kg
4 loading doses followed by weekly injections of 0.2, 2, and 20 ng/kg
5 for 135 days, calculating to 4.9 (0.49) ng/kg body burden (assuming
6 100% absorption, 10 day halflife). ED01 value calculated for
7 CYP1A1 EROD induction of 0.003 ng/kg body burden using EPA
8 Benchmark Dose Software Version 1.3.
Vogel et al. 1997: EROD Induction
7
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
20. van Birgelen et al. (1995). Significant increases in CYP1A1
(graph of liver EROD data from publication table 3 attached) and
CYP1A2, plus decreased relative thymus weights and loss of hepatic
retinoids, at all doses tested in 8 week old female Sprague-Dawley
rats exposed to dietary matrix intakes of 0, 0.2, 0.4, 0.7, 5, 20
HgTCDD/kg diet, corresponding to 0, 13.5, 26.4, 46.9, 320 and 1024
ng/kg/day oral intake. LOAEL (no NOAEL) calculated from 13.5
ng/kg/day dose to be 243 ng/kg body burden (50% absorption from
dietary matrix, 25 day halflife). Calculated no effect levels (CNEL)
by the authors of 0.7 to 4 ng TCDD/kg/day (Hill and Weibull
models, based on the measured control value plus twice the standard
deviation: mathematical calculated corresponding body burdens are
13 and 72 ng/kg at 50% absorption, halflife 25 days). Measured
levels by authors in liver and fat of 1400 and 620 ppt, respectively.
ED01 value calculated for CYP1A1 of 19 ng/kg body burden using
EPA Benchmark Dose Software Version 1.3.
21. Schrenk et al. (1994). CYP1A1 induction in female Wistar rats
exposed via biweekly subcutaneous injection to average daily doses
of 2, 20, and 200 ng/kg/day for 13 weeks. LOAEL (no NOAEL) for
CYP1A1 EROD induction of 2 ng/kg/day (graph of publication
table 1 data attached), approximating to 72 ng/kg body burden (25
day halflife, 100% absorption). ED01 for CYP1A1 induction of 26
ng/kg body burden using EPA Benchmark Dose Software Version
1.3.
van Birgelen et al. 1995: EROD
Induction
ID -i—
SB inn
urn
Schrenk et al. 1994: EROD
Induction
T3E
o
E
n_
i' -3E
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13
Table A-l. Body burdens for critical endpoints in animals with human
equivalent daily intake (continued)
1 22. Walker et al. (1999). Dose-dependent expression of CYP1A1
2 (graph of publication table 3 data attached) and CYP1A2 RNA
3 (CYP1B1 less sensitive) in female Sprague-Dawley rats gavage fed
4 biweekly for 30 weeks to average daily doses of 3.5 - 125
5 ng/kg/day. LOAEL value of 3.5 ng/kg/day (no NOAEL), calculates
6 to 76 ng/kg body burden (assuming halflife of 25 days, 60%
7 absorption following gavage). Measured liver level of 447 ng/kg.
8 ED01 values calculated for CYP1A1 mRNA and CYP1A2 mRNA at
9 59 and 270 ng/kg body burdens, respectively, using EPA
1 0 Benchmark Dose Software Version 1.3. High maximal induction
1 1 potential of enzymes contributes to high 1% effective dose (ED01).
12
Walker et al. 1999: CYP1A1 mRNA
an
0 X 50 It CD 12 1SI
1EBMN
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1 GLOSSARY
2
3 Adverse effect: A biochemical change, functional impairment, or pathologic lesion that affects
4 the performance of the whole organism or reduces an organism's ability to respond to an
5 additional environmental challenge.
6
7 Area Under the Curve (AUC): Area under the concentration versus time curve. The AUC is a
8 summary measure that integrates serial assessments of a dose over the duration of the study.
9
10 Aryl hydrocarbon receptor (AhR): An intracellular protein that is a ligand-dependent
11 transcription factor that functions in partnership with a second protein, the aryl hydrocarbon
1 2 receptor nuclear translocator (Arnt).
13
14 Aryl hydrocarbon receptor nuclear translocator (Arnt): An intracellular protein that
1 5 functions as a transcription factor in the cell in partnership with a second protein, the aryl
1 6 hydrocarbon receptor (the AhR).
17
1 8 Background exposure: The exposure that regularly occurs to members of the general
1 9 population from exposure media (food, air, soil, etc.) that have dioxin concentrations within
20 the normal background range. Most (> 95%) of background exposure results from the
21 presence of minute amounts of dioxin-like compounds in dietary fat, primarily from the
22 commercial food supply. The origin of this background exposure is from three categories of
23 sources: naturally formed dioxins, anthropogenic dioxins from contemporary sources, and
24 dioxins from reservoir sources. The term "background exposure" as used in this document
25 should not be interpreted as indicating the significance or acceptability of risk associated with
26 such exposures.
27
28 Benchmark dose (BMD): A statistical lower confidence limit on the dose that produces a
29 predetermined change in response rate of an adverse effect, typically 1-10%, compared to
30 background.
31
32 Body burden: Body burden is defined as the concentration of TCDD and related chemicals in
33 the body and is typically expressed as ng/kg body weight. In animals, these values are
34 calculated from studies at or approaching steady-state and are associated with either
35 biochemical or toxicological responses. In addition, these values are calculated on the basis
36 of knowledge of the species-specific half-life and the exposure, or they are estimated on the
37 basis of the TCDD tissue concentration, the size of the tissues, and the weight of the animal.
38 In humans the values are typically presented as steady-state body burdens and are estimated
39 on the basis of an intake rate and the half-life of TCDD in humans. Alternatively, body
40 burdens in humans are estimated on the basis of lipid adjusted serum or adipose tissue TCDD
41 or TEQ concentrations.
42
43 Cancer: A family of diseases affecting cell growth and differentiation, characterized by an
44 abnormal, uncontrolled growth of cells.
45
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1 Carcinogen: An agent capable of inducing cancer.
2
3 Carcinogenesis: The origin or production of a benign or malignant tumor. The carcinogenic
4 event modifies the genome and/or other molecular control mechanisms of the target cells,
5 giving rise to a population of altered cells.
6
7 Chronic effect: An effect that occurs as a result of repeated exposures over a long period of
8 time in relation to the lifetime of the organism.
9
10 Chronic exposure: Multiple exposures occurring over an extended period of time or a
11 significant fraction of the animal's or the individual's lifetime.
12
1 3 Chronic study: A toxicity study designed to measure the (toxic) effects of chronic exposure to a
14 chemical.
15
16 Chronic toxicity: The capacity of a substance to cause adverse human health effects as a result
1 7 of chronic exposure.
18
1 9 Cohort: A group of animals of the same species, including humans, that is identified by a
20 common characteristic and that is studied over a period of time as part of a scientific or
21 medical investigation.
22
23 Confidence interval (CI): A range of values for a variable of interest, for example, a rate,
24 constructed so that this range has a specified probability of including the true value of the
25 variable.
26
27 Confounder: A condition or variable that is both a risk factor for disease and is associated with
28 an exposure of interest. This association between the exposure of interest and the confounder
29 (a true risk factor for disease) may make it falsely appear that the exposure of interest is
30 associated with disease.
31
32 Congeners: Compounds that have similar chemical structures or belong to closely related
33 chemical families
34
35 Coplanar: Descriptive term referring to the fact that multi-ringed chemical structures can
36 assume a flat configuration, with rings in the same spatial plane.
37
38 Dioxin-like: An adjective that describes compounds that have similar chemical structure and
39 physical-chemical properties and invoke a common battery of toxic responses as does
40 2,3,7,8-TCDD. Because of their hydrophobic nature and resistance towards metabolism,
41 these chemicals persist and bioaccumulate in fatty tissues of animals and humans. Certain
42 members of the dioxin, furan, and PCB family are termed "dioxin-like" in this reassessment.
43
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1 Effective dose (ED): The dose that corresponds to an increase, expressed as a percent response,
2 in relation to expected levels of an adverse effect that can be defined as a percent increase
3 over background rates or a percent increase between background and maximal rates.
4
5 Effective dose01 (ED01): The dose corresponding to a 1% increase in an adverse effect.
6 Effective dose evaluation at the 10% response level (ED10 or lower bound on ED10 [LED10])
7 is somewhat the norm, given the power of most chronic toxicology studies to detect an effect.
8 In cases where the data allow evaluation at a lower effective dose level, the Agency suggests
9 using the lower value. Such is the case for 2,3,7,8-TCDD.
10
11 Epidermal growth factor (EGF): A mitogenic polypeptide active on a variety of cell types,
1 2 especially, but not exclusively, epithelial.
13
1 4 Follicle stimulating hormone (FSH): FSH is an acidic glycoprotein secreted by the anterior
1 5 pituitary gland. In women, follicle stimulating hormone stimulates the development of
1 6 ovarian follicles (eggs) and stimulates the release of estrogens. In men, follicle stimulating
1 7 hormone stimulates the production of sperm.
18
1 9 Half-life: A measure of the time required to reduce to one-half the original concentration of a
20 specified chemical in the body.
21
22 Hormone: Control chemicals produced by tissues or organs specialized for that function and
23 that exert their highly specific effects on other tissues of the body.
24
25 Latency Period: The time between first exposure to an agent and manifestation or detection of a
26 health effect of interest.
27
28 Ligand: Any molecule that binds to another. In normal usage, a soluble molecule
29 such as a hormone or neurotransmitter that binds to a receptor, usually with high affinity.
30
31 Lower limit on effective dose01 (LED01): The 95% lower confidence limit of the dose of a
32 chemical needed to produce a 1% increase of an adverse effect in those exposed to the
33 chemical or to 1% of the maximal response relative to control.
34
35 Lowest-observed adverse effect level (LOAEL): The lowest exposure level at which there are
36 statistically significant increases in frequency or severity of adverse effects between the
37 exposed population and its appropriate control group.
38
39 Luteinizing hormone (LH): A hormone that acts with the follicle stimulating hormone (FSH)
40 to stimulate sex hormone release.
41
42 Margin of exposure (MOE): The LED10, LED01, or other point of departure divided by the
43 actual or projected environmental exposure/dose of interest, expressed as a ratio.
44
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1 Minimal risk level (MRL): An estimate of daily human exposure to a hazardous substance that
2 is likely to be without appreciable risk of adverse noncancer health effects over a specified
3 route and duration of exposure.
4
5 No-observed-adverse effect level (NOAEL): The highest exposure level at which there are no
6 statistically significant increases in the frequency or severity of adverse effect between the
7 exposed population and its appropriate control; some effects may be produced at this level,
8 but they are not considered adverse or to be precursors to adverse effects.
9
10 No-observed-effect level (NOEL): An exposure level at which there are no statistically
11 significant increases in the frequency or severity of any effect between the exposed
1 2 population and its appropriate control.
13
14 Pharmacokinetics: The quantitative description of the process of chemical disposition:
1 5 absorption, distribution, metabolism, and excretion (metabolism and excretion equal
16 elimination).
17
18 Physiologically based pharmacokinetic (PBPK) model: Physiologically based model used to
1 9 characterize pharmacokinetic behavior of a chemical. Available data on blood flow rates and
20 metabolic and other processes that the chemical undergoes within each compartment are used
21 to construct a mass-balance framework for the PBPK model.
22
23 Point of departure (POD): The dose-response point that marks the lower end of the range of
24 observation and the beginning of a low-dose extrapolation. This point is most often the upper
25 bound on an observed incidence or on an estimated incidence from a dose-response model or
26 the lower bound on the dose associated with such an incidence.
27
28 Promoter: An agent that is not carcinogenic itself but that when administered after an initiator
29 of carcinogenesis stimulates the clonal expansion of the initiated cell to produce a neoplasm.
30
31 Receptor: A molecular structure within a cell or on the cell's surface that is characterized by
32 selective binding of a specific substance and a specific physiologic effect that accompanies
33 the binding (for example, see aryl hydrocarbon receptor).
34
35 Receptor site: The portion of the receptor molecule or structure with which the compound
36 (ligand) interacts.
37
38 Reference dose (RfD): An estimate (with uncertainty spanning perhaps an order of magnitude)
39 of a daily oral exposure to the human population (including sensitive subgroups) that is likely
40 to be without an appreciable risk of deleterious effects during a lifetime. It can be derived
41 from a NOAEL, a LOAEL, or a benchmark dose, with uncertainty factors generally applied
42 to reflect limitations of the data used. Generally used in EPA's noncancer health
43 assessments.
44
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1 Relative potency (REP): The ratio of the potency of the congener to the standard
2 toxicant in that specific study; a concept similar to toxic equivalency but based on a single
3 study, species, or matrix, etc., and not averaged to obtain a general toxic equivalency value.
4
5 Relative risk (RR): The relative measure of the difference in risk between the exposed and
6 unexposed populations in a cohort study. The relative risk is defined as the rate of disease
7 among the exposed divided by the rate of the disease among the unexposed. A relative risk
8 of 2 means that the exposed group has twice the disease risk as the unexposed group.
9
10 Reservoir sources: Reservoirs are materials or places that contain previously formed
11 CDD/CDFs or dioxin-like PCBs and have the potential for redistribution and circulation of
1 2 these compounds into the environment. Potential reservoirs include soils, sediments, biota,
13 water, and some anthropogenic materials. Reservoirs become sources when they have
14 releases to the circulating environment.
15
1 6 Risk (in the context of human health): The probability of injury, disease, or death from
1 7 exposure to a chemical agent or a mixture of chemicals. In quantitative terms, risk is
1 8 expressed in values ranging from zero (representing the certainty that harm will not occur) to
1 9 one (representing the certainty that harm will occur).
20
21 Slope factor: An upper bound, generally approximating or exceeding a 95% confidence limit,
22 on the increased cancer risk from a lifetime exposure to an agent. This estimate, usually
23 expressed in units of proportion (of a population) affected per mg/kg/day, is generally
24 reserved for use in the low-dose region of the dose-response relationship, that is, for
25 exposures corresponding to risks less than 1 in 100.
26
27 Standardized mortality ratio (SMR): This is the relative measure of the difference in risk
28 between the exposed and unexposed populations in a cohort study. The SMR is similar to
29 the relative risk in both definition and interpretation. This measure is usually standardized to
30 control for any differences in age, sex, and/or race between the exposed and the reference
31 populations. It is frequently converted to a percent by multiplying the ratio by 100.
32
33 Statistical significance: The probability that a result may be due to chance alone. By
34 convention, a difference between two groups is usually considered statistically significant if
35 chance could explain it only 5% of the time or less. Study design considerations may
36 influence the a priori choice of a different statistical significance level.
37
38 Thyroid stimulating hormone (TSH): A hormone secreted by the anterior pituitary gland that
39 activates certain actions in thyroid cells leading to production and release of the thyroid
40 hormones (T3 and T4). T3 and T4 blood levels feed back on the hypothalmus/pituitary gland
41 and decrease TSH production when T3 and T4 levels are high.
42
43 Tolerable daily intake (TDI): A TDI is an estimate of the amount of a contaminant in food or
44 drinking water that can be ingested daily over a lifetime without a significant health risk.
45 The term is used frequently in World Health Organization (WHO) health assessments. The
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1 term "tolerable" is used, as contaminants do not serve an intended function and as intake is
2 unavoidably associated with the basic consumption of food and water. Tolerable does not
3 generally connote "acceptable" or "risk free."
4
5 Toxic equivalence (TEQ): The toxic equivalency factor (TEF) of each dioxin-like compound
6 present in a mixture multiplied by the respective mass concentration. The products are
7 summed to represent the 2,3,7,8-TCDD toxic equivalence of the mixture.
8
9 Toxic equivalency factor (TEF): TEFs compare the potential toxicity of each dioxin-like
10 compound present in a mixture to the well-studied and well-understood toxicity of 2,3,7,8-
1 1 TCDD, the most toxic member of the group, with the TEF of 2,3,7,8-TCDD being 1. TEFs
1 2 are the result of expert scientific judgment using all of the available data and taking into
1 3 account uncertainties in the available data.
14
1 5 Transcription: The process of constructing a messenger RNA molecule using a DNA molecule
16 as a template, with resulting transfer of genetic information to the messenger RNA.
17
1 8 Transcription factor: A substance, usually a protein, that is developed within the organism and
1 9 that is effective in the initiation, stimulation, or termination of the genetic transcription
20 process.
21
22 Upper bound: A plausible upper limit to the true value of a quantity or response. This is
23 usually not a true statistical confidence limit.
24
25 Weight-of-evidence: An approach used for characterizing the extent to which the available data,
26 including human, animal, and mechanism of action, support the hypothesis that an agent
27 causes an adverse effect, such as cancer, in humans. The approach considers all scientific
28 information, both positive and negative, in determining whether and under what conditions
29 an agent may cause disease in humans.
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